diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 0000000..d5d207e --- /dev/null +++ b/.editorconfig @@ -0,0 +1,14 @@ +# top-most EditorConfig file +root = true + +# global definitions +[*] +end_of_line = lf +insert_final_newline = true +trim_trailing_whitespace = true +indent_size = 4 +indent_style = space + +# override indents for specific filetypes +[*.{md,yml,yaml,html,css,scss,js,cff}] +indent_size = 2 diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000..07b99f5 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,26 @@ +--- +name: Bug report +about: Create a report to help us improve +title: '' +labels: bug +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Environment used** +OS, relevant tool versions, etc. + +**Additional context** +Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000..11fc491 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,20 @@ +--- +name: Feature request +about: Suggest an idea for this project +title: '' +labels: enhancement +assignees: '' + +--- + +**Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + +**Describe the solution you'd like** +A clear and concise description of what you want to happen. + +**Describe alternatives you've considered** +A clear and concise description of any alternative solutions or features you've considered. + +**Additional context** +Add any other context or screenshots about the feature request here. diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 0000000..c371bee --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,22 @@ +* **Please check if the PR fulfills these requirements** +- [ ] Tested as per the documentation and they passed +- [ ] Docs have been added / updated (for bug fixes / features) + + +* **What kind of change does this PR introduce?** (Bug fix, feature, docs update, ...) + + + +* **What is the current behavior?** (You can also link to an open issue here) + + + +* **What is the new behavior (if this is a feature change)?** + + + +* **Does this PR introduce a breaking change?** (What changes might users need to make in their application due to this PR?) + + + +* **Other information**: diff --git a/.github/workflows/linting.yml b/.github/workflows/linting.yml new file mode 100644 index 0000000..233be9f --- /dev/null +++ b/.github/workflows/linting.yml @@ -0,0 +1,40 @@ +name: Linting- Markdown,Shell + +on: + push: + workflow_dispatch: + +jobs: + markdown-linting: + name: Markdown lint + runs-on: ubuntu-22.04 + + steps: + - name: Checkout Code + uses: actions/checkout@v3 + + - name: markdownlint-cli + uses: nosborn/github-action-markdown-cli@v3.3.0 + with: + files: . + config_file: ".markdownlint.json" + # dot: false + # ignore_files: '".git*/**"' + + + markdown-check-links: + name: Markdown - checking links + runs-on: ubuntu-22.04 + steps: + - uses: actions/checkout@v3 + - uses: gaurav-nelson/github-action-markdown-link-check@v1 + with: + config-file: ".markdownlint.json" + + shellcheck: + name: Shellcheck + runs-on: ubuntu-22.04 + steps: + - uses: actions/checkout@v3 + - name: Run ShellCheck + uses: ludeeus/action-shellcheck@master diff --git a/.gitignore b/.gitignore index a1adfcd..8e01e18 100644 --- a/.gitignore +++ b/.gitignore @@ -25,12 +25,7 @@ dask-worker-space/ *.egg *.err *.out -*.db -*.py*.sh -*.tsv -*.csv -*.gz* - +.DS_Store # PyInstaller # Usually these files are written by a python script from a template @@ -51,7 +46,6 @@ htmlcov/ nosetests.xml coverage.xml *,cover -*.pdf # Translations *.mo @@ -72,9 +66,6 @@ target/ # conda .conda -# Database -*.db -*.rdb # Pycharm .idea @@ -83,17 +74,13 @@ target/ .ipynb_checkpoints/ # exclude data from source control by default -/data/ -cagi*/ +work -#snakemake +#snakemake or nextflow .snakemake/ -# data/ -variant_annotation/data/ - -# exclude test data used for development -to_be_deleted/test_data/data/ref -to_be_deleted/test_data/data/reads +.nextflow/ +.nextflow* +report* #logs logs/ @@ -103,5 +90,3 @@ logs/ # .java/fonts dir get created when creating fastqc conda env .java/ - -/.vscode/settings.json diff --git a/.markdownlint.json b/.markdownlint.json new file mode 100644 index 0000000..e953d26 --- /dev/null +++ b/.markdownlint.json @@ -0,0 +1,10 @@ +{ + "default": true, + "MD007": { + "indent": 2 + }, + "MD013": { + "line_length": 120, + "tables": false + } +} diff --git a/.pylintrc b/.pylintrc new file mode 100644 index 0000000..ba10934 --- /dev/null +++ b/.pylintrc @@ -0,0 +1,571 @@ +[MASTER] + +# A comma-separated list of package or module names from where C extensions may +# be loaded. Extensions are loading into the active Python interpreter and may +# run arbitrary code. +extension-pkg-whitelist= + +# Add files or directories to the blacklist. They should be base names, not +# paths. +ignore=CVS,lib,.local + +# Add files or directories matching the regex patterns to the blacklist. The +# regex matches against base names, not paths. +ignore-patterns= + +# Python code to execute, usually for sys.path manipulation such as +# pygtk.require(). +#init-hook='import sys;' + +# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the +# number of processors available to use. +jobs=0 + +# Control the amount of potential inferred values when inferring a single +# object. This can help the performance when dealing with large functions or +# complex, nested conditions. +limit-inference-results=100 + +# Pickle collected data for later comparisons. +persistent=yes + +# Specify a configuration file. +#rcfile= + +# When enabled, pylint would attempt to guess common misconfiguration and emit +# user-friendly hints instead of false-positive error messages. +suggestion-mode=yes + +# Allow loading of arbitrary C extensions. Extensions are imported into the +# active Python interpreter and may run arbitrary code. +unsafe-load-any-extension=no + + +[MESSAGES CONTROL] + +# Only show warnings with the listed confidence levels. Leave empty to show +# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED. +confidence= + +# Disable the message, report, category or checker with the given id(s). You +# can either give multiple identifiers separated by comma (,) or put this +# option multiple times (only on the command line, not in the configuration +# file where it should appear only once). You can also use "--disable=all" to +# disable everything first and then reenable specific checks. For example, if +# you want to run only the similarities checker, you can use "--disable=all +# --enable=similarities". If you want to run only the classes checker, but have +# no Warning level messages displayed, use "--disable=all --enable=classes +# --disable=W". +disable=useless-return, + logging-fstring-interpolation, + import-error, + print-statement, + parameter-unpacking, + unpacking-in-except, + old-raise-syntax, + backtick, + long-suffix, + old-ne-operator, + old-octal-literal, + import-star-module-level, + non-ascii-bytes-literal, + raw-checker-failed, + bad-inline-option, + locally-disabled, + file-ignored, + suppressed-message, + useless-suppression, + deprecated-pragma, + use-symbolic-message-instead, + apply-builtin, + basestring-builtin, + buffer-builtin, + cmp-builtin, + coerce-builtin, + execfile-builtin, + file-builtin, + long-builtin, + raw_input-builtin, + reduce-builtin, + standarderror-builtin, + unicode-builtin, + xrange-builtin, + coerce-method, + delslice-method, + getslice-method, + setslice-method, + no-absolute-import, + old-division, + dict-iter-method, + dict-view-method, + next-method-called, + metaclass-assignment, + indexing-exception, + reload-builtin, + oct-method, + hex-method, + nonzero-method, + cmp-method, + input-builtin, + round-builtin, + intern-builtin, + unichr-builtin, + map-builtin-not-iterating, + zip-builtin-not-iterating, + range-builtin-not-iterating, + filter-builtin-not-iterating, + using-cmp-argument, + eq-without-hash, + div-method, + idiv-method, + rdiv-method, + exception-message-attribute, + invalid-str-codec, + sys-max-int, + bad-python3-import, + deprecated-string-function, + deprecated-str-translate-call, + deprecated-itertools-function, + deprecated-types-field, + next-method-defined, + dict-items-not-iterating, + dict-keys-not-iterating, + dict-values-not-iterating, + deprecated-operator-function, + deprecated-urllib-function, + xreadlines-attribute, + deprecated-sys-function, + exception-escape, + comprehension-escape, + assignment-from-none + +# Enable the message, report, category or checker with the given id(s). You can +# either give multiple identifier separated by comma (,) or put this option +# multiple time (only on the command line, not in the configuration file where +# it should appear only once). See also the "--disable" option for examples. +enable=c-extension-no-member + + +[REPORTS] + +# Python expression which should return a note less than 10 (10 is the highest +# note). You have access to the variables errors warning, statement which +# respectively contain the number of errors / warnings messages and the total +# number of statements analyzed. This is used by the global evaluation report +# (RP0004). +evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10) + +# Template used to display messages. This is a python new-style format string +# used to format the message information. See doc for all details. +#msg-template= + +# Set the output format. Available formats are text, parseable, colorized, json +# and msvs (visual studio). You can also give a reporter class, e.g. +# mypackage.mymodule.MyReporterClass. +output-format=text + +# Tells whether to display a full report or only the messages. +reports=no + +# Activate the evaluation score. +score=yes + + +[REFACTORING] + +# Maximum number of nested blocks for function / method body +max-nested-blocks=5 + +# Complete name of functions that never returns. When checking for +# inconsistent-return-statements if a never returning function is called then +# it will be considered as an explicit return statement and no message will be +# printed. +never-returning-functions=sys.exit + + +[LOGGING] + +# Format style used to check logging format string. `old` means using % +# formatting, while `new` is for `{}` formatting. +logging-format-style=new + +# Logging modules to check that the string format arguments are in logging +# function parameter format. +logging-modules=logging + + +[SPELLING] + +# Limits count of emitted suggestions for spelling mistakes. +max-spelling-suggestions=10 + +# Spelling dictionary name. Available dictionaries: none. To make it working +# install python-enchant package.. +spelling-dict= + +# List of comma separated words that should not be checked. +spelling-ignore-words= + +# A path to a file that contains private dictionary; one word per line. +spelling-private-dict-file= + +# Tells whether to store unknown words to indicated private dictionary in +# --spelling-private-dict-file option instead of raising a message. +spelling-store-unknown-words=no + + +[MISCELLANEOUS] + +# List of note tags to take in consideration, separated by a comma. +notes=FIXME, + XXX, + TODO + + +[TYPECHECK] + +# List of decorators that produce context managers, such as +# contextlib.contextmanager. Add to this list to register other decorators that +# produce valid context managers. +contextmanager-decorators=contextlib.contextmanager + +# List of members which are set dynamically and missed by pylint inference +# system, and so shouldn't trigger E1101 when accessed. Python regular +# expressions are accepted. +generated-members= + +# Tells whether missing members accessed in mixin class should be ignored. A +# mixin class is detected if its name ends with "mixin" (case insensitive). +ignore-mixin-members=yes + +# Tells whether to warn about missing members when the owner of the attribute +# is inferred to be None. +ignore-none=yes + +# This flag controls whether pylint should warn about no-member and similar +# checks whenever an opaque object is returned when inferring. The inference +# can return multiple potential results while evaluating a Python object, but +# some branches might not be evaluated, which results in partial inference. In +# that case, it might be useful to still emit no-member and other checks for +# the rest of the inferred objects. +ignore-on-opaque-inference=yes + +# List of class names for which member attributes should not be checked (useful +# for classes with dynamically set attributes). This supports the use of +# qualified names. +ignored-classes=optparse.Values,thread._local,_thread._local + +# List of module names for which member attributes should not be checked +# (useful for modules/projects where namespaces are manipulated during runtime +# and thus existing member attributes cannot be deduced by static analysis. It +# supports qualified module names, as well as Unix pattern matching. +ignored-modules= + +# Show a hint with possible names when a member name was not found. The aspect +# of finding the hint is based on edit distance. +missing-member-hint=yes + +# The minimum edit distance a name should have in order to be considered a +# similar match for a missing member name. +missing-member-hint-distance=1 + +# The total number of similar names that should be taken in consideration when +# showing a hint for a missing member. +missing-member-max-choices=1 + + +[VARIABLES] + +# List of additional names supposed to be defined in builtins. Remember that +# you should avoid defining new builtins when possible. +additional-builtins= + +# Tells whether unused global variables should be treated as a violation. +allow-global-unused-variables=yes + +# List of strings which can identify a callback function by name. A callback +# name must start or end with one of those strings. +callbacks=cb_, + _cb + +# A regular expression matching the name of dummy variables (i.e. expected to +# not be used). +dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_ + +# Argument names that match this expression will be ignored. Default to name +# with leading underscore. +ignored-argument-names=_.*|^ignored_|^unused_ + +# Tells whether we should check for unused import in __init__ files. +init-import=yes + +# List of qualified module names which can have objects that can redefine +# builtins. +redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io + + +[FORMAT] + +# Expected format of line ending, e.g. empty (any line ending), LF or CRLF. +expected-line-ending-format=LF + +# Regexp for a line that is allowed to be longer than the limit. +ignore-long-lines=^\s*(# )??$ + +# Number of spaces of indent required inside a hanging or continued line. +indent-after-paren=4 + +# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 +# tab). +indent-string=' ' + +# Maximum number of characters on a single line. +max-line-length=120 + +# Maximum number of lines in a module. +max-module-lines=1000 + +# List of optional constructs for which whitespace checking is disabled. `dict- +# separator` is used to allow tabulation in dicts, etc.: {1 : 1,\n222: 2}. +# `trailing-comma` allows a space between comma and closing bracket: (a, ). +# `empty-line` allows space-only lines. +no-space-check=trailing-comma + +# Allow the body of a class to be on the same line as the declaration if body +# contains single statement. +single-line-class-stmt=no + +# Allow the body of an if to be on the same line as the test if there is no +# else. +single-line-if-stmt=no + + +[SIMILARITIES] + +# Ignore comments when computing similarities. +ignore-comments=yes + +# Ignore docstrings when computing similarities. +ignore-docstrings=yes + +# Ignore imports when computing similarities. +ignore-imports=no + +# Minimum lines number of a similarity. +min-similarity-lines=4 + + +[BASIC] + +# Naming style matching correct argument names. +argument-naming-style=snake_case + +# Regular expression matching correct argument names. Overrides argument- +# naming-style. +#argument-rgx= + +# Naming style matching correct attribute names. +attr-naming-style=snake_case + +# Regular expression matching correct attribute names. Overrides attr-naming- +# style. +#attr-rgx= + +# Bad variable names which should always be refused, separated by a comma. +bad-names=foo, + bar, + baz, + toto, + tutu, + tata + +# Naming style matching correct class attribute names. +class-attribute-naming-style=any + +# Regular expression matching correct class attribute names. Overrides class- +# attribute-naming-style. +#class-attribute-rgx= + +# Naming style matching correct class names. +class-naming-style=PascalCase + +# Regular expression matching correct class names. Overrides class-naming- +# style. +#class-rgx= + +# Naming style matching correct constant names. +const-naming-style=UPPER_CASE + +# Regular expression matching correct constant names. Overrides const-naming- +# style. +#const-rgx= + +# Minimum line length for functions/classes that require docstrings, shorter +# ones are exempt. +docstring-min-length=-1 + +# Naming style matching correct function names. +function-naming-style=snake_case + +# Regular expression matching correct function names. Overrides function- +# naming-style. +#function-rgx= + +# Good variable names which should always be accepted, separated by a comma. +good-names=i, + j, + k, + ex, + Run, + _, + df, + x, + y + +# Include a hint for the correct naming format with invalid-name. +include-naming-hint=yes + +# Naming style matching correct inline iteration names. +inlinevar-naming-style=any + +# Regular expression matching correct inline iteration names. Overrides +# inlinevar-naming-style. +#inlinevar-rgx= + +# Naming style matching correct method names. +method-naming-style=snake_case + +# Regular expression matching correct method names. Overrides method-naming- +# style. +#method-rgx= + +# Naming style matching correct module names. +module-naming-style=snake_case + +# Regular expression matching correct module names. Overrides module-naming- +# style. +#module-rgx= + +# Colon-delimited sets of names that determine each other's naming style when +# the name regexes allow several styles. +name-group= + +# Regular expression which should only match function or class names that do +# not require a docstring. +no-docstring-rgx=^_ + +# List of decorators that produce properties, such as abc.abstractproperty. Add +# to this list to register other decorators that produce valid properties. +# These decorators are taken in consideration only for invalid-name. +property-classes=abc.abstractproperty + +# Naming style matching correct variable names. +variable-naming-style=snake_case + +# Regular expression matching correct variable names. Overrides variable- +# naming-style. +#variable-rgx= + + +[STRING] + +# This flag controls whether the implicit-str-concat-in-sequence should +# generate a warning on implicit string concatenation in sequences defined over +# several lines. +check-str-concat-over-line-jumps=no + + +[IMPORTS] + +# Allow wildcard imports from modules that define __all__. +allow-wildcard-with-all=no + +# Analyse import fallback blocks. This can be used to support both Python 2 and +# 3 compatible code, which means that the block might have code that exists +# only in one or another interpreter, leading to false positives when analysed. +analyse-fallback-blocks=no + +# Deprecated modules which should not be used, separated by a comma. +deprecated-modules=optparse,tkinter.tix + +# Create a graph of external dependencies in the given file (report RP0402 must +# not be disabled). +ext-import-graph= + +# Create a graph of every (i.e. internal and external) dependencies in the +# given file (report RP0402 must not be disabled). +import-graph= + +# Create a graph of internal dependencies in the given file (report RP0402 must +# not be disabled). +int-import-graph= + +# Force import order to recognize a module as part of the standard +# compatibility libraries. +known-standard-library= + +# Force import order to recognize a module as part of a third party library. +known-third-party=enchant + + +[CLASSES] + +# List of method names used to declare (i.e. assign) instance attributes. +defining-attr-methods=__init__, + __new__, + setUp + +# List of member names, which should be excluded from the protected access +# warning. +exclude-protected=_asdict, + _fields, + _replace, + _source, + _make + +# List of valid names for the first argument in a class method. +valid-classmethod-first-arg=cls + +# List of valid names for the first argument in a metaclass class method. +valid-metaclass-classmethod-first-arg=cls + + +[DESIGN] + +# Maximum number of arguments for function / method. +max-args=6 + +# Maximum number of attributes for a class (see R0902). +max-attributes=10 + +# Maximum number of boolean expressions in an if statement. +max-bool-expr=5 + +# Maximum number of branch for function / method body. +max-branches=12 + +# Maximum number of locals for function / method body. +max-locals=15 + +# Maximum number of parents for a class (see R0901). +max-parents=7 + +# Maximum number of public methods for a class (see R0904). +max-public-methods=20 + +# Maximum number of return / yield for function / method body. +max-returns=6 + +# Maximum number of statements in function / method body. +max-statements=50 + +# Minimum number of public methods for a class (see R0903). +min-public-methods=2 + + +[EXCEPTIONS] + +# Exceptions that will emit a warning when being caught. Defaults to +# "BaseException, Exception". +overgeneral-exceptions=BaseException, + Exception diff --git a/.test_data/README b/.test_data/README new file mode 100644 index 0000000..c0fa03a --- /dev/null +++ b/.test_data/README @@ -0,0 +1,7 @@ +This directory has 3 files - + +`oc_test_data.vcf.gz` - test multi-sample VCF data from OpenCRAVAT + +`testing_variants_hg38.vcf.gz` - We custom made a test VCF file with few variants from every chromosome (1-22,X,Y) + +`file_list.txt` - contains list of above 2 test vcf files with relative path. This file is used to test nextflow pipeline diff --git a/.test_data/file_list.txt b/.test_data/file_list.txt new file mode 100644 index 0000000..f2548e9 --- /dev/null +++ b/.test_data/file_list.txt @@ -0,0 +1,2 @@ +.test_data/oc_test_data.vcf.gz +.test_data/testing_variants_hg38.vcf.gz diff --git a/.test_data/oc_test_data.vcf.gz b/.test_data/oc_test_data.vcf.gz new file mode 100644 index 0000000..74158e1 Binary files /dev/null and b/.test_data/oc_test_data.vcf.gz differ diff --git a/.test_data/testing_variants_hg38.vcf.gz b/.test_data/testing_variants_hg38.vcf.gz new file mode 100644 index 0000000..7d1eb26 Binary files /dev/null and b/.test_data/testing_variants_hg38.vcf.gz differ diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..1b1911b --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,20 @@ +# CHANGELOG + +Here is a changelog format you could use. Feel free to choose a format that works for the repo. + +```txt +YYYY-MM-DD John Doe + +* Big Change 1 +* Another Change 2 +``` + +--- + +```txt +2024-02-01 Tarun Mamidi + +* Uses OpenCRAVAT for annotations +* Uses Neural Networks from keras instead of traditional scikit-learn models +* Nextflow pipeline to annotate, parse and DITTO predictions +``` diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..50e17fc --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,42 @@ +# Contributing Guidelines + +๐Ÿ˜ ๐ŸŽ‰ Thank you for taking the time to contribute! ๐Ÿ˜ ๐ŸŽ‰ + +The following is a set of guidelines for contributing to this repo. + +- [Contributing Guidelines](#contributing-guidelines) + - [Contributing](#contributing) + - [Reporting Issues](#reporting-issues) + - [Seeking Support](#seeking-support) + +--- + +## Contributing + +To get started on contributing to this repo: + +- Follow [Code of Conduct](https://www.contributor-covenant.org/version/2/1/code_of_conduct/) +- Read these **Contributing Guidelines** to completion +- Choose an existing feature/bug listed under GitHub issues for the repo. If the feature/bug is not listed under issues, + create a new issue. +- Fork and create a new branch for your work. +- Submit a pull request with adequate documentation of functionality and changes made. Ensure the PR description clearly +describes the problem and solution. Include the relevant issue number. + +## Reporting Issues + +If you encounter a bug while using the project, we want to hear about it! Here's how to report a bug: + + 1. Check the existing GitHub issues for the repo to see if the bug has already been reported. + 2. If the bug has not already been reported, create a new issue by clicking the "New Issue" button on the GitHub + issues for the repo and click "Get started" for a "๐Ÿž Bug Report". + 3. In the ๐Ÿž Bug Report template, provide a clear and concise description of the bug, including any error messages + that you encountered and steps to reproduce the bug in a specific environment. + 4. If possible, include any relevant details such as the version of the project you are using, your operating system, + and any other relevant information that may help to reproduce and fix the bug. + +--- + +## Seeking Support + +For support in setting up and using this repo, please contact us via GitHub issues for the repo diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..78651f6 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +the GNU General Public License is intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. We, the Free Software Foundation, use the +GNU General Public License for most of our software; it applies also to +any other work released this way by its authors. You can apply it to +your programs, too. + + When we speak of free software, we are referring to freedom, not +price. Our General Public Licenses are designed to make sure that you +have the freedom to distribute copies of free software (and charge for +them if you wish), that you receive source code or can get it if you +want it, that you can change the software or use pieces of it in new +free programs, and that you know you can do these things. + + To protect your rights, we need to prevent others from denying you +these rights or asking you to surrender the rights. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md index 304c4a1..2693914 100644 --- a/README.md +++ b/README.md @@ -1,144 +1,125 @@ # DITTO + +[![Perform linting - +Markdown](https://github.com/uab-cgds-worthey/DITTO/actions/workflows/linting.yml/badge.svg)](https://github.com/uab-cgds-worthey/DITTO/actions/workflows/linting.yml) + + ***!!! For research purposes only !!!*** -> **_NOTE:_** In a past life, DITTO used a different remote Git management provider, [UAB +> ***NOTE:*** In a past life, DITTO used a different remote Git management provider, [UAB > Gitlab](https://gitlab.rc.uab.edu/center-for-computational-genomics-and-data-science/sciops/ditto). It was migrated to > Github in April 2023, and the Gitlab version has been archived. -- [DITTO](#ditto) - - [Data](#data) - - [Usage](#usage) - - [Installation](#installation) - - [Requirements](#requirements) - - [Activate conda environment](#activate-conda-environment) - - [Steps to run DITTO predictions](#steps-to-run-ditto-predictions) - - [Run VEP annotation](#run-vep-annotation) - - [Parse VEP annotations](#parse-vep-annotations) - - [Filter variants for Ditto prediction](#filter-variants-for-ditto-prediction) - - [DITTO prediction](#ditto-prediction) - - [Combine with Exomiser scores](#combine-with-exomiser-scores) - - [Cohort level analysis](#cohort-level-analysis) - - [Contact information](#contact-information) - -**Aim:** We aim to develop a pipeline for accurate and rapid prioritization of variants using patientโ€™s genotype (VCF) and/or phenotype (HPO) information. - -## Data +DITTO is an explainable neural network that can be helpful for accurate and rapid interpretation of small +genetic variants for pathogenicity using patientโ€™s genotype (VCF) information. -Input for this project is a single sample VCF file. This will be annotated using VEP and given to Ditto for predictions. +## Using DITTO -## Usage +DITTO scores for variants can be obtained by the below 3 ways. Webapp and API are for single variant analysis and the +local setup is for batch/bulk variant predictions. -### Installation +### Webapp -Installation simply requires fetching the source code. Following are required: + +DITTO is available for public use at this [website](https://cgds-ditto.streamlit.app). + -- Git - -To fetch source code, change in to directory of your choice and run: +### API -```sh -git clone -b master \ - --recurse-submodules \ - git@gitlab.rc.uab.edu:center-for-computational-genomics-and-data-science/sciops/ditto.git -``` +DITTO is not hosted as a public API but one can serve up locally to query DITTO scores. Please follow the instructions +in this [GitHub repo](https://github.com/uab-cgds-worthey/DITTO-API). -### Requirements +### Setting up to use locally -*OS:* +> ***NOTE:*** This setup will allow one to annotate a VCF sample and make DITTO predictions. Currently tested only in +> Cheaha (UAB HPC) because of resource limitations to download datasets from OpenCRAVAT. +> Docker versions may need to be explored later to make it useable in Mac and Windows. -Currently works only in Linux OS. Docker versions may need to be explored later to make it useable in Mac (and -potentially Windows). +#### System Requirements *Tools:* - Anaconda3 - - Tested with version: 2020.02 - -### Activate conda environment +- OpenCravat-2.4.1 +- Git -Change in to root directory and run the commands below: +*Resources:* -```sh -# create conda environment. Needed only the first time. -conda env create --file configs/envs/testing.yaml +- CPU: > 2 +- Storage: ~1TB +- RAM: ~25GB for a WGS VCF sample -# if you need to update existing environment -conda env update --file configs/envs/testing.yaml +#### Installation -# activate conda environment -conda activate testing -``` +Requirements: -### Steps to run DITTO predictions +- DITTO repo from GitHub +- OpenCravat with databases to annotate +- Nextflow >=22.10.7 -Remove variants with `*` in `ALT Allele` column. These are called "Spanning or overlapping deletions" introduced in the VCF v4.3 specification. More on this [here](https://gatk.broadinstitute.org/hc/en-us/articles/360035531912-Spanning-or-overlapping-deletions-allele-). -Current version of VEP that we're using doesn't support these variants. We will work on this in our future release. +To fetch DITTO source code, change in to directory of your choice and run: ```sh -bcftools annotate -e'ALT="*" || type!="snp"' path/to/indexed_vcf.gz -Oz -o path/to/indexed_vcf_filtered.vcf.gz +git clone https://github.com/uab-cgds-worthey/DITTO.git ``` -#### Run VEP annotation - -Please look at the steps to run VEP [here](variant_annotation/README.md) +#### Setup OpenCravat (only one-time installation) +Please follow the steps mentioned in [install_openCravat.md](docs/install_openCravat.md). -#### Parse VEP annotations +> ***NOTE:*** Current version of OpenCravat that we're using doesn't support "Spanning or overlapping deletions" +> variants i.e. variants with `*` in `ALT Allele` column. More on these variants + +> [here](https://gatk.broadinstitute.org/hc/en-us/articles/360035531912-Spanning-or-overlapping-deletions-allele). + +> These will be ignored when running the pipeline. -Please look at the steps to parse VEP annotations [here](annotation_parsing/README.md) +#### Run DITTO pipeline +Create an environment via conda. Below is an example to install `nextflow`. -#### Filter variants for Ditto prediction - -Filtering step includes imputation and one-hot encoding of columns. +- [Anaconda virtual environment](https://docs.anaconda.com/free/anaconda/install/index.html) ```sh -python src/Ditto/filter.py -i path/to/parsed_vcf_file.tsv -O path/to/output_directory -``` - -Output from this step includes - - -```directory -output_directory/ -โ”œโ”€โ”€ data.csv <--- used for Ditto predictions -โ”œโ”€โ”€ Nulls.csv - indicates number of Nulls in each column -โ”œโ”€โ”€ stats_nssnv.csv - variant stats from the vcf -โ”œโ”€โ”€ correlation_plot.pdf- Plot to check if any columns are directly correlated (cutoff >0.95) -โ””โ”€โ”€ columns.csv - columns before and after filtering step - -``` +# create environment. Needed only the first time. Please use the above link if you're not using Mac. +conda create --name ditto-env -#### Ditto prediction +conda activate ditto-env -```sh -python src/Ditto/predict.py -i path/to/output_directory/data.csv --sample sample_name -o path/to/output_directory/ditto_predictions.csv -o100 .path/to/output_directory/ditto_predictions_100.csv +# Install nextflow +conda install bioconda::nextflow ``` -#### Combine with Exomiser scores - -If phenotype terms are present for the sample, one could use Exomiser to rank genes and then prioritize Ditto predictions according to the phenotype. Once you have Exomiser scores, please run the following command to combine Exomiser and Ditto scores +Please make a samplesheet with VCF files (incl. path). Please make sure to edit the directory paths as needed and run +the pipeline as shown below. ```sh -python src/Ditto/combine_scores.py --raw .path/to/parsed_vcf_file.tsv --sample sample_name --ditto path/to/output_directory/ditto_predictions.csv -ep path/to/exomiser_scores/directory -o .path/to/output_directory/predictions_with_exomiser.csv -o100 path/to/output_directory/predictions_with_exomiser_100.csv +nextflow run pipeline.nf \ + --outdir ./data/ \ + -work-dir ./wor_dir \ + --build hg38 -with-report \ + --oc_modules /data/opencravat/modules \ + --sample_sheet .test_data/file_list ``` - -### Cohort level analysis - -Please refer to [CAGI6-RGP](https://gitlab.rc.uab.edu/center-for-computational-genomics-and-data-science/sciops/mana/mini_projects/rgp_cagi6) project for filtering and annotation of variants as done above for single sample VCF along with calculating Exomiser scores. - -For predictions, make necessary directory edits to the snakemake [workflow](workflow/Snakefile) and run the following command. +To run on UAB cheaha, please update the `model.job` file and submit a slurm job using the command below ```sh -sbatch src/predict_variant_score.sh +sbatch model.job ``` -**Note**: The commit used for CAGI6 challenge pipeline is [be97cf5d](https://gitlab.rc.uab.edu/center-for-computational-genomics-and-data-science/sciops/ditto/-/merge_requests/3/diffs?commit_id=be97cf5dbfcb099ac82ef28d5d8b0919f28aed99). It was used along with annotated VCFs and exomiser scores obtained from [rgp_cagi6 workflow](https://gitlab.rc.uab.edu/center-for-computational-genomics-and-data-science/sciops/mana/mini_projects/rgp_cagi6). +## Reproducing the DITTO model +Detailed instructions on reproducing the model is explained in [build_DITTO.md](docs/build_DITTO.md) ## Contact information -For issues, please send an email with clear description to +For queries, please open a GitHub issue. + +For urgent queries, send an email with clear description to -Tarun Mamidi - tmamidi@uab.edu +|Name | Email | +|------|--------| +|Tarun Mamidi | | +|Liz Worthey | | diff --git a/cheaha.config b/cheaha.config new file mode 100644 index 0000000..4692aa3 --- /dev/null +++ b/cheaha.config @@ -0,0 +1,61 @@ +conda { + enabled = true + cacheDir = '/nextflow/nextflow-conda-env-cache/' +} + +// Define the Scratch directory +def scratch_dir = System.getenv("USER_SCRATCH") ?: "/tmp" + +params { + config_profile_name = 'cheaha' + config_profile_description = 'University of Alabama at Birmingham Cheaha HPC' + config_profile_contact = 'Tarun Mamidi (tmamidi@uab.edu)' +} + +env { + TMPDIR="$scratch_dir" + SINGULARITY_TMPDIR="$scratch_dir" +} + +process { + executor = 'slurm' + + withName: runOC { + cpus = 2 + memory = 10.GB + time = 2.h + queue = 'amd-hdr100' + //clusterOptions = '--reservation=wortheylab' + } + withName: parseAnnotation { + cpus = 1 + memory = 4.GB + time = 5.h + queue = 'amd-hdr100' + } + withName: prediction { + cpus = 1 + memory = 40.GB + time = 10.h + queue = 'amd-hdr100' + } + + errorStrategy = 'retry' + maxRetries = 2 +} + +params { + max_memory = 3072.GB + max_cpus = 264 +} + +// https://www.nextflow.io/docs/latest/config.html#scope-executor +executor { + name = 'slurm' + submitRateLimit = '10/1sec' + pollInterval = '120 sec' + + queueSize = 200 +} + +// cleanup = 'eager' diff --git a/configs/col_config.yaml b/configs/col_config.yaml new file mode 100644 index 0000000..c9293ce --- /dev/null +++ b/configs/col_config.yaml @@ -0,0 +1,610 @@ +raw_cols: + - transcript + - gene + - consequence + - protein_hgvs + - cdna_hgvs + - chrom + - pos + - ref_base + - alt_base + - coding + - aloft.tolerant + - aloft.recessive + - aloft.dominant + - aloft.pred + - aloft.conf + - cadd.phred + - cgd.inheritance + - chasmplus.score + - chasmplus.pval + - civic.molecular_profile_score + - cosmic.variant_count + - cosmic_gene.occurrences + - cscape.score + - cgc.class + - cgc.inheritance + - cancer_genome_interpreter.resistant + - cancer_genome_interpreter.responsive + - cancer_genome_interpreter.other + - ccre_screen._group + - ccre_screen.bound + - clingen.disease + - clingen.classification + - clinpred.score + - clinvar.sig + - clinvar.id + - clinvar.rev_stat + - clinvar.sig_conf + - dann.score + - dann_coding.dann_coding_score + - dgi.interaction + - dgi.score + - ensembl_regulatory_build.region + - ess_gene.indispensability_score + - exac_gene.exac_pli + - exac_gene.exac_pnull + - exac_gene.exac_del_score + - exac_gene.exac_dup_score + - exac_gene.exac_cnv_score + - exac_gene.exac_cnv_flag + - fathmm.fathmm_score + - fathmm_xf_coding.fathmm_xf_coding_score + - funseq2.score + - gerp.gerp_rs + - ghis.ghis + - gtex.gtex_tissue + - gwas_catalog.pval + - genehancer.feature_name + - genehancer.score + - linsight.value + - lrt.lrt_score + - lrt.lrt_omega + - loftool.loftool_score + - mavedb.score + - metalr.score + - metasvm.score + - mutpred1.mutpred_general_score + - mutpred_indel.score + - mutation_assessor.score + - mutationtaster.score + - mutationtaster.prediction + - mutationtaster.model + - ncbigene.entrez + - ndex_chd.numhit + - ndex.numhit + - ndex_signor.numhit + - omim.omim_id + - prec.prec + - prec.stat + - provean.score + - pangalodb.sensitivity + - pangalodb.specificity + - phdsnpg.score + - phastcons.phastcons100_vert + - phastcons.phastcons30_mamm + - phastcons.phastcons17way_primate + - phylop.phylop100_vert + - phylop.phylop30_mamm + - phylop.phylop17_primate + - polyphen2.hdiv_rank + - polyphen2.hvar_rank + - revel.score + - rvis.rvis_evs + - repeat.repeatclass + - sift.score + - sift.med + - sift.confidence + - sift.seqs + - segway.mean_score + - siphy.logodds_rank + - spliceai.ds_ag + - spliceai.ds_al + - spliceai.ds_dg + - spliceai.ds_dl + - spliceai.dp_ag + - spliceai.dp_al + - spliceai.dp_dg + - spliceai.dp_dl + - uniprot.acc + - varity_r.varity_r_loo + - varity_r.varity_er_loo + - vest.score + - dbsnp.rsid + - dbscsnv.ada_score + - dbscsnv.rf_score + - gnomad.af + - gnomad_gene.oe_lof + - gnomad_gene.oe_mis + - gnomad_gene.oe_syn + - gnomad_gene.lof_z + - gnomad_gene.mis_z + - gnomad_gene.syn_z + - gnomad_gene.pLI + - gnomad_gene.pRec + - gnomad_gene.pNull + - gnomad3.af + - phi.phi + +train_cols: + - transcript + - gene + - consequence + - protein_hgvs + - cdna_hgvs + - chrom + - pos + - ref_base + - alt_base + - clingen.disease + - clingen.classification + - ncbigene.entrez + - omim.omim_id + - uniprot.acc + - dbsnp.rsid + +id_cols: + - transcript + - gene + - consequence + - protein_hgvs + - cdna_hgvs + - chrom + - pos + - ref_base + - alt_base + - clingen.disease + - clingen.classification + - clinvar.id + - clinvar.sig + - clinvar.rev_stat + - clinvar.sig_conf + - ncbigene.entrez + - omim.omim_id + - uniprot.acc + - dbsnp.rsid + +train_ClinicalSignificance: + - Uncertain_significance + - Pathogenic + - Likely_pathogenic + - Benign + - Likely_benign + - Benign/Likely_benign + - Pathogenic/Likely_pathogenic + - Conflicting_interpretations_of_pathogenicity + +BenchmarkSignificance: + - Pathogenic + - Likely_pathogenic + - Likely_benign + - Benign + - Benign/Likely_benign + - Pathogenic/Likely_pathogenic + - Pathogenic,_other + - Pathogenic/Likely_pathogenic,_other + - Pathogenic,_drug_response + +CLNREVSTAT: #https://www.ncbi.nlm.nih.gov/clinvar/docs/review_status/ + - practice_guideline + - reviewed_by_expert_panel + - criteria_provided,_multiple_submitters,_no_conflicts + +dummies_sep: + gtex.gtex_tissue: '|' + dgi.interaction: ',' + cgd.inheritance: '/' + so: ',' + repeat.repeatclass: ',' + genehancer.feature_name: '/' + cgc.inheritance: '/' + cgc.class: ', ' + +median_scores: + aloft.tolerant: 0.03175 + aloft.recessive: 0.5456 + aloft.dominant: 0.38385 + cadd.phred: 9.129 + chasmplus.score: 0.073 + chasmplus.pval: 0.277 + civic.molecular_profile_score: 7.5 + cosmic.variant_count: 1.0 + cosmic_gene.occurrences: 1725.0 + cscape.score: 0.436793 + cancer_genome_interpreter.resistant: 0.0 + cancer_genome_interpreter.responsive: 2.0 + cancer_genome_interpreter.other: 0.0 + clinpred.score: 0.109 + dann.score: 0.6540942459117577 + dann_coding.dann_coding_score: 0.994319113925808 + dgi.score: 3.55 + ess_gene.indispensability_score: 0.863945523287189 + exac_gene.exac_pli: 0.0614137585139882 + exac_gene.exac_pnull: 1.01047322872054e-05 + exac_gene.exac_del_score: 0.2455437788049 + exac_gene.exac_dup_score: 0.194157677715669 + exac_gene.exac_cnv_score: 0.034861123316654 + fathmm.fathmm_score: -1.59 + fathmm_xf_coding.fathmm_xf_coding_score: 0.346275 + funseq2.score: 0.498146534129084 + gerp.gerp_rs: 4.66 + ghis.ghis: 0.561316286 + gwas_catalog.pval: 2e-09 + genehancer.score: 1.09 + linsight.value: 0.142985 + lrt.lrt_score: 4.2e-05 + lrt.lrt_omega: 0.099477 + loftool.loftool_score: 0.101 + mavedb.score: 0.8677136380949829 + metalr.score: 0.4031 + metasvm.score: -0.3355 + mutpred1.mutpred_general_score: 0.7979999999999999 + mutpred_indel.score: 0.581 + mutation_assessor.score: 2.0 + mutationtaster.score: 1.0 + ndex_chd.numhit: 2.0 + ndex.numhit: 4.0 + ndex_signor.numhit: 1.0 + prec.prec: 0.36264 + provean.score: -2.3 + pangalodb.sensitivity: 0.0 + pangalodb.specificity: 0.0112782 + phdsnpg.score: 0.93 + phastcons.phastcons100_vert: 1.0 + phastcons.phastcons30_mamm: 0.987 + phastcons.phastcons17way_primate: 0.961 + phylop.phylop100_vert: 4.239 + phylop.phylop30_mamm: 1.026 + phylop.phylop17_primate: 0.599 + polyphen2.hdiv_rank: 0.55554 + polyphen2.hvar_rank: 0.53365 + revel.score: 0.38 + rvis.rvis_evs: -0.41 + sift.score: 1.0 + sift.med: 2.74 + sift.seqs: 46.0 + segway.mean_score: 0.0858073982609 + siphy.logodds_rank: 0.62632 + spliceai.ds_ag: 0.0004 + spliceai.ds_al: 0.0 + spliceai.ds_dg: 0.0004 + spliceai.ds_dl: 0.0 + spliceai.dp_ag: -1.0 + spliceai.dp_al: 0.0 + spliceai.dp_dg: 0.0 + spliceai.dp_dl: 0.0 + varity_r.varity_r_loo: 0.3558284342288971 + varity_r.varity_er_loo: 0.29223165 + vest.score: 0.754 + dbscsnv.ada_score: 0.5035390098589105 + dbscsnv.rf_score: 0.516 + gnomad.af: 0.000116777782496 + gnomad_gene.oe_lof: 0.34946 + gnomad_gene.oe_mis: 0.88424 + gnomad_gene.oe_syn: 1.0119 + gnomad_gene.lof_z: 3.7638 + gnomad_gene.mis_z: 0.92308 + gnomad_gene.syn_z: -0.1187 + gnomad_gene.pLI: 7.8292e-05 + gnomad_gene.pRec: 0.30551 + gnomad_gene.pNull: 1.1292e-05 + gnomad3.af: 0.000167535 + phi.phi: 0.48298 + +consequence_cols: + - 2kb_downstream_variant + - 2kb_upstream_variant + - 3_prime_UTR_variant + - 5_prime_UTR_variant + - NMD_transcript_variant + - NSD_transcript + - complex_substitution + - exon_loss_variant + - frameshift_elongation + - frameshift_truncation + - inframe_deletion + - inframe_insertion + - intron_variant + - lnc_RNA + - miRNA + - misc_RNA + - missense_variant + - polymorphic_pseudogene + - processed_transcript + - rRNA + - ribozyme + - scaRNA + - snRNA + - snoRNA + - splice_site_variant + - start_lost + - start_retained_variant + - stop_gained + - stop_lost + - stop_retained_variant + - synonymous_variant + - transcript_ablation + +filtered_cols: + - transcript + - gene + - consequence + - protein_hgvs + - cdna_hgvs + - chrom + - pos + - ref_base + - alt_base + - clingen.disease + - clingen.classification + - ncbigene.entrez + - omim.omim_id + - uniprot.acc + - dbsnp.rsid + - aloft.tolerant + - aloft.recessive + - aloft.dominant + - cadd.phred + - chasmplus.score + - chasmplus.pval + - civic.molecular_profile_score + - cosmic.variant_count + - cosmic_gene.occurrences + - cscape.score + - cancer_genome_interpreter.resistant + - cancer_genome_interpreter.responsive + - clinpred.score + - dann.score + - dann_coding.dann_coding_score + - dgi.score + - ess_gene.indispensability_score + - exac_gene.exac_pli + - exac_gene.exac_pnull + - exac_gene.exac_del_score + - exac_gene.exac_dup_score + - exac_gene.exac_cnv_score + - fathmm.fathmm_score + - fathmm_xf_coding.fathmm_xf_coding_score + - funseq2.score + - gerp.gerp_rs + - ghis.ghis + - gwas_catalog.pval + - genehancer.score + - linsight.value + - lrt.lrt_score + - lrt.lrt_omega + - loftool.loftool_score + - mavedb.score + - metalr.score + - metasvm.score + - mutpred1.mutpred_general_score + - mutpred_indel.score + - mutation_assessor.score + - mutationtaster.score + - ndex_chd.numhit + - ndex.numhit + - ndex_signor.numhit + - prec.prec + - provean.score + - pangalodb.sensitivity + - pangalodb.specificity + - phdsnpg.score + - phastcons.phastcons100_vert + - phastcons.phastcons30_mamm + - phastcons.phastcons17way_primate + - phylop.phylop100_vert + - phylop.phylop30_mamm + - phylop.phylop17_primate + - polyphen2.hdiv_rank + - polyphen2.hvar_rank + - revel.score + - rvis.rvis_evs + - sift.score + - sift.med + - sift.seqs + - segway.mean_score + - siphy.logodds_rank + - spliceai.ds_ag + - spliceai.ds_al + - spliceai.ds_dg + - spliceai.ds_dl + - spliceai.dp_ag + - spliceai.dp_al + - spliceai.dp_dg + - spliceai.dp_dl + - varity_r.varity_r_loo + - varity_r.varity_er_loo + - vest.score + - dbscsnv.ada_score + - dbscsnv.rf_score + - gnomad.af + - gnomad_gene.oe_lof + - gnomad_gene.oe_mis + - gnomad_gene.oe_syn + - gnomad_gene.lof_z + - gnomad_gene.mis_z + - gnomad_gene.syn_z + - gnomad_gene.pLI + - gnomad_gene.pRec + - gnomad_gene.pNull + - gnomad3.af + - phi.phi + - Adipose_Subcutaneous + - Adipose_Visceral_Omentum + - Adrenal_Gland + - Artery_Aorta + - Artery_Coronary + - Artery_Tibial + - Brain_Amygdala + - Brain_Anterior_cingulate_cortex_BA24 + - Brain_Caudate_basal_ganglia + - Brain_Cerebellar_Hemisphere + - Brain_Cerebellum + - Brain_Cortex + - Brain_Frontal_Cortex_BA9 + - Brain_Hippocampus + - Brain_Hypothalamus + - Brain_Nucleus_accumbens_basal_ganglia + - Brain_Putamen_basal_ganglia + - Brain_Spinal_cord_cervical_c-1 + - Brain_Substantia_nigra + - Breast_Mammary_Tissue + - Cells_EBV-transformed_lymphocytes + - Cells_Transformed_fibroblasts + - Colon_Sigmoid + - Colon_Transverse + - Esophagus_Gastroesophageal_Junction + - Esophagus_Mucosa + - Esophagus_Muscularis + - Heart_Atrial_Appendage + - Heart_Left_Ventricle + - Liver + - Lung + - Minor_Salivary_Gland + - Muscle_Skeletal + - Nerve_Tibial + - Ovary + - Pancreas + - Pituitary + - Prostate + - Skin_Not_Sun_Exposed_Suprapubic + - Skin_Sun_Exposed_Lower_leg + - Small_Intestine_Terminal_Ileum + - Spleen + - Stomach + - Testis + - Thyroid + - Uterus + - Vagina + - Whole_Blood + - activator + - adduct + - agonist + - allosteric modulator + - antagonist + - antibody + - binder + - blocker + - chaperone + - cofactor + - inducer + - inhibitor + - ligand + - modulator + - negative modulator + - positive modulator + - potentiator + - product of + - stimulator + - substrate + - vaccine + - AD + - AR + - AR + - BG + - Digenic + - XL + - 2kb_downstream_variant + - 2kb_upstream_variant + - 3_prime_UTR_variant + - 5_prime_UTR_variant + - NMD_transcript_variant + - NSD_transcript + - complex_substitution + - exon_loss_variant + - frameshift_elongation + - frameshift_truncation + - inframe_deletion + - inframe_insertion + - intron_variant + - lnc_RNA + - miRNA + - misc_RNA + - missense_variant + - polymorphic_pseudogene + - processed_transcript + - rRNA + - ribozyme + - scaRNA + - snRNA + - snoRNA + - splice_site_variant + - start_lost + - start_retained_variant + - stop_gained + - stop_lost + - stop_retained_variant + - synonymous_variant + - transcript_ablation + - LINE + - LTR + - Low_complexity + - SINE + - Satellite + - Simple_repeat + - Enhancer + - Promoter + - germline + - somatic + - Oncogene + - TSG + - fusion + - coding_Yes + - aloft.pred_Dominant + - aloft.pred_Recessive + - aloft.pred_Tolerant + - aloft.conf_High + - aloft.conf_Low + - ccre_screen._group_CTCF-only + - ccre_screen._group_DNase-H3K4me3 + - ccre_screen._group_PLS + - ccre_screen._group_dELS + - ccre_screen._group_pELS + - ccre_screen.bound_Yes + - ensembl_regulatory_build.region_CTCF_binding_site + - ensembl_regulatory_build.region_TF_binding_site + - ensembl_regulatory_build.region_enhancer + - ensembl_regulatory_build.region_open_chromatin_region + - ensembl_regulatory_build.region_promoter + - ensembl_regulatory_build.region_promoter_flanking_region + - exac_gene.exac_cnv_flag_N + - exac_gene.exac_cnv_flag_Y + - mutationtaster.prediction_Automatic Disease Causing + - mutationtaster.prediction_Automatic Polymorphism + - mutationtaster.prediction_Damaging + - mutationtaster.prediction_Polymorphism + - mutationtaster.model_complex_aae + - mutationtaster.model_simple_aae + - mutationtaster.model_without_aae + - prec.stat_lof-tolerant + - prec.stat_recessive + - sift.confidence_High + - sift.confidence_Low + +Benchmark_cols: + cadd.phred: 'CADD' + cscape.score: "CSCAPE" + clinpred.score: 'ClinPred' + dann.score: 'DANN' + dann_coding.dann_coding_score: 'DANN_Coding' + dgi.score: 'DGI_score' + fathmm_xf_coding.fathmm_xf_coding_score: 'FATHMM_coding' + funseq2.score: 'Funseq2' + linsight.value: 'Linsight' + lrt.lrt_score: 'LRT' + loftool.loftool_score: 'Loftool' + metasvm.score: 'MetaSVM' + metalr.score: 'MetaLR' + mutpred1.mutpred_general_score: 'MutPred' + mutpred_indel.score: 'MutPred_indel' + mutation_assessor.score: 'Mutation_Assessor' + mutationtaster.score: 'Mutation_Taster' + provean.score: 'Provean' + phdsnpg.score: 'PHDSNPG' + revel.score: 'Revel' + sift.score: 'SIFT' + vest.score: 'VEST' + dbscsnv.ada_score: 'dbSCSNV_ADA' + dbscsnv.rf_score: 'dbSCSNV_RF' + varity_r.varity_r_loo: 'varity_r' + varity_r.varity_er_loo: 'varity_er' diff --git a/configs/envs/ditto-nf.yaml b/configs/envs/ditto-nf.yaml new file mode 100644 index 0000000..9241ba7 --- /dev/null +++ b/configs/envs/ditto-nf.yaml @@ -0,0 +1,10 @@ +channels: + - conda-forge +dependencies: + - python=3.10.11 + - pandas=2.0.1 + - numpy=1.23.5 + - pyaml=23.7.0 + - pip=23.2.1 + - pip: + - tensorflow==2.11 diff --git a/configs/envs/environment.yaml b/configs/envs/environment.yaml index 8bdfe13..56fa943 100644 --- a/configs/envs/environment.yaml +++ b/configs/envs/environment.yaml @@ -5,22 +5,18 @@ channels: - anaconda dependencies: - - python=3.8.5 - - pandas=1.2.1 - - numpy=1.18.5 - - optuna=2.5.0 - - scikit-learn=0.24.1 - - imbalanced-learn=0.7.0 - - scipy=1.4.1 - - shap=0.37.0 + - python=3.10.11 + - pandas=2.0.1 + - numpy=1.23.5 + - optuna=3.1.1 + - scikit-learn=1.2.2 + - scipy=1.10.1 + - shap=0.41.0 - pip - - gpy=1.9.9 - - scikit-optimize=0.8.1 - - hyperopt=0.2.5 - - tune-sklearn=0.2.1 - - seaborn=0.11.2 + - gpy=1.10.0 + - scikit-optimize=0.9.0 + - hyperopt=0.2.7 + - seaborn=0.12.2 + - ipykernel=6.23.1 - pip: - - ray==1.6.0 - - ray[tune] - - tensorflow-gpu==2.3 - - lz4==3.1.3 + - tensorflow==2.11 diff --git a/configs/envs/open-cravat.yaml b/configs/envs/open-cravat.yaml new file mode 100644 index 0000000..b7f3d32 --- /dev/null +++ b/configs/envs/open-cravat.yaml @@ -0,0 +1,7 @@ +dependencies: + - pip + - pip: + - pytabix==0.1 + - open-cravat==2.4.1 + #- joblib==1.3.2 + #- git+https://github.com/tkmamidi/open-cravat.git diff --git a/.gitmodules b/configs/mypackage/mypackage.py similarity index 100% rename from .gitmodules rename to configs/mypackage/mypackage.py diff --git a/configs/mypackage/mypackage.yml b/configs/mypackage/mypackage.yml new file mode 100644 index 0000000..c8ce08f --- /dev/null +++ b/configs/mypackage/mypackage.yml @@ -0,0 +1,153 @@ +title: DITTO Package +description: Annotating variants with aloft cadd cadd_exome cancer_genome_interpreter ccre_screen chasmplus civic clingen clinpred clinvar cosmic cosmic_gene cscape dann dann_coding dbscsnv dbsnp dgi ensembl_regulatory_build ess_gene exac_gene fathmm fathmm_xf_coding funseq2 genehancer gerp ghis gnomad gnomad3 gnomad_gene gtex gwas_catalog linsight loftool lrt mavedb metalr metasvm mutation_assessor mutationtaster mutpred1 mutpred_indel ncbigene ndex ndex_chd ndex_signor omim pangalodb phastcons phdsnpg phi phylop polyphen2 prec provean repeat revel rvis segway sift siphy spliceai uniprot vest cgc cgd varity_r +type: package +version: 0.0.1 +# Modules for this package +requires: +- aloft +- cadd +- cadd_exome +- cancer_genome_interpreter +- ccre_screen +- chasmplus +- civic +- clingen +- clinpred +- clinvar +- cosmic +- cosmic_gene +- cscape +- dann +- dann_coding +- dbscsnv +- dbsnp +- dgi +- ensembl_regulatory_build +- ess_gene +- exac_gene +- fathmm +- fathmm_xf_coding +- funseq2 +- genehancer +- gerp +- ghis +- gnomad +- gnomad3 +- gnomad_gene +- gtex +- gwas_catalog +- linsight +- loftool +- lrt +- mavedb +- metalr +- metasvm +- mutation_assessor +- mutationtaster +- mutpred1 +- mutpred_indel +- ncbigene +- ndex +- ndex_chd +- ndex_signor +- omim +- pangalodb +- phastcons +- phdsnpg +- phi +- phylop +- polyphen2 +- prec +- provean +- repeat +- revel +- rvis +- segway +- sift +- siphy +- spliceai +- uniprot +- vest +- cgc +- cgd +- varity_r +# oc run and oc report settings +run: + # Annotators + annotators: + - aloft + - cadd + - cadd_exome + - cancer_genome_interpreter + - ccre_screen + - chasmplus + - civic + - clingen + - clinpred + - clinvar + - cosmic + - cosmic_gene + - cscape + - dann + - dann_coding + - dbscsnv + - dbsnp + - dgi + - ensembl_regulatory_build + - ess_gene + - exac_gene + - fathmm + - fathmm_xf_coding + - funseq2 + - genehancer + - gerp + - ghis + - gnomad + - gnomad3 + - gnomad_gene + - gtex + - gwas_catalog + - linsight + - loftool + - lrt + - mavedb + - metalr + - metasvm + - mutation_assessor + - mutationtaster + - mutpred1 + - mutpred_indel + - ncbigene + - ndex + - ndex_chd + - ndex_signor + - omim + - pangalodb + - phastcons + - phdsnpg + - phi + - phylop + - polyphen2 + - prec + - provean + - repeat + - revel + - rvis + - segway + - sift + - siphy + - spliceai + - uniprot + - vest + - cgc + - cgd + - varity_r + # Reports + reports: + - csv + # Filters + #filtersql: + #includesample: + #- sample1 + #excludesample: + #- sample2 diff --git a/configs/opencravat_test_config.json b/configs/opencravat_test_config.json new file mode 100644 index 0000000..237c5ba --- /dev/null +++ b/configs/opencravat_test_config.json @@ -0,0 +1,701 @@ +[ + { + "col_id": "chrom", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "pos", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "ref_base", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "alt_base", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "coding", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "aloft.all", + "parse_type": { + "list-o-dicts": { + 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+ { + "col_id": "gnomad_gene.all", + "parse_type": { + "list-o-dicts": { + "dict_index": { + "1": "gnomad_gene.oe_lof", + "2": "gnomad_gene.oe_mis", + "3": "gnomad_gene.oe_syn", + "4": "gnomad_gene.lof_z", + "5": "gnomad_gene.mis_z", + "6": "gnomad_gene.syn_z", + "7": "gnomad_gene.pLI", + "8": "gnomad_gene.pRec", + "9": "gnomad_gene.pNull" + }, + "trx_mapping_col_index": 0 + } + } + }, + { + "col_id": "gnomad3.af", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "phi.phi", + "parse_type": { + "none": "none" + } + } +] diff --git a/configs/opencravat_train_config.json b/configs/opencravat_train_config.json new file mode 100644 index 0000000..5c12657 --- /dev/null +++ b/configs/opencravat_train_config.json @@ -0,0 +1,725 @@ +[ + { + "col_id": "chrom", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "pos", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "ref_base", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "alt_base", + "parse_type": { 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"chasmplus.pval" + }, + "trx_mapping_col_index": 0 + } + } + }, + { + "col_id": "civic.molecular_profile_score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "cosmic.transcript", + "parse_type": { + "list": { + "trx_index_col": "cosmic.transcript", + "column_list": [ + "cosmic.variant_count" + ], + "separator": ";" + } + } + }, + { + "col_id": "cosmic_gene.occurrences", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "cscape.score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "cgc.class", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "cgc.inheritance", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "cancer_genome_interpreter.resistant", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "cancer_genome_interpreter.responsive", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "cancer_genome_interpreter.other", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "ccre_screen._group", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "ccre_screen.bound", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "clingen.disease", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "clingen.classification", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "clinpred.score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "clinvar.sig", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "clinvar.id", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "clinvar.rev_stat", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "clinvar.sig_conf", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "dann.score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "dann_coding.dann_coding_score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "dgi.interaction", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "dgi.score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "ensembl_regulatory_build.region", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "ess_gene.indispensability_score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "exac_gene.exac_pli", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "exac_gene.exac_pnull", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "exac_gene.exac_del_score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "exac_gene.exac_dup_score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "exac_gene.exac_cnv_score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "exac_gene.exac_cnv_flag", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "fathmm.ens_tid", + "parse_type": { + "list": { + "trx_index_col": "fathmm.ens_tid", + "column_list": [ + "fathmm.fathmm_score" + ], + "separator": ";" + } + } + }, + { + "col_id": "fathmm_xf_coding.fathmm_xf_coding_score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "funseq2.score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "gerp.gerp_rs", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "ghis.ghis", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "gtex.gtex_tissue", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "gwas_catalog.pval", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "genehancer.feature_name", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "genehancer.score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "linsight.value", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "lrt.lrt_score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "lrt.lrt_omega", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "loftool.loftool_score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "mavedb.score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "metalr.score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "metasvm.score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "mutpred1.mutpred_top5_mechanisms", + "parse_type": { + "list-o-dicts": { + "dict_index": { + "4": "mutpred1.mutpred_general_score" + }, + "trx_mapping_col_index": 0 + } + } + }, + { + "col_id": "mutpred_indel.score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "mutation_assessor.all", + "parse_type": { + "list-o-dicts": { + "dict_index": { + "2": "mutation_assessor.score" + }, + "trx_mapping_col_index": 0 + } + } + }, + { + "col_id": "mutationtaster.all", + "parse_type": { + "list-o-dicts": { + "dict_index": { + "1": "mutationtaster.score", + "3": "mutationtaster.prediction", + "4": "mutationtaster.model" + }, + "trx_mapping_col_index": 0 + } + } + }, + { + "col_id": "ncbigene.entrez", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "ndex_chd.numhit", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "ndex.numhit", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "ndex_signor.numhit", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "omim.omim_id", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "prec.prec", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "prec.stat", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "provean.all", + "parse_type": { + "list-o-dicts": { + "dict_index": { + "2": "provean.score" + }, + "trx_mapping_col_index": 0 + } + } + }, + { + "col_id": "pangalodb.sensitivity", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "pangalodb.specificity", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "phdsnpg.score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "phastcons.phastcons100_vert", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "phastcons.phastcons30_mamm", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "phastcons.phastcons17way_primate", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "phylop.phylop100_vert", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "phylop.phylop30_mamm", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "phylop.phylop17_primate", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "polyphen2.all", + "parse_type": { + "list-o-dicts": { + "dict_index": { + "3": "polyphen2.hdiv_rank", + "6": "polyphen2.hvar_rank" + }, + "trx_mapping_col_index": 0 + } + } + }, + { + "col_id": "revel.all", + "parse_type": { + "list-o-dicts": { + "dict_index": { + "1": "revel.score" + }, + "trx_mapping_col_index": 0 + } + } + }, + { + "col_id": "rvis.rvis_evs", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "repeat.repeatclass", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "sift.all", + "parse_type": { + "list-o-dicts": { + "dict_index": { + "1": "sift.score", + "4": "sift.med", + "5": "sift.confidence", + "6": "sift.seqs" + }, + "trx_mapping_col_index": 0 + } + } + }, + { + "col_id": "segway.mean_score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "siphy.logodds_rank", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "spliceai.ds_ag", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "spliceai.ds_al", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "spliceai.ds_dg", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "spliceai.ds_dl", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "spliceai.dp_ag", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "spliceai.dp_al", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "spliceai.dp_dg", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "spliceai.dp_dl", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "uniprot.acc", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "varity_r.varity_r_loo", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "varity_r.varity_er_loo", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "vest.all", + "parse_type": { + "list-o-dicts": { + "dict_index": { + "1": "vest.score" + }, + "trx_mapping_col_index": 0 + } + } + }, + { + "col_id": "dbsnp.rsid", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "dbscsnv.ada_score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "dbscsnv.rf_score", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "gnomad.af", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "gnomad_gene.all", + "parse_type": { + "list-o-dicts": { + "dict_index": { + "1": "gnomad_gene.oe_lof", + "2": "gnomad_gene.oe_mis", + "3": "gnomad_gene.oe_syn", + "4": "gnomad_gene.lof_z", + "5": "gnomad_gene.mis_z", + "6": "gnomad_gene.syn_z", + "7": "gnomad_gene.pLI", + "8": "gnomad_gene.pRec", + "9": "gnomad_gene.pNull" + }, + "trx_mapping_col_index": 0 + } + } + }, + { + "col_id": "gnomad3.af", + "parse_type": { + "none": "none" + } + }, + { + "col_id": "phi.phi", + "parse_type": { + "none": "none" + } + } +] diff --git a/configs/var_class.yaml b/configs/var_class.yaml new file mode 100644 index 0000000..94f0e7c --- /dev/null +++ b/configs/var_class.yaml @@ -0,0 +1,105 @@ + NMD_transcript_variant,synonymous_variant: NMD + synonymous_variant: synonymous + 2kb_downstream_variant,NMD_transcript_variant: NMD + frameshift_elongation: frameshift elongation + processed_transcript: other RNA + stop_gained: stop gained + 2kb_downstream_variant,miRNA: other RNA + 2kb_downstream_variant,lnc_RNA: other RNA + intron_variant: intron + intron_variant,processed_transcript: intron + missense_variant: missense + frameshift_truncation: frameshift truncation + NMD_transcript_variant,3_prime_UTR_variant: NMD + 3_prime_UTR_variant: 3 prime UTR + 5_prime_UTR_variant: 5 prime UTR + 2kb_downstream_variant,processed_transcript: other RNA + intron_variant,splice_site_variant: splice site + intron_variant,lnc_RNA: other RNA + 2kb_upstream_variant,lnc_RNA: other RNA + 2kb_upstream_variant: intergenic + 2kb_upstream_variant,processed_transcript: other RNA + intron_variant,NMD_transcript_variant: NMD + lnc_RNA: other RNA + NMD_transcript_variant,stop_gained: NMD + missense_variant,NMD_transcript_variant: NMD + 2kb_downstream_variant,NSD_transcript: NMD + 2kb_downstream_variant: intergenic + complex_substitution,missense_variant: missense + complex_substitution,frameshift_truncation: frameshift truncation + complex_substitution,frameshift_truncation,NMD_transcript_variant: NMD + intron_variant,NMD_transcript_variant,splice_site_variant: NMD + 2kb_upstream_variant,NMD_transcript_variant: NMD + 2kb_upstream_variant,misc_RNA: other RNA + complex_substitution,frameshift_elongation,intron_variant: frameshift elongation + 2kb_downstream_variant,misc_RNA: other RNA + missense_variant,start_lost: start lost + NSD_transcript: NMD + NMD_transcript_variant,5_prime_UTR_variant: NMD + splice_site_variant: splice site + stop_retained_variant: stop retained + 2kb_upstream_variant,miRNA: other RNA + inframe_insertion: inframe insertion + frameshift_truncation,NMD_transcript_variant: NMD + frameshift_elongation,NMD_transcript_variant: NMD + frameshift_truncation,stop_gained: frameshift truncation + inframe_deletion: inframe deletion + 2kb_downstream_variant,snRNA: other RNA + 2kb_upstream_variant,NSD_transcript: NMD + inframe_deletion,NMD_transcript_variant: NMD + frameshift_truncation,NMD_transcript_variant,stop_gained: NMD + missense_variant,start_lost,NMD_transcript_variant: NMD + 2kb_upstream_variant,snRNA: other RNA + intron_variant,NSD_transcript: NMD + frameshift_elongation,stop_gained: frameshift elongation + NMD_transcript_variant,splice_site_variant: NMD + frameshift_truncation,stop_lost: frameshift truncation + 2kb_downstream_variant,snoRNA: other RNA + inframe_insertion,stop_gained: stop gained + ribozyme: other RNA + NSD_transcript,5_prime_UTR_variant: NMD + complex_substitution,frameshift_elongation: frameshift elongation + polymorphic_pseudogene,5_prime_UTR_variant: other RNA + polymorphic_pseudogene: other RNA + NMD_transcript_variant: NMD + frameshift_truncation,stop_lost,stop_retained_variant,3_prime_UTR_variant: frameshift truncation + 2kb_downstream_variant,stop_lost: stop lost + complex_substitution,inframe_deletion: inframe deletion + complex_substitution: complex substitution + NMD_transcript_variant,stop_lost: NMD + inframe_insertion,NMD_transcript_variant: NMD + 2kb_upstream_variant,ribozyme: other RNA + frameshift_elongation,NMD_transcript_variant,stop_gained: NMD + complex_substitution,stop_gained: stop gained + exon_loss_variant,frameshift_truncation: frameshift truncation + 2kb_downstream_variant,stop_lost,3_prime_UTR_variant: stop lost + inframe_deletion,stop_gained: stop gained + polymorphic_pseudogene,3_prime_UTR_variant: other RNA + complex_substitution,missense_variant,NMD_transcript_variant: NMD + snRNA: other RNA + 2kb_upstream_variant,snoRNA: other RNA + exon_loss_variant,frameshift_truncation,NMD_transcript_variant: NMD + miRNA: other RNA + complex_substitution,inframe_insertion,intron_variant: complex substitution + stop_lost: stop lost + complex_substitution,frameshift_elongation,NMD_transcript_variant: NMD + exon_loss_variant,inframe_deletion: exon loss variant + frameshift_truncation,start_lost: frameshift truncation + complex_substitution,inframe_insertion,stop_gained: stop gained + misc_RNA: other RNA + NMD_transcript_variant,stop_retained_variant: NMD + start_lost,5_prime_UTR_variant: start lost + complex_substitution,inframe_insertion,intron_variant,missense_variant: complex substitution + complex_substitution,frameshift_elongation,intron_variant,NMD_transcript_variant: NMD + complex_substitution,inframe_insertion,intron_variant,NMD_transcript_variant: NMD + intron_variant,polymorphic_pseudogene: other RNA + 2kb_downstream_variant,intron_variant,stop_lost,3_prime_UTR_variant: stop lost + complex_substitution,inframe_insertion: complex substitution + start_retained_variant: start retained + 2kb_upstream_variant,rRNA: other RNA + complex_substitution,inframe_deletion,missense_variant: complex substitution + snoRNA: other RNA + inframe_insertion,NMD_transcript_variant,stop_gained: NMD + complex_substitution,synonymous_variant: complex substitution + NMD_transcript_variant,stop_lost,3_prime_UTR_variant: NMD + frameshift_elongation,stop_lost: frameshift elongation diff --git a/data/background.pkl b/data/background.pkl new file mode 100644 index 0000000..9767d31 Binary files /dev/null and b/data/background.pkl differ diff --git a/data/test_class_data_20.csv.gz b/data/test_class_data_20.csv.gz new file mode 100644 index 0000000..84c072e Binary files /dev/null and b/data/test_class_data_20.csv.gz differ diff --git a/data/train_class_data_80.csv.gz b/data/train_class_data_80.csv.gz new file mode 100644 index 0000000..2dcb41b Binary files /dev/null and b/data/train_class_data_80.csv.gz differ diff --git a/data/tuned_parameters.csv b/data/tuned_parameters.csv new file mode 100644 index 0000000..297643e --- /dev/null +++ b/data/tuned_parameters.csv @@ -0,0 +1 @@ +NeuralNetwork: {'activation': 'elu', 'activation_l0': 'elu', 'batch_size': 267, 'dropout_l0': 0.8082860537327723, 'kernel_initializer': 'glorot_normal', 'kernel_initializer_l0': 'he_normal', 'n_layers': 1, 'n_units_l0': 161, 'optimizer': 'Adamax'} diff --git a/data/webapp.png b/data/webapp.png new file mode 100644 index 0000000..bb1f393 Binary files /dev/null and b/data/webapp.png differ diff --git a/docs/Notes.txt b/docs/Notes.txt deleted file mode 100644 index ab32976..0000000 --- a/docs/Notes.txt +++ /dev/null @@ -1,678 +0,0 @@ -Getting started -=============== - -############# 6/5/20 ################################### -srun --ntasks=1 --cpus-per-task=1 --mem-per-cpu=4G --partition=express --job-name=classify --pty /bin/bash -srun --ntasks=1 --cpus-per-task=2 --mem=30G --gres=gpu:4 --partition=pascalnodes --job-name=image_GPU --pty /bin/bash -Using conda envi to install and work on tools - module load Anaconda3/2020.02 - source activate testing #for GPU Neural Networks - conda create --name envi #first time use only - source activate envi - -ClinVar data (GRCh37) is downloaded to /data/external/ directory using the following command on 6/4/20. - wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/clinvar.vcf.gz - -To unzip it, use the below command - gunzip clinvar.vcf.gz - -Output CSV file is saved as clinvar.csv under "data/processed" - -############# 6/6/20 ######################################### -Using Annovar to annotate clinvar variants - -Download Annovar - wget http://www.openbioinformatics.org/annovar/download/0wgxR2rIVP/annovar.latest.tar.gz -Unzip Annovar - tar xvfz annovar.latest.tar.gz - -Download all annotation databases using the following commands - perl annotate_variation.pl -buildver hg19 -downdb -webfrom annovar refGene humandb/ - perl annotate_variation.pl -buildver hg19 -downdb cytoBand humandb/ - perl annotate_variation.pl -buildver hg19 -downdb -webfrom annovar exac03 humandb/ - perl annotate_variation.pl -buildver hg19 -downdb -webfrom annovar avsnp147 humandb/ - perl annotate_variation.pl -buildver hg19 -downdb -webfrom annovar dbnsfp30a humandb/ - -SLURM interactive session before annotating variants - alias SRUN_EXPRESS="srun --ntasks=1 --cpus-per-task=4 --mem-per-cpu=4096 --partition=express --pty /bin/bash" - SRUN_EXPRESS - -Annotate variants- - Tab delimited input - - perl table_annovar.pl ../../processed/clinvar.avinput humandb/ -buildver hg19 -out ../myanno -remove -protocol refGene,cytoBand,exac03,avsnp147,dbnsfp30a -operation gx,r,f,f,f -nastring . -csvout -polish -xref example/gene_xref.txt - VCF file input - - perl table_annovar.pl ../../external/clinvar.vcf humandb/ -buildver hg19 -out ../myanno -remove -protocol refGene,cytoBand,exac03,avsnp147,dbnsfp30a -operation gx,r,f,f,f -nastring . -vcfinput -polish -xref example/gene_xref.txt - - -Install scikit-allel to extract info from vcf as csv. - conda install -c conda-forge scikit-allel -Run "parse_clinvar.py" code to convert vcf annotations to txt format. - - -################## 6/8/20 ########################################### - -Ran Brandon's script for annotation - -Script is located in this path - /data/project/worthey_lab/projects/experimental_pipelines/annovar_vcf_annotation - -Commands to Run - module load Python/3.6.6-foss-2018b - python /data/project/worthey_lab/projects/experimental_pipelines/annovar_vcf_annotation/Annotate.py --req_AD --clean_homo_ref --clean_no_call /data/project/worthey_lab/projects/experimental_pipelines/tarun/Varsight/data/external/clinvar_20200602.vcf /data/scratch/tmamidi/ /data/project/worthey_lab/projects/experimental_pipelines/tarun/Varsight/data/interim/annov/ /data/project/worthey_lab/tools/annovar/annovar_hg19_db - - -################## 6/24/20 ########################################## - -Again, Downloaded the "updated" clinvar vcf file and ran Brandon's script for annotation. - --Commands to Run - module load Python/3.6.6-foss-2018b - python /data/project/worthey_lab/projects/experimental_pipelines/annovar_vcf_annotation/Annotate_Sample_Annovar.py /data/project/worthey_lab/projects/experimental_pipelines/tarun/Varsight/data/external/clinvar.vcf /data/scratch/tmamidi/ /data/project/worthey_lab/projects/experimental_pipelines/tarun/Varsight/data/interim/ /data/project/worthey_lab/tools/annovar/annovar_hg19_db - --Ran "parse_clinvar.py" code to convert vcf annotations to txt format. --Working on "Filter_variants.py" to filter all the variants and their annotations for ML models - -################### 7/8/20 ############################################ - -data/processed/class-count.csv has the summary statistics of all variants categorized according to ACMG guidelines. -Filtering out only the necessary columns - - Polyphen-2 HDIV vs HVAR - While PolyPhen-2 HDIV uses alleles encoding human proteins and their closely related mammalian homologs as TN observations, PolyPhen-2 HVAR applies common human nsSNVs as TN observations. - LRTori is a two-sided P-value of the likelihood ratio test of codon constraint. Each LRTori is associated with an estimated nonsynonymous-to-synonymous-rate ratio (ฯ‰) and an amino acid alignment of 31 species at the test codon. The score ranges from 0 to 1 and a larger score signifies that the codon is more constrained or a NS is more likely to be deleterious. - Eigen-PC-raw: Eigen PC score for genome-wide SNVs. A functional prediction score based on conservation, allele frequencies, deleteriousness prediction (for missense SNVs) and epigenomic signals (for synonymous and non-coding SNVs) using an unsupervised learning method (doi: 10.1038/ng.3477). - GenoCanyon_score: a functional prediction score based on conservation and biochemical annotations using unsupervised statistical learning. (doi:10.1038/srep10576) - GenoCanyon_rankscore: rank of the GenoCanyon_score among all GenoCanyon_scores in genome - -For details on annotations, please refer to this pdf document - https://softgenetics.com/PDF/GeneticistAssistant-Variant-Reference-Fields.pdf -It covers most of the annotations but not all. - - -### Variants per each ACMG category ######### ->>> df[['InterVar_automated','ID']].groupby('InterVar_automated').count() - ID -InterVar_automated Count -Benign 20859 -Likely_benign 187722 -Likely_pathogenic 23539 -Pathogenic 18587 -Uncertain_significance 244031 -Total 494738 - ->>> df[['CLNSIG','ID']].groupby('CLNSIG').count() - -CLNSIG -Affects 112 -Benign 29704 -Benign/Likely_benign 13909 -Conflicting_interpretations_of_pathogenicity 20654 -Likely_benign 80901 -Likely_pathogenic 25774 -Pathogenic 59416 -Pathogenic/Likely_pathogenic 4072 -Uncertain_significance 193444 -association 186 -drug_response 305 -not_provided 9409 -other 1720 -protective 36 -risk_factor 411 -Total 440053 - -################# 7/16/20 ##################################################################################### - -There are features annotated multiple times by different databases (using Annovar). Scikit-allel cannot account for multiple features and just omits all other instances except the last one. -To overcome this problem, I'm writing a parsing code for extracting info from vcf - "parse_vcf.py" -Looks like that's not the way to go. It didn't work. - -So, the three databases (gnomad211_exome,clinvar_20190305,mitimpact24) while running Annovar are creating duplicate annotations. So, I removed them and ran "Annovar.py" again and it worked. --Commands to Run - module load Python/3.6.6-foss-2018b - python /data/project/worthey_lab/projects/experimental_pipelines/annovar_vcf_annotation/Annovar.py /data/project/worthey_lab/projects/experimental_pipelines/tarun/Varsight/data/external/clinvar.vcf /data/scratch/tmamidi/ /data/project/worthey_lab/projects/experimental_pipelines/tarun/Varsight/data/interim/ /data/project/worthey_lab/tools/annovar/annovar_hg19_db - -Then run "parse_clinvar.py" to convert annotated vcf to tab-delimited format. -Finally, got "raw-features.csv" to further transform to categorical and use ML. - -CLNSIG ID -Affects 132 -Benign 92165 -Benign/Likely_benign 19060 -Conflicting_interpretations_of_pathogenicity 41019 -Likely_benign 152762 -Likely_pathogenic 34029 -Pathogenic 74857 -Pathogenic/Likely_pathogenic 6724 -Uncertain_significance 294609 -association 216 -association_not_found 2 -drug_response 1979 -not_provided 11110 -other 1660 -protective 39 -risk_factor 441 - -################# 7/21/20 ###################################################################### - -Extract rows with only 5 ACMG classified annotations. - - (648422, 141) - -Impute values to NAs - -################# 7/29/20 ####################################################################### -Ran the code multiclass.py with SVM classifier for first 10000 rows. Complete dataset takes way too long to output result. - -Confusion matrix: Benign. Likely_benign, VUS, Likely_pathogenic, Pathogenic -array([[ 874, 324, 0, 0, 88], - [ 76, 1570, 1, 3, 248], - [ 0, 7, 91, 96, 83], - [ 2, 9, 98, 317, 85], - [ 28, 449, 41, 65, 2945]]) - -################## 8/1/20 & 8/2/20 ############################################################### -Wrote code for keras using tensorflow (keras-seq) and 5 models from sklearn (models.py). -Using model.job for submitting slurm job. - -Keras results - keras.csv, losskeras.jpg and accuracy-keras.jpg - stored in data/processed directory - 5 layers with 141,70,45,23,11,5 neurons and batch size = 100, epoch=10 - looks like the highest is 80% accuracy with almost 60% loss. - -sklearn 5 models results - model-scores-.csv - - for first 1000 lines - repeats=3; split=15 - SVM 0.704 (0.049) - KNN 0.749 (0.045) - BAG 0.869 (0.040) - RF 0.874 (0.042) - ET 0.869 (0.044) - -for first 10000 lines - repeats=3; split=50 - SVM 0.748 (0.014) - KNN 0.709 (0.015) - BAG 0.806 (0.010) - RF 0.807 (0.013) - ET 0.807 (0.012) - -################### 8/21/20 ##################################################################### -Cleaned the src directory and made subfolders accordingly. -Under classification, developing sklearn pipeline - refer to models.py - -################### 8/27/20 ###################################################################### -Started working on JSON parsing - output from CodiCem and input for this tool. -Trying out multiprocessing for our models. Ref - https://www.youtube.com/watch?v=fKl2JW_qrso -Parameter tuning - - https://towardsdatascience.com/credit-risk-management-classification-models-hyperparameter-tuning-d3785edd8371 - -################### 8/31 & 9/1 ################################################################### -Using Ray, ran multiple classifiers for 5 ACMG classes. results are stored in "sklearn results.xlsx" in prossessed directory. - code used for this is models.py. - On a separate context, trying to run commands separately and debug syntax errors in multiclass.py script. - -################### 9/2/20 ###################################################################### -Start trying out hyperparameter tuning. - - Trying out Random forest and Decision tree. - -################### 9/9/20 ###################################################################### -Trying out ELI5 - https://eli5.readthedocs.io/en/latest/overview.html#installation - - package to calc weights -Better to use feature_importances_ function from classifiers - - https://machinelearningmastery.com/calculate-feature-importance-with-python/ - -################### 9/10/20 ###################################################################### -Looking more towards Neural Networks which can be easily customizable. -Hyperparameter tuning for NN - - https://www.youtube.com/watch?v=OSJOBH2-a9Y - - Also look in to Brandon's code - -################### 9/11/20 ###################################################################### -Plan for today - - - Work on creating neural Network - - use standard scalar for NN (mandatory). - - Parameter tuning as mentioned by youtube video above and parameters from Brandon's code - - backpropagation is being taken care by keras. So, no need to implement that separately. - - select different drop-outs with parameter tuning - - Use ELI5 package to calculate weights of features once the NN model is trained - - Use average_precision_score function from sklearn to calculate precision scores. -Also, refer to this for all types of parameters - https://github.com/keras-team/keras/issues/13586 -Something to look at for parameter setup - https://towardsdatascience.com/hyperparameter-optimization-with-keras-b82e6364ca53 -Breaking down parameters for neural network - https://machinelearningmastery.com/grid-search-hyperparameters-deep-learning-models-python-keras/ -Building layers and neurons - https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_08_4_bayesian_hyperparameter_opt.ipynb - -################### 9/14/20 ###################################################################### -Converting keras script from CPU to GPU usage on cheaha. This needs Tensorflow-gpu and CUDA to be installed to work. - - Using the environment 'testing' where I installed tensorflow-gpu. - - use the below modules on cheaha - also add them to the .job file. - - module load CUDA/10.1.243 - - module load cuDNN/7.6.2.24-CUDA-10.1.243 -Next step is to modify the NN builder definition to create layers and neurons on its own. Use this tutorial for this - https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_08_4_bayesian_hyperparameter_opt.ipynb -Note- I saw a worker stopping and errors out the script. This is cause of njobs=-1 (according to web), where parallel fits take up all the memory and this might exit out? A proposed solution is to update matplotlib (pip install -U matplotlib). -I know it's strange, but whatever works and doesn't throw an error, lol. I have to look up how Cheaha's GPU split jobs among them and how ntasks work. - -Cool plots for parameter tuning using hiplot - https://medium.com/roonyx/neural-network-hyper-parameter-tuning-with-keras-tuner-and-hiplot-7637677821fa -Try this for tomorrow. looks promising. - - https://medium.com/python-data/hyperparameter-tuning-tensorflow-2-models-with-keras-tuner-81f36f801040 - - -################### 9/15/20 ###################################################################### -Looks like hyperopt is the way to go. Use this video to build your model - https://www.youtube.com/watch?v=RublDm4J1vY -Probably this helps to copy+paste - https://www.kaggle.com/inspector/keras-hyperopt-example-sketch - -################### 9/16/20 ###################################################################### -**Ignore everything above about tuning parameters** -BayesSearchCV works using this tutorial - https://github.com/M-e-r-c-u-r-y/Machine-Learning/blob/master/Kaggle/Titanic/Neural%20Network%20Classifier%20using%20Bayesian%20Search.ipynb - - make sure you have the latest skopt installed. - -Now that everything is working, let's move to faster tuning algorithms like Ray.tune - https://docs.ray.io/en/master/tune/examples/index.html -Here is an example of how it's done - https://docs.ray.io/en/master/tune/examples/tune_mnist_keras.html -Didn't understand this, but might be useful, lol - https://medium.com/rapids-ai/30x-faster-hyperparameter-search-with-raytune-and-rapids-403013fbefc5 - -################### 9/18/20 ###################################################################### -Example given by Ray folk - https://colab.research.google.com/github/ray-project/tutorial/blob/master/tune_exercises/exercise_1_basics.ipynb#scrollTo=m1YHm_CyFWS9 -Video presentation of the same thing - https://www.youtube.com/watch?v=VX7HvEoMrsA -probably use this - https://medium.com/@himanshurawlani/hyperparameter-tuning-with-keras-and-ray-tune-1353e6586fda - -################### 9/19/20 ###################################################################### -Build layers and neurons chosing randomly for parameter tuning - https://www.curiousily.com/posts/hackers-guide-to-hyperparameter-tuning/ -and this - https://keras-team.github.io/keras-tuner/ -but these are available in keras-tuner. Ray doesn't support keras tuner yet. -TuneReporter function definition - https://medium.com/@himanshurawlani/hyperparameter-tuning-with-keras-and-ray-tune-1353e6586fda -Ray tune and this might be a good combination - https://www.justintodata.com/hyperparameter-tuning-with-python-keras-guide/ - -################### 9/28/20 ###################################################################### -As per internet, the way to go is to build the keras model as we already did but use early stopping and population based method to tune parameters (cause it works well with large data and large range of parameters.) -Use this link for Ray tune FAQs - https://github.com/krfricke/ray/blob/3636f77e15f15732ef576d4782705dd1d47b4d6a/doc/source/tune/_tutorials/_faq.rst#id32 - -################### 9/30/20 ###################################################################### -Working with OPTUNA. This allows us to use define-by-run search space. Created a sub-folder under src for optuna runs. - -################### 10/1/20 ###################################################################### -Optuna is running on cluster. Study name is Ditto_v0 -Have to try Axclient - https://www.justintodata.com/hyperparameter-tuning-with-python-keras-guide/ -Have to try Ray-tune Dragonfly and Axclient and compare the results. Also use Novorad? amd wandb. - -################### 10/2/20 ###################################################################### -export CUDA_VISIBLE_DEVICES=4 -running the above line gave me a ray hostname to look up for how much I'm using. -Made wrike tasks to use keras-tuner, Ray tune algorithms like hyper-opt and dragonfly along with schedulers. -Also have to tune ML models on the side. - -################### 12/16/20 ###################################################################### -Making a pipeline of the whole process. Here's the plan - - - We need to have a vcf file to start with. I'm taking this one from UDN solves. - ------------- - 52 Proband UDN308376 C1019-HJ-0012 SL156674 GABRG2 GABRG2-related encephalopathy GABRG2(NM_00081.3,c.316G>A,p.Ala106Thr,Pathogenic) - ------------- - - We have an annotated vcf file for this in our lab workspace on cheaha. - `python /data/project/worthey_lab/projects/experimental_pipelines/annovar_vcf_annotation/Annovar.py /data/project/worthey_lab/projects/PyxisMap/analysis/UDN/SL156674.vcf.gz /data/scratch/tmamidi/ /data/project/worthey_lab/projects/experimental_pipelines/tarun/Varsight/data/interim/ /data/project/worthey_lab/tools/annovar/annovar_hg19_db` - - Once we have the annotated vcf file, I'm going to run the following python scripts for annotation and feature extraction. Basically, prepping the data for prediction. - - vcf to csv conversion - parse_clinvar.py - - csv to test file - filter.py - - Now, we'll have the test csv for prediction. We'll be using predict.py for predictions. - -For training the classifier/model, I'll be using `parse_clinvar.py` and `filter.py` for prepping the clinvar dataset. Then I'll be running Optuna for hyper-parameter tuning `optuna-tpe.ipy`. -Copy the best parameters and run the final code for model performance stats. - -Below are the other cases to work on. ------------ -63 Proband UDN587784 C1006-HJ-0022 SL201103 ENG TELANGIECTASIA, HEREDITARY HEMORRHAGIC, TYPE 1; HHT1; SYSTEMIC LUPUS ERYTHEMATOSIS (NO MOLEC DIAGNOSIS) ENG(NM_000118.3,c.816+6T>C,N/A,Research) -68 Proband UDN021466 C1012-HJ-0066 SL202061 KMT2B KMT2B-related dystonia KMT2B(NM_014727.2,c.7613delC,p.Thr2539Profs*75,Research) SHANK2(NM_133266.4,c.902dupC,p.Pro302Serfs*32,Pathogenic) ------------ - -################### 12/21/20 ###################################################################### -*Testing a sample and ranking the variants* -module load Anaconda3/2020.02 -source activate envi -cp /data/project/worthey_lab/projects/PyxisMap/analysis/UDN/SL156674.vcf.gz /data/project/worthey_lab/projects/experimental_pipelines/tarun/Varsight/data/interim/ -gunzip /data/project/worthey_lab/projects/experimental_pipelines/tarun/Varsight/data/interim/SL156674.vcf.gz - -We will need to filter the vcf before annotating and filtering as the files are really huge to work with. Using vcflib package to filter. -`conda install -c conda-forge -c bioconda -c defaults vcflib` -https://github.com/vcflib/vcflib - `vcffilter -f "DP > 10 & MQ > 30 & QD > 20" SL156674.vcf > filtered_SL156674.vcf` - -python /data/project/worthey_lab/projects/experimental_pipelines/annovar_vcf_annotation/Annovar_Tarun.py /data/project/worthey_lab/projects/experimental_pipelines/tarun/Varsight/data/interim/filtered_SL156674.vcf /data/scratch/tmamidi/ /data/project/worthey_lab/projects/experimental_pipelines/tarun/Varsight/data/interim/ /data/project/worthey_lab/tools/annovar/annovar_hg19_db -KeyError: "['CLNSIG', 'MC', 'CLNVC', 'AF_ESP', 'AF_TGP', 'CLNHGVS', 'CLNREVSTAT', 'AF_EXAC'] not in index" - -################### 1/9/21 ###################################################################### -Use Annovar.py for Clinvar and Annovar_Tarun.py for samples cause clinvar is not updted and downloaded clinvar has info. -python /data/project/worthey_lab/projects/experimental_pipelines/annovar_vcf_annotation/Annovar.py /data/project/worthey_lab/projects/experimental_pipelines/tarun/Varsight/data/external/clinvar.vcf /data/scratch/tmamidi/ /data/project/worthey_lab/projects/experimental_pipelines/tarun/Varsight/data/interim/ /data/project/worthey_lab/tools/annovar/annovar_hg19_db - -Imputation notes -Using this for now - logistic regression for transform - https://medium.com/nerd-for-tech/how-to-implement-mice-algorithm-using-iterative-imputer-to-handle-missing-values-3d6792d4ba7f -Alternate resources - - https://towardsdatascience.com/6-different-ways-to-compensate-for-missing-values-data-imputation-with-examples-6022d9ca0779 - https://www.analyticsvidhya.com/blog/2020/07/knnimputer-a-robust-way-to-impute-missing-values-using-scikit-learn/ - -Something to read on Overfitting - https://elitedatascience.com/overfitting-in-machine-learning - -################### 1/12/21 ###################################################################### -Installed Hazel on to my laptop. -Hazel is a gene prioritizing tool given phenotype as input. - -################### 1/13/21 ###################################################################### -Testing Ditto - -Sample - SL212589 IQCB1 Senior-Loken syndrome5 IQCB1(NM_001023570.3,c.1518_1519delCA,p.His506GlnfsTer13,Pathogenic) IQCB1(NM_001023570.3,c.1465C>T,p.Arg489Ter,Pathogenic) -HPO - HP:0000510, HP:0003774, HP:0000090 -Command for Hazel - -curl -d '{"terms": ["HP:0000479", "HP:0001129", "HP:0003774", "HP:0007481"]}' -H "Content-Type: application/json" \ --X GET http://localhost:5000/v1/cosine > SL212589_genes.yaml - -################### 1/18/21 ###################################################################### -For faster queries, tabix the file - https://pypi.org/project/pytabix/ - -################### 1/19/21 ###################################################################### -ping Hazel in cheaha - curl -d '{"terms": ["HP:0001337", "HP:0010242"]}' -H "Content-Type: application/json" -X GET http://10.111.161.203:5000/v1/cosine -Looking in to other annotation tools - Trying out Exomiser -Download Exomiser zip from the release page mentioned here and follow the readme in that folder - https://github.com/exomiser/Exomiser -prioritizing variants - http://exomiser.github.io/Exomiser/manual/7/analysis_file_config/ - -################### 2/10/21 ###################################################################### -Started from scratch and using VEP for annotation. -Downloaded new clinvar db and annotated using VEP along with parser that Brandon wrote. -Filtered the data and used it for classification. -Realized that gnomad allele frequencies and CADD scores are available for all variants but dbnsfp have more than 80% missing data. - -################### 2/11/21 ###################################################################### -Variant annotation on HGMD variants since Clinvar variants have 100% accuracy. -Had to tabix index the HGMD variant file. -`./src/run_pipeline.sh -s -v ../../../../../manual_datasets_central/hgmd/2020q4/hgmd_pro_2020.4_hg38.vcf.gz -o ../data/processed/HGMD -d ~/.ditto_datasets.yaml` - -################### 2/15-21/21 ###################################################################### -So Tarun has been having trouble annotating HGMD dataset, and after a bit of troubleshooting, it appears this is (highly) likely due to SVs present in them. -Some background: VEP by default can annotate SVs and the max size they allow by default is 10Mb. Also of note is that, VEP by default annotates 5000 variants in parallel. Such parallelization appears to be true for SVs as well. -Taken together, it appears parallelizing SV annotation requires large memory. We tried 100G mem but that was not sufficient either. -I believe we can make the VEP annotation work for SVs by tweaking some settings but my current thinking is that SVs should be out of scope for ditto. At the least, some profiling/analysis on HGMD SVs must be performed before the decision to include them in ditto. -Also, I looked for SVs in clinvar (by searching for SVTYPE tag), and I'm surprised they didn't have any. -PS- I looked at the one of the deletion SVs (size 637Kb), which was successfully annotated. That particular line in VCF had 14578054 characters. Yup, 14.5 million characters - -One more error - -So here is the issue that is causing trouble when annotating HGMD vcf with VEP. (To be more specific, bcftools is the one that has trouble). Info field PHEN is not supposed to include semicolon character, - -Removing SV from HGMD and working with modified VCF- -Converting VCF of chromosomes '1' to 'chr1' and removing special characters from INFO column - -` -module load Anaconda3/2020.02 -module load tabix -module load BCFtools - -cp /data/project/worthey_lab/manual_datasets_central/hgmd/2020q4/hgmd_pro_2020.4_hg38.vcf ./` -check all the chromosomes present in the VCF - -`grep -v ^# clinvar.vcf | cut -f1 -d$'\t' | sort -u - -sed -E -i 's/(^[^#]+)/chr\1/' -sed -i 's/^chrMT/chrM/g' clinvar.vcf | grep -v ^chrNW -sed -E 's/(^[^#]+)(=")([^;"]+)(;)+([^;]*?)(")/\1\2\3%3B\5\6/' hgmd_pro_2020.4_hg38.vcf > hgmd_pro_2020.4_hg38_fixed_info.vcf` - -or use the VCF that was editied by Brandon -cp /data/project/worthey_lab/projects/experimental_pipelines/brandon_test/hgmd_fix/hgmd_pro_2020.4_hg38_fixed_info.vcf ./ -grep -v SVTYPE hgmd_pro_2020.4_hg38_fixed_info.vcf > modified_hgmd.vcf -bcftools norm -f /data/project/worthey_lab/datasets_central/human_reference_genome/processed/GRCh38/no_alt_rel20190408/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna hgmd_pro_2020.4_hg38_edited.vcf -Oz -o hgmd_pro_2020.4_hg38_edited.vcf.gz -#bgzip -c modified_hgmd.vcf > modified_hgmd.vcf.gz -tabix -fp vcf hgmd_pro_2020.4_hg38_edited.vcf.gz` - - -Run the pipeline for annotation- -`./src/run_pipeline.sh -s -v ../data/external/hgmd_pro_2020.4_hg38_edited.vcf.gz -o ../data/processed/HGMD -d ~/.ditto_datasets.yaml` - -Things to note down - -However there is one more thing. Briefly looking at the HGMD dataset, I noticed a deletion SV of size 30 bases. And this makes me wonder what's their min. size definition for a SV. More common limit is 50 bases, but this is opinionated and hence not a surprise they have smaller ones included. -So removing variants based on string SVTYPE would also remove "large" indels. I think this is acceptable but this needs to be kept in mind. Or better yet, log these decisions somewhere. - -################### 2/21/21 ###################################################################### -Current workflow: -`module load Anaconda3/2020.02 -module load tabix -module load BCFtools` -Download Clinvar - - `wget -P /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/external/ https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - tabix -fp vcf clinvar.vcf.gz ` -Check chromosomes and keep note for modifications - - `zgrep -v ^# clinvar.vcf.gz | cut -f1 -d$'\t' | sort -u` -Copy HGMD VCF - - `cp /data/project/worthey_lab/manual_datasets_central/hgmd/2020q4/hgmd_pro_2020.4_hg38.vcf ./` -Check chromosomes and keep note for modifications - - `grep -v ^# hgmd_pro_2020.4_hg38.vcf | cut -f1 -d$'\t' | sort -u` -Fix the INFO column and index for merging - - `sed -E 's/(^[^#]+)(=")([^;"]+)(;)+([^;]*?)(")/\1\2\3%3B\5\6/' hgmd_pro_2020.4_hg38.vcf > hgmd_pro_2020.4_hg38_fixed_info.vcf` - bgzip -c hgmd_pro_2020.4_hg38_fixed_info.vcf > hgmd_pro_2020.4_hg38_fixed_info.vcf.gz - tabix -fp vcf hgmd_pro_2020.4_hg38_fixed_info.vcf.gz ` -Merge Clinvar and HGMD - - `bcftools merge clinvar.vcf.gz hgmd_pro_2020.4_hg38_fixed_info.vcf.gz -Ov -o ../interim/merged.vcf` -Add `chr` to chromosomes columns - - `sed -E -i 's/(^[^#]+)/chr\1/' ../interim/merged.vcf ` -Fix chromosome issues noted before - - `sed -i 's/^chrMT/chrM/g' ../interim/merged.vcf - grep -v ^chrNW ../interim/merged.vcf > ../interim/merged_chr_fix.vcf` -Check chromosomes and fix any remaining issues - - `grep -v ^# ../interim/merged_chr_fix.vcf | cut -f1 -d$'\t' | sort -u` -Normalize the variants using reference genome - - `bcftools norm -f /data/project/worthey_lab/datasets_central/human_reference_genome/processed/GRCh38/no_alt_rel20190408/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna ../interim/merged_chr_fix.vcf -Oz -o ../interim/merged_norm.vcf.gz` -Filter variants by size (<30kb) and class - - `python ../../src/training/data-prep/extract_variants.py` - Clinvar variants: 300280 - HGMD variants: 156402 -bgzip and Tabix index the file - - `bgzip -c ../interim/merged_sig_norm.vcf > ../interim/merged_sig_norm.vcf.gz - tabix -fp vcf ../interim/merged_sig_norm.vcf.gz` -Run variant annotation as shown in ReadMe file - - `./src/run_pipeline.sh -s -v ../data/interim/merged_sig_norm.vcf.gz -o ../data/interim -d ~/.ditto_datasets.yaml` -Parse the annotated vcf file - - `python parse_annotated_vars.py -i ../data/processed/merged_sig_norm_vep-annotated.vcf.gz -o ../data/processed/merged_sig_norm_vep-annotated.tsv` -Extract Class information for all these variants - - `python extract_class.py` -Filter, stats and prep the data - - `python filter.py` - -################### 2/22/21 ###################################################################### -Working with cross validation and SHAP feature importances. No matter how I filter the data, accuracy remains 99%. - - -Go ontology, PPI, regulatory network, enzyme activity, binding pockets - - -################### 2/24/21 ###################################################################### -Switching from pandas and scikit-learn to RAPIDS - -conda create -n rapids-0.17 -c rapidsai -c nvidia -c conda-forge \ - -c defaults rapids-blazing=0.17 python=3.7 cudatoolkit=10.1 -conda activate rapids-0.17 - -RAPIDS didn't actually work. So exiting from it. -However, there is a good documentation on it - https://towardsdatascience.com/how-to-use-gpus-for-machine-learning-with-the-new-nvidia-data-science-workstation-64ef37460fa0 - -One more thing to checkout, "pycaret" - https://github.com/pycaret/pycaret - -Voting classifier is working!!!! -` -Model: Ditto_snv -Train_mean_score(train_data)[min, max]: 0.9895633354353022[0.9891826455224171, 0.9897574431754781] -Test_mean_score(train_data)[min, max]: 0.9553676109956326[0.9553676109956326, 0.9553676109956326] -Precision(test_data): 0.9940970722021246 -Recall: 0.8649082940969867 -roc_auc: 0.9292058306929102 -Accuracy: 0.8649082940969867 -Confusion_matrix[low_impact, high_impact]: -[[1254381 197263] - [ 51 8898]] -` -` -Model: Ditto_non_snv -Train_mean_score(train_data)[min, max]: 0.9935592590759252[0.9930853864700828, 0.9939860042957113] -Test_mean_score(train_data)[min, max]: 0.9376182240663085[0.9376182240663085, 0.9376182240663085] -Precision(test_data): 0.8891183584829964 -Recall: 0.5681611375869461 -roc_auc: 0.7449727925202043 -Accuracy: 0.5681611375869461 -Confusion_matrix[low_impact, high_impact]: -[[27399 27952] - [ 48 9440]] - ` -################### 3/05/21 ###################################################################### -Working the past week to tune the classifiers. -Finally, I can make classifiers to tune with Ray Tune PB2. Only downside is that the default parameters perform better than tuned models, lol - -################### 3/17/21 ###################################################################### -Tuning all the models with TuneSearchCV. They're all under the folder Tuning. Code to tune them - - `for FILE in Tuning/*.py; do python slurm-launch.py --exp-name $FILE --command "python $FILE --vtype snv_protein_coding" ; done` - -################### 6/27/21 ###################################################################### -Working on stabilizing the tuning process the last 2 weeks. -Shifted from `voting classifier` to `stacking classifier` cause it can tune the weightsfor itself. We don't need to manually set them. -Shifted from `tune` parameters to `hyperopt` parametes as they work with conditional search spaces. -Final working script for tuning is at `src/training/training/Tune_hp_stacking.py`. - -There was a problem with dbNSFP database used by VEP. VEP doesn't parse out the transcript info from the database but just replaces `;` with `&` and combines all transcripts in to a single line. -We don't want this to happen as we can't work with prediction score from this database for ML. Remember the complex columns that we tried to take mean off? That approach is wrong. -So, Brandon wrote a formatting script to run before running the annotation scripts. -Running the annotation process after formatting the database. Hopefully, it works this time :) - -################### 9/3/21 ###################################################################### -working on a bunch filters and correlation plots for the last couple weeks. -Also, the ML_models script can plot ROC and AUC curves for each dataset. - -Different combinations of filters - -``` -python slurm-launch.py --exp-name F_1_0_0_nssnv --command "python training/data-prep/filter.py -v F_1_0_0_nssnv -af 1 --cutoff 1 -afv 0 -otv 0" --mem 30G -python slurm-launch.py --exp-name F_7_0_0_nssnv --command "python training/data-prep/filter.py -v F_7_0_0_nssnv -af 1 --cutoff 0.7 -afv 0 -otv 0" --mem 30G -python slurm-launch.py --exp-name F_3_0_0_nssnv --command "python training/data-prep/filter.py -v F_3_0_0_nssnv -af 1 --cutoff 0.3 -afv 0 -otv 0" --mem 30G -python slurm-launch.py --exp-name F_1_005_0_nssnv --command "python training/data-prep/filter.py -v F_1_005_0_nssnv -af 1 --cutoff 1 -afv 0.005 -otv 0" --mem 30G -python slurm-launch.py --exp-name F_1_0005_0_nssnv --command "python training/data-prep/filter.py -v F_1_0005_0_nssnv -af 1 --cutoff 1 -afv 0.0005 -otv 0" --mem 30G -python slurm-launch.py --exp-name F_1_00005_0_nssnv --command "python training/data-prep/filter.py -v F_1_00005_0_nssnv -af 1 --cutoff 1 -afv 0.00005 -otv 0" --mem 30G -python slurm-launch.py --exp-name F_7_005_0_nssnv --command "python training/data-prep/filter.py -v F_7_005_0_nssnv -af 1 --cutoff 0.7 -afv 0.005 -otv 0" --mem 30G -python slurm-launch.py --exp-name F_7_0005_0_nssnv --command "python training/data-prep/filter.py -v F_7_0005_0_nssnv -af 1 --cutoff 0.7 -afv 0.0005 -otv 0" --mem 30G -python slurm-launch.py --exp-name F_7_00005_0_nssnv --command "python training/data-prep/filter.py -v F_7_00005_0_nssnv -af 1 --cutoff 0.7 -afv 0.00005 -otv 0" --mem 30G -python slurm-launch.py --exp-name F_3_005_0_nssnv --command "python training/data-prep/filter.py -v F_3_005_0_nssnv -af 1 --cutoff 0.3 -afv 0.005 -otv 0" --mem 30G -python slurm-launch.py --exp-name F_3_0005_0_nssnv --command "python training/data-prep/filter.py -v F_3_0005_0_nssnv -af 1 --cutoff 0.3 -afv 0.0005 -otv 0" --mem 30G -python slurm-launch.py --exp-name F_3_00005_0_nssnv --command "python training/data-prep/filter.py -v F_3_00005_0_nssnv -af 1 --cutoff 0.3 -afv 0.00005 -otv 0" --mem 30G -python slurm-launch.py --exp-name F_1_0_1_nssnv --command "python training/data-prep/filter.py -v F_1_0_1_nssnv -af 1 --cutoff 1 -afv 0 -otv 1" --mem 30G -python slurm-launch.py --exp-name F_7_0_1_nssnv --command "python training/data-prep/filter.py -v F_7_0_1_nssnv -af 1 --cutoff 0.7 -afv 0 -otv 1" --mem 30G -python slurm-launch.py --exp-name F_3_0_1_nssnv --command "python training/data-prep/filter.py -v F_3_0_1_nssnv -af 1 --cutoff 0.3 -afv 0 -otv 1" --mem 30G -python slurm-launch.py --exp-name F_1_005_1_nssnv --command "python training/data-prep/filter.py -v F_1_005_1_nssnv -af 1 --cutoff 1 -afv 0.005 -otv 1" --mem 30G -python slurm-launch.py --exp-name F_1_0005_1_nssnv --command "python training/data-prep/filter.py -v F_1_0005_1_nssnv -af 1 --cutoff 1 -afv 0.0005 -otv 1" --mem 30G -python slurm-launch.py --exp-name F_1_00005_1_nssnv --command "python training/data-prep/filter.py -v F_1_00005_1_nssnv -af 1 --cutoff 1 -afv 0.00005 -otv 1" --mem 30G -python slurm-launch.py --exp-name F_7_005_1_nssnv --command "python training/data-prep/filter.py -v F_7_005_1_nssnv -af 1 --cutoff 0.7 -afv 0.005 -otv 1" --mem 30G -python slurm-launch.py --exp-name F_7_0005_1_nssnv --command "python training/data-prep/filter.py -v F_7_0005_1_nssnv -af 1 --cutoff 0.7 -afv 0.0005 -otv 1" --mem 30G -python slurm-launch.py --exp-name F_7_00005_1_nssnv --command "python training/data-prep/filter.py -v F_7_00005_1_nssnv -af 1 --cutoff 0.7 -afv 0.00005 -otv 1" --mem 30G -python slurm-launch.py --exp-name F_3_005_1_nssnv --command "python training/data-prep/filter.py -v F_3_005_1_nssnv -af 1 --cutoff 0.3 -afv 0.005 -otv 1" --mem 30G -python slurm-launch.py --exp-name F_3_0005_1_nssnv --command "python training/data-prep/filter.py -v F_3_0005_1_nssnv -af 1 --cutoff 0.3 -afv 0.0005 -otv 1" --mem 30G -python slurm-launch.py --exp-name F_3_00005_1_nssnv --command "python training/data-prep/filter.py -v F_3_00005_1_nssnv -af 1 --cutoff 0.3 -afv 0.00005 -otv 1" --mem 30G -python slurm-launch.py --exp-name F_3_no_0_nssnv --command "python training/data-prep/filter.py -v F_3_no_0_nssnv -af 0 --cutoff 0.3 -afv 0.00005 -otv 0" --mem 30G -python slurm-launch.py --exp-name F_3_no_1_nssnv --command "python training/data-prep/filter.py -v F_3_no_1_nssnv -af 0 --cutoff 0.3 -afv 0.00005 -otv 1" --mem 30G -``` - -Running ML models for different combinations of filters - -``` -python slurm-launch.py --exp-name F_1_0_0_nssnv --command "python training/training/ML_models.py -v F_1_0_0_nssnv" -python slurm-launch.py --exp-name F_7_0_0_nssnv --command "python training/training/ML_models.py -v F_7_0_0_nssnv" -python slurm-launch.py --exp-name F_3_0_0_nssnv --command "python training/training/ML_models.py -v F_3_0_0_nssnv" -python slurm-launch.py --exp-name F_1_005_0_nssnv --command "python training/training/ML_models.py -v F_1_005_0_nssnv" -python slurm-launch.py --exp-name F_1_0005_0_nssnv --command "python training/training/ML_models.py -v F_1_0005_0_nssnv" -python slurm-launch.py --exp-name F_1_00005_0_nssnv --command "python training/training/ML_models.py -v F_1_00005_0_nssnv" -python slurm-launch.py --exp-name F_7_005_0_nssnv --command "python training/training/ML_models.py -v F_7_005_0_nssnv" -python slurm-launch.py --exp-name F_7_0005_0_nssnv --command "python training/training/ML_models.py -v F_7_0005_0_nssnv" -python slurm-launch.py --exp-name F_7_00005_0_nssnv --command "python training/training/ML_models.py -v F_7_00005_0_nssnv" -python slurm-launch.py --exp-name F_3_005_0_nssnv --command "python training/training/ML_models.py -v F_3_005_0_nssnv" -python slurm-launch.py --exp-name F_3_0005_0_nssnv --command "python training/training/ML_models.py -v F_3_0005_0_nssnv" -python slurm-launch.py --exp-name F_3_00005_0_nssnv --command "python training/training/ML_models.py -v F_3_00005_0_nssnv" -python slurm-launch.py --exp-name F_1_0_1_nssnv --command "python training/training/ML_models.py -v F_1_0_1_nssnv" -python slurm-launch.py --exp-name F_7_0_1_nssnv --command "python training/training/ML_models.py -v F_7_0_1_nssnv" -python slurm-launch.py --exp-name F_3_0_1_nssnv --command "python training/training/ML_models.py -v F_3_0_1_nssnv" -python slurm-launch.py --exp-name F_1_005_1_nssnv --command "python training/training/ML_models.py -v F_1_005_1_nssnv" -python slurm-launch.py --exp-name F_1_0005_1_nssnv --command "python training/training/ML_models.py -v F_1_0005_1_nssnv" -python slurm-launch.py --exp-name F_1_00005_1_nssnv --command "python training/training/ML_models.py -v F_1_00005_1_nssnv" -python slurm-launch.py --exp-name F_7_005_1_nssnv --command "python training/training/ML_models.py -v F_7_005_1_nssnv" -python slurm-launch.py --exp-name F_7_0005_1_nssnv --command "python training/training/ML_models.py -v F_7_0005_1_nssnv" -python slurm-launch.py --exp-name F_7_00005_1_nssnv --command "python training/training/ML_models.py -v F_7_00005_1_nssnv" -python slurm-launch.py --exp-name F_3_005_1_nssnv --command "python training/training/ML_models.py -v F_3_005_1_nssnv" -python slurm-launch.py --exp-name F_3_0005_1_nssnv --command "python training/training/ML_models.py -v F_3_0005_1_nssnv" -python slurm-launch.py --exp-name F_3_00005_1_nssnv --command "python training/training/ML_models.py -v F_3_00005_1_nssnv" -python slurm-launch.py --exp-name F_3_no_0_nssnv --command "python training/training/ML_models.py -v F_3_no_0_nssnv" -python slurm-launch.py --exp-name F_3_no_1_nssnv --command "python training/training/ML_models.py -v F_3_no_1_nssnv" -``` -Tune all classifier - -``` -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_7_0_0_nssnv_$FILE --command "python $FILE -v F_7_0_0_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_1_0_0_nssnv_$FILE --command "python $FILE -v F_1_0_0_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_1_005_0_nssnv_$FILE --command "python $FILE -v F_1_005_0_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_1_0005_0_nssnv_$FILE --command "python $FILE -v F_1_0005_0_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_1_00005_0_nssnv_$FILE --command "python $FILE -v F_1_00005_0_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_7_005_0_nssnv_$FILE --command "python $FILE -v F_7_005_0_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_7_0005_0_nssnv_$FILE --command "python $FILE -v F_7_0005_0_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_7_00005_0_nssnv_$FILE --command "python $FILE -v F_7_00005_0_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_1_0_1_nssnv_$FILE --command "python $FILE -v F_1_0_1_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_7_0_1_nssnv_$FILE --command "python $FILE -v F_7_0_1_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_1_005_1_nssnv_$FILE --command "python $FILE -v F_1_005_1_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_1_0005_1_nssnv_$FILE --command "python $FILE -v F_1_0005_1_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_1_00005_1_nssnv_$FILE --command "python $FILE -v F_1_00005_1_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_7_005_1_nssnv_$FILE --command "python $FILE -v F_7_005_1_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_7_0005_1_nssnv_$FILE --command "python $FILE -v F_7_0005_1_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_7_00005_1_nssnv_$FILE --command "python $FILE -v F_7_00005_1_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_3_0_0_nssnv_$FILE --command "python $FILE -v F_3_0_0_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_3_005_0_nssnv_$FILE --command "python $FILE -v F_3_005_0_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_3_0005_0_nssnv_$FILE --command "python $FILE -v F_3_0005_0_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_3_00005_0_nssnv_$FILE --command "python $FILE -v F_3_00005_0_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_3_0_1_nssnv_$FILE --command "python $FILE -v F_3_0_1_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_3_005_1_nssnv_$FILE --command "python $FILE -v F_3_005_1_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_3_0005_1_nssnv_$FILE --command "python $FILE -v F_3_0005_1_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_3_00005_1_nssnv_$FILE --command "python $FILE -v F_3_00005_1_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_3_no_0_nssnv_$FILE --command "python $FILE -v F_3_no_0_nssnv" ; done -for FILE in *.py; do python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_3_no_1_nssnv_$FILE --command "python $FILE -v F_3_no_1_nssnv" ; done -``` - -Running Stacking Classifier for different combinations of filters - -``` -python slurm-launch.py --exp-name F_1_0_0_nssnv_stacking --command "python training/training/stacking.py -v F_1_0_0_nssnv" -python slurm-launch.py --exp-name F_7_0_0_nssnv_stacking --command "python training/training/stacking.py -v F_7_0_0_nssnv" -python slurm-launch.py --exp-name F_3_0_0_nssnv_stacking --command "python training/training/stacking.py -v F_3_0_0_nssnv" -python slurm-launch.py --exp-name F_1_005_0_nssnv_stacking --command "python training/training/stacking.py -v F_1_005_0_nssnv" -python slurm-launch.py --exp-name F_1_0005_0_nssnv_stacking --command "python training/training/stacking.py -v F_1_0005_0_nssnv" -python slurm-launch.py --exp-name F_1_00005_0_nssnv_stacking --command "python training/training/stacking.py -v F_1_00005_0_nssnv" -python slurm-launch.py --exp-name F_7_005_0_nssnv_stacking --command "python training/training/stacking.py -v F_7_005_0_nssnv" -python slurm-launch.py --exp-name F_7_0005_0_nssnv_stacking --command "python training/training/stacking.py -v F_7_0005_0_nssnv" -python slurm-launch.py --exp-name F_7_00005_0_nssnv_stacking --command "python training/training/stacking.py -v F_7_00005_0_nssnv" -python slurm-launch.py --exp-name F_3_005_0_nssnv_stacking --command "python training/training/stacking.py -v F_3_005_0_nssnv" -python slurm-launch.py --exp-name F_3_0005_0_nssnv_stacking --command "python training/training/stacking.py -v F_3_0005_0_nssnv" -python slurm-launch.py --exp-name F_3_00005_0_nssnv_stacking --command "python training/training/stacking.py -v F_3_00005_0_nssnv" -python slurm-launch.py --exp-name F_1_0_1_nssnv_stacking --command "python training/training/stacking.py -v F_1_0_1_nssnv" -python slurm-launch.py --exp-name F_7_0_1_nssnv_stacking --command "python training/training/stacking.py -v F_7_0_1_nssnv" -python slurm-launch.py --exp-name F_3_0_1_nssnv_stacking --command "python training/training/stacking.py -v F_3_0_1_nssnv" -python slurm-launch.py --exp-name F_1_005_1_nssnv_stacking --command "python training/training/stacking.py -v F_1_005_1_nssnv" -python slurm-launch.py --exp-name F_1_0005_1_nssnv_stacking --command "python training/training/stacking.py -v F_1_0005_1_nssnv" -python slurm-launch.py --exp-name F_1_00005_1_nssnv_stacking --command "python training/training/stacking.py -v F_1_00005_1_nssnv" -python slurm-launch.py --exp-name F_7_005_1_nssnv_stacking --command "python training/training/stacking.py -v F_7_005_1_nssnv" -python slurm-launch.py --exp-name F_7_0005_1_nssnv_stacking --command "python training/training/stacking.py -v F_7_0005_1_nssnv" -python slurm-launch.py --exp-name F_7_00005_1_nssnv_stacking --command "python training/training/stacking.py -v F_7_00005_1_nssnv" -python slurm-launch.py --exp-name F_3_005_1_nssnv_stacking --command "python training/training/stacking.py -v F_3_005_1_nssnv" -python slurm-launch.py --exp-name F_3_0005_1_nssnv_stacking --command "python training/training/stacking.py -v F_3_0005_1_nssnv" -python slurm-launch.py --exp-name F_3_00005_1_nssnv_stacking --command "python training/training/stacking.py -v F_3_00005_1_nssnv" -python slurm-launch.py --exp-name F_3_no_0_nssnv_stacking --command "python training/training/stacking.py -v F_3_no_0_nssnv" -python slurm-launch.py --exp-name F_3_no_1_nssnv_stacking --command "python training/training/stacking.py -v F_3_no_1_nssnv" -``` - -python ../../../slurm-launch.py --num-cpus 20 --mem 250G --partition long -temp ../../../ --exp-name F_3_00005_1_nssnv_stacking --command "python stacking.py -v F_3_00005_1_nssnv" - -For testing Ditto - -``` - cp /data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/annotated_vcf/train/CAGI6_RGP_TRAIN_12_PROBAND_vep-annotated.vcf.gz ./data/processed/testing/ - module load BCFtools - bcftools annotate -e'ALT="*" || type!="snp"' CAGI6_RGP_TRAIN_12_PROBAND_vep-annotated.vcf.gz -Oz -o CAGI6_RGP_TRAIN_12_PROBAND_vep-annotated_filtered.vcf.gz - python parse_annotated_vars.py -i ../data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND_vep-annotated_filtered.vcf.gz -o ../data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND_vep-annotated_filtered.tsv - python slurm-launch.py --exp-name testing --command "python Ditto/filter.py -i ../data/processed/predictions/CAGI6_RGP_TRAIN_18_PROBAND_vep-annotated_filtered.tsv -O ../data/processed/predictions/CAGI6_RGP_TRAIN_18_PROBAND" - python slurm-launch.py --exp-name testing --command "python Ditto/predict.py -i ../data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND/data.csv -o ../data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND/predictions.csv -o5 ../data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND/predictions_500.csv --variant chrX,101412604,C,T" - python slurm-launch.py --exp-name testing --command "python Ditto/combine_scores.py --sample CAGI6_RGP_TRAIN_12_PROBAND --raw ../data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND_vep-annotated_filtered.tsv --ditto ../data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND/ditto_predictions.csv -ep /data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/exomiser/train/CAGI6_RGP_TRAIN_12_PROBAND -o ../data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND/predictions_with_exomiser.csv -o100 ../data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND/predictions_with_exomiser_100.csv" - python slurm-launch.py --exp-name testing --mem 4G --command "python Ditto/ranks.py -id /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/filter_vcf_by_DP6_AB_hpo_removed --json /data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/metadata/train_test_metadata_original.json" - python slurm-launch.py --exp-name testing --command "python Ditto/submission.py" -``` - - - - -################### 1/31/22 ###################################################################### - -Download dbSNP database -curl -L https://ftp.ncbi.nih.gov/snp/organisms/human_9606_b151_GRCh38p7/VCF/All_20180418.vcf.gz - data/external/dbsnp_12122_All_20180418.vcf.gz - -Creating Ditto db using test vcf data - -```sh - python src/Ditto/filter.py -i annotation_parsing/.test/testing_variants_hg38_vep-annotated.tsv -O ./data/processed/ditto_db - -``` - -################### 9/21/22 ###################################################################### - -Running VEP for PKD variants from dbNSFP - -```sh -./src/run_pipeline.sh -s -v /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/Ditto/dbnsfp_pkd_for_VEP.vcf -o /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/Ditto/ -d ~/.ditto_datasets.yaml -``` diff --git a/docs/Pipeline.txt b/docs/Pipeline.txt deleted file mode 100644 index b5d90c1..0000000 --- a/docs/Pipeline.txt +++ /dev/null @@ -1,67 +0,0 @@ -** Current workflow: ** -`module load Anaconda3/2020.02 -module load tabix -module load BCFtools` -Download Clinvar (2/21/21 ) - - `wget -P /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/external/ https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - tabix -fp vcf clinvar.vcf.gz ` -Check chromosomes and keep note for modifications - - `zgrep -v ^# clinvar.vcf.gz | cut -f1 -d$'\t' | sort -u` -Copy HGMD VCF - - `cp /data/project/worthey_lab/manual_datasets_central/hgmd/2020q4/hgmd_pro_2020.4_hg38.vcf ./` -Check chromosomes and keep note for modifications - - `grep -v ^# hgmd_pro_2020.4_hg38.vcf | cut -f1 -d$'\t' | sort -u` -Fix the INFO column and index for merging - - `sed -E 's/(^[^#]+)(=")([^;"]+)(;)+([^;]*?)(")/\1\2\3%3B\5\6/' hgmd_pro_2020.4_hg38.vcf > hgmd_pro_2020.4_hg38_fixed_info.vcf` - bgzip -c hgmd_pro_2020.4_hg38_fixed_info.vcf > hgmd_pro_2020.4_hg38_fixed_info.vcf.gz - tabix -fp vcf hgmd_pro_2020.4_hg38_fixed_info.vcf.gz ` -Merge Clinvar and HGMD - - `bcftools merge clinvar.vcf.gz hgmd_pro_2020.4_hg38_fixed_info.vcf.gz -Ov -o ../interim/merged.vcf` -Add `chr` to chromosomes columns - - `sed -E -i 's/(^[^#]+)/chr\1/' ../interim/merged.vcf ` -Fix chromosome issues noted before - - `sed -i 's/^chrMT/chrM/g' ../interim/merged.vcf - grep -v ^chrNW ../interim/merged.vcf > ../interim/merged_chr_fix.vcf` -Check chromosomes and fix any remaining issues - - `grep -v ^# ../interim/merged_chr_fix.vcf | cut -f1 -d$'\t' | sort -u` -Normalize the variants using reference genome - - `bcftools norm -f /data/project/worthey_lab/datasets_central/human_reference_genome/processed/GRCh38/no_alt_rel20190408/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna ../interim/merged_chr_fix.vcf -Oz -o ../interim/merged_norm.vcf.gz` -Filter variants by size (<30kb) and class - - `python ../../src/training/data-prep/extract_variants.py` - Clinvar variants: 305386 - HGMD variants: 156402 -bgzip and Tabix index the file - - `bgzip -c merged_sig_norm.vcf > merged_sig_norm.vcf.gz - tabix -fp vcf ../interim/merged_sig_norm.vcf.gz` -Copy paths to dataset yaml file (~/.ditto_datasets.yaml) - - ``` - cadd_snv: "/data/project/worthey_lab/temp_datasets_central/tarun/cadd/raw/GRCh38/v1.6_rel20220331/whole_genome_SNVs_inclAnno.tsv.gz" - cadd_indel: "/data/project/worthey_lab/temp_datasets_central/tarun/cadd/raw/GRCh38/v1.6_rel20220331/gnomad.genomes.r3.0.indel_inclAnno.tsv.gz" - gerp: "/data/project/worthey_lab/temp_datasets_central/mana/gerp/processed/hg38/v1.6/gerp_score_hg38.bg.gz" - gnomad_genomes: "/data/project/worthey_lab/temp_datasets_central/mana/gnomad/v3.0/data/gnomad.genomes.r3.0.sites.vcf.bgz" - clinvar: "/data/project/worthey_lab/temp_datasets_central/tarun/clinvar/grch38/20221001/clinvar_20221001.vcf.gz" - dbNSFP: "/data/project/worthey_lab/temp_datasets_central/tarun/dbNSFP/v4.3_20220319/dbNSFP4.3a_variant.complete.parsed.sorted.header.tsv.gz" - ``` -Run variant annotation as shown in ReadMe file - - `./src/run_pipeline.sh -s -v ../data/interim/merged_sig_norm.vcf.gz -o ../data/interim -d ~/.ditto_datasets.yaml` -Parse the annotated vcf file - - `python parse_annotated_vars.py -i ../data/interim/merged_sig_norm_vep-annotated.vcf.gz -o ../data/interim/merged_sig_norm_vep-annotated.tsv` -Extract Class information for all these variants - - `python extract_class.py` -Filter, stats and prep the data - - `python filter.py` - - -For testing Ditto - -```sh - cp /data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/annotated_vcf/train/CAGI6_RGP_TRAIN_12_PROBAND_vep-annotated.vcf.gz ./data/processed/testing/ - module load BCFtools - bcftools annotate -e'ALT="*" || type!="snp"' ./data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND_vep-annotated.vcf.gz -Oz -o ./data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND_vep-annotated_filtered.vcf.gz - python annotation_parsing/parse_annotated_vars.py -i ./data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND_vep-annotated_filtered.vcf.gz -o ./data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND_vep-annotated_filtered.tsv - python src/Ditto/filter.py -i ./data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND_vep-annotated_filtered.tsv -O ./data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND - python src/Ditto/predict.py -i ./data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND/data.csv --sample CAGI6_RGP_TRAIN_12_PROBAND -o ./data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND/ditto_predictions.csv -o100 ./data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND/ditto_predictions_100.csv - python src/Ditto/combine_scores.py --raw ./data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND_vep-annotated_filtered.tsv --sample CAGI6_RGP_TRAIN_12_PROBAND --ditto ./data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND/ditto_predictions.csv -ep /data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/exomiser/train/CAGI6_RGP_TRAIN_12_PROBAND -o ./data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND/predictions_with_exomiser.csv -o100 ./data/processed/testing/CAGI6_RGP_TRAIN_12_PROBAND/predictions_with_exomiser_100.csv - -``` -python Ditto/ranks.py -id /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/filter_vcf_by_DP6_AB --json /data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/metadata/train_test_metadata_original.json -python Ditto/Exomiser_ranks.py -id /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/filter_vcf_by_DP6_AB_hpo_removed --json /data/project/worthey_lab/projects/experimental_pipelines/mana/small_tasks/cagi6/rgp/data/processed/metadata/train_test_metadata_original.json -o exomiser_ranks.csv diff --git a/docs/Plan-for-project.txt b/docs/Plan-for-project.txt deleted file mode 100644 index b7e5fdf..0000000 --- a/docs/Plan-for-project.txt +++ /dev/null @@ -1,85 +0,0 @@ -Plan for Variant prioritization tool - -Objective: -Application of classification algorithms that ingest variant annotations along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. It should also predict and classify the variants based on ACMG guidelines. -Background: In the field of genomic medicine, the primary goal of diagnosing rare disease patient is to identify one or more genomic variants that may be responsible for a particular phenotype. This process is typically through annotation, filtering and ranking/prioritizing variants for manual curation. These curated variants are then clinically reported back to the patient. -Problem: Manual curation of thousands of variants even after filtering is time consuming process. Average time of curation is 100 variants per man-hour. Thus, methods/tools that can identify variants to be clinically reported, even in presence of high degree of variability in phenotype presentation, are of critical importance. -Solution: Develop a tool for accurate prioritization of variants, reduces time to review and diagnose rare disease patients. -Dataset: -Genotypes โ€“ -โ€ข SNVs โ€“ -o allele frequency, -o sequence based, structure-based predictions (ClinVarRVRD), -o sequence conservation, splice factor motifs, splice donor/acceptor sites, RNA folding energy, codon usage, CpG content (SilVa tool) -o splice donors and acceptors in pre-mRNA transcripts (SpliceAI) -โ€ข oligogenic or multilocus genetic pattern (VarCoPP) -โ€ข CNVs (CNVdigest) -โ€ข Gene expression pathway level training (MuliPLIER) โ€“ database (recount2) -โ€ข Protein-chaperon interaction (DeepNEU) -โ€ข eQTL as feature? -โ€ข Can we use polygenic risk scores? probably to identify modifier variants? -Note โ€“ population based = improved prediction? MAF filter? - -Genotype-Phenotype โ€“ -โ€ข Variant pathogenicity prediction and annotation (eDiVA) for WES datasets -โ€ข Genotypic and phenotypic features (Xrare and DeepPVP โ€“ deep phenomeNET variant predictor) -Note - HPO terms for human and orthologous genes from model organisms (mouse, zebrafish etc.,) and phenotypes from protein-protein interactions. OMIM orphaned, IMPC, string DB. Monarch -Phenotype โ€“ -โ€ข Phenotypes (HPO, upheno), diseases (Mondo), genes and pathways (HANRD - heterogeneous association network for rare diseases, Orphamizer) -โ€ข EHR data (Ada XD, Dr. Warehouse) โ€“ this is not HPO -โ€ข Image based (Face2Gene, DeepGestalt) -โ€ข IR fingerprint (artificial neural networks) -Variants โ€“ ClinVar, InterVar, CancerVar, CNVinter, HGMD, DGV (db of genomic variants), dbSNP, Varibench, HumVar, ExoVar, predictSNP, and SwissVar -Features โ€“ allele frequency, specific populations, evolutionary conservation, functional impact (SIFT), segmental duplication, simple sequence repeats, ClinVar and OMIM; local context: GC content within 10 flanking bases on the reference genome; amino acid constraint, including blosum62 and pam250; Protein structure, interaction, and modifications, including predicted secondary structures, number of protein interactions from the BioPlex 2.0 Network, whether the protein is involved in complexes formation from CORUM database, number of high-confidence interacting proteins by PrePPI , probability of a residue being located the interaction interface by PrePPI (based on PPISP, PINUP, PredU), predicted accessible surface areas were obtained from dbPTM, SUMO scores in 7-amino acids neighborhood by GPS-SUMO, phosphorylation sites predictions within 7 amino acids neighborhood by GPS3.0, and ubiquitination scores within 14- amino acids neighborhood by UbiProber ; Gene mutation intolerance, including ExAC metrics โ€“ loss of function (check mvp paper) -Algorithms โ€“ -โ€ข Machine learning โ€“ sklearn, imblearn -โ€ข Deep learning/neural networks -โ€ข Convolutional Neural networks -โ€ข AI ?? -Training and Testing -โ€ข Probably use 80:20 from all clinvar variants -Tuning parameters -โ€ข Use population based tuning for large parameter space and large train data - https://deepmind.com/blog/article/population-based-training-neural-networks -โ€ข Use Optuna for define-by-run search space and parallelize it. -โ€ข ELI5 or SHAP for feature importance -Simulation data- -โ€ข Use 1000genome project VCF and add variants with HPO terms and create multiple vcf files. Use this as test. -โ€ข Use SNV vcfs and compare to SNV prioritization tools. Same with other type of variants and tools. Finally, combine all types of variants and test the model. -โ€ข Also simulate using inheritance patterns โ€“ a bit complicated โ€“ refer to eDiVA paper -Real data โ€“ -โ€ข Use UDN phase-1 data as a test set. -Results โ€“ -Variant ranking โ€“ -โ€ข Top 1 variant predicted by tool -โ€ข In top 10 list predicted by tool -โ€ข In top 20-30% list predicted by tool -โ€ข Not predicted/ unpredictable -Things to keep in mind โ€“ -โ€ข When seeing new values in some categorical columns, look for additive smoothing, also called Laplace smoothing -โ€ข Nuclear variants vs mitochondrial variants -โ€ข Hyperparameter tuning of features and check for F1 score -โ€ข Sequencing errors -โ€ข Default features and in combination with other predictors? -โ€ข Similar phenotype โ€“ similar mutation in the same gene? -โ€ข Inheritance patterns? a) dominant de novo, (b) autosomal dominant inherited, (c) autosomal recessive homozygous, (d) autosomal recessive compound heterozygous, or (e) Xโ€linked. -โ€ข Xu et al. developed Dic-Att-BiLSTM-CRF (DABLC), a deep attention NN method. By incorporating dictionary-based (using disease ontology) and document-based attention mechanisms, this new method outperformed existing ones at identifying rare and complex disease names - - -Tools to look/read: -1. rvtests - https://github.com/zhanxw/rvtests -2. favor - http://favor.genohub.org/ -3. LINSIGHT - https://www.nature.com/articles/ng.3810 -4. STAAR - https://github.com/xihaoli/STAAR ; https://www.nature.com/articles/s41588-020-0676-4 -5. Methylome mappability - https://bismap.hoffmanlab.org/ -6. Linkage disequilibrium - https://www.nature.com/articles/ng.3954 -7. Assay info - Encode (https://www.encodeproject.org/report/?type=Experiment) -8. Phen2Gene - https://academic.oup.com/nargab/article/2/2/lqaa032/5843800 -9. Hyperas - Hyperparameter tuning using the treestructured Parzen estimator (TPE) algorithm (DeepPVP). -10. Miscastv1.0 - Protein surface prediction - - dbs to consider - PhyreRisk, VarMap -11. AMELIE - page-13 in https://stm.sciencemag.org/content/scitransmed/suppl/2020/05/18/12.544.eaau9113.DC1/aau9113_SM.pdf -12. Cool plots for parameter tuning using hiplot - https://medium.com/roonyx/neural-network-hyper-parameter-tuning-with-keras-tuner-and-hiplot-7637677821fa - -Bluesky/User requirements/ideas: -1. Can we give 2 scores per variant: One as driver (monogenic), one as modifier (complex diseases). -2. Can it also predict if a variant is protective? \ No newline at end of file diff --git a/docs/ToDo.txt b/docs/ToDo.txt deleted file mode 100644 index 8d06b63..0000000 --- a/docs/ToDo.txt +++ /dev/null @@ -1,2 +0,0 @@ -Try combinations of Classifiers for stacking - https://towardsdatascience.com/stacking-classifiers-for-higher-predictive-performance-566f963e4840 -Dependence plots for SHAP -https://docs.seldon.io/projects/alibi/en/latest/examples/interventional_tree_shap_adult_xgb.html diff --git a/docs/build_DITTO.md b/docs/build_DITTO.md new file mode 100644 index 0000000..e948c63 --- /dev/null +++ b/docs/build_DITTO.md @@ -0,0 +1,123 @@ +# DITTO + +:fire: DITTO (inspired by pokemon) is a tool for exploring any type of small genetic variant and their predicted functional +impact on transcript(s). + +:fire: DITTO uses an explainable neural network model to predict the functional impact of variants and utilizes SHAP to +explain the model's predictions. + +:fire: DITTO provides annotations from OpenCravat, a tool for annotating variants with information from multiple data +sources. + +:fire: DITTO is currently trained on variants from ClinVar and is not intended for clinical use. + +## System Requirements + +*OS:* + + Currently tested only in Cheaha (UAB HPC). Docker versions may need to be explored later to make it useable in Mac and Windows. + +*Tools:* + +- Anaconda3 +- OpenCravat-2.4.1 +- Git + +*Resources:* + +- CPU: > 2 +- Storage: ~1TB (includes annotation databases from OpenCravat) +- RAM: ~50GB + +> ***NOTE:*** We used 10 CPU cores, 50GB memory for training DITTO. The tuning and training process took ~16 hrs. Since +> DITTO uses tensorflow architecture, this process can be potentially accelerated using GPUs. + +## Installation + +### Requirements + +- DITTO repo from GitHub +- OpenCravat with databases to annotate + +To fetch DITTO source code, change in to directory of your choice and run: + +```sh +git clone https://github.com/uab-cgds-worthey/DITTO.git +``` + +Create environment and install dependencies + +```sh +# create conda environment. Needed only the first time. +conda env create --file configs/envs/environment.yaml + +# if you need to update existing environment +conda env update --file configs/envs/environment.yaml + +# activate conda environment +conda activate training +``` + +### Setup OpenCravat (ignore if already installed) + +Please follow the steps mentioned in [install_openCravat.md](../docs/install_openCravat.md). + +## Data + +Download the latest clinVar variants: [Download VCF](https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz) + +## Annotation + +```sh +oc run clinvar.vcf.gz -l hg38 -t csv --package mypackage -d path/to/output/directory/ +``` + +> ***NOTE:*** By default OpenCravat uses all available CPUs. Please specify the number of CPU cores using this parameter +> in the above command `--mp 2`. Minimum number of CPUs to use is 2. Also, please make sure to setup `mypackage` from +> `configs` directory to your modules directory. To learn more about it, please review [OpenCravat's package](https://open-cravat.readthedocs.io/en/latest/Package.html). + +## Preprocessing + +By default, OpenCravat annotates all transcript level annotations for each variant in a single row. DITTO makes +transcript level predictions for each variant. To parse out each transcript level annotations to different rows, use +the below command + +```sh +python src/annotation_parsing/parse_single_sample.py -i clinvar.vcf.gz.variant.csv -e parse \ + -o clinvar.vcf.gz.variant.csv_parsed.csv.gz -c configs/opencravat_train_config.json +``` + +Filter and process the annotations as shown in this [python +notebook](../src/annotation_parsing/opencravat_clinvar_filtering_80-20-20.ipynb). This will output training and testing +data to train the neural network. + +## Tune and Train DITTO + +The below script uses the training data to tune the neural network by splitting it to train and validation data. It then +uses the testing data to calculate accuracy, roc, and prc metrics along with a SHAP plot showing the top features used +to train the model. Please modify the data path accordingly or use the published training and testing data from this repo. + +```sh +python training/NN.py --train_x /data/train_class_data_80.csv.gz \ + --test_x /data/test_class_data_20.csv.gz -c configs/col_config.yaml -o /data/ +``` + +This script took 10 CPU cores, 100 GB memory and ~17 hrs to tune and train DITTO. + +## Adding more databases (features) to DITTO + +Follow the below steps to install and add more databases for annotation and before training: + +1. Install the annotator/database using OpenCravat. + +2. Add the annotator to `mypackage/mypackage.yml` and reannotate the clinvar VCF file. + +3. Add the annotator to the [train config](../configs/opencravat_train_config.json) and specify how to parse the + annotation. + +4. Follow the steps from Preprocessing above to parse, filter, process, tune and train DITTO. + +## Benchmarking + +Please follow the [python notebook](../src/analysis/opencravat_latest_benchmarking-Consequence_80_20.ipynb) to benchmark +DITTO with other pathogenicity predition tools. It also has code snippets to generate testing metrics and SHAP plots. diff --git a/docs/install_openCravat.logfile b/docs/install_openCravat.logfile new file mode 100644 index 0000000..a95de19 --- /dev/null +++ b/docs/install_openCravat.logfile @@ -0,0 +1,962 @@ +$ oc module install clinvar +Installing: clinvar:2023.02.01 +Proceed? ([y]/n) > +[2023:06:01 09:31:01] Starting to install clinvar:2023.02.01... +[2023:06:01 09:31:01] Downloading code archive of clinvar:2023.02.01... +[**************************************************] 323.7 kB / 323.7 kB (100%) +[2023:06:01 09:31:01] Extracting code archive of clinvar:2023.02.01... +[2023:06:01 09:31:01] Verifying code integrity of clinvar:2023.02.01... +[2023:06:01 09:31:01] Downloading data of clinvar:2023.02.01... +[**************************************************] 49.0 MB / 49.0 MB (100%) +[2023:06:01 09:31:02] Extracting data of clinvar:2023.02.01... +[2023:06:01 09:31:03] Verifying data integrity of clinvar:2023.02.01... +[2023:06:01 09:31:04] Finished installation of clinvar:2023.02.01 +(opencravat) [tmamidi@c0174 opencravat]$ oc module install cancer_genome_interpreter civic +Installing: cancer_genome_interpreter:1.3.0, civic:2023.04.01, wgcancer_genome_interpreter:1.0.0, wgcivic:1.1.0 +Proceed? ([y]/n) > +[2023:06:01 09:33:34] Starting to install cancer_genome_interpreter:1.3.0... +[2023:06:01 09:33:34] Downloading code archive of cancer_genome_interpreter:1.3.0... +[**************************************************] 340.5 kB / 340.5 kB (100%) +[2023:06:01 09:33:34] Extracting code archive of cancer_genome_interpreter:1.3.0... +[2023:06:01 09:33:34] Verifying code integrity of cancer_genome_interpreter:1.3.0... +[2023:06:01 09:33:34] Downloading data of cancer_genome_interpreter:1.3.0... +[**************************************************] 21.9 kB / 21.9 kB (100%) +[2023:06:01 09:33:34] Extracting data of cancer_genome_interpreter:1.3.0... +[2023:06:01 09:33:34] Verifying data integrity of cancer_genome_interpreter:1.3.0... +[2023:06:01 09:33:35] Finished installation of cancer_genome_interpreter:1.3.0 +[2023:06:01 09:33:35] Starting to install civic:2023.04.01... +[2023:06:01 09:33:35] Downloading code archive of civic:2023.04.01... +[**************************************************] 74.2 kB / 74.2 kB (100%) +[2023:06:01 09:33:35] Extracting code archive of civic:2023.04.01... +[2023:06:01 09:33:35] Verifying code integrity of civic:2023.04.01... +[2023:06:01 09:33:35] Downloading data of civic:2023.04.01... +[**************************************************] 38.4 kB / 38.4 kB (100%) +[2023:06:01 09:33:35] Extracting data of civic:2023.04.01... +[2023:06:01 09:33:35] Verifying data integrity of civic:2023.04.01... +[2023:06:01 09:33:35] Finished installation of civic:2023.04.01 +[2023:06:01 09:33:35] Starting to install wgcancer_genome_interpreter:1.0.0... +[2023:06:01 09:33:35] Downloading code archive of wgcancer_genome_interpreter:1.0.0... +[**************************************************] 961 B / 961 B (100%) +[2023:06:01 09:33:35] Extracting code archive of wgcancer_genome_interpreter:1.0.0... +[2023:06:01 09:33:35] Verifying code integrity of wgcancer_genome_interpreter:1.0.0... +[2023:06:01 09:33:35] Finished installation of wgcancer_genome_interpreter:1.0.0 +[2023:06:01 09:33:35] Starting to install wgcivic:1.1.0... +[2023:06:01 09:33:35] Downloading code archive of wgcivic:1.1.0... +[**************************************************] 45.0 kB / 45.0 kB (100%) +[2023:06:01 09:33:35] Extracting code archive of wgcivic:1.1.0... +[2023:06:01 09:33:35] Verifying code integrity of wgcivic:1.1.0... +[2023:06:01 09:33:36] Finished installation of wgcivic:1.1.0 +(opencravat) [tmamidi@c0174 opencravat]$ oc module install aloft cadd cadd_exome cancer_genome_interpreter ccre_screen chasmplus civic clingen clinpred clinvar cosmic cosmic_gene cscape dann dann_coding dbscsnv dbsnp dgi ensembl_regulatory_build ess_gene exac_gene fathmm fathmm_xf_coding funseq2 genehancer gerp ghis gnomad gnomad3 gnomad_gene gtex gwas_catalog linsight loftool lrt mavedb metalr metasvm mutation_assessor mutationtaster mutpred1 mutpred_indel ncbigene ndex ndex_chd ndex_signor omim pangalodb phastcons phdsnpg phi phylop polyphen2 prec provean repeat revel rvis segway sift siphy spliceai uniprot vest +cancer_genome_interpreter: latest (1.3.0) is already installed. Use -f/--force to overwrite +civic: latest (2023.04.01) is already installed. Use -f/--force to overwrite +clinvar: latest (2023.02.01) is already installed. Use -f/--force to overwrite +Installing: aloft:1.1.0, cadd:1.0.1, cadd_exome:1.6.1, ccre_screen:1.0.1, chasmplus:1.3.0, chasmplus_group:1.2.0, clingen:1.0.1, clinpred:1.0.0, cosmic:94.0.0, cosmic_gene:94.0.0, cscape:1.0.1, dann:1.0.2, dann_coding:1.0.0, dbscsnv:1.1.0, dbsnp:154.0.2, dgi:1.0.0, ensembl_regulatory_build:1.0.3, ess_gene:3.5.8, exac_gene:0.4.0, fathmm:2.3.7, fathmm_xf_coding:1.0.0, funseq2:1.0.1, genehancer:1.1.0, gerp:3.6.2, ghis:3.6.0, gnomad:2.2.0, gnomad3:1.1.0, gnomad_gene:2.2.1, gtex:7.0.4, gwas_catalog:1.0.0, linsight:2021.01.08, loftool:3.6.1, lrt:1.0.0, mavedb:1.0.1, metalr:1.0.3, metasvm:1.0.1, mutation_assessor:3.1.0, mutationtaster:1.0.0, mutpred1:1.4.0, mutpred_indel:1.1.0, ncbigene:2022.08.30, ndex:4.0.11, ndex_chd:1.0.2, ndex_group:1.0.0, ndex_signor:1.0.2, omim:1.0.0, pangalodb:1.0.0, phastcons:3.5.9, phdsnpg:0.0.9, phi:3.6.1, phylop:3.5.10, polyphen2:2022.10.13, prec:3.6.0, provean:1.1.0, repeat:2020.10.16, revel:2022.11.29, rvis:3.1.0, segway:1.2.1, segway_group:1.0.0, sift:1.2.0, siphy:3.5.5, spliceai:1.0.0, uniprot:2020.08.10, vest:4.4.0, wgallelefrequency:1.1.0, wgaloft:1.0.0, wgccre_screen:1.0.0, wgchasmplus:1.1.0, wgclingen:1.1.1, wgcosmic:1.1.2, wgcosmic_gene:1.1.1, wgdgi:1.0.0, wgenhancer:1.1.0, wgensembl_regulatory_build:1.0.0, wgfathmm:1.1.0, wgfunseq2:1.0.0, wggenehancer:1.0.1, wggerp:1.0.3, wgghis:1.0.3, wggnomad:1.1.1, wggnomad3:1.1.0, wggnomad_gene:1.2.1, wggtex:1.1.1, wggwas_catalog:1.1.1, wgmutation_assessor:1.2.0, wgmutationtaster:1.0.0, wgmutpred1:1.1.2, wgncbigene:1.1.0, wgndex_chd:1.0.0, wgndex_chdsummary:1.0.1, wgndex_signor:1.0.0, wgndex_signorsummary:1.0.2, wgndexchasmplussummary:1.1.1, wgndexsummary:1.0.1, wgpangalodb:1.0.0, wgphastcons:1.1.0, wgphdsnpg:1.1.0, wgphylop:1.1.0, wgprec:1.1.0, wgprovean:1.0.0, wgrevel:1.2.1, wgrvis:1.1.0, wgsift:1.0.0, wgsiphy:1.1.1, wgvest:1.2.0 +Proceed? ([y]/n) > +[2023:06:01 09:36:15] Starting to install aloft:1.1.0... +[2023:06:01 09:36:15] Downloading code archive of aloft:1.1.0... +[**************************************************] 110.5 kB / 110.5 kB (100%) +[2023:06:01 09:36:15] Extracting code archive of aloft:1.1.0... +[2023:06:01 09:36:15] Verifying code integrity of aloft:1.1.0... +[2023:06:01 09:36:15] Downloading data of aloft:1.1.0... +[**************************************************] 91.2 MB / 91.2 MB (100%) +[2023:06:01 09:36:16] Extracting data of aloft:1.1.0... +[2023:06:01 09:36:17] Verifying data integrity of aloft:1.1.0... +[2023:06:01 09:36:18] Finished installation of aloft:1.1.0 +[2023:06:01 09:36:18] Starting to install cadd:1.0.1... +Following PyPI dependencies should be met before installing cadd. +- pytabix +Trying to install required PyPI packages... +Defaulting to user installation because normal site-packages is not writeable +Collecting pytabix + Downloading pytabix-0.1.tar.gz (45 kB) + 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clinpred:1.0.0... +[2023:06:01 09:57:52] Finished installation of clinpred:1.0.0 +[2023:06:01 09:57:52] Starting to install cosmic:94.0.0... +[2023:06:01 09:57:52] Downloading code archive of cosmic:94.0.0... +[**************************************************] 2.6 MB / 2.6 MB (100%) +[2023:06:01 09:57:53] Extracting code archive of cosmic:94.0.0... +[2023:06:01 09:57:53] Verifying code integrity of cosmic:94.0.0... +[2023:06:01 09:57:53] Downloading data of cosmic:94.0.0... +[**************************************************] 406.5 MB / 406.5 MB (100%) +[2023:06:01 09:57:56] Extracting data of cosmic:94.0.0... +[2023:06:01 09:58:02] Verifying data integrity of cosmic:94.0.0... +[2023:06:01 09:58:05] Finished installation of cosmic:94.0.0 +[2023:06:01 09:58:05] Starting to install cosmic_gene:94.0.0... +[2023:06:01 09:58:05] Downloading code archive of cosmic_gene:94.0.0... +[**************************************************] 2.7 MB / 2.7 MB (100%) +[2023:06:01 09:58:05] Extracting code archive of cosmic_gene:94.0.0... +[2023:06:01 09:58:05] Verifying code integrity of cosmic_gene:94.0.0... +[2023:06:01 09:58:05] Downloading data of cosmic_gene:94.0.0... +[**************************************************] 30.2 MB / 30.2 MB (100%) +[2023:06:01 09:58:06] Extracting data of cosmic_gene:94.0.0... +[2023:06:01 09:58:06] Verifying data integrity of cosmic_gene:94.0.0... +[2023:06:01 09:58:06] Finished installation of cosmic_gene:94.0.0 +[2023:06:01 09:58:06] Starting to install cscape:1.0.1... +[2023:06:01 09:58:17] Downloading code archive of cscape:1.0.1... +[**************************************************] 41.4 kB / 41.4 kB (100%) +[2023:06:01 09:58:17] Extracting code archive of cscape:1.0.1... +[2023:06:01 09:58:17] Verifying code integrity of cscape:1.0.1... +[2023:06:01 09:58:17] Downloading data of cscape:1.0.1... +[**************************************************] 56.4 GB / 56.4 GB (100%) +[2023:06:01 10:06:13] Extracting data of cscape:1.0.1... +[2023:06:01 10:12:01] Verifying data integrity of cscape:1.0.1... +[2023:06:01 10:14:14] Finished installation of cscape:1.0.1 +[2023:06:01 10:14:14] Starting to install dann:1.0.2... +[2023:06:01 10:14:24] Downloading code archive of dann:1.0.2... +[**************************************************] 527.7 kB / 527.7 kB (100%) +[2023:06:01 10:14:24] Extracting code archive of dann:1.0.2... +[2023:06:01 10:14:24] Verifying code integrity of dann:1.0.2... +[2023:06:01 10:14:24] Downloading data of dann:1.0.2... +[**************************************************] 109.6 GB / 109.6 GB (100%) +[2023:06:01 10:30:10] Extracting data of dann:1.0.2... +[2023:06:01 10:40:16] Verifying data integrity of dann:1.0.2... +[2023:06:01 10:43:22] Finished installation of dann:1.0.2 +[2023:06:01 10:43:22] Starting to install dann_coding:1.0.0... +[2023:06:01 10:43:22] Downloading code archive of dann_coding:1.0.0... +[**************************************************] 117.8 kB / 117.8 kB (100%) +[2023:06:01 10:43:22] Extracting code archive of dann_coding:1.0.0... +[2023:06:01 10:43:22] Verifying code integrity of dann_coding:1.0.0... +[2023:06:01 10:43:22] Downloading data of dann_coding:1.0.0... +[**************************************************] 1.9 GB / 1.9 GB (100%) +[2023:06:01 10:43:37] Extracting data of dann_coding:1.0.0... +[2023:06:01 10:43:55] Verifying data integrity of dann_coding:1.0.0... +[2023:06:01 10:44:02] Finished installation of dann_coding:1.0.0 +[2023:06:01 10:44:02] Starting to install dbscsnv:1.1.0... +[2023:06:01 10:44:02] Downloading code archive of dbscsnv:1.1.0... +[**************************************************] 103.5 kB / 103.5 kB (100%) +[2023:06:01 10:44:02] Extracting code archive of dbscsnv:1.1.0... +[2023:06:01 10:44:02] Verifying code integrity of dbscsnv:1.1.0... +[2023:06:01 10:44:02] Downloading data of dbscsnv:1.1.0... +[**************************************************] 285.6 MB / 285.6 MB (100%) +[2023:06:01 10:44:04] Extracting data of dbscsnv:1.1.0... +[2023:06:01 10:44:07] Verifying data integrity of dbscsnv:1.1.0... +[2023:06:01 10:44:09] Finished installation of dbscsnv:1.1.0 +[2023:06:01 10:44:09] Starting to install dbsnp:154.0.2... +[2023:06:01 10:44:09] Downloading code archive of dbsnp:154.0.2... +[**************************************************] 212.7 kB / 212.7 kB (100%) +[2023:06:01 10:44:09] Extracting code archive of dbsnp:154.0.2... +[2023:06:01 10:44:09] Verifying code integrity of dbsnp:154.0.2... +[2023:06:01 10:44:09] Downloading data of dbsnp:154.0.2... +[**************************************************] 15.5 GB / 15.5 GB (100%) +[2023:06:01 10:46:10] Extracting data of dbsnp:154.0.2... +[2023:06:01 10:48:51] Verifying data integrity of dbsnp:154.0.2... +[2023:06:01 10:50:50] Finished installation of dbsnp:154.0.2 +[2023:06:01 10:50:50] Starting to install dgi:1.0.0... +[2023:06:01 10:50:50] Downloading code archive of dgi:1.0.0... +[**************************************************] 170.9 kB / 170.9 kB (100%) +[2023:06:01 10:50:51] Extracting code archive of dgi:1.0.0... +[2023:06:01 10:50:51] Verifying code integrity of dgi:1.0.0... +[2023:06:01 10:50:51] Downloading data of dgi:1.0.0... +[**************************************************] 3.2 MB / 3.2 MB (100%) +[2023:06:01 10:50:51] Extracting data of dgi:1.0.0... +[2023:06:01 10:50:51] Verifying data integrity of dgi:1.0.0... +[2023:06:01 10:50:51] Finished installation of dgi:1.0.0 +[2023:06:01 10:50:51] Starting to install ensembl_regulatory_build:1.0.3... +[2023:06:01 10:50:51] Downloading code archive of ensembl_regulatory_build:1.0.3... +[**************************************************] 79.0 kB / 79.0 kB (100%) +[2023:06:01 10:50:51] Extracting code archive of ensembl_regulatory_build:1.0.3... +[2023:06:01 10:50:51] Verifying code integrity of ensembl_regulatory_build:1.0.3... +[2023:06:01 10:50:51] Downloading data of ensembl_regulatory_build:1.0.3... +[**************************************************] 15.4 MB / 15.4 MB (100%) +[2023:06:01 10:50:52] Extracting data of ensembl_regulatory_build:1.0.3... +[2023:06:01 10:50:52] Verifying data integrity of ensembl_regulatory_build:1.0.3... +[2023:06:01 10:50:52] Finished installation of ensembl_regulatory_build:1.0.3 +[2023:06:01 10:50:52] Starting to install ess_gene:3.5.8... +[2023:06:01 10:50:52] Downloading code archive of ess_gene:3.5.8... +[**************************************************] 15.2 kB / 15.2 kB (100%) +[2023:06:01 10:50:52] Extracting code archive of ess_gene:3.5.8... +[2023:06:01 10:50:52] Verifying code integrity of ess_gene:3.5.8... +[2023:06:01 10:50:52] Downloading data of ess_gene:3.5.8... +[**************************************************] 146.0 kB / 146.0 kB (100%) +[2023:06:01 10:50:52] Extracting data of ess_gene:3.5.8... +[2023:06:01 10:50:52] Verifying data integrity of ess_gene:3.5.8... +[2023:06:01 10:50:52] Finished installation of ess_gene:3.5.8 +[2023:06:01 10:50:52] Starting to install exac_gene:0.4.0... +[2023:06:01 10:50:52] Downloading code archive of exac_gene:0.4.0... +[**************************************************] 9.3 kB / 9.3 kB (100%) +[2023:06:01 10:50:53] Extracting code archive of exac_gene:0.4.0... +[2023:06:01 10:50:53] Verifying code integrity of exac_gene:0.4.0... +[2023:06:01 10:50:53] Downloading data of exac_gene:0.4.0... +[**************************************************] 1.9 MB / 1.9 MB (100%) +[2023:06:01 10:50:53] Extracting data of exac_gene:0.4.0... +[2023:06:01 10:50:53] Verifying data integrity of exac_gene:0.4.0... +[2023:06:01 10:50:53] Finished installation of exac_gene:0.4.0 +[2023:06:01 10:50:53] Starting to install fathmm:2.3.7... +[2023:06:01 10:50:53] Downloading code archive of fathmm:2.3.7... +[**************************************************] 16.6 kB / 16.6 kB (100%) +[2023:06:01 10:50:53] Extracting code archive of fathmm:2.3.7... +[2023:06:01 10:50:53] Verifying code integrity of fathmm:2.3.7... +[2023:06:01 10:50:53] Downloading data of fathmm:2.3.7... +[**************************************************] 1.2 GB / 1.2 GB (100%) +[2023:06:01 10:51:03] Extracting data of fathmm:2.3.7... +[2023:06:01 10:51:26] Verifying data integrity of fathmm:2.3.7... +[2023:06:01 10:51:43] Finished installation of fathmm:2.3.7 +[2023:06:01 10:51:43] Starting to install fathmm_xf_coding:1.0.0... +[2023:06:01 10:51:43] Downloading code archive of fathmm_xf_coding:1.0.0... +[**************************************************] 99.7 kB / 99.7 kB (100%) +[2023:06:01 10:51:43] Extracting code archive of fathmm_xf_coding:1.0.0... +[2023:06:01 10:51:43] Verifying code integrity of fathmm_xf_coding:1.0.0... +[2023:06:01 10:51:43] Downloading data of fathmm_xf_coding:1.0.0... +[**************************************************] 1.6 GB / 1.6 GB (100%) +[2023:06:01 10:52:00] Extracting data of fathmm_xf_coding:1.0.0... +[2023:06:01 10:52:17] Verifying data integrity of fathmm_xf_coding:1.0.0... +[2023:06:01 10:52:24] Finished installation of fathmm_xf_coding:1.0.0 +[2023:06:01 10:52:24] Starting to install funseq2:1.0.1... +[2023:06:01 10:52:33] Downloading code archive of funseq2:1.0.1... +[**************************************************] 68.0 kB / 68.0 kB (100%) +[2023:06:01 10:52:33] Extracting code archive of funseq2:1.0.1... +[2023:06:01 10:52:33] Verifying code integrity of funseq2:1.0.1... +[2023:06:01 10:52:33] Downloading data of funseq2:1.0.1... +[**************************************************] 31.7 GB / 31.7 GB (100%) +[2023:06:01 10:56:39] Extracting data of funseq2:1.0.1... +[2023:06:01 10:59:21] Verifying data integrity of funseq2:1.0.1... +[2023:06:01 11:00:14] Finished installation of funseq2:1.0.1 +[2023:06:01 11:00:14] Starting to install genehancer:1.1.0... +[2023:06:01 11:00:14] Downloading code archive of genehancer:1.1.0... +[**************************************************] 38.4 kB / 38.4 kB (100%) +[2023:06:01 11:00:14] Extracting code archive of genehancer:1.1.0... +[2023:06:01 11:00:14] Verifying code integrity of genehancer:1.1.0... +[2023:06:01 11:00:14] Downloading data of genehancer:1.1.0... +[**************************************************] 17.9 MB / 17.9 MB (100%) +[2023:06:01 11:00:14] Extracting data of genehancer:1.1.0... +[2023:06:01 11:00:15] Verifying data integrity of genehancer:1.1.0... +[2023:06:01 11:00:15] Finished installation of genehancer:1.1.0 +[2023:06:01 11:00:15] Starting to install gerp:3.6.2... +[2023:06:01 11:00:15] Downloading code archive of gerp:3.6.2... +[**************************************************] 12.1 kB / 12.1 kB (100%) +[2023:06:01 11:00:15] Extracting code archive of gerp:3.6.2... +[2023:06:01 11:00:15] Verifying code integrity of gerp:3.6.2... +[2023:06:01 11:00:15] Downloading data of gerp:3.6.2... +[**************************************************] 972.3 MB / 972.3 MB (100%) +[2023:06:01 11:00:23] Extracting data of gerp:3.6.2... +[2023:06:01 11:00:35] Verifying data integrity of gerp:3.6.2... +[2023:06:01 11:00:43] Finished installation of gerp:3.6.2 +[2023:06:01 11:00:43] Starting to install ghis:3.6.0... +[2023:06:01 11:00:43] Downloading code archive of ghis:3.6.0... +[**************************************************] 21.1 kB / 21.1 kB (100%) +[2023:06:01 11:00:43] Extracting code archive of ghis:3.6.0... +[2023:06:01 11:00:43] Verifying code integrity of ghis:3.6.0... +[2023:06:01 11:00:43] Downloading data of ghis:3.6.0... +[**************************************************] 384.8 kB / 384.8 kB (100%) +[2023:06:01 11:00:43] Extracting data of ghis:3.6.0... +[2023:06:01 11:00:43] Verifying data integrity of ghis:3.6.0... +[2023:06:01 11:00:43] Finished installation of ghis:3.6.0 +[2023:06:01 11:00:43] Starting to install gnomad:2.2.0... +[2023:06:01 11:00:43] Downloading code archive of gnomad:2.2.0... +[**************************************************] 149.6 kB / 149.6 kB (100%) +[2023:06:01 11:00:43] Extracting code archive of gnomad:2.2.0... +[2023:06:01 11:00:43] Verifying code integrity of gnomad:2.2.0... +[2023:06:01 11:00:43] Downloading data of gnomad:2.2.0... +[**************************************************] 7.8 GB / 7.8 GB (100%) +[2023:06:01 11:01:44] Extracting data of gnomad:2.2.0... +[2023:06:01 11:02:59] Verifying data integrity of gnomad:2.2.0... +[2023:06:01 11:03:27] Finished installation of gnomad:2.2.0 +[2023:06:01 11:03:27] Starting to install gnomad3:1.1.0... +[2023:06:01 11:03:27] Downloading code archive of gnomad3:1.1.0... +[**************************************************] 156.3 kB / 156.3 kB (100%) +[2023:06:01 11:03:27] Extracting code archive of gnomad3:1.1.0... +[2023:06:01 11:03:27] Verifying code integrity of gnomad3:1.1.0... +[2023:06:01 11:03:27] Downloading data of gnomad3:1.1.0... +[**************************************************] 20.1 GB / 20.1 GB (100%) +[2023:06:01 11:06:55] Extracting data of gnomad3:1.1.0... +[2023:06:01 11:10:06] Verifying data integrity of gnomad3:1.1.0... +[2023:06:01 11:11:16] Finished installation of gnomad3:1.1.0 +[2023:06:01 11:11:16] Starting to install gnomad_gene:2.2.1... +[2023:06:01 11:11:16] Downloading code archive of gnomad_gene:2.2.1... +[**************************************************] 152.8 kB / 152.8 kB (100%) +[2023:06:01 11:11:16] Extracting code archive of gnomad_gene:2.2.1... +[2023:06:01 11:11:16] Verifying code integrity of gnomad_gene:2.2.1... +[2023:06:01 11:11:16] Downloading data of gnomad_gene:2.2.1... +[**************************************************] 4.9 MB / 4.9 MB (100%) +[2023:06:01 11:11:16] Extracting data of gnomad_gene:2.2.1... +[2023:06:01 11:11:16] Verifying data integrity of gnomad_gene:2.2.1... +[2023:06:01 11:11:17] Finished installation of gnomad_gene:2.2.1 +[2023:06:01 11:11:17] Starting to install gtex:7.0.4... +[2023:06:01 11:11:17] Downloading code archive of gtex:7.0.4... +[**************************************************] 14.3 kB / 14.3 kB (100%) +[2023:06:01 11:11:17] Extracting code archive of gtex:7.0.4... +[2023:06:01 11:11:17] Verifying code integrity of gtex:7.0.4... +[2023:06:01 11:11:17] Downloading data of gtex:7.0.4... +[**************************************************] 1.0 MB / 1.0 MB (100%) +[2023:06:01 11:11:17] Extracting data of gtex:7.0.4... +[2023:06:01 11:11:17] Verifying data integrity of gtex:7.0.4... +[2023:06:01 11:11:17] Finished installation of gtex:7.0.4 +[2023:06:01 11:11:17] Starting to install gwas_catalog:1.0.0... +[2023:06:01 11:11:17] Downloading code archive of gwas_catalog:1.0.0... +[**************************************************] 113.5 kB / 113.5 kB (100%) +[2023:06:01 11:11:17] Extracting code archive of gwas_catalog:1.0.0... +[2023:06:01 11:11:17] Verifying code integrity of gwas_catalog:1.0.0... +[2023:06:01 11:11:17] Downloading data of gwas_catalog:1.0.0... +[**************************************************] 5.2 MB / 5.2 MB (100%) +[2023:06:01 11:11:18] Extracting data of gwas_catalog:1.0.0... +[2023:06:01 11:11:18] Verifying data integrity of gwas_catalog:1.0.0... +[2023:06:01 11:11:18] Finished installation of gwas_catalog:1.0.0 +[2023:06:01 11:11:18] Starting to install linsight:2021.01.08... +[2023:06:01 11:11:18] Downloading code archive of linsight:2021.01.08... +[**************************************************] 138.1 kB / 138.1 kB (100%) +[2023:06:01 11:11:18] Extracting code archive of linsight:2021.01.08... +[2023:06:01 11:11:18] Verifying code integrity of linsight:2021.01.08... +[2023:06:01 11:11:18] Downloading data of linsight:2021.01.08... +[**************************************************] 3.2 GB / 3.2 GB (100%) +[2023:06:01 11:11:43] Extracting data of linsight:2021.01.08... +[2023:06:01 11:12:16] Verifying data integrity of linsight:2021.01.08... +[2023:06:01 11:12:31] Finished installation of linsight:2021.01.08 +[2023:06:01 11:12:31] Starting to install loftool:3.6.1... +[2023:06:01 11:12:31] Downloading code archive of loftool:3.6.1... +[**************************************************] 12.5 kB / 12.5 kB (100%) +[2023:06:01 11:12:31] Extracting code archive of loftool:3.6.1... +[2023:06:01 11:12:31] Verifying code integrity of loftool:3.6.1... +[2023:06:01 11:12:31] Downloading data of loftool:3.6.1... +[**************************************************] 254.6 kB / 254.6 kB (100%) +[2023:06:01 11:12:31] Extracting data of loftool:3.6.1... +[2023:06:01 11:12:31] Verifying data integrity of loftool:3.6.1... +[2023:06:01 11:12:31] Finished installation of loftool:3.6.1 +[2023:06:01 11:12:31] Starting to install lrt:1.0.0... +[2023:06:01 11:12:31] Downloading code archive of lrt:1.0.0... +[**************************************************] 73.3 kB / 73.3 kB (100%) +[2023:06:01 11:12:32] Extracting code archive of lrt:1.0.0... +[2023:06:01 11:12:32] Verifying code integrity of lrt:1.0.0... +[2023:06:01 11:12:32] Downloading data of lrt:1.0.0... +[**************************************************] 916.8 MB / 916.8 MB (100%) +[2023:06:01 11:12:39] Extracting data of lrt:1.0.0... +[2023:06:01 11:12:50] Verifying data integrity of lrt:1.0.0... +[2023:06:01 11:12:56] Finished installation of lrt:1.0.0 +[2023:06:01 11:12:56] Starting to install mavedb:1.0.1... +[2023:06:01 11:12:56] Downloading code archive of mavedb:1.0.1... +[**************************************************] 43.4 kB / 43.4 kB (100%) +[2023:06:01 11:12:56] Extracting code archive of mavedb:1.0.1... +[2023:06:01 11:12:56] Verifying code integrity of mavedb:1.0.1... +[2023:06:01 11:12:56] Downloading data of mavedb:1.0.1... +[**************************************************] 720.4 kB / 720.4 kB (100%) +[2023:06:01 11:12:56] Extracting data of mavedb:1.0.1... +[2023:06:01 11:12:56] Verifying data integrity of mavedb:1.0.1... +[2023:06:01 11:12:56] Finished installation of mavedb:1.0.1 +[2023:06:01 11:12:56] Starting to install metalr:1.0.3... +[2023:06:01 11:12:56] Downloading code archive of metalr:1.0.3... +[**************************************************] 234.5 kB / 234.5 kB (100%) +[2023:06:01 11:12:56] Extracting code archive of metalr:1.0.3... +[2023:06:01 11:12:56] Verifying code integrity of metalr:1.0.3... +[2023:06:01 11:12:57] Downloading data of metalr:1.0.3... +[**************************************************] 1.3 GB / 1.3 GB (100%) +[2023:06:01 11:13:07] Extracting data of metalr:1.0.3... +[2023:06:01 11:13:23] Verifying data integrity of metalr:1.0.3... +[2023:06:01 11:13:29] Finished installation of metalr:1.0.3 +[2023:06:01 11:13:29] Starting to install metasvm:1.0.1... +[2023:06:01 11:13:29] Downloading code archive of metasvm:1.0.1... +[**************************************************] 264.1 kB / 264.1 kB (100%) +[2023:06:01 11:13:29] Extracting code archive of metasvm:1.0.1... +[2023:06:01 11:13:29] Verifying code integrity of metasvm:1.0.1... +[2023:06:01 11:13:29] Downloading data of metasvm:1.0.1... +[**************************************************] 1.3 GB / 1.3 GB (100%) +[2023:06:01 11:13:40] Extracting data of metasvm:1.0.1... +[2023:06:01 11:13:56] Verifying data integrity of metasvm:1.0.1... +[2023:06:01 11:14:02] Finished installation of metasvm:1.0.1 +[2023:06:01 11:14:02] Starting to install mutation_assessor:3.1.0... +[2023:06:01 11:14:02] Downloading code archive of mutation_assessor:3.1.0... +[**************************************************] 13.4 kB / 13.4 kB (100%) +[2023:06:01 11:14:02] Extracting code archive of mutation_assessor:3.1.0... +[2023:06:01 11:14:02] Verifying code integrity of mutation_assessor:3.1.0... +[2023:06:01 11:14:02] Downloading data of mutation_assessor:3.1.0... +[**************************************************] 1.2 GB / 1.2 GB (100%) +[2023:06:01 11:14:11] Extracting data of mutation_assessor:3.1.0... +[2023:06:01 11:14:26] Verifying data integrity of mutation_assessor:3.1.0... +[2023:06:01 11:14:33] Finished installation of mutation_assessor:3.1.0 +[2023:06:01 11:14:33] Starting to install mutationtaster:1.0.0... +[2023:06:01 11:14:33] Downloading code archive of mutationtaster:1.0.0... +[**************************************************] 194.2 kB / 194.2 kB (100%) +[2023:06:01 11:14:33] Extracting code archive of mutationtaster:1.0.0... +[2023:06:01 11:14:33] Verifying code integrity of mutationtaster:1.0.0... +[2023:06:01 11:14:33] Downloading data of mutationtaster:1.0.0... +[**************************************************] 1.9 GB / 1.9 GB (100%) +[2023:06:01 11:14:48] Extracting data of mutationtaster:1.0.0... +[2023:06:01 11:15:15] Verifying data integrity of mutationtaster:1.0.0... +[2023:06:01 11:15:34] Finished installation of mutationtaster:1.0.0 +[2023:06:01 11:15:34] Starting to install mutpred1:1.4.0... +[2023:06:01 11:15:34] Downloading code archive of mutpred1:1.4.0... +[**************************************************] 70.2 kB / 70.2 kB (100%) +[2023:06:01 11:15:34] Extracting code archive of mutpred1:1.4.0... +[2023:06:01 11:15:34] Verifying code integrity of mutpred1:1.4.0... +[2023:06:01 11:15:34] Downloading data of mutpred1:1.4.0... +[**************************************************] 3.2 GB / 3.2 GB (100%) +[2023:06:01 11:15:59] Extracting data of mutpred1:1.4.0... +[2023:06:01 11:16:47] Verifying data integrity of mutpred1:1.4.0... +[2023:06:01 11:17:12] Finished installation of mutpred1:1.4.0 +[2023:06:01 11:17:12] Starting to install mutpred_indel:1.1.0... +[2023:06:01 11:17:12] Downloading code archive of mutpred_indel:1.1.0... +[**************************************************] 398.7 kB / 398.7 kB (100%) +[2023:06:01 11:17:12] Extracting code archive of mutpred_indel:1.1.0... +[2023:06:01 11:17:12] Verifying code integrity of mutpred_indel:1.1.0... +[2023:06:01 11:17:13] Downloading data of mutpred_indel:1.1.0... +[**************************************************] 3.0 GB / 3.0 GB (100%) +[2023:06:01 11:17:36] Extracting data of mutpred_indel:1.1.0... +[2023:06:01 11:18:21] Verifying data integrity of mutpred_indel:1.1.0... +[2023:06:01 11:18:48] Finished installation of mutpred_indel:1.1.0 +[2023:06:01 11:18:48] Starting to install ncbigene:2022.08.30... +[2023:06:01 11:18:48] Downloading code archive of ncbigene:2022.08.30... +[**************************************************] 102.3 kB / 102.3 kB (100%) +[2023:06:01 11:18:48] Extracting code archive of ncbigene:2022.08.30... +[2023:06:01 11:18:48] Verifying code integrity of ncbigene:2022.08.30... +[2023:06:01 11:18:48] Downloading data of ncbigene:2022.08.30... +[**************************************************] 3.3 MB / 3.3 MB (100%) +[2023:06:01 11:18:49] Extracting data of ncbigene:2022.08.30... +[2023:06:01 11:18:49] Verifying data integrity of ncbigene:2022.08.30... +[2023:06:01 11:18:49] Finished installation of ncbigene:2022.08.30 +[2023:06:01 11:18:49] Starting to install ndex:4.0.11... +[2023:06:01 11:18:49] Downloading code archive of ndex:4.0.11... +[**************************************************] 200.6 kB / 200.6 kB (100%) +[2023:06:01 11:18:49] Extracting code archive of ndex:4.0.11... +[2023:06:01 11:18:49] Verifying code integrity of ndex:4.0.11... +[2023:06:01 11:18:49] Downloading data of ndex:4.0.11... +[**************************************************] 138.7 kB / 138.7 kB (100%) +[2023:06:01 11:18:49] Extracting data of ndex:4.0.11... +[2023:06:01 11:18:49] Verifying data integrity of ndex:4.0.11... +[2023:06:01 11:18:50] Finished installation of ndex:4.0.11 +[2023:06:01 11:18:50] Starting to install ndex_chd:1.0.2... +[2023:06:01 11:18:50] Downloading code archive of ndex_chd:1.0.2... +[**************************************************] 274.4 kB / 274.4 kB (100%) +[2023:06:01 11:18:50] Extracting code archive of ndex_chd:1.0.2... +[2023:06:01 11:18:50] Verifying code integrity of ndex_chd:1.0.2... +[2023:06:01 11:18:50] Downloading data of ndex_chd:1.0.2... +[**************************************************] 23.0 kB / 23.0 kB (100%) +[2023:06:01 11:18:50] Extracting data of ndex_chd:1.0.2... +[2023:06:01 11:18:50] Verifying data integrity of ndex_chd:1.0.2... +[2023:06:01 11:18:50] Finished installation of ndex_chd:1.0.2 +[2023:06:01 11:18:50] Starting to install ndex_group:1.0.0... +[2023:06:01 11:18:50] Downloading code archive of ndex_group:1.0.0... +[**************************************************] 74.6 kB / 74.6 kB (100%) +[2023:06:01 11:18:50] Extracting code archive of ndex_group:1.0.0... +[2023:06:01 11:18:50] Verifying code integrity of ndex_group:1.0.0... +[2023:06:01 11:18:51] Finished installation of ndex_group:1.0.0 +[2023:06:01 11:18:51] Starting to install ndex_signor:1.0.2... +[2023:06:01 11:18:51] Downloading code archive of ndex_signor:1.0.2... +[**************************************************] 203.3 kB / 203.3 kB (100%) +[2023:06:01 11:18:51] Extracting code archive of ndex_signor:1.0.2... +[2023:06:01 11:18:51] Verifying code integrity of ndex_signor:1.0.2... +[2023:06:01 11:18:51] Downloading data of ndex_signor:1.0.2... +[**************************************************] 22.5 kB / 22.5 kB (100%) +[2023:06:01 11:18:51] Extracting data of ndex_signor:1.0.2... +[2023:06:01 11:18:51] Verifying data integrity of ndex_signor:1.0.2... +[2023:06:01 11:18:51] Finished installation of ndex_signor:1.0.2 +[2023:06:01 11:18:51] Starting to install omim:1.0.0... +[2023:06:01 11:18:51] Downloading code archive of omim:1.0.0... +[**************************************************] 12.5 kB / 12.5 kB (100%) +[2023:06:01 11:18:51] Extracting code archive of omim:1.0.0... +[2023:06:01 11:18:51] Verifying code integrity of omim:1.0.0... +[2023:06:01 11:18:51] Downloading data of omim:1.0.0... +[**************************************************] 2.7 MB / 2.7 MB (100%) +[2023:06:01 11:18:51] Extracting data of omim:1.0.0... +[2023:06:01 11:18:51] Verifying data integrity of omim:1.0.0... +[2023:06:01 11:18:52] Finished installation of omim:1.0.0 +[2023:06:01 11:18:52] Starting to install pangalodb:1.0.0... +[2023:06:01 11:18:52] Downloading code archive of pangalodb:1.0.0... +[**************************************************] 141.8 kB / 141.8 kB (100%) +[2023:06:01 11:18:52] Extracting code archive of pangalodb:1.0.0... +[2023:06:01 11:18:52] Verifying code integrity of pangalodb:1.0.0... +[2023:06:01 11:18:52] Downloading data of pangalodb:1.0.0... +[**************************************************] 309.2 kB / 309.2 kB (100%) +[2023:06:01 11:18:52] Extracting data of pangalodb:1.0.0... +[2023:06:01 11:18:52] Verifying data integrity of pangalodb:1.0.0... +[2023:06:01 11:18:52] Finished installation of pangalodb:1.0.0 +[2023:06:01 11:18:52] Starting to install phastcons:3.5.9... +[2023:06:01 11:18:52] Downloading code archive of phastcons:3.5.9... +[**************************************************] 116.0 kB / 116.0 kB (100%) +[2023:06:01 11:18:52] Extracting code archive of phastcons:3.5.9... +[2023:06:01 11:18:52] Verifying code integrity of phastcons:3.5.9... +[2023:06:01 11:18:52] Downloading data of phastcons:3.5.9... +[**************************************************] 1.4 GB / 1.4 GB (100%) +[2023:06:01 11:19:03] Extracting data of phastcons:3.5.9... +[2023:06:01 11:19:21] Verifying data integrity of phastcons:3.5.9... +[2023:06:01 11:19:31] Finished installation of phastcons:3.5.9 +[2023:06:01 11:19:31] Starting to install phdsnpg:0.0.9... +[2023:06:01 11:19:31] Downloading code archive of phdsnpg:0.0.9... +[**************************************************] 16.4 kB / 16.4 kB (100%) +[2023:06:01 11:19:31] Extracting code archive of phdsnpg:0.0.9... +[2023:06:01 11:19:31] Verifying code integrity of phdsnpg:0.0.9... +[2023:06:01 11:19:31] Downloading data of phdsnpg:0.0.9... +[**************************************************] 1.2 GB / 1.2 GB (100%) +[2023:06:01 11:19:41] Extracting data of phdsnpg:0.0.9... +[2023:06:01 11:19:57] Verifying data integrity of phdsnpg:0.0.9... +[2023:06:01 11:20:06] Finished installation of phdsnpg:0.0.9 +[2023:06:01 11:20:06] Starting to install phi:3.6.1... +[2023:06:01 11:20:06] Downloading code archive of phi:3.6.1... +[**************************************************] 14.5 kB / 14.5 kB (100%) +[2023:06:01 11:20:06] Extracting code archive of phi:3.6.1... +[2023:06:01 11:20:06] Verifying code integrity of phi:3.6.1... +[2023:06:01 11:20:06] Downloading data of phi:3.6.1... +[**************************************************] 362.1 kB / 362.1 kB (100%) +[2023:06:01 11:20:06] Extracting data of phi:3.6.1... +[2023:06:01 11:20:06] Verifying data integrity of phi:3.6.1... +[2023:06:01 11:20:06] Finished installation of phi:3.6.1 +[2023:06:01 11:20:06] Starting to install phylop:3.5.10... +[2023:06:01 11:20:06] Downloading code archive of phylop:3.5.10... +[**************************************************] 230.2 kB / 230.2 kB (100%) +[2023:06:01 11:20:06] Extracting code archive of phylop:3.5.10... +[2023:06:01 11:20:06] Verifying code integrity of phylop:3.5.10... +[2023:06:01 11:20:06] Downloading data of phylop:3.5.10... +[**************************************************] 2.8 GB / 2.8 GB (100%) +[2023:06:01 11:20:29] Extracting data of phylop:3.5.10... +[2023:06:01 11:21:04] Verifying data integrity of phylop:3.5.10... +[2023:06:01 11:21:27] Finished installation of phylop:3.5.10 +[2023:06:01 11:21:27] Starting to install polyphen2:2022.10.13... +[2023:06:01 11:21:27] Downloading code archive of polyphen2:2022.10.13... +[**************************************************] 61.8 kB / 61.8 kB (100%) +[2023:06:01 11:21:27] Extracting code archive of polyphen2:2022.10.13... +[2023:06:01 11:21:27] Verifying code integrity of polyphen2:2022.10.13... +[2023:06:01 11:21:27] Downloading data of polyphen2:2022.10.13... +[**************************************************] 1.6 GB / 1.6 GB (100%) +[2023:06:01 11:21:40] Extracting data of polyphen2:2022.10.13... +[2023:06:01 11:22:02] Verifying data integrity of polyphen2:2022.10.13... +[2023:06:01 11:22:15] Finished installation of polyphen2:2022.10.13 +[2023:06:01 11:22:15] Starting to install prec:3.6.0... +[2023:06:01 11:22:15] Downloading code archive of prec:3.6.0... +[**************************************************] 18.9 kB / 18.9 kB (100%) +[2023:06:01 11:22:15] Extracting code archive of prec:3.6.0... +[2023:06:01 11:22:15] Verifying code integrity of prec:3.6.0... +[2023:06:01 11:22:15] Downloading data of prec:3.6.0... +[**************************************************] 295.6 kB / 295.6 kB (100%) +[2023:06:01 11:22:15] Extracting data of prec:3.6.0... +[2023:06:01 11:22:15] Verifying data integrity of prec:3.6.0... +[2023:06:01 11:22:15] Finished installation of prec:3.6.0 +[2023:06:01 11:22:15] Starting to install provean:1.1.0... +[2023:06:01 11:22:15] Downloading code archive of provean:1.1.0... +[**************************************************] 70.5 kB / 70.5 kB (100%) +[2023:06:01 11:22:16] Extracting code archive of provean:1.1.0... +[2023:06:01 11:22:16] Verifying code integrity of provean:1.1.0... +[2023:06:01 11:22:16] Downloading data of provean:1.1.0... +[**************************************************] 2.4 GB / 2.4 GB (100%) +[2023:06:01 11:22:35] Extracting data of provean:1.1.0... +[2023:06:01 11:23:07] Verifying data integrity of provean:1.1.0... +[2023:06:01 11:23:27] Finished installation of provean:1.1.0 +[2023:06:01 11:23:27] Starting to install repeat:2020.10.16... +[2023:06:01 11:23:27] Downloading code archive of repeat:2020.10.16... +[**************************************************] 66.7 kB / 66.7 kB (100%) +[2023:06:01 11:23:27] Extracting code archive of repeat:2020.10.16... +[2023:06:01 11:23:27] Verifying code integrity of repeat:2020.10.16... +[2023:06:01 11:23:28] Downloading data of repeat:2020.10.16... +[**************************************************] 112.1 MB / 112.1 MB (100%) +[2023:06:01 11:23:29] Extracting data of repeat:2020.10.16... +[2023:06:01 11:23:30] Verifying data integrity of repeat:2020.10.16... +[2023:06:01 11:23:31] Finished installation of repeat:2020.10.16 +[2023:06:01 11:23:31] Starting to install revel:2022.11.29... +[2023:06:01 11:23:31] Downloading code archive of revel:2022.11.29... +[**************************************************] 97.8 kB / 97.8 kB (100%) +[2023:06:01 11:23:31] Extracting code archive of revel:2022.11.29... +[2023:06:01 11:23:31] Verifying code integrity of revel:2022.11.29... +[2023:06:01 11:23:31] Downloading data of revel:2022.11.29... +[**************************************************] 1.3 GB / 1.3 GB (100%) +[2023:06:01 11:23:41] Extracting data of revel:2022.11.29... +[2023:06:01 11:23:58] Verifying data integrity of revel:2022.11.29... +[2023:06:01 11:24:09] Finished installation of revel:2022.11.29 +[2023:06:01 11:24:09] Starting to install rvis:3.1.0... +[2023:06:01 11:24:09] Downloading code archive of rvis:3.1.0... +[**************************************************] 11.7 kB / 11.7 kB (100%) +[2023:06:01 11:24:10] Extracting code archive of rvis:3.1.0... +[2023:06:01 11:24:10] Verifying code integrity of rvis:3.1.0... +[2023:06:01 11:24:10] Downloading data of rvis:3.1.0... +[**************************************************] 768.0 kB / 768.0 kB (100%) +[2023:06:01 11:24:10] Extracting data of rvis:3.1.0... +[2023:06:01 11:24:10] Verifying data integrity of rvis:3.1.0... +[2023:06:01 11:24:10] Finished installation of rvis:3.1.0 +[2023:06:01 11:24:10] Starting to install segway:1.2.1... +[2023:06:01 11:24:10] Downloading code archive of segway:1.2.1... +[**************************************************] 144.5 kB / 144.5 kB (100%) +[2023:06:01 11:24:10] Extracting code archive of segway:1.2.1... +[2023:06:01 11:24:10] Verifying code integrity of segway:1.2.1... +[2023:06:01 11:24:10] Downloading data of segway:1.2.1... +[**************************************************] 4.6 MB / 4.6 MB (100%) +[2023:06:01 11:24:10] Extracting data of segway:1.2.1... +[2023:06:01 11:24:10] Verifying data integrity of segway:1.2.1... +[2023:06:01 11:24:10] Finished installation of segway:1.2.1 +[2023:06:01 11:24:10] Starting to install segway_group:1.0.0... +[2023:06:01 11:24:11] Downloading code archive of segway_group:1.0.0... +[**************************************************] 7.0 kB / 7.0 kB (100%) +[2023:06:01 11:24:11] Extracting code archive of segway_group:1.0.0... +[2023:06:01 11:24:11] Verifying code integrity of segway_group:1.0.0... +[2023:06:01 11:24:11] Finished installation of segway_group:1.0.0 +[2023:06:01 11:24:11] Starting to install sift:1.2.0... +[2023:06:01 11:24:11] Downloading code archive of sift:1.2.0... +[**************************************************] 11.1 kB / 11.1 kB (100%) +[2023:06:01 11:24:11] Extracting code archive of sift:1.2.0... +[2023:06:01 11:24:11] Verifying code integrity of sift:1.2.0... +[2023:06:01 11:24:11] Downloading data of sift:1.2.0... +[**************************************************] 4.4 GB / 4.4 GB (100%) +[2023:06:01 11:24:43] Extracting data of sift:1.2.0... +[2023:06:01 11:25:43] Verifying data integrity of sift:1.2.0... +[2023:06:01 11:26:18] Finished installation of sift:1.2.0 +[2023:06:01 11:26:18] Starting to install siphy:3.5.5... +[2023:06:01 11:26:18] Downloading code archive of siphy:3.5.5... +[**************************************************] 44.3 kB / 44.3 kB (100%) +[2023:06:01 11:26:18] Extracting code archive of siphy:3.5.5... +[2023:06:01 11:26:18] Verifying code integrity of siphy:3.5.5... +[2023:06:01 11:26:18] Downloading data of siphy:3.5.5... +[**************************************************] 1.5 GB / 1.5 GB (100%) +[2023:06:01 11:26:30] Extracting data of siphy:3.5.5... +[2023:06:01 11:26:47] Verifying data integrity of siphy:3.5.5... +[2023:06:01 11:26:56] Finished installation of siphy:3.5.5 +[2023:06:01 11:26:56] Starting to install spliceai:1.0.0... +[2023:06:01 11:26:56] Downloading code archive of spliceai:1.0.0... +[**************************************************] 198.4 kB / 198.4 kB (100%) +[2023:06:01 11:26:56] Extracting code archive of spliceai:1.0.0... +[2023:06:01 11:26:56] Verifying code integrity of spliceai:1.0.0... +[2023:06:01 11:26:56] Downloading data of spliceai:1.0.0... +[**************************************************] 718.0 MB / 718.0 MB (100%) +[2023:06:01 11:27:02] Extracting data of spliceai:1.0.0... +[2023:06:01 11:27:10] Verifying data integrity of spliceai:1.0.0... +[2023:06:01 11:27:13] Finished installation of spliceai:1.0.0 +[2023:06:01 11:27:13] Starting to install uniprot:2020.08.10... +[2023:06:01 11:27:13] Downloading code archive of uniprot:2020.08.10... +[**************************************************] 8.5 kB / 8.5 kB (100%) +[2023:06:01 11:27:13] Extracting code archive of uniprot:2020.08.10... +[2023:06:01 11:27:13] Verifying code integrity of uniprot:2020.08.10... +[2023:06:01 11:27:13] Downloading data of uniprot:2020.08.10... +[**************************************************] 372.3 kB / 372.3 kB (100%) +[2023:06:01 11:27:13] Extracting data of uniprot:2020.08.10... +[2023:06:01 11:27:13] Verifying data integrity of uniprot:2020.08.10... +[2023:06:01 11:27:13] Finished installation of uniprot:2020.08.10 +[2023:06:01 11:27:13] Starting to install vest:4.4.0... +[2023:06:01 11:27:13] Downloading code archive of vest:4.4.0... +[**************************************************] 1.4 MB / 1.4 MB (100%) +[2023:06:01 11:27:13] Extracting code archive of vest:4.4.0... +[2023:06:01 11:27:14] Verifying code integrity of vest:4.4.0... +[2023:06:01 11:27:14] Downloading data of vest:4.4.0... +[**************************************************] 2.1 GB / 2.1 GB (100%) +[2023:06:01 11:27:30] Extracting data of vest:4.4.0... +[2023:06:01 11:27:56] Verifying data integrity of vest:4.4.0... +[2023:06:01 11:28:12] Finished installation of vest:4.4.0 +[2023:06:01 11:28:12] Starting to install wgallelefrequency:1.1.0... +[2023:06:01 11:28:12] Downloading code archive of wgallelefrequency:1.1.0... +[**************************************************] 83.9 kB / 83.9 kB (100%) +[2023:06:01 11:28:12] Extracting code archive of wgallelefrequency:1.1.0... +[2023:06:01 11:28:12] Verifying code integrity of wgallelefrequency:1.1.0... +[2023:06:01 11:28:12] Finished installation of wgallelefrequency:1.1.0 +[2023:06:01 11:28:12] Starting to install wgaloft:1.0.0... +[2023:06:01 11:28:12] Downloading code archive of wgaloft:1.0.0... +[**************************************************] 951 B / 951 B (100%) +[2023:06:01 11:28:12] Extracting code archive of wgaloft:1.0.0... +[2023:06:01 11:28:12] Verifying code integrity of wgaloft:1.0.0... +[2023:06:01 11:28:12] Finished installation of wgaloft:1.0.0 +[2023:06:01 11:28:12] Starting to install wgccre_screen:1.0.0... +[2023:06:01 11:28:12] Downloading code archive of wgccre_screen:1.0.0... +[**************************************************] 947 B / 947 B (100%) +[2023:06:01 11:28:12] Extracting code archive of wgccre_screen:1.0.0... +[2023:06:01 11:28:12] Verifying code integrity of wgccre_screen:1.0.0... +[2023:06:01 11:28:13] Finished installation of wgccre_screen:1.0.0 +[2023:06:01 11:28:13] Starting to install wgchasmplus:1.1.0... +[2023:06:01 11:28:13] Downloading code archive of wgchasmplus:1.1.0... +[**************************************************] 75.8 kB / 75.8 kB (100%) +[2023:06:01 11:28:13] Extracting code archive of wgchasmplus:1.1.0... +[2023:06:01 11:28:13] Verifying code integrity of wgchasmplus:1.1.0... +[2023:06:01 11:28:13] Finished installation of wgchasmplus:1.1.0 +[2023:06:01 11:28:13] Starting to install wgclingen:1.1.1... +[2023:06:01 11:28:13] Downloading code archive of wgclingen:1.1.1... +[**************************************************] 1.1 kB / 1.1 kB (100%) +[2023:06:01 11:28:13] Extracting code archive of wgclingen:1.1.1... +[2023:06:01 11:28:13] Verifying code integrity of wgclingen:1.1.1... +[2023:06:01 11:28:13] Finished installation of wgclingen:1.1.1 +[2023:06:01 11:28:13] Starting to install wgcosmic:1.1.2... +[2023:06:01 11:28:13] Downloading code archive of wgcosmic:1.1.2... +[**************************************************] 14.5 kB / 14.5 kB (100%) +[2023:06:01 11:28:13] Extracting code archive of wgcosmic:1.1.2... +[2023:06:01 11:28:13] Verifying code integrity of wgcosmic:1.1.2... +[2023:06:01 11:28:13] Finished installation of wgcosmic:1.1.2 +[2023:06:01 11:28:13] Starting to install wgcosmic_gene:1.1.1... +[2023:06:01 11:28:13] Downloading code archive of wgcosmic_gene:1.1.1... +[**************************************************] 14.6 kB / 14.6 kB (100%) +[2023:06:01 11:28:14] Extracting code archive of wgcosmic_gene:1.1.1... +[2023:06:01 11:28:14] Verifying code integrity of wgcosmic_gene:1.1.1... +[2023:06:01 11:28:14] Finished installation of wgcosmic_gene:1.1.1 +[2023:06:01 11:28:14] Starting to install wgdgi:1.0.0... +[2023:06:01 11:28:14] Downloading code archive of wgdgi:1.0.0... +[**************************************************] 1.0 kB / 1.0 kB (100%) +[2023:06:01 11:28:14] Extracting code archive of wgdgi:1.0.0... +[2023:06:01 11:28:14] Verifying code integrity of wgdgi:1.0.0... +[2023:06:01 11:28:14] Finished installation of wgdgi:1.0.0 +[2023:06:01 11:28:14] Starting to install wgenhancer:1.1.0... +[2023:06:01 11:28:14] Downloading code archive of wgenhancer:1.1.0... +[**************************************************] 40.1 kB / 40.1 kB (100%) +[2023:06:01 11:28:14] Extracting code archive of wgenhancer:1.1.0... +[2023:06:01 11:28:14] Verifying code integrity of wgenhancer:1.1.0... +[2023:06:01 11:28:14] Finished installation of wgenhancer:1.1.0 +[2023:06:01 11:28:14] Starting to install wgensembl_regulatory_build:1.0.0... +[2023:06:01 11:28:14] Downloading code archive of wgensembl_regulatory_build:1.0.0... +[**************************************************] 945 B / 945 B (100%) +[2023:06:01 11:28:14] Extracting code archive of wgensembl_regulatory_build:1.0.0... +[2023:06:01 11:28:14] Verifying code integrity of wgensembl_regulatory_build:1.0.0... +[2023:06:01 11:28:14] Finished installation of wgensembl_regulatory_build:1.0.0 +[2023:06:01 11:28:14] Starting to install wgfathmm:1.1.0... +[2023:06:01 11:28:14] Downloading code archive of wgfathmm:1.1.0... +[**************************************************] 1.1 kB / 1.1 kB (100%) +[2023:06:01 11:28:15] Extracting code archive of wgfathmm:1.1.0... +[2023:06:01 11:28:15] Verifying code integrity of wgfathmm:1.1.0... +[2023:06:01 11:28:15] Finished installation of wgfathmm:1.1.0 +[2023:06:01 11:28:15] Starting to install wgfunseq2:1.0.0... +[2023:06:01 11:28:15] Downloading code archive of wgfunseq2:1.0.0... +[**************************************************] 960 B / 960 B (100%) +[2023:06:01 11:28:15] Extracting code archive of wgfunseq2:1.0.0... +[2023:06:01 11:28:15] Verifying code integrity of wgfunseq2:1.0.0... +[2023:06:01 11:28:15] Finished installation of wgfunseq2:1.0.0 +[2023:06:01 11:28:15] Starting to install wggenehancer:1.0.1... +[2023:06:01 11:28:15] Downloading code archive of wggenehancer:1.0.1... +[**************************************************] 952 B / 952 B (100%) +[2023:06:01 11:28:15] Extracting code archive of wggenehancer:1.0.1... +[2023:06:01 11:28:15] Verifying code integrity of wggenehancer:1.0.1... +[2023:06:01 11:28:15] Finished installation of wggenehancer:1.0.1 +[2023:06:01 11:28:15] Starting to install wggerp:1.0.3... +[2023:06:01 11:28:15] Downloading code archive of wggerp:1.0.3... +[**************************************************] 620 B / 620 B (100%) +[2023:06:01 11:28:15] Extracting code archive of wggerp:1.0.3... +[2023:06:01 11:28:15] Verifying code integrity of wggerp:1.0.3... +[2023:06:01 11:28:15] Finished installation of wggerp:1.0.3 +[2023:06:01 11:28:15] Starting to install wgghis:1.0.3... +[2023:06:01 11:28:15] Downloading code archive of wgghis:1.0.3... +[**************************************************] 582 B / 582 B (100%) +[2023:06:01 11:28:15] Extracting code archive of wgghis:1.0.3... +[2023:06:01 11:28:15] Verifying code integrity of wgghis:1.0.3... +[2023:06:01 11:28:16] Finished installation of wgghis:1.0.3 +[2023:06:01 11:28:16] Starting to install wggnomad:1.1.1... +[2023:06:01 11:28:16] Downloading code archive of wggnomad:1.1.1... +[**************************************************] 22.4 kB / 22.4 kB (100%) +[2023:06:01 11:28:16] Extracting code archive of wggnomad:1.1.1... +[2023:06:01 11:28:16] Verifying code integrity of wggnomad:1.1.1... +[2023:06:01 11:28:16] Finished installation of wggnomad:1.1.1 +[2023:06:01 11:28:16] Starting to install wggnomad3:1.1.0... +[2023:06:01 11:28:16] Downloading code archive of wggnomad3:1.1.0... +[**************************************************] 22.4 kB / 22.4 kB (100%) +[2023:06:01 11:28:16] Extracting code archive of wggnomad3:1.1.0... +[2023:06:01 11:28:16] Verifying code integrity of wggnomad3:1.1.0... +[2023:06:01 11:28:16] Finished installation of wggnomad3:1.1.0 +[2023:06:01 11:28:16] Starting to install wggnomad_gene:1.2.1... +[2023:06:01 11:28:16] Downloading code archive of wggnomad_gene:1.2.1... +[**************************************************] 22.7 kB / 22.7 kB (100%) +[2023:06:01 11:28:16] Extracting code archive of wggnomad_gene:1.2.1... +[2023:06:01 11:28:16] Verifying code integrity of wggnomad_gene:1.2.1... +[2023:06:01 11:28:16] Finished installation of wggnomad_gene:1.2.1 +[2023:06:01 11:28:16] Starting to install wggtex:1.1.1... +[2023:06:01 11:28:16] Downloading code archive of wggtex:1.1.1... +[**************************************************] 12.3 kB / 12.3 kB (100%) +[2023:06:01 11:28:17] Extracting code archive of wggtex:1.1.1... +[2023:06:01 11:28:17] Verifying code integrity of wggtex:1.1.1... +[2023:06:01 11:28:17] Finished installation of wggtex:1.1.1 +[2023:06:01 11:28:17] Starting to install wggwas_catalog:1.1.1... +[2023:06:01 11:28:17] Downloading code archive of wggwas_catalog:1.1.1... +[**************************************************] 964 B / 964 B (100%) +[2023:06:01 11:28:17] Extracting code archive of wggwas_catalog:1.1.1... +[2023:06:01 11:28:17] Verifying code integrity of wggwas_catalog:1.1.1... +[2023:06:01 11:28:17] Finished installation of wggwas_catalog:1.1.1 +[2023:06:01 11:28:17] Starting to install wgmutation_assessor:1.2.0... +[2023:06:01 11:28:17] Downloading code archive of wgmutation_assessor:1.2.0... +[**************************************************] 970 B / 970 B (100%) +[2023:06:01 11:28:17] Extracting code archive of wgmutation_assessor:1.2.0... +[2023:06:01 11:28:17] Verifying code integrity of wgmutation_assessor:1.2.0... +[2023:06:01 11:28:17] Finished installation of wgmutation_assessor:1.2.0 +[2023:06:01 11:28:17] Starting to install wgmutationtaster:1.0.0... +[2023:06:01 11:28:17] Downloading code archive of wgmutationtaster:1.0.0... +[**************************************************] 938 B / 938 B (100%) +[2023:06:01 11:28:17] Extracting code archive of wgmutationtaster:1.0.0... +[2023:06:01 11:28:17] Verifying code integrity of wgmutationtaster:1.0.0... +[2023:06:01 11:28:17] Finished installation of wgmutationtaster:1.0.0 +[2023:06:01 11:28:17] Starting to install wgmutpred1:1.1.2... +[2023:06:01 11:28:17] Downloading code archive of wgmutpred1:1.1.2... +[**************************************************] 1.1 kB / 1.1 kB (100%) +[2023:06:01 11:28:17] Extracting code archive of wgmutpred1:1.1.2... +[2023:06:01 11:28:17] Verifying code integrity of wgmutpred1:1.1.2... +[2023:06:01 11:28:18] Finished installation of wgmutpred1:1.1.2 +[2023:06:01 11:28:18] Starting to install wgncbigene:1.1.0... +[2023:06:01 11:28:18] Downloading code archive of wgncbigene:1.1.0... +[**************************************************] 60.0 kB / 60.0 kB (100%) +[2023:06:01 11:28:18] Extracting code archive of wgncbigene:1.1.0... +[2023:06:01 11:28:18] Verifying code integrity of wgncbigene:1.1.0... +[2023:06:01 11:28:18] Finished installation of wgncbigene:1.1.0 +[2023:06:01 11:28:18] Starting to install wgndex_chd:1.0.0... +[2023:06:01 11:28:18] Downloading code archive of wgndex_chd:1.0.0... +[**************************************************] 251.5 kB / 251.5 kB (100%) +[2023:06:01 11:28:18] Extracting code archive of wgndex_chd:1.0.0... +[2023:06:01 11:28:18] Verifying code integrity of wgndex_chd:1.0.0... +[2023:06:01 11:28:18] Downloading data of wgndex_chd:1.0.0... +[**************************************************] 23.0 kB / 23.0 kB (100%) +[2023:06:01 11:28:18] Extracting data of wgndex_chd:1.0.0... +[2023:06:01 11:28:18] Verifying data integrity of wgndex_chd:1.0.0... +[2023:06:01 11:28:18] Finished installation of wgndex_chd:1.0.0 +[2023:06:01 11:28:18] Starting to install wgndex_chdsummary:1.0.1... +[2023:06:01 11:28:18] Downloading code archive of wgndex_chdsummary:1.0.1... +[**************************************************] 226.7 kB / 226.7 kB (100%) +[2023:06:01 11:28:19] Extracting code archive of wgndex_chdsummary:1.0.1... +[2023:06:01 11:28:19] Verifying code integrity of wgndex_chdsummary:1.0.1... +[2023:06:01 11:28:19] Downloading data of wgndex_chdsummary:1.0.1... +[**************************************************] 23.0 kB / 23.0 kB (100%) +[2023:06:01 11:28:19] Extracting data of wgndex_chdsummary:1.0.1... +[2023:06:01 11:28:19] Verifying data integrity of wgndex_chdsummary:1.0.1... +[2023:06:01 11:28:19] Finished installation of wgndex_chdsummary:1.0.1 +[2023:06:01 11:28:19] Starting to install wgndex_signor:1.0.0... +[2023:06:01 11:28:19] Downloading code archive of wgndex_signor:1.0.0... +[**************************************************] 251.5 kB / 251.5 kB (100%) +[2023:06:01 11:28:19] Extracting code archive of wgndex_signor:1.0.0... +[2023:06:01 11:28:19] Verifying code integrity of wgndex_signor:1.0.0... +[2023:06:01 11:28:19] Finished installation of wgndex_signor:1.0.0 +[2023:06:01 11:28:19] Starting to install wgndex_signorsummary:1.0.2... +[2023:06:01 11:28:19] Downloading code archive of wgndex_signorsummary:1.0.2... +[**************************************************] 245.2 kB / 245.2 kB (100%) +[2023:06:01 11:28:19] Extracting code archive of wgndex_signorsummary:1.0.2... +[2023:06:01 11:28:19] Verifying code integrity of wgndex_signorsummary:1.0.2... +[2023:06:01 11:28:20] Downloading data of wgndex_signorsummary:1.0.2... +[**************************************************] 22.5 kB / 22.5 kB (100%) +[2023:06:01 11:28:20] Extracting data of wgndex_signorsummary:1.0.2... +[2023:06:01 11:28:20] Verifying data integrity of wgndex_signorsummary:1.0.2... +[2023:06:01 11:28:20] Finished installation of wgndex_signorsummary:1.0.2 +[2023:06:01 11:28:20] Starting to install wgndexchasmplussummary:1.1.1... +[2023:06:01 11:28:20] Downloading code archive of wgndexchasmplussummary:1.1.1... +[**************************************************] 1.7 MB / 1.7 MB (100%) +[2023:06:01 11:28:20] Extracting code archive of wgndexchasmplussummary:1.1.1... +[2023:06:01 11:28:20] Verifying code integrity of wgndexchasmplussummary:1.1.1... +[2023:06:01 11:28:20] Downloading data of wgndexchasmplussummary:1.1.1... +[**************************************************] 104.9 kB / 104.9 kB (100%) +[2023:06:01 11:28:20] Extracting data of wgndexchasmplussummary:1.1.1... +[2023:06:01 11:28:20] Verifying data integrity of wgndexchasmplussummary:1.1.1... +[2023:06:01 11:28:21] Finished installation of wgndexchasmplussummary:1.1.1 +[2023:06:01 11:28:21] Starting to install wgndexsummary:1.0.1... +[2023:06:01 11:28:21] Downloading code archive of wgndexsummary:1.0.1... +[**************************************************] 244.7 kB / 244.7 kB (100%) +[2023:06:01 11:28:21] Extracting code archive of wgndexsummary:1.0.1... +[2023:06:01 11:28:21] Verifying code integrity of wgndexsummary:1.0.1... +[2023:06:01 11:28:21] Downloading data of wgndexsummary:1.0.1... +[**************************************************] 138.8 kB / 138.8 kB (100%) +[2023:06:01 11:28:21] Extracting data of wgndexsummary:1.0.1... +[2023:06:01 11:28:21] Verifying data integrity of wgndexsummary:1.0.1... +[2023:06:01 11:28:21] Finished installation of wgndexsummary:1.0.1 +[2023:06:01 11:28:21] Starting to install wgpangalodb:1.0.0... +[2023:06:01 11:28:21] Downloading code archive of wgpangalodb:1.0.0... +[**************************************************] 995 B / 995 B (100%) +[2023:06:01 11:28:21] Extracting code archive of wgpangalodb:1.0.0... +[2023:06:01 11:28:21] Verifying code integrity of wgpangalodb:1.0.0... +[2023:06:01 11:28:21] Finished installation of wgpangalodb:1.0.0 +[2023:06:01 11:28:21] Starting to install wgphastcons:1.1.0... +[2023:06:01 11:28:21] Downloading code archive of wgphastcons:1.1.0... +[**************************************************] 739 B / 739 B (100%) +[2023:06:01 11:28:22] Extracting code archive of wgphastcons:1.1.0... +[2023:06:01 11:28:22] Verifying code integrity of wgphastcons:1.1.0... +[2023:06:01 11:28:22] Finished installation of wgphastcons:1.1.0 +[2023:06:01 11:28:22] Starting to install wgphdsnpg:1.1.0... +[2023:06:01 11:28:22] Downloading code archive of wgphdsnpg:1.1.0... +[**************************************************] 6.9 kB / 6.9 kB (100%) +[2023:06:01 11:28:22] Extracting code archive of wgphdsnpg:1.1.0... +[2023:06:01 11:28:22] Verifying code integrity of wgphdsnpg:1.1.0... +[2023:06:01 11:28:22] Finished installation of wgphdsnpg:1.1.0 +[2023:06:01 11:28:22] Starting to install wgphylop:1.1.0... +[2023:06:01 11:28:22] Downloading code archive of wgphylop:1.1.0... +[**************************************************] 716 B / 716 B (100%) +[2023:06:01 11:28:22] Extracting code archive of wgphylop:1.1.0... +[2023:06:01 11:28:22] Verifying code integrity of wgphylop:1.1.0... +[2023:06:01 11:28:22] Finished installation of wgphylop:1.1.0 +[2023:06:01 11:28:22] Starting to install wgprec:1.1.0... +[2023:06:01 11:28:22] Downloading code archive of wgprec:1.1.0... +[**************************************************] 726 B / 726 B (100%) +[2023:06:01 11:28:22] Extracting code archive of wgprec:1.1.0... +[2023:06:01 11:28:22] Verifying code integrity of wgprec:1.1.0... +[2023:06:01 11:28:22] Finished installation of wgprec:1.1.0 +[2023:06:01 11:28:22] Starting to install wgprovean:1.0.0... +[2023:06:01 11:28:22] Downloading code archive of wgprovean:1.0.0... +[**************************************************] 879 B / 879 B (100%) +[2023:06:01 11:28:23] Extracting code archive of wgprovean:1.0.0... +[2023:06:01 11:28:23] Verifying code integrity of wgprovean:1.0.0... +[2023:06:01 11:28:23] Finished installation of wgprovean:1.0.0 +[2023:06:01 11:28:23] Starting to install wgrevel:1.2.1... +[2023:06:01 11:28:23] Downloading code archive of wgrevel:1.2.1... +[**************************************************] 34.4 kB / 34.4 kB (100%) +[2023:06:01 11:28:23] Extracting code archive of wgrevel:1.2.1... +[2023:06:01 11:28:23] Verifying code integrity of wgrevel:1.2.1... +[2023:06:01 11:28:23] Finished installation of wgrevel:1.2.1 +[2023:06:01 11:28:23] Starting to install wgrvis:1.1.0... +[2023:06:01 11:28:23] Downloading code archive of wgrvis:1.1.0... +[**************************************************] 734 B / 734 B (100%) +[2023:06:01 11:28:23] Extracting code archive of wgrvis:1.1.0... +[2023:06:01 11:28:23] Verifying code integrity of wgrvis:1.1.0... +[2023:06:01 11:28:23] Finished installation of wgrvis:1.1.0 +[2023:06:01 11:28:23] Starting to install wgsift:1.0.0... +[2023:06:01 11:28:23] Downloading code archive of wgsift:1.0.0... +[**************************************************] 925 B / 925 B (100%) +[2023:06:01 11:28:23] Extracting code archive of wgsift:1.0.0... +[2023:06:01 11:28:23] Verifying code integrity of wgsift:1.0.0... +[2023:06:01 11:28:23] Finished installation of wgsift:1.0.0 +[2023:06:01 11:28:23] Starting to install wgsiphy:1.1.1... +[2023:06:01 11:28:23] Downloading code archive of wgsiphy:1.1.1... +[**************************************************] 997 B / 997 B (100%) +[2023:06:01 11:28:24] Extracting code archive of wgsiphy:1.1.1... +[2023:06:01 11:28:24] Verifying code integrity of wgsiphy:1.1.1... +[2023:06:01 11:28:24] Finished installation of wgsiphy:1.1.1 +[2023:06:01 11:28:24] Starting to install wgvest:1.2.0... +[2023:06:01 11:28:24] Downloading code archive of wgvest:1.2.0... +[**************************************************] 162.1 kB / 162.1 kB (100%) +[2023:06:01 11:28:24] Extracting code archive of wgvest:1.2.0... +[2023:06:01 11:28:24] Verifying code integrity of wgvest:1.2.0... +[2023:06:01 11:28:24] Finished installation of wgvest:1.2.0 diff --git a/docs/install_openCravat.md b/docs/install_openCravat.md new file mode 100644 index 0000000..2a0109a --- /dev/null +++ b/docs/install_openCravat.md @@ -0,0 +1,75 @@ +# OpenCravat + +Original documentation for OpenCravat can be found [here](https://open-cravat.readthedocs.io/en/latest/index.html). + +## Installation + +### Create conda environment + +```sh +# create conda environment. Needed only the first time. +conda create -n opencravat + +# activate environment +conda activate opencravat +``` + +### Install openCravat + +```sh +pip3 install open-cravat==2.4.1 +``` + +### Set Modules Directory + +Use `oc config md` to see where modules directory is currently pointed to. To change the modules directory, use `oc +config md [new directory]` to point OpencRAVAT to the new directory. + +Test it by using `oc config md` command. It should output the new modules directory. + +### Install necessary modules for DITTO + +```sh +oc module install-base + +oc module install aloft cadd cadd_exome cancer_genome_interpreter ccre_screen chasmplus civic clingen clinpred clinvar \ +cosmic cosmic_gene cscape dann dann_coding dbscsnv dbsnp dgi ensembl_regulatory_build ess_gene exac_gene fathmm \ +fathmm_xf_coding funseq2 genehancer gerp ghis gnomad gnomad3 gnomad_gene gtex gwas_catalog linsight loftool lrt mavedb \ +metalr metasvm mutation_assessor mutationtaster mutpred1 mutpred_indel ncbigene ndex ndex_chd ndex_signor omim \ +pangalodb phastcons phdsnpg phi phylop polyphen2 prec provean repeat revel rvis segway sift siphy spliceai uniprot \ +vest cgc cgd varity_r +``` + +Please look at the [install logs](../docs/install_openCravat.logfile) for the versions of all the above annotators used to +train the current DITTO model. + +### Install reporter modules for DITTO + +#### List available reporters + +```sh +oc module ls -a -t reporter +``` + +#### Install reporters + +```sh +oc module install vcfreporter csvreporter tsvreporter -y +``` + +## Setup modules package for DITTO pipeline + +Package is a module which defines module installation and job parameters. To learn more about OpenCravat's package, +please click [here](https://open-cravat.readthedocs.io/en/latest/Package.html). + +Here's the package for DITTO - `configs/mypackage/mypackage.yml` + +Copy the package directory to the modules directory. + +```sh +# Use this to check the modules directory +oc config md + +# copy the package to the modules directory +cp -r configs/mypackage path/to/modules/directory/ +``` diff --git a/model.job b/model.job new file mode 100644 index 0000000..b25aefa --- /dev/null +++ b/model.job @@ -0,0 +1,33 @@ +#!/bin/bash +# +#SBATCH --job-name=DITTO +#SBATCH --output=DITTO_logs.out +# +# Number of tasks needed for this job. Generally, used with MPI jobs +#SBATCH --ntasks=1 +#SBATCH --partition=amd-hdr100-res +#SBATCH --time=06:00:00 +# +# Number of CPUs allocated to each task. +#SBATCH --cpus-per-task=1 +# +# Mimimum memory required per allocated CPU in MegaBytes. +#SBATCH --mem=10G +# +# Send mail to the email address when the job fails +#SBATCH --mail-type=FAIL + +#Set your environment here +module reset +module load Java/13.0.2 +module load Anaconda3 +#conda activate nextflow + +#Modify paths and run the pipeline here +/data/project/worthey_lab/tools/nextflow/nextflow-22.10.7/nextflow run ../pipeline.nf \ + --outdir /data/results \ + -work-dir .work_dir/ \ + --build hg38 -c cheaha.config -with-report \ + --sample_sheet .test_data/file_list.txt -resume + +#https://training.nextflow.io/basic_training/cache_and_resume/#how-to-organize-in-silico-experiments diff --git a/model/Neural_network.db b/model/Neural_network.db new file mode 100644 index 0000000..0ad4d20 Binary files /dev/null and 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"list-o-dicts,list,none" - }, - { - "col_num": "540", - "col_id": "siphy.logodds", - "description": "Score", - "parse": "false", - "parse_type": "none" - }, - { - "col_num": "541", - "col_id": "siphy.logodds_rank", - "description": "Rank", - "parse": "true", - "parse_type": "none" - }, - { - "col_num": "542", - "col_id": "spliceai.ds_ag", - "description": "Acceptor", - "parse": "true", - "parse_type": "none" - }, - { - "col_num": "543", - "col_id": "spliceai.ds_al", - "description": "Acceptor", - "parse": "true", - "parse_type": "none" - }, - { - "col_num": "544", - "col_id": "spliceai.ds_dg", - "description": "Donor", - "parse": "true", - "parse_type": "none" - }, - { - "col_num": "545", - "col_id": "spliceai.ds_dl", - "description": "Donor", - "parse": "true", - "parse_type": "none" - }, - { - "col_num": "546", - "col_id": "spliceai.dp_ag", - "description": "Acceptor", - "parse": "true", - "parse_type": "none" - }, - { - "col_num": "547", - "col_id": "spliceai.dp_al", - "description": "Acceptor", - "parse": "true", - "parse_type": "none" - }, - { - "col_num": "548", - "col_id": "spliceai.dp_dg", - "description": "Donor", - "parse": "true", - "parse_type": "none" - }, - { - "col_num": "549", - "col_id": "spliceai.dp_dl", - "description": "Donor", - "parse": "true", - "parse_type": "none" - }, - { - "col_num": "550", - "col_id": "swissprot_ptm.uniprotkb", - "description": "UniProtKB", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "551", - "col_id": "swissprot_ptm.crosslnk", - "description": "Crosslink", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "552", - "col_id": "swissprot_ptm.disulfid", - "description": "Disulfid", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "553", - "col_id": "swissprot_ptm.carbohyd", - "description": "Glycosylation", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "554", - "col_id": "swissprot_ptm.init", - "description": "Initiator", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "555", - "col_id": "swissprot_ptm.lipid", - "description": "Lipidation", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "556", - "col_id": "swissprot_ptm.mod", - "description": "Modified", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "557", - "col_id": "swissprot_ptm.propep", - "description": "Polypeptide", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "558", - "col_id": "swissprot_ptm.signal", - "description": "Signal", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "559", - "col_id": "swissprot_ptm.transit", - "description": "Transit", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "560", - "col_id": "swissprot_ptm.pubmed", - "description": "Pubmed", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "561", - "col_id": "swissprot_ptm.all", - "description": "All", - "parse": "true", - "parse_type": "list-o-dicts" - }, - { - "col_num": "562", - "col_id": "target.therapy", - "description": "Recommended", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "563", - "col_id": "target.rationale", - "description": "Rationale", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "564", - "col_id": "trinity.Rnaedit", - "description": "RNA", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "565", - "col_id": "uk10k_cohort.uk10k_twins_ac", - "description": "Twins", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "566", - "col_id": "uk10k_cohort.uk10k_twins_af", - "description": "Twins", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "567", - "col_id": "uk10k_cohort.uk10k_alspac_ac", - "description": "ALSPAC", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "568", - "col_id": "uk10k_cohort.uk10k_alspac_af", - "description": "ALSPAC", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "569", - "col_id": "uk10k_cohort.uk10k_ac", - "description": "UK10K", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "570", - "col_id": "uk10k_cohort.uk10k_af", - "description": "UK10K", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "571", - "col_id": "uniprot.acc", - "description": "Accession", - "parse": "true", - "parse_type": "none" - }, - { - "col_num": "572", - "col_id": "vest.transcript", - "description": "Score", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "573", - "col_id": "vest.score", - "description": "Score", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "574", - "col_id": "vest.pval", - "description": "P-value", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "575", - "col_id": "vest.all", - "description": "All", - "parse": "true", - "parse_type": "list-o-dicts" - }, - { - "col_num": "576", - "col_id": "vista_enhancer.element", - "description": "Element", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "577", - "col_id": "vista_enhancer.features", - "description": "Features", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "578", - "col_id": "dbcid.evidence", - "description": "Levels", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "579", - "col_id": "dbsnp.rsid", - "description": "rsID", - "parse": "true", - "parse_type": "none" - }, - { - "col_num": "580", - "col_id": "dbsnp_common.rsid", - "description": "rsID", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "581", - "col_id": "dbscsnv.ada_score", - "description": "AdaBoost", - "parse": "true", - "parse_type": "none" - }, - { - "col_num": "582", - "col_id": "dbscsnv.rf_score", - "description": "Random", - "parse": "true", - "parse_type": "none" - }, - { - "col_num": "583", - "col_id": "gnomad.af", - "description": "Global", - "parse": "true", - "parse_type": "none" - }, - { - "col_num": "584", - "col_id": "gnomad.af_afr", - "description": "African", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "585", - "col_id": "gnomad.af_amr", - "description": "American", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "586", - "col_id": "gnomad.af_asj", - "description": "Ashkenazi", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "587", - "col_id": "gnomad.af_eas", - "description": "East", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "588", - "col_id": "gnomad.af_fin", - "description": "Finnish", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "589", - "col_id": "gnomad.af_nfe", - "description": "Non-Fin", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "590", - "col_id": "gnomad.af_oth", - "description": "Other", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "591", - "col_id": "gnomad.af_sas", - "description": "South", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "592", - "col_id": "gnomad_gene.transcript", - "description": "Transcript", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "593", - "col_id": "gnomad_gene.oe_lof", - "description": "Obv/Exp", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "594", - "col_id": "gnomad_gene.oe_mis", - "description": "Obv/Exp", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "595", - "col_id": "gnomad_gene.oe_syn", - "description": "Obv/Exp", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "596", - "col_id": "gnomad_gene.lof_z", - "description": "LoF", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "597", - "col_id": "gnomad_gene.mis_z", - "description": "Mis", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "598", - "col_id": "gnomad_gene.syn_z", - "description": "Syn", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "599", - "col_id": "gnomad_gene.pLI", - "description": "pLI", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "600", - "col_id": "gnomad_gene.pRec", - "description": "pRec", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "601", - "col_id": "gnomad_gene.pNull", - "description": "pNull", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "602", - "col_id": "gnomad_gene.all", - "description": "All", - "parse": "true", - "parse_type": "list-o-dicts" - }, - { - "col_num": "603", - "col_id": "gnomad3.af", - "description": "Global", - "parse": "true", - "parse_type": "none" - }, - { - "col_num": "604", - "col_id": "gnomad3.af_afr", - "description": "African", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "605", - "col_id": "gnomad3.af_asj", - "description": "Ashkenazi", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "606", - "col_id": "gnomad3.af_eas", - "description": "East", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "607", - "col_id": "gnomad3.af_fin", - "description": "Finnish", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "608", - "col_id": "gnomad3.af_lat", - "description": "Latino", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "609", - "col_id": "gnomad3.af_nfe", - "description": "Non-Fin", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "610", - "col_id": "gnomad3.af_oth", - "description": "Other", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "611", - "col_id": "gnomad3.af_sas", - "description": "South", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "612", - "col_id": "mirbase.transcript", - "description": "Transcript", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "613", - "col_id": "mirbase.id", - "description": "Accession", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "614", - "col_id": "mirbase.name", - "description": "Name", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "615", - "col_id": "mirbase.derives_from", - "description": "Derives", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "616", - "col_id": "ncrna.ncrnaclass", - "description": "Class", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "617", - "col_id": "ncrna.ncrnaname", - "description": "Name", - "parse": "false", - "parse_type": "list-o-dicts,list,none" - }, - { - "col_num": "618", - "col_id": "phi.phi", - "description": "P(HI)", - "parse": "true", - "parse_type": "none" - } -] diff --git a/opencravat_test.test_config.json b/opencravat_test.test_config.json deleted file mode 100644 index ac3b4c7..0000000 --- a/opencravat_test.test_config.json +++ /dev/null @@ -1,118 +0,0 @@ -[ - { - "col_id": "chrom", - "parse_type": { - "none": "none" - } - }, - { - "col_id": "pos", - "parse_type": { - "none": "none" - } - }, - { - "col_id": "ref_base", - "parse_type": { - "none": "none" - } - }, - { - "col_id": "alt_base", - "parse_type": { - "none": "none" - } - }, - { - "col_id": "thousandgenomes.af", - "parse_type": { - "none": "none" - } - }, - { - "col_id": "fathmm.ens_tid", - "parse_type": { - "list": { - "trx_index_col": "fathmm.ens_tid", - "column_list": ["fathmm.fathmm_score"], - "separator": ";" - } - } - }, - { - "col_id": "fathmm_mkl.fathmm_mkl_coding_score", - "parse_type": { - "none": "none" - } - }, - { - "col_id": "gerp.gerp_nr", - "parse_type": { - "none": "none" - } - }, - { - "col_id": "gerp.gerp_rs", - "parse_type": { - "none": "none" - } - }, - { - "col_id": "gerp.gerp_rs_rank", - "parse_type": { - "none": "none" - } - }, - { - "col_id": "mutation_assessor.all", - "parse_type": { - "list-o-dicts": { - "dict_index": { - "1": "mutation_assessor.impact", - "2": "mutation_assessor.score", - "3": "mutation_assessor.rankscore" - }, - "trx_mapping_col_index": 0 - } - } - }, - { - "col_id": "mutationtaster.all", - "parse_type": { - "list-o-dicts": { - "dict_index": { - "1": "mutationtaster.score", - "2": "mutationtaster.rankscore", - "3": "mutationtaster.prediction", - "4": "mutationtaster.model" - }, - "trx_mapping_col_index": 0 - } - } - }, - { - "col_id": "gnomad_gene.all", - "parse_type": { - "list-o-dicts": { - "dict_index": { - "1": "gnomad_gene.oe_lof", - "2": "gnomad_gene.oe_mis", - "3": "gnomad_gene.oe_syn", - "4": "gnomad_gene.lof_z", - "5": "gnomad_gene.mis_z", - "6": "gnomad_gene.syn_z", - "7": "gnomad_gene.pLI", - "8": "gnomad_gene.pRec", - "9": "gnomad_gene.pNull" - }, - "trx_mapping_col_index": 0 - } - } - }, - { - "col_id": "gnomad3.af", - "parse_type": { - "none": "none" - } - } -] diff --git a/pipeline.nf b/pipeline.nf new file mode 100644 index 0000000..76c6023 --- /dev/null +++ b/pipeline.nf @@ -0,0 +1,102 @@ +#!/usr/bin/env nextflow +nextflow.enable.dsl=2 + +// Define the command-line options to specify the path to VCF files +params.sample_sheet = '.test_data/file_list.txt' +params.build = "hg38" +params.oc_modules = "/data/project/worthey_lab/projects/experimental_pipelines/tarun/opencravat/modules" +// Define the Scratch directory +def scratch_dir = System.getenv("USER_SCRATCH") ?: "/tmp" + +params.outdir = "${scratch_dir}" + +// Define the output directory for intermediate and final results +output_dir = params.outdir + +log.info """\ + + D I T T O - N F P I P E L I N E + =================================== + Parameters: + build : ${params.build} + sample_sheet : ${params.sample_sheet} + output_dir : ${output_dir} + oc_modules : ${params.oc_modules} + """ + .stripIndent() + + +// Define the process to run 'oc' with the specified parameters +process runOC { + + // Define the conda environment file to be used + conda '../configs/envs/open-cravat.yaml' + + input: + path var_ch + val var_build + val oc_mod_path + + output: + path "${var_ch}.variant.csv" + + script: + """ + oc config md ${oc_mod_path} + oc run ${var_ch} -l ${var_build} -t csv --mp 2 --package mypackage -d . + rm -rf ${var_ch}.sqlite ${var_ch}.err + cp ${var_ch}.variant.csv ${output_dir}/${var_ch}.variant.csv + """ + +} + +// Define the process to parse the annotation +process parseAnnotation { + + // Define the conda environment file to be used + conda 'python=3.10' + + input: + path var_ann_ch + + output: + path "${var_ann_ch}_parsed.csv.gz" + + script: + """ + python ${baseDir}/src/annotation_parsing/parse_single_sample.py -i ${var_ann_ch} -e parse -o ${var_ann_ch}_parsed.csv.gz -c ${baseDir}/configs/opencravat_test_config.json + """ +} + +// Define the process for prediction +process prediction { + + // Define the conda environment file to be used + conda '../configs/envs/ditto-nf.yaml' + + input: + path var_parse_ch + + script: + """ + python ${baseDir}/src/predict/predict.py -i ${var_parse_ch} -o ${output_dir} -c ${baseDir}/configs/col_config.yaml -d ${baseDir}/model/ + """ +} + +// Define the workflow by connecting the processes +// 'vcfFile' will be the channel containing the input VCF files +// Each file in the channel will be processed through the steps defined above. +workflow { + + // Define input channels for the VCF files + vcfFile = Channel.fromPath(params.sample_sheet).splitCsv(header: false) + vcfBuild = params.build + oc_mod_path = params.oc_modules + + // Run processes + runOC(vcfFile,vcfBuild,oc_mod_path ) + parseAnnotation(runOC.out) + // Scatter the output of parseAnnotation to process each file separately + parseAnnotation.out.flatten().set { parsed_files } + prediction(parsed_files) +} diff --git a/shap_plots/NMD_SHAP.pdf b/shap_plots/NMD_SHAP.pdf new file mode 100644 index 0000000..6af0c2a Binary files /dev/null and b/shap_plots/NMD_SHAP.pdf differ diff --git a/shap_plots/Neural_network_features.pdf b/shap_plots/Neural_network_features.pdf new file mode 100644 index 0000000..0d39dcc Binary files /dev/null and b/shap_plots/Neural_network_features.pdf differ diff --git a/shap_plots/intergenic_SHAP.pdf b/shap_plots/intergenic_SHAP.pdf new file mode 100644 index 0000000..60a2769 Binary files /dev/null and b/shap_plots/intergenic_SHAP.pdf differ diff --git a/shap_plots/intron_SHAP.pdf b/shap_plots/intron_SHAP.pdf new file mode 100644 index 0000000..e9e6599 Binary files /dev/null and b/shap_plots/intron_SHAP.pdf differ diff --git a/shap_plots/missense_SHAP.pdf b/shap_plots/missense_SHAP.pdf new file mode 100644 index 0000000..1fcd038 Binary files /dev/null and b/shap_plots/missense_SHAP.pdf differ diff --git a/shap_plots/splice site_SHAP.pdf b/shap_plots/splice site_SHAP.pdf new file mode 100644 index 0000000..aa68529 Binary files /dev/null and b/shap_plots/splice site_SHAP.pdf differ diff --git a/src/analysis/array_script.job b/src/analysis/array_script.job new file mode 100644 index 0000000..60d0087 --- /dev/null +++ b/src/analysis/array_script.job @@ -0,0 +1,26 @@ +#!/bin/bash +#SBATCH --job-name=sort +#SBATCH --ntasks=1 +#SBATCH --mem=10G +#SBATCH --partition=amd-hdr100 +#SBATCH --time=06-06:00:00 +#SBATCH --mail-type=FAIL +#SBATCH --output=logs/%x_%A_%a.log +#SBATCH --array=0-23 + +# module reset +# module load Anaconda3/2020.02 +module load BCFtools/1.12-GCC-10.2.0 +# source activate training + +n=$SLURM_ARRAY_TASK_ID # number of jobs in the array +FILES=(/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/processed/sorted/*) +gene=${FILES[$SLURM_ARRAY_TASK_ID]} + +echo "${gene##*/}" + +# sort, bgzip and tabix index the predictions +sort -t$'\t' -k1,1 -k2,2n -T $USER_SCRATCH /data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/processed/all_snv/${gene##*/} >/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/processed/sorted/${gene##*/} + +# TO-DO: bgzip and tabix index the predictions +bgzip ${gene##*/} diff --git a/src/analysis/filter.sh b/src/analysis/filter.sh new file mode 100644 index 0000000..077e797 --- /dev/null +++ b/src/analysis/filter.sh @@ -0,0 +1,23 @@ +#!/bin/bash +set -euo pipefail +# Filter the DITTO scores and other annotations after running the pipeline. Example tested on CAGI project + +# Specify the input folder containing the CSV files +input_folder="/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/processed/CAGI_TR/" + +# Specify the output folder where filtered files will be saved +output_folder="/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/processed/cagi_filtered/" + +# Loop through all CSV.gz files in the input folder +for input_file in "$input_folder"*DITTO_scores.csv.gz; do + # Extract the base filename (without path and extension) + base_filename=$(basename "${input_file}" .csv.gz) + + # Define the output file path + output_file="${output_folder}${base_filename}_filtered.csv" + + # Use zcat, awk, and redirection to filter the data and save to the output file + zcat "${input_file}" | awk -F',' 'NR == 1 || ($NF > 0.9 && $(NF-1) < 0.00001) {print}' > "${output_file}" + + echo "Filtered ${input_file} and saved as ${output_file}" +done diff --git a/src/analysis/opencravat_latest_benchmarking-Consequence_80_20.ipynb b/src/analysis/opencravat_latest_benchmarking-Consequence_80_20.ipynb new file mode 100644 index 0000000..d9000f4 --- /dev/null +++ b/src/analysis/opencravat_latest_benchmarking-Consequence_80_20.ipynb @@ -0,0 +1,44455 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "id": "14382f60", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: nnv in /Users/tarunmamidi/opt/anaconda3/envs/ditto/lib/python3.8/site-packages (0.0.5)\n" + ] + } + ], + "source": [ + "#!pip install tensorflow\n", + "#!pip install scikit-learn\n", + "#!pip install matplotlib\n", + "#!pip install pandas\n", + "#!pip install pyyaml\n", + "#!pip install shap\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2b03269e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-22 22:41:34.162729: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "pd.set_option('display.max_rows', None)\n", + "import yaml\n", + "import warnings\n", + "warnings.simplefilter(\"ignore\")\n", + "#from joblib import load, dump\n", + "import argparse\n", + "#import shap\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import functools\n", + "print = functools.partial(print, flush=True)\n", + "from sklearn.preprocessing import label_binarize, MinMaxScaler\n", + "from tensorflow import keras\n", + "from sklearn.metrics import (\n", + " roc_curve,precision_score,\n", + " precision_recall_curve,roc_auc_score,\n", + " f1_score,accuracy_score, confusion_matrix, ConfusionMatrixDisplay,\n", + " confusion_matrix,\n", + " average_precision_score,\n", + " recall_score\n", + ")\n", + "\n", + "from sklearn.utils import class_weight\n", + "import shap\n", + "# from keras_sequential_ascii import keras2ascii\n", + "# from nnv import NNV\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "880823ee", + "metadata": {}, + "outputs": [], + "source": [ + "warnings.simplefilter(\"ignore\", category=DeprecationWarning)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "22e98d97", + "metadata": {}, + "outputs": [], + "source": [ + "with open(\n", + " \"../../configs/col_config.yaml\"\n", + " ) as fh:\n", + " config_dict = yaml.safe_load(fh)\n", + "\n", + "with open(\n", + " \"../../configs/var_class.yaml\"\n", + " ) as fh1:\n", + " var_dict = yaml.safe_load(fh1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'3 prime UTR',\n", + " '5 prime UTR',\n", + " 'NMD',\n", + " 'complex substitution',\n", + " 'exon loss variant',\n", + " 'frameshift elongation',\n", + " 'frameshift truncation',\n", + " 'inframe deletion',\n", + " 'inframe insertion',\n", + " 'intergenic',\n", + " 'intron',\n", + " 'missense',\n", + " 'other RNA',\n", + " 'splice site',\n", + " 'start lost',\n", + " 'start retained',\n", + " 'stop gained',\n", + " 'stop lost',\n", + " 'stop retained',\n", + " 'synonymous'}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set(var_dict.values())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3a4ff3de", + "metadata": {}, + "outputs": [], + "source": [ + "#amis = pd.read_csv(\"/Users/tarunmamidi/Downloads/AlphaMissense_hg38.tsv\", low_memory=False, skiprows=3, sep='\\t')\n", + "#amis.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4bcc801e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-22 22:41:40.803645: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " dense (Dense) (None, 239) 57360 \n", + " \n", + " dense_l0 (Dense) (None, 161) 38640 \n", + " \n", + " dropout (Dropout) (None, 161) 0 \n", + " \n", + " dense_last (Dense) (None, 1) 162 \n", + " \n", + "=================================================================\n", + "Total params: 96,162\n", + "Trainable params: 96,162\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "clf = keras.models.load_model('../../model/Neural_network/')\n", + "clf.load_weights(\"../../model/weights.h5\")\n", + "clf.summary()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "#keras2ascii(clf)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# layersList = [\n", + "# {\"title\":\"Input\\n(239 n)\\n(elu)\", \"units\": 239, \"color\": \"green\", \"edges_color\":\"darkBlue\", \"edges_width\":2},\n", + "# {\"title\":\"Dense\\n(161 n)\\n(elu)\", \"units\": 161, \"edges_color\":\"darkBlue\", \"edges_width\":2,\"color\": \"orange\"},\n", + "# #{\"title\":\"Dropout\", \"units\": 161, \"edges_color\":\"red\", \"edges_width\":2},\n", + "# {\"title\":\"output\\n(1 n)\\n(sigmoid)\", \"units\": 1,\"color\": \"red\"},\n", + "# ]\n", + "\n", + "# NNV(layersList).render(save_to_file=\"DITTO.png\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "276a7133", + "metadata": {}, + "outputs": [], + "source": [ + "#X_train = pd.read_csv(f\"../../data/train_class_data_80.csv.gz\")\n", + "#train.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0a8776c9", + "metadata": {}, + "outputs": [], + "source": [ + "#X_train.chrom.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bd9d4901", + "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details." + ] + } + ], + "source": [ + "X_train = pd.read_csv(f\"../../data/train_class_data_80.csv.gz\")\n", + "conq_class = X_train['consequence'].unique().tolist()\n", + "X_train = X_train.drop(config_dict[\"train_cols\"]+['class'], axis=1)\n", + "feature_names = X_train.columns.tolist()\n", + "#X_train = X_train.sample(frac=1).reset_index(drop=True)\n", + "X_train = X_train.values\n", + "background = shap.kmeans(X_train, 10)\n", + "explainer = shap.KernelExplainer(clf.predict, background)\n", + "print(explainer.expected_value)\n", + "del X_train, background\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['synonymous_variant',\n", + " 'missense_variant',\n", + " 'intron_variant',\n", + " 'intron_variant,lnc_RNA',\n", + " 'intron_variant,NMD_transcript_variant',\n", + " 'intron_variant,processed_transcript',\n", + " 'NMD_transcript_variant,3_prime_UTR_variant',\n", + " 'processed_transcript',\n", + " '2kb_downstream_variant,processed_transcript',\n", + " 'stop_gained',\n", + " '3_prime_UTR_variant',\n", + " '2kb_upstream_variant,lnc_RNA',\n", + " '2kb_upstream_variant,processed_transcript',\n", + " '5_prime_UTR_variant',\n", + " 'lnc_RNA',\n", + " 'NMD_transcript_variant,synonymous_variant',\n", + " 'missense_variant,NMD_transcript_variant',\n", + " 'frameshift_elongation',\n", + " '2kb_downstream_variant,NMD_transcript_variant',\n", + " 'frameshift_truncation',\n", + " 'intron_variant,splice_site_variant',\n", + " 'NMD_transcript_variant,5_prime_UTR_variant',\n", + " '2kb_downstream_variant',\n", + " 'inframe_deletion',\n", + " '2kb_downstream_variant,miRNA',\n", + " 'frameshift_truncation,NMD_transcript_variant',\n", + " '2kb_downstream_variant,lnc_RNA',\n", + " '2kb_upstream_variant',\n", + " '2kb_upstream_variant,NMD_transcript_variant',\n", + " '2kb_downstream_variant,misc_RNA',\n", + " '2kb_downstream_variant,NSD_transcript',\n", + " 'intron_variant,NMD_transcript_variant,splice_site_variant',\n", + " 'frameshift_truncation,stop_gained',\n", + " 'NSD_transcript',\n", + " 'NMD_transcript_variant,stop_gained',\n", + " 'splice_site_variant',\n", + " 'missense_variant,start_lost',\n", + " 'complex_substitution,frameshift_truncation',\n", + " 'stop_lost',\n", + " 'frameshift_elongation,NMD_transcript_variant',\n", + " '2kb_upstream_variant,miRNA',\n", + " 'complex_substitution,frameshift_elongation,intron_variant',\n", + " 'missense_variant,start_lost,NMD_transcript_variant',\n", + " 'exon_loss_variant,frameshift_truncation',\n", + " 'complex_substitution,frameshift_elongation',\n", + " 'complex_substitution,missense_variant',\n", + " 'ribozyme',\n", + " '2kb_upstream_variant,misc_RNA',\n", + " 'frameshift_elongation,stop_gained',\n", + " 'inframe_insertion',\n", + " 'stop_retained_variant',\n", + " '2kb_downstream_variant,snRNA',\n", + " '2kb_downstream_variant,snoRNA',\n", + " '2kb_upstream_variant,ribozyme',\n", + " 'inframe_deletion,NMD_transcript_variant',\n", + " 'intron_variant,NSD_transcript',\n", + " 'complex_substitution,inframe_insertion,intron_variant',\n", + " 'polymorphic_pseudogene,5_prime_UTR_variant',\n", + " 'polymorphic_pseudogene',\n", + " 'intron_variant,polymorphic_pseudogene',\n", + " 'complex_substitution',\n", + " 'complex_substitution,stop_gained',\n", + " 'NMD_transcript_variant,splice_site_variant',\n", + " 'inframe_deletion,stop_gained',\n", + " 'inframe_deletion,NMD_transcript_variant,stop_gained',\n", + " '2kb_upstream_variant,snRNA',\n", + " '2kb_upstream_variant,snoRNA',\n", + " 'exon_loss_variant,intron_variant',\n", + " 'NMD_transcript_variant',\n", + " 'complex_substitution,missense_variant,NMD_transcript_variant',\n", + " 'frameshift_truncation,NMD_transcript_variant,stop_gained',\n", + " 'complex_substitution,synonymous_variant',\n", + " '2kb_upstream_variant,NSD_transcript',\n", + " 'start_lost,5_prime_UTR_variant',\n", + " 'start_lost,NMD_transcript_variant,5_prime_UTR_variant',\n", + " 'complex_substitution,frameshift_truncation,NMD_transcript_variant',\n", + " 'NMD_transcript_variant,stop_lost',\n", + " 'frameshift_elongation,start_lost',\n", + " 'inframe_insertion,NMD_transcript_variant',\n", + " '2kb_upstream_variant,rRNA',\n", + " 'complex_substitution,inframe_insertion',\n", + " 'complex_substitution,inframe_deletion,missense_variant',\n", + " 'frameshift_elongation,NMD_transcript_variant,stop_gained',\n", + " 'polymorphic_pseudogene,3_prime_UTR_variant',\n", + " 'miRNA',\n", + " 'complex_substitution,NMD_transcript_variant,stop_gained',\n", + " 'NMD_transcript_variant,stop_retained_variant',\n", + " 'frameshift_truncation,NMD_transcript_variant,stop_lost',\n", + " 'frameshift_truncation,stop_lost',\n", + " 'frameshift_truncation,start_lost',\n", + " 'complex_substitution,frameshift_elongation,intron_variant,NMD_transcript_variant',\n", + " 'snoRNA',\n", + " '2kb_downstream_variant,polymorphic_pseudogene',\n", + " 'complex_substitution,inframe_deletion',\n", + " 'inframe_deletion,stop_lost,3_prime_UTR_variant',\n", + " 'exon_loss_variant,frameshift_truncation,NMD_transcript_variant',\n", + " 'complex_substitution,start_lost,start_retained_variant',\n", + " 'misc_RNA',\n", + " 'frameshift_elongation,NMD_transcript_variant,stop_retained_variant',\n", + " '2kb_downstream_variant,scaRNA',\n", + " 'inframe_deletion,stop_lost,stop_retained_variant,3_prime_UTR_variant',\n", + " 'snRNA',\n", + " 'intron_variant,start_lost',\n", + " 'inframe_insertion,stop_gained',\n", + " 'complex_substitution,frameshift_elongation,intron_variant,NMD_transcript_variant,stop_lost',\n", + " 'complex_substitution,inframe_insertion,missense_variant',\n", + " 'complex_substitution,frameshift_elongation,intron_variant,start_lost,start_retained_variant,synonymous_variant',\n", + " 'NSD_transcript,5_prime_UTR_variant',\n", + " 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'complex_substitution,frameshift_truncation,intron_variant',\n", + " 'frameshift_truncation,start_lost,NMD_transcript_variant',\n", + " 'complex_substitution,inframe_insertion,stop_gained',\n", + " '2kb_upstream_variant,NMD_transcript_variant,5_prime_UTR_variant',\n", + " '2kb_upstream_variant,start_lost,NMD_transcript_variant,stop_lost,transcript_ablation,3_prime_UTR_variant,5_prime_UTR_variant',\n", + " '2kb_upstream_variant,5_prime_UTR_variant',\n", + " '2kb_upstream_variant,start_lost,NMD_transcript_variant,transcript_ablation,5_prime_UTR_variant',\n", + " '2kb_upstream_variant,scaRNA',\n", + " '2kb_downstream_variant,stop_lost,3_prime_UTR_variant',\n", + " 'start_retained_variant',\n", + " 'inframe_insertion,NMD_transcript_variant,stop_gained',\n", + " 'frameshift_elongation,stop_retained_variant',\n", + " '2kb_downstream_variant,NMD_transcript_variant,3_prime_UTR_variant',\n", + " 'complex_substitution,inframe_insertion,NMD_transcript_variant,stop_gained',\n", + " 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1ENST00000418600SYNGAP1synonymous_variantp.Pro1066=c.3198T>Gchr633443750TGcomplex neurodevelopmental disorderDefinitive8831.0NaNQ96PV0rs781201249
2ENST00000428982SYNGAP1synonymous_variantp.Pro1007=c.3021T>Gchr633443750TGcomplex neurodevelopmental disorderDefinitive8831.0NaNQ96PV0rs781201249
3ENST00000449372SYNGAP1synonymous_variantp.Pro1052=c.3156T>Gchr633443750TGcomplex neurodevelopmental disorderDefinitive8831.0NaNQ96PV0rs781201249
4ENST00000628646SYNGAP1synonymous_variantp.Pro1066=c.3198T>Gchr633443750TGcomplex neurodevelopmental disorderDefinitive8831.0NaNQ96PV0rs781201249
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transcriptgeneconsequenceprotein_hgvscdna_hgvschromposref_basealt_baseclingen.diseaseclingen.classificationncbigene.entrezomim.omim_iduniprot.accdbsnp.rsid
0ENST00000293748SYNGAP1NMD_transcript_variant,synonymous_variantp.Pro1051=c.3153T>Gchr633443750TGcomplex neurodevelopmental disorderDefinitive8831.0NaNQ96PV0rs781201249
1ENST00000418600SYNGAP1synonymous_variantp.Pro1066=c.3198T>Gchr633443750TGcomplex neurodevelopmental disorderDefinitive8831.0NaNQ96PV0rs781201249
2ENST00000428982SYNGAP1synonymous_variantp.Pro1007=c.3021T>Gchr633443750TGcomplex neurodevelopmental disorderDefinitive8831.0NaNQ96PV0rs781201249
3ENST00000449372SYNGAP1synonymous_variantp.Pro1052=c.3156T>Gchr633443750TGcomplex neurodevelopmental disorderDefinitive8831.0NaNQ96PV0rs781201249
4ENST00000628646SYNGAP1synonymous_variantp.Pro1066=c.3198T>Gchr633443750TGcomplex neurodevelopmental disorderDefinitive8831.0NaNQ96PV0rs781201249
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'exac_gene.exac_cnv_flag_N',\n", + " 'exac_gene.exac_cnv_flag_Y',\n", + " 'mutationtaster.prediction_Automatic Disease Causing',\n", + " 'mutationtaster.prediction_Automatic Polymorphism',\n", + " 'mutationtaster.prediction_Damaging',\n", + " 'mutationtaster.prediction_Polymorphism',\n", + " 'mutationtaster.model_complex_aae',\n", + " 'mutationtaster.model_simple_aae',\n", + " 'mutationtaster.model_without_aae',\n", + " 'prec.stat_lof-tolerant',\n", + " 'prec.stat_recessive',\n", + " 'sift.confidence_High',\n", + " 'sift.confidence_Low']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feature_names\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "40fcf264", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "#var['so'] = var['consequence']\n", + "#pd.pivot_table(var, values='consequence', index='so', columns='class',\n", + "# aggfunc='count').sort_values(by=['low_impact','high_impact'], ascending=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6fc8e643", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6506/6506 [==============================] - 7s 1ms/step\n" + ] + } + ], + "source": [ + "y_score = clf.predict(X_test.drop(\"class\", axis=1))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "45412118", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.0000000e+00],\n", + " [0.0000000e+00],\n", + " [0.0000000e+00],\n", + " ...,\n", + " [4.4107437e-06],\n", + " [4.4107437e-06],\n", + " [1.0000000e+00]], dtype=float32)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_score = 1 - y_score\n", + "y_score\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ba1c77ca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.],\n", + " [0.],\n", + " [0.],\n", + " ...,\n", + " [0.],\n", + " [0.],\n", + " [1.]], dtype=float32)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_score.round()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "879f3398", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "208167" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(y_score)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "720f2350", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(208167, 240)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test.shape\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0.0004\n", + "1 0.0004\n", + "2 0.0004\n", + "3 0.0004\n", + "4 0.0004\n", + "Name: spliceai, dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get maximum value from 4 spliceai columns\n", + "\n", + "X_test['spliceai'] = X_test[['spliceai.ds_ag','spliceai.ds_al','spliceai.ds_dg','spliceai.ds_dl']].max(axis=1)\n", + "X_test['spliceai'].head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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transcriptgeneconsequenceprotein_hgvscdna_hgvschromposref_basealt_baseclingen.diseaseclingen.classificationncbigene.entrezomim.omim_iduniprot.accdbsnp.rsidDITTO
0ENST00000293748SYNGAP1NMD_transcript_variant,synonymous_variantp.Pro1051=c.3153T>Gchr633443750TGcomplex neurodevelopmental disorderDefinitive8831.0NaNQ96PV0rs7812012490.0
1ENST00000418600SYNGAP1synonymous_variantp.Pro1066=c.3198T>Gchr633443750TGcomplex neurodevelopmental disorderDefinitive8831.0NaNQ96PV0rs7812012490.0
2ENST00000428982SYNGAP1synonymous_variantp.Pro1007=c.3021T>Gchr633443750TGcomplex neurodevelopmental disorderDefinitive8831.0NaNQ96PV0rs7812012490.0
3ENST00000449372SYNGAP1synonymous_variantp.Pro1052=c.3156T>Gchr633443750TGcomplex neurodevelopmental disorderDefinitive8831.0NaNQ96PV0rs7812012490.0
4ENST00000628646SYNGAP1synonymous_variantp.Pro1066=c.3198T>Gchr633443750TGcomplex neurodevelopmental disorderDefinitive8831.0NaNQ96PV0rs7812012490.0
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transcriptgeneconsequenceprotein_hgvscdna_hgvschromposref_basealt_baseclingen.disease...mutationtaster.prediction_Polymorphismmutationtaster.model_complex_aaemutationtaster.model_simple_aaemutationtaster.model_without_aaeprec.stat_lof-tolerantprec.stat_recessivesift.confidence_Highsift.confidence_Lowclassspliceai
0ENST00000293748SYNGAP1NMD_transcript_variant,synonymous_variantp.Pro1051=c.3153T>Gchr633443750TGcomplex neurodevelopmental disorder...0000001000.0004
1ENST00000418600SYNGAP1synonymous_variantp.Pro1066=c.3198T>Gchr633443750TGcomplex neurodevelopmental disorder...0000001000.0004
2ENST00000428982SYNGAP1synonymous_variantp.Pro1007=c.3021T>Gchr633443750TGcomplex neurodevelopmental disorder...0000001000.0004
3ENST00000449372SYNGAP1synonymous_variantp.Pro1052=c.3156T>Gchr633443750TGcomplex neurodevelopmental disorder...0000001000.0004
4ENST00000628646SYNGAP1synonymous_variantp.Pro1066=c.3198T>Gchr633443750TGcomplex neurodevelopmental disorder...0000001000.0004
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5 rows ร— 257 columns

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DITTOCADDClinPredMetaSVMGERPspliceaiRevel
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" + ], + "text/plain": [ + " DITTO CADD ClinPred MetaSVM GERP spliceai Revel\n", + "0 0.0 4.416 0.109 -0.3355 4.66 0.0004 0.38\n", + "1 0.0 4.416 0.109 -0.3355 4.66 0.0004 0.38\n", + "2 0.0 4.416 0.109 -0.3355 4.66 0.0004 0.38\n", + "3 0.0 4.416 0.109 -0.3355 4.66 0.0004 0.38\n", + "4 0.0 4.416 0.109 -0.3355 4.66 0.0004 0.38" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bench = var[['DITTO','cadd.phred','clinpred.score','metasvm.score', 'gerp.gerp_rs','spliceai','revel.score']]#,'sift.score','dann.score']]\n", + "bench.columns = ['DITTO','CADD','ClinPred','MetaSVM','GERP','spliceai','Revel']#,'SIFT','DANN']\n", + "bench.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "fe4b101b", + "metadata": {}, + "outputs": [], + "source": [ + "default_colors = {\n", + " \"DANN\": \"#de77ae\",\n", + " \"SIFT\": \"#fee090\",\n", + " \"Revel\": \"#542788\",\n", + " \"VEST\": \"#5ab4ac\",\n", + " \"GERP\": \"#d8b365\",\n", + " \"MetaSVM\": \"#3182bd\",\n", + " \"ClinPred\": \"#969696\",\n", + " \"CADD\": \"#D55E00\",\n", + " \"DITTO\": \"#b2182b\",\n", + " \"spliceai\": \"#5ab4ac\",\n", + " }\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ba0f56c0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#bench = var[['cadd.phred','clinpred.score', 'chasmplus.score','dann.score','revel.score','metalr.score','metasvm.score','mutation_assessor.score','mutationtaster.score','sift.score','provean.score','vest.score','gerp.gerp_rs','DITTO']]\n", + "#bench.columns = ['CADD','ClinPred','chasmplus','DANN','Revel','MetaLR','MetaSVM','mutation_assessor','mutationtaster','SIFT','provean','VEST','GERP','DITTO']\n", + "Y_test = var['class']\n", + "\n", + "fig, [ax_roc, ax_prc] = plt.subplots(1, 2, figsize=(30, 13))\n", + "\n", + "fig.suptitle(f\"DITTO Benchmarking\", fontsize=40)\n", + "fsize = 22\n", + "ax_roc.tick_params(axis='both', which='major', labelsize=fsize)\n", + "ax_prc.tick_params(axis='both', which='major', labelsize=fsize)\n", + "ax_roc.set_xlabel(\"False Positive Rate\", fontsize=fsize)\n", + "ax_roc.set_ylabel(\"True Positive Rate\", fontsize=fsize)\n", + "ax_roc.set_title(\"Receiver Operating Characteristic (ROC) curves\", fontsize=fsize)\n", + "ax_roc.grid(linestyle=\"--\")\n", + "ax_prc.set_xlabel(\"Recall\", fontsize=fsize)\n", + "ax_prc.set_ylabel(\"Precision\", fontsize=fsize)\n", + "ax_prc.set_title(\"Precision Recall (PRC) curves\", fontsize=fsize)\n", + "ax_prc.grid(linestyle=\"--\")\n", + "\n", + "scores = {}\n", + "scores['roc'] = {}\n", + "scores['prc'] = {}\n", + "scores['f1'] = {}\n", + "for name in list(bench.columns):\n", + " x = bench[name].values\n", + " if np.unique(x).size == 1 and name in ['CADD', 'MetaSVM','mutation_assessor','provean','GERP']:\n", + " y_true = np.zeros_like(x)\n", + " elif name in ['CADD', 'MetaSVM','mutation_assessor','provean','GERP']:\n", + " y_true = (x-np.min(x))/(np.max(x)-np.min(x))\n", + " else:\n", + " y_true = x\n", + " fpr, tpr, _ = roc_curve(Y_test, y_true)\n", + " auc = roc_auc_score(Y_test, y_true, average='weighted')\n", + " auc = \"{:.2f}\".format(auc)\n", + " scores['roc'][name] = auc\n", + " ax_roc.plot(fpr,tpr,label=str(name)+\" = \"+str(auc), linewidth=5, c= default_colors[name])\n", + " precision, recall, _ = precision_recall_curve(Y_test, y_true)\n", + " prc = average_precision_score(Y_test, y_true, average='weighted')\n", + " prc = \"{:.2f}\".format(prc)\n", + " scores['prc'][name] = prc\n", + " #f1 = f1_score(Y_test, y_true, sample_weight= weights, average='weighted')\n", + " #scores['f1'][name] = \"{:.2f}\".format(np.nanmean(f1))\n", + " ax_prc.plot(recall,precision,label=str(name)+\" = \"+str(prc), linewidth=5, c= default_colors[name])\n", + "\n", + "# ax_prc.legend( bbox_to_anchor=(1,0.5), loc=\"center left\", fontsize=fsize)\n", + "# ax_roc.legend( bbox_to_anchor=(1,0.5), loc=\"center left\", fontsize=fsize)\n", + "ax_prc.legend(fontsize=fsize)\n", + "ax_roc.legend(fontsize=fsize)\n", + "fig.tight_layout()\n", + "#plt.savefig(\n", + "# f\"/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/processed/train_data_3_star/benchmarking/DITTO_ROC_PRC_benchmarking.pdf\",\n", + "# format=\"pdf\",\n", + "# dpi=1000,\n", + "# bbox_inches=\"tight\",\n", + "# )\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "661de33c", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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transcriptgeneconsequenceprotein_hgvscdna_hgvschromposref_basealt_baseclingen.disease...mutationtaster.prediction_Damagingmutationtaster.prediction_Polymorphismmutationtaster.model_complex_aaemutationtaster.model_simple_aaemutationtaster.model_without_aaeprec.stat_lof-tolerantprec.stat_recessivesift.confidence_Highsift.confidence_Lowclass
208162ENST00000413539DYSFintron_variantNaNc.457+150A>Gchr271512071AGNaN...0000001001
208163ENST00000429174DYSFintron_variantNaNc.457+150A>Gchr271512071AGNaN...0000001001
208164ENST00000288135KITsynonymous_variantp.Thr666=c.1998C>Tchr454729342CTNaN...0000000101
208165ENST00000412167KITsynonymous_variantp.Thr662=c.1986C>Tchr454729342CTNaN...0000000101
208166ENST00000328333COL7A1missense_variantp.Gly2037Gluc.6110G>Achr348575409CTNaN...0001000100
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transcriptgeneconsequenceprotein_hgvscdna_hgvschromposref_basealt_baseclingen.disease...mutationtaster.prediction_Polymorphismmutationtaster.model_complex_aaemutationtaster.model_simple_aaemutationtaster.model_without_aaeprec.stat_lof-tolerantprec.stat_recessivesift.confidence_Highsift.confidence_Lowclassspliceai
37022ENST00000591909TUBB63_prime_UTR_variantNaNc.*354C>Gchr1812329537CGNaN...0000000000.0004
37023ENST00000289672PKD1L1intron_variantNaNc.4961-38C>Tchr747847109GANaN...0000000000.0004
37024ENST00000437951PTCH1synonymous_variantp.Ser1169=c.3507C>Tchr995449168GAnevoid basal cell carcinoma syndrome...0000001000.0004
37025ENST00000644285ANKRD11intron_variantNaNc.745-6013G>Achr1689281204CTNaN...0000000000.0004
37026ENST00000436005PNKDintron_variantNaNc.173-24C>Tchr2218340005CTNaN...0000000000.0004
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" + ], + "text/plain": [ + " transcript gene consequence protein_hgvs \n", + "37022 ENST00000591909 TUBB6 3_prime_UTR_variant NaN \\\n", + "37023 ENST00000289672 PKD1L1 intron_variant NaN \n", + "37024 ENST00000437951 PTCH1 synonymous_variant p.Ser1169= \n", + "37025 ENST00000644285 ANKRD11 intron_variant NaN \n", + "37026 ENST00000436005 PNKD intron_variant NaN \n", + "\n", + " cdna_hgvs chrom pos ref_base alt_base \n", + "37022 c.*354C>G chr18 12329537 C G \\\n", + "37023 c.4961-38C>T chr7 47847109 G A \n", + "37024 c.3507C>T chr9 95449168 G A \n", + "37025 c.745-6013G>A chr16 89281204 C T \n", + "37026 c.173-24C>T chr2 218340005 C T \n", + "\n", + " clingen.disease ... \n", + "37022 NaN ... \\\n", + "37023 NaN ... \n", + "37024 nevoid basal cell carcinoma syndrome ... \n", + "37025 NaN ... \n", + "37026 NaN ... \n", + "\n", + " mutationtaster.prediction_Polymorphism \n", + "37022 0 \\\n", + "37023 0 \n", + "37024 0 \n", + "37025 0 \n", + "37026 0 \n", + "\n", + " 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"cell_type": "code", + "execution_count": 28, + "id": "180040dd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "chr2 3648\n", + "chr17 3168\n", + "chr1 2792\n", + "chr11 2221\n", + "chr16 2155\n", + "chr5 1953\n", + "chr3 1857\n", + "chr19 1839\n", + "chr12 1777\n", + "chr9 1730\n", + "chr7 1677\n", + "chr13 1611\n", + "chrX 1546\n", + "chr6 1244\n", + "chr10 1227\n", + "chr15 1198\n", + "chr14 1088\n", + "chr4 1027\n", + "chr8 1007\n", + "chr22 706\n", + "chr20 616\n", + "chr18 555\n", + "chr21 385\n", + "Name: chrom, dtype: int64" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "var1.chrom.value_counts()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "80d5c0cc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "synonymous_variant 8683\n", + "intron_variant 6928\n", + "missense_variant 4066\n", + "processed_transcript 2703\n", + "stop_gained 1944\n", + "frameshift_truncation 1907\n", + "NMD_transcript_variant,3_prime_UTR_variant 1405\n", + "intron_variant,splice_site_variant 919\n", + "frameshift_elongation 900\n", + "intron_variant,NMD_transcript_variant 647\n", + "2kb_downstream_variant 624\n", + "2kb_upstream_variant 616\n", + "3_prime_UTR_variant 600\n", + "intron_variant,processed_transcript 488\n", + "2kb_downstream_variant,processed_transcript 487\n", + "intron_variant,lnc_RNA 487\n", + "5_prime_UTR_variant 487\n", + "2kb_upstream_variant,processed_transcript 477\n", + "NMD_transcript_variant,synonymous_variant 354\n", + "2kb_downstream_variant,NMD_transcript_variant 343\n", + "missense_variant,NMD_transcript_variant 229\n", + "lnc_RNA 203\n", + "2kb_upstream_variant,lnc_RNA 196\n", + "2kb_downstream_variant,lnc_RNA 146\n", + "frameshift_truncation,stop_gained 132\n", + "inframe_deletion 111\n", + "intron_variant,NMD_transcript_variant,splice_site_variant 109\n", + "NMD_transcript_variant,stop_gained 95\n", + "missense_variant,start_lost 58\n", + "complex_substitution,frameshift_truncation 57\n", + "frameshift_truncation,NMD_transcript_variant 53\n", + "2kb_upstream_variant,NMD_transcript_variant 49\n", + "inframe_insertion 45\n", + "frameshift_elongation,stop_gained 44\n", + "2kb_downstream_variant,miRNA 41\n", + "2kb_upstream_variant,miRNA 39\n", + "splice_site_variant 35\n", + "frameshift_elongation,NMD_transcript_variant 35\n", + "NSD_transcript 30\n", + "2kb_downstream_variant,misc_RNA 22\n", + "complex_substitution,frameshift_elongation 21\n", + "complex_substitution,frameshift_elongation,intron_variant 18\n", + "NMD_transcript_variant,5_prime_UTR_variant 18\n", + "polymorphic_pseudogene 17\n", + "complex_substitution,missense_variant 16\n", + "2kb_upstream_variant,misc_RNA 10\n", + "miRNA 10\n", + "complex_substitution,stop_gained 9\n", + "exon_loss_variant,frameshift_truncation 8\n", + "2kb_upstream_variant,NSD_transcript 7\n", + "complex_substitution,inframe_insertion,intron_variant 7\n", + "frameshift_truncation,NMD_transcript_variant,stop_gained 6\n", + "missense_variant,start_lost,NMD_transcript_variant 5\n", + "NMD_transcript_variant 5\n", + "stop_retained_variant 5\n", + "intron_variant,NSD_transcript 4\n", + "2kb_downstream_variant,NSD_transcript 4\n", + "2kb_downstream_variant,snRNA 4\n", + "complex_substitution 3\n", + "complex_substitution,frameshift_truncation,NMD_transcript_variant 3\n", + "stop_lost 3\n", + "polymorphic_pseudogene,5_prime_UTR_variant 3\n", + "inframe_insertion,stop_gained 3\n", + "inframe_deletion,NMD_transcript_variant 2\n", + "complex_substitution,frameshift_elongation,intron_variant,NMD_transcript_variant 2\n", + "2kb_upstream_variant,ribozyme 2\n", + "exon_loss_variant,inframe_deletion 2\n", + "frameshift_elongation,NMD_transcript_variant,stop_gained 2\n", + "inframe_insertion,NMD_transcript_variant 2\n", + "2kb_upstream_variant,snRNA 2\n", + "frameshift_truncation,stop_lost 2\n", + "2kb_downstream_variant,snoRNA 2\n", + "NMD_transcript_variant,stop_lost 2\n", + "complex_substitution,inframe_deletion,missense_variant 2\n", + "inframe_deletion,stop_gained 2\n", + "start_retained_variant 1\n", + "complex_substitution,inframe_insertion 1\n", + "NMD_transcript_variant,splice_site_variant 1\n", + "complex_substitution,frameshift_elongation,NMD_transcript_variant 1\n", + "start_lost,5_prime_UTR_variant 1\n", + "complex_substitution,inframe_deletion 1\n", + "frameshift_elongation,stop_lost 1\n", + "intron_variant,polymorphic_pseudogene 1\n", + "complex_substitution,synonymous_variant 1\n", + "frameshift_truncation,stop_lost,stop_retained_variant,3_prime_UTR_variant 1\n", + "2kb_downstream_variant,intron_variant,stop_lost,3_prime_UTR_variant 1\n", + "complex_substitution,inframe_insertion,stop_gained 1\n", + "snRNA 1\n", + "2kb_upstream_variant,snoRNA 1\n", + "exon_loss_variant,frameshift_truncation,NMD_transcript_variant 1\n", + "NSD_transcript,5_prime_UTR_variant 1\n", + "2kb_upstream_variant,rRNA 1\n", + "ribozyme 1\n", + "frameshift_truncation,start_lost 1\n", + "polymorphic_pseudogene,3_prime_UTR_variant 1\n", + "Name: consequence, dtype: int64" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "var1['consequence'].value_counts()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "361115a7", + "metadata": {}, + "outputs": [], + "source": [ + "#var1 = var1.merge(amis, left_on=['chrom','pos','ref_base','alt_base'], right_on=['#CHROM','POS','REF','ALT'], how='left')\n", + "#var1.shape\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "fb43779b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "class\n", + "0 147809\n", + "1 60358\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "var['class'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "70f39086", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Precision: 0.9900747951197025\n", + "Recall: 0.9900752761004386\n", + "ROC_AUC: 0.9971943867614745\n", + "PRC_AUC: 0.9929252877057522\n", + "Accuracy: 0.9900752761004386\n", + "Confusion matrix:\n", + "[[146781 1028]\n", + " [ 1038 59320]]\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Confusion matrix for test variants')" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "prc = precision_score(Y_test, y_score.round(), average=\"weighted\")\n", + "recall = recall_score(Y_test, y_score.round(), average=\"weighted\")\n", + "roc_auc = roc_auc_score(Y_test, y_score)\n", + "prc_auc = average_precision_score(Y_test, y_score, average=\"weighted\")\n", + "# roc_auc = roc_auc_score(Y_test, np.argmax(y_score, axis=1))\n", + "accuracy = accuracy_score(Y_test, y_score.round())\n", + "# score = clf.score(X_train, Y_train)\n", + "matrix = confusion_matrix(Y_test, y_score.round())\n", + "cm = confusion_matrix(Y_test, y_score.round())\n", + "print(f\"Precision: {prc}\\nRecall: {recall}\\nROC_AUC: {roc_auc}\\nPRC_AUC: {prc_auc}\\nAccuracy: {accuracy}\\nConfusion matrix:\\n{matrix}\")\n", + "cm = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[ 'Benign', 'Pathogenic'])\n", + "cm.plot()\n", + "plt.title(f\"Confusion matrix for test variants\", fontsize=12)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3cb17f0d", + "metadata": {}, + "outputs": [], + "source": [ + "bench = var1[['DITTO','cadd.phred','clinpred.score','metasvm.score', 'gerp.gerp_rs','spliceai','revel.score','dann.score','sift.score']]\n", + "bench.columns = ['DITTO','CADD','ClinPred','MetaSVM','GERP','spliceai','Revel','DANN','SIFT']\n", + "Y_test = var1['class']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "id": "c4691444", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig, [ax_roc, ax_prc] = plt.subplots(1, 2, figsize=(30, 13))\n", + "\n", + "fig.suptitle(f\"DITTO Benchmarking\", fontsize=40)\n", + "fsize = 22\n", + "ax_roc.tick_params(axis='both', which='major', labelsize=fsize)\n", + "ax_prc.tick_params(axis='both', which='major', labelsize=fsize)\n", + "ax_roc.set_xlabel(\"False Positive Rate\", fontsize=fsize)\n", + "ax_roc.set_ylabel(\"True Positive Rate\", fontsize=fsize)\n", + "ax_roc.set_title(\"Receiver Operating Characteristic (ROC) curves\", fontsize=fsize)\n", + "ax_roc.grid(linestyle=\"--\")\n", + "ax_prc.set_xlabel(\"Recall\", fontsize=fsize)\n", + "ax_prc.set_ylabel(\"Precision\", fontsize=fsize)\n", + "ax_prc.set_title(\"Precision Recall (PRC) curves\", fontsize=fsize)\n", + "ax_prc.grid(linestyle=\"--\")\n", + "\n", + "scores = {}\n", + "scores['roc'] = {}\n", + "scores['prc'] = {}\n", + "scores['f1'] = {}\n", + "for name in list(bench.columns):\n", + " x = bench[name].values\n", + " if np.unique(x).size == 1 and name in ['CADD', 'MetaSVM','mutation_assessor','provean','GERP']:\n", + " y_true = np.zeros_like(x)\n", + " elif name in ['CADD', 'MetaSVM','mutation_assessor','provean','GERP']:\n", + " y_true = (x-np.min(x))/(np.max(x)-np.min(x))\n", + " else:\n", + " y_true = x\n", + " fpr, tpr, _ = roc_curve(Y_test, y_true)\n", + " auc = roc_auc_score(Y_test, y_true, average='weighted')\n", + " auc = \"{:.2f}\".format(auc)\n", + " scores['roc'][name] = auc\n", + " ax_roc.plot(fpr,tpr,label=str(name)+\" = \"+str(auc), linewidth=5, c= default_colors[name])\n", + " precision, recall, _ = precision_recall_curve(Y_test, y_true)\n", + " prc = average_precision_score(Y_test, y_true, average='weighted')\n", + " prc = \"{:.2f}\".format(prc)\n", + " scores['prc'][name] = prc\n", + " #f1 = f1_score(Y_test, y_true, sample_weight= weights, average='weighted')\n", + " #scores['f1'][name] = \"{:.2f}\".format(np.nanmean(f1))\n", + " ax_prc.plot(recall,precision,label=str(name)+\" = \"+str(prc), linewidth=5, c= default_colors[name])\n", + "\n", + "ax_prc.legend(fontsize=fsize)# bbox_to_anchor=(1,0.5), loc=\"center left\",\n", + "ax_roc.legend(fontsize=fsize)# bbox_to_anchor=(1,0.5), loc=\"center left\",\n", + "fig.tight_layout()\n", + "#plt.savefig(\n", + "# f\"/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/processed/train_data_3_star/benchmarking/DITTO_ROC_PRC_benchmarking.pdf\",\n", + "# format=\"pdf\",\n", + "# dpi=1000,\n", + "# bbox_inches=\"tight\",\n", + "# )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "2f326fb2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['chr6', 'chr13', 'chrX', 'chr14', 'chr11', 'chr18', 'chr12',\n", + " 'chr17', 'chr2', 'chr15', 'chr9', 'chr1', 'chr8', 'chr4', 'chr16',\n", + " 'chr21', 'chr5', 'chr19', 'chr3', 'chr10', 'chr7', 'chr20',\n", + " 'chr22'], dtype=object)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "var.chrom.unique()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "id": "64fb18cd", + "metadata": {}, + "outputs": [], + "source": [ + "acc_scores_chr = {}\n", + "prc_scores_chr = {}\n", + "f1_scores_chr = {}\n", + "for name in var1.chrom.unique():\n", + " acc_scores_chr[name] = {}\n", + " prc_scores_chr[name] = {}\n", + " f1_scores_chr[name] = {}\n", + " for clf in bench.columns:\n", + " index_list = var1[var1.chrom == name].index\n", + " x = bench[bench.index.isin(index_list)][clf].values\n", + " if np.unique(x).size == 1 and clf in ['CADD', 'MetaSVM','mutation_assessor','provean','GERP']:\n", + " x_norm = np.zeros_like(x)\n", + " elif clf in ['CADD', 'MetaSVM','mutation_assessor','provean','GERP']:\n", + " x_norm = (x-np.min(x))/(np.max(x)-np.min(x))\n", + " else:\n", + " x_norm = x\n", + "\n", + " acc = accuracy_score(Y_test[index_list], x_norm.round())\n", + " acc = \"{:.2f}\".format(acc)\n", + " acc_scores_chr[name][clf] = acc\n", + "\n", + " prc = precision_score(Y_test[index_list], x_norm.round())\n", + " prc = \"{:.2f}\".format(prc)\n", + " prc_scores_chr[name][clf] = prc\n", + "\n", + " f1 = f1_score(Y_test[index_list], x_norm.round())\n", + " f1_scores_chr[name][clf] = \"{:.2f}\".format(f1)\n", + "\n", + "pd.DataFrame(f1_scores_chr).to_csv(\"/Users/tarunmamidi/Documents/Development/DITTO/data/processed/f1_scores_chr_overall_1_transcript.csv\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "id": "e6fc0509", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'chr3': {'DITTO': '0.99',\n", + " 'CADD': '0.71',\n", + " 'ClinPred': '0.51',\n", + " 'MetaSVM': '0.40',\n", + " 'GERP': '0.45',\n", + " 'spliceai': '0.29',\n", + " 'Revel': '0.32',\n", + " 'DANN': '0.43',\n", + " 'SIFT': '0.39'},\n", + " 'chr19': {'DITTO': '0.98',\n", + " 'CADD': '0.51',\n", + " 'ClinPred': '0.59',\n", + " 'MetaSVM': '0.54',\n", + " 'GERP': '0.34',\n", + " 'spliceai': '0.22',\n", + " 'Revel': '0.35',\n", + " 'DANN': '0.35',\n", + " 'SIFT': '0.27'},\n", + " 'chr11': {'DITTO': '0.98',\n", + " 'CADD': '0.63',\n", + " 'ClinPred': '0.41',\n", + " 'MetaSVM': '0.37',\n", + " 'GERP': '0.49',\n", + " 'spliceai': '0.30',\n", + " 'Revel': '0.28',\n", + " 'DANN': '0.46',\n", + " 'SIFT': '0.43'},\n", + " 'chr1': {'DITTO': '0.98',\n", + " 'CADD': '0.71',\n", + " 'ClinPred': '0.50',\n", + " 'MetaSVM': '0.42',\n", + " 'GERP': '0.40',\n", + " 'spliceai': '0.23',\n", + " 'Revel': '0.36',\n", + " 'DANN': '0.40',\n", + " 'SIFT': '0.33'},\n", + " 'chr17': {'DITTO': '0.98',\n", + " 'CADD': '0.52',\n", + " 'ClinPred': '0.33',\n", + " 'MetaSVM': '0.27',\n", + " 'GERP': '0.59',\n", + " 'spliceai': '0.28',\n", + " 'Revel': '0.19',\n", + " 'DANN': '0.58',\n", + " 'SIFT': '0.53'},\n", + " 'chr8': {'DITTO': '0.98',\n", + " 'CADD': '0.74',\n", + " 'ClinPred': '0.28',\n", + " 'MetaSVM': '0.18',\n", + " 'GERP': '0.38',\n", + " 'spliceai': '0.35',\n", + " 'Revel': '0.15',\n", + " 'DANN': '0.36',\n", + " 'SIFT': '0.36'},\n", + " 'chrX': {'DITTO': '0.97',\n", + " 'CADD': '0.79',\n", + " 'ClinPred': '0.60',\n", + " 'MetaSVM': '0.50',\n", + " 'GERP': '0.50',\n", + " 'spliceai': '0.27',\n", + " 'Revel': '0.38',\n", + " 'DANN': '0.48',\n", + " 'SIFT': '0.42'},\n", + " 'chr7': {'DITTO': '0.98',\n", + " 'CADD': '0.62',\n", + " 'ClinPred': '0.43',\n", + " 'MetaSVM': '0.37',\n", + " 'GERP': '0.43',\n", + " 'spliceai': '0.29',\n", + " 'Revel': '0.16',\n", + " 'DANN': '0.42',\n", + " 'SIFT': '0.39'},\n", + " 'chr2': {'DITTO': '0.99',\n", + " 'CADD': '0.19',\n", + " 'ClinPred': '0.34',\n", + " 'MetaSVM': '0.31',\n", + " 'GERP': '0.41',\n", + " 'spliceai': '0.28',\n", + " 'Revel': '0.19',\n", + " 'DANN': '0.42',\n", + " 'SIFT': '0.39'},\n", + " 'chr16': {'DITTO': '0.98',\n", + " 'CADD': '0.61',\n", + " 'ClinPred': '0.30',\n", + " 'MetaSVM': '0.23',\n", + " 'GERP': '0.38',\n", + " 'spliceai': '0.29',\n", + " 'Revel': '0.16',\n", + " 'DANN': '0.39',\n", + " 'SIFT': '0.36'},\n", + " 'chr12': {'DITTO': '0.98',\n", + " 'CADD': '0.74',\n", + " 'ClinPred': '0.63',\n", + " 'MetaSVM': '0.56',\n", + " 'GERP': '0.40',\n", + " 'spliceai': '0.21',\n", + " 'Revel': '0.52',\n", + " 'DANN': '0.39',\n", + " 'SIFT': '0.28'},\n", + " 'chr6': {'DITTO': '0.98',\n", + " 'CADD': '0.72',\n", + " 'ClinPred': '0.34',\n", + " 'MetaSVM': '0.23',\n", + " 'GERP': '0.41',\n", + " 'spliceai': '0.25',\n", + " 'Revel': '0.24',\n", + " 'DANN': '0.39',\n", + " 'SIFT': '0.37'},\n", + " 'chr15': {'DITTO': '0.98',\n", + " 'CADD': '0.72',\n", + " 'ClinPred': '0.52',\n", + " 'MetaSVM': '0.49',\n", + " 'GERP': '0.45',\n", + " 'spliceai': '0.26',\n", + " 'Revel': '0.44',\n", + " 'DANN': '0.44',\n", + " 'SIFT': '0.36'},\n", + " 'chr9': {'DITTO': '0.98',\n", + " 'CADD': '0.64',\n", + " 'ClinPred': '0.38',\n", + " 'MetaSVM': '0.33',\n", + " 'GERP': '0.31',\n", + " 'spliceai': '0.28',\n", + " 'Revel': '0.23',\n", + " 'DANN': '0.32',\n", + " 'SIFT': '0.29'},\n", + " 'chr21': {'DITTO': '0.98',\n", + " 'CADD': '0.71',\n", + " 'ClinPred': '0.48',\n", + " 'MetaSVM': '0.42',\n", + " 'GERP': '0.38',\n", + " 'spliceai': '0.31',\n", + " 'Revel': '0.32',\n", + " 'DANN': '0.36',\n", + " 'SIFT': '0.34'},\n", + " 'chr5': {'DITTO': '0.99',\n", + " 'CADD': '0.65',\n", + " 'ClinPred': '0.27',\n", + " 'MetaSVM': '0.22',\n", + " 'GERP': '0.35',\n", + " 'spliceai': '0.27',\n", + " 'Revel': '0.14',\n", + " 'DANN': '0.35',\n", + " 'SIFT': '0.33'},\n", + " 'chr18': {'DITTO': '0.97',\n", + " 'CADD': '0.72',\n", + " 'ClinPred': '0.37',\n", + " 'MetaSVM': '0.34',\n", + " 'GERP': '0.31',\n", + " 'spliceai': '0.26',\n", + " 'Revel': '0.18',\n", + " 'DANN': '0.32',\n", + " 'SIFT': '0.29'},\n", + " 'chr13': {'DITTO': '0.99',\n", + " 'CADD': '0.51',\n", + " 'ClinPred': '0.14',\n", + " 'MetaSVM': '0.13',\n", + " 'GERP': '0.67',\n", + " 'spliceai': '0.11',\n", + " 'Revel': '0.11',\n", + " 'DANN': '0.66',\n", + " 'SIFT': '0.65'},\n", + " 'chr10': {'DITTO': '0.97',\n", + " 'CADD': '0.70',\n", + " 'ClinPred': '0.43',\n", + " 'MetaSVM': '0.38',\n", + " 'GERP': '0.34',\n", + " 'spliceai': '0.26',\n", + " 'Revel': '0.33',\n", + " 'DANN': '0.34',\n", + " 'SIFT': '0.29'},\n", + " 'chr4': {'DITTO': '0.98',\n", + " 'CADD': '0.67',\n", + " 'ClinPred': '0.37',\n", + " 'MetaSVM': '0.30',\n", + " 'GERP': '0.34',\n", + " 'spliceai': '0.28',\n", + " 'Revel': '0.23',\n", + " 'DANN': '0.34',\n", + " 'SIFT': '0.32'},\n", + " 'chr20': {'DITTO': '0.94',\n", + " 'CADD': '0.68',\n", + " 'ClinPred': '0.50',\n", + " 'MetaSVM': '0.43',\n", + " 'GERP': '0.28',\n", + " 'spliceai': '0.27',\n", + " 'Revel': '0.35',\n", + " 'DANN': '0.29',\n", + " 'SIFT': '0.23'},\n", + " 'chr22': {'DITTO': '0.99',\n", + " 'CADD': '0.76',\n", + " 'ClinPred': '0.48',\n", + " 'MetaSVM': '0.43',\n", + " 'GERP': '0.38',\n", + " 'spliceai': '0.35',\n", + " 'Revel': '0.32',\n", + " 'DANN': '0.38',\n", + " 'SIFT': '0.33'},\n", + " 'chr14': {'DITTO': '0.97',\n", + " 'CADD': '0.75',\n", + " 'ClinPred': '0.57',\n", + " 'MetaSVM': '0.49',\n", + " 'GERP': '0.33',\n", + " 'spliceai': '0.19',\n", + " 'Revel': '0.43',\n", + " 'DANN': '0.32',\n", + " 'SIFT': '0.26'}}" + ] + }, + "execution_count": 184, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f1_scores_chr\n" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "id": "228893c8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Extract the chromosome names and values in the specified order\n", + "chromosomes = ['chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8', 'chr9', 'chr10', 'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'chr18', 'chr19',\n", + " 'chr20', 'chr21', 'chr22', 'chrX']\n", + "CADD_values = [float(acc_scores_chr[chr]['CADD']) for chr in chromosomes]\n", + "ClinPred_values = [float(acc_scores_chr[chr]['ClinPred']) for chr in chromosomes]\n", + "Revel_values = [float(acc_scores_chr[chr]['Revel']) for chr in chromosomes]\n", + "MetaSVM_values = [float(acc_scores_chr[chr]['MetaSVM']) for chr in chromosomes]\n", + "GERP_values = [float(acc_scores_chr[chr]['GERP']) for chr in chromosomes]\n", + "DITTO_values = [float(acc_scores_chr[chr]['DITTO']) for chr in chromosomes]\n", + "spliceai_values = [float(acc_scores_chr[chr]['spliceai']) for chr in chromosomes]\n", + "\n", + "# Create a Manhattan-style bar plot\n", + "plt.figure(figsize=(17, 6))\n", + "plt.rcParams.update({'font.size': 12})\n", + "plt.plot(chromosomes, DITTO_values, marker='d', label='DITTO', linestyle='-', c= default_colors['DITTO'])\n", + "plt.plot(chromosomes, CADD_values, marker='o', label='CADD', linestyle='-', c= default_colors['CADD'])\n", + "plt.plot(chromosomes, ClinPred_values, marker='s', label='ClinPred', linestyle='-', c= default_colors['ClinPred'])\n", + "plt.plot(chromosomes, MetaSVM_values, marker='s', label='MetaSVM', linestyle='-', c= default_colors['MetaSVM'])\n", + "plt.plot(chromosomes, GERP_values, marker='^', label='GERP', linestyle='-', c= default_colors['GERP'])\n", + "plt.plot(chromosomes, Revel_values, marker='s', label='Revel', linestyle='-', c= default_colors['Revel'])\n", + "plt.plot(chromosomes, spliceai_values, marker='s', label='spliceai', linestyle='-', c= default_colors['spliceai'])\n", + "\n", + "plt.xlabel('Chromosome')\n", + "plt.ylabel('Accuracy')\n", + "plt.title('Accuracy per Chromosome')\n", + "plt.legend()\n", + "plt.grid(axis='y')\n", + "\n", + "# Add shaded rectangles instead of grid lines\n", + "ax = plt.gca()\n", + "for i in range(len(chromosomes)):\n", + " if i % 2 == 0: # Shade every other chromosome\n", + " ax.axvspan(i - 0.5, i + 0.5, facecolor='lightgray', alpha=0.5)\n", + "\n", + "plt.xticks(range(len(chromosomes)), chromosomes, rotation=45) # Set x-axis labels\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "id": "f8d642bd", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Extract the chromosome names and values in the specified order\n", + "chromosomes = ['chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8', 'chr9', 'chr10', 'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'chr18', 'chr19',\n", + " 'chr20', 'chr21', 'chr22', 'chrX']\n", + "CADD_values = [float(f1_scores_chr[chr]['CADD']) for chr in chromosomes]\n", + "ClinPred_values = [float(f1_scores_chr[chr]['ClinPred']) for chr in chromosomes]\n", + "Revel_values = [float(f1_scores_chr[chr]['Revel']) for chr in chromosomes]\n", + "MetaSVM_values = [float(f1_scores_chr[chr]['MetaSVM']) for chr in chromosomes]\n", + "GERP_values = [float(f1_scores_chr[chr]['GERP']) for chr in chromosomes]\n", + "DITTO_values = [float(f1_scores_chr[chr]['DITTO']) for chr in chromosomes]\n", + "spliceai_values = [float(f1_scores_chr[chr]['spliceai']) for chr in chromosomes]\n", + "\n", + "# Create a Manhattan-style bar plot\n", + "plt.figure(figsize=(17, 4.5))\n", + "plt.rcParams.update({'font.size': 12})\n", + "plt.plot(chromosomes, DITTO_values, marker='d', label='DITTO', linestyle='-', c= default_colors['DITTO'])\n", + "plt.plot(chromosomes, CADD_values, marker='o', label='CADD', linestyle='-', c= default_colors['CADD'])\n", + "plt.plot(chromosomes, ClinPred_values, marker='s', label='ClinPred', linestyle='-', c= default_colors['ClinPred'])\n", + "plt.plot(chromosomes, MetaSVM_values, marker='s', label='MetaSVM', linestyle='-', c= default_colors['MetaSVM'])\n", + "plt.plot(chromosomes, GERP_values, marker='^', label='GERP', linestyle='-', c= default_colors['GERP'])\n", + "plt.plot(chromosomes, Revel_values, marker='s', label='Revel', linestyle='-', c= default_colors['Revel'])\n", + "plt.plot(chromosomes, spliceai_values, marker='s', label='spliceai', linestyle='-', c= default_colors['spliceai'])\n", + "\n", + "plt.xlabel('Chromosome')\n", + "plt.ylabel('F1 score')\n", + "plt.title('F1 score per Chromosome')\n", + "# plt.legend(bbox_to_anchor=(1,0.5), loc=\"center left\")\n", + "plt.grid(axis='y')\n", + "\n", + "# Add shaded rectangles instead of grid lines\n", + "ax = plt.gca()\n", + "for i in range(len(chromosomes)):\n", + " if i % 2 == 0: # Shade every other chromosome\n", + " ax.axvspan(i - 0.5, i + 0.5, facecolor='lightgray', alpha=0.5)\n", + "\n", + "plt.xticks(range(len(chromosomes)), chromosomes, rotation=45) # Set x-axis labels\n", + "plt.ylim(-0.05, 1.05)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Assuming you have a DataFrame called df with a column called 'column1'\n", + "# and a dictionary called my_dict\n", + "\n", + "var1['so'] = var1['consequence'].map(var_dict)\n", + "var1['so1'] = var1['so']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "pd.pivot_table(var1, index='so', columns='chrom', values='so1',\n", + " aggfunc='count').T.to_csv(\"/Users/tarunmamidi/Documents/Development/DITTO/data/processed/var_number_type_chr.csv\") #.sort_values(by=['low_impact','high_impact'], ascending=False)\n", + "#var1['chromo'] = var1['chrom']\n", + "#pd.pivot_table(var1, index='chromo', columns='so', values='chrom',\n", + "# aggfunc='count')#.sort_values(by=['low_impact','high_impact'], ascending=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pd.pivot_table(var1, index='so', columns='chrom', values='so1',\n", + " aggfunc='count').plot(kind='bar', figsize=(20,10), linewidth=3)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "da5e15a5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "breast-ovarian cancer, familial, susceptibility to, 2;Fanconi anemia complementation group D1 1137\n", + "Wilson disease 70\n", + "autosomal recessive nonsyndromic deafness;syndromic genetic deafness 26\n", + "xeroderma pigmentosum group G 9\n", + "auditory neuropathy 8\n", + "Waardenburg syndrome type 4A;Waardenburg syndrome type 4A 4\n", + "nonsyndromic genetic deafness;Clouston syndrome 4\n", + "Leigh syndrome 2\n", + "factor VII deficiency 2\n", + "high myopia-sensorineural deafness syndrome 1\n", + "Name: clingen.disease, dtype: int64" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "var1[var1['chrom']=='chr13']['clingen.disease'].value_counts()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "2111b336", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Confusion matrix of DITTO for chr13 variants')" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "name = 'chr13'\n", + "clf = 'DITTO'\n", + "index_list = var1[var1.chrom == name].index\n", + "x = bench[bench.index.isin(index_list)][clf].values\n", + "\n", + "cm = confusion_matrix(Y_test[index_list],x.round())\n", + "cm = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[ 'Benign', 'Pathogenic'])\n", + "cm.plot()\n", + "plt.title(f\"Confusion matrix of DITTO for chr13 variants\", fontsize=12)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "ce2499ce", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "synonymous_variant 8683\n", + "intron_variant 6928\n", + "missense_variant 4066\n", + "processed_transcript 2703\n", + "stop_gained 1944\n", + "frameshift_truncation 1907\n", + "NMD_transcript_variant,3_prime_UTR_variant 1405\n", + "intron_variant,splice_site_variant 919\n", + "frameshift_elongation 900\n", + "intron_variant,NMD_transcript_variant 647\n", + "2kb_downstream_variant 624\n", + "2kb_upstream_variant 616\n", + "3_prime_UTR_variant 600\n", + "intron_variant,processed_transcript 488\n", + "2kb_downstream_variant,processed_transcript 487\n", + "5_prime_UTR_variant 487\n", + "intron_variant,lnc_RNA 487\n", + "2kb_upstream_variant,processed_transcript 477\n", + "NMD_transcript_variant,synonymous_variant 354\n", + "2kb_downstream_variant,NMD_transcript_variant 343\n", + "missense_variant,NMD_transcript_variant 229\n", + "lnc_RNA 203\n", + "2kb_upstream_variant,lnc_RNA 196\n", + "2kb_downstream_variant,lnc_RNA 146\n", + "frameshift_truncation,stop_gained 132\n", + "inframe_deletion 111\n", + "intron_variant,NMD_transcript_variant,splice_site_variant 109\n", + "NMD_transcript_variant,stop_gained 95\n", + "missense_variant,start_lost 58\n", + "complex_substitution,frameshift_truncation 57\n", + "frameshift_truncation,NMD_transcript_variant 53\n", + "2kb_upstream_variant,NMD_transcript_variant 49\n", + "inframe_insertion 45\n", + "frameshift_elongation,stop_gained 44\n", + "2kb_downstream_variant,miRNA 41\n", + "2kb_upstream_variant,miRNA 39\n", + "splice_site_variant 35\n", + "frameshift_elongation,NMD_transcript_variant 35\n", + "NSD_transcript 30\n", + "2kb_downstream_variant,misc_RNA 22\n", + "complex_substitution,frameshift_elongation 21\n", + "NMD_transcript_variant,5_prime_UTR_variant 18\n", + "complex_substitution,frameshift_elongation,intron_variant 18\n", + "polymorphic_pseudogene 17\n", + "complex_substitution,missense_variant 16\n", + "miRNA 10\n", + "2kb_upstream_variant,misc_RNA 10\n", + "complex_substitution,stop_gained 9\n", + "exon_loss_variant,frameshift_truncation 8\n", + "2kb_upstream_variant,NSD_transcript 7\n", + "complex_substitution,inframe_insertion,intron_variant 7\n", + "frameshift_truncation,NMD_transcript_variant,stop_gained 6\n", + "stop_retained_variant 5\n", + "NMD_transcript_variant 5\n", + "missense_variant,start_lost,NMD_transcript_variant 5\n", + "2kb_downstream_variant,snRNA 4\n", + "intron_variant,NSD_transcript 4\n", + "2kb_downstream_variant,NSD_transcript 4\n", + "stop_lost 3\n", + "polymorphic_pseudogene,5_prime_UTR_variant 3\n", + "complex_substitution 3\n", + "complex_substitution,frameshift_truncation,NMD_transcript_variant 3\n", + "inframe_insertion,stop_gained 3\n", + "complex_substitution,inframe_deletion,missense_variant 2\n", + "NMD_transcript_variant,stop_lost 2\n", + "2kb_upstream_variant,snRNA 2\n", + "inframe_deletion,NMD_transcript_variant 2\n", + "2kb_upstream_variant,ribozyme 2\n", + "exon_loss_variant,inframe_deletion 2\n", + "frameshift_elongation,NMD_transcript_variant,stop_gained 2\n", + "inframe_insertion,NMD_transcript_variant 2\n", + "frameshift_truncation,stop_lost 2\n", + "complex_substitution,frameshift_elongation,intron_variant,NMD_transcript_variant 2\n", + "inframe_deletion,stop_gained 2\n", + "2kb_downstream_variant,snoRNA 2\n", + "start_retained_variant 1\n", + "NMD_transcript_variant,splice_site_variant 1\n", + "2kb_upstream_variant,snoRNA 1\n", + "2kb_downstream_variant,intron_variant,stop_lost,3_prime_UTR_variant 1\n", + "complex_substitution,inframe_deletion 1\n", + "complex_substitution,inframe_insertion 1\n", + "complex_substitution,inframe_insertion,stop_gained 1\n", + "frameshift_truncation,stop_lost,stop_retained_variant,3_prime_UTR_variant 1\n", + "frameshift_elongation,stop_lost 1\n", + "frameshift_truncation,start_lost 1\n", + "intron_variant,polymorphic_pseudogene 1\n", + "complex_substitution,frameshift_elongation,NMD_transcript_variant 1\n", + "NSD_transcript,5_prime_UTR_variant 1\n", + "exon_loss_variant,frameshift_truncation,NMD_transcript_variant 1\n", + "complex_substitution,synonymous_variant 1\n", + "2kb_upstream_variant,rRNA 1\n", + "polymorphic_pseudogene,3_prime_UTR_variant 1\n", + "snRNA 1\n", + "start_lost,5_prime_UTR_variant 1\n", + "ribozyme 1\n", + "Name: consequence, dtype: int64" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "var1.consequence.value_counts()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "var_type_order = ['NMD', 'missense', 'stop lost','stop gained', 'start lost','synonymous', 'splice site','frameshift truncation','frameshift elongation', 'complex substitution', 'exon loss variant', 'inframe insertion','inframe deletion','intron', '3 prime UTR', '5 prime UTR', 'other RNA','intergenic']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f5ef2e63", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Confusion matrix of DITTO for intergenic variants')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "name = 'intergenic'\n", + "clf = 'DITTO'\n", + "index_list = var1[var1.so == name].index\n", + "x = bench[bench.index.isin(index_list)][clf].values\n", + "\n", + "cm = confusion_matrix(Y_test[index_list],x.round())\n", + "cm = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[ 'Benign', 'Pathogenic'])\n", + "cm.plot()\n", + "plt.title(f\"Confusion matrix of DITTO for intergenic variants\", fontsize=12)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "afdea2e6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1221" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1106+115" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "6a2dfb47", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9846774193548387" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1221/1240" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "roc_scores = {}\n", + "prc_scores = {}\n", + "f1_scores = {}\n", + "\n", + "for consq in var_type_order:\n", + " roc_scores[consq] = {}\n", + " prc_scores[consq] = {}\n", + " f1_scores[consq] = {}\n", + "\n", + " index_list = var1[var1.so == consq].index\n", + " missense = bench[bench.index.isin(index_list)]\n", + "\n", + " missense_y = var1[var1.index.isin(index_list)]['class'].values\n", + "\n", + " for name in list(missense.columns):\n", + " if name in ['CADD', 'MetaSVM','mutation_assessor','provean','GERP']:\n", + " x_norm = (missense-np.min(missense))/(np.max(missense)-np.min(missense))\n", + " else:\n", + " x_norm = missense\n", + " try:\n", + " auc = accuracy_score(missense_y, x_norm[name].fillna(0).values)\n", + " except:\n", + " auc=0\n", + " roc_scores[consq][name] = round(auc,2)\n", + " try:\n", + " prc = precision_score(missense_y, x_norm[name].fillna(0).values)\n", + " except:\n", + " prc=0\n", + " prc_scores[consq][name] = round(prc,2)\n", + " try:\n", + " f1 = f1_score(missense_y, x_norm[name].fillna(0).values.round())\n", + " except:\n", + " f1=0\n", + " f1_scores[consq][name] = round(f1,2)\n", + "\n", + "#pd.DataFrame(roc_scores).to_csv(\"/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO4NF/data/processed/tuning/NN_roc_scores.csv\")\n", + "#pd.DataFrame(prc_scores).to_csv(\"/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO4NF/data/processed/tuning/NN_prc_scores.csv\")\n", + "# pd.DataFrame(f1_scores).to_csv(\"/Users/tarunmamidi/Documents/Development/DITTO/data/processed/f1_scores_type_overall_1_transcript.csv\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "id": "f38dba1f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'NMD': {'DITTO': 0.98,\n", + " 'CADD': 0.81,\n", + " 'ClinPred': 0.42,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 0.0,\n", + " 'spliceai': 0.38,\n", + " 'Revel': 0.01,\n", + " 'DANN': 0.33,\n", + " 'SIFT': 0.33},\n", + " 'missense': {'DITTO': 0.98,\n", + " 'CADD': 0.73,\n", + " 'ClinPred': 0.97,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 0.0,\n", + " 'spliceai': 0.07,\n", + " 'Revel': 0.83,\n", + " 'DANN': 0.61,\n", + " 'SIFT': 0.15},\n", + " 'stop lost': {'DITTO': 0.86,\n", + " 'CADD': 1.0,\n", + " 'ClinPred': 0.0,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 0.0,\n", + " 'spliceai': 0.0,\n", + " 'Revel': 0.0,\n", + " 'DANN': 1.0,\n", + " 'SIFT': 1.0},\n", + " 'stop gained': {'DITTO': 1.0,\n", + " 'CADD': 0.99,\n", + " 'ClinPred': 0.08,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 0.0,\n", + " 'spliceai': 0.04,\n", + " 'Revel': 0.0,\n", + " 'DANN': 0.89,\n", + " 'SIFT': 1.0},\n", + " 'start lost': {'DITTO': 0.98,\n", + " 'CADD': 0.96,\n", + " 'ClinPred': 0.97,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 0.0,\n", + " 'spliceai': 0.03,\n", + " 'Revel': 0.67,\n", + " 'DANN': 0.9,\n", + " 'SIFT': 0.24},\n", + " 'synonymous': {'DITTO': 0.83,\n", + " 'CADD': 0.01,\n", + " 'ClinPred': 0.17,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 0.0,\n", + " 'spliceai': 0.56,\n", + " 'Revel': 0.0,\n", + " 'DANN': 0.0,\n", + " 'SIFT': 0.0},\n", + " 'splice site': {'DITTO': 1.0,\n", + " 'CADD': 0.98,\n", + " 'ClinPred': 0.0,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 0.0,\n", + " 'spliceai': 0.97,\n", + " 'Revel': 0.0,\n", + " 'DANN': 0.9,\n", + " 'SIFT': 0.99},\n", + " 'frameshift truncation': {'DITTO': 1.0,\n", + " 'CADD': 0.01,\n", + " 'ClinPred': 0.0,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 0.0,\n", + " 'spliceai': 0.0,\n", + " 'Revel': 0.0,\n", + " 'DANN': 1.0,\n", + " 'SIFT': 1.0},\n", + " 'frameshift elongation': {'DITTO': 1.0,\n", + " 'CADD': 0.08,\n", + " 'ClinPred': 0.0,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 0.0,\n", + " 'spliceai': 0.0,\n", + " 'Revel': 0.0,\n", + " 'DANN': 0.93,\n", + " 'SIFT': 1.0},\n", + " 'complex substitution': {'DITTO': 0.96,\n", + " 'CADD': 0.14,\n", + " 'ClinPred': 0.0,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 0.0,\n", + " 'spliceai': 0.0,\n", + " 'Revel': 0.0,\n", + " 'DANN': 0.96,\n", + " 'SIFT': 0.96},\n", + " 'exon loss variant': {'DITTO': 1.0,\n", + " 'CADD': 1.0,\n", + " 'ClinPred': 0.0,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 1.0,\n", + " 'spliceai': 0.0,\n", + " 'Revel': 0.0,\n", + " 'DANN': 1.0,\n", + " 'SIFT': 1.0},\n", + " 'inframe insertion': {'DITTO': 0.56,\n", + " 'CADD': 0.3,\n", + " 'ClinPred': 0.0,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 0.3,\n", + " 'spliceai': 0.0,\n", + " 'Revel': 0.0,\n", + " 'DANN': 0.3,\n", + " 'SIFT': 0.3},\n", + " 'inframe deletion': {'DITTO': 0.83,\n", + " 'CADD': 0.69,\n", + " 'ClinPred': 0.0,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 0.69,\n", + " 'spliceai': 0.0,\n", + " 'Revel': 0.0,\n", + " 'DANN': 0.69,\n", + " 'SIFT': 0.69},\n", + " 'intron': {'DITTO': 0.88,\n", + " 'CADD': 0.69,\n", + " 'ClinPred': 0.26,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 0.0,\n", + " 'spliceai': 0.63,\n", + " 'Revel': 0.0,\n", + " 'DANN': 0.11,\n", + " 'SIFT': 0.11},\n", + " '3 prime UTR': {'DITTO': 0.88,\n", + " 'CADD': 0.41,\n", + " 'ClinPred': 0.26,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 0.0,\n", + " 'spliceai': 0.33,\n", + " 'Revel': 0.0,\n", + " 'DANN': 0.07,\n", + " 'SIFT': 0.06},\n", + " '5 prime UTR': {'DITTO': 0.92,\n", + " 'CADD': 0.41,\n", + " 'ClinPred': 0.27,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 0.0,\n", + " 'spliceai': 0.03,\n", + " 'Revel': 0.0,\n", + " 'DANN': 0.24,\n", + " 'SIFT': 0.23},\n", + " 'other RNA': {'DITTO': 0.95,\n", + " 'CADD': 0.53,\n", + " 'ClinPred': 0.58,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 0.0,\n", + " 'spliceai': 0.29,\n", + " 'Revel': 0.01,\n", + " 'DANN': 0.23,\n", + " 'SIFT': 0.23},\n", + " 'intergenic': {'DITTO': 0.92,\n", + " 'CADD': 0.7,\n", + " 'ClinPred': 0.51,\n", + " 'MetaSVM': 0.0,\n", + " 'GERP': 0.0,\n", + " 'spliceai': 0.33,\n", + " 'Revel': 0.0,\n", + " 'DANN': 0.17,\n", + " 'SIFT': 0.18}}" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f1_scores\n" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "id": "d03ce7b6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Extract the chromosome names and values in the specified order\n", + "chromosomes = f1_scores.keys()\n", + "CADD_values = [float(f1_scores[chr]['CADD']) for chr in chromosomes]\n", + "ClinPred_values = [float(f1_scores[chr]['ClinPred']) for chr in chromosomes]\n", + "Revel_values = [float(f1_scores[chr]['Revel']) for chr in chromosomes]\n", + "MetaSVM_values = [float(f1_scores[chr]['MetaSVM']) for chr in chromosomes]\n", + "GERP_values = [float(f1_scores[chr]['GERP']) for chr in chromosomes]\n", + "DITTO_values = [float(f1_scores[chr]['DITTO']) for chr in chromosomes]\n", + "spliceai_values = [float(f1_scores[chr]['spliceai']) for chr in chromosomes]\n", + "\n", + "# Create a Manhattan-style bar plot\n", + "plt.figure(figsize=(17, 6))\n", + "plt.rcParams.update({'font.size': 12})\n", + "plt.plot(chromosomes, DITTO_values, marker='d', label='DITTO', linestyle='-', c= default_colors['DITTO'])\n", + "plt.plot(chromosomes, CADD_values, marker='o', label='CADD', linestyle='-', c= default_colors['CADD'])\n", + "plt.plot(chromosomes, ClinPred_values, marker='s', label='ClinPred', linestyle='-', c= default_colors['ClinPred'])\n", + "plt.plot(chromosomes, MetaSVM_values, marker='s', label='MetaSVM', linestyle='-', c= default_colors['MetaSVM'])\n", + "plt.plot(chromosomes, GERP_values, marker='^', label='GERP', linestyle='-', c= default_colors['GERP'])\n", + "plt.plot(chromosomes, Revel_values, marker='s', label='Revel', linestyle='-', c= default_colors['Revel'])\n", + "plt.plot(chromosomes, spliceai_values, marker='s', label='spliceAi', linestyle='-', c= default_colors['spliceai'])\n", + "\n", + "plt.xlabel('Variant consequence')\n", + "plt.ylabel('F1 score')\n", + "plt.title('F1 score per Variant consequence')\n", + "#plt.legend(bbox_to_anchor=(1,0.5), loc=\"center left\")\n", + "plt.grid(axis='y')\n", + "\n", + "# Add shaded rectangles instead of grid lines\n", + "ax = plt.gca()\n", + "for i in range(len(chromosomes)):\n", + " if i % 2 == 0: # Shade every other chromosome\n", + " ax.axvspan(i - 0.5, i + 0.5, facecolor='lightgray', alpha=0.5)\n", + "\n", + "plt.xticks(range(len(chromosomes)), chromosomes, rotation=90) # Set x-axis labels\n", + "#plt.gca().set_xticklabels(var_type_order)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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consequencesoDITTOcadd.phredspliceaigeneprotein_hgvscdna_hgvschromposref_basealt_basetranscriptclingen.classificationclass
11729start_retained_variantstart retained2.516013e-029.1290.0004DOLKp.Met1=c.1dupchr9128947303-TENST00000372586NaN0
25154stop_retained_variantstop retained5.960464e-086.8650.0004RESTp.Ter315=c.944A>Gchr456932151AGENST00000622863NaN0
30215stop_retained_variantstop retained0.000000e+004.4010.0004SLC26A2p.Ter740=c.2220A>Gchr5149981813AGENST00000286298NaN0
31259stop_retained_variantstop retained0.000000e+006.7620.0004GM2Ap.Ter194=c.582A>Gchr5151267451AGENST00000357164NaN0
35592stop_retained_variantstop retained0.000000e+007.3770.0004CEACAM16p.Ter426=c.1278A>Gchr1944710506AGENST00000587331Moderate0
35803stop_retained_variantstop retained0.000000e+008.2600.0004BLOC1S3p.Ter203=c.609G>Achr1945179905GAENST00000587722NaN0
\n", + "
" + ], + "text/plain": [ + " consequence so DITTO cadd.phred \n", + "11729 start_retained_variant start retained 2.516013e-02 9.129 \\\n", + "25154 stop_retained_variant stop retained 5.960464e-08 6.865 \n", + "30215 stop_retained_variant stop retained 0.000000e+00 4.401 \n", + "31259 stop_retained_variant stop retained 0.000000e+00 6.762 \n", + "35592 stop_retained_variant stop retained 0.000000e+00 7.377 \n", + "35803 stop_retained_variant stop retained 0.000000e+00 8.260 \n", + "\n", + " spliceai gene protein_hgvs cdna_hgvs chrom pos ref_base \n", + "11729 0.0004 DOLK p.Met1= c.1dup chr9 128947303 - \\\n", + "25154 0.0004 REST p.Ter315= c.944A>G chr4 56932151 A \n", + "30215 0.0004 SLC26A2 p.Ter740= c.2220A>G chr5 149981813 A \n", + "31259 0.0004 GM2A p.Ter194= c.582A>G chr5 151267451 A \n", + "35592 0.0004 CEACAM16 p.Ter426= c.1278A>G chr19 44710506 A \n", + "35803 0.0004 BLOC1S3 p.Ter203= c.609G>A chr19 45179905 G \n", + "\n", + " alt_base transcript clingen.classification class \n", + "11729 T ENST00000372586 NaN 0 \n", + "25154 G ENST00000622863 NaN 0 \n", + "30215 G ENST00000286298 NaN 0 \n", + "31259 G ENST00000357164 NaN 0 \n", + "35592 G ENST00000587331 Moderate 0 \n", + "35803 A ENST00000587722 NaN 0 " + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "var1[var1['so'].isin(['start retained', 'stop retained'])][['consequence','so','DITTO','cadd.phred','spliceai','gene','protein_hgvs','cdna_hgvs','chrom','pos','ref_base','alt_base','transcript','clingen.classification','class']]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "59ef2d72", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4376, 256)\n" + ] + }, + { + "data": { + "text/plain": [ + "chr1 405\n", + "chr2 329\n", + "chr17 320\n", + "chrX 298\n", + "chr12 296\n", + "chr3 252\n", + "chr19 249\n", + "chr11 249\n", + "chr16 188\n", + "chr15 186\n", + "chr7 185\n", + "chr6 176\n", + "chr5 173\n", + "chr9 169\n", + "chr10 144\n", + "chr4 143\n", + "chr14 123\n", + "chr8 119\n", + "chr13 99\n", + "chr22 85\n", + "chr20 75\n", + "chr18 64\n", + "chr21 49\n", + "Name: chrom, dtype: int64" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(var1[var1['consequence'].str.contains(\"missense_variant\")].shape)\n", + "var1[var1['consequence'].str.contains(\"missense_variant\")].chrom.value_counts()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "bench = var1[['DITTO','clinpred.score','metasvm.score','vest.score','revel.score','cadd.phred','gerp.gerp_rs','dann.score','sift.score','spliceai']]\n", + "bench.columns = ['DITTO','ClinPred','MetaSVM','VEST','Revel','CADD','GERP','DANN','SIFT','spliceai']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "4d70b402", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 1, ..., 0, 0, 0])" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "var1[var1.index.isin(index_list)]['class'].values" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "fa3741d2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ubVFg3V7VPbPa1vzyz8yR28/yNAAMRGY0JJkha9DT/78TAAAAAAAkXscGb0awqZOZPeNyeWR6C6VI/aFguE7KqEjI9VPtuIohOq5iiCQpZBhx56yojn2ODwBSwTRNmUZQRrhN0XBARqRN0XCbopHAgVikTW6PX3mVs+TNyHc6XQAAAAAAgH4hJy9D3374k/rFN5/RohfXH4ybpvSHe/+n+ppWXXrDSXK5BlZnML/XrTpvmXb5R2hoaPvBeOvTtyl0wifkLxtp+1qzBlfqE+Mm6t8b11niv165RFk+r2aUVyYq7RjeiKGI13PwODLAfh8BAADSDW/QAUngcrmUl5fX5QfX/tbgbWHVTstxhsejb806QYs3xX9obk5Rhn5z+/PatMraaXz00YN15U2nJS3PdNLw1p9kNFdbYiXnf8uhbADn2KmZwEC3f+0/4jZ3yy2fqsFTP8OfnwGEmgkA9lEz0Z/sam7S/Yvnq669zRLP9fn1zVknaGxRsSN5mRFDjW9uUuvq3QrtaexybtHZkyRJ7gyvsiYMlq+YRj7phJoJIKWMltiYJzf1efQSNRMAAAAA0oenQ6OfSIIavEmSfMWxDd4ADHjJXBsyQi2q3/a6AtXvyZtZpOzSCcounSh/TlnC75UqUSOsUPNutTduV3vjDkWCjR80bzvQxO3DZm6S2e216ja/pOEn3CRfljPfiwHoOdbTAcA+aiYA2EfNBBLH5/fqhp98TH+89xW9+Ngyy9h/frtQDTUt+uKd58jjdTuUoTPK8vz6S+lXdGvVjQdjPqNNWx/6rI669RXb9cflcumioyZpVfU+bWqwfsfwk6UL9ZdzL5THnZxfW68RtRyHB9ZvIQAAQNqhwRuQBF6vV6ed1nWTMpev//zxi5qmlu6tssQuGjdJ44tL9eCLK2PmXzG6ULf+3yMKNAdjxs6/aqbc7v6/uGYaEdU+/yNLLGfy2coaMc2hjADn2KmZwEDWsP0NNWx71Rp0uVU28WIVjTqdL6UGGGomANhHzUR/sbG+Vj9c8rZaw2FLvCQzSzcdd6KG5OV3cmbytK7ZrYbXNyi0q8HW/GG3nitvfmZyk8IRoWYCSKlIq/XYnSGXu+9sikPNBAAAANB/df3dc2hfk1pW7JR/cIFypgxJi++qvZkFluNIsCFxF/cVS22bDx338wZvwxxYawb6omSsDYXb6lW/5SU17HhLphE6GG/es1SS5MsqUXbpRGWXTVR2yQR5M/ISev9EMaOGgs1Vam/Y+kFDt+0KNu2WTCMh14+012v3kgc1fO635PZmJOSaAJLrSGpmJNikQO16mVFDWUWj5c8ZlODsACC98B0kANhHzQQSy+Nx63O3n6Gishw98fO3LGOv/Wu1mura9PWffEwZWX3n+a4jde8FR+uav4b0Sv5HdHrTMwfjxobXtPPVP2n46Vf36HpXHT1Vt7/9akz8mS0b9PGxE44433i8EeuaXCRJjeQAAABgT//pMAWkkWg0qp07d2rYsGFyd/Khx+XxpDir5Klvb1NLOGSJzaoYIklqC8c+mLHpj4vjNnfz+T2aOGNocpJMM01L/qlw9RZLrPT8mxzKBnCWnZoJDFSBmvXat+ZxS8zl9mro8V9Tdsl4h7KCk6iZAGAfNRP9wYr9e/Tzd99RKGpdX6nMzdPNx52okqzslORhRk2F9jbKaA4qGgiq+m/v2j6X5m59AzUTQEoZLdZjT64zefQSNRMAAADAQBRpbFPVL1+TGYlKkqJtIeXPHu1wVpI3w9rgzQg2Je7ivmLrcT9v8OZx8RkXsCORa0Ohlr2q2/yiGnct6rIJWritVo0756tx53xJUkb+0AMN30onKLv4KEeanZmmqXBb7YFmbg3bPvhxu8xouPuTj0Cwaaf2LP+9KmdeK9dhdcs0o2qv36rmvcsVqH1fbk+misecpZxBk9OiISkwUPW0ZobbatWyZ4Wa9y5TW90mSebBMV92mXLKjlZO2SRll06Q25ue30GbZlSRtjqFWvYq1LJPwda9CrXsUbh1v9zeTOWWH6uC4SfLn1PmdKoA0gzfQQKAfdRMIPFcLpcu+tJcFZRk66HvviQzeujz2LuvbdLdn/ubbvrVRcorzHIwy9SZObJIf/zMDF33h2s0NfCOSiLVB8dq//MdDT35Mrl99tfkRhcW6Z4TT9Nt861N3pburUpagze3aVqOo0m5CwAAAOyiwRuQBIZhaMWKFaqsrBwQDd6iHT7oSVJBRvwPp+cVZWr9rsaY+LBxpfrCHWepbEhBnLP6F9M0Vfvs/ZZY5qhZyp4wz5mEAIfZqZnAQBRqrdbud38jmdYl1PIpn6a52wBGzQQA+6iZ6Ove2rVdD616N2bdZWxhsW6cNVd5/tS8rGOapvY/uliB96rsn+R2Kf+EMSo4aSzN3foIaiaAlIpp8JbjTB69RM0EAAAA0H/FPgP2ofqX1x1s7iZJtU+uTJMGb/mW40giG7z5OzR4izTJjIblcvsSdw8H+NxuFWZkqiHY7nQqQJ+UiLWh9obtqt30vFr2LldXtbczwaZdCjbtUv2Wl+XyZCivYroKhs5VVsk4S9OzRDJCLWpv2Ka2hm1qb9ii9oZtMkIt3Z/YCy5Phjy+bLl9WXJ7sxQJ1CoSbDg43rJvhWrWP6WScR9RoPZ9texdoZZ9K2OafO6u26Ds0okaNOmTysgfGJtPo+8xQq2q2/yimna/I7cvW7nlU5Q3eLoyCob3i+aE3dVM0zQVatmrlr3L1bJ3udobt3d6rXCgWg3bX1fD9tcll1tZRWMONnw78OuV2vV60zQVaatTW8NWhZqrFGrde7CpW1fNLuta9qpu80vKKZukwhGnKKd8SspzB5Ce+A4SAOyjZgLJc8anpiq/OFs/+8Z/FQ5GDsbXL9ut71zxqG57+FMqGZzfxRX6j2OG5KvNnaM/l31V/2/P7QfjGc27VP/qr1Vy9g09ut6ogiJNH1ShZfv3HIxtaazX6up9mlxWnqi0O2XG6QMAAACA1KHBG+AQl2dgLh41L9waE/vk9Sfo4utPkNvd97+ItqN1zctq37HCEis9/6Z+8UU8ACAxArUbtGf57xUNt1rixWPOUcHQOQ5lBQAAgFR5bssGPbpudUz82LJyfXX6bGV6U7esG65psd3crejsScoaW6aMYcXdTwYADFyGdb1Dnlxn8gAAAAAA2Na+pcbpFOLyZFo3E42GA4oaIbk9/iO/uLcoNhapl/yDjvzaDnK5XLpi0hQ9sHyx06kAA4ppmmqrXa/aTc8rULOu84kut/IqZkqSAjXrZISau76uEVTTroVq2rVQvqwS5Q+do/yhc+TPKet1rlEjpGDTTrXVb1V7wza1N2xVOFDd6+tJki9nkDILRsifW3GweZvHm/VBE7dseT5o5ub2ZcU0Ogq17NP2t7+vaDhwMFa36XnVb31VphHs8r6BmnXa9ubdKhh+okrHfzymMWhPmWZURrBJkWCjIu0f/BM88I/L5VHhiFOUkVdxRPfAwBA1gqrf+qrqNr946N/t9nrVNe9W3abn5c0qVt7gacodPE1ZxWOT2gAsGgl+8O90g1xujzLyh8vtSV5DWzNqqK1+k1r2rVLL3pUKB/b35iJqq9uotrqNqln/pDz+XGWXTjqsmaMpmaY+bKJ56CX2D+OS25spT0aevP48efx58mQc+LGzn3s00v5Bo8utaq/foraGrTGNJXvwE1Br9XtqrX5P3swiFQw/SXmVM+TLKk3qr30ymGZU4UCNQs1VCrbslRmNyOX2yu32yeXxyuX2yeX+4McPjt1un7xZxfJlxfn7NgAAAADHHXfGUbr995/S/df9S61Nh9Zedm2q1bcvfUS3PfwpDR1T6mCGqeFxu/T2TafohPtMvZ85WRPaDz3XXfP0PSo86Wp5sgu6uEKsYwcNtjR4k6QfL12g7590hipy8xKSNwAAANITDd4Ah7g8HqdTcESwOfZhio9//vgB09xNkmqevc9y7B98lPJmXOBMMgCAtGJGI6rd+KxqNz6njjv05pRPUemECxzJCwAAAKlhmqaeeH+NntmyIWbshCHDdc2UGfKmeMfJaEvXL8ZIUuX18+QfUijXAFrfAQAcgUiL9dhLgzcAAAAASHdmxHA6hbi8GbEvTxnBJrmzj/zlMpfbJ9ObL0UOa9wRquvzDd4kaU7lMIUMQw+tevdgbEdzo4MZAf2XaUbVsm+l6jY9r/aGbZ3Oc7l9Khh2gorGnCX/BzXMNKMKNlcpULPuwD+1G7tsaBZuq1XtxmdUu/EZZRWPU17FjAO1LBqWGQ0raoQ/+P8RmUbHWFimEZERblGwuUoyo73+Ofuyy5RZOFKZBSOUWTBCGQXD5PFl9/p6/txyDZlxrXa+8zNLXt01dzvEVOOOt9RctUTFY89T0ajTbTdQigSb1Vq9Rq37V6utbpMi7Y3q+EzX4Rp3zlfljC8qd9Bkm7lhoDGjhhp3vq3aDc8oEmzodF6krU71W19R/dZX5PHnyZ9b/kEzxGy5fR80RfRlf9Ao8cNY9mGxLJlG6IMmhA2HmhG2NxyMGR/EopF2y709/jwVjjxVRSPnyeNPzPp9NNKm5uqVatm3Ui37V1saNnbF5cmQ2+PvttmlEWpRc9ViNVcdeQNbtzfzYMM3rz9PLm+mgk27FGquUld//nsr0l6v2g1Pq3bD05IkT0aBfFkl8mUXy5dVeqARWnaJMnIr5csuSfj97TrQyK1WoZYqBZurFGreo2DLgR/NaLhX18woGK68ihnKq5ghf07f/zs2AAAA0J9MnDlMdz1yue75/N9Vv//Qs161e5p1+2WP6ubfXKzx04Y4mGFqZPk9Om/KYD3R/gV9d/dXD8aNllrVPvdDDbr4nh5db0icJm7haFTv1e6nwRsAAEA/R4M3IAlcLpfKysrkcnX+UqsrxS/jpg3T+sXmrDPGKSOrb+00dSTatixRYN1rlljJud+Uyz0wG/4Bkr2aCQwEodZq7Vn+e7U3bIkZ8+dVqnLa55O6Eyf6BmomANhHzURfY0Sj+v3qZXpj1/aYsXNHjdNlEyfL7cC/z61rrTvFySV58rMkSf6KfJVeMFXewt6/lIP0QM0EkFJGhwZvnhxn8uglaiYAAACAgShdG7y5vVlyub0yo5GDsUiwSb4ENHiTJPmKrQ3ewnWJuW4a6LjeHDVNraut1sSSMocyAvoGu2tDphlV0+53VLfpBYVa9nQ6z+3NPNBIadTp8mbkd7iXW5n5Q5WZP1TFo8+UGY2orWGrAtUHGr611W9RZ82G2uo2qq1uY49/fj3l8eceaOZWOOqDH0fK60/8y6jZpRNUfsxl2rf6kS7n+fMqlVM6Uc1V78Y0z4pG2lXz/r/VuP0NlU686EADvA6/j6YZVbBxp1r2r1br/lVqb9iunjR0Mo2Qdi95UIOnXKmCYXNtn4f+zzRNtexZpur1Tyrcuq9H5xqhZrXVdd3gLJGMULNqNzytus0vqGDYiSoafcbBxpM9EW6rVVPVCo3OWK5tr74gmfb+Pun25Si3/FjlVUxTdulEudxeBZt2q7X6PQWq31OgbpPta/VGNNKuaKRd4UD1EV3H7c2UP7dC/tzBB/7JLlOgbpOadi1UNNLW6XlGsFFGsDHuc6TZpRNVOv5jyioac0S52WGE2xSoWavW/e+pvWmnQi17ZBqhhN4j2LhDwcYdqnn/Px80e5upvMqZvfr3ra8zQq0K1L5/oJmraSgjd7D8eZXKyBsijz8v7t87TNOUaQRlhFplhAOKhltlhD/8/4HD4oHD4q2S3CoccbKKRp/BM8lphu8gAcA+aiaQGsOPKtO9j1+hez7/d1VtPbQ23tLYrruufkL/72cXaMa85H8+cZrX7damrKO1JOdEzWqdfzBe//pDKrvo7h7VovHFpTpzxGi9vN36me+Pa1botOGjk/qcOBUTAADAWTR4A5LA6/Vq7tyuv5h3eQZgQy/TVOt+64tbs88a71Ayzqh59n7LsbewQgUnfNqhbID0YKdmAv1d0653tG/NozG7YUpSdskEVUz/vNzeTAcyQ7qhZgKAfdRM9CUhw9Avl7+jZftiX/C5ZMIx+sjooxx5GMk0TTW9tckSc2f6NPyWc1KeC5KLmgkgpYxW67En15k8eomaCQAAAKD/6nwN0oxEU5iHfS6XS96MAoXbag/GIu2NibuBr1hq23bouB81eItnYdVOGrwB3bCzNtRa876q1/5dwaZdnc7x+PNUNPoMFY44RR6fvY10XG6vsovHKbt4nDT+Y4q0N6hp92I17lqgUHNVj34eveFy+5RZMPxQM7ei0fJllaTsO6zCEScr1LJX9Vv/Z4lnFo1W3uBpyi2fKn9uuSSpdPwFqtvykuo2vxjTkCjcVqs9yx5SQ9EYlU36lPy55QpUr1NL9Wq17l8jI9ikI2JGtXflnxQJNqp4zDk0HIBaa9apZt2/1d4Yu9HXh/IqZ8qbWayWvcuPuLFYophGSA3bXlXDtteUVzlD+UOOlxmNKmoEZRpBRSNBRY2Q5f9HjXaZkZDC7fUKNe+WJPlt3MubWajc8qnKrZiu7OJxMRunZxYMU2bBMJWMPUfRSLsCtRvUWr1WrdXv9bhhXqK5fTnKLByhjA+bueUcaOjmyciP+fOfVzlTZRMvVPPuJWrY/kaX/07EE6hZpx0165RTdoxKx39MmYUjE/gzObBJcev+VWrZt0qB2g1JbaTX0aFmb/9WdskEFQw/UbmDp8nt8aUsh1Qyo8YHTVvXqrV6rdobtqqzhqIeX478eZVyezKsDdzCrZLZu89p1ev+qba6jRo89Wrbfw9B8vEdJADYR80EUqdsSIHufuxy3XftP7Vx5aFnnEPtEf3g+n/p2rvP1amfmOxghsk3e3SRnl65R08XXWZp8GY0V8sMtcmVYf/v1G6XS585Zpr2BVq1qtr6efbt3Tt00tARCcu7I9aoAAAAnEWDNyAJDMPQxo0bNW7cOHk6aeTm8gy83W58ze1qb7Q2rhl3bKVD2aRecO8GNb/7b0us+Oyvy+3LcCgjID3YqZlAf9a8513tWfH72AGXW6XjL1DxmLPYJQ8HUTMBwD5qJvqK1nBIP16yQOvray1xl6QvTJmhU4aNdCQvqZOXNvmCv1+iZgJIFdOM9vkGb9RMAAAAAANRujZ4kyRPxwZvwQQ3eDtcP2rw1hoOx8SW7dujz/bvd/GAI9bV2lCoZZ+q1/1TLftWdnq+L7tURaPPUsGwuXJ77LQ96pw3s1DFY85S0egzFWzcocZdC9S0e7Gi4dbuT+6WS/68CmUVjj7QzK1wlDLyKuRyO/vqQdmkTyqjYJiCjTvkzx2s3PJj5c0sjJnn9mao9KiPqmDYiapZ/6Sadi2MmdNWv1k73v6+5HL3okHOgQajnswCeTMKFDXa1Va7wTKj5v3/KNLeqEFHf4pnvwao9sbtql73bwVq1nU6J7t0osomfEKZhQde4C6beJFCzbvVvHe5WvYu77JRZKK5PBkyjWCcEVPNVUvVXLU0offLyB+m3PJjlTv4WGXkD7f9ornbm6nc8inKLZ8iSQoHatRavVaB2g2KRgI68E2764P/fXhN1wffc7sO9jSOhttkBJsVCTXJCLXYqwMutzLzhx1odFk0WlmFo+TLGdSjl+TdngwVDD9RBcNPVHvDNjVsf1Mt+1YcyMGm1uo1aq1eo9zyY1U46jS53X6ZpiGZ0QPfw3zw44H/b8g0zQ9+jMbMkRlVuK1erftXK9QSuyGd7Z+XL0cZeZXy+HJkRiMyoxFFo+EP/n9YpvHBj9GIopF2mdHYv4t+KFD7vgK178vty1b+kNkqHH6iMvKHdptD1Agr0lancFvtBz8e+P/RcED+vErllk9VZuFIx5oahFqrFag50NAtULMu7obQ8RjhVrXVbUx4Pi37Vmr7/O9pyMzrlJE3cN7pSWd8BwkA9lEzgdTKL8rWd/54iX5yw1Na/uaWg/GoYepXtz6nxtpWffzzx/fbBmJnH12uW/+zVi2e/JixV9/frzOOHdnja04tGxzT4O0/G9fpxCH2Px/3VPyW0gAAAEgVGrwBSRCNRrV+/XqNGTNGHo9H0WiHL/xMxews1R9FTVPvbm84eJy1r9kyXlyeq8HDC1OblIPqXviJZB76GOzOLlDRqV90MCMgPXSsmcBAYpqmat5/Mibuyx6kiumfV1aCd1hE30fNBAD7qJnoC+rb23Tf4vna1dxkifvcbn1l+vGaUe7cQ7SmEVXds6tj4sXnHu1ANkg2aiaAlDECinlczJvjSCq9Rc0EAAAA0H918XpPNH1f/fFmWF+qMoJNnczshY4N3iINMk1DLlff/zyY549tLDUsv8CBTIC+Jd7akBFqVe3GZ1S/7bVOGwT58ypVMuZc5VXOTPjzwy6XS5mFI5RZOEJlEy9W6/7Vaq5aolDrfrncPrk9PrncPrkO/uiV290h5vZ+MM8vf84gZRaOkNubmdA8E8Hlcqlg6Bxp6Bxb831ZRaqYerWKRp6q/Wv/Eb9BTjdNnVyeDOWUTlTOoMnKLBwub0ahPBl5lqZtphlV9bp/q37LS5ZzG7a9KiPYpMFTr5bb47OVM/q+UOt+1ax/ssuGaJkFI1Q68RPKKZ1oibtcLmXkD1VG/lCVHvVRhVr3q71hm4xwQNFw4NCPkYCi4bYOsba4/z67vZnyZhZ+0JTwwI/eD5oTeg87dnsz1d64U3VbXlJz1ZJeND7shsuj7NIJB5q6lU+WL6skIZf1ZZeqcMTJKhxxcq+vYZpRRcNtB5q9BVtkhJoUCTbLCDUrGg7Im1WsrMLRyigYdsTNOQ+XWThSgwtHSrpSRjigcFudIm21CgdqFf7gx0hbrUKt++I2A2vZt7LLpqLJ4PZlKyOvUv7cygM/5h340ePPs92EwIwaCtRtUHPVu2rZu1xGqDnuvGg4oIZtr6ph26vKLByl/CHHy+XxHWjO9+G/96GWA79WbbVdfw7Yt1J1m56XN7NQueVTlVsxXdnF45L6To8RblOgdr0C1QeauoUD+5N2r94Kt+7X9rfuVdHoM1Uy9py0/G//QMJ3kABgHzUTSL3MbL++9eAn9JvbXtAbT62xjD364zdUX92iq24+XW53/2vy5nK59NtPT9Ntf4htiv3jlzf2qsHbWSPHaFNDnRZU7TwY2xdoVVMoqIKM5Py93DTT93seAACAgYAGb0AKtLZad8NzB9rk8vSf3dg6+1j30nvWDuK+JusXi+OnD+23Xdk7MqNRNS39tyVWdOqX5MmK7doOABg4go07FGq1/vcyf+gclR9zKQ9KAAAA9HN7Wpp13+L5qmkLWOLZXp++MWuuJhSXOpTZAU2Ltqp50daYeOboMgeyAQD0G0ZLh4Bbcmc7kgoAAAAAoH/wZlqbkkWCjf+fvfsOj6M62wZ+T9letOrNsmRL7r0Xegs9EEgglDeVkC+khwTyBt4kJJCQAukVUkhCCRBSSKihGxsXcLdxtyWrS1u0vczM94dsWaNdSStbq92V7t916ZLmTNlnjTjanT3nPqN38YEBb9CAuBcwjk4gSTbVuwY+N8BhGL3AEqKJQFMT8B5+DV37noYaD6U8xuioQumMK2Ern68LBMsUUTLAUbkYjsrFGX+sfGJ21aFm1ZcRaHsHnbv/hnioa8jjDbZy2MvmwlY2D5aiacOGswmCiLLZ74dsLkDnrid0+/ytm6DE/Kha+ilIBt4LHc8SER+69/0b3sY3Bg1HM9jKUDrjStgrl6Q1ht5oK4PRVpbW42uaBk2JQjkW/CZIBsimAoiyKe3nYC6oQdWijyM+80p4Dr4Eb+Mb0JRo2ucPJBps8ERdmLboIjgr5kMyWE76WpkkCCIkow2S0QbYs1ODZLD29hHOSUn7VCUG75HX4d7/7KBhaBkhSLAWT4etbG5v8KC9CpLJecrzPwRR6g3OLJkFbe51fWFv/ta3ocaDKc+JeA8h4k0evzBSiYgX3iOvwnvkVYgGG2yls2EtmQlbySwYrCc/RkNNRJCIeBGPeBHxHECwcxfCngMjCkoURAMsxdMgmwoQ9bcg5m+BpsbTPRuSwQrRYOv9brT1/U4db5MMVmjQ0PXu36HETnxep6lxuPc/A1/jGyiefjlck0+HIOqnGmqaBjURhhLtQSLq6w1AjPqQiPYca+uBEgvCaC9H6ez3QzY60n7eRERERJQ/ZIOET997CVylNvzzwfW6fc/86W34ukP49HcvhcE4/oIXl9UV4ttXzAZ+qm/3BOPYetSHBZNGtniKIAj4yNyFuoA3AFAYwkZEREQ0bjHgjWgMDAx4k3oCEMbR6gCHfB7dtiQIkAURu9v0E7XkYEy3bbWn/4Fxvos0boHi79S1FZ79iSxVQ0REuaKnWX9D22ApRsWCj0yYAFQiIiKiieqQz4Pvb3gTPTH9YHiXyYzbl5+Oyc6RfdA/mjRFRejdNrif3pa8UwBE69ATWIiIiIakDJiYI1l5H4SIiIiIiIhOiWTKXMCbIJqgSXZ9YHncPS4C3ipsdlTZHGgJjmFICNG4oSHYsQ3uvf9AfMDCjsdJRgdKZrwXBTWnQxDHz3jhfCYIAhyVS2Armw/v4VfQve/fUBO9C1cLogxL8XTYy+bBVjYv7UCtgYqmXgDZ5ETrlj8CmtLXHureg6Z1P8Sk5Z+DbHaNwrOhXKFpKqI9zfC3boLn0EvQlFjK4yRTAUqmX4aCmtOSgpNGiyAIEGRz78KylsJTupbBUoyyOdegeNql8Da+Dn/zRiRiPRAlM0TZCEEyQZSMECUTBNkEUTJBlHvbhGM/mxzVkO2Tseu557GoYjEkAz9nPlmiZETR1PPhmnwGPIdfgefA81AGCUI7VZLRAVvZPNjL58FWOifjCxX3D3srm3MtAm2b4Wtag1DXuxl9XABQ40H4WzbC37IRAGCwlsJ6rBZryQyIBhvURBiJiK831Czi7Q01i/iQiHp7vx/bd7JBiEZHNWyls2ErnQNLUQNE6UTgsqapiIe6EPO3IBpoA3A8CNAG0Wg98bPBClE2px0kayudjZZNv0bEd0TXrsT86NjxCLyHXoKlaBoSsX7hbdEeaGpi2GtHvAcR9beg9rTbM9bXEREREVF2CYKAG798NlwlNjx078u6fW/+Zzd6PCF85afvg2Uczh1fWFuE/QPanIoXT73dPOKAt8H8bts7+OziFTDLfD1NRERENN7wFR5RBoiiiMmTJ0MURcTjcUSj+g9segPeMr8S31hZ33pUtz2ruBSSKAL90sLFSBzWFv3gueKKibMyT3DHC7ptY/k0GMumZqkaotzSv88kmkg0TUXPsYEhxzmql3NSMw2JfSYRUfrYZ1Ku2tHVjh9tegsRRT/4tcJmx1eXn45Sqy1LlfXq/tdW+NcfTrnPsXwKJIsx5T7Kb+wziWjMKPqFYSDZs1PHKWCfSURERERE41d+flYtm5y6bSXSM7oPYChMDngbJ5ZUVKHlwJ5sl0GUV+KBFkyzbkX75mdS7hdEGYVTzkdRw8WQDJYxro7SIUoGFNW/BwU1qxHo2A7JYIO1eAZEeXQmHjurV0AyOtC86Ve6wJ9oz1EcefN7mLT8szA5qkblsWjsqYkIwt5DCLsPIOzeh4j3UF9QYCqibEFRw0UonHIuRCn/JrdLRhuKGy5GccPFJ3W+oii8nz6KRNmE4oaLUFh3NjyHXoLv6DokIr7eMaeC1BvwJYgQjn2h3/bA9hPfJQiiDFNBDezlC2B21aUdFDbqz08ywFm9HM7q5YgFO+FrWgNf01ooIwxwFg1WGCxFMFiKIVuKIQgiAh3bBw1lPS4e6oSvsRO+xtcBCBBEGZoaP4VnlEwyOnpD5MrmwFYya8jQT0EQYbSVwWgrw2h+mmawFKNm9W3o3P03eI+8Cmiqbn8s2I7YMP9WQ4n6jqBr79Monfm+U6x04uFnkERE6WOfSZR9l31kGQqKrfjF/z4DJXHiNeX2tUfwzQ8/hq/99v0oKM7ueOjRZiisBiwFQPjEe5QVgdfgDs0ZtcfY0tmGJ/fuwo2z54/aNY/jfEUiIiKi7GLAG1EGSJKERYsWAQD8/uQVLqWeIDCOVuTb3d2l215WUZ10jKUzAFHRf/hz5ntH741rrgtsf163bZv7nixVQpR7+veZRBNJqHtP0sATZ/WKLFVD+YJ9JhFR+thnUi56q+UofrllA5R+ofgAMKXAha8sOw0Fpsyufj0UTdXgeW7noOFulpnlKL5ywdgWRWOGfSYRjZlEUL8t51/AG/tMIiIiIiKi3CKbC3TbiREGQAzLUAxEmk5sj6OANyJKXyLiQ9eef8LX9CYkaCmPcVQuRcmsq2C0loxxdXQyJKMdBZNWZeTattLZmLz6yzi64WdQoieCRxPhbhx+7ZswF9bDWbkU9srFMFgKM1IDjY542IOw5wDC7v0Iew4g2tOUFIaUiiAa4Ko7B8UNF0Myjq8J7SPB++mZIcpmFE+7FMXTLs12KRljtJWidOb7UDL9vQh27oCvaR1iwTaIkhGiwQpJtkI0WCAZrJCPhbkZLEWQLcUpA1ZLZ38AsUArAm2b4W/bjKivcZgKtNEJdxMkWIsaYC2dA1vpbJick7IWoNefKBlQPveDcNWehc53/4Zg+7ZRvb57/3Owlc2FtWjaqF53vGOfSUSUPvaZRLnhjMvnwFloxQ8+93dEQydePx/c2YY7r38Ydz54DcprXNkrcJQJkgzXsvfD+/rv+tqWBNfgJdxyUtczSzJsBgOCcf17jxcO789MwNuoX5GIiIiIRoIBb0QZoCgKtm3bhvnz5yMQCOj2CeEIxHgcgpT9D2ZGQ0ugBz2xqK6tyu5IOk5IKLrtyrqicfXmfChqJIDQvjd1bfZ5F2apGqLc07/PlKTxE35JNJye5vW6bZOzhquz0rDYZxIRpY99JuWaFw8fwEM7tyRN+ZlTXIovLl0Fi2zISl3H+dcfgu/1fYPuLzitgau3jWPsM4lozCj6z0wg5V/AG/tMIiIiIiKiXt5X96DgrOlZv28omQYGvPVA09QhgxM0NYGOnX9FsHMnrCWzUTbnWojSIPdoDQOCd+Ldp1oyEeURVYnBc/BFdO9/DpoSTXmM2VWHstnXwlJUP8bVUS4zF9Ri8urbcXT9TxAPdej2RTwHEPEcQMeuv8JS1ABH5RI4KpdANruyU+w4o2ka1HgIiaiv9yvig3LseyLqgxoPQVMVaJoCTU30fmkKNFUB+tp6v6uJ8AgfXUBBzWkonn45w/vA++l06gRRgr18Aezlp7YYnSAIMDmqYHJUoXjapYiHuhHs3IVQ126Eut+FEgsMf5E0iLIFBksxLMXTYSudA2vxdIiyaVSunQkmRyUmLfsMQt170LnrSUR8R4Y9R5TNkExOyKYCSCYHZFMBRNkC94Hn+gVgamjb/HvUnfUNiHL2FjrMN+wziYjSxz6TKHcsOH0KvvnQdfjOzU/A7znxHrrtiAd3XPcX3PHABzBlVnkWKxxdtjnn6wLeChMn/3mBJIq4atps/HnXVl27omnY1tmG+aUVJ33tVFIvWUFEREREY4UBb0QZoKoqGhsbMXfuXASDQd0+qad3WxgHN49aAn7c89YbSe02Q+9gN09o8FWLzNbsTloeS8F3XwWUfv8WkgG2WedkrR6iXNO/z+SNdZooVCWOQOs7ujZn9YosVUP5hH0mEVH62GdSrtA0DU/t242n9u1O2reichI+tWApDFn+HVVjCXT/c2vKfeapJXCsnALLtLIxrorGEvtMIhozSQFvtuzUcQrYZxIRERER0fg1suk9nud2wTy1FObJRRmqJz2yyalv0BSo8RAk4+Ch4r6mtfAeea3358bXYS6ohav2jNQHG4r123HvsAFyRJT/NE2Dv2UDOnc/hUTEk/IY2VyI0llXwVG1jH0CpWS0lWLyabehecPPBg3NCbv3I+zej46dj/cLe1s84rA3TdMATYUgTox7lpqqIOw5gLB7PxIRb1+QWyLaG+amqYkxrcdgLYW1ZCYKp5wPk6NyTB87l/F+OuUqg7UYrtoz4Ko9A5qmItrTjFDXbgS7diPcvReaqp8HIxqskE0FkM0FkE2uvu+SueBEu7kAopS7YW5DsRbPwOTT/xeBti0Idb0LAMdC3Jx934//LErGlNcQZRO63v1733Y83A33gRdQMuO9Y/IcxgP2mURE6WOfSZRbGuZV4u5HbsTdNz2OzmZfX7uvK4hv3PgIbvvFVZi7sjaLFY4e0ezQbQunGJt20ZQGmCQJD27XzzH89dZN+Pl5l0IcxQV2NI0Rb0RERETZxIA3ogwLBPSTleSe3m1BzO/BHJFEAt/bsAbeaETXPquoFDWOAgSiCfx7W1uWqsstge3P67at01ZDNA8+gJCIiMa/YPs2qIn+f0MFOKqWZa0eIiIiIsoMVdPwxx2b8VLjoaR959dOxYfnLBzVD99PVnhve8p2+9JalL5/8RhXQ0RE45qiXxQHEu+VExERERER5TPPsztR+clBgtHGSFLAG4BExJcy4C0WaIO38Q14Dr6oa3cfeG6IgLeBAXYKkPABhsKTLZmIclzYcwAdOx9HxJv8+Q4AqJqE4mkXo2TahXkb5EJjRzY5UbPqVnTt+Sd8R9dBjYcGOVJD2L0PYfc+dOz8KyxF0+CoWgpzwWQosSCUeABKLHDs5yCUmB9qLNhvXxCamoDRUQnX5DPgnLQKksE6ps8105RYEMHOnQi0b0Owc8cQ/5YZJogwF0yGpbABlqJ6WAobIJsLslMLEZ0yQRBhLqiBuaAGRfXvgarEEe05Ck1TIJtdkIcINRtPBEGEo3IxHJUnN0akqP5CBDt2IOze19fmPvgiXHVnp3zPRkRERETjS9WUItzz6I245xOP48iezr72cDCGez7xBD73g8uw6qKZWaxwdIgW/WvbwkQXDInAIEen5+yaOjx3aD+OBnr62nzRKHZ3d2JOCRfnJiIiIhovGPBGlGGRiD4ATQz2fpgs5PnqAHs9XegK6z8Yr3cV4UtLV0EUBDy09sRKc3IgivK1qQe6TATBAQFv9rkXZqkSIiLKFT3N63XbluJpMFg4+JuIiIhoPIkrCn65ZSM2tDUn7btq2ixcNW0WhBwIdwMAJRBNanOeVo+iS+dloRoiIhrXBg5okxnwRkRERERElM/USDzbJUAQZUgGG5T4iVDxRNQHE6r7tlUlDvf+Z+A+8Dw0NZF0jXioM6mt7/qSBZpoAdRwvxM84zLgTct2AURZFg91ofPdp+Bv2TTIEQIc1SvxTqMVDfUXQ5QMY1of5S9RNqNszrUonXU1gp274W/dhEDbFqiJ8CBnaAi79yLs3jvix4r5W9Cx86/ofPcfcE5aicLas2FyVg9/4knSNA1KLAA1HoKqxKAqEWiJGFQl2vuViEJTYlATEahKDNqxNlWJQjLYYC+fD1vZPAhi8rh6TdMQC7Yh2L4dgfatCHsOAJqasecyGFG2wFI4FZaiBlgKG2AurGO4I9E4JkoGWAqnZLuMvCMIIsrn3YDDr92F4+8sNCWKI2u+C2vxjL5ATIOlEImoH0qsB4lIT+/3aO+XEu2BpibgqFwC56RVOTOmhoiIiIjSU1hmx11/uQHfv+Vv2LWxqa89EVfwoy/+E77uEC66Ib8XnbbULYEqGiGqMQCABBW+na8grqyCQRJP6pqCIOCbp52Nm57/l679cI+XAW9ERERE4wgD3ogyQBRFzJgxA6IoIhrVT5AVw73bwkm+WcsFB70efG/Dm0ntX11+OqwGAxKqit+tORHwVvbWxA13i3UeQqx9n67NNo8Bb0T99e8ziSaC3pVEd+janNUrslQN5Rv2mURE6WOfSdkUisfx47fXYWe3flKgAOAjcxfh/Nqp2SlsBIounQdB5GDZiYJ9JhGNBU2NAVpM3yjZslPMKWCfSURERERE49fI7wcKhtxY5FQyFwwIeOvp+znYuQvt2x9BPNQx6PkGW/nQD2AoAqL9FvOIdwPI/fu8wzENWKS2PRQc5Eii8U1V4nAfeA7u/c+mDIEEAEvxdJTNvgYGezUaLPt4b4hOiiDKsJfPg718HlQljlDXbvhbNiHQvgVqIjL8BUZAU6LwHXkNviOvwVI0Da66c+CoWAhBPLXpI4loDyLeI4j4jiDiPYyI7zCUfn93R8rXtAaSqQAFk1ahoOY0GKzFCHXvRbBjOwLt24YMYU2HaLBBNhdANjkhmwogmQogGe0QRQMgShBECYIoQxCO/Sz0bkOUIAgyJKMNRnsFBIH/z58M3k8nmlhMjioU1KyGr+nEXKNEuBs9R9ei5+jatK8T7NiOUPcelM+7AaJkzESpOYl9JhFR+thnEuUum8OEOx68Bj/98tNY/+KJ4HpNA3737Rfh7Qrg2s+dkbdhvqLJikDFYjhb3uprmxt6By/t7sBFcytO+roW2YDZxaXY1X1q90GGkq//5kRERETjBQPeiDJAkiTMnDkTABCJ6D9wF8O924KUG4PbTsafd21NaptS4ILV0Lsa4W1P6ENrTN3JA78cLktmissxwR0v6LYlRynMkxdmpxiiHNW/zySaCPytm3SDQQVRhqNySRYronzCPpOIKH3sMylbfNEIvr/hTRzu8eraZVHELQuXYUXlpOwUNgKmumKGu00w7DOJaEwoKSaJS/axr+MUsc8kIiIiIiI6IVcC3mRTAWL+lr5tJepDItqDjp2Pw9+yYdjzjdaSoQ9ICnhzn2ypOWVaYZFu+6DXjWA8Bpth4oQoEAU7dqB9x6ODhkgZbGUonfV+2MsX9E2C5L0hGg2iZIC9fD7s5fOPhb3tOhb2tnXUw97C7n0Iu/ehw1QAV+2ZcE0+A7LZpTtGUxVoahyqEoOmxKGqvd+VmB8RX+OxMLcjSIRH/2+gEvX1hiweeA6CZISmxIY/6RiTswbmglrI5t7wtt4wt4JjYW5OiJJh1Oul9PF+OtHEUzz9cvQ0b4Cmxk/pOj1H1yHiOdgbsikZIYgGCJIBoihDEA0QZTNMzmqYXVMgm5yjVH12sc8kIkof+0yi3GY0yfjij6/A7779Il58bItu399+tQ7erhA+8Y33QJLzM6QxXrMK6BfwVh5vwe5W/ykFvAG948wziaPCiYiIiLKLAW9EGZBIJLBhwwYsX74c0WhUt+94wBvyOO26PRRIantPXQMA4JV3O/Hq3q4TO1StN159gEv+Z2IE2QS2P6/bts29AAJXhyDS6d9nyjJfmtD4FvW3oHP333RttrJ5kAzWLFVE+YZ9JhFR+thnUjZ0hIK4d/0baA/pA2zMkowvLV2FOSVlWaqMaGjsM4loTCQGfLYg9E7GyDfsM4mIiIiIaPxKHuM1HMGQG+OgBk7o72nZiO59z0BNhNM6XxvuuRv0QWjjJeBtemEJDKKIuKoC6P0N+NzLz+Lbp52LKrsju8URZVg87EHHrr8i0PpOyv2iwYriaZehsO5sCOKJe0C8N0SZ0Bv2tgD28gVQlTiCnTvhb9mEUOdOqEoUktEOyWCHZLRBNNqO/dy73buv97saD8Pb+DoC7VsBTU16HCXqQ/fep9G97z+QzYXQlFhfqFuq47NhuHA3QTTAWjIT9vL5sJXNg8FSNOTxlF3sM4kmHoOlCBULP4qO7Q9DiadY/GkEYsF2xILtwz+mtQRm11RYCnu/TM5Jutdv+YJ9JhFR+thnEuU+SRLxiW+8B4Wldjz+szW6fS89sRU97hA+f9/lMJnzb+xYbVUZ9KPgNPz5rSZ85tx6GKTc+MwkFS3FPH8iIiIiGjt890qUAZqmobOzE5qmIRLRr6ImhnsD3wQpN1YvHQ1Ly6tw5qRaAMC/t7We2KFqKFt7EKKif+N32y+uwpJzGsayxKzQlASCu17StdnnXpilaohyV/8+k2g8S0R70LzhZ0krrBbUrM5SRZSP2GcSEaWPfSaNtcYeH763YQ28Uf3rPafRhNuWn4YpBYVZqmx4Wjw3JmxQ9rDPJKIxoQwIeJPt2anjFLHPJCIiIiIiOkEw5MYYOMlUoNuO+hpHdL6mxIc+ICngzQNN0yDk8SKvAGCUJMwoKsGOro6+tkgiga+89gLOnTwFN8yaDzMnydI4o6kJeA69jK69T0NToskHCCJctWejZPplkIzJ9694b4gyTZQMcFQshKNi4Umdbyubg3jYA1/j6/AeeR1KzJ98kKYiEe4+tUJTEUSIkhGCZIIomyBKpt5t2QxRMvZuyyYIkgmCKCPUtRsR76FhLyubXbCVzYe9fD6sJTMgSqbRr50ygn0m0cTkrFoKR8UiRP1HEXYfQNizH2H3ASQinr5jBNEA2eSEZHL2fRdlC7xHXkv9Gm0I8VAX4qEu+Fs29F3b7KqDrXQObGVzYXLW5MV7N/aZRETpY59JlB8EQcAHPn0aXMU2PPCtF6CpJ/6f3fjSPtz98cdx+y+vhr3AnMUqR85qlAcEvPV6p9GLFVNGL4j+kd3bsaS8ChW20Rljxx6TiIiIKLs48oIow6JR/YcLYrh3kq+Qw0ncI/WeunoAQDSh4JU9XX3tzv2dcBzRr1bqcFkwd2XtmNaXLeED66GGe3RttrkXZKkaIiLKJlWJoXnjLxEfMDDOUbkUtrL5WaqKiIiIiEbL7u5O3L9pHUIJ/STAUosVX11xOipsjixVNrh4dxCRQ13oevKdbJdCREQTxcCANyk/A96IiIiIiIgmmqEmSopybgS8yeaCIfeLBiuKpr4HXXv+kXK/po4w4E2LA4ofkJ0jqDI3zSsp0wW8Hfdy4yEIAD42b/HYF0WUISH3PrRvfxgxf0vK/ebCepTPux5mZ80YV0Y0ugyWQpTMuALF0y6Fv/UdeA6/gojnwKhdX5CMMBdMhrmgDmZXLcwFtZAtxRBEeYQBOlci2tMMX9Ob6Gl+C0rsxD1ks6uuL9QtX4J5iIjoBEGUYC7o/RtROOVcAEAi4oWqxCAZHRBlc8q+vaBmNZo3/RLxYPJ7lHRpahxh9z6E3fvQtecfkExO2Mrmwl46F9bS2RAlIxJRP5SoD4loDxLRnmM/+6FEewBRgtFWDpO9AkZ7BQy2coiS4aTrISIiIproLvjgQjiLrfjJrf9CPKb0tb/79lF8/caHcceD16C4PPfGWY9Uqy8y/EEj9P0Na3Df2RfyvggRERHROMCAN6IM0jRtiIC33BjcNpqe3d7e97OgqCjcoR8EYzTL+NKPr4DFZhzr0rIisON53bapZj4MrsosVUNERNmiaSratv4REe9BXbvZNQUVCz/Cm6xEREREeW5TWwt+vnk94qqqa5/sKMBty09DodmSpcpS01QNR3/4AhLuULZLISKiiUYJ6rclW3bqICIiIiIiopFRBg94Ewy5MQZONg0etOasXoHS2R8AgMED3pTE0A8g2QDBCGixE21x97gIeJtbUg5gR8p9e9zdKduJRoOqxOFv2Yh4uBv28gUwF0zO2GMloj3o3P0Ueo6uTblfMthQOutqOGtWQxDGz+LNRIIow1m9HM7q5Yj4muA9/Ap6mtcPH2x64gIQJROM9orekB5XHcyuOhjtFaP2/4rJWY2yOdegdNZVCLv3Q1WiMBfUDRveSkRE+Uc2u4Y9xuSowpSzvolAxw7Egx3Q1DhUJQ5NjUM79l1V4khEvIj4jkBTosNeU4n2oKdpLXqa1gIQAAz+Hjc1AQZrMYz2ChjtlTDayiAZHZCMNkgGG0SDDZLRzhA4IiIioiGsuGA67vzdtfjeLX9DyH/iNVzTvi7ced1fcOeD16B6anEWK8y+AqMpqa09FIQ7EkaxxZqFioiIiIhoNDHgjSgDJEnCwoULoaoq1AGTe8VwFBCEvA5zUQdZlbWx38Rgc2cAclg/AODzP7wcc1fWZrS2XBLa87pu2z7vwixVQpTbjveZ0jgMviQCgO69T8PfsknXJluKUb3s0xCliRF6SqOHfSYRUfrYZ9JYeLXxEB7c/k7S0M8ZRSW4dekq2Ay593qv8/FNw4a7yYUcCDDRsM8kojGhBPTbkj07dZwi9plERERERDR+pR7PpinK4GfkSMCb0Z686KbBVobyeTfAVjILQG/A02DUYYJuBEGAZigCYm0nGuNuwFJ3UvXmksnOAtgMBgTjyf8GRwM9SKgqZJGBVzR6NE1DoPUddL77N8RDXQB6x9bYyxegePp7YS6oSes6vaEeTVDiASixINR4EEos2LetxINQj39PRAa9TsHkM1A6832QjOndq+K9IcpX5oIaVCz4EEpnvx8RzyFomgJBMkAUjRAkg+5nUTJCEA0QxLH7PRdEGdaSmWP2eDQ22GcS0ckQRBmOioXDHqepCqKBFkQ8BxH2HETEcxCxYPtwZ51ERRrioS7EQ10IdqQOxwYAQTRAMtohGWyQzS4YrMUwWEtgsJT0freWQDRYB51Lxj6TiCh97DOJ8tPsZTX41l+uxz03PQFP54lxZF0tPfi/6x/G//7m/Zi2oCqLFWbXebVT8WZLU9L8/XBimAVq0pTPmQZERERE4wED3ogyQBRF1NbWoqcneVCYGI5AyOObR93hEPyxmK5NTPHGToroB3y5Sm1Yfv70jNaWaxK+Nt22uW5pliohym3H+0yi8aineQO69/1H1ybKZkxa/tkhVxAnGgz7TCKi9LHPpEzSNA1PH9iLv+5JHri5pLwSn1m0AsYcuv8TOdwN70vvIuELI97hH/JY2WWF65yJdQ+H2GcS0RhJBPXbcn4GvLHPJCIiIiKiiUaLq4Puy5WAN5OzBs7qlehpfguCZERR/YUoqr8IomRI63xtQMCbEg9DMlj0B6UKeBsHREHAZVNnpLzfDQAvHj6Ai6dOG+OqaLyKeI+gY9fjCLv3Je0LtG9FoH0r7BWLUTL9cpic1UnHaJqGsHs/PIf+i0DbFpxcQEcvk7MG5fNugKVw6ojO470hyneSwQpb2Zxsl0ETBPtMIsokQZRgdtbA7KyBq/YsAIASCyLsOYBg504EO7b3BQqPBU2NIxHxIBHxIOo/mrpmydQb/GYugmwpGvBzESbXTILAgG0iomHxdSZR/qqdUYa7H70Rd9/0OFoPn7jH7veGcddHHsOtP7kCi86sz2KF2TOtsBj3nX0hvvjKc7r2327bhG+ddu4pX5/xbkRERETZxYA3ogxIJBJ4/fXXMXv2bP0ORYEQi0MwGbNT2Ch4qfGQblsWRUx2FiQdZ/DrVzwsKnNktK5cpIZ8um3J6spOIUQ57nifeeaZZ0KW+dKExo9ITxPatj6kbxREVC3+JEyOibuiCJ0a9plEROljn0mZomoaHt69Dc8d2p+076xJdfj4vEWQcmiwpZZQ0f7HdVAHhPH3J5hkOJZMRsGZ0yAVWLhK2wTEPpOIxoQS0G9L+Rnwxj6TiIiIiIjGr9RBSVpCGfQMQc6NgDdBEFC56GMonXUVRIMFomQa0fmamgAAxMMeNL11P+LBdtjK5qNiwYchm46NezMU6U8aJwFvAHDxlIZBA972erpxMRjwRqcmEfGi892/o+foWxgulC3Q9g4CbZvhqFqC4mmXw+SohKYm0NOyCZ5DLyHqO3JKtYiyGSUzroCr9mwI4sj7MN4bIiJKH/tMIhprktEGe/l82MvnQ9M+iHiwA8GOHQh07kC4e0/fez8AgCBCNjkhmQogmxyQTQWQTE6oiQhigTbEAm1IRDyjWp+mRBHztyDmb0m9HwIMZhcM1mLI5iIYLEWQjDZAECFABASh92dBgGx2weScDNns4jgfIppw+DqTKL+VTSrAtx+5Aff+vyexf1trX3s0HMe9n/obbrnnEpx15dwsVji0ga+9KuNHIWmJQY4emUKTOantgHd0XpNq2skvlkFEREREp47vXokyQNM0+P1+RCL6kDMxHIUAnNSgkFyxpaNVt72soho2Q3Jgna3Jq9tumF+ZybJykhLWB7yJ1uQgPCI60WfyJhGNJ0osiJZNv0pa5btszge5AiqdEvaZRETpY59JmZBQVfx26ya82dKUtO+99TNwzYw5OTdoMt4dGDLcrepz58JUxXsWEx37TCLKNE1TASWob5Rs2SnmFLHPJCIiIiKiiUZLqIPuEwy5s9gFAMhm10mdpym991A7dv4V8WA7ACDYsQ1Na7+PSSu/AIOlOGXAm6ZpOXdP+GRIogiDKCKuJv+33jxgvCBNTEo8hGhPE2RzIQzW0rR/71UlCveBF+E+8Bw0JZbyGEGU9UEbAAAN/pZN8Le8DVv5PES8R6BEfSnPT5sgwlG5FGWz33/SfQXAe0NERCPBPpOIskkQBBjt5TDay1E49TyoShQxfxsEyQDZ5IRosEIQhn5P2xv21t4b+BbsDX2Lh7qhxINQ4kGo8dDo1gwNiYhnRMFykskJs3MyTAWTYT72JVuKx8V7VSKiwfB1JlH+Kyiy4ut/+CDu+/w/sHXNob52VdHw86/+B97uIN77seU5+ZrGNHmBbrs83oJzfU8DOPVQOoMkwWYwIBjXj/s+6u/BJIfzlK7NHpOIiIgouxjwRpRBiqJfvVSI9w5CEaTcGtiWLk8kjCM9+kEytc7kCcCmrgBMHv0HFasunJHR2nKNlohDi4V1bZKFk6WJiCYCTU2gZfMDiIe6dO0Fk89AYd3Z2SmKiIiIiE5ZJJHAT955C9s625P23Th7Pi6eMi0LVQ0ttLcd3pfeHXS/bV41w92IiGhsKGEkDROT7VkphYiIiIiIiEZmyIA3OX8XOu1PU+NQExEEO7bp2mPBdjS+2RvyZjQNCHhTo4AaytsA8/5EQcDyykl4s7kxad/Csom3sCvphb2H0bzhZ1BifgCAZCqAtagBlqJpsBRPg8lRnRSMoWka/C0b0Ln7qUHDKQyWYpTOej+spbPhPfwy3AdfTBGQoSHYvi3l+QAgSCYYraWQjDaIBhskow2S0Q7p+M8GG0SjDZLBDtnsgmSwnNK/BRERERHlL1EyweyqHdk5shlmV+2g52maCjUeghLrDXxTYkEoMT/iYTfioS7Ew12Ih7qRCLuRqUgNJdqDYOcOBDt3nKjbYOsLe7OVzoGleEZOhqMQERHRxGaxGfHVX12NX97xLN74107dvr/84FV4OgL40O3nQhRz63WMbda5ME9ZisihTX1tV7sfQlv00wCqTvn63znjfHz+5Wd1bbe//iIevPC9sMiGU74+EREREWUHA96IxlTvDXlBys+BbS81Hkpqq7I7ktpcu/SrdrpKbZi1rCZjdeUiJZy8WqRo5YRpIqLxLtS9B+07HkXM36JrN7umomzOB7NUFRERERGdKn8sih9sXIsDXreuXRIEfHLBUpxWPTlLlZ2gKSq0uAL3szsQPeJGrK1n0GNLr18GucACU03RoMcQERGNKiUwoEEERGtWSiEiIiIiIqKR0RLK8AflAUEYfMyepiYQaN8GTU0k7UtEPGh68/uYtPxzMAkyoPU7JuYGLPkf8AYAN89fgmmuIvxx5xZdu8QQgAkt6m9F8/qfQIkH+9qUqA/+1rfhb30bACDKFliK6nsD34qmAdDQuetviHgPprymKJtR1HAxCqecD1HqnZBYPO1SuOrOgefQS/AcfBFqIjJkXQZrCVx156Kg5jSGthERERFR1giC2BswbBx6YStNTSAe8SIR7kY87EYi7O4NgQu7+9o0JTZqdanxIEJduxHq2g33gefhqFqKigUfgSgZR+0xiIiIiEaDbJDwmXsvhavEhqd/v0G37z8PbYKvO4RbvnMJDMbcmZMviCIqrv8RDt9zRl+bXfXD0rQWWDHzlK9fYrFiTnEpdnZ36tpfaTyES6ZOP+XrExEREVF2MOCNKAMkScKqVasQDAZTHyCKqdtz3MBJzHVOFxb3W6FT0zSY2/2wN3l1x1143SJIUn4+55OlhpMnUUsWBrwRpXK8z5TyNPySCAASER86dz+Jnub1SfskkxPVS/9f36BUolPBPpOIKH3sM2m0dIdDuHfDGrQE/Lp2kyTh84tXYkFZRZYq6xU51IXW37yR9vGCUYJ9/qQMVkT5iH0mEWXcwIA3yZq3q8SzzyQiIiIiovEr9fu08RLwJhltsBTWI+w5kHJ/T/OGlO0AoMSDaHzrftTPXQRR63evOOEGMD4WPpVFERfU1aM16Mfzh1P/G9HEEg934+j6H+vC3VJRE2EEO3Yg2LFjmCsKKJh8OkpmXAHZ5EzaKxmsKJl+OQrrzoXn0H/hOfRSUtCbpWgaCqecD3vFAghCdsak8t4QEVH62GcSEfUSRBlGawmM1pKU+zVNQyLqR2frQdhNKpSo51gAXDe0RAwaVEBTAU2DpqnQ1ARigdZhg5GP87dsQjzYheplt0A2u0bxmRERZQdfZxKNL6Io4EO3nQNXsRV//sGrun1r/r0Lfk8It/70fbDYcies1jr9dLTZZ6AisKevTYp4R+36F9TVJwW8Nfb4Tuma+TpWj4iIiGi8YMAbUQaIooiysjIcPnw45X4hD8PONE3DYZ9X13ZZ/fS+N3VKQsWuX69F9Y62pHNnLRkfg9hGQgkPeLMsyRCMXCmSKJXjfSZRPlJiQbgPvgDPoZehKdGk/YIoo2rJJ/lhOI0a9plEROljn0mj4ai/B9/bsAbuSFjXbjcY8ZVlp6GhsChLlfWKNntHFO4GALa51RmqhvIZ+0wiyrikgLehV7HPZewziYiIiIhootESarZLGDXVyz4Dz6GXkIh44Wtao9sX7NSHU4kGK9R4qG9bU6IIuJvhdPULporpF0wdD6wyF68jIBH1o+mtHyMR8YzK9azFM1A65xqYncOPJZWMNpTMuAKFU86D59BLCHbthsleBVfdWTAX1I5KPaeC94aIiNLHPpOIKD2CIMBgdqJqysK0z9E0FfFQFyK+RkR9RxDxNSLia4Q6SEBzxHcYR9Z8F5ULPwpL8QwGfBBRXuPrTKLx6b0fX4GCEht+dcezUPp9LrH1zcO46yOP4X9/834UFFmzWKGeImTuXvr80vJRvyZf/RERERFlV/6lTBHlgXg8jv/85z9QlNSrlwp5uDqAOxJGT0wfXDOloBAAEI3E8c0PPwp3inA3ACif7Mp0eTlHDekD3iRLAT8AIRrE8T4zHo9nuxSitCmxIDrf/QcOvvy/cO9/NmW4m8lZg5rVt8FaNC0LFdJ4xT6TiCh97DPpVO3zdOPb615LCncrMlvw9VVnZT3cDQB8b+xP+1jLjHKUXL0IJVctymBFlK/YZxJRxiUGTCSQbdmpYxSwzyQiIiIiovFLS906jgLeeoOj3ovSWVcl79T6P08BdWfcCWvJLN0hsQH3ixEffwFvA73VehQJdfz8DpyMeKgLgfbtCLn3IxZohxILQtPG77+JEg/j6IafIh5s17WbXVNQd+bXUTH/Q3BOWgWDtWTYaxmsZahaegsmrfxSWuFu/UlGO0pmXIHa076KigUfyolwN4D3hoiIRoJ9JhFR+kbaZwqCCKOtDM6qpSiddTVqVn4RDe+5H1PP/S6qlnwKRQ0XQ5TNunMSEQ+a3rofjWu+g57m9dDURCaeChFRxvF1JtH4ddYVc3H7r66GyaIPTzuwvRX/d91f0H7Um53CxphJknFB7VRd2xvNjQidQr+naqk/AyIiIiKisSFnuwCi8SqRGPxGtyDlX7bifq9+MJpVNqDcaoPPHcIPPv0U9mxuTnne1DkVKKl0ptw3nqlhfcCbaCnIUiVE+WGoPpMolyixIDyH/gvPoZegJiIpjxFlC0pmXglX7VkQhPz7m0+5j30mEVH62GfSydrS0YafvvMWogPC+6vsDnx1+ekotmR/BThN0xDc0jTkMSUfWAzRZIC5rhiS3TRGlVG+Yp9JRBmlBPTbkj07dYwS9plERERERDSRaPHUi5zmM0EceuistXg6DNYSVC/7DI6u/zHC7n0AgFh0wOJvEyDgDQDebm/BispJ2S5jzGmaBs+hl9C5+8kBAYAABBGSwQbJaIdkdEAQRGhqHJqagKYqfT+ragI4/l1TYHRUo3LhR2FyVGXnSQ1DVeJo2fRLRH1HdO1GeyUmLf8sJKMdJuckFEw+HQCQiHgRcu9DuHsfwu79iPqbAWgQZQuKp1+Gwrpzhv3/LR/x3hARUfrYZxIRpe9U+0xBEGCwFsNgLYajchGc1SvQvPHniIe6dMdFfEfQuvl36Nz9N7jqzoWr9ixIBsspPTYR0Vjj60yi8WvRGVPxjT9+EN/95JPwe08sutJ6xIM7r/sL7njgGtTNLMtihWPDJCXfV/3aG//FD8++ELLI+YpERERE+Wb8fWpOlAcEUcp2CSP2brf+hv5UVyHWv7AXD9z1AnrcoZTnFJ42Bfc++IGxKC/nKCF9wJtkmXghd0RE44kSD8Fz8L/wHPrvoMFugADnpFUonXUVZBP7fSIiIqJ89WZzI36zdROUASuV1buK8JVlq+Ew5kZQWmhXa1KbaDWg6pazIZpkSA5zirOIiIiyZJwFvBEREREREU0kWkId/qA8I4iGIffbK5cAAETJAEvRtL6At2hkQMCbGoKmhCFI42cifCgRT2rb53FPuIA3TVXQsfOv8B55dbADoMT8UGJ+AMn36wcT9R1B88ZfYMrZd+Vc8JmmKmh95wGEuvfo2mVLMSat+AIkY/L9HNnsgrNqGZxVywD0LpwYD3XCaK+EKOfG5ylERERERBOVyVGFyaf/L1o2/brvfW1/iYgXXe8+Bc+hl1A+54OwVy6GIAh9+zVNgxL1IRpoQzzYDlG2wFG5OOfeyxAREdH4M21BFb79yA2456bH0dnS09fu7Qzi6zc+jNt/cTXmrJicxQozr9RqS2rrDIewx92FOSXjP+COiIiIaLzhHTWiLBCk/EvH3u3u1G3Hn+3CfY+tTXlstMiKlnNnYPkZdbqb+xOJGtYHvInWgixVQkREp0KJh+A59BI8B/8LNREe5CgBjqplKJl+GYz2ijGtj4iIiIhG17MH9+Evu7cltc8vLcfnF6+EWc6N26laQoX3v+8mtRdfPh+GEgbmEBFRDlKC+m0GvBEREREREeUNLaFku4RRJ4gSIIiAliq8ToCjYmHflmx29f0cj8WgaZp+TFzcDUjVGat1rIXiyQFvUSWRhUqyR01E0PLObxHs2JGR68dDnehpXo+CmtMycv2ToWka2rb/GYH2Lbp2yehAzYovwGApTOs6ktEGyZg88ZCIiIiIiLJDNjpQs/KL8B55DZ6D/0U83J10jBL1oeWd38BWNh+WwimIBdp6v4LtSQujexuno2bFFxjyRkRERBlXPbUYdz96I+75xBNo3Htifns4EMPdNz2Oz//wcqy8cEYWK8yslZWT8PrRIzjgdevaN3e0MuCNiIiIKA/lX8oUUR6QZRnnnHMOJElKuV8YpD1XeSJhNPlPpJxrMRU7Hk9evQUAvLPK0XzBTKgmGe9dUDlWJeYcJTQg4M3CgDeiwRzvM+UcCUogAgAlHkbX3n/j4MtfQ/fepwcJdxPgqFqOurO+iarFNzHcjcYE+0wiovSxz6SR0DQNj727I2W422lVNbh16eqcCnc7fOc/EWv1Je2zTC/PQkU0HrDPJKKMSwT023L+TvRln0lERERERONX6oU8tUSqELT8J4iGlO2Wwqm6ULeBwVbx2ICws7hntEvLqobCoqS2QrMlC5VkRzzsQePa76cMdxMk06g9Tve+Z6CpuRGeqGkaOnf/DT1N+gV/RdmMSSs+D6Odnz30x3tDRETpY59JRJS+TPaZgiijcMp5mHLO3aha8klYCutTHhfs2IauPf9ET/N6RHxHksLdACDcvRfd+54Z9RqJiEaCrzOJJo6icge+9ZfrMWvpJF17Iq7g/i/8Ay88tjlLlSV7dkcbNE0btevZjUZ8Y9VZsBn0n2W83HgIijryz210C9cQERER0ZjjO1iiDLFYBh/UJIj5la24sa1Fty3vjSKu6t9oTlpQhTX1ZVAsvW8WnWYZMyocY1ZjrlHD+knWEgPeiIY0VJ9JNJaUeBjewy/DffBFqPHQIEcJcFQtQfG0y2ByVI1pfUQA+0wiopFgn0npUFQVv9u+Ga8dPZy07+IpDbh+1nyIOfShdsdjG1O2W+dWQbKN3uQymnjYZxJRpmhqHNBi+kbJnp1iRgn7TCIiIiIimki0RG6EUI02UZShKNGkdnvlEt12/7A3AIiGQzCa+o0Fi3dnorysWVE5CX/YsSXbZWRFxNeI5g0/RyLq1bULooyKBR+Bs3o5VCUGJRY49uXv+xkaIEgyBFGGIBz7Lp34ORZsQ/v2h/uuGQ91oqd5PQpqVo/xs0zmPvA8PAdf0LUJogHVyz4Dc8HkLFWV23hviIgofewziYjSl+k+UxAlOCqXwFG5BGHPIXTv/ReCnTtHfJ3u/c/AVjYXlsKpGaiSiCg9fJ1JNHHYnGbc8eA1+MmXn8bG/+7ra9c04IFvvgBvZxAf+MxpWQ8wiysaPvnnzfjthxaP2jUlUcRXlp2Gb659ta8tqih4o7kRZ9fUjdrjEBEREVHm5VfKFFGeSCQSeOaZZ6AoqQe3CZI0xhWdmo1tzX0/a+44wn9oTjpGuXJeX7gbABTbjWNSW65SQvqAN9HKgDeiwRzvMxOJxPAHE2WImoige98zOPjy19C155+Dhrs5Kpei7qxvoGrxzQx3o6xgn0lElD72mZSOmKLgx++8lTLc7YMz5+KGHAt3UwJRhHa0pNxnnVE+xtXQeMI+k4gySgkkt+VxwBv7TCIiIiIiGr+01K0JdYzrGBuCZEjZ7qhcpNuWzYW67Vh0QChc3DOqdWWbw2jC4vLKbJcx5gLt29C49gdJ4W6SwYZJK78EZ/VyAIAoGWGwFMFcMBm20jlwVq9A4ZTzUDj1PLhqz0JBzWlwTloBR9US2MsXwFY2B9aSGSiYfCbMrjrdtbv3/Qeamt0ARe+RN9D17lP6RkFE1ZKbYS2enp2ichzvDRERpY99JhFR+sa6z7QUTkH18s+hcvEnIJmcQx4rSEZA6DcFVVPRvOmXaHnnQXS++w94G99AxHcEmpb6vgIR0Wjj60yiicdkNuDWn1yJ869ZkLTviV+8id9+43koyth+liGL+vHlM8PbsemIF09vbR3Vx5lSUJjU9uSekYf08rUaERERUXbJ2S6AaCISpPzJVlRUFfs8vauMamEF8V8lh7s1XDUfz+93j3VpOU0N6wPeJAsD3oiIcpGaiMBz+BW4D7wANR4c9Dh75WKUTLscJmf1GFZHRERERJkUjMdw36Z12OPu0rULAG6atxhnT56SncKG4H353ZTtRZfOhX1p7RhXQ0RElKaBAW+CEYKYehI9ERERERER5Z5xG/CW4r2puaAOBkuxrk0y2iGIMjS1d8JoNCngrTtjNWaLJOTP+MbR4Dn8Cjp2PIaBIYcGWxkmLf8cjLayU34MQRBQPO1yNG/8WV9bPNSJnpYNKJi06pSvfzL8LW+jfftfktorFnwY9vLkiZJERERERDS+CIIAZ9Uy2Epmo3vfvxHs2g3Z5ITRVgGj/cSXbHbBvf9ZdO35Z9+5SrQH/pYNuusZLMWwVy6Bo3IxzK4pEHJoUU0iIiLKf5Ik4ua7LkRhqR1P/OJN3b7/Pr4VPZ4wPv/Dy2E0jU10RrHdCPSbRn6m/3m45RJ8418fwzkzS2EfpTpkUYRJkhBVTiwW4o/HRuXaRERERDR2GPBGlA2ilO0K0tYc8COu9g7SUzf5AXfyygYvx1XAom8rd5rHorycpQwIeBOtDHgjIsolvcFur8Jz4HkoQwW7VSxG8fTLYHZOGsPqiIiIiCjTPJEwvrdhDZr8Pbp2gyjiM4tWYGlFVZYqG1ys04+etQeT2qu/eB6M5UOvpEtERJRViQH3XmR7duogIiIiIiKik6IllOEPykOCmDx81l65OPk4QYRsKkA83BvkFhsY8KYEoKkxCKIxI3VS5miais5dT8Bz6KWkfZaiaahe+ilIxtG7j2ErmwtzQR0ivsN9bd17/wNn1XIIYzymNNi5C61bfoeBoXals6/JWuAcERERERFlh2S0oWzOtUMeU1R/EQId2xHxJI9dOi4e7obn4AvwHHwBsrkQjsrFsJcvBEQJaiwAJR6EEgtC6f9zPAAlFoSmxGC0lcHsqoO5oBZmVx1ks2t0nygRERHlPUEQcM1nT4er1IYH73oBWr/bmxte3Iu7P/44bv/lVbCNwfx2V2kVAs1v69ou9P4dfy/6EB5Z34Sbzxy9hcY/MX8Jfr55w/AHEhEREVHOYsAbURYIUv6scHnI5+n7WQsmD9bzTStF3JH8ZveTo/jmMx+pIX3Am2RhwBsRUS5QE1F4Dr8Cz8EXoMQCgx5nr1h0LNitZgyrIyIiIqKx0Bb04971a9AZDunarbIBty5bjZlFJVmqbHBKKIbWX7+ech/D3YiIKOcpA+7BSAx4IyIiIiIiyidaXM12CRkhSIakNkeKgDcAkC2FfQFv8WgMmgYIQr8D4m7AVJGJMilD1EQUrZsfRKB9a9I+Z/UKlM//EMQUvyOnQhAEFE+/DM0bf97XFg91oKdlw5iFqinxMAJtm9G+41Foqn6x36KGS1A09fwxqYOIiIiIiPKLIEqoXPhxNK39ARJR77DHJyIeeA69lDJQezDxUCeCnTv7tmWTC6aCGkhGO0TJBFE2QZBMEEQZmhKDqsSgKVGoSgxqIgpNiUIQZciWYhgsRb1ftlKYnDUQhPyZx0dERETDe88HF8FZaMVPvvw0EvET8953b2rC1298BHc88AEUlTsyWkPhuf8PgS1P69osWghGLYotTb5Bzjo5lbbMPhciIiIiyjwGvBFlgCzLuOSSS9DU1KTfcSwNXJDGdrXFU3HY5+37WX3br9tXMKUIB5bXJZ3z6pfPgNMyuoOb8o0S1r8BF60MeCMazPE+U5b5soQyK9S9Dy1v/xpKzD/oMfbyhSiefjnMBQx2o9zEPpOIKH3sMymVQz4Pvr/hTfTEorp2l8mM25efjsnO3Hj/rsYSiHf2huHE2nrQ9cTbKY8rvnLBWJZF4xj7TCLKqKSAN1t26hgl7DOJiIiIiGj8ElK2akryoqDjgSDqx7eZnJNgtJWlPFY2u/p+1jQNiiZBFvr9uzDgLa8kIl4c3fgLRH1HkvYVT78cxdMugyCk/v/hVNnK5sFcUItIv8fu3vcfOKuWQxAzM65UTUQQaN8Gf8smBDt3JAW7AUBB7VkomXFFRh5/vOG9ISKi9LHPJCJKXz70mUZbKaac8y2EuvciHupEPNSNeKgLUf9RxENdo/54iagXiQ7vKV/HYC1BweQzUDBpNWRzbowNI6JTkw99JhFl3soLZ8DhsuB7n/4bwoFYX3vj3k7ced1fcMeD16B6anHGHt+x4BLUfvVlHLn33BR7tYw97snK1D1vIiIiIkoP38ESZUg4HEY0qp8sLIYjAABByp+VP7ojIQCA1hYD3PqBPYYye9Lx37pi1oQPdwMANaQPeJMs/BCAaCjhcBgOB1cSoMxJRLxo3vQLqPFQyv328gXHgt0mj3FlRCPHPpOIKH3sM6m/HV0d+NGmdYgo+vsb5VYbvrriDJRZcyNsJrSnDR1/Xg8toQ55nGCUYGlIPdmQ6GSwzySijEkKeEv+bCHfsM8kIiIiIqKJRIsPfa8yX8lG/ftTe8XiwY81F+q2EwlANvZriLtHszTKoED7NrRv+wsSUa9+hyChYsGHUDBpVUYfXxAEFE+/HM0bf97XFg92oKd5PQpqVo/a46iJCAId23tD3Tp2QFPjgx7rqFqK8rnXcYLfCPDeEBFR+thnEhGlLx/6TFE2w14+X9emaRqi/qMItL4Df8smxILtWaoutXioC13v/h1de/4Je/lCuGrPhLVkJgQhf+b2EVGyfOgziSjz5qyYjG/95Qbc84nH4e0M9rV3tvTg/65/GP/72/dj2vyqjD2+sXJmUpsz4QEwKWOPSURERET5iQFvRBmQSCTwyiuvoLq6WtfeF/CWoZUWM0lzJw/w2eq06ranlthw2fzKsSoppylhfcCbaGXAG9FgjveZl1xyCQwGBkTS6NM0DW3b/5Iy3M1WPh8l0y6H2VWbhcqIRo59JhFR+thnUn/rW4/il1s2IqHqJyLWOV24bflpKDCZs1SZXqTRjfY/rEvr2KpPnQVDSf4H5FBuYJ9JRBmVCOq35dwIVT1Z7DOJiIiIiGj80lK3JpQxrmNsFNSchkDHdkBTIZmccNWdNeixstml245FYzAb+40BHOcBb2uOHsGqykmosNnzNgQsEfWjY+df4W/ZkLRPNFhRvfRTsBbPGJNabGXzYCqoRdR3pK+tbdufoMbDcE0596T/jdVEFMGO7ehp3YRg+/YhQ91O1DIXlQs/xmCDEeC9ISKi9LHPJCJKXz73mYIgwOysgdlZg+Lp70Us0Ap/69vwt76NmL8VomyCaLBBMtogGWyQjPZj322QDHaIRhsEQUDE14SI7zCivkaoicjoF6qpCLS9g0DbO5BMBTBYiiGZ7JCNzt6aTA5IRjtkowOCbIYomSBKRoiyCYJkhCiZMj4fUFN7Fy4VRE75JRpKPveZRDT66maW4e5Hb8Q9H38crUc8fe1+bxh3ffgx3PrTK7HojKkZeWzZWQ7JWQalp6OvbWFoPXyYl5HHOxWalvozICIiIiIaG7zbQ5RB0WhUty2FjgW8Sfk/GEY2SYiU6Vc5mFnJScUAoCkJaFH9hDXJwoA3IqJs8TdvQLB9m67NUtSAstnXwOyqy05RRERERDRm/nvkAP64Y0vS1MQ5xaX4wpJVsObIAB/fmv1w/3t7WsdW3HwGjJW810BERHlCCei3JX6WQERERERElE+0hDr8QXnIXrEQtaffgai/GfayeZCMgweSG8yFuu1oKAg4nCcaxnnAW3soiC+/9gJsBgPqXUVocBWh3lWEelchHEZTtssbkqZp8LdsRMfOx6DEAkn7DdYSTFr+ORjtFWNWkyAIKJl+GZo3/qJfoSo6dv0VIc8+VMz/MCSDJa1rqUoUwY4d8Le8jUDHNmhKLI0CRNhKZ8NZvQKOqmUMdyMiIiIiolEjCAJMjiqYHFUomX45NE1LO8TaWb0CAKBpKmLBdkS8hxEPdUNNRKApUaiJCNREFJqa6A1ck3sD2ATp+HcjNCWKeNiNeLgb8WAnEhFPysdSoj4oUd/In59khNFWBqO9EkZ7xbGvShhtZRAlY99xmqZCjYegxIJQ4kEoscCx70GoA7b7/6wpUQiiAbbSOSibcw0M1pIR10hERDQRlU9y4duP3ojv3vwEDuxo62uPhuP43qf+hk/dczHOumLuqD+uIIpwLLgU3jf+0Ne2MPgWXsNNo/5YRERERJTfGPBGlEGRiH7FEDF8POAtsyt2ZMSAWdBWV/IAooU1rrGpJcep4Z6kNpEBb0REWZGI+NC+81Fdm2RyonrpLZCMnExMRERENJ5pmoan9u3GU/t2J+1bXlGNWxYugyFH7tEogejQ4W6SCCgqnKfVo+jiuRBkTrYiIqL8oGkqoOgXRGHAGxERERERUX4ZrwFvAGAuqIG5oGbY4+QBAW/hgBso7xfwlujpnWAujo8huZFEPGV7MB7Hts52bOts72ursNn7Qt8aXEWY7CyALObGPex42IP2HQ8nLQp4nLV0NioXfhyyyZFyfybZyubDUbkE/ta3de2B1ndwpOcoqpb8P5idk1KeqyoxBDt2wt+6CYH2bdCUaMrjdAQRtpJZcFQthb184ZCBhkRERERERKMl3XA3/TkiTPZKmOyVp/TYmqYh4j0Eb+Pr8DdvhKamfq87omsqMUR7jiLac3TAHgEGazEAEUo8CDUeQtJEvHQfQ40j0L4Fwa7dKJ35Prjqzj6pYG5NU6FEexAPe5CIuJEIe5GI+iDKZhht5TDay2EYEExHRESUzwqKrPjGQ9fhvs/9HVvfPNzXriRU/Pz2/6CnO4TLP7Z81B/XvvAyXcDbrPA2rE0kLzZCRERERBPb+BhNQpSDZFlGNKofOCOGjgW85cgAppHQWvXPRTYldx9XLa4aq3JymhJOXsVFsjLgjWgossyXJDT6NE1D+/aHj31AekL53BsY7kZ5jX0mEVH62GdOXKqm4aEdW/DfxoNJ+86fPBUfnrsQ4kkMIMyUaIs3ZbvkNGPSly+AaOTvMmUe+0wiygg1jKSB6+Mg4I19Zv5SFAWKokBVx29ABRERUT4QRRGyLEPMw/EzRBORllCyXULWyRaXbjsaCQ84QgMSHsBYOmY1ZdL2ro60j20LBtAWDODN5kYAgEEUUVdQiAZXIRpcxah3FaLEYj2pSf0nS9M0+BrXoHP3E1ATkaT9osGKstnXwDlp1ZjW1Z8gCKhcdBMMtjK49z+r2xcPdqBxzXdRPu8GFNSsBgCoShzBzp3wt2xCoH1r2qFu1pKZcFQuhaNiIcfKjCLeGyIiSh/7TCKi9LHPHF2CIMBSOBWWwqkom30NeprXw3vkdcT8zRl4NA3xUNfoXlGJomPnY/C3bET5/P+ByaGfs6dpGpRoD2LBdsSC7YgHOxEPu5GIeJAIuxGPeAFtuHs6AmRLYW/gm60cJuckOKtXQJRNo/pciDKBfSYRpWKxGXH7r96PX37tGaz59y7dvj99/xV4uoK48ctnQxRH776wbc4FUEUjRDUGAJCRwOSutwCcMWqPQRMXx7oRERHlhtEY68Z3sUQZYDAYcOmll+Lvf/+7rl0MHwt4k6RslHVK1K36xPCqufqVUCYXWXJqYnQ2qaEBAW+iBMHEFS+JBnO8zyQabf6WjQi0b9G1OaqWwlG5KDsFEY0C9plEROljnzlxxRUFv9q6EetbkwfjXTVtFq6aNitrE7YGo4aTV4i1TC9D8fsWMdyNxgT7TCLKmKTVSAVAsmSllNHCPjO/aJqGcDiMQCAAv9+PWCyW7ZKIiIioH5vNBofDAYfDwclYRDkh9X1TBrwBsqkAvf8+vSHmmqpCEy0Q1H5Bb3H3uAl4W1U1Cetajp7UuXFVxT5PN/Z5ugHsBwC4TGbUuwpR7ypCg6sIU12FsMiGUaz4hFiwA+3b/oxQ956U++0Vi1A+9zrIZldGHn8kBFFC6cz3wVJYj9Ytv9ctYKipcbRt/SNCXbsBAIH2rSnD6lJctS/UzV65ELLRkaHqJy7eGyIiSh/7TCKi9LHPzCzJYEVh3Tlw1Z6NqL8Z8WA7lFgAiagfSuzYVzSARMwPJRaApkShJqJIWswsC8KeAzj82jdhKZoOS1ED4qGuY4Fu7Wm+TxyKhkTYjUTY3ff+s2vv06hZ+cWkQDmiXMI+k4iGYjBK+Oz3L0NBsRX/eWiTbt/Tv98Ab1cQt9xzMWTD6MzzlywOBKpXwtn0el/brJZ/AfjKqFx/tHD+f37gWDciIqLcdipj3TgyjigDVFVFV1cXIhH9jVIxdDzgLb9WINYCCrQW/ZuA+lV1wLuju7rIeKGE9QFvosWZcxPHiXLJ8T6zpKSEK7TTqElEe9Cx41Fdm2R0oGzOdVmqiGh0sM8kIkof+8yJKZyI40eb1mFnd6euXQDw4TkLcUFdfXYKG0bnoxuT2io+dloWKqGJin0mEWWMEtRvSzYIQn73M+wz80cikUBTUxMikQhkWYbdbkdpaSkkSYIoivzshoiIKEs0TYOqqojFYvD7/Whra0N7eztqampgs3HxPKJcpCXUbJeQdYIoQzI5oER7+toUWCBjQMDbODGvpLwv4E0A8H+rzkKJxYr9Xjf2e9w44HXjoM+DuJre74Y3GsHb7a14u72175qTHM6+wLeGwiJU252nNMFM01R4Dv4XXXv+CU1NXlRFMjlRPvc6OCqXnPRjZIq9fD7qzrgTLW//FhHfYd2+nub1aVxBgLV4BhxVS2CvWAzZxFC3TOK9ISKi9LHPJCJKH/vMsSEIAszOSTA7Jw17rKZp0NREb9ibEoWaiCER9SEWaDv21YpYoA2JiGfoxxQNkIw2SAY7JKMNosHWu220Qzr+s8EG0WiHJJvhOfwKfI1vJF0n7N6LsHvvST/3dClRH5rW3YeaVV+GyVGZ8ccjOhnsM4loOKIo4MNfPReFZXb85Qev6va98a+d8HtC+NKPr4TFZhyVx/PVna8LeKvrWoPAjhdhn3vBqFx/NGQ/tpaGw7FuREREuWm0xrox4I0oAxRFwbp161IEvB0b0CWOTrL3WFFe1t9sFgSgtL6YAW+DUEP6gDfJUpClSojyw/E+85JLLuGNdRo1XXufhhLXTyAun3s9B7FS3mOfSUSUPvaZE48vGsH3N7yJwz1eXbskCLhl4XKsrBp+YF42RI8mD/KTnOYsVEITGftMIsoYJaDfluzZqWMUsc/MD9FoFE1NTdA0DbW1tbBYLBzkRkRElGNsNhsKCwuRSCTQ2tqKpqYmhrwRZV3q6T0MeOslmwv1AW+qpB+AO44C3s6cVAsNwD5PN5ZXVGNGUQkAoNhixYrK3nvtCVVFk9/XF/i23+tGazAwxFVP0AA0+XvQ5O/Bq02HAQBmScZUVyEaXEV9wW8uc3r3yiPew2jf/khSONpxzkmrUDb7GkjG3P0bY7CWoGb1V9C56wl4j7yaxhkCrMXTYa9cAkflYsgmZ6ZLpGN4b4iIKH3sM4mI0sc+M/cIggBBMgCSARJ6P+c2OSphK5mpO05NRBALtCMWbIcgSBCNNn2gmzSy4JiK+f8DR9UytG/7E+KhUZi3J0gwmF2QLUWQzQVQogHEgu1DBtMpMT+a3roPNatuhcnOkDfKPewziSgdgiDgio+vgKvYhl/e8QxU5cRnIFveOIRvffQx/O9v3g9nofWUH8s9/So4196PAsV7ou3Fn2Us4C2R5uIr/WkaI95yGce6ERER5b5THevGgDeiDNE0DdFoVNcmhnsD3wQpf24cBQ8HoL7h1bXNWloDo3V0ksnHIyWsD3gTrQx4IyIaS5qmItD6jq7NXrkYjqrcW4WZiIiIiEZHRyiIe9e/gfaQPuTXLMn44tJVmFtSlqXKhqaEYmj5+atJ7ZaG3KyXiIhoxBIDJnbLuTuRmsYPTdPQ2NgIURRRW1sLg8GQ7ZKIiIhoCLIso7q6Gs3NzWhqakJDQwNkmUPaiHKJllCyXUJOMJhdiPqO9G3H4xpM/d9ujKOAN0EQcHZNHc6uqRv0GFkUMaWgEFMKCnEB6gEAwXjsWNibB/s93djv7kRQSW+iWURJYFd3J3Z1d/a1lVisqHcVYdqx0Le6AheM0onFdSPeI+ja+zSCHdtS12gpQsW8/4GtbE5aNWSbKBlQPu96WIoa0Lbtz9CU6IAjBFiKpsFRtRSOikWQzRyXSERERERElG2ibIbZVQuzq3bUrmkrmYm6M7+B7n3/gbfxDagDFr7vTxANMNjKYLSVwWAtgcFcBNnigmwugsFSCMnkhCAkz2NUE1HEgh2IBdsRC7Qh0LYZ0Z6mvv1KtAdN6+7H5FVfhtFePmrPjYiIaKyddeVcOAotuP8L/0Q0HO9r37+tFXde9zDufPAalE06tXutqsmJvxV9BB/r/HFfW2DbM4h7W2FwZSYs9YXDB/CeuvqMXJvGFse6ERER5ZeTHevG0XBEGaJpWlKitRg6HvAmpTolJx185CDQb4yVIAn46B3n41D2Ssp50eZdum3ZyRvZRERjKdrTDCXm17WVzrwqS9UQERERUaY19vjwvQ1r4I1GdO1Oowm3LT8NUwoKs1RZapqqIbC5EaF32xDa3pLymOL3LRzbooiIiDJFGRDwJtmzUwdNKIFAAIlEAlOmTOGANyIiojwhiiIqKyuxb98++P1+FBbm1v0coolOi6cX0DXeyWZ93xSLRABdwJsXmqZAEPJnbOBosxmMmF9agfmlFejY+Ve4O16CG1Z0WOrhLT8dB7weHOnxQhkwrnIwXeEQusIhrG89CgCQBAG1ThfqrDLKgvtQ7NuOIoQgJJ0pwFV3Nkpnvg+ibB7V5zgWnNXLYXLWoH3Hw4h4j8BcUANH5VI4KhdDNruyXR4RERERERGNAVE2oXTWVSiefjmC7dvgb98CNR6G0VYKg60cRlsZjPZyyObClAFu6VzfXFADc0ENAKBwynk4uv4niHgP9h2jRH1oWncfalbdypA3IiLKa4vPqsfX/3AtvvvJJxHwnRhv3nrYjTuv+wvuePADqJ1xaotzr3Wch+u7fg2zduz6qgLfm39CyaW3n9J1AcBuNCa1PbRzC6YVFuXcOHkaOY51IyIiyj8nM9aNAW9EGSAIAmw2W1K7GE/07pdGfuM0GzasO4DALp+ubfn1c1A3swwvrzmcnaLyQPjget22ZcrSLFVClB8EQYDD4YAgJA+3JDoZoS590KbBWgKj7dRushLlCvaZRETpY585Mbzr7sJ9G9cilIjr2kstVnx1xemosDmyVFmyyOEu+N7Yj9DO1iGPM08rg2iYuBMAKTvYZxJRxigDVhIfBwFv7DNzn8/ng9lshtmcf5P4iYiIJjJZlmGz2RjwRpSDNEXJdgk5Qbbo+6ZwKADobkGrQMIHGIrGtK5cFOk5Cs+hlyAAKEYIxeHtmFRxPmxzz0VMUXDY58UBrxv7j311hUNpXVfRNBz0eXDQBwAuQDgDFi2GaviOfXkxxWpC3cIbYC2alsFnmHkmRyUmr/pytsugFHhviIgofewziYjSxz6TBiNKBjiqlsBRtSSjjyMZLJi04nM4uv7HiHgP97Unol40vXUfalZ9mXMyKGewzySikzF9YTW+/cgNuPumx9Hd6u9r93QG8PUbH8Htv7was5fVnNS1q11mREQr1tvPxln+5/rava//HsWX3HbK/VWJxZqy/a61r+Ke089DtcM57DXYY+YujnUjIiLKTyMd65YfKVNEeUaWZZx++umD7hfE/Phf748/fkW3LTglfOIL50LTNPz8lYODnDWxaaqCyKGNujbL1BVZqoYoP8iyjHPPPReyzNxZGh3BTn3Am7V0TpYqIRp97DOJiNLHPnP8e7u9BfeufyMp3K3G4cQ3Vp+dU+FuoV2taP31G8OGuwFA2bUMiqexxz6TiDJGCei3x0HAG/vM3KaqKgKBAAoKCrJdChEREZ0Eh8OBYDAIVVWzXQrRBJV6eo8W5/+TACCbXbrteNgDSAMmVcXdY1dQDuve88+ktlDXuwAAoyRhelExLp46DZ9dvAI/Ofdi/OK8S/HFJavw3voZmFNcCrOU/nvusGDEfqEUrwkNeERYinvC8/D1bQfx6y0b8eLhAzjk8yDBvys0inhviIgofewziYjSxz6TcoFksGLSii/AXFCna09EvGhadx9iwY7sFEY0APtMIjpZk+pLcM+jN6JmWomuPeSP4u6P/xXrX9x7UtddVd+78Murzot17bG2vYi17D65Ygf44pKVSW1xVcXWzra0zmcoZm7iWDciIqL8NpKxbvmRMkWUZ1RVxdGjRwfdL0jSGFYzcpqm4Y+vbUTn1m5d+/Sr6lFgs6ArEEs6xyCxOwGAaMtuqBH9hDVLPQPeiIaiqiqOHDnCQfo0KlQlhrB7n67NVjIrS9UQjT72mURE6WOfOb692nQYP9q0DvEB/31nFBbj/1adhUKzJUuV9Yp19CCwuRH+txvhf/sI2v/0VlrnVXz8NEh2U4arI0rGPpOIMiYxMODNlp06RhH7zNyWSCSgaRpMJr6mIiIiykdGoxFA7990IsodWkLJdgk5QTbrV1xOhD2AoUh/UIwBb2HPQQTatya1hzoHn8TmMpuxtKIK186ci6+tPBMPXPhe3Hvm+bhp3mKcXVOHSoMKaFraNbQGA3ijuRF/3LkFd655GTc9/0/ctfZVPLxrG9a3HkVXOARtBNcj6o/3hoiI0sc+k4gofewzKVdIBismrfwCTAW1uvZExHMs5K0zS5URncA+k4hORXGFE9/6yw2YuWSSrj0eU3Df5/+BFx/bMuJrmmQJ71tUiX3mOQgL+oVhvG/+6VTK7TO3pByOY5+l9hdR0vsMh3fEcxPHuhEREeW3kYx1Y0Q5UQYoioIdO3YMul/I8TC0Fw4fwPObBgyoMou48SOnAQDUFIObrlhYORal5bzwgfW6bUNJLeSC8ixVQ5QfFEXBli1bUFVVBVHM7f6Rcl/YvR+a2v9FsABrycys1UM02thnEhGlj33m+KRpGv59cC8eezf5vsvi8kp8dtEKGLMcrN/11Gb4NxxO+3jH8jpYppfDNrcqc0URDYN9JhFlgqbGAW3AgjGyPTvFjCL2mbnt+ABu/rchIiLKT8f/hnNSFlG2JI8J0xSVs36OMQwIeFMTYWhSAQT0Wwg2zoC3rj3/TNke8R2BEgtCMg4f/i4KAmocBahxFGCVS8aRxt8iChEtWgGacexLKEQAyZPZUomrKvZ6urHX0w0c6m1zmcxocBWhobAI9a4iTC0ohFnmkGoaHu8NERGlj30mEVH62GdSLpEMVtSs+AKa1v8IUV9jX3si4kHTW/dh8qovw2AtyWKFNNGxzySiU2UvMOPO312Dn9z6NDa+tK+vXVM1/Pabz8PbHcT7b1kNQRDSvubkIisgCFAESfe5Svd/voey938Hwin2V2ZZxvfOvAC3/Pc/uvbnD+3HwtIKTHUVDnJmLy56kps41o2IiCi/jWSsG0cjEGWBIGZ3ovFwXm48lNQmW2XMKC8FAPgjyemRVy7iJGQgOeDNMnVFliohIpqYgp27dNtm1xRIBusgRxMRERFRPlE1DY/s3oZnD+1P2nfWpFp8fN5iSFn8cDPa5Ebrg29Ciw6/6oaxsgCu82bCMq0Moom3aImIaJxSAslt0vCTuIlGw0gGWBIREVHu4N9wotyjJRi4eJxsSZ4cpWhG/SDcCR7wFureg1DX7kH2agh174GjcnHa19M0DZ27ngSgwQQFU+DGVCmA4oYFcNWdA3dcxQGvB/u9buz3unHY50E8zZBQbzSCTe0t2NTeAgAQANQ4ClDvKkRDYTEaXEWosjsg8m8TERERERERTUCS0YaaFV9E01v3I9rT1NeeCLvRtO4+1Kz6MgzW4ixWSEREdGpMZgNu/cmVeOCuF/DSE1t1+x7/2Rp4u4L42J3nQ5LSG5v+3oWV+MlLB1KumaOEPJDtp/53s8BkxvsaZuLv+9/tawvEY7j/7XX4yTkXZXUcPZ0afk5ORESUn0byN5yzB4myQEjzDV22RJTkicgOg7Gvc7n7P+8m7ade4YMDAt7qGfBGRDSWQl36gDdb6awsVUJEREREoymhqvjttrfxZnNj0r7L66fj2hlzs/bBZsIXRsfDGxBtTG/inmg1oOpz5/CDWCIiGv+UoH5bMEIQjdmphYiIiIiIiE6KllCyXULOECUjJKMdSuxEoHkiIegH4Sbc0DQVgpDb4wOHoqkK3Aeeh79tM6xFDSiddTUEcfihxpqmoWvPP4c8JtT17ogC3kKdO5MC44obLkbxtEsAAKUGoNRqw8qqSQB6P0to7PFhv9eNA8dC39qCKQLoU9UPoNHvQ6Pfh1eaDgMAzLKM+oJCNLiK0FBYhHpXEQpM5rTrJyIiIiIiIspnktGGmpXHQ96O9rXHw91oeus+1Ky6FQYLQ96IiCh/SbKIT37rQrhKbfjbL9fq9r3w6Gb4uoP43A8uhzGNxbwLrUbcuLIGz7o/gGvcv89UybisfgZ2dHdin6e7r80TCaMzHESFzZGxxyUiIiKiU8OAN6IMEAQBxcXFaG9vT71fksa4opHRAGDAQpaieGLS8ZHuUNI5Jjl/B6WNFjUSQPToDl2bZSoD3oiGIwgCSktLGW5ApywR7dF9cAgA1pLZWaqGKDPYZxIRpY995vgRSSTw03fewtbO5PssN86aj4unTstCVSe4n9kxZLib5LJAspkAALLLisILZvL3knIO+0wiyojEgAnUsj07dYwy9plERERERDSRaAl1+IMmENlSpAt4i8Xi0MV9aQqQ8AOGgjGvbTRoqoLWzb+Dv3UTACDqOwJNU1E+97phzw117kTYvV/XZrRXIhZo7dsODghrG66Wjt1P6tpkswuFU88f9BxZFDHVVYiprkIA9QAAfyyKA15PX+DbAa8bwXg8rRoiiQR2dndiZ3dnX1upxdoX9tbgKkKt0wVjjo8HpdHFe0NEROljn0lElD72mZSrJKMdk1Z+EU3r7kfM39zXHg91oWndfahZ9WUYLEVZrJAmIvaZRDSaBEHABz93BlzFNvz+7hehaSf2rX9hL+7xPI7bfnk1bA7TsNe6+cwpuHzNxUkBb1r/i54isyzj9uWn4abn/6VrV4d5DPaYRERERNnFgDeiDJBlGcuWLcOuXbtS7hek3A1DS6gqvOEwEk916tptjhND0WRRX//kIgsMOfycxkr48NuA1m9QoyTDXJf+iqNEE5Usy1i9enW2y6BxIOw5qNsWJBMshVOyVA1RZrDPJCJKH/vM8cEfi+KHG9div1cfoCYJAm6evxSnT5qcpcpOCG49Oui+sv9ZAducqjGshujksM8kooxQBywWI1mzU8coY59JRERERETjV/L0Hk1hwFt/BnMhor7Gvu14pAewmgE1cuKgeHdeBrwNDHc7znv4VRRMPh1mZ83g52oaOvf8U9dmsBSjYsGH0Pjm9/ra4sF2xMPutCZ++46uRczfomsrmXElRGn4SXT9OYwmLCyrwMKyir5a24IB7O8X+NbY44OS5gS7znAIneEQ1rX0fjYgCQJqnS40FPYGvtW7ilButXGC8TjGe0NEROljn0lElD72mZTLZKMDNSu/hKa37tO9V4+HunBkzXdQtfgTsBbPyGKFNNGwzySiTLjohsUoKLbip1/5NxJxpa9918YmfON/HsEdv/0ACsuGXuDUbpJhMyYvCOIPJ1DkGL1aTVJyPEhsmM9zeM+aiIiIKLuYyESUAYqiYN++fYMfIObuio1Nfh+iz3QBnoSufeEZgwfkfPVi3oQFgPCB9bpt86T5EI2WLFVDlD8URcG7774LRVGGP5hoCJEBAW8W1xQIIvOMaXxhn0lElD72mfmvOxzCt9a9lhTuZhQlfGnp6pwIdwvv60jZbplZgZr/vYjhbpQ32GcSUUYoAwLexPER8MY+k4iIiIiIJhItwYC3/mRLsW47HvYAhkL9QXH9Pe18MFi427G96NjxGLQhAtACbVsQ9R3RtRVPvxxm1xSIBpuuPdT17rD1qIkIuvb8S9dmck6Cc9LKYc8djiAIqLQ7cMakWnx07iLcffp5ePDCK/D1VWfhhlnzsKKyGsWW9O9hKJqGgz4PXjh8AL/cshG3vvo8/t+L/8YPNryJv+/bjW2d7QjGY6dcN+UO3hsiIkof+0wiovSxz6RcJ5t6Q96MDv14OCXag6a3foSOnX+Fv20LEhFfliqkiYR9JhFlyqqLZuKOBz4Ai82oaz/ybgfuuO4vaDk0/P3/i+eVZ6q8Id2x5iUc8nkG3Z/eEidERERElClMvCDKAFVVceDAgUH3C1LuZise9nmhbg8kta+6aCYAwB9JoDuoH3AkMrgbABA+qA94s9SvyFIlRPlFVVXs2bMH9fX1kKTcDcCk3BceEPBmLhw8nJQoX7HPJCJKH/vM/Nbs78G9G9bAHQnr2m0GA76y7DRMKywe5MyxFdzVmtRW8v7FcCytzUI1RCePfSYRZYSi/zsOaXwsiMI+k4iIiIiIxq8U03sUBrz1Z7Dow9wSYQ9gmAxE+90rzrOAt95wtwfhb3170GPC7n3wt2yEs3p58vmaiq69/9S1GW3lcFavgCCIsBbPQKDtnb59wa7dKKhZPWRN7oMvQonqJ4SXzno/BCEz4y6NkoQZRSWYUVTS1+aJhHHA68Z+rwf7PW4c9LkRTXPScCAew5bONmzpbOtrq7I5UF9YhAZX71eNwwlJzN1xpDQ43hsiIkof+0wiovSxz6R8IJucqFn5JTStuw+xQL97IZoKz6GX4Dn0Uu9xlmJYCuthLZ4Oa/EMGGxlEIT0Jx9qmgo1HoYgGSFKhtF+GjQOsM8kokyau7IWd/35etxz8xPwdQX72jubfbjz+r/ga7/9ABrmVQ56/vuXVKP7b5mvUxZFJFT9Zzj/3L8HX1iSeqGUoRZxISIiIqLMY8AbURYIOXzj6LDPC8ST36jVz60AAPx+zeGxLSiPhA8w4I2IKFs0VUHEd1jXZimsz04xRERERHRK9nvc+MHGNxGI6wPmi8wW3L78dExyOLNUmZ6WUOBfdzCp3TojOyuvERER5Rx1QMCbOD4C3oiIiIiIiCYSLcGAt/5kS5FuOx52AwZ9Wz4FvA0W7iaIBogGqy5krXPXk7CXz4com3XH+ps3IuZv0bUVz3gvBLF3jKStdJYu4C3UtRuapg06sTsR8cJ94Hldm610Lmyls0f+BE9BodmCpRXVWFpRDQBQNQ1H/T3Y73X3Br953GgO9KSKRUypJehHS9CPN44eAQAYRQlTXIVocBWiwVWMelchii3WDD0bIiIiIiIiotEhm5yYvPoraN38OwQ7d6Y8JhHuhj/cDX/LBgCAZHLCWjwDlqIGCIIIJR6CGg9BiYegxIN9P5/4HgagAYIEe9k8lM5+P4y2sjF8lkRENNFNmV2Oex69EXff9Djajnj62v2eML754UfxlZ++DwtOn5K1+kRBwOKySmxoa9a1twb8WaqIiIiIiIbDgDeiLBByeOVFXyya1PaZ713aN6DqQL/E8eNKHaaM15Xr4u5mJDz6N8OWqQx4IyIaK1F/MzRFHwBidmXvRikRERERnZytHW34yTtvIaoouvYqmwO3rzgdJTk0uSmw5WhSm3laGSSHOcXRREREE5AyIOBNYsAbERENr7OzE5s3b8bBgwfh8/kQj8dht9tRXV2N2bNnY/bs2YOGgdD4884772Dfvn1obu79LL66uhrTp0/HokWLRvVxvF4v1q5di+bmZnR1daGkpATV1dVYvXo1XC7XqD4WEVG+0RQGvPVnMOvD3BIRNyAX6g+Ke4YMMMsVSiyItq1/RKB9q65dEA2oXv4ZqPEIWt7+VV97IupF975nUDrrqr42TU2ga+/TuvNNjklwVC7p27aWzNI/brQHsUArTI6qlHV17X16wPgPAaWzrh7p0xt1oiBgsrMAk50FOHdy73iUUDyOQz4P9nvdvV8eN3pSjL9MJaYq2OPuwh53F4B9AIBCkxkNhUWodxWhwVWEKQWFMMsc5k1ERERERES5RTLaUb38s+je9wy69z4NDBN/rkR74G/ZCH/LxpE9kKYg0L4FgY7tKJp6PoqnXZoUPE9ERJQp5TUu3P3IDfjOzU/i4M62vvZoKI7v/r8n8envXoozLh/bhUn6+9TCZWh504+j/p6s1UBERBzrNtYOHTqELVu2oKWlBYFAAJWVlaitrcXq1athMBiyWltzczPWrVuH9vZ2+Hw+lJSUoKqqCitWrEBpaemoPU48Hsf69euxf/9+dHZ2QhRFVFVVob6+HsuWLePvWw7jJ/9EGSCKIqqrq9He3p5yvyBJY1zRqXG4hp50VVecO5OrsyV8cL1uW7QWwFgxPUvVEOUXURQxefJkiDkcfkm5L+w5qNs2WMsgmxxZqoYoc9hnEhGlj31m/nmzuRG/2boJiqYf8FXvKsJXlq2Gw5g7AfOaqqHryXeS2m1zUk9II8p17DOJKCPUAQFv4vgIeGOfSTQ+1NXV4ciRI0MeYzKZYDKZUFxcjPLyctTX12POnDlYtWoVVq5cCbP55CeQnH322Xjttdf6tv/whz/gIx/5SN/2Rz7yETz00EMnff1cUVtbi8OHDw97XE9PD37729/i4YcfxpYtW4Y8tri4GFdddRU+/elPY8GCBaNT6ARz8OBBbNy4EZs2bcLGjRvxzjvvwO8/sZJ3uv/dMiUej+O+++7Dgw8+iAMHDqQ8pqGhATfddBO+9KUvndLgvM2bN+Nb3/oWnnnmGcRisaT9JpMJF198Mb7xjW9g4cKFJ/04RET5I3mgLwPe9GSLPuBNUxNQYIRuNKAWA5QAIOfumIVAx3a0b/0zElGvrr033O2zsJXMhKZpsJbORqhzV99+98EXUVBzGoz2cgCA7+g6xEMdumuUzHgvBOHEe2aDtRSypQiJsLuvLdS5O2XAW7SnGb7GNbq2gprTYHJWn/RzzSSrwYA5JWWYU1IGANA0DV3hUF/g2wGPG4d7vIir6f1/5IlGsLGtBRvbWgD0hspNcjjRcCzwrd5VhCq7AyIH5WcV7w0REaWPfSYRUfrYZ1K+EQQRJdMvg6NiIXqaNyDiPYSw9zA0Jb3g8xHRFLgPPA/f0XVw1Z4NS1EDLK66kwp709QE4qFuxENdiIU6IQgirMUz+u51UH5gn0lEY6Wg2IZvPvRB/OBzf8f2tSfGmCgJFT/9ytPwdQdx2UeWZaU2oyThyoaZ+PnmDWkdz7vKRPmNY93GBse65a4nn3wS999/P9atW5dyf1FREa699lp861vfQklJyZjVpaoqHn30UXz/+9/Htm3bUh4jSRLOPfdc3HbbbTj//PNP+rGOHDmCu+++G3/96191Y/36q66uxo033og77rgDDkfujleYqBjwRpQBkiRh3rx5eOed5Im+ACBIuXvzKOANA55E2sd/7rx6pngCCB/QB7xZpiyHwJuERGmRJAmLFi3KdhmUx9REFD1H1+raLIVTslQNUWaxzyQiSh/7zPzy3KF9+POu5JvZ80vL8fnFK2GWc+s2Zrwj9c1w+5LJY1wJ0ehgn0lEGaEMCHiTxsdiMewziSaOaDSKaDSKnp4eHDp0CG+99VbfPrvdjssvvxy33HILTj/99CxWmd80TcOvfvUr3HHHHfB6vWmd093djQceeAAPPPAArrvuOvzoRz9CeTkn3Qzn1VdfxXe/+11s2rQJbrd7+BOyZN++ffjgBz846FiL4/bv34+vfvWreOKJJ/DYY4+hoaFhxI9177334utf/zri8figx0SjUfzjH//AM888g29/+9u47bbbRvw4RET5Tksw4K0/2VwACCKgnfh3SURjkAQDoPX7mxJ352TAm5qIoGPn4/A1rUna1z/cDQAEQUDZnGtx+LW7TjxfTUHHzr+ievlnoakJdO/9t+4aZlcdbOX6iQmCIMBWMgu+pjf72oJdu1E49bykGjp3/w3AiUVgBMmIkhnvPdmnO+YEQUCp1YZSqw2rqmoAAAlVxZEeLw543djv6Q1+aw8F07qeqmlo7PGhsceHlxsPAQAssoz6Y2Fvx4PfnKbcWSBnIuC9ISKi9LHPJCJKH/tMylcm5ySUOicBADRNRczfirBnP0Ld+xDq3gMl6hu1x1KiPeje+69jWwJMzkmwFNbDUb0M1qJpumNVJY5oTxMi3sOI9DQiHuxCPNSJRMSL/vcejl/LWbMKJdMuh8FaPLKa4iEAgGQY2XgINRGFqsYgG3Pv/lE+YJ9JRGPJYjfhf3/9Afziq//Bm8/s1u176N6X4e0K4oZbzxpynn3syCag7LJMl6qTVA1zAIjGPY51yzyOdRt7gUAAn/jEJ/DYY48NeZzb7cavfvUrPPXUU3jooYdw4YUXZry2trY2XH311Vi7du2QxymKghdffBEvvvgiPvnJT+InP/kJTCP8fPfBBx/Epz/96ZQLmPbX3NyM733ve3jsscfwpz/9CWeeeeaIHocyK7dmRhKNE4qiYPv27YPuF0Rp0H3Z5HMHseOerdkuIy+FDw4IeKtfkaVKiPKPoijYtm0b5s+fD0nKzf6RcpeaiOLoxp8h4j2sazcX1menIKIMY59JRJQ+9pn5QdM0PL5nJ/51YE/SvtVVNfjkgqWQczBA3fPf3UltRZfNg2jg7xrlJ/aZRDTaNC0BaAM+RJYs2SlmlLHPJCKgd+DQo48+ikcffRTnnXcefv7zn2PmzJnZLiuvBAIBXH/99Xj66aeT9hmNRsyfPx+VlZWwWq1obW3Fvn370Nraqjvu0UcfxauvvoqnnnoKK1euHKvS89KWLVvwwgsvZLuMIbW1teGCCy5IWnG4oaEBc+bMgaZp2LlzJw4cONC37+2338Z73vMevPXWWygrK0v7sb7zne/gjjvu0LVZLBYsW7YMlZWVaGlpwcaNGxGJRAAAsVgMt99+OwRBwFe+8pVTeJZERPlHUxjw1p8giJDNLiTCJwJT4xEPTIYiINZ+4sC4G7DUjspjxkNdaNv+MJSYH8UNl8JReXKTV0Nde9C29Y+Ih7uT9okGK6qX/D9YS/SvaU32ShROOR+egydeRwQ7dyDYsQ3xUDcSEY/u+JIZV6acxGYdEPAW6t4DTU1AEE8MYQ527kKwc4fuvKKp74Fsdo3oeeYaWRT7AtneU9fb5o9FewPfvB7s93TjgNeDUGLw0Nn+wokEdnR1YEdXR19bmdV2LPCtEA2uYtQ6C2DgPYuM4b0hIqL0sc8kIkof+0waDwRBhMlZDZOzGq7as6BpGuLBDoS69yLUvQexQCsE0QDJYIVosEIyWCEZbX0/n/hugygZ4T3yGjyHXtIF7Z+gIdrThGhPE7xHXoW1dA4cFYsQ7WlC2HsY0Z6jgKakWbmGnqa16Dm6Hq7as1A87RLIJmfqI9UEwp4DCHbsRLBzJ6I9TYAgoaj+QpTMeC8EQT/WUdM0xENdx2o9iqi/GdGeo4iHugBosJXOQdG0S2C0lUMQpN45p4IIQTj+nWE8qbDPJKKxZjBK+NwPL0dBiRXP/Olt3b5/Prge3s4g/t/dF0E2SCn7bv/Td6Fi2dgGvCXRBgacEtFEwrFup45j3caeoii49tpr8cwzz+jaS0tLsWjRIhQUFODAgQPYvHkztGN/59rb23HFFVfgv//9b0bDDFtaWrBy5Uo0NTXp2l0uF1asWIGioiK43W689dZb8PlOBJ//5je/gcfjwWOPPZb2+71vf/vb+PrXv57UvmDBgr5FUffv34+tW0/kBB05cgQXX3wxXnnlFSxfvvxkniJlAAPeiDJAVVU0NzcPul+Qcm9ispJQcffNT0BpiSbtKyy1Z6Gi/KGpCsKHNunaGPBGlD5VVdHY2Ii5c+fyxjqNSCzYgdZ3HkTEd1jXLpmcKJjEN7c0PrHPJCJKH/vM3KeoKn6/YzNebTqctO+iugbcMHs+xBwcoBTc1YrQjpakdudpDBmm/MU+k4hGnRJJbhPHR8Ab+0yi8enRRx9NGjQVj8fh9Xrh9Xpx5MgRrF+/Hm+++SZ279YHPr/00ktYsmQJ/vCHP+Caa64ZlXp++MMf4pvf/GZax55++um6z6ZTPZdUjh49ioqKCsjy0MNGjh49ijPOOEPXdujQobRqG+zawWAQF154YdLqlVOnTsU3vvENXHHFFSgoKNDt0zQN69atw89+9jPdipytra244IIL8Oyzz3KF2ZNgMpkwadIkXWhaNqiqiiuvvFIX7lZZWYk//vGPeM973qM79rnnnsNHP/pRtLW1Aej9fXzf+96HNWvWpDXw7d///jfuvPNOXdvNN9+Me+65ByUlJX1tnZ2d+NrXvoYHH3ywr+3222/HvHnzcNFFF53U8yQiyn0pJvckGPA2kMFcqAt4S0TcgCNFwNso0DQVzZt+2TspGUDrlt/BUnj3iELPVCWKrnf/0TshOgVr6RxUzP8QDJbClPuLp12Knua3oER7+to6dv4VqqIPdrcUTYe1ZFbqxxgQHKcpUUS8R2Ap6r2vrmkqOnc/qTtGMjlRVK9/HTBeOIwmLCyrxMKySgCAqmloCwaw3+vGAY8b+71uNPp9UNOccNcRCqIjFMS6lt4JBLIootbpQoOrEPWuIkwrLEKpxcZJ4aOE94aIiNLHPpOIKH3sM2k8EgQBRns5jPZyuGrPGP6EAcpmfwCuyWegY9fjCHbsGPLYUOdOhDp3nmypvTQF3sMvw9f4BoyOSkiyBaLBCtFggSRbEA91I9T9LtREJOk89/5nkAh3o2TGlYj0NCLiPYyI7wgi3iNQ48FBHzLY2RsUl5oAk7Ma9vIFsJbMgiibIAgSRIMFBkvxqT3XPMc+k4iyQRQFfOR/z0NhqR0P3/eabt9r/9yBHm8IX/rRFRAcyQuTxY9sgvf1P6DgjI9k7T6txoA3onGFY92GPo5j3caHr371q7pwN4PBgPvvvx8333wzjEZjX/uuXbtw0003Yd26dQCAaDSKK6+8Etu3b0dlZeWo1xWPx3H11Vfrwt3sdju+//3v46abboLBYNAd+8ADD+C2225DMNj73vDxxx/HjBkz8K1vfWvYx/rXv/6Fb3zjG7q2888/H7/4xS8wffp0XfuePXtwyy234OWXXwYAhEIhXHbZZdi2bRsqKipO+vnS6GHAG1EWCDl44+jgzjYc3tGe1L7iPdNRO/PEG8o1+5JX8Jzookd3Qovqb7ZapjLgjYgoUzRNQ0/zW2jf/gg0RR9MKhlsmLTiCxBlc5aqIyIiIqJ0xBQFP9+8Hm+3tybtu2bGHLy3fkbOTjTqWZs86V2QuVomERGRjhoe0CAAIu/XEFHuqqioQF1d3ZDH3HTTTQCAtWvX4v7778ff/va3vn2hUAjXXXcdVFXFBz/4wVOup6SkRBc0NZSBA8vSeS4A0jomE+cCwGc+85mkAW+f+tSncN9998FiSR0IKggCVq9ejdWrV+PDH/4wrr32WvT09IaMBAIBfOADH8DWrVtRVpY8WJp6GQwGzJkzB0uXLsWyZcuwdOlSzJs3D2+++SbOOeecrNb28MMPY/369X3bRUVFWLt2bcrftYsuughr167FkiVL4PF4APT+f/nXv/512P//FEXBl7/8Zd3g9S9+8Yu4//77k44tLS3FAw88ALvdjh//+McAej+juvXWW3HBBRdwwhARTRiawoC3gWRLMeA5cZ84HnYDRVP0B41SwFuwY0dfuBsAaEoMgfZtcNWemdb5Yc9BtG75A+LB5HGBgmRC2ewPoGDyGUPe35YMFpTOej/atvy+ry0e6ko6rmTmFYNeRzY5YXRUI+Y/MVkj2LW7L+Ct5+hbuucJACUzrpgwYz9EQUCV3YEquwNnTqoFAESVBA77vNh/LPDtgNeN7sjA+y2pJVQVB46dA/T+rjqNJtQfC3xrcBWh3lUEa7/JBURERERERES5yGivwKTln0Ooaw8C7VsQdh9ApKcR0E7tnpVosMFgLUEs0JY0J0VT44j6Gkd8zZ7m9ehpXj/8gWnTEO05imjPUXTv+49uj7mgDhULPgSTc9IoPh4REQ1HEARc+YmVKCiy4tdffw6qcuJz582vHcQXLnkQt/3pOjxWfBM+2P2g7tyW330MkrMUjoWXjXXZRDQOcazbyHCsW/45ePAgfvKTn+jannjiCVxxxRVJx86ePRsvvfQSzjvvvL6Qt+7ubtx111349a9/Peq1/f73v8dbb73Vt20ymfDCCy9g1apVSccaDAbccsstmD9/Ps477zzEYr2LqH3/+9/Hxz72sSF/N+PxOD772c/qxrm9733vw+OPP54yjHDGjBl4/vnncfXVV+Nf//oXgN4FTr/5zW9m5N+BRk7MdgFEE5KYexN+dzQnT6ie+pXZuPUnV0I8Vu8dfz/FlSzGqfBB/c1XQ+kUyM7SLFVDRDS+KfEQWjf/Dm1b/pD0QZposGHSqi/BzA+piIiIiHJaMB7DvRvWJIW7CQBumrcYVzTMzNmwNC2hIrK/M6m94uOnZaEaIiKiHKaE9NuiOWf/vhMRjdTq1avx5JNP4uGHH4bdbu9rV1UVH/vYx7Bjx44sVpf7HnnkEfzxj3/UtX3uc5/DL3/5y0EHvA100UUX4dlnn9X9+7e1teHDH/4wV50exIc//GH09PRg8+bNeOCBB3DzzTdj8eLFuhVDs0VRlKSVRu+///4hB7BNmTIlKZTtzjvvhKoOPaHrT3/6E/bs2dO3PWPGDHz3u98d8px7770XM2bM6NvetWsXHn744SHPISIaT7SEku0Sco7BUqjbToQ9gKFIf1DcPSqvS9wHX0hqC3YOP4ZPVeLofPfvaHzzeynD3SxF01B35tfhqj0zrffrzuoVsBTWD7rfVjoH1qJpQ17DVjJLtx3q2n2s1ii69vxDt8/oqEJBzcS+726SZMwoKsGl9dPx+SUr8dPzLsHPz7sEX1iyEpdNnY5ZRSUwjSBwticWxeaONjy5dxfu3bAGN7/wL3zltRfwm62b8NKRgzjs80IZ5rUUERERERERUbZYS2agbM61qD3ja5h20U9Qs+rLKJlxBSSTc4izBBjtlXBOWoXSWVejasknUXvGnWi48MeYduGPUHfGHZh67j0onHIeBDF5cnwui/gO48ia78J3dF22SyEimpDOuXo+bvv5VTCa9X8/utv8uP09v8UudX7K84I7XhyL8lLjWAqiCYtj3U4Nx7plx1133YV4PN63/ZGPfCRluNtxFosFf/zjH2E0Gvvafve73+HgwYOjWpemafje976na7vzzjtThrv1d/rpp+NrX/ta33Y0GsXXv/71Ic95+OGH0dh4Iny8tLQUv/vd71KGux0nyzL+8Ic/oLi4uK/twQcfxN69e4d8LBobDHgjygBRFFFfP/iAplybxKRpGv7x8jZ9o1PCZRfO76u1xRvGszuSB3vZTfl1AzMTwgf0AW+WqSuyVAlRfhJFETNmzIAo8mUJDS3sPoAjr38b/pYNSfsMtjJMXnUrzM6aLFRGNHbYZxIRpY99Zm7yRMK4e93r2OPu0rUbRBFfWLIS50yekqXKhqcpKg7f+c+kdsfKKTBPSW+1IaJcxT6TiEadGtZvS+kNYsgH7DOJ6Ljrr78ef//733X9QTgcxqc//eksVpXbIpEIvvjFL+raVq9ejR//+Mcjvtbq1avxox/9SNf23HPP4R//+McpVDh+FRYWwmw2Z7uMlNasWYNDhw71bVdXV+PGG28c9rz/+Z//QXV1dd/2gQMHklbLHehPf/qTbvuLX/wiTCbTkOeYTCZ8/vOfH/I6RETjR/KYNk3hgPKBZIs+zC0edicHvKmR5PfGIxTxHka4O3mgdajrXWjq4MF7kZ4mNK75Ltz7nwWg/+8niAaUzr4GNatuhdGW/gKmgiCgbO51SPU7AgAlM64c9hrWAQFvYc9BqIkIPAdfQiLi1e0rnXU1BIHvuwcqNFuwrKIa182ahztXnYUH3vNefOeM8/DxeYtw1qRaVNsdg/wXSqYBaAn48frRI/j9js24Y81L+MQL/8K3172GR3Zvx8bWZrgjp/Y7PF7x3hARUfrYZxIRpY99JlH6RMkEa/F0FE+7FFPPuQclM98Hg6UYsqUY9opFKJl5FWpWfgkNF/4YU86+C5ULP4qi+gvhqFwCc8FkSAZr37VkkxNlc67FlHO+fSxsPr131qaCyShquBjl826AIA6/mI5ksMFaMhOFU85HxYIPo2blrXDVng2xXy0jpalxtG35A9q2/QVKLIhYsAOh7n3oadkI98H/omPXk2jZ/Ds0vXU/jq7/KTp3/w09zesR9bcMeW8pH7DPJKJcsOScBnz9Dx9MuU94Loh3jfOS2jU1kemy+hWRWzkGRJR9HOs2chzrlh3hcBhPPvmkru32228f9rzp06fjyiuv7NtOJBJ45JFHRrW2HTt26Ma5mc1mfOYzn0nr3M9+9rO68XtPPPEEAoHAoMc//fTTuu2Pf/zjKCwsHOToE4qKivCxj32sb1tRFPz5z39Oq0bKLCYzEWWAJEmYNm3a4IOHc2zw0cv/2o7Qvzt0bSZJwmnVk/u293cEU557xrTilO0TSfjggIC3ega8EY2EJEmYOXNmtsugHBf2HETjuh8AWvJqwQU1p6NszjUQ5dycmEQ0mthnEhGlj31m7mkL+nHv+jXoDId07RZZxq1LV2NWcfqTybIhfKAzZbt1duUYV0I0+thnEtGoUwZMABbHT8Ab+0wi6u/888/H//3f/+Guu+7qa3v99dfxyiuv4JxzzsliZbnpoYceQkfHic+lZVnGb3/725NeIO3jH/84HnroIaxZs6av7Yc//CHe9773nXKtNHb+/ve/67Y/9KEPQZKkYc+TJAk33nijblXUp556CqeffnrK47u7u/HGG2/0bRuNRlx//fVp1XjDDTfg85//fN/KsK+99hrcbjeKioqGOZOIKP9pSvJn9BOdwawfNJ2IuAHZCUAC0G9ybNwNSCc/Sdd98MWU7WoijIj3MCxF+gVoExEf3Aeeg+fwq4CWPEnX7KpDxcKPwmQ/uXva5oLJKKg9E74jr+na7RWLYHbVDnu+tXha77jJ4+M+NAX+1nfQvf9Z/XElM2ErnXtSNU40kiii1ulCrdOFcydPBQCE4nEc9Lmx3+PBfm83Dng96IlF07peVFHwrrsL7/ZbpKfIbEG9qwgNrkI0FBZjSoELJmliDz3nvSEiovSxzyQiSh/7TKKTI8omFDdcjOKGi0/pOgZLMSoWfBglM65A2HsIajwMNR6GkghBjYegJMIQBAmWogbYSudANjn7zjU5J6F506+gRHuO1WSGuaAOZldt33fZUpz0eZi1ZAbK5lwLNRGBpqmApkDTlN7wNU1FIuJDoGMbgu3behcYgHYsmE0f6O9rfB2+xteHfY7Bzh36BkHsDdgXJAiCCNnkhHPSShROvQCiZDypf8exwj6TiHLFjEXV+M5f/wdfu3ZgaIqAX4a+jK9ZvoaKeHNWahvoZMdlENH4wrFuI8Oxbtnx/PPPIxQ6Me9s1apVab/+/+hHP4rHH3+8b/upp57CnXfeOWq1vf66/r3XihUr4HK50jq3qKgIy5Yt6xu/FolE8J///AfXXnttWo914YUXpl3nRRddhB/84Ad9208++SS+/e1vp30+ZUZupUwRjROJRAIbN24c/IAceyP0wsObk9rqGsogDlPnb/5nEcqdEztMRwn7EW3eqWtjwBvRyCQSCaxduxaJxBiuQEB5p3P3k0nhbqLBiqrFN6NiwYcY7kYTBvtMIqL0sc/MLYd8Hty19rWkcDeXyYz/W3VWzoe7AUDCG0rZbplaMsaVEI0+9plENOoGBrxJ4yfgjX0mEQ106623oqCgQNf229/+NkvV5LaBq5decsklmDNnzklfTxAE3Hrrrbq2tWvXYsOGDSd9TRp7zz33nG777LPPTvvcgcc+++yzqQ8E8OKLL0JRToTdLFmyBA6HI63HcTqdWLx4cd92IpHAiy+mDt0hIspvWnJLIjkobKKTLfoFURMRX+94BsOA1bLj7pN+jHioC/7WtwfdH+w8MV4tEfWjY9eTOPjy1+A59FJyuJsgoWTGlZi8+vaTDnc7rnTGFRANtv4XR8n096Z1riibYSmcqmtr3/EINKV/+JiA0lkf4ESzU2A1GDC3pBxXTpuJLy87Db88/1L86JyL8OmFy3FRXQPqXUWQxfSHjrsjYWxsa8aj7+7At9e9hpue/xfueOMl/H77O3it6TBaAj1Q/z979x0eR3muj/+eme270hZ1WZZtFfcGbrhgbIohhIRQEnoNJwnhSxolCeEECCQn4UAOISG/JBAgIWAghCT0mGbAuGHccLck27J62aK6deb3h8zas7uSVrJWW3R/rssXft9pzxr70e7sM8+rROeOTMZ7Q0RE8WPOJCKKH3MmUWrQGGzIKjwF1vFLYC87C7mTv4T8GZehaM71KJx9Dawli1XN3QDAaC9H2coHMH7xbZi04n5UnPsIxi/+AfKmXYKs4nnQmnL7vdchiBIknRkafRY0Bhu0xhzozPnQWQr7GsBN/yomrbwfk89/DJPP/z0qv/A72CacMTIvVpGhyEEoIR/kYC/83c1o2/9vHHr/brTsfgHdrXugyKmZk5gziSiVVM4pxhevmx81b/+0Efvk1FnMRBlj93GJqH+sdYsfa92S42RqyU4//XRoNMcXq9q2bRuam5tHKjTU1dWpxjNnDu1n/axZs1Tj119/PeZ+Pp8PbW1tqrmhXCvyOvv27UNNTU3cx1NijO1l1IgSRFEUtLe397s91QqQuly9UXNfu+101fjTIy7VOC9LhwUTI4rSxiDv4S3AiR9sJS0MpackLyCiNKQoClpbW3mTiPoV6HWh11mlmjM6KlF0yo3QRhROE2U65kwiovgxZ6aO3W0t+PWnG+CNKKYpMJnxo0XLkG+yJCmyoenZ3Rg1N+FnX4KgkZIQDdHIYs4kohEnR3zvIGZOgzfmTCKKlJWVhRtuuEFV0PXmm28iGAyqioXGuiNHjmDfvn2quZtuuumkz3vBBRegoKBAVYj11ltvYeHChSd9bko8n8+Hqir1d0CnnXZa3McvWbJENT548CD8fj90Ol3Uvrt27VKNFy9ePIRI+661adOm8Hj37t0D7E1ElEFC/OwTSWuMrJlTEPR6oNE6gMAJRdYn0eCtr1Gb3O/27tY9sE86E87qNXAdfj+iSdpx+qwSFJ5yAwzZ44cdy4kknQXj5t+Mpu1PQQ75kDv1Iuizx8V9vCl3mqr+Qwn5VduzS06DwToysVIfQRCQbzIj32TGknF9f7aBUAi1nR4cdDlR7e771dzTHdf5ZEXB4Q43Dne48W7tIQCASaNFuc2OCpsD5TYHKuwOZOn0CXtNycZ7Q0RE8WPOJCKKH3MmUXoTNQaYcqYk/jqSFgWzroLBXo7mnc9AkQMjfo2g1w3XoXfhOvRu3+vKnQaDbSIEQQIE4dh/RegthTDmTIYgxN9IfqQwZxJRqrni+8vx+l+2RM2bGjxAQRICioU5k4iOYa1bfFjrljwnU+NlNpsxa9YsbNu2LTy3e/duFBSMzA9kp1NdA2Cz2YZ0fOT+n332WVzXGeq1Yu372WefoaysLHpnGjXMsETJIKZWg7dI0tUFGD81LzyWFQXPbDyq2kcnjf7Nt1TUW71JNTaUzoGoMyQpGiKizNTVtFU1FjUGlCz6HkRJm6SIiIiIiChemxrr8PvtnyAoqx9Em5htw50Ll8KqT4/P0LI/iN796lVb9KUOiDreXiUiIoopssGblDkN3oiIYjnnnHNURW8ejwe7du3C3LlzkxZTqvnwww9VY0mScNZZZ530eTUaDVauXInnn38+PPfRRx+d9HlpdOzfvx+hUCg8zs/PR3Z2dtzHZ2dnIzc3N7xiaSgUwoEDB2KuWLpnzx7VuKKiYkixlpeXD3g+IqJMpQRDg+80xohaMwRJp2pOFuh19jV4O9EwG7yF/N1w165Tzemzx8PXcbx+z+s+hJr37oIc9MY+iSDCUX4uciovGPHaClPOZJSd9T9QFGXIC92acqei/cCrMbcJoha5U74yAhHSYLSShPJjzdg+1+HzodrtRNWxXzVuF3qC8T0k3hMM4LO2FnzW1hKeKzCZ+5q9HWv4NiHbBo3ImlMiIiIiIiKikWAtOQ2G7PFo2Pon+LuOL1wrSHpoDDZo9Na+/xqs0OitCAV64Os4Cl/HUQS97rivIwe96Grahq6mbTG367PHI3/GZTDlTD7Zl0RElNb0Bi3Ou+pUvPXs1sF3Hi1DvH9PRGMLa90Gx1q35Nm7d69qPJwarxMbvO3ZswdnnnnmiMQWueiozxd7Ibb+RO6/b98+yLIMMeJ71FiLm/p8vribMMaKa8+ePbjwwguHEC2NND6BSJQEQy1sGm2CVv0DoL3LH7WPXiONVjgpLbLBm7FsUZIiISLKXJ2Nn6rGloK5bO5GRERElAbeOVKDp3dtQ+R6X9Nz8vD9eYth0qbPe7pAa1fUnJSlT0IkREREaSIU0eBNZIM3Ispsp512GgRBUK0Sv2PHDha9nWD9+vWq8bRp02AymUbk3PPmzVMVvW3YsGFYDUcG0tTUBK+3nwYqIyg3NxcWiyXh10kVVVVVqnFpaemQz1FaWhpu8AYABw8ejNng7WSvFbn/wYMHh3Q8EVG6UkLy4DuNMYIgQGuww999fFGQoNcJ2EamwZu79kMooRMKrgURxaf+Fw5/eD8U+fOGW0rs5m6CCGvJEuRUng+tKXdY14/XcN5rGW2TIEh69es7xl52NrRG+0iERsOQrdfjlIIinFJQBKBvUeDGrk5UuZ3hxm9HOzsgK5Hf+sTW3NON5p5urG/oa0yoFUVMyLaFG76V2xzIM5pSvpaWiIiIiIiIKFXps8dh4vKfwtfZAFHSQmOwQdQMvuBu0N+JkLcDiiIDigxFCSHQ0wb3kQ/R6zwwpBh8HUdxdMNDsBTMgc5SCDkUgCIHj/0KQNQYYMqZDHPeDEi6sfP9FxGNTfNXVqRWgzciogGw1m1wrHWLz0jXujmdTjid6u/ZU6nGKycnRzVubGzsZ8/YIvf3er2ora3FxIkTVfN2ux2iKEKWZdWx8Ta7ixXX/v37hxQrjTw2eCNKAEmSMHPmTDQ3N8feIc2KUv7wQU3U3PVLhl5YnWkURUFvTUSDt3I2eCMaKkmSMHfuXEgSG0dStKDXjV5ntWouq3hekqIhSj7mTCKi+DFnJo+iKHj54F68fHBv1LYFhePw7bkLoEuj/y9KSIb7vegb2dbllUmIhigxmDOJaMRFNniTMqfBG3MmEcXicDiQl5eHlpaW8FxtbW0SI0o9dXV1qvGMGTNG7NyRzby6u7vhdrtht49cg5DLL78cH3zwwYidrz9PPfUUrr/++oRfJ1W43W7VOD8/f8jniDzG4/Ek5FrxXoeIKL1F17QpQTZ4i0VjdKgavAV6nUBeRDF1qBuK7IMgxr9YiBwKwHXoPdVcVtE86CyFMOZMRk/r7n6OFJBdchpyKr8InXnoP09HiyBqYMqZjO6Wz1Tzki4LjvLzkhQVxSIKAsZlZWNcVjbOGD8RAOANBnHI4zrW8M2FKrcTLm/vwCc6JiDLqDrWKA6H++aydXpU2PqavVXYHSiz2tNicSDeGyIiih9zJhFR/JgziWg4BFGCwTp+SMdodFnQ6LJUc0Z7GbKKF8DXcRRdzTvR3bITXvfhuM/Z1bwDaN4Rc5un9iMAAgy2STDnz4A+ezx05gJoTbkQpeHdB2DOJKJUNGfZJNz1+Ffxi//6e7/7NB1xIdcfglbH/EVEycVat8Gx1i0+I13rFlnfZTKZYDabh3SORNZ4TZs2TTXeuHHjkI6PtX+s+ERRxOTJk7Fv3z7VsfE2eNuwYUNc16HRxQZvRAkgiiJKSkr63S6I4ihGc3J6/SH8c1t0h86VU/OSEE1qCTrrEHSr/2zY4I1o6ERRxIQJE5IdBqWozsatAI53gRc1BphypycvIKIkY84kIoofc2ZyyIqCv+zejneORDeLP6t0Eq6feQrENGp8r4RkHP7Jv2NuM0zIiTlPlI6YM4loxMkRD/eKmdPgjTkzcymyDL/TnewwMpbOYUur70iHw263q4reWltbkxhN6olcWXMkC9JsNlvM643kNSgxurq6VGOjcejvGSKP6ezsTMi14r0OEVGmUUJs8BaL1uhQjYO9LkBrAyACOOHPLOAE9EVxn7ez4ROEfOrCakfZOQAAS97MGA3eBGSPW4icygugsxTE/wKSyJQ7NarBW87kL0HSZs69g0xl0GgwLScP03KO14629/ag+lizt2q3EzVuF/xyKK7zdfh92NrSiK0tfTWYAoBxluxjDd/sqLDloCQrO+W+V+K9ISKi+DFnEhHFjzmTiJJNEAQYrKUwWEuRO/kCBL0edLfuQk/bPoQC3VAUBVBkQJER6G1HoKdtCGdX4HXXwOs+sa5UgM6cD721FPrs8eFrSzrLoGdjziSiVHXK6WV4+P1v4aZvvgzrgZao7Ts+PoR/3/Qi7vnL5RBS7L4npR/WuiUWa92ItW7JMZq1ZMNxxhlnqMbV1dXYsGEDFi9ePOix69atQ01N9LN2/cV3xhlnqBq8PfPMM7j66qvjivOvf/1r3Neh0cMGb0QJEAwGsW7duv53SJPPXb2BEJb+KnZnVrOe6aO3ZpNqLJrt0BVUJikaovQVDAbx4YcfYvny5dBomFtIrbPxU9XYUjBn2Kv0EGUC5kwiovgxZ46+QCiE/2/HFmxqrIvadnHlNFxcOS3tvozu2Hgo5rxo0o1yJESJxZxJRCNJUWRA9qonpcx5SJs5M3P5nW6smbU82WFkrFWffQh9rmPwHdNYZIFVb29vP3uOTZFFb7EK1YYr1rna29tRXl4+YtegxIgsyjMYDEM+R2RRXuQ5R+pa8V6HiCi9KdEzbPAWkyaiwVug1wlBkKBorEDQdXyDvz3uBm+KHIKz+i3VnDFnMgy2iQAAa+kyuI68j0B3CwABWcXzkVN5AfRZ8TeQSwWWgjlo3fuPvgeCAejMBbCVLktyVDRcOUYTcowmLCwaBwAIyTKOdnaEG75VuZ1o6IrvYQEFQF1XB+q6OvBB3WEAgF6SUGa1o8Lu6Gv8ZnPAbkjufSbeGyIiih9zJhFR/JgziSjVaAxWWMcvhXX80qhtihyCu/ZDtO1/BXKge5hXUODvboa/uxmdDZ+EZ815M1Ew+ypojf0vwMucSUSpzJRlQNuCCXDNKAK2/Stq++7Ntag90IoJU/JH/Noh5YTvdCJL9pXo74AovbHWLbFY60asdUuO0awlG47CwkKcddZZePfdd8Nzt912Gz766CNIktTvccFgELfddlvMbf01Xrvmmmvwxz/+MTxes2YN3njjDZx//vkDxvjqq6+q4hvsOjR6+OmVKAEURRkk0afHw8wvfhL9QDYA/H9Xzx3dQFJUT9VG1dhYtjDtHlQnSgWKoqCzs7NvNReiEwS9bvQ6q1RzWUXzkhQNUWpgziQiih9z5ujqDQbwf1s2YHe7etUeAcC1M+Zi1cT0+6JB9gfhfHVnzG2WeaWjHA1RYjFnEtGIimzuBgCiafTjSBDmTCLqjyyrm5/we8PRMxp/1mvXrk34NWh4/y+H+/9/qMfx3zQRjVlBNniLRWtQP/AQ9B4r8Nc51A3eTvz9INy1H8Hf1aSac5StCv9e1Ogxcfk96HVVQ2fOh9aYng+V6Mz5KJx1Ndqr3oBGb0Xh3BsgiCxlzhSSKGKi1YaJVhvOnlAGAOgO+FHjdqHqWMO3arcTnX5/XOfzhULY62zDXmdbeC7HYOxr9mbva/g20WqDXhq9v0O8N0REFD/mTCKi+DFnElE6EUQJ9okrkV28EO7aj+DrOApBECGIWgiSFoKogSBq4OuoR0/7Piih+O4DAEB36y4c3fh/KF3yQ2j0WTH3Yc4konQQMungzTXH3NbpSkwTpcbuLnT4fMjW66O2MWUSUSTWuiUPa93iN5q1ZPH6yU9+omqgtmHDBlxzzTV46qmnoI/xM9jr9eK6667D5s2bY55PFMWY80uXLsUZZ5yBDz74IDx35ZVX4pVXXsHy5bEbfK5duxZXX331kK5Do4dVEURJIKRY8ov1waze1YvfvFsdc/8FE+0x58ea3urIBm+LkhQJEVFm6mzahhNXCBc1BpjyZiQvICIiIiKKqcPnw4OfrMMhj1s1LwkCvj13IU4rLklOYCfJW9Uacz7/6kUwTS8a5WiIiIjSSChGAZo09BXUiIjSjdvtVo0jV4Ic6xwOdQMSj8czYueO/LOPdT1KTRaLRTUezmrAkcdEnvPEeZfreJOdoV4r3usQEWUaJcQGb7FoIpqrBXqPNXjT5gA4oebO3x7X+UKBHrQfeFU1p7eWwpw/UzUnSlqYc6cOOd5UYy1dBmvpsmSHQaPErNVhVl4BZuUVAOh7CLulpzvc7K3K7cRhjxuhOJ8wbPf2or2pHpub6gH0fR81PsuKimNN38ptDhSaLRD5IBIRERERERFRQkk6M3IqzhtwHzkUQK/zALpbdsHrqYW/uxkhX8eAxwS6W1C/+bcoXXonFwYgorRz4n1Jv90EnJDysnV9dRKP3PYKbrjrLMw/sxJ6o3bY1zJrdVFzr9ccwBXTZkFA5P1RdngjIjXWug2MtW7JMZq1ZMO1cuVKfOc738Gjjz4anlu9ejU2btyIW265BcuWLYPD4YDT6cRHH32Exx57DIcPHwYAGAwGmM1mtLcfryOw2Wz9XuvJJ5/E/PnzwzVvHo8HK1euxBVXXIGvfvWrqKyshKIoqKqqwosvvojVq1eHG2GXlJSgrq4uruvQ6OCnW6JkSLHCkc5A9CoINzy9FUB0I7pNd61g4QsA2e+F99AnqjlTxeIkRUNElJk6Gz5Vjc35syFKw79pSUREREQjr7WnG7/cvA5N3V2qeYOkwffnn4aZuQVJiuzkdX5aGzWXe8kpMM8sTkI0REREaUSO+DJd1EMQpOTEQkQ0ik5sHAUAeXl5SYokNUUWoUX+eZ2MWOfKyckZsfNT4rDBGxFR6lOCbPAWizaiwZsc6IEc9ELQRiycGojvPY+z6i2E/J2qufzpX4UgpNZCskQjQRAEFJgtKDBbsHRcKQDAHwrhSIf7WMM3F6pc7Wjt7YnrfCFFweEONw53uPFObQ0AwKzVotzW1+ytwuZAuc2OLJ0+Ya+JiIiIiIiIiGITJS3MeTNgzpsRngsFeuHvaoSv4yi8nqPweWrh66yHIgfC+3g9h+Gp2wBb6enJCJuIaNjsphOffVQ/iz/VvhdmTSc87cAjt72K/BIr7nn6CuSXWId1rek5edBLEnyhUHiutbd7WOciorGHtW4DY61bcqRDgzcAeOihh9Da2orVq1eH5w4dOoTbb7+932NEUcQzzzyD22+/Pe4Gb2VlZfjXv/6Fiy66CE5n36Jzsizj2WefxbPPPtvvcYsWLcK1116LW265Ja7r0OhggzeiBJAkCfPnz8frr78ee4cU648WlEOqsVGrQag7Osh7vjQVWolFYwDQW7MZSvCExniCCGPlkuQFRJTGJEnC4sWLIUl8yJOOC3o96HUeVM1lFc9LUjREqYM5k4gofsyZiVfb4cGvNq+D2+dVzWfpdLhzwTKU2ez9HJmaFFmBv9GDoKcXPbsa0LO7IWofy7wJSYiMKPGYM4loRIUiG7xl1qp+zJmZS+ewYdVnHyY7jIylc9iSHUJCtbW1obW1VTU3YQI/P5xo3LhxqvHu3btH7NyR5zKZTCxIShNWq7pQPfLfUTxaWlpU4/7+31utVhw9enTY14r3OkRE6S26XkwJscFbLBpj9P3vQK8LOn1E4X2oA4ocgCD2v5hdoKcNrkPvqOYsBXNgypkyIrESpQOdJKHSnoNK+/F/Qx6fN9zwrdrtRLXbid5gMK7zdQcC2NnajJ2tzeG5ApMZFfYcVNjsqLDloDTbCo049HpY3hsiIoofcyYRUfyYM4loLJG0RhjtZTDay8JzQa8bRz7+FYK9xxsduA+/j+xxp0GU1PfWmDOJKJUJgoC7zp+CX7yxH0qMXgIVtgPY0db3jGRLnQdrnt+Gq29fMaxraUQRK8ZPxH8OVw8e17CuQKmMtW6JxVo3Yq1bckTWkvX09KC7uxtmsznuc4xGjZdWq8Vzzz2HhQsX4v777w83X+vPpEmT8OSTT+KMM87AVVddpdpWUFAw4LHLly/Hpk2bcPPNN+Odd94ZcF9BEHDzzTfjwQcfxCOPPKLaVlhYOOCxlHhs8EaUAKIoIjc3t9/twjCKQkbTnNwibO9WVHMOsxZfnlOUpIhST89+9YceQ+lcSMbsJEVDlN5EUUR+fn6yw6AU09m0DcDxn0WCpFet2EM0VjFnEhHFjzkzsfY72/DQJ+vREwyo5nONJvxo4TIUWbKSFNnwKLKCI/e8CiUQ6ncf49RCCCK/3qbMxJxJRCNK7lGPpcxq8MacmbkEUYQ+1zH4jkQxbNy4MWpuzpw5SYgkdS1ZsgR//OMfw+M9e/agp6cHJpPppM+9ZcsW1Xjx4sUQhJH9/NbU1ASv1zv4jicpNzc3IauGpqrKykrV+MiRI0M+R+Qxkec8cX7Xrl3Dvla81yEiyjRs8BabKOkhac0IBbrDc0GvEzpzjKZsASeg778ou3Xfv6DIJzStEkTkTbt0JMMlSktWvQGnFhTj1IJiAICsKGjo6jzW9K3v19EOD5RBzvO55p5uNPd04+P6WgCAVhQxMduGCrsD5TYHKmwO5BpNg36W4L0hIqL4MWcSEcWPOZOIxjqNwYaCmVeg/pPfhed8HXU4uuEh2CauhDl3GjSGvmYPzJlElOpKHX31ctX6aTgHr6i26SW/avzvJzbh4m8thsmiH9a1TFpdP1tY857pWOtGJ4O1boNjrVt8RrrWLScnB3a7HS6XKzxXW1uLadOmxX2O0azx+t73vocbb7wRq1evxpo1a7B9+3a0trYiEAiguLgY06dPx2WXXYZLLrkERqMRVVVV8PuPvxeoqKiIqwFdRUUF3n77bWzYsAEvv/wy1q5di6NHj8LlciE7Oxvjx4/HOeecg2uuuQYzZ84EAOzdu1d1jvnz54/oa6ehY4M3ogQIBAIDd78c4R+wI82s0QNQ/8B+4RuLRvyNQTrrOfCRamyacnqSIiFKf4FAAGvWrMGqVaug1fa/YjGNLV2Nn6rGloI5EKX+bjgSjR3MmURE8WPOTJytzQ14dOsmBGT1g30lWdn40cJlsBvSr4lL64tbBmzuBgDW5XxwmzIXcyYRjahQr3ospt97g4EwZxJRLG+//bZqbLfbMWMGFy050emnq79PDYVCePfdd/GlL33ppM4bDAbx/vvvD3itkXD55Zfjgw8+GPHzRnrqqadw/fXXJ/w6qWLKlCmQJAmhUN9n8paWFnR2diIrK77G8R0dHWhrawuPJUnqtyhv2rRp+Oc//xkeV1VVDSnWmpqaqPMREWWe6DZJSpAN3vqjMTpUDd4CvU4IogaKxgoEPcd3DLj6bfDW6zqEzobNqjnbhBXQWQZepZtoLBIFASVZ2SjJysYZ4ycCALzBIA55XH0N31x9Td/cvvgeVgnIMg66nTjodobnrHp9uNlbhc2BMpsdRo36/g/vDRERxY85k4gofsyZRESAOX8mtKZcBHqOf/fjdR9C0/ZDAAB9dgnMeTNhHrcE73/0KXMmEaUsq7EvN32cdRa+1fKrQfffurYayy6YPrJBRLQDUJR4l8ogorGAtW6DY61bfBJR6zZt2jSsX78+PK6qqhpSndZo13hlZ2fjm9/8Jr75zW8Ouu+GDRtU40WLFg3pWosXL8bixYvj2vdkr0Ujjw3eiBIkGAz2uy2VGqX1BAKQIz6YbaxxAlB3j02hkJNOCQXRW7VeNWeazAZvRCdjoJxJY0/Q14Ge9gOquayieUmKhij1MGcSEcWPOXPkfXD0MJ74bGvUvYQp9hzctmAJzP2uApbaunfUDbg977J5MJbljlI0RMnBnElEI0aOaPAmZVaDN4A5k4jUOjo68PTTT6vmzj//fEiSlJyAUtSkSZMwZcoU7N+/Pzz35z//+aSL3l5//XU0Nzer5s4777yTOieNHr1ej/Lychw4cPx7oQ0bNmDVqlVxHX9iMR/Qt+KqXh97ZfPPVyc98TpD8fHHHw94PiKijBVig7f+aI0O+DqOhsfB3mOriGsdEQ3e2mMerygKWve8qJoTNUbkTP7iiMdKlKkMGg2m5eRhWk4egL5/V05vL6rcTlQfa/p2yOOGXx54kZ/PeXw+bG1uxNbmRgB9z0COy8oON3wrtzlQYDDy3hAR0RAwZxIRxY85k4jGOkEQUTjnetRtfhRKyB+13ddRB19HHZw1a5AbKkagey60tnEAgJC/C15PLbyeWvg8R+DvaobGaEfBrKugNTpG+6UQ0RhXkW8BACiChL2GOZjm3RHeduGZbmxerd7f4+wZzfCIaIxjrVt8WOuWPDNnzlTVhG3YsCHuP/fu7m7s3Lkz6nyp4t1331WNV6xYkZDrHDp0SNXorqSkBBUVFQm5FsWPDd6IkiFFuqUFZRkPbl4HOaLxdmunD7CoG7wZtOIoRpbavLXbIXu7VHOmKWzwRkQ0UrqatuHElcEFSQ9zPru/ExERESWToih4reYAnt+3K2rbqflFuPXURdCl6Rc6gbauE99+hmlyzBD1Gji+MBPGyvzRD4yIiChdhSIavImZ1+CNiOhEDz/8MDo6OlRz3/jGN5IUTWr77ne/i29/+9vh8euvv449e/Zg+vThrUStKAoeeugh1dxpp53G1SbTzHnnnadq8LZ27dq4G7ytXbtWNf7CF77Q777nnHMOJElCKNTX3OPTTz9FZ2cnsrKyBr1OZ2cntm7dGh5rNBqcc845ccVIRJTulCAbvPVHE/FgaKDX2fcbrQPoPXTCBmfM47uatqLXVa2ay6n8IjS6wX82EVFsgiAgx2hCjtGERUUlAPrqZI92evoavrldqHK1o7G7a5Az9VEA1HV2oK6zA2uPHgYAGCQJ2aKM7oN7MNmRhwqbAzaDIUGviIiIiIiIiGhsMeVMRunSH6L+k98j2Bt74QQoMmxiHY6u+xmMOZUI9LTH3NfXWYcjH/0c5Wf/CoLIR+mJaPRIooDvn12B/3unKmpbsOYDlJZ+GbW1gSRERkTEWrehYK1bcpx33nn405/+FB5H1ocN5KOPPlI10D/llFNQUFAwkuENW2dnJ1566aXw2GKx4LLLLkvItZ588knV+Otf/3pCrkNDw0+lRMmQIg3eNjXW4aA7uoBMCahTg0ErwqRjuvhcz/4PVWNd0RRosvmgNxHRSOls+FQ1thTMgijpkhQNEREREcmKguf2foY3Dx2M2ra8ZAJumnUqJDF9G8O739sXNVdw4xKYJqfGTXwiIqK0E9ngTWKDNyLKXO+88w4eeOAB1dyKFSuwfPnyJEWU2q6//nr89Kc/RVtbGwAgGAziv/7rv7Bu3ToIw/gO/c9//jPWrVunmrv99ttHJNZIQykUo6G56KKL8Oijj4bHzzzzDO6///5BVwYOhUL429/+FnWu/uTm5mLZsmX44IMPAAB+vx/PPfccvvnNbw4a47PPPotA4HiB/fLly+FwOAY4gogocyghNnjrj8ZgV42D3hMavJ0oRoM3ORRA695/qOa0plzYJq4c0RiJCNCIIiZZ7ZhktePsCX1z3QF/uOFbtcuJKrcTXQF/XOfzhkLwisDrh6rw+qG+BzRzjCZU2ByosNlRbnNgktWetgsjERERERERESWbIXs8Jp1xDzrqN6G7ZRd62vdDDnpj7Kmgt/1AjPnjQv5OHHjj28gqno/scYthzp8BQUjfelciSh9aTV+u6ZHMUdsqit2orY2eJyJKNNa6DQ1r3ZLj3HPPhdFoRG9vXz36hg0bsG/fPkydOnXQY59++mnVeKBastH26KOPoru7Ozy+8sor41qYdKhcLpeqQZ4kSWzwliL4SZQoATQaDZYuXdrv9uH8wE4EtzfWjS0Aijo1PPCVGaMQTfro2f+RamyazDetRCdDo9Fg5cqV0GjYSJKAoNeNnvb9qrmsonlJioYo9TBnEhHFjzlzZARlGX/csSVmc7cLyibjG7PnpVVzN2+tE61//xTNf9kQ/tW19WjUfvpxttEPjiiJmDOJaETJEQ3eRFNy4kgQ5kwi+tzq1atx0UUXQZaPNz0xmUz4/e9/n8SoUpvRaMSvf/1r1dz69evxve99b8jn2rBhA77//e+r5s455xxcfPHFJxMiJcHpp5+OSZMmhcd1dXVRjdti+dvf/ob6+vrwuLy8fMA6DQC49tprVeP/+7//g8/nG/AYn8+HRx55RDV33XXXDRofEVF6iq5pY4O3/mmN6kZugd5+GrwFPVCUoGrKfWQtAj1tqrm8qRdDlLQjHicRRTNrdZidV4iLK6fhjoVL8YdzLsDDK87Ft+cuwKoJ5Siz2iENoc63vbcHmxrr8Ozez/CzDR/gpv/8G3evexdP79qGj+qOoLGrE4qiJPAVERGlJt5PJyKKH3MmEZGaqDHANuEMjFtwCypW/Rrjl9wB28QzIYjDu3/W2bAF9Z/8FofX3gNP3YYRjpaIqH8fZZ0bY5b3Colo9LHWbehY65YcJpMJl156qWruV7/61aDHHThwAP/85z/DY41GgyuvvHLE4xuOffv24ec//3l4bLFYcNdddyXkWrfddhtaWlrC45tvvhnjx49PyLVoaNLn6UuiNGMwGPrfmCIN3uKlk9Ir3kRSFAU9ByIavE05PUnREGUOo9GY7BAoBSiKguZdq3HiTUpB0sGcPzN5QRGlIOZMIqL4MWeeHG8wiP/bsgHr6mujtl01bRaumDYrZZrYD0T2B9H64qc4fPe/0fj7D9D1aS169jaFf0XSOMyQzPokREqUXMyZRDRiQhEN3qTMyy/MmURj24YNG/DVr34VV155Jbq6usLzkiThqaeewrRp05IYXeq75pprcM0116jmHn30Udxyyy3hVTcH85///Adf+MIXVH/++fn5eOaZZ9Lic2qmEwRB9WuwFWElScJ9992nmvvBD36Aw4cP93vM4cOHo4oeH3jgAYiDNKG/7rrrMGXKlPB4//79gxbL/fjHP8b+/ccXJ5o+fTquuuqqAY8hIsokSnDgBm/OV3ei6en1CDi7B9wvE2kiGrwFe119DZy09og9FSDgPr6f1432g6+r9jDYy2Hh4ndESSMIAgrNFiwdV4rrZs7F/cvOxBPnXoh7l6zA1dNn47SiEuQZ41/EIKQoOORx4+0jNfjDji24/YM1+Nbbr+FXm9fhHwf2YEdLE7r8/gS+IiKi1MH76URE8WPOJCKKTRA1MDkqUTDzcpSd9T+wl58LQYqu8RREDQy2idBZCvs9l7+7GU3bn0LbgdcSGTIRUdgWy7LkXJi1E0R0DGvdTg5r3ZLj3nvvhVZ7vLnz008/jVdeeaXf/b1eL2644Qb4T/j+7etf/zrKy8sHvdZQa90AIBgMDrrP5/bv34+zzz5b9fflf/7nfzBhwoRBjw2FQnFfR1EU3HHHHXjqqafCcxMmTMD//M//xH0OSiwu60CUAMFgEO+++26/24VBiopHk+KTAY/6B4gipU58qcbfsBehrnbVnGkyG7wRnYxgMIg33ngD559/vurNNo09nY2foqtpm2ouu3gBxBhfvBCNVcyZRETxY848OV1+P/73k49R5Xaq5kVBwDdmz8PpJYPfSE4Vrrf2oGtrdJO6/tjOmprAaIhSE3MmEY0URVEAOaJgQcysBxGYM4kyU1NTU1QzqWAwCI/HA7fbjSNHjmDTpk1Yt24d9uzZE3W82WzG008/HbVyJMX2+9//HgcOHMCmTZtUc2+99RbuvfdeXHjhhcjOzlYdoygKNm3ahEcffRSrV69WbTOZTPj73/+OgoKCUYk/XdXV1cUsLmtqUjc/DwaD/TZXs1gsyM3NHfHYrrrqKjz22GPhvxNOpxNLlizB008/jVWrVqn2/c9//oPrr78eLpcrPLdkyRJcdtllg15HkiQ89NBD+PKXv9z3vgXAr3/9a3R1deEXv/gFcnJywvu2tbXhrrvuwuOPPx6eEwQBDz/8MCRJOqnXS0SUupTomdDADd4AoHdfM9qVHSi8YUkigkpZ2ogGb4ocQNDrhtZohyJZgNDxAn0EnIAuF13NO9G04y+QAz2qY/Onf5XF+0QpRidJqLTnoNKeA0zqm/P4vNjf1oo1Wz8BcnNwqMMNb5wPcHQF/NjZ2oydrc3huSKzBeU2BypsDlTYHRifZYUmheqLiYhOFu+nExHFjzmTiCg+Gn027BVfxqYDMpbMyoHs90BnzofeOgF6SyEEse+Rec/Rj9Fe9SYC3S0xz9N+8DWYHJUQJC2CPg905kLos4pG86UQUYarzDeHf++RbLCG3MkLBgh/P05EmYG1bqOLtW6jr6ysDN/97nfx0EMPhecuvfRS/PrXv8Y3vvEN6HS68PzevXtx0003Yf369eG5nJwc3HPPPQmL7/rrr4eiKLjiiitw1llnxWza39DQgD/96U948MEHVc3dvvjFL+KWW26J6zp79+7FpZdeiq9//eu48MILMXny5Kh9gsEg1q5di7vvvlv1d1Sv1+OZZ56BxWIZxiukRGCDN6JkSJFarKAiQ97TDQSPfzCTNCLkfAvgi7+b51jSc+Aj1VjjGA9tbvo81E5ElKqCvg607HpONSfpLMidelGSIiIiIiIau9p7e/CrzetQ39WpmteJEr47bxHm5qdPEYuvwY2O9dVx7y9Z9DBOzk9gRERERBlO9iGqIYCUWQ3eiCgzXXHFFcM+9uyzz8bvfvc7TJkyZQQjymwWiwVr1qzBFVdcgTfeeCM8X1NTg2uvvRZ6vR6zZ89GUVERjEYjmpubceDAATQ0NESdq6CgAP/4xz+wdOnS0XwJaWnZsmU4cuTIoPvV19dj0qRJMbddd911ePrpp0c4MkAURfzzn//EaaedhtravibtjY2NOPfcc1FZWYkZM2ZAURTs3r0bVVVVqmMnTpyIl19+Oe6mOBdccAEeeOAB/OQnPwnP/elPf8IzzzyDRYsWobCwEI2Njdi8eXPUSru//OUvcd55553kqyUiSjPBiAZvogDI0Q8B9e5vjprLdBq9FYKkgxI6vgq4+8gHyJv6FUDrUDV4U3wtaK75BJ4jH0SdJ6t4Poz2stEImYhOklVvwCn5hWiUJZw/fwkkjQb1XR2odjlR5e77VdfZEaNdZmyN3V1o7O7Cuvq+98BaUcQkqx3lNgcq7Q6U2xzIMRjZAJKIiIiIiIgoggwdssef3m9TTOv4pbCOX4pATxu6WnbBfWQt/J0nfNeoyDi68eETjhBQdMrXkT1uYWIDJ6IxY+54W7JDUGODN6KMwlq30cVat+T45S9/id27d+PNN98EAAQCAdx66624//77ceqppyIrKws1NTXYunWrqpGpTqfDP//5TxQVJe7ZN6/Xi3/84x947rnnoNVqMW3aNEycOBFZWVlwu92ora3Frl27ohqsrlq1Ci+++OKQvvvbv38/7rzzTtx5553Iz8/HzJkzwwu0Njc3Y+fOnaqFUgHAaDTihRdewOmnn37yL5ZGDBu8ESVDihRb7G5rgXJQXZBcMrsY77G5W7+693+oGpumnM7iGSKiEdCyazVC/i7VXP7MK6DRZ/dzBBERERElQkNXB365aR3aver7BWatFncsWIpKe06SIhsa2R+E+5298HxY1e8++gkOmKYWhseiQQvTtEJosgyjESIREVFmknuj59jgjYgykMViwZe+9CV8+9vfxrJly5IdTlrKzs7Ga6+9ht/97nf47//+b3g8nvA2n8+HTz75ZNBzfO1rX8NvfvMbFBYWDrovpb6ioiK8/fbbuPzyy7Ft27bw/MGDB3Hw4MGYx5x66ql44YUXhryi7V133QVBEHDPPfcgEAgAAHp7e7F27dqY+2u1Wtx///248847h3QdIqJMoMjqBm+CJEKRWVsGAIIoIatoPjrqjq8E7j78Phzl50LUOgBvbXi+u3E9PEei71drDHbkTfvqqMRLRCNPFASMz7JifJYVK0r7miT3BgM45HGjyuVE9bGmb26fN67zBWQZB1ztOOBqx5uH+uZsegMqbH3N3sptdpTZ7DBqYj+8TkRERERERERqWlMu7BNXwDbhDLTt/xecVW/2s6eC9gOvssEbEY0YSRTw1Xnj8PdP65MdChERa91GAGvdRp8kSXjxxRdx00034YUXXgjPt7S04K233op5TH5+Pv7yl7+MamOzQCCAnTt3YufOnf3uo9FocMcdd+BnP/sZNJrht/lqaWnBe++9N+A+06dPx9/+9jeccsopw74OJQYbvBElgSCKyQ4BnX4f9rS3QukMqua3e4P9HEGKoqAnosGbecryJEVDRJQ5Ohs+RWfjp6o5S+EpyCqan6SIiIiIiMamKpcT//vJx+gK+FXzdoMRP1q4DCVZqd98VwnJCHZ4Ufer//S7j+3MKcheUg7Joh/FyIiIiMaIUI96LGghCPw6kojSk1arhcFggMPhQGFhIcrLyzFjxgwsXrwYixcvhsHA5tAnSxAE3Hrrrbj22mvxpz/9CX/7298GLHQCAIfDgYsvvhi33HIL5s6dOzqB0qiZPHkyNm3ahIcffhiPP/44ampqYu5XXl6Om266Cbfddhu02uE1t/jxj3+M8847D/fddx/efPNN+P3+qH10Oh2+8IUv4N577+XfNyIas5RQjAZvATZ4+5yj4lx01G0A0LfythzshfvIB3AUjFPtp9UoUceaC2ajcPa1XPiOKMMYNVpMz8nD9Jw8AH11p+3eXlS7nTh4rOnbIY8LgYgGmv1x+7zY0tyALc0NAAABQElWNipsjr7Gb3YHxlmyIXKhYiIiIiIiIqJ+CYKAnMoL0NW0Hf6uxpj7+Lub4XUfhsE2cXSDI6Ixp1xZg/fxFShIYK8B3i8kGnNY65Z4rHUbfRaLBc8//zwuvfRSPPzww9i4cWPM/RwOBy677DLcd999yMvLS3hcl19+OVwuF9avXw+vt/+FnrKysvC1r30Nd9xxB6ZMmTLk6xQXF+Nb3/oW3nrrLRw+fHjAfU899VTcfPPNuP7660+qiRwljqAoSnTlCI1JHR0dsFqt8Hg8yM5m0dDJUBQFHR0deO6551TzxQ8+AdHrw7zHf43iC1YlKbo++5xt+Nna9xG4/zDQe7xQpH1uCdwzilT7Pn7tKZg3wT7KEaYef+thVN0+STVX/ovd0I+bnqSIiDKDoigIBoPQaDQQeONozAn6O3F47b0I+TvDc6LWjEkr7mMRM1EMzJlERPFjzhyana1NeOTTjfCF1A/mFZkt+NGi05FrNCUpsvjI3gDaX9mBrq1HB9xPN86GcbeuHKWoiNIHcyYRjRSlpwpoff34hCYbwrgbkhdQAjBnjqyR/n7S6/Xi0KFDmDRpEguSiNJUS0sLtm7dikOHDsHtdiMUCsFkMmHcuHGYMWMGpk+fDjEFFlSj0fHpp5/iwIEDaGjoa2ZRXFyMyZMnY968eSN6nc8L7urr69He3o6cnByMGzcOS5Ysgd3OWonRxJ/lNBSsdTt5Pfub0fzU+vBYshpR+uPzwmNFUXD4x/9SHSOadZC7o5tiAsCkX16UkDhTXf2WP6CraWt4LOmyUDr/Wmg714bnZFlG1e69AABB0iF/+mWwli7j50qiNDQS94aCsoyjnR5UuZyocvc1fWvs7hp2TAaNBuVWe7jhW4XNAaue76WIKPl4P52IKH7MmURE8TuZnOn1HEXdpkdUzzBFyqm8AFnjFkBnLmROJqKT8j9v7MffP63HY4cugTXkjtr+6I7b0O7Nw/V3nYUvXjt/WNd46cAe/PPg3vB4UdE4fOfU0/DSw79Bq0kXni9xdeFLd90xrGvQcax1I6JIrHUbXYcOHcLWrVvR0NCA7u5uFBYWYsKECVi6dCl0Ot3gJxhhfr8fO3fuxMGDB9HY2Iienh5oNBrk5+dj+vTpmDdv3rAXL43U0NCAnTt34vDhw3C73QgGg7BYLJg0aRIWLFiA4uLiEbkODc1Qfpaz7R5RggzUabNvDb3kkhUF8p5uVXM3AOgpUn+gKHUYMXMciyABoOfAR6qxZMmBrnhakqIhyiy9vb3IyspKdhiUBC27Xoj6YqRg5uVs7kY0AOZMIqL4MWfGZ339UfxhxycIRawDUW6z444FS5Gl0ycpsvh0f1aPlmc3D7qfrjAbxf9vReIDIkpTzJlENCJCveqxaExOHAnGnElElDj5+fk477zzBt+RxoR58+aNeDO3WOx2O774xS8m/DpERGlHjl47WJBYfB4pp+ILqgZvIX8njmz6IyqmH1+BWxRFaHU6SKZiFM39OnSWgmSESkQj5GTvDWlEEZOsdkyy2nEOygEAXX4/qt19Dd8+b/rWHQjEdT5vMIjd7a3Y3d4ansszmlBuc6DiWMO3Cdk26CRp2DETEQ0X76cTEcWPOZOIKH7DzZkG63iUnflz+LtbIGnNaDvwCjrqNqj2aT/4GtoPvgZJb0X2uIXIm3oRBJGP4hPRyPvOnIfxq0/vTnYYRER0EljrNromTZqESZMmJTuMMJ1Oh/nz52P+/OE1ah2K4uJiNnFLc6y2IUqAYDCIjz/+uN/tgpj8Bm8AAHdQNfRbjfA7zOHxJacW46nr50GvYVEHAPTsVzd4M00+naswEI2AYDCI999/H8FgcPCdKaN4PUfQ2aBuxGEpmIOs4oVJiogo9TFnEhHFjzkzPm8dqsJj2zdHNXebnVeAuxYtT/nmboG2rriauxXcuATF3z2Tn+OJ+sGcSUQjRo5o8CZlXoM35kwiIiIiIspc6vvESkiO2oMN3qIZbBNgypuumpNDQQQD6s+NOZOWonTJnWzuRpTmEnVvyKLTYU5+IS6ZPB0/XLgMfzznS3h4xSp8a858nDOhDJOsNkhD+J6rtbcHGxvr8Lc9O3Hv+rW46T//xn+vew9/2bUd6+pq0dTdBUWJbuRJRDSSeD+diCh+zJlERPE72ZwpagwwWEuhNeXANuGMfvcL+Txw1byN5s+eHW6oREQAgFpdWb/b5ucPXgc/EpQYi/oQERER0ehh23CiZEiVh4kD6g9kAbMu/PuXvrUIZXnmyCPGtJ4DH6rGpimnJykSIqLM0H7gNdVY1JpQMOsqNt0gIiIiGgWKouDv+3fj39X7o7YtLh6Pb82ZD42Y+g/q9expHHC7NteC4u+eCVHL5vVERESjIhTR4E3MvAZvREREREREYwUbvMUvp/wL6Gndo5rz+3zQaI+X6GbnV0AQWbJLRPERBAGF5iwUmrNweskEAIA/FMJhjxtV7nZUuV2odjvR1tsT1/lCioIajws1HhdwpBoAYNHqUGFzoNzuQIXNjnKbA2atbpAzEREREREREWUOo70MJYu+B9ehd9DdshuRi2AAgOfox+hu3YOsolPhKD8XGoNt1OMkovT217xb8V8t/4vJ3j1R20ostUmIiIiIiIhGG6tFiJIgVRrXyPvVhR3KCQV4WQamhxMFO1rgb1Q/9G6asjxJ0RARpT+vpxZdzTtUc/yig4iIiGh0hGQZT+7ahrVHD0dtO3diOa6ePgdiity7GEjX9qNwvrEr5raC6xfDMCkXop73N4iIiEZVZIM3yZScOIiIiIiIiOjkhaIfZoSU+veOk8GYMxkGWxm87prwnN/ng8lywgKrAWcSIiOiTKKTJEx25GCyIyc85/Z6UeV2osrtRLXbiRq3C95QMK7zdQX82N7ahO2tTeG5IrMFFTYHKuwOlNscGJ9lTYtFoYiIiIiIiIiGy5w3Hea86fB11MF56B10N+9EyN+l2ifodcF16F101G9G0SlfhzlvepKiJaJ01Kgrxc9Kfouvzh+HKz/7f+je/U542xT7PtR6W0Yhihjf+RARERHRqOETjkQJotEM8M8rBYodmqudUA57VXM9RdlJiib19ez/SDUWDRYYSucmJxiiDDRgzqSM1H7gNdVY1Jphn7gySdEQpRfmTCKi+DFnRvOHQnhs22ZsaW6I2va1KTPw5fIpKdOYfiBKSEbr81ui5gWNiIkPXJiEiIjSH3MmEY0IOaLBm2hMThwJxpxJRERERERjgRKSo+YEKfl1b6lIEAQUzLoStev/F0rIB0lrhjF/ARCqPr4TG7wRZYxUujdkMxgwv7AY8wuLAQCyoqCuswPVx5q+VbmdqO/siPvxzcbuLjR2d+Gj+loAgE6UMMlqQ/mxpm8VNgccBmNafJ9IRKkhlXImEVGqY84kIopfInKmPrsERXOuh6IoaNz6J3Q2fhq1T8jfibpNv0FO5ReRU/lFCKI04nEQUWYzTDhF1eANAIzt6wGcPyLnr+3wICTLQMT9O7Z3IyIiIkou3vkjSgCtVouzzz4bf/nLX2JuT4W6hupPIh7kzpLQWZabnGDSQM8BdYM3Y/liCBJTKNFI0Gq1+OIXv5jsMGgUeTuOoqt5u2rOUXYORI0hOQERpRHmTCKi+DFnRusJBPDrLeux19mmmhcAfH3WqVhZOik5gQ1R78EWNP3545jbsk4rG+VoiDIDcyYRjZhQRIM3KfMavDFnEhERERFR5op42IcN3obEYC1F+Vn/A19HPQz2SRD8TUCLusGboihsikSU5lL93pAoCCjNtqI02xr+7q8nEMAhjyvc8K3a7YTH54vrfH45hP2udux3tQOH+ubseoOq4dskqx0GNiMhohhSPWcSEaUS5kwiovglOmcKgoDCuTdC1FnQWb8ZcjCiFgYK2g++Bmf1W9Ca8qAz50NrLoDBNhEmRyU0BmvCYiOi9Jd16lfQ/sb/qubEyJq7k9DY3YUHP/kY5RHz/G6CiIiIKLn4bSpRAsiyjLa2tv53SIEPQke2N6knppgBFuD1q2f/h6qxacryJEVClHk+z5m5ubkQReahsaD9wGuqsag1wTZxZZKiIUovzJlERPFjzlRzeXvx4Ccfo7bDo5rXiiL+3ykLMb9wXJIii0/Q3YPWl7bCW9Xa7z4ahwnW5RWjGBVR5mDOJKIRI0cUm4mZ1+CNOZOIiIiIiMaKWA3eWF82MElngSl3CgBA0eaoNypBINQJaLKTEBkRjZR0vDdk0moxIzcfM3LzAQCKoqCttyfc7K3K7cJhjwsBOUbej8Hl82JLcwO2NPctNC0AGJ9tRYXN0df4zeZAsSULYgrUShNRcqVjziQiShbmTCKi+I1GzhQlLQpnXYWCmVfA13EUbQdeRXfzTtU+ihyEv6sR/q5G1bw+uwS5Uy+CJX9WQmIjovRmqlyCIPTQIL4FGAajE6WouV1tLbAbdQCU45OKErUfEREREY0eNngjSoBQKIQtW7b0v0OSixYURYlq8CaUZ95DViMl1OOBt3aHas405fQkRUOUeUKhEDZs2IDzzz+fX0aOAV5PLbqatqnmHGXnQNLy5xBRPJgziYjix5x5XFN3F3656SO09vao5o0aDW6bvwTTcvKSFNnglKCMhsfWwt/oGXTf8XeeOwoREWUm5kwiGgmKogCRq4lKmXfPhzmTiIiIiIgyV8TDPTEavAkim/XETTIBgg5Q/MfnAk42eCNKc5lwb0gQBOSZzMgzmbG4eDwAICjLqO3woMrdjiq3C9VuJ5q6u+I6nwKgtsOD2g4P3qs9BKDve8gyqx0V9hxU2Owotzlg1RsS9ZKIKEVlQs4kIhotzJlERPEbzZwpCCIM1gkYN/8WuGrWoHXfPwFl4Abpvo461G/+LYyOShjt5ZB0ZkhaM0StEUGvB4GeVgR62iFqTcipPB86c35CXwMRpZ52VKIAu0bkXHPzC/HywT1Rizd4NRL0wWB4rLDBGxEREVFSscEbURIIQnJvtnt7Aujt9KsnSwxAU+z9x7re6o2qG2+CRgdj2cIkRkRElJ4URUHrnpdUc6LWBNvEM5MUEREREVHmO+xx41eb16HDr17ly6rX484FyzDRaktOYHFQgjIO3/3vuPYtuf2cBEdDREREg1L8AELqOTHzGrwRERERERGNFUoo4mEfic3dhkIQBCi6HMDXeHwy4ASME5MWExFRfzSiiDKbHWU2O1Ydm+v0+1DtdqHK7US124kqlxM9wUBc5+sNBrG7vRW721vDc3lGEyrsDlTYHCi3OTAx2watJCXg1RARERERERGNLEEQ4Cg/FwZbGVp2rYavs27QY3qdB9HrPDjgPl5XNSauuC/pzxsTUXLlHPw1fE3fhL5w8pCPLc224seLTsfPNnyQgMiIiIiIaKSwwRtRMgjJLXZrPuqOntSyAK8/3sNbVWPDxHkQdXwojYhoqLpbdqGnfZ9qzlF+LiQtcyoRERFRIuxua8GvP90A7wmrbwFAgcmMHy1ahnyTJUmRxafhD4N/0WyZVwrHF2ZCsuhHISIiIiIaUKg3ek7ifR8iIiIiIqJ0pYRk1ViQ+JDhkGns6gZv/vbkxUJENERZOj3m5hdibn4hAEBWFDR1d+tnm6MAAQAASURBVIWbvVW5nTja6UFIUQY5U5/W3h609vZgQ0PfQ/AaUcSEbCvKbX1N3ypsDuSbzBCSXGNNRERERERE1B9TTiUmLP9vhPyd8Hc3I9DdAn93C3yeWvQ4q6CEfIOf5AT+7mbUvPtjmPNmIHvcQphypyYociJKdbUPn4+KBw8O697YFEcuii1ZaOjq7H+n+G7hEREREVGCsMEbUQIIggCLxYLm5uZ+dhjdeE4kKwr++If31JMGEYpFnQ4kkQUSn/PWbleNDRNOSU4gRBlKEARkZWWxMCvDKXIIrXtfUs1pDHbYJ52VpIiI0hNzJhFR/MZ6ztzcWI/Htm9GUFY/gDch24ofLlwGq96QpMjiowRD8Ne5Y27TOMzIuXA29OMdkEy60Q2MKEON9ZxJRCNEjmzwJgGCNimhJBJzJhERERERjRls8HbydDlA9wnjoCtpoRDRyBjL94ZEQUCxJQvFliycXjIBAOALBXHY40bVsaZv1W4n2r0xFoKIISjLqHa7UO12YQ2qAQBZOp2q4VuZzQ6zlt8HEqWrsZwziYiGijmTiCh+yc6ZgiBAo8+GRp8NOCrD84ocguvQu2jd909ACcV9vqDXBc/RdfDUrcfEM+6B3lI0ovHKQS/8Xc19Del62hDoaUXQ64bGYEdW0TyYcqdCEKURvSYRDSyI6EVTAy3VCHmaobEVDuucfBdJRERElNrY4I0oATQaDZYtW4bq6uqY2wUxOcVugVAIj27dhOrtjap5cUEWunuPP2RVnmeGw8yCiM9FNXgrnZuUOIgylUajwZlnnpnsMCjBPEfXw9+l/vmTO/UrECX+vCEaCuZMIqL4jeWc+e6RGjy1a1vUQlvTHHn4wfzFMGlTu9FKqNuHpifXx9xWevf5kCz6UY6IKPON5ZxJRCMoFPHgpmTMyAcQmDOJiIiIiChzqT/DKREN3sAGb0OndajH/nYoipKRn5eJxgreG1LTSxpMceRiiiM3POfy9qLK3dfsrcrlQo3HCV8ovgfbO/1+bG9pwvaWpvBcsSULFTZHuPHb+KxsSEmqwyaioWHOJCKKH3MmEVH8UjVnCqIER/kqWApPQWfjFgR7XQgFuhHydyEU6IYc6IGks8DrPhz7BIqMzoYt0JavgiDqhnQPUZFDCPS2H2vk1qT6b8jn6fc4z9F1kHRZyCpeAPukM6Ez5w/xVRPRcDhzL8C4tk+i5hU5OHIXiUgh/FqCiIiIKLnY4I0oAWRZRl1dXf87JOmT0Op9n2FrSyOUkPoRb3++GU1HTQCA+RNtuPdL05IRXkqSfd3wNx9UzbHBG9HIkmUZR48exfjx4yGy8CojyUEv2g78WzWnzx6P7HGLkhQRUfpiziQiit9YzJmKouBfVfvw0oE9UdsWFBbj23MXQiel/ip7HR9Xw1/vjpof9/2z2NyNKEHGYs4kogSQoxu8ZSLmTCIiIiIiGisiG7wJbPA2dJEN3hQ/EOoBNObkxENEJ433hgZnNxixoHAcFhSOAwCEZBn1XZ2ocrejyuVCtduJ+q6OqMWq+tPQ1YmGrk58WHcEAKCXJEy02lFxrOFbhd0BhyEz70USpTvmTCKi+DFnEhHFL9Vzps6ch5yKL/S7PeTvwqEP7kXI1xG1rf3Aq2g/8CoAAaJGD1EyQNSZoDXYoTE6oNFnAwAURYYiBxHoboW/uxmBntZhN4YK+TvhPvwe3Ec+gH3iCmQVLwCgQFFkaA0OaE05wzovEfVv5fe/i19c2IxvzPy9ar715Z+i+KYnE3JNJd6bcURERESUEGzwRpQAoVAIu3bt6nd7Mlbg/KSpHv85XB1zW1uLEXKpiCKrAX+65tRRjiy1eY9+pv7kKojQl8xMXkBEGSgUCmH79u0oLi5OyRvrdPKc1WuivnjIm34pBIH/v4mGijmTiCh+Yy1nyoqCv+7ejreP1ERtO7N0Em6YeQrENFl6K+DsjpoTNCI0Dj7wRpQoYy1nElGChCIavImZ+VAlcyYREREREWUu9dM9kYuICtLA95g7txxB1vwJIx5VWpOyAEEDKCc8XBloZ4M3ojTGe0NDJ4kiSrOtKM224szSvrmeQAA1nr5mb1UuJ6rcTnT4fXGdzxcKYb+zDfudbeE5u8GICltf07dymwOTrHYYNHxMgijZmDOJiOLHnElEFL90z5mSzoKSRd+F58hHcB9Z289eCuSgF3LQC/jc8Hc2JD4wJQTXoXfhOvSuatpcMBu5k78MQdJCFHWQ9NkQJW3i4yHKYPklVnRL46LmPRtXJ6zBGzu8ERERESUXv7kkSgZxdB+obu3pxp92fHrCRCDmfpX5llGKKH14a7erxrqiKRB1mflQGhFRIvS0H4Cz+j+qOXPeTJhzpyUpIiIiIqLMEwiF8IcdW7CxsS5q20WV03BJ5bSkNJsfSY4LZkHUSskOg4iIiAYiRzR4k3gvnYiIiIiIKK2FZNVQkAZ+WLLtpa1s8BZBEAQoWgfgbzk+GXACxtLkBUVElAJMWi1m5uZjZm4+AEBRFLT29vQ1fDvW9O1whxtBWR7kTH1c3l580tSLT5r6HngXBQHjs7JRbnOg4tivIktW2iyIRURERERERJnNkD0ehllXAsAATd5GkgCtKQdaUx60pjxo9FnobtkNr+fwoEd2N+9Ed/PO42cStbCOXwJHxfnQGu0JjJkosxkdeVFzSsCLUG8HJGP2SZ9fAO+DEREREaUSNngjSorR/WD0avV+9AQDUEIKQn9v6Xe/+RNtoxdUmvDV7lCNDaVzkxMIEVEa6mk/iLrNv4Uin9hYVEDetEuSFhMRERFRpukNBvDIpxuxq039eV8AcO2MOVg1sSI5gQ1DqNePQFMHQl0+1Xz2sgpkn1aWpKiIiIgobqGIBm8iG7wRERERERGlMyWiwRsGafBG/YjV4I2IiFQEQUC+yYx8kxmLi8cD6FvkqrbTgyqXM9z4rbmnO67zyYqCIx0eHOnw4L3aQwAAk0aLMps93PCtwu5Alk6fsNdERERERERENJicyV+Cv6cFPW37ASV00ucTtSbozIXQWQqgsxRCZ+77r9aUB1HSqvbNnXIh/F3NcNd+CPfh96HIwbiuocgBuI98AM/Rj2GbcAYc5edBY7CedOxEY80ZF87Av5+7GBeWvayab/n7XSi69ncjfj1lxM9IREREREPBBm9ECSAIAnJyctDc3Nzv9tF0wNUOAFAO9ED+pDNqe8igxQ/OqcCVi8aPalzpwFu7XTVmgzeikScIAvLy8kY9N1Ji9TgPom7zo1BC6uYctokroM8el6SoiNIfcyYRUfzGQs7s8Pnwv598jBqPSzUvCQJunrsg/PBDOug92ILmv2yAEpSjtgkaPjRIlGhjIWcS0SiQIxq8SZnZ4I05k4iIiIiIxorIBm8CG7wNj9ahHrPBG1Fa472h0aOVJJTbHCi3Hc+jHT4fqt3HG75Vu13oCQYGOMtxPcEAdrW1qBbOyjeZUXHsGpV2B0qzrNBK0oi/FqKxijmTiCh+zJlERPHLpJyp0Wdh/KLvQVEUKHIActAHOeiFHPJCDvqgBL0I+jsR7HUh6HUh6O+EABEQRAiiBI0+GzpzAbSWvqZuGl3WkK6vsxQgf/pXYZ+4Em0HXkV3y2d915UH/6ytyEG4Dr0L95EPYS09HY6ys6E15Q73j4JozLn4W4tx9W/nRDV48x7ZmpgLKmzxRkRERJRMbPBGlAAajQYLFizAnj17Yu8gjm6x2+efu5TW6BsrvXkW3PuNRVg1q3BUY0oHihyC9+hO1ZyhdE6SoiHKXBqNBkuWLEl2GDSCepxVqNsU3dzNUngK8qd/NUlREWUG5kwiovhles5s7enGrzavQ2N3l2peL0n4/rzFmJVXkKTIhs57pB1Nf/442WEQjWmZnjOJaJSEIhq8iabkxJFgzJlERERERJS51A9ERjd4S/8HJpOCDd6IMgrvDSVXtl6PUwqKcEpBEQBAVhQ0dXeiyu1Clasd1W4Xajs9kON8YLWlpxstPd1Y33AUAKARRUzItqHyWNO3CrsDeUZTRjQNIEoG5kwiovgxZxIRxS8Tc6YgCBAkHURJB+iH1qRtJGhNuSiae4Nqzn3kIzir/4Ogz9PX8E2JXsAZABQ5APfh9+A+shZZRfNhn3QmDLZJ/CxNNAhBEPD9312J7Q/9C3PztoXn/d74FjMY/ALR1yMiIiKi5GGDN6IECIVCOHjwYL/bk/U5SN7XEzXXeNYUSBqurhqLv7kKil/9Z2YonZucYIgy2Oc5s7KyEhJXv0x7vc5q1G36TXRzt4K5KD71vyCIfPtJdDKYM4mI4pfJObO2w4MHN6+Dy+dVzWfpdLhjwVLVKvapLtDaiabH1w24j67IOkrREI1dmZwziWgURTZ4k4zJiSPBmDOJiIiIiGjMCKmb4wgSa8yGJbLBm9wLJdQLIUM/NxNlOt4bSi2iIKDYko1iSzaWl0wAAPhCQRzyuFHtcuKg24lqtxNOb+8gZ+oTlGVUHzvmc9k6PcptdlTYclBht6PM6oBJq03I6yHKNMyZRETxY84kIoofc+bosE04HbYJp4fHihxC0OuCs/o/cNeuA5SQ+gBFRmfDZnQ2bIbGmIOsonnIKjoF+uxSiBI/RxPFkmUz4EjnJFWDt1DdloRcS4lzQQQiIiIiSgx22CBKAFmWUV1d3f8Oo9zhTYECpSsEZb+6WVn3OBsUFt71y1u7QzXWWAuhsRYkKRqizCXLMvbv34/y8nLeWE9zXvdh1G2O1dxtDornfYPN3YhGAHMmEVH8MjVn7ne24aFP1qMnqF6dK9dowg8XLkOxZfRX7jsZvTVtUIKxV/WTrEZY5pTAPGvcKEdFNPZkas4kolEmRzwkKWbmg+rMmURERERElLnUD/cooYh7t5IIyHwAaMg0VgASgBMeeAw4AYn3vonSEe8NpT69pMFURy6mOnLDc05vL6pdTlS5+34d8rjgC4UGOMtxHX4ftrU0YVtLEwBAAFBsyUKFzYEKuwPlNgdKLNmQRNZjE0ViziQiih9zJhFR/Jgzk0MQJWhNuSiYdRUc5eeh/eDr8NStB5ToGuBgbztcNWvgqlkDQdRAn10Ko30SDLZJMNrLoDHmQBjlZ6yJUlHxpJyY810734Jl9nkjezE2eCMiIiJKKnbZIEoCYRS/xO8JBNDc0w2lwRe1rXu8bdTiSEfe2u2qsb50blLiICJKB4ocROO2P0MOelXz5oLZKJ73TTZ3IyIiIhoBW5sb8ejWjQjI6mKIkqxs/HDhMjgM6ddEJVZzNynbgHHfPwuSUZeEiIiIiGg4FDkIKOoGtJDS770JERERERERHRfZ4E2QRChyfM1w6DhBEKFobUCg/fhkwAkY2OCNiGi0OAxGOIrGYUFRX+4NyTKOdnag2u1EtduJg24nGro64zqXAqC+qxP1XZ34oO4IAEAvSSiz2lF+rOlbhc0Bexp+d0tEREREREQUD60pB4VzrkVO5RfhOvQO3LUfQQn5Y+6ryEF43TXwumvCc5I+G0bbJGSNW4isonkQBDZNp7Epy25ETsVUIKKc/uhvLsTUx3sgiMNvYrk134rT6tpOMkIiIiIiGinstEGUDKPYXf7DusMIyjJCa5xR2zrLcmMcQZ/zRTR4M5TOSU4gRERpwFnzDvzdzao5c/5sFJ/K5m5EREREI+GDo4fxxGdbIUesnjXZnoPbFyyBWZs5zdCKv30Gm7sRERGlG7knek7kA4xERERERERpLaS+Hy1IIpQAG7wNi9YR3eCNiIiSRhJFTLTaMNFqw1kTygD0Lahd43HioKuv6VuV24lOf+yH0yP5QiHsdbZhr/P4Q7MOgxEVtr5mb+V2ByZZbdBLrKMjIiIiIiKizKE15SB/xmXIqbwA7iMfwFO3AYGIZ8tiCfk60NW8A13NO9Ce9Qayxy2EpLMg5OtA0NcBUdLDnD8DRkclm79Rxis/72LgjV+o5pSgH51b/43s+RfHfR4xom9BUBThF0Xo5OjF2ImIiIho9PFbQqIEEEUR48aNQ3NzPzcjRqHBW1N3J363bTMOedxQQgqUI17Vdq/DNKqN5tKRt3aHamwonZucQIgynCiKKC0thSjyhmu6CvS60H7wddWcwToBxfO+CVHSJikqoszEnElEFL9MypmvVe/H6n27ouZPyS/EracuyqgHAQxludDYTMkOg2jMyaScSURJEuqNmBABUZ+UUBKNOZOIiIiIiDKXupZMCUU89COx1mzYtA71mA3eiNIW7w1lLpNWi5m5BZiZWwAAUBQFrb3dqHK5UOVuR5XbhSMdbgTjfCjW6e3F5qZ6bG6qB9D3kG1pljXc8K3CZkehOSvq4VuiTMKcSUQUP+ZMIqL4MWemHklnRk7l+XBUfAG+jjp0Nm5BV9N2+LsaBz3W31mPtn3/jJp3Vr8Fjd6GrOJ5yCpeAINtEgR+hqZMJGrwf9vvxPfnPqiaDrQeGtJppthzcbSzQ30OSYCO/d2IiIiIUkLmPP1JlEIkScKsWbOwdevWmNtH4z7Cb7duxuEOd99AVoCID2HuGUWJDyKNBTtaEHQ3qObY4I0oMSRJwimnnJLsMOgktO79O5SQ74QZAfmzrmJzN6IEYM4kIopfJuRMWVHw/L7P8HrNwahtp5dMwE2zToUmzQtU5G7f4DsRUcJlQs4koiSTIxq8SYaMLapkziQiIiIiorEissGbIKX3/eikYoM3oozBe0NjhyAIyDdZkG+yYMm48QCAQCiEIx0eVLmdqHI7Ue12oqWnO67zyYqCwx1uHO5w453aGgCASaNFuc2OCnsOKmx2lNscyNJl5sIZNDYxZxIRxY85k4gofsyZqUsQBBis42Gwjkfe1IsQCvTA6z6MXlcNvO4aeF2HEArE9zkaAII+N1yH3oXr0LvQGnNgLpgDnaUQOlMutKY8aIwOPrtGGcHtc6ChexyKzfXDPscV02aF7zkRERERUephgzeiBAiFQvjss8/630FIbLFbSJaPN3frh89uSmgM6c5bu0M1FnRG6AorkxQNUWYLhULYuXMnZs+eDUmSkh0ODZGvox6dDVtUc9bSZTDaJiYnIKIMx5xJRBS/dM+ZQVnGEzs/xUf1tVHbLiibjMunzkz7pikdG2rgfm9/ssMgIqR/ziSiFBCKaPAmGpMTxyhgziQiIiIiorEiqsFbmi84klSRDd5CXVBkHwSRTXyI0g3vDY1tWklChd2BCvvxvN7h84WbvX3+395gMK7z9QQD+KytBZ+1tYTnCkxmVNj6rlFuc2BCti3tF/2isYs5k4gofsyZRETxY85MH5LWBHPedJjzpgMAFEVBoKcVvc6DcNa8DX9nQ9znCvS2w334vYhZAfrsEuRUXoCsIjb9o/TWHTCf1PEGzSAtQ5STOj0RERERnSQ2eCNKAFmWUV8/QKfsNH8AeyyIbPCmL5kFQeQNP6JEkGUZtbW1mDlzJm+spyFP3XrVWNSakDv1K8kJhmgMYM4kIopfOudMXyiIR7duwvaWpqhtV06dhS+WT05CVCNLURS43tmb7DCI6Jh0zplElCLkiAZvUuY2eGPOJCIiIiKizBXxdE9kgzcNm8sMm9YGQIDqzzjgAvSFSQqIiIaL94YoUrZej1MLinBqQREAQFYUNHR1hhu+VbmdONrhifsZ2uaebjT3dOPjhqMAAK0oYmK2DeV2R1/jN5sDuUZT2i8GRmMDcyYRUfyYM4mI4secmb4EQYDOnA+dOR/ZJYvRUb8JHfWbEfJ1QBAlSDoLJF02fJ5a+Drr4jijAl/HUTR8+v/B6KiEo/xcWApmJ/x1EI2k0il5Meeb/34Xcr5w24hdR1HY4Y2IiIgomdjgjSgJ+KV66vPVbleNDaVzkxIHEVEqU+QQOuo3qeZspcuh0WUlKSIiIiKi9Nfl9+OhLetx0NWumhcFAd+YPQ+nl0xIUmQjTFYgd/ujpnWF1iQEQ0RERCctFNHgTTQlJw4iIiIiIiIaMUpEgzdIbPA2XIKggaKxAkH38cmAkw3eiIgykCgIKMnKRklWNs4YPxEA4A0GccjjQpXb2df4zeWEy+eN63wBWcZBtxMH3c7wXLZOj4pjDd/KbQ6UWe0wabWJeDlERERERERECSEIIqwli2EtWRxzu6+zAZ0NW9DRsBmB7pZBz9frPIh650EUzr0BloK5CPk8kEMB6Cz5ECX9SIdPNGIqZxfDYNJFbwj54avfA/246SN0JTZ4IyIiIkomNngjSgaRDd5SnZcN3oiIBtXduhshX4dqLrufG+tERERENLj23h48uPlj1HWp32PpRAnfOXURTjm26num0hZkwXb21GSHQURERMMhRzR4k4zJiYOIiIiIiIhGjBJSP+wjDFbzxpK4gWlzohu8ERHRmGDQaDAtJw/TcvLCc+29Pah2u1DlbkeV24VDbhf8ciiu83X4fdja3IitzY0A+n4Ej7Nko8Le1/CtwuZASVY2RC5ITkRERERERGlKn1UM/ZQvI2fyl+DrOIqupu3wdzch0N0Gf08r5EB3zOOatj+lGguSHpbCucgung+jYzIkLWuaKHm21bpjzj+w+mq8c+s/UGk7oJr3tx0ZwQZvRERERJRMbPBGlACiKKK8vBzNzc0xtwv8wjylyX4vfI37VHOGCXOTEwzRGCCKIqZMmQJR5ErP6aajboNqbLBNhD4rs5uOECUbcyYRUfzSLWc2dHXgl5s/Rntvj2rerNXi9vlLMdmRk6TIRk/B1YsgxVqBjIgSLt1yJhGloFBEgzcxc4shmTOJiIiIiChzqWvalJCs3qoZ5HOQMvDmMU9rB078+MwGb0RpifeGaKTkGE3IMZqwsGgcACAoy6jr7ECV24kqtxPVLicaujvjOpcCoK6rA3VdHVh79DAAwCBpMMlqR4XdgQqbHeU2B+yGzL1vS6mJOZOIKH7MmURE8WPOHFsEQYDBWgqDtVQ1Hwr0oKN+M1p2PTfg8UrIh876Teis3wRRY0Dxqd+AOX9mIkMm6ldVSzcOtXVjUq5ZNT9hSh72ipdiMT5OUmRERERElGhs8EaUAJIkobKyEuvXr4+9Axu8pTRf/W7gxFXwBAGGklnJC4gow0mShKlTpyY7DBqikL8bXc07VHPZJUuSFA3R2MGcSUQUv3TKmdVuJx7c/DG6An7VvN1gxI8WLkNJVnaSIiOisSKdciYRpSg5osGblLkPCjJnEhERERHRmBHZ4E0a/IHJ3po2GMtyExVRetNGLOTCBm9EaYn3hihRNKKIiVYbJlptOHtCGQCgO+BHtduFarcTVa6+xm+R3yn3xxsKYq+zFXudreG5HKMJFTY7KmwOlNscmGS1QydJCXk9RABzJhHRUDBnEhHFjzmTAEDSmmCfuAIABm3y9jk56EXrvpejGryFAj3wddbD11F37NdRBHraIWqM0OizIOmzIWnNEDV6SHorsopOhc6cP9IviTKQRR/dxmNrrTuqwRsA2EqK4e6ywqb3JCQWdjUgIiIiSi42eCNKgGAwiE8++aTf7cJorw7glQffh8K8tdtVY11+BUSDJTnBEI0BwWAQmzdvxsKFC6HR8K1Juuho2AxFDobHgqhBdvGCJEZENDYwZxIRxS9dcubO1mY88ukG+EIh1XyR2YIfLlyGPFP0F7hERCMtXXImEaWwUI96LGZugzfmTKKxo7q6Grt378bRo0fR2dkJWZZhs9lgs9kwZcoUzJo1CzqdLu7zCScsAnbGGWdg7dq1/e5777334r777guP33//faxYsWI4L4NSzNNPP40bbrghPH7qqadw/fXXJy+gNBIIBPDxxx+jtrYWjY2NsFgsKC4uximnnIKJEycmPbZNmzahqqoKra2tEEURxcXFKC8vx4IFC1T//k9WfX09NmzYgObmZng8HuTm5qK4uBiLFi1CXl7eiF2HiMYqRT2KaPCGOGrenG/swrj/t2IEY8ogWrt6HPRAkYMQRH62JEonvDdEo8ms1WF2XgFm5xUAABRFQXNPd1/Dt2NN3450uBFSlEHO1Ke9twftvT3Y1FgPAJAEAaXZVpTbHKg49qvQbBnRzzA0tjFnEhHFjzmTiCh+zJl0ItuEMyBq9Oio3wyv+zDkQPeA+/s66nD4g58BAqDIIchBL4JeV8x9Q/5OBHpaouad1W9hwul3Q2fiYic0sC/OLsRT64+o5oKh+O7jjLQ4bx8RURpjrRslAmvdho+1bn1Y63YcP70SJYCiKGhvbx9gj9H94jv06kCxUKTIBm+G0rlJiYNorFAUBa2trVB4lyhtKHIQruo1qjlzwWxIOjYfIUo05kwiovilQ85cX38Uf9jxSVTBfZnVjjsXLkWWTp+kyBJPkVP3/wvRWJQOOZOIUlyoVz2WMrfBG3MmUWbbtGkTnnjiCbzyyitoaYku1D6RTqfDwoULccUVV+Cyyy5DTk7OKEWZPJEFW/0xGo2wWq0oKirCvHnzsHz5clx66aUwGjP35wMlTmtrK+655x688MILcDqdMfdZsmQJfvCDH+CSSy4Z1diOHDmCBx54AC+88AI6Oztj7jNu3DhcffXV+MlPfoKsrKxhXUeWZaxevRoPPvggdu7cGXMfSZJw5pln4s4778TZZ589rOsQEUWKbPAmaAavefPXxX4QjgBoHdFzQRegG1tFy0TpjveGKJkEQUCh2YJCswVLx5UCAPyhEI50uFHldvY1fnM50drbM8iZ+oQUBYc8bhzyuPHOkRoAgFmrDTd8+/y/liE89Ed0IuZMIqL4MWcSEcWPOZNOJAgCrCWLYS1ZDEVREPS6oMghaAzZkIM+NO34C7pbPlMd4+usO6lryoEe1G/+LSYu/28u4EEDKsszY1KuCYfa4rtXk1jMmUSZiLVuA2OtGyUDa91Y69YffnIgSoZRXthMqfdFzck6/vPvj692h2qsL52TpEiIiFKTp24jAr3q5qG20uVJioaIiIgoPb11qArP7NkRNT8zNx/fn7cYhgxcVVCRFXgPtaH3YAs8aw8kOxwiIiIaIYoSAhS/elJkUQMRpZc9e/bgu9/9Lt555524j/H7/Vi3bh3WrVuHH/zgB7j55pvxk5/8BLm5XKW7t7cXvb29aGpqwrZt2/DEE0/gO9/5Dn74wx/ijjvugCRJyQ6R0sSbb76J66+/ftAi1PXr12P9+vW46qqr8Mc//hFmc+IXJXriiSdwyy23wO/3D7hffX09fvWrX+H555/HX//6VyxfPrTv1JqamnDJJZdg/fr1A+4XCoXw9ttv4+2338Y3v/lN/OY3v4Fen7mLBxDRKAmpH/YRRDFJgWQGQdRCkbKBUMfxyUA7G7wREdFJ0UkSKu05qLQffxjP4/OGm71VuZ2o9rjgDQbjOl93IICdrc3Y2docnis0W1QN30qzrdDwfQERERERERGlGEEQoDUeX2hDlPQomHU1at77MaDIAxw5dP6uRlS9fTuyiuZB0poRCnQjFOiGHPTBkF2CnMovQtQYRvSalJ4cZt2wG7zJ8sn9vVVObGbA/m5EGYW1biOLtW40Uljrxlq3gWTek6JEaSDZxW6KJEDW859/LIosw1u7XTVnKJ2blFiIiFKRIgfhrHpDNWewl8GUOy1JERERERGlF0VR8PcDe/Dvqn1R2xYXl+BbcxZkZDG8oiiofeANyD0D3wgmIiKiNBTqjZ6T2OCNiNLHE088gVtvvRVerzdqW1ZWFqZNm4a8vDxYrVa0tbWhqakJBw8eRG/v8fzn8/nwyCOP4M0338S+fdGf9whwu9348Y9/jDVr1uD111/nCqc0qLVr1+IrX/mKqqhMEASceuqpKCsrg9vtxrZt29DW1hbe/uyzz6KjowP/+te/ICbw/sr999+Pn/70p1Hzc+bMQUVFBQCgqqoKO3Ycb+5/5MgRfOELX8D777+PhQsXxnWdhoYGnHbaaTh69Khq3mazYdGiRXA4HHA6ndi4cSM8Hk94+x//+Ee4XC48//zzEIRRXoGQiDKKEop4cEjKvHvXo05rj2jw5kpeLERElLGsegNOLSjGqQXFAABZUdDQ1YkqtxNVrnZUu1042umJ+7nepu4uNHV3YV19LQBAK4qYaLWj4ljDt3KbHblGEz9/EBERERERUcrRGu0omnsj2g+8Cn93c/87CiJ0liLos0tgyC6BzlIEJeRH0N+JkK8Dvs4GdDVtUx0iB3rgqf0o6lQ9rbvhPvIBik65CZaC2SP9kihDWbINQJd6bteGI1h6SvznkAQBIeX4HR+XUQdLIL6m/0SUPljrNjpY60ZDxVo31roNhh2eiBJAkiTMnDkTzc39fOBPclJRzp6a1OunMu/RHZC9nao5NngjSixJkjB37lx2sE4THfWbEOhpU83lVn4po98wE6US5kwiovilYs6UFQVPfrYV7x89HLVt1cRyXDN9DsQMfF+lKAqO3PsaFF//XxALWgkau2kUoyKiE6ViziSiNCLHaPAmZu4KtMyZRJnll7/8JX784x+r5kRRxFVXXYUbb7wRS5cuhVarjTqut7cX7777Ll566SU8++yzCAb7Pu/EKpzLZIsWLcLzzz8fNd/V1YXa2lqsWbMGTz75JDo7j3//+v777+Paa6/F3//+99EMldJMXV0dLr74YlXB29KlS/H4449j2rTjiw75fD788Y9/xO23345AIAAAePXVV3H33XfjF7/4RUJie+WVV3DPPfeo5s4++2w89thjmDx5smp+//79+Pa3v4333nsPANDT04MLLrgAO3fuRGFh4YDXCQQCuOSSS1QFbxaLBQ8++CBuuukmVW4KBAJ4/PHHceedd6K7uxsA8OKLL2LKlCn42c9+dlKvl4jGGvX96cgGbwIbvJ08XQ7gPXJ8HGhPXixENCy8N0TpSBQElGRloyQrGyvGTwQAeINB1HhcqHY7UeVyosrthNsX332NgCzjoKsdB13Hf47Z9AaU2+woP9b0rcxmh1ETfU+FxhbmTCKi+DFnEhHFjzmThip73EJkj1uIkL8b3o5aBHtdEAQJgigBogZaYw50lkKI0sCfYxu3P4WOug1xXVMOelH/yWMYv+R2mByVI/EyKMOd/bU56HhEPffaX7Zgyc1K3M9tTnXkYnd7a3jcbDZgfEfPCEZJRMnGWreTw1o3ShTWurHWLR5s8EaUAKIooqSkpP8dEvywdmvvIB+4Mu9Z8RHTsekF1VibOxEae3GSoiEaG0RRxIQJE5IdBsUhFOhB2/5XVHMG2ySY8qYnKSKisYc5k4gofqmWM/2hEB7bthlbmhuitn118gxcWDElI5vmKoqCo794c8DmbgCQd8UCCBoW2xAlS6rlTCJKM6GIBm+iEYKQuQ/9M2cSZY5XXnkFd911l2pu6tSpeOGFFzB79sCraBuNRlxwwQW44IIL8JOf/AR33303XnzxxRGL7d5778W99947YudLFIPBgIkTJ8bcNnPmTJx//vm4/fbb8eUvfxnbth1fzfyll17Ce++9hzPPPHOUIqV0c88998DlcoXHS5YswTvvvAODQd1EVq/X4zvf+Q5KS0tx0UUXhed//etf45vf/OaI/8wOBAK49dZboZyw6vlFF12EF198ERpNdAnWlClT8J///AeXXHIJXnml7zu21tZW3HvvvfjDH/4w4LWefPJJbNy4MTzW6/VYs2YNFi9eHLWvVqvFt7/9bcyePRtnnXVWuFjwwQcfxI033tjvv1MiokHJimooSJl3D3vUaezqsd+ZnDiIaNh4b4gyhUGjwfScPEzPyQPQ972u09uLqmMN36rdTtR4XAjI8iBn6uP2efFpcyM+bW4E0FeqXpKVHW74VmF3YJwlOyMXPKP+MWcSEcWPOZOIKH7MmTRcks4Mc+60wXfsR8HMK6A15qCreQd8HUcHPwAKjq7/X0g6CyRdFvTZ4yAIEnxdjZA0Rjgqzz+peCizzDxtAtbHmG9v6kRuUXZc5zhrQpmqwZtXq4ECthQgyhSsdTt5rHWjRGGtG2vd4pG5T1cQJVEwGMS6dev63S6Iif04tLmxXjXml+HxURQFHZvUXXezF12ekQ/YE6WSYDCI9957L9zxm1KToiho/uxZBL0u1Xzu5C8xTxKNIuZMIqL4pVLO7AkE8ODmdVHN3QQAX591Kr5SOTVj31N5D7Uj1OmLuc1YmY+ci+di0i8vgnl60ShHRkQnSqWcSURpSI5o8CYZkxPHKGHOJMoMNTU1uO6661SFKwsWLMC6desGLXiLVFlZiRdeeAFPPvkkjMbMzoHDUVJSgtdeew3Z2eqC49/85jdJiohS3cGDB/GXv/wlPNbpdHj66aejCt5O9JWvfAXXXXddeOzz+XDfffeNeGzPPvssamtrw+O8vDz8+c9/jlnw9jmNRoOnnnoKOTk54bknnngCBw4c6PcYRVHwq1/9SjV39913xyx4O9GyZctUxbw+nw8//elPBzyGiEhN3dBNCUU0dBEHLzcVdFxzeEC6HPU46IGihJITCxENC+8NUaYSBAE5RhMWFZXgqumz8dMlK/DEuRfigWVn4voZc3H6uFIUmS1xn08BcLSzA2uPHsYTn23Fjz58B//1n1fw840f4oV9u7ClqQFurzdxL4hSAnMmEVH8mDOJiOLHnEnJImoMyJ3yZUxc/t+YtPLnyJ16ESxFpyKreCFsE1bAUjA35nEhfxf8XY3obNiCjvpN8Hlq0dO+H3Ub/w9NO/4CRebfZepjsuij5pSIxXgGUmIZoBGcEv95iCj1sNZt9LDWjYaKtW6sdYsXG7wRJYCiKOjq6up/hwQ/tL2ztVk1Ng6QYOm43upNCLQdUc1ZT7s8SdEQjR2KoqCzs1P1wZJST0f9JnQ2fKKaM+VMhSlvRpIiIhqbmDOJiOKXKjnT7fXi/o0fYK+zTTWvFUV899TTcGbppCRFNjoCbZ0x50vuXIXCry9F9sLMfv1E6SJVciYRpalQRIM3MbMLPpgziTLDD3/4Q7jd7vDYZrPh5ZdfVhWlDNUNN9yA1157bQSiyzzFxcX4r//6L9Xc+++/j0AgkKSIKJU999xzCIWON7q5+OKLUVlZOehxP/zhD1XjF198Ed4Rbhbw6quvqsZf//rXYbfbBz3O4XDgxhtvDI9DoRCeeeaZfvfftWsXDh06FB4bDAb8v//3/+KK8dZbb1UVCP79738fuH6FiGgASkj9uUeQBq95E3VSosLJDFpHxIQMBNzJiISIhon3hmgs0YgiJlntOGdiOb41dwEeWnEu/rTqS/jhwqW4uHIa5uQVwKLVxX0+byiIPe2teKV6P/7v0w245d3X8d333sSjWzfhzZqDOOBshz/ExqeZhDmTiCh+zJlERPFjzqRUoDPnIafiCxg371soPvUmFMy6EuMWfBvlqx4e0nk8Rz+Gs+adBEVJ6cYYo8HbSGHGJEpvrHUbXax1o6FgrRtr3eLFrk9ESSAkuMFbd9CvGuslDTIzhY2sjk3Pq8a6oqnQjx9a12Iiokzk72lDy67nVHOi1oTCudcn/GcaERERUTpr7u7CLzevQ0tPt2reqNHgB/OXYHpOXpIiGz2+WlfUXMmdq6B1mJMQDRERESWEHNHgTcrsBm9ElP4OHjyIl19+WTX361//GiUlJSd97vLy8pM+RyIcOHAAO3bsQF1dHYLBIPLy8jBv3jzMmjVr1GJYtWoVHn74eDF7Z2cn6urqMGlS/I2/ZVnG5s2bUVNTg6amJvh8PkyYMAFXXnnloMfW1NRg69ataG1thcvlgtVqRWFhIRYvXozi4uJhvaYTdXV1Ye3ataitrUVHRweKiopQWlqKZcuWQavVnvT5x5J//vOfqvENN9wQ13HTpk3DokWLsGnTJgBAd3c31qxZgy9/+csjFtuHH36oGp977rlxH3veeefhf//3f8Pjl156Cffff39c11m0aBFsNltc13E4HFiwYAE++ugjAIDX68Xrr7+Oyy67LO5YiYjCZFk1FKTB1xMWdCxJHYgg6qFIZiB0wvcGASegG/7DF0RERKPJrNVhdl4hZucVAuhrrNDc04UqlwtV7nZUu1040uFGKM5GC229PWjr7cGmxjoAgCQImJBtQ7nNjgpbDirsdhSYLKxTJCIiIiIiorSg0WUhf+aVUc/hDaTXeQDAeYkLiggA2BSTKG2x1o21bqx1S22sdWOtW7xYTUOUDPySOeUocggdm15QzVkXXc6CACIa8xQ5hMZtf4YcVHd9Lpx1NbTGyJWliYiIiOhzhz1uPPjJOnh8PtW8Va/HnQuWYaLVlpzAEkgJyvC3dMD1nz3o3d8ccx99qZ3N3YiIiDJNqEc9FtngjYhS2yOPPAL5hGYlBQUFuPrqq5MYUbR7770X9913X3j8/vvvY8WKFTH3PXz4sKpw7LrrrsPTTz8NAHjttdfw85//HBs3box5bFlZGX72s5/hqquuGrHY+zN+/Piouba2NlXsTz/9tKrA6amnnsL111+P3t5e3H///fjLX/6ChoYG1TmsVmu/RW/d3d149NFH8eSTT6Kqqqrf2ObOnYu77roLl1566ZC/H25sbMQPf/hD/P3vf4+5gmZubi6uu+463H333XEXLY1lTU1N2LFjR3is0WiwdOnSuI9fsWJFuOgNAN58880RK3rz+Xxoa2tTzc2cOTPu4yOLTPft24eamhqUlZVF7VtXVzfs63x+rc+L3gBkbNEbESWeEop42Ecc/OekqJMSFE0G0TqiG7wRERGlKUEQUGjOQqE5C8tKSgEA/lAIhz1uVLudqDr2q623Z5Az9QkpCmo8LtR4XHj7SA0AwKLVodxmR7nNgUq7A+U2B8xaXcJeExEREREREdHJsE9cAX1WMXwddZC0Zkg6CwK9bfB6aiEHvehs+ES1f3frXoT83ZB0rK8mtYvLX0Sn+xfIG2dNdihElESsdTuOtW59WOuWOljr1oe1bvFhgzeiBJAkCfPnz8frr78eewdh8NVMTwobaQ9Zz/4PEfQ0qeayF2Ve0idKRZIkYfHixZAkFvmmImf1W/C6qlVz2SVLkFU8P0kREY1tzJlERPFLZs7c096Kh7eshzcYVM3nm8z40cJlKDBbRj2mROvZ04jmv8b+EuVEopGF9kSpiO8zieikyL3qsZTZDd6YM4nS31tvvaUa33DDDRm36mQoFML3vvc9/O53vxtwv5qaGlx99dXYsGEDfvvb3yZ08StlmKtB7927FxdffDH27ds3pOP+85//4Nprr0VLS8ug+27fvh1f+9rXcPbZZ+Oll16C1RpfcfQ777yDSy+9FB6Pp9992tra8PDDD+Oll16K+rtH0Xbt2qUaz549G2Zz/A8yLFmyRDXevXv3iMQFAE5ndPOdoRQyxtr3s88+i1n0FnmtoRZMRu7/2WefDel4IhrL1O8FlJCs3ioNXvMm6FiSOiitA/AePT5mgzeitMJ7Q0SD00kSJjtyMNmRE55ze72ocjtRHf7lgjcUHOAsx3UF/NjR2owdrccXGSsyW1Bh62v2VmF3YHyWFRoxwfX5NGTMmURE8WPOJCKKH3MmpQNTzmSYcibH3NYzYTmObnj4+IQSQvOuZ1F0yk0QEv3sOaU4dV2FWduNDfd/E2XP/ztJ8RBRKmCt23GsdevDWrfUwVq32NdirVtsrKYhSgBRFJGbm9vv9gS+V0BQltHU3RVxvQReMEN4Nj6vGhtK50JfPDVJ0RCNLaIoIj8/P9lhUAxyyA9n9X9Uc1pTLgpmXp6kiIiIOZOIKH7JypmfNNbjd9s3IyirH3ybkG3FnQuWwWYwjHpMieZv6YiruRsAZC+aNPhORDTq+D6TiE5KKKLBm5jZDd6YM4nSW11dHWpqalRzZ555ZpKiSZzvfve7eOyxxwD0fVc8e/ZslJWVQa/X48iRI/jkk08QPKEp+WOPPYYZM2bg5ptvTlhMkas0AhjwO3Wgr/DnggsuCP8/0+l0WLBgAcaNGwe/34+amhrU1tZGHff444/j5ptvRigUUs1PnDgRM2bMgNVqRUdHB3bs2IGjR483V3nnnXewbNkyrF+/HllZWQPGtnbtWnz5y19Gb6/65+CECRMwa9YsmM1m1NXVYdOmTQgGgzhy5AjOPfdcfOc73xnwvGPdnj17VOOKioohHV9eXj7g+U6GThfdtN7n80Gjia/0yufzRc3t2bMHF1544aDXinXsUK61b98+yLIMkc0OiGioIu5zQxQGXXhU0PGhykFpHeoxG7wRpRXeGyIaHpvBgPmFxZhfWAwAkBUF9V0dqHI5w43f6jo74l7jvLG7C43dXfiovu++gFYUMclqR8Wxhm8VNgccBiNr6JOMOZOIKH7MmURE8WPOpHRndEyGOX8muluONwTpbNgCRZZRNPcGiBp9EqOjZNI6xiPkUtdWVIrvj8i5eYeEKD2x1o21bqx1S22sdYt9Lda6xcYGb0QJEAgE8M477/S/QwK/LK52O+GX1T+89aK6cK6lyw/kgI5RggF0fvKSai77NDYvIhotgUAAa9aswapVqzKua3i6627ZDTnoPWFGQNEpN0HUZF5TEqJ0wZxJRBS/ZOTM92oP4cnPtkYVnU9z5OEH8xfDlKG5u3tHfVz72c+dDtP0ogRHQ0TDwfeZRHRSIhu8SZnd4I05kyi9ffzxx6qxIAiYP39+kqJJjNdffx1tbW0AgJtuugn33HMPSkpKVPvU19fjG9/4Bt54443w3I9+9CNce+21Q1pBcijefvtt1dhisUTFFenee+9FZ2cnDAYDfvrTn+LWW2+FxWJR7XPo0CHV+L333osqeLvqqqtw1113Yfr06VHXWLt2LW699dbwapq7du3CN77xDaxevbrfuNxuN66++mpVwdvkyZPx+9//HmeddZZq3/b2dtx777343e9+h9raWvz85z8f8DWPBK/Xi6ampoRfR6PRDPr/cKiqqqpU49LS0iEdP2HCBNW4vb0dLpcLdrv9pGOz2+0QRRHyCc2OGhsb4y7Ma2xsjJrbv39/zH1zctQFHbGOHcq1vF4vamtrMXHixCGdh4jGouN3t4OeXoQ61UW0giRCCYYiD1KRewMJiSyjRDV4c0FRZAhCZhUnE2Uq3hsiGhmiIGB8lhXjs6xYWdq3SFhvMIAatyvc8K3K7YQnzoeAArKMA652HHC1A8duFdj0hnDDt3KbA2VWOwxxPrhEI4M5k4gofsyZRETxY86kdCcIAgpmXYPDH94HOdATnu9q2oq6TR6ULPoun90bo/IvuR+1D56tmtOJXnS6eiGIAszZ+mE3s1fibqtPRKmEtW59WOt2HGvdho+1bqx1SzZ+Q0WUICd2gY0kJLBT5N72NtV4vDEbTW0RHWJF9to+UdfudxDqVq+Emr3wsiRFQzQ2DZQzKXk6Gz9VjU05k2G0lyUpGiL6HHMmEVH8RitnKoqCf1Xtw0sHolfKWFBYjG/PXQidJMU4Mv0pigL3u/tibpMseuRdNh9SlgEahwmijrciiVIZ32cS0bDJXvVYzOwGbwBzZqZSFAVer3fwHWlYDAbDsItMR1J9vbpBdUFBwYgUw6SSzwvefv/73/e7Sum4cePw73//G6eddho+/bTvu4COjg48//zz+PrXvz7iMTU1NeHxxx9Xza1YsWLQBy46Ozuh0+nw5ptvYsWKFTH3mTRpUvj3XV1duPLKK8MFb4Ig4IknnsCNN97Y7zVWrFiBDRs2YNWqVdiwYQMA4Pnnn8fNN9+M5cuXxzzmpz/9qerv0vTp0/Hhhx9GFSoBfcVLv/3tb1FeXo7vf//7cLlcA77mkbBx40asXLky4deZMGECDh8+PKLndLvdqnF+fv6QjrdYLDAYDKp87vF4RuTfuSiKmDx5MvbtO34fZOPGjXEXvX3+9+tEHo8n5r7Tpk1TjTdu3DiESGPv39+1iIhiCXX5UP+bd6PmBUmAEhi4wZu/3o1ghxeabD581q/IBm8IAcEOQGtLRjRENAy8N0SUGEaNFjNy8zEjt++zoKIoaOvtQbXbhSp3O6rcLhz2uBA44WGkgbh9XmxpbsCW5gYAgABgfJYV5TY7Kuw5qLA5UGzJgpgC96wyGXMmEVH8mDOJiOLHnEnpTmu0o3D2tWjY+idAOf45t9dVjdoNDyOrcC4Mtokw5UyFIGZmDTpFs8w4C9rLX0bg+YtV8zcufhQAcOals/Gt+89LifobSj7WuiUWa91GD2vdWOvGWjfWuo2VWjc+VUmUDAl8Q1ftVjcqy2kQUO1Rv0H3OUyqcbZxbK9U0LHpedXYWH4adHkTkxMMEVGKkEN+dDXvUM1ZiuYlKRoiIiKi1CUrCp7ZvQNrjlRHbTuzdBJumHlKRheE++vdUXOiWYcJ//3F0Q+GiIiIRp2iyIDcq56UMr/BG2Umr9eLp59+OtlhZKzrr78eRmPy84PTqf4u1WazJSeQBLvyyiv7LXj7nEajwX333YcLLrggPPfee++NeNFbQ0MDvvzlL0cV3Nx6661xHX/33Xf3W/AW6Q9/+AOam5vD4zvuuGPAgrfPWSwWvPDCC6isrITP5wMAPPTQQzGL3jo7O/HUU0+Fx5Ik4dlnn41Z8Hai733ve3j33Xfx2muvxfVaxqquri7VeDh5w2g0qoreOjs7Tzquz51xxhmqordnnnkGV199dVzH/vWvf42a6y+2M844QzWurq7Ghg0bsHjx4kGvs27dOtTU1MR9LSKiWNzv74fcE4jeIIqQgwM3eAOAjo+r4PjCzAREliFEIyAa1A3TA042eCMiIoogCALyTGbkmcw4rbgEABCUZdR2eFDldqLa7USV24mm7q5BztRHAVDb6UFtpwfvHz0MADBqNCiz2lFhc6DC7kC5zQGrno1qiYiIiIiIKPGyik5FycLvoGHrnyAHesLzPs8R+DxHwmNJnw37xJXQGOwQJB0sBXMgSmP7mfB0tq9p4O9t80rz0dDPtvde2onzr5mPCVPyhn5hZeiHUGpjrVtisdZtdLHWrQ9r3dIPa92OX+dErHWLTUx2AERjUgIf7D7kUXdB9X7WoR7nmBE061Vzs0uyExZPqpP9XnR++k/VXPZplycpGiKi1NHduhtKyHfCjICsolOTFg8RERFRKgrKMn6/fXPM5m5fqZiKGzO8uRsAeGvaouayF5clIRIiIiJKCjnGCpBi8otaiIj6097erhpnatHbf//3f8e136pVq6DT6cLjbdu2Dek6Xq8Xhw8fjvq1Z88evPXWW/jBD36AadOmhVdO/dxXvvIVrFq1atDzm0wmfOc734k7nt/+9rfh31sslrj/HABg/Pjx+NrXvhYer1mzJlwAd6J//OMfqsKsiy++GHPnzo3rGg888EDc8YxVkUVvBsPQH6qPLJSLPOfJuOaaa1TjNWvW4I033hj0uFdffRXvvvtu1Hx/hWiFhYU466yzVHO33XZbeMXe/gSDQdx2220xt2Vi0RsRJU7P7saY84IkQgnIgx+/J/bx1EcQBEDrUE8GnLF3JiIiIhWNKKLMZseqieW4ee4CPLziXPzhnAtwx4KluLhyGmbnFcCsjf8h995gELvbW/Hv6v14eMsGfPud1/G9997E77ZtwpuHDuKgqx3+QT6LEREREREREQ2XOW86ShffAUlr7nefkK8Dbfv/jaYdT6Nx659w5KMH4OuoH8UoaST9e3sjegP932vQG3X9bgOA1nrPgNv7o7DDG1FaYq2bGmvdjmOtW2pgrVsf1rrFR5PsAIgykUajwdKlS/Hyyy/H3C6IiXm42+31wuVTP0zlPqj+sNZdYlONr1hYAr1GSkg86aBr55uQvSckd0FA9oKvJi8gojFIo9Fg5cqV0Gj4tiSVdDaoP4Aacyqh0Y/dhqBEqYI5k4gofonOmd5gEI98ugGftbWo5gUA10yfg3MnVSTkuqlCCcpwvb0Hng8ORm3LWjhx9AMiopPC95lENGyh3ug5KbMbvDFnElGqKysrw9SpU+PaV6vVory8HHv37gUAtLS0DHKE2qZNmzBp0qQhHXP66afj2WefjWvflStXwmq1xrXv/v37UVtbGx6ff/75sFgsQ4pt5cqVeOaZZwAAPp8Pmzdvxumnn67aZ926darxFVdcEff558yZgxkzZmD37t1DimuoVqxYAUXJjOJsYRiN84dzTLyWLl2KM844Ax988EF47sorr8Qrr7wScxVcAFi7dm2/K5+KYv/rcv7kJz9RFcpt2LAB11xzDZ566ino9fqo/b1eL6677jps3rx5yNciIjquL4cqcuwmboIkQBngoaPwfjp+XhqU1gH4Go6P2eCNKG3w3hBR6snS6TE3vxBz8wsBAIqioKm7C1VuJ6rcTlS7najt8CAU52fl1t4etPb2YENDHQBAEgRMyLahwu5Aha3vV77JnNDPn5mCOZOIKH7MmURE8WPOpEyjzx6HktO+h7rNv0XI1zHo/v6uRhz+8D5YCuagYPY1fN4vxek10d/T7m3sxKmltriO10kBZOvc6PDHt39/BPA+BhGlJta69WGtG2vdEoG1bqmFn2CJEmTg7pqJSbKHPC7VWA8JjVXqzry+XPUPdqsx/lXKMlHHpudVY9OUM6C1FycpGqKxK7K7MCWXHAqgu2Wnai6raF6SoiGiSMyZRETxS1TO7PD58L+ffIyaiM/hkiDg5rkLsLh4fEKum0ra/rkNXZ/WRs3rx9uhyebPKqJ0xPeZRDQsckSDN0EHQcj8RWWYM4nSl8PhUI09nuGtbJzKpk+fPqT97XZ7+PeJ/PPIzs7GHXfcgR/96EdxP2xxyimnxH3+yGK00tJSHD58eCghRhUFVVdXRxW9bdmyRTVetGjRkK6xaNGihBe9pbPIQsXe3hjNZAcRecxQix8H8+STT2L+/PlwufruC3k8HqxcuRJXXHEFvvrVr6KyshKKoqCqqgovvvgiVq9eHS5CLCkpQV1dXfhcA62svHLlSnznO9/Bo48+Gp5bvXo1Nm7ciFtuuQXLli2Dw+GA0+nERx99hMceeyz8d95gMMBsNqtWcs7UVZyJKEHkfoqnRTGuBm8iG7wNTqt+X8oGb0TphfeGiFKbIAgosmShyJKF00smAAD8oRAOeVyoPtb0rcrtQntvT1znCykKajwu1HhcWINqAECWTodyqwMVdgfKbQ6U2+wwa3UJe03pjDmTiCh+zJlERPFjzqRMY7BOQNnKB9DRsAU+Ty26W3ch0NM24DFdzTvQ9fYOmPNmImfyF2G0l49StDQUK6bkYX21+juAUH/fw/R3jnHv4JVDl55kJJnROIdorGGtWzTWuh3HWrfkY63bcax1GxwraYgSIBgMqrpLRklQF02nV5287c1ArV9dVOezm8K/14gCllXkJCSWdCB7u9C5/VXVnPW0y5MUDdHYFQwG8cYbb+D888+HVju2m06miu7W3ZCD3hNmBGQVnpq0eIjoOOZMIqL4JSpntvZ041eb16Gxu0s1r5ckfG/eYszOKxixa6UiJSSjZfUn6NnVEHO7aQabphOlI77PJKJhC0V8ES1lfgEtc2bmMhgMuP7665MdRsYaeHGs0ZOTo/5u1O12JyeQBDqxiC0eJ+ayYDA4IjEYDAZYrVYUFhZi3rx5WL58OS699FKYzeYhnSc/Pz/ufY8ePaoaP/TQQ3jooYeGdL1ITmd0o5Xm5ubw700mE4qLh/Y5ePLkyScVU6ZLh6K3srIy/Otf/8JFF10U/jsiyzKeffbZAVfsXbRoEa699lrccsst4bnBCtEeeughtLa2YvXq1eG5Q4cO4fbbb+/3GFEU8cwzz+D222/P+KI3IkqEviLd/lbHFiQBSnDwBm+CLvMbf5+0GA3eFEVJ6OrcRDQyeG+IKD3pJAlTHLmY4sgNz7m8vccavrlQ5XKixuOELzT4ex0A6PT7sb21Cdtbm8JzxeYslNsdqLD1/RqflQ0p4gG7sYY5k4gofsyZRETxY86kTCVqDLCVLgMAKIqMnrZ9aP7sOQS9fc04FDkQ87ju1l3odddg0oqfQaPPHrV4KT6XzhuHX7yxP+79pazcqLlCU1OMPYemv+9+KH2x1i2xWOs2eljr1oe1bumJtW5qrHUbGBu8ESWBkKAva0/8eKXIClpWq3+wByx6yPrj/+wfv/ZUTC8eux/YO7e9CsV/wg88UULW/EuSFxARUYrobPxUNTY6KqAxWJMUDREREVHqqOvswC83fQSXz6uat2h1uHPhUpTbHP0cmTl8tc5+m7tJViOsZ1SOckRERESUVHJkgzdT7P2I0oAgCFzlewyILFBqamqC2+3OqGKQyJU5E+mMM87A2rVrE3b+rKysuPc9sbhnpHR2dkbNfb6SJdC3UutQWa38vmUgkX8+ra2tQzq+q6srqugtEf++ly9fjk2bNuHmm2/GO++8M+C+giDg5ptvxoMPPohHHnlEta2wsHDAY7VaLZ577jksXLgQ999/f8xCzBNNmjQJTz75JM444wxcddVVqm0FBZm9KAERjbBQPw/5iKK6QK0fop5lqYOKbPCmBIBQF6CJ//0PERERnRy7wYj5heMwv3AcAEBWFNR1dqDK7exr/OZyor6rI563PwCAhu5ONHR34qO6IwAAnShhks2OCpsdFbYclNvsyDHyHjoRERERERENThBEmPOmo+zMB8JzQX8nnAffgPvIh1HN3uRAD5o/ew55Uy+CHPJBZymCKLEBYqqwm7Rw9cRu0BdJlzdpwO3dnd4Bt9PYwVq3sYG1biOLtW6sdRtprHVTY63bwFhJQ5QMo7DQptLsh/doj2qup+j4D91sgwZzxo/tH6ieTc+rxuYZ50ATo7s5EdFYEuh1obt5h2ouq2hekqIhIiIiSh0HnO14aMvH6A6ov1zNMZrwo4XLUGwZGw9d9exvjjlvO2cabGdOgSCMwk0PIiIiSh2hiAZvIguGiCi1LV26VDVWFAWffPIJzjnnnCRFRCPF7/eP+DkHW8E6VT8De71eNDWd/Areg9FoNCgpKRnRc1ZWqhvHHzlyZEjHR+7vcDiGvNJvvCoqKvD2229jw4YNePnll7F27VocPXoULpcL2dnZGD9+PM455xxcc801mDlzJgBg7969qnPMnz8/rmt973vfw4033ojVq1djzZo12L59O1r/f/buOzyqKv0D+PdOSS8zk0oKqfTeuzRBRexYsCLYVl10V2XVn921omtfV12liCgryoroKghIr1JCD2m09EwmvUy5vz8CQ+7MJJkkM5n2/TwPj55zy7yHCSd37rz3PSUl0Ov1iIuLQ9++fXHzzTfjhhtuQGBgILKysiT/JtLT070quZeInE802f4dKMjt+90n+DEttU3yEEDwA8Rm1zB6LQu8ERERuZBMENA9LBzdw8IxpXvTw9S1ej1yK8qRpdM2/SnXorKxwa7zNZqMOKEtxQltKYCTAAC1fwDS1RqkqTRIV2mQEq5GgILXTkRERERERNQ2hV8oovvdDHXKVBQcXIy6skzJ9urCfagu3AcA8Avphrih90EZFAGZIsAV4VIr7v9yP1Y9OBpJEbYLwXeb9zkKPp9nc9uHf/sJl1zdr/35CvZWsCcit8JcN+/FXLeLmOt2EXPdLsbqjblu/DaIyAW65Jdfo/Uv4PL+cTZ29E3GmnJUZ/xP0hc++hYXRUNE5B6M+jqc2/MBTIbmKzkICO021GUxEREREbmD/UUFeH/fLjSajJL+hJAw/G3UeGgCfKOQiWg0oeL3TKv+8Ik9WNyNiIjIV5ksCrzJfeO6iIg8V2JiIlJSUpCbm2vu27hxI5PevEBkpHQhr+XLl2P27NkOfx21Wm1OKKuoqGj38R05pr127tyJyZMnO/11kpKSkJeX59Bz9unTR9LOyspq1/E5OTmSdt++fTsdU1vGjBmDMWPG2LXvjh07JO1Ro0bZ/TphYWG4//77cf/99zv1dYiIAAAtJH4Ldq6eLmOBtzYJggBRqQYamy2qotcCgUmuC4qIiIisBCmV6BcZjX6R0QCaHpArras1F3zLLtcir1IHvclk1/nKG+qxpzAfewrzATQVlUsIDUP6+YJv6SoNuoWEQsbv3omIiIiIiKgFyqBIJI5+DBWnt6Do0DKb+zRWFyBv84sQ5H6I6nMDVEmTmOftZv7+03F8dqftZzYFmbzVY8uLq6GJ4YIxRL6AuW7ei7luFzHX7SLmurX/dTyJfRk3RNQuCoUCU6dObXkHO5PdHEnpr4AxyK/LX9ddVf3xX8CoN7cFhR9Ch17rsniIfJlCocCMGTOg4CqELiWaDMjf9ykaKs9K+kNiB0MRoHJNUERkhXMmEZH9HDVnbj57Cv/4Y4dVcbce6gg8O2aizxR3ayyqRN7//WDV799dA80V/fmlP5GH43UmEXWY0aLAm8z7r404ZxJ5vssvv1zSXrRoEfR6fQt7k6eIiYmRtDMzrQuUO/p1amtrkZ+f367jnRWXt7iw+ucFGRkZqK2ttfv4bdu2tXo+V8rNzZUk5SUkJCA9Pd0pr7V+/XpJe9KkSU55HSLyRk33ecWWCpTI7bsPLPi3/uARnafUSNt6rWviIKJ24b0hIt8mCAKigoIxJi4Rd/QdhBfGTca/L7sGL42bjLv6DcK4uETEBAXbfT6TKOJ0ZQU2nM7Fpxl/YMHmdbh/7Y94bdcWfHviCPYVFaCyocGJI3IuzplERPbjnElEZD/OmURNn0/Du4+Hf3jri4aIxkYUH/4ahQeXQBTtK05OjpcUEWTVl1Vc3eHzNTYY2n8QU/2JPBZz3bwTc928A3PdHMNXct1Y4I3ISerr61vc5oqHngU7E+x8RcXOryXtkIEzIA8Kd1E0RFRXV9f2TuQ0oiii6PDXqC05IulXBscgduAdLoqKiFrCOZOIyH6dnTN/ys7EJwf3wiSKkv7B0bF4atR4hPh5fyF1Y20jKracxLl31tvcHjoyuWsDIiKn4XUmEXWIyeJLaLn3F3gDOGcSebpHH30UsmYLchUWFuKrr75yYUTkCGPHjpW0165d65TXGT58uKS9c+fOdh2/a9cuR4bjdbp164aBAwea2waDAVu3brX7+N9//13SvuKKKxwVWqd98cUXkva8efOc8jpVVVVYuXKluR0SEoKbb77ZKa9FRF7MJNrsFuT2pZvK/PhgpV2UEdK2vsw1cRBRu/HeEBE1p5DJkKbSYHpyOh4cMhL/mHw5/jVtJp4YMRbX9eiDAZHRCFIo7T5frUGPw6XF+G/Wcby9dzv+9Nsa/GXjL/hw/278kpuFrHIt9EZj2ydyE5wziYjsxzmTiMh+nDOJAEGQIWHEw9CkXQa5X2ir+1ae3Y7ynN+6KDKy9OQVvVwdAmD7qx8i8gDMdfNOzHXzDsx16zxfynVjgTciJzAYDFbVMiVcUOCthbw7n2SoLEbNUemD6WGjb3FRNERkMBiwceNGGAwdWDmAHEKb/QsqTm+R9Mn9QpAw8s+Q+4W4KCoisoVzJhGR/TozZ4qiiOXHDmH58UNW2yYkJOEvw8bAX+6dD6g1llSheMVe5P9rM/L/tRmnX/oJ2p8Ot7h/6PDWV34jIs/A60wi6jCjRcKszPsLvHHOJPJ8PXv2xHXXXSfp++tf/9ru1Sltyc7O7vQ5qGOGDh2KyMhIc3vHjh04fLjlz7MdNX78eEn766+/bmFPawcPHsSRI0fa3rGTJk2aBFEUnf4nLy/PKfFb/vtctGiRXccdP35cklQYHByM6dOnOzS2jiovL8enn35qbsvlcqclvb3//vuoqakxt2+99VaEhrb+QAcR0UWi5D+WBJl96aaCgmmpdlGqpW29FqLIJD8id8d7Q0Rkj1A/fwyO7oZZPfviyVET8Mn0q7Bw4nTcP2g4Lu2eiuQwFWTteJ6guLYGO/LP4MujB/H89o24Z+1qPLdtI5YeOYBt506juLbaLa8jOGcSEdmPcyYRkf04ZxJdpAgIR1SfG5A69XVE9ZnV6r4lx1aisbqoiyKj5nrGhOCzO4e4Ngg3vG9ARPZhrpt3Yq7bRcx1a8JctybenOvGTBoiV+j6+m5o1EtX6lLauaKqN6o5vA4QTea24B+M0MEzXRgREZHrVJ7bjdLjqyR9gkyJ+BEPwy842kVREREREbmOwWTCJxl/4KecTKttV6b2wP0Dh0Fh50NsnqaxuArn3v4NNfvPoCGvDA15Za3uH3vfhC6KjIiIiNyWyaLAm9z7C7wRkXd4/fXXERYWZm6Xl5fjhhtuQHl5eYfPuWjRIsycye8cXUUmk+HBBx80t0VRxPz58x3+YMf111+PkJCLi+OsWrUKBw8etOvYZ555xqGxeKvbbrsNcrnc3P7+++9x8uTJNo974403JO2bbroJAQEBDo+vIx577DEUFxeb23/605+QmJjo8Nc5fvw4XnnlFXM7JCQETz/9tMNfh4h8mNzepDcXJMd5ImWEtG1qAEy1romFiIiInEomCIgLCcUlCUm4e8AQvDJhKv592dV4bsxE3Np7AEbGxkMTYP/9dYPJhGydFr/mZeOfB/bgLxt/xYO//YS39mzDqpPHcKikCLV6vRNHRERERERERO5EJldCkzYd6dPfQfdxTyJl0kvwC42z2i/392eR+fODyFr7GAoPLoXJyM+ORESegLlu3oe5bt6DuW4d52u5bt75NCqRmxME1//T6xcX1vZOXqo2c4ukHdxnCmT+wS6KhojIderKs1F4cLFFr4BuQ+YiUJ3qipCIiIiIXKrBaMC7f+zAlrOnrLbN7t0ft/YZCKEdq2i7G0NFHaoPnkX1/tM2/5z7x292nUdQypHw+DQEpka2vTMRERF5LVEUAWO9tFPGAm9E5BnS09OtVkrcuXMnJkyY0O6VMLOysnDzzTdj7ty5qKura/sAcprHHntMsrLpxo0bcffdd6O+vr6Vo6ydPn0a//vf/2xuCwsLw5w5c8xto9GI2267DVqtttVzvvvuu1izZk274vBVPXr0wF133WVuNzY2Ys6cOa2+jz/88AMWL15sbvv5+eH5559v87VeeOEFCIJg/jNp0qQ2jzEajW3uc4EoinjiiSck801SUhJee+01u45vT9LmiRMncOmll0rmoddeew1JSUl2n4OIqFUywaPvj7slRSggyKV9+tavKYiIiMh7+MsV6KWJxJVpPfHIsNH4YOoMfDh1Bh4dNhozU3uijyYS/nJ52yc6r7KxAfuLC7Ey8yhe370V961djSc2rcWnB/di/akc5FXoYDSZ2j4REREREREReSy5XzAC1anwC4lF/LAHIFNaPzctmgwwNlah4sxWlB7/3gVRkiuIEF0dAhF1AnPdvBNz3bwDc90uYq5b61xfZYrISykUipY3ukGy2/9d2cvVIbhMzYnNknZw74kuioSILmh1ziSnEE0GFB5cAtEkvViO6jsLod2GuSgqIrIH50wiIvu1Z86s0Tfi9V1bsb+4UNIvEwTcP2g4ZqZ57udoQ0UdipbuxJnXfkHJ13tQsuIPm3/sETYhHd3unwBlZEjbOxORR+F1JhG1m6kBgMXDYHLfKPDGOZPIO1x//fV4+eWXJX1HjhzBoEGDMGfOHGzevBl6ve3Vquvq6vDTTz9hzpw56NOnD/7zn/90RcjUhrCwMKxYsUIyTy9btgxDhw7FV1991WrSVEFBARYtWoSrr74aqampWLFiRYv7vvzyy4iLu7ja+ZEjRzBmzBisX7/eal+tVov58+fjr3/9KwBArVZ3ZGg+58UXX5T8XW3fvh2XXnopjh8/LtmvoaEBH3zwAW688UZJ/2OPPea0ZK9jx46hd+/eWLhwITIzM23uYzAY8Ntvv2HMmDF46623zP3+/v748ssvJSvjtmbOnDm47bbbsGbNmhaTavPz8/HCCy9gyJAhOHfunLn/yiuvxEMPPdSOkRERtU6QuT7fzdsIggxQaKSdLPBG5BF4b4iInEUdEIgRsfGY3WcAnhkzEZ9NvxqvTpiKeQOGYGJCEuJDQmHvVZkIIL+6CpvOnsIXh/fj/7aux71rV+PlHZvw9bFD2FNwDtp65z/AyTmTiMh+nDOJiOzHOZOobX4hsUga/zT8w7u3uE957nro68q7MCpyhBP7zrW9kyXWdyPyeMx18z7MdfMezHVrwly31vFTLJETKJVKXHrppViyZIntHVxc4C05IghRof4ujcFVDJUlaMw/JukL6jnBRdEQEdA0Z1555ZWuDsPnlOeuR2O1tHiJKnky1CmXuigiIrIH50wiIvu1Z87U1tfhjV1bcba6UtLvJ5Pjz0NHYWhMN2eE2CVMeiPyP94Eo65jidkR1w0GACgjQhCQGsmH+Ii8FK8ziahDTDauL2RBXR9HF+OcSeRdnnnmGURGRuLRRx9FQ0MDAMBkMmHJkiVYsmQJQkND0bdvX0RFRSEsLAxlZWUoLCxEZmamzQSUoCDvnwfd3ZQpU7B48WLMmzfP/J4eO3YMt99+O+bOnYvBgwcjLi4OISEhqK6uhlarxfHjx1FcXGz3a6hUKnz11Ve44oorzIl0mZmZuPTSS5GUlISBAwciODgY586dw86dO83Jk4mJiZg/fz6eeOIJxw/cyyQkJOD777/HZZddhsbGRgDAtm3b0LdvXwwbNgypqamoqKjAvn37UFJSIjl25syZVgmtjnbixAksWLAACxYsQHR0NPr3729eUbeoqAgZGRkoL5c+CBEYGIgVK1ZgwgT78xPq6+vx3XffYfny5VAqlejTpw+Sk5MRGhoKnU6H06dP4/DhwxBFaTb+9OnT8Z///AeCGyw+SESeppV5Q8a1hJ1CqQH0zX6XscAbkdvjvSEi6kpymQxJYSokhakwpXsqAKBWr0dOhRZZ5eXI0pUhW1eOysYGu87XYDTiuLYUx7Wl5j5NQCDSVBr0UGmQptYgJVwFf7ljHjPinElEZD/OmURE9uOcSWQ/v+AoJI5+DCVHv0XF2e2AaLLap6Y4A6qkiS6IzrdV1BlgNImQdyA3/+t3N2Pitf3bdQy/OybyDsx18z7MdfMOzHVrwly31rHAG5ETmEwmlJaWtri9Sx6GNrZcTnv+1DTnv76bqs3cKmkL/sEISBriomiICLg4Z0ZGRkLGhOAuoa8rR2nmGklfQHgSovvd7PUXv0SejnMmEZH97J0z86ur8PrurSirq5X0BymUeHzEWPTSRDo7VKdqPFfe4eJu3Z+7EvIgPwdHRETuiNeZRNQhRotrDEEJQeb9Xz1yziTyPg888ADGjx+P+fPnY+PGjZJtVVVV2LVrV5vnCAwMxPz58/Hkk086K0xqh9tuuw29evXC7NmzkZWVZe5vbGzE7t277TpHW6uPTpo0CatXr8asWbNQWXmxYPypU6dw6tQpq/0TExPxyy+/2P361PR3vGrVKsyZM8ec2CaKIvbu3Yu9e/faPGb27Nn47LPPIJfLuyzO4uJibNiwodV9+vbti2XLlmHIkI7nJuj1emRkZCAjI6PFfRQKBZ544gm89NJLktV9iYgcQZAzl8AplBppmwXeiNwe7w0RkasFKZXoHxmD/pExAJo+K5fU1SKrXItsnRYndVqcqtTBYLJ+aN8WbX0dtIXnsKfwHABAJgjoHhqONJW6qfCbWoPY4FDIOpBbyjmTiMh+nDOJiOzHOZOofeTKQMQOuhNRfW+EvrYYp7a8ItluMthXNJwcb8n2U5g7PrnN/eRK6VxXVliFxgYD/Pzb8Z2w2HLNASLyLMx18z7MdfMOzHWTYq6bNX56JXICo9HY4iQLAHBy8RzT6XoYPjrX4vZBieFOfX13Vntis6QdlD4WgkLpomiICGiaM3fs2AGj0ejqULyeKJpQXZSB/L3/hGiU3nyN7n8rBIGXhkTujnMmEZH97Jkzs3VavLTjd6vibmr/ADw3dqLHF3cTRRHl60+0uN2vW7jNP4E9oxF7zzgWdyPyIbzOJKIOMVkUeJMFuiaOLsY5k8g79e/fHxs2bMD27dsxb948REVFtXmMv78/Jk2ahE8++QT5+fl4/fXXoVKpnB8s2WX48OE4fvw4li1bhjFjxrSZACSTyTBkyBAsWLAA+/btwzvvvNPma0ybNg3Hjx/HHXfcgYCAAJv7aDQazJ8/HwcOHEDfvn07NBZfNmPGDBw+fBgPPPBAq4mIo0ePxsqVK7F8+XIEBwc7Naa4uDg88MADSE5ObnPfoUOH4rPPPsPBgwc7lPB2yy23YMqUKS3+fF0QGhqKefPm4fDhw3j11Vd9IuGNiJyllYd75MwncAo/Fngj8jS8N0RE7kYQBEQHBWNsfCLu6DcIL42bjH9PvxovjZuMO/oOwti4REQH2f9Z2SSKyKvUYf3pXHya8Qee2LQO96/9Ea/v2oKVJ45gf1EBqhrte/ifcyYRkf04ZxIR2Y9zJlHHyJWBCAhPQlBkH0l/ybGVaKjKd1FUvsNPYf09y85c+74TiElQWfWteH9Lu15fZIE3Iq/CXDfvw1w378BcN+a6tUYQeUVG51VWViI8PBwVFRUICwtzdTgeTa/X48cff0RRUZGkP+7Nf0NW34CZZw9CcEIVzd9O5WDR4f3Qf54P8aj04XSTQobcm4dhfHoE3p89yOGv7SlynhuG+lP7zO2o619C1DXPujAiItLr9fj5558xY8YMKJUsuOgMRn0dKs9uR3nuRuhri622hyeOR+ygO10QGRG1F+dMIiL7tTVnHiopwjt/7ECDRYJHt+AQ/G3keES1I8HZHYmiiPJfj6Li90yrbd0emICAZM8uXkdEjsXrTCLqCLHqMKBdf7HDLwZCt1tcF1AX4ZzpWI7+frK+vh65ublISUlpM0GAqC0nT57EkSNHcPbsWVRVVcFkMkGlUkGj0aBXr14YMGAA5wEPUlVVhZ07d+Ls2bPQarWor69HSEgIIiIi0KNHD/Tp06dT81BVVRU2btyIM2fOoKqqCjExMUhKSsL48ePh58cC6o7Q2NiIbdu24dSpUygsLERwcDDi4+MxZMgQpKSkuCSm/Px8ZGRkIC8vDzqdDgaDASEhIUhJScGIESMQFxfnkNdpbGxERkYGTp48iYKCAtTW1kKhUCA6Ohp9+/bFsGHDvGY+4u9yag/munVe7YkiFC3abm7LwwPQ/akrkPvkKqt95WEB6P607W2WNFcOQPiEdIfG6q1EvRbI/1LamXA/BDnnQCJ3xXtDROSpKhsakK3TIuv8n2ydFnUGQ4fPFxMUjHSVBmkqDdLVGiSFqaCQSR9W55xJRGQ/zplERPbjnEnUOWd2vYfakiNW/T0u/wAyhb8LIvINeqMJo179XdI3ODEcX8wZZrWvbusS5H82x9z2TxqGp769Ec0rgqT1j8XrK++SHHeuqhILNq+T9I07XQwBQFTWGcx6+9XODsPnMdeN3Blz3bwLc908H3PdmOtmyTfK2BG5G5mTVzTVWX/Z2hjWNBmkR3v2A+qdYayrRP3pA5K+oF6XuCYYIqIuIJoMKM38Ebq8jTAZ6m3uI1MGIbL3dV0cGREREZFr7cg/g48P7IHRYt2D1HA1nhgxDmH+nv3ltCiKKP/fYVRszrLaFnXrCBZ3IyIiIscwSReagTzQNXEQETlJjx490KNHD1eHQQ4SGhqKadOmOfX8V199tdPOT4Cfnx8mT57s6jAk4uLiHJbY1ho/Pz8MHz4cw4cPd/prERG1RJA7Od/NVynCAcgAmC726bWA3Pm/X4iIiMi3hPn7Y0hMNwyJ6QYAMIkiCqqrJAXfTldWQGzjPBcU1dagqLYG2/LPAACUMhmSwlRIV2vMhd9UCu94QIuIiIiIiMibKANUNvtP/vJnRPWZhfDEcZD7+e5z6M6ilMtw99gkLNp+qt3HCjIBl906FL98tc/cZzSaWjmCiHwRc928C3PdPB9z3ZjrZokF3oicQBAEhISEoKioqMXtXa10ZBIAoH98eJe/truoO7kdEC9+aBUUfghMHenCiIgIaJoTQ0NDXTI3eruyrP9Bm/W/FrfL/cMQP+xPUPiHdmFURNQZnDOJiOzX0pz5a24Wvjx60CopuX9kNB4dNhqBHp5gLIoitGsOoXJbtnSDAERePwQhAxNcExgRuTVeZxJRhxjrpG2ZbxR445xJREREREQ+Sd70GUh1aW/ofjtu7lZd2ge63465KiqPJwhyiEpVU1G3C/RaIIAF3ojcFe8NEZG3kAkC4kPDEB8ahomJyQCAeoMBuRXlyD5f9C1LV47y+rrWT3Se3mQyF4u7IMzPHyo/AcacTPSMiEJquBpBSs/OySAichZeZxIR2Y9zJlHnaNIuR03xERgadFbbSo6tRMmxlVAEqBCgSoUm7TIEqlO6Pkgv1U0V0OFjew6OkxR4az97S7oTERERkTOwwBuREygUCowfPx7Z2dlt7+wEYrVR0i4am4qGiBAAwOReka4IyS3UnNgsaQekjoTMzzceOCNyZwqFAlOmTHF1GF6pquAPm/3+oQlQp0xBaPxIyOR+XRwVEXUG50wiIvtZzpmiKOK7zKNYlXXcat/R3RLwp8EjoJDJujJEhxNFEdofM1C5PUe6QQAibxiK0OFJrgmMiNwerzOJqENMFg91yX3jfjvnTCIiIiIi8l4tPxApnL9/Hj6xJ8RGAxoLKxEyLAnBA+NZ4K2zlBrrAm9E5LZ4b4iIvFmAQoE+EVHoExFl7iurq0W2rhxZOi2ydVrk6MrRaDK2cpaLKhsbUAngdNZxIOs4BADxIWFIU2mQrlYjXRWBhNAwyFiYg4iI15lERO3AOZOoc/xCYpA69TWc2fEW6sptPwNvqNehunAfqgv3ITRuJKL6XA9loKaLIyXH4mdvIiIiIldigTciJzCZTDh79qztjU5+WNxU2ABU2v7SdHiyyqdXJqjN3CJpB/Wc4KJIiKg5k8mEM2fOIDExETIPL6jhToz6WjRWFUj6gqMHQpM2DYGanj79+4DIk3HOJCKyX/M5E4KARYf3Y8PpXKv9piel4Y5+gzw+YVg0iShbfRBVOy3GKABRNw1DyJDurgmMiDwCrzOJqEOMFgXeZL5R4I1zJhERERER+SJB1nQPXaaUQzNjgGRbYI9o1J0sdkVY3kFp8VAcC7wRuTXeGyIiXxMRGISIwCCM7BYPADCaTDhTVWku+Jal0yK/usquc4kAzlZX4mx1JTadzQMA+MvlSA1vKviWptIgXaWBOsA3vm8gImqO15lERPbjnEnUeYJMjsSxC1CVvwcVZ7ahtrTlhVyq8nejtuw4ksY/zSJvnkwUXR0BERERkU9jgTciJzAajTh8+LDNbc4sqGMSTTD9Vm7Vrw/xc9pregpTYx3qc3ZL+oJ7XeKiaIioOaPRiAMHDiAuLo431h2oXpeHpnSY8wQ54obdD5lc6aqQiMgBOGcSEdnvwpwZGRODz47sx57CfKt9ZvXsi2vTe3t88VvRJKLshwOo2pUn3SAAUTcPR8jgRJfERUSeg9eZRNQhJosCb3LfeOCKcyYREREREXmvVh7ukfPzj9OwwBuRR+G9ISLydXKZDMnhKiSHq3BpUioAoEbfiBxdObLOF3zL1mlR1dho1/kajEYc05bgmLbE3BcREIh0tcZc8C0lXA0/udwp4yEiche8ziQish/nTCLHEAQBYfEjERY/EnXl2Sg9/gNqy07A1ncFxoZKVJ7diYgeM7o+UHIIlncjIiIici0WeCPqak58aLyoohqmozWSvupEFRoiQ5z2mp6iLmc3REOzL4oFGQJ7jHVdQERETlZXniNpB4R3Z3E3IiIi8jmNEPGPfTtxvLxM0i8AuLv/EEw9n2zsyUSTiNJV+1G955R0g0xoKu42KME1gREREZH3M1oUeJP5RoE3IiIiIiIiXyTIPXuhFLdmWeDNWAXR1AhBxkVdiYiIyDMEK/0wICoGA6JiAACiKKK4tgYnykqw/uB+mFThOFVVAaNo3+PkZfV1KCs4h10F5wAAckFAYmg40lUac+G32OAQyDx8MT8iIiIiIiJ3EahOQ+KYv8Jk1ENfU4zq4kMoPf69ZJ/SE/9FoDoNgRE9PX5xdZ/ECm9ERERELsUCb0RdTJA574Pryb3ngIZmn7IEoHR4klOLynmK2hNbJO2A7oMhDwxzUTRERM5Xr5MWeAtUe37xEiIiIqL2qGiox+9yI3QWxd0UMhkeHjwSI7rFuygyxxFNIkq/24fqP05LN8gERM8egeABnj9GIiIick+iKFoXeJMHuSYYIiIiIiIicj6ZzNUReC+FGk1L0zTL+9OXA/4xroqIiIiIqFMEQUBMcAg0fv4o35+BGaMvgSiT4VSlDtk6LbJ0WmSVa1FSV2vX+YyiiLxKHfIqdfjtdFNubLBSiTRVU7G3dJUGaSo1Qv38nTksIiIiIiIiryeTK+EfFg//sHg0VJ5BVf4eyfYzO99GgDoV0X1v4rOKRERERETtwAJvRE4gCAIiIiJQVFRka6PTXrckv0LS1kcFwhjElTwBoPbEZkk7qNclLoqEiCwJgoCoqCiu3OBAoiiirjxX0hfAm6ZEXoFzJhGRfYprq/Ha7m3QWUyXAQoF/jpsDPpFRrsmMAcSTSJKv/0D1fvPSDfIBETfOhLB/eNcExgReSReZxJRu4l6AEZpnyzQJaF0Nc6ZRERERETkiwQ5C7w5iyBTQFSEAYZmuX96LQu8Ebkp3hsiIrJf8zlTKZejhzoCPdQR5u0VDfXnC76VI1unRbZOizqDwa5z1+j1yCgpQkbJxec1YoNDzhd8UyNdFYHuYeFQsFAxEXkIXmcSEdmPcyZR14jqc71VgTcAqC/PwentbyJu6P0I7TbEBZERAOQdK4ZBb4RCKbfzCLHtXYiIiIjIaVjgjcgJFAoFRowYgaNHj1pvdNKNI5MooqqhUdpn8YWkzEdvWokGPWqztkv6WOCNyH0oFAqMHTvW1WF4laqCvTDpayR9gSoWeCPyBpwziYjallehw5t7tqKioUHSH+bnj7+NHI/kcJVrAnMg0WhCybd/oObAWekGuYDo20YhuG831wRGRB6L15lE1G7GOus+uW8UeOOcSURERERE3qvl3DJB7pt5Z11GqbEu8EZEbon3hoiI7NfWnBnuH4ChMXEYGtO0gJ1JFJFfXYWs88XesnRanKmssPsR9MKaahTWVGPbudMAAKVMhuQwFdLVmvOF3zSIDAxiIRAicku8ziQish/nTKKuoQyMgCbtcmizf7HeKJqQ/8fHiOx9HTRpl0EQWFzbFX75ah9mzhlh1778JExERETkWizwRuQERqMRJ0+ebGGrcz4G6RrqYRBNkj5RlL5Wv25hTnltd1d/aj/EBmmho6Ce410UDRFZujBn9ujRA3K5vSsGkC2iaEJZ5hqUnVwj6Zf7h0MRqHFRVETkSJwziYhad6ysBG/v3W61onN0UDD+NnI8YoNDXBSZ44hGE0pW7EVNxjnpBrkMMXeMQlDvWNcERkQejdeZRNRuJssCb3JAULoklK7GOZOIiIiIiHySjA9nOZVSA9TlXmyzwBuR2+K9ISIi+7V3zpQJAhJCw5AQGoZJickAgHqDAbkV5cjSaZFV3lT0TddQb9fr600mnNRpcVJ38doq3N/fXOwtXaVBqkqNQIVvfL9BRO6N15lERPbjnEnUdSJ7Xwu/0DjUlh1HbfFRGBp0ku2lx1ehvjwH8SMeck2APiQ8ItiqL/twod3Hi/ZWTyciIiIip2CBNyInMJlMyM7OtrlNkDmnwFuFjS8qLeq94d5Lkp3y2u6uNnOLpO0X1weKsCgXRUNElkwmE06cOIG0tDTeWO8Eo74OBQc+R01RhtU2dfJkrjhI5CU4ZxIRtWxP4Tl8tH839Cbph+HE0DA8OXICVAEBLorMcUSjCcXf7EHtoXxJv6CQIfqOUQjqxeJuRNQxvM4konYz1krb8kCfuf/EOZOIiIiIiLxXy0/3CHLf+MznMkqLRftY4I3IbfHeEBGR/RwxZwYoFOgTEYU+EU25/6IoQltfZy74lq3TIrdCh0aT0a7zVTQ0YF9RAfYVFQAABADxoWHmgm9pKg0SQsMg85HvPIjIffA6k4jIfpwzibqOIMgQnjAa4QmjIZoMOLX1NTRUnpHsU110EIZ6HRQBKtcE6SP6jki06hPbVbWNFd6IiIiIXIkF3oi6mpO+7Gvrc1iwnxwBSt+8YVVzYrOkHdzrEhdFQkTkHIaGSpzZ+Q80VuVbbQtLGANN2nQXREVERETUdTaezsXnh/ZZfe0YJQJPDR+HcG8o7mYwofjr3ag9UiDpFxQyRN85GkE9Y1wUGREREfkkU520LQt0TRxERERERETUJQSZzNUheDfLAm+GCoiiAYLAFF8iIiKi5gRBQERgECICgzCqWwIAwGAy4UxVBbJ1WmTpypFVXoaCmmq7zicCOFtVibNVlfj9TB4AIECuQKpKjbTzRd/SVRqvWFSQiIiIiIioMwSZAnHDHsC5PR+gsbpQsu3cH59AkzoNfqHd4Bcc6zMLhXYlhVKO6+4bjVWf7uzYCVjfjYiIiMilmP1B1MVc9cHUYPLNT1+iyYTazC2SviAWeCMiL2JoqMSZHW+jsVpa6AOCDNF9b4IqeTJvihIREZHXEkURP2SdwLeZR6y2DY2ORUp+CYKUShdE5liiwYTi5btRe9SiuJtSjpg7RyOwR7SLIiMiIiKfZbQo8CZngTciIiIiIiKvJmfegVNZFniDCOjLAb8ol4RDRERE5EkUMhlSwtVICVfj0qSmvhp9o7ngW3a5Flk6Lar1jXadr95owNGyEhwtKzH3RQQGnS/2pka6KgLJ4Sr4yeXOGA4REREREZHb8guOQtKEZ3Hyfw9J+uvLs5H/RzYAwD88CTEDbkOgKtkFEXo5i2dEC0/rXBMHEREREbUbC7wROYFMJkN8fDyKiopsbXTKazYYDa1uVwd5/gPtHdGQfxSmmnJJX1DPCS6Khohskclk6N69O2Rc7bndDA1VOLPzH1bF3eR+IYgbej+CInu5KDIichbOmUREF5lEEcuOHsSvedlW2yYlJuOuPgNxRHHY4+dM0WBE0bLdqDsuXelMUMoRM2cMAtP4cBcRdR6vM4mo3Uz10rbMdwq8cc4kIiIiIiLv1XIRN4GfgZxKkPlBlIcAxuqLnSzwRuSWeG+IiMh+rpwzg5V+GBgVi4FRsQCaFhAsqq1BVrn2fOE3LU5V6mAURbvOV1ZXi7K6WuwqOAsAkAsCuoeFI12lQZpKgx5qDWKCQrggMxF1GK8ziYjsxzmTyLVkciXUKVNRnrve5vaGilM4s30h4kc8hOCovl0cnW/JPlSAgjwtuiVbLiJDRERERO6GBd6InEAul2PAgAHYt2+f1TZnfWl3qlLX6vaFNw5wyuu6u9oTmyVtZWQylBGJLoqGiGyRy+UYMmSIq8PwOE3F3d5GY1W+pF8ZFInE0X+FMijSRZERkTNxziQiamIwmfCvg3uxI/+M1bZr0nvjxp59IQiCx8+ZJr0Rxct2oe6EtIC84CdHzJyxCEzlNR8ROQavM4mo3Yy10rbcdwq8cc4kIiIiIiKfJGehCqdTRlgUeCtzXSxE1CLeGyIisp87zZmCICA2OASxwSEYn9AdANBoNOJUpQ5Z5U0F37J1WpTU1bZxpiZGUURuhQ65FTqsO5UDAAhR+iFVpUa6SmMu/Bbi5+e0MRGRd3GnOZOIyN1xziRyvai+N0IURejyNtjcLpr0KMxYirSpr3dxZN7Nz19u1ffH79mYOYcF3oiIiIjcHQu8ETmB0WjEoUOHbG90Uq5bboUOolbf4vZ+cWHOeWE3Z1ngLajXJS6KhIhaYjQakZGRgYEDB0Iut77JRNYMjVU4s/Mftou7jXkMysAIF0VGRM7GOZOICKg3GPDuHztwqLTYatsdfQfh8pR0AJ4/Z5r0RhQv3Ym6k9JxCn4KxM4dg4BkFncjIsfx9DmTiFzAVCdt+1CBN86ZRERERETkiwS5zNUheD+lGqg/dbGtL3ddLETUIt4bIiKyn7vPmX5yOXqoI9BDfTHntqKhvqnY24WibxXlqDcY7Dpftb4RGSVFyCi5uIhft+AQpJ0v+Jau1iAxNBwKGa+ticiau8+ZRETuhHMmkesJggwx/W+BOmUKqvL3oLb0OGrLTkj2MdRpUXxkBaL63ABBxnIWjjDkkjSseH+rpK+hruW6AlKi4wMiIiIiIrvxipjICUwmE86dO2d7o+CcL+RyyrUw/VEl6TOE+AMAnr+qt1Ne092JoojaE1skfUE9J7goGiJqiclkwunTp9G/f3/eWLdDTfERFGYshaFemsjL4m5EvoFzJhH5uqrGBizcsw3ZOum1kFwQcP+g4RgX393c58lzpqnRgKKlO1GfVSLpF/wViJ07FgFJvOYjIsfy5DmTiFzEaFHgTeY7Bd44ZxIRERERkfdq+eEeQeakVU3pIqXFvX+91jVxEFGreG+IiMh+njhnhvsHYFhMHIbFxAEATKKIc9WV5oJvWTotzlZV2v1YfEFNNQpqqrH13GkAgFImQ0q4GmkqDXqoNUhTaRAREAhB4PU2ka/zxDmTiMhVOGcSuQ+/4GhE9LgSET2uRGNtKXI3PC3ZXp67HkZ9LWIHzeHnHgdI6x8LdVQIykuq232syPpuRERERC7FAm9EXcxZH0Lzc7VApVHSV5kWBQAYlBDulNd0d/riHBh0+ZK+oN6XuCgaIqLOMerrUHJsJSpOb7HapgyMQOJoFncjIiIi71ZaV4vXd21BQY30C0l/uRyPDhuNgVGxLorMsUyNBhQt3oH6nFJJv+CvQOy8cQjornFRZERERETNmCwKvMl9p8AbERERERGRT5I5Z1FTakZpcf9fXw5RNEFw0oKyRERERNQ2mSAgMTQciaHhmNQ9BQBQZ9AjR1eObF05ss8XfdM11Nt1Pr3JhMzyMmSWl+F/uU19Kv8ApKuair2lqzVIDVcjQMFHvYiIiIiIyLMoAyMQoE5FfXmOpL/y7A6Ed5+AIE26iyLzLumDumHPbyc7cCQrvBERERG5klPv+uv1euzYsQNbtmxBdnY2tFotqqqqAADr16935ksTuS8nFHgTRRGmBpNVf31MKDTBSiRHBjv8NT1B7YnNkrY8PAZ+MT1cFA0RUcfVlBxFYcZSGOqsV2dWBEYgccxjUAaxuBsRERF5r7NVlXh991aU10sLiYQo/bBg5Dikqbyj6Jmp4Xxxt1xpcTdZgBKx88bBP1HtosiIiIiILBgtCrzJWOCNiIiIiIjImwly5yxqSs1YFniDCTBUAEp+N0BERETkTgIVSvSLjEa/yGgATc9ylNXXIVunxclyLbJ1WuRWlENvsn6+wxZdQz32FuVjb1HTwvYCgITQMKSrNE1/1BGICwmFzAnPoRARERERETmKIAiIH/EQ8vd+gjptpmSb9uTPCBo130WRua8DZyrs2q8+d6+TIyEiIiIiZ3NKgbeamhq88847+PDDD1FSUiLZJooihBa+WPj666/xf//3fwAAjUaDPXv2tLgvkTuTyWRIS0tDUVGR1TZB5vifaW19HUybdJI+0/mkuiv6xzr89TxF7cltknZwz0s4pxC5IZlMhl69ekHG1Z5t0p3egqKML21uC4rsjdhBd0MZyGReIl/BOZOIfFGmtgxv7d2GGr1e0h8REIgnR41HXEiYzeM8bc40NehRuGgHGvLKJP2ywPPF3RJ4zUdEzuNpcyYRuZYoGgBRem0GeZBrgnEBzplEREREROS9Wsmr4mcgpxPkARBlQYCp9mKnvowF3ojcDO8NERHZz1fmTEEQEBkYhMjAIIzqlgAAMJhMOFNVgaxyLbJ0TUXfCmqq7TqfCOBMVSXOVFVi45k8AECAQoG0cDXSVRqkqZsKv4X7BzhpRETkCr4yZxIROQLnTCL3pfALReKYx5C97nEYG6vM/TUlh1FxdifCE0a7MDr3tP5YMab2iW5zv+rD6xDSf1rHX0js+KFERERE1HkOL/CWkZGBm266CSdPnoQoNl3t2VtQ6aqrrsIDDzyAqqoqnDp1CuvWrcP06dMdHSKR08nlcvTo0QPbt2+33uiEAmO5FTqYjtdK+gxBfg5/HU/TWJwlaQekjnRRJETUGrlcjt69e7s6DLfUWF2I4sPfWPULcn9E952F8O4sXEnkazhnEpGv2V9UgPf37UKjySjpjw8Jxd9GjkdEYMuFRDxpzjTV61H4xXY0nNZK+mVBSsTOGw//eJVrAiMin+FJcyYRuQFjnXWfLLDr43ARzplEREREROSLnLGoKdmg1AANzQu8aVvel4hcgveGiIjs58tzpkImQ0q4GinhakxDGgCgurER2bqmgm8Xir5ZLnbYknqDAUfKSnCkrMTcFxUYhDSVBunnC74lhangJ5c7ZTxE5Hy+PGcSEbUX50wi9yYIAkK7DYPu1O+S/sIDX6DwwBeI6Hk1QmIGIiC8u2sCdKFQf+uyHr8cLrIu8CZYf7ar2vt95wq8EREREZFLObRE+dGjRzFx4kRzcbcLBUdEUTQXe2tNSEgIbrzxRnP7u+++c2R4RF3GYDBgz549tjc6oRBPXkU5UG+S9FX2aLtit7fTl52WtP0ik1wUCRG1xmAwYPv27TAYDK4Oxa2IogkFB5dANEmTN4IieiF54vNQJU1kcTciH8Q5k4h8yZazp/CPP3ZYFXfrodLguTGTWi3uBnjOnGmq16Pw823Wxd2C/RB7zwQWdyOiLuEpcyYRuQmrAm8CIPN3SSiuwDmTiIiIiIi8Vys5nu0o8Kb96ZADYvFRSo20zQJvRG6H94aIiOzHOVMqxM8Pg6JjcUPPvvjbyPH4ZNpVeHvSdDwwaDimJaUiJVwFeTvygkvqarGz4CyWHc3AC9t/xz2//oBnt27AksMHsO3caRTWVNv1HBcRuQfOmURE9uOcSeT+whLHQpBZFzMDgLLM1Ti15e/I3/85TMbGLo7Mtcakaaz61h8vgbZG+vcQ1GOs1X6mxlqrPgDYsuaoY4IjIiIiIqeyfXXcAfX19Zg5cyYqKirMxUYGDBiARx55BJMnT0ZDQwP69OnT5nmuueYafPHFFwCA9evXOyo8oi4liiLKyspsbhNkDq2rCADI0ZVb9dXGhTv8dTyJaDJBrz0r6VNE+F5FdyJPIIoiSkpKmERgoTx3A+rLsyV94UkTEdN/NgTB8b9LiMgzcM4kIl/xU04mlh+zfgBscFQs5g8bBX9527e0PGHONNY1ovDz7Wg8K/1cLwv2Q7d7J8AvNsxFkRGRr/GEOZOI3IjJosCbLNCnFiLgnElERERERN5MNNn+rCPI2/e5T19aDWVkiCNC8i1+LPBG5O54b4iIyH6cM1snCAJig0MRGxyKCQlNC9k3Go3Iq9AhS1eGLF05snRalNXZfojfklEUkVNRjpyKcqw91ZR/HKL0Q7pKgzS1pum/KjWClX5OGxMRdRznTCIi+3HOJHJ/gapkpEx6GTkbnmpxn6pzu1B1bhd6XP4+ZIqALozOdcIClbhmcDf8cKBA0r8tqwxXDepmbvtFp8I/ri8a8q2LtymUckn7XHYZCk6Vo1uSuvUX55xJRERE5FIOK/D2/vvvIy8vz/wAx/z58/GPf/wDsvPFrE6dOmXXeSZPngxBECCKInJzc1FcXIzo6GhHhUnkek54xqnouHUxOZPFhzRfY6wsBox6SZ9Sk+iiaIiI2qexugilx1dJ+pRBkYjuM4vF3YiIiMiriaKIr48fxk85mVbbxsd3x70Dh0HhhMLprmCsbUTh59vQeE4n6ZeH+CP23vHwi2FxNyIiInJTRosCb/JA18RBREREREREjtdSgbd23puv2pMHzRX9HRGRb1FYFngrhyiKPlVYnYiIiMiX+cnl6KmJQE9NhLlPV1+PLJ0WWTotss//aTAa7Tpftb4RB0oKcaCk0NzXLTgE6eoIpKvUSFdFIDE0DHIvycUhIiIiIiL3oQyKQOygOSg8uLjV/U7+8ghSp74OZWAbBcq8xLDuKqsCb1X1Bqv9QgbPtFngbdikNOz433FJ3+nMkrYLvBERERGRSzmswNsHH3xgTiK59tpr8e6773boPCEhIUhOTkZubi4A4NixYyzwRl7G8clWVXvLJW2D2h/GQKXDX8eT6MtOSzvkCijCY1wTDBFRO4iiCYUZSyGapEUqYwfdBZnC30VRERERETmf0WTCvw/tw+az1osEzEjpgdl9BkDmJQ8wGWsaUPjvbWgsqJD0y0MDmoq7RYe6KDIiIiIiO5hqpW0ZC7wRERERERF5C1G0XeANsvbdnxf8HJaa6lv8LAq8iQbAUAkow10TDxERERG5nCogAMNj4zA8Ng4AYBJFnK2qRPb5om9ZOi3OVVWihSt5KwU11SioqcaW8/k5fjI5UsJVSFdrkKbSIF2lgSYgkEWGiYiIiIio08ITxyI4uj+02WtRV3YCkClQX55tsZeInPV/Q9qlb0IRoHJFmF3qsv4xeG71sQ4ff8nV/fDh336Sdrb03Q4RERERuQ2HZNEcPXoU586dAwAIgoCFCxd26nxpaWnmAm85OTmYOHFip2NsS25uLg4cOID8/HxUV1ejW7duSEpKwtixY6FUur5QlsFgwL59+3DkyBGUlJSgsbERISEhiI+PR8+ePdGvXz8oFEyKchdyuRz9+/dHUVGR1TZnfNFVl1MtaTemhwM+/oWaXntG0laqEyDI5C6KhohaI5fLMXjwYMjl/DcKALq831GnPSnpUyVNQlBELxdFRETuhHMmEXmrBqMBH+7bjX3FBVbbZvfuj5lp7b8Wctc501jdgMJ/b0VjYaWkXx4WgG73jocyisXdiKjrueucSURuylgnbct9q8Ab50wiIiIicgfMdSPnEACTyfYWuazFo0Sj9TEyf76/HSILAmT+gKnhYp9eywJvRG6E94aIiOzHOdM5ZIKA7mHh6B4WjsndUwAAtXo9civKzQXfsnVaVDQ0tHGmJo0mI06Ul+FEeZm5T+0f0FTsTd1U8C0lXI0AfoYjcirOmURE9uOcSeRZFP5hiO47y9w+s+Nt1JadsNovb8vf0X3sAvgFR3dleF1OKZdhZIoau3PLO3S8IAjolqxBQZ7WwZERERERkTM55A77gQMHADRdFPbv3x+pqamdOp9KpTL/f0VFRafO1ZaVK1fiH//4B3bs2GFzu0ajwc0334yXXnoJkZGRTo3FlpMnT2LhwoVYsWIFKisrW9wvMDAQ48ePx5/+9Cdcd911XRgh2SKTyZCQkNDSRoe/nmiQVtc2hvs7/DU8jVWBN02iiyIhorbIZDIkJSW5Ogy30FhTjJLj30v6lIERiOpzvYsiIiJ3wzmTiLxRjb4Rb+3ZjsxmiaJAU0LqPQOGYmJicofO645zprG6AQWfbYW+yKK4W3hgU3G3yBAXRUZEvs4d50wicmMm3y7wxjmTiIiIiFyJuW7kbKJRtL1B1vJio6LeaL07C7x1iCAIEJUaoKHZgjh6LYAUl8VERFK8N0REZD/OmV0nSKlEv8ho9ItsKoQgiiJK62rNxd6yyrXIq9RB30JBZ0vlDfXYW5SPvUX5AAABQGJYONJVmqbCbyoN4kJCIRNa/pxARO3DOZOIyH6cM4k8W7eh9+LU1ldhqJMWKDM2VKIqfw8ielzposi8nNjC9z9ERERE1CUcUmmqpKTE/P89evTo9Pn8/S8Wp6qtre30+Wyprq7G7NmzceONN7aY8AYAWq0WH3/8Mfr3749ff/3VKbHYYjAY8Nxzz6Fv37747LPPWk14A4C6ujqsW7cOK1as6KIIqTUGgwFbt261uU3gl1hdQl92WtJWRLDAG5G7MhgM2LBhAwwGg6tDcSlRNKHw4FKIxkZJf8yguyBTBLgoKiJyN5wzicjbaOvr8NKOTVbF3ZQyGf4ybHSHi7sB7jdnGqvqUfDpFuvibqpAdLtvAou7EZFLuducSURuzmhR4E3mWwXeOGcSERERkSsw1426htjiAz5CKwXeTA3Wn48EPxZ46zClRtrWa23vR0QuwXtDRET245zpOoIgICooGGPiEnF730F4Ydxk/Puya/DyuCm4q98gjIvvjpigYLvPJwI4XVmBDadz8VnGH/jb5nW4b+1qvLZrC/5z4gj2FeWjoqHeeQMi8gGcM4mI7Mc5k8izKfzDkDrlVQRF9rHaZmysdkFEvoHl3YiIiIhcyyFZNPX1F2/ENy/O1lEVFRXm/w8NDe30+SwZjUbcfPPN+PnnnyX9UVFRGDJkCMLDw5GdnY39+/dDPJ+wVFRUhGuuuQa//fYbxo8f7/CYmqurq8OsWbOs4hMEAf369UP37t2hUqlQXV2NnJwcHD9+nDcj3IwoiqiubuGDpDPqu5n40cqSXntG0lZqursoEiJqiyiKqKqqMv/O9VW6U5tRp82U9IUnTURwZG8XRURE7ohzJhF5k4LqKry+eytK66TF/YMUSjw+Yix6aSI7dX53mjMNlfUo/GwL9CXSewUKVRBi7xsPpcb+pFkiImdwpzmTiDyAyaLAm9y3CrxxziQico0LORwlJSXQ6XQAgODgYERERCA5ORnp6emIiYlxbZDk0Y4dO4YjR47g3LlzaGxsRFxcHFJTUzFq1CjIZA5ZP7PDcnJysHv3bpSUlKC6uhoxMTGIi4vD2LFjERYW5tLYqGsw1426kthSHlprBd4ard9PmZ/cUSH5HhZ4I3JrvDdERGQ/zpnuRSGTIVWlRqpKjenn+6oaG5CtK0eWTotsnRZZ5VrUGvR2na/OYMDh0mIcLi0290UFBiFdHYF0lRppKg2Sw1RQyvnZgMgenDOJiOzHOZPI8wmCDAmjHkHu789DX1Nk7i/PXQ9V8mT4BUe7MDoiIqLOY64bORtz3TyPQwq8RUZefOC2tLS00+fLyckx/39ERESnz2fpySeflCSUKZVK/OMf/8B9990HPz8/c//Ro0dxzz33mFc9bWhowLXXXotDhw6hW7duDo8LaLq5cMstt0jiCwgIwIIFC3DfffchPj7e6pja2lqsW7cO33zzjSR+ck+CgyfDmsp6NORLVzsS+R0YDJYF3iISXRQJEVHbDA1VKDn2naRPEahBdJ8bXBQRERERkXPl6Mrx5p6tqGpslPSr/QOwYOR4dA8Ld1FkjmeoqEPhZ1uhL7Uo7qYJQuy9E6BUB7koMiIiIqIOMloUeJPxeoaIPE9ycjJOnTpl1f/xxx/jgQce6NA5v/jiC8ybN8+qPykpCXl5eR06p6/LycnBxx9/jC+//BJFRUVt7h8fH4+RI0di2rRpuOKKK5CcnCzZ/sorr+CZZ54xtwcNGoQDBw44JNYff/wRV199tbmtVqtRUFAgWSRx8eLFuPvuu23GferUKcg78LCrKIpITU21+TO2aNEizJkzp93n9CWiKOKzzz7DRx99hIyMDJv7xMXF4c4778QzzzyD4OCuK9Lf2NiIzz77DO+88w6ys7Nt7uPv748rrrgCzzzzDIYNG+aUOJ588km88cYbVv18aKprMdeNulQLBd5ay3kTbRR4g9y1CcMezUaBN1EUIQjOWFmWiIiIiHxZqJ8/BkfHYnB0LADAJIoorKk2F3vL0mlxpqoCRjvvA5TU1aKkrhY78pue5VDIZEgKC0e6SoM0lQbpKg2ig4J5bUtERERERBAEGYIieqKiRpoLcGrL39F9/FPwD3HOd1uequbYxk6egd/vEnky5rp5Bua6tY25bp3DXLe2uXOum0OyaGJjm27mi6KI/fv3d+pcZWVlOHbsmLmdnp7eqfNZysnJwXvvvSfp+/bbb/Hwww9bJYz17dsX69evx5gxYyTxvfjiiw6Nqbl//vOfWL16tbndrVs37Nu3Dy+++KLNhDcACAoKwjXXXIOvv/4an3/+udNiIwdx8JdRG/97GDBKJxN9dNdNtO5KX3Za0lZqWOCNiNxXxenNEI0Nkr7YgXdCpghwUUREREREznO4tAiv7NxsVdytW3AInh87yeuKuxV8usW6uFtEMLrdx+JuRERE5KFMFgXe5IGuiYOIyAkWLVrU4WO/+OILB0bieC+88AIEQTD/+f33310dUotEUcRrr72Gfv364a233rIr4Q0Azp07h1WrVuHBBx/Etddea7V9zpw5ksSygwcPYt++fQ6J2fL9v+222yQJb605d+4c1q5d26HXXb9+PZMqO6ioqAjTp0/H/fff32LCGwDk5+fj9ddfx6BBg7B3794uiS0zMxODBg3Cww8/3GLCG9BUvOu///0vRo8ejeeff97hiWj79u3D22+/7dBzUvsx1426mthCgTfIW855MzUYnRSNj7Is8CY2AsYa18RCRERERD5FJgiICwnFhIQk3D1gCF6ZMBWfXXY1nhszEbf2GYCRsfGICLD/eyGDyYRsXTl+zcvGPw/swV9//xV/+m0NFu7ZhlUnj+FQSRFq9I1tn4iIiIiIiLySX3CMVZ/JUI+8359HfcUZtyjE4S4M2jOoP3u44yfgXyWRV2Kum3tgrpv9mOvWccx1a5u757opHHGSsWPHQiaTwWQyoaysDBs2bMCUKVM6dK4vvvjC/CYEBwdj+PDhjgjR7MUXX4Rerze358yZg2uuuabF/QMDA7F48WIMGDAAjecfPP7888+xYMECpKamOjS206dP48knnzS3AwIC8Ntvv6FPnz52n0OhcMhbSp0kl8sxfPhw/PTTT9YbHVzg7fCRM9LTpweiWh4MmBz6Mh5FNDTCUFEo6VNGdHdRNETUFrlcjjFjxnSoUrM3EE0G6E5tkvSFxo1EcFRfF0VERO7M1+dMIvJ8O/LP4OMDe6xW9k0NV+OJEeMQZufNYHu4es406GpR8OlWGLTSh64UkcHodu8EKMJZCIWI3Ier50wi8hyiaARM0oUKIPOt6xrOmUTebffu3Thy5Aj69evXruMyMzOxbds2J0XlW0RRxL333muz4I9KpcKAAQMQHR2NgIAA6HQ6FBQU4MiRI2hoaLBxNqn4+Hhcdtll+Pnnn819ixYtwtChQzsVc0lJiVVugK0VblvzxRdf4Iorrmj3a7t7sqW7qqmpwYwZM6ySHhMSEjBw4EAEBATgxIkTOHLkiHlbdnY2pk+fjh07dqBXr15Oi+3QoUO45JJLoNPpJP3R0dEYMWIEwsLCUFRUhJ07d6K2thYAYDAY8NJLL6G2thYLFy50SBx6vR5z586FwWBwyPmo45jrRl1LAEy2k84EWSs5b0YfTlRzBnkoICgB8eK/fei1gCLEdTERkRnvDRER2Y9zpnfwlyvQSxOJXppIc195fR2ydFpk67TIKtcip6IcDUb7Cj9XNTbiQHEhDhRffN4jLiQU6SoN0lQapKs0SAwNg1wmc/hYiNwZ50wiIvtxziTyHqrkSaguOog67Umrbae2vIzgqP7oNvQeyJW+t6i6X7T193o1R35DQEJ/F0RDRO6KuW6ux1y39mGuW8cw161tnpDr5pA73mq1GiNGjDC3n3322Q5Vyjt37hxef/11cxXNadOmQebAm/J1dXVYuXKlpO9vf/tbm8f17NlTUvHSYDBg+fLlDovrgldeeQXV1dXm9v/93/+hb18Wd/FEMpkMkZGRNrcJDi7wVlRj8aB4TBAMet/+Mktfng9YzEFKTaKLoiGitshkMkRHRzv0d74nqS48AEO9TtKnSb3UNcEQkdvz9TmTiDzb2rxsfLR/t1Vxt/6R0Xh69ASHFncDXDtn6rU1KPhki1VxN2VUCLrdx+JuROR+eJ1JRHYz1lv3yX3r2oZzJpF3UiqV5v/vyMqmzROPmp+L2u/999+3SnibNGkS1q9fj7KyMmzevBkrV67EsmXLsGbNGvzxxx+oqqrCzp078dRTT6Fnz56tnt8yGW358uV2Jcy15ssvv5QUXxo6dCgGDx7c5nHNf1ZWr16NsrKydr2uTqfDqlWrbJ6PWjdnzhxJwltoaCiWL1+OU6dO4aeffsJ3332Hw4cPY+fOnZIEt/Lyclx55ZWoq6tzSlw6nQ7XXnutJOEtOjoaK1asQEFBAdasWYPly5dj/fr1KCkpwYsvvih5eOmtt95yWCLk66+/joMHDwJo+vsh12CuG7mCaGoh75OfgbqMIAiAUi3t1GtdEwwRWeG9ISIi+3HO9F7qgECMiI3HLb0H4JkxE/HZ9Kvx2oRLMW/AEExMSEZ8SCja89RMfnUVNp89hUWH9+P/tq7HvWtX4+Udm7D82CHsLjgHbb1z7sUQuRPOmURE9uOcSeQ9ZHI/JIx6FBE9r7K5vabkMMpz13dxVO4hbNQtVn2ioXO5DUTkPZjr5j6Y62Y/5rp1HHPd2uYJuW4O+wT7yCOPmP9/586deOCBB9p1fFFREa6++mqUl5ebi8P99a9/dVR4AIBff/3VXNEPAMaMGYPevXvbdezdd98taX///fcOja2qqkqSSBccHCz5OyXPotfr8dtvv9ne6MACbwaTCSV1tZI+P/g57PyeSl92WtIW/IIgC1a3sDcRuZper8dPP/0k+TDgS8rzNkraAapUBKiSXRMMEbk9X58zicgziaKIlZlHseTIAVg+Fja6WwIeHz4WgQrH35R11ZypL6tBwadbYCiXfl5XRoci9t4JUIT5VgEUIvIMvM4kIruZaq37ZAFdH4cLcc4k8k4zZ840//+yZcvatYqf0WjE0qVLze2rrrKdeExt0+l0eO655yR98+fPx4YNGzBlypQWH9JQKpUYNWoUXn31VZw4cQLr1q3D5ZdfbnPfq666CtHR0ea2VqvFDz/80Km4LRMl7V3RdMaMGeYxNTY2trv40vLly1Ff31R8VSaTYcaMGe063ldt3bpVUjDLz88PGzZswOzZs61+xkaNGoVt27YhLS3N3JednY333nvPKbG98cYbyMnJMbcjIiKwZcsW3HTTTVaxBQUF4bnnnrP6uXnyySdRWVnZqTiOHj2Kv//97+b2q6++2qnzUccx1426ngi0UOBNkDl2UVNqg1IjbevblxxPRM7De0NERPbjnOk75DIZuoeFY0r3VNw3aBjenDgdn06/Gk+NmoAbe/bD0OhuCPOzf+HHBqMRx7Wl+CknE+/t24k/r/8ZD6//Ge/+sRNrsk/gWFkJ6ttxD5fIE3DOJCKyH+dMIu8ikysR2fMqJI5dAJkyyGp7WeaPMDR07vtPTyQPCkfIwCscd0KxhQV+iMgjMdfNPTDXjbluXYG5bm3zlFw3hxV4u+WWW8xVGUVRxL///W9MmDABW7ZsafW4mpoa/Otf/8LgwYNx4MABCIIAQRAwffp0jBs3zlHhAQB++eUXSXvSpEl2HzthwgQoFApze//+/SgqKnJUaFixYoVkRdMbbrjBbasCkn1avBByYIG3E9pS6E1GSZ8fWKnUoD0jaSsjujetbEpEbqs9Hx69SX3lGdRpT0r61CmTXRQNEXkKX50zicgzmUQRXxzej1Unj1ltm5aUioeGjISy2coLjtbVc6a+rBoFn26BUSdd2UIZE4pu946HIsy3ip8QkWfhdSYR2cVosYKXLACC4HsrInPOJPI+c+fONf9/UVERfvrpJ7uP/d///oeCggKb56L2WbNmjSRZp3fv3nj77bfb/V3vpZdeitdff93mNqVSiTvuuEPS15lVIHfv3o3Dhw+b2wEBAbj11lvtOjYhIQHTpk3rcBzN958+fToSEhLadbyv+r//+z9J++mnn8bw4cNb3D8iIgL//ve/JX1vvPFGpxPLLFVVVeGjjz6S9L377rttrtR70003Yc6cOeZ2SUkJ3nrrrQ7HYTKZMHfuXDQ2NprP3zwxmLoWc93IJVoo8AYWeOtaVgXeyl0TBxHZxHtDRET245zpu4KUSvSPjMa1PXrjsRFj8c9Lr8Q7ky/Hw0NG4vLkdKSrNFC08JCrLeX1ddhTeA5fHz+Mv+/cjHvXrsbTW37D54f2YdOZPJyrqoSJBQvIw3HOJCKyH+dMIu8TpElH8iXP2dxWU3Kki6NxEzLnPWdBRJ6NuW7ugbluzHXrCsx1a50n5bo59ImLlStXIiIiwjzhbNu2DZMmTUJ8fDzuvPNOyb5/+tOfMHXqVERFReGhhx4yJ5CJooi4uDh8+eWXjgwNACQTDdC0qqm9goODMWDAAEnfkSOO+0CwceNGSbv5xEbeRWjHl1BtKa6tseqTgR/Y9GWnJW2lJtFFkRARtU578mdJW+4fhtBuw1wUDREREZFj6Y1GvL9vFzaczrXadkPPvrir32DIvKgYt760GgWfbIGxwqK4W2wYut07AfJQFncjIiIiL2CyLPAW6Jo4iIgcbPTo0ejTp4+5bblKZWuaJx716dMHo0aNcmhsvsRyAcEbb7xRUpzIUSxXHV23bh3Onj3boXNZ/qxcf/31UKlUdh/fPEnywIEDOHDggF3HHTp0CH/88Ye5fffdd9v9mr7s1KlT2Lx5s7kdGBiI+fPnt3ncpEmTMHLkSHNbp9Nh9erVDo3t999/R1VVlbkdHx+P2bNn23XsE088IWkvXrwYYgcfaH733Xexa9cuAIBarcb777/fofOQYzDXjVxBbKHAmyNz3sgOyghpW691TRxERERERA4iCAKig4IxJi4Rd/QbhBfHTca/p1+Nl8ZNxp19B2FcXCJigoLtPp9JFHGqsgIbTufi04w/sGDzOty/9ke8tmsLvj1xBPuLClDV2ODEERERERERkaMpAzWIH/lnq/6q/D0uiMb9NJw93PZOROQTmOvmHpjrxlw3Z2OuW9s8KdfNobNDamoq1qxZg+uuuw4FBQUQBAGiKKKgoACFhYXm/URRxKeffmr+fwDmfRMSErBmzRpERkY6MjQAwLFjxyTt9PT0dh2flpaG/fv3m9tHjx7FlClTHBLb7t27Je0LCXl1dXVYtWoVvvnmGxw5cgT5+fnw9/dHZGQkhgwZgmnTpmH27NlcAdWTeM+z625Lrz0jaSsjWOCNiNxPnTYbVQV/SPpUSRMhyBz/4Y2IiIioq9Xq9Xjnjx04WlYi6RcAzOk/BJcmpbomMCdpLKlC4adbYayql/T7dQtH7D3jIA/2d1FkRERERA5mtCjwJmeBNyLyHnfffTcWLFgAAPjpp59QXFyM6OjoVo8pKSnBmjVrzG1Hr2h69OhRHDp0CCUlJaisrIRGo0G3bt0wfvx4REREtH0CJzKZTDhx4gROnDiBs2fPoqqqCgqFAmq1Gt27d8eoUaMQHh7ernPm5+dL2s5apbNPnz4YM2YMduzYAaBpLEuWLLFa7bIt9fX1+OabbyR9lgl1bbnmmmug0Wig1TYVbVm0aBHee++9No/7/PPPzf+v0WhwzTXXSJK5yLZVq1ZJ2tdeey3UarVdx959992S3J7vv/8et99+u8Nis3z/Lr30Usjl9i3w17dvXyQmJuLMmaZciTNnzmD37t3tTsLNzs7Gs88+a24vXLgQMTExyMvLa9d5yHGY60Yu0VKBNzmT3rqU0uL3k6kOorEWgjzINfEQERERETmBUi5HmkqDNJXG3FfZ0IBsnRbZOi2ydFpk68pRa9Dbdb5agx6HS4txuLTY3BcTFGx+jR5qDbqHhkNp5z0XIiIiIiLqeiHRA9D01MHF7ytqig+j4vRWhCWM9ulnLyu2L0PsbW3nE9jS0aIpROS+mOvWPsx1Y66bJ2KuW+s8LdfN4VexI0eOxL59+zB37lz873//A9BUvK35f5u7UNhNFEVMmzYNS5YsQWxsrKPDglarNU8SF3Tv3r1d57Dc/+TJk52OC2iqdpiVlWVu+/n5ITU1FZs2bcLdd9+N3Nxcyf719fWoqKhAdnY2Vq5ciaeffhrPPfecXZUWqWsoFAqMGzcO33//vdU2W/8OyLGsCrxp2vdvnYi6lkKhwOTJk51SldpdiaIJxUdXSPpkymCokx2TTE9E3ssX50wi8jwVDfV4c/c25FXqJP0KmQwPDR6Jkd3iuySOrpozG4srUfjZVhirpKv++sWFI/ae8ZAH+Tn19YmIHIHXmURkNxMLvHHOJPJed955J55++mkYDAYYDAZ8+eWXeOyxx1o9ZunSpdDrmx4uVCgUuOOOOzodR1lZGRYuXIhly5bh3LlzNveRyWQYO3Ysnn/+eVx66aUtnuv333/H5MmTbW5rqf8CW8m95eXlWLVqFX744Qds3rwZOp2uxeNlMhnGjx+PJ554AjNnzmz1tS4wGAyStmWOhyPNmzfPnPQGNK0C2d6kt++++07yd5CSktLm36slf39/3Hrrrfjwww8BAF999RUWLlwIP7+W7yc0NjZi2bJl5vatt94Kf38Wl7fHL7/8ImlPmjTJ7mMt9127di1MJhNkMpkDIoPVyrr9+/dv1/EDBgwwJ70BTcm77Ul6E0UR9957L2prawE0jdfRibzUPsx1I9cQIJpMtjfJmPPWpRThAOQAjBf79OUAC7wRuRzvDRER2Y9zJnVEmL8/hsR0w5CYbgAAkyiisKYKJ8u15wu/leN0VQVMdhYnKKqtQVFtDbbnN903UchkSA5TIf180bd0tQZRgUF8zodcjnMmEZH9OGcSeb+EUY/g7K53JX2FGUtRdHg5QuNHInbA7T5R6E0WYL1gUl3uHqu+X7/Zj1HTe7V6Ln7kIfI+zHWTYq5b25jr5nmY69YyT8x1c8zfvIWYmBj89NNP2LNnD26//XbExsaai7hZ/gkLC8P111+PjRs34tdff3VKcTcAVpNtUFAQgoOD23UOy4qlFRUVnQ0LAFBYWChpx8XF4fvvv8eUKVOsEt5sKSsrwyOPPII77rjD6pcAuU5AQIDtDQ6a8ACgoqGh7Z18kL7stKStiEh0USREZK/AQN96ELQqfy/qdXmSvsieMyH3a9+1CRH5Jl+bM4nIsxTXVuPF7b9bFXcLUCiwYMS4LivudoGz58zGokoUfGqjuFuCisXdiMjj8DqTiOxitCjwJvPNuYNzJpF3iomJwYwZM8ztRYsWtXlM831mzJiBmJiYTsWwdOlSpKam4o033mgx4Q1oWoVz69atmDZtGu644w40NjZ26nXtNXHiRMybNw+rV69uNeHtQoybN2/GVVddhdmzZ6OmpqbN81vmZPzwww+dCbdVN998M0JCQsztrKysdq8KavkzMnfu3A49CNo8saisrAyrV69udf8ffvgBZWVlNo+n1h0+fFjSHjNmjN3H9u7dGxqNxtyuqalx6GqflkmeKpWqXcdb7n/o0KF2Hf/pp59i48aNAJryXT755BM+2OxizHUjlzHZLpIgODDnjdomCDJAqZJ26sts7ktEXY/3hoiI7Mc5kzpLJgiICwnDxMRkzB0wFK9MmIrPpl+NZ8dMxOze/TEiNh6aAPt/zgwmE7J0WvySl4WPDuzGXzb+ggd/+wlv7dmG/548jsOlRag9/6AzUVfjnElEZD/OmUTeLTiqr80CbqLJgMoz21GY8aULoup6qnF3WvWJhkYEBkufUTi0/RRqq9uoN2BfjWwi8iDMdWsbc92kmOvmeZjr1jJPzHVzatbNsGHDsHTpUpw7dw5ZWVnYuHEjVq5cieXLl2Pt2rU4ePAgysrKsHLlSkycONGZoaC6ulrS7sgHeMtjqqqqOhXTBZa/DKqrq3H77bfDdH41zKSkJLz55pvYvn07Tpw4gR07dmDhwoVITk6WHLds2TI8+eSTDomJOsdgMGD9+vUtbHXcpHBMWwJRy0RHS3rtGUlbqWGBNyJ3ZjAY8PPPP/tM4rbJ2IiS499L+pTB0VAlOfdaiIi8g6/NmUTkWU5V6vDi9k0oqpXexA7z88czoy9Bv8joFo50DmfPmY2FFSj4dAtMFl+G+ieqETtvHIu7EZFH4XUmEdnNZFHgTR7kmjhciHMmkXdrnjx05MgR7NljvfLxBbt27cKRI0dsHtsRzz33HO666y5UVlaa+wRBQO/evXHVVVfh1ltvxRVXXIGoqCjJccuWLcOMGTO6ZF66kMNwgUajwfjx43Httdfi9ttvx3XXXYchQ4ZAqVRK9vvmm29wyy232FwptTnLBKSdO3fiqaeegtFodMwAmgkJCcFNN90k6bMn0fGCU6dOYcOGDea2TCbDnDlzOhTLkCFDMHjwYLvj+OKLL8z/P2jQIAwZMqRDr+trKisrrZJJ09LS2nWO1NRUSfvo0aOdjusCy5VsG9q54J/l/u2J7ezZs1iwYIG5/eyzz6Jnz57ten1yPOa6kWuIEFso8AaZeyfCeiVlhLStL3dNHEQkwXtDRET245xJzhKgUKC3JhIz03rh0WGj8cHUGfhg6gw8OnQ0rkztid6aSPjL5Xafr7KxAfuLC/Ft5hG8tmsr7lu7Ggs2rcWnB/diw+kcnKrUwWhxb5TI0ThnEhHZj3MmkW8IjurX4rbKszvQWFPShdG4RsjAy232T7zG+u+mvLjaxp4XiazwRuSVmOvWOua6XcRcN8/DXLeWeWqum3X5YidJTU21evO7kmXSW0BAQLvPYZn0ZnnOjrJMeistLTX//4033oglS5ZYvfbo0aPx8MMP484778S3335r7n/77bdxzTXXYMKECW2+bkNDg+SH/sIvT71eD/35FWdkMhnkcjmMRqPkF9iFfoPBIPnFJJfLIZPJWuzXW6xko1A0/Qha/gJuqV+pVMJkMkl+qQmCAIVC0WJ/S7E7c0xt/bJufkxHx9RoMOD4qSKIJ2ol57b12r70PjXoimCqkSazKSO6e/SYvPF94pg4puaxNz+ft4ypeeyW/bqctTDUSasSa3peC5MoQA545Ji88X3imDgmdx4TAKvrdU8fkze+TxwTx+RrYzqhLcO7B3ahzmKfqMAgPD5sNGKDQwF07fVe83E4+n1qLKhE6eIdMNVKz+uXqELkXaMgD/Rzy/eptTEBnvmzxzFxTByTY8Z0Yf8L//WGMXnj+8QxcUzuMCa5oVayjI1R9IOo13v0mID2vU8XjrVMwPDkMbnzd2pEXe3KK69ETEwMioqKADQlF40YMcLmvs0Tj6KjoyUrorbXkiVL8PLLL5vbMpkMDz30EB5//HF0795dsq8oivjhhx/wyCOP4PTp0wCA9evX49lnn8Vrr70m2Xf06NHIzc0FALz77rt47733zNu+/vprjB49ul1xCoKA8ePH45ZbbsGMGTOQkpJic7/y8nJ8/vnneOmll8zFhNasWYNPPvkEDzzwQIvnv/766/HYY4+htvbid+Cvv/46vv/+e9xzzz246qqr0Lt373bF3Jp58+ZJ3sdvv/0WH3zwgWS105YsWrRIModNnz4dCQkJHY5l7ty5mD9/PgDg119/RX5+PuLi4qz2O3fuHNatWyc5rqudPXvW6veOM8TGxnYot6clWVlZknZkZCSCgtpXrLZ79+7Yu3evuX3y5EmHxAYAERHSIj4FBQXtOt5y/+zsbBiNRsjlbT/M/MADD5hzhgYMGIAnnniiXa9NzsFcN2vMdXP8mIxGabyiCBgsxmze12SEXq+3OSab+xsMMBgMXveZsCvfJ0EeDsksri+ze6zuOiZ7YueYOCZ3H5Ov5bpxTBwTx8QxdXZMAHPdOKauGVOoXIGh0bEY0S0eRqMReoMBZ6urkFNRjpxKHbIrypFfbV/hcxHAueoqnKuuwqazpwAA/nI5UsLVSFdpkBIajtRwNdTnP6vzfeKYHDEmZ+a6uWpMzfs5Jo6JY+KYHDkm5rpxTByTb4wpqv/tkCmD0FCZj4bKU7BUVXQIYYkXv1/yhDFd6LdcbGf1wQLMHplg831SRqVCX5Jj7jMYDLjs1qFY9Op6yb4GgwEKSAsUAUC9Qo5AgxEQpdeZ/Nljrht5B+a6tY65bhcx1425bgBz3VxN4eoAXEUQ2r+iZEeOsUfzi8TmRowYgeXLl5svVi0FBARg+fLlyMvLk1RT/fvf/45ff/21zdd97bXX8OKLL1r1r1271vwPu3v37hgyZAgyMjLMvzABoFevXujduzd2796NkpKLVa4HDx6MpKQkbN68WbLq65gxYxAdHY21a9dKJr/JkycjMDAQP//8sySGGTNmoK6uDhs3bjT3KRQKXHnllSgtLcWOHTvM/aGhoZgyZQrOnDmDAwcOmPujoqIwduxYnDx5EidOnDD3d8WYWnrPAKCissI83s6MaeeJ42g4bp14Wetn/cvFl96nP777AKrmQSoDoYxMxv88eEze+D5xTByT5Zgu8KYx2Xqf5GhAuvx3yUOwNaIGm/aeRffuco8cE+B97xPHxDG585gurJJw4aaGN4zJG98njolj8rUxLft9PXbITDBZ3DZRicDoqgb88ftml43pAke+T4HVIlKOiVBYfPyuCQUOx1YgImO/W75P3vizxzFxTByT48e0bt06rxsT4H3vE8fEMblyTJN7lyC02XfPBzKO45zurEePqb3vU2RkJICmL5Kbf3nuyWNy5fvUFUkTRO2hUChw++234+233wbQtBrnO++8Y5V4U1dXhxUrVpjbd9xxh9VKnvY6deoU/vSnP5nb/v7++O9//4vLL7e9ErMgCLj22msxduxYjBs3zjwXLVy4EPfdd58kES0gIADJyckAAJVKJTlPbGyseZu9fvzxR7uOUavVePzxxzFt2jRMnDgRFRUVkhhlMpnN46Kjo/Hss8/iqaeekvRnZmZiwYIFWLBgASIiIjBy5EiMHDkSI0aMwNixY6FWq9s1jgvGjh2L3r174/jx4wCAmpoa/Oc//2kzkUwURSxZskTSN2/evA7FcMFtt92GJ554Ag0NDTAajVi6dCmefPJJq/0WL15sTlz28/PDbbfd1qnX7Yjx48fj1CnrBHpH27hxIyZNmuSw81kWpIqOjm73OSyPufCz7Qh9+vSRtHfu3Gn3sQ0NDdi/f7+kz2g0orq6GuHh4a0eu2zZMvz0008AmhJuP/vssw7PZ+RczHVjrpszxhRSLqL5kr319fXYvXO3pO+Czdu2wBiqtDmm+J4BiMisl+y/e88eBOqive4zYVe+T1HBFRjR7BkDsVHr8WPyxveJY/LNMV3gTWPyxveJY+KYOCbXj4m5bhyTu4xpUq9eeGDwCGzcuhWZ2hJoAZQJIiqUctQa7fueosFoxHFtKY5rLxY5DxSBCFHAuN59kBAQhNx9+6E4nyXO94lj6siYLvCmMXnj+8QxcUwck/uMibluHBPH5P1j6jb4bvz000/wN0YhUX6xOAkAHDmcgfJDF/f3lDFFR0ejTFsGNFviJbOoGkUVtdi16TfJGGfMmAGTKP1ebv+B/Zg49BpY2rt3L0ZfZb2gUo46BP1KKmAymSQ/B/zZY64beQfmurWOuW5NmOvGXLcLmOvmWoLooFKxS5cuNf//rFmz2l3574Kamhp899135vadd97Z6dgAICMjA4MGDTK3IyIiJKuH2uODDz4wV5EEmipqNo+1ozZv3oyJEyda9W/atAmXXHJJm8dv3brVahXToqKiNv+B2lrVNDExEaWlpQgLCwPACswdHZNer8eaNWvM1W4viHvz31D3SseYH5d1ekw788/gg083wPhtieQ1+r46Az8eudh326hEPDIlxWfep7Of3IHK7Rf/foMHXoGkx3726DF54/vEMXFMzWPX6/VYt24dZsyYIVmxz5PH1Dz25v0lR5aj6uy2ZkcKiB+zAP5h3T12TM37veV94pg4JnceU319PX799VdMmzYNSqXSK8bkje8Tx8Qx+dKYNp7JxReH9sPy5lIvdQTmDx6J4PM3yFy1qumF60xLHX2f6k6XoWTxLoj10r8jvyQNIu8YCZm/wi3fp9bG5Kk/exwTx8QxOXZMdXV1WLdunfk60xvG5I3vE8fEMbnDmOSFX0AwXXxo36i5CmJAokePCWjf+2QwGLB27VpcdtllkMsvJtp58phc+T5VVlZCpVKhoqLC/P1kZ9TX1yM3NxcpKSntXgnPJIqoqNW3vSN1SHiQEjInFZnpiOTkZEnSTklJibmA49GjR9GvXz/ztuXLl2P27NmS47/88ktJLsPhw4fNx5SWliIqKsq8LSkpCXl5eS3G8vDDD+Ojjz4yt//5z39KkuBas2fPHowcOdLc/vOf/4z333/f5r4vvPCCpCiOoxOKWvL222/j8ccfN7e3bt2KcePGtXrMn//8Z3z44Yd2nV8QBAwYMAA33HAD7rjjjhZXWm3JW2+9JVnBcdy4cdi6dWurx6xfvx6XXnqpuR0ZGYlz587Bz8+v1eMWL16Mu+++29x+6KGHJOO86aab8O233wIAevbsKUlEvqBHjx7mRMdZs2aZ9wesf5YWLVqEOXPmtBpTR1j++3EWR/+Mrl69GtdcczHRfdiwYZIVSu3x17/+Fe+8846kfSFJtrN27tyJMWPGmNtyuRy5ublITExs89hly5bhjjvusOo/c+ZMq6vtFhcXo2/fvigrKwMAzJ8/X7L68QV5eXlW/7actTJ5Z36Xexvmulljrpvjx1R/shilS3dfPGdYANRXD0DZsosF+C6IfXQyFBHBNscEETj73BrJ/pF3jkRgzxiv+0zYpe+TXgtFyTeSWA2x8wCZv+eOqRmveZ84Jp8bky/lunFMHBPHxDF1dkzMdeOY3H1MMpkMZQ11OFFaiuwKLbIrdDhdWQGDRQEFe8kEAYkhYUgLVyM1XIVekdGIDgyC2CwWvk8cU0tjckaum6vH1LzfW94njolj4pjcY0zMdeOYOCbfHFPOrw9JYo3oPQvhSZM9ckxPfJuB9cel3/t9dOsgDO8uzZ1SKBTIeiId+pIcc1+3h79H2NBrcEu/hZJ931o9B1EpGty7drWkP1BvwLACLdT7j+GGjy5+t82fPea6UeuY65bXYizMdbPGXDfmugHMdZPLmevW1u9yhaNedM6cORDO/5KaNGkSunfv3qHzlJaWSs7lqAJvISEhknZdXV27z2F5jOU5O8rWeZKSkuxKeAOaKkampqYiJ+fiBfqmTZtw4403tnqcv78//P39rfov3NRoTi6XQy6XW+174ULa3v6WKh+2p18mk0Ems65y2lJ/S7E7c0xKpRJTp07F8uXLrbYJMpnVMR0ZU0l9vVV/j0Fx0DZYf5nkK++TaDKh5sg6SV/owBntjr2lfk/42WtvP8fEMQGuH5NCocCMGTOgUDQVw/CGMdnqb6g8i6qz2yXbwhLGICQiTdLnSWOy5A3vkyWOiWNqKcb29jtqTP7+/pI5s63YPWFM3vg+cUwcU3v7PXFMoihidfYJ/OfEEav9hsfE4aEhI+FnI5auHJPldWZbY2qrX39Oh1Ibxd0CUiMRM2cMZH7SmNzhfWqr3xN/9trq55g4JoBjainG1voDAwOt5kxPH5M3vk8cE8fk6jGJogkwSb+bkPuHQmh2nKeNqTl736e2rjM9cUwXuOJ9aula3RUqavWY+o/WE12o49b/dTzUwa0nBLmLvn37YtSoUdi1axcA4IsvvrBKevviiy/M/z9y5EhJklx71NTUSM6VmpqK+++/3+7jR4wYgQkTJmDLli0AmpJ6Wkp6c5UbbrhBkvS2ffv2NpPePvjgA0yePBlPPfUUMjMzW91XFEVkZGQgIyMDL730EubOnYtXXnlFknjYmjvvvBNPPfWUOXl427ZtyMzMRM+ePVs8pvl7BjStattWwps95s6da05iy8zMxLZt2yR/V5s2bTInvF3Yn+xXXV0taXekeFhgYGCr5+yMkSNHomfPnuafeaPRiMceewz/+c9/Wj2uqqoKzzzzTIvbWvPwww+bE94SExPxyiuvdCBychbmulljrpvjx6SXS/cRBAFymfVrAIDC7+Lfs63Y5eGBMFZc/JmTKxTmGLzpM+EFXTEmUREJQACaLbOjQBUEpfW/QU8ZU2f7OSaOCXD9mHwl180Sx8QxtRRje/s5Jt8aE3PdOKb29rtiTNFBIYjuHoIJSAYA6I1GnKqsQJauDFm6cmTrtCiurbE5BksmUcSpqgqcqqrAhrNNfUEKJdJUaqSrI5CuUiNNpUEoFHyfWun31TE5OtfNHcbUVozt7eeYOCaAY2opxvb2e/qYmOvGMXFMvjmmoMjeqC09fnGDsd5m/J4wpuuGxlsVeBMEocX3yd7XClIq0UsTiRNaG4tGtXB+/uwx141sY66bbcx1s425bsx1cwbmulnz9Fw366uZTnBkxTpHV78LDw+XtGtra1FTY99N9guKi4slbZVK1dmwWjzP6NGj23WOUaNGSdrHjh3rTEjkAPU2CrABABx08V5Uaz15GkUR27LKHHJ+T1R/+gCMFUWSvpCBl7soGiJqj44ko3ua4mMr0TzpVpD7IbLXNS0fQETUAl+YM4nIvZlEEcuOZtgs7jYpMRnzh46yWdzNFRw1Z9afKkPBv7fBZFncLS3KZnE3IiJPxOtMImqTycb3HrJA6z4fwDmTyPs1TybasGEDTp8+bW7n5ORg06ZN5nbzVSrba+vWrZI5ZdasWTaTUlszefLFFalPnTolibWrGI1GVFRU4OzZs8jLy5P8ab6KMmB/LsP111+Po0ePYs2aNbj11lsRERFhVxyfffYZhg0bhv3799v1OtHR0Zg5c6akb9GiRS3uX1FRgVWrVkn6HJV8Nn36dMkKlJZxNE+2i4+Px/Tp0x3yuu2Vl5cHURSd/sfZq+52JPHamcnaMpkMTz31lKTv22+/xRNPPGH17+gCnU6HmTNntrjKbGvzyX//+1/Jqrj//Oc/HVb8ixyDuW7kMibbeZuC3KGppmQHQZADCpW0U691SSxEJMV7Q0RE9uOcSZ5GKZcjXa3B5Sk98PCQkXhn8uX4+NKZeGz4WFyb3hv9I6MR2MLD/7bUGvQ4VFqMVSePYeGe7Xhg3Rr8deMv+Of+3Vibl4VsnRYGk8mJIyJPwjmTiMh+nDOJqOzkGlQVHnB1GB0yNq3t/IeOujw53fYGB9ftICL3wly39mGuG3PdmOvWecx1az+fybqJiIiAWq2W9LV3srf8IenRo0en4wKaVjC1XF20W7du7TpHXFycpH2h6iC5hsFgwLZt22xuE2QOKvBWY13grareYNUnd9DreYLqjP9J2n4x6fCLaeHDKBG5DYPBgI0bN5qrRXujmuLDqC05KunTpE6HMlDdwhFERLb5wpxJRO7NYDLh4wN78EteltW2a9J64Z4BQyFv5815Z3HUnFmfV4bCL7ZDbJCeJ6BHNGLuGs3ibkTkFXidSUR2sVXgTd7+1cA8HedMIt9wyy23mFcvNJlMWLJkiXnbF198YV6wLjAw0GrF0/bYulW6km5cXJxV0lhbfyxX08zJyelwPPYqLy/HF198gRtuuAE9evSAn58fVCoVEhMTkZKSIvmTnp5uday95HI5rrzySnz11VcoKSnB4cOH8fnnn+Ohhx7CmDFjWlxJ9MyZM7jiiitw7tw5u15n3rx5kvbSpUtbTDL6+uuvJYmKI0eORP/+/e0cUetkMhnuvPNOc/s///mPuZhTZWUlVq5cad5255132lxxmlpmmdDVkQd/LI9xdJLYXXfdhWuvvVbS99Zbb2Ho0KH4+OOP8ccff+DkyZPYunUrXnrpJfTs2RObN28GAGg0GqtVV1sq5lVeXo4HH3zQ3L7pppuskj/J9ZjrRq4htvyAjw/loLkVpUbaZoE3IpfjvSEiIvtxziRvEebvj6Ex3XBjr354atQEfDr9arxxyTTcN3AYpnRPQfewcLTnE1NRbQ225Z/BkiMH8dy2jbjn1x/wwraN+PLoQezIP4OS2hrzPWjyHZwziYjsxzmTyDcJgvX344UHF0MUPbNgcmSI7XwHIqKOYK5b65jrxlw3T8Rct4u8JdfN7Z5AbX4T2tHVAPv06YPt27eb21lZWejTp4/dx1v+cmjPsa2Ry+Xo1asXMjIyzH2WSXBtsdy/vt7GQzbkHhz0c11Ya13gzWTjS5wRyb5TPKj60C+SdsjAK1wUCRHRRaLJiOKj30r65P7h0KRd5qKIiIiIiDqm3mDAe/t2IqOkyGrbHX0H4vIUxzwc6E7qc0tRuGg7xEbpje7AnjGIvmMUZEreXCYiIiIfYrT4YljwgyC43VeNREQOERYWhhtuuAHLli0DACxevBjPPPMMRFHE0qVLzftdf/31CA8P7/DrnDlzRtJ+9NFH8eijj3b4fACg1Tqv6IfRaMTChQvxyiuvoLra+vtqe1RUVHToOEEQ0K9fP/Tr18+8imh9fT3Wrl2Lf//73/jxxx8l+xcVFeGxxx7DN9980+a5r7jiCsTFxSE/Px8AkJ+fj19//RUzZsyw2rf5yqKAdcJcZ82dOxevvfYaRFFEVVUVVq5cibvuugvffPMNamtrzft1ZjVdX+UJSW+CIGDJkiW47rrrsGHDBnN/RkaGJEnNUkBAAFatWoUpU6ZI+ltKevvrX/+KgoICAIBarcb777/f+eDJKZjrRq4gmmwXEXDUoqbUTkoNUJd9sc0Cb0RERERELicTBCSEhiEhNAwTE5MBNOVV5VaUI0unRbZOi6xyLcob7PusqzeZcFKnxUndxev9MD9/pKs1SFc1/UlVqRGoUDpjOEREREREHiEoqi9qSo5I+kz6WhQd/hrRfW6ATOF7C5USEV3AXDfbmOvGXDdPxly3i7wl183tnrq4UJERAIKCghx67v79+0uS3nbs2IGrrrrK7riaJ6VdOJ+jDBw4UHJ+nU7XruMt94+IiHBAVOQcjkl2q2pstGu/cem+8bNgrNGhLmuHpC94wOUuioaI6KKKM1vRWF0g6YvqfR1kivYluBMRERG5UlVjAxbu2YZsnXTlEbkg4P5BwzEuvruLInOeuuwSFC3eAVFvUdytVwyib2dxNyIiIvJBxlppWx5oez8iDxUepMT6v453dRheKzzI8x48mzt3rjnpLScnB5s2bUJ9fb0kUa2ziUdlZWWdOt6Wqqoqh58TAAwGA2bPni1ZWbMjTCbHrZ4dEBCAq6++GldffTXWrVuHG2+8UZJU9+2332LhwoVITExs9TxyuRx33XUXXnvtNXPfokWLrJLejhw5gj179pjbQUFBuOWWWxw0miZpaWmYMGGCeaXKRYsW4a677pIk240fPx49enhfoX1ns0xQLSkpafc5iouLJe2Wkso6IywsDL/++iuef/55vPvuu5JkR1sGDx6MxYsXIzIyUrIar0qlsllwa+3atVi8eLG5vXDhQsTExDgsfnIs5rqRS9hYYBQABLmsiwMhAE0F3ppjgTciIiIiIrcUoFCgT0QU+kREmfvK6movFnzTlSNXV45Gk7GVs1xU2diAfUUF2FfUlIMuAIgPCUO6WoO080XfEkLDIBNYjJuIiIiIfIM6ZSpEkwGlx1dJ+itObUK9NhuJYx+HXOnYuhTeRGzh+x/yXMx1cy7mutnGXLfOYa4btQdz3Zp4U66b2xV4O3LkYvVktVrt0HNffvnl+PTTT83t33//3e5jt2zZAoPBYG4PGTLEoW/6jBkzzL8wAenfgz0OHz4saSckJDgkLuo4hcL2Py9B5rxkt0aD9AJhSPeOV9D1NDVH1gHNvuwSlP4I7j3JdQERUbu0NGd6OqO+FqUnVkv6/MMSEZYw2kUREZE38NY5k4jcV2ldLd7YtRX5NdKb5v5yOR4ZOhqDomNdFFnbOjpn1mWVoGiJdXG3oD6xiL5tJAQFi7sRkffhdSYRtclksfKXzHcLvHHO9E4yQYA62M/VYZAbmTRpElJSUpCbmwugKfmo+YqGSUlJVisItlejnYt6tYezknLfeecdq4S30aNH44YbbsDw4cORmJiIqKgoBAQEwM9P+m9J6IKHDKdNm4ZFixbh+uuvN/eZTCb89ttvdiUnzp07F6+//rr572/16tUoKyuTFByyXNH0xhtvRFhYmINGII3lQtLb5s2bsWbNGuzatUuy3ZXOnj0ryZ9xltjYWAQEOG6VdctEwZKSEtTW1rZr8clTp061ek5HUSgUeOWVV/DII4/gq6++wrp163D06FGUlpZCFEXEx8dj8ODBuPXWWzFz5kwoFAr89ttvknMMHz7c5rmff/558/8PHDgQU6ZMQV5eXqvxnD171qrP8pjo6GiHL+RJzHUj1xBNLVxLyFg0wCX8LAq8GSohmvQQZJ73UAmRN+G9ISIi+3HOJF8WERiEiMAgjOrW9JnXYDLhbFUlsnTapsJv5VqrfKyWiADOVlfibHUlfj+TBwAIkCuQEq5GulqDdJUaaSoN1AG++/2dN+CcSURkP86ZRL5HEGTQpE5DWeYaiCa9ZFtD1VmU525AZM+ZLoqOqOsx140sMddNirluzHW7gLluLZ/TUZjrZh+3+hRbWVmJd955B0DTpNe7d2+Hnv+yyy5DYGCg+RfRjh07cPz4cbtep3lFPwC47rrrHBrbzJkz4e/vj4aGBgDAnj17oNVqodFo2jgSKC8vx+7duyV9EyZMcGh81D5KpRKXXnoplixZYr3RQb/QTWV6GL+VVtksqWpwyLk9UfWhXyTtoN6TIPNn8jCRJ1AqlbjyyitdHYZTaLN+gbFR+sV7dN8bIQhc2ZqIOsab50wick9nqyrxxu6t0NZLi3mEKP3wxIhxSFe3fd/CVTo6Z9adLG4q7mZRRD2obzdE3zoSgoLXckTkfXidSUR2MVoUeJP75gMinDOJfIcgCJgzZ445QWTlypWSFQPnzJnT6WSuyMhISXv79u0YM2ZMp87pDI2NjXjllVfMbUEQzKtttqWystKZoUlcd911SE1NRU5Ojrnv+PHjdh2bnp6OSy65BJs2bQLQNOavvvoK8+fPBwDo9XpJISPAeclns2bNwp///GdUVVVBFEXceeed5m0hISG48cYbnfK69ho/frxV8pczbNy4EZMmTXLY+cLCwhAXF4f8/HxzX3Z2NgYMGGD3OS4kwV7Qp08fh8VnS3R0NP7yl7/gL3/5S5v77tixQ9IeNWqUzf2aJ+9mZGQgNTW1Q7GlpKRI2qtWrcK1117boXNRy5jrRl1PAFoo8CawwJtrKGwskmwoB/yiuz4WIgLAe0NERO3BOZNISiGTITlcheRwFS5NaronU6NvRLauHNk6LbLKmwq/Vevte1i63mjAMW0JjmkvPlcUERiEdJUa6SoN0lQapISr4SfnQpaegHMmEZH9OGcS+S5BpkBEjxkoPfGD1bayzNWI6HFllxTlcYbqeucUnnFO2SQickfMdbuIuW7MdWuOuW4XMdftIlfkurWrwJu9/2Aff/xxhISE2H3ehoYGFBQUYM+ePaitrTX3X3LJJe0Jr01BQUGYNWsWvvzyS3PfG2+8gUWLFrV6XGZmJlatWmVuKxQK3HrrrQ6NLTQ0FLNmzcJXX30FoOnv5MMPP8Rzzz3X5rEffvgh6uvrze2kpCT079/fofFR+5hMJpSWltrc5qjPhvqvCq36RLn0IfMApW98ESOKIqozpAXeQgZc7qJoiKi9LsyZkZGRkMm8p1hGY20pynOl1YNDYgYhKNKxBWyJyLd465xJRO7pZHkZFu7Zhhq9dIWriIBA/G3keMSHOn7VDkfqyJxZe6IIxV/utC7u1j8O0bNHQJBz7iUi78TrTCKyi8miwJvMNwu8cc4k8i1z5szBiy++CJPJJMlluJAQ11kxMTGSdmZmplsmvW3atAkVFRXm9h133GFXwhsASYJRVxg8eLAk6a28vNzuY+fNm2dOegOaVjG9kPS2Zs0aFBcXm7f16NHD4TktFwQHB+Omm27C559/DkA6hhtvvLFd+Tgk1b9/f8nP5I4dO+xOejt+/DjKysrM7aCgIKvEL1dav369pO3IhEFyHea6kUu0tEI6P/+4hCBTQlSEAYZmDxI0alngjciFeG+IiMh+nDOJ2has9MPAqBgMjGq6VyyKIopqa5oKvp0v+naqUgdjS5/VLJTV1aKsrha7Cs4BAOSCgO5h4UhTaZB+/k9scIjHFr3wZpwziYjsxzmTyLdF9LgSYfGjkbflZZj0tZJtulOboE6e5JrAOmnBd4exr+8Uh5+3XqmAgdf/RD6DuW5NmOvGXDdvwVw379KuT6+LFy/GkiVLbP65QBRFfPfddy3uZ+vPN998g02bNqGmpsZ8noCAAEllRkd54YUXoFQqJWNavXp1i/vX19fj7rvvRmPjxRVQ5s2bh7S0tFZfRxAEyZ/ff/+9zdhefvll+Pn5mduvvvqqVdVBSzt27MDf//53Sd9TTz3Fm+0uZjQasXfvXtsbHXTTSDxdb9VXkyBdsXN6X99I5Go4kwGDTnqxFDLwChdFQ0TtZTQasWPHDkklcG9Qevx7iKZmKycIMkT1meW6gIjIK3jrnElE7udAcSFe3bnFqrhbXEgonh87ye2LuwHtnzNrjxeiaKl1cbfgAfEs7kZEXo/XmURkF6NFgTe5bxZ445xJ5Fu6d++OqVOnWvVPnjwZycnJnT7/2LFjJe21a9d2+pwt6UwOwcmTJyXtmTNn2n3s9u3bO/y6HSGXSxdBCw8Pt/vYWbNmSfY/ePAg9u3bBwBWxZSctaJpW+e/++67nfq63u7yy6ULxdmTy9PSvpdddpnbPDSUnZ2NzZs3m9spKSmYMsXxyf/kGsx1o64lQjS1UDSAb5HrKDXStl7rmjiICADvDRERtQfnTKL2EwQBscEhGBffHXf1G4yXx0/Bvy+7Bi+MnYTb+w7EmLgERAUG2X0+oygit0KH307l4F8H9+LxTWtx/7of8cburfgu8ygOFheiutk9BHIdzplERPbjnElEyqAIRKRbP0NefHg5GqoLXBBR+xltfB+jq9Xb2LN9AhQKq75zYUEtL/BDRF6FuW5NmOvGXDdvwVw37+Ief/vnCYIAURShUCjwz3/+E4mJiQ5/jdTUVDzyyCOSvlmzZuHDDz+UJLYBwLFjxzB16lTJJBwREYHnn3/e4XEBTT90CxYsMLcbGhowffp0fPzxx9BbPFBtMBjwySefYPr06ZK4R44cyUnOzTkrITF+UCwqekkLul0zOM4pr+VuqjP+J2kro1LgF9vTRdEQEQF15dmoypcW+lQlTYJfSEwLRxARERG5j61nT+Mfe7ej0SRNfEhXafDcmImIaEeSoKeoPVqAoi93AUaL4m6DEhB1y3AWdyMiIiICAJNlgTfvuy4kIrLFVvKRoxKepk6dKknSWr16tWTlTEfy9/eXtBsaGuw+VqfTSdrtSSRbvHix3fs6wtGjRyXt+Ph4u48NDAzE7NmzJX2LFi1CYWEh/ve/i99Jy+Vyu1d17aixY8eid+/ekr4ePXpgwoQJTn1de+Tl5UEURaf/ccaqnNddd52k/d///tfq57sllj/Lludypddffx1is4T8e++9t8WEvAMHDrT7vcjNzbU6j+U+1157rbOG5/OY60ZdroUHfFiEz4VY4I2IiIiIyKf5yeXooY7AFSk98PCQUXh3yhX456VX4rHhY3BNWi/0i4iyWcShJTV6PTJKivD9yWN4c8823L/uRzz2+6/4+MAerM3LRm5FOQwmU9snIiIiIiJyobCEsTb7835/XvLdqbsamGCdd9FoaPs63FRf3er2PppI+FsUC6pVylvYm4i8EXPdmOvGXDcp5rrZPpcr+Wqum/13cM+z56K2oxe+ycnJmDx5MubPn49BgwZ16Bz2eP3113HkyBHzpKTX6/HnP/8ZL7/8MoYOHYrQ0FDk5ORg3759krH4+flh1apV6Natm9Nie+mll3DixAl8++23AIDq6mo8+OCDePrppzF69GhoNBpotVrs3LnT6h9efHw8vvvuO8nKqOSGnJTsNu7eYdi84+IFUIDCdx4+rz70i6QdMvAKJhUSkcuIoojioyslfTJlECJ72l/hm4iIiMhVfs7JxFfHDln1D4qKwfyho9uVDOgpao7ko3j5bsAovZ8VPCQRUbOGsrgbERER0QVGiwJvskDXxEFE1MWuv/56bNmyRdI3YsQIh5xbrVbjtttuw9KlSwE05Qc8/vjj5rYjqVQqSbugwP5Vq9VqtaR9/PhxTJ8+vc3jfvjhB6u/u9YcOnQIffr0gaKD9x+2bduGI0eOSPrau7LjvHnz8K9//cvcXr58OaKjo2EwGMx9M2bMcGreyAU//vgjCgsLze2YGC4k1FnJycmYMGGC+eeyrq4O7733XpvFrzZt2oRdu3aZ2yqVCldffbVTY7XX77//js8//9zcjo+Px4MPPujCiMgZmOtGXcrk/g89+RwWeCMiIiIiIgvh/gEYGhOHoTFxAACTKCK/ugpZOi2yysuQrSvHmaoK2PsJr7CmGoU11dh67jQAQCmTITlcjXSVBukqDdJUakQGBvE5HSIiIiJyGwr/UMQNewD5f/zLapuhXgtlYIQLorLfk1f0xKbM0nYfd+5ft6LXyJtb3K6UyzExMRlr87I7Ex4ReTDmujHXjblu3oO5bt6lXTOFrSp1QFMhk9TUVABNKzVu3rwZCQkJdp1TEAT4+/tDpVJZVdF0Frlcjv/85z+45557sGLFCnN/cXExfvnlF5vHREdHY8mSJU6vECkIAr788ktoNBp88skn5n6dTtdibEDTaqarVq1CXFycU+Mj+wiCgJCQEBQVFdna2PUBeTFjXSVqT26T9IUMuNxF0RBRRwiCgNDQUK/5wre25Cjqy6U3wCJ6XAm5X4iLIiIib+JtcyYRuQ9RFLHixGH8mJ1ptW1cfHfcN3AYFC2shOCu7Jkzaw6fQ/HyPVYPrIUM7Y7IWUMhyDjfEpFv4HUmEdnFZFHgTe6bBd44ZxL5Hj8/P4wfP95p53/hhRewYsUK8yqjX375Jbp164ZXX31VsuJpW44ePYrS0lJccsklNrf36dNH0l67di3mzJlj17ktF+j78MMPMW/ePAQHB7d4zN69e3H33Xfbdf4L3n77bWzZsgVPP/00brvtNgQEBNh9bE5ODu644w5JX79+/TBw4MB2xTB8+HAMHDgQGRkZAACtVou///3vkn0ctaptW9LT05Gent4lr+VLXn31VUnuz6uvvoorr7wSw4cPt7m/VqvFvHnzJH1/+9vf2lzdNy8vDykpKZK+3NxcJCcnt3qcwWCwO/Fz+/btuOaaayQFvT7++ON2rTxMnoG5btSVRBZ4cz+WBd4MOoiiEYJg/7UiETkO7w0REdmPcyZR15EJAhJCw5AQGoZJickAgHqDATkV5cjWaZFVrkWWTgtdQ71d59ObTDhZXoaT5WXmPpV/ANJUaqSrIpCmUiNVpUagQumM4fgkzplERPbjnElEF4R2G4rg6P6oKT4s6S8+8h/ED/+Ti6KyT5Cfvff4rb+3MehaL3IU5mejXofI73+IfAVz3Zjrxlw378JcN+/Rridzk5KSbP658IZc+ECcmJjY4r6Wf7p3746YmJguK+52QUhICL755ht8++23GD16dIv7aTQa/OlPf8Lhw4dx+eVdUzTK398f//rXv/Dbb79h2rRprf4i69+/PxYvXozt27cz4c2NKBSKFi98eOPIsWqO/AYYL1aQFRR+CO7bvsq0RORaCoUCU6ZM6XCFanciiiJKT/4o6VMGRkCdPNlFERGRt/GmOZOI3IfRZMJnGftsFne7IqUHHhg03OOKuwFtz5k1GS0UdxuexOJuRORzeJ1JRG0RRREwWhR4k/lmgTfOmUTkaCkpKfj0008lfW+++SbGjx+PH3/8UbKapqW8vDx89NFHmDJlCvr164cNGza0uO/w4cMREnJxMZpvvvkGf/7zn7FhwwZkZmYiLy9P8qe5UaNGISkpydw+efIkpk2bhmPHjlm9TnV1Nd58801MmjQJ5eXliIqKauuvQCInJwf33HMPYmJicM899+CXX36BTqdrcf+CggK89tprGDp0qNWihR988EG7XvsCywSnxsZG8//HxMRg5syZHTovuYfx48dj1qxZ5nZjYyOmTp2Kb775BiaTSbLvrl27MHbsWGRnX1zcKS0tDfPnz3dafNOmTcMDDzyA9evXQ6/X29wnJycH8+fPx8SJE1FZWWnuf/DBB3HVVVc5LTZyLea6UdcQ+ICPO7Is8AYR0OtcEQkRgfeGiIjag3MmkWsFKBToGxGFq9J64S/Dx+DDqTPw/pQrMH/oKMxI6YGe6ggo25ETpmuoxx9FBVhx4jBe3bUF9/66Gk9uXofPMv7A76dzcaaqAiZ+puwwzplERPbjnElEzcWP+LNVX3XhfpTnrIcoihBFEUZ9HUTRZONo9xeYZv29oKmx1gWREBE1Ya6bFHPdyNmY6+Y9HPYJtnv37ubCVZ70wXjWrFmYNWsWcnNzsW/fPuTn56OmpgaxsbFISkrCuHHj4Ofn1+7zig64KT116lRMnToVJSUl2LlzJwoKClBaWorQ0FDExMRg7NixSEhI6PTrkOOZTCacPXvW9kYWeHOo6gzpar9BvS6BzL/l6rlE5H5MJhPOnDmDxMREyDywcEhztaXHUF+eI+nT9JgBQeY510ZE5N68ac4kIvfQaDTig/27sK/IehWnW3r3x8zUnh5bqLy1ObP64FmUrNhrVdwtdGQyIq4dzOJuRORzeJ1JRG0SGwFYJLnJfbPAG+dMInKGO++8E4WFhXjqqafMSTc7d+7E1VdfjaCgIAwZMgQxMTEIDAxEVVUVSktLcfTo0VaTwSwFBwfj/vvvx9tvvw2gKafhww8/xIcffmhz/+Y5D3K5HG+++SZuvvlmc9+OHTvMq4b26tULgiAgPz8fu3fvNq/QqlAosHTpUlxxxRXt/StBZWUlPv/8c3z++ecQBAE9e/ZEQkICIiIiIJfLUVFRgZMnTyIrK8tmfsZ7772HyZM7tgDP7bffjgULFpjH0dydd97pUTkxZNvixYuRnZ2N/fv3A2j6eZs9ezYWLFiAQYMGwc/PD5mZmTh8WLrSu1qtxk8//YSgoCCnxVZVVYVPPvkEn3zyCQICAtC/f38kJCQgODgYZWVlyMnJQWam9UINd911F95//32nxUXug7lu5GyiiQ/juxtB5g9RHgIYqy926rWAX4TrgiLyYbw3RERkP86ZRO5FEAREBAYhIjAIo7o1fT43mEw4U1WBrHItsnVaZOm0KKipbuNMTUQAZ6oqcaaqEr+fyQPQVFQuLVyNNJUG6WoN0lUahPsHOGlE3oVzJhGR/ThnElFzgiAgoudVKMv8UdJffHQFio+ugCD3h2hsgCBTImH0XxCkSXdRpB0Te9u7qNz5tavDICKSYK6bNea6kTMx1807OOxfomVVS0+TkpKClJQUV4dhU1RUlE9VHfQGRqPRavIz89AH492RKIqoPvQ/SV/IgK5ZfZiIHMdoNOLAgQOIi4vz6Bvroiha3QhUBEYgPGGMiyIiIm/kLXMmEbmHGn0j3t6zHSfKyyT9AoB7Bw7DxMRkl8TlKC3NmdX7z6DkP3ubsgybCR2VgohrBrG4GxH5JF5nElGbjDZW/ZT5ZoE3zplE5CwLFizAwIEDcffdd6OwsNDcX1tbi23bttl1DrVa3er2V155BSdPnsTq1avbHd9NN92EzMxMPPfcc+YkM1EUcfDgQRw8eNBq/8DAQCxduhSXX27/97dpaWlQKpVWKzmKoogTJ07gxIkTbZ4jPj4e7733Hm644Qa7X9eSRqPBtddeixUrVlhtmzt3bofPS+4jODgYP//8M26//XasX7/e3H/mzBmcOXPG5jFpaWn4+uuv0atXr64KE/X19di7dy/27t3b4j6BgYF49dVX8eijj3ZZXOQemOtGziECDij2R06gVFsXeCMil+C9ISIi+3HOJHJ/CpkMKeFqpISrMQ1pAIDqxkbkVGhx8nzRt2xdOar1jXadr95gwJGyEhwpKzH3RQYGIV3VVOwtTaVBcrgKfnK5U8bjyThnEhHZj3MmEVkKjupv9VznBaKxqdiNaNLjzPY3IfcPR6AqGcHR/RGWMAYyefsXTupKssBwh5yHX/8QkaMx1425btR1mOvmHfjplaiL8UFxx2k4dwQG7VlJX8jA9lfEJSJyhNqy46grz5b0RaTPgCBjZWsiIiJyP+X1dXh5xyar4m5KmQx/GT7G44u7taTqj9M2i7uFjUlFxLUs7kZERETUImOdtC0oIMiUromFiMiLXX755cjNzcVHH32EwYMHQ2hj8TClUomxY8fihRdeQGZmJh555JFW9/f398cPP/yA3377Dffddx+GDRuGiIgI+PnZl7D8zDPP4Oeff8bQoUNb3CcsLAx33XUXDh06hFmzZtl13gueffZZlJSU4Ouvv8a8efPQu3fvNv8OAEAmk2HcuHH46KOPcPz48U4lvF0wb948q76xY8eid+/enT43uYfY2FisW7cO//rXvzBgwIAW9+vWrRv+9re/4eDBgxgxYoTT47r33nsxZsyYNlfPjYyMxKOPPoqTJ0/6ZMIbETmRydUBkE3KCGmbBd6IiIiIiMhJQvz8MDAqFjf07IsFI8fjX9Nm4u1J0/GnQSMwLSkVqeFqyO24b3tBaV0tdhacxbJjGXhxx++459cf8OzWDVh8eD+2nj2Nwpoq84PWREREREQd4RcSCwj2FRE2NlSguuggig59hbO733dyZERE3o25bsx1o67DXDfPx4ojRF2tHV9kUOuqM36RtJUR3eEX18dF0RCRLxNF0WqVB0WgBuGJY1wUEREREVHLCqqr8MburSipq5X0BymUeGzEWPTWRLooMueq2nsKpd/tsy7uNi4NmpkD7LqJTkREROSzTBYF3mSBromDiMhB8vLynHbuyMjITj2MFhAQgAcffBAPPvggtFotdu7ciYKCAmi1Wuj1eoSEhCA6Oho9e/ZE7969ERQU9P/s3Xd4W+XdPvD7HA3L8tDwjp1pJ84mhOwFhDASZoFSWlqghNX+Wigto0Bb4H2hBVoob0spZQYolLbsMsoOZBEyyXZiJ7ETb1uSh2Rb6/z+cKLkSHJ8bEs6GvfnunK9Ps8Z+pq3+UY6es79DPg1zjjjDJxxxhmDqu+cc87BOeecg7179+Krr75CY2MjJElCQUEBSkpKMH/+fBgMBtk5A/nvYTKZcPnll+Pyyy8HALS3t2Pv3r2orKxES0sL2tvbIQgCsrKyYDKZUF5ejsmTJw/qv8OJnHnmmRF/qPDqq6/G1VdfHdFrHu/xxx/H448/HrXrJyNBEHDDDTfghhtuwK5du7Bjxw7U1dXB7XZj2LBhGDNmDObMmQNRHPj6maNGjRrU/4aO1tPV1YWtW7eisrISjY2N6OrqQlpaGoqKijB58mScdNJJg6prMAb7uxBRYpL8/Psel3RBq9d7WsMfR0REREREFGGCIKAwIwuFGVlYUDICAOD2+XCwzYEqhw37HDZUOWxoCZqL1hefJGF/mx372+z4uHo/ACBTp0ep2YJSsxVjLVaUmq3I0Cl7WJuIiIiISKNLR+FJV6Gl4i14u5QvkNLVuhe2/R/DOubMKFZHRDR0nOvGuW794Vy31MC5bsrE61w3BrwRRYEgCMjJyUFjY2PYfRQZnds/kG1nTDmH/32JEpAgCMjLy0vov7+u1gp02SplYzllSyGIfKtFRJGVDD2TiNR1oM2Oh79eg3Z3j2zcnGbAHbMWYES2SaXKIu/4ntnx9UG0vLEl5JjsBWWwnjuZfZWIUh7fZxJRv3xBAW+a1A14Y88koliyWq1YtmyZ2mWENW7cOIwbNy7qr5OdnY0ZM2ZgxowZUX8tSm0TJ07ExIkT1S4jID09HXPnzsXcuVxQiohiLA4nuRIAXY582+OAJPkhCLGZAE1Ex/DeEBGRcuyZRMlLr9FgnDUH46w5WHpkzNHdjcojYW+VDhv2O+zo9nkVXa/T48Y3zY34pvnYM1hFGZkoM/eGvZVZrBieZYI2Rg+BqoE9k4hIOfZMIgrHVDIHppI58Ps8aKl4C92OauiMOdDoM9HZuA0eZ+jz/gDQvOvfSLeUId0yOsYVxxi//yGiGOBcN851o9jhXLfEE5PUkcOHD8Nut6OtrQ1+v39A5y5atChKVRFFj1arxcyZM7Fr167QnUn8hUIs+bo64KpYJRvLnLq0j6OJKJ5ptVrMmzdP7TIGTZIktFa8LRvTGiwwDZ+vUkVElMwSvWcSkbp2tDTijxu/Cpk4V5iRiV/OWoA8Y4ZKlUXH0Z7Zvv4AWt/cGrLfdOpYWM6ZxAkeRETg+0wiUsAfFPAmpm7AG3smERERERElLwGSnw/4xCWdNWjAB3jbAJ1FlXKIUhnvDRERKceeSZRazAYDZhQOw4zCYQAAvyShtrMdlfbewLdKhw21He1Q+qmz3tmJemcnVtXWAAD0ogajTeZA4FuZ2QqrIT1p5n6xZxIRKceeSUQnImp0yJ/4bdlY/sRvw+Nqgat1Lxq+WRFyTuOOlzFq4a9iVCERERERpaKoBLx5vV688sorePnll7F+/Xp0dHQM6jqCIMDrVbZaB1E88fl82LdvXx97k+PLA7W5dn8G+DzHBjRaZEw8Q72CiGjQjvbMsWPHQqPRqF3OgHXUfY0ue5VszFq2FIIYkxxdIkoxid4ziUg9X9UdxhNbv4YvaOWl0SYzbps5H6Y0g0qVRY/P50PVO+uhWx+62pbptHGwnD0xaSb4ERENFd9nElG/fEEBbxqjOnXEAfZMIiIiIiJKahID3uKRoEmHJKbLA9g9dga8EamA94aIiJRjzyRKbaIgYHiWCcOzTDh9xGgAQJfXg/0OOyodNlQdCX1r6+lRdD2334cKeysq7K3Agd4xc5oBZUcC30rNVowxWWDQJuYcfvZMIiLl2DOJaDB0xlyYjLnw+3rQtOMfsn09bTXobj8EQ/ZwlaojIiIiomQX8buW69evx+WXX46amt4VMiRO+KEU5Pf7UVVVFXYfnx2PjM5t/5VtG8cugCY9S6VqiGgo/H4/KioqUFpamnA31n2eLjTtek02pk3PgWn4fJUqIqJkl8g9k4jU8/HBKrywc2vI6qeTcvJwy4y5SNfqVKkr2trWVIUNdzMvLof5zAkMdyMiOg7fZxJRv/zBAW/p6tQRB9gziYiIiIgoeUmAn/M945bOCvTUHtv2tAIYo1o5RKmK94aIiJRjzySiYOlaHSbl5mNSbj6A3mcOW7pcqHLYUeloRaXDjoNtdnj8fkXXc/R0Y2NjHTY21gEABADDs0woNVtQZslBmdmKYZlZEBNgnhh7JhGRcuyZRDQUllGnw9tlh61K/oy6s2mH6gFv5/zfGnx+60KY0qPwfAfzPoiIiIhUFdGAt08++QTnnXcePB4PJEmSPSh7/M/Hh74FP0zLQDhKeqKodgUJT5IkdG7/QDaWOXWpStUQUSpr3fcufD1tsrH8SZdB1CRnSAoRERElFkmS8Ma+3Xhj3+6QfbOLivGjk2ZCl6QTG9pWVaLt/Z0h4+Yl42FZMkGFioiIiIgSnC8o4E1M3YA3IiIiIiKiZCYx4C1+6SxBAW829WohIiIiIiKKAEEQkGfMQJ4xA3OGlQAAvH4/atrbUOmwodJhQ5XDhgZnp6LrSQBqOtpQ09GGzw8dBACka7UYY7KgzGxFmcWKUrMVpjRDlH4jIiIiIkoE2SVzQgLeJJ9HpWrk/vbFAdx+zrgBn1d/0I7iMTlRqIiIiIiIIiFiAW+NjY347ne/C7fbHQhty8vLw9KlS5GVlYXHH38cQO/N13vuuQft7e2oq6vDunXrUFNTE9iXn5+P66+/nsnplLSCQw0Ho9PZDQQtSCPG/4IyEeOu3wNPS7VsjAFvRBRrPR11sB/4VDaWkTcJmQXT1CmIiIiI6Dh+ScILO7bik5r9IfuWjByDqyZNS4iVSQfD8cVe2D8IE+525gRYzhivQkVEREREScAfFPCmYcAbERERERFRUorgAr0+lxv2D3fB2+aCaeFYpJfmRezaKUkX9GAWA96IiIiIiCgJaUURY8wWjDFbcBZKAQAd7h5UOeyoOi70zelRFsDR5fViZ2szdrY2B8by0o2BsLcysxWjss1Ju1AqEREREYVKyxoGfWYR3J31gbHOpu1Iyy5BumUMtAZz1GvISAsf8bGrvr3fcwVBgCAKskV7XnjoM8xYXBax+oiIiIgosiIW8PbYY4+htbU1EF71wx/+EI8//jjS09NRXV0dCHgDgHvuuUd27meffYY777wTGzZsQHNzM1avXo133nkHmZmZkSqPKKZEUURxcTEaGxtDd0bgAfrPV+8JuiZQXJwLoGnI104EndvkyehaSzHSSiarVA0RDZUoihgxYgREUVS7FMUkSULTjlcB6VjapiBqkT/p8ogEeRIR9SUReyYRxZ7H58MTWzfg64bakH0Xj52Ai8dOSNr3LI6VFbD/d1fIuOWciTCfVq5CRUREiYHvM4moXz6XfFtM3YA39kwiIiIiIkpeAuCPXMCb7b3t6NzUu/hv194mjPjVMmiM+ohdP+XorPJtjx2SJCXtdz5E8Yr3hoiIlGPPJKJIydKnYVp+IablFwLoncvf4OxE5ZHAt0q7DYc62uBTGFre3OVCc5cL6+oOAwA0goBRJnMg8K3MbEW+MSOmn7fYM4mIlGPPJKJI0GfJA9562qpRt+lJQBAx7JQbkFV4clRfXxQEPHjxJPzyDfnC9kre0oqCgMLhZtRX2wNjDdX8zoCIiIgonkUs4O2ZZ54JvOk7/fTT8eyzzyo+d/HixVizZg2uv/56rFixAl988QUuvfRS/Pe//+3/ZKI4pNFoMGXKFGzevDlknyAO/cPRpnUHZNuGkRnIzjYO+bqJonPbB7LtzCnn8EMnUQLTaDQ4+eTo3vCKtI76TXC1ysM2LWPOhD6zQKWKiChVJGLPJKLYcnk8eGzTOtmKowAgALh68slYMnKMOoXFgP3TPXB8vDtk3LJ0EsynjlOhIiKixMH3mUR0IpIkAf4u+aAmdQPe2DOJiIiIiCiZSWGeHNLmZAzqWkfD3QAAfgmdG6thWjR2sKVRcMCb5AF8HYA2W516iFIU7w0RESnHnklE0SIIAooys1CUmYWFJSMBAG6fDwfa7Kh02FDlsKHSYUdrl6ufK/XySRKqHHZUOez4CFUAgCy9PhD4Vmq2otRsQYYueqHl7JlERMqxZxJRJIiiLvwOyQ9b5YdRD3gDgLMmFcDmdOPhD/cFxrbXtis697r7zsb/XP2qbGzt+7sx/9yJ4U9QGIZMRERERNERkYjy3bt3o7W1NTC554EHHhjwNbRaLZ555hksXLgQkiTh448/HlBIHFE88fl82L59e/idEQgiq9neKNsePi11AoUkrweuvV/KxjKnLlWpGiKKBJ/Phy1btsDn86ldiiJ+bzead/1bNqY1WJBTtkyliogolSRazySi2Grr6cYDX30ZEu6mFUX8dPrs5A53+2R32HC3rmkWZC0oVaEiIqLEwveZRHRCkgeQgvqDmLoBb+yZRERERESU1PyhD/jkXhyZh5h8LndErpOyNBmAEBQm4LGpUwtRCuO9ISIi5dgziSiW9BoNyq25OHfMONw0fQ7+tHgpHj9jGW45ZQ7OLy3HBGse0jQaxdfrcLuxtakBr+3dhYe+Xo3rP/oPblv5EZ78ZiM+qd6Pg20O+Pz+iNXPnklEpBx7JhFFQmbhtD73dTv2w+fp6nN/JKXpQt+jfr6nOcyRcpNmjYAlP1M29tnrfeQaEBEREZHqIhLwtnXr1sDPhYWFmD179uCKEUX84Q9/CGz/9a9/HWppRKrw+/2ora0NvzMCAW+eLq9su2S0tY8jk4+ntQaSp0c2ZixfpFI1RBQJfr8fNTU18EfwC85osu3/GN5uu2wsf+JlELVpKlVERKkk0XomEcVOk8uJ+9auxMF2h2zcoNHi9pnzMbuoRJ3CokySJNg/2gXHJ3tC9pmWTcK+9Db2TCIiBfg+k4hOyBdmspomdQPe2DOJiIiIiCh5SZCCAt6ME4uQXpo38EuFCYoTDbrBFkYABEEAdEHzBBnwRhRzvDdERKQceyYRqc1iSMeMwmJcPn4yfjV3EZ45+0L8buESLJ8yHacNH4WSzGwM5AmvOmcHVh2uxvM7tuDu1Z/iuo/ewf+s+wKv7N6Gr+tr0drlGnSt7JlERMqxZxJRJGQVnYKCKVdA0IR/JrTyw5vRZa+Keh06MfQd6br9/d/7F0UBC86bKBuzN3f2eXzotzZEREREFEvaSFyktbUVQO8EkilTpoTsF4ICrbq7u2EwGMJea+bMmRg1ahQOHjyILVu2YP/+/RgzZkwkyiSKC4IQkVxFuTAf4JKVu3m/bFs0mqDJylWpGiJKNZIkof3wOtmYMXcCMoumq1QREREREVDT3oaHvl4NR0+3bDxbn4bbZ83HaJNFpcqiS5Ik2D/chbaVe0P25Vx4EtJnDAfe361CZURERERJxh8c8KYBBL0qpRAREREREVGUSfJHfLTWjEFdxt/jDRljwFsE6KyAu+HYNgPeiIiIiIiIFBMFASOyTRiRbcLiEaMBAC6PB/vb7Khy2FDpsKHSbkO7u0fR9Xp8PlTYWlBhawGwD0BvqFyZ2YJSsxVlZitGmywwaCPy+CYRERERRZh55KnIGjYT7s4G1Kx5MGT/oXWPYvTp90OXHr3nMU4ZFXptj09ZgGXp5ELlLyQx4o2IiIhITRG5Q9je3h74OScnJ2S/0WiUbXd2dvYZ8AYAkydPxsGDBwEA33zzDQPeKLmkThZbVHiCAt70eWNCQiSJiKLF3VkPj6tFNpY3/lvsQ0RERKSa3a3NeHTjOri8Htl4XroRv5y9AIUZWSpVFl2SJMH+351o+2JfyL6ci6Yhe85oeDyeMGcSERER0YD5ggLeNOm8H0ZERERERJSs/PIHfIRBLjzq7w69Ry8a+ED7kOms8m0GvBEREREREQ2JUafD5Nx8TM7NB9A7L62ly9Ub9uawocpuw8F2Bzx+ZSEb9u4ubGjowoaGOgC9oXIlWdkoOxL4Vma2oigzCyK/byUiIiKKCxqdEemWMTCNWIS2mi9l+yS/Bx31m2AdsyRqr19kMmBqSTa2HW7v/2AiIiIiSlgRmTFzfFibFCbBNytL/jBxXV0dcnNz+7yeyWQK/NzQ0NDncUTxShRFlJaWorGxMXQnb8IPibtJHvCmy2MAJFGiE0UR5eXlEEVR7VL61dm4TbatNViQZhqpUjVElIoSqWcSUfRtbKjD41vWh0weG5Flwu2z5sNiSFepsuiSJAm293agfXWlfIcA5H7rZGTNGgWAPZOIaCDYM4nohPxBAW9icr7PVIo9k4iIiIiIkpcQOv8zkgFvega8DVmYgDdJkhjEThRDvDdERKQceyYRJSJBEJBnzECeMQNzhw0HAHj9flS3O1Bpt6HqSPBbo8up6Hp+SUJNextq2tvwWc0BAIBRq8MYswVlZitKj4S+ZWi17JlERArxfSYRRUPuuPPhbNoOb7ddNt6861+wjF4MQYhez5lSbGLAGxEREVGSi8iMGav12KSR9vbQN5BpaWkwmUxoa2sDAOzZswdTp07t83qtra2Bn4+eQ5RINBoNxo4di7Vr14bsE3jjaEjczfKAN30+A96IEp1Go8H48ePVLkMRZ9N22XZGwVROkiWimEqknklE0bXy0EE8s20TgmP2y625+MWMucjQ6VWpK9okSYLt3e1oX1Ml3yEAuZdMR9aMY+G77JlERMqxZxLRCfmCAt40hvDHpQj2TCIiIiIiSmr+oG8eBjknwt/tDR0cZFgcHSc44M3fA/hdgCZDnXqIUhDvDRERKceeSUTJQiuKKD0SxnZUh7vnSNibHZX2VlQ57HB5Q8POw3F5PdjR0oQdLU2BsXxjBkrNVhysOYAysxUjs03QaTQR/12IiJIB32cSUTRoDSaMPu1/sO+/Pw3Z5+5sRFpWkQpVhedz2fs/KFjwgydEREREFFMRSZoaO3Zs4OcDBw6EPWbSpEmBn1euXNnntTweD9avXx/Yzs7OHnqBRDHm9XqxYcOG8DsZBDQknmZ5j9HlMeCNKNF5vV6sXbsWXm+Yyb1xxOfuRJetUjaWmd93YC0RUTQkSs8kouiRJAnvVFbg6TDhbqcUFOGXsxYkdbhb6zvbwoe7XXqKLNwNYM8kIhoI9kwiOiF/UMCbmK5OHXGCPZOIiIiIiJKXBEmSf/sgDDKUTepW9lA7DZA2GxCC1nV229SphShF8d4QEZFy7JlElMyy9GmYll+ES8dNxC9nL8Tfzjofvz/1TNxw0gwsGTEGo7LNEAfw/FiTy4l1dYfw0q5vcM/az3HtR+/gN2s+x4s7t2Jt7SE0uTpDPrMTEaUqvs8komgRtWmwlp4TMt5l2wtJ8sesjlpH14n3/+0Hg7gq30sSERERqUnb/yH9mzhxIgRBgCRJ2LdvH9xuN/R6+cPEc+fOxdq1ayFJEl599VXcf//9sFqtIdd68sknYbcfSw4uLy+PRIlEMSVJElpbW8PuExjwNiTu5v2ybT0D3ogSniRJaG5ujvsvHJ1NO3H8jSxB1MGYy/cpRBRbidIziSg6/JKEl3dvw38PVIbsO7VkFJZPORkaMSJZ/nFH8ktoffsbdKwPWlhAAPIum4HMk4eHnsOeSUSkGHsmEZ2QzyXf1hjVqSNOsGcSEREREVFS8wd91hnkXDd/DwPeokEQBEg6C+BuPjbotQEI/Z6EiKKD94aIiJRjzySiVCIKAoZlZmNYZjYWlfQuVNrj8+JAmwNVdhsqHb1/bN0nDus4yuv3o8phQ5XDhg/RuyBqtj4NpWYLSs1WjLVYMcZkhVGni9rvREQUr/g+k4iiKbf8Atiq/isba9z+Mhw1q1F8yo3QGXOiXsPGgw40tHWj0GSAIIQ+H+Ku3xP1GoiIiIgosiIS8GaxWDB58mRs374dPp8PX375JZYsWSI75tvf/jYeeeQRCIKAtrY2nH/++XjllVcwcuTIwDHPPPMMbr311kBYnNFoxLx58yJRIlH8YL7boPmcdviddtmYPp8Bb0QUG51N22TbxrwJEDX6Po4mIiIiiiyv34+nvtmINXWHQvZdUFqOy8onJW2guOSX0PrWVnR8fVC+QxSQ950ZyDypRJW6iIiIiFKGP+ghAzFdnTqIiIiIiIgo+oIC3gRxkAFv3d5IVEPh6KzygDe3Tb1aiIiIiIiIqE9pGi3GW3Mx3pobGLN1dwUC36ocNuxvs6PH51N0vXZ3D7Y0NWBLUwOA3sfTijKzMNZsRemRP8OzspN2kVgiIiKiWBBELbQGC7zd8mfZe9qq4aheibwJl0T8NcN9FbOmqhWXTC+GoNVBay2B13b4WI1aPtNKRERElGgiEvAGAGeeeSa2b98OAHj33XdDAt5mzZqFhQsXYvXq1QCAdevWobS0FBMmTIDJZMK+ffvQ0tISSE0XBAHXXnst0tP5kAglFyECN8r9TvkEuOR8hD+Uu/mAfEAQocsZoU4xRJRSfB4XnE07ZGOZ+SepVA0RERGlmm6vF/+3+Stsa24M2ff9iVOxdPRYFaqKDckvoeWNLejcWC3fIQrIv3wmMqYWq1MYERERUSrxdcu3NfzujoiIiIiIKFkdnb8ZMMipbv5uz9CLofB0Vvm2hwFvREREREREicJqSIe1qBgzi3rnvfn8fhzubMfe1mas3LEd7qxM1Ds7IPVzHQCQANR1dqCuswNfHO6dX5em0WC0yYIysxVlZitKLVZYDfx+l4iIiGggcsaei8btLwNB78p6Ouuj8npTik0ADsnGXD3HQoBLfvQqDj6wYGgvEvz9DxERERHFVMQC3i677DI8+uijkCQJL730Eh588EEYDAbZMX/5y18wf/58dHZ2QhAE+P1+7Ny5E4IgyILdJElCaWkp/vd//zdS5RHFlEajweTJk9HYGPrw/VDj2A5VtsDd2CMbs4zIHtI1E4Wneb9sW5cznEnjRElAo9Fg2rRp0Gg0apcSliRJaPjmBfi9XbLxjPzJKlVERKks3nsmEUVeh7sHv9+wFlUO+cM5GkHADSfNwPzi5A29lvwSWl7bjM7NNfIdooD8781ExuQTh7uxZxIRKceeSUQn5JffF0v1gDf2TCIiIiIiSl4C4A8eGtxcNwa8RRED3ohUxXtDRETKsWcSEfVPI4oYmW3G8MxsjBP1GD58OLp9Puxvs6HSbkeloxVVDjva3T39XwxAj8+HPbYW7LG1BMashnSUmq0YeyTwbbTJjDRNxB4pJSKKOb7PJKJoM49cBIN5FKpX3S8bdzZui8rrLZ6Qd8L9QpoxKq9LRERERLETsbtxs2bNwuuvvw6/v3eGj9PpDAl4mzx5Mt577z1cdtllaGhoANAb6Hb0/0qSBEmSMHXqVPznP/9BZmZmpMojiilRFFFSUhJ+5yAnvR216fNK+YBJg4LxOUO6ZqJwNwUFvOWOVqkSIookURQxcuRItcvok+Pg5+hs2CIby8ibDF26RaWKiCiVxXvPJKLIau1y4cGvV6Ous0M2nqbR4Obpc3BSfqFKlUWf5JfQ/O9NcG6Rr0QFjYD8781CxqRh/V6DPZOISDn2TCI6IV9QwJuY2gFv7JlERERERJTMji7Ue5QgDjLgrccbiXIoHF3QXEG/C5KvG4LGEP54Iooo3hsiIlKOPZOISLnje6ZRFDE5twCTcwsA9H5Wb+5yotJuR5XDhn0OG6rbHfD6g1Paw7N1d8HWUIsNDbW9ryUIGJFlQqnZgjJLDsrMFhRmZEEc4vNuRESxwveZRBQLBtMI5I6/GC173pCN2/Z/DOuYMyP6WqIgYG6pFeuquKALERERUbKK6HIL3/rWt/o9ZsGCBaioqMBf//pXvPPOO9i3bx8cDgcsFgtOOukkfOc738FVV13F9HRKaF6vF6tXrw67b7CT3o5qqZc/2C+OMw75monC0ywPeNPnj1GpEiKKJK/Xiy+//BKLFi2CVhtfK0F1t1WjefdrsjFRl4GCqd9XqSIiSnXx3DOJKLJqO9rx4NerYeuWh2lk6vS4deY8jLUkb9C35POj+V+b4PzmsHyHRkTBFbNgnFik6DrsmUREyrFnElFfJMkLSG75oCa1A97YM4mIiIiIKHlJgF8e8DbYxUz93Z4I1ENhaU0ARADHPcjvsQGa/hfHIaKh470hIiLl2DOJiJQ7Uc8UBAH5xkzkGzMxr3g4AMDj86G6vQ2VDhuqHDZUOmxocjkVvZZfknCw3YGD7Q58WnMAAGDU6noD38xWlFmsKDVbkaVPi+wvSUQUIXyfSUSxkm4eHTLWvOvfyC6eA21algoVEREREVGiUuXTa1ZWFm6//Xbcfvvtarw8UdRJkoTOzs7wOyO9ookuNcLdAMDdJA940+Ux4I0oGUiShI6OjpBVoNXm83ShbtNTkPzyVaWLpv0QunSrSlURUaqL155JRJG1z96KP2xYi06PPEjDakjHL2ctQHFWtkqVRZ/k86P51Y1wbq+VjQtaEfnfnw3j+ELl12LPJCJSjD2TiPrk6wodE1M74I09k4iIiIiIklpQwNtgFx5lwFv0CIIISWcBPK3HBj02wMCAN6JY4L0hIiLl2DOJiJQbaM/UaTQos/SGsR3V3tMjC3yrctjQ5fWe4CrHuLwebG9pwvaWpsBYgTEDZebesLcyixUjs83QiuLAfjEioijg+0wiihVjbjm0Bgu83XbZeM2ahzBm8f0qVUVEREREiYjx5ESxFumAtxTibpYHvOkZ8EZEUSJJEhq3vwSPq1k2bhlzFjILpqpUFREREaWCrU0N+NPmr9Dj88nGh2Vm4ZezFiAn3ahSZdEn+fxo+scGuHbUycYFrYj8K+fAOK5ApcqIiIiIUpg/OOBNAESDKqUQERERERFR9IU8FDnIgDf4+HBlVOmsQQFvrX0fS0RERERERCkhOy0N0wuKML2gCADglyTUd3ag8rjAt5r2Nij9xN7ocqLR5cSaukMAAJ0oYmS2uTdY7kjwW166EQKfkyMiIqIkNnzurTjw+d2yMY+rCZ4uO3TpFpWqGgSGYhIRERGpKq4D3jweD3Q6ndplEEWUEKXVSt7b3hCV68YLyeeFp7VaNqbLZ8AbEUVHW80qdNRtlI0ZzGOQN/4idQoiIiKilLCmtgZ/+2YjfEFfnpWarbht5jxk6dNUqiz6JK8fTa98Ddeuetm4oBVRcNVcpI/NV6kyIiIiohTnCwp4Ew2coE9ERERERJTM/EEP+PAzYHzSBT005rGrUwcRERERERHFLVEQUJyVjeKsbJw6fBQAoNvrxYE2O6qOhL5VOuywdwcv+hWex+8PhMUdla1PQ5nZijJLb+DbGJMFRj4LSkRERElEn5GHgqk/QOO2l2Tjvp62uAt4C1nE5/h9MayDiIiIiELFZcCbz+fDM888g9/+9reorq7u/wSiOKPRaDBjxgy89957oTujNOntH18flm0n24ctj+0w4PPKxvR5DHgjSgYajQZz586FRqNRuxQAgNvZjKZd/5aNiTojhk2/DoIYl2+diCiFxFvPJKLI+eDAPvx917aQ8al5Bbh5+hwYtMn7PkTy+tD08tdw7ZYHlws6TW+4W1neoK7LnklEpBx7JhH1KTjgTWNUp444wp5JRERERETJyu/yoKfNJhsTRAa8xSVdjnzb06pOHUQpiPeGiIiUY88kIlIuVj3ToNViQk4eJuQcm5PX2uVClcMeCG874LDD7fcpul67uwebm+qxual3YVcBQHFmNkqPhL6Vma0oycqGyAB5Ioogvs8kolgzj1gYEvDWsO0lDJ/zc2j0GVF5Tf8Jwtr6criyFY2HHJEvhoiIiIiGLK6eDvb7/XjhhRdw//334+DBg2qXQzRooigiNzc37L5Y3ZMexGe3uOZpOSDbFg2Z0GSF/29MRIlFFEXk5+erXQYAQJL8aNz2IiRfj2y88KSroTPm9HEWEVHsxFPPJKLIkCQJ/6zYif9UVYTsmz9sOK4/aQa0oqhCZbHh9/jQ9Pf16KpolI0LOg0KfjgP6WMG/7mPPZOISDn2TCLqkz8o4E1MV6eOOMKeSUREREREyUryhHl4mw9gxyedRb7t64Tkd0MQ9erUQ5RCeG+IiEg59kwiIuXU7Jk56UbkpBsxq6gYAODz+3Goox2VDhuqHDZU2m2oc3YoupYE4HBnOw53tuOLwwcBAGkaDcaYrCizWFBmtqLUbIXFwO+diWjw+D6TiNQgiDpIfk9gu6f9EBp3/APDpl8bldf7v0+rMHFYNmaOsvR5jBhmkZ6fnPk3XPqPpbIxjygmX+gAERERUYKJiyeEJUnC3//+d4wfPx7XXnstDhw4AIlvFCmBeTwefPLJJ+F3CrH5a3f3ueUxeZ1YcTftl23r8sZA4ARCoqTg8Xjw3nvvwePx9H9wlLXVrIKrVR6uYhqxCFmF09QpiIgoSDz1TCIaOp/fj6e3bQ4b7rZ0dBlunDYz+cPdXgoT7qbXoPCaoYW7AeyZREQDwZ5JRH3yBQW8aTjRnj2TiIiIiIhSSpiHgygO6CwAgv5/47GpUgpRquG9ISIi5dgziYiUi6eeqRFFjDKZsWTkGNxw0gz8/rSz8NRZ5+OXsxbg0nETMS2/EJk65QHjPT4fdtua8Z+qvfjjpq/wk0/fx02fvo8/bf4K7+3fiwpbC9y+MKHzRER9iKeeSUSpQ5OWHTLW7dgf5sjIueGlLfD5Q/M2JK8bADB+eknY86pX1cq22w16dKenRb5AIiIiIlJMG4mLuN1u2Gw2GAwGmM3mAZ37z3/+E/fddx8qKioCoW6CIDDgjRKe1+sNvyMKoWSaMNecNCz0w2Ii8zTLP+jq88aoVAkRRUOfPTOGPK4WNO16TTamTbcif+KlKlVERBRePPRMIho6t8+HP29Zj82N9SH7Lh8/GeeNGZfUodZ+txeNL61H974m2biQpkXhD+fBMConIq/DnklEpBx7JhGF5Q8KeBMZ8AawZxIRERERUeoQGPAWlwRBC0lrAryOY4MeG5BWqFpNRKmE94aIiJRjzyQiUi6ee2aGTo8peQWYklcAAJAkCU0uJyodNlQ6bKhy2HCwzQGfwudBW7u70Fpfi/X1veEjGkHA8CwTyixWlJmtKDVbUZiRCTGJ51AS0dDEc88kouSUO/ZcNGx7UTbm93T1cfTAjbQasa4qdCGXxvZuWMIc377xTVhmfAs/fmApnrj7A9k+oSM0PLdu1LBIlUpEREREgzDogLcPP/wQK1aswGeffYaWlpbAuNlsxqmnnoof//jHWLJkSZ/nv//++7jzzjuxY8cOWbAb0HuTz2w24+b5f3JwAAEAAElEQVSbbx5seURxKxoP6FsMhohfM964m+QBb7p8BrwRUeRIkoSGbS9B8vXIxgunXglRm/w9loiIiGLL6XHjkY3rUGFrkY0LAK6dMh2njRitTmEx4nd70fjCV+iuapaNC2laFF4zD4aRkQl3IyIiIqII8AVNQtMw4I2IiIiIiCilRHKuGxf9jSydNTTgjYiIiIiIiCgGBEFAQUYmCjIyMb94BIDeRW+r2x2oOhL6Vmm3obnLpeh6PknCwXYHDrY78El17/NjGTodSs3HAt9KzRZk6dOi9jsRERERnYhpxAIAkIW8+TxOdLfVwGAaMeTrf3/OcLy64bDi4+2fPYHsGd/C6ZdMxYf/2IKqHQ2BfYaO0OOd2RlDrpGIiIiIBm/AAW8dHR247LLL8NFHHwFAIJztKLvdjrfffhtvv/02Lr/8cjz//PPQ6/WB/Y2Njbj++uvx7rvvBs4/PtjNYrHgZz/7GW6++WZkZ2cP+hcjiltDXNXU5wtNzrakJf8DVe5mecCbPo8Bb0QUOW01q+Fq2S0bMw1fgIy8iSpVRERERMnK3t2Fh79eg5qONtm4ThTxk5NnY0Zhcq+M5Hd70bhiHbr3y8PtRIMOhcvnIW24VaXKiIiIiCgsf9CEewa8ERElJIfDgY0bN6K6uhoOhwNdXV0wGo3Izs7G8OHDMXr0aJSVlUEURbVLpRNwOBxYu3Ytamtr0dLSgtzcXBQXF2PevHkwm82q1tbR0YHVq1fj8OHDaGlpQVZWFoYNG4bJkydj3LhxqtZGRERDIwxxrtvxWv+zHcYJRYqP93t8gM8PIU0blUVVE57OCnQdN6eOAW9ERERERESkIr1Gg7GWHIy1HFvgta2n+0jgmx2V9lZUtdnR7fUqup7T48G25kZsa24MjBVmZAZC38rMVozINkHL7zaIiIgoRjRpppAx+4FPUTTth0O+9jBzOtbccSrmP/RFyL60grEhY76OY89jlJTmygLe1n+4F8NnjcChtGPfrdSPLILH54NOoxlyrURE0ca5bskh1ee6HTp0CPv27Qu8RldXF/R6PUwmE0aMGIFTTjkFeXl5EXktSgwDCnjr6enBaaedhq1btwaC3cJNnDm679VXX4UkSXjllVcAABs2bMCFF16IxsbGQLCbIAiBYLdbbrkFN998M7Kysob6exGpSqvVYv78+XjjjTdC9g11stma9/eEjFnTk/+BKk9QwJuOAW9ESUOr1eL000+HVjvg3NmI8HS1onn3v+U1GSzIm3ipKvUQEZ2I2j2TiIamwdmBB9evDlmV0qjV4Rcz52G8NVelymLD3+NBw/Pr0HOwVTYuGnQovHY+0kosEX099kwiIuXYM4moT74u+baY/N9H9Ic9kygxPfDAA/jVr34V2D7ppJOwdevWiFz7P//5Dy644ILAtsViQX19PdLS0gJjK1aswA9/OPQJvYDy2ru7u/Hiiy/imWeewcaNG0MW7wuWkZGBk08+GaeddhrOPvtszJ07F5rjJvaedtpp+OKL0InEkdZfnaloy5Yt+J//+R+8//77cLvdIfvT0tKwdOlS3HPPPZg2bVpMa1uzZg3+93//F59++im8fTyUd9JJJ+HGG2/EDTfcEJVwnu3bt+OUU06Bx+ORjT///PO4+uqrI/56REQpJ4K922tzwufsgSYjrd9ju2tsaHppPXwd3cg8eThyv31KRMPmkoIuaNEcBrwRxQTvDRERKceeSUSkXLL2TFOaAdMLhmF6Qe/Ct35JQl1nByodtiPBbzYcam+D0m8GGpydaHB2Yk1tDYDehXVHmSwoM1sCwW+56UYGxRMluWTtmUQU/9KyS0LGnM07o/66oiETmVOXonPbB2H3zzyjDF+8vSOw3dPlQca+LmCyMTDm0+mwx9aCKXkFUa+XiIaOc9041y2Rpepct3379uH555/HunXrsGXLFrS1tfV7zsknn4zly5fj2muvlf0dpOQ0oEjKe++9F1u2bAEAWThb8J/j9/3zn//EBx98gB07dmDJkiVoaGgInH802O3+++/HwYMH8atf/YrhbpQ0DAZD+B1DuElcsfkwujp6ZGNagxbpWt2gr5kIfF3tsjRxANDnM+CNKJmkqxhU2bLnLfi93bKxwqk/gEZn7OMMIiJ1qdkziWjwDrTZcd/aL0LC3cxpBvx67qnJH+7W7UHDc2tDw92MOhRetyDi4W5HsWcSESnHnklEYfmDAt407BUAeyZRIrr66qtlE7i++eYbbN68OSLXfu6552TbV1xxheqTbdauXYuTTjoJN9xwAzZs2KBoIpnT6cTq1atx//33Y+HChdi+fXsMKqX+PPjgg5g9ezbeeuutsBPegN7FGt966y3Mnj0bDz/8cEzq8ng8uPHGG7FgwQJ8+OGHfU54A3r/vv3oRz/CaaedhkOHDkW0Dp/Ph2uuuSYk3I2IiCIowqFqnlanouNs7++Ar6N3LkfnlkNw19ojWkdSCA5487ZD8vf9bzIRRQ7vDRERKceeSUSkXCr0TFEQUJKVjdOGj8LyKdPxu4VL8MzZF+JXcxbh8vGTMaNgGMxpfTyPF4bH78c+eys+OFCJx7d8jZ99/l/8v0/fwyMb1+Ltyj3Y2dKELi/vHxMlo1TomUQUf3TpFhgs8mfbfT3t6G6rifprm+Ze0ee+WWeOg6iRf58j1nSHHNfeEzpGRPGJc9041y1RpfJcty+++AK/+93vsHLlSkXhbkBvGN5PfvITTJ06FevXr1f8WpSYFEeUd3R04E9/+lMgYVCSJFitVlx11VWYM2cOLBYL2tvbsWnTJrzwwguoq6sLnPvYY4+hra0NHR0dgWC3jIwM/OIXv8Ctt96KzMzMyP9mRCryer349NNPw+8cQsBb3cHQiWo5c/IGfb1E4W6slA8IAnQ5I9Uphogizuv14v3338eyZcug08U2sNLTZUd73QbZWPbwecjInxzTOoiIlFKzZxLR4O1sacKjG9eh2ye/+VdgzMAvZy9EvjFDpcpiIxDuVmOTjYtGPQqvXYC0YaaovC57JhGRcuyZRBSOJPkAv3zRGYicIMueSZSYiouLcfbZZ+P9998PjD3//POYPn36kK7b3NyM9957Tza2fPnyIV1zqD766CNceOGF6O6WT8zV6XQoLy/HmDFjkJWVBZfLBZvNhp07d6KlpaWPq5Gafvvb3+Luu++WjaWnp2PmzJkoKipCXV0dNmzYEPj/tdvtxh133AFBEHDbbbdFrS6v14vzzz8fH374oWxcp9Nh9uzZKCkpgdPpxDfffIOammMT2b/88kuceeaZWLNmDXJyciJSyyOPPIKNGzdG5FpERBSeEOGAN9Gg7HNU8IIx7V8fRN5wax9Hpyhd8OI5EuC1A/rkn09IpCbeGyIiUo49k4hIuVTumQatFhNy8jAhp/fzrCRJsHV3odJhQ6XdhiqHDQfaHHD7fYqu19bTg82N9djcWA8AEAAUZ2WjzGxFmdmKUrMVJVnZEIfwfB8RqSuVeyYRqc888lQ02PfLxqpX3Y+07BL4vW6kW0tRMPm7ELXKQ2uHShAEnHJ6GTZ8si8wtm9TO9IXZ6LLoD92oILAJCKKD5zrxrluiYhz3eQEQcCwYcNQVlaGnJwcGI1GOJ1OHDhwADt37pQtaLp3716cccYZeO+993DqqacO/peluKY44O3f//43urq6AgFvp556Kt58802YzWbZcRdffDF+/etf48orr8Rrr70GAPjkk08AIBDutmTJEjz77LMYPnx4hH4NosQhRPIGcLYGReWRmfgcz7oq18m2dbmjIOpj9+GWiJKXo3olIPkD24KoQ96ES9QriIiIiJLO+vrDeGLrBnj9ftn4qGwzbp81H6YBrDaZiHxdbjQ+txY9h+SB5WKGHkXXLYC+MDrhbkREREQUAf4wK3ZqGPBGRIlr+fLlsklvr7zyCv7whz8MaQXSl156STbRZvr06Zg2bVq/582ePRuvvvrqoF5Tr9f3ua+2thaXXHKJbMJbQUEBfvOb3+B73/teyPyOo6qrq/HBBx/gzTffxKeffgqfL/QhqVdffTVkIl1fRo8eLdtetWoVSkpKFJ1Lvd5991386le/ko1df/31eOCBB5CbmxsYa25uxl133YVnnnkmMHbHHXdgypQpOOecc6JS2x133BEy4e2mm27CvffeC4tFHjTz0Ucf4Uc/+hH27++d4F5RUYGLL74YK1euHPLciX379uGee+4JbGdlZaGjo2NI1yQiojAi/LCzoBX7PUbyhz5gpDQYLpUIoh6SJgvwHffvn8fGgDciIiIiIiJKaIIgICfdiJx0I2YX9X634PX7caijDVVHQt8qHTbUOzsVXU8CcLijHYc72rHy0EEAgEGjxRizBaVHQt/KzFaYDck9l5OIiIgiIzN/atjxnvbDAACPqwnaNBPyJlwcy7JwzvemywLenA4vsmxeYNixOSbMdyNKLJzrxrluiYRz3XrvZ5SXl+P888/Haaedhnnz5oVc/yiHw4HnnnsO9913H9rb2wEATqcT3/ve97Br1y6YTHzeMhkpDnhbs2YNgN5VEHJzc/H666/32RANBgP+/ve/Y8eOHaioqIB05B2fIAj44Q9/iKeffhqi2P9EHaKkFMFJb4JJC4shHXWOrohdMx65Kr6UbRvHLVSpEiJKJn5fDxzV8v6SXTIXWn2WShURERFRsvmkugordmxF8Pdgk3Ly8LNT5sKY5KvG+VxuNDy3Bu7DDtm4mJnWG+5WkK1OYURERESkjC/Mdw8iA96IKHGdf/75yM/PR1NTEwDAZrPh7bffxmWXXTboaz7//POybaUrmhoMBowaNWrQr9uXu+66C52dxx5oKi8vx+eff46ioqITnjdy5EjceOONuPHGG1FTU4O//OUvyMjIkB1TWFg46LpKSkqi8vsmK5/Ph1tvvTUw1wYAbrnlFjz66KMhx+bl5eHpp59GZmYmHnvsMQC983p+8Ytf4Mwzz4RGo4lobbt37w68zlGPPPIIfv7zn4c9/qyzzsKaNWswf/78wMS3L7/8Ev/85z9x+eWXD7oOSZKwfPnywETMSy+9FM3Nzfjiiy8GfU0iIgpPECMb8KaE3+UOGdNm8SHrsHTW0IA3IiIiIiIioiSjFUWMNlkw2mTBkpGlAACnx90b+Oawo9LeiiqHHZ2e0HsK4XT7vNjV2oxdrc2Bsdx045HANwvKzDkYZTJDH+F77ERERJT4NPoMZA2bhY66r/s8pru9JoYV9Zoyd2TooJeJbkSJjHPd+sa5bvGFc916XXnllYr/TpnNZvz85z/H4sWLsWDBAjidTgBAXV0dnnzySdxxxx2KrkOJRXHK2tatWwH0hrRdc801sFqtJzxer9fjpz/9KSRJCiQRjhs3Dk899RTD3Si1RXhVU6shHY9/tj9k3KBLjr9nkiTBtXeVbIwBb0QUCe2Hv4Lf45SNWcacoVI1RERElEwkScLre3fh+TDhbrMKi3HbzPnJH+7m7EHDM6tDwt00WWkoup7hbkREREQJITjgTTRAEJLjuwciSk06nQ4/+MEPZGPPPffcoK/39ddfY8eOHYFtg8GA733ve4O+3lB1d3fjjTfekI09++yz/U54CzZixAg89NBDGDt2bCTLowF48cUXUVFREdguLy/H7373uxOe8+CDD6K8vDywvWvXLrz88ssRr+2hhx6C3+8PbC9ZsqTPCW9HFRYWhvxdu+uuu8KunqvUE088gVWreucRmEwm/OlPfxr0tYiIqB8RnuumhK8zdCV1MXPwK9EnNV3QPF4GvBEREREREVGKyNDpMTWvEBePnYDbZy3Ak2eeh0dOOxs/OmkmzhpZijEmCzQDuK/R0uXC+vrDeHn3dty3biWu/fBt/Hr1Z1ixYwtWHa5Gg7ND9rA6ERERpa6M/Ekn3O9q3oUux8HYFHOEoOh9D9/LECUSznVThnPd1Me5br10g3hWdNq0aSH1vPnmmwO+DiUGxU9h1NfXB34+/fTTFZ2zePHiwM+CIODaa6+NeGIiUTzSarU444zwIUFChAMOrQYDvtjbIhvLydCjyJQcq5Z6mvbD66iXjRnHL1KpGiKKBq1Wi2XLlkGr1cbsNSXJD/v+T2RjGXmTkZY5sA9+RESxpkbPJKKB8UsSVuzcijf27Q7Zt2TEGPx0+mzokvzeSG+42xq469pk45osAwqvXwh9fmzC3dgziYiUY88korD8wQFv6erUEWfYM4kSW/AKiR9//DEOHz48qGsFr2h68cUXw2w2D7a0IduyZYtsRdPhw4dj/vz5qtVDg/fiiy/Ktm+55RakpZ041CYtLQ0333zzCa8zVJIk4b333pON3XrrrYrOPfXUUzFz5szA9oEDB7By5cpB1VFTU4Nf/vKXge2HH354wJM7iYhoAMTIBrx5W50n3O/3+FD3xJch44KGgeNhMeCNKOZ4b4iISDn2TCIi5dgzh04QBBRmZGJByQhcNXka/nfBYjxz9oW4d95p+P6EqZhTVIK8dKPi6/kkCfvb7Pi4ej+e/GYjfrHyI9z48bt46OvVeH3vLnzT1IBOtzuKvxER9YU9k4jUll08B/mTLkdG/hRkFk4Pe8yhtb+H29USdl+suL8J+k6G+W5ECYdz3SgRcK7b0Cxbtky2XVlZGZXXIfUpnvXS3t4e+Hn48OGKzjl63NHVCebOnTuQ2ogSWnd36EqiACK+qmlzmw9dHnna5zULRipM245/rgr5hD1Ndj70BUzQJUo2XV1d/R8UQc6mnXA7G2VjljFLYloDEdFgxbpnEpFyHp8Pj29Zj0+q94fsu3jsBFw9eRrEJPms1hdfZw/qn14Nd31QuFu2AUU3LIQ+Lyum9bBnEhEpx55JRCF8QX1Bw4C3o9gziRLXhAkTZPMW/H4/XnjhhQFfp7u7G6+++qpsLHhCXazV1dXJtktKSlSqhIaitbUVq1atCmzr9XrFq+VeccUVspVAv/jiC9hskQt52bVrF1pajk1A1+v1OO200xSff84558i2X3vttUHVcf311wcmeC5cuBDXXXfdoK5DRETKCBHOVWt4dg1cu+r73O/aUQvJ7Y3siyazkIA3ByTpxCuHE9HQ8d4QEZFy7JlERMqxZ0aeXqPBWEsOlo4Zi59On43HFi/FE0vOxc9nzMUFpeWYlJMHwwACojo9bmxrbsQb+3bj4Q1rcMPH/8GtKz/EX7duwMcHq3CgzQ6v3x/F34iIjmLPJCI1CYIAy+jFKJn1UxTPuBGFU68MOUbye9C6710VqjumZ2V7IOODiBIT57pRvONct6GzWuXf+Xd0dETldUh9iqffuFyuwM+ZmZmKzjEa5asaFBQUKH05ooTm9XqxZs2asPuG8iy/P8wHqb11oTejZoy0DP5F4oxr7yrZtnHcwqQJryOiXl6vF59//jm83thM0HW27EbDNytkY/qsYTDmTojJ6xMRDUWseyYRKdfl9eD3G9ZgfX2tbFwAcPWkabhk3MSk/yzj6+hG/dOr4Glol41rTOkoumEhdLnK7idFCnsmEZFy7JlEFJbfJd8WGfAGsGcSJYPgyWkrVqwY8DVef/11OByOwPbo0aNx+umnD7GyoQnuS5Gc7ESx8/HHH8PnOxbKcsoppyArS1lgfnZ2NqZPP7Y6uNfrxccffxyx2oJXAB47dmy/q60eb8qUKbLt4BVSlVixYgU+/PBDAL0ruT711FNJf8+NiEh1Ueiztg929Lmv+Z+bIv56SS044A1+wNsW9lAiigzeGyIiUo49k4hIOfbM2DGlGXBKwTB8Z/xk3DVnEZ4+6wI8uGgJrpsyHacNH4XhWdkYyN2QemcnVtfWYMXOrfjV6s9w7Ydv4761K/H3Xduwvv4wWrpcDFYhijD2TCKKNxn5U6BJM4WMtx9aB5/HFeaM6BDEMO9ipON/5HsSokTEuW4UzzjXbeiqq6tl28OGDYvK65D6Iry+4okdn55IlLLEwf+16/J6Qgd9GtlmicWAsQWxfWg+mpwVX8q2jeWLVKqEiBKdJPnRsvc/OPzVY/C55enFltFL+PAHERERDVpbTzce+OpL7Gxtlo1rBAE/OXk2zhxVqlJlsePt6Eb906vhaZS/z9KY01F0/ULocpLncyoRERFRyvAFLTCjYcAbESWH73znO7JF7SorK/Hll1+e4IxQzz//vGz7mmuuUf17hvz8fNl2RUUF9uzZo1I1NFg7dsgDb45fhVeJefPmybZ37tw55JqOCp5IaTabB3R+8PGHDh1CW5vyAJqGhgb8/Oc/D2zffffdGD9+/IBqICKigQv7MNAQeZo7I37NVCVoDIBGvhAzPHz4gYiIiIiIiEgpURAwPMuE00aMxnVTT8GDi87E02dfgLtmL8R3yifjlIIimNMMiq/n8fux196KDw7sw582r8fNn32An3z6Pv64cR3eqazArtZmdDOUioiIKKloDSaMWvRr6DOLgvZIcHfUx6yOH92/9MQHMN+NKCFxrhvFM851G7oXX3xRtq12+CJFT0wD3ogIQ3qz4/SEBrxpIA94W1CWO+jrxxuPvQ6epirZmLF8oUrVEFGia971Glr3/gfBd6LSTCOQXTxbnaKIiIgo4TW5nPifdV/gQJtDNm7QaHH7rAWYM6xEncJiyNvehYanVsHTJA9301qMR8LdMlSqjIiIiIiGxB8U8CYy4I2IkkNmZiYuu+wy2VjwJLYTqa6uxmeffRbYFkURV199daTKG7QZM2aELLr3ve99D7W1tSpVRIOxa9cu2XZZWdmAzi8tlS80EHy9odDr9bLtnp6eAZ0f7viB1Pf//t//g91uBwBMmjQJd9xxx4Ben4iIBomL5cU/nVW+7WbAGxEREREREdFQpGt1mJSbjwvKyvHzGfPw+BnL8H+Ll+KnJ8/G0tFjMc6SA52o/LFcR083NjbW4Z8VO/DAV1/i2g/fxp1ffoJntm3CypoDONzRDr/ExBUiIqJEpk3LxqhT7wkZb69dP6Trvr6pTvGxY08KDpgjomTAuW4UzzjXbWj+8pe/4O9//3tgW6vV4mc/+1lEX4Pih1btAoiSlVbb11+voQS8ueWvIYoQhnC9eOfau0q2LaZnwzB8qkrVEFE09d0zI8PT1Qr7gU9CxjPyp6Bo2g8hanRhziIiik/R7plEpFxNexse+no1HD3dsvFsfRpunzUfo00WlSqLHW9bF+qfXgVvi1M2rrVmoOj6BdCajSpVdqQO9kwiIsXYM4kohC8o4E3DgLej2DOJEt/y5cvx3HPPBbb//e9/489//rNstdO+PP/885COe8jmrLPOQkmJ+gHvWVlZuPDCC/Haa68FxrZs2YLy8nL84Ac/wKWXXooFCxYgLS1NxSpjw+FwwOFwRP11MjMzkZsb2QXYKisrZdsjRowY0PnBx+/bt2/INR2Vk5Mj266vH9hK4+GOr6ioULRy67///W+88cYbAHoX1XvqqadCJuEREVGUiMk7Ny1p6KxA9+Fj2x4GvBFFG+8NEREpx55JRKQce2b8EgQBuelG5KYbA4v+ev1+HOpoQ6XdhkqHDVUOG+qdnYquJwGo6WhDTUcbPj90EABg0GpRarKgzGxFqcWKMrMVpjRDlH4josTHnklE8UgQRIg6I/weV2DMUb0S+ZMvhyD0Hw6r1YR+J/P82mp8Z2YJIjLTg4GyRAmLc92SG+e6HZMsc9364nQ6cfjwYXz11Vd47rnn8OWXX8r2/+53v8PUqczTSVb8FEsUBTqdDkuWLMELL7wQunMIk946wwS8JTNXhTzgzTh2PgRRo1I1RBQtOp0O5557blRfo7Nxu3xAEJFbfhGspWcpujlGRBQvYtEziUiZPbYWPLJhLVxej2w8L92IX85egMKMLJUqix2vw4X6p1fD2xoU7paTgaLr1A93Y88kIlKOPZOIwvIz4C0c9kyi5DBv3jyMHz8ee/bsAdA7ceZf//oXrrnmmhOeJ0lSyHfAy5cvH/Drd3d34+DBgwM+DwCGDRvWZ7DVAw88gA8//BAdHR2BMafTiSeffBJPPvkk9Ho9TjrpJMyaNQszZ87E3LlzMW7cuEHVEc8ee+wx3HfffVF/nauuugorVqyI6DWDJ+vl5+cP6Pzg49va2oZaUsD48eNl27W1tTh8+LDiSZ/r1q0LGVNSn81mw09+8pPA9o9//GPMmzdP0WsSEdHQCUrnuvn5UJBqdFb5NgPeiKKK94aIiJRjzyQiUo49M/FoRRGjTRaMNllwJkoBAJ1uN6ocvYFvR0PfnB5PP1fq1e31YmdrM3a2NgfG8tKNKDVbUXYk8G1kthl6DZ+rI2LPJKJ4JiD0exW/xwWNvv8QJp1GRLHZgFpHt2z8T59V4raBTR3oA7/LIUpUnOvGuW6RwLlusZnrdpTD4YDFYlF0bGZmJh599FFcd911iq9PiWdAAW+C0Pum8tZbb1WU5hlsoOcJgoBnn312wK9DpDa/34+Wlpaw+47+PRoMV9BNXa0gwuFSdqM3Ebn2BgW8lS9SqRIiiqajPTM3NxdilIIrnU3bZNtZw2Yip+ycqLwWEVE0xaJnElH/NjXW4c+b18Pj98vGh2dl445ZC2AxJH/whcfuQsPTq+C1uWTjutxMFF63AFqT+v8N2DOJiJRjzySisHxBAW+i+u/x4gF7ZvLySxI63e7+D6RBydTrIQ7he9JoWL58OW677bbA9nPPPdfvpLfPPvtMNlktNzcXF1xwwYBfe/369Rg9evSAzwN6VyqdNm1a2H3jxo3DG2+8gW9/+9thV/V0u93YsGEDNmzYEBgrKCjAOeecgx/84AdYvHjxkL7PpqHr7OyUbaenD+zf3+Djj58AOVSFhYUoLy9HRUVFYOyll17CnXfe2e+5TqcTb7zxRsi4kvpuvvlmNDU1AQCKi4vx29/+dgBVExHRkCl8b+Dr6O7/oH5IPn//B1Go4IA3rw2S5OeCh0RRwntDRETKsWcSESnHnpkcMvV6nJRfiJPyCwH0hik0ODsDYW+VDhtq2tvgk5SFqzR3udDc5cJX9YcBABpBwMhsM8qOhL6Vmq0oMGbwux1KOeyZRBTPrGVL0bz7tUGf/9SV03Hun9bKxg62uICgvBzJ64bk9UDQ6gb9WpTYONctujjXTY5z3agvnOs2NAUFBbjppptw/fXXIzc3N6LXpvgzoIA3oPfG0uuvvz7gFxroeZIkMeCNEpbP58PGjRvD7xzCm4ROj/yNtigI+HRPcx9HJzaf046ew9tlY8ZxC1WqhoiiyefzYd26dVi2bFlUbqz7vT1wteyRjWUWTI346xARxUK0eyYR9e+LQwfxzPbN8AdNsCm35OAXM+chQxd+VZFk4rE50fDUangdQeFueZkovG4htNkGlSqTY88kIlKOPZOIgkmSBPiDHs7XGNUpJs6wZyavTrcbP/rkXbXLSFp/XXIestPS1C5D5sorr8Sdd94Jr9cLAFizZg327t17wlU+n3vuOdn2D37wgz5XGFXLkiVLsHnzZtx222144403env6CTQ2NuKFF17ACy+8gOnTp+PRRx/FqaeeGqNqKVjwpDeDYWD3WYInvQVfb6i+//3v49e//nVg++GHH8aVV16J4uLiE57361//OuwKpv1Nenv//ffx97//PbD9l7/8BdnZ2QOsmoiIhkSM3YR4n4sPoQxKcMCb5AO8HYDOpE49REmO94aIiJRjzyQiUo49MzkJgoCizCwUZWZhYclIAIDb58OBNvuRwDc7Kh02tHa5+rlSL58kYX+bHfvb7PiougoAkKnTo8xsRanF2vt/zZaUmMdKqY09k4jiWXbx7JCAN1vVh8ibcImi84tMBhRmp6GhveeEx/XU7ULF/8uBcfypSB89E+llcyFpxg66bko8nOsWXZzrFjuc65bYONdtaBobG/Hkk0/C5/Ph5ptv5ry4JDfgT68DTbAUBCHwh4gAYQg3jZzd8g9k4RYtLTLFx4P0Q+XatwY47g2YoDPAMHqGihURUaJyteyG5PceGxBEZORNUq8gIiIiSkiSJOE/VRV4atumkHC36QVF+OXshSkxKcbT2on6p1aFhrvlZ6Hw+vgJdyMiIiKiIfJ3AwiaJCHyvR4RJZf8/Hycd955srHnn3++z+Pb2trw5ptvysb6WwVVLaNHj8Zrr72GXbt24e6778aECRMUnbd582YsXrwYDz/8cJQrjK57770XkiRF/c+KFSui/rsMZo5ONP3kJz+ByXQsLMbhcGDp0qWora3t85xHH30Ujz32WNh9J3ropr29HTfeeGNg+5JLLsGFF1448KKJiGhIYjnv09fR98NKHV/th+QNM1mOANEY+pnd06pOLURERERERER0QnqNBuXWXCwbMw43TZ+NPy1eisfPWIZbTpmD80vLMTEnD2kajeLrdXrc2NrcgNf37sJDX6/G9R/9B7eu/BBPfrMRn1RX4WCbAz4/76kQERGpyVb1IdydjYqP/9FpY2TbzZ09EA2ZIcf5uzvQufVdNL95D2p+fxY6HhyNHENz3xc+cWYSEcU5znULxblunOvWn1jOdQuWnZ2NAwcOBP5UVVVh8+bNeOONN3DLLbcgLy8PAHDo0CH85je/wZQpU7BhwwbF16fEM6CkqVg0haN/iJLWEP6Rse12yLZFTehf4UtOGTbo68cTV8WXsu300tkQdfGVdExEiaGzabtsO91aBo3OqFI1RERElIj8koSXd2/Dq3t2hOw7tWQkfjZ9DvQDmFCTqDwtnah/ajV8ji7ZuK4gG0XXL4Q2i4EfREREREnD3xU6pkkPHSMiSnDLly+Xbb/44ovw+Xxhj/3HP/6Brq5j/XHWrFmYPHnyoF731FNPHfRcimnTpil+nfHjx+P+++/Hrl270NTUhLfeegu/+c1vcP755wcmCAXz+/2444478Mwzzwzqd6OhycyUT8o+/n9zSgQfH3y9oTKbzSGr+27fvh0TJkzA7bffjs8//xwVFRXYunUrVqxYgYULF+IXv/hFYB5QSUlJyPX6cvvtt+PQoUMAAJPJhD//+c8R/V2IiEghMYYBb53dfe7rPtCK5n9vilktiUQQBEBnkQ967OoUQ0REREREREQDZjGkY0ZhMS4fPxl3z1mEZ86+EL9buATXTpmO04aPQklWNgZyh6be2YlVh6vx/I6tuHv1p7j2w3fwP2tX4pXd27C+/jBau1z9X4SIiIgGRZOWHXbctv+jQV+zucONupxZ0GTl9nvsjPz1g34dIop/nOsWinPd1MW5bn0TRRGjRo0K/BkzZgxOPvlkfOtb38Kjjz6KgwcP4ic/+Ung+JqaGixZsgQ7doQ+v0rJQav0wHvuuSeadRAlFUEQkJmZicbGMInag5zz1uXsgWujQzZmHSd/IzJpWBaMesV/reOaq2KVbNs4bqFKlRBRtAmCgKysrKikLEuSH52N22RjmQUnRfx1iIhiJZo9k4jC8/r9eGrbJqyprQnZd37pOHynfHJK/J30NHeg/unV8LXLH67SF2aj8NoF0GTGXyA3eyYRkXLsmUQUwhf0BbughyAkx/cPQ8WeSZRcli5dimHDhqGurg4AUFdXhw8//BDLli0LOTZ4ok/whLl4l5eXhwsvvBAXXnhhYGzbtm145ZVX8OSTT6KtrU12/C233IKLLroIubn9TxCmyMnMzITdfiyQJd4mvQHAxRdfjP/7v//DLbfcAr/fDwDo6OjA73//e/z+97/v87ybbroJbW1teOGFFwJjfU16W7lyJZ566qnA9sMPP4yioqLI/AJERDQgQiwD3jp6Trjf+c1hSN+ZEdOaEoYuB+ipP7btaVWvFqIkx3tDRETKsWcSESnHnknHEwUBI7JNGJFtwukjRgMAXB4PDrTZUemwodJhQ5XDhraeE99LOcrt96HC3ooK+7H7BZY0A0rNVpRZrCgzWzHaZIFByzkBlBjYM4kongmCgMKTrkLDNy/Ixt3OMHkDA/De3k7cdP92tHzwezh3fIyew9vDHpetbws7DgA4ElZDRImLc9041y3ecK7b4BmNRvz5z3+GTqfDH//4RwBAe3s7rrzySmzatImfd5IQA96IokCr1WLBggWoqqoK2TfYRnrwYAvgk394GnfOeHy6viGwrUmSyWv+Hie6Dm6UjRnLF6lUDRFFm1arxeLFi6Ny7Z62Gvh65B/SMvOnRuW1iIhiIZo9k4hCdXu9+NPmr/BNc+iXad+fMBVLx4xVoarYczd1oOHpVSEPVumLTCi8dj40GfEX7gawZxIRDQR7JhGF8AWt2K1JV6eOOMSembwy9Xr8dcl5apeRtDL1erVLCEuj0eCqq67C7373u8DY888/HzLpbefOndiwYUNg22g04vLLL49ZndEydepUTJ06FT//+c/xne98BytXrgzs6+zsxN/+9jfcfffd6hWYgkwmEw4dOhTYbm5uHtD5TU1Nsu1ITio73k033YTx48fjpptuQkVFxQmPzczMxEMPPYQf/ehHOOuss2T7CgsLQ47v6urCtddeG1gJdeHChbjuuusiVzwREQ1MNCYN93FJX2d3+B3Hk6S+L5DKdFb5tsemTh1EKYD3hoiIlGPPJCJSjj2T+mPU6TApNx+TcvMBAJIkoaXLFQh7q7TbcLDdAc+Rh9X7Y+/pxsbGOmxs7A2GEAAMzzahzNwb+FZqtmJYZhZEPlBOcYg9k4jiXXbJvJCAN4+rFZIkKcoYKMsPDbdxuDzQmgtR+N1HAABeRwOcuz5F7d++r7guCQx4Syac6xZdnOsWnzjXLf5wrtvQPfDAA/jnP/8ZCG7csmULPvnkE5x55pkRfy1SF2P1iaLA7/fj8OHD4XcK4qCuaQ+TVmrNzQbQEHpwguuqWg/4vMcGRA2MZXPVK4iIosrv9+PQoUMYPnw4RHFwPTIcSZLQvOdN2ZguIx/6zIKIvQYRUaxFq2cSUagOdw/+sGEtKh3yB2A0goDrp87AgpIRKlUWW+7GdjQ8vRq+zqBwt2IzCpfPh8YYn19aAOyZREQDwZ5JRCH8QQ/Viwx4O4o9M3mJgoDstPgMsKbouuaaa/Dggw8GwqTeeecdtLa2IicnJ3BM8Iqm3/72t5GdnR3TOqMpPz8f77zzDiZMmIDa2trA+EcffZSQk94cDgccDkfUXyczMzPiq76OHTsWO3bsCGxXV1cP6Pzg48eOjd4CBWeddRZ27tyJt99+G++//z7Wrl2LxsZGdHR0ID8/H6NHj8ZFF12EK664IjC5bc+ePbJrzJgxI+S6L774YmAxPVEUcddddyn679DdLX//0tLSgoMHDwa2jUYj8vPzB/prEhFRFBYcFdN1YceDv4ugAQgJeLMrflCMiAaG94aIiJRjzyQiUo49kwZKEATkGTOQZ8zA3GHDAQBevx/V7Y7ewDeHHZX2VjS6nIquJwGoaW9DTXsbPqs5AABI12pReiTs7WjwG79PpXjAnklE8U4QBORPuhxNO18NjHm7WnH4q0dRPOunEDUnfgZjfGFowFswrbkQpnlXoKdhL1re/p8h10yJh3PdUhfnunGu22Bxrlt05roNVXp6Oi666CI88cQTgbH//ve/DHhLQgx4I4oCn88n+4dIZpCTpnZuOCQf0AoQBxkWF++cFV/Ktg0jp0M09P+BlIgSk8/nw9atWzFs2LCI3lhvO7QGrpbdsrGsolMidn0iIjVEq2cSkVxrlwsPfr0adZ0dsnG9qMHNp8zBtPzIr7YQj9wN7ah/ehX8TrdsXF9iQeE18+I63A1gzyQiGgj2TCIK4QtadEbDgLej2DOJkk9ZWRkWLVqEL774AgDgdrvx8ssv46abbgIAeDwe/P3vf5edc80118S8zmjLysrCDTfcgN/85jeBseAJSonisccew3333Rf117nqqquwYsWKiF5zwoQJePPNYwsYVVZWDuj8/fv3h1wvmjQaDS6++GJcfPHF/R576NAh2UJ5xcXFKC4uDjmu67jF7/x+P5YuXTqo2m677Tbcdtttge0LL7wQb7311qCuRUSUyoSoBLyF/36BAW9DEBzwJrkBXyegzVKnHqIkxntDRETKsWcSESnHnkmRoBXFQCDbWUfGOtw9qHLYUemw9Qa/2W1weT2Krtfl9WJHSxN2tDQFxvLSjSiz5KDMbEGp2YpR2WboNJoo/DZEfWPPJKJE5WqtgLN5F7IKp53wOEEQcNXcEXhhXU1g7N1tDfifCycOrQBpaKcTUXzgXLdenOs2cJzrFp25bpFQXl4u2x7of0dKDAx4I4qxwa6KufndvbJtY1nyBp659q6SbRvLF6pUCRElKk+XDc27/iUb0xrMsJaerVJFRERElChqO9rx0Ner0dotD7TI0Olw28z5GGvJ6ePM5NJT14aGZ1eHhLulDbegcPl8iAadSpURERERUUz4gwLeRAa8EVFyW758eWDSG9C7iunRSW/vvvsumpqOPbgyduxYLFq0KOY1xsK0adNk23a7XZ1CUtjkyZNl2+vWrRvQ+WvWrDnh9dT06aefyrZPO+00dQohIqKBGeRctxMR9eGnrTLgbQg0mYCgA6TjHtD22BjwRkRERERERJTisvRpmJZfGFjY2C9JaHB2BsLeKh021HS0wS8pS3xp7nKhucuFdXWHAPSGyo3MNqHsSLBcmdmKfGPGoJ+fJCIiShY6Y27Y8bbqL/oNeOvL5hoHpo8wD74ohf/eE1H841y3Xpzrpj7OdYsMnU7+nGZPD+dOJCMGvBHF2iBXNbXVtMu2i04bFolq4o7kdaOrUv4Pd0Z5cr5pJKLokPw+NG57CX5vt2y8YMoPoNEZVaqKiIiIEkGl3Ybfb1iDTo881MxqSMcdsxagJCtbpcpiq6fW0Rvu5pKv0pg20orCH85juBsRERFRKvC55NsaBrwRUXK79NJL8dOf/hRtbW0AgG+++QabN2/G9OnT8fzzz8uOTcYVTY/SaDSybZPJpFIlqevMM8+ERqOBz+cDAGzatAkdHR3Iyuo/nKWjowObN28ObGu1Wpx55plRq3Wgnn32Wdn2tddeq1IlREQ0EMIg57qd+KLhh/0d3eF3UL8EQYCkswLuxmODHhuQPlK9ooiIiIiIiIgo7oiCgGGZWRiWmYWFJb33DXp8Xhxsc6DySOhblcMWskhyX7x+P6ocdlQ57ACqAABZen0g7O1o8JtRx3mnRESUWjLyJiJr2Ax01G2UjTubd6LLvh/pljEnPN9kDP23c/vhtqEFvBFR0uBct16c66Y+znWLjMOHD8u2CwoKovZapB5R7QKIkpEgCMjJyelzXyQUjrZE5DrxpuvgZkhu+U3g9HELVKqGiGJBEATk5eUNuT96umxoqXgH+z+7E87mnbJ92SXzkFkwZUjXJyKKB5HqmUQU6pumBvx2/Zch4W7DMrJwz7zTUifc7bAdDc+ECXcblYPCaxIr3I09k4hIOfZMIgrhD5qsLTLg7Sj2TKLklJ6eju9+97uyseeffx4NDQ344IMPAmMajQZXXXVVrMuLmV27dsm2i4uLVapkaO69915IkhT1PytWrIh47bm5uViw4Nj34263G6+88oqic19++WV4PMfu6SxatAhWqzXiNQ7G6tWrsXr16sB2eXl5n6ua/uxnPxvU/z9OPfVU2XWef/552f633norir8hEVESi+FnH18nV6EeEl3Qv/semzp1ECU53hsiIlKOPZOISDn2TFJTmkaLcmsuzh0zDjefMgd/OmMZHj9jGX52yhycXzoOE6y5SAsKTjiRDrcbW5sa8NreXXjw69W47qN3cNsXH+Fv32zEp9X7cbDNAZ/fH8XfiJIdeyYRJQJB1GLY9OthzCkP2Vez5kFIknTC88+eFBrs8n+fVqG+bfCL5Zz4FYkokXCuWy/OdeNct2gZyFy3SPjoo49k22PHjo3aa5F6tGoXQJSMtFotZs6cGfKmAMCgJ71JQR+dstPSsHpfy6CuFc9ce1fJttOKJ0GbGT4sj4iSg1arxbx58wZ9fndbDVr3vYfOhq0Id5tJk2ZC/sRvD75AIqI4MtSeSUThramtwd++2Qhf0JdkpWYrbps5D1n6NJUqi62eQ3Y0PLsG/m55uJthdA4Krp4HMS2xbiOxZxIRKceeSUQhfEEBbxoGvB3FnkmUvJYvX44nn3wysP3KK68gPz8fXq83MLZs2TIUFRWpUV6/GhoaIEnSoOvzeDwhK7guXrw4EqXRAF155ZX44osvAtt//OMfcfXVVyMtre97VD09PXjsscdkY/EyQdPlcuHGG2+UjT3wwAMqVUNERAMWo2ckJb8En5MBb0PCgDeimOC9ISIi5dgziYiUY8+keGMxpGNmYTFmFvYGJPj8fhzubEeVw4ZKux2VjlbUdXYoDoqp6+xAXWcHvjxcDQBI02gw2mRBqdmKMrMVZRYrrAbOSyBl2DOJKJGYR54KV2tFyLjP3QltWlaf5xWZDJg+wozNNQ7Z+I1/34K3fjxHecil1MfPRJTwONeNc93iBee6Dc17772HjRs3ysYuvPDCqL0eqUdUuwCiZOTz+bBv376w+wRxkH/tgj44GbV6fF6RhAFvFfKAN+O4hSpVQkSx4vP5sGfPHvh8vgGd191+CLUbnkD1qvvR2bAF4e8wCSic+gNo9BkRqZWISG2D7ZlE1Lf/HtiHJ7ZuCAl3m5pXgLtmL0yZcLfuGhvqn1kdGu42JhcFP0y8cDeAPZOIaCDYM4kohJ8Bb31hzyRKXjNmzMDUqVMD2zabDffff7/smGuuuSbWZSm2Z88ejBkzBjfddBOqq6sHdK7P58ONN94YsoBZ8EqvFBtXXXUVysuPreJdUVGBu+6664Tn3HnnnaioODYpfOLEibjiiiv6fa3TTjsNgiAE/tx77739nnP8RND+dHZ24txzz8XOnTsDY5dccgkuueQSxdcgIiJ1CWJsEt78LjcfLBqqMAFvksT/qESRxntDRETKsWcSESnHnknxTiOKGJltxuIRY3D9Safg4VPPwlNnXYA7Zy/Et8dNwvT8ImQPYL5tj8+HPbYWvLd/L/5v81f46afv4yefvo/HNn2Fd6sqsLu1Gd0D+D6CUgt7JhElkqxhM6DPGhZmT//370ssoXP2Dtm60NCufMEc3yfHLQbD7wyIkgrnunGuW7zgXDdg48aNePPNNxW/zlEbNmzAD37wA9nYokWLMGXKlAFfi+Jf4j2dS5QA/H4/qqqqwu8cxJw3SZJCPqp5PaHHWY36gV88znia98u200tnq1QJEcWK3+9HRUUFSktLodFo+j3e3dmA5j1vHgl165vBNAq54y9CRt7ESJVKRKS6gfZMIuqbJEn4V8VOvFMVuhrSvGHDccNJM6AdbEB3gumubkXDc2sh9chvWBrK8lBw5RyI+sS8fcSeSUSkHHsmER1P8nsAn1M+KHIBhaPYM4mS2/Lly3HzzTcHtt1ud+DngoICnHfeeRF5ne7ubhw8eHDQ5w8bNgx6feh3w93d3fjzn/+Mxx9/HKeeeiquuOIKnH766SgtLQ17HbfbjQ8++AD33XcftmyRf+9y1VVXYebMmYOukQZPo9HgD3/4Ay644IJAKMujjz6Kzs5O/Pa3v0VOTk7g2JaWFtx11114+umnA2OCIOCRRx6J2r9Tf/vb3/D666/jyiuvxLnnnou8vLyQYzo7O/H666/j7rvvRm1tbWB81KhReOKJJ6JSF1G0uVwutLW1weMJM2GnHyNGjIhCRUQxIsQm4M3X2R2T10lqwQFv/u7eAHeNUZ16iJIU7w0RESnHnklEpBx7JiUio06Hybn5mJybD6B3Xm5zlwtVDhsq7TZUOmw42O6A1+9XdD17dxc2NNRiQ0Pv9wqiIGB4VjbKzFaUmq0oM1tRlJkFMUb3qyh+sWcSUaIZPucXqPr4FwM+71snD8P72xvg9csTBrw+Zf+2AoC0wwmcndP/gUSUkDjX7RjOdVMP57oBhw8fxsUXX4zJkyfjiiuuwEUXXYTy8nIIfXx+3bVrF/72t7/hiSeekAXQGQwGzq1LYon5hC5RQhv4TcR2d2iadqbWEDJ2xZzhg6oonvicdtm21lSoUiVEFI88rlYcXPUAJF/4VQYETRqyi2fDPHIRDCZOkiciIqLwfH4/ntuxBSsPHQzZd86oMlwxcWrKTADpPtiChufWQXLLw93Sx+Yj/8o5EHWc+EBERESUcjytoWM6S+zrICJSwfe//33cfvvt6OkJ/R7iyiuvhFYbmSkW69evx+jRowd9/pYtWzBt2rQ+90uShJUrV2LlypUAgLy8PJSXlyMnJwdZWVlwOp2ora3Fjh074HK5Qs5ftGgRHn/88UHXR0N33nnn4f7778fdd98dGHvqqafw0ksvYfbs2SgsLER9fT2+/vprdHV1yc598MEHcc4550StNkmS8Pnnn+Pzzz+HIAgYPXo0ysvLYbFY4HK50NDQgM2bN8smjQLA6NGj8dFHHyE/Pz9qtRFFUk1NDZ599ll89tln2Lp1a9h+qYQgCANaDZgo7sQq4K0j/DwQGgBtNgANAN+xMY+NAW9EREREREREFBOCICDfmIF8YwbmDut9xtHj86Gmoy0Q+FblsKHR5eznSr38koTq9jZUt7fh05oDAACjVocxZgvGHg19s1iRpU+L2u9EREQUEYP8quWk4Sb884ZZuOSv6wf90pLn+HA4qc/jiCgxca5bL851Ux/nuvXasWMH7rzzTtx5553IysrC5MmTkZubi+zsbLjdbthsNuzYsQONjY0h56anp+Odd97BpEmThvw7U3xiwBtRjAmiOOBzDne0h4yFu/l4ysjEf8DK57TJtsWMxP+diChy2g6tCRvuJuoyYB1zJsyjTodGl65CZURERJQo3D4fHt+yHpsa60P2XVY+CReU9r06QrLp2t+CxhVrIbl9svH08gLkf382w92IiIiIUlVwwJvWDEHUqVMLEVGMWa1WXHTRRfjnP/8Zsu+aa65RoSLlrFYr8vPz0dTUFLKvubkZzc3N/V5Do9Hg//2//4ff/e53MBoZBKK2u+66C4Ig4J577oHH4wEAdHV1BSYzBtPpdPjf//1f3H777TGrUZIk7N+/H/v37z/hcRdccAGeeeaZsCugEsUbr9eLX//613jkkUfg8/XeOz26wjBRSorRVya+Tga8DZUgiJB0FsDTcmzQYwMMJeoVRUREREREREQpTafRoPRIGNvZR8bae3pQdSTsbZ/Dhv0OO1xej6Lrubwe7Ghpwo6WY9+HFRgzesPejgS+jcw2QzuI5zeJiIjiUbF5YM/KjplUCFT2sZNfeRIlHc5141y3eMK5bnIdHR1Yt26domPnzJmDv/3tb5g6deqgX4/iHwPeiKJAFEUUFxeHTc4czKqmtZ0doa8RdB2tmPgBBH53FyRPt2xMk2FVqRoiihVRFDFixAiICr5A6WzaLj9XZzwS7LaYwW5ElBIG0jOJKJTT48ajG9dhj61FNi4AWD5lOk4fMfjVRBJNV1UzGlesg+QJCncbX4CC78+GoE38cDf2TCIi5dgziUjGLX+/DH2OOnXEKfZMouS3fPnykElv8+bNw/jx41WqSJmpU6eivr4e69atw/vvv4/Vq1djw4YNIStehjN8+HBcdtllWL58OSZMmBCDakmpO++8E+eccw7uu+8+fPDBByErhQKAXq/H0qVLce+9955wtdtIWbBgAb797W/jk08+gd1u7/M4rVaLM888Ez//+c+xZMmSqNdFFAmSJOE73/kO3nrrrUComyAIEASBIW+UsmK1KI6vs7v/g6h/OmtowBsRRRTvDRERKceeSUSkHHsmpZLstDScXFCEkwuKAAB+SUKDswP77L2hb5UOGw51tMOv8J5so8uJRpcTa+sOAQC0oohR2WaUHQmWK7NYkZduTJnFn1MBeyYRJYMuWxWyik6O+HV1+sR/DoSIBoZz3TjXLZ6k6ly3xYsXY8WKFfjwww+xatUqHD58uN9zjEYjli1bhquuugrnnnsuP7OmAAa8EUWBRqPBlClTsHnz5tCdg+irtR3tQy8qAficof8gajIZ8EaU7DQaDU4+uf+bUd5uB3raqmVjw065ERm58f0hk4gokpT2TCIKZe/uwsNfr0FNR5tsXCeK+MnJszCjsFilymKvq7IJjS98FRLuZpxQiPwrZiVFuBvAnklENBDsmUQk42mVb+ty1akjTrFnEiW/M888M+IhPldffTWuvvrqiF4zHFEUMX/+fMyfPx8A4PV6UVVVhcrKShw+fBjt7e3o6elBRkYGsrKyMHz4cEyZMgXDhg2LaB0MQYqsk08+GW+99RbsdjvWrl2L2tpatLa2IicnB8XFxZg3bx4sFsugrt3XCqknMm3aNPzrX/+CJEnYu3cvdu3aFfjflyAIMJvNGDduHGbPno2srKxB1TUYg/ldiIL95S9/wZtvvikLdZMkCaNGjcKECRNgsVig0+nULpMoKfk6e9QuITnogubaMeCNKOJ4b4iISDn2TCIi5dgzKZWJgoBhmdkYlpmNU4ePAgB0e7042O5Apb0VlQ47qhw22Lr7D3oAAK/fj8ojQXFHZevTjgt8s2CMyQoj7/UmLPZMIkoGdZv+ivLznorxq3IuB1Ey4ly3oeNct8hKxblu2dnZuOqqq3DVVVcBABobG7F7925UV1ejtbUVLpcLOp0O2dnZyMnJweTJk1FeXg6NJjme4SRlGPBGFAU+nw/bt28Pu08YxMoAwQEEycrnDJ1QpjGaY18IEcWUz+fDtm3bMHXq1BO+EXU27ZBti1oDjNayaJdHRBRXlPZMIpJrcHbiwfWr0Nzlko2na7X4xYx5mJCTp1Jlsefa24imF7+C5PXLxo2TipD/3VkQtMmzmh17JhGRcuyZRCTjbpFv63LUqSNOsWcSUSLRarUoLy9HeXm52qVQBFgsFpx77rlqlxEgCAL/90VJRZIkPPDAA4FgNwC48MIL8cADD2DixIkqV0cU/7LmjEbHVwcGfb6vgwFvEcGAN6Ko470hIiLl2DOJiJRjzySSM2i1GG/NxXjrsQXpbN1dqLLbsM9hQ5XDhgNtdvT4fCe4yjHt7h5sbqrH5qZ6AIAAYFhmFsrMOSizWFBqtqIkMxuaQTz3SbHHnklEiUbUGMKOO2pWwTxiYewKYYAREcU5znVLLqk8162goAAFBQVRfx1KLAx4I4oCv9+P2tra8DsFYUDX6vF5sb/NHoGq4p/PKf89RaMJgsibbETJzu/3o6amBpMnTz7hjfXOJnlwpjF3IgSRb2WIKLUo7ZlEdMyBNjse/noN2t3yB4PMaQbcPms+Rmab1SlMBa6KBjS9tD403G3KMORfPhOCJrkmprBnEhEpx55JREdJPhfgD1r1Wp8b/uAUxZ5JRERElJw2btyIxsZGCIIAQRBw9dVX49lnn1W7LKKEYT1n0tAC3joZ8BYRwQFvPickfw8EMU2deoiSEO8NEREpx55JRKQceyZR/6yGdFiLijGzqBgA4PP7caijHVUOGyqP/Knr7FB0LQlAbWcHajs78MXhgwCANI0GY0wWlFmsKDVbUWa2wmJIj9JvQ0PBnklEiUbU6GDMnQBXy27ZeEf9ptgGvBERERGRapiKQhRjwgAD3irtNnj9/v4PTAJ+p3zFUI3RolIlRBRvJL835AZWZsEUlaohIiKiRLGzpQmPblqHbq9XNl5gzMAvZy9AvjFTpcpiz7W7Ho1//xrwyT9fZkwtRt53ZiRduBsRERERDZK7Rb4taAGtSZ1aiIiIiIhiaPv23gXHJElCeno6Hn30UZUrIkoskl8a0vm+zu4IVZLidGYAIoDjvg/y2IC0IpUKIiIiIiIiIiKKDo0oYpTJjFEmM84YOQYA4PS4sd9hDwS+VTls6HC7FV2vx+fDblsLdtuOzZvIMaQHwt7KLFaMMpmRpuEj2URENHDFM36Mff/9qWysq3Uf/D43RI1+QNdyuX2DqmFo3+QQERER0VDwbgJRrA0w4C1wU1BK/o9OPqddtq3JsPZxJBGlGpetEn6vfDJvRt5klaohIiKiRLC+/jCe2LohJDB7VLYZt8+aD1OaQaXKYs+5qx5NL68HfPLPlRnTSpD37VMY7kZEREREx3iCAt50VggC3y8SERERUfJrael9LywIAubMmQOTiUHHRAMh6DRDOt/X2ROhSlKbIGggaU2A97h5eO5WBrwRERERERERUUrI0OkxJa8AU/IKAPQu6NHc5cQ+e2/YW6XDjup2R8jc4r60dnehtaEWXzfUAgBEQcCILBPKzFaUWnqD3wozMiEO8HlRIiJKPaI2DdnFc9Be+1VgTPJ7ULPmIQyfeys0unTF17rvP7vxynWzBl5E8scUEBEREcUtBrwRRYEoiigtLUVjY2PozoEGvLU2Q2rzAsGB2kl438/ntMm2NRkWlSoholgSRRHl5eUQxb4fFHU2bZdtG0wjoTVwQj0RpR4lPZOIgE+q92PFji0h3z9NzMnDLafMhVGnU6UuNTh31KHpla8Bv/y/RubJw5H77VMgiEn44fII9kwiIuXYM4kowN0q39blqFNHHGPPJCIiIkpO6enHHpooKmIQEtFAiToNtGYjvA6XbFzQaSB5gie+yUl+KSTgLXthGdpXVUa8zpSgt8oD3rz2vo8logHjvSEiIuXYM4mIlGPPJIoOQRCQb8xEvjET84tHAAA8Ph+q29tQ6WhFpcOOKocNTS6nouv5JQkH2x042O7AJzX7AQBGrQ6lZgvKLDkoM1tQarYiS58Wtd+J2DOJKHGlW0tlAW8A0NN+CO2H18EyenHYc8K1uj0NnX2+hq7mv9AK8+GVjjwz0+yB5JeS+rkRIiIiokTAgDeiKNBoNBg7dizWrl0bsk8YQMCb2+dDlcMG32qH/Po6ETnFyRds5HPKJ5NpMqwqVUJEsaTRaDB+/PgTHuNs3CbbzsifEs2SiIjilpKeSZTKJEnCm5V78PreXSH7ZhYW48fTZkKv0ahQmTqc22vR9I8NoeFup4xA7iXTk/5LOvZMIiLl2DOJKMDTIt/W56pTRxxjzyQiIiJKTiUlJYGfnU5lD/MRkZx5yXi0vLZZNpZ76XQ0/2PDCc/zd3tCvsswzS+FYYQVTS9/HfE6k54uB0DVse3gMHciGhLeGyIiUo49k4hIOfZMotjRaTQos1hRZjn23GJbTzeqHHZUOmyoOvKny+tVdD2X14PtLU3Y3tIUGCswZqDM3PsapWYrRmaboWUYWcSwZxJRotKkhc8FaNr5KswjF0EQQ2M/tKKIsfmZ2NfUd6hbsHLLLuy0nRTY9q9vh2auCYDU90lEREREFFVRDXhramrCf//7X6xatQpVVVWw2Wzo6OgAAFRVVfVzNlHi8nq92LChj4lpAwh4q7C1wOP3Q6rulo3PO2c80oz6oZQYl3xOm2xbzLCoVAkRxZLX68XXX3+NWbNmQasNfWvidjbB7WyUjTHgjYhSVX89kyiV+SUJL+7cio+r94fsO2PEaFw9+WSIA/g8lug6vzmM5n9uDA13mzkSud86OenD3QD2TCKigWDPJCIAkCQ/4JHfp+99KJyOx55JRERElJxmzpwJURQhSRIqKirULocoIQm60EV2Mk8qgeT1oeXfm8Oc0cvn7AkZEzPSoCvMjmh9KUMXNOfOYw9/HBENCu8NEREpx55JRKQceyaRukxpBkwvKML0giIAvXOS6zo7UOWwofLIn0PtbYpjcRpdTjS6nFhTdwgAoBNFjMo2o9Ri7Q1+M1uRm26EkELzmiOJPZOIElVG3gQYLGPQbQ995qXbUY10a2nY8351Xjmuem6TbGxTtR2njLRAayoMOb4goxk7j5sGKO3rAuaaAIkBb0RERERqicqn1/r6evz617/Gyy+/DLfbLdsnSVKfNx5eeOEFXHPNNQAAi8WC+vp66HS6aJRIFFWSJKG1NfzKl8IAVlv4rOZA7w8++Xj5ycWDLS2u+TrlD45pMqx9HElEyUSSJDQ3N0Pq4waRs2m7bFujz4LBPDIWpRERxZ3+eiZRqvL4fPjrNxuxvv5wyL6Lx07AxWMnpNQkiM6th3rD3YJaRdbsUci5cFpKhLsB7JlERAPBnklEAABvGyAFrUCtz1WnljjGnklERESUnEpKSnD66afj008/RUVFBfbu3Ytx48apXRZRQjFOLIKg10By9052Sx9X0Lujn+9o/EEBb4JeCzFMWBwpFBzW7muH5PdAEDkXlygSeG+IiEg59kwiIuXYM4niiygIKMnKRklWNk4dPgoA0O314kCbHZUOW2/wm90Ge0+3out5/H7sc9iwz3Hs2UlTWhpKzccC38aYLUjX8v6NEuyZRJSoRE0aRsy9FfVbnkVHvTywrWbtQxh37pMQhNAMAl2YXIIX19XglJEWZM+8BA0v/li2b/jYPODQsW3Jz35JREREpLaIB7x9/PHH+P73v4+WlpbAB2SlD1F/97vfxe23347m5mbY7Xb85z//wcUXXxzpEonUpfA5+tYuFzY21oXdJ2qUh8QlEp9LvlqoJsPSx5FElEo6gwLeMvInh71RRURERKmpy+vBY5u+wo6WJtm4AODKSdNw1qjwqxglq47NNWj596bQcLc5o5FzwUkpE+5GRERERIPgCVq4RkwHRKM6tRARERERqeC+++7DypUr4ff7cdddd+G1115TuySihCLqNCi4eh4cn+2BmK5DzrlTFJ3n65QHvGky9YpfU/L54e/2QEzX8zuQo7Rh5tx5bEBaQexrISIiIiIiIiJKEgatFhNy8jAhJy8w1trlOhb45rDjgMMOt9+n6HptPT3Y3FiPzY31AHrnPRdnZqPMYg0Ev5VkZUNMoQWuiYhSgSBqUTjtmpCANwDY+96NGH36/dBn5MvGi8yGkGOb2nu/W9Fm5yNz6lJ0bvsgsC8rzPEAQp4xISIiIqLYiWjA26pVq3D++efD7XbLQt00Gg3MZjNaWlpOeL5er8fll1+OP//5zwCAt956iwFvlHwU3lT7pHo//Cm2ioCv0ybb1mRYVaqEiOKF39uDrta9srHMfGUTgImIiCj5tff04Pcb1mB/W1BYtCDgx9NmYc6wEpUqU0fHxmq0vL455Iu37HljYD1/quIAfiIiIiJKUe6g7/F0OXwPSUREREQpZd68ebjvvvvwq1/9Cm+++Sbuuusu/Pa3v1W7LKKEkj4mF+ljFpzwGK/DJdv2Od2ybU1GmqLX8rZ3ofG5tXA3tCNthBUFV8+Fxqg8HC5ZCaIWktYEeNuODXrsDHgjIiIiIiIiIoqwnHQjctKNmF3UO1/Z6/fjcEc7Kh2tqHTYUWW3oc7ZoehaEoDDne043NmOlYcOAgAMGi1Gmywos1hRZrag1GyFxZAepd+GiIhiRdToYDCPQbdjf8g+W+UHKDzpKtmYKV2HsrwMVDY7w15PSMtQ+MqplVlAREREFE8iFvDmcDhw8cUXB8LdJEnCsmXL8Itf/AILFixAbW0txowZ0+91LrjggkDA2+effx6p8ohiSqPRYPLkyWhsbAzZJ4hiv+e7fT58fuhANEqLa35XUChDJgPeiFKBRqPBtGnToNFoZOOSJKF5zxuQ/N5jg4IIY97EGFdIRBQ/+uqZRKmo2eXEg1+vRoOzUzZu0Ghxy4w5mJybWg+pdGw4iJY3toSGu80vhfW8KSkZzMGeSUSkHHsmEQEAPK3ybX2uOnXEOfZMIiIiouR21113IS0tDXfccQceeughrF27FnfffTdOP/10aLURXUuVKGX5XR70HLYjrcQCAPB19sj2iwoD3tq+2Ad3QzsAoKfGBue2w8ie0/8c1ZSgswYFvLX2fSwRDQjvDRERKceeSUSkHHsmUXLQiiJGmcwYZTJjycjeMafHjSqHHZUOG6rsNlQ6bOj0uE98oSO6fV7stjVjt605MJaTbkSZ2YIysxWlZitGmyzQp1jvYM8komRQMPUKVH/5vyHjPR31YY+/ev5I/OqtXYqunWX/EkD5UMojIiIiogiL2Kyzhx56CK2txyaBPPLII7jlllsC20ofJJ4/fz40Gg18Ph/q6upQW1uL4uLiSJVJFBOiKKKkpCT8TgV/F9bVHUKHW9mNumTi67TJtjUZFpUqIaJYEkURI0eOlI1JkoTmXf+C46A87NVoHQuNzhjL8oiI4kq4nkmUimra2/DQ16vh6OmWjWfp9bh95gKMMafWZ4n29QfQ+ubWkHHTojJYlk5OyXA3gD2TiGgg2DOJCADgbpFv63LUqSPOsWcSERERJa/FixcHfjabzbDZbFi1ahXOOeccpKeno7S0FBaLBaKCxR2PEgQBn376aTTKJUpojs8rUPCDOQAAv1Me8KbJ0Cu6RvuaKtm27f0dDHg7SmcFuo5bYNZj7/tYIhoQ3hsiIlKOPZOISDn2TKLklaHTY2peAabm9S5cLUkSGl1OVDlsqDwS+Fbd7oBPkvq5Uq/WLhdau1xYX18LANAIAkZkm1BqtqLsyJ/CjMyknjvMnklEycCQPRxjFv8O+z+7M/LXdlUhW+9Au9ssG1f4Tw0RERERRUFEAt4kScKzzz4b+NB/3XXXycLdBsJgMKCsrAwVFRUAgN27dzPgjRKO1+vF6tWr+9jb/82xzY3yhG2jTgcnuvs4OjlIfj98LodsTJNhVacYIoopr9eLL7/8EosWLYJWqw2Eu9kPBE1yF0TkjDtfnSKJiOJEcM8kSkUVthb8YcNauLwe2XhuuhG/nLUARZlZKlWmjvav9qP1rW9Cxk2njoXlnElJPUGjP+yZRETKsWcSkeT3At42+aA+V51i4hx7JhEREVHyWrlypeyeqiAIkI486eByubB9+/YB3XOVJCml79ESnYhr57H5cT6nfCFUTWbaoK4p6PkZLUAXNO/O0xr+OCIaMN4bIiJSjj2TiEg59kyi1CEIAgozMlGYkYn5xSMAAG6fD9XtDlQ6bIHgt+Yul6Lr+SQJB9ocONDmwCfV+wEAmTo9xpgtgcC3UrMVmXpliyokAvZMIkoWOmMOCqZ8H43b/z6k64hpmSFjJZmHsMtmBgBITb3fw0h+/5Beh4iIiIgGLyKfXjdv3oyWlhYAgEajwX333Tek640aNSoQ8Hbw4MGhlkcUc5IkobOzM+w+Qex/4qbTI5+0lqnTwxmRyuKXv6sdkOQfDjUZFpWqIaJYkiQJHR0dkCSpN9xt92thw92KTl4OY844dYokIooTx/dMolS0ubEOf9q8Hp6gL5ZKsrLxy1kLYDGkq1SZOtrWVMH2n20h46bTy2E5a0LKPzjInklEpBx7JhH1Pugd1AOCHwYnAOyZRERERKkm1e+zEsWCz9kj29ZkDC7gTWtOre+JTij4M723DZLkhSDwgVeioeK9ISIi5dgziYiUY88kSm16jQZjLTkYa8kJjLX1dAfC3iodNlS12dHt9Sq6XqfHjW3NjdjW3BgYK8zIDIS9jbVYMTzLBK0oRvx3iQX2TCJKKhH4LtI0+ztoW71CNjbJuh27bFN6Nxo98B/qhreIAW9EREREaonIbI3du3cD6J3QNn36dBQUFAzpeiaTKfBze3v7kK5FFHc48TMsn9MWMqbJ4MNjRKlEkvxo2vlPOA5+FrRHQNG0a5A9bKYqdREREVF8+OLQQTyzfTP8QV/Gl1ty8IuZ85ChS56V5ZRoW10J27vbQ8bNZ4yHecl4PnRIRERERAPjaZVva00QxNR6j01EREREBIAPhBHFkO39HbAumwx/pzzgTVQQ8Obv9oSMaU0MeAvQBS+sKgEeB6DPVaMaIiIiIiIiIiIaIFOaAdMLhmF6wTAAgF+SUNfZgUqHDZX2VlQ57DjU0Ra8lF+fGpydaHB2YnVtDQBAJ4oYZbKgzGzt/WOxIseQzvnHREQJKHPqOSFjk3O24c2qb8Mr6QAA0m4XvKcoCwolIiIiosiLSMBbU1NT4OdRo0YN+Xpa7bGyuru7h3w9onjCm1zh+Zx2+YBGCyEtQ51iiCjmRLjRsOkJdLXuDtojoOjk5cgunqVKXURERBQf/lNVgVf37AgZn55fhJ9Onw29RqNCVepp+3IfbO+H/vcwL5kAy5LxKlRERERERAnP3SLf1vGBbyIiIiJKPX4/V60niqW2L/chc8ZI+JzygDdNZv+B415HV8gYA96OEcQ0SJpMwNd5bNBjY8AbEREREREREVGCEgUBJVnZKMnKxmnDRwEAur1e7G+zo8phQ6XdhkqHDY4eZc9je/x+7LO3Yp/92IKA5jQDSs0WlJlzUGq2YIzZgnStLhq/DhER9cHT1dL/QWEYxy2Ea+8q2Vi+sRF1zhIAgOST4PMx4I2IiIhILREJePP5fIGfNRF4qNrhcAR+NpvNQ74eUaxpNBrMmDED7733XuhOUYx9QQnA57TJtjUZVobhEaUIr7MB440b0dUaFPQIAUUnX8NwNyKi42g0GsydOzcin7uIEoFfkvCP3dvx/oF9IfsWlYzEtVOmQ5Nin7EcK/fC/t+dIeOWsyfCfHq5ChXFL/ZMIiLl2DOJCJ5W+bY+R506EgB7JhERERERUeR0rD8An9MtG9NkpPV7ntfhChkTtPycJqOzhga8EdGQ8d4QEZFy7JlERMqxZxLRYBi0WkzMycPEnDwAgCRJaO3ukgW+HWizw6NwcRNHTzc2NdZjU2M9AEAAUJKVjVKzFWPNVpRarCjOzIao8jOf7JlElMx8Pe3obquBwTRiQOdZzvhxSMDbt0r/hb9s+3lg23tcHggRERERxVZEAt7y8vICPzc2Ng75env27An8bLVah3w9olgTRRG5ueFXu2RmWXg+pzzYSZNhUakSIoolZ9MO1G76GySffDVmCBoUTbsa2cWz1SmMiChOiaKI/Px8tcsgigmv34+nt23C6tqakH3njRmHy8dPTrlQaMfnFbB/uCtk3HLOJJhPG6dCRfGNPZOISDn2TCKCJ2jlT1347ziIPZOIiIiIiCiS3I3tgF+SjWkylQS8dUWrpOShswLdx33PxoA3oojgvSEiIuXYM4mIlGPPJKJIEAQBuelG5KYbMbuoBEDvfOxDHW2otNt6g98cNtQ7O/u5Ui8JwKGOdhzqaMfKQwcB9IbKlZosKDVbUWaxosxshSnNEKXfKDz2TCJKdu21Xw844M0053LU/vW7srH89CYI8EOCCIABb0RERERqikjA27BhwwD0Jrxv2rQJkiQN+iHrw4cPY//+/YHtiRMnRqJEopjyeDz45JNPwu9MsQACpbprtsq2NZl8eIwo2fncTtRteTYk3E2Tlo3iU36EdGupSpUREcUvj8eDjz76CGeddRZ0Op3a5RBFTbfXiz9vXo+tzQ0h+66YMAXLxqRemJn9kz1wfLI7ZNy6bDJMi8aqUFH8Y88kIlKOPZMotUm+LsDnkg/qc9QpJgGwZxIREREREQ2Q1Peu7srmkDExQ0HAW5ur32NSni5ocWUGvBFFBO8NEREpx55JRKQceyYRRYtWFDHaZMFokwVnovc5rU63G/vbbNh3JPStymFHp8et6HrdXi92tjZjZ+ux+3q56UaUmXvD3sosVozMNkOv0UTl9wHYM4kouRiyQ4Pc/B7noK6ltRTDa6/tc7/Pz4A3IiIiIrVEJOBt/vz50Ol08Hg86OjowNtvv42LLrpoUNd68sknAz9bLBZMnTo1EiUSxZzX6w2/gwFvISRJQsfG12VjxrHzVKqGiGKls/GbkJtNBvNoFM/4EbQGszpFERElgD7fZxIliU63G3/YsAb7HPKHTERBwPVTT8HCkpEqVaYOSZLg+GQPHJ/uCdlnPW8KTAvKVKgqcbBnEhEpx55JlMI8LUEDGkBrVqOShMGeSUREREREpJzkVv4ZStBrIer6f/jT6+gaSkmpISTgzQFJ8kMQRHXqIUoivDdERKQceyYRkXLsmUQUK5l6PabmFWJqXiGA3rnKja5OVNrtqHS0osphR3W7Az7pBCs3HKely4WWLhe+qj8MANAIAkZmm1FmtqLUbEWZxYICYyaECD5Xy55JRMkizRQa8NZ2aA3yJ38PomZgIZbFP/oHqn+7qO8DlLV1IiIiIoqCiAS8ZWRkYP78+Vi5ciUkScJdd92FpUuXIi2t/5UUj7d792788Y9/DHxQP/fccyNRHlFc4QSpUO663XA37JWNZc24RKVqiChWOhu2yLYNlrEYPudnA77xRERERMmjtcuFh75ejdrODtm4XtTg5lNmY1p+kUqVqUOSJNg/2o22zytC9uVcMBXZ80pVqIqIiIiIko67Vb6tt/K7DCIiIiIiIooYn8ut+FhNpr7fY/zdHnjq24ZSUmoIDniDD/C2ATqLKuUQEREREREREVF8EgQBhRlZKMzIwoKS3qAht8+Hg20OVDls2OewocphQ0uXS9H1fJKE/W127G+zA9VVAIBMnR6lZgvKLDkoM1tQarYiQ9f/vUAiomQnCAIso5fAfuAT2fjBL+/DmNPvH9C1tFm5kSyNiIiIiCIoIgFvAHDHHXdg5cqVEAQBFRUVuPTSS/Hvf/8bBoNB0fm7d+/GsmXL0N3dDUmSIAgCbrvttkiVRxQ/IrDSgNvnx11v7oxAMfGhfeMbsm2tpRjpo2eqVA0RxYLf2w1ns7yPZZXMZbgbERFRCqvrbMeD61ejtbtLNp6h0+G2mfMx1pKjUmXqkCQJ9v/uRNsX+0L25Vx0ErLnjFGhKiIiIiJKSp4W+baOE72IiIiIiI5yOp148803sXr1amzYsAFNTU2w2Wy9D1tYLMjPz8fMmTOxYMECXHTRRcjMzFS7ZKK44+/yKD5Wk3HiRYWdO+rQ8voWSG7vUMtKeoImHZKYDviP++7NY2PAGxERERERERER9Uuv0WCcNQfjrDlYemTM0d2NyiNhb5UOG/Y77Oj2KbtP1+lx45vmRnzT3BgYK8rIRJnZijKLFaVmK4ZnmaAVuSAhEaUercEcMuZxNqGnow5pWcMi90JS5C5FRERERAMTsYC3s88+G2eccQY+/fRTCIKA999/H5MmTcK9996LSy65pM/zqqqq8PTTT+PPf/6zLNztiiuuwOTJkyNVHlFMabVazJ8/H2+88UbozqHnu+HzPc04oJOHIKVpE/fmVccm+X+nrFO+BYE344iSmrNpJyT/cTfxBRHZRdNUq4eIKFFotVqcfvrp0Goj9lGOKC5U2m34/YY16PS4ZeMWQzp+OWsBSrKyVapMHZIkwf7BDrR9WRmyL+db05A9e7QKVSUe9kwiIuXYM4lSnLtVvq1LrXDlgWLPJCIiIkoNLpcL9913H5566im0t7cD6L13G3xMbW0ttm7diqeffhrZ2dm4/vrr8Zvf/AYZGRlqlE0Ul/xd7v4POkLM0J9wP8PdBkhnBXpqj217bABKVSuHKBnw3hARkXLsmUREyrFnElEiMBsMmFE4DDMKe8OG/JKE2s52VNp7A98qHTbUdrQrzg+qd3ai3tmJVbU1AAC9qMFokxmlR0LfysxWWA3pEAT5A7nsmUSUbLKGzUTz7tdCxqtX3Y9xy55QoSIiIiIiirSIfoJ99dVXMWvWLBw8eBAAcODAAVx99dVYvnw5hg8fLjt26dKl2Lt3b+DYo8FuADB+/Hg8+eSTkSyNKOYMBkPY8UgEl9U7uoE8ecDb6ePzhnxdNbibD6C7eotsLHvGxSpVQ0Sx0tEg/3ufbh0HjY4T3ImIlEhPT1e7BKKI2tbcgMc2fYUen082XpSRiV/OXojcdKNKlalDkiTY3t2O9jVV8h0CkHvxyciaOUqVuhIVeyYRkXLsmUSpSZIkwBMU8KbPVaeYBMKeSURERJTctm/fjssuuwx79+4NhLoJghDyENnxJElCW1sb/vCHP+Dtt9/Gv/4/e/cdHlWZtgH8PlPTJlPSEwKBhN57RxQrYHdVFCtYd21rwXXdZXV3/dTFhrprA7ss6tpFUUCkV0HpkEAgAVJnJpm0qef7IzDkZFJOkplMZub+XRfX8r6nPeMmDzNnnvO8H3+MIUOGdFbIRIGh8MNKpgA8tU7Z+ypjtS1uZ3O3NmqywRsRdRTvDRERycecSUQkH3MmEYUahSAgU6dHpk6Ps7vXL15d63LisNWCXKsZeaeavlXY7bLO5/C4ccBSjgOWcuBI/ZxBG4WcUw3fsg0m9NIboVUqmTOJKKyoo43oPmEejm14RjIvelxwO6qg1MQFKTIiIiIi8peOd5pqICEhAcuXL8eAAQO8DdtEUYTL5cKRI0e8+4miiB9++AFHjhyBKIqSfYcMGYLly5cjJiayHuCm8OJyubBy5cqmN7ZQ7ClXU6sY/Gl63w6fNxhs2z+XjJW6RMT0mRykaIioM4geF6pLdknmjpQq4XKxCJeIqDUulwvLli1jzqSwseF4ARZs3eDT3C3bYMT8CVMjs7nb17813dztyhFs7tZGzJlERPIxZxJFMFclIDZ60F6dEJxYQgRzJhEREVF4y83NxbRp07zN3U43dTtd4yaKIpRKJZRKpWQOgLf+7eDBg5g2bRpyc3OD+VKIOs4//d0QO7Sb7H2VcS03eKM2UpukYzZ4I+ow3hsiIpKPOZOISD7mTCIKF9EqNQYmJuPSnH7446gJeHXaDLx49oW4Z/hYXNQzB72NCVAr5D/WbrXXYVvxCfx3/278c9MazF3+Jf60ZgWe/P5rrDp6GIW2SnjEpp62JSIKLdGmbESbcnzmPS55TTKb09twoMGI+ZKIiIgoWFT+PmFOTg62bNmChx56CIsWLYLD4QCAJlcwbVgAp1Qqccstt+D5559HbGysv8Mi6jJaWs23vf4ysx+i1Uq/n7czVG77TDLWDb8UgtLvqYmIupCasgPwuGolc1ViapCiISIiomD5/kgu3t/7q8/8kKQU3DdiHKJUkfW5QPSIKP/qV9g2HZFuEICkq0cibnj34ARGREREROHNWSYdK6IAJb+nIyIiIqLI5HK5cPHFF6OsrAyCIHgbto0aNQo33ngjJkyYgH79+nkXLq2pqcGBAwewYcMGvP/++9iyZYu3Lqi8vBwXX3wxdu3aBVWE3e+m8OGvOreYAWmy91XGssGbXzXR4K1h80oiIiIiIiIiIqJAEgQBSTGxSIqJxbj0+oUgXB4PjlVWINdqRq7VjDyrGUXVVbLOJwIoqKoEFMDhPTsBANEqFXrpjcgxJiDHYES2wQS9NipAr4iIKHBSh9yEI6v/IpmzndwOU/b5PvtW2eU1Bp6QtgYHrf1PjdjgjYiIiChYAlI9Fh0djVdffRWPP/44XnvtNfz444/Yvn07nE6nz74DBw7EBRdcgLvuugvZ2dmBCIeoa1H4vzgqVOutnNaTqM3dIJnTjboiSNEQUWexFe2QjLX6LLjMvHFOREQUKURRxCcH9uDLvAM+28anZ+LOoaOgasPKbOFA9Igo/3InbJvzpRsEIOmaUYgblhmUuIiIiIgoAjjKpWN1Ah/yJiIiIqKI9cYbb+DAgQPexm6JiYl47bXXcMUVTdeyxMTEYPjw4Rg+fDh+//vf4/PPP8ddd92F0tJSAMDBgwfxxhtv4O677+7Ml0HkP376fKhQKxE/IRuVG/Ja37cDDd4q1hyC8aKB/FzbkDpBOhZdgNsGqOKDEw8REREREREREUU8lUKBXgYjehmMOB/1z5TbHHbkWS3Ia9D0rbqJ59GbUutyYU95KfaUl3rnkqJjkGM0IdtgQo7BhKx4A9RKZUBeDxGRv6iiDD5zpfs+RVzqcJ/549Y65JZUISc5zjunNvk+d6JT2/waIxERERG1T0CXB01LS8MTTzyBJ554Ana7HUVFRSgvL4fD4UBiYiJSUlKg0+kCGQJRF8QCstNsv3wJiGc6fiuidIgdMC2IERFRoImiB1XFOyVzsclDAbO8FQOIiIgotLk9HizevQOrC/J9tl2QlY3ZA4ZCEWEP3YgeEWWf7UDVtqPSDQqhvrnb0G7BCYyIiIiIIoOzTDrWJAYnDiIiIiKiLmDhwoXe5m5JSUn4+eef0a9fP9nHX3755RgwYACmTJmCsrIyiKKIhQsXssEbhS4/fmcjaOQ9PKmM03ToOmUfb0fSNaM6dI6woowBBA0gOs7MOc1s8EZERERERERERF2KTqPFsORUDEtOBVC/oHhRdRVyTzV8y7WYUWCrgLvBs6gtKa2tQWltDTaeKAQAKAUBWXqDt+FbjsGE5JhYLhZBRF2KoFQ3OX9s3VMQ0uf5zH++4wQevqCPd6yIikPswHNRvWeFd05s2NNAXgolIiIiogAIaIO3hrRaLXr06IEePXp01iWJgkalUmHatGn46KOPfLbxps8Ztm2fScZxw2ZCoW7/KqxE1PXVWY7Aba+UzMWnj8T0XglQqTrtbQkRUchSqVSYPn06cyaFJIfbjVd3bMG24hM+267uOxCXZPeNuM9LokdE2f9+QdX2Y9INCgHJs0YjdnBGcAILE8yZRETyMWcSRTBnuXSsTghOHCGEOZOIiIgoPOXn5+PgwYMQBAGCIOCFF15oU3O30/r27Yvnn38eN9xwAwDg0KFDyM/PR1ZWlp8jJgo8QdH539soYztWO1a1owCmmYM7fJ5wIQgCRE0CYD95ZtJpBqKzghYTUajjvSEiIvmYM4mI5GPOJCKSEgQBaXE6pMXpMLlb/TPpDrcbRyosyLWYcchSjsMVFpTX1co6n1sUkWe1IM9qwQ/IAwDoNBpvw7dsgwnZBiNi1R1bgIKIqCMEQYGEPpeg/OBXknm3sxp9oo767G+pdvrMGc++U9LgTYIN3oiIiIiChnf9iAKkrq6u6Q0KRZvOIzo9qDxR1egcod/0wF1lRvX+nyRz8aOuCFI0RNRZbEU7JGNNXBo0cSmw2WzQ6XRBioqIKLTU1tYyZ1LIqXE68fy2DdhnLpPMCwDmDB6Bs7v3DE5gQSR6RJR9sh1VOwqkGxQCkq8bg9hB6cEJLMwwZxIRycecSRR5RNEFOC3SSU1icIIJMcyZRESBkZ+fj549z9wnuummm/DOO+8EL6AQs2/fPuzZswfHjx+Hw+FAeno6evXqhbFjx0LRxjoFfzt8+DC2bNmC0tJSVFVVISUlBenp6ZgwYQLi4+M7fH5RFHHkyBHs3bsXBQUFsFqtUKlUMBqN6N69O8aMGQODwdDxF0JhbevWrQDqf54SExNx7bXXtvtc1157LR544AGUlZV5z80GbxSSglCipvBDY7a6vDLEDuEiOl4qo7TBm6O8+X2JSBbeGyIiko85k4hIPuZMIqKWaZRK9DUloo8xATZbKnQ6Haz2OuRZzci1mpFrseBwhRl2t1vW+WwOB3aWFGFnSZF3Lj1Whxyjydv4LVMXD2WQv2cjosiS2GemT4M3AIitPYg0fTZOVjTTu4CIiDoda906JpJr3Sgy+a3B24kTJ5CezoePiQDA5XJh/fr1TW4T2lj45tlYiVqrXTKnSNUBztBulW3b+TXgdnnHgjoKcYMvDGJERBRooiiiqlGDt7jU4XC5XPjpp58wffp0qNXqIEVHRBQamDMpFFnr6vDM1nU4VlkhmVcrFPjD8DEYlRp5D9iIbg9KP9mO6p2F0g1KAcnXj0XsgLTgBBZmmDOJiORjziSKUE4LfJblVCcEJZRQwpxJFB6ysrJw9Kjv6sYNKRQK6HQ66PV69O7dG8OGDcOMGTMwdepUCG390pcoQERRxJtvvolXX30Vv/32W5P7pKen48Ybb8Tjjz+O2NjYTovN4XDgzTffxAsvvIC8vLwm99Fqtbjooovw+OOPY+TIkW06f0lJCb744gv88MMPWL16NcrLm29WIwgCJkyYgLvvvhvXXntt0IsAqWsqLS0FUP/zMmrUqA79nCiVSowaNQrff/89gPqfV6KQFIT3PMpYTYfPoYjhZzUJTQJQ3WDssjS7KxG1jveGiIjkY84kIpKPOZOISL6GOdMYFY1RqRneenSPKKLQVnmq4ZsZeVYzjldVNq6OadaJahtOVNuwprD+u2StUoksvRE5pxq+ZRuMSIiOCdArIyKq1+uc/8PhVX+SzIkeFy4alILF61uudWksKboUaoUD8lpfElFXxVo3CheRXOvW0PHjx7F161Zs3boV27Ztw7Zt22A2myX7iGJo9xMiX35r8NajRw9ceOGFmDNnDi6++GIolUp/nZoovLThDZDo9MC9SlpQNWRCFg4l64Djlf6OrFNVbvtMMo4bfAEUUXFBioaIOoPDdhzOmlLJnC51WHCCISIiok5RXF2Fp7esQ0lNtWQ+WqXCg6MmoH9CUpAiCx7R7UHp0m2o/u24dINSgZTZYxDTn83diIiIiKiTOMqkY2U8BEXHH6QnIgoXHo8HFRUVqKiowLFjx7By5Uo899xzyM7OxsKFCzF9+vRgh0gRrri4GLNnz8aKFSta3O/EiRN4+umn8cknn+C///0vRo0aFfDYDh48iEsvvRT79+9vcT+73Y4vvvgC33zzDR577DH87W9/k1VUOm/ePDz33HNwu+WVoYuiiPXr12P9+vV45ZVX8N577yEnJ0fWsRQ5KirOLFJiMpk6fD6j0ej9u81m6/D5iIJC0bmF/oJGCYWm4yWtgh/OEVbUjXKaoxyiKPJBDiIiIiIiIiIiCjsKQUD3eD26x+txTveeAIAapxOHKyzIO9X0LddqRqXDLut8drcbB8xlOGA+U2NjjIpGjsGI7FNN33rqjYhS8Z4kEfmPOiYBUfos1FXk++V8Y1M2YAMul93skohCE2vdqKuL5Fo3ANi1axf+/Oc/Y+vWrSgqKvJH2BRi/Pap0e12Y9myZVi2bBmSkpJw00034dZbb0Xfvn39dQmisCC0YYVfcW8NYJMWJP/uDxPx1M7QTtieuipU714umdONvCJI0RBRZ7EV7ZSMVVFGaPU94HK5ghMQERERBVR+hRXPbFnn8wW4XqvFI6MnIUtvCE5gQSS6PSj571bU7DohmRdUCiTfMBYxfVODFBkRERERRSRnuXSsSQhOHEREISYvLw8zZszA3/72N8yfPz/Y4VCEqq6uxvTp0/HLL79I5rt164YhQ4YgKioKBw4cwJ49e7zb8vLycP7552Pjxo0BreXZtWsXpkyZAqvVKplPTk7G6NGjER8fj+LiYmzatAk1NTUAAJfLhSeffBI1NTX417/+1eo18vLymmzulpiYiAEDBiApKQkKhQIFBQXYvn07nE6nd5+NGzdi4sSJ+Pnnn9GvX7+OvVgKKw0bspWUlHT4fGVlZx72MhgMHT4fUTD4tf+Xx9PqLspYrR8vSF6NG7yJDsBdA6g6b7VzIiIiIiIiIiKiYIlRqzEoMRmDEpMB1C8MVFZbg1xrfbO3PIsZ+ZVWOGXcwwQAS10tthbVYmtRfT24QhCQqYv3NnzLMZiQFqeDggssEFEHxCT2bVeDN0V0vM9cRlyBHyIiolDFWjfqCiK91g2ofz1ff/21v8OnEOL3tuCiKKKkpAQLFizAggULMGHCBNx222343e9+h+joaH9fjqjLUjXXdb8NN2Y8uTWScf9R3dBvRDcgxBu8Vf32HURngyYPShV0wy8OXkBE1CmqinZIxnGpw71diZvNmURE5IM5k0LBnrISPL99I+oaNXJNiYnFo2MnITkmLkiRBY/o8qBkyRbU7DkpmRdUCiTfOA4xfVKCFFl4Y84kIpKPOZMoAjnKpGN1YnDiCEHMmUThZ8mSJRg3bpxkzu12o6KiAnv27MFnn32Gr776Cp4GRf1/+9vf0KdPH8yaNauzwyXCzTffLCl40+l0eP3113HNNddA0WDRuc2bN+Omm27CgQMHAAAWiwUzZszArl27AlLDY7Vacdlll0kK3pKTk/Hyyy/jqquuksRWU1ODBQsW4Mknn/Q2a1uwYAH69++PW2+9VfY1+/Tpg1tuuQUXX3wxBgwY4LMqqtlsxoIFC/Dss896r1NSUoKZM2di9+7diIqK6sArpnCSklJ/j1YURWzZsgUOhwMajaZd53I4HNi8ebPPuYlCjh8fQHTXOlvdRxnHBm8BodQBggoQG3xv5yxngzeiDuC9ISIi+ZgziYjkY84kIpKvIzlTEAQkxcQiKSYW49MzAQAujwdHK63ItZiRd6rxW3FNtazzeUQRRysrcLSyAquOHQEAxKjU6GUwIsdg8jZ+i9fy/icRBV5Mn8ktbBU7LQ4iCizWulGoYa1b81QqFbKzs72vmcKXovVd5OnVqxdEsf6N3eliSVEUsWHDBtxyyy1IS0vDXXfdha1bt/rrkkRdllqtxrnnntv0xrY0eMurlYxHTs3pSFhdRuW2zyTj2P7nQBlrbGZvIgoHjpoy2Culnf51qcMA1OfMGTNmQK1WByEyIqLQwpxJoWDryeN4dut6n+ZuPeL1mD9haoQ2d3Oj5MPNvs3d1Eqk3DSezd0ChDmTiEg+5kyiCOUsl441CcGJI8QwZxKFp9TUVGRlZUn+ZGdnY8SIEbjhhhvw+eefY8WKFYiLk97XmDdvHux2ezNnJQqMdevW4dNPP/WONRoNVq1ahVmzZkmKygBg7NixWL9+PbKzs71zeXl5eOmllwIS2zPPPIPDhw97xwkJCVi7di2uvvpqn9hiYmLw17/+FR999JFk/tFHH0VlZWWr15o0aRJWrFiBAwcO4NFHH8XAgQN9mrsBgMlkwlNPPYXPPvsMSqXSO5+Xl4cXX3yxja+Qwtnp4mdBEGCz2fDmm2+2+1yLFi2S/Bw3LqwmChl+bPDmqWm9wZsitn1NFallgiAAapN00mkOTjBEYYD3hoiI5GPOJCKSjzmTiEi+QORMlUKBbIMJF/TMwd3Dx+D5sy/Ea+fNxMOjJ+DynH4YnJiMGJX869W4nNhdVoIvcvfjuW0bcNeKb/DAT9/j1R1b8P2RXORazHCeagpBRORPCk0UTOff3/RGkQ3eiMIFa90olLDW7QyFQoH+/fvjxhtvxMKFC7FhwwbYbDZ8//33HX8x1OX5rcFbbm4ufvrpJ1x//fWIioqCKIqSRm+VlZV44403MG7cOAwdOhQvv/wyzGYWiVB48ng8KCsra3Kb3Lo3sdoNFEsL2waMyexoaEHncdSh6tdvJHPxo64IUjRE1FmqinZIxkp1LKJNvQHU58ySkhJJJ3AiImoacyZ1dauOHcZLv2yCq9HPaH9TEh4fdxb02qggRRY8osuN4g82o2ZfkWReUCuRcvN4RPdODlJk4Y85k4hIPuZMosgjuusAd5V0Up0YnGBCDHMmUeQ6++yz8corr0jmCgoKsGrVqiBFRJHqz3/+s2T82GOPYdSoUc3un5CQgLfeeksy98wzz8guLJPLZrPh1Vdflcy9+OKL6NOnT4vHXX311bj55pu949LSUixYsKDFYxYsWIC1a9di2rRpsuO75JJLcP/990vm3n//fdnHU/hLS0vD0KFDAdTXuj322GPYsmVLm8+zdetW/OlPf4IgCBAEAUOGDEFaWpq/wyXqFE01zmwvT62j1X2UMVq/XY8aYYM3Ir/hvSEiIvmYM4mI5GPOJCKSr7Nypk6jxbDkNFzVdyAeHTsZr59/Mf511nm4Y+goTOveE1nxBijacA+1pKYaG04U4P29v2L+hp8w94ev8Nf1P+G9PTux4XgBSmqqILL5EhE1o6Zsv+x9lXGm1nciorDHWjfqKljrVm/atGmoqKjA3r178e677+Kee+7B+PHjERUVec/bRiq/NXgDgLPOOgvvv/8+Tp48iVdffRUjR470fqBs2Oxt165duP/++5GRkYHrrrsOK1as8GcYREHndruxbdu2pjcKMn/tqnw78Gf1S+lAVF1D9d6V8NQ1eGhMEKAbfmnwAiKigBM9LliP/iyZi00ZCkGhBFCfMzdu3Ag3Vx4hImoVcyZ1VaIo4vND+7Bo1w40/lp5dGo6HhkzETERuLKkx+lG8fubUbu/WDIvaJRIuWUCorOTghRZZGDOJCKSjzmTKAI5yxtNKAC1IRiRhBzmTKLIdsMNNyAxUdoQk0Vv1JmOHj2KNWvWeMfR0dG49957Wz1u6tSpGDNmjHdstVrx1Vdf+TW21atXw2azeccZGRmYNWuWrGMffvhhyfidd95p8QGWrKysdsX4+9//XjLeu3cvSkpK2nUuCk8PPfSQd1FTm82Gc889F//5z39kH//666/jvPPOg81m8/4MP/jgg4EKlyjwFP5s8OZsdR9lnMZv16NG2OCNyG94b4iISD7mTCIi+ZgziYjkC1bOVAgC0uPiMaVbD9w6eAT+OXka3rrgEvxl/Fm4rt9gjE7NgCkqWvb5XB4P8qxmLM/Pw6s7t+CBn5bj7hXfYsHW9fj80D7sLitGjbP1+6pEFBlcdRbEeUqDHQYRhRjWulGwsdbtDJ1Oh7i4uLYHSmFDFYiTxsfH46677sJdd92FXbt24a233sJHH32E8vL6h0UEQYAoirDb7Vi6dCmWLl2KHj164NZbb8XNN9+Mbt26BSIsoq6hAyubKlV+7ckYFLbtn0nG0b0nQmVIDVI0RNQZLPk/wVktfTBClzYySNEQERGRv3lEEe/t2Ykfjx722XZO9564ZdDwNq1OFi48TjdK3tuE2kPS90GCRoXUW8cjKiuxmSOJiIiIiDqBs0w6VpsgCMrgxEJEFEIUCgVGjx6N7777zjtXUFDQrnMdPnwYv/zyC0pLS2GxWKDX65Gamorx48cjPT3dXyEHnNlsxoYNG1BUVISysjJERUUhKSkJw4YNw8CBA4MdXtj5/PPPJePLLrsMRqNR1rG33HILtmzZ4h1/9tlnmD17tt9ia1iMBwDnnnsulEp57y8GDBiAzMxM7+9TQUEBtmzZgrFjx/otPgDo2bMn9Ho9KioqvHMnTpxAcnKyX69Doeu6667DwoULsXXrVgiCgKqqKvzhD3/A008/jVmzZmHChAno06cP9Ho9BEFARUUFDh48iA0bNmDJkiUoKCjwNogTBAEjR4706+8ZUafz49c77hpHq/soYrRtPm9Ur0TUHS5rfcdIxwZvREREREREREREfqdVqtDPlIh+pjN14ea6WuRZzMi1mpFnNeNwhQV2mc3oKh127Cgpwo6SIgD1t2jT4nTobTAh22BCjtGEbnHxUCpC/zljImqZoPBtAWJy5wPowHfbLfedIaIwxFo3X6x161ysdSM6IyAN3hoaPHgwXnrpJSxYsACff/45Fi9ejBUrVniL2QBAFEXk5+dj/vz5eOKJJ3Deeefhtttuw8UXXwyVKuAhEnUqIQIbG5wmul2w/fKlZC5+5BVBioaIOoPLYUP5wW8kc1GGnohN5occIiKicODyePCfnVux6WShz7bLc/rhyj4DIvIzkMfhQvF7m1CXK10hSdCqkHrrBET1SAhSZEREREREpzgaPfyu4XtUIiK5GhcYmc3ym2NUV1dj4cKFWLx4MXJzc5vdb9iwYXjsscdw1VVXtXhvZciQIdi1a5d3fPDgQfTu3Vt2PKdjSktL865GqdfrcfLkSURHN7+6vCiKWLp0KV588UVs3boVHo+nyf0yMjJwzz334N57723xfCTf999/LxlPnTpV9rGN9/3hhx/g8Xig8NMDIIWF0nuEgwYNatPxgwcPlhSRfvvttwEpemtch+RwtN5wiCKHIAj44osvMH78eBw7dsy7iGlBQQH+9a9/tXjs6ZV4Tx/TvXt3v68eTNTZBIX/vuPx1Laeb5WxmjafV5Omh73QAtEh7wHJiNW4wZunFqK7FoKS79GIiIiIiIiIiIj8yRQVDVNaBkanZQAA3B4PCqsqkWupb/iWazXjeJVN1rlEACeqbDhRZcPPhUcBAFqlEj31RuQYTPV/jCYYo3ifjyjcxCT2R/mhbyVzCvC7ECJqO9a6sdYtmFjrRnRGp7XpVqvVuPrqq/H999/jyJEjmD9/Pnr06CEpbgMAt9uN5cuX46qrrkK3bt06KzwivxIEAXFxcU1v9GPhW6ipObgW7qpyyZxu5OVBioaIOkP5ga/hcdVK5pIHXg1BOPMWRBAE6HS6iGz+QkTUVsyZ1JXUupz419b1Ps3dBAA3DRyKq/oOjMifVY/DheJ3Njbd3G3ORDZ360TMmURE8jFnEkUgp/RePdSJTe9HPpgziaiyslIyjoqKknXc8uXL0atXLzz22GMtFrwBwM6dO3H11Vfj/PPPR0VFRbP7zZkzRzJevHixrFga+uSTT7wFbwBw3XXXtVigdvjwYYwcORKzZs3C5s2bmy14A4Djx4/j0UcfxYABA7Bnz542x0a+du/eLRmPHz9e9rH9+vWDyXSmuUt1dTXy8/P9FZpPAajBYGjT8Y33b1jQ6S9WqxXl5dL3QWlpaX6/DoW2tLQ0rFmzBuPGjfMuYNpwEdOm/gDw7ieKIsaOHYvVq1cjNTU1mC+FqOP8+LlHTgM2Ray2zedVJzVTo0dSKj2ARquNO+U/vEFEZ/DeEBGRfMyZRETyMWcSEckXajlTqVCgR7wB03r0wu1DR+HZs87Hm+dfgj+NnYSr+gzA8ORUxGvk3xu1u93Yby7DN4cP4sVfNuEPK5fhnpXL8OL2Tfg27yD2m8tgd7sC+IqIqDPEJPTxmVOJ9iBEQkShjrVurHULJta6EZ3RaQ3eGsrMzMT8+fNx+PBh/Pjjj7jmmmug0Wi8BW9AfUFcaWlpC2ch6rpUKhUmTZrU5Da5N47EuubfHOw+Xtnstq6scttnknFUjxHQJGUFJxgiCji77QSsx9ZI5nTpoxFtzJbMqVQqnHPOOVCpVJ0ZHhFRSGLOpK6i0m7HU5vWYndZiWReKQj4/fAxOD8rJ0iRBZfH7kLx2xtQd7hMMq+IUiNt7iREdTc1cyQFAnMmEZF8zJlEkUUURcDRqMGbho2I5WLOJKJffvlFMu7Vq1erx7z55puYMWMGSkqk91KysrIwY8YMXHfddZg5cyYyMzMl21esWIFJkyZJitIamj17NrTaM8X+7777Ltzutq3YvGjRIsm4cSFdQ1u2bMHYsWOxY8cOyXxCQgLOPfdcXHvttbj88st9VrPMz8/HxIkTsXPnzjbFRlKVlZU4fvy4ZC47O7uZvZvW+Od17969HY7rNI1GIxnb7W0rLm+8vz9jO+3TTz+VjNPS0rj4JDWpe/fuWLt2LV588UVkZWVJGrkBkDR9A840fsvKysKLL76IdevWISsrKwiRE/mZHx+QTLh8WKv7KGM1re7TkKBVIW5YZus7Uv1CjGqDdJIN3ojahfeGiIjkY84kIpKPOZOISL5wyJkxajUGJabg8t798dDoifj3uTPwwtkX4PfDxuDCrBxkG0xQKeQ//m+uq8XWouP4aP8u/H3jz5i7/Cv8ee1KLN71C9YUHsWJqkp4GnzPQUShITZ5iGTc07UJl6a0v+GQCOYBokjEWjfWugULa92IpIL+CXbatGmYNm0arFYrPvzwQzz55JMoKytr/UCiLszj8aCwsLDpjTIL39zfSx+uOn3YliOhWVglejywbf9cMqcbdUWQoiGizlB24EtAPNOsUlCokdTP9/fe4/GgoKAAmZmZULTh5jMRUSRizqSuoLSmGs9sWYeT1VWSea1SiQdGjsfgpJQgRRZcHrsTRW9vhD1f+llOEa1G6pyJ0HYzBimyyMWcSUQkH3MmUYRx2wDRIZ1TJwYnlhDEnEkU2b766iucOHFCMjd16tQWj1m1ahXuuusuSTHa9ddfj8ceewwDBgzw2X/16tW45557vKtX7t69G7fffjuWLFnis29CQgIuu+wyLF26FABw8uRJLFu2DBdffLGs13Pw4EGsW7fOOx42bBhGjhzZ5L5FRUW47LLLJPUcY8eOxd///nece+65Pgud5ebm4uGHH8YXX3wBAKioqMDVV1+N7du3Q6fTyYovUAoLC+FyBX7V+tTUVNmr3srReDXcxMRExMTEtOkc3bt3x7Zt27zjQ4cO+SU2oP7nsaGTJ0+26fjG++fl5cHtdkOpVHY4NqD+3/CXX35ZMnfZZZfJXqSPIo9SqcS9996Le+65B2vWrMG6deuwbds2lJSUwGKxQBRFmEwmJCcnY9SoUZg0aRKmTJnCnykKK/78cY4dnIGqX47BfrT52jdlTNsavCXPGg1FlLqjoUUOtQlwNvgeiw3eiNqF94aIiORjziQiko85k4hIvnDMmYIgIDkmDskxcZiQUd8oxel242hlBXKtZuRZzci1mlFSUy3rfB5RRH6lFfmVVqw8dgQAEKNSI9tgRI7BhByjCdkGE3QabStnIqKu5qrUPfi+tA/snnZ8P8L+bkQRh7VurHVrC9a6da1aNwo/QW/wBgBmsxnvv/8+Fi9ezOZuFBbcbrf3TUhjgoybRjaHA2KBtGNnek8TFAoB63LLffaPUnX9G1F1+dvhskg7rMazwRtR2BJFEdUl0jxo7HUe1DEJPvu63W7s3LkT6enpYXNjnYgoUJgzKdgKbBV4ZvM6WOx1knmdRoOHR09EtsEUpMiCy1PnRNHiDbAfkz4Io4hRI3XOJGgzDMEJLMIxZxIRycecSRRhHI2+ixM0gDIuOLGEIObM8OXxiLBZa4MdRtjSGaKhUIR2w5t9+/bh9ttvl8x1794d06dPb/aYqqoqXHfddd6CN0EQ8NZbb+HWW29t9pipU6di48aNOP/887Fx40YAwH//+1/cddddmDJlis/+c+bM8Ra9AfWrlMotemvLiqZz5syRFCXNmTMHr7/+erMFSTk5Ofj8889x3333YeHChQDqC6xeeOEF/PWvf5UVX6BMmjQJR48eDfh1fvrpp1aLItvCarVKxsnJyW0+R+NjKioqOhKSRP/+/SXjTZs2yT7Wbrf7rJbrdrtRVVUFvV7vl/heeOEF/Pbbb96xSqXC/fff75dzU3gTBAFnnXUWzjrrrGCHQtT5/NjhTRmjQdodU5D/2BfN7qOIlf8wYeafLoRKH+2HyCKIutH3eGzwRtQuvDdERCQfcyYRkXzMmURE8kVKzlQrlcgx1jdjO63Sbpc0fMuzmlErs9lHjcuJXWUl2FVW4p1LiYlFjqG+2VuO0YQe8Qaowvi/KVGoUUf7Pp+jUnigV9WhxMEFcCIda90Ci7VurHUDWOvWUax1I5IKaoO3H374AYsWLcJXX30Fh8MRzFCIOlHLb+ZsDjsKqyoBt7QV9hV3TgAAOFwen2NG9jD6L7wAqSv4TTLWpPSGNr1/M3sTUajzOKshepySOUP3yUGKhoiIiPzhgLkMC7ZuQI1L+m98YnQM5o2ZhPS44K5GESyeOieKFq2HvcAimVfEauqbu6XzphwRERERdTHORgvJaBJ9VqIjikQ2ay3mTng52GGErbc23AO9qW2rLwabx+OB1WrF3r178dlnn+H1119HTU2Nd7tCocB//vMfaDSaZs/x2muvobi42Dt++OGHWyx4Oy0uLg5Lly5F7969YbfXLwy2YMGCJovezj33XGRlZSE/Px8A8O2336K4uBgpKSktXsPlcuG9997zjqOionD99dc3ue+2bduwbNky73j8+PF44403ZD0w8fzzz2P16tXexlqvvPIK5s2bB62Wq8G3VVVVlWQcHd32pjaNj7HZbB2KqaHGza9Wr16NgoICZGZmtnrsJ598gtpa38Jjm83ml6K3LVu24LHHHpPMPfDAA+jTp0+Hz01EFNb8/NCCoBBgmNYP1pX7m75clMyHkQRAGdf8ewnHyQqoE+OgjGn+fVpEYoM3IiIiIiIiIiKikBav1WJEShpGpKQBADyiiJNVNuQ2aPh2rLICYivnOa24phrFNdVYf6IAAKBWKNAj3lDfWM5Q/ycxOoZ1RURBYuw5DbaiX+C2V/rnhHKTA4UE1roFFmvd6rHWjbVuHcFaNyKpTm+lnZ+fj/nz56NHjx646KKL8Omnn8Jut0MU698ViqIIURSRkpKChx56qLPDIwq8Vu5l7C8va3I+LavpJm6jsgxI0nX9NwTuyhLJWJPaO0iREFFncNZZG80IUEXxDSkREVGo+qX4JP5v81qf5m7ddPGYP2FqxDZ3c9c4cPKtppu7pd02mc3diIiIiKhrcjT6HkKdEJw4iIi6kLPPPhuCIEj+KJVKJCQkYPLkyXjhhRckBW/R0dF4//33W1zRFABefvlMIWVcXBz+8pe/yI4pMzMTV199tXf8ww8/eAvgGhIEAbfccot33LiYrTnLli1DUVGRd3zFFVfAaGz6O+nTq5Ke9tRTT8kqeAMApVKJ++67zzsuLS31rtZKbdO46C0qKqrN52hc9Nb4nB0xZswYScM0t9uNBx98sNXjbDYbHn/88Wa3dVRhYSEuv/xyycKTgwcPxpNPPtnhcxMRhb0APLRnOLcfEn83ounLyWwop9RFQVA2/16k/POdKFzwA+yFlmb3iUiNG7y5qyB6fN9fEhERERERERERUWhQCAIydPE4KzMLcwaPwFOTz8VbF1yKx8dNwbX9BmF0ajqMWvnfKTo9HuRazfj+SC5e2bEF9//0Pe5e8S2e27oBX+bux+6yEtQ4na2fiIj8QhOXgl7nPOXHM7LDG1G4YK0ba91CAWvdiKQ6pcGbw+HAkiVLcN555yEnJwf/+Mc/UFBQ4G3qdrp7t0KhwIwZM/DZZ5+hsLAQzzzzTGeER+R3giAgIaHpB6KEVv7xP1LRtsKyngmxbdo/WFyNGrwpdclBioSIOoOrTprLlFodBIWqyX0FQUBSUhJX8yAikoE5k4JhTeFRvLB9I5wej2S+jzEBfx1/FkxRbV89IRy4axwoWrQejkYPBynjtEi7fTI0qfFBioxOY84kIpKPOZMowjjLpWNNYnDiCFHMmUSRTa/X46677sK+fftw3XXXtbjvgQMHcOzYMe94+vTpiIuLa9P1zj77bO/f7XY7tmzZ0uR+t9xyi6QIbdGiRa2eu/E+c+fObXbfFStWeP+emprqs3plaxq+DgBYu3Ztm473t/z8fO/ig4H8M3Xq1IC+jvb8WxTIf78UCgX+9Kc/SeY++eQTPPzww3C73U0eY7VaMXPmTBw9erTZc3aE1WrF9OnTceLECe+cwWDAp59+2q6iQSKiSCMEoLpUEAToRvaA8YIB7T6HMr7176Y8NU5Ubjzc7muEJbUBPqvTOtkEj6iteG+IiEg+5kwiIvmYM4mI5GPObFmUSoX+CUm4OLsv7h85Hq+cOwMLz7kI940Yhxm9+qCvKREahVL2+SoddvxSchIfH9iD/9u8Frf/8BXm/fwj3vh1O1YdO4JjlRXwiGwaRRQ4zee67/cUe3t1EBE1hbVurHVjrVvzumKtG4W3pjut+MnOnTuxaNEifPTRR7BarQAAURS9XUBP/6L36tULt956K26++Wakp6cHMiSiTqFSqTB69Gjs3bvXd2Mr/4g0bpoQLlw2aYM3VTwbvBGFM1ddhWSs0uqb3VelUmHChAmBDomIKCwwZ1Jn+ybvAJbs3+0zPzw5FfeMGAutMqC3Fbosd7UdRW+th+Ok9D2PUheF1NsmQZOsC1Jk1BBzJhGRfMyZRJFDFN2+D2+rm16whprGnEkU2WpqaqBQKJCSktLqvuvWrZOMu3fvjvz8/DZdr3HBT15eHiZPnuyzX2ZmJs4//3x8//33AOoL7tavX4+JEyc2ed7i4mIsW7bMO+7Vq1ezBVp5eXk4efKkd5yTk9NsgVJzHA6Hzzmp7RoXTdbW1rb5HI2PaWshZmtuuukmfPnll/jiiy+8cwsWLMAPP/yAO++8E2PGjEF8fDyKi4uxatUqvPLKKygtLQUAmEwm1NbWSmI0GAztjqW6uhozZszArl27vHOxsbFYtmyZZPVVIiJqQQCLpXWjs2BZsR9w19fK6c+Wn5tVenlNOqu2H0PS70a2K75wJAgqiCo94LKemXSaAW1q0GIiCkW8N0REJB9zJhGRfMyZRETyMWe2XUJ0DBKiYzAmLQMA4PZ4UGCrRK7VjDyrGbkWM05U22SdSwRQWFWJwqpK/FyYDwDQKpXopTchx2hEjsGEbIMJxghdRJ6oM7w08Fu8nD8OGyxZ+K2wEkMzm392l4giG2vdWOsWTKx1I5Ly+5PYVqsVH374IRYtWoRff/0VALzdfxs2douKisLll1+OuXPn+nSwJAp1brcbhw4danpjhK4M4K5kgzeiSOKqs0rGqihjs/uezpm9e/eGUil/BRAiokjEnEmdxSOK+O/+Xfj2sO/nmsndemDu4BFQReiKAu4qO4reWgdHUaVkXhkfhbTbJkGdxOZuXQVzJhGRfMyZRBHEaQHQaKEZTWJQQglVzJnhS2eIxlsb7gl2GGFLZ+jaxdtLlizBuHHjJHPV1dU4fPgwvv/+e7z99tuora2F0+nEq6++il27duH7779HdHTzr6ugoEAyXrBgARYsWNChOM1mc7Pb5s6d6y16A4DFixc3W/T27rvvwuVyecdz5sxpdrXLxq9j3bp16NmzZ1vC9tHS66DmhULRmyAIePfdd3H55Zdj1apV3vnffvsNd999d7PHRUVF4fPPP8c555wjmW9v0VtdXR0uvfRSbNiwwTsXHR2Nr7/+GuPHj2/XOYmIIlIA69yUcVqk3zkFtq35UJlioZ+UI/tYVXzXfm/ZpalNvg3eiKhNeG+IiEg+5kwiIvmYM4mI5GPO7DilQoEsvQFZegPO7dELAFDtdOCw1YJcq7n+j8WMKqejlTPVs7vd2GcuxT5zqXcuISoaOcb6Zm85BhN66o3Q8P8vIr+5J2sTbC4tth21sMFbhGKtW2Cx1o21bg2x1q19WOtGJOW3Bm+rVq3CokWL8Pnnn8NutzfZ1E0URQwdOhRz5szB7Nmz+cNJYcvj8TTbiVVQRGaDN1ejBm9KNngjCmuuOotkrIoyNLuvx+PBgQMHkJ2dzRvrREStYM6kzuDyePDWb9ux9vgxn20ze/XBtf0GNXsTNNy5q+w4+eY6OIsbNXfTR9c3d0v0701C6hjmTCIi+ZgziSKIs0w6VsZBUGiDE0uIYs4MXwqFAL0pJthhUJCkpqYiKyvLZ37gwIG4+OKLcf/99+OCCy7AkSNHAABr1qzBnXfeiXfffbfZc5aXl/s9Tput+VXTL7nkEiQlJXlXiPz444/x0ksvNVnUtHjxYu/flUolbrrppmbP29mvg5qn10uLsk//f90WJSXS7+0DUbcTHx+P5cuXY/78+XjxxRdRU1PT4v7Dhg3DO++8g8TERLjdbklsWm3b36c4HA5ceeWVWLlypXdOq9Xiiy++4AKUEa7xezdBECQFwE3t4w9NXYcoVAT6+yBtphHazOYXDGyOUt+1H6jo0tQmoPbwmTEbvBG1Ge8NERHJx5xJRCQfcyYRkXzMmYERq9ZgcFIKBielAABEUURJTbW34Vue1Yz8Civcp3oHtKa8rhblJ49j88njAAClICBTp0eOsb7hW47BhNTYuIh9LoFILkHRfCuQcYYCbz8PijysdYtsrHVjrVsoYK0bkZTfGryde+653kZuACRN3fR6PWbNmoW5c+dixIgR/rokUWiK0BsO7kYN3lRs8EYU1lx1Vsm4pQZvRERE1HXY3S4s/GUzdpYU+Wy7rt9gzMjuE4Souga3ra6+uVuJ9KasUh+NtNsnQZ3A5m5EREREFAIcjQoXNInBiYOIKMT07t0b33zzDcaMGYPq6moAwHvvvYdLLrkEV155ZZPHOBzyVjNvi5YKc9VqNW644QY8//zzAICqqiosXboUc+bMkey3bt06HDhwwDu+6KKLkJGR0ex5O/t1dIbCwsJOabaUmpqKqKgov52vd+/eknFpaSlqamoQEyO/YPfo0aMtntNfVCoV/vnPf+K+++7Dhx9+iB9//BF79+5FWVkZRFFERkYGhg0bhuuuuw4zZ86ESqXCihUrJOcYNWpUm6/rcrlw9dVXY9myZd45tVqNTz75BOeff36HXxeFNjm5J9j5iajL6aJlbiq9zH9fu2j8QaVOkI7Z4I2IiIiIiIiIiIiaIAgCUmLjkBIbh4kZ3QEADrcbRyutyDvV9C3XYkZpbcsNME5ziyLyK63Ir7RixdH6RShi1Wpkn2r2lm0wIdtghE7DphhEDQmCgLQRt+HkL2/6bNMqXPC05WT8LpQoYrDWzb+CXUvCWrfmz+kvwap1o8jitwZvpzVs7DZlyhTMmTMHv/vd7/z6i0wUylrrKO/ytOnjVEgQPR64bNKOqko2eCMKa40bvKnZ4I2IiKjLq3I4sGDbBhyySBs+KAQBtw8ZicndegQpsuBzVdah6M21cJZWSeZVhhik3j4JalNskCIjIiIiImojZ5l03PjBbiIiataAAQPw5JNP4sEHH/TOPfDAA5g+fTqio6N99k9MlDbR/OijjzBr1qyAxjh37lxv0RsALFq0yKfobdGiRZJx4+2NNX4dt99+O15//fUORhpckyZN8in+CoSffvoJU6dO9dv54uPjkZ6ejhMnTnjn8vLyMHjwYNnnOL0y72n9+/f3W3xNSU5OxgMPPIAHHnig1X03btwoGY8dO7ZN13K73Zg1axa+/PJL75xKpcJ///tfXHzxxW06F4Wv0zU7LRXfNlzgtKPXCnaRL1GHKbpmhzRlvO97r6aojPz+xofaKB27KiB6XBAUfi8lJiIiIiIiIiIiojCjUSrR25iA3sYz9UYV9rpTDd8syLWUI6/CgjqZDUiqnU78VlqM30qLvXOpsXHepm85BhO6x+uhUij8/lqIQkl8+mjEpQzDoe9+H+xQiCiEsNatHmvd5GOtW+fWulHk8WtVhiiKSE1NxU033YQ5c+YgJyfHn6cnChkKhQIZGRkoLi723dhKg7eyOnkd60OJu8YCeNySOZWODd6IwlnjBm+qKGPTO6I+Z3bv3h0K3mwlImoVcyYFSnltDZ7dsh6FVZWSeY1CiXtHjMXwlLQgRRZ8ropaFL25Ds6yRs3dTDFIvW0y1Eb5q0ZQ52LOJCKSjzmTKII4pA2doU5sej9qFnMmUWS755578J///Ae5ubkAgIKCArzyyit4+OGHffZNSUmRjA8ePBjw+Pr374/x48d7i4c2btyIffv2eQubbDYbPvnkE0mMM2fObPGcwXgd1LxBgwZJit42btwou+ht//79KC8/814gJiYGPXv29HuM7bVy5UrJuC0Fgx6PBzfccAM+/fRT75xSqcQHH3yAK664wl8hUoibMmVKq4syytmHKJJ01d8HlV5mgzeZ+0UUtcl3zmUBNEmdHwtRiOK9ISIi+ZgziYjkY84kIpKPObNr0WujMCIlHSNS0gEAHlHEiSobcq3m+sZvFjMKbBWQuyROUXUViqqrsP74MQCAWqFAlt6IHIMROYYEZBuMSIyO6bL3r4kCRaFUw5RzEcy533nnopUuVAcxJiLq+ljrxlq3YGOtG9EZfmvwNnPmTMydOxczZsyAUqn012mJQpJSqcTgwYPxyy+/+G4UWr5xVFYbhg3eKkt85lTxLAojCleixwW3wyaZU0Xpm91fqVRi+PDhgQ6LiCgsMGdSIJyoqsTTW9ajvNFnkRiVGg+Pnog+poRmjgx/ropanHxjLVzl0q+9VAmxSLttElQGNnfrypgziYjkY84kigyixw64pfftoInc9/vtxZxJFNnUajWeeOIJXH/99d65Z555BnfeeSd0Op1k3wkTJkjGP/zwA+bPnx/wGOfOnStZHXLx4sX417/+BQBYunQpqqvP3Oe46aaboFK1XDYycOBA6PV6VFRUAKgvsrLZbD6vlzrHhRdeiB9++ME7Xr16NW6//XZZx65evVoyvuCCC7rMgy95eXlYs2aNd9yzZ0+cc845so71eDy45ZZbsGTJEu+cQqHAO++8g2uuucbvsVLoavw70N59iCJKF31AThkfJWs/QdU1/p3rSgSFGqIyHnA3WPTJWc4Gb0RtwHtDRETyMWcSEcnHnElEJB9zZtemEAR008Wjmy4eUzOzAAB1LheOVFiQe6rhW67VDKu9Ttb5nB4PDlnKcchSDqC+OY1eq0WOwYRsgwk5BhN6GYyIVqkD9IqIuq4R+hPY5GlDize5nRaJKGyw1o21bsHGWjeiM/z20/vVV1/hkksuYXM3IgButxu7du1qcltrdW9lNeHX4M3VqMGbIkYPQaUJUjREFGiuugqfOVWUsdn93W43duzYAbfbHciwiIjCAnMm+Vue1YwnNvzs09zNqI3CXyecFdnN3aw1OPl6E83dEmORdvtkNncLAcyZRETyMWcSRQhHeaMJBaA2BSWUUMacSUTXXnst+vbt6x2Xl5dj4cKFPvuNGDECiYmJ3vHGjRuxe/fugMd3zTXXSArS3nvvPTidTgDAokWLJPveeuutrZ5PqVRi2rRp3rHdbsf777/vp2iDIz8/H6IoBvxPIFblvPzyyyXjL774AlarVdax77zzTovnCqann34aonimmvy2226TVZAniiLuuOMOvPfee945QRDw5ptvYvbs2QGJlYgoonSN2mgJQauCQuO3dY0DxmmuRvnXv8G8fC88dmeww5FSN6rhcVqCEwdRiOK9ISIi+ZgziYjkY84kIpKPOTP0RKlU6J+QhIuz++KBUePxyrTpWHjORbh3xFhM79kbfY0JULehWUeF3Y7txSfx8YE9eGrzWty2/CvMW/Mj3vxtO346dgTHKivgEdnJiiJDgutgG/bm7wVRJGKtG2vdWOvmf+2tdaPIxp8QogDweDw4fvx40xtb6PBW7XSgxtXFCrr8wN2owZtKlxykSIioM7jqrJKxoFBDoW6+AYrH48GxY8fg8XgCHBkRUehjziR/+q20GP/ctAZVTodkPi02DvMnTEWmTh+kyILPaa6ub+5mljZ3UyfF1Td300cHKTJqC+ZMIiL5mDOJIoSzTDpWGyEIXLiprZgziUihUODxxx+XzD333HPeVT8b7nf33Xd7x6Io4t5774XL5QpofLGxsbjmmmu845KSEnzzzTfYt28fNm3a5J2fPHmypHivJffcc49k/MQTT+DkyZP+CZjaJCsrC5MnT/aOa2tr8dJLL7V63M8//4zNmzd7xwaDAZdccklAYmyr1atXSwoyMzIyJL87LfnDH/6At956SzL3n//8R1ZBJxERtU5obSXTIFDFRwU7hFaJbg+KF29A5fo8VPx0ACVLtgU7JClNowWenI0bwhNRS3hviIhIPuZMIiL5mDOJiORjzgx9giAgIToGY9O64foBQ/DXCVPx1gWX4h+TzsEtg4ZhckZ3pMXGyT6fCKDQVonVBfl4a9cv+NPaFbht+Vf456Y1WLp/N7YVnYC1ri5wL4iok2h16T5zvWu/lzSYISJqjLVurHULJta6EZ3BBm9EnUxoofNmaU1NJ0bSeVyNGrwp49ngjSicNW7wpooydMmiXyIioki24XgBFmxdD3ujlct66Y2YP2EqkmJigxRZ8DnLq3HyjbVwWaSfz9TJOqTeNhmqeDZ3IyIiIqIQ5Wj0wLY6oen9iIioVbNmzUKfPn28Y4vFghdeeMFnvwcffFCysulPP/2EW265BXVtLB4/duwYvvvuO9n7z5kzRzJevHixz4qmjfdpydSpU3Heeed5xyUlJZg+fToKCwtlnwMAbDYbPvroozYdQ76eeuopn/G2bc03jjGbzT7/f8+bNw96fcsLPOTn50MQBMmf/Pz8VuNrS2Hnhg0bcOmll0oKzv/zn/+0GhtQ//v173//WzL38ssv44477pB9fSIiakUXrPVQhsD3NNV7TsBZVuUd1+4vCmI0TVAZpWOHOThxEBEREREREREREZ2iUijQU2/EuT2yceew0Vgw9QK8cf7FmDdmIq7sMwBDk1IQp9bIPl+d24W95aX4Ku8AXti+Eb9f+S3uW/UdFv6yGcsOH8RBczkcjZ6lIOrq4lKHNzlvryxo8bhoVf2zMWwDRxS5WOvGWrdgYq0bUT02eCPqbC0UvpXVhmmDN1upZKxigzeisNZUgzciIiLqOr4/kotXd26Bu9EqPYMSk/HncVOg02iDFFnwOcuqcPKNtXBbayXz6hQd0m6bBFV8VJAiIyIiIiLyA2eZdKxhgzciovZSKpU+K5u+8MILsFgskrn4+HgsXboUKpXKO/fBBx9gxIgR+PDDD1ssfjt58iTefvttXHLJJejVqxeWLl0qO75x48Zh4MCB3vF3332Ht99+WxLX7373O9nnA4B3330X3bp184537tyJIUOG4Nlnn0VZWVmzx9lsNnz11VeYM2cOMjIy8Nhjj7XpuuRr0qRJuOqqq7xjh8OBadOm4b///S88Ho9k382bN2PChAnIy8vzzmVnZ+Pee+8NWHznnXce7rzzTqxcuRJOp7PJfQ4fPox7770XZ511FiorK73zd999Ny6++OJWrzF//nw8//zzkrl7770XM2fORH5+fpv+VFVVNXMVIiLqmg3euvZ3NaLLA9uW/GCH0bLG9wNcFRBFPshIREREREREREREXUusWoMhSam4ond/PDJmEl47byaem3oB7ho6Guf3yEYvvRHKNtzHLqutweaThfhw3y48sXE15i7/En9Ztwrv7N6BtYVHUVRtkzTrIOpqFEoNdjmH+swfXfsPFG5ZiPJD30IUPT7be8Yfgc5jAfjzTRSxWOvGWrdgYq3bGUVFRU3WrzXVfLC5Wreioi62wBzJpmp9FyJqK4VCgezsbBQXF/tubOGGgdMTnoVS7soSyZgN3ojCm6Namvtaa/CmUCjQt29fKBTsO0tE1BrmTOoIURTx6cG9+CJ3v8+28endcOfQ0VBF8M+Wt7lbpfRmszo1HmlzJ0EZF7mN70IVcyYRkXzMmUThTxRFwFkunVQnNr0ztYg5k4hOu+666/D3v/8dhw4dAgBUVlZiwYIF+Oc//ynZ75xzzsE777yDOXPmwG63AwD27duH2bNn49Zbb8WwYcOQnp6OuLg4VFVVwWw2Y//+/SgpKfG5ZlvMmTMHf/zjHwEAbrcbZrPZu23WrFmIiYlp0/nS0tLwzTffYPr06Thx4gSA+tVc582bh0cffRT9+/dHr169oNfrYbfbYbVakZeXh/z8fEkRvMlk6tDronrvvPMO8vLysGPHDgD1P3+zZs3CI488gqFDh0Kj0eDgwYPYvXu35Dij0Yhvv/22zf//t4XNZsPrr7+O119/HVFRURg0aBC6deuG2NhYlJeX4/Dhwzh48KDPcTfddBMWLlwo6xrvvvuuz9zChQtlH9/Q22+/jZtvvrnNxxE1tH37dhw5cgRarRb9+/dHTk5OsEMi8gtB0fUavDW5GE8zzyOJbt+HmALJWV6Nwn/90KnXbBd14/djHsBpZSN4Ipl4b4iISD7mTCIi+ZgziYjkY86MXIIgIDU2DqmxcZjUrTsAwOF242ilFbkWM3Kt9X/Kamtknc8tijhcYcHhCgt+PHoYABCn1iDbYES2wYQcgwnZBhPiNJqAvSaittrrGozB6l995qtLdqO6ZDcU6tgmjxvg+gXAwCa3EVFkYK0ba92CKdJr3U679tpr8fPPP8vat2fPnk3On3XWWVi9enWbrktdg6wGb0qlUjIWBAEul6vFffyhqesQhQKlUonevXtjw4YNPtu6YuFboLkaNXhTssEbUdgSRRHVpdI3z9r4bs3sXU+pVKJfv36BDIuIKGwwZ1J7eUQRb+/egVXHjvhsOz8rGzcMGApFG1avCjeOUhuK3lgHt03a3E2Tpkfq3IlQxrK5WyhiziQiko85kygCuKsAj106p2GDt/ZgziSi05RKJf785z9LGkMtXLgQDzzwABITpTn2+uuvR9++fTFr1izk5uZ65x0OB7Zs2SLrekajsU3x3XjjjXj00UfhcDh8ts2dO7dN5zpt6NCh2LFjB2644Qb88MOZ5iWiKGLv3r3Yu3dvq+do6+ugpsXGxmLZsmWYPXs2Vq5c6Z0vKChAQUFBk8dkZ2djyZIl6Nu3b2eFibq6Omzbtg3btm1rdp/o6Gg89dRTuP/++zstLqLm1NXVeQt7AaBHjx4t1sR99dVXuPfee31+78aPH4833ngDAwYMCFisRJ2iC351pNT5NngTnU0vqlp3uAzuKnunLeJjWb6n2W1VOwoQNzyzU+JojaDQQlTGAu7qM5NOMxu8EcnEe0NERPIxZxIRycecSUQkH3MmNaRRKtHbmIDexjP3NyvsdfXN3ixm5FnNyLNaUOeW16ugyunAr6XF+LW02DuXFhvnbfaWYzQhU6eHig0GKUi6pXXH/sJE9Isra3J7rfkQ4jIG+czHiLZAh0ZEXRxr3VjrFkysdSMCZH2CEEXR54+cffzxhygUuVwubN26tZmtXbDyLcDcjRq8qXRs8EYUrhxVRXDWSG8OxSb73hBqyOVyYcOGDWzqSkQkA3MmtYfD7cZL2zc12dztd30G4sZIb+5WUomi19f6NndL1yP1tkls7hbCmDOJiORjziSKAM5y6VjQAEpdcGIJccyZRNTQ7NmzkZOT4x1XVVXh2WefbXLfUaNGYf/+/fjggw8wfvx4qFQtr8enUCgwfPhwPPLII/jll1/wwgsvtCm2hIQEXHrppT7zQ4YMwahRo9p0roaSk5OxfPlyrFmzBpdddhliY5te/bmhnj17Yu7cufjuu+9aLH6itklNTcWPP/6I1157DYMHD252v7S0NMybNw+//vorRo8eHfC4brvtNlk/44mJibj//vtx6NAhFrxRl/Hcc8+hd+/e6N27N84++2woWng45+OPP8YVV1yBgoICn3q3DRs2YOzYsdi+fXsnRk8UAF3w+yNVfHSb9q/a2XQxeCBU/3a82W2Vmw53WhyyqButNO80BycOohDEe0NERPIxZxIRycecSUQkH3MmtUavjcLIlHRc028QHhs3BW9ecAmennIubhs8AlMzs5Cpi2/TU94nq6uw9vgxvLNnJx5ftwpzl3+JJzasxod7f8Pmk4Uoq61hPwTqNLPGZuKD48Ob30H0QDdsRucFREQhhbVurHULJta6UaRr+SesAeFUsU5LHzIEQfDLhxB/nYcoWERRRHl5edMbO1j4dsxc06Hjg8FlkzZ4U8azwRtRuKou2SUZq6KM0Oq6tXiMKIooLS3lv/1ERDIwZ1Jb1TideH7bBuwzSxuwCgBuHTwC53TvGZzAughHcSVOvrkOniq7ZF7TzYDUWydCGaMJUmTkD8yZRETyMWcSRQBHoxU7NQne7/6obZgzicJDfn6+X86jVCpx6NChNu1//fXX4/rrr4fNZsOmTZtQWFgIs9mMuro6xMXFISEhAb1790b//v0RHx/fofg+/vjjDh3fksmTJ2Py5MlwuVzYtm0bcnNzUV5eDpvNhpiYGOj1evTq1Qv9+/dHamqqrHNmZWUxv7aRIAi44447cMcdd2Dv3r3YvXs3Tpw4AYfDgfT0dPTq1Qvjxo1rsUlVc9r7/8fpeGpra7Fz507k5uaiuLgYtbW10Gq1SEtLw6BBgzB06NB2xQX473eYqLEvvvgCoihCEATMmTOn2ffMFosFd9xxBzweDwRBkNS6nT6muroaV1xxBQ4cOICoqKhOew1EfqUI8ufGJv4ZUsa37ffJ/N1u6CfltL5jB3nqnC1utx/tYg3U1CagrkHzOzZ4I5KN94aIiORjziQiko85k4hIPuZMaiuFICBTp0emTo+pp56fqHU5cdhqQZ7VglxrOfKsFljtda2cqZ7T48FBSzkOWsqBI/VzBm0UcgwmZBtMyDGa0EtvRFQrTUKI2iNarYQrKhN/PXAuHslegziVw2cfQaXBSc1kpDnWBiFCIgoE1rp1HGvduoZIrXU7bfXq1R06nkKbrE8HU6ZMafUhDzn7EBEgdCBpl1XZsemwxY/RdA5XpbTBm4oN3ojCVuMGb7HJg/j+gIiIKEisdXV4dus6HK2skMyrFQr8ftgYjE7LCFJkXYOjqKK+uVu19AstbaYRKbdOgDKazd2IiIiIKIw4Gy1Ko04IThxEROSl0+lw3nnnBTuMDlOpVBg3bhzGjRsX7FAi3oABAzBgwIBgh+EVHR2N8ePHY/z48cEOhUiW04Wap7/fnjlzZrP7vvzyy6ioqPA2dsvIyMAVV1wBlUqFzz77DEePHgUAFBYWYuHChXjkkUc65TUQ+Vuwyz08tb4PJbW1wZtCq/ZXOC2yH7d2ynX8Rm2SjtngjYiIiIiIiIiIiCJItEqNgYnJGJhY/6yzKIoor6tFrsWMXGv9n/wKC5wej6zzWe112FZ8AtuKTwAABACZOj2yDUbkGEzIMSYgPU4HRbBvvFPYOFSTiNt2XYGrUnfhyrQ9wQ6HiCIAa93I31jrRpFGVoM3OV0A2SmQSKYOfP7eftTqM6dWdazLZ6CJLgc81dKmdGzwRhSe3M4a1JilnbtjkwcHKRoiIqLIVlxdhae3rENJTbVkPlqlwh9HTcCAhKQgRdY12E9UoOitdfDUNGru1t2I1FsnQhHVOQ/7EBERERF1GkeZdKxJDE4cRERERERd1K5du+B2uwEAcXFxGDFiRLP7fvDBB97mbn379sWmTZug1+sBAE888QSmTZuGrVu3QhRFvPPOO2zwRqEryA+aqfTRvnNtbPCmMsX6K5wW2QtDbNFWnwZvFoiiB4LQtWsRiYiIiIiIiIiIiAJBEAQkRscgMToG49K7AQBcHg8KbBU4ZDEjz1r/52R1lazziQCO2SpwzFaBnwryAQBRKhWy9fUN37KNJuQYTNBr23bPmwgARIjtPZCIiIiIgkRWgzciahulUolBgwahuLjYZ5sgo/BNFMUmPyjZXb7d3ifnJLQrxs7ispX5zCnZ4I0oLNWU7gPEM3lKUKgQm9iv1eOUSiWGDRsGpVIZyPCIiMICcybJkV9hxbNb16HCbpfM67VaPDJ6ErL0huAE1kXYj1tRtGgdPDVOyby2hwmpt0xgc7cwwpxJRCQfcyZReBNFN+A0SyfVbPDWXsyZREREROHpyJEjAOrrelpaIXj//v3Izc2FIAgQBAFPPvmkt7kbUN8c7uWXX/au9nzgwAEUFBQgMzMzsC+AKBCC3eDNEIPovimoPVBfgxc/oRcEVds+i6lNMYEIzYfjuLXF7UqdtlPikK1xgze4AVcloDYEIxqikMJ7Q0RE8jFnEhHJx5xJRCQfcyZ1FpVCgZ56I3rqjQCyAQBVDgfyrGbknvqTZzWj2uls+USn1Llc2FNeij3lpd65pOgY5BhNyDbUN3zrEW+Ahj/b1IqU+CgcM9c2uc1ZZ23hSHZ4IyIiIgoWNngjCgCFQoFu3bo1t7HV48XjDsAp/aAUq4sCKu0++47t1bjYqmtxV5ZIJwQFlLFdO2Yiap+qkl2ScXRCHyhUra8ioVAo0KNHj0CFRUQUVpgzqTV7y0vx/LYNqHW5JPPJMbF4dMwkpMTGBSmyrsFeaEHRW+vhqWvU3C0rAam3jIdCy+Zu4YQ5k4hIPuZMojDntAJotICMumsvHtOVMWcSERERhaeGizimpaU1u9/atWsB1C/eqNPpcPnll/vsM2bMGHTr1g2FhYUAgN9++40N3igkCYrgNngDgJQbx6Fm70kIaiWi+6a0+XhB3TkPwtkLLS1uV0RrOiUO2RTRgCIK8NSdmXOa2eCNSAbeGyIiko85k4hIPuZMIiL5mDMpmOI0GgxNTsXQ5FQA9d8XFVVXSRq+HausgFuU10irtLYGpbU12Hii/jslpSCgR7wBOQaTt/FbSkwshCAvyEJdy9zJWdia3/T3EnWWPBT99kEnR0RERERErWGDN6IAcLlcWLduXZPb5HyQ9vxWJRknd9MjLcsI/FYkmR+Yrmt/kJ3E1ajBmzI+CYKMJndEFFpE0YPqRg3e4pIHyzrW5XJhzZo1mDJlClQqvjUhImoJcya1ZOvJ43h15xY4PdLGDT3i9Xhk9CQYolpvvBrO7AVmFC3a4NPcLapXIlJuHg+Fhr9T4YY5k4hIPuZMojDnLJOOlXEQlJH9+aAjmDOJiIiIwlNNTY337zpd8/U469evB1Bf/zNt2rRm3xMOGjTI2+Dt2LFjfoyUqBN1gQfGBKUCsYMz2n181fZjcJZVQZ0YB/1ZveGpdUKTGu/XRX/c1Xa4zDWSOXVqPJxFlWf2qXH47Xr+IAgCRLUJsJ84M+k0A+gVtJiIQgXvDRERycecSUQkH3MmEZF8zJnUlQiCgLQ4HdLidJjcrb7xoMPtxpEKC/KsZuRaLci1mlFeW9PKmeq5RRGHKyw4XGHBD0fzAABxag1yDCZkG031/2swIlbdxRYVoU41oruhxe0Vx9ZAFacBzNJ5UWbjQSIiIiLyP799en3vvfe8f7/qqqsQExPTrvNUV1fjf//7n3d84403djg2os4miiKqqqqa3iij8E08VicZj7ugb8h2WG/c4E2lSw5SJEQUSHUVx+B22CRzsTIbvImiCJvNxhtEREQyMGdSc1YdO4LFu35B45+M/qYk/HHUeMSo/feQSiiqO1qOosUbINpdkvmo7CSk3DSOzd3CFHMmEZF8zJlEYc5RLh2rE4ITR5hgziQiIiIKTw3f3zmdzmb327Bhg/fvkydPbna/hIQz77srKyub3Y+oSwuRcrXYod1Q/Wths9vtR82wHzWjavuZZovaHiZo0w1Qp8ZDNyarQ7V59uNWyVhQKWCY2gel/93mnfNU2WE/UQFtur7d1/G7Jhu8EVFreG+IiEg+5kwiIvmYM4mI5GPOpK5Oo1SirykRfU2J3jlLXa234Vue1Yw8qxl2t1vW+aqcDuwsLcLO0iLvXHqs7lTDNyNyDAnI1MVDqVD4/bVQ13ekxtTkvDpe69PgjYiIiIiCx29PMN98883eIpepU6eie/fu7TpPWVmZ5Fxs8EZhR04tmEd6cyk5owsVdrWR29aowVs8G7wRhaPq4t8kY01sCjSx/H0nIiIKNFEU8UXufnx6cK/PtlEp6fj98DHQKJVBiKzrqMsvR9HbTTR3652MlBvGsrkbEREREYU/Z5l0rElsej8iIiIiogim0+m8fy8uLm5yn6KiIuTm5nrHEyZMaPZ8LteZe9J8yIxClaAIjQ5viVcOhzpJB1d5Fap2FMg65nTTNwAQHW7oJ+e0+/qORg3eNOkGCE08SGfbmg/tpUPbfR2/Uzd66IsN3oiIiIiIiIiIiIgCwhgVjVGpGRiVmgEA8IgiCm2VyD3V7C3XasZxWyXkfqN0otqGE9U2rC08CgDQKJToaTAix2BEtsGEHIMJCdExAXo11JXsqEzDLlsKBuua/n6TiIiIiLoGvz7FLIpih1YyDNS5iLoSQWi+C7onDAs6XZXSBm9KXVKQIiGiQKou2S0ZxyYPDlIkREREkcMjinh/76/4IT/PZ9vZmVm4dfAIKCL8c3Xt4TIUv7MBokO6ulV0n2Qk3zAOCnVkN78jIiIiogjhKJeO1QnBiYOIiIiIqAvLyKh/oEYURezatavJfZYtW+b9u1arxYgRI5o9n9Vq9f49NjbWP0ESdbYQ+Z5JoVHBeG4/AIC71oHa/W17iMm6cn+HGrzZCyySsbabocn9bBsPIzEIDd5ElweiKPp+L9ZEgzfW7RIREREREREREREFnkIQ0D1ej+7xepzTvScAoMbpxJEKC3JPNXzLtZhR6bDLOp/D48YBcxkOmM8sBGrURtU3ezPWN3zrqTciSuXXthIUJA1v43ugwFO5Z2PRkP8hRuk8s0+ILOJDREREFCn4TpwoAJRKJUaNGoVvv/3Wd2MLBVDltbUBjCo43I0avKnik4MUCREFisteibqKfMlcbIr8Bm9KpRLjx4+HUskGK0RErWHOpNNcHg9e+3UrNp4o9Nl2WU4/XNVnQMQ/fFGbV4ridzZCdDZq7tY3Bcmzx7K5WwRgziQiko85kyh8iR4H4K6UTmoSgxNMmGDOJCIiIgpPQ4YM8f7dbDZj+fLluOCCCyT7vP322wAAQRAwZswYqNXqZs93+PBh799TU1P9HC1RJwnBr5o8NY62H1PnbH2nFtiPWyVjTYYRIppe6NVjd0FQKzvtwaq6/DKUfLgF7io74idmI2HmmVzn0+BNdALuKkCl65TYiEIV7w0REcnHnElEJB9zJhGRfMyZFK5i1GoMTEzGwMT6Z7BFUURZbY234VuexYz8SiucHo+s81nsddhWfALbik8AqL/lnxmvR46hvuFbtsGE9DgdFBH+3EkoUggCJuYkYH3umYVfzY5oxESf+b7DNDId7qI4oKbqzIFi099dEBEREVHgdbkGb2KDN4eR/jA6hS6FQoHExGYekGrh5/p41amHrGqkH7AVCoW/Qut0rkYN3pRs8EYUdmwntkrGClUUYky9ZR+vUCiQnMzcQEQkB3MmAUCdy4UXt2/ErjLpe20BwA0DhuKCnjnBCawLqc0tQfG7m3yau8X0T0Xy9WMgqPiFfiRgziQiko85kyiMOcsbTQiA2hiUUMIFcyYRERFReMrOzkbv3r2Rm5sLURRx9913Y8WKFejZsycA4LnnnsP69eu9+1966aXNnquqqgq5ubmScxOFos5qQuZPTnNNp17PZauDu0K6qKs204CavSeb3P/o/K8hqBRIunY0YgelBzy+ss93wm2zAwAq1+UhfmxPqJNONXBTxgGCBhAbNMVzmtngjagVvDdERCQfcyYRkXzMmURE8jFnUqQQBAFJMbFIionF+PRMAIDL48HRSivyrGbkWi3ItZSjuKZa1vlEAMcqK3CssgKrjh0BAESrVMg+1eztdOO3eK02UC+J/Oiec7IlDd7EJlbtUZx9MTzfLunMsIiIiIioGV2ua1R19ZkPEjExMUGMhKj9nE4nVqxY0eS2lgrfCm2VEGvcEIukK4lmZCf4Nb7O1LjBm4oN3ojCirPWjLIDX0rmYhL7Q1DI7yHrdDrx7bffwuns2IrIRESRgDmTKu12/HPTGp/mbkpBwO+Hj2FzNwC1h0pQ/M5G3+ZuA9KQfP1YNneLIMyZRETyMWcShTFHmXSsNkIQutz6TyGFOZOIiIgofM2dOxeiKEIQBBw5cgT9+vXDmDFjkJWVhUceecS7WGlUVBRmz57d7HlWr17tXeRUpVJh4MCBnRI/kd+F4AK9nip7p17PcdwqGQsaFdSJOqj00c0eI7o8MH+zS7IYsj+Ibg+qdhagYm0uXLY6OMuq4Cy2Sfaxn6g4E6vQRBN4p9mvMRGFI94bIiKSjzmTiEg+5kwiIvmYMymSqRQKZBtMOD8rB3cPG43nz74Qr503Ew+PnoDLe/fHkKQUxKjUss9X63Jhd1kJvszdj+e2bcBdK77B/au+wys7tuD7I4eQazHD6Xa3fiLqdI2/wTlc47voqxATJ53w79cSRERERNQGXe4Jjj179nj/bjT6vpkkChUul6vpDc0UvnlEESeqbBAP10k+JKm1KvQemhaACDuH28YGb0ThShRFFP/2PjyuOsm8ocdZbT5XszmTiIh8MGdGrtKaajyzZR1OVldJ5rVKJe4fOR5DklKCFFnXUXOgGCXvb4Lo8kjmYwalI3nWaAjKLtfnngKMOZOISD7mTKIw5SyXjtWhu6BMV8KcSURERBSe7rvvPrz99ts4cOAABEGA0+nE9u3bvU2YBEGAIAj44x//iKSkpGbP8/nnn3v3Hzp0KLRabafET+R3IdjgrT0UcdLfUdEjovrXAristRDUSmhS4hGVk+Rt8tiQvcAiGWsz9BAUAqL7tPy9nctaA3hEQOm//8aWH/ai4udDAIDKDXnQjevps49C26hkWG0CHMVnxmzwRiQL7w0REcnHnElEJB9zJhGRfMyZRGfoNFoMS07DsOT659A9ooii6irkWs3Is5iRazXjmK0CHpmLjpTW1qC0tgYbTxQAqG8q1yNejxyDCdkGE3obTUiKjm3ynjkFz5ITw3BWQn6wwyAiIiKiZnSpBm+VlZV44YUXANQXuPXr1y/IERH5X3MfWi11tXB43PAUSBsl9RmWDrWmS/2qtomrslQyVrLBG1HYqCzcgOrSPZK5+G4TEJs0IEgRERERha9CWyWe3rIOlrpayXycWoNHxkxEtsEUpMi6jpr9RSh+fzPgljZ3ix2cgaRrR7G5GxERERFFJkeZdKxODE4cREREREQhQKPRYPny5bjwwguxb98+APULnwmCAFEUIYoirrzySjzxxBPNnqOqqgr/+9//vPVB06ZN65TYiQIiBB/OMk0fBPOy3W06Rm2KkYwtP+5FxU8HJXOGaf1gPK+/z7H249IGb5pu9YsaK2O10HQzwlFo8TnmNLetDipDTLPb20J0ub3N3QDAZamBdcV+3x0b/3+qbvQdY+NG8UREREREREREREQUUhSCgPQ4HdLjdJjSrQcAwO52Ib/CitxTDd/yrGaUN3o2pTkujwd5VgvyrBYAeQAAnUaDbIMJOaf+ZBtMiFGrA/WSqAndE6Il4wpXFBYWXY57Uz8PUkRERERE1JI2dY269dZbZe330EMPIS4uTvZ57XY7Tp48ia1bt6KmpsY7P2XKlLaERxQaFE03FXB6TjUhcEq7oCdn6AMdUcB47NUQHTWSOZWODd6IwoGz1oKSPR9L5pRaPZIH/C5IEREREYWvg+ZyLNi2HtVOp2Q+IToGj46ZhPQ4XZAi6zpq9p5E8YdbfJu7De2GpKtHsrkbEREREUUkURR9H8zWJAQnGCIiIiKiEJGZmYmdO3di8eLF+Oqrr3D06FEAQL9+/XDdddfhiiuuaPH4d955B5WVld7xjBkzAhovUSAJitBr8BY3qkebG7w1/h6pcXM3AKjckOfT4E0URTgKrZI57akGbwB8vrdqrPC5FUi7fRK0mU0v5CS63Cj7fCdq80oR0zcVCRcPARQCrKv2+zRvU8ZH+R7vdLd4fQBNNHgzextbEhEREREREREREVF40CpV6GtKRF/TmcVBLXW13mZvuRYzDldYYHfLuK8MwOZwYGdJEXaWFHnn0uN0koZvmbp4KJt5np46TqtSYsbgVHy768z/ByJa++8ttrKdiIiIiAKlTQ3e3nnnnVYLN0RRxP/+9792BdOwMCQqKgo33nhju85DFGwqlQoTJ07EZ5995rMtkoqfXJUlPnPKeDZ4Iwp1oiiieNf78LikqzSkDpkNpSa2zedTqVQ4++yzoVK16W0JEVFEYs6MPDuKT2LhL5vh8Ei/KOsWF495YyfBFBXdzJGRo3rPCZR8tAVwS79sih2eiaSrRrC5WwRjziQiko85kyhMuasBT510Tp3Y9L4kG3MmERERUfhTq9W44447cMcdd7T52Dlz5uCGG27wjvX60F3YkQghWOamjNF06Hh3lb3JeU+t02fOXVnns782w+D9e0z/VDhOVjR7LdHpRuWWfCQ10+CtcsNhVG0/BgCwbT4CbXcjAMGnudvpWNpF3agRvMcOeGoAZdvrf4giBe8NERHJx5xJRCQfcyYRkXzMmUT+YYyKxujUDIxOzQAAuD0eFFZVnmr4ZkGutRwnqmyyW4KdqLLhRJUNawrrF0/SKpXoqTci22BCb4MJ2UYTn3/xs0m9EyQN3lrE3m5EREREQdWlPsEKggBRFKFSqfDvf/8bmZmZwQ6JqN2ionxXxYw0jRu8CeooKKLighQNEflL5fFNqC6RrnYcnzEOcSlD233O6GjenCMikos5M3KsLTyKN37bDo8o/SaltzEBD42agDhNxx5QCQfVu46jZMlWwCP9bxQ3ojsSrxoBQRGCTx6RXzFnEhHJx5xJFIac5dKxoAZU8cGJJcwwZxIRERFRc6Kjo/l+kcJHBC1kepqjqFL2vvZCi2SsiFJDlXCmMVrMoAxYVx1o8RxVW48i6coRTW4zL5PW5lTtKEBdbqns+GRR6QBBCYgNFptymtngjagV/LeeiEg+5kwiIvmYM4mI5GPOJPI/pUKBHvEG9Ig34Jzu9XM1TicOV9Q3fMuzmpFrNaPS0fRCKY3Z3W7sN5dhv7nMO2eMikaOwYQcgxE5xgT01BugVXapVhfhy+ZufR8iIiIiCpg2v+sVxdZb9MrZpylZWVk4++yzce+992Lo0PY3iSEKNpfLhZUrV/puUCg6P5ggcjdq8KaKT4YQgYV/ROHEWWtByZ6lkjmlNh7JA69p9zldLheWLVuG6dOnQ61WdzREIqKwxpwZOb7NO4iP9u/ymR+WnIp7R4zll1gAqn87jpL/NtHcbVQPJF4xnM3diDmTiKgNmDOJwpSjTDpWJ/AevR8wZxIRERERUaSIlO9a6o6U49j/fY/Um8fDUdz+Bm+abgbJ525tuh5pd07GydfWtnie0v/9Av3k3qjLL0PN7hOoPVjS5H4uS43s2OQSBAVElQlwNmgc5zQDUVycmag5vDdERCQfcyYRkXzMmURE8jFnEnWeGLUagxJTMCgxBUB9/4jS2hrkWszehm/5lVa4PB5Z57PU1WJr0XFsLToOAFAIAjJ18cgxmJBtMKG30YTUWB0UrHHzO7HcibpKV7DDICIiIopYbXoi/MiRI03Oi6KIXr16AQAEQcCaNWvQrVs3WecUBAFarRYGgwFarbYt4RCFngj7UOlq1OBNGZ8cpEiIyB9E0YOinYvhcUoLRlMGz4ZSw5V7iYiI/EEURSzZvxvfHj7os21ytx6YO3gEVBHWOLopVb8WonTpNp/mbroxWUi4bFjEPHBERERERNQiZ7l0rEkIThxEREREREQUmiLo6xZ3RS2Ov7QKccPlNzZzFFolY203o88+UVmJrZ6nautRVG09Kvu6fqduosEbEREREREREREREVEjgiAgOSYWyTGxmJBRfz/d6XbjmK0CuZb6hm95VjOKa6plnc8jijhaWYGjlRVYeay+h0WMSo1eBiN6n2r6lmM0Qadh/wl/KD/i/4VkiIiIiEieNjV469GjR4vbT68+mJmZie7du7c/KqJwFWEN3tyNGryp2OCNKKRZDv+ImvIDkjld+hjoUocFJyAiIqIw4/Z48OauX7C20PcBjhm9emNWv8Hez92RrGpHAUo/3gZIe7tBN7YnEi4dyuZuRERERESnOcukY3XrD5UTERERERERnabNNAU7hHbRdDPCUWhp17FVOwpk7SeKIuzHrZI5bYahyX21WQmw55c3ua0tXOXyHohrM3Wj/5/Z4I2IiIiIiIiIiIiIZFIrlcg+1YztglNzlXY78k41eztkNeOw1YIal1PW+WpcTuwuK8HusjPPp6fExNY3ezvV8K1HvAEqhSIArya8iR6x9Z2IiIiIKCDa1OCtJd27d/c+aK5S+e20RGEl0hoNuBo1eFPq2OCNKFTVVRxF6f4vJHOqKCNSBs0KTkBERERhxu524eVfNmNHSZHPtln9BmFmdt8gRNX12LYfRdmnv/g2dxvfCwmXDGEDPCIiIiKiU0TRAzgaPZCtSQhOMERERERERBSSonqFZqNww5TeKPloi1/PKWiUkrG7ohaeGodkTtNMgzfdqB5+afAWMBo2eCMiIiIiIiIiIiIi/4nXajE8JQ3DU9IAAB5RRFG1DYcs9U3fcq1mFNgq4RHlNRwrrqlGcU01NpyoX6RFpVAgK97gbfiWbTAhKTqGz5O0QuZ/biIiIiIKAL91YsvPz/fXqYhCnkqlwrRp0/DRRx9JN0TYh0OXTdrgTRXPBm9EocjjtuPkjkWA6G4wKyBt+K1QamI7fH6VSoXp06ezQSwRkQzMmeGp2unAgq0bcNAifbBDIQi4bchITOnWI0iRdS22rfko+2yHT3O3+InZMM0czC/jyAdzJhGRfMyZRGHIZQXgls6pQ/PB/K6GOZOIiIgoNJ1zzjmSsSAIWLlyZYv7+ENT1yEKBQmXDQvZ715ih2Qg3TQVlZuOQJMaD4ginOXViB/bE+5qB4reWteOs0r/W9iPWyVjRbQaKmNMk0fqRvUABAFln2xvx3U7gbpRgzd3DUR3HQRlVHDiIerieG+IiEg+5kwiIvmYM4mI5GPOJAo9CkFAelw80uPicVZmFgCgzuVCfqUVuZZy5FotyLWaYamrlXU+l8eD3FON4pBfPxev0SLHUN/sLcdoQi+9ETFqdWBeUBd1zFwDpLa0Bzu8EREREQULP8ESBUhtre8HyZZK3pwedwtbQ5O7UtrgTalLClIkRNQRpXs/haOqSDJnyr4AMQl9/XaN2tpa6HQ6v52PiCicMWeGF3NdLZ7ZvA6FVZWSebVCgXtHjMOIUysWRbrKLUdQ/tlOn/n4STkwzRgUsg8YUeAxZxIRycecSRRmHNIG0lDGQFBGByeWMMScSURERBR6Vq9e7b2XLIpik/eVG+7jD81dhygUCCpFsEPoEG03I5KuMja5zXBOX1hXHWjT+USHC47iSmhS4gEAjkYN3jQZhhZ/37UZhjZdr1Op9AAUADxn5pxmQJkerIiIujzeGyIiko85k4hIPuZMIiL5mDOJQl+USoV+pkT0M51ZtNRcV4s8ixmHrGbkWc04UmGB3S3v2ftKhx2/lJzELyUnAdQ/z58ep0OOIQE5RiNyDAnopouHgt/dEREREVEQhHYVDlEX5XK5sH79et8Nnua7W5+sqgpgRMHhatTgTRWfHKRIiKi97LaTsB79WTKn1fdAYt9L/HYNl8uFn376CS6Xy2/nJCIKV8yZ4eVElQ1/27Dap7lbjEqNP42dzOZup1Ruarq5m/6s3mzuRi1iziQiko85kygMOcukY3Vi0/tRmzFnEhERERFRRAjjr1+cpe2r07P+dND7d3ujBm/adEOLx6pMMZ3fNK+J/w8tP+713U1QAmqDdNJpDkxMRGGA94aIiORjziQiko85k4hIPuZMovBliorG6LQMXNd/MP4y/iy8ef4l+Oekabh10HBM6dYD6XHyGzuKAI5X2fBzYT4W7dqBP61dgbnLv8Q/Nv6M/+7fha1Fx2Gpqw3ci+kEGiXbhBARERGFClWwAyCKJC31HWjc1CEcuNngjSjkVRXvlIwFpQbpw+dAUPAtBBERUUfkWc3419b1sDkcknmjNgrzxk5Cpk4fpMi6lsqNh1H+5a8+8/qpfWC8YACbuxERERERNcdRLh2rE4ITBxERERFRFyKKzS/M2JZ9iCJD+H4Ho9RHt+u46p0FwLWjAACOE1bJNk2GocVjFRoVTBcPqf/eq4VFYuWKGZiG6D4piO6djLr8cjhLbdCN7AEAcFfVQRGlhiJKjcLnVkB0ur3HOQqt8NhdUGgb1f2oTdKmbmzwRkRERERERERERERBplQokKU3IEtvwLQevQAA1U4HDlstyLWakWs1I89q9nkupzl2txv7zGXYZz6zeGpCVDSyDSbkGE3IMZiQpTdAqwyNZ2dH9DC0aX9+DUpEREQUPLLeYR47dsxnrnv37q3u4w+Nr0MUyoQWPvwct4VXgzfR44HLViqZU7LBG1HIqSndJxnru42HJi41SNEQERGFh12lxXhh+0bY3W7JfFpsHOaNmYSkmNggRda1VKzPg/nr33zmDef0heG8/mzuRkRERETUEmeZdKxJDE4cRERERERdhMfj8cs+RBEjjL+G0Y3qgcp1ue0+3lVZB7fNLpnTpre+eFP82J7QjclC/p++kHWdqJwk1OWWNrnNMK2/95pqk/S7RXVinPfvDZu7neaucTTd4K0hNngjIiIiIiIiIiIioi4oVq3B4KQUDE5KAVC/eFNpbTUOWeqbveVazcivsMIts5tZeV0tyouOY0vRcQCAQhDQXadHjsGE7FNN31Jj46Dogs+v6KPVePPG4bjtvR3N75SQApQXd15QRERERNQkWQ3esrKyJA9OC4IAl8vV4j7+0NR1iEKFUqn0mWvpN6Swqr7Bm1julMwrlAp/htVp3DUWwCMtEFOxwRtRSPG47ai1SAtaY5IGBORaKlVorGpARNQVMGeGto0nCvCfnVt9vizqpTfi4dETEa/VBimyrqVibS7M3+7ymTec2w/Gc/sHISIKVcyZRETyMWcShQ/R4wRcFdJJdUJwgglTzJlERERERBTuwnmhHU1qPJKvG4OSj7a0+dgjj37uMydoVVAlxDWxty9BEKAbmwXb5vwW9zOc1x+q+KgmG7wpYjXQpMbLul7TMTQxyQZvRG3Ce0NERPIxZxIRycecSUQkH3MmEZ0mCAKSY+KQHBOHiRndAQBOtxtHKyuQay1HrtWCXEs5SmtrZJ3PI4rIr7Qiv9KKFccOAwBiVGrkGE3INpiQYzAi22CCTtM1nv0Z2cOIs/sm4qcDZU1uVwwYAc/a7zo5KiIiIiJqrE2fYkUZ3Yrl7EMU7tRqNaZMmYKlS5dKN7RQ+GaurYVY6YK4t1oynz0oNRAhBpy7ssRnTqlLCkIkRNReteWHIHoaNFoVFIhJ6Ov366jVasyYMcPv5yUiCkfMmaHth/xcvLfnVzT+1DwoMRn3jxyHaJU6KHF1NdafD8Ly3R6fecN5/WGc1i8IEVGoYs4kIpKPOZMozDjLG00IbPDmR8yZREREREQUEcK3vxsAIHZIBnoOubzJhm1tpU3XQ1DI/w8WO7gbbFuPAp6ma2213Y3QT8qBoFai7H87fLZH5yTLvp6gUUJ0uFvfsXGDN7cNoscBQaGRdR2iSMJ7Q0RE8jFnEhHJx5xJRCQfcyYRtUatVCLHaEKO8cy97wp7HfKsFuRazcg79afW5WrhLGfUuJz4rbQYv5UWe+dSYmKRY0zwNnzrEW+ASqHw+2uRY1SWET8dKIPVFe27MVbeAjVEREREFFhsU04UAB6PB+Xl5Y0noWhlZVPP1krAc2asjVZjwvT+AYgw8FyNGrwpYvRQqLtGR3Iikqe6bJ9kHGXoCaU6xu/X8Xg8KCsrQ2JiIhRBuolFRBQqmDNDkyiK+N/Bvfg8d7/PtnFp3XDXsNFB+yKnq7H+dACW5Xt95o0XDIDhbP83mqXwxpxJRCQfcyZRmHE0Wo1TpYeg4FeC/sKcSURERERERA1p0g1t2j86Jwnpd03BiVd/9tlmPH8A9Gf3gXCqzlCbaYS9wOJzvFwxA9JRvbOg9R1VRtR39WvQdM5pAbQpsq9FFCl4b4iISD7mTCIi+ZgziYjkY84kovbQa6MwIiUNI1LSAAAeUcSJKhvyrGbknvpTUFmBppdm8VVcU43immqsP34MAKBWKJAVb0C20YQcQ/2fxOgY7/3+QPrdqAxYa5x4c20+jtXq0T26wrtN0BkCfn0iIiIiap2spzluuukmv+xDFCncbjd27twpnfSILa5sKooi3JsqJXMTpvdDrC40m6K5KookY5UuOUiREFF7VZdKm6vEJgam4aTb7cbGjRsxffp03lgnImoFc2bo8Ygi3t69A6uOHfHZdn6PbNwwcGirjaAjhWXlflh/3Oczb7xoIAxn9QlCRBTqmDOJiORjziQKM85GC9BoEoMTR5hiziSKHHl5edizZw8KCgpgs9ng8XhgMBhgMBjQt29fDB48GBqNJthhEslitVqxYcMGHD9+3PvAS0ZGBiZMmACDwRDU2Gw2G9atW4fCwkKUlZVBp9MhPT0dgwYNQp8+/rkvaLFYsGfPHhw6dAhmsxl1dXUwGAxISkrCyJEjkZ2d7ZfrEBGFlQj//krTzQhHoaX1HQFoMgxtPr8209TkfOzgdMnDXvYTFT77RPeWX4eXcPFgWQ3eBIUKoioecDW4ntPMBm9ETeC9ISIi+ZgziYjkY84kIpKPOZOI/EEhCOimi0c3XTzOyswCANS5XDhSYalv+GYxI89qhsVeJ+t8To8Hh6xmHLKavXN6rRbZhjMN33oZjIhWqf3+WlQKBe6a2gtphij8b00BHui1XrpDUhpw3O+XJaIuhrVuFE4ivdbtNKfTifXr1+PYsWM4efIk4uLikJ6ejuHDhyMrK8uv16LAk9Xg7e233/bLPkSRTBA9gND8DSOxwgWYXZK5c383NNBhBYyjJE8yViVkBikSImoPV50VDpv0rk1s0sAgRUNERBSaHG43/r1zC7YWnfDZdlWfAbgsp1+nrMYTCiwr9sG6Yr/PvGnGYOgn5wQhIiIiIiKiEOYsk47VCcGJg4goBG3evBlvvfUWvvrqK5SUlLS4r0ajwZgxYzBr1ixcc801SEhof749ePAg+vbtK5nLzMxEfn5+ux9KWL16Nc4+++wW91EoFNBqtYiNjUVSUhLS09PRt29fDBs2DBMnTsSAAQPadW0AyM/PR8+ePVvcRxAEaLVaREdHIykpCWlpaejTpw+GDBmCiRMnYtiwYbx/1kE7duzAk08+iWXLlsHhcPhs12q1uOiiizB//nwMGzasU2Nbv349/v73v2PlypVwuVxN7jN06FDceeeduOOOO9r0s+B0OrFq1Sp8/fXXWL16Nfbs2dPi/unp6ZgzZw7uvvtupKamtul1EBGFrQj5JzhmUDpqdvt+l6dN1yOqZwIq1+a2eg5tOxq8AUBUdhLq8kq9Y02aHqrEOOlObo/PcSpDjOxrCCrf95Ki043KDXlwVdQiblQPaJJ09RvUJt8Gb0REREREREREREREESJKpUL/hCT0T0jyzpXX1iDXWt/sLddixpEKKxwet6zzVdjt+KX4JH4pPgmg/quXDF08cgwmb+O3brp4KPxUFzEkQ4/vmpgXevQGdvrlEkTUxbDWjbVu4SZSa90aKy0txfz587F06VKYzU1/bz9hwgT88Y9/xJVXXtnu61DnktXgjYj8wCO2mIRFp+gz13NA6BYOO4oOScbalN5BioSI2qO6bJ9krFBFIcqQFZxgiIiIQlCN04nnt23EPnOpZF4AcMug4ZjWo1dwAutiRFGE9cd9sK464LPNdPEQ6CdmByEqIiIiIqIQ5yiXjjWJwYmDiCiE7N27F/fddx9WrFgh+xiHw4F169Zh3bp1+OMf/4i77roLf/7zn5GY2Pa8u3jxYp+5goIC/Pjjj7jgggvafD65PB4PamtrUVtbi7KyMuzbtw8rV670bs/OzsZ1110XsKZXoiiirq4OdXV1sFgsOHjwIH7++Wfv9vT0dFxzzTX4wx/+gF69eD+trZ5++mn89a9/hdPpbHYfu92OL774AsuWLcPf//53PPLIIwGPy+l04p577sHrr7/e6r6//vor7rrrLixZsgQffPABMjNbX1hu8+bNuOiii2CxWGTHdOLECfz973/Hyy+/jJdffhmzZ8+WfSyFl+LiYsnP5l133YWkpKQWjvBVUlKC1157zTv+wx/+AJPJ5LcYiTqLECEd3hIuHdpkgzd1SjzcVXZZ51A3bsomU9I1o1Dy0RbY88uhyTAg5cZxrRZ5C9qOl/yal+/1vmbblnxkPnohFFpVfYO32iNndmSDNyIiIiIiIiIiIiKKcAnRMUiIjsHYtG4AAJfHg0JbJXKt5ci1WpBrKcfJ6ipZ5xIBFNoqUWirxOqCfABAlFKFXgbjqYZvRuQYEmCIimp3vEdqfL+XFNQaAPKa0hFRaGCtG2vdwlGk1ro19t133+Hmm29utWnjhg0bsGHDBlx//fV4/fXXERsb2+ZrUedigzeiABAEAdHR0dJJUQQiqOOqo1ja4E2T2idIkRBRe1SXSFexj0noB0GhDMi1BEGATqdjV2oiIhmYM0NDhb0Oz25Zj/xKq2RepVDgD8PGYHRaRnAC62JEUYRl+V5UrD7osy3h0qGIH88bmdQxzJlERPIxZxKFD9FdDXhqpZPq9q+yR76YM4nCz1tvvYV77rkHdXV1Ptt0Oh369++PpKQk6PV6lJWVoaioCIcOHUJt7Zl8a7fb8eKLL+K7777D/v3723R9l8uFd999t8ltixYtCmjRW2vy8vLw97//Hc8++yweeughPP7444jqQCFxW504cQIvvPACXnrpJdx+++34v//7PxgMhk67fih76qmn8Oc//1kyFx0djdGjRyMtLQ0nTpzA1q1bvT/3DocD8+bNgyAIePjhhwMWl8vlwsUXX4zly5dL5tVqNcaOHYtu3bqhuroav/76K44dO+bdvmbNGpx33nlYv359qysIl5aWNtncTaPRYPDgwUhNTYVer0d5eTm2bduG8vIzzXGtVituuOEGlJSU4I9//GMHXy2FokWLFuFvf/sbBEHAgAED8Ne//rXN50hOTsYnn3yCvXv3AgDi4uL480ShKUI+8gjKpleQ16ToUF1qa/V4lSm22XO0emx8FNLvnNLiPnGjeqBq21HvOGX22HZdq6GGDe08tU5U7z4O3cge9Q3eGmKDN6Im8d4QEZF8zJlERPIxZxIRycecSUTBpFIokKU3IEtvwLk96ueqnQ7kWS3ItZqRZzEj12pGldMh63x1bhf2lpdib3mpdy4hOgY5pxq+ZRtM6Kk3QqNs/dleY6waZc5YFNtjkaKtbtfrI6Kuj7VuzWOtW+iK5Fq3hlavXo3LLrsMDseZ9xGCIGDEiBHo1asXrFYrduzYgbKyMu/2Dz/8EJWVlfjiiy+gULSvdoE6Bxu8EQWASqXCiBEjUFBQ4J0TPCIEReTcNHIUSZs0aFJ6BykSImorj9uJ6pLfJHMxSQMCdj2VSoVzzjknYOcnIgonzJldX0lNFZ7evA7FNdIvQ6JUKvxx5HgMTEwOUmRdiyiKsHy/BxU/H/LZlnDZMMSP6xmEqCjcMGcSEcnHnEkURhzl0rGgAlT64MQSppgzicLL008/jT/96U+SOYVCgeuvvx633norJk6cCLVa7XNcbW0tVq5ciU8//RQffvghXC4XADRZONeaZcuWoaioqMltX375JcrKytq1UmpjGRkZWLdunc98dXU1rFYrLBYLdu/ejc2bN2PNmjUwm8809LDb7fjnP/+JZcuW4csvv2zXypIAcOTIEZ+52tpaWK1WWK1W7Nu3D1u2bMGaNWtw8uRJ7z4ejwevvfYali9fji+//BKDBw9u1/UjxTfffIPHH39cMnf77bfjn//8p+RnqbS0FI899hjeeust79y8efMwePBgXHjhhQGJbd68eT4Fb/feey/+9re/wWg0SuZ/+OEH3HXXXTh8+DAA4MCBA7jiiiuwevVq2Q/sxMXF4eqrr8Z1112HCRMm+CzUJ4oivvjiC9x///2SIrsHH3wQgwcPxnnnndeel0khbOnSpd6/33777e0+z2233Yb7778fALBkyRI2eKPQFDllbk3SpMQDCgVsm3zfvzSUeNWIgMZhPLcfnKU2OItt0I3NQnRv/3/XWPbJL4BHRNyQRg3eXBUQRRcEgWXGRA3x3hARkXzMmURE8jFnEhHJx5xJRF1NrFqDIUkpGJKUAqD+e+jimmrkWc3IPdXw7WilFW5RlHW+8toalNfWYPPJQgCAUhDQPV6PbIPpVOM3E1Jj43y+NzfGaDAqy4Cfy3vi6vTdkm1KwQPIvD4RdV2sdWOtWzhirVu9wsJCXHHFFZLmbhMnTsSbb76J/v37e+fsdjtef/11PPTQQ3A6nQCAr7/+Go8//jieeuqpdr9WCjxWXhAFgMfj8X1j4vEAEbIqgLvaCretVDKnSe0TpGiIqK1qSvfA42r4oUyALnVYwK7n8XhQUFCAzMxMdgYmImoFc2bXll9hxbNb16HCbpfMx2u0mDdmErL0huAE1sWIogjzt7tRuS5XukEAEi8fDt2YrKDEReGHOZOISD7mTKIw4iyTjtUmCAJ/r/2JOZMofHz11Vd47LHHJHP9+vXD0qVLMWTIkBaPjY6OxsyZMzFz5kz8+c9/xuOPP46PP/64XXEsWrRIMp45cya++eYbAPUrTX7wwQfeJkEdoVKpkJWV1eI+M2fOBFBfiPbhhx/i2WefxaFDZxr079ixA1OnTsX69euRmpra5hhau/5FF10EoH7ly88++wzPPvsstm/f7t1+5MgRnHPOOVizZo2kaInOcLvdeOihhyA2KMx+4IEH8Pzzz/vsm5SUhDfffBNxcXF48cUXAdTfu3vwwQdx3nnnQSljBfC22Ldvn/c6pz333HPNNr46//zzsX79ekycONFb+LZmzRosXboU1157bYvXSk5OxiOPPII777wTsbGxze4nCAIuv/xyTJkyBZMnT8a+ffu82+69917s3btXdjM5Cn2lpaXYtWuXd3zllVe2+1xXXnmlN3fv2LEDFovFp7CTqMuLkPwnqJr+XKeI0yIqRoPYwRmo3nVcsk1lioV+Sm/EDsmAMkYT0PhUhhik33VWQK8BAObv9yBuZOPGpiLgtACapIBfnyiU8N4QEZF8zJlERPIxZxIRycecSURdnSAISI2NQ2psHCZmdAcAONxuHK20IvdU07c8qxmltTWyzucWRRypsOJIhRUrjtZ/dx6n1qCXweht+JZtMCFOo8Hrs4fjH2/6NkQyRZWhjP3diEIaa918sdYt9LHW7Yz58+fDYrF4xxMmTMCKFSsQFRUl2U+r1eLee+9F9+7dcfnll3vnn3/+edxxxx3o0aOH3JdInSyon14rKyvxv//9DwsWLMDLL7+MFStWwOPxBDMkIr9wu92Sf/wB1He2jpDCN0dxo9cuKKBJ6hmcYIiozWwnt0vG0aYcqKIMAbue2+3Gzp074Xa7A3YNIqJwwZzZde0rL8U/Nv3s09wtOSYW8ydMZXO3U0RRhPmbXU03d7tyBJu7kV8xZxIRycecSRRGHOXSsbrjq+CRFHMmUXg4fPgwbrrpJklh0OjRo7Fu3bpWC94a6927N5YuXYrFixcjOjq6TccWFRVh2bJl3nGfPn3w73//W/IgwuLFi9t0Tn+Ijo7G3LlzsWPHDtxwww2SbYcPH8a1114r+W/nbyqVCldffTU2bdqEhx9+WNJgq6ysDFdccQVqauQVOkea9957DwcOHPCO+/bti//7v/9r8Zinn34affv29Y737t2LDz/80O+xPfPMM5KaoHPPPbfZgrfTUlNTfX4HHnvssRb/HR47diwOHz6MBx98sMXmbg0lJCRgyZIlkt+9/fv3Y9u2bbKOp/Dw66+/Aqh/4CIzMxPp6entPldGRga6d69/WEMURezcudMfIRJRACg0KsQMlv6+J1w+DIIgQFAqkHz9GPT4+yXI+uel6Pn05ej59OXIfOR8xI/rGfDmbp3JU+2AoNAAyjjpBqel6QOIIhjvDRERycecSUQkH3MmEZF8zJlEFIo0SiV6GxNwUc/euGfEWLx4zkX497kz8MdR43Fpdl8MTEhClEol+3xVTgd+Ky3GZ4f24dmt63HHj1/jodXL8dqv26DvkYQTiIcb9bUWQreeSIkpArMmUehirVvLWOsWuljrVu/QoUN49913vWONRoN33nnHp7lbQ5dddhluuukm79hut+OJJ55o8ToUXH5r8FZWVoZVq1Z5/zidzhb3f+WVV5CZmYmrr74a8+bNw/33348LLrgAvXr1wsqVK/0VFlGXIXg8ECJkRYDGDd7UST0hqMKnoI0onHncDlQV/yqZ06WNDFI0REREoWFr0XE8s2Udal0uyXz3eD3mj5+K1Ni4Zo6MLKIoovyr31C5Pk+6QQASrxoJ3SiuDkBERERE1GHOMulYkxCcOIiIurh58+bBarV6xwaDAZ999hkSEtqfN2+55RbvaqRyvfvuu3A1uKd00003ITMzE+ecc453bteuXdi6dWu74+qI2NhYvPfee7jxxhsl8z///DPefvvtgF9fpVLh2WefxV/+8hfJ/P79+/H0008H/Pqh6L333pOMH3jgAWi12haP0Wq1uO+++1o8T0eJoohvv/1WMvfQQw/JOvass87C6NGjveMjR45g9erVze6flJQku7FbQ0OHDsWkSZMkcz/99FObz0OhKy/vzL3rAQMGdPh8DVdfbnhuopARIQuZAkDytaOR+LsR0I3vhYwHpiF+rHQhU4VaCUEZGXV/UDd6P+wsb3q/ECGKIhzFlfDUtVzPTERERERERERERETU2fTaKIxMScfV/QbhsXFT8Ob5l+CZKefhtsEjcHZmFrrr9GjLtzUnq6uw7vgxbHZp8JYwHs/gXLwvTMGGqAnAff8Hq8B75UShirVu8rDWLfSw1q3eRx99JGkCd8UVV6B3796tXmvevHmS8ccff4y6ujpZcVLn81vVyfPPP4/zzjsP5513Hv7whz9ArVY3u+/ChQtx3333wWazQRRFyZ9jx47hoosuwvfff++v0Ii6BlFEmz5JhTBHkbTBmyal9X88iKhrKNmzFB5XwzduAnRpI4IWDxERUVf307EjeGn7JjgbdOMHgP6mRPxl3FkwtNAlP5KIHhHlX/wK28bD0g0CkHT1KOhGdg9OYEREREREYUQUPYDTLJ1UJwYnGCKiLuzQoUP47LPPJHPPP/88unXr1uFzZ2dnt2n/hqs1KhQKb3HZLbfcItlv0aJFHY6tI1577TVJoyIA+Mc//iFZoTKQ5s+fLykEBIAXX3wRlZWVnXL9UFFeXo61a9d6xxqNBtddd52sY6+//npJnc/PP/8Ms9ncwhFts3fvXpSVnWlEq9FoMHXqVNnHX3jhhZLxp59+6q/QJIYPHy4ZnzhxIiDXoa6pYTG0yWTq8PkanqPhuYlCRQT1d4OgVEA3sgcSLx0KTUp8sMMJGvOyXag57JBOOi3BCcYPRJcHRYvW4/gLK3HsH8tQe6hEul0UgxQZEREREREREREREYU6l7UGtl+OoeSjLTjy6OeSP+ble+Cx1zdA8tidcJqrIbrqm7Z46pyo2V8E83e7Uf71byj571aUf7MLti35cJdVIcWpxJi6GFxdZ8I8Vyaeru2BuysScEGhEv3LgDhHS1E1ilFQ4AiiscKuw2sWE1R9VYH4T0FEAcZat7ZjrVtoYK3bGZ9//rlk3Ph3qjn9+/fH2LFjvePq6mr88MMPMqOkzua3d2Jff/01RFGEIAiYM2dOs/sdP37c2wVQOFUF1LBQQhAEuFwuzJ49G7m5uTAYDP4KkajTCIKA+Ph46T8CHhGR0uHNXnxQMtamssEbUSioKFiPimNrJXMxiX2hijIE9LqCICApKcn7voCIiJrHnNl1iKKIr/IO4OMDe3y2jUpJx++Hj4FGqQxCZF1PfXO3nbBtyZduUAhIumYU4oZ2/IYyUVOYM4mI5GPOJAoTrgpAdEnnNO1fnY+axpxJFPpefPFFSbFWSkoKZs+e3elxrF27FgcPnvleddq0ad7Cu8svvxx6vR4VFRUAgCVLluD5559HTExMp8cJANHR0Xj00Udx0003eeeOHDmCH3/8ERdccEHAr69QKDB//nysWrXKO2ez2bBkyRLccccdAb9+qPjxxx8lK3mOHDkSOp1O1rHx8fEYMWIENm/eDABwuVz48ccfcc011/gltsLCQsm4d+/era622tDgwYMl48YrpPqLSiUto3I42lAdTyGv4fs7u93e4fM1/PlpuII1UcjgZ56IU7EmF3H97YhJbzDZuJF8iPDUOWH+bjfqcksBnGn21ljG/dOgSY3cpn7UPrw3REQkH3MmEZF8zJlERPIxZxJRZxNFES5zDeqOlKHucBnqjpTBZalpdv+Knw6i4qeDzW5vq/RTfwAFRIio0AKFOuC40YNCkweFGgWcMvoXDInq+HegRNT5WOvWdqx1Cw2sdatXVFSEX3/91TtWqVSYOHGi7GtNnTrV+98BAL777jtccsklso+nzqPwx0kqKyuxd+9e7wfi6dOnN7vviy++6C2CE0URw4YNw3PPPYeXXnoJY8eO9TZ7s1gsWLBggT/CI+p0KpUKff+fvfsOj6O61wf+zs5W7ar3asu9Y2Ns44IxBDtACDX0AKaGQC4thF8SQoDAJZAEws1NbkLvPZSYXm2MCy4Y915k9S6tVtq+c35/rL3yaFfSrrTS7krv53n8oDkzc+YrIR/vzp55z/jxqjbpSADicOCu3afa1ueOi1ElRBQup7UCddteVbVJsh45ky8Z8GtrtVrMmzcv6IEJIiIKxjEzPihC4KWdW0KGuy0qHolbjp/DcLcjhCLQ+PamkOFuOZfMYrgbDSiOmURE4eOYSTREuBvU2xoTJNkcm1qGMI6ZRInvk08+UW1fffXVqtUcB0vXlUqXLl0a+NpkMuGiiy4KbLe1tfW6iuNAu/TSS5GTk6NqW7Zs2aBdf+HChZg+fXrMrp8Itm/frtqeO3duROfPmzdPtb1jR/D9z77qukJqpIs9dj2+oqIiMCk0mvbv36/azs/Pj/o1KH5lZWUFvq6tre13f8f2kZGR0e/+iIgGg6dF27UBQgzOSvb94TjQgJqnVqHupXVoW38Ih//wIWzryno9r/Wr3WFfQwgBx7561L28DvWvrofjYGNY5xy7ADYNDbw3REQUPo6ZRETh45hJRBQ+jplEFG2K0wPF5YGr2ormT3eg9tnVaHhzI6r/sQKHfv0uyn7zHir//Bka/70J7ZvKewx3G0gavQLzSAdGzWrBkh/U4menVeGB42vw1OQqPDC6Dlflt2BBWgfy9J6Q509Lj//7/UQUjHPd+oZz3eIf57r5df05TJs2DWZz+PP/B/LnQNEVlXew27ZtC0xCSE9Px8SJE7s99vXXXw+EXM2ePRsrV66EXq8HANx8880466yz8Mknn0AIgVdeeQUPPvhgNEokGlQ+ny8olROKAmiikqkY14QQcNepk8X1eWNjVA0RhcPnsaP6u39CKOobN3nTroQhuaCbs6J4fZ8P+/btw9ixYyEzDIeIqEccM2PPqyj415aNWFtdEbTvnDETcOG4ScMm2Lk3QhFo/Pd3aN/U5WelkZBz6SyYpxbGpjAaNjhmEhGFj2Mm0RDh6hJCoc8JfRz1C8dMosRWWVmJgwcPqtpOPfXUQa/DZrPhrbfeCmynpKTgvPPOUx2zdOlSPPXUU4HtZ599FldeeeWg1diVTqfDySefrKp79erVg1rD4sWLsXnz5sD2mjVrIIbRQmu92blzp2p7zJgxEZ0/evToHvvrj6Pzgo46ujBkuEIdv3Pnzogn9vWkra0Nn3/+uapt9uzZUeuf4l9eXh4A/7yX7777Di6XK6LVd4/lcrnw3XffBba7ThomSgj893VY8rR0fRhEAbxWQJc+6LW4623w2ZwwjsiApO3+/be3zYm6F7+FcHkBAPYd1WFfw747vEBPIQQa3vwOHd93fu7ZsbUKOZfPDvmZp1AEmpZtQft35dDlJCPnslnQZVrCroviG+8NERGFj2MmEVH4OGZSXwlFwFnWBHd1K7TpSUiakAdJHhrPLwoh4Gt3wVXeDMe+eth31UKxu2Eal4uMM6dAl8kF72LJ2+aEfUc1Wr7YBePILGScNRXaFCMUpwcagw6SduB+DzlmElGkjp1XoLg8cJY1w3mwAdav98W4sp5JWgWGfBdMhU4YC13QZ7tDfnyjlYBSkwelJg8WowMA0O6VcMChxz6rAftsOlR6tKjYa8fUHwzyN0FE/cK5bn3HuW7xj3PdOtuPFU8/B4quqAS8lZWVAQAkScKkSZO6PW7Lli2oqqoKHHv//ferfrE1Gg0effTRQIpoeXk5Dhw4EPQLRRTvFEVBdXWXiULK8PjH1mdrhGJXp4fq88bFqBoi6o0QCmo2PwuPXb2qbNrIU5BSODgPLCiKgj179mD06NG8sU5E1AuOmbHl9Hrx+Hdrsa2xPmjfFZOOw+mlkd08GcqET0HDW5vQsblLuJssIeey2TBPHvgQWSKOmURE4eOYSTREuLs8lGzIi00dQxzHTKLE1nWSliRJOOGEEwa9jtdeew12e+eKzhdddBFMJpPqmHnz5mHcuHHYu9e/uNbXX3+N/fv3RzyBJ5rmzp2rmvS2Y8cOeL1eaLVRmXoS1vWP1draivLycowYMSJq12htbUVra2vU+uuOxWJBVlZWVPvcv3+/arukpCSi87sev29f9CazZ2ZmqrZramoiOj/U8Xv27IlqwNsTTzyh+nuZmpqKU045JWr9U/w78cQTA/N6XC4X3nzzTVxxxRV96uvNN9+E0+lU9U2UcIb+NLdhTZ+fCndN8ArhikuGz66BnKR0NnqaIgp4E14FTcu2wHmwEaaJecg4YwokTWS/UG3rDqHp3c0AAOOoLORdt6DbPpo/2BoId4uUcPt6PcZV0YLqf6wIua99S2Ug4E0IAZ/NBVdlCxr//R0Uu3+hTXdVKyr//DlyrpjDz0iHCN4bIiIKH8dMIqLwccykcPnsbjgPNcJV0QLrN/sBnxLyuJwrT4R5Un6PfQlFQHh9gCRBeHzQGHW9vodXPD449tah9as9cFe1ImXuKKSfOQUaXWS/t8KnwNNgg6u8Ba4aKySNBE2SHrLZAMfeOnjbnBBODzyN7UHn2ndUBwLei39zOrSppqBjKDqEIuBptMFV0Qrb+kNwHW6GNsMMb3OH6rhj/5+Eos0yI+P0KQAAT4MN2vQkeBra4Wlsh/NgI3w2Z9A5edfOh2ls6MVTuhszPY3taN9cAdv6MlhmjkDaKeOg0Q/O55hEFFtH/31s/64c9p2RfRbdH5JOhvD0fp85bBoBQ25noJsh1wUp0peGGhNgLEKSyMWUzCJMHpeOjz7+GHeecUZQWA0RxT/OdesfznWLHs51G7i5bv39OXT9fWpqakJLSwvS0wd/ETfqWVRGnoaGhsDXPa04unLlysDXGRkZWLJkSdAxEydOxOjRo3HgwAEAwNatWxnwRglJCKHaloQYFhPf3LV7VduSVg9dZmT/iBDR4Gk+8Ck66raq2oxppciZdGGMKiIiIopPNrcLf96wGgdaW1TtsiThZ8edgPmFfM17lPAp/hXst1Sqd8ga5F4+G0m9TBghIiIiIqLICeED3A3qRga8EREFObog3VG5ubkxmcjyzDPPqLaXLl0a8rirrroKd999d2D72WefxUMPPTSQpfVowoQJqm2v14uamhoUFxfH5PoAoj7p7fHHH8f9998ftf66c9VVV+H555+Pap9dJ+v1NH8nlK7HW63BoS991fX/XVVVFSorK1FUVBTW+WvXrg1qi2Z9ZWVleOCBB1Rtt956Kye4DzMZGRk47rjjsGXLFggh8Pvf/x4//vGPkZaWFlE/ra2tuPfeeyFJEoQQmDRpEvLy+NqciOJL8qwRaFq2NeQ+d4sOpqRjVhT3NEfUt3XlPtjWl/lP/WY/DIVpsEwP//Wi4vQEwt0AwHmwEe4aKwyFacHHenzo2FoV1B6JtrUH0fSfLQCAtB9MQNop4yBp/U/MOfbVo/aZ1d2ea99ejUO/fjes69S/tA6Fd/wA+pyUftVLREREREREA0sIEVgIIta8rXZ0bK+Gu8YKxeWFfXv3AVpd1b/4beDrol8tgS7TDMXjg6fWCmdZE+y7auE82Kg6xzAyE7mXz4Zk0MJZ1oS2NQfhqmyB0u7q2n1A29qDaFt7ECX3nAnF4YHzcDPkZAOSxuUGHSt8CpwHG9G+tRL2HdWBcPT+qPjjJ4GvNUk6ZF90AkzjcqHY3f6fXa0VxtIsQFHgONAI4VOQtmgcdDnJ8NTbYPv2EGwbymCamAd9birsO6oDofhFdy6GLsvS7xrjnVAEoCjw2d1o/74C3qYOyKkm+KwO2HfVwGdT///vGu4WDm9jB+pfXhfRObXPrEbBLxbBUOT/LNdndwOKgGwxQHG4YWkRsH6xB/bvK6AxaOFpUIcBWpfvgXX5HiRNzofrcDMga6DLsvh/BxaOhbfNAW2GGabR2ZBkTcTfE8UPxeODJElwljfDeagREIChOA3uaitaPt0ZOM4wMhOZP54W8j5jONcQXn+Il8bQexjmsTzNHfDU+4MN9bn9vzcYq3+nfO0uOA82om3tATgPNQEaCVA6n6EvuHkRDMX9m3vhs7vhrmqFZNDCUJzu/6xNEfA2d0DSywAkOA81wnmwAY599fA222EoTofwKnDXWgHR6yX6TWPWwzgqC6bSLBhHZUGXkxL0+9CxrQru2jZ/eKUkQXF5IHwKdOlmaMx6GArSYBiZCUmrAYQCuBugtB+GsB6CRm6EJEUYGCfpAWMhYCwGjEWALguSJOFoLpzH4//3Nl5e3xBRZDjXrX841y16ONdt4Oa69ffnYLFYYDQaVQtRWq1WBrzFoagEvB2btmmxdH/TYs2aNQD8LwIXL17c7YvBiRMnBgLeqqvDv/FEFNcUBZJm6N/scdepk011OaMhabh6DFE8sjfuRuPu91Rtst6Cgpk/g6ThCiVERERHNTrseHjdN6jpUH/wa5Bl3DbzREzL5oNZRwmfgobXN6Jjm/oGsqTVIOenc5A0gT8rIiIiIqIB4WkChFfdpg+eME1E3ROKAl97U6zLGLJkS2ZcfFba3KwOp4g0tCcatm/fjvXr1we2x44di/nz54c89sorr8Q999wDRVEAAC+88AIeeOAB1Ur0gynUpJ+GhoZBm/TW3fXJr71dff+y60q5vel6vM1m63dNR+Xl5WH8+PHYs2dPoO2ll17Cb37zm17P7ejowDvvvBPUHq363G43Lr74YlV/I0eOxF133RWV/imx/PznP8fPfvYzSJKE8vJynHPOOXjvvffCnvRotVpx3nnnoaysDIB/jtyNN944gBUTDRw+6DN0SXoZllkjuw1487ToYCrsfHDXXV6GjookpMwfDW1qz68vhCLQ8tlOVZvzYGPYAW++dhfKH/woZHsoHZsrwuq3J0fD3QCg9cvd8HW4kHXudAhF9Bju1hfOA40MeCMiIiIiIooD3lY72r+vgG1dGbyt9qD92kwzCm46GZ6mDih2N1zlzWj9yn9/O+0HE5B26vioB0IJRcB5sAHWb/bDsacuqn1X/vmzsI5zlTWh/L8/7tM1yh8Ifj8P+O9DZF98Ahx769CxvRpKh7tP/YdDsXtQ93zwQ/S2bw+ptju+D76fYN9WDfs29XPMlX/5HEV3LYGrogW2jYfh3FcPy6wRyDrnuEA4POD/f6c43JD0Wmh0ne2+Dhechxoh6bQwlmZCox+857SEV4Hi9EBj0gX9rgqvAuehRrR+vRfO/Q1BIU3xpPrvK0LvkIBRArDt9j/H2lMUkn1HTeBrn9UBAKgrU88LKPndmZAthv6USkcoLg88De1w11hhXX0Anto2AEDWhTORPLMkor68Vgcc++ph31ML54FGJE3Mgy7LAnedDd5WO1xlkc3vcJU1ofp/lwe2LSeMQOaPp0HSyXBVNMO+swaehnbYd9b00Itfxo+mQtLLcNdYISfpYRiZCdPobLiqWuHc3xB0j7QnKfNGw2dzomNbFSStBsLrnxuQNLkA6UsmQvgEnAfq0fzJTsCnALIGGoMW6YsnQtLJcOxvgHB74TjQAOHyzx3T5SQj9aQx0OUkQ5dlgWz2/34Lnz/M0VNvg+Jww9PUgY4tlXBX+8NGJIM20EfqwjEwFGfAWdYEx4GGwP/LgC7jRvU/VgT9jFJPGhPyexaKgLu2Dc6DDWj/rhzuGit0uf7gzUhD2lwVLZGdEAHDyEwYR2YCwh8uaRqXC11Ocq+fn5inFsI8tTDkPiGEf1EVxzbAWQm4KgHFBQ0QfuKHJAOGAn+Ym7EE0OdAkmI/H4cGH+e6DSzOdevEuW7Rvz75ca6bX39/DkfPOTbgLZo/C4qeqNwVOHZAdbm6T+Q/GvAGACeddFK3xx37Dwt/cSgRaTQaZGRkqNMyhQCGwcS3rgFv+tyxMaqEiHric7ej+vtnoL7rJSF/xnXQmTIGtRaNRoOSkhJo4uDNLhFRvOOYOfgqbW14eP0qtDgdqnaLTo+7Zs/H6LTB/XczngmfgvrXNgStEChpNci58sSQq/ERDSSOmURE4eOYSTQEuLpM8NamQpIj/4CXescxc+jytTdh739FtvIdhW/c/9ZDm5Id6zLQ1KSe2BiLSW9dVzS96qqruj22qKgIp512Gj77zP/gTXV1NT7++GOcddZZA1pjd0JNOnM4HCGOHJrXj3ddJ3sZjcaIzu86Oaxrf/3105/+FPfcc09g+09/+hOuvPJKFBaGntx+1D333BNyBdNozSe67rrrVBNRZVnGCy+8ALPZHJX+KbFcffXVeOihh1BeXg4AWLVqFY477jj893//Ny655BLodLqQ53m9Xrz++uv43e9+h4qKCkiSBCEEiouLccMNNwzmt0AUPUN/mtuwZRqXC41ORtZPjkfjvzcF7fc0q8c64WqEdeU+dOyoRtGd6oWd29YcCATFJZ9YiqRJ+SH66wi7tqYPt4VslzTBv5BCCFhX7Q+773DZvj2ElHmjJZrniAABAABJREFU4a6J3grvRykub+8HUZ/4HG44DzZCl5MMfXbygF6L94aIiMLHMZOIKHwcM6PLH5bWiPZN5WjfVK7apy9Mg7u6tccAGW9TR7eBZa1f7kbrl7uD2i3HlyDrwuN7DX0RioBid0N4fRBeBR3bqtDyafhBQIlGuH2of2ldrMvos8o/qcPx2jcchnN/A3KvPBG2DWVoW3Mwov7kVBPybzgJuszIPoMQioC3xQ6fzQHF7YNsMUKflwJJI8HT2I72zZVo/WJXyHPTfjABQhGw76yGpy7EZytxGu7WoyiXXP7gR5DTTCj65WJVSN9wp7g8cOz3B096GmxQ7O4+/+wb3/oOjW9959+QgORZI2HfUxcI3dPlpkC4vfC2BIduHtX+XXm3+/qifeNhtG883Kdzm7u5j9kXbWsOBL4+Gu4GAPYd1bDvqA4+wadAsbtVC1d05am3ofHt71Vt+rwUuLuGtHUhjrl/aV3Zv3uvzR9uC/ycLLNGQJtshNfq6Pb/Y8jxaSDJEuATgEZC+pJJkGQNtOkmGIozel3oJFLCYwWcFUf+VAJK97/noUmAIQ8wFvtD3Qz5kKTw40H4OnPo4ly3gcW5bp041y1xrx/vONfNr78/B8D/s2hp6Qy+jfbPgqIjKgFvycmdH4ZXV4d4wwCgrKwMFRWd6fZz587ttr+eQuKIEoEsyyguLsbBg5036SRFGRYBb67avaptBrwRxR8hBGq3vgSfS/3iMGv82TBnTxr0emRZxowZMwb9ukREiYhj5uDa19KEP29YjQ6PR9WeaTTh13MWoMDC1dWPEl4F9a+uD1qxStJqkHvVXJjG8kMDGnwcM4mIwscxk2gIcNWqt/V5saljGOCYSUT94Xa78fLLLwe2NRoNrrzyyh7PWbp0aWDSGwA8++yzMZv0dnR11WP19qBSol3/vvvuw3333RfVPmMl0p/NQP+//MUvfoG//OUvgQlsra2tOOOMM/Dxxx93O/Htsccew+OPPx5yXzQmoN9zzz146aWXVG1//OMfsXDhwn73TYlJq9Xi9ddfx6JFi+B2uwEAlZWVWLp0KW699VaceOKJmDx5MtLT0yFJEpqbm7Fz506sXbsWVqsVQohAuJvRaMQbb7zRbSgcUfwb+vPchivzZH8Im2lM6IdSPK3qcUuX5gUg4G3qgLepA7osCwDAvrcuEO4G+IPRbN8eCurPUJAaVl2K04OO7yt6P/AI5/6GAXvor+qxLwak39YvdyN51gjIZsOA9D9cOQ40oPaZ1YAiBuXzad4bIiIKH8dMIqLwxcuYKYQAfMIfPubxQfEqEB5fYFt4FQiXF86KFliX7wEAGEoykHfdfGj0UXlMtV+1exrbj4S6VQRCg7pyV7UOyPXbN5XDXW+DsTQTngYbHLs7F2lLP2MyhFdB6+ehA7j6RNYAigLLjBKkzB8Nb3MH6l9Z3/t5CUJONsA8vRjGERkwlGRCm2KEq7IFroqWHkONBoO3xY6q//mqT+f6rA5U/vkzSAatKkipK31eCtz1NkARMI3PhbvaCp/NGXScLssCT2PPD+6HCiTsD/P0IshmA0zjc2EalQ37nlq0fr4LnsZ2aDPM8NT779f09j0CgDbDDF+Hq9fjBoOv1YHap1Yh/8aFIRcb6CvF5YGv3QXZbIDGGJ+fFygeH5wHGuAqb4a31QH7zhooTk/vJ/aVAGzry1RNnrqeg8eo/3oLdxtI7Rv6FqQXDXKqCcaRmXAeaoRw+5AybxSMo7JhKEkfsNcuwtfRGebmrAC8ffjZ67L9YW6mYsBQCEmj73M98fI6k4gSE+e6xf/1Oddt4MRqrltfvq/B/L2mvovKq8+SkhIA/hthW7Zsgc/ngyyrk8rff//9wNdmsxnTpk3rtr9jkwGPDY8jShQ+ny+wmm/AkUmcQ527bp9q25A3LkaVEFF3rBWr0F6rXoUhKXsSMsacEZN6fD4ftm7dimnTpgW9fiAiIjWOmYNnc30N/ue7dXArPlV7oSUZ/2/2AmSakmJUWfwRXh/qX1kP+y51oISkk/2T57t5OIRooHHMJCIKH8dMoiHAXafeNjDgbaBwzCRKbBkZGartUCslDqRly5ahsbExsH3qqaeiuLi4x3POO+88pKamBmr94IMPUF9fj5ycwQ/Ub21tDWrruhLmUL5+vLNYLKr5NpGu+Nr1eIvFEpW6jkpLS8Ozzz6LCy64INC2bds2TJw4ETfeeCPOOOMMFBQUwOFwYPPmzXjmmWewatWqwLFFRUWorKxU9dcfjz/+OB588EFV2x133IFf/epX/eqXEt+cOXPw3HPP4eqrr4bb7Q4EtrW2tuLTTz/Fp59+GnSOEAIAVOFuL774IubMmTPY5RNFz9Cf5jYsSHot5GRj4AFgSSfDNN5/z0CbloS8G05C87Itqof7PM3qB001OgFtsg9emxbC638IQQiBumfXRLXW9gjC3QDAump/t/uSJufDcnwJNAYdap9ZBYgj7ZPygxasGkzC40P5Ax+h4OaTYSjO6P0E6pEQAs0fbEPb6gOdbV4Fto2HBzTgjfeGiIjCxzGTiCh8XcfMsILWAtudXyuh9oU4RwnVfmTf0fdQ4XKVN6PhjY3IveLEgfnhdEMoAs5Djf5At+/Kez9hELgrW+CubAlqb/l4R7/71uUmQ9LJSD6+BEmTCyBbDJDkzofDDYVpKPp/P0TLx9vhaWiHu6bnz8DMUwuhMengONAAb1NH7wXIEpJnjYS+IBVJR+4tVPzxkz59L/qiNJinFsI4MhO6bP+zw+7aNgivD8aSjG5DsAxF6TAUpSNl7ih07KhG49vfQ7G7+1RDKBqjbmBDrY7RW6DZsfdqHHvquj2ut3C3/jCMzIR5Uj6Mo7Kgy7ZAYwj9/8U8uQDmyQXd9iN8CnwdLnib7dCmJ0G4vRBCQJeVHBSkpri8OHzv+9301D3LCSMgWwxImpQPbaqpz7+brvJmlP32PeTdcBKMpZlhPQvstTpg31UDV2UrACBpQi7kFBMce+vh2FMLV0XnmJB53nSkzCntU22h+GxOOPY3wF3TirZ1ZZBNenhb7YH9pnG5MIzIgLfFDkmWYD6uGMLthfNQIxx763sdJ4h6YplZEpN/f7uGRxpHZ8M4KgumUVkwFKdD0g7+e0/hcwKuqiOhbhWApznyTrRpgLH4yJ8iSHL05kHwvTlRYuNct/6J9VyzWF8/3nGuW+i6I/05hDon2j8Lio6oBLxNnz4dgH+SWnt7O9566y1ccsklqmOeeeaZwDHz58/vMV1w377OgKiCgu7f3BPFK0VR0Nzc5U2YIoAhHvAmhIC7Vh3wps8bG6NqiCgUd3sd6ne8oWqTdWbkH7cUktT/Ve77QlEUlJeXY8qUKbxJRETUC46Zg+ObysN4cut3UIR6lsrYtAzcOWs+LPq+r34z1CgeH+pfXhc0eUDSychdOhem0Qx3o9jhmElEFD6OmUSJTSguwNOkbjTkxqaYYYBjJlFiy8zMVG2HmkQ1kI7Omzhq6dKlvZ5jNBpx8cUX48knnwQAeDwevPjii7jzzjsHosQeHTuh6qjs7MG7/xPr68e7eJ/0BgDnn38+/ud//ge33357YJVam82GP//5z/jzn//c7Xm33HILrFYrXnjhhUBbfwLennrqKdxxxx2qtp///Od49NFH+9wnDS2XXHIJSktLcfHFF6O8vDzoIa5jA92O/lcIASEERo8ejddffx0zZ84c9LqJomqIz3MbLiRJQtZ509HwzveAIpD542mQkzo/6zSNykLhbT9A80fbYV3pn/fnc2jgc0mQDZ2flerSPfDatIHgv/qX10WlPufhJrRvKod9dx181vBfu7gbbN0+3GyeVoicy2YHtnOvngf7zhoYR2bCNC4X5X/4MOI6JYMWI37/I9jWHULTsq3dHienmWCeVADjmGwYSzNR//J6OA80BB1X/Y+vkX3xCbDM6PkBGOqZbd0hVbjbUc6yphBHRw/vDRERhY9jJhENBd0GrfUaoNZ70JrqGI8XSe12VL5fFTg20qC1WLLviDxMWwgBd2UrrKsPQGPUInXBGOiygu9LC58CCEDS+p938TS2+0Pdvq+At8UedHykdNkW6HJS4G3uiKuwI31+KtIXT4RpXG7ge++NLj1J9Z64Y0c16l/yv4eXU01InukPh9NlW6DRdz5W7LM50fLFbihuL5QOF0xjcmCamAf9kfC17oz4w4/R8OZ38NS1HQkBS4arogWeelvQz1Jf6A91M08thC7THNSXaVRWWN/jUceGivk6XGj+cDvaN5XDcnwxdFnJ0OenwDgqC16rA4rdDV1OCtzVrbBtPAyf1QFddjIsM0tgKM6Ap64NwqdAn5+K1i93o/WrPRHVksj0hWnQZVmgTTPBNCYHxtJM+Npd/kUDkqIzX16SNdCmmKBN6T3IQmPQYuQfz0XH5go0vLUJUIQ/3FCjgbveBl2WGcYRmdCPzcbKPRux5MdnQm8IrrP04fPgbXNAkiTIycZAu+L2wlXeAsXhRv0r67uto/bJb0K2J88eiaRJ+XDXWOG1OuA63Bz0u96+8XC3/Ta9uxnepna0b62Cr9V/LyzznOOgzTTDVd4CZ1kjkmePhKEoHVAEtGkmABI8DTYoHh88dW3o2FYFx976kP17uwQHOvbWwbG38x6abV1Zt7UlEjnFCEnWwNtqh2VGMTQmPQwlGTCNzoanwQZfhwuGonRo05IAAN5WOyof/QLC4wv/GhYDfO0uyBYDkueOgpykR9N/tkRYqAYZP5wEOc0E2/oyOPcH3yeMNW16Uo//lkpaDQwlGTCOzoZpTDYMRemqcNHsC2fCa3Oi8a1Nqt+1SOmL0pA0Pg+GojTY99ajfUMZDCMyoTFokTQhD942J+QkHQzFGdAXpKpqiAWheABXdWegmzv038keyRZ1oJu2539z+4PvzYkSG+e69U+s55rF+vrxjnPd/BjwNnxEJeCtqKgI06dPx5YtWyCEwC233ILCwkKcdNJJcLvduOuuu7B1a+ekhvPOO6/bvlpaWnD4cOeb2NGjR0ejRKJBJ7qEQUhi6Ae8eVuqIdzqN/T63HExqoaIuhKKFzXfPw3hU69Qk3vcVdAa02JTFBERUZz58OBevLprW1D79Ow83DJzDgxyVN5GDwmKx4f6l9YFfRgn6WXkXT0PxtLIJlsQEREREVEfBU0S0wB6TgAgipRsycS4/+3DpEsKi2zJ7P2gQdB1gbna2lq0trb2KygqXJWVlfjss88C2ykpKTj//PPDOvfqq68OTHoD/JPnYjHpbefOnaptnU6H/Pz8mF0fAEaMGDFo1493qampqKioCGw3NET2kEB9vXoMHKi/F7fccgsmTJiAW265BXv29PyQlMViwSOPPIKf//znWLJkiWpfXl5en67/0ksv4cYbb1TN6bj66qvxj3/8o0/90dA1Z84c7Nu3D88//zz+9a9/YfPmzUFzgY5uazQazJw5EzfffDN++tOf8uEIIoorSZPyMWJSPoQiIGlCz19MP30yfHb3kQdQJXiadZDzO+cX6dI9cJT7H8J1ljf36cH9rhwHG1H71Dd9CiwIFeqVevJYyMlGJM8eqWpPGpeLpHGdQfgFNy9C9T9WRHS9rPNnQJI1MI7NCbnfOCYbeUvnBT1wr3R5qPZYDW9shHF0NrQpxm6Poe65qlvR9F7oh2p9VgfcdW3Q56YMclVERERENJCCgtbCCFDrNmgt8LV/X/Ax6u3BClrTA1DcnsG52AA49Ot3YRyTjdyr5kKj898fEz4FzsPN8DZ3QNLLkDQaKA43Wr7YHRT0bfv2EJIm5cNQkgHb+jJ4mzuCrmEYkQHX4ebwi9JIgKL+H2iakAvL8SVImpgfqPMooQg4DzXCvr0aGpMO2vQkaDPMMI7IACQJbWsPovmD4DnG0ZA8eyRSFoyGPid672XMkwtQ+nD3z/EeJScbkXXe9Ij71+i1yP3pnG73O/bVw2tzwliSETK8L1pkswHZF81E9kXBi27oc3SBr01jcmAaE/zeXp+fGvg6bfFE6AvT4G2xw9fmhKE4HcYx2bDvrEXjW9+FvH7KvNHo2F4FX5sz0GYoTkfK/NFwHGjwh97VtvXnW4yInGJU1QIA2kwzNEYdNAYtzNOKkDQpr9vAtaOhWLEiSRIsM0pgmVHS7TEejwe+g1K397oAhPz+NHotTGP882lKHz4PHdurUP9y90FvXdnWl8G2vizs40Oxrtyv2u4aGhaPIWCAevyVDFqIY+576fNT4Wlsh/m4IiRNyIM+P8X/e6SRYNtQhqZ3Ngf3NzITrvLmoDFa0skQHh+SZ4+E5YQRMBSmQZI18LW7oLi80KYn9fj/XbYYgtq0aUkY+cDZqjZneTOaP9gGbYYZpjHZMI3NgTa15xDClLmjAPhDJX0dLugyLPDZ3Wj9Yhe8VgfM04pgGpMdsh/LtKKgNiEEhMcHSdYEBZYpTg86tlVB0sowlGQEgjEVjw8d31dAeH0wlGRAX5AW9PMQXh+8bU4oTi9qn/wGitMDfUEqTONyoc1Igj4vFRqTDhqDrtt7k54GGxSnF7q8lKB/K7vSJhuRd828zuv7FFhX7UfLxztUx2lMOsipJkARSF041v+zCjHeJE3MR9Y5x/V4zcEmhA9w1XYGurlqASiRdaIxAcaizlA3bWrQAlNEkeJct4HFuW6c6zYQ1wc41+1YnOvml5qaqtqO9OfQ3t4eFPA2GGMERS5qT6bfdNNNuOGGGyBJEhobG7Fo0SJkZmaira0NHo8nsFJpamoqLr300m77+fzzzwNfG41GTJ48OVolEsWWokDSxDYZfKC56/aptiV9ErTpBd0cTUSDrWn/J3Ba1SuBpJYsRHLe9NgUREREFEeEEHh993Z8cHBv0L4FhSW4ftpMaIf46/lIKG4v6l5aB+c+9Y0wyaD1h7uNjI8b2UREREREw4KrVr2tz4IkMZyaKFKSRgNtCsMRh7r58+ertoUQ2LBhAxYvXjzg137uuecCqzgCQFtbG5KS+vaAxO7du7FmzRrMmzev94Oj6Ntvv1VtD/YKz12vn5GRgeLi4qheo7W1dVBWu7VYLMjKiu4CCWPHjsX27dsD28curhiOrsePHTs2KnWFsmTJEuzYsQP/+c9/8NFHH2HNmjWoq6uDzWZDTk4OSktLce655+Lyyy8PTG7bvXu3qo8TTjgh4uu+/vrruPrqq1V/Fy+//HI8/fTTnEBPIel0Olx//fW4/vrr0dLSgrVr16KmpgZNTU0AgKysLOTn52Pu3LmcHElDjsSPxYacnh58lDQSUuaNPhLwBnhadDB2CXg7yroi+PPUSAkhUPvkN2Ef37bmAOy7a2EcnQ3jyEy0f1eu2p968lhknDElrL4Mxeko/s3pqHtpHdyVLTCNzYGhJAOtX+4OebwuJxnmqYUAAH12MvKunQ/bd+UwFKTCMDITwuWFYWRmULgbAHgb23uspX3TYaQtGh9W3dRJcXpQ/0rPD19X/fVLmKcXIfPH0yCbgx/sJSIiIqK+60vQmtJLCFu8Ba1RCBoJklaGpNNA0spB4WxHOfc34PA9ywAJffr/Zd9ZA/vO7kPFwwl301gMsEwvguX4EhgK0gD457x6mzogpxh7fI8gaSSYRmfDNDr0Z4apC8YgdcEYVVvl418GhXfpcpPhqbOF7MM8tRDa9CRAlmAszoBpXG7I95RDgamboPR4JkkSzJODn4VMnlkC89QCOPbWwV1nQ9L4XOgL0wKfLWSePS1kf5bp/s+xbBsPo+n9rapQrHDpi9JhnpIPQ1EGfB0uNLy5EfAJaDPNME8uQNLkfBiKM4Lu/SgeH3w2JzQGLTRJen4OEkLS5AKknDQGbd/s7/3gYcI0IRfCJ6BNM8E8MR/G0dnQGPo+BylldilSZpcGtoVXASAgaSP7jFm2GEKGt/WVsSQDBTed3KdzZbMh8G+JNsWIrPNn9KkfSZIg6UP/bDVGHZJnjQxu18lBC1wE9auVocvwB8KNuO+sPtWmy07u03kAIMkapJ08DmknjwPgH4sUpwey2dDjPep4IoQCuBuOCXSrBkSE47ekB4yFRwLdigBdFsdhijrOdRseONetfzjXLXo4123g5rp1rbu/P4eMjAykp6dH1AcNjqg93XHttdfixRdfxKpVqwJhbo2NjQAQeNEpSRLuu+8+JCd3/+L+nXfeCRx7/PHHc0VTSkgajQbZ2dloazvmBq0Q/hvU/fDd4Zb+dTDA3LXqyVv6vLF800kUJxSfCy2HvlC16c25yJl0YYwq6qTRaDB+/HhoGJpDRNQrjpkDw6coeGbbJnxdGXzz48zSsbh04lRo+Lo2QHF7UffCt3AeUK8GIBm0yLtmHowjGO5G8YFjJhFR+DhmEiU4V5162xB6hS+KDo6ZRImtuLgYpaWlOHToUKBt+fLlAz7pTQiB5557Lqp9Pvvss4M66c3tdmPlypWqtgULFgza9QHgiy/Un3V1ncQYDY8//jjuv//+qPfb1VVXXYXnn38+qn1OnDgR7777bmB7//7IHkY5ePBgUH8DSZZlnH/++WGt7ltRUYHKysrAdmFhIQoLCyO63ttvv40rrrgCPp8v0HbhhRfihRde4L/rFJb09HSceeaZsS6DaBDxs7FhR3Q+ee9p0al2BQLeBHp80D6syygCVX/9ovcDj2Hf5Q+3b1t9AJJOhvB0/nuOI+F0kdCmmlBw08kQPgUanQzF44PzYCOchxpVx5mnFSLrguNVDx6axuaE/YC6sTQzUHsoLZ/shGV6MbRpfXsYZjgSQqDh7U3wNnX0emzH5kp4mzpQcPOiqNbAe0OJTwgB4VUgaSRIcv//PwqfEpV+iIYijplEAyuyoDX/dv+C1jrPY9BaDHUJWtPoZEhaDSSdfKT92G11u6aHfcdua3TH7vNfp+vrHXddG6r++mX3dQ7i74gu2wLF5YVpTA7M0wphGpsTVK9Gr4U+P3VArp9/3QJYV+4DAJjG58JYmgVJ43+21tvYDq/NBdmihy7DHHGgEcUXjV4L85RCmMPLmFdJPmEEkk8YEZU6zFML/e9DtJoen9vU6GRojoQ8DTXRep0pSRIyfzQVKSeOQuWfP4tSdX7aLDNMo3NgW3eo94OjRJ+XAneXwMmQxxWmQZtqgrOsEdrUJKSdMh7GMdmQk/QDXuNQDbSk7mmOvAaJZ0IIwNN8JNCtEnBVAoorsk4kGTAU+MPcjCWAPgdSnKykw/fmRImNc936jnPdootz3QZurlvXuvv7c5g0aVJE59PgiVrAmyRJeP/993HhhRcGDTTiyMSP22+/Hbfccku3fTQ1NWHZsmWBGwuDkRxKNBBkWUZWVhYOHDgQaJMUpV9hZ26vgve3dD/ZJx646/aptvW5A5dySkSRsVVvhOKxq9ryZ1wHjTb2K5TKsowJEybEugwiooTAMTP6XD4v/r5pPTbVBz+IcOmEKThrNFdMP5bi8qLuhbVwHlQ/3KAx6pB37TwYijNiVBlRMI6ZRETh45hJlODcXT470DPgbSBxzCRKfKeffjr++c9/Brafe+453H///dDpdD2c1T9fffWVaqJdNLzxxht4/PHHYbFYotpvd1599VU0NKgD/88+++xBuTYArFixAlu2bInZ9RPBlCnqJ4rWrl0b0fmrV6/usb9Y+vJL9YOCixYtiuj8ZcuW4dJLL4XX27my+rnnnotXX32VC08SEXWH+W7Dji6r83Vl14A3fboHgEDrV7vRXx1bKuBpaO/z+apwN/gfatammiLuR9JIkDT+1wEanYz8n50Exe2FRh+1adUwTyvqMeANACoe/hSpi8ZBn5sM8/TioDmm7vo22L4tg74oDZbjioZ1iJSvw4WKRz6DcHvVO2QN4FNCnuOqaIHi9EBjjN77Pd4bSkyKywtJJ8NV3oyaf3U+0JV++iSkLerbvBBPcwca3/4ezoMNMI7JQc5lsyCbBv5h+FgSilCFXhL1hmMmDRf+oDWlMxgtKEStb0FrSi9hbQxai7FwgtaOhqMFBaj1JWjtyL44eU+gy06GnGKEr80Zk+trkvSwTC+C5fgS6AvT+vW8Xn/JFgMyzgy+ny5JEnTZydBlJ8egKhrKjr2nMVxF+3WmLtOMkX88F/ApsO+pQ/PH2+Ft7DlcXpebjKQJeTCNz4NxRAYkWQPHvnr4OlwwFKUH7rVp05PQ8smOzvOyLX2+NyYZtBAu/30R83FFSJ490r8wuywFjYPC64Pi8kJj0MFV2QzhUSDpZegyzJCTjX26PtFQIjzWI4FuR0LdFHvvJ6lI/kVYjcX+UDdDPiQpeveWo4nvzYkSH+e69Q3nusU/znXz61r31q1bYbfbkZQU3iJh8fxzILWovlpMTU3FZ599hs8++wz/+c9/cPjwYQDAhAkTcOmll2LmzJk9nv/yyy/DYDDAYPCHzZx11lnRLI9o0Hi93sDvf4AigH4kXFc0B79BTBuEVPxIuGr3qrYNeeNiVAkRddVatkK1bc6eDGNadFZ/6S+v14v169dj9uzZ0Grj80YWEVG84JgZXR0eN/6yYQ32tjSp2jWShOumHo+Ti0fGprA4pbg8qH1uLVxlXX5eRh3yrpsPQ1F6jCojCo1jJhFR+DhmEiUu4bUBvi6TSg0MeBtIHDOJEt9tt92GJ554AoriDwCora3FK6+8gqVLlw7YNZ955hnV9sMPP4yLL7444n7uvvtuvPrqqwCA9vZ2vPnmm7jmmmuiUmNPHA4HHnnkEVXb6NGjceqppw74tQFAUZSglUZTUlJwySWXDMr1E8XixYshyzJ8Pn/oyXfffQebzYbk5N4fHrPZbNi0aVNgW6vVxtWijF3/Dl133XVhn/vRRx/hwgsvhMfjCbT96Ec/whtvvMF/y4mIehLDB6MpNjQGLUwT8uDYXQt3l4A3jUFANvvQsbWqT3172xxw7K6DviAVDW9t6vFYOdUEn9URdt8p80f3qaZQohnuBgCWGcWQLQa469qgMerQ+O/Q37t1hX/epbvGiowzpwbabZvK0fjmd4Htjs0VyLtmflRrjHe+Dhdcla2w76yGbV1Z0H6NSYeCW05F1aOfQ3hDh7z5bM6oBrzx3lBi8bba0fDWJjgPNoQMwGn5ZCeSZ5dC7mE+thAC7po2yBY9tCn+QElXRTNqX/gWSrsLAODcV4/y+z9E4R0/gD4nZUC+l1gSXgUtn++E9Wv/QuBpp45H+pJJMa6KEgHHTBps0QtaU28zaC3OHRu0ppOhCRGUFlHQWpdAtZDBbQMQtJZoY6akkZB/w0loXb4HHVsqu3093hN9URoyzpgCx776wPsiAJD0Wn9YkkELb4sd7qrWwL6kSfmwzCxB0vg8SNr4CLsjosE3EGOmJEmAVoZ5cgHMkwuC9gtFwF3VAkmvhS4nOWSwpGlsTlBb2qJxsMwsga/VDm26GbLFEOhPeLxw19kgJ+mhzTDDvqsGwu2DpNPAXdMGT4MNxlFZ0KaYICcboS9IDfvfH0krQ9b6gwCNI7Mi+VEQDUnC19EZ5uasALxtkXeiy/aHuZmKAUMhJE18Pd/fnUR7nUlEwTjXLXKc65YYONfNLz8/H9OmTcPWrVsB+P/tXrVqFZYsWRLWtVasWKHaPuOMMyIrlgbNgLwSW7JkSdi/LMe69dZbceuttw5ARUSDSwiBjg71A1WSEOjP0qYdbl9Q2yWzivrc30Bw1+1Tbetzx8aoEiI6lqO1DE6rOnQybeSi2BQTghACDQ0N/g/ViYioRxwzo6fF6cDD61eh0qb+YEan0eCW4+fg+NzgD2aHM8XpQe1za+A63Kxq1yTpkHftAhgK02JTGFEPOGYSEYWPYyZRAnPXqbclPaBNi0kpwwXHTKLEN27cOJx33nl4++23A2133HEHlixZgoKC/t0TOnDgAEaPVgdMtLS04N133w1sa7VaXHvttcjKinwi/dKlSwOT3gD/RKDBmPR24403Yvfu3aq2e+65B5p+LHAWifvuuy9oItIdd9wxICu63nfffbjvvvui3u9gyMrKwoIFC/D1118DANxuN1599VX87Gc/6/XcV155RRWAtnDhQmRkZAxYrZFYtWoVVq1aFdgeP358j6uaHuvzzz/HBRdcALfbHWhbsmQJ3n77bej1iTHhnoiIaDBZphfBsbsWvnYZikeCRtf53leX5oWvQz3lWF+UDndlS499tn9fCdvGcih2d4/HHZVz6Qmo+dc3YR0rpxhhLImP1yzdMY3NCTzc69jfgI7NFd0ea125H5YZJdDnp6Lli11o/UL9Gtyxtx5emxPaZOOA1hxrrsoWVP99RVjHZl98AnTpSbCcMAK2bw+FPKbxP1uQf92CqNXHe0OJwdPcgZonvgkrMLL8Dx8CAORkI0xjsuGqaoWn3ob00ychaWI+Gt/dHLQQXneqHvsSAJCyYAwyzpgc9eCXWPC22lH/6nq4yjvH+9av9sC+pw45l8+GLsMMxemBZNCGDDig4Y1j5vDVe9CaD+KY0DT/Md2HsIUOWlP3waC1OKCRVEFp4QataXoJYRvsoLVYScQxU5dlQfaFM5F94UxYv9mP5g+3BR2TMn80jCMyobEYAJ8CxemBsTQrEHAEAKbR2bDMLIFjVy20aUkwTcgNCuD2OdyBUD4ioliMmZJGgqG4b/ehtMnGoPs5kkaCZNCp7m0dGyxnnlLYt0KJCAAgfE7AVXUk1K0C8DT3flJX2jTAWHzkTxEk2RT1OgdDIr7OJCI1znWLHOe6JQbOdet03nnnBQLeAOC5554LK7Nr9+7dWLduXWDbbDb3KeuLBgejdokGSNCbHUX064Pr+9/fFdQ2f0xmn/uLNqH44Kk/oGpjwBtRfGgtW6Ha1poyYM6ZGvpgIiKiYaCm3YaH169Co8Ouak/S6nDnrHkYn8FVqo6lOD2ofXa1arIuAGiS9Mi7bgEMBakxqoyIiIiIiOCqVW8b8vgQHRFRGB5++GF8/vnnaGvzh/+3tLTgggsuwEcffYT09PQ+9fncc8/hT3/6E3btUn+u+8orr8DpdAa2Fy9e3KcJbwBw6qmnIi8vD7W1/vF/zZo12L17NyZMmNCn/nrT0dGBm266CS+++GJQHVdeeeWAXPNYXq8Xd999N/785z+r2idNmoS77rprwK+fiK688srApDcA+Otf/4qlS5fCYDB0e47L5cLjjz+uarvqqqsGqsSI2O123Hjjjaq2//7v/w7r3K+//hrnnHOO6u/fqaeeivfee6/HnwdRV5s3b8ayZcvwzTff4MCBA2hubobNZoMkSfB6vUHHt7a2Bv59MRgMyM3NHeySiaKCby2HJ1+768hXEjwtOhhyOkPZdBkeOKvUD6LmXD4b7upWOA81wVCUBn1hGqzL96J9U3lnnzYnwpW6aByMI7NgHJ0N54GGXo9P+8HAvA4eMGE8QNf+fTk0u/RB4W6BLpweYIgGvAkh0PLpTlhX7A3r+NSFY5A0IQ8AYBqT023AW7jBXBQ/hCLQtmo/bBsPQ5+bgszzpkNO6j2gWXF50PLpTrStOdin6/psTrR/3xnC2PLJTrR8srNPfbWt2g+NXkb6kkl9Oj/ahCL8wUguL9pW70fH1iroC9KQ9ZMZkE3+n63i9gYFqdj31KHhjY0hQzrdVa2o/NNngW051YS8pXOhz+ccEqJ4cjRoTekmDI1Ba0NUb0FrPQaoRRi0dnTfEApao75JPWkMUub7QwEkTeQ3FfTZydBnJ3e7/+hrFiIiIqJQhOIBXNWdgW7u+sg7kS3qQDdt969NiIgGG+e6hYdz3RIP57r5XX755XjwwQfh8/kAAO+88w727duHsWN7zut55JFHVNsXXXQRjMah+VnyUMCAN6LBIhSgDzepjzrUaO/9oBjyNJVDeNUf4OvzxsWoGiI6yuduh616g6otbcTJkCR+gEpERMPTwdYW/GnDKtjc6teu6QYj7pq9ACUpnGh6LJ/Djbpn18BV0SXczaxH/vULoM/jz4uIiIiIKKZcdeptPcMjiIjCMWbMGDz33HO44IILAm3ffvstTjrpJLz++uuYMmVK2H3t378fd999N958802MGDEiaP8zzzyj2r7sssv6XLcsy7jooovwt7/9LdD27LPP4k9/+lOf+wzF6XTi1VdfxSOPPIK9e9XhCmPGjMGrr746oIGiXq8X7733Hh555BFs3LhRtS87Oxtvv/02TKbEXB17oF111VX405/+hD179gAA9uzZg9/+9rd49NFHuz3nN7/5TeB4wD+p8PLLL+/1WosWLVJNsLv33nt7XRHW6/VCqw1vqlJ7ezt+/OMfY8eOHYG2Cy64QPX3tjtr167FWWedBYfDEWhbuHAh3n//ff7uUNi2bduG22+/HcuXLw+0hbOy/fLly/GTn/wEgH9l3NraWiQlJQ1YnUQDhglvw5Iuu3PVeE+LVhXwpk/3BB+fngRdehLMkwsCbYonOPyyJ+lLJiFpagG0KUZoDDoAQObZ01D11y97PE8yaJF8QvDr73imyzT3eox15f5BqCQ+tXy8PezvX5ebgvQfTg5sm6cUIPvimbDvqkXH1irVscKrQAgB535/aKBxTDYXSIhTropmVP/ja1Wbp94GjcWArHOOA+D//+nYXw/n4WbosswwTykANBo0f7it25C/WHEeGphwQcXpCQTpdMfX4ULrir1oW7W/24Alb4sd5TuqAVkD+BQAgC7LgvTTJ6Hxne+h2IPH/Z74rA60frUHOZfPjug8ouGif0FrXc/p3GbQWpzrGrQWUYBaN8cd2afprg8GrVEM9SXYjYiIiKgvhPD5F0Y9GujmqgWgRNaJxgQYizpD3bSpvG9IRHGLc916xrluiYtz3fzGjh2Lq666Cs8++ywAwO12Y+nSpfjyyy+7DWz7z3/+g+effz6wrdfrce+994ZVK8UGA96IBoAsy8jJyUF7e3ugTVJEv/7h18sauH2dbzDH51p6OHrwuWv3qbY1SamQk/uWxktE0WOtWONfgeAoSUZq8fzYFRSCLMuYPn06ZFmOdSlERHGPY2b/bG+sw183fgunT/1QQZ7Zgl/PXoDspN4n1A8nPrsbtc+uhruyVdWusRj84W65KbEpjChMHDOJiMLHMZMoMQmhAO4uAW+GvNgUM4xwzCQaOs4//3w88MADuOeeewJtO3bswHHHHYcrrrgC11xzDebOnQudThd0rsPhwFdffYW33noLr7zyCrze0CEWmzZtwubNmwPbSUlJOPfcc/tV92WXXaaa9Pbiiy/ioYce6nUikdfrRVlZWVC73W6H1WpFS0sLtm3bhnXr1uHrr79Gc3Nz0LHHH388li1bhtzcvgWKhrq+0+mE1WpFa2srdu3aFbh+TU1N0LGjRo3CsmXLBmwV16FAlmX85S9/wdlnnx0IonrsscfQ3t6Ohx56CJmZmYFjGxsb8dvf/hZPPfVUoE2SJDz66KMD9u/cE088gbfffhtXXnklfvSjHyE7OzvomPb2drz99tu4++67UVXVGc4xcuRI/N///V+v1/j+++9xxhlnqOZrjB8/Hv/4xz9QXx/ZSu1GoxF5eXx9NRw9//zzuPnmm+F0OiGEer6PJEk9Br2dc845KCkpweHDh9HR0YG3334bV1xxxWCUTUTUb8bSLOiyLfA0tMPTon4drOsS8Jb1k+ND9uHc1xD29QpuXgRDcXpQuz43BSMfOhfV/7sc7hpr8P6iNOReeWLCBVckTSlA69d7AR9TVrqy760LO9zNPKMY2T85Puj/v2VGCSwzSlBe/gl8rZ1Bv9BIqH95Pew7qgEAybNHIuv8GX2ulfeGBoZjfwNqn14Vcp9t7UFknDkF1uV70PrVHtW+xrc2DUZ5fSKUnh8qVpwetC7fA8eeOhhHZ8M0Lgcakx7GkozQ/XkVNH+0HW1rDgAATGNzkHv1PPg6XHCVN0OXkwzh9qHmyW8gXBGEbR4zN93T2I76l9eHf24XHduq4KpsgeNAA5z7GyBbDDCOyUbyTP9Dgp7mDjj21cNQlA5DYVqfr0OJIx7HzLCC1o7sU3oJYesxaK3rOQxaiy1ZCj9Aretx3exj0BpFWzyOmURE8YpjJtHw458n13BMoFs1ICJbbASSHjAWHgl0KwJ0WcMi0I1jJtHQwblufpzrNrRwrlun+++/H++++y5aWloAAGvWrMFpp52Gp59+WvU75HK58OSTT+KXv/yl6vxf/vKXIUMbKX4MSsBbbW0tNmzYgPr6ejQ3N0OSJKSnpyMnJwezZs3iZEgacjQaDZKTk9WNihLVlU1/+6PxUesrGtz1B1Tb+pwxw+LNLVE8E0JB62H1ipbJ+TOhNcRXGItGo+ELRiKiMHHM7Ltvqyvxf5vXw9floavS1DT8atZ8pBpCJ9kPV74OF2qfWQ13tfqBCTnZgLzrF0CfE1+vJ4hC4ZhJRBQ+jplECcrTDAj1w9Uw9G0SAoWPYybR0PK73/0OWVlZuO222+ByuQAAiqLghRdewAsvvIDk5GRMmjQJ2dnZSElJQVNTE2pra7F37144HI6g/pKSklTbXVc0/fGPfwyLpX8Lec2ZMwejR4/GgQP+z2fr6urwwQcf9DqZrqqqCqWlpX26ptFoxJ133onf/e53MBgMfeoDQJ+vL8sybrjhBvzxj39Eampqn68/XJx11ll48MEHcffddwfannzySbz00kuYM2cO8vLyUFNTg/Xr1wf9Hj/88MM4/fTTB6w2IQSWL1+O5cuXQ5IklJaWYvz48UhPT4fdbkdtbS02bdoEt9utOq+0tBSfffYZcnJyer3Gf/7zH1it6vuae/bswdSpUyOu9+STT8aKFSsiPo8S29tvv41rr71WFewmhEBJSQkyMjJUk5lD0Wg0uPjiiwMrTi9btowBb5SYOO9rWNLotci/6WQ4DzRAl9YM+L4K7Ds24M08rRCWmSUh+zBPK4RtfVmv19Lnp0JflNbtfkkjIe+GBbDvrIGcpIehOAMasz6h5yQaCtJQ+F+nourxL2NdSlzxNNhQ9/zasI7Nu+EkmEb1vPBw9gXHo/aZ1Z0NigiEuwGAbX0ZbOvLkHHmFKQsGANJE9nvFO8N9Z/wKfC22KFNM0HSyrDvqkH9Kz2Hih2+Z1m/r6sx65FxxhR46m2wrtzX+wm9yPiR/3fItr4MTe9uDtrvszph21AGZ1kTAECfl4KU+WMAALaNZWh6p/Mcd20b2lb73+ennjIOGT+crO7L5kTdy+vgOtz5gJpjXz3Kfvtev7+PcMnJRvhszl6Pq/77CtV2+/cVaHxrE0wTcuHYrV6wpPi3p0ObYopmmRRnehozVUFrvQSohRW0dmRfj8FtDFqLvbCC1tTb3QaohRO0ppMhyQxao8TA15lEROHjmEk09Akh/HPjnBWAsxJwVQKKK7JOJBkwFPjD3IwlgD4HkjT83htwzCQaWjjXLTyc65ZYhvtct6OKiorwzjvv4Ic//GGgv9WrV2PSpEmYOXMmRo0aBavVik2bNqGhQb3o2llnnYUHHnig/98wDagBC3hramrCP//5Tzz//PM4dOhQj8eWlpZi6dKluPHGG5GV1fMH70SJwOv1ory8XN0oRI8T33pa3TcUrSa+3kh6Gg6qtvU5o2NUCREdZW/YBY9d/QItbeSi2BTTA6/Xi5UrV2LhwoW9Jm4TEQ13HDP75rOyA3hxx+aguXlTsnJw28wTYdIGr0wxnPk6XKh9ejXcNV3D3YzIu2EB9NnJ3ZxJFF84ZhIRhY9jJlGCctWqt+VkSLI5NrUMIxwziYaeG2+8EQsWLMAtt9yC5cuXq/bZbDasW7eu1z5MJhNuueUW/PrXvw60OZ1OvPrqq6rjLrvssqjUfOmll+LBBx8MbD/zzDP9Xi01lNGjR+Pyyy/Hz3/+85gs3FdQUICLL74Yv/jFLzBq1KhBv34i++1vfwtJknDvvffC4/GHsTgcjm7DynQ6HR544AHcddddg1ajEAIHDx7EwYMHezzu7LPPxtNPPx1yBVSiaKupqcFVV10FAIEAoZtuugm//OUvUVpairKysrDGo3POOQd/+tOfIITA119/3evxRHEpcTO0qJ9kkx7mKYUQniSg+th2BRqjD3nX/QCGovRuzzeNzQkr4C151ohew9pkkx7JM4fWg2f6vBQkTcyDfVdt7weHYN9Th9Qh8nmtq7oV1X9b3u3+7EtnwTy5AJI2wrmyYY5fzR9th5xqguW4orCOd1W0oG39IcgZSdjsq8LCRSfz3lCEhCLQtvYgmt/fCgDQZVuQPKcUzR9tB5Topy0ZRmQg56dzoE02QigCUAQkrQZCCBhHZaFjWxW0GWa4q1th31HTY1/6/FS4a6xHQtpGwzJzRCAcMGVOKVLmlMK6cp//eznC22pH49vfq/pp/nA7emNdvhdJ4/NgGJEBV3kzbN8eQvv3FX34CUSJhEAgonXFXrR8urNP3XQNdwOAioc+QdYFM5A8a6Sq3dfugvWbfbBtOAzF7n+IyTJrBDLOnALZpO/T9al7QohA8FlEQWtd9nUNVFM8/j+2FivMxiTAy6C1uHJM0JpGFagWpaC1bvZFGqxKNJzwM0giovBxzCQamoTHeiTQ7Uiom2KPsAcJMOQBxmJ/qJshH5LEMYJjJtHQw7lu3eNct8TFuW5+ixYtwrvvvoulS5cGQtyEENi4cSM2btwY8pxLL70UTz31FGRZjvh6NLgG5JXY008/jdtvvx12uz2s0KqDBw/i3nvvxcMPP4y//vWvuP766weiLKJBI4QIStmUFNHtB1JeRYFL8Q5GaQPG3SXgTZfNFx1EsdZavlK1bUgugik9/sIXhRCw2WwRB10SEQ1HHDMjI4TA2/t24d19u4L2nZhfhBuPOwE63rhQ8bW7UPP0Knhq21TtcooR+TecBF1W/1bdIBpMHDOJiMLHMZMoQbm7PBBnGPzJCMMRx0yioWnKlCn46quvsHbtWjzzzDNYtmxZ0CqHXRkMBsydOxeXXnopLrroIqSlpan2v/3222htbQ1sp6enR221yMsuu0w16e3jjz9GdXU1CgoKIupHo9FAp9PBbDYjOzsbRUVFGDduHKZPn46TTjoJEydOjEq93ZEkCXq9HiaTCVlZWSgoKMC4ceMwbdo0LFiwANOnT+819IO695vf/Aann3467r//fnz88cdBcxgAQK/X44wzzsB9992H6dOnD3hNCxYswIUXXogvvvgCLS0t3R6n1WqxePFi3HHHHTjttNMGvC6io/7whz/Abvc/LCLLMl5//XVccMEFgf3hjkmzZs2CTqeDx+NBU1MTDh061OeVnYlih/8GD3vaVAgvcOzzb7p0D/SFaT2eZp5aGFb35unF/SgusaWfPhlemxM+mwuGgtRuw94yz5+Opnc2q9qaP96O1AVjBqHKgeXrcPUY7lZ01xLoMgZ+IYOG1zbAPLWwx7AZ+946tG88jI6tVYG2UgDKPC/AByLD5mt3ofy/P1KFOXka2tH8wbboXkgjIfeqE2EozoCc1BkEJmkk4Mj/Z0mSkDQhD0kTOu+nOssa4WloR9LkAmiMOlhX7oXzQCNM43ORMm90WIFEkj56vw91L34L2ayHp6E9an32Vf6NC2EckQkASDtlPNJOGQ93fRvctW1QnJ6gcSpSjW9/HwjCk7QaCK8S8rj2DYfhbepA/g0nhdyvePzBYcf+f+8Ld10bmj/YBse+egCA5YQRyDh9MmSLoV/9hiPsoDWPAqWHELZQQWtB7V3OGUgGAN4224BeI6H1O2it67EMWiNKVPwMkogofBwziYYG4evoDHNzVgDett5P6kqX7Q9zMxUDhkJIGgbDd8Uxk2ho4lw3znUbijjXze/MM8/E9u3bce+99+KNN97o9ronnngi7rzzTtW8JopvUf9k+Wc/+xmefvrpwAu9owNQdy/8jt1vt9tx4403Yv369XjqqaeiXRrRoAr6nRcC6OYfZJvbNQgVDSxPvTrgTZ/DgDeiWBJCgb1xt6otbeTJfGNARETDhiIEnt/+Pb4sPxS0b/GIUbhy8nRo+O+iis/m9Ie71aknVcqpJuTfsAC6TIa7ERERERHFFVeXh38Z8EZE1G9z587F3LlzAQD79u3Djh07UFlZCZvNBkVRkJaWhoyMDIwfPx5Tp06FTqfrtq/LL78cl19++YDUOXHixF4n3y5atCimE3RHjhzJCcJxYMaMGXjvvffQ0tKCNWvWoKqqCk1NTcjMzERhYSHmzZuH9PT0PvXd3QqpPZk+fTrefPNNCCGwd+9e7Ny5E5WVlWhra4MkSUhLS8O4ceMwZ84cJCcn96mu++67D/fdd1+fzqXhzefz4bXXXgt8pv7//t//6/MkSK1WiwkTJmDbNn9gye7duxnwRglH0sS6Aoo1SdLAbdVBn+kJtJknm8Oae5R14fFofGtTt/v1Ren9DuBJZPrcFBT+4hQARx6uW1+Gpnc3AwC0aUlIPnEkLMePgGzWBwcn+QSaP96O9MUTAUmCu7YNslkPbVrS4H4T/eCub0PVY192u7/kd2cOSpDSUXUvrIXwKUiZNxrmSfmqfU3LtqJtzYGQ59X+79fIOuc4mMblMiSnF942J6r/sUIV7tYdy8wSZJ03HWW/WxbZRTQSCm87FfqclD7VaByZBePIrMB22qLxwKLxEfUh6aK3wKBid0OxBz+4FAnj6GyYpxZAm26GxiBDl5MC+BR425xo/mQHnPvqkTynFIbidDT+Wz1m5y6dqwrA60qfkxL4WXsb22Fdub9ftR7VW9CY82AjrCv3IXXh2M5zFAHryn1oXb4HwuWFZdYIZF9wfMTX7dhZjYZXNwTta994GO0bDyP99MkwjckOCkoLN2hNCblvcIPWqBdhB635g9I0up5D2Bi0RkREREREpCZ8TsBVdSTUrQLwNEfeiTYNMBYf+VMESTZFvU4iokTCuW7Rw7lu8WE4znULJScnB//85z/xP//zP1i9ejUOHz6M2tpamM1mFBYWYsaMGZyHlICiGvB2//33B4LZJEnyryIkBLKzszFnzhxMmDABqampAACr1Yo9e/Zg3bp1qK+vVwW9PfvssygoKMD9998fzfKIYktR0N3Kpm0JHvAmhIC7QR3wpstmwBtRLLlt1VC8DlWbOWdajKohIiIaXB6fD/+3eQPW11YF7btg3CScN2YCQ0+78NqcqH1qFTz1XcLd0kzIv/4k6DIHfnV0IiIiIiIKn1A8gKdJ3ajPjU0xRERD1NixYzF27NjeDyRKAOnp6fjRj34U6zICJEnC+PHjMX58ZIEJRAPp22+/RVtbGwD/ir933XVXv/orKioKBLxVVFT0uz6iwcfP0gjw2ozqgLfxxrDOS545oseAt5xLTuh3bUOFJElImVOKlDnBE/CFEvohEuvX+yCbDbDvqYPzQAMAIPuyWbBMKxrQWqPBVd2K6r8t73Z/3vUL+h3ups2I7LNtx546AIBzf0OgLesnx0N4fd2GuwGAr8WOuufXImlyAXKvmNO3YhOQu8EGSStDl54EX4cL1pX7oDg9SD1pLHRZ6kXjhNeHpmVbYVtfFlbfKXNHIePH0yBpJGT8eBqa398adEz2pbNgnlYIn9WBpve3QtLKSD15HAwFqdH49vrFNDZ7wK9hLM2CviAVbatD/25KOhnpP5yElLmjIMmh01rlZCPyr52vakuamAfnoUbo81KD/j/2JuPMqdBmWYIDKQdI80fb0fzRdmRfOgttaw7AdVj9MHj7hsOQzQZknD65176EImD79iCalgX/rnXV8skOtPS5aopIt0FrXYLR+hS01kN/DFojIiIiIiKKKqF4AFd1Z6Cbuz7yTmSLOtBNG73gFCKioYZz3Wgo4Vw3P71ej1NOOWVQr0kDJ2oBb7t378aDDz6oCmqbPn06HnzwQZx++unQaEJ/SKgoCj799FP87ne/w/fffx8IhnvooYdwySWXYOLEidEqkWjQyLKMnJwclJWVBdokRUDq5u+BLcED3nwdzVAcbao2fQ4D3ohiydGinsCjNWVCZ+pbIvFAk2UZc+fOhSxHbwVLIqKhimNm7+weD/763VrsbGpQtUsAlk6ZgdNG8HVqV942hz/craFd1a5NT0Le9Qugi3ACPFG84JhJRBQ+jplECchdD+DYh3wlQJ8Tq2qGFY6ZREREREPT/v37AfgnZc6aNQspKSn96u/Y848GxxElFOZbEAA5qxDA7s5tfXv3B3eRdeFMNP77O0AA+vxU6HJTAEVB5tnH9TvAa7iQNBKMpZlwHmoK2tf80XbVdsOrG2AoSIs4mGkwKW5vj+Fu2ZecANPo/gdk6TLMSF04BtaV+/vcR+O/uw8o7Mq+oxrV/1qJvKVzoTHq+nzNnrjrbejYUgl9fgrMUwrDPs/b5kTD6xvgPNiItMUTkXJiKYRXgZxiDMz3d9daUfX4V4FzUuaNRurCMdCmJQXaFLcXnsZ2WJfvRcc2/0J7qQvHomNHNbxNHQAA27oypP1gAtIX++f9Kx4f6l9eFwjQ603qonFI/+GkQF2W6UWwrtgDn80/v1qfn4r8n50U+Blr05KQe8WJYf8sBoM2xYT8m05Gzf99HdbxOVfMQdKkfEARsH69Dy2f7ezx+OS5o5B51lRIsgbpiyfCebARkl6GNtMC29oDgCwjZd4oaJPDC+M8lmw2RPS71VXK7FIYCtLgqm5F0rhcaNOSIISAz+ZEx+ZKWL/Zj6TJ+UhfPBHC40PFw5/2+VpHNby2odt91hV7kb54YsiQO5/dDeuKvbCu3NfvGoY8WdMZoNbHoDVoJbQ77UhJTzuyn0FrRETd4WeQRETh45hJFJ+E8AGu2s5AN1ctACWyTjQmwFjUGeqmTQ3cL6K+4ZhJREREFB+iFvB27733wufzAfBPdPvFL36Bxx9/vNtgt6M0Gg3OOOMM/PCHP8Qdd9yBv/3tb5AkCYqi4L777sMbb7wRrRKJBo1Go4HR2OUDciGAbt5IWl2JHfDmqT+obtDI0GUUx6YYIgIAOJrVAW+m9NExqqR3Go0GOTl8+JSIKBwcM3tmdTnxp/WrUdbWqmrXajS4efpszM7v+2TUocprdaDmqW/gbexQtWszzMi/YYFq0jZRouGYSUQUPo6ZRAnI3eXBSF0mJM3APEBKahwziYiIiIamhobOhWOKi/s/5+XYOXNer7ff/RENOj4vRQAMI0cBjZ0Bb/C0hH1u8swSJI3PheL0xHXoWLzLPOc4VfhWT9q+PYTMs6YOcEV91zWU7qicn86BeUpBVK+VceZUpJ02EZAkaHQyFI8PtvVl8NmcsK7YG9VrAYCrrAlNH2xD9k+O79P5issD68r98LW74Gt3wXW4CfqCNGRfNBOu6lbUvbAW8PkXe8g6fwZ0ef4gWUNxOnxWB5o/3gGfzYnUReOQNC4X9j11qHtujeoarZ/vQuvnuwAAcqoJ+vxUOHbXBtXStuYA2tYcCGrvKlQ4VuuXu9H27UFkXzgTDW99B6XDHdb3n/7DSUg7ZbyqTTYbUPzbM6DY3dAYdSGDuuKRsSQDpQ+fB8Xt9YdVHZk37rU6UPPESkAAybNHInXh2M7vSZaQMn9UtwFvpgm5SF8yCYaCtECbxqjzh8MdkXFm7P/uG4rSYSjqXIRYkiRoU0xIXTgWqQvHqo5NO3U8Wr/a02N/KfNHI2lCHmqfWd2nemqfWwPL9GIknzACAGDfU4u659b2qa+YiyRoTaeBpocQtq7tgxG0lhqVXoiIhj5+BklEFD6OmUTxQQgFcDccE+hWDYgIPxOT9ICx8EigWxGgy2KgW5RxzCQiIiKKD1EJeHO73fjwww8DL5rPP/98/O1vf4uoD41Gg8cffxxVVVV4++23AQAffvgh3G439Hp9NMokGjQejwcVFRXqRqF0l+8GmzuxA97cDYdU27rMEZDkqOVHElEfOFrUK5CaMuI34M3j8eCzzz7DkiVLoNPxIVQiop5wzOxevb0dD69bhTq7OqjMqNXijplzMTmLH0h05W21o+apVYEVtY/SZpqRfz3D3SjxccwkIgofx0yiBOTq8vClIS82dQxDHDOJiIiIhqZjHxY5ushpfzQ3Nwe+TktL63d/RIOPD1ARAF2GetvXDqG4IGkMYZ0uWwyQLeEdS6HpspPDPtZT39btPqEIeFvt8LU5oc9PgcYwOPc0hCLQ/OE2tK0OHRiWMn901MPdjtLoO+ewanQyUuf758/Zd9XCU9f9z6qv2jceRvvGwwCApCkFyDpvOmRz77//itODmie+gbvGqmp37K1D/Svr4TzUqGpvfOf7wNdJUwvgrmiFt9UOAHAeVB/bHZ/VAYfVEdaxkVI63Kh7PnSIli7bgrzrFsC2oQytX/jDI3N+OhvmKaEX65MkKayfYTw69vcPALSpJhTf9cPujzfokHftfFWYmeWEEcg69zhIWnnA6oyVtMUTYRqfC8Xp9f87IQQc++qhODwwH1cEQ2Fa57GnTUTrF7sivoZzfwOc+xvQ+O9N0FgMUNrDn6ufd+18aJL0qHniGwj3MQ+mhwpaCxmgFlnQmqaXELZoBa3FAu+nExGFj2MmEVH4OGYSxYYQAvA0Hwl0qwRclYAS4bPxkgwYCvxhbsYSQJ8DSUqMYP9ExTGTiIiIKD5EJYFpzZo1sNv9Hw5rNBo89thjfe7rsccewzvvvAMhBBwOB1avXo1TTjklGmUSDSpFUVTbkiIATeg3mm3u8Fapi1eehoOqbX3OqBhVQkQA4HVa4bGrJ2uZ0sfEqJrwcMVyIqLwccwMdritFX9avxqtLqeqPUVvwF2z56M0Nb2bM4cvT4sdtU99A2+zXdWuy7Ig7/oF0KaaYlQZUXRxzCQiCh/HTKIE0zXgTZ8bmzqGKY6ZRERERENPdnZ24Ovq6up+97d9+/bA15mZmf3uj2jQJW6OCEWTLg3+XwbR2eZpBgz5MSpo+JFkDfJ/dhJqnvim12MVZ+j7Fa3L96Dl052qtuLfnD6gnwkLIeDc34DmT3bAXdUa8pj0H05C6qJxA1ZDd+RkAzx14R8v6WRk/ngaJL2Mhtc3hnWOfXs16p0e5F+3IGifr90Fr9UBd40VzoONaN9U3m0/XcPdgq6zrf+vWQaLxqxH/s8WQrYYkH7aRKSfNjHWJcUd09gc5P98IRx76mAYkQnTuBxVCPFQIkkSjCPUr5ENRaHn9qSfNgEpc0tR86+V8DS0hzzGUJIBV3lzyH0Awgp3M43PRdb5M1Rj48g//BhCERBeX8IHrcUS76cTEYWPYyYRUfg4ZhINDuGxHgl0OxLqpth7P0lF8i9caiz2h7oZ8iFJUYm2oAhwzCQiIiKKvai8Ci4rKwPg/7BtxowZKC4u7nNfxcXFmDlzJjZu9H8Ifvjw4WiUSBR7QnT7QbvNHWFKeZxx16sD3nRZpTGqhIgAwNGiXvFUozXCkBJ6lUsiIqJEt6upAY9uXANHlw8csk1J+PWck5BntsSosvjlae5A7ZOrAqt4H6XLtiDv+pOgTTHGqDIiIiIiIgqH8HUAPpu60ZAXm2KIiIiIiIaIkpISAP5Amu+//x4ej6fPq9jv3bsXVVVVge1p06ZFpUaiwTRUA2UoMpIkQ2jTAG9LZyMD3gadsTQLaaeOR+tXe3o8Tk5Wf84rFIGG1zegY2tV0LEdWyuRetLYftUlfAo8je1Q7G7oi9Kh0cn+diHQ+O9NaP+u++CyzPOnI2V2bOaZGkdkwrm/Iaxj5WQDiu5cDI1BB6EIeK0OdOysgftw90FSRzn3N0AcM29Y+BTYd9ag/pX1/ao/ERXcvAiGYi7MFw7jiMyg4DMCZLMBedefhNpnVsNT1+ZvSzUh4/TJME8rhOL0oPyBj/rUt3lqITLPnw7ZpA+5X9JIkPR88JyIiIiIiIiGB+Hr6Axzc1YA3rbIO9Fl+8PcTMWAoRCSJvR7biIiIiKi4SQqnzY1NHR+0D1q1Kh+91daWhoIeDu2b6JEIoRQNygKIMkhj21zuVSLXCYaT4M64E2f0/9xgIj6rmvAmzGtFJKkiVE1REREA2djbRX+/v16eBRF1V6Skoq7Zs1HunHgVhxPVJ6mdtQ8tQq+VoeqXZeTjLzrF0CbzHA3IiIiIqK456pTb0s6QJcRm1qIiIiIiIaIuXPnwmQywel0wuFw4LXXXsOVV17Zp77+9re/Bb7Ozc3F+PHjo1UmEdHg02UEB7zRoFPc3t4PAtCxtQrWVfsgp5rga3PC1U0QmbfZHrI9XF6bExX//XFgW5OkQ/FdP4TGqINtQ1mP4W6m8blInjWyX9fvj9RF46A4PXDXWGGZWQLzlEI4DzcBQqDuubWB4zRJeuT//GRoDP7AV0kjIe3kcTDPK8VHH36IhdmT0fr+9h6v1b7hMDp2VMOxp67H42JNYzEAXgWK0xPRebosC3KuOhFVj37R7TEFt5wCQ0FaPyskArQpRhTediqUDjc0Zr0qjFY2G6DLTYanztZDD51SF45B2qkToDH2LdCZiIiIiIiIaKgQPifgqjoS6lbRt/u/2jTAWHzkTxEkmc/xEBERERF1FZWAN1nuDK3yesObRNATn88Xsm+iRKHVapGdnY2KiopAm6QIoJuVTR1eD0S1W92HToZGToyVUN1dAt502Qx4I4olR/N+1bYpfXSMKgmPVqvFKaecAq2WqxwSEfWGY2anFeWH8PS2TUE5yRMysnDHCXNh1nGVn648jUfC3axdwt1yU5B//QLIFkOMKiMaGBwziYjCxzGTKMG4atXb+lwu8DCIOGYSERERDU0GgwE/+MEP8MEHHwAA7r77bpx99tlIS0uLqJ/Vq1fjiSeeCAROnH/++dEulWhwJMa0NRoMugzAccxikwx4iwnZHPxZbsrcUWhb2zl3076jGvYd1Ue2WoKOV9FE9pdceBX4Olxw7KtH4783Be1X7B40/HsTMk6fjKb3tnTbj6TVIPviE1TBTINNo5OR+eNpqrakcbkAgOLfnoHWL3ZB+BSknToBugxz0PlarRannHoqkpOTYVu+D752V7fXanzn++gWHwHZYkDWRTMh3D7Uv7wu5DHG0kxknjcD+pxkAIDi9MC+pw4tH++At7UzBFCXk4y0U8ah+eMd8LU5A21HF5ErvOM0VD2mDnkzjctB1oUzucgcRZUkSd3Obcm7bgGalm2BfVt1yP3GMdnIOnc6dFmWgSyRuuD9dCKi8HHMJCIKH8dMor4TigdwVXcGurnrI+9EtqgD3bTJ0S+UooZjJhEREVF8iMqrsezs7MDX+/bt63d/x/aRlZXV7/6IYiFoAooQkHqYFKNsaVdtT5pVDFmO/weyhNcDT5N6tUV9DgPeiGJF8bnhtKr/TpoyxsSomvCZTFyZgYgoXMN9zBRCYNmBPXhzz46gfSfkFuDmGbOhZ1B4EE+DzR/udmSy9VH6vBTkXcdwNxq6hvuYSUQUCY6ZRAnE3SXgzZAbmzqGMY6ZREREREPT3XffjQ8++ACSJKGqqgpLlizBBx98gJycnLDOX758OX7yk59AURQIIaDVanHnnXcOcNVEAySG4UsUZ3QZ6m0GvMVE0sR8tHy+C1D8S6BlXXg8vC2OXs7qntTL4sNCEbBtLIOvzQnH/ga4ypp67dO+vRr27aGDlQCg4L9OgT4/tcd5tLGmTTEi6/wZvR539N5Q3rXzUfX35YCv69J0kcu96kTospNR+ZfPg/YlTcqHfWdN9/VMyIVjd53/2Mn5SF8yCfrclMD+0ofPg6vaivpX18HX6kDKgjFIXzIp6P+FxqiD5bgiWI4rgvAqsO+qgaSXYRqXC0mSYBqXi7a1ByFpZaScWAqNUQcA0Ocko+AXi2DfWQN9QRqSJufHNMSPhidtshG5l88JbHta7Gh6dzN8NieS54xE8uzSuB5/hjLeTyciCh/HTCKi8HHMJAqPED7/YqJHA91ctQCUyDrRmABjUWeomzaV934SDMdMIiIiotiLSsDb6NGjAfgftN+xYwd2796NCRMm9Kmv3bt3Y9u2bYHtMWPiP5SGqCuv14u6ujp1o6IA2h4C3g6qJ9vMPX38QJQWdZ7mCkDxqdr02Qx4I4oVl60aEMf+nZRgTCuNWT3h8Hq9+Oijj3DmmWdCp9PFuhwiorg23MdMRQi8snMrPinbH7RvUfFIXDNlBmRN/IckDzZ3vQ21T30Dn029crg+PxV5180Pudo70VAw3MdMIqJIcMwkShxCCMDV5fMHQ15sihmmOGYSERERDV1z5szBJZdcgtdffx2SJGHjxo2YMGECbr/9dlx00UXQ6/VB5/h8PqxYsQJPPfUU3nrrLf9rdvgXhrz11lsxcuTIQf4uiIiiTN8l4M3bBqF4IGn4nngw6fNSUHCzP0DLUJKOpPF5aPlid5/7s67cD/veeqQvnghPvQ1taw7CND4X6YsnQk4xouHNjejYXBmV2iWDFoW3ngpdhjkq/cXasfeG9PmpKLhxIey766AxatH84faI+9MXpSFv6bzAomzFvzkd7ZvK4etwI2lSPowjMyFpJLgqmuE42AjhVdD6+a7A+YW3/0AV5tYdQ0Eqiu9cAiFEWA/gSloNzFMLVW2y2YD00yaG7r8oHYai9F77JRosuvQk5F0zL9ZlDHu8n05EFD6OmURE4eOYSdQ9IRTA3XBMoFs1ILyRdSLpAWPhkUC3IkCXxUC3BMYxk4iIiCg+RCXgbc6cOUhNTUVbWxsA4L/+67/w2WefRfyCXQiBW265JbCdkpKCOXPm9HAGUeKQhIDUU9iER72CX/GYrAGuKDrcDQdV25qkVGjMnKRBFCteu3q1VJ0pA7KOCftERJT4vIqCJ7ZsxJrqiqB954wejwvHT+aHRiG469pQ+9Qq+Nq7hLsVpiHv2vmQk4IfRiMiIiIiojjmbQGEW92mZ8AbEREREVG0PPPMM9izZw++//57SJKE1tZW3HfffbjvvvuCAt4mTpyIQ4cOwePxAEAgMEUIgXnz5uHhhx+OxbdAFB383I2O0oaYC+htAfQ5g1/LMGcoTIOhMK1P5+pyU+Cpa1O1eWrbUP/SusB2+8bDaN94uD8lBjGWZiHz7GlDJtwtFENxBgzFGfA0d4Qd8JZ6yjjo81Ohy7QE/T/VppqQdkrwAtFHrwMA6T/o2yLsADivgoiIiIiIiIhoCBFCAJ7mI4FulYCrElBcvZ94LEkGDAX+MDdjCaDPgST18Cw8ERERERFFLCoBb7Is4/zzz8dzzz0HSZLw1Vdf4bLLLsOzzz4Lkym8UBmn04nrrrsOX3zxReDD4/PPPx+yLEejRKLYUwQwBOdFeOrVAW/67FGcAEIUQx5ni2pba8ro5kgiIqLE4fR68T+bvsXWhrqgfVdMmobTS8fGoKr4565tQ81T30DpUIc/6IvSkXfNPIa7ERERERElIleX90WyGZLWEptaiIiIiIiGIJPJhE8//RSXXHIJvvrqq8AcGCEEXC5XIMBNCIE9e/YEzpMkKbBvyZIlePPNNznvjRIap3/RUZJGB6FNAbzHhIO5mxnwFgeE1xfWcUV3LUHrl7uDAt4GWv7PF8I4InNQrxlL2vQk6AtS4a62dntM6sljkXbKeGiMukGsjIiIiIiIiIiIhhLhsR4JdDsS6qbYI+xBAgx5gLHYH+pmyIckRSVugoiIiIiIuhG1V9z33nsvXn31Vbjdbggh8Oabb2LdunW4++67cfHFF8NiCf1wSXt7O95880089NBDOHToUGCim8FgwO9///tolUc06IQQqm1JUbqd+Wb3egajpAHhblAHvOmyR8WoEiICAG/XgDdjiFV0iYiIEojN7cKfN6zBgdZmVbssSfjZcSdgfmFJjCqLb65qK2qfWRUU7mYoTkfetfM5YZyIiIiIKFG5atXb+rzY1EFERERENIRlZWXh888/x1/+8hf85S9/QUNDAwAEwt66Lnx4NPAtLS0Nv/rVr3DXXXcx3I0SHxPe6Fi6DHXAm6e5+2Np0GgM3U8Bl5MNSD15HCwzSyCb9PC1u6J6bUmrgZxshLcl9MOj6UsmDatwN8D/+iD/hpNgW18GoSjQ56RAk6SHoTgdkqyJdXlERERERERERJSghK+jM8zNWaG+VxsuXbY/zM1UDBgKIWn00S+UiIiIiIi6FbWAt5KSEjz66KP4xS9+EQhpKysrww033ICbbroJkydPxrhx45CamgpJkmC1WrF3715s374dXq83EIZ1dDXTv/zlLxgxYkS0yiMaVFqtFhkZGaitPeZBKyGCJnge1ex09Nify+uD26dEs8So8dSrA970OQx4I4olr0Md8KYzxX/Am1arxZlnngmtlis9EBH1ZriNmY0OOx5ZtwrVHTZVu0GWcevxJ+K4HAYZhOKqavWHu9nVQdKGERnIu3oew91o2BhuYyYRUX9wzCRKIO4uAW+G3NjUMYxxzCQiIiIaHiRJwq9+9Sv813/9F1577TV8/vnnWLVqFaqrq6EonXN40tPTMW/ePPzwhz/EFVdcgdTU1BhWTUQ0QHQZgKOsc5sBb3HBNDYHLZ/uDGynnjIO6UsmhZynKlsMUbmmoSQDOVfMgTbZCE9jO+pe/BaeevXn+ZnnHoeUE4fuPNKe7g1pjDqkLhwbg6qIiOIT76cTEYWPYyYRUfg4ZtJQJ3xOwFV1JNStom/3Y7VpgLH4yJ8iSLIp6nVSYuCYSURERBQfovpq7KabbkJDQwPuv//+wAQBIQQ8Hg82b96MLVu2qI4/GuoGIBAKJ4TAPffcg5tvvjmapRENOp/Pp25QBKAJXoXPqyiwunpeHfGfKw5Fs7SocjeoA9502UN3Yg5RIvA41QFvWmP8B7wBgMPhQHJycqzLICJKCMNlzKy0teGR9auCwpAtOj1+NWs+xqRnxKiy+OaqbEHtM6uhOLqEu43MRN7Vc6ExMNyNhpfhMmYSEUUDx0yi+CeEF3A3qhsNDL6OBY6ZRERERMOH0WjE1VdfjauvvhqAf75bS0sL3G43MjMzodPxvjsNUd0sZErDlK7LZ7MMeIsLhqJ05F49Dx3bqmAoSUfyrJHdLkKsL0gDvivv03WSphYg+4LjAY0ESStD0vivocuyoPC2H8Bd3YrmT3YAikDGGVNgKE6M+Wr9wXtDRETh45hJRBQ+jplEROHjmElDiVA8gKu6M9DNXR95J7JFHeim5d8P6sQxk4iIiCj2gtOm+unee+/FBx98gLy8vECAmyRJIScNHNsuhEBeXh6WLVuG+++/P9plEQ0qr9eL1tZWVZskFADBfw+6BlaEsq3KGtSWYoyPtOyuAW/67NIYVUJEAODtGvBmiv8Jc16vF8uXL4fX6411KUREcW+4jJn7WprwwNqvg14rZxpN+P3ckxnu1g1XRQtqnw4OdzOWZiLv6nkMd6NhZ7iMmURE0cAxkyhBuBsAKOo2fU5MShnOOGYSERERDU379u3D3/72t8Cf2trakMdJkoSMjAzk5eUx3I2GNua70bG6Brx5WyGEL/SxNKiSxuci+yfHI2V2abfhbgCQfEIJkqYUhNxnnl4csj3nyhNR+vB5yL18DjRGHTR6bSDc7ShJI8FQlI786xYg/4aThkW4G+8NERGFj2MmEVH4OGYSEYWPYyYlOiF8EM4qiNZvIWrfAir+BdS/B7R9F364m8YEJI0FMk4FCq4CCq+BlLUEkmUiw91IhWMmERERUXwYkISoM888E4cOHcIrr7yCF198EevXr4fDETrEymQyYfbs2bjyyitx+eWXQ6/XD0RJRLGniJALm4YT8HYkK1GlIM0YhaL6x9fRAqVDHSalyx4Vo2qIyOexw+tsVbXpjEN/0hwREQ0tm+tr8T/ffQu3on4goMCSjF/PXoBMU1KMKotvzvJm1D6zGsKl/tDFOCoLuUvnQqOPj4BoIiIiIiLqB1eXgAldJiSNITa1EBERERENMZ988gluv/12AEB6ejp+/vOfx7giothivhupdA14gwA8rYA+MxbVUB9oDDrk/nQOhBCo+uuX8NTbAADaLDOyzp+OzB9NQevyvRBeH4yjsmAozoAu0xzjqomIiIiIiIiIiBKfEIp/YU9nhf+PqxoQEYZtSXrAWAgYiwFjEaDL6nHBByIiIiIiii8D9oS3Xq/H1Vdfjauvvhperxc7duxAfX09WlpaIIRARkYGcnJyMHnyZGi1fNCchgEhAI0mqLm5m/DDntx1+ri4ePPtbjikbpAk6LNGxKYYIoKteiMglM4GSQOdOSd2BREREUVoVWU5nty6Eb4uCcdj0jJw56x5SNYzuCAU5+Em1D67JjjcbUw2cq88keFuRERERERDhatOva3PjU0dRERERERDUHt7O4QQkCQJM2bMgE6ni3VJRLEVB3PTKH5IGgOEbAZ8HZ2NnmYGvCUgSZKQd808tH61B9BISD9tov/zZL0WmWdPi3V5RERERERERERECU8I4b9/6qwAnJWAqxJQXJF1IsmAocAf5mYsAfQ5kKTg59OJiIiIiCgxDMpT3lqtFscdd9xgXIoobkmKEjKUrclpj7gvnRwfE+g8DQdV27qMYkhafYyqISJrxRrVtiVnGmRdUoyqiQzDXomIwjdUx8yPD+7Dy7u2BrUfl52LW44/EcYh+n33l7OsEbXProVwq8PdTGNzkHPlidDo5BhVRhQfhuqYSUQ0EDhmEiUAd61625AXmzqIYyYRERHREJSVlRX4OjeXYcpEiI/paRRPdBnBAW+UkLRpScg6f0asy0hovDdERBQ+jplEROHjmElEFD6OmRRvhMd6JNDtSKibEulz45J/Lpix2B/qZsiHJPH3nKKDYyYRERFR7PXrFZkQAhs3bsTOnTvR2NgIwD/ZbeLEiZg1a1bIMCui4UCn08FisaClpaWzUREhVzZtdjogmj2AU1G1603xvxKwu75LwFv2qBhVQkQuWw2creq/k6nF82JUTWR0Oh1+9KMfxboMIqKEMBTHTCEE3tizHe8f2Bu0b35hCW6YNhNaDVcaCsVxsBF1z6+BcPtU7abxucj56RyGu9GwNxTHTCKigcIxkyj+CZ8D8FrVjQaGTsQCx0wiIiKioSk/Pz/wdVtbWwwrISKKU7pM/8OJRzHgjYYp3hsiIgofx0wiovBxzCQiCh/HTIoHwtsBuI6EuTkqAF8fPlvSZfvD3EzFgKEQkkYf/UJp2OOYSURERBQf+hTw1t7ejj//+c/4xz/+oQ6wOkZaWhpuuukm/OpXv0JKSkq/iiRKNIqiwOv1qhuFgKQJEfDmcEDZ0q5qS04zoWRs9kCWGBW+9kbVti6jKEaVEFFb5RrVtqxPhjlnSoyqiYyiKGhsbERWVhY0DPAhIurRUBszfYqCZ7Z9j68ry4L2nVE6FpdNnAoNg8NDchxoQN3zayE8XcLdJuQi96dzIGkZ7kY01MZMIqKBxDGTKAG469Tbktb/YDUNOo6ZREREREPTnDlzIMsyFEXB9u3bY10OUezxMzrqSpeu3vY0xaYOohjjvSEiovBxzCQiCh/HTCKi8HHMpFgQPifgqvIvguGs6NsCGNo0wFh85E8RJNkU9TqJuuKYSURERBQfIn4ldvDgQRx//PF48MEH0dzcDCFEyD8tLS146KGHcPzxx+PAgQMDUTtR3PL5fLDb7ao2SVFCTnxrdjqgHHSo2mYvHgtZG/9vlBRXh2pbY7DEqBKi4U0oPlgr16raUopOhKTpU47roPP5fFi7di18Pl/vBxMRDXNDacx0+3x4fNO3IcPdLpkwBZcz3K1bjn31IcPdkibmMdyN6BhDacwkIhpoHDOJEoCrVr2tz4Yk8bV/LHDMJCIiIhqasrOzsWjRIgghcPjwYWzcuDHWJRHFFj+mo666Bs17WiGEEptaiGKI94aIiMLHMZOIKHwcM4mIwscxkwaDUDwQjsMQLasgal4DKp8AGj4AbFvCD3eTLYB5IpC5BCi8BlLhVZAyT4VkHstwNxo0HDOJiIiI4kNEyS9WqxWLFi1CZWUlAEDq5WF7IQQOHjyIU045BVu3bkVaWlqfCyVKeEJ0G/AGj1C1FY/NHqyq+kVxtqu2JYM5RpUQDW8dDTvgc7Wp2lKL58eoGiIiot51eNx4dMMa7GlRr+ouAbh+2kycXDwyJnUlAvveOtS/+C2EV/2wRNLkfORcOhtSAgRFExERERFRHwQFvOXFpg4iIiIioiHs97//PZYvXw4hBG6//XYsX74cWm1iLKxGFG29zQ2lYUiX0aXBB3itgC49JuUQERERERERERERRYsQPv/8LGeF/4+rFkCEC1xoTICxCDAW+/9oU3mvnYiIiIiIAAARPfl9++23o7KyEpIkQZIkCCEghIBGo0F2djaysrKg0WgC7UffeFRVVeG2224biPqJEociIEnqv3JeRYHV5Qw6NFHesiuuDtW2hgFvRDFhrVyj2jamjoQhuSBG1RAREfWsxenAA2u/Dgp302k0uP2EuQx364F9T23ocLepBci5jOFuRERERERDlRACcNepGw0MeCMiIiIiiraTTjoJv/3tbyGEwJo1a/CTn/wEra2tsS6LKDYSZQIbDRpJNvkfUDyWpyU2xRARERERERERERH1gxAKhKsOwroRou5doOJfQN2/Aes6wFWNsMLdJD1gKgXSFwL5lwFF10PKPhNS8lRIujSGuxERERERUUDYT3/X1dXhlVdeCbyhEEJgzpw5eP/999HW1oba2lrU1dXBZrPh/fffx9y5cwMhb0IIvPbaa6itrR2wb4QonhwNQVS1CSVo4luL0wExiHVFGwPeiGLP525HR91WVVtK8bwYVdM3kiQhOTmZNy2JiMKQ6GNmTbsN969ZgQpbm6o9SavDr+echJm5DCjtjn1XDepeXBcU7maeVoicS2ZBkhnuRtRVoo+ZRESDiWMmUZzzWgGly2IxDHiLGY6ZREREREPbH/7wB/z1r3+FLMt4//33MWHCBNx7773YtGkTvF5vrMsjGkR8z0Mh6DLU256m0McRDWG8N0REFD6OmURE4eOYSUQUPo6Z1BdCCAh3E0TbZoj6D4DKJ4Ha14HW1YCzHBBhfAYkyYCxGEibC+RdDBT/DFLO2ZBSZkDSZ/N3kuISx0wiIiKi+KAN98DXX38dHo8n8ALuiiuuwLPPPgtZllXHGY1G/OhHP8IZZ5yBa6+9Fi+88AIAwOv14vXXX8dtt90WveqJ4pRWq4XBYFBP7BQAurwBanY6BrewKBMMeCOKubaq9RBK51gjabRIKZgVw4oip9Vqceqpp8a6DCKihJDIY+Yhawv+tH412twuVXuawYj/N3sBSlJSY1RZ/OvYWYP6V9YBPnU8tHl6EbIvnMlwN6JuJPKYSUQ02DhmEsU5d5cFlDQmQE6OTS3EMZOIiIhoCBs1alTga51OB6/Xi/r6ejz44IN48MEHIcsyUlNTkZwc/utxSZJw4MCBgSiXaGDxOR8KRZcOuKo6tz3NsauFKEZ4b4iIKHwcM4mIwscxk4gofBwzKVzCYwWcFUf+VAKKPcIeJP8inMZiwFgEGPIhSWHHMhDFBY6ZRERERPEh7HcS3377LQB/SvXIkSPx1FNPBYW7HUuj0eCJJ57AypUrcejQIQDA2rVrGfBGw4KiKPD5fEHtkkYdvNDmcgUdk0gUBrwRxZy1YrVq25I3HbI+sf4uKoqCiooKFBcXQ6NhQA0RUU8Sdczc3liPv25cC6dPvapRntmC/zd7AXKSEuvfrsHUsb0a9a+uBxR1uJtlRjGyLpwJScOna4i6k6hjJhFRLHDMJIpzrjr1tiGPK2rGEMdMIiIioqGrrKxM9Vr76NdC+O/Re71eNDU1oampKew++dqdEhZ/dykUXaZ6mwFvNAzx3hARUfg4ZhIRhY9jJhFR+DhmUneEtwNwHQlzc1QAvrbIO9Fl+8PcTMWAoRCSRh/9QokGEcdMIiIiovgQ9iuxLVu2APBPOrvhhhug1/f+pkSv1+OGG24IbG/durUPJRIlHp/PB7fbHbyjy8Q3BSL4mASiuBnwRhRLzrYKuNoqVG2pRfNjVE3f+Xw+bN68OWQwJhERqSXimLmuphJ/3rA6KNytNDUNv597MsPdetCxrSp0uNvMEoa7EYUhEcdMIqJY4ZhJFOdcteptfV5s6iAAHDOJiIiIhiNJkvr0h4hoyNFlqLc9LYEQTKLhgveGiIjCxzGTiCh8HDOJiMLHMZOOEj4nhP0ARPMKiOqXgKqngcZPgfYd4Ye7adMBy1Qg60yg6AZIBZdBylgIyVTKcDcaEjhmEhEREcUHbbgHHrvy6IIFC8K+wPz5/qAZIUREq5cSDUVDbfKm4lIHvEkMeCMaVG0Va1TbWmM6krInxqgaIiKiYJ+XHcALOzYHxRpPzszG7SfMhUmri0ldiaB9SyUa3tgYHO52wghknT+D4W5ERERERMOEED7A3aBuNOTGphgiIiIioiGupKRkyM3tIeor/l2gkLoGvAkP4LMB2pTY1ENERERERERERETDhlA8gKsacFb4/7jrI+9EtgDG4iN/iiBpk6NfKBERERERURdhB7y1trYGvs7Lywv7Arm5nQ+ZWK3WsM8jGpKG2Ly3rgFvGga8EQ0aoXjRVrVO1ZZSdCIkSROjioiIiDoJIfDOvl14Z9+uoH1z8gvx8+NmQSfLMagsMbRvrvCHu3VJxkuePRKZ505nuBsRERER0XDibgTQZfVMPQPeiIgGQ1VVFbZt24aKigq0trbC5XLBYrEgNTUVWVlZmDp1KkaOHBnrMolQW1uLDRs2oKqqCq2trcjNzUVRURHmz5+PpKSkWJc36Jqbm7F+/Xrs378fVqsVsiwjNTUVpaWlmDJlCoqKimJdIsWxsrKyWJdAFD/4cRSFIpsBSQ8Id2ebp5kBb0RERERERERERBR1QvgAV21noJurFoASWScaE2As6gx106ZygRMiIhqSONeNEkUizHUTQmDr1q3Ytm0bampq4HK5kJSUhLy8PIwdOxbTpk2DwWDoc/+KouD777/Hjh070NjYCJ/Ph/z8fIwYMQJz586FVht2FBglkLD/r3o8nsDXer0+7Asce6zX6w37PKJEJkkSZFmGoqhvFkiaoRW8JBjwRhQz7XVb4XO3q9pSi+bFqJr+kSQJ2dnZvDlKRBSGRBgzFSHwwvbN+KL8YNC+00aMwlWTp0MTx/XHmm1TORrf+i443O3EUmSefRzD3YgikAhjJhFRvOCYSRTH3LXqbW0aJNkYm1oIAMdMoqFu8+bNeOaZZ7Bs2TKUl5f3enxqairmz5+PCy64AOeccw4yMzPDus7SpUvxwgsv9LdcAMCtt96Kxx9/POS+FStW4JRTTum1D71ej9TUVGRmZmLq1KmYNWsWLr74YpSUlERcT7jXNBgMSE1NRU5ODmbMmIH58+fj4osvRlpaWsTXHK6++uor/PGPf8Ty5cvh8/mC9lssFpx77rn4wx/+gNLS0gGtZdGiRfj666+j0tfJJ5+MFStWRHzeO++8g//93//FypUrg+ZqHKugoAA//OEPce+992LEiBH9qJSIiGj4kSQJQpehvl/haQZMI2NWE9Fg470hIqLwccwkIgofx0wiovBxzBy6hFAAd8MxgW7VgIgwl0DSA8bCI4FuRYAui78rNKxxzCQa2jjXjXPdEkk8zXXrTk1NDf7yl7/gpZdeQkNDQ7fH6fV6zJ49G1dddRWuu+66sPtvaGjAI488gueffx5NTU0hj8nMzMRFF12Ee++9F7m5XBR+KBlaaVNEcUKr1XYThDh03gAJnxfC61a1aQyWGFVDNPxYK9aotk0ZY6C3JOaLNK1Wi3nz5jFNmIgoDPE+Znp8PvzvpnUhw93OHzsRSxnu1iPbxsMhw91S5o1C5jkMdyOKVLyPmURE8YRjJlEcc9Wptw15samDAjhmEg1Nu3btwplnnokZM2bg73//e1gT3gDAarXio48+wrXXXov8/HzccMMNOHTo0ABXG31utxsNDQ3YvXs33nrrLdx1110oLS3FBRdcgKqqqgG5psvlQn19PbZv346XXnoJN954IwoLC3HnnXfC6XQOyDWHCq/Xi9tuuw2nnXYavvjii5AT3gCgvb0dL7/8Mo477ji89NJLg1xl35lMpoiOLysrw8KFC3HBBRdgxYoVPYa7AUB1dTWee+457Nq1qz9lEhENffxMj7qjy1Bve5pjUwdRjPDeEBFR+DhmEhGFj2MmEVH4OGYOHUIICHcTRNtmiPoPgMongdrXgdbVgLM8vHA3SfaHuaXNBfIuBop/BinnbEgpMyDpGWpFxDGTaGjiXDfOdUskiTLX7e9//zvGjBmDxx57rMdwN8D/O7hq1So8/fTTYff/wQcfYMyYMXj00Ue7DXcDgKamJvzzn//E5MmT8c4774TdP8U/vhojGgA+nw9eb4ibB0PoZoDi6ghq0xjMMaiEaPjx2JvQ0bBd1ZZaNC9G1fSfz+fDvn37MHbsWMiyHOtyiIjiWjyPmXaPB49/txY7mtQ3LyQAS6fMwGkjRsWmsARh21CGxne+Dw53mz8aGWdN5QeLRH0Qz2MmEVG84ZhJFMdcteptPQPeYo1jJtHQ8+STT+LWW28NOdFKp9Nh8uTJyMrKQlZWVmCiVnV1ddDkNo/Hg6eeegrPPfccGhoaEn51TkVR8M4772D58uX46quvMH369AG/pt1ux6OPPopPP/0Uy5cvR1ZW1oBfMxHdfPPNePLJJ1VtqampmDlzJrKyslBeXo4NGzYEJsPZbDZcddVVMBgMuOiii2JRckQuuOCCsI/duHEjzjzzzKBJdWazGdOnT0dubi70ej2ampqwY8cOVFdXR7tcGgK2bNmCzz77DDt37kRjYyMAICsrCxMnTsTixYsxY8aMGFdIFCP8aIq6w4A3GuZ4b4iIKHwcM4mIwscxk4gofBwzE5vwWAFnxZE/lYBij7AHyb9AprEYMBYBhnxIEqMSiLrDMZNo6OFct9A41y1+xftcN0VRcP311+PZZ58N2jd27FiMGjUKmZmZ6OjoQGVlJbZv3w6XyxXRNV544QVcc801QQuXjhs3DpMmTYJer0dZWRk2btwYOKapqQkXXXQR3nnnHZx99tl9/wYpbvBdC9EAUBQFHo8nqF3SDJ2Zbwx4I4oNxetE1cZ/AKLzBZwk65FccEIMq+ofRVGwZ88ejB49mjeJiIh6Ea9jptXlxJ/Wr0ZZW6uqXavR4KbpszAnvyg2hSWItnWH0PTu5qD21IVjkH7GFIa7EfVRvI6ZRETxiGMmUXwSigvwtqgbDbmxKYYCOGYSDS1/+MMfcO+996raJEnCBRdcgOuuuw4LFiyA2Rz6M9Cqqip89tln+Pe//42PPvoo0O71ekMvBtaDW2+9FbfddlvE9QNASkpK2McWFhZi1apVQe0ulwt1dXVYt24dnn32WezevTuwr6WlBaeffjp27dqF9PT0iOvr7poOhwNVVVVYvnw5nnrqKVVA1/bt23HWWWdh9erVHGu7+Mc//qGa8CZJEn73u9/hrrvugsViCbQfPnwYt9xyC5YtWwYAEELgqquuwoQJEzBt2rSo1/X666/3aTXa9957D7fffntg22w245JLLgnr3L179+L0009XrWg6btw4/PGPf8SZZ54Jo9EYdM7hw4fx/vvvh5yQR8PPpk2bcPvtt4cco476zW9+g/nz5+Oxxx7DCSck7mfyRH3Bz6eoWyEC3oQQ/J2hYYP3hoiIwscxk4gofBwziYjCxzEzsQhvB+A6EubmqAB8bZF3osv2h7mZigFDISSNPvqFEg1RHDOJhhbOdfPjXLfEEa9z3Y516623quaSybKMn//857jtttswevTooOPdbje+/vprvPXWWzh48GCv/W/YsAE/+9nPVOFuM2fOxD//+U/MmjVLdWxVVRXuuOMOvPnmmwD8Qa2XXHIJ1q9fjylTpvT1W6Q40aeAN07EIOqjIfR3J1TAm8SAN6IBJYSC6k1Pw9VWqWpPKZwDjTb4AQUiIqLBUG/vwMPrvkGdXf360ChrcccJczE5KydGlSWGtrUH0fSfLUHtqSePRfrpk/n+m4iIiIhoOHPVdWmQAT1XdyMiipa33noraMLbpEmT8Nprr4U1KaiwsBBXX301rr76amzduhUPPfQQ3njjjT7VkpaWhpEjR/bp3EhotdpurzN+/HgsXLgQt99+O375y1/ib3/7W2BfXV0dHn74YTzyyCNRvebEiRNx2mmn4Ve/+hUuuugifP7554F969atw/PPP49rr7024msOVVarFb///e9VbY8//jhuueWWoGNHjBiBd999FxdddBHefvttAIDT6cRdd92FTz75JOq15eXl9em8rrVcfPHFqsl73VEUBVdeeaUq3O2iiy7CSy+9BL2++wc6RowYgV/84hf4xS9+EfHkVBpa/vOf/+Cyyy6D0+mEECLQfvSe/LFtq1atwsKFC/Hqq6/i3HPPHexSiYjiT9eAN8UFKHZA5txBIiIiIiIiIiKi4U74nICrCnBW+P94miPvRJvuD3QzFgPGIkiyKfqFEhERJRjOdVPjXLf4F89z3Y768MMP8fe//z2wnZycjA8//BAnnXRSt+fo9XosXrwYixcvDmv+2a233gqXyxXYnjt3Lj7//POQYYyFhYV44403kJWVhf/7v/8D4A8XvPPOOwf050CDQxPJwZIkQQiBkSNHQpblsP6MGjUqcL4QIuzzZFmGVtun/Dmi+DWEwhlE14A3jQxJy+R7ooHUsPMtdNRvVbXpzLnInnB+jCoiIqLhrrzNivvXrAgKd0vRG/C7uQsZ7tYL6+oDocPdThnPcDciIiIiIgLcteptfTYkiZ+dERFFw549e3DNNdeo2hYuXIjVq1f3acXHadOm4fXXX8eyZcuQk5PY98S0Wi0ef/xxnHLKKar2F198URV4FE1paWl4++23gybHHTvxjoDHHnsMzc2dD0CccsopISe8HaXRaPCvf/0LmZmZgbZPP/0UK1euHNA6w1VRUaGa6Agg7EmOjz/+ONatWxfYPvXUU/Hqq6/2GO7WFeckDV+7d+/GpZdeCofDASEEJElSBbsdHeuOtkuSBKfTicsuuwy7du2KZelERPFBmwJ0vT/h7sNDmkRERERERERERJTwhOKBcByGaFkFUfMaUPkE0PABYNsSfribbAHME4HMJUDhNZAKr4SUeSok81iGuxEREYFz3XrCuW7xK97nurW1teHGG28MbEuShPfee6/HcLeuept/tmLFCqxduzawrdfr8fLLL4cMdzvWX//6V4wfPz6w/emnn+Krr74Kuy6KTxEFvB11dDJbuH+idS5RotBoNCEHY0nTp79ycUnpEvCm+f/s3Xd8U/X6B/DPyWjSvQelZcoegrKHCOK86nUjOFBwcN33OnAzRFGv+nNdvYIIuEDcW1GuiOwtFGQXuigt3Stpxvf3RyHtSTpO2iQnST/v16sv+X5zxvM17dP05MlzDOFsQEHkRSVHf0NJ5irZnFYfjrRh90IbEth3wNVoNOjUqRM0QZQjiYi8xZ9y5l9FhXhmw+8oNZtk84mhYZg1ahy6RseqFFlgKFt7CMXf7nKZjzmvN2Iv6MPX1kQe4E85k4jI3zFnEvkps1ODN0OyOnGQDHMmUXB49NFHUVlZ6RgnJSXhs88+Q0xMTJuOe9lll2H37t2IjIxsY4TqkiQJDzzwgGwuPz8fe/fu9do5IyMj8eCDD8rmdu3ahfz8/Cb2aH8++OAD2XjmzJkt7pOQkODSNO3999/3aFyttWTJEtjtdse4d+/eGDVqVIv71dTU4JlnnnGMDQYD3nnnHWi1Wq/EScFnxowZMJlMjuZtQgjodDqMHDkS1113Ha699lqMHDkSer1e1uzNZDLhzjvvVDl6IiL1SZIE6J3eC7aywRu1H7w2RESkHHMmEZFyzJlERMoxZ6pLCBuEKReidCNE/qdA9n+Bgq+A8m1AbYGyg2hCgbAeQNwEIHVqXVO3hAsgRfSBpAvs99qJ/A1zJlFwYK1b81jr5p/8vdbtP//5D3JychzjadOmYcKECR49x7fffisbX3XVVejWrVuL+4WEhLg0w1u8eLFHYyPfc/vVWMO7k3rziyiQabVa6PV61weC6Hu7sQZvROQdVQUZKMhYLpuTNDqkDrkLIeGB3RkbqMuZgwcP5ocuiIgU8JecuTU/Dy9sXotqq0U23ykyGrNGnYuU8MC+qOdtZWsOovi73S7zMRP7IPZ8Nncj8hR/yZlERIGAOZPI/wghAPMJ+WRIijrBkAxzJlHg++uvv/D111/L5t544w0kJiZ65PhJSUkwGAweOZaaRowY4TKXnZ3t1XNecMEFLnN//fWXV88ZKHbu3InMzEzHODU1tdH/X4259dZbZeNvvvkGNpvNo/G5SwjhUnTmXJzXlE8++QSlpaWO8XXXXYczzjjDk+FREMvIyMCaNWscjd0A4MEHH0R+fj7WrVuH5cuX45NPPsG6deuQn5+Phx9+WLb/unXrsGuX681biIjaHX2cfFzLBm/UfvDaEBGRcsyZRETKMWcSESnHnOlbQtghzCcgyrZCnPiyrqHbic+Ask2AOQ+AvcVjQAoBQrsCsecAHaYAabdDSrwEUuQASPoYfn6CyIuYM4kCH2vdlGGtm3/x91o3IQQWLlzoGEuShMcee8yj5wCANWvWyMYXXnih4n0vuugi2fjbb79FbW2tR+IidbjV4E0I4bMvokBms9lgsVhc5oPpOoPdVCEbs8EbkXfYaquQt+NdAPLfjckDb0ZYfA91gvIwm82GHTt2qP5BEiKiQOAPOXN19lG8um0DLHb5G3G94hLw5MhzEGsMVSmywFC6+gCKf8hwmY+9sC9iJ/ZWISKi4OUPOZOIKFAwZxL5IVsFYK+WzxmS1YmFZJgziQLfq6++KqtJSE9Px9VXX61iRP4pNjbWZa642LuNO9LT013mTp486dVzBoqffvpJNh43bpziDzr07t0bKSn1jWILCwuxdetWj8bnrv/973+yIj69Xo+bb75Z0b7vvvuubOxc1EfUnM8//xxAXQ2cJEl4/fXX8e9//7vRnBcTE4MXXngB//nPfxzbA8AXX3zh05iJiPySc4M3Cxu8UfvBa0NERMoxZxIRKcecSUSkHHOmdwkhIGqLIMp3QhR8B+QsAPKXA6XrAFMWIKwtH0TSAsZ0IGYkkDIJSL8TUtLlkKIGQwpJZEM3Ih9iziQKfKx1U4a1bv7F32vdVq1aJatdGzt2LLp37+7RcwBATk6ObNy/f3/F+3br1g1hYWGOcVlZGdauXeux2Mj3dEo3bPjNSUTNs9vtsFobuVAhudVT0a9Zy0/IxtpIz3T5JSK5ksxVsFvkH+SM73EpotNcO0kHKrvdjqysLPTv3593AiAiaoGaOVMIgW8PH8An+12bk52d3AH3DB6OEObxZpX+th8lP+91mY+9qB9izu2pQkREwY2vM4mIlGPOJPJDZvk1eGgMgC5GlVBIjjmTKPCtXLlSNr711lv589yI8vJylzmj0ahCJAQAGRny67IjR450a/+RI0fiyy+/dIz37NmD4cOHeyS21njvvfdk48suuwxJSUkt7ldTU4NNmzY5xkajEWPGjPF4fBS8tmzZAqDuzrsjRozA3Xff3eI+M2bMwEcffYR169ZBkiRs3rzZ22ESEfk/NnijdozXhoiIlGPOJCJSjjmTiEg55kzPE5YywJR96ivH9aaULZIAQ0pdUzdjGmDoAElS3L6AiLyIOZMo8LHWTRnWuvkXf691++2332Tj888/32PHbsi5yWBMTIxb+8fExKC6uv61+e7duzFhwgRPhEYqUPwXUufOnb0ZB1H7EESd5a1l8g+X6aKSVYqEKHjZLNUoyVwlmwtPHoj4npepFBEREbVXdiHw8V+78GPmIZfHxqV1wfQBg6HVBE8zY28o+XUfSn/9y2U+7pL+iD6nhwoRERERERGRX6vNl49DUnj3WiIiD8jKysLRo0dlc+PGjVMnGD+3bds2l7lu3bp59ZzZ2dkucwkJCV49Z6DYu1d+44gzzjjDrf2d7zDqfDxfKi0txRdffCGbmz59uqJ9t2/fLrvZ3pAhQ6DX6wHU3bjyww8/xPfff49jx46huLgYMTExSE1NxdixY3H55Zdj4sSJnlsIBaS//qq/Tj916lTF+918881Yt24dAGDfvn0ej4uIKODo4+VjezWEzQRJyw9JEBERERERERER+TthrQLMp5q51WQDNteGKC3SJ9Y1cwtNBwwdIWlCPB8oERFRO8daN+VY6+Zf/L3Wzfnmjqcb0FmtVnz//ff4+OOPsXPnTuTm5kKSJCQmJqJv3744//zzMWXKFCQmJio6T0hICGprax1js9nsVpzO26tZ80dtxxbYRD4UTB++spU7NXiLZoM3Ik8ryVwFu7WmwYyExN5XBVUuISIi/2e127Fg1zasy81yeeyy7r0wqVc//m5qhhACpb/uQ+kq1w98xV06ANFj3Ls4RURERERE7YTZqcGbgdfgiYg8Yf369bKxJEkYMmSIStH4t//+97+ycXx8PPr37+/Vc/7yyy8uc7179/bY8U+ePInKykqPHa8pMTExbt9tsyWHDslvvtGpUye39nfe/uDBg22OqbU++ugjmEwmx7hjx4648MILFe27detW2bhPnz6wWq147rnnMG/ePFgsFtnjBQUFKCgowM6dO/HGG29g+PDheOutt3DWWWe1fSEUkEpLSx3/duf74PS2QgjZMYiI2i1dNAANAHv9nKUY0KaqFRERERERERERERE1QdhMgDkXMGXXfVmK3T+ILrauoZsxHTCmQdKGej5QIiIikmGtm3KsdWu99ljr1lgN2q5duzB16lTs3LnTZfvKykpkZmbi+++/xxNPPIF//etfmDVrFrRabbPniY+Plz2Hx48fR79+/RTFWFNT41KjtH//fkX7kn9igzciL9BoNNDr9a4dNDXB0/jCWiZv8KaN4ofLiDzJZqlGSeYq2Vxkh7NgiAy+QkiNRoNevXpBo9GoHQoRkd/zdc40Wa14bftG7Co84fLYjX0H4uKuPXwSR6ASQqBk5V8o+831wkn85QMRNap7I3sRkafwdSYRkXLMmUT+RQg7UFsgnwxJUScYcsGcSRTY8vLyZOOUlBRERUWpFE290tJSl7utKqHRaNwuflLilVdewZdffimbu/3226HTea/EpKqqCq+88opsrn///ujQoYPHzvHQQw9h6dKlHjteU2bNmoXZs2d77Hh2ux0VFRWyuaSkJLeO4bx9WVlZm+Nqrffee082vvXWW1ssdjvt+PHjsnFKSgomTZqEL774QtH+mzZtwtixY/Hhhx/iyiuvVBYwBZWG3/vx8fGK94uNjXX82/nnkYioPZIkDYQ+FrAU1U9aigFj8NU1ETnjtSEiIuWYM4mIlGPOJCJSjjmzZcJuAcx59Q3dnOuQlNBGnGrmdqqhmy7S84ESkdcxZxIFNta6KcNat7Zpb7VuZrNZ1jhNq9Xi8OHDuPDCC1FTU9Pi/lVVVXjmmWewceNGfP7554iMbPp1cp8+fXDs2DHHeOPGjZg4caKiOLds2QKbzSabU7Pmj9qODd6IvECr1UKn07k0eJOkJhq81Qr5/jr//0PJWi5v8qGL5ofLiDypJPN/sFuqZXPxPS5VKRrv0mq1Hu1ITUQUzHyZMytqzfj3lvU4XCq/O5NWknDnmUMwuqPnL6YFEyEESn7ag7LfXe8OEH/FmYga0U2FqIjaF77OJCJSjjmTyM9YigBhlc8ZeJMVf8GcGbyEsMNWW6V2GEFLGxIOSVL/PdDiYvm1Lk/febK1XnvtNbz22mtu7xcdHe1yl8bWMJvNOHHiBDZt2oQFCxbg119/lT3es2dPPPbYY20+T1PKyspw/fXX48iRI7L5e++912vnDCSN3Yk1NDTUrWM4b69Wg6qdO3di+/btjrEkSbj11lsV7+/8/b548WLk5OQ4xhdffDFuuukm9O3bFyEhITh69Ci++eYbLFq0CBaLBQBQXV2NyZMnY926dTj77LPbtiAKOHa73fFvpY0FnbdteAwionbNpcFbUdPbEgURXhsiIlKOOZOISDnmTCIi5ZgzXQlhA8z59Q3dzPkA3Hw/QxMKGNPqm7rpopv+PDQRBQzmzODFWjfvYq1b81jrxlq35vh7rVtJSYlsHBISgiuvvNLR3C0hIQF33303JkyYgJSUFJSXl2PTpk145513sHv3bsd+v/zyC6ZNm4ZPP/20yXONGzcOP/30k2P84Ycf4oknnlD0Ovv99993meNNKQMbG7wReYHVakVtba3rA00kWlFikY3jU9TvnNsSa5lzgzd+uIzIU2yWapRkyv+YiOhwFgxRHVWKyLusVis2b96MYcOGebUbNhFRMPBVziyqqcbzm9cir1L+B79Bq8X9Z43AmUls7tscIQRKfsxA2ZpDLo/FXzkIUcO7qhAVUfvD15lERMoxZxL5GXO+fKyLgqQNUycWcsGcGbxstVU4/MuDaocRtLqf/zJ0BvXv6F1UJG86ER0d7db+CQkJLsdoTOfOnVt1l1JvO3bsmNtF+AMHDsQ333zT6ru/Wq3WRv9fmEwm5ObmYvXq1Vi4cCFOnJC//zxkyBBMmzatVecMNo0VvRmNRreO4Vz01tgxfWHRokWy8fjx49Gtm/KbYTgXeZ5u7qbX67F06VJMnjxZ9nifPn1w8cUX46677sJFF13kuLOx2WzGlClTsHfvXreafBEREVED+jj52FLS+HZEQYbXhoiIlGPOJCJSjjmTiEg55sy6pj6oLWzQ0C3P9WaSLZFCAGPHUw3d0gB9Ahu6EQUh5szgxVo372Ktm39grVtg8vdaN+f6s5qaGkdzt7Fjx+Lrr79GbGysbJshQ4ZgxowZeOihh/Dqq6865j/77DN8+OGHuPHGGxs91/XXX4+nnnoKVmvda/X9+/fj7bffxl133dVsjNu3b8fSpUtd5tngLbDxlRiRFwghYLPZXB9o5AWEsAqgXL5tYkf3XlypwVom/4CZLooN3og8pfTob7BbqmVzCT0uVSka7xNCoLCwEEIItUMhIvJ7vsiZuRXleH7zWhSbamTzEfoQPDR0FHrExnvt3MFACIHi73ajfN1h+QMSkHDVYEQO7aJKXETtEV9nEhEpx5xJ5GdqjsnHIWyy7U+YM4movejXrx9mzJiBO+64AyEhIa0+Tm5uLrp2de+GB3369MH333/v8eLiJUuWYMmSJR49plrcLV70hw9jmM1mfPzxx7K56dOnu3UMu93e6PxLL73k0tytoQEDBuCbb77BiBEjHAVzBw4cwIoVK5rdj4iIiJqhd3rf2NLyh0OIggGvDRERKcecSUSkHHMmEZFy7TFnCiEAS/Gphm45gDkHsJvdO4ikBQyppxq6pQMhSZAkjXcCJiK/0R5zJhG1T6x182/+VOvWVP1Z586d8d133zXZHFCr1eL//u//cOzYMXz55ZeO+WeffRZTpkyBRuP62rpLly644YYbZM3a/vnPfyIxMRHXXntto+fJyMjAZZdd5qhxa6ixc1Dg4LNH5EPOv0hMVitQYnHZLrFj6zrC+ordVAlRK28+pYtmgzciT7DUFKP4yC+yuYiUwTBEpakUERERtScHS4owd8PvLs3d4oyheHrkODZ3a4EQAsXf7mq8udvVZ7G5GxERERERtUhYq4CaTPmkkdcGiYg8JS4uTjYuKytTKRK5WbNmQQjh9pfz3SQ9qbS0FKmpqW0qeHOX0WjE/fffj61btyIpKcln5/V3ERERLnOn7xqqlPP2jR3T27788ksUFxc7xrGxsbjqqqvcOkZjcXfv3h333ntvi/ueffbZmDp1qmzuww8/dOv8RERE1IBeftdy2Coh7LXqxEJERERERERERBRkhKUMoiIDovBHIOdd4PiHQMnvQM1hhc3dJMDQAYgeBiRfBaTPgJR8FaTooZAMKWzuRkREFABY66Yca938h7/XujV1rLlz5zbZ3K2h//u//5M1Wtu3bx+2bdvW5PavvPKKrGlgbW0trrvuOlx22WX45JNPsHv3buzbtw8//fQT/vGPf+Dss89GXl4eACAtTf4ZgpiYmBbjI//l2RaQRNQ8pwZvWeVlEMXyzpnh0UaERRh8GZXbrGUnXOa0UWzwRtRWwm5F3vaFsFvkDRTje16qUkRERNSe7CzIx+vbN8Jss8nmUyMi8eiwMYgPDVMpssAg7AJF3/yJio1OjRgkIOHasxF5Vid1AiMiIiIiosBStRdAgzuDSTogvKdq4RARBZv4ePkNDNwtetuxYwdsTtfPAGDjxo2YPHlym2LzhY4dO2Lt2rWyOZvNhpKSEvz555/48MMPsXr1agB1dyO9+uqr8eabb+Luu+/2eCwhISGIjo5GYmIiBg8ejNGjR+P6669HbGxsyzu3M00VvblTuOYPDd4WLVokG99www0wGo1uHaOxuG+88UbFd229+eabZXGsXbsWNpsNWq3WrTgosJ3+ftm4cSOOHj2qaJ/8/HzZ+I8//nDrLvfnnHOO4m2JiAKGPhaABKBBPrQUA4YUtSIiIiIiIiIiIiIKWMJaBZizAVMOUJMN2MrdP4g+EQhNr7uZpKEjJI3vGpwQERGR57HWjbVugcjfa90aO5bBYMB1112naP/OnTtj3Lhx+O233xxzq1evxtChQxvdPi4uDt9++y0uueQSZGVlOea/++47fPfdd02ep3v37njuuecwadIkxxwbvAU2Nngj8gKtVouQkBCYTCb5A05FxZllJRAlFtlcUsdob4fXZtZyeYM3KSQUGqPvC8CJgs3J/d/AVHJYNheZOgzGqHSVIvINrVaLQYMG8YMTREQKeCtnrsvNwjt/boXN6cNI3WPi8PDQUYgM8e8GxGoTdoGir3aiYvNR+QMSkDhpCCIGBffvciJ/xdeZRETKMWcS+QchBFCZIZ8M6wlJw7/J/AlzZvDShoSj+/kvqx1G0NKGhKsdAgCgQ4cOsnF+fj7Ky8sV3XkRANLTG7/Oo7RBkNp0Oh26dOnS6GNDhgzB9OnTsWDBAsyYMcPRuOi+++5Dr169MHHixFads3PnzgHz/8dfaTQaREREoLKy0jFXWFiIxMRExccoKCiQjX1d7HXs2DH873//k81Nnz7d7eM0FveIESMU7z906FBotVpH8Wp5eTlyc3PRqRNv0NHeCCFaXawshMC5556reHtJkmC1WlvekIgowEiSDkIXDVhL6yfZ4I3aAV4bIiJSjjmTiEg55kwiIuWCJWcKmwkw5wKm7LovS7H7B9HF1jVzM9Y1dZO0oZ4PlIgCWrDkTHLFWjfvYq2bf2CtW2Dy91q3qKgoaDQa2O31N2QfNGiQWzcqHTFihKzB219//dXs9v369cOWLVtwzz334LPPPmvxppLXXnst3nrrLfz888+y+ZQUvhcfyNjgjcgLNBpNo3/sSBqN4992IXC0vBSiVF7EmZCq7AWVmqxl8gZvuqhkxXfEJqLGVRbsRvHhn2Rz+rAEJA+YolJEvqPRaNC5c2e1wyAiCgjeyJk/Zh7Eh3t3ucwPTEzG/WeNgFHHPxubI+wCJ7/Ygcqtx+QPaKS65m5npqkTGBHxdSYRkRuYM4n8hCkbsDrdfTeyvzqxUJOYM4OXJGmgM0SqHQZ52ejRo2Vju92Obdu2Yfz48SpF5H/uuOMO5OfnY9asWQDq/h/dcsst2Lt3r+LiQH918uRJWeGYt8TExHi8gVqPHj2wY8cOx/jYsWPo27ev4v2PHZNfv+zRo4fHYlNi8eLFsqK4s846C4MGDXL7OD179nSZcy5mbU5oaChiYmJQVFTkmCsqKmKDt3ZIkqQWiyUb2+c0d/clIgpa+jjXBm9EQY7XhoiIlGPOJCJSjjmTiEi5QM2Zwm4BzHn1Dd1qC1reyZk24lQzt1MN3XSscSCi5gVqzqSWsdatfWCtW8tY69Z27a3WTafToWvXrjh8+LBjzp36MwBITU2VjRvWojUlKSkJK1asQEZGBlasWIFVq1bh6NGjKCoqQmhoKNLT0zF27FjcdNNNjhueOjeOGzJkiFtxkn/hJ/WJvMBqtcJsNrs+0KDYs8RUA7PNBljlRZ+h4SHeDq/NrOVODd6ik1WKhCg4WGpKkL9zsXxS0iL1rDug1YepE5QPWa1WrFmzBueccw50bCJERNQsT+ZMIQQ+2b8H3x7e7/LY6NR03HHmEOgaNCgmV8IucPLz7ajcliV/QCMhafJQhA/oqE5gRASArzOJiNzBnEnkJyoz5GN9PBDCO235G+ZMosDWqVMndOnSRXaXzd9//51Fb06eeOIJfPXVV44iq9zcXMydOxcvvfSSypG1zUMPPYSlS5d6/TyzZs3C7NmzPXrMPn36yIreDh065Nb+R44ccTmerwghsGTJEtnc9OnTW3Wsfv36ucwZDAa3juG8vclkalUsFPjaciNDpfuyERwRBT19HFDT4HUGG7xRO8BrQ0REyjFnEhEpx5xJRKRcoORMIWyAOb++oZs5H4C9xf1kNKGAMa2+qZsuuk3vbxBR+xMoOZOIGsdaN2VY69Y27bHWrV+/frIGb76sP+vfvz/69++PuXPntrjthg0bZOPhw4crPg/5H74SI/ICIYTs7tOnNbx2Yg/gIk5bmbzBmzaKDd6IWkvYbTi+413YauUdlJP6XANjTBd1gvIxIQQqKipY3E5EpICncqbNbsei3Tvwe85Rl8cu7noGpvQZCA3f+GuWsAuc/HQbKndkyx/QSEiaMgzh/VMb35GIfIavM4mIlGPOJFKfsFUB1YflkxH9WZTph5gziQLfBRdcgAULFjjGixcvxlNPPQWtVqtiVP5Fq9Xi5ZdfxoQJExxzb775Ju6//36kp6erGFn71b9/f9l4w4YNuPfeexXvv379+maP502//vqr7K6qoaGhmDJlSquO1adPH+j1elgsFsdcaWmpW8dw3j4+Pr5VsVBg6tSpE19jExF5kj5OPmaDN2oHeG2IiEg55kwiIuWYM4mIlPPXnCmEHagtbNDQLQ8QVvcOIoUAxo6nGrqlAfoEvq9BRG3irzmTiJRjrVvLWOvmf/y91m3gwIH45ptvHGN/rD+rqqrCpk2bHOPQ0FA2eAtwbPBG5EsajdoReIS1LF821kWnqBQJUeArPvwTaooPyuYikgchpuuEJvYgIiJqm1qbDW/s2ITtJ467PHZ97/64tFtPvgnYAmGzo/DTbajamSN/QCsh6YbhCO/bQZ3AiIiIiIgocFX+BdldeiUtEN5btXCIiILZAw88gIULFzqKV7OysvDFF1/g2muvVTky/zJ+/Hicf/75+OWXXwAAZrMZ8+bNwzvvvKNyZO3TRRddhMcff9wxXrNmDYQQiq7l7tu3D/n59e/xJyQkYMiQIV6JszGLFi2Sja+++mrExMS06lhGoxHjx4/HypUrHXN79uzBiBEjFO2fmZmJ6upqx1ij0aBDB17PbU8a3tWaiIg8wLnBm7Ucwm6FpGFpMhERERERERERBTchRN0ND0zZgCkHMOcAdrN7B5G0gCH1VEO3dCAkCZIUHJ9BJiIiIs9grZsyrHXzL/5e63bJJZdg3rx5jvGePXvc2j8jI0M2TktL80hcDS1fvhxVVVWO8XXXXYfIyEiPn4d8h3/pEflQsDTKsJafkI110ckqRUIU2ISwo/jIr7I5XWg8Us6cGjT5goiI/EuVpRbPb17r0txNAnD7gLNwWfde/B3UAmGzo/CTrY00d9Mg+UY2dyMiIiIiIvcJIYBK+Ru9COsJSWtUJyAioiDXp08fXHHFFbK5e++9F4WFheoE5MdmzZolGy9evBiZmZkqRdN2S5YsgRDC61+zZ8/2eOyDBw9Gly5dHOPc3FxZk7OW1t3Q5Zdf7rO7+BYXF+Orr76SzU2fPr1Nx7zmmmtk459++knxvs7bDho0iIVvREREbaGPdZoQgLVElVCIiIiIiIiIiIi8TVjKICoyIAp/BHLeBY5/CJT8DtQcVtjcTQIMHYDoYUDyVUD6DEjJV0GKHgrJkMLmbkREROSCtW7KsdaNtW5KjRgxQtaULScnR3GTN6vVilWrVsnmxo4d69H4LBYLXnnlFdncnXfe6dFzkO/xrz0iL9BqtTAYDI08EhzNMqxlTg3eotjgjag1zOW5sFuqZHOpZ90GbUi4ShGpQ6vVYuTIkT77IAkRUSBrS84sMdVg3oY12F98Ujav12jwwNkjcW6nrp4KM2gJmx0Fy7egaleubF7SaZB883CE9WFzNyJ/wteZRETKMWcSqcycA1jL5HMR/dWJhVrEnEkUHJ5//nlZU6cTJ07gmmuuQVlZWTN7tT+jR4/Geeed5xhbLBY888wzKkbUvt10002y8QsvvNDiPkVFRXj33XdlczfffLNH42rORx99BLO5/sMs3bt3x7hx49p0zKuvvhpRUVGO8ddff43Dhw+3uF9tbS1ef/112dyll17apliIiIjaO0kTAmidmqVaitUJhshHeG2IiEg55kwiIuWYM4mIlPNlzhTWKoiqfRBFv0LkLAbylgDFq4DqA4C9WtlB9IlA1FlA0uV1Dd1SroMUMxKSMR2SpPNq/EREfJ1JFBxY66YMa938iz/XukmShKlTp8rmXn75ZUX7Llu2DLm59Z8vjoiIwLnnnuvJ8PDcc89h7969jvGll16KkSNHevQc5Hts8EbkBRqNBhpNIz9emiBp8Fbu1OAtmg3eiFqjumi/bKwPS0RobHeVolGPRqNBUlJS43mTiIhkWpsz86sqMGf9amRVyC/ahen0mDlsDIakpHoyzKAkrHYUfLwZ1bvzZPOSToOkm0cgrFeKSpERUVP4OpOISDnmTCKVVWTIx/q4urv1kl9iziQKDj179nS50+OaNWswZswYZGRkNL5TM0pKSjwUmf9xvrPpBx98gEOHDqkUTfv24IMPIi4uzjH+7bff8MYbbzS5vd1ux4wZM1BUVOSYu/DCCxU1WOvSpQskSXJ8Of+8KLVo0SLZeNq0aZCkttVNxMXFYebMmY6xxWLBjTfeiKqqqmb2Ah566CHs27fPMY6IiMC9997bpliIiIgIddcxGmKDNwpyvDZERKQccyYRkXLMmUREynkzZwqbCaL6METxaoi8D4Dcd4GTPwOVewBbubKD6GKBiAFAwiVA2h2QUqdAih0LKbRr3Q0TiIh8iK8ziYIDa92UY62b//D3WrdHHnkECQkJjvHixYvx+eefN7vPwYMH8cADD8jm7rnnHlkDRmdCCNhsthbjOe2NN97A7NmzHeOoqCj897//Vbw/+S++GiPyAovFApPJ5DLf1kJlfyDsNliLs2Vzumh+0IyoNWqcGryFxfdUKRJ1WSwWfP/997BYLGqHQkTk91qTMzPLSjBn/e8orJHfISrGYMRTI8ehT3yip8MMOsJqQ8FHm1C957hsXtJrkTx1JMJ6suExkT/i60wiIuWYM4nUI2w1QPVh+WRE/6B4PyFYMWcSBY+rrroK8+bNk81lZGTgzDPPxOTJk/HLL7+gpqamyf0tFgtWrlyJm266Cddee22bYiktLcXRo0db9ZWVldWmc7dk7NixmDBhgmNstVoxZ84cr56TGhcdHY25c+fK5u6//37MmjULlZWVsvmsrCxceeWV+OyzzxxzBoMBL774ok9iBYBt27bhzz//dIy1Wi1uueUWjxz7n//8J3r2rH9vdePGjRg3bhy2bNnism1eXh6mTJniUiA4b948WZEeERERtRIbvFE7w2tDRETKMWcSESnHnElEpJwnc6awWyBqjkGUrIU4vgzIeQco/A6o+FP5dS5tBBDeB4i/AOg4DVLHmyHFT4AU3gOSNrTNMRIRtQVfZxIFD9a6KcNaN//h77VuUVFReP7552Vz119/PebNm4fqavlnsYUQ+PTTTzF27FgUF9f/ndC1a1c88sgjzZ6nqqoKXbp0waxZs7Bz585GtxFCYNOmTbj44otx3333OeYlScKCBQvQsWNHN1dH/kindgBEwUoI4Trp9IEsYRcQmSanTfz7Q1uWk8cgrLWyuZCU9tmUiqgthLCjuvigbC60nTZ4A+r+QCIiImXcyZl7ThbglW0bYHLaJzksHI8OH4uksHBPhxd07Ja65m41+07I5iW9Fsm3jERodzbII/JnfJ1JRKQccyaRSqr+AtDwrlxaILy3WtGQQsyZRMHjiSeeQHJyMu677z5HgZvdbsfy5cuxfPlyGAwG9OvXD4mJiYiLi4MQAhUVFcjKysKBAwdgNptdjmk0GnHrrbe6Fcdrr72G1157rVVriI6ORmlpaav2VWrWrFn43//+5xh//PHHeOKJJ9C7N39n+drdd9+NP//8EwsXLgRQV5cwd+5cvP766xgyZAji4+ORnZ2NzZs3y35fSZKEpUuXYuDAgT6L9b333pONL774YqSmpnrk2KGhofjxxx8xcuRIFBQUAKhrKDds2DD06tULffv2RUhICI4dO4YtW7a43AV16tSpuP/++z0SCxERUbvHBm/UDvHaEBGRcsyZRETKMWcSESnX2pwphA0w5wOm7Lovcz4Au3sH0YQCxjTAmF73pYv2+88EE1H7xteZRMGDtW7KsNbNf/h7rdv06dPx119/4eWXXwZQ9zvzqaeewvz58zFy5EgkJyejvLwcW7ZswYkT8s8XR0dH48svv0RsbGyL58nJycHcuXMxd+5cxMTEYMCAAUhOToZOp0NBQQH27NnjcnytVou33noLkyZN8tyCSVU+afBmtVqRl5eH4uJiVFRUQAiBc845xxenJvIrkkYjG9vXlUEclTd46zGwgy9Dcltt/gHZWBMWA20k72pN5C5zRS7sFnn33rB23OCNiIg8b9PxHLy1cwusdvkbjl2iYvDIsNGINhhViixw2C02FHywCTUHnJq7hWiRfMsohHbj62AiIiIiImo9IQRQkSGfDD+Dd+8lIvKx2267DWPGjMG//vUv/Pjjj7LHzGYztm/frug4oaGhmDRpEubMmYNOnTp5I1TVnHPOOTj33HOxevVqAHWFgbNnz8by5cvVDaydeuuttxAaGoo33njDceO50tJS/Prrr41uHxERgTfffNOnxV4mkwkff/yxbG769OkePUe3bt2watUqTJkyBbt373bM79+/H/v37290H0mS8MQTT7jcHZaIiIjawKXBWymEsEGStOrEQ0REREREREREdIoQdqC2sEFDtzxAuNnoSAoBjB1PNXRLA/QJbOhGREREqmGtW8tY6+Zf/L3W7d///jfCwsIwf/58R5O56upqrFq1qsl9evTogW+++aZVTQNLS0vxxx9/NLtNWloalixZgvPOO8/t45P/0rS8SetUVVXh1VdfxYQJExAdHY2uXbvi7LPPxrnnnosJEyY0us/OnTvx/vvv4/3338fXX3/trdCI1NPgwo3dZoft1xLZw0np0Rh3RX9fR+UWs1ODt5CUnrwgRdQKNSflP0v6sAToQ+NVioaIiILNr8cO443tm1yau/WLT8QTI85hczcF7BYbCt7f2EhzNx1SprG5GxEREREReYA5D7DK3ydAxAB1YiEiaud69+6NH374ATt27MA999yjuGgtLi4OF110Ed59913k5+dj8eLFQVfwdtqsWbNk408//RQZGRlNbE3epNPp8Nprr+HXX3/FeeedB42m8dKf8PBw3Hjjjdi1axemTp3q0xg///xz2d12k5OTcemll3r8PP3798fWrVsxf/589OjRo8ntDAYDrrrqKuzYsQPPPPMMaxyIiIg8ybnBG+yAtUyVUIiIiIiIiIiIqH0TQkDUFkGU74Qo+A7IWQDkLwdK1wGmLGXN3SRtXTO3mFFAyiQg/U5ISZdDihoMKSSR7zMRERGR6ljr1jLWuvkPf691kyQJc+fOxaZNm3DFFVfAYDA0uW3Xrl3x6quvYteuXYqbuxmNRvzrX/9StH2vXr3w0ksvYf/+/WzuFoQkcbrFoQctXLgQM2fORFlZXZGG8ykkSYLNZnPZb/PmzRgxYgQkSYJGo0FWVhY6dOjg6fCoCeXl5YiOjkZZWRmioqLUDiegCSGwdOlS1NTUOOYSPvwGg2+4FmfcNQ0AsH1fFuZfsUy23xPvXYdBo7q6HG/akm3YmV1f9PTE33rh6rM6ein65h1//x6UrPqPYxw96kZ0vPMDVWIhCmS5W99GZf4OxzgqfRQ6nHmLegGpSAiBiooKREZG8iI3EVELWsqZQgh8cfAvfHHwL5fHhqV0xF2DhkKv5Z3SW2KvteLE+xthOlQom5cMdc3djJ3ZlJUoEPB1JhGRcsyZROoQJ38CqvbXT+higdSb+HPo55gzPcvT70+aTCZkZmaia9euMBrZ4J7aJicnB7t370ZWVhZKS0tRW1uLyMhIxMTEID4+Hv369UO3bt3UDpMIx48fx+bNm5Gbm4uysjIkJSUhPT0do0ePRnh4uNrh+dSff/6Jffv24fjx4zCZTIiPj0eXLl0wevRohIWFqR0eKcDf5eQO1rq1XfX+EzixeH2jj3V9/kofR0OBTOQsBGzV9ROJf4MUdoZ6ARF5Ea8NEREpx5xJRKQccyYRkXLOOVNYygBT9qmvHMBe3fJBZCTAkFLX1M2YBhg6QJJ0XomdiMjX+DrTs1jrRv6MtW4UKPy91q28vBzr1q1DXl4eCgoKEBYWhqSkJAwZMqTZG5AqUVRUhB07duDo0aMoKipCbW0twsPD0alTJ5x11ln8GQ1A7vwu9/hfmdOmTcPSpUshhHB5oSdJkkuzt4aGDRuGoUOHYsuWLbDb7Vi2bBn+9a9/eTpEIp9o7A+dhnOF1a4XivoMTvNqTJ5Qm79fNg5J6alSJESBSwg7qosOyObC4tr3z1JoaKjaIRARBYymcqZdCCzdsxO/Hjvi8tjETt0wtf8gaHgxvkX2WitOLNkA05GTsnnJoEPK9NEwdnK+Az0R+TO+ziQiUo45k8i3hM0EVB2ST0b2ZxFVgGDOJGof0tLSkJbm/+/fEnXo0AF///vf1Q7DL5x55pk488wz1Q6DiIio/dHHyRu81RYD7K1KQYzXhoiIlGPOJCJSjjmTiEghWxXCRDZQlAdhygFs5e4fQ58IhJ5u6NYRkibE83ESEfkJvs4kah9Y60aBwt9r3aKionDxxRd75djx8fGYOHGiV45N/k/jyYPNnDkTS5YscTR3E0KgU6dOuPnmmzFjxoxmm7uddu211zr+/eOPP3oyPCKfsVqtMJlMrg9I9T9yjTV4CwS1+fKmVCEpvVSKhChw1VbkwW6pks2FxbffBm9WqxU//PADrFar2qEQEfm9pnKmxWbDmzs2Ndrc7aoefXALm7spYjdbcWLxepfmbhqjHh1uG8PmbkQBhq8ziYiUY84kUkHVXwBsDSa0QHgftaIhNzBnEhERERERETnRO72PailWJw4iH+C1ISIi5ZgziYiUY84kImqasJkgqg9DFK+GyPsAyF0EbemvQNVe5c3ddLFAxAAg4RIg7Q5IqVMgxY6FFNqVzd2IKKjxdSYRERGRf9B56kDbtm3DSy+9BOlU04C4uDi8/fbbuOaaawAAx44dw3//+98Wj3PZZZfhkUcegRAC69evh9VqhU7nsTCJ1NWgqcbJmqpmNvRP9toaWIqyZHOGlPbblIqotWpK5M13dKHx0IclqBQNEREFuhqrBf+3dQP2FBXK5iUAU/sNwvlduqsTWICxmy3IX7wB5qNFsnlNqB4p00fDkBarUmRERERERBRshBBAZYZ8Mqw7JC3vlElEREREREREAYgN3oiIiIiIiIiIyEOE3QKY8wBTdt1XbYH7B9FGAMb0U19pkHSRng+UiIiIiIiISCGPdU57+umn6z6QgrrmbuvXr0ePHj3cPk6vXr0QERGByspKmEwm7N+/H/369fNUmESqkmQN3qpVjKR1ak8ccpkLST5DhUiIApup7JhsHBrbVaVIiIgo0JWZTfj3lnXILCuVzWslCXcNGoYRqWnqBBZg7CYL8t9bD3OW/IMGmjA9UqaPgaFjjDqBERERERFRcDIfd/2gc0R/dWIhIiIiIiIiImor5wZv1mIIYYckadSJh4iIiIiIiIiIAoYQNsCcX9/QzZwPwO7eQTShgDGtvqmbLlr2WV4iIiIiIiIiNXmkwVtlZSV+/fVXxx+8L7/8cquau53Wt29fbN68GQDY4I2Ci6b+olBRIDZ4yz8gG+tiO0JjjFApGqLAZSrLko2N0Z1VioSIiAJZQXUVXti8FvlVlbJ5o1aHfw4Zif4JSSpFFljsJgvyF62DObtENq8JD6lr7pYarVJkREREREQUtCoz5GNddF2RKRERERERERFRIHJu8CZsgLUC0PO9ViIiIiIiIiIikhPCDtQWNmjolgcIq1vHsNgkaMM6QxPWqa7mRp/Ahm5ERERERETktzzS4G3t2rWwWCwAgJiYGNx0001tOl5SUn0jgvz8/DYdi0gNOp0ORqMR1dXyJm4NLxJZ7G7eRcAPODd4C0npqVIkRIFL2K2orciVzRnaeYM3nU6HSy65BDqdR16WEBEFtdM583h1FV7Ysg6lZpPs8agQAx4ZNhpdo2NVijCw2Kprkf/eetTmuDZ363D7WISkRKkUGRF5Al9nEhEpx5xJ5DvCZgKq5dfaEdGfRaYBhDmTiIiIiIiIyIkmDNAYAXuD968tRWzwRkGJ14aIiJRjziQiUo45k4iCmRACsBSfauiWA5hzALvZvYNIWsCQChjTAUMaJE0cNPoQ1tsQEbWArzOJiIiI/INHXo3l5OQAqGteNWzYMGg0mjYdLyqq/kP0FRUVbToWkVqEEK6TAX7ByJy/XzY2sMEbkdvMFXkQdvldRYzRnVSKxn/U1NQgMjJS7TCIiALC7vw8vL33T1RbLbL5xNAwPDp8DFLCmU+VsFXXIn/ROtTmlsrmtREGpNw+BiHJbO5GFAz4OpOISDnmTCIfqdoPCFuDCQ0Q0Ve1cKh1mDOJiIiIiIiI6kmSBKGPBczH6yctJU3vQBTgeG2IiEg55kwiIuWYM4komAhL2amGbqeautmr3TyCBBhS6hq6GdMBQwokqe7j8EII1FRUIFIf4vnAiYiCEF9nEhEREamvbZ3YTjl58qTj34mJiW0+nsVS36igrc3iiNRgtVphNrveRUAK8O/n2vwDsnEIG7wRuc1Udkw21oclQKsPUyka/2C1WvHbb7/BarW2vDERUTu3JS8Hr/651aW5W3pkFGaNOpfN3RSyVZmRv3Cta3O3SCNS7hjL5m5EQYKvM4mIlGPOJPINIQRQuVs+GdYdkrZ9Xx8MNMyZRERERERERI3Qx8vHliJ14iDyMl4bIiJSjjmTiEg55kwiCnTCWgVRtQ+i6FeInMVA3hKgeBVQfUB5czd9IhB1FpB0OZA+A1LKdZBiRkIypjmauwHMmURE7mDOJCIiIvIPupY3aVl4eLjj31VVVW0+XmFhoePfcXFxbT4ekd+QGvzbrloUreba4K2XSpEQBS5TWZZsbIzurFIkREQUaH7PPop3d2+HXZLP94qNx4NDRyGcd6BSxFZpRv67a1GbXy6b10YZ0eH2MdAnskkeERERERF5SW2+64ebI/qpEwsRERERERERkSfpY+VjS7E6cRARERERERERkdcJmwkw5wKm7Lqv1lwL0sUCxjTAmA4Y0yBpQz0fKBEREREREZEf8EiDt8TERMe/jx492qZjCSGwY8cOxzgpKalNxyPyL/XdOOylFtkjeqMOIUaP/Eh6hbWyCLZK+QfPQlJ6qhQNUeAylR6VjQ1s8EZERC0QQuC7IwewfF+Gy2NnJXXAvWcNR4hWq0JkgcdWacbxhWthOeHU3C06tK65W0KESpEREREREVG7UOH0d50uCjB2UicWIiIiIiIiIiJP0sfLx5YSCCEgSVLj2xMRERERERERUcAQdgtgzqtv6FZb4P5BtBGnmrmdauim443ZiYiIiIiIqH3wSDepXr16AahrPLB7924UFRUhPj6+hb0at2bNGpSVlTnGw4YN80SIRD7XWGGSpNE4/m0rrJU9FpcW6dfFTLX5B+UTWh1CErqoEgtRoDKVZ8Ncdkw2Z4zmBzgBQKfz3waXRERqsguBj//ajR8zD7o8Ni6tM6YPOAvaBq8xqWnWChPyF66FpaBCNq+NDkWHO8ZAH8/mbkTBiK8ziYiUY84k8i5hNwPVB+STEf39+n0BahpzJhEREREREZETfZx8LGoBWyXAD+pSEOK1ISIi5ZgziYiUY84kIn8ihA0w59c3dDPnA7C7dxBNKGBMq2/qpov2WJ0McyYRkXLMmURERETq88grssGDByMhIQFFRUWw2+1466238NRTT7XqWC+88ILj37169UJycrInQiTyKb1eD6PRiKqqKvkDDS5A2U/KG7zFd4zyRWitZi3JkY31cZ0g6fQqRUMUmEoOr5SNtSERCI3roVI0/kOv1+Nvf/ub2mEQEfkdq92OBbu2YV1ulstjl3XviUm92AhAKWu5CfkL/4ClsFI2r4sJQ8odY6CPC1cpMiLyJr7OJCJSjjmTyAeq9gPC2mBCA0T0VS0caj3mTCIiIiIiIqJGaCMASQ8IS/2cpZgN3ijo8NoQEZFyzJlERMoxZxKR2oSwA7WFDRq65TnVuSgghQDGjqcauqUB+gSvfN6BOZOISDnmTCIiIiL/oPHUga6++moIISCEwPz587Fr1y63j/Gf//wHP/30EwBAkiRMnTrVU+ER+ZTdbofd3sgdCRpcj7IVyhu8xaX5d4M3W1WJbKyNiFcpEqLAZKkpQnneFtlcTJcJ0GjZKNFut6OgoKDxvElE1E6ZrFa8snV9o83d/p7eDdf17MfmbgpZy2qQv6CR5m5xYUi5cyybuxEFMb7OJCJSjjmTyLuEEEDFbvlkaFdIWv49EoiYM4mIiIiIiIhcSZIE6OPkk5ZidYIh8iJeGyIiUo45k4hIOeZMIvI1IQREbRFE+U6Igu+AnAVA/nKgdB1gylLW3E3S1jVzixkFpEwC0u+ElHQ5pKjBkEISvfZ5B+ZMIiLlmDOJiIiI/IPHGrw98cQTMBgMkCQJJpMJEyZMwMqVKxXtW1tbi9mzZ+O+++5z/NEeFRWFu+66y1PhEfmUzWaD2Wx2mZc09T9ydqcGb/GB1uAtPFalSIgCU8mRVYCovwgiaUMQ2+Vc9QLyIzabDRs2bIDNZlM7FCIiv1BZW4v5m/7An4UnZPNaScId/QfDkJnFnKmQtawGxxf8ActJp+Zu8eHocMdY6GPDVIqMiHyBrzOJiJRjziTystoTgOWkfC6yvzqxUJsxZxIRERERERE1gQ3eqB3gtSEiIuWYM4mIlGPOJCJfEJYyiIoMiMIfgZx3geMfAiW/AzWHAbvrZ2FdSYChAxA9DEi+GkifASn5KkjRQyEZUiBJHvu4erOYM4mIlGPOJCIiIvIPOk8dKC0tDbNnz8Zjjz0GSZJQXFyMiy++GOPGjcOkSZOQlJQk276goAD79+/HypUr8cEHHyA7OxtCCAB1d/J74403EBkZ6anwiPxDg7sO2MvkdzGI6RDh62jcwgZvRK1nq61CadYfsrno9NHQhvj3zz0REfleUU01Xti8FrmVFbL5EI0W9589Av1i4/HDzt0qRRdYrKXVOL5gLazFVbJ5XUI4Otw+FrroUJUiIyIiIiKidqdyj3ysjQSMndSJhYiIiIiIiIjIW9jgjYiIiIiIiIjIbwhrFWDOBkw5QE02YCt3/yD6RCA0HTCmAYaOkDQhng+UiIiIiIiIKMh5rMEbAMycORN//fUX3n//fUiSBCEEfv/9d/z++++y7YQQ6NChg2wMwLHPPffcgxtvvNGToRH5BalBgzdnWr3Wh5G4z15dKhtrw9jgjUip0qw1ELaGdzKRENvtfNXiISIi/5RbUY4XNq9FkalGNh+u1+PhoaPRIzYeFotFpegCi6W4CvkL18JaUi2b1ydGIOX2MdBFsbkbERERERH5hrDXAlX75ZMR/X1212IiIiIiIiIiIp9ppMGbEKLZukkiIiIiIiIiIvIMYTMB5py6hm6m7NY139fF1jVzM9Y1dZO0rLsnIiIiIiIiaiuPNngDgMWLF6Nr16545plnHI3bGjZwO63h3OnGbgAwZ84cPPXUU54Oi8inJEmCRqOB3W53fkCdgDzAVl0iG2vC2eCNSAm7zYKSzFWyucjUsxESlqBSRP5HkiRERkaymJOI2rVDJcX495Z1qLTUyubjjKGYOWwM0iKjADBnKmEpqsLxhX/AVipvlKdPikTKbWOgizKqFBkR+RpzJhGRcsyZRF5UtR8QDZt1S0BEX9XCobZjziQiIiIiIiJqgnODN7sJsNcA2jB14iHyAl4bIiJSjjmTiEg55kwiag1htwDmvLpmbqZsoLbA/YNoI041c6v7knQRng/Uw5gziYiUY84kIiIi8g8eb/AmSRJmzZqFiy66CM888wx++OEHx2PNNXobN24cnn32WYwaNcrTIRH5nE6ng8FggNVqlT8QwH8A2arkDd60bPBGpEh57kbYzOWyubhuF6oUjX/S6XSYMGGC2mEQEalmV2E+Xt22EWabTTafGh6JmcPHICG0vtidObN5lpOVOL5wLWxlTs3dkiPR4bYx0EayuRtRe8KcSUSkHHMmkRdVZsjHoV0DohiWmsacSURERERERNQEXRQALYAG731bitngjYIKrw0RESnHnElEpBxzJhEpIYQNMOfXN3Qz5wOwu3cQTShgTKtv6qaLDrimP8yZRETKMWcSERER+QePN3g7bfjw4fjuu+9w5MgR/Prrr1i7di2ys7NRVFSE2tpaJCQkIDk5GaNGjcKFF16I/v37eysUIp+z2+2wOTXoABBwF7sacmnwFsYGb0QtEcKOksMrZXNh8b1hjOmsUkT+yW63Izs7G+np6dBoNGqHQ0TkU+tys/DOn1thO9X4+rTuMXF4eOgoRIYYZPPMmU2znKzE8QV/wFZuks3rU6LqmrtFGJrYk4iCFXMmEZFyzJlE3iHMBa53R47g+2GBjjmTiIiIiIiIqHGSpIHQxwKWk/WTluK6Dw0TBQleGyIiUo45k4hIOeZMImqMEHagtrBBQ7c8QFjdO4gUAhg7nmrolgboEwL6M64AcyYRkTuYM4mIiIj8g9cavJ3WrVs33HHHHbjjjju8fSoiv2Gz2VBbW+v6QABf/LJXOzV4C2eDN6KWVJ7YhdqqE7K5uO4XqhSN/7LZbNi5cydSU1N5kYiI2pWfMg/ig727XOYHJibj/rNGwKhz/XONObNxtYUVyF+wFrYKeXO3kA7RSLltNLThbO5G1B4xZxIRKcecSeQllRnysTYCCOXNHwIdcyYRERERERFRM/Rxrg3eiIIIrw0RESnHnElEpBxzJhEBgBCi7lqKKRsw5QDmHMBudu8gkhYwpJ5q6JYOhCRBkoIrrzBnEhEpx5xJRERE5B+83uCNiOpJAfzHj62KDd6I3FV69DfZ2BCZhrDEvipFQ0RE/kIIgU/378HXh/e7PDYqNR13njkEugB+3ehrtSfKkb9wLWyV8jevQ1KjkXLbGGjDQlSKjIiIiIiI2jNhrwWqnP7ui+gXdEWzREREREREREQy+jj5mA3eiIiIiIiIiIiaJCxlpxq6nWrqZq928wgSYEipb+hmSIEk8WPjRERERERERP6Ef6kT+ZKkdgCtI4SArbpUNqdhgzeiZlmqi1B9cp9sLrb7+ZCkAE0ERETkETa7He9l7MDq7KMuj13U5Qzc0HcgNPxdoVhtfjmOv7sWdufmbmkxSJk2ms3diIiIiIhIPVUHAFHbYEICIvqpFg4RERERERERkU+wwRsRERERERERUZOEtQown2rmVpMN2MrdP4g+EQhNB4xpgKEjJA1r5omIiIiIiIj8GRu8EXmBJEnQarWw2+3OD6gTUBvZTRWA3Sab04axwRtRc8pyNgAQjrFGF4rIDmerF5AfkyQJiYmJbH5HREGv1mbDf3ZsxtYTeS6PXderHy7v3qvFXMicWa/2eFldc7eqWtm8IT0WydNGQRvKN6qJ2jvmTCIi5ZgzibygMkM+Du0MSRepTizkUcyZRERERERERM1wbvBmq4KwmyFpDOrEQ+RhvDZERKQccyYRkXLMmUTBS9hMgDmnrqGbKbt1zfB1sXXN3Ix1Td0kbajnAw0gzJlERMoxZxIRERH5BzZ4I/ICnU6HkJAQWCwW2byk0agUUdvYqkpc5rThbPBG1BQh7CjPXiebi+o4DBotG800RqfTYdSoUWqHQUTkVVWWWryydQP2FZ+UzUsApg84C+M7dVV0HObMOua8UuS/uw72aqfmbp1ikTJtNDRGvUqREZE/Yc4kIlKOOZPIs0RtIVB7Qj4ZMUCdYMjjmDOJiIiIiIiImqGPAaAB0ODmuJZiwNBBpYCIPIvXhoiIlGPOJCJSjjmTKHgIuwUw59U1czNlA7UF7h9EG3GqmVvdl6SL8HygAYw5k4hIOeZMIiIiIv/gsQZvWVlZnjqUTKdOnbxyXCJvstlssFqtrg8EaIdru3ODN0kDjTFSnWCIAkB10QFYaopkc9Hpo1WKxv/ZbDYcPHgQPXr0gFarVTscIiKPKzHV4MXN65BVUSab12s0uGfwMAxJ6aj4WMyZgDm3FPmL1sJeLW+mbOgch5RbR7G5GxE5MGcSESnHnEnkYZUZ8rE2HAjtokoo5HnMmURERERERERNkyQthC4asDaoOawtYoM3Chq8NkREpBxzJhGRcsyZRIFLCBtgzq9v6GbOh6zxvRKaUMCYVt/UTRcNKUA/h+oLzJlERMoxZxIRERH5B481eOvSpYvHLxpIktR4kywiP2e322GxWBp5JDAvrNmq5Q3etGExkDQalaIh8n9l2etk45DIjjBEd1YpGv9nt9uxf/9+dO/enReJiCjo5FdV4vlNf6Cwplo2H6rT4cEho9AnPtGt47X3nGnOKUH+u+tgNzk1d+sSj5RbR0JjYHM3IqrX3nMmEZE7mDOJPEfYLUDlPvlkRD9IEq+pBwvmTCIiIiIiIqIWhMTJG7xZS5relijA8NoQEZFyzJlERMoxZxIFDiHsQG1hg4ZueYBw8zPQUghg7Fjf0E0fz4ZubmDOJCJSjjmTiIiIyD94rMHbaUIITx+SKGhImtZdaLPY3Lxrg4fZquQFVprwWJUiIfJ/Nks1Ko9vl81Fp4/mhXYionYos6wEL25eh/Jas2w+xmDEI8NGo3NUjDqBBShzdjHyF613ae5m7JaA5FtGQhPi8T9viYiIiIiI3Fd9EBC18rmIfurEQkRERERERESkBn08gMP149oi1UIhIiIiIiIiImoLIQRgKT7V0C0HMOcAdnPLOzYkaQFDan1Dt5Ak3iiQiIiIiIiIqB1R/RPwzg1v2CCOglorGjwVlJuxJ6/CC8Eo59zgTcsGb0RNqsjbAmFv0HhG0iKq43D1AiIiIlXsOVmAV7ZtgMkqvxtXclg4Hh0+BklhESpFFphMx4qQ/956CLP8/6exeyKSp45gczciIiIiIvIflRnysbEzJF2UOrEQEREREREREalB71RfaClpfDsiIiIiIiIiIj8kLGWnGrqdaupmr3bzCBJgSKlv6GZIgSSx3p2IiIiIiIiovfLYVYGpU6e6tb3NZkNJSQn27NmDo0ePAqhr9hYXF4fLLrvMU2ERqUKj0UCn06G2tlY279zQUInvdx/3VFit5tLgLYwN3oiaUpa1TjaOSD4TOkOkStEEBo1Gg06dOkGj4d1niCg4bD6ei//s3Ayr3S6b7xIVg0eGjUa0wdjqY7fHnGk6eqq5W61Tc7ceSUi+aTibuxFRk9pjziQiai3mTCLPELUnAbPTNf3I/uoEQ17DnElERERERETUAn28fGwrh7BbIGn06sRD5EG8NkREpBxzJhGRcsyZROoS1irAfKqZW002YCt3/yD6RCA0HTCmAYaOkDQhng+UADBnEhG5gzmTiIiIyD947JPwixcvbvW++/btw5w5c/DJJ5+gpKQEVqsVS5YsgVar9VR4RD6l1Wqh1+tdGryhFX8AnaysdZnrkRTR2tBaxV7t1OAtnA3eiBpjLs+BqeyobC46fZQ6wQQQrVaLwYMHqx0GEZFHrDp2BIszdkA4zfeNT8Q/zx6JMH3bCtbbW86sOXISJ5ash6i1yeZDeyYh6aYR0Oj5NyMRNa295UwiorZgziTykIpd8rE2DAjtqk4s5DXMmUREFIgsFgvWrVuHrKwsHD9+HBEREUhNTcXgwYPRpUsXtcPzmdzcXGzYsAEnTpxAWVkZEhISkJqaiuHDhyMxMVHt8IiIiIKHrpH6QksxYEj2fSxEHsZrQ0REyjFnEhEpx5xJ5FvCZgLMOXUN3UzZddct3KWLrWvmZqxr6iZpQz0fKDWKOZOISDnmTCIiCkSsdatTXFyMrVu3IjMzE6WlpRBCIDo6GmlpaRg6dChSUlLUDpHc4LEGb23Ru3dvLFu2DKNGjcL999+Pjz/+GDqdrk1N44jUZLPZYLFYXOYlqe6/Qji3/AAkN44/oGNUKyNrHVsVG7wRKVGS+T/ZWGuIRnhiP5WiCRw2mw27du3CwIED2dyViAKWEAJfHtqHzw/sdXlsaEpH3DVoKEI8kOPaU86sOVyIE0s2QFicmrv1SkbSjcPZ3I2IWtSeciYRUVsxZxK1nbDXAlX75JMR/SFJ/JkKNsyZRMGhS5cuOHbsWLPbGAwGGAwGxMfHIzk5Gd27d0e/fv0wcuRIjBgxAkaj0eNx2e12dO7cGTk5ObL5AwcOoEePHq0+riS5vhs9evRorF27tk3HCg8PR2VlZbPbN/b/umvXrti3bx9CQkLcOrfzsQoLC5GQkODWMdqbwsJCzJo1C5988gmKixv/cNKoUaPwr3/9C1dffbWPo2vaf//7X/zjH/9wmc/MzHS7SM9ut2PZsmV48cUXsWvXrka30Wq1mDBhAh555BFMnDixNSETERFRA5JGB6GLBqxl9ZOWEjZ4o6DAa0NERMoxZxIRKcecSeRdwm4BzHl1zdxM2UBtgfsH0UacauZW9yXpIjwfKCnCnElEpBxzJlFwYK0ba93aC9a61X1W/ZNPPsF//vOfFr/nBw8ejBkzZmDatGnQ6fyifRg1Q6N2AA3de++9mDZtGoQQeP/99/HZZ5+pHRJRq9jtdlitVtcHTr0AqLJYXJq8RYUYFB37ysGpjb4o8SZbdalsrAljgzciZzUlR1CWvU42F502EpKGFz1aYrfbkZWVBbvdrnYoREStYhcCS/fsbLS523mduuK+s4Z7pLkb0H5yZs2hgkabu4X1SUHyTWzuRkTKtJecSUTkCcyZRB5QtQ8QDW/8IgER/VULh7yHOZOo/TCbzSgvL0dmZiY2btyIjz76CI8//jjGjx+PxMRETJkypVVFY81ZuXKlS8EbALz33nsePQ8ArFu3Dt98843Hj6tEZmYm/vvf/6py7vbkxx9/RP/+/fH22283WfAGAOvXr8c111yDG2+8EVVVVT6MsHHZ2dmYOXOmR46Vn5+PsWPH4sYbb2yyuRtQV9T+yy+/4Pzzz8eMGTNgNps9cn4iIqJ2TR8nH1uK1ImDyMN4bYiISDnmTCIi5ZgziTxLCBuEKReidCNE/qdA9n+Bgq+A8m3Km7tpQoGwHkDcBCB1KtBxGqSECyBF9GFzN5UxZxIRKcecSdR+sNbNe1jr5husdaurdTvvvPMwefJkRT+vO3bswJ133okRI0bg0KFDHomBvMevGrwBwOzZsx3Nq1588UWVoyHysFPf24U1rr8oogzKGrypwVZVIhtrw9ngjaghYbfhxO6PANQ3bpS0IYjpMl69oIiIyCcsNhve3LEZvxw74vLYVT364Nb+g6HxcXPeQFd94ETjzd36dkDSDcMh6djcjYiIiIiI/IsQAqjYLZ8M7QpJF6lOQERE5HWVlZVYtmwZxo4di4kTJ2Lfvn0eOe6iRYsanV+6dClsNlujj7XF448/7pXjKjFv3jxUVFSocu72YPXq1bjiiitQUFD/ISVJknD22Wfj2muvxfnnn+9yR9iPPvoIkydPVr2we8aMGSgvL2/zcfLy8jBs2DCsX79eNh8TE4MLL7wQkydPxoUXXojo6GjZ4++88w5uvvlml5v2ERERkZtcGryVNL4dEREREREREVEbCWGHMJ+AKNsKceLLuoZuJz4DyjYB5jwACt77kEKA0K5A7DlAhxuAtNshJV4CKXIAJH2M4zPPRERERESBhrVubcdaN+9irRtQWFiI8ePH47fffpPN6/V6DBs2DNdeey0mTZqE0aNHw2g0yrbZtm0bxo8fj2PHjrU5DvIev2vwlpaWhjPPPBNCCGzbtg0HDhxQOyQij5Gkuh+5k9XVLo9pJL/7cXSwV7PBG1FzSo+thrk8WzYX3+NS6EP5s0JEFMxqrBa8tHU9Nh2X32VAAjC13yBc3bMv38h1U/X+Eyh4fyOEVX5RJax/KpJuGAZJ57+vmYmIiIiIqB0zHwcsJ+VzkQPUiYWIiFpl2bJlyMzMlH0dOHAAmzdvxsqVK7Fw4ULcdttt6NOnj8u+q1atwtlnn40VK1a0KYaTJ082eZfR48eP44cffmjT8RuzZ88evP/++x4/rhKFhYV46aWXVDl3sMvJycFVV12F2tpax9zo0aOxZ88ebN26FStWrHDcQfe1116DXq93bPftt9/iySefVCNsAMAHH3zg+F6PjGx9s1yLxYKrr74a2dn17+FGRETgrbfeQkFBAX766Sd8/PHH+Omnn1BYWIj//Oc/CA8Pd2y7YsUKzJo1q/ULISIiokYavBWpEwcRERERERERBR0hBERtEUT5ToiC74CcBUD+cqB0HWDKAoS15YNIWsCYDsSMAlImAel3Qkq6HFLUYEghCfwcABERERH5Lda6+R5r3byHtW51HnjgAZfmizNmzEBOTg42bdqEFStWYPny5Vi7di2OHz+ORx99FBpN/eetc3JycOedd7YpBvIuv/x0fLdu3Rz//vPPP1WMhKh1NBqN7BeDw6kLezkFJYBJ3rRCp/fLH0cAgK2KDd6ImmI1leLk/q9lcyGRqYjrNlGliAKPRqNBr169ZC8iiYj8XbnZjOc2/oGMkwWyea0k4Z7Bw3FBl+5eOW8w58zqffk40Uhzt/ABHZE0eSgkbfCtmYi8K5hzJhGRpzFnErVR5W75WBcNGDurEwt5HXMmUXBKSUlBly5dZF89evTA0KFDcf755+O2227DwoULsXfvXqxbtw5XX321bP/q6mpMnjwZy5cvb3UMH3zwgaxI6dJLL5U93tQdT9tq1qxZMJlMXjl2S1555RWcOHFClXMHs1mzZqGkpP49/lGjRuHXX391Kdo0GAy47777XAo2X3nlFVXu5llQUIAHHnjAMZ4/f36rj/Xee+9h48aNjrHBYMDKlSvxj3/8w6WWQ6/X46677sJPP/2EkJAQx/yLL76Io0ePtjoGIiKids+5wZu1DELJh6uJ/ByvDRERKcecSUSkHHMmUcuEpQyiIgOi8Ecg513g+IdAye9AzWHAblZwBAkwdACihwHJVwPpMyAlXwUpeigkQwokiT9/gYI5k4hIOeZMouDEWjfWugUT1roBR48exccffyybe+yxx/D2228jKSnJZfuYmBjMnz8fr732mmz+559/xqZNm1odB3mXX74aMxgMjn/n5uaqGAlR62i1Wuh0OtcHTt24IeP3o0CDvhUavQbpPRJ9EltrWMudGpeExzWxJVH7U7D3U9it8j8EkvvfAEnTSA6gRmm1WvTu3RtarVbtUIiIFCmsrsLcDatxpEzeBNeo1eGRYaMxIjXNa+cO1pxZvfc4TnywEbA5NXc7Mw2J1w9hczciapVgzZlERN7AnEnUesJWDVQdlE9GDOCdnIMYcyYRjRo1Cp999hk++ugjREREOObtdjumTZuGjIyMVh23YVGbRqPBW2+9hR49ejjmvv/+e48UiIWHh2PixPobFWVnZ+ONN95o83GVuuKKKxz/rqysxNy5c3127vbg4MGDWLp0qWMcEhKCJUuWwGg0NrnPFVdcgalTpzrGZrMZc+bM8Wqcjbn77rtRXFwMABgxYgT+8Y9/tOo4Qgi88MILsrknn3wSI0eObHa/MWPG4PHHH3eMzWYznn766VbFQERERAD0zjeRFYClVI1IiDyK14aIiJRjziQiUo45k8iVsFZBVO2DKPoVImcxkLcEKF4FVB8A7NXKDhKSCESdBSRdXtfQLeU6SDEjIRnTIEn83FegYs4kIlKOOZOIWOvWNqx18y7WutX59ttvZePk5GTMmjVLUQwDBw5s9ljkP/zyU/JZWVmOf1utvGMfBR6r1SrrOHuadKrDde52+YuR1CFJCA0PcdneH9hNlbBXl8rmdHHea1pCFEiqCveiIm+LbC4qbSTC4ns0sQc1xmq1Yv369fydT0QBIau8DHPWr8bxqkrZfGRICJ4YcQ76JyR79fzBmDOr9uThxEebAJuQzYcPSkfidWezuRsRtVow5kwiIm9hziRqg8q9AGwNJrRARF+1oiEfYM4kotOmTJmCL7/8UnaX45qaGtx9991uH2vTpk3Ys2ePY3zeeechPT1dVohktVrx/vvvty3oU1544QVZM9L58+ejtLTUI8duyZw5cxAaGuoYL1y4EIcOHfLJuduDjz/+GDZb/WuTq666SlY82ZSZM2fKxitWrPDp3W6/+OILfPbZZwAAvV6PhQsXtvoO4hkZGcjMzHSMjUYj7rnnHkX73nvvvbICwU8//RSVlZXN7EFERERNkTQGQBshn7QUqxMMkQfx2hARkXLMmUREyjFnEgHCZoKoPgRRvBoi7wMg913g5M9A5R7AVq7sILpYIGIAkHAJkHYHpA5TIMWOhRTaFZLGPz+7Se5jziQiUo45k4hOY61b67DWzbtY61bnyJEjsvEFF1wAg8HQ4n6SJOGyyy6TzR08eLCJrUltfvdJ+ePHj2PTpk2OBJuYmKhyRETuE0LIfpE4nPq+tlTJ/xBK6Zvgi7BaxVKS6zKnj2WDNyK7zYITGR/L5jT6MCT2uUaliAKXEAKFhYUQQrS8MRGRivYXn8QzG35HiVn+h35CaBhmjTwX3WKc70DuecGWM6t256Lgo80uzd0izmJzNyJqu2DLmURE3sScSdQ6Qgig0unOdeE9IGlDG9+BggJzJhE1NHHiRDz11FOyuTVr1uC3335z6zgN72gKALfccgsA4Oabb5YV/jhv11pnnXUWJk2a5BiXlJTg+eef98ixW5Kamor777/fMbZYLHjyySd9cu724Msvv5SNb731VkX79enTB8OHD3eMq6qqsHLlSo/G1pSSkhJZsejMmTPRv3//Vh9vzZo1svHw4cMRExOjaN+4uDgMHTrUMTaZTPj+++9bHQsREVG7p4+Tj9ngjYIArw0RESnHnElEpBxzJrVHwm6BqDkGUbIW4vgyIOcdoPB7oOJP5dcQtBFAeB8g/gKg43RIHW+GFD8BEms3ghpzJhGRcsyZRNQQa93cx1o372KtW52qqirZOC1NeT+f9PR0l/jIP/nVp+XtdjvuuOMOWK1WxwvFIUOGqBwVkec07Awrn/dxIG6wFOfIxtrwOGgMYSpFQ+Q/ig//DEtVgWwusfeV0BkiVYqIiIi8afuJPMzf9AeqrRbZfFpkFGaPOhcdIpj/3VW1KxcFy7YAdqfmbkM6I+GasyFp/PhFMhEREREREQCYjgHWMvlcxAB1YiEiItU8+OCDiI6Ols0tWLBA8f5VVVVYvny5YxwVFYUrr7wSQF3xzXnnned4bP/+/Vi3bl0bI64zb9486PV6x/j1119Hbq7rzb+84dFHH0VcXH2zjxUrVmDbtm0+OXcwy8/Px59//ukY63Q6jB49WvH+5557rmz8448/eiq0Zv3zn/9Efn4+AKBnz55tLoLMyZHXOLhbQDdggPz1HBu8ERERtQEbvBERERERERHRKULYIEy5EKUbIfI/BbL/CxR8BZRvA2oLWtwfAKAJBcJ6AHETgNSpQMdpkBIugBTRB5IuwqvxExEREREFA9a6uY+1bt7BWrd6KSkpsrHJZFK8r/O2Db9Xyb/4RYM3m82GH3/8ESNHjsQPP/zgaILVvXt39OvXT+XoiDwoAJtUWIuzZWNdnPJun0TBqraqAMWHfpDNGWO6IrrTWJUiIiIib/o9+yj+b9tGWOx22Xyv2Hg8PXIcYo28u5e7Kndmo2C5a3O3yGFdkHDVYDZ3IyIiIiKiwFCxWz7WJwCGDurEQkREqomMjHS5c+SPP/4Iq9WqaP9PP/0UFRUVjvGkSZMQGlp/zfH0HU5P89SdTbt374477rjDMa6pqcGsWbM8cuyWREdH47HHHnOMhRCYOXOmT84dzDIyMmTjgQMHIjw8XPH+o0aNko337Nnjkbia8/PPP2Pp0qUA6m6Yt2DBAhgMhjYds7hY3jgmJibGrf2dt9+9e3fjGxIREVHL2OCNiIiIiIiIqN0Swg5hPgFRthXixJd1Dd1OfAaUbQLMeQDsLR4DUggQ2hWIPQfocAOQdjukxEsgRQ6ApI9xfA6ZiIiIiIiUYa2b+1jr5h2sdas3dqy8R8n27dsV7+vcbHDo0KFtjoe8w2MN3iZMmOD21znnnIOBAwciIiICl156KbZs2QIhBIQQkCQJc+fO9VR4RD6l1WoREhLSyCOBd9HQUiy/u7WeDd6IULj3Uwh7wz9UJCQPuAGS5Bd9UwOOVqvFoEGDoNVq1Q6FiMjFd4f3Y8GubbALeSOys5I64NHhYxGub+w1n/cEQ86s3JGFwk+2ujZ3G94V8VcMYnM3IvKYYMiZRES+wpxJ5D5hLQdqMuWTkQNYPNwOMGcSUWPOP/982bisrMylAKkpzkVsU6dOlY2vvPJK2V1TV6xYgcrKylZGKvf0008jIiLCMV6yZAn27dvnkWO35N5770WnTp0c41WrVuGXX37xybmD1d69e2XjM844w639u3fv3uzxPK2yslJWeHnbbbdh3LhxbT6uc62G2Wx2a3/n7fft2we7XcGHzIiIiMiVS4O3UgjB36sU2HhtiIhIOeZMIiLlmDMpGAghIGqLIMp3QhR8C2S/A+QvB0rXAaYsQChoGCFpAWM6EDMKSJkEpN8JKelySFGDIYUksCaDADBnEhG5gzmTiBrDWjf3sdbN81jrVu+8885Dr169HOM//vgDu3btanG/3NxcfP75546xXq/H5MmTPRITeZ7OUwdavXp1qy6QiAaNEk7vL4TA3Xffjeuvv95T4bUoMzMTO3fuRF5eHiorK9GhQwd07twZo0aNgl6v91kcFBw0Gk2jf+wE4kVEa4lzg7d0lSIh8g82Sw0qT8hfEMV0GQ9jdKcm9qCWaDQadO7cWe0wiIhk7EJg2V+78UPmQZfHzknrjNsGnAWtxveNPQM9Z1ZsO4aTn20H5L3dEDmyG+IvHxiQr5eJyH8Fes4kIvIl5kyiVqjcA9kfN5IeCO+tWjjkO8yZRNSYESNGQJIkWf3Dn3/+iUGDBjW734EDB7B27VrHuEePHhg9erRsm9DQUFx33XVYuHAhAKCqqgqffPIJpk+f3ua4k5KS8OCDD2LOnDkAAJvNhsceewxffvllm4/dEoPBgDlz5sjuCPvoo49i4sSJql4nNJlMyM/P9/p5dDod0tI8e3O1Q4cOycYNiwqVcP79VlRUhJKSEsTGxrY5tsbMnDkTWVlZAICUlBS8+OKLHjlufHy8bHz8+HG39nfe3mQyISsrC126dGlraEQexVo3IgoIzg3eYAOsZYDeO68viHyB14aIiJRjziQiUo45kwKVsJQBpuxTXzmAvdrNI0iAIaWuqZsxHTCkQJI89pFnClLMmUREyjFnElFjWOvmPta6sdbNW7VuQN3v6/feew8TJkyA2WyG3W7HNddcg5UrVzZZs3bixAlcccUVqK6u/zv8ySefRGpqqsfiIs9S/WpHw6ZuQghERUVh/vz5+Mc//uGT83/22Wd45ZVXsGHDhkYfj4uLw6RJkzB37lwkJCT4JKbmVFdXY8CAAThy5IhsfurUqViyZIk6QZELq9Xa+F2gVWgC0laW4mzZWBfr2V+8RIHGUnMS8q40EhJ6Xa5WOEHBarVizZo1OOecc6DTqf7ShIgIVrsdC3dtw9rcLJfHLu3WE9f37q/aRZdAzpkVW47i5Bc7XJq7RY3ujrhLB7C5GxF5XCDnTCIiX2POJHKPEDag0ulOdeG9IWlC1AmIfIo5k4gaExcXh8TERBQUFDjmThfzNKelO5qedssttziK3k7v54miNwB46KGH8Pbbbzti/+qrr7BhwwaMHDnSI8dvzs0334yXX37ZcQfY7du3Y/ny5areRXLjxo0YP36818/TuXNnHD161KPHLC0tlY2TkpLc2j8iIgJGoxEmk8kxV1ZW5pWitz/++ANvv/22Y/zGG28gJibGI8fu06ePbLxx40a39m9s+7KysjbFRORJrHUjokAiaUMhNKGAvaZ+0lLMBm8U0HhtiIhIOeZMIiLlmDMpUAhrFWA+1cytJhuwlbt/kJDEUw3d0gBDR9ZakNuYM4mIlGPOJKLGsNatdVjrdtSjx2Stm9yoUaPw3XffYcqUKSgsLMTBgwcxcOBATJ8+HRdddBE6d+4MSZKQk5ODVatWYcGCBSgqKnLsf+edd+Kpp57yaEzkWR59JdawQ6cSWq0WUVFRSEpKwllnnYXzzjsPkyZNQnh4uCfDalRlZSVuv/12LF++vNntiouL8fbbb+OLL77A0qVLceGFF3o9tuY88cQTLgVv5H+EELDb7S7zgdizwlKcIxvr49jgjdo3S3WRbKwzRkOrD1MpmuAghEBFRYXbryOIiLzBbLPi9W2bsLPQtWv9lD4D8LduPVWIql6g5szyzZko+mKny3zUmDMQ9zf1GuYRUXAL1JxJRKQG5kwiN1UfAWxOd52OHKhOLORzzJnBS9gF7NW1aocRtDRhIZA0wX0NKDY2Vlb0VlhY2Oz2VqsV77//vmOs0Whw8803N7rtqFGj0KtXL+zfvx8AsGHDBvz1118ujaxaIyIiAk8++STuu+8+x9zMmTOxZs2aNh+7JRqNBs899xwuv7z+ZkpPPvkkrrnmGuj1eq+fP9hUVlbKxqGhoW4fIzQ0VFb0VlFR0ea4nJlMJtx2222O36WXXXYZrrnmGo8df9y4cbLx4cOHFRdyrl27ttGaHG/8fyByF2vdiChg6eMAc2792FIMoLtq4RC1Fa8NEREpx5xJRKQccyb5K2EzAeacuoZupuxTf9e7SRdb18ztVFM3Sev++xdEDTFnEhEpx5wZvFjr5l2sdXPFWjfWunkaa91cTZw4EX/99RdeffVVfPTRR8jMzMSrr76KV199tcl9evfujblz5+Laa6/1SkzkOR5r8NZYMyt/ZbPZMGnSJPzwww+y+cTERAwePBjR0dE4fPgwduzY4fghO3HiBP7+97/j119/xZgxY9QIGxs3bsTrr7+uyrnJQwKwcYXVqcGbLi5dpUiI/IPVqcGbPjRepUiIiMjTKmtr8dKWdThYKn/jWSNJuGPg2Rib1lmlyAJb+cZMFH2102U++pweiL24H5u7ERERERFR4KnYJR8bOkAKSVAnFiLyGHt1LbLm/dDyhtQqnZ68BNoIg9pheJXznR9ramqa3f77779Hfn79jSYmTJiA9PSm34udOnUqHn/8ccd40aJFeOmll1oZrdyMGTPw6quvOhrw/PHHH/juu+9w6aWXeuT4zbnsssswduxY/PHHHwCAI0eO4J133sE999zj9XMHG+eiN6PR6PYxQkNDUVJS0uQxPeHpp5/GgQMHAACRkZF46623PHr8lJQUnHfeeVi1apVj7sEHH8Qff/wBrVbb5H5WqxUPPvhgo4+xwRupjbVuRBTQGm3wRkRERERERET+SNgtgDmvrpmbKRuoLWh5J2faiFPN3Oq+JF2E5wMlIiIiaudY6+ZdrHVzxVq3Oqx18xzWujXOarUCAAyGlnPQqFGjMHv2bEycONGrMZFnaNQOQA2PPvqorOBNr9fjjTfeQE5ODn7++WesWLEC27ZtQ0ZGhuwOvmazGVdccQWOHz/u85hra2sxffp0RyO9yMhIn8dAbSdpAutHzm6uhq1KXlClj0tTKRoi/2CpOSkb68L4wU0iomBQVFONuRtWuzR3C9Fo8eCQkWzu1krlG4403tzt3J5s7kZERERERAFJWIrr7k7dUORAdYIhIiK/4nxTvJaufS1atEg2vuWWW5rd/uabb4amwfvNH3zwASwWi3tBNkGv12PevHmyuccee8xnN/p74YUXZONnnnnGK8VWSpx77rkQQnj96+jRo15fS2uuv3r7mu3WrVvxyiuvOMbPPfcc0tI8X4PwxBNPyMYbNmzATTfdBLPZ3Oj2JpMJN9xwAzZv3tzo45oAq/Wg4MNaNyIKaPo4+ZgN3oiIiIiIiIj8hhA2CFMuROlGiPxPgez/AgVfAeXblDd304QCYT2AuAlA6lSg4zRICRdAiujD5m5ERERERH6KtW6tx1o372jPtW6nLVy4EN27d8e8efOwb9++Frdfv349LrjgAgwcOBDr1q3zWlzkGe2uAvHIkSN47bXXZHOffvop7rnnHoSEhMjm+/bti1WrVskK34qKijBnzhyfxNrQ3LlzsXfvXgBA586dceedd/o8BlJOq9U23hHz1C8Iq5sveNRiKclxmdPHdlQhEiL/Yakuko31oXFNbElKabVajBw5ElqtVu1QiKidyqssx5z1q5FbWSGbD9fr8fiIsRiU1EGlyFwFUs4sW3sIRV//6TIfM6EXYi/s67evgYkoeARSziQiUhtzJpEbKnbLx5pQIOwMdWIhVTBnElFTSktLZePQ0NAmt83Pz8ePP/7oGEdFReGqq65q9vgdO3aU3WmxoKAA3333XeuCbcT111+PwYMHO8YZGRl4//33PXb85owcORJ///vfHeOCggKP3bG1PYmIkH9IqqU76zbGeR/nY7aFxWLBtGnTYLPZAAAjRozAXXfd5bHjNzR+/Hjcd999srlly5ahT58+ePnll7Fp0yYcPHgQmzZtwksvvYQ+ffpgxYoVAOruBhsfHy/bNyYmxitxEinBWjciCniNNHgTQqgTC5EH8NoQEZFyzJlERMoxZ5KvCGGHMJ+AKNsKceLLuoZuJz4DyjYB5jwAChoiSCFAaFcg9hygww1A2u2QEi+BFDkAkj6GNeLkdcyZRETKMWcSUVNY69Z6rHXzDNa6yT377LO44447UFVV5ZgbMmQI3nvvPRw8eBBVVVWoqalBZmYmli1bhvHjxzu2y8jIwLhx47B06VKvxUdt55EGbwcPHsTrr7/u+MrPz/fEYb1izpw5ss6et9xyiyx5OgsNDcWSJUtkBXGLFi3CkSNHvBpnQ3/++aesi+fbb7+N8PBwn52f3KfRaBq/g7MkodpiQY1Td9kYg9FHkbnHWixv8KYJi4HGyDtnUPtmqXFu8JagUiTBQ6PRICkpqfG8SUTkZYdLizFn/e8oMsn/kI81huLpkeeiR2x8E3uqI1ByZtkfB1H83W6X+ZiJvRF7AZu7EZFvBErOJCLyB8yZRMoIuwWo3CufjOgLSdKpExCpgjmTiJpSUlIiGycmJja57dKlS2G1Wh3j6667rtkiudOc73zqfGfUtpAkCc8//7xsbtasWTCbzR47R3Pmz58vKyh++eWXUVBQ4JNzBwt/L3p77rnnsHt33XVjvV6PhQsXevX36UsvvYTJkyfL5jIzM/HQQw9hxIgR6NmzJ0aMGIGHH37YcZdZjUaDDz74wGXdbPBGamKtGxEFPOcGb8IK2Coa35YoAPDaEBGRcsyZRETKMWeStwghIGqLIMp3QhR8C2S/A+QvB0rXAaasur/TWyJpAWM6EDMKSJkEpN8JKelySFGDIYUksC6cfI45k4hIOeZMImoKa93ahrVubcdat3r/+9//8NRTT8nmZs+ejc2bN+PWW2/FGWecgbCwMBiNRnTp0gXXX389/ve//+Gdd95x/E1us9kwffp0rFu3zisxUtt55BMnP/30E/75z38CAGJjY/GPf/zDE4f1uJqaGnz22WeyuZkzZ7a4X8+ePXHFFVc47tZrtVrx8ccf48knn/RKnA1ZrVZMmzbN8Qtv8uTJuPjii7Fp0yavn5taz2KxwGQyucxLEnCg5CRQI7+bRce4aF+F5haLU4M3fVy6SpEQ+QchBCzVhbI5fZh/Nf4JRBaLBStXrsQFF1wAvV6vdjhE1I7sKjyBV7dtgPlUB/XTOoRH4NHhY5EQGqZSZE0LhJxZ+vsBlPy4x2U+5vw+iD2vtwoREVF7FQg5k4jIXzBnEilUtR8QtfK5iAHqxEKqYc4MXpqwEHR68hK1wwhamrCQljcKYCdPnkRhofw9pM6dOze5/XvvvScbOxezNeXKK69ETEyM4w6qP/30E/Ly8pCamupWvE254IILcN5552HVqlUAgKysLLz55pt48MEHPXL85vTp0we33HKLo5CvsrISzzzzDN544w2vnztYREfL6w6cvydbUllZ6VL05qnGZhkZGXjuuecc45kzZ6J///4eOXZT9Ho9Pv74YwwbNgzPPPMMiouLm92+a9eueO+99zBu3DjccMMNsseSk5O9GSpRk1jrRkRBQRsOSCHyayqWYkAXpV5MRG3Aa0NERMoxZxIRKcecSZ4kLGWAKfvUVw5gr3bzCBJgSKlr6mZMBwwpvPEd+RXmTCIi5Zgzgxdr3byLtW5yrHVzxVq3tmOtW70nnngCQgjHeOrUqZg1a1aL+91xxx3Izs7GvHnzANQ1ebv//vuxdetWr8VKreeRKyuVlZUQQkCSJAwePNhvX+D9/PPPqK6uvyA1cuRI9O6trMHArbfe6ih6A4AvvvjCJ0Vv//73v7F9+3YAQFxcHF599VWvn5M8o2ECdZA0yCwrhSiR392iQ1qMb4Jyk6U4WzbWx6WpFAmRf7DVVsBulTdvDAlPUima4NKwczcRkS+sz83Gf//cApvTa7buMbF4eOhoRIYYVIqsZf6cM0t/24+Sn/e6zMde2Bcx43upEBERtXf+nDOJiPwNcyaRApW75WNjZ0h6/7yBC3kXc2ZwkjQStBH+e02I/NvGjRtd5s4888xGt/3jjz9w4MAB2dyYMWNadV6bzYYlS5bg8ccfb9X+jXnhhRcwdOhQx/vdzz33HG677TaXgipvmDNnDj7++GNH4dU777yDBx54AN27d/f6uU8zmUzIz8/3+nl0Oh3S0jz7/nuPHj1k42PHjrm1v/P2cXFxiI2NbXNcAPDss8+itrauqUuHDh1w44034ujRo24fJydHfpO6uLg4REU13xzmgQcewLRp07Bs2TKsXLkSO3fuRGFhISwWC1JTU9G3b19MmjQJV199NUJDQ3Ho0CFHrABwxhlneKz4j8hdrHUjomAgSRJESDxgPl4/aSkGQruoFhNRW/HaEBGRcsyZRETKMWdSawlbDWA6VtfMrSYbsJW7f5CQxFMN3dIAQ0dImuBuaEGBjzmTiEg55szgxFo3agvWunkGa93ahrVudXJzc11+JpU0dzvt0Ucfxcsvv+z4Pty2bRt27dqFgQMHuh0veZdHGrwlJCQ4/u3Pd6396aefZONzzz1X8b5jx46FTqdzvIjdsWMHTpw44dX17t+/H3PmzHGMX375ZSQlsZFQQJMk1FTXApU22XRiR//8AJi1WP4LQxfLBm/Uvlmq5J1/JY0OutA4laIhIqLW+inzED7Y+6fL/MDEZNx/1ggYdbzDWGuUrNqH0l/+cpmPvbgfYsb1VCEiIiIiIiIizxHmfKC2QD4ZyTc+iYiozi+//CIbx8bGol+/fo1ue/qunZ6yePFiPPbYY5AkySPHO/vss3Httdc6mgIVFxfj+eefx/z58z1y/OZ07NgR9957L1588UUAdXeSfvLJJ7Fs2TKvn/u0jRs3Yvz48V4/T+fOnVtV9NWcPn36yMaHDh1ya/8jR47Ixn379m1zTKc1vFvq8ePHFTeocjZ27FjZ+P/+7//wwAMPtLhfVFQU7rzzTtx5550tbrthwwbZePjw4W7FSORJrHUjoqChi5U3eKstUi8WIiIiIiIioiAg7LVA9RGgej9QkwXA7t4BdLF1zdxONXWTtKFeiZOIiIiIiPwPa908g7VubcNatzo7d+6Ujbt164auXbsqPkd4eDhGjBiB3377zTG3adMmNnjzQxpPHKRDhw6Of5eXt6LDv49kZGTIxiNHjlS8b3h4OAYMGCCb27Nnj0fiaozdbsf06dNhNpsBABMmTMAtt9zitfORb0iShOrCGpf5xFT/bPBmKZE3eNPHscEbtW+1VSdkY31YAiTJI79KiYjIB4QQWLF/T6PN3UampuPBIaPY3K0VhBAo+eWvRpu7xf1tAJu7ERERERFRcKjYLR9rI4HQLqqEQkRE/qW8vBxLliyRzV1yySXQarUu21ZUVODTTz/16PkPHTqENWvWePSYzz77LPR6vWP82muvIS8vz6PnaMpjjz0mu5PmJ598gu3bt/vk3IGuf//+svGuXbtQXV2teP9169Y1e7z2YtWqVbKxOw21iDyNtW5EFDRC4uVja4k6cRAREREREREFMCGsENWHIAq/B3IWAEU/AzVHoai5mzYCCO8DxF8AdJwOqePNkOInQArvweZuRERERETtCGvdPIu1bq3HWrc6paWlsnFKSorbx3De5+TJk20JibzEI11phg8f7kjYzoVl/uSvv+QNB8444wy39u/evbtsvHfv3jbH1JQ333zTkVBCQ0PxzjvveO1c5Hk6nQ4Gg8H1AY2EqgJ5gzd9lB6GUL3rtn7AWuzc4C1dpUiI/IOlulA21ofxTtOeoNPpMH78eOjYVImIvMhmt2PR7u34+tA+l8cu7NIddw0aCp3G/5t2+lvOFEKg9Je/ULrK9f9r3GUDET3Wvb+5iIg8yd9yJhGRP2POJGqesJmA6gPyycj+vPlDO8WcSUTOXn75ZZcb4d1xxx2Nbrts2TJZEdIVV1yBzMxMt79ef/112XE9fafUM844A7fffrtjXFNTg9mzZ3v0HE2JiYnBY4895hgLIfDoo4/65NyBrkOHDrI7b1qtVqxdu1bx/qtXr5aNL774Yk+FFjAqKirw2WefOcYRERGYNGmSihFRe8daNyIKGvo4+bi2CEIIdWIhaiNeGyIiUo45k4hIOeZMaooQdoiaYxAnVwLZC4HC74HqQ4CwNb+jJhQI6wHETQBSpwIdp0FKuABSRB9IugjfBE/kJcyZRETKMWcSkTPWunkWa91aj7VudWJiYmTjqqoqt49RWVkpG0dE8O9+f+SRT54kJibi3HPPhRACx44dw9atWz1xWI8qLi5GcXGxbK5Tp05uHcN5+4MHD7Y5rsYcPXoUjz/+uGM8a9Ystwv0SH2SJDU6V10g7xpqTDT6KiS3WYqzZWNdXJpKkRD5h9qqAtk4JDxRpUiCT2go7/hDRN5Ta7Ph9e2b8Fv2UZfHruvVDzf1PROaRl67+St/yZlCCJT8vBel/9vv8lj8389E9OjujexFRORb/pIziYgCAXMmUTOq/gKEtcGEBojop1o4pD7mTCI67ddff8W8efNkc+eeey7OOeecRrd3Lk6bOnUqunTp4vbXLbfcAqOx/n3mzz77DGVlZR5d29NPP43w8HDH+L333sP+/a7XAr3h3nvvRVpa/XvTv/zyC3799VefnPt07Yu3v44ePeqV+K+88krZePHixYr227dvHzZt2uQYh4eH44ILLvBYXF999VWr/j85y8zMlD3+wAMPeCxGAHj99ddlRXJTpkxBZGSkR89BpBRr3YgoqDg3eBO1gE353deJ/A2vDRERKcecSUSkHHMmnSaEgDDlQhT9D8h5Fyj46lTdQm3TO0laILQrEHsO0OEGIO12SImXQIocAEkf0+jnHIkCGXMmEZFyzJlEdBpr3byDtW6tx1o3IDU1VTbev3+/rLGiEtu3b5eNU1JS3NqffMMjDd6AuoSn0dQd7p///CesVmsLe/hWaWmpbBwWFiZL0EokJSXJxp7+pXHa7bff7igYPfPMM/Hggw965TzkPVarFSaTyfUBSUJNsVk2ZYj3zwZv9toa2CqLZHN6Nnijds7i1OBNH56sUiTBxWq14ocffvC71w5EFByqLRa8uHkttp7Ik81LAG4bcBb+fkbvgHrD2l9yphACJT/uQdnqAy6PxV8xCFEju6kQFRGRnL/kTCKiQMCcSdQ0IQRQsVs+GXYGJK177zFR8GDOJKLTli1bhiuvvBJ2u90xFxYWhrfeeqvR7TMyMrB582bHOCYmBpdcckmrzh0ZGYlLL73UMa6pqcGyZctadaymJCcn41//+pdjbLPZZHcb9Saj0Yg5c+bI5h599NFGi6BI7oYbboBWq3WMv/jiC0UNnV544QXZ+LrrrpMVVrYH+/btw7PPPusYR0REyBpWEfkaa92IKKhoIwFJJ5+zFDW+LZGf47UhIiLlmDOJiJRjziQhBIT5BETJH0Due8CJz4DK3YC9ppm9NEBoFyD+QiDtDkhJl0OKGgwpJCGg6uOJ3MWcSUSkHHMmEZ3GWjfvYa1b67HWDRg4cCBiY2MdY5PJhA8++EDx/t999x1yc3Nlc2PGjPFYfOQ5HmvwNnbsWDz++OMQQmD9+vW45pprXArN1FRZWSkbt6bbsPM+FRUVbYqpMYsWLXJ049RoNFi4cCF0Ol0Le1GgkDQawOkXsUbnnxdMrSW5LnO6WDZ4o/ZLCIFapwZvIeGJKkVDRERKlJpMeGbj7/ir+KRsXq/R4IGzR2B8p64qRRbYhBAo/j4DZWtcL5TEXzUIUSP4/5WIiIiIiIKIKQewlsjnIgeoEwsREfmFDRs24Nprr8WUKVNkdQharRaLFy9Gnz59Gt3P+Y6mV199NUJCQlodx5QpU2Tj9957r9XHasrDDz+MxMT698O+/PJLj5+jKVOnTkW/fv0c423btiErK8tn5w9UPXr0wNSpUx3j2tpa3HLLLY3foO6Ur7/+GkuWLHGMQ0JCMGvWrBbPNXv2bEiS5Pg699xz2xK6x7lToL5//35MnDgRNTX1H5SbP38+Onfu7I3QiBRhrRsRBRNJkgB9nHzSUqxOMERERERERER+RFiKIUo3AHnvA/nLgfLtgK2y+Z0MaUDcBCDtNkhJf4cU0RuSpvXvORERERERUfBirZtvsNatdVjrVvezeM0118jmHn30UWRkZLS4b1ZWFmbMmCGbGz16NDp06ODRGMkzPFpNNXfuXMTHx+Phhx/Gt99+i969e+POO+/E3//+dwwcOFDV4i3norfWdF90LnpzPmZb5eXl4aGHHnKM77vvPgwdOtSj52jIbDbDbDY7xuXl5QAAi8UCi8UCoK7wTqvVwmazybqxnp63Wq2yzqFarRYajabJ+dPHPe3094RzYW1T83q9Hna7HTabzTEnSRJ0Ol2T803F7s01NdVNVQCNPtbcmhrGeJov1lRz4ojsnJrQaNh1RtgbrDfQn6fGYueauKam1iSs1bBb5Xe+kULiYLfbA3ZN/vI8NTxesKypYexcE9fENamzppOmGjy/6Q8U1FTLjh+q0+GBwcPRKzbeEWugrOn08wTA5fW6r54nIQTKftyLyg2Zsu0gAbFXnImoYV3b/fce18Q1cU3+s6aG6wiWNTWc55q4Jq6Ja/Lkmk5vf/q/wbCmYHyeuCZ11mQv3ym/W5I+DghJVbxWf1xTMD5PvlzT6X0bzgX6mvzxPTUiX8vPz8fRo0dlc1arFWVlZSgtLcWxY8ewadMmrF27Fnv37nXZPzw8HEuWLHEptDmttrYWH374oWzOuWjNXZdccgliYmIcN97bsmULdu/ejQEDPNeINDIyEk8++STuv/9+jx1TKa1Wi+eeew5///vffX7uQDdnzhx8+eWXKCmpa1K7fv16TJw4Ee+++y569+7t2M5sNmPBggV48MEHZfs/+OCDQdHY7JZbboEQApMnT8Z5553XaHOsvLw8LFiwAC+++KKsudvf/vY33H333b4Ml8gFa91csdbN82uy2Zpuhnl630BbExB8z1OwrMmujYEGDW6uaSmGECKg1xSMzxPXxFo3rolr4pq4pmCqdfPWmoLxeeKauCauSf01sdatna3JWgFd7RGIqv2QLPIbmTcpJBn20B6wG7sB2oi62KGBFk1/zpDPE9cUrGtirRvXxDVxTVwTa93Uep5Y60b+grVujWOtW2BirRvw9NNP48MPP3TUr5WWlmLUqFF47rnnMG3aNISFhcm2r62txbJly/DQQw/h5En5dYX58+f7LG5yj07phtOmTXP8+6WXXkJcnPxuet26dXP8W6/Xw2q1oqCgAPPmzcO8efOg1WoRHR2NyMhIxcFJkoTDhw8r3t4dkiT5ZB933HXXXY5fSJ07d8a8efO8er758+djzpw5LvMrV650/IB36tQJgwcPxq5du2QdQnv16oXevXtj8+bNKCwsdMwPGjQInTt3xpo1a2R3fR05ciSSkpKwcuVK2Yvu8ePHIzQ0FD/88IMshksuuQQ1NTX47bffHHM6nQ5/+9vfcPLkSWzYsMExHxkZiQkTJiA7Oxs7d+50zCcmJmLUqFE4ePAg9u/f75j3xZpO/4EhIwSqqqtRXFLs8lBza8rMPApA3s3WF2sK3/8NkhqcU5/QJeiep2D83uOavLimzpFo+FtACAm/rN6IQYPOCtw1+dHzdFowrSkYnyeuiWsKlDUZOiTj67KTKK+t/4AHAIRrdXh8xDjk7dmLHwo3BdSaTj9Pe/bsAQD88ssvAHz8PAmB1EyBhBOyTSAAZHeXkFl1FBPQrV1/73FNXBPX5H9rOi2Y1hSMzxPXxDVxTf6zpl9++SXo1gQE3/PENfluTYlxYUDNETS8MGjS9YDOZgvYNQXj8+TrNSUkJAAADh8+jEOHDgXFmtR8npyL9YjUMnny5FbvO3HiRLz55pvo1atXk9t8/fXXskKa1NRUnNvGO0AaDAZcddVVsruZLlq0CK+++mqbjutsxowZePXVV5GZmenR4ypx+eWXY/To0Vi3bp3Pzx3I0tLS8MUXX+DCCy9EbW0tAGDdunXo27cvzj77bHTr1g1lZWXYvn27LI8DwKWXXopnnnlGjbA9zmQy4fPPP8fHH38MvV6PPn36oEuXLoiMjERpaSmysrKQkZHhUoB9wQUXYMWKFV6vESJyF2vdWOvmjTVFlAjUV33Wy0+TkGa1BuSagOB7noJlTQczT6JXSn3sVlMhYLUG9JqC8XnimljrxjVxTVwT1xQ0tW5eWlMwPk9cE9fENfnPmk4LpjUF4/PU2jWt/+NXdIipQlpMFeIi6mrcW7p6V16jR25pOExSOs4aPgEH9+3D/v1r/GZNwfg8cU2BtSbWunFNXBPXxDWx1o21btResdataax1Czysdav7f/DRRx/h2muvdTQ1raiowL333otHHnkEZ599NlJTU6HRaJCfn4+tW7c2epPHZ599FmPHjvV1+KSQJBS2itVoNI6ir8zMTHTq1KnJxxtqSydaSZJcOgK31q5du3DmmWc6xvHx8S6dCFvyxhtv4L777nOMr7rqKnz++eceiW/58uWyX6Q//PADLr744ia3nz17tqxgberUqViyZIlb52zsrqbp6ek4efIkoqKiALADc2vXJITAkiVLZP9/E97/Che9/zZeXLod+z6rb1yYNCoJb7w7tck1TXh5LcpN9f8frhyciscv7uH1NRV8eC/KVr/jeCxq5I1Inl7/gun0WoHAfZ4ai51r4pqaWlPl8S0o2LWk/lxhSUgfOyug1+Qvz5MQAjabDUaj0fHvQF9Tw9iD5XnimrimQFnTX8Un8dqOzTA53W0+KTQMjwwdjQ6RUQG3JkD+fNTW1kKn00GSJN/d5arWgtLvMlC15ZjscUhA3NWDEHZmWrv/3uOauCauyf/WJISAEAIhISGKY/f3NTWcD5bniWvimrgm/1iTxWKB1Wp1vM4MhjUF4/PENfl+TVL5FqBso2NOSDpIHacDGkPArikYnydfr6nh4w1jCeQ1qfk8lZeXIyYmBmVlZY73J9vCZDIhMzMTXbt2hdFobPPxKHh16dIFx44da3nDJkREROCyyy7DXXfdhTFjxrS4/UUXXYSff/7ZMf7nP/+JV155pdXnP23VqlWYOHGiYxwfH4+8vDyEhMhvINawliM8PLzR4p7mfPTRR7jxxhtd5pUcy/n/dWFhoaOAWIl169Y1+f/Y3WO1Nz/88ANuueUWl8K2pkyePBkLFy5EeHi4ou1nO9WtjBs3DqtXr25NqE1yrkPKzMxEly5dFO17zTXXuFXTo9Pp8PDDD2Pu3LmO36/ke/xdXo+1bq5Y6+b5NZkOFuDk+5vhLOXB82BMiAzINQHB9zwFy5qs5fuhLfmpPkhNKJB2e0CvKRifJ66JtW7B8jxxTVwT1+Qfa7Ja1al14/PENXFNXFMgrom1bkG6JrsZutqjQPUBCFMOJCj4nK0uCiK0B2zGMwB9vP+tqZn5gH2euKaAWxNr3bgmrolr4ppY66bW88RaN1ILa91Y69YetPdaNwD4/vvvMX36dJw4ccKt84aHh+P555/HPffc49Z+1Hbu/C53qyJRCOHyDdUSd7dveC5PioiIkI1ramrcPobzPs7HbK2TJ0/KiukmT57cbMGbpxgMBhgMBpd5vV4PvV4vm9NqtdBqtS7bnn4hrXTe+bitmddoNNBoNIrnm4rd22ty/h6WhICkabwRYlOxZ+RVyJq7AUBYiNYna7Lk7ZXNGdP7B+XzxDVxTUrXVHNyj+zxkIgU2fkDcU2nqf08CSFgMplgNBqDZk1K5rkmrqmpGN2d55rqY99yPBdv7twMa4OLsADQOSoaM4eNQbTB2Gzs/rimxuatVitCQ0Nlryu9uSZhFyhrrLmbRkLipCGIODNNPt0Ov/eUznNNXFNT81yTd9YkhEBFRQVCQkKCZk0txejuPNfENQFcU1MxujsfDGsymUyy15nBsCZnXBPXBChfkxB2oDJDNieF94KkNTYZe1Pz/rKm5mJ0d749r+n068zIyMhG3/MJxDWdpsbz1Nr3kol8Qa/Xw2g0Ii4uDikpKejevTv69euHkSNHYuTIkYoLK7Ozs/HLL7/I5qZMmeKRGMePH48OHTrg+PHjAICioiJ89dVXuO666zxy/NOmTJmCl156SXZHZF8ZPXo0Lr/8cnzzzTc+P3egu+SSS5CRkYFZs2bhk08+QUlJSaPbjRgxAg899BCuvvpqH0foXddffz1KSkqwfv16mEymJreLjIzEddddh4cffrjZuxMT+Rpr3Vyx1s3za7JoG9/GEBXmeK0eaGtq7TzX5P01aUOTgIYvR+w1gN0EvT600e0DYU3B+DxxTax145q4pubmuSauqTVr8nWtW1PzfJ64JoBraipGd+e5Jta6OceuZL49rUmnBVBzBKg6ANQcA1DXjKTZd0W1YUBYTyC8FxCSDI0kwXVFfJ4ArqmpGN2dD4Y1sdaNa+KauCauibVuajxPrHUjf8Zat3qsdQtM7b3WDQD+9re/Ye/evXjnnXewaNEiHD58uNntk5OTcdNNN+Gee+5B586dfRQltVbjrzhaoVOnTn79oiQ6Olo2rq6uRlVVleJujABQUFAgG8fExHgiNNx3332OLpJxcXF49dVXPXJcUo/VanXpzAy7ANz8Gckudi3OvGRASltCU0QIAVOu/INrho79vH5eIn9lt5pQmb9TNheexJ8JT7Farfjtt99wySWXNHnBhoioJf/LOoL3du9wua9Zn7hE/GvISIQFSX7xdc4UdoGTn29H5bYs+QMaCUnXD0X4wI5ej4GIqLX4OpOISDnmTKJG1GQCtkr5XMRAdWIhv8KcSRQcjh496rNzpaenN88kWmQAAQAASURBVHp3ZE/QaDTIy8trcbu23mBPkiTs2LGjVft64v/1119/3eZjtFdJSUl4++238dprr2HdunU4duwY8vPzER4ejo4dO2Lw4MHo2rVrq449e/ZszJ4927MBO2nL9+4111yDa665BrW1tdi1axcOHjyI48ePo7q6GjqdDklJSejbty/OPvts/k4nv8RaN1KV/5aBUiDTRQPQ4vQH2wEAlmJAy/ecKbDw2hARkXLMmUREyjFnBjYhbHXN3Kr219UaCEvLO2kMQNgZQFgvwNgRktRYSzciagxzJhGRcsyZRMGBtW7uYa1b4GrPtW6nxcXF4bHHHsNjjz2GnJwcbNu2DcePH0dpaSmEEIiOjkZiYiIGDx6MM844wwNRk694rMGbL38ptEZ8fDxiY2NlXRqzsrLQp08fxcc4duyYbNyjR482x7V//34sW7bMMX7ggQdQXV3d4v/P0tJS2biyslK2j0ajQadOndocH3mQsEPStK36LSEiBH06RHoooKZZS4/DXiXvaGro2N/r5yXyVxX5OyDsDd5gkTSI7DBEvYCIiMhBCIGvDu3DZwf2ujw2NCUVdw0ahpBG7rpBLRN2gZOfbUPl9mz5AxoJSZOHInwAC+2JiIiIiCiIVeySj0OSIRmS1ImFiIiIqI1CQkIwfvx4tcNQRUhICIYMGYIhQ/j+LgUW1roRUbCRJA2EPgawFNVPWooBI993JiIiIiIiosAjhB0w5QDVB4DqQ4Dd3PJOkh4I7QaE9wRCO0OSWONORERERERE5Kw917o1lJaWhrS0NLXDIA/xWIO3QNCnTx+sX7/eMT506JBbRW9HjhxxOV5b1dTUyMZPP/00nn76abeP8/nnn+Pzzz93jKOjo10K48jHnLprSnYBSG1v8OYL5pwM2VhjjIA+nkWU1H6V526WjcMT+0Jn8H6zRSIiap5dCHyw50+sPHbY5bEJnbri1v6DoWnj66/2StjsKPx0O6p2OjV300pImjIM4f1S1QmMiIiIiIjIB4SlFDBlyScjB6oSCxERERERtV+sdSPV8D1W8hZ9nGuDNyIiIiIiIqIAIYQAavOBqgN1jd1s1Qr20gKhXU41desKSaP3dphERERERERERORnNGoH4Ev9+/eXjTds2KB436qqKuzatavZ4xE1JFwmBKQAKX4z58obvBk69oOkaVfpgsjBai5HdeFe2VxUx+EqRRO8dLp21XOWiDzAarfjrZ2bG23uduUZvTEtiJu7eTtnCpsdhSu2NdLcTYPkG4azuRsRBRS+ziQiUo45k6iByt3yscYAhPVUJxbyS8yZREREROQLrHUjoqCjj5OP2eCNAhSvDRERKcecSUSkHHOmfxJCQNQWQpSsA3KXAPkrgIqdLTR3kwBjJyB+IpB+O6SkSyGF92RzNyIPYs4kIlKOOZOIiIhIfe3qFdlFF12EBQsWOMarV69WvO8ff/wBq9XqGA8ePBjJycmeDI+CiF6vhyRJdXfmOM0uAubupuYc5wZvLPCk9qsibwsatmyUtCGISB6kWjzBSK/X429/+5vaYRBRAKmxWvDqto3IOFkgm5cA3NzvTFzQ5Qx1AvMBb+dMYbOjcPlWVO3Olc1LOg2SbhyOsN4pXjs3EZGn8XUmEZFyzJlE9YTdClTKb/iA8L6QNO3qLTVqBnMmEREREfkKa92IKOiwwRsFAV4bIiJSjjmTiEg55kz/IyylQNUBoHq/8r9fDR2A8F5AWA9I2jCvxkfUnjFnEhEpx5xJRERE5B80agfgSxdeeCFCQ0Md4w0bNmDfvn2K9l2yZIlsfOWVV3okpkGDBtXdycHNr1mzZsmOM3XqVNnjpaWlHomPWsdut8ubuwGQhB2QAuNHzpS7RzY2pLHBG7Vf5bmbZOOI5EHQ6AwqRROc7HY7CgoKYLfb1Q6FiAJAudmM5zb+4dLcTStJuHvwsKBu7gZ4N2cKqx0FH29pvLnbzSPY3I2IAg5fZxIRKcecSdRA+XbAbpLPRQ5QJxbyS8yZREREROQrrHUjtQTIPUz/n737jpOrLvc4/j1TtvdsSSO9E0ISSCWhRQEpAoog1YR2VRS8XgWsBAVEVBS9KlIkVEHgoihVqkBCCKRASO+kbMr2na0z87t/rGxyZraczc7smZ39vF8vXvp75vzOPAvZJzNnnnkOeqPIAW+hWplwozu5AIeJa0MA4Bw1EwCco2YmBhOslaleLrPnL9LuB6WqJZ0Pd0spkvLmSIMWyOp/vqzsoxnuBsQZNRMAnKNmAgAAJIbeMW0qRjIyMnTeeefZYj//+c873bdhwwY988wzrWufz6eLLroo5vkheRx6B9xWpnc0v5lwWI2RA94GHelSNoC7mmr3qqFymy2WM2iGO8kksVAopCVLligUCrmdCoAEt78uoJ8seUNbqips8VSvV9+ddpxmDTzCpcx6TrxqZstwt/dU9/FuW9zyeVTylVnKGFMS0+cDgJ7A60wAcI6aCbQwwRqpepk9mDZUlj/fnYSQkKiZAAAA6Cn0usE9vaDJDb2TP09Rf76aK9o6EkhYXBsCAOeomQDgHDXTPSZUL1PzoUzpU9Ku+6WKt6SmfR1v8uVJuTOkgZfKGnCRrNxjZPlyeiRfANRMAOgKaiYAAEBi6FMD3iRp4cKF8vv9retFixbp2Wefbff4hoYGLViwQE1NTa2xK664QiNHjuzweSzLsv3zxhtvdDt39B7GmOhgONwrJrw1l22XaQzYYqmDJ7qUDeCu6t3v2dbelCxlFo13KRsA6Ns+qanSzYvf0J5ArS2enZKiH8w8XkcVMYDscJlgSPseXaq6NXtsccvvVcn82UofXexSZgAAAADQwyrfkcyhN3CxpPzjXEsHAAAAAOh1gysSv8UNvZRl+SRfrj3YXO5OMgAAAACAPsuEm2Rq18rs/Zu08z6p/HWpcVfHm7xZUs4xUv8LpYGXycqbKctf0CP5AgAAAAAAoHfzdeVg6z/Dqb785S8rLS0tLglFPt+rr74a03OOGDFC1113nX75y1+2xs477zzdeeeduvrqq5WSktIaX7t2ra688kotXry4NdavXz/ddNNNMc0JyafNAW/GyPIk/kzFxp2rbWtvZoF8uf1dygZwjzFG1bvsA96yBxwry9OlvzoBADGwvvyAfrlsseqCzbZ4YXqGbpg+RwOzsl3KrPcLN4e075Glql+/1xZvGe42S+kji1zKDAAAAAB6lmnYLQXW24NZR8pK4X0RAAAAAPfQ6wYg6fgLpGDlwTUD3gAAAAAAPcCEg1L9VqluQ8v/mlDnmzzpUsZoKXOMlDqw9bu1AAAAAAAAQFd0eUqNMUZLly6NRy5RzxOvi1633367Pv74Y73wwguSpObmZn3zm9/UT3/6U02dOlXZ2dnasmWLli9fbhvUlZKSomeeeUYDBgyIS15IblbYSL3gQm7kgLfUwRO5AI0+qb58g5oD9mE3OYNnuJRNcrMsS9nZ2dQaAG1avnePfrv8XTWHw7b44Owc3TB9jgrS0l3KzB2xrJnh5pD2PbxU9RsihruleNV/wWylDS/s9nMAgJt4nQkAzlEz0dcZY6SKN+1BK0XKm+VOQkho1EwAAAD0NHrdACQVf4FUv+XgmgFv6GW4NgQAzlEzAcA5amZ8GBOSGj6RAhukus2Saep8k5UiZYyUMsdKaUfIsjzxTxRAl1AzAcA5aiYAAEBi6PKAt2Tg9Xr117/+VVdeeaWeeOKJ1vi+ffv04osvtrmnuLhYDz74oObOndtTaaIX83q90UHTOwa8NeyKHvAG9DWh5jqVrlxki/kzCpWWN8KdhJKcz+fTySef7HYaABLQv3du170ffqDwIV9EkaQx+f30nWmzlelPcSkz98SqZoabgtr78FI1bNxni1upvpbhbsP6dfs5AMBtvM4EAOeomejzAmukJvv7I+XNlOXNcCcfJDRqJgAAAHoavW7ocb2gxw29mD/is2gGvKGX4doQADhHzQQA56iZsWOMkRp3S4H1Ut0mKVzf+SbLK6WPkDLHSOnDZFl98iu3QK9BzQQA56iZAAAAieGwbiFgjIn7P/GWlZWlxx9/XE8++aRmzpzZ7nEFBQX62te+ptWrV+u0006Le15IDsFgMDoYDsvyJH7zW+POiAFvgxjwhr7FGKPSVQ+qub7MFs8begJT6uMkHA5r+/btCofDbqcCIIH8c/N6/WnV+1HD3aYU99eNM+b0yeFuUmxqZrgpqL0Pvtv2cLfLGe4GIHnwOhMAnKNmoi8z4Uap4h170JcvZU9yJyEkPGomAAAA3ECvG4Ck4c+3r4NVMuE2+i2BBMW1IQBwjpoJAM5RM7vHGCPTuFem4i1p15+lvU9JtR91MtzNI6UPk/qdKg2+WlbR6bIyRjHcDegFqJkA4Bw1EwAAIDF0+YqTZVm64447VFhYGI98etx5552n8847T1u3btXy5cu1e/duBQIB9e/fX0OHDtVxxx2nlJSuD4+I95C6hQsXauHChXF9Dhy+tga8WcZISuzhUCYUVNOedbZY6qAjXcoGcEflttdVW7rCFksvGKX84Z9xKaPkFwqFtHLlSg0cOFAez2HNngWQRMLG6PF1H+m5LRujHps7eKiuPGqqfH24VnS3ZoYbg9r74BI1bDlgi3vS/Op/xWylHlEQq1QBwHW8zgQA56iZ6NOq3otu6i44QZbldScfJDxqJgAAANxErxt6RGK3uKG387fxmXSwQkop6vlcgMPAtSEAcI6aCQDOUTMPj2kulwLrpcAGKVjpbFPqYClzjJQxSpY3Pa75AYgPaiYAOEfNBAAASAxdGvBmjJFlWfrSl76kIUOGxCsnVwwfPlzDhw93Ow0kM2MkK7G735r2bpIJNtliqYMZ8Ia+o6Fyu/avfcoW8/ozNWDKVbI8fKETAOItGA7rvo+W662d26MeO2PEGF04bqKsBH89lcjCjc0qfWCJGreV2eKeNL/6X3mcUgfnt7MTAAAAAJKTaa6Qqlfag+nDZaUPdSUfAAAAAHCKXjfEE5/JIp4sj1/Gmy2Fag4Gm8sY8AYAAAAAcMQEqw8OdWs+0PkGSUopkTLHShmjZfmy4psgAAAAAAAAEKFLA94AOBMOh9sIGlkJPt26cddq29qXN0C+rH4uZQP0rFBznXYv/5NMOGiL9598ufzpDLwBgHhrDAX12+VLtXJfadRjF407SmeMHONCVskj3NCs0gcWq3F7uS3uSfer/5VzlDooz53EAAAAAMBNFf+WdOj1fI+UP9etbAAAAAAAAPoGf0HEgLcK93IBAAAAACQ8EwpIgY1S3QapcY+zTf5+UuYYKWOMLH9eXPMDAAAAAAAAOsKAN6CHWMZICX5z04ad9gFvqYMmupQJ0PP2fvSomuvsd+/JH3GKskqOcimjvsOyLBUVFXEHaKAPq21q0i/fX6yNFWW2uMeydPWkYzR38FCXMks8h1Mzww3NKv3zO2rcYW+K92SkqP+Vxyl1YF6MswSAxMDrTABwjpqJvsjUb5Pqt9mDOVNk+bnZAzpGzQQAAAAAoJtS+kkN2w+um8vaPxZIMFwbAgDnqJkA4Bw1M5oJNUh1m1qGujXslGQ63+TLlTLGSJljZKUUxj1HAO6gZgKAc9RMAACAxMCANyAOvF5vdDAc7vIboJqG5hhl5Exj5IC3wQx4Q9/QWL1LNbuX2WJpeSNUNO4cdxLqY3w+n2bPnu12GgBcUlZfpzvee0c7a6tt8RSPV9dOnaEpJQNcyiwxdbVmhuqbtPfPi9X4ScRwt8wUDbhqjlL658Y6RQBIGLzOBADnqJnoa4wJSeX/tgc9GVLuNHcSQq9CzQQAAACQ1Ph+D3qCL2LAflO5O3kAh4FrQwDgHDUTAJyjZrYw4WapfosU2PCfG7aFO9/kzZQyRkuZY6WUEoaXAH0ANRMAnKNmAgAAJAaP2wkAySgYDEYHjZE8zn/ljDG646WNMcyqc427GPCGvqmmdIVt7fFnaODUq2R5mIPaE0KhkNatW6dQKOR2KgB62O7aGt285M2o4W4ZPr++N2Muw93a0JWaGaprUul970QPd8tK1YCr5zLcDUDS43UmADhHzUSfU7NKCtrfKyn/OFmeVHfyQa9CzQQAAAAAoJtS+tnXwaqWgfxAL8C1IQBwjpoJAM715ZppTFCmbovM/heknfdIB15sGfLW0XA3T6qUNVEq+YI06HJZBSfISu3PcDegj+jLNRMAuoqaCQAAkBgY8AbEQVtvdKyw6dKF4tLqxqiYzxu/X9lwU4OaSu0D5VIHHRm35wMSSW3EgLfcwbPkz+jXztGItXA4rPXr1yscdnB3JQBJY3NluX6y5A2V1dfZ4vmpafrx7BM0poA63BanNTMUaFTpfW+raVelLe7NTtWAq+copSQnjlkCQGLgdSYAOEfNRF9iQnVS5VJ7MKVYyhzvTkLodaiZAAAAAJIaX4RGT/AXRATCUnOlG5kAXca1IQBwjpoJAM71tZppTFimfodM2SvSzvuk/f+Q6jZIJtj+JssvZY6Vij4vDb5KVr95stKOkGXx9Vigr+lrNRMAuoOaCQAAkBh8bicAJCNjTFvBLjXANTRHD4mbPTKysSl2mvask4z9DVrqwAlxez4gUTTXl6mx+hNbLKtksjvJAEAf8eH+vfrNB0vUGDEUd0Bmlm6YPkdFGZkuZZYcWoa7vaOmPVW2uDc7Tf2vnqOUomyXMgMAAACABFC5RDJN9ljBidzJGwAAAAAAoIdYnlQZb6YUChwMNpdLKdwIDgAAAACSlTFGaiqVAuulwEYpXNf5Jnml9GFS5hgpfbgsjz/eaQIAAAAAAAAxx4A3IA7aHPAWDkvd/H7YlXOHde8EHWgu22Fb+woGy5vO8A8kv9rSVba1x5+p9IJRLmUDAMlvye5P9MeVyxSKeL00Ijdf108/TtkpqS5llhxCtY3ac9/bai6ttsW9OWkacPVc+QuzXMoMAAAAANxnmvZJtavtwcxxslIHuJMQAAAAAABAX+UviB7wBgAAAABIKsYYqfnAf4a6bZBCNQ52WVLaEVLmWCljpCwPveUAAAAAAADo3bo04M2yujmdCujjLMvTrf2eOP4OBqv32da+3P5xey4gkdTuXWlbZ5VMkuXxupNMH+XxeDRkyBB5PN2rkQAS30tbN+nhNasUOQp3YmGx/vuYWUrzMX+6Mx3VzFBNQ8twt7325gdvbroGXD1H/n4MdwPQt/A6EwCco2aiLzDGSOVv2oOWX8o7zp2E0GtRMwEAAAAkNVpE0VP8BVLDJwfXDHhDL8G1IQBwjpoJAM4lW800zRUtA93qNjh/v5c6UMocI2WMluXNiG+CAHq1ZKuZABBP1EwAAIDE0KUJCsYYhrwBDkS90QmHW3rfEvj3J1gTMeAtp8SlTICeE2oKqK5sgy2WVTLZnWT6MK/XqylTpridBoA4MsboqQ1r9LdN66IemzVwsL569DT5uFDsSHs1M1jdoNJ731Lz/lr78XnpGnDVXPn7ZfZUigCQMHidCQDOUTPRJ9RtlBp322O5x8ryMQwbXUPNBAAAAJDcEre/DUnGX2BfM+ANvQTXhgDAOWomADiXDDXTBGtaPpcPrJea9nW+QZJSiqWMMVLmaFm+nPgmCCBpJEPNBICeQs0EAABIDI6nKGzdulVbt27Vli1bNGjQoHjmBPR6wWDQHjCm5X8TeMBbqGqvbe3LKXYpE6DnBPatlky4dW15/MosmuBiRn1TKBTSihUrFAqF3E4FQByEjdGfV69oc7jbKcNG6uuTpzPcrQvaqpnB6nqV3hM93M2Xn6EBVzPcDUDfxetMAHCOmolkZ8LNUsVb9qA3R8qZ6k5C6NWomQAAAACSWQK3tyHZRA14q5A5pI8LSFRcGwIA56iZAOBcb62ZJlQnU/OhTOlT0q4/t3wu39lwN1++lDtDGniZrAEXyso9huFuALqkt9ZMAHADNRMAACAxOJ6kMHTo0NZ/vF5vPHMCer2oNzrhlgFvlidxO+CC1fYL6F4GvKEPqNm7wrbOKBovjy/VpWz6rnA4rB07digcpkkTSDZNoZB+u/xdvbZja9RjXxpzpC6bcLQ8fEOgSyJrZrCqXnvueUvNByKGuxVkasB/zZW/gOFuAPouXmcCgHPUTCS96g+kkP19kwrmyrJ87uSDXo2aCQAAAABADEQOeFNICla7kgrQFVwbAgDnqJkA4Fxvqpkm3ChTu1Zm79+knfdJ5a9Ljbs63uTNlnKOkQZcJA28VFbeTFn+/B7JF0Dy6U01EwDcRs0EAABIDHxzBYgDY4xtbX26TuABJsEa+4A3HwPekOTCoWYF9n1si2WVTHYnGQBIQnXNzbrz/SVaW77fFrckXX7UVJ08ZLg7iSWRYGWd9tz7toJlAVvc1y9TA66aI19ehkuZAQAAAEDiMMFqqfp9ezBtsJQ+0p2EAAAAAAAAIHnSJU+aFG44GGsul/x5rqUEAAAAAGibCQel+q1S3XqpbpukUOebPOlS5mgpY6yUOkBWAn+nDgAAAAAAAIgnBrwBcRA54E3/mWydyBejQ9X2AW/ebAa8IbnVHVgnE2o8JGIpq2SSa/kAQDKpbGjQHcve1vbqKlvc7/HomsnTNW3AIJcySx7ByjodeOBdBcvrbHF/YZb6XzVHvtx0lzIDAAAAgART8bZkDm0ut6T8ExL6ej0AAAAAAK7h/TJ6iGVZMv4CqXH3wWBzuaQRruUEAAAAADjImJDU8IkUWC/VbZFMU+ebrBQpY5SUOUZKO0KW5Yl/ogAAAAAAAECCY8AbEAdRXwz7dOCbJ3EvTAcjBrz5chjwhuRWu3elbZ2eP0K+1Bx3kunjPB6Pxo4dK08C10gAzu0N1Or2997WvrqALZ7u8+nbx87WhH5FLmWWHDwej8YOHK799y9RqLLe9pi/KEv9r5orX06aS9kBQGLhdSYAOEfNRLIyDbukuo32YPZRslIK3UkISYGaCQAAAABAjLQ54A1IbFwbAgDnqJkA4Fyi1ExjjNS46z9D3TZJ4YbON1k+KX1Ey1C39KGyLL6uCiC+EqVmAkBvQM0EAABIDFwxA+IgcsCb9Z8Bb1GD3xKECYcUqjlgizHgDcnMmLBq966yxbL6T3EpG3i9Xo0bN87tNADEwLaqSt2x7G1VNTba4rmpqbp+2hwNy81zJ7EkEq6sV8YruxWqihjuVpyt/lfNkS+b4W4A8CleZwKAc9RMJCNjwlL5G/agJ1XKnelKPkge1EwAAAAASS0x29uQrPwF9jUD3tALcG0IAJyjZgKAc27WTGOM1LRXCmxouYFaqNbBLo+UPvQ/Q91GyPKkxD1PAPgUrzMBwDlqJgAAQGJg3C4QB83NzfZA2LiTiEOh2nLJhG0xb26JS9kA8Ve3f61CjdW2WFbJ0S5lg2AwqMWLFysYDLqdCoBuWFO2X7e8+2bUcLfijEzdNOtEhrvFQPOBWu3501vRw91KcjTg6rkMdwOACLzOBADnqJlISrUfS832G5sob5Ysb7o7+SBpUDMBAAAAJDcmvKEHtTHgzZjE7rUEuDYEAM5RMwHAOTdqpmkqk6lcIu1+UCp9QqpZ0flwt7TBUsE8afBVsoo/LytzHMPdAPQ4XmcCgHPUTAAAgMTgczsBIBmFw/ZhaTJG8iTuPMVg9b6omC+r0IVMgJ5RsfUV2zole5BSshhq6BZjjPbv30+DJtCLLduzS79f+Z6aI14DDc3J1fXT5igvjcFj3dW0v0al976tUHWDLZ7SP0f9r5wjb1aqS5kBQOLidSYAOEfNRLIxoQapcok96O8nZR3lTkJIKtRMIHkFAgEtX75cmzZtUmVlpQKBgNLS0pSdna3Bgwdr2LBhGjNmjPx+v9upohfZunWrVq5cqd27d6u2tlYDBgzQ0KFDNXv27D7zZykUCmnt2rVatWqVDhw4oNraWmVkZKigoEATJ07UpEmT+sy/CwAAECFywJtpbhkm4Mt2Jx/AAa4NAYBz1EwAcK6naqZprpLqNkiBDdE3TGtPSn8pc4yUMUaWLzOu+QGAE7zOBADnqJlA8qLXDfGQiL1u5eXlWrdunT755BPt3btXgUBAkpSbm6uSkhJNmTJFI0aMiMtz79mzR++//762bt2qmpoa+Xw+5efna8SIEZo0aZKKi4vj8rxITgx4A+Ig8o2OFQ7LshL37qah6r22tTezQJaPF2tITo21exTY/7Etlj/sJJeyAYDe77UdW/Xnj5Yr8jLv+IIiffvYWcrgAlC3Ne2rUem9bylU02iLpwzIVf8rj5M3k+FuAAAAAGBTtVQK19tj+cfLshL3RiwAAHeEw2E98cQTuvfee/Xmm29G38grQmpqqiZNmqTjjz9ep556qo4//nilprZ/fW7RokVasGBB6/qEE07QG2+80eFzxOJz5V//+tf61re+1SOfUTv5mfqip556SnfeeaeWLFnS5uMFBQW64IIL9JOf/ESFhT1387VgMKiPP/5Yy5Yt0/vvv69ly5bpo48+UnNzc+sxX/nKV7Ro0aJuP9eOHTt055136uGHH1Z5eXm7x2VmZurCCy/Ut7/9bY0fP77bzwsA6KbEbXFDMvJmSVaKZJoOxprLGfAGAAAAADFkQgEpsFEKrJeaSp1t8veTMse2DHXz58Y3QQAAAACAI/S6dR+9bm1LpF632tpa/e///q+WLFmiZcuWac+ePZ3uGTx4sC677DJde+21Kikp6dbzh0IhPfTQQ/rDH/6g999/v8NjR4wYoc997nO65ZZblJeX163nRfLjmyxAHERNsjZGSuABb8Hqfba1N4dJoUhelVtft609/kzlDJ7hUjYA0HsZY/S3jet0fxvD3Y4tGajrpx/HcLcYaNpbrdJ7ooe7+Qfmqv9VcxjuBgAAAAARTHO5VPOhPZg+Ulb6EHcSAgAkrLVr12rWrFm66KKL9Prrr3fa8CZJjY2NWrZsmX71q1/plFNO0QsvvNADmaI3qa2t1YUXXqgvfelL7Ta8SS13Fv3jH/+oiRMn6qWXXop7Xn/84x81e/Zs5eTkaPLkybrqqqv0pz/9ScuXL7cNd4uV+++/X0ceeaTuuuuuDoe7SS13FL7vvvs0efJk/fznP495LgAAIHFZliX58+3B5o5fOwAAAAAAOmdCDTI1q2X2Pi3tvF+qeLPz4W6+XClnmjTgElkDL5GVO43hbgAAAACQIOh1QzwkYq9baWmpvve97+nZZ591NNxNknbu3KnbbrtNY8eO7daNTT/88ENNnjxZl19+eafD3SRpy5Yt+v3vf6/SUocD9dGn+dxOAEhGUdNhw0aWp/cMePMx4A1JKtQUUNXOxbZY3pC58ngZjuMmr9eryZMny+v1up0KAIfCxujhNav08rbNUY+ddMQwXX7UVHkSeLhtb9FUWq09976lcKDJ/kBxhkqumC1vRoo7iQFAL8HrTABwjpqJpFLxlqRDmxa8Uv5ct7JBEqJmAslh5cqVmjdvXtTgKY/Ho9GjR2v06NHKyclRU1OTysvLtW7dOu3evdulbNFbhEIhXXDBBXr++edt8aKiIk2ZMkW5ubnavHmzVqxY0XrTuL179+rss8/WK6+8ojlz5sQtt5deeqnDJrxY+t3vfqdrr702Kj5gwABNnTpVeXl5qq6u1ocffqjt27e3Pt7U1KQbb7xRgUBAP/nJT3okVwBAtJ64Kzpg4y+QmvYeXDPgDQmOa0MA4Bw1EwCci0XNNOEmqX6rFFgv1W+X/XPz9p44U8oYI2WOkVJKuC4AoFfgdSYAOEfNBJIDvW6Ih0TudYtUUFCg0aNHq3///srKylJjY6NKS0u1atUq1dTUtB5XVVWlBQsWqKysTP/zP//Tped4/vnn9aUvfUl1dXW2eH5+vo466iiVlJRIkg4cOKDVq1dr//793f/B0Kcw4A2Ig8gL2pYxUgJf5A5FDHjzMuANSarqk3dkQocMybE8yht2omv5oIXH49HQoUPdTgOAQ8FwWHevWqYlu3dGPXbOqHE6b8wEPtyPgcbdVSq9/+2o4W6pR+Sr5PLZ8qYz3A0AOsPrTABwjpqJZGGCtVL9NnswZyp3FUdMUTOB3i8QCOiss86yNbzl5OTohhtu0OWXX67+/fu3ua+0tFQvv/yynnnmGb3wwgtqbGzskXzfeustDR48uEt7CgoKJElbt251dPy7776rCy+8sHU9aNAgvf322472pqWldSm3ZHbjjTfaGt78fr/uvPNOXX311UpJOXhNd82aNbryyitbB641NjbqnHPO0UcffaQBAwb0aM55eXnKzMzUrl27YnK+NWvWRDXHDRkyRH/4wx90+umnR31+8Oabb+prX/ua1q5d2xq75ZZbdNppp2n27NkxyQkAACQ4f4F93VzmTh6AQ1wbAgDnqJkA4Nzh1kxjgi3D3AIbpPotkgk6eLI0KWOUlDlWSh0oy/IcRsYA4B5eZwKAc9RMoPej1y0avW6xkci9bsXFxTrjjDP02c9+VrNnz27377Lm5mY9++yzuuGGG7R58+bW+PXXX685c+ZoxowZjp7vnXfe0Re/+EU1NDS0xqZNm6Zbb71VJ510kny+6NFc69at09///nfdf//9Xfzp0Fcx4A2Ig2Aw4oJ4gg94C0YMePPllLiUCRA/JhxSxbbXbbHs/lPlTy9oZwd6SjAY1L///W8df/zxbb7ABZA4GoJB/eaDJfrogP21gyXp0glH69Tho9xJLMk07qpsGe5W12yLpw4tUOGl0/XmkrepmQDgAK8zAcA5aiaSRv1m+9pKkXKPdScXJC1qJtD73XHHHdq58+ANLIqLi/XGG29o/PjxHe7r37+/LrvsMl122WXav3+/7rnnHhUWFsY7XQ0ePFjDhg07rL1O923bts229vl8h/2cfdWWLVt011132WJPPvmkzj777KhjJ0yYoFdffVXz5s1rbXwrKyvTzTffrLvvvjtuOWZlZWnKlCk69thjNW3aNB177LEaNWqUbr75Zt18880xeY477rhDzc0Hr20XFxfrnXfeabdx84QTTtA777yjGTNmaOPGjZIkY4x++tOf6oUXXohJTgAAIMFFDXgrlzGGG8shYXFtCACco2YCgHNdqZnGhKWGT/4z1G2zFHbwJX3LL2WMlDLHSGlDZFneGGUOAD2P15kA4Bw1E+j96HWLRq9b9yVyr9vw4cO1Z88eeTydD6T3+/364he/qJNPPlnHH3+8Vq9eLUkKh8NauHCho/6zQCCgSy65xDbc7dvf/rZ++ctfdviZ9bhx4zRu3Dhdf/31CofDDn4y9HW8EgPiIKoAh01C39EkVBM54K3YpUyA+Kndu1LBevsdXvNHzHMpGxzKGKOamhoZY9xOBUAHapoa9Ytl72hzZYUt7rUsfW3yNM0aeIRLmSWXxp0VKr3/HYXrI4a7Deun/gtmKeQRNRMAHOJ1JgA4R81E0qiLGPCWMUKWJ6XtY4HDRM0Eer/HHnvMtr7zzjs7bXiLVFRUpB/84AexTAu93M0332wbbDZ//vw2G94+lZ6erkWLFumoo45SU1OTJOn+++/X9ddfrxEjRsQ8vz/96U8qKipy1PjWHf/4xz9s6+9///ud3pU3Pz9fP/vZz3Teeee1xl577TUFAgFlZmbGJU8AQAeYqYWe5u9nX4cbpXCd5OV1ABIT14YAwDlqJgA411nNNMZIjXukug0tg93C9Q7O6pUyhkkZY6X0YbI8/pjmDABu4XUmADhHzQR6P3rdEA+J3Ovm9XZ9KH1+fr7uuusuzZt3cHbIK6+8opqaGmVnZ3e493vf+55taOBll12mX/3qV46f27Ksw8oZfU/iTpwCerHINzqWCbc2v4UT8E1QsNo+4M3LgDckoYqtr9nWabnDlJYX+y9HAEAy2l8X0M2L34ga7pbq9eo7045juFuMNH5SodL7ooe7pQ3vp/4LZsuTSmMFAAAAALTHhOqlhp32YMZId5IBACSsPXv2aNOmTa1rv99vGyoFHI76+no99dRTttgNN9zQ6b4xY8bonHPOaV0Hg8GopsxYKSkpiftwt6qqKpWXl9tiZ511lqO9p59+uu1u4U1NTdqxY0dM8wMAOMSAN/Q0X7ZkRTS8N1e0fSwAAAAA9CHGGJmm/TIVb0u7HpD2PinVrOpkuJslpQ2V+n1WOuIqWUVnysoczXA3AAAAAOiF6HVDPPSGXrfDceKJJyo9Pb11HQwGtX379g737Ny5U3/4wx9a10VFRfr1r38dtxzRtzHgDYiDcDgcETCS1dL9VtXYaHsoJQGmcUYOePMx4A1JpqFqu+rLN9pi+cPnybLoSgWAzuysqdbNS97UnkCtLZ7lT9EPZh6vSUUlLmWWXBp2lGvPfW8r3BAx3G1EoUoWzJYn1dfOTgAAAACAJKl+q6RDbrBi+Voa1wEAOMTu3btt68LCQqWmprqUDZLFSy+9pLq6utb1rFmzNG7cOEd7FyxYYFv/3//9X0xz60mBQCAqNnjwYEd709PTVVhYaItVVDDYBQDcQS8NepZleSRfgT3YXOZOMgAAAACQAExzhUzlUmn3w9Kex6TqD6RQTcebUgdKBSdJg6+UVXKOrKwJsjx8/gEAAAAAvRm9boiHZO1183g8ysvLs8Vqajq+nnLfffcpFAq1rr/61a+qoKCggx3A4WNKABAHUUOjjJFlWQobo4pG+51Ssv3uv4gKVe+1rRnwhmRTsfU129qbmqvsgce4lA0ieb1ezZo1S94EGHgJwG5DeZl++f47CjTbh471S8/QjdPnaGBWtkuZJZeG7WUq/fNimcagLZ42qkgll82UJ+Xg2zZqJgA4R80EAOeomUgKdZvs67Sh3IUccUHNBHq3YNB+Da6qqkqhUIjfaXTLiy++aFufeOKJjvfOnTtXPp+v9c/mihUrtHfvXpWU9L6bq/Tr18/2s0hSQ0ODUlJSHO1vaGiwrWmWAwCXMN8NbvDnS837D66by93LBegE14YAwDlqJgA45zV1OvnYPHn3P2l/f9SRlGIpY4yUOUaWj55uAH0HrzMBwDlqJtC70euGeEjWXre6ujrt32+/pjJw4MAO99x///22deQAOyCWPG4nAPQJxkgej3bX1qg5HLY9lO2wmTlewo11CjfU2mJeBrwhiQQbq1Wze5ktlj/sRFkeZpwmCo/Ho+LiYnk8vCwBEsmKvXv0s6VvRQ13G5yVo4WzT2S4W4w0bDug0vujh7uljy5WyVdm2Ya7SdRMAOgKaiYAOEfNRG9nwk1S/Q57MGOUO8kg6VEzgd6tuNj+OWhdXZ1eeeUVl7JBsli9erVtPWvWLMd7MzMzddRRR9liH3/8cUzy6mmpqamaPn26LbZ8+XJHe7ds2aLKysrWdU5OjkaPHh3L9AAAQCLz97OvGfCGBMa1IQBwjpoJAB0zoTqZmg9lSp+UtWeRsoIrZXU23M2XL+XOlAZeJmvAhbJyj2G4G4A+h9eZAOAcNRPo3eh1Qzwka6/bX/7yF9tQxOHDh2vo0KHtHr9x40bt3LmzdT1y5EgNHz48rjmib+PVGBAHkdNwrXBYlmVpY0VZ1LF+lyfkBmuiL/77shnwhuRRue11mfDB30nL41PukONdzAiRmpub9dxzz6k5YogUAPe8tXO77vxgiZrCIVt8dH4//WjWCSpIS3cps+RSv+WASv+8WKYpYrjb2BIVXzZTHn/060RqJgA4R80EAOeomej16rdLOvQ9rEdKH+ZSMkh21Eygdxs+fLj69+9vi/3Xf/2X1q5d61JGSAaRf35GjeraoNmRI0fa1mvWrOl2Tm655pprbOv//d//dbTvrrvusq0vvfRS7jYMAEBfklJgXzPgDQmMa0MA4Bw1EwCimXCjTO0amb1/k3beJ5W/LjXu7niTN1vKOUYacJE08FJZeTNk+fN7JF8ASES8zgQA56iZQO9GrxviIRl73d555x195zvfscUi15Hee+892/rQQXcff/yxbrzxRk2dOlVFRUVKTU3VwIEDNX36dN1www1aunRp7JJHn+FzOwEgGRljIgOSJW2qTLzGo1D1Ptva8qXIk5HrUjZAbDUF9qt888u2WM6gGfKlcoeiRBM5GBOAe57bvEGPrfsoKj65uL+unTpDqV7eQsRC/eb92rtoiUyzfYhe+rgSlVwyQ5av/S+uUTMBwDlqJgA4R81Er1a3yb5OGyzLm+ZOLugTqJlA73bJJZfol7/8Zet6+/btmjx5sr70pS/py1/+sk488URlZWW5mGHfVFpaqoaGhrg/T2FhYUz/+5aXl6u83N4HMGTIkC6dI/L4jRs3djsvt1x00UV64okn9Oyzz0qSnn76ad122236/ve/3+6e++67T7/73e9a18XFxbrpppvinisAoB2W5XYG6Iv8EQPeQnUyoQau7yBhcW0IAJyjZgKAZMJBqX6rVLdeqtsm+83L2uFJlzLHSBljpNQBsni/DgA2vM4EAOeomUDvRq9bYqLX7SA3et0aGxu1f/9+rVixQk888YT+8pe/KBwOtz5+1lln6Wtf+1qH53j//fdt6/HjxysQCOjGG2/U73//+6iZQXv27NGePXu0bNky3XHHHTr11FP1hz/8QSNGjIjdD4akxnQGIA7aHvDm0caKMncS6kAwYsCbN6eYC/9ICsYY7Vv9mEz40MnylvKHf8a1nAAgkRlj9Jd1q/Xclg1Rj80dNERXTjpGPo/HhcyST/3Gfdr70LtRw90yxvdX8cXTOxzuBgAAAACwM+Y/zfCHyujancQAAH3LDTfcoL/85S/atWtXa6ypqUmPPvqoHn30UXm9Xh155JGaPn26pk2bppkzZ2rixInyuHR9dPjw4V06fuvWrRo2bFh8komjL3/5y3rzzTfj/jwPPPCA5s+fH7PzVVZW2tYZGRnKzMzs0jmKi4tt66qqqu6m5aonnnhCCxYs0OOPPy5J+sEPfqB//OMfuvzyyzV16lTl5uaqtrZWq1at0sMPP6xXX321dW9xcbFefPFFFRUVuZU+APR5dI3BFb5cSR5JB5vu1VwueQe6lREAAAAAdIsxIalhhxTYINVtlkxzp3uaQ5a82WPlyRrfclMzi75tAAAAAOjr6HVLTPS6HdQTvW6TJ0/WqlWrOj3Osix9/etf15133tnpzJw9e/bY1vn5+frsZz+rJUuWOMrppZde0vTp0/X3v/9dxx13nKM96NsY8AbEQeSANytsFExL0a7aGpcyal8oYsCbL7u4nSOB3qVmzwcK7P/YFssbdqJScwa5lBEAJK5QOKz7Plquf+/cHvXYGSNG68vjjpKHAbAxUbdhr/Y99K5MMGyLZxw5QMUXTpfloxkDAAAAALqk/pPoZvgM7oQFoOuMMVK43u00kpcnPWFuMlVYWKjnnntOZ5xxhq3x7VOhUEgffvihPvzwQ913332SWpp35s2bp0suuUSnn366/H5/T6eNBFVbW2tbp6end/kckXtqahKvr6Ar0tLS9Je//EULFizQXXfdpVdeeUXvvvuu3n333Xb3pKSk6NJLL9Wtt96qkpKSHswWABAlMV6yoY+xLK+MP69lqNunmsulNAa8AQAAAOg9jDFS4y4psF6q2ySFGzrfZPmk9BEKpY7US2+s0WmfO1lePoMAAAAA0Al63eKMXjckqWTtdUtJSdFVV12lr3/965owYYKjPZHD7m677Tbt3LlTUsuguAsuuEDnn3++Ro8eLcuytHHjRj355JP6y1/+0jpPqKysTGeffbY++OADDR06NKY/E5IPA96AOIh6wWaMmtLT3EmmE8Ea+4A3bw4D3tD7hZrrtO/jx20xb2quCsee7VJG6IjP59NJJ50kn4+XJYAbGkNB/W75Uq3YVxr12IXjJurMkWNdyCo51a0v1b6Hl0YPdztqoIq/PE2Wt/PhbtRMAHCOmgkAzlEz0avVbbKvUwfK8nbtTmJAV1Azk1i4Xtp5r9tZJK/BV0neDLezaHX00UdrxYoV+v73v69FixYpGAx2eHxFRYWeeuopPfXUUxo1apTuuOMOnXvuuT2ULRJZZNNbWlrX+wIim94iz9lbBYNB+f1++Xw+NTU1tXtcRkaGbrjhBl199dUMdwMAoC/zF0QPeAMSENeGAMA5aiaAvsAYIzXtlQIbpLoNUijgYJdHSh8qZY6V0ofL8qTIa4xOOLE/NRMAHOB1JgA4R81MYvS6xRe9bkhSydrr1tTUpEceeURNTU264YYbNHLkyE73RA54+3S4W05Ojv72t7/ppJNOsj1+5JFH6pxzztGVV16ps88+u3WwXVlZma644gq98sorsflhkLR4NQbEwacTN1uFwwkzpTdSsGqvbe1jwBuSwIF1f1OosdoWKznyAnn9ifNmCnaHM+EZQPcFmpv0y2WLtaGizBb3WJaumnSMjh/MxPBYqVu7R3sfeU8K2Ye7ZU4apKILjnU03O1T1EwAcI6aCQDOUTPRGxkTluq32IMZnX8gC3QXNRNIDkVFRbr33nv1ox/9SI8++qieeuoprVy5UuFwuMN9mzZt0he+8AV985vf1G9+8xt5PM6v7R2Ot956S4MHD3Z8fFeOTSRvvPGG2ynExOH0BSRqL8Hh2rVrly699FK9/vrrjo6vq6vTTTfdpNtuu03XXHONbrnlFv6uBQBXJdffS+hF/AX2NQPekMB4vQoAzlEzASQr01QmBda3DHULVjnYYUlpg6WMMVLGKFne6C9QUzMBwDlqJgA4R80EkgO9bomFXree9fzzz9tuMFpTU6PS0lK99957evTRR7V27VpVVVXp3nvv1aOPPqrf//73mj9/fofnbO9356GHHooa7naok046SY888ojOPvvs1tirr76qJUuWaNasWV37wdCnxLf6An1U5NRby5iE7X0LVe+zrRnwht6uvnKbKre/aYtlFk9U1oBjXMoInQkGg3r++ec7nRgOILbKG+r1kyVvRg1383s8+u9jZjHcLYYCH+/W3keWRg93mzy4y8PdqJkA4Bw1EwCco2ai12rcLYUb7LF0BrwhvqiZQPIZMmSIvve97+mDDz5QeXm5XnjhBd1yyy364he/2GED2e9+9zv9+Mc/jnt+gwcP1rBhwxz/w12Xe1ZWVpZtXV9f3+VzRO6JPGdvsnPnTs2ZM8c23C0jI0PXXnutXn/9dR04cEDNzc0qLy/X4sWL9f3vf1/5+fmSpMbGRt1555064YQTVFFR4daPAABI0B439AEMeEMvwbUhAHCOmgkg2ZjmKpmq92R2PyLteUSqXtb5cLeU/lL+CdLgK2SVfEFW9sQ2h7tRMwHAOWomADhHzQSSD71u6I7e2us2cOBA25+bo446Sp/97Gf1gx/8QGvWrNE999yjtLSW6y11dXW6/PLL9dBDD3V4zrbyPumkk2yD29rz+c9/XvPmzbPFHnnkkS78ROiLqHZATwgbKUHvuh2MGPDmZcAbernKbW9IMq1ry+NXycSLXJkGDACJandtjX7+3ts6UF9ni2f4/PrOtNkaW1DoUmbJJ7B6t/Y99l7L68FDZE05QoVfOkaWh7+fAAAAAOCw1W2yr1OKZPlz3ckFAJAUcnNzddppp+m0005rjW3atElPPPGEfve732nv3r2243/2s5/poosu0oQJE3o6VSSI3tr0Fi+XXHKJtm3b1roeNWqUnn/+eY0ePdp2XH5+vmbNmqVZs2bpmmuu0TnnnKNly5ZJkpYtW6aLL75Yzz33HJ/xAgDQl0QOeAvVyISbZHlS3MkHAAAAACSZYECq2yAFNkhNpc42+QulzDFSxhg+vwYAAAAAxA29buiqZO11u+qqq1RSUtI6nM0Yo69//euaN2+eBg0a1OaetvK+7LLLHD/nZZddpldffbV1/cYbb3QtafQ5DHgD4iAcDtsDxsjyeNxJphPBGvuAN19OiUuZALHRHLC/2cgf8Vn5MxhUBACf2lxZrl8se0c1TU22eH5qmm6YMUdHZNNIECuBj3Zp31+WRQ93O2aICr84leFuAAAAANANxhipbrM9mD7KnWQAJAdPujT4KrezSF6edLczOGyjRo3SD37wA1133XW64oor9Ne//rX1sXA4rF//+te69957XcwQbsrNtV9Tr6urUyAQUGZmpuNz7Ntn/8w+Ly8vFqn1uJdeeklvvvlm6zolJUXPPfdc1HC3SAMHDtRzzz2ncePGqby8XJL0wgsv6J///KfOOuusuOYMAGgDwzXhFl++JEuH3thTzRVSKv2MAAAAAHqWCTW03GwssF5q3Olsky9XyhzbMtQtpV98EwQAAAAAet3ii143JKlk7nX7/Oc/r3PPPVfPPPOMJCkQCOgPf/iDbr311jaPbyvvmTNnOn6+yGPXr18vYww3NEW7GPAGxIEx9iEeljEtvUcJKFQdOeCt2KVMgPjwpxd0fhAA9BEf7d+rX3+wRI2hkC0+IDNLN0yfo6IM52/C0bHaVTu1/4n3o4e7HTtUhV+YwnA3AAAAAOiupr1SqNYeyxjpTi4AkoJlWZI3w+00kMCysrL06KOPavPmzfrggw9a4y+//LKLWSWP0tJSNTQ0xP15CgsLY3rX0H79+ik/P18VFRWtsR07dmj8+PGOz7F9+3bburOBaInqySeftK0vvPBCjRkzxtHeoqIiXXPNNfrpT3/aGnvggQcY8AYAQB9ieXwyvhwpWHUw2FzOgDcAAAAAPcKEm6T6LVJgg1S/XVK4803eLCljdMtgt5RivsALAAAAoMfQ64bO0OsWX/S6HZRIvW4XXnhh64A3SXrxxRfbHfDWVl/bgAEDHD/XwIEDbetQKKTKykrl5+c7Pgf6Fga8AXEQdVE+bJSIE95MOKxgzX5bzMuANwA9zOfz6fTTT5fPx8sSIJ6W7P5Ef1y5TKGIQbQjcvP13WnHKSc11aXMkk/tyk9ahrvZ/1Ure/ow9TtncreGu1EzAcA5aiYAOEfNRK9Ut8m+9uVJfm72gPijZgJ9m8/n03XXXafLLrusNbZjxw7V19crPb333rk1EXz5y1/Wm2++GffneeCBBzR//vyYnnP8+PFavHhx63rTpk1danrbsmVL1Pl6o1WrVtnW8+bN69L+z3zmM7YBb0uXLo1JXgAAoBfxF0QPeAMSDNeGAMA5aiaARGdMsGWYW2C9VL9VMsHON3nS/jPUbYyUOihmQ92omQDgHDUTAJyjZgJ9G71u8UOvm/18iWLs2LG29aZNm9o5UjryyCOjYqld+J59W8f2xNA/9F4etxMAkpGJGJwiE5Y8iffrFq6rlEL2DyB8DHgD4IL6+nq3UwCS2svbNun3K96LGu42sbBY3585l+FuMVSzfEfbw91mDu/2cLdPUTMBwDlqJgA4R81Eb2KMkeo224MZo7gjOnoMNRPo2yZPnhwVO/SOluh7Jk6caFsvWbLE8d5AIKAPP/yww/P1FpWVlbZ1//79u7Q/8vgDBw50NyUAwOHgrTXcFDm8nwFvSFBcGwIA56iZABKNMWGZ+u0yB/4lfXKvtP+fUt3Gjoe7WX4pc5xUfLY0+EpZ/U6WlTY45p9PUzMBwDlqJgA4R80E+jZ63RApmXvd/H6/bd3Y2NjusZMmTYqKRfa/daStY/v16+d4P/qexJs4BSSBUChkW1tho7qcLJeyaV+wel9UzJtd5EImAPqyYDCo119/XcGggzteAegSY4yeWv+xHvx4VeS8Mc0cMFjfnXac0n3+Nvei62re364DT34QNdwtZ/YI9Tv76JgMd6NmAoBz1EwAcI6aiV6nuVwKVtpjGSNdSQV9DzUTgNfrjYrl5ua6kAkSxWmnnWZbv/HGG473vvXWW7a/U6ZMmaKSkpJYpdaj8vLybOtAINCl/bW1tbZ1Vlbi9VgAQF9gMeENbmLAG3oBrg0BgHPUTACJwhgj07Bbpvx1aed90r6/SYE1kmnqYJdXyhglFZ4uDb5aVuGpstKHybKiPyOIBWomADhHzQQA56iZAOh1Q6Rk7nXbuXOnbd1RbiNHjtTYsWNtsY8//tjxc61evdq2LioqUkpKiuP96Ht8bicAJKNwOGwPGKOq4oK2D3ZRsHqvbe3JyJXHn+pSNgAAIJbCxuiB1Sv02o6tUY+dMnSkLj3yaHlifOe4vqxm2TYd+L8V0cPdjhupgjOPivld+gAAAACgT6vbZF97s6SUxPlwGACQ3NasWWNb5+bmKjMz06VskkdXGsUSzamnnqr09PTWu14vWbJE69at07hx4zrdu2jRItv63HPPjUeKPWLgwIG29YoVK3TOOec43v/BBx/Y1v37949FWgCAruJjTbgpcsBbsErGBGVZtDoDAAAA6BpjjNS8XwpsaPknVONglyWlDZEyx0oZI2R5+H4VAAAAAKB3o9ctPuh1a5FovW4vv/yybT169OgOj//iF7+o2267rXX94osvat68eY6e68UXX7St586d6zBL9FUetxMA+oSwUUVRnttZRAlV77OtfTl8AQ4AgGTQFArpt8uXtjnc7bwxE3QZw91iqnrpVh14Onq4W+7xoxjuBgAAAADxULfZvs4YyXsvAIAjNTU12rRpU+cHduCee+6xrU8++eRunQ+9X0ZGhs477zxb7Oc//3mn+zZs2KBnnnmmde3z+XTRRRfFPL+ecuKJJ9rWDz74oJqamhztNcbo3nvvtcVoegMAoA+KHPAmIzVXuJIKAAAAgN7JNFfIVL4r7X5Y2vMXqfqDzoe7pQ6SCk6SBl8pq+QcWVnjGe4GAAAAAHAdvW6Ih2TtdduzZ0/Un/ezzz67wz2XXXaZvF5v6/qBBx5QRUXnn09XVFTo/vvvt8XOPPPMLmSLvogBb0AcGGMiA6os/k/zUSBkeygl1b27SwYjBrx5c4pdygRAX+fzcaddIFbqmpt1x3vvaFnpLlvcknT5xCk6d/R4vvQeQ9VLtqjsmZVR8dwTRiv/cxPj8u+amgkAzlEzAcA5aiZ6C9Owu+VO64fKGOVOMuizqJlA71VWVqZx48bpsssu08cff9zl/QsXLtS//vUvWyyRmpTgnoULF8rv97euFy1apGeffbbd4xsaGrRgwQLbALQrrrhCI0eO7PS5LMuy/ZMod4Q999xzbf8Otm/frm984xvR/RNt+PGPf6xly5bZYpGNhACAHsJHyXCR5UmRvFn2IAPekIC4NgQAzlEzAfQEE6yRqfpAZs9j0u6HpKqlUrCT9xIpxVL+XGnQ5bL6nycre5Isb0bPJNwOaiYAOEfNBADnqJlA70WvG+IlUXvdAoGA7rzzTtXX1zv7Qf5j//79OuOMM1RdXd0aKygo0IUXXtjhvrFjx+ryyy9vXZeVlemKK65QMBhsd08wGNQVV1yhsrKy1tiQIUN08cUXdyln9D0MeAPiIHKQh2WMqvrlSpLMXvsdqgcOj7zrZM8JN9n/YvOkZrVzJADEj9/v1xlnnGF7IwDg8FQ1NujWd/+tteX2L7r7PB5dN3Wm5g0d4VJmyanqnc0q+/uqqHjuSWOVf9qRcRnuRs0EAOeomQDgHDUTvYUxYaniDXvQky6lDnQlH/RN1Eyg9wuFQnr44Yc1ceJETZs2Tb/97W/18ccftzuEKhQK6dVXX9W8efN088032x474YQTGEIFSdKIESN03XXX2WLnnXee/vd//9fW2CZJa9eu1bx587R48eLWWL9+/XTTTTfFLb9gMKht27a1+U9lZaXt2Nra2naPra2tbfc5hg0bpq9+9au22L333qvPfe5zWrlyZZt7NmzYoPPPP1+33HKLLX7yySfrM5/5zGH9rACA7mLCG1zm72dfN5e1fRzgEq4NAYBz1EwA8WRCdTI1q2RKn5R2/VmqfFtq2t/xJn+BlDtTGniZrAEXysqZKsuX3TMJd4KaCQDOUTMBwDlqJtD70euGeEjUXrfm5mb9z//8j0aMGKFvf/vbWrJkSVQ+h9q7d69+9atfafz48VqxYoXtsV/84hcqLCzs9Dl/8pOfqKioqHX9zDPP6HOf+5zWr18fdezGjRt1+umn65lnnmmNWZal3/zmN0pJSXHyI6IPY+QuEAehUMgeCIcV8nlljJEptf8FMnhk538p9JR4DCEBgM6Ew2EdOHBAhYWF8niYPQscrn11tbp96dvaWxewxdN8Pn37mFk6srDYpcySU9Vbm1T+3EdR8bx545T3mXFxe11FzQQA56iZAOAcNRO9Ru1H0U35ucfKsvhzi55DzQSSy/vvv6/3339fkpSbm6vx48ersLBQeXl5qq+vV2lpqT766CPbnR0/NWHCBD3++OM9nTIS2O23366PP/5YL7zwgqSWhrNvfvOb+ulPf6qpU6cqOztbW7Zs0fLly21NlikpKXrmmWc0YMCAuOW2c+dODR8+3NGxTz/9tJ5++uk2H3vggQc0f/78dvf+/Oc/1/Lly/XOO++0xl566SW99NJLGj58uCZOnKicnBzV1tZq3bp1bTbCDRs2TA899JCjXAEAcUD7GNzmz5cath9cN1e4lwvQBq4NAYBz1EwAsWbCjVLdZimwXmr4RFLbX2a38WZLmWOkzLGSvzBhvzdFzQQA56iZAOAcNRNILvS6IZYSudettLRUv/71r/XrX/9aKSkpmjBhggYMGKC8vDwZY1RVVaUNGzZoy5YtbQ47vPXWW3X55Zc7eq7+/fvrn//8p0466STV1dVJkl555RWNGzdORx99tEaPHi3LsrRx48Y2b3L64x//WOeee263fl70DQx4A+IgHA7bA8ZIsqTakFRnf+yI0Ykz4A0A3BAKhbRkyRKdfvrpXCQCDtP26krd8d47qmxssMVzUlJ1w/Q5Gpab505iSarq3xtV/vzqqHjeZ8Yr/zPj4vrc1EwAcI6aCQDOUTPRG5hQnVS5xB7095Oyj3YnIfRZ1Eygd8vIyNCQIUO0Y8eOqMeqqqr07rvvOjrPxRdfrN/85jeO7vCIvsPr9eqvf/2rrrzySj3xxBOt8X379unFF19sc09xcbEefPBBzZ07t6fSjKv09HQ999xzuuaaa/Too4/aHtu6dau2bt3a4f7jjz9eDz30kAYNGhTPNAEAQCLz97Ovm8vdyQNoB9eGAMA5aiaAWDDhZql+qxTYINVvkxTqfJMnQ8oc3TLULaV/wg51OxQ1EwCco2YCgHPUTKB3o9cN8dRbet2ampq0cuXKNoerRRo8eLB++9vfdnng2vTp0/XCCy/osssu0/btB29GtmrVKq1atarNPX6/X3fddZe+9rWvdem50HfxSgyIg8gpn1a4ZW32NtniKWk+FQ3K7bG8AABA8llbtl8/XfJm1HC34oxM3TT7RIa7xVjlGxvaHO6Wf+qEuA93AwAAAIA+rXKxFG60xwpOlGV53ckHANArFRcXa/v27Vq+fLl+8pOf6LOf/axycnIc7S0sLNRXv/pVvffee3rkkUdoeEObsrKy9Pjjj+vJJ5/UzJkz2z2uoKBAX/va17R69WqddtppPZhh/OXm5uqRRx7Ra6+9pi984QtKSUnp8HiPx6OTTz5ZTzzxhN544w0NHTq0hzIFAAAJyZ9vXzdXyJhw28cCAAAASErGhGTqtsoceFHaea904AWpfrM6HO7mSZWyjpSKz5UGXyGr4ERZqQN6xXA3AAAAAADaQq8b4i3Ret1ycnL07LPP6utf/7omTJjgaDipz+fT3Llzdc8992jt2rVdHu72qeOPP14fffSRbrzxxg5vTpqZmakFCxZo3bp1DHdDl/jcTgBIRpED3vTput7eaFRQki2Phw8LAADA4Xm/dJf+d8V7ag7bX2MMycnVDdPmKC8tzaXMklPla+tV8fKaqHj+aUcq78QxLmQEAAAAAH2DaSyVaj+2BzPGyEob7E5CAIBeb8qUKZoyZYokKRwOa9u2bdq4caN27Nih6upq1dXVKSMjQzk5Oerfv78mTZp0WEOn5s+fr/nz53dpT9RnzT3gxBNPdOV5k9l5552n8847T1u3btXy5cu1e/duBQIB9e/fX0OHDtVxxx3X6eCz9hzOf6thw4b1+H/jk046SSeddJIaGxu1atUqrV27VhUVFaqtrVVGRoby8vI0atQoTZ06VVlZWT2aGwCgA7SywW3+gohAWApWRQ9+AwAAAJBUjAlLjbukwAapbpMUbuh8k+WT0kdImWOl9CGyLL4mCQAAAABIPvS62dHrFnuJ0uvm8Xh01lln6ayzzpIk1dTUaM2aNdq2bZtKS0sVCAQktQyCy83N1dixYzVp0iSlxei79NnZ2frZz36m2267Te+99542b96sPXv2KBQKqbCwUKNGjdKsWbPk9/tj8nzoW7hyCcRB9IC3cJvNb/TDAYBkWZays7O5OxbQRa/v2Kr7P1quyLe24woK9T/HzlYGbxBjquKVdap8ZW1UvOD0ico9fnSP5UHNBADnqJkA4Bw1E4nMGCOVv24PWn4pf447CaHPo2YCycfj8WjEiBEaMWKE26kgCQ0fPlzDhw93Ow1Xpaamavr06Zo+fbrbqQAAHLDoaIPLLG+6jCdDCtcdDDaXMeANCYNrQwDgHDUTQGeMMVLTXimwXqrbKIUCDnZ5pPRhUuYYKX2ELE9y9EtTMwHAOWomADhHzQSSD71uiKdE63XLzs7WjBkzNGPGjB59XsuyXHleJDcGvAFxEPlGx2ICLAC0y+fz6eSTT3Y7DaDXMMbo2c3r9df1H0c9dmzJQF0zZbpSvF4XMktOxhhVvrJOla+ui3qs4MyjlDtnVI/mQ80EAOeomQDgHDUTCa32Y6lpnz2WO12WL9udfNDnUTMBAAAAJDW+34NE4C+QGg8d8FbuXi5ABK4NAYBz1EwA7TFNB6TABqlugxSscrDDktIGSxljpIxRsrxpcc+xp1EzAcA5aiYAOEfNBAAASAwetxMAklE4HI4IMOAN6CnN9faGPsubHHdkSmbhcFjbt2+Prp0AooSN0cNrVrU53O3EI4bp2qkzGO4WQ8YYVby8tu3hbmdN6vHhbhI1EwC6gpoJAM5RM5GoTKhBqnzHHvTlSzlT3EkIEDUTAAAAAIC48xfY1wx4QwLh2hAAOEfNBHAo01wlU/WezO5HpD2PStXLOh/uljpAyj9BGnyFrJIvyMqemJTD3SRqJgB0BTUTAJyjZgIAACQGBrwBcRAKhewBw4A3oCc015cr2FBhi6XlDnUpGzgVCoW0cuXK6NoJwCYYDusPK5fppW2box47e9Q4XXnUVHk9vLyPFWOMKl78WFWvr496rN/ZRyv3uJEuZEXNBICuoGYCgHPUTCSsysVSuMEeKzhBlsVwc7iHmgkAAAAgqVmW2xkADHhDQuPaEAA4R80EYIK1MtUrZPY8Lu1eJFUukZrLOt7kL5TyjpMGLZDV/3xZOZNleTN7JF83UTMBwDlqJgA4R80EAABIDD63EwCSkYkY6GaFGfAG9IT6CvvQI48/QylZ/V3KBgBipyEY1F3L39WH+/dGPXbphKN12vBRLmSVvIwxKn9+tarf2hT1WL9zJytnxnAXsgIAAACAvsU07pNqP7IHM0bJSueGDgAAAAAAAEktasBbhYwxshhACAAAACQ8E6qX6jZLgfVS405nm3y5UuZYKXOsrMj3AwAAAAAAAADQyzHgDYiDyAFvMuEun+OjXdUxygboO+rL7QPe0vNHyLI8LmUDALFR09SoXyx7R5srK2xxr2Xpv44+VscNGuJSZsnJGKPyf36k6nfsf6fIkgq/MEXZ04a5khcAAAAA9CXGGKniDXvQ8kn5c13JBwAAAAAAAD0oJWKggwlKwWrJn+tOPgAAAAA6ZExIqt8q1a6R6rdLcvAdKm+WlDlGyhgjpRQz0BkAAAAAAABA0mLAGxAHUQPewqbtAztw+/PrY5QN0HfUV0QOeBvlUiboCsuyVFRUxIeyQBsO1Nfp9qVvaU+g1hZP9Xr1rWNmalJRf5cyS07GGJX/40NVL95if8CSCr84VdnHDnUnsUNToWYCgGPUTABwjpqJhBNYKzXuscdyp8ny5biTD3AIaiYAAACApMZbHSQCT4bkSZXCjQdjzeUMeENC4NoQADhHzQSSn2na1zLULbBeCjd0vsGTLmWMkjLHSqkDqQ+HoGYCgHPUTABwjpoJAACQGBjwBvQAyxh1ufutjcN5+wS0LxxsUGP1J7ZYev5Il7JBV/h8Ps2ePdvtNICEs7OmWre/97YqGupt8Sx/iq6ffpxG5hW0sxOHw4SNyp5dpZp3t9ofsKTCLx2j7KlD3EksAjUTAJyjZgKAc9RMJBITbpQq3rYHfblSzlR3EgIiUDMBAAAAJDc61OA+y7Jk/AX2GwA0l0sa7lpOwKe4NgQAzlEzgeRkQnUtA91q10jNBzrfYKVIGSOlzDFS2hGyLG/8k+yFqJkA4Bw1EwCco2YCAAAkBo/bCQDJyBgTGTiM+W72DZfMPIIJ2UAH6iu3SSZ8MGB5lJY/zK100AWhUEjr1q1TKBRyOxUgYWysKNNPlrwRNdytX1q6bpp9AsPdYsyEjcr+trLN4W5FFxybMMPdJGomAHQFNRMAnKNmIqFUviuF7e+HVXCiLIt7FiExUDMBAAAAJDXa05Ao/BF9Ec3l7uQBRODaEAA4R80EkocxIZm6LTL7/intvF+q+HfHw90sr5QxSio6Qxp8lazCU2SlD2O4WweomQDgHDUTAJyjZgIAACQGBrwBcRAOhyMCpu0DO9AUtJ/jlAkl3UkJSHoNFZtt67ScIfJ4U13KBl0RDoe1fv366NoJ9FEr9+3Rbe++pUBzsy0+KCtbN80+UQOzclzKLPmYYEiNn1Rozz1vqea9bfYHPZaKvjxNWZOPcCW39lAzAcA5aiYAOEfNRKIwTfulmlX2YPoIWenDXMkHaAs1EwAAAACAHsCANyQorg0BgHPUTKD3M00HZMr/3TLUbf8/pPrNkjr4nU4dKPX7TMtQt6IzZGWMkuXhRl5OUDMBwDlqJgA4R80EAABIDFwlBeLAGBMZ6NL+NXuqFeriHqCvqy/fZFunF4x0KRMAOHxv7dyuez78QOGI1wGj8wr0nWnHKSslxaXMkku4oVmVb25U1evr2z7AY6n4wmnKPGpQzyYGAAAAAH2YMUYqf0PSIe+JLa+Uf7xbKQEAAAAA0OdYbicAfKqNAW/GGFkWf0oBAACAeDKhBqluvVS7Rmra1/kGb5aUNUHKHC/Lnxf3/AAAAAAAAACgt2HAGxAHkZOsrS5Otn54yY5YpgMkPWPCqq/YYoul5zPgDUDv8vyWDXp07UdR8clF/XXtMTOU6uWleywEK+v0ye0vtX+Ax1LxRdOVOXFgzyUFAAAAAJAC66XG3fZYzrGy/Lnu5AMAAAAAQF/E8CwkisgBb6ZJCgUkX5Y7+QAAAABJzJiw1LBdql0r1W2RFOp4g+WVMkZJmROktCMYxAwAAAAAAAAAHWBKBNATjOnS4Qdqm6JiQ/tlxCobIOnUl29SOFhvi6UXjHIpG3SVx+PRkCFD5PF43E4FcIUxRo+vW61/btkQ9dicQUN01aRj5OP3o9tMKKyKl9eo6s2N7R/ktVR88QxlThjQc4l1ETUTAJyjZgKAc9RMuM2EG6XKt+xBX46Uc6w7CQEdoGYCAAAAANADvNmS5ZdM88FYczkD3uA6rg0BgHPUTCDxmeZyqXaNFFjXMlC5M6kDWoa6ZY6W5UmNf4J9CDUTAJyjZgKAc9RMAACAxMCANyAOTORAt7DzAW+NwZA+2F5pi00+IlfZafy6Au2p+mSxbZ2SNUC+tDx3kkGXeb1eTZkyxe00AFeEwmHd/9Fyvblze9Rjpw8frQvHHyUPd7WLiao3N3Y83E1S//mzlT66uIcyOjzUTABwjpoJAM5RM+G6qvekUJ09ln+CLA/XxZF4qJkAAAAAkhofTyNBWJYl48+XmvYdDDaXS+lD3EsKENeGAKArqJlAYjLhRimwoWWwW1Np5xu8mVLmeClrgix/fvwT7KOomQDgHDUTAJyjZgIAACQGxu0CcRAOh21rK3LgWwee+zD6A5LTJpZ0O6e2NO/fYltbKelxeR4gnsLBBtXsed8Wyz3iOJeyweEIhUJasWKFQqGQ26kAPaopFNJvPni3zeFuF46bqIsnTGK4WwwF1uxp97Hii6dr+O3nJvxwN4maCQBdQc0EAOeomXCTCdVJ1SvtwfRhUvpwN9IBOkXNBAAAAACgh/gL7OvmcnfyAA7BtSEAcI6aCSQOY8Iy9dtl9r8g7bxXKn+tk+FuXiljjFR8tjTocln5xzHcLc6omQDgHDUTAJyjZgIAACQGBrwBcWAiB7p1YcDb9rK6qFhJTlp3U2pTYM2rtnX6iBlxeR4gnqp3vy8TajoYsDzKGcSf5d4kHA5rx44dUcMxgWQWaG7S7Uvf0vJ99qFjHsvS1ZOO0Zkjx7qUWXJqLg+oaWdFVNybnaphPztHmUcNciGrw0PNBADnqJkA4Bw1E64KbJR06J89r5R/giyGniNBUTMBAAAAJDXejyORMOANCYhrQwDgHDUTcJ9prpCpWCztekDa9zepboNkOhjskFIiFZwkDb5SVtHnZKUPk2Xx1cOeQM0EAOeomQDgHDUTAAAgMfjcTgBIRlED3rr5xmfWiILOD+qi5vKdairdYItlTpgX8+cB4q3qk3ds66zio+RLy3UpGwDoXEVDvW5/723trKm2xf0ej66dOkNTSwa6lFnyKntmZVQs86hBKrpwGgMDAAAAAMBtdevt64xRsvx5rqQCAAAAAACABMKANwAAAKDLTLhJCmyQAmukxj2db/BkSFnjpMwJslL6xT9BAAAAAAAAAOhjGPAGxEHkgDfLmMMeHvL5owcoxRf7O94E1rxqW3sy85U2dHLMnweIp8baPWqo2GyL5RxxnEvZAEDn9tTW6Pb33taB+jpbPMPn13emzdbYgkKXMktewZoG1W/cFxXPmTtKlofhbgAAAADgJtNcFf2lgsyx7iQDAAAAAAAkPkJFIokc8BaulwnVyfJmuJMPAAAAkKCMMVLjTql2jVS3STLBTnZ4pIzhUuYEKX2oLMvbI3kCAAAAAAAAQF/EgDcgDiIHvCls2j7QRYGP7QPeMsefJMvDhzLoXao/WWxbe1OylVU80aVscLg8Ho/Gjh0rjyf2wyyBRLKlskJ3LHtbNU1Ntnh+apqunz5HQ3JyXcosuTV9UhEV86T5lTakoI2jEx81EwCco2YCgHPUTLimboN97UmT0oe4kwvgEDUTAAAAQHJjwhsSiC9XkldS6GCsuUJiwBtcxLUhAHCOmgnEn2mukgJrpdq1Uqi68w0pRS1D3TLHyvKmxz9BOEbNBADnqJkA4Bw1EwAAIDEw4A2Ig6gBb5FrlxljFFgTMeBtwjyXsgEOjwmHVLVziS2WM3iWLA9/tfU2Xq9X48aNczsNIK5WH9irX7//rhpC9jvi9c/M0o3T56goI9OlzJKbCYW196F3o+Ill892IZvYoGYCgHPUTABwjpoJ1wTW29cZo7g7PBIeNRMAAAAAgJ5hWR4Zf57UXHYw2FwmpQ1yLSeAa0MA4Bw1E4gPE26W6ja2DHVr3Nn5Bk+6lDlWypogK6Uo/gnisFAzAcA5aiYAOEfNBAAASAyM2wXiIBwO29ZWxNptTXvWK1i52xZjwBt6m8D+jxVqtN9lKveI3juwpy8LBoNavHixgsFg5wcDvdC7u3fqjvfeiRruNjw3Tz+edQLD3eKo7O+rooMeS2lDCno+mRihZgKAc9RMAHCOmgk3mKYD9i/nSlImjURIfNRMAAAAAMnMstzOAIjg72dfN1e4kwfwH1wbAgDnqJlA7BhjZBp2yRz4l7TzXqnsX50Md7Ok9BFS0ZnS4CtkFZzAcLcER80EAOeomQDgHDUTAAAgMfjcTgBIRsaYyIA7ibQjsOZV29qXP0gp/ce4lA1weKo+ece2TssbrtTsgS5lg+4wxmj//v3RtRNIAi9v26yHPl6pyD/dEwuL9a1jZird53clr74gVNekmve2RcUzjhzQ88nEEDUTAJyjZgKAc9RMuCKw3r72ZkmpXN9D4qNmAgAAAADQg/wRN3CLvGEA0MO4NgQAzlEzge4zwWqpdp0UWCMFqzrf4O8nZU2QMsfK8nID6t6EmgkAzlEzAcA5aiYAAEBiYMAbEAe9bcBb5oR5srj9KnqRYFONaveussVyjzjOpWwAIJoxRk9vXKtnNq6NemzmgMH66tHHyu/1upBZ31H+/Oo240XnTe3hTAAAAAAAkYwx0QPeMsdwnRoAAAAAALfx1hyJJmrAW4U7eQAAAAA9xISbpfrNUu0aqeGTzjd4UqXMsVLmBCmlmM9cAQAAAAAAACDBMOANiINEHvBmwiEF1r5ui2VOmOdSNsDhqS1dKZlw69ry+JU98Fj3EgKAQ4SN0aLVK/Tqjq1Rj3126AhdduRkeWieiKtgZZ1q398eFR/w9RPkSfW7kBEAAAAAwKZxjxSqsccyx7qTCwAAAAAAOASfZSPBRA54C9XKhBtleVLdyQcAAACIA2OM1FTaMtQtsEEyTZ3ssKT0oS1D3TKGy7L4eiAAAAAAAAAAJCqu4AJxEDngzQonzoC3hu0rFK6rtMUY8IbepmbPB7Z1ZvFR8vozXMoG3eX1ejV58mR5vV63UwG6rTkU0h9WLtN7pbuiHvvimAk6d9Q47ozXA+rW7GkznnpEfg9nEnvUTABwjpoJAM5RM9Hj6tbb1758yV/kTi5AF1EzAQAAAADoQf48tQwePKQHs7lcSh3gUkLo67g2BADOUTOBzplgrRRYK9WulYIVnW/w5UtZE6TMcbJ8WfFPED2GmgkAzlEzAcA5aiYAAEBiYMAbEAeRA94UuXZRYM2rtnXKgLHyFwxyKRug60JNtao7sM4Wyx54jEvZIBY8Ho+GDh3qdhpAt9U1N+vXHyzRmrL9trglaf7EKfrM0BHuJNbHhJtDathWFhXvf/XcpBiuR80EAOeomQDgHDUTPcmYkBTYaA9mjk2K92zoG6iZAAAAAJIab8+RYCzLK+PLsw+7YMAbXMS1IQBwjpoJtM2YoFS3RapdIzXskG2YcVusFClzTMtgt5T+fK6apKiZAOAcNRMAnKNmAgAAJAaP2wkAyShqwFs4LCXIhyiBj+0D3jInzHMpE+Dw1JSulEy4dW15/MoqPsq9hNBtwWBQr732moLBoNupAIetqrFBt77776jhbj6PR9dOnclwtx5S8942bf/Rswp8uCvqsfQRhS5kFHvUTABwjpoJAM5RM9GjGj6RwvX2WOZYd3IBDgM1EwAAAACAHuYvsK+by93JAxDXhgCgK6iZwEHGGJnGvTJlr0s775MOvCA1bFeHw93ShkiFp0mDr5LVb56s1AEMd0ti1EwAcI6aCQDOUTMBAAASg8/tBIBkFDngzYoc+NaBbWV1sU6nVbi5UXUb37bFGPCG3qZ2zwe2dWbxRHl8aS5lg1gwxqimpiZ6OCbQS+yrC+j2pW9pb13AFk/z+fTtY2bpyMJilzLrG8INzapZtl21y3eoaU9Vm8dkHJk8d+6mZgKAc9RMAHCOmokeFVhnX6eUyPLnuZIKcDiomQAAAACSGsMCkIj8BVL95oNrBrzBRVwbAgDnqJmAZEIBKbBeql0jNZd1vsGXK2VNkDLHy/Jlxz9BJAxqJgA4R80EAOeomQAAAImBAW9AHITD4YiAszc+FXVNemujgw9tDlP9piUyTfUHA5alzHEnxu35gFgLNQUUOLDWFssecIxL2QCAtL26Une8944qGxts8ZyUVF0//TgNz813KbO+Y99j76l+w74Oj/EX0+QCAAAAAInANHzS8gWGQ2WOdScZAAAAAAAA9A7+AvuaAW8AAABIYMaEpPqtLUPd6rdJ6uT7RJZfyhwjZY6XUgfKYvA2AAAAAAAAACQVBrwBPcHhZOuVO6qiYqk+T8zSaNi+wrZOGzJF3qyCdo4GEk/t3pWSOThA0fL4lFUyyb2EAPRpa8v261fvL1Z9MGiLF6Vn6MYZc9U/M8ulzJKTMUbBynqZxoP/vpv2Vnc63C19XH/lHj863ukBAAAAADphwo3SgZcjot6WLysAAIAuGTZsmLZv3y5JGjp0qLZt29busYsWLdKCBQta1w888IDmz58f5wyTX1f+G8CutLRUy5Yt065du1RZWamSkhINHjxYxx13nDIyMtxOL+727t2rNWvWaPPmzaqoqFAwGFR+fr5KSko0bdo0DR482O0UAQBIPCkRPY7BaplwsyyP3518AAAAgDaYpv0tQ90C66RwQ+cbUgdLWROkjFG8tgUAAAAAAEmLXjf30et2+Oh1o9ctVhjwBsSBiRjoZjkc8Pb717dExY4fUxiTnCQpWLPftk4pGRWzcwM9oWb3B7Z1ZtFEeXxpLmWDWPF6vZo1a5a8Xq/bqQCOvV+6W/+7Yqmaw2FbfEh2rq6ffpzy09Jdyiz5NO2tVsO2MpU9s7JL+wrOmKjcuck32I2aCQDOUTMBwDlqJnpE+RtSqNYey58ty5vpSjrA4aJmAgBweF577TX97Gc/0+uvv65QKBT1eFZWls455xz95Cc/0fDhw+Oez/z58/Xggw8e9v6bbrpJCxcu7PS4+vp6vfDCC3r++ef1+uuva8uW6L6QQ40aNUr/9V//pauuukq5ubmHnR8AHC7LcjsDoA2+/OhYsEJKKe75XNDncW0IAJyjZqIvMKE6KbC+ZbBb84HON3hzpKzxUtYEWb6c+CeIXoOaCQDOUTMBwDlqJgAAh4deN3rdYo0Bb0AchCOGvShy3YZAY1BbDgSi4seN6hertBQOlNvW3qzYnRuIt1BTQIEDa22x7IHHuJQNYsnj8ai4mIZL9B5v7Niq+z5arsjxreMKCvXtY2cp05/iSl7JqHblJ9r/+Ptd2pMza4QypxyhtCEFnR/cC1EzAcA5aiYAOEfNRLyZwMaWO9UfKnWwlD3FnYSAbqBmAgDQNcFgUN/5znf029/+NupmeYeqra3VI488or///e/6/e9/r0svvbQHs4yPZ555RpdeeqkCgehekPZs2rRJ3/3ud/Wb3/xG999/v0499dQ4ZggAbWHCGxKP5fHL+HKkYPXBYFM5A97gCq4NAYBz1EwkK2NCUv32lqFu9VsldfJ9IcsnZYyWsiZIqYNkMVkbbaBmAoBz1EwAcI6aCQBA19DrRq9bvHjcTgBIRlGFuoPC/amyQFNU7MQxhbFKSZIUrC2zrRnwht6kdu8qyRycbmt5fMoqOdrFjBArzc3Neu6559Tc3Ox2KkCHjDH6+6Z1ureN4W7HlgzUDdPnMNwtxqrf3dql47OOHap+Zx+dtMPdJGomAHQFNRMAnKNmIp5MsFYqf80etFKkws/y5QX0StRMAAC65pprrtFdd91l66PIzc3VySefrPPPP18zZ8603S28pqZGX/nKV/TXv/7VjXRjateuXW02vKWnp2vGjBn6/Oc/rwsvvFDz5s1TTk5O1N4zzjhDTz75ZE+lCwAteKuOROWP6ANoLm/7OCDOuDYEAM5RM5FsTNMBmfJ/Szvvl/b/Q6rfrA6Hu6UOlPp9Rhp8lazCU2SlDebzUbSLmgkAzlEzAcA5aiYAAF1Drxu9bvHiczsBIBlFDnizwp0PeGvLdZ8ZFYt0WoUY8IZerGbPB7Z1ZtGR8vjSXMoGsRYMBt1OAehQ2Bg9uvZDvbh1U9RjJx4xTJdPnCKvh9nJsRRuCqpxW1n7Bxza42Kk3BPHqOC0I+OeVyKgZgKAc9RMAHCOmol4MMZIZa9I4Qb7AwUnyfLltL0J6AWomQAAOPP73/9e99xzT+vasiz98Ic/1PXXX6+srKzW+Pbt23Xttdfq2WefldTyOvIrX/mKxo0bp0mTJvVIrm+99ZYGDx7s+Pi8vLwunb9fv366+OKLdf7552vatGlKSbHfNKi5uVkPP/ywvvvd76q8vGVgTSgU0sUXX6wjjzxSEyZM6NLzAQCQdPwFUv22g2sGvMFFXBsCAOeomejtTKhBqlsv1a6RmvZ1vsGbJWVNkDLHy/LnxT0/JBdqJgA4R80EAOeomQAAOEOv20H0usUeA96AGIsc7vaf4GGda2BebIdXMeANvVU42KDA/jW2WPaAY13KBkBfEwyHdc+q9/XO7k+iHjt75Fh9aeyR3FEvDipf39BmPPeE0cr/7ARZPgbqAQAAAEDCq/1Qathuj2WMljLHupMPAAB90Pz58zV//ny300g627ZtczuFhFdVVaUf//jHtthvfvMbXXvttVHHDh06VM8884zOP/98Pf3005KkhoYGXX/99XrxxRd7JN/Bgwdr2LBhMT/vsGHD9KMf/UiXXHJJVKPbofx+vy6//HKdcMIJmjNnjkpLSyW1NMN9+9vf7rF/DwAAJCx/gX3NgDcAAADEiTFhqWFHy1C3ui2SQh1vsLxS+qiWwW5pg2VZ9LcCAAAAAAAcDnrd4oNet87R69aCXrf44aoxEGPhcLitYM8n0gYGvKG3aqjaLplDPhi1PMosOcq9hAD0GQ3BoH71/uI2h7tdOmGSzh83keFucdC0t1pVr6+Pig/46lwVfG4iw90AAAAAoBcwTWVSxdv2oDdTKjiJ99IAAAB9wJ133tl6d05JOumkk9psePuUx+PR3XffrX79DvYxvPTSS/r3v/8d1zzj6dxzz9WGDRt0+eWXd9jwdqiRI0fq3nvvtcX+9a9/ae/evfFIEQCi8Z4diSpywFuwUsZ0MmgDAAAA6ALTXC5T8ba068/Svr9LdRvV4XC31AFSwcnS4KtkFZ0mK30Iw90AAAAAAACAXoheN3rd4o0rx0APsIwkSzJ19kFvaZnOilqshAL2u1Z6MwvaORJILA1VO2zr1OxB8vozXMoGsebz+XTSSSfJ5/O5nQpgU9PUqNuWvqUP99vfRHgtS1+fPE2nDR/tUmbJzYTC2vXrV9t8LPUIXrtQMwHAOWomADhHzUSsmXCjtP+fkgnaH+j3WVnedHeSAmKEmgkAgDMPP/ywbX3DDTd0uqewsFBXXHGFLfbQQw/FNK+eNGjQIPn9/i7vO/PMMzVkyJDWdTgc1ptvvhnL1ACgfcx3Q6KKHPAmIzVXupEJ+jiuDQGAc9RM9AYm3ChT85HMniek3Q9L1R9IoUD7G7yZUs6x0sBLZfU/X1b2UbI8qT2XMJIWNRMAnKNmAoBz1EwAAJyh141et3jj1RgQY+FwODpowpIsqdr+Zba8osyeSUpSuKlepqneFvNm9WvnaCCxNFRut63Tcoe6lAniJT2dL/YisRyor9PPl76t3YEaWzzV69V1U2fq6OL+LmWWnMJNQR14arkCH+2STNvHFF86Q5aX+dQSNRMAuoKaCQDOUTMRK8YY6cC/pGCl/YGsSbLSua6H5EDNBJJLRUWFVq5cqY0bN6qqqkqNjY1KT09XXl6ehg4dqvHjx2vQoEHdfp76+nq99dZb2rlzp/bt26f8/HwNHTpUJ554otLS0mLwk8RXWVmZ3n33XZWWlurAgQMyxigvL08jR47U0UcfreLi4sM+95YtW7R8+XLt379fFRUVys3NVf/+/TVr1iwNHDiwW3k3NDRozZo1Wr9+vfbt26fa2lplZGQoPz9fY8eO1THHHOP4bpNwbuXKldq6dWvreuDAgTrllFMc7V2wYIHuuOOO1vWzzz6rUCgkr9cb8zwT2ZQpU7Rjx8Eboe3evdvFbAAAcJ/lSZXxZtqHbTSXSyn0QKLncW0IAJyjZiIRGROWGj6RatdI9ZslE+pkh1fKGCFlTZDShsiy6GVFfFAzAcA5aiYAOEfNBJILvW7O0OuGrqDXrfvodescA96AGDOmjakk4ZaYqbZ/8FNQnNUTKUmSQrVlUTEfA97QSzRW2Qe8peYOaedI9EbBYFDPP/+8Tj/99MOa6gvE2s6aav38vbdV3mAfjJrlT9F3px2nUfmRd4VGd1S8uk6V/1rb4TH+oixlTBjQQxklNmomADhHzQQA56iZiKnqD1q+CHGolGIpf647+QAxRs0Eksfrr7+u22+/Xa+++qpCoY6/wDdo0CB97nOf0ze+8Q0dffTRbR6zcOFC3Xzzzbbzn3jiidq9e7cWLlyoxx9/XDU1NVH7srKydMkll+jWW29VQUHsrz8vWrRICxYsaF0/8MADmj9/vqO9zc3NWrRokf7whz9o1apVbX8W/h9HHXWULrjgAl1xxRXq37/zm6QEAgH99re/1Z///Gdt2rSp3eMmT56s73//+zrvvPNkWZajvLds2aK//vWvev7557V06VI1NTW1e2xaWpq+8IUv6IYbbtCkSZMcnV+Shg0bpu3bWz7DHDp0qLZt2+Z4b1/w4osv2tYnnHCC4/9+48aNU//+/VVaWipJ2r9/v95//33NmDEj5nkmssg7qHf05xgAYsphvQZc4S+IHvAG9DCuDQGAc9RMJBrTXNky1C2wVgrVdr4hpbhlqFvGWFnexP/iMno3aiYAOEfNBADnqJlA8qDXrXP0urWPXreO0evWffS6dY4Bb0CMtfUXvWU+HfAWtMXzilwc8GZ55MnI67HnBw5XONigpsA+Wywtd6hL2QBIdhsryvTLZYtV22x/49AvLV03TJ+jQdk5LmWWHIwxqvt4jxp3lMuEjerW7FGwPNDpvkHf/ozjN8MAAAAAAPeY+h1S5WJ70JMmFZ0hy8NHUgCAxGCM0be+9S399re/dbxn165duu+++1RSUtJu01tbFi9erLPOOkvl5e0PnqitrdXdd9+tp59+Wv/4xz8SprFn6dKl+vKXv+y4meujjz7SRx99pObmZi1cuLDDY1966SVddtll2rdvX4fHSS13xzz//PP1mc98Rk899ZRyc3M7PL68vFwjR450lLPUctfTxx57TE888YR+9rOf6bvf/a7jvWjf6tWrbetZs2Z1af+sWbP0zDPPtK4//vjjhPnd6CmRzaADBnAjHAAA5O8nNXxycM2ANwAAAHTChJukuo0tg90ad3e+wZMuZY2XMsfLSimMf4IAAAAAAAAdoNfNGXrd0B30unUfvW6d49s0QIy1Ock1/J9YxIC3guKDA96q6+2PxVrkgDdvZr4sjyeuzwnEQkPVDkmH/F5ZHqXmDHYtHwDJa+W+Ut31wbtqCtun1w/MytaN0+eoX3qGS5klB2OMdv3mVTXvjZ7a3y6PpaILjmW4GwAAAAD0AiZYLR14UbZreZJUeJosHwPTAQCJ47bbbotqePP5fJo0aZKGDh2qzMxM1dfXq6KiQuvWrdPu3Q6+9NeGTZs26bvf/a4qKyslSenp6Zo5c6ZKSkpUWVmp5cuX25q+9u/fr1NOOUWvvvqqjj322MP++WLh8ccf1/z589XY2GiLp6am6phjjlH//v2Vmpqq8vJyrVmzRp988kk7Z4p277336mtf+1rUnWSHDRumI488Urm5uaqurtaqVats533llVc0Z84cLV68WNnZ2e2ePxwOR8WOOOIIjR07Vrm5uUpLS1NVVZXWrVunzZs3t36+HwqFdP3118uyLH3nO99x/POgbWvWrLGtR40a1aX9kY2LkeeLh1tvvVVr167V5s2bVV5ervT0dBUUFGjUqFGaO3euzjzzTE2ZMiXueUgt9WPVqlW22PTp03vkuQGAT2aR0Pz59jUD3gAAANAGY4zUuFOqXdsy3M109l0dj5Q+XMqaIKUPlWV5eyRPAAAAAACAztDr1jl63eh16y563bqHXjdnGPAGxFhbf4nKtMRMtf0v7vyigwPebv7H2rjmFT3grSCuzwfESkPVdts6NXugPF6/S9kASFZv79yhez58X6GIQa2j8gr0nWmzlZ2S6lJmycEYo113vqLm/bWdH2xJxZfMkC8nTf6ibHnSqPkAAAAAkOiMCUr7n5PC9fYH8mbJSh/qTlIAALShqqpKt9xyS+va6/XqRz/6ka677jrl5eW1uWfPnj168cUX9dBDD3XpZhTXX3+9KisrlZKSoh/96Ef67//+b2VmZrY+HgqF9NRTT+m6667T3r17JUnV1dW66KKLtGrVKqWnpx/eD9lNS5cujWp4GzJkiG6++WZdcMEFbea1c+dOPf3007rnnns6PPdrr70W1fB28cUX6/vf/74mTJgQdfwbb7yhb37zm613yFy9erWuvvpq/eUvf+nwefx+v8455xx94Qtf0CmnnKKCgrY/G9+yZYt+8Ytf6E9/+lNr89v3vvc9nXrqqTrqqKM6fI6ecODAAdXWOriu3k15eXnt/vk/XJF35BwyZEiX9kcev3Hjxm7n1Jn77rvPtm5qalJVVZW2bt2qf/3rX/rxj3+sk08+WXfccYeOOeaYuOby61//2rYeN26cxo4dG9fnBIBWTHhDIvP3s6+bK2RMWJbFjW4BAAAgmeYqKbC2ZbBbqLrzDf4iKWu8lDlWlpebQAMAAAAAgMRCr1vn6HWj1y0W6HXrHnrdnGHAGxBjJmIwjCRZYSOFjVRjv/NPfvHBAW+7Kuojt8nThRdNnQkF7Her9Gb1a+dIILE0VO2wrdNy+UJosvH5fDr99NPl8/GyBO54YctGPbL2w6j40UUlunbqTKXxZ7Pbdv78ZQUr6zo9rviS6cqcOKgHMuq9qJkA4Bw1EwCco2ai28rfkJr22WPpI6Scaa6kA8QTNRPo3V566SU1NDS0rn/4wx/qpptu6nDPgAEDtGDBAi1YsED19dGf6banoqJCXq9XTzzxhM4555yox71ery644AJNnTpVc+fObW1827hxo2677Tb99Kc/dfxcsdLU1KQLLrjA1vB23HHH6dlnn223cUySBg8erOuuu07XXnut7U6th6qtrdVFF13U2vBmWZbuu+8+XX755e2e98QTT9SSJUt0yimnaMmSJZJa7rj6ta99Tccff3ybe3JycrRt2zYNHDiw0593xIgR+uMf/6gZM2ZowYIFkqRgMKhf/epXWrRoUaf74+073/mOHnzwwbg/z0033aSFCxfG7HzhcFg1NTW2WHFxcZfOEXl8VVVVt/OKhddee02zZ8/WnXfeqWuuuSYuz7F06VL96U9/ssWuv/76uDwXALSNCW9IYP7I16QhKVgl+fNdSQd9E9eGAMA5aiZ6ggk3S3WbpNo1UuPOzjd40qXMsVLWBFkpRfFPEHCImgkAzlEzAcA5aibQu9Hr1jF63eh1iwV63bqHXjfneDUGxFhbA95kjEx9WArZw4cOePN5PVIw3LoePyBbXk/sGuaCtWW2NQPe0Fs0Vm23rVMZ8JaU6uvrlZ2d7XYa6GOMMXpi/Wr9Y/OGqMeOGzREV086Rj4Pd3nurvIXVrc73C3jyAHypKfIk+5X1pQhSh2Y28PZ9U7UTABwjpoJAM5RM3G4TM1qqfZje9CXKxWe0qU7vwG9CTUzOYWNUW1Tk9tpJK2slJSY3tzqcG3fbv/c6bzzzuvS/q7eafSb3/xmmw1vhxo9erT++Mc/6gtf+EJr7O6779YPf/hDpaamdun5uuvBBx+0/TsaNGiQ/v73v3fY8HYoy7JUUlLS5mN33313a2OfJH33u9/tsOHtU1lZWXriiSc0evTo1ma8X/7yl+02vaWkpDhqeDvU/Pnz9fTTT+uf//ynJOmvf/2r7r33Xvn9/i6dBy3auhNrV393Io+PbKKLpVGjRun000/XMccco7Fjxyo3N1fGGO3fv1/Lli3T//3f/2nx4sWtxzc1Nekb3/iGfD6f/uu//iumuVRUVOjCCy+03fl3xowZ+spXvhLT5wEAoLeyvOkynnQpfMiXUZorGPCGHse1IQBwjpqJeDDGSI27W4a61W2UTHMnOywpfZiUNUFKHy7L8vZEmkCXUTMBwDlqJgA4R81MTvS6xRe9bu2j161j9Lr1PvS6HT563bqGAW9AjLU34E3B6Hh6Zkq75/nOKaNjmZZCDHhDLxQONqipdq8tlpY7xKVsEC/BYFCvv/66Tj/9dN48oMeEwmHd/9EKvblzW9Rjnxs+WheNPyohLsD0diYUVtWbG9t8bNB1JytlAAPduoqaCQDOUTMBwDlqJg6XaSyVyt+wBy2fVHSmLE/PfkgP9BRqZvKqbWrS1175p9tpJK0/fuZM5fRwA5cT7d2BMxZSUlL0wx/+0NGx5557rqZOnarly5dLkg4cOKDnnnvO1gjXE/74xz/a1rfccov69YvN58q/+93vWv9/VlaWfvSjHznee8QRR+j888/Xww8/LEl6+eWX1djYGNOmwC9+8YutTW/19fVauXKlpk2bFrPz9yVtNb2lpaV16RyRTW9tnbO7Tj31VH31q1/VzJkz23x8/PjxOv744/U///M/eumll/SVr3zF1rh5zTXXaNq0aZo6dWpM8mlubtZ5552nrVu3tsYyMzP10EMPycMNiQAAOMhfIDXuOrhuLpM0wrV00PdwbQgAnKNmItZMsEYKrG0Z7Bas6nyDv1/LULfMsbK8mfFPEOgGaiYAOEfNBADnqJnJi163+KLXrWP0unWMXrfehV63w0OvW9fxbwWIsTYHvIWN1MUZMbEeKsOAN/RGDdWfSDrkd8ryKDVnsGv5AEgOTaGQfrP83TaHu3153ERdzHC3mCn7x4dtxgd/9xSGuwEAAABAL2dCddL+5ySF7A/0+4yslEJXcgIAoDPjxo2zrX/wgx/EpZlGammo6UrD2EUXXWRbv/3227FOqUPl5eVauXJl6zo3N1cXXnhhTM69fv167dixo3V9+umnKysrq0vnOOmkk1r/f2Njo957770u59Hc3Kzy8nLt2LFD27Zts/0T2ci8du3aLp8/1hYtWiRjTNz/WbhwYdx/FquLn7t09fjDceGFF7bb8Bbp1FNP1ZIlS1RcXNwaC4VCuvHGG2OSizFGV155pV577bXWmGVZuv/++zVmzJiYPAcAOMZH5Uh0/nz7urncnTwAAADQI0w4KBNYJ7P3GWnXn6XKJR0Pd/OkSlmTpP5flgZcLCtnKsPdAAAAAABAr0SvW/vodaPXLZ7odesYvW6Hx+d2AkCyaWvAm2XCLmRix4A39EaBvfbBQKlZA+XxpriUDYBkEGhu0q/eX6L15QdscUvSlZOO0YlHDHMlr2RjgmHtvPNfCpbXRT3W/8rj5O9HswwAAAAA9GbGhKUDL0qhiCaB7MmyMse6kxQAAA7MmzdPxcXFrXczfffddzV69GhdccUVOvfcczVlypSY3T1wxowZ3Tp+2bJlMcnDqSVLltg+6545c2bM7hoa2cA3ZMgQbdu2rUvniPzvsnnzZs2dO7fDPVu3btUTTzyh1157TatXr9aePXscP19FRUWX8sNBbTU01tfXd6nRsb6+vtNz9rThw4fr7rvvtt1t+F//+pc2bdqkUaNGdevc//3f/62HHnrIFrvrrrt0wQUXdOu8AAAkJX9E3yMD3gAAAJKSadov1XwkBdZLpqmToy0pbYiUNUHKGCHL4qtyAAAAAACg96PXrX30utnR63b46HXrOnrdDg9XrYEYC4fbGOYWNnL79qahWnsjkzezwKVMAGcaqrarfMu/bLG0/OEuZYN48/l4SYL4q2io18/fe1uf1FTb4n6PR9+cOkPHlAx0KbPkYkJhbfvh39t9PG1kUQ9mk5yomQDgHDUTAJyjZqJLKpdIDZ/YY6mDpPw57uQD9DBqJtB7ZWRk6O6779Z5553X+rluaWmpbr31Vt16663Ky8vTrFmzNGvWLM2dO1czZ85UWlraYT1XV+9EOHr0aNv608a8nhLZEHbkkUfG7NyffGJ/3fDLX/5Sv/zlL7t1zvLy9gd5lJWV6frrr9eiRYva/vzegaqqqsNNrc9L1qY3STr33HM1btw4rVu3rjX24osv6hvf+MZhn/MHP/iB7rrrLlvs9ttv1ze/+c3DPicAAEnNH9H32FwhY0yP3BUd+BTXhgDAOWomusKEm6W6DVLNaqmptPMNvvyWoW6Z42T5EuP6EdAd1EwAcI6aCQDOUTOB3otet/bR62ZHr9vho9eta+h1O3y8IgNi7NBJr4cEez6RCKFAmW3tzerXzpGA+0w4qNKVD0rmkBehlkf5w05yLynEjd/v1xlnnOF2GkhypYEa3b70be2vr7PFM3x+/c+02RpXUOhSZsml5oPtOvDk8nYfL5k/i6bqbqJmAoBz1EwAcI6aia4wdZuk6vftQW+mVPQ5WZbXnaSAHkTNTF5ZKSn642fOdDuNpJWVkuJ2Cq3OPfdcvfjii/r617+uTZs22R6rrKzUCy+8oBdeeEFSS6PN5z//eX3rW9/StGnTuvQ8OTk5XTo+NzfXtu6oqSseysrsnyfn5+fH7dyxUFNT02Z8z549mjdvntauXdut8x9usxxa7kCblZWl2tra1tj+/ftVVOT8BjCRTZ95eXmxSq/bTj31VFvT24cffnjY57rlllt022232WILFy7UDTfccNjnBAAg6UUOeDPNUqhG8nXt9TdwuLg2BADOUTPhlGk6INWulmrXSqap44OtFClzTMtgt5T+9KQiaVAzAcA5aiYAOEfNTF70usUXvW6do9eta+h1S1z0ujlHr1v3MOANiLGoAW/GKBE+MgrVMuANvUfZxufVWLPTFus36nSl5gx2KSPEUzgc1oEDB1RYWCiPx+N2OkhCW6sqdMd776i6qdEWz0tN0w3T52hITm47O+GEMUahqnrVrS1V2d9XtXtcznEjlT6mpAczS07UTABwjpoJAM5RM+GUaS6XDvwrIuqRCk+X5c10JSegp1Ezk5fHspSTmup2Gughn/3sZ7V27Vr94x//0F//+le9/vrr2rt3b9RxtbW1euyxx/TYY49pwYIF+v3vf6/09HRHz9HVLxZGHu/2FxNj+fxNTZ18KfMwtHnjNUlXXnmlreHN5/PprLPO0qmnnqrJkydr0KBBysvLU1pamu0u1W+88YZOOimxbjZ14MABW+NYvOTl5cW8qWz06NFasWJF63r79u2aMGGC4/3bt2+POl+iGDZsmG29f//+wzrPL37xC/3oRz+yxW688UbddNNNh5saAMRGIjS6AR3xZrYM9Th08EdzOQPe0GO4NgQAzlEz0RETDkp1G6Xaj6TGPZ1vSBsiZY2X0kfJ8vBVOCQfaiYAOEfNBADnqJnJi163voVet87R6+Y+et3s50sU9LolDq5qAzEW9ZdrOCxjWWpy8Y2PCYcVqrVPvfUx4A0JqqH6E5Vtet4WS8kepH6jT3cpI8RbKBTSkiVLdPrpp3ORCDG3+sA+/fr9JWoIBW3x/plZumH6HBVn8OXzw2WCIdV8sENlz6zs9NghN50hb3ri3LWgN6NmAoBz1EwAcI6aCSdMuEna/5z9y7OSVHC8rLSB7iQFuICaCSQPn8+nc889V+eee64kadOmTVqyZIneeecd/etf/9KWLVtsxz/wwAOqrKzU//3f/zk6f1VVVZfyqaystK1jeVdRJwoLC23rWN5VNfLcjz32mC688MKYnf9T7733np5//uDnjIWFhXrppZc0derUTvdWV1fHPJ/u+s53vqMHH3ww7s9z0003aeHChTE95/jx421Nb5F3EO5M5O/f+PHjY5JXLEQ2vtbX13f5HHfddZeuv/56W+y///u/9bOf/axbuQEA0BdYliXjL5CaSg8Gm8ul9GGu5YS+hWtDAOAcNRNtMc3lUs1HUmCtFG7s+GBvppR1pJR1pCwG+iLJUTMBwDlqJgA4R80Ekge9bnb0utHrFkv0unWMXrfY4JUYEGNRA96MFOiXq6DXvV+3xp0fSSZsi/nyBriUDdCxsg3/tP95tTwaMHk+d9oC0GVL9+zUL5a9EzXcbXhunn486wSGux0mEzaqeX+7tv3wWUfD3YYuPJPhbgAAAACQDCoXt3xZ9lCZ46SsSe7kAwBAjI0aNUqXXnqp7r77bm3evFkrVqyIasx65pln9Morrzg634YNG7r0/Bs3brSti4uLu7S/uwYMsH9+vGbNmpidu6SkxLbu6r8bp/7+97/b1nfccYejhjdJ2r17dzxS6rMmTpxoWy9ZsqRL+xcvXtzh+dx04MAB2zqyqbMzf/jDH/Stb33LFrvmmmt05513djc1AAD6Dn+BfR15zQoAAAAJxZigTGCdTOmT0u6HpZqVHQ93SxsqFZ0pDbpcVt4shrsBAAAAAABEoNeNXjfEDr1u7aPXLXYY8AbEWOSAN8uEVT2gSIoc/HaIUNiotjHY7uPdFVj3hm3tLxohX25J2wcDLgo21ah274e2WMHI05SWO9SljAD0Vq9s36zfLV+qYNg+4PTIfkX6wczjlZua5lJmvVvtqp3a9v2/6cBTyzs9NmVQnob+5Cx50vw9kBkAAAAAIJ5Mw06pZpU96C+UCk6WZVnuJAUAQJxNnjxZjz32mL7+9a/b4s8884yj/e+++26Xnm/p0qW29bRp07q0v7tmzZplu1vzkiVL1NTUFJNzz54927Z++eWXY3LeSJGNg2eeeabjvZFNVuie0047zbb+97//HX2zvHasW7dOpaWlrevCwkIde+yxMc2vOyJ/VwcOHOh473333advfOMbttjVV1+t3/3udzHJDQBig/f56AUY8AYAANArmOYKmYq3pJ33Swdekho7+OKpN0PKmSYNWiCr5BxZGSNlWXzlDQAAAAAAwAl63eh1w+Gj161t9LrFFle7gRgLRwySUdioalCxTCBkC3u8llLTWwaePLh4e1xzqlv3pm2dOf7EuD4fcLhqdi2TzMHfFcvjV8HIU1zMCD3BsixlZ2fzZWDEhDFGT29YowdWr1TkW6cZAwbpu9OOU7qPgWOHo37Tfu3/y7LOD7Sk9DElGnDVHHlSfPFPrI+hZgKAc9RMAHCOmomOmHCzVBZx9zbLKxWdLsvDe2z0PdRMoO+54oorbOutW7c62vfyyy+rrKzM8fM89thjtvWcOXMc742F/Px82x1Aq6qq9Pjjj8fk3FOnTrXd+XHJkiVavXp1TM59qMrKSts6NzfX0b6qqir97W9/i3k+3bVo0SIZY+L+z8KFC2Oe+5QpUzRs2LDW9a5duxw3Oy5atMi2/vznPy+v1xvD7A7fvn379Oqrr9piJ554oqO9Dz74oK6++mpb89+CBQt0991387oCAICuamPAm9MGe6C7uDYEAM5RM/smY0IygQ0ypU9Lux+SqpdL4Yb2N6QNkQpPlwZdLit/tixfTs8lCyQQaiYAOEfNBADnqJlA30OvW/fR63Z46HVrQa8b2sOANyDGohqFjFHVgCKp2j7gLa8wU15vy6/gBzsqo86TmRqbom3C4agBbxnjTozJuYFYq9q5xLbO6j9ZXn+GS9mgp/h8Pp188sny+RgEhe4JG6NFq1fq/zaujXrsM0NH6BtTZsifIG+KeqP6Tfs6fLzgrEka+tPPa9hPz1b/y2fLk8aX/OOBmgkAzlEzAcA5aiY6VLlYClbZY3mzZfnz3ckHcBk1E+h7In/fU1NTHe1ramrSLbfc4ujYZ555RsuXL29dFxYW6owzznCeZIxcc801tvUPf/hDVVRUdPu8Ho/HdndYY4yuvfZaBYPBbp/7UPn59tcn69atc7Rv4cKFqqmpiWkukC699FLb+uc//3mne8rKynTffffZYpdddllM8+qOG264QXV1da3rnJwcR01vjz/+uC6//HJbP8nFF1+s++67j4Y3AAmHsoReIXLAW7hRCte1fSwQY1wbAgDnqJl9i2mulKl4W9p5v3TgBalxZ/sHe9KlnGOkgV+RVXKurMzRsiz6e9G3UTMBwDlqJgA4R80E+h563eh1w+Gh1+0get3igwFvQIxFDnizwkbVA4tkqu1/YecXZbX+/1A4+u6RI4oyY5JP487VCgXKbbHMcSfE5NxALDVW71Jj1XZbLHfwbJeyQU8Kh8Pavn27wuGw26mgF2sOhfS75Uv1yo4tUY99YfR4zT9ysjy8aTgsJmxUu+ITVb2xoc3HC878f/buOzyO8z73/j2zDcDuAgRAgL2IFHsRSbGIEhUVt0SOE7fYKe524hzHJzmJ4/L6JI7jxDkuKS5JnJPjEttJbMdOc2zLsWVJlihSIsUuNpFiBwtA9N0FsGWe9w/YEgeLMgAXO1u+n+vSFc8zs7t3JPLGYnb2N+u0+KO/qIa7lsoOBWQF+RVjOtGZAOAdnQkA3tGZGIsZbJP6D7oXI3Ok+AY/4gAlgc4Eyts//dM/6fjx/JuEjOcrX/mKa3vVqlWeH/vZz35W//mf/znuMadPn3ZdECZJ73znOz1fXFdIb3jDG7R06dLnty9evKhXvvKVni98M8bo2rVro+57z3ve47qz6SOPPKK3vvWtGhwcnFTGCxcu6MEHHxx132233eba/tjHPjbh833hC1/Qpz/96UllgDfvec971NT0wvCVRx55RJ/97GfHPN5xHP3mb/6m627AL3vZy3TPPRNf37B48WJZlvX8PyPvjDrS3//936u3t3fcY25kjNGHPvShvOd973vfq7q68W9Y9u///u964xvf6Hrv8LrXvU5f/vKXZdt8pgIAwJQE6yVrxJfR0l2jHwsUGOeGAMA7OrPyGZOTSZ6SufZv0uUvS337JGdg7AdE5kszf06a/3ZZjTtkhWYULStQ6uhMAPCOzgQA7+hMoLxxrdv4uNYNhcS1bsO41m368G8PKLCRA96McdQ3p0XqzbnWG1tjGsu77l1SsCE0qZM/dm2HWm5RqHlhQZ4bKKTeS7td28HIDNW1eP+lAeUrl8vp4MGDyuVyEx8MjGIgm9En9z6hPVfbXOuWpLeu3ajXLF/NROgpMo7R+Q99Wx3feDp/pyUt+vDPq2HHrbIC/FpRLHQmAHhHZwKAd3QmRmOcjNT5kHvRCkjNL5Fl8XsgqhedCZS3b37zm1qzZo3uu+8+/c3f/I3OnTs35rEdHR36/d//ff3lX/7l82u2besNb3iDp9dqbGxULpfT6173On30ox9VMpl07c/lcvqXf/kX7dixQ1evXn1+/dZbb9UHP/jByf0/ViDBYFBf//rXVVNT8/zaY489pk2bNukrX/nKmBeoXbp0SZ/5zGe0bt06fe5znxv1mPr6en3jG99w3SX2H//xH7Vp0yb90z/907gXv125ckVf+tKX9Au/8AtasmSJvvGNb4x63Gtf+1oFAoHnt7/2ta/p13/9110XUd2Y+dd//df1jne8Q8YYtbS0jPn6mJqGhgZ95CMfca39zu/8jv7oj/5IiUTCtX7hwgW96lWv0re+9a3n1yKRiD7xiU9MS7Y/+7M/0+LFi/Wud71LjzzyiIaGhkY9zhijRx55RPfff7/+5E/+xLVv3bp1+r3f+71xX+e///u/9cu//MuuO/jeeeed+uhHP6qLFy/q3Llznv+5fv36zf8/DgBAhbAsSwq572ivLAPeUBycGwIA7+jMymUyvTLdT0iXvihd/540eHHsg+0aqX6TNPdNsma/RlZ0uSwrMPbxQJWiMwHAOzoTALyjM4HyxrVu4+NaNxQS17pxrdt0C058CIDJGDngbSgQULYmItPvnojZ2DL2gLdopHAfWCVPPOp+7pX3Fuy5gUIxTk59bU+61urn38EXRQFMqHdoUJ/Y84TO9fW41oO2rXdt2KJtc+b7E6zMDZ69rq7vPaOhi2NPqm96+TrZNaEipgIAAAAAFFXPbinb415r2C5r5JdnAQAoM8YYPfroo3r00Uf17ne/W83NzVqzZo2am5sVjUaVSqV05swZHTlyJO8C1w9+8INavXq1p9f52Mc+pve9733q7e3VH/zBH+jP/uzPdMcdd2jWrFnq7e3Vvn378u4AWl9fr3/+53+e8C6J02nz5s36yle+oje+8Y3PXwh07tw5vfnNb9Y73/lO3X777Zo9e7bC4bC6urp0/PhxXbhw4fnHv/a1rx3zue+//379wz/8g97+9rc//9zHjx/XG97wBr3tbW/Thg0bNHfuXMViMSUSCXV1denEiRNqb2/3lH358uX6jd/4DdeFd5///Of11a9+VVu3btWCBQs0ODioM2fO6NChQ89/tj979mx98pOf1Bvf+MZJ//vC+H7rt35Lhw4d0v/7f/9P0vDfv4985CP6zGc+o82bN6u5uVkXL17Unj17XBeGWZalL3/5y1q/fv20Zevp6dHnPvc5fe5zn1MwGNSqVas0f/58NTQ0yBij69eva//+/aPe1feWW27R97///Qn/rn7ta19TOp12re3atUvLli2bdN43v/nNE96tFQCAqhJqktIdL2ynGfAGAAAwnYxxpIGzUv8RafD8xA+IzJPia6W6W2VZfHUNAAAAAABgKrjWbXxc64ZC4lo3rnWbTpwlBwps5IC3VHj4r5npzbrWG1vHHvBWsCyOo9SJH7vW6lbeM+2vC0xWsuOYckN9rrX6+dt9SgOgXLSnkvrYU4/rWso9Cb4mENTvbd6uNTNbfUpW3nKJIV35v49PeFz99iVFSAMAAAAA8IMZvCz1H3AvhmdL9Rv9CQQAwDTq7OzUY489Nu4xwWBQf/iHf6gPfehDnp93+fLl+s53vqNXvOIV6unpUSqV0sMPPzzm8S0tLfr2t7+tLVu2eH6N6fJLv/RLmj9/vl7/+tfr4sWLz68PDg7qiSeeuKnn/rVf+zWtWLFCv/Irv6LTp08/v55Op7Vnzx5Pz9HYOPbA2U996lO6cOGCvvvd7z6/NjQ0pMcfH/289+LFi/Wd73xHHR0do+7Hzfvbv/1b1dbW6rOf/ezz11P09PTooYceGvX4WCymv/7rv9brX//6omXMZrM6cuSIjhw5MuGxr3/96/W5z31u3D+HAACgCEJN7u0MA94AAACmg8n2SYmjw//kkuMfbEek6Copvk7WyPdrAAAAAAAAuGlc65aPa91QSFzrhuli+x0AqDSO47i2U5HQ8P/oc0+8bWyJTnuWocvHlEt0utbqVjDgDaWn79Ju13ZNw2JF4nN8SoNisyxLLS0tsizL7ygoIxf6evXHux7NG+5WH47oD7b/DMPdpmjoYrcu/On3xj2m8efWaPFHf1FWgF8l/EBnAoB3dCYAeEdn4kbGyUqdIz+ADEgzXyLL4ndBgM4EytunP/1pfeYzn9EDDzzg6YKV+vp6vfnNb9bhw4cndcHbT+3YsUOHDx/WW97yFsVio98ALBaL6Z3vfKdOnDihO+64Y9KvMV22b9+uU6dO6TOf+cyEd3K1LEubNm3SJz7xCb373e+e8Lk3b96sEydO6B//8R+1fft2BYPj35vQtm1t3LhR73vf+7R//3791V/91ZjHhsNhffvb39anPvUpzZ07d8zjFixYoA9+8IM6dOiQ1qxZM2FmTF0wGNSnP/1pPfTQQ3rRi14k2x79PWU0GtUb3vAGHT58WG9+85unNdOHPvQhvfrVr9acOd4+k25oaNCb3vQm7dmzR1//+te54A1AZeN3HZQLBrzBJ5wbAgDv6MzyZYwjkzoj0/6fUts/SL17xh/uFpkjNb9UmvcOWU33MNwNmAI6EwC8ozMBwDs6EyhvXOvmHde6oVC41g3TxTI/HRmIqtfX16eGhgb19vaqvr7e7zhl69y5c3rwwQef374YDur87CalP3JO6s0+v/6Bz71Gt993qyTpN//xgPac7X5+33tftky/snXBTWfp+uFf6+o//s/nt0MzF2nZX5y76ecFCimXTuq5h947/MXRn2hd+6tqXHyvf6EAlLQTXdf1F3t3KZXNuNZbauv0gW07NDsa9ylZ+ev41n4lnj4/5v7573+ZQo11RUwEAAAAACg20/Vjqf+ge3HGXbIaNvuSB0BlK/Tnk4ODgzp79qxuueUW1dTUFCAhKpkxRqdPn9apU6d04cIF9fb2KpPJKBaLqbm5WWvWrNHatWsVDoc9Pd+HP/xh/fEf//Hz24888ojuvffe57eTyaR27typixcvqqOjQ42NjVq0aJHuvfde1dbWFvr/vYK7fPmynnzySbW3t6urq0vBYFAzZszQ0qVLtWHDBjU3N0/5ufv7+/Xkk0/q0qVL6urq0uDg4PP/HZYtW6ZVq1ZNqSOy2az279+vgwcPqrOzUzU1NZo9e7ZuvfVWbd68mQuYfXLlyhXt2bNHbW1t6u3tVWtrqxYsWKC77rpL0ej03yhvpPb2dh0/flyXLl1SR0eHUqmULMvSjBkz1NTUpHXr1mnVqlX8eSkSfpZjMrjW7ealTl7TtS/tcq3VrZ6jWW8qnQvxgbGYTLd0+SvuxfnvlBXg5wcAAMBUmWy/lDg6/E8uMf7BVliKrZJia2WFZxYnIAAAAAAAPuJaN/iJa90mh2vdUChc64bxTOZn+fijIQFMWv7MxJ9sO+71cG1o2rMkTzzq2q5bee+0vyYwWf1XnnYNd7PsoOrnbvExEYotl8vp1KlTWrZsmQKBgN9xUOL2Xbusz+5/ShnHca0viNfr/Vt3qLGm9E8MlKLksSvq+vZhZXtSo+6f++57FZ7TICsw+qRxFA+dCQDe0ZkA4B2diZ8yqdP5w93Cs6T6Tb7kAUoRnQlUDsuytGzZMi1btqworxeNRvWyl72sKK81HebOnatXv/rV0/Lc8XhcL3nJSwr+vMFgUFu3btXWrVsL/tyYujlz5ugXf/EX/Y7xvNbWVrW2tvodAwAATFawQZIt6YbrRzJdUmDsO9sDhcC5IQDwjs4sD8Y40uAFqf+INHBWz3//ZSzh2VJ8rVS3XJY9/d+JAaoFnQkA3tGZAOAdnQlUDq51mxyudUOhcK0bCoUJDUCB5Q14yxv4VrwcqZM/dq1FGfCGEtR7cbdrOzprvQLh4k+rhX8cx9HJkyfljBjYBYz06MVz+qund+cNd1vRNFN/uP0ehrt5lEullTp5TaljV9T35Fmd/cC/q/0rT4493O2371NkfiPD3UoEnQkA3tGZAOAdnQlJMplu6foPR6wGpOaXyLL4nRD4KToTAAAAAAD/WZYthRrdi5kuf8KgqnBuCAC8ozNLm8kmZXr3SG3/ILX/pzRwRmMOd7PCUmydNOdXZc15vazYGoa7AQVGZwKAd3QmAHhHZwIAAJSGoN8BgEqTP+DNnxxDbceU67/uWqtbeY8/YYAxpBNXNdhzxrXWMP9On9IAKFXGGH3nzLP6+oln8vbdPmuO3r1xm8LcQcKTocu9uvr3j8sZzHg6fuZrNioyd8b0hgIAAAAA+M44Wanje5JJu3c03SMr3OxPKAAAAAAAAGA8oUYp0/nC9o3/GwAAAHmMMdLgBSlxREqdlTTBF/zDrcOD3aLLZdnhomQEAAAAAAAAAKDSMOANKLD8AW/+THhLnXjUtR1qXqjQzMW+ZAHG0nf5add2IFKvaMtqn9IAKEWOMfrn44f14NnTefvumb9Yb1+3UQHb9iFZ+Rm63KvLn3nY8/Gh1rjq1sydxkQAAAAAgJLR/aiUcd8wRNGVUmytL3EAAAAAAIBPLL8DAJMQanJvZ7r9yQEAAFDiTC4pJY5JiaNStnf8g62QFF0hxdbJirQWJyAAAAAAAAAAABWMAW9AgTmO+y5Gfl3zljrxY9d23cp7ZVlcgYfSkrp+zLUdn7NZls2Ppmpj27YWLlwomyFdGCHrOPr7w/v0RNuFvH2vWLpCr1+xhp9tHmV7Up6Hu8W2LFJ8y2JF5s2QFeDvZamhMwHAOzoTALyjM6ubSRwd/jLHjULNUtP9/N4NjILOBAAAAACgRISa3duZTn9yoKpwbggAvKMz/WWMkQYvSolnpNRzkpzxHxBukWLrpOgKWXa4KBkBvIDOBADv6EwA8I7OBAAAKA1M0QEKzBgzcsGXHMlnH3Nt1628x5ccwFic7KAGus+41qIta3xKAz8FAgFt3LjR7xgoMYPZrD69/0kd7riWt+8Nq9fr525Z5kOq8pPrH9TVLz+p9KWx71IdbIpKthRdO0+NL10ty+bL+6WMzgQA7+hMAPCOzqxeJt0hdT3iXrRCUssDsuyQP6GAEkdnAgAAAABQIkKN7u1cQsZJM5AE04pzQwDgHZ3pD5MbkBLHpMQRKds7/sFWUKpbLsXXSeFZ3PwJ8BGdCQDe0ZkA4B2dCQAAUBoYtwsUWCkMeDO5rHK97oE4tbdsKXoOYDypzpOSueFOYFZAdc3L/QsE3+RyOR04cEC5XM7vKCgR/ekh/dlTj+cNdwtYlt61YQvD3SZgjFHq2Wvq/tEJXfjog2MOd7MiQc1/z4u14H0v1YLff6mafnYNw93KAJ0JAN7RmQDgHZ1ZnYwzJHV8TzIj/rs3v0hWqMmfUEAZoDMBAAAAVDY+M0YZCTUq789spsuXKKgenBsCAO/ozOIxxsgMXpLpeFC69AWpZ+f4w91CzVLTvdL8d8ia+RJZkdkMdwN8RmcCgHd0JgB4R2cCAACUBga8AQWWN+CtRFihiN8RAJdkxzHXdm3TUtlB/pxWI8dxdOHCBTmOM/HBqHidAyl9ZPeP9VyP+4LbSCCg92y+U3fNW+hTsvIwcLpd5/6//9C1L+5Szw+Pj3vsvN++T6GWeJGSoVDoTADwjs4EAO/ozOpjjJE6H5KyPe4d8fWyoit8yQSUCzoTAAAAAIDSYFlBKdjgXmTAG6YZ54YAwDs6c/qZ3KBM337p8lela/8qpZ6VNMaX9q2AFF0lzX6dNOfXZMVvk2Vz7T5QKuhMAPCOzgQA7+hMAACA0hD0OwBQafIGvJXowDfAb6kRA96iM1f7lARAqWjr79PH9uxU1+CAaz0WCuv3t9ypZY3NPiUrD+lrfbr6xV0THhdoqFXrr25RqDlWhFQAAAAAgJLUf1BKnXavhWdJjXf7EgcAgErw4Q9/WB/+8If9jgEAAHBzLL8DAJMUanLfxIABbwAAoMIZY6Shy1LiGSl5SmMOdPupUJMUWydFV8oK1BQlIwAAAAAAAEob17oBQHEx4A0osLwp1sx3A/JkBjqVTl5zrdW1MOANqGanu7v0yb1PKJFJu9abamr1ga07NC9e71Oy8jF4rlNyxn/j0frGbapbPUeWxVX5AAAAAFCtzNBVqXune9GOSC0PyLL42AgAAAAAAABlJNQkDZx5YZsBbwAAoEKZ3KCUPCEljnh4zxOQosuk2FopMpdrRgEAAAAAAAAA8BHf1AEKzBj3YBWLCW9AnmTHcde2HYqqpmGhT2ngN9u2tWLFCtm27XcU+ORQ+1V9ev+TGsq57yI4NxbXB7buUHNtnU/JykzOGXW55tYWBeI1iq2bp7rVc4ocCoVGZwKAd3QmAHhHZ1YPY4zU9WNJI36HbH6ZrCDD1QEv6EwAAAAAAEpIqMm9zYA3TDPODQGAd3TmzTPGSOmrUv8RKXVKMtnxHxBslOJrpegqWYHa4oQEUBB0JgB4R2cCgHd0JgAAQGlgwBtQYCMHvDHfDciXun7MtR2duVKWxQmCahUIBLRy5Uq/Y8AnT7Rd0P899LRyI35+Lp3RpPduuVPxcMSnZJVh4f/+OQXiNX7HQAHRmQDgHZ0JAN7RmVVk6PLwl0BuVL9ZVt0t/uQByhCdCQAAAKCiWX4HACZp5IC3bJ+Mk5Vlc3k0pgfnhgDAOzpz6owzJCVPSP3PSJnrExxtS3W3SvF1UmSeLIs39UA5ojMBwDs6EwC8ozMBAABKA9N0gALLH/DGhDfgRk52SIlrR1xrdTNX+5QGpSCbzWrXrl3KZie4qxwqzoNnT+lvD+7NG+62vmWWPrjtboa7TVLyuPsL+jVLZjLcrQLRmQDgHZ0JAN7RmVWkb597OxCXZtzhTxagTNGZAAAAAACUkFDjiAUjZbt9iYLqwLkhAPCOzpwcY4zM0FWZzoekS5+Xuh4df7hbsEGasUOa/3ZZLT8nq2Y+w92AMkZnAoB3dCYAeEdnAgAAlAZuUQcUWN6ANzHgDbhR/5V9MrmhG1YsxWat8y0P/GeMUUdHxyj9iUpljNG/nDyqbz93Mm/fXXMX6Ddu26ygzRziyUgcuKjBU+1+x0AR0JkA4B2dCQDe0ZnVwaQ7pYGz7sX6TbKsgD+BgDJFZwIAAACoZJYYCIHyYtlhmUBcyvW/sJjpksIt/oVCRePcEAB4R2d6Y5whKfmslDgipTsmONqW6pZKsbVSzQIGugEVhM4EAO/oTADwjs4EAAAoDQx4Awos75ccfucBXHo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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig, [ax_roc, ax_prc] = plt.subplots(1, 2, figsize=(50, 20))\n", + "\n", + "fig.suptitle(f\"DITTO Benchmarking missense variants\", fontsize=40)\n", + "fsize = 30\n", + "ax_roc.tick_params(axis='both', which='major', labelsize=fsize)\n", + "ax_prc.tick_params(axis='both', which='major', labelsize=fsize)\n", + "ax_roc.set_xlabel(\"False Positive Rate\", fontsize=fsize)\n", + "ax_roc.set_ylabel(\"True Positive Rate\", fontsize=fsize)\n", + "ax_roc.set_title(\"Receiver Operating Characteristic (ROC) curves\", fontsize=fsize)\n", + "ax_roc.grid(linestyle=\"--\")\n", + "ax_prc.set_xlabel(\"Recall\", fontsize=fsize)\n", + "ax_prc.set_ylabel(\"Precision\", fontsize=fsize)\n", + "ax_prc.set_title(\"Precision Recall (PRC) curves\", fontsize=fsize)\n", + "ax_prc.grid(linestyle=\"--\")\n", + "\n", + "scores = {}\n", + "scores['roc'] = {}\n", + "scores['prc'] = {}\n", + "scores['f1'] = {}\n", + "for name in list(bench.columns):\n", + " index_list = var1[var1.so == 'missense'].index\n", + " x = bench[bench.index.isin(index_list)][name].values\n", + " Y_test1 = var1[var1.index.isin(index_list)]['class'].values\n", + " if name in ['CADD', 'MetaSVM','mutation_assessor','provean','GERP']:\n", + " y_true = (x-np.min(x))/(np.max(x)-np.min(x))\n", + " else:\n", + " y_true = x\n", + " fpr, tpr, _ = roc_curve(Y_test1, y_true)\n", + " auc = roc_auc_score(Y_test1, y_true, average='weighted')\n", + " auc = \"{:.2f}\".format(auc)\n", + " scores['roc'][name] = auc\n", + " ax_roc.plot(fpr,tpr,label=str(name)+\" = \"+str(auc), linewidth=3, c= default_colors[name])\n", + " precision, recall, _ = precision_recall_curve(Y_test1, y_true)\n", + " prc = average_precision_score(Y_test1, y_true, average='weighted')\n", + " prc = \"{:.2f}\".format(prc)\n", + " scores['prc'][name] = prc\n", + " #f1 = f1_score(Y_test, y_true, sample_weight= weights, average='weighted')\n", + " #scores['f1'][name] = \"{:.2f}\".format(np.nanmean(f1))\n", + "\n", + " ax_prc.plot(recall,precision,label=str(name)+\" = \"+str(prc), linewidth=3, c= default_colors[name])\n", + "ax_prc.legend( bbox_to_anchor=(1,0.5), loc=\"center left\", fontsize=fsize)\n", + "ax_roc.legend( bbox_to_anchor=(1,0.5), loc=\"center left\", fontsize=fsize)\n", + "fig.tight_layout()\n", + "#plt.savefig(\n", + "# f\"/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/processed/train_data_3_star/benchmarking/DITTO_ROC_PRC_benchmarking.pdf\",\n", + "# format=\"pdf\",\n", + "# dpi=1000,\n", + "# bbox_inches=\"tight\",\n", + "# )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "var1['class1'] = var1['class']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [], + "source": [ + "def f1plot(var_type):\n", + " acc_scores_chr = {}\n", + " prc_scores_chr = {}\n", + " f1_scores_chr = {}\n", + " for name in var1.chrom.unique():\n", + " acc_scores_chr[name] = {}\n", + " prc_scores_chr[name] = {}\n", + " f1_scores_chr[name] = {}\n", + " for clf in bench.columns:\n", + " index_list = var1[(var1.chrom == name) & (var1['so'] == var_type)].index\n", + " x = bench[bench.index.isin(index_list)][clf].values\n", + " if np.unique(x).size == 1 and clf in ['CADD', 'MetaSVM','mutation_assessor','provean','GERP']:\n", + " x_norm = np.zeros_like(x)\n", + " elif clf in ['CADD', 'MetaSVM','mutation_assessor','provean','GERP']:\n", + " x_norm = (x-np.min(x))/(np.max(x)-np.min(x))\n", + " else:\n", + " x_norm = x\n", + "\n", + " acc = accuracy_score(var1[var1.index.isin(index_list)]['class'], x_norm.round())\n", + " acc = \"{:.2f}\".format(acc)\n", + " acc_scores_chr[name][clf] = acc\n", + "\n", + " prc = precision_score(var1[var1.index.isin(index_list)]['class'], x_norm.round())\n", + " prc = \"{:.2f}\".format(prc)\n", + " prc_scores_chr[name][clf] = prc\n", + "\n", + " f1 = f1_score(var1[var1.index.isin(index_list)]['class'], x_norm.round())\n", + " f1_scores_chr[name][clf] = \"{:.2f}\".format(f1)\n", + " pd.DataFrame(f1_scores_chr).to_csv(f\"/Users/tarunmamidi/Documents/Development/DITTO/data/processed/f1_scores_{var_type}_chr_1_transcript.csv\")\n", + " pd.DataFrame(acc_scores_chr).to_csv(f\"/Users/tarunmamidi/Documents/Development/DITTO/data/processed/accuracy_scores_{var_type}_chr_1_transcript.csv\")\n", + " pd.DataFrame(prc_scores_chr).to_csv(f\"/Users/tarunmamidi/Documents/Development/DITTO/data/processed/precision_scores_{var_type}_chr_1_transcript.csv\")\n", + "\n", + "\n", + "\n", + " # Extract the chromosome names and values in the specified order\n", + " chromosomes = ['chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8', 'chr9', 'chr10', 'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'chr18', 'chr19',\n", + " 'chr20', 'chr21', 'chr22', 'chrX']\n", + " CADD_values = [float(f1_scores_chr[chr]['CADD']) for chr in chromosomes]\n", + " ClinPred_values = [float(f1_scores_chr[chr]['ClinPred']) for chr in chromosomes]\n", + " Revel_values = [float(f1_scores_chr[chr]['Revel']) for chr in chromosomes]\n", + " MetaSVM_values = [float(f1_scores_chr[chr]['MetaSVM']) for chr in chromosomes]\n", + " GERP_values = [float(f1_scores_chr[chr]['GERP']) for chr in chromosomes]\n", + " DITTO_values = [float(f1_scores_chr[chr]['DITTO']) for chr in chromosomes]\n", + " spliceai_values = [float(f1_scores_chr[chr]['spliceai']) for chr in chromosomes]\n", + "\n", + " # Create a Manhattan-style bar plot\n", + " plt.figure(figsize=(17, 4.5))\n", + " plt.rcParams.update({'font.size': 12})\n", + " plt.plot(chromosomes, DITTO_values, marker='d', label='DITTO', linestyle='-', c= default_colors['DITTO'])\n", + " plt.plot(chromosomes, CADD_values, marker='o', label='CADD', linestyle='-', c= default_colors['CADD'])\n", + " plt.plot(chromosomes, ClinPred_values, marker='s', label='ClinPred', linestyle='-', c= default_colors['ClinPred'])\n", + " plt.plot(chromosomes, MetaSVM_values, marker='s', label='MetaSVM', linestyle='-', c= default_colors['MetaSVM'])\n", + " plt.plot(chromosomes, GERP_values, marker='^', label='GERP', linestyle='-', c= default_colors['GERP'])\n", + " plt.plot(chromosomes, Revel_values, marker='s', label='Revel', linestyle='-', c= default_colors['Revel'])\n", + " plt.plot(chromosomes, spliceai_values, marker='s', label='spliceAI', linestyle='-', c= default_colors['spliceai'])\n", + "\n", + " plt.xlabel('Chromosome')\n", + " plt.ylabel('F1 score')\n", + " plt.title(f'F1 score per Chromosome for {var_type} variants')\n", + " plt.legend()\n", + " plt.grid(axis='y')\n", + "\n", + " # Add shaded rectangles instead of grid lines\n", + " ax = plt.gca()\n", + " for i in range(len(chromosomes)):\n", + " if i % 2 == 0: # Shade every other chromosome\n", + " ax.axvspan(i - 0.5, i + 0.5, facecolor='lightgray', alpha=0.5)\n", + "\n", + " plt.xticks(range(len(chromosomes)), chromosomes, rotation=45) # Set x-axis labels\n", + " plt.ylim(-0.05, 1.05)\n", + " plt.tight_layout()\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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consequencesoDITTOclingen.classificationclasscadd.phredspliceaigeneprotein_hgvscdna_hgvschromposref_basealt_basetranscript
112552kb_downstream_variantintergenic0.051278Definitive04.8720.0004TMPRSS3NaNc.*1810C>Tchr2142380172GAENST00000398397
115322kb_downstream_variantintergenic0.032473NaN017.2300.0004ITGB2NaNc.*989G>Achr2144907014CTENST00000397846
136602kb_downstream_variantintergenic0.002689NaN09.2000.0004DYRK1ANaNc.*394C>Tchr2137512348CTENST00000398956
139102kb_downstream_variantintergenic0.002124NaN09.5590.0004DYRK1ANaNc.*253A>Gchr2137512207AGENST00000398956
151312kb_downstream_variantintergenic0.000795NaN017.7500.0004DYRK1ANaNc.*500A>Gchr2137512454AGENST00000398956
152142kb_downstream_variantintergenic0.000741Moderate02.0170.0004KCNE1NaNc.*1815A>Gchr2134447430TCENST00000399284
154942kb_upstream_variantintergenic0.000593NaN05.6900.0004SOD1NaNc.-156G>Cchr2131659614GCENST00000389995
192242kb_downstream_variantintergenic0.000025NaN021.0000.0004SLC19A1NaNc.*3457C>Achr2145512201GTENST00000311124
335812kb_upstream_variantintergenic0.000000NaN010.4400.0004CRYAANaNc.-620C>Tchr2143169105CTENST00000398133
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" + ], + "text/plain": [ + " consequence so DITTO clingen.classification \n", + "11255 2kb_downstream_variant intergenic 0.051278 Definitive \\\n", + "11532 2kb_downstream_variant intergenic 0.032473 NaN \n", + "13660 2kb_downstream_variant intergenic 0.002689 NaN \n", + "13910 2kb_downstream_variant intergenic 0.002124 NaN \n", + "15131 2kb_downstream_variant intergenic 0.000795 NaN \n", + "15214 2kb_downstream_variant intergenic 0.000741 Moderate \n", + "15494 2kb_upstream_variant intergenic 0.000593 NaN \n", + "19224 2kb_downstream_variant intergenic 0.000025 NaN \n", + "33581 2kb_upstream_variant intergenic 0.000000 NaN \n", + "\n", + " class cadd.phred spliceai gene protein_hgvs cdna_hgvs chrom \n", + "11255 0 4.872 0.0004 TMPRSS3 NaN c.*1810C>T chr21 \\\n", + "11532 0 17.230 0.0004 ITGB2 NaN c.*989G>A chr21 \n", + "13660 0 9.200 0.0004 DYRK1A NaN c.*394C>T chr21 \n", + "13910 0 9.559 0.0004 DYRK1A NaN c.*253A>G chr21 \n", + "15131 0 17.750 0.0004 DYRK1A NaN c.*500A>G chr21 \n", + "15214 0 2.017 0.0004 KCNE1 NaN c.*1815A>G chr21 \n", + "15494 0 5.690 0.0004 SOD1 NaN c.-156G>C chr21 \n", + "19224 0 21.000 0.0004 SLC19A1 NaN c.*3457C>A chr21 \n", + "33581 0 10.440 0.0004 CRYAA NaN c.-620C>T chr21 \n", + "\n", + " pos ref_base alt_base transcript \n", + "11255 42380172 G A ENST00000398397 \n", + "11532 44907014 C T ENST00000397846 \n", + "13660 37512348 C T ENST00000398956 \n", + "13910 37512207 A G ENST00000398956 \n", + "15131 37512454 A G ENST00000398956 \n", + "15214 34447430 T C ENST00000399284 \n", + "15494 31659614 G C ENST00000389995 \n", + "19224 45512201 G T ENST00000311124 \n", + "33581 43169105 C T ENST00000398133 " + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "var1[(var1['so'] == \"intergenic\") & (var1['chrom']=='chr21')][['consequence','so','DITTO','clingen.classification','class','cadd.phred','spliceai','gene','protein_hgvs','cdna_hgvs','chrom','pos','ref_base','alt_base','transcript']]" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "id": "6b8f9839", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.912" + ] + }, + "execution_count": 171, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "index_list = var1[(var1['consequence'] == '2kb_upstream_variant')].index\n", + "x = bench[bench.index.isin(index_list)][clf].values\n", + "f1_score(var1[var1.index.isin(index_list)]['class'], x.round())" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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nZ1+/fv2UvLy8yuUGg0Hp3LmzolarlczMzGqxjRgxotp5FEVR+vbtq7i5uVX53HKq6TEQQgghhGjpJKEkhBBCCNFCOW/A1fSvtptfTmeaUNq1a5cCKFOmTDnltnfffbcCKG+88cYpt50+fboCKB9//HG1dfv371fUarXSsWPHKsudN1xPTio4vfXWWwqgvPfeezWe7/7771eAGhNd/+W8Yf3fpJGT87F0Jj1sNpsSFBSkhIWFVbkh7lRQUKCoVCpl8uTJlcucN4NDQkKUsrKyU8bklJWVVdnm/71ZfirOG9nXXXddtXWHDx9WAGXSpElVljtv6taUvFizZk3lTfOaDB06VAGUVatWVS5z3tB/6qmnqm0/Z86cyhvM//XFF18ogPL5559XLvvqq68UQJk6dWq17cvLyyufL6mpqYqiKMrChQvrncAbM2aMAihLly6ttm7JkiUKoIwaNarKckDR6/VVElOK4kgqarVaBVAOHTpU7XgdO3ZUYmJiqixLSEhQdDpdjTf/rVarEhQUpAwYMOCU16EotSdgzGaz4unpqXh7e1dJDDs9/vjjCqDMmTOn2rHi4+Prde6T1ZVQqulxUxRFGT58uAJUWXeqhNKZPHZArUmC5557rtrj4JSamqpoNJpqCaXLLrtMAZQ9e/bUGmO7du2qLHM+X2tKmFx//fUKoOzevbtyWU0JpaysLEWtVithYWGK0Wis8dz14Uz4n/yepSiKkpmZqWg0GqVv3771PpbzcyEtLa3K8roec0WpPaFUm59//lkBlC+++KLKcuf79X+T6IqiKE8//XSNibO6PlP79eun6PX6Gl8zQgghhBCtkZS8E0IIIYRo4ZT/zKXRmHr06EHfvn357rvvSE9P55JLLmHYsGEMGDCgWomrDRs2AHDBBRec8rjbt28HqJwP5WRdu3YlMjKSlJQUCgsL8ff3r1zn7u5OfHx8tX3Wr18PwI4dO6rMdeHknM9n//799OzZ85TxAZx33nmo1dWnIB05ciSrVq1i+/btjBgxgoMHD5KXl0dcXBzPPfdcjcfy9PRk//791ZbHx8fXWJauNg3R9gMGDKi2LCoqCnCUy6pJTWXr6mpDgLFjx7Ju3Tq2bdvG8OHDq6w7uUyik3MuqLrWHT16tF7n1+l0jBgxgi+//JLt27cTHR3NiBEjiIiI4KWXXmL79u1MnDiRoUOHkpCQUFnC7+Rjq9XqyvmHTjZq1Cg0Gg3btm2rtq5Lly7VShZqNBpCQkIwGo2V5ff+e20bN26s/N1kMrFz506Cg4N56623qm0PjtdBTc+n03HgwAFKS0s599xzCQgIqLZ+7NixvPDCCzVeZ03Ph7NR0+MGJ56XhYWFpywFCWf32MXExNRYbs35PDv33HOrrYuOjiYqKqraPD/r169Hp9PVOG8cQHl5OTk5OeTl5REUFFS53N/fv0rZRadTvT6dNm/ejN1uZ/jw4ej1+jq3rcuwYcOIi4vj999/p6CgoPL58fXXX2Oz2Wqct2nt2rW8/fbbrF+/nuzsbMrLy6usz8jIoEOHDlWW1faY1+XIkSO8/PLLLFu2jCNHjlBaWlrtPDU5k/e9mkydOpUHH3yQnj17cs011zB8+HCGDRtWY6lQIYQQQojWQBJKQgghhBCi3jQaDcuWLePZZ5/l559/ZtasWQD4+vpy44038sILL1TO4+OcaD4iIuKUxy0qKgIgNDS0xvVhYWEcOXKEoqKiKgmlkJCQyknTT5aXlwecmF+oNgaD4ZSxnXyumjhjdl6D89xJSUl1znFS07lru/7aBAUF4ebmRnl5ORkZGTXefD6VmuZ8cc6FYrPZatynpjjr04Ynb1ffGOpaZ7FYzvj8vr6+bNiwgdmzZ7Nw4UIWLVoEQLt27bjrrrt44oknKs9TVFREYGBgjXMUabXaynlT6nNdzn3qWme1Wit/LygoQFEUcnJy6jVnzpk6m/Y73eftqdT12EDtz8v/OpvHrrZrcl5/be8HISEh1RJKeXl5WK3WU8ZgMBiqJJTO9nE4nffgU7n++ut56qmn+P7775kxYwYAX375JTqdjilTplTZdv78+UyePBkPDw/GjRtH586d8fLyQq1Ws3LlSlatWoXZbK52jtN9Hh0+fJhzzjmHgoICzjvvPMaPH4+fnx8ajaZyrrCazgNn9r5Xk5kzZxIcHMz777/P22+/zZtvvolKpWLUqFG8+uqrpzVPnxBCCCFES1C9i6UQQgghhBB1CAgI4M033yQ9PZ2kpCQ++eQTunbtyjvvvMOdd95ZuZ0z8VNbD/GTOW/uHT9+vMb1x44dq7KdU03JpJO327lzJ4qjzHON/2644YZTxuaUlZVV43JnzM5zOn9efvnldZ47JSWl2rFqu57aaLVaBg8eDMCyZctOa9+zUVOcZ9qGDeVMzh8ZGcmnn35KdnY2iYmJvPPOOwQGBvLMM89UGV3m5+dHfn5+lQSWk9VqJTc3F19f34a8nCrnBujbt2+dz6ezHa12Nu13us/bpnI2j11t1+Rs59reD2pa7ufnR0BAwCljiI6OPtNLrdHpvAefyvXXX49KpeKLL74AYNu2bSQmJjJx4sRqo3Geeuop3Nzc2LJlC7/99huvv/46zz77LM888wxdu3at9Ryn+zx64403yMvL49NPP2XlypW88847PPfcczzzzDNMmDDh9C/yDF1//fVs2LCBvLw8/vzzT6ZPn87KlSsZP348OTk5TRaHEEIIIURTkISSEEIIIYQ4Y7GxsUyfPp1Vq1bh7e3N/PnzK9c5Ex2LFy8+5XH69u0LwMqVK6utS05O5ujRo3Ts2LHK6KS6OM+9Zs2aem1fH//++y92u73acmfMzmvo1q0b/v7+bNiwocYEREO77bbbAHjttdcwmUx1bltbb/2GUFcbnry8sXrs13V+q9XKv//+W+v5VSoVPXv25J577mHp0qUAVZ7Lffv2xW63s3r16mr7rl69GpvN1mjX5e3tTc+ePdmzZw/5+fmNcg5wlJbU6/Xs2LGjxpJfK1asABqv/c6UszxhTaNKGuOxcz7PnM+nk6WlpZGenl5t+eDBgykoKGDPnj0NEkN9nXPOOajVatasWXPK94ZT6dChAyNHjmTjxo0cOHCgMrFUU1I+OTmZHj160L179yrL7XZ7jY/bmUpOTgZg0qRJ1datWrWqwc6jVqvrNWrJ39+fiRMn8vHHH3PjjTeSl5fXoJ9BQgghhBDNgSSUhBBCCCFEvaWkpNR4U7SgoACz2YyHh0flshkzZqDVann22WdrnKPk5Plvbr75ZgDmzp1bpUe3zWbjoYcewm63M3369HrHedNNN+Hv78+cOXPYtGlTtfV2u73WxEdtkpKSeP/996ssW7BgAatWrSI2NpbzzjsPcIwauueeezh27Bj33ntvtTk9wDHaY+/evad1/tpMmTKFCRMmkJSUxKWXXlo5kuRk5eXlvPfeezz44IMNcs6aDBs2jK5du/Lvv//y888/V1n3888/s3r1arp06VLj3DMN4bLLLiMwMJDvvvuucv4up7feeovDhw8zduzYynlbEhMTq5UmgxMjTE5+Ljufn4899liVG/Mmk4lHH30U4LSen6dr5syZlJeXc/PNN1eWMTtZQUFBjXMbnQ43NzemTp2KwWDg6aefrrLu0KFDvPPOO+h0OqZNm3ZW52lozhJxNSVyoOEfu2uvvRatVsu7775b5ZyKovDYY4/VmHh44IEHALj11lvJzMystt5oNFZ7zjaEdu3acc0115CZmckjjzxSbSSWwWCosYRhbZxzJX366ad89913BAUFcdFFF1XbLiYmhqSkpCojoxRFYc6cOQ32vuc8D5xIdjotXryYTz75pMHOExQUVOvza9GiRVVKVDo5S2Ce/D4ihBBCCNEayBxKQgghhBBtxMkTpzsTPI888kjl5Pa33HLLKW/279y5k8svv5z+/fvTq1cvwsPDycnJYcGCBVgsFh555JHKbXv06MH777/PHXfcQUJCApdccglxcXHk5uayefNm/Pz8Km8EDh06lFmzZvHKK6/Qq1cvJk+ejJeXF3///TeJiYmce+65PPzww/W+1qCgIH7++Wcuv/xyBg8ezJgxY+jZsydqtZojR46wfv168vLyKCsrq/cxzz//fB588EH+/vtv4uPjSU5O5tdff8XDw4NPP/0UtfpEX62nnnqKnTt38uGHH/L7778zevRoIiIiyM7OJikpibVr1/L888/To0ePep+/Nmq1mp9++olp06axYMECOnXqxJgxY+jevTsajYa0tDSWLVtGTk4ODz300FmfrzbOcljjxo3j6quv5tJLL6Vbt24cOHCA3377DR8fH7788ssqj1ND8vb25v/+7/+48sorGTFiBFdeeSUdOnRg69atLFmyhNDQUP73v/9Vbv/PP/8wc+ZMhg4dSrdu3Wjfvj1Hjx5lwYIFqFSqKs+3a6+9lgULFvDjjz/Ss2dPLrvsMlQqFb/99hspKSlcddVVTJ06tVGuCxwJra1bt/L+++/TuXNnJkyYQIcOHcjPzyclJYXVq1dz00038eGHH57VeV566SXWrFnDvHnz2Lx5M6NGjSI3N5cff/yRkpIS5s2bR8eOHRvoqhpG165diYiI4Pvvv0en09GhQwdUKhXTpk0jOjq6wR+7zp078+yzz/L4448THx/P1VdfjZ+fH0uXLiU/P5/4+Hh27dpVZZ8xY8bw0ksv8dhjjxEXF8fEiRPp2LEjBoOBtLQ0Vq1axbnnnls5j1dDmjdvHomJicybN49ly5Yxfvx43NzcSElJYfHixSxcuJCRI0fW61iTJk3irrvu4q233sJisXDPPffUOK/YAw88wB133EG/fv2YNGkSOp2OtWvXsnfvXi6++GJ+//33Brm2O++8k88++4yrrrqKSZMmERERQWJiIosWLeKqq67ihx9+aJDzjBkzhu+//55LL72Uvn37otVqGT58OMOHD+eaa67Bw8ODc889l5iYGBRFYc2aNWzevJl+/foxduzYBolBCCGEEKLZUIQQQgghRIsEKKfz55xz+9r+ffbZZ6c8Rnp6uvLYY48pQ4cOVUJCQhQ3NzclIiJCOf/885W//vqrxn3WrVunXHHFFUq7du0UnU6nhIWFKRMmTFB++umnatt+9913yrBhwxRvb2/F3d1d6dGjhzJ37lyltLS02rbR0dFKdHR0nfGmpKQod911lxIbG6u4u7srPj4+SteuXZXrrrtOmT9//imvV1EUZcWKFQqgzJ49W1m3bp0yZswYxcfHR/H29lbGjRunbNq0qcb97Ha78uWXXyqjR49WAgICFJ1Op4SHhyvDhg1Tnn/+eeXIkSNV4gSUG264oV4x1Wbx4sXKlClTlJiYGMXDw0Nxd3dXOnbsqEyZMkX5+++/q2z72Wef1dnugDJixIgqy2644QYFUFJSUmqNYf/+/cp1112nhIaGKlqtVgkNDVWmTp2q7N+/v9q2s2fPVgBlxYoV1dbVFd/JbfJfmzZtUi677DIlODhY0el0SlRUlHLHHXcoGRkZVbbbu3ev8sADDyj9+/dXgoODFTc3NyU6OlqZNGmSsnbt2mrHtdlsynvvvaf0799f8fT0VDw9PZV+/fop8+bNU2w2W7Xta3r8nOp67o4YMaLW1/Xvv/+uXHjhhZWvpZCQEGXgwIHKE088oezbt6/Gff6rrsdOURSloKBAmTVrlhIbG6u4ubkpfn5+ytixY5XFixef9rHqUlvb1/W41fb827RpkzJ69GjF19dXUalUNR73dB67umJw+vLLL5WEhATF3d1dCQ4OVqZOnapkZGQoPXv2VPz9/WvcZ82aNcqVV16phIWFKTqdTgkODlbi4+OVBx54QNm8eXOVbet6jtT02NX1HmIwGJS5c+cqvXv3Vjw9PRVvb2+le/fuyn333adkZWXVeZ3/5WwDQNmyZUut23322WdKfHy8otfrlaCgIOWyyy5Tdu3adUbtfvJ5/9v2a9euVUaNGqX4+/sr3t7eyrBhw5T58+fX+tys6/VV23tOVlaWMmXKFKV9+/aKWq2uctwPPvhAueyyy5SOHTsqnp6eSkBAgJKQkKC8/PLLSnFxca3XI4QQQgjRUqkU5SxnbxVCCCGEEKIVW7lyJaNGjWL27Nk888wzrg5HCNFMFRcXExISQkJCAuvXr3d1OEIIIYQQQjQ4mUNJCCGEEEIIIYSop5ycHCwWS5VlVquVBx98kLKyMiZNmuSiyIQQQgghhGhcMoeSEEIIIYQQQghRT7/88gtPP/00Y8eOJSoqivz8fFavXs3Bgwfp168fd999t6tDFEIIIYQQolFIQkkIIYQQQgghhKinQYMGMWLECNatW0d2djaKotCxY0eefPJJHnnkETw8PFwdohBCCCGEEI1C5lASQgghhBBCCCGEEEIIIYQQdZI5lIQQQgghhBBCCCGEEEIIIUSdJKEkhBBCCCGEEEIIIYQQQggh6iRzKFWw2+1kZmbi4+ODSqVydThCCCGEEEIIIYQQQgghhBCNSlEUSkpKCA8PR62uewySJJQqZGZmEhUV5eowhBBCCCGEEEIIIYQQQgghmlR6ejqRkZF1biMJpQo+Pj6A40Hz9fV1cTTNV3Z2tqtDcJn27du7OgSXkrZvu6Tt26623PbQtttf2l7avq2Stm+7pO3bLmn7tkvavu2Stm+7pO3brrbc9vVRXFxMVFRUZY6kLpJQquAsc+fr6ysJpTqUlpa6OgSXaevPC2n7tkvavu1qy20Pbbv9pe2l7dsqafu2S9q+7ZK2b7uk7dsuafu2S9q+7WrLbX866jMVUN0F8YQQQgghhBBCCCGEEEIIIUSbJwklIYQQQgghhBBCCCGEEEIIUSdJKAkhhBBCCCGEEEIIIYQQQog6SUJJCCGEEEIIIYQQQgghhBBC1EkSSkIIIYQQQgghhBBCCCGEEKJOklASQgghhBBCCCGEEEIIIYQQdXJ5QqmkpIRZs2Yxfvx42rVrh0ql4plnnqn3/tnZ2dx4440EBwej1+sZMmQIy5Yta7yAhRBCCCEaUebCRSzpM4LM3xe7OhQhRBOR171oq/IWr2T7mMnkL1nl6lCEEEIIIUQ9uDyhlJeXx0cffYTZbOayyy47rX3NZjNjxoxh2bJlvP322yxYsICQkBDOP/98Vq2SP0iFEEII0bKUZWez/7mZ6DSHOfDsTMqys10dkhCikZlz89j58BzMOXnsengO5tw8V4ckRKMymUwUFBSQk3aEAx99jlGnYf//Picn7QgFBQWYTCZXhyiEEA0qt9RESlFBrf9yS+V9TwjRcmhdHUB0dDQFBQWoVCpyc3P55JNP6r3vp59+SmJiIuvWrWPIkCEAjBo1ivj4eGbNmsXGjRsbK2whhBBCiAZVtPkX0ufdSGgPQ+Wy5Ic70+GeL/AdcIULIxNCNBZFUdg161lsRseNJKvByK5HnmPgp2+5NjAhGonJZGLJkiXY7XbHgqmXVK47vnULAGq1mvHjx6PX610RohBCNKjcUhMPrVyMxfm+VwOdWs1rIycQ7Cnve0KI5s/lI5RUKhUqleqM9p0/fz5du3atTCYBaLVarrvuOjZt2kRGRkZDhSmEEEII0WiKt/xKxrzJqBRDleUqxcDRdydRvOVXF0UmhGhMmQsXcfzvZSg2GwCKzcbxv/4hc+EiF0cmROMwm80nkkm1sNvtmM3mJopICCEaV0m5uc5kEoDFbqekXN73hBAtg8sTSmcjMTGRPn36VFvuXLZnz56mDkkIIYQQ4rQodhvHvrwHRYH/9rFRqUBR4NhX96LYba4JUAjRKMy5eeya9WyNL/xds56V0ndCCCGEEEKIZsflJe/ORl5eHoGBgdWWO5fl5dX+JcxsNlfp9VRcXAyAxWLBYrE0cKSth9VqdXUILtPWnxfS9m2XtH3b1ZbbHpqu/Y37V2Iryqx2T9lJpQJbYQbH//iCwHFTUGsb/883afu2+9qXtm/4trebyzEdzaQ0LR3TkQxMaUcxph6hYMPWylJ3VSgKVoOBnbPm0Pd/rzd4PLWRtpfXfXM6l9VqbbK4pO3bLmn7tqsp2/503veaIi5pe3ndt1Vtue3r43QenxadUALqLJdX17oXX3yROXPmVFu+ZMkSqdUshBBCiCbjvfN3wuqx3f7Zsym5dx7ERELnDiidOkDHDuDl2egxCiFOwVgKuXmQkw+5BahyKv4/Jx8Ki1EpymkdTrHZyfp7OX998hmEhzRS0EI0vfKM41CPivf775iFW0g7lB6x0K0zeHs1fnBCCNEIClDqdfd17b9r2VufN0ghhGgEJlMNHd1q0aITSkFBQTWOQsrPzweocfSS02OPPcbMmTMrfy8uLiYqKorx48fj6+vb8MG2EtnZ2a4OwWXat2/v6hBcStq+7ZK2b7vacttD07V/UXuF7EOfnnI7ReuDylwOBw7DgcOVXze94zrh378P/v3j8e8fj1fnmDOen9JJ2r7tvval7Wtue8Vup+xYFqa0o5QeOYopzfkvndIjGViKius8rkbviT46Es8OkRU/Izj22yIKt+2CGuZVUGnUtB8/kr633NQg11Uf0vbyum8K+TG5/Ltu3Sm3UxlNqNZtRbVuK6hU6LvF4jOoH76D+uEV3wO1m1uDxSRt33ZJ27ddTdn2qcWFLN2w+pTbDTt3GDG+/o0ej7S9vO7bqrbc9vXhrN5WHy06odS7d292795dbblzWa9evWrd193dHXd392rLdTodOp2u4YJsZbRNUGanuWrrzwtp+7ZL2r7tasttD03T/oU7Etlx73u076xB42arseydooA2IIKR63diPJRG/uYdFGzZQf6WHRgPpWJIOowh6TBHv//NEXeAHwH94wkckEDAwAT8E3qhPc3R19L2bfe178q2z1u8kiOvzCP6kXsIHD+iyc9vLzNTdjgNY9pRTKnpGNPSK34epTT9KPbyustAuLcPRh8dhVd0JPqYKLyio9BHR+IVE4VbcFC1RG/UpRewfNhFWEsMjhf6SVRaLfGvzG7S56K87uV139gspaXs+HMxBPicctvox+5F2bqbovVbKE1KwbQvCdO+JLI+/wG1hwc+/XvjN3gAvkP643mWHSmk7V3D1e/5IG3fljXHz1etVtskcUnby+u+rWrLbV8fp/P4tOhn0uWXX86dd97Jxo0bGTRoEOCoB/n1118zaNAgwsPDXRyhEEIIIUR1iqKQ9uWP7Hn6JbCXoYrToFLZUBSqJJWcv4dNewe1VodP11h8usYSfd1kAMx5BRRs3UnB5u2On9sTsRQUkf3ParL/cfSEVGk0+PboSsDABEeSaUACnpFhZz2KSYiGZMkvIHXuG9hKjKQ89wY+A/qgCwxo0HMoioK1oAhzxjHK0jMxH83EnJ5Z+bslp/b5VwFUOi36qAj0HRxJIn10VMVPx6ij003cugcH0eeVp9l2x8PV1tnLLZQezcQ9OOi0jilEc1VWWMTKn37BFORfr+29+/QgYMQwAMpz8ijeuI2iDVsp3rAVS24+RWs3U7R2MwC6dkH4DuqH35AB+A3uhy6o9kolonloivd8IYQQQjSOZpFQ+vvvvzEajZSUlACwd+9efv75ZwAmTpyIXq9n+vTpfPHFFxw6dIjo6GgAbr75Zt577z2uvPJKXnrpJdq3b8/777/PgQMH+Oeff1x2PUIIIYQQtbGaTOya9SwZv/wBKESPN6OhHI1veyzFRlQYT2ys0hJ5zw/4DriixmO5BwUQOn4koeNHAmC3WCjec4D8LRWjmDbvoCzzOEW791K0ey+p//ctAB6h7QmoSC4FDkzAr1d31G7SY0u4hqIopM59C5upFACbyUTq828T9/ozp38sqw3z8SzMR49hPprpSBxlHMOcfoyyo5nYjXXXBtf6+lQZWXRy0sgzPBSVRnMml1ir8EvOJ3PBIrKWrESx2VBpNLiHBFOWmcX2+55k+OIf0XhUr6ogREtSnHmMNX8vxhzkD+UWVG466ppVTKVSVakm4tYuiOCLxhF80TgURaE0OYWi9Vso3rCN4q07seTkkffHUvL+WAqAvktnfAf3x29If3z69kYtr6FmpSHf84VoCXzc3NGp1VhqKHHrpFOr8XGT9yohRMvQLBJKM2bMIC0trfL3n376iZ9++gmAlJQUYmJisNls2Gw2lJPKQbi7u7Ns2TJmzZrFPffcg8lkIiEhgb///psRI1wzZFoI0fCaQzkEIYRoCIbkFLbc8gAlB5JRaTTE3dgVW/pPqLRudJj5FyqvKNZdOBKdWy5BsTmoPfT49L2k3sdX63T4J/TCP6EX3HIdAKUZxyjYurOyVF5R4n7Kjmdz7I8lHPtjiWM/dzf8+vQkcKAjyWSLDpce3qLJ5C9ZScHyf08ssNkpWLaGvMUrCZowstr2NlOpI1l09JhjhJHz/49mUn4sC8Vqq/N8biHtcI8Mwz0yHI/IcNyjwnCPcPyM6BLXpKP3VCoVfV55muVrN2EtLkHr7cXg7z9h/aQbMRw8xMHX36f7Ew80WTxCNLS8g4dYu24d1gBf1KZSBvXohV+3OMxmM9biEpJmPo3dWIraS4/3Y3eTfMRxX8Bmq/l1rFKp0Md1Qh/XibDrr8JuLqdkRyLF67dQtGErpv3JmA4ewnTwEMe//BGVmw6ffn3wG9wfvyED8IzriEqtbsqHQPzH6b7nC9HSBXvqeW3kBN7csp7U4kIGh0Wy4dhR3DUanhw8HJVKhY+bO8GepzfSWQghXEWlKEpdnYPajOLiYvz8/CgqKsLX19fV4TRbWVlZrg7BZUJCQlwdgku5qu0t+QXsuvQGbCVGND7e9FnweZOXQ5C2l9d9W9WW2x4avv0zf1/MjgeewmY04d4+mN6zp1Lw+wyw2wi78UMCRt3u2G7hIhKffIGoc7ajWAzEPLUefezgBovDaiqlaNfeynmYCjbvoDy/oNp27lHheMf3xCe+J97xPRxzVDTw6Izmqi2/9pv6dV/5OW8wVZ1HSAVqT0+i7pmOpaJMnTk9k7KjmVjzC+s8pspN50gYRYRVJIwqEkcVy9TubrXu66q2d7zuX6TX848TfvEEji9azuab7gW1mnN//5qAfn0aPQZ5z5fXfUPL3LaTTXv3YNd7oC02MGzYuQTFdaqyzckdxwLGDWft2rVkZ2cTGBjIiBEjTjvBa8kvoHjjdoo2bKVo/RYs2blV1msD/fEb3N8xgmlwf9zaB0vbN6Ha3/NVaLy9mvy7nrR929XUbW+z27l1yULMNhsvnjeWZ9atwGyz8dLwsUT5+DVpLNL28rpvq9py29fH6eRGmsUIJSGEqImUQxBCtAb2cgt7n3udlE++BiBoyADiX3uEjLfHgt2G35Cp+I+8rXL78EvOJ/yS80mfdyUlm3/GmLikQRNKWr0nQYP7EzS4P+B4rzWmHKkskVewZQclB5IdIz/SMytLCKm99Hj36oZ3fE+8E3ri3as7Wl/vBotLtD1VPuf/28dNAbuplLSX59W4r9bfF/eKJJEjWeRMHIWhaxfU4kYgOF/3TqHnjyZi0kVk/PIHO+57guFLf5bSd6JFObxyDTuPZ6LoPXAvKGbExPPxDguttl3QhJFVRqX069ePf/75h/z8fJKTk4mLizut8+oCAwi6YDRBF4xGURTKUo5QVDF6qWTLTqz5heT9tYy8v5YB4Nk5hpzR59FuxFCChvQ/7bnQRP0oNhvmY9kcfuql6skkAEXBZpTveqL1Si8pxmyzodfqiPTxpbN/IHvzckguyG/yhJIQQpwtSSgJ0QL8t9dqWyHlEIQQLV1p5nG23v4gBVt2AhB793S6PHwnR9+8EGthJm7h3Qm78cMae2B79xxHyeafMSQuod1lTzdajCqVCu9O0Xh3iibqqksByEg+hHH3fkp27sGwcw+G3fuwG00Ub9xG8cZtzh3x7ByDd3yPypFM7h0izrpcmJQ5bTtKD6VW/ZyvRcDY4Xj17HJilFFkOFqf1p/M7PXcY+Su2YAhOYUDr86jx1MPujokIepl3x+L2FdaAm46vPIKGXHlJDz863fDVK/X07t3b7Zv386ePXsIDQ3Fx8fnjOJQqVR4dorGs1M0oVMnYbdYMOzcWzH/0laMew9SeiiVlEOppHz8FWo3HQED+9Ju+BDajRiKX+/uLS457Uq20tKK+escc9aZK+awK0vPpDwzC8VqrfsAdsd3PVNyKvrYmCaJWYimklSQB0Bn/wDUKhWxFQmlpIJ8RnXo6OLohBDi9EhCSYhmzpybx86H52AtLmHXw3MIGjIA9+AgV4fV6Cz5BaTOfRNUqmrlEFLnvonvwPgmL30nhBCnI2f1erbNmEV5fgFaXx/6vvMCoRNGkf3r0xj3LkPlpifq7p9Re9R8Y9yr1zgASg9twGYqQqNvut6LWh9v/IYOwG/oAMDRs7j0UCqGnXsrk0zm9ExKk1MoTU4h55c/HfsF+OHdx5Fg8o7viVePLmg8Pep9Xsd7/xvYSoykPPcGPgP6yHt9K+bZOYaA0edSsGod2GqYqFqjJmDkMOJem930wTUDbgF+9Hn1GTbfcDeHPvic0AvGEDggwdVhCVGnbd//QqqbCjQa/PKKGH7dFHSenqd1jJiYGDIyMsjOzmbr1q1nVPquJmqdDt8B8fgOiId7pmMpLKJk0w7Kd+whZ/V6So9mkrd2E3lrN7H/xbfRBfjTbvhg2g0fQvDwoegjw846hpZMURSs+QWUpR/DnJGJOb0icVQxh50lN7/uA2i1qN102CuqT1RT8Z4vySTRGiUXOl4fcQFBFT8DK5bnuSwmIYQ4U5JQEqIZUxSFXbOexWY0AWA1GNn1yHMM/PQt1wZWD4qioJjLsZlKsZeWYSut+GkqxW4qxVZahr2WdVZTKYbtu7GVGGs6sJRDEEI0a4rdTtJbH3HgtfdAUfDt1Y0BH7+BV0wHDLsXk7twLgDhN32Ee0SPWo/j1q4jbiFxlGclYdy3At/+lzXRFVSn0mjQd+mMvktn2l95MeBI/hh27sWwcw8lO/dg3HMAa0ERhavWU7hqvWM/rQZ919jKBJN3fA/cQ9vXeA4pc9r2qFQqYp68n+LN26t/5qtUaPR6Yp64zzXBNROh40cSeeUlHP1pITvuf5IRS38+rSStEE3Fbrez4cvvOO7rSB61KzQw7ObrUZ/B3HsqlapK6bukpCS6dOnS0CGj8/cjcPwIQqZdVVn+NWfVOnJWrSdv7UYsBYVkLlhE5oJFAHh17kj7kY7kUvDQgWi9vRo8JlezW6yUH8s6McqoYqRRWYZj5FGtyaAKGl+fk0aThuERFV5ZntStfTDWouLa51CS93zRijlHKDkTSbH+jsRShqEEo6UcL13t8zsKIURzIwklIZqxzIWLOP73ssrfFZuN43/9Q+bCRVXq7J8NRVGwlZZhM5mwmUqxGk/8PPn/C7OzHYkg00nJn7Iy7Kayasucv2OvobdxQ5ByCEKIZqo8v5Dt9zxG9vI1AHS4dhK95j6GxtMDS146GR9OBUUhYNQd+A2desrjefUa70goJS5xaUKpJrrAAAJGDSNg1DAA7BYLpv3JGHbsqRzFZMnJw7jnAMY9B8j69lcA3ELaVSaXvON7ou8ai1qnlTKnbZQuMIDox+/n8GPPV12hKMQ8+YCMUAN6PvsIuWs2YDyUyv6X36XnMw+7OiQhqrCay/n3y2/JD3JM4BxVaqH/jdehPotycXq9nj59+rBt2zb27t1LaGjoKSeIPhsnl3/teNMU7BYLhdt3OxJMq9dTsG03xkMppBxKIeXTb1FptQT0j3eUxxs5FP/4nqhOM3nmqrLmlhIDptR0jGlHyU7ce9Ioo2OYj2fVPGLUSaXCLbQd7hEnJ4vCK5NHWt+6yxPqAgOIefIBDj0yt+oKec8XrVix2UyWydFxprO/I6Hk6+5OiN6LLJORQ4X59GlXfY45IYRoriShJEQzZc7NY9esZ2ss+bbzoWdwCwpE7aarlgSymUxYTaXYjDX9rJ40qnEi7Aam9nBH7emB2tMTjd4TtafHiZ+ejp9q/Yllag8PchcuxnQgGew1xCblEIQQzVDhjt1suWUmpRnHUHu40+elp4i6+jIAFKuFo+9fjc2Qh0d0P0KufbNex/TuNZ6CZe9hSFzSiJE3DLVOh3fv7nj37k7otMkoikL58WzHHEwVpfJMB5Ipz8ohf8lK8pesdOzn4Y5nl86Y9idVP6iUOW0T9F06VV1Q8TkviUQHN38/+rz2DJuuu5PDH31J2MQxBJ7Tz9VhCQGA2WBk1bc/YAj2B7udOI07vade0SDHjo6OJiMjg6ysrMrSd2eTpDodap2OwHP6EXhOP7o+fDeWomJy126qTDCZUtPJ37iV/I1bOfDqPHR+vgSfO4jg4UNoP3Io+g6RdR6/McuaK3Y7ZVk5mFLTMR05ijE1HVOaI4FkSk2nPL+g7mv3cMc9IqxyzjqPqHDH71HhuIeHoHY7u5EUgeNHkr94JQUr11V2QPQfMUTe80Wr5Sx3F+7tU2UkUmxAEFkmI0kFklASQrQsklASohmqUuruv8keRcFaYmD95Jsb/LwaT080Xp5o9fpqPy1qFWq9J5paEkO1rvNwP+3eegBBE0ZKOQQhRIugKAppX/7Anqdfxl5uwatjB/p//AZ+PbtVbpP14yOUJq9Hrfcj8u6fULvVr2SVvvtI0GixZB+iPPswbu07nXKf5kKlUuEeFoJ7WAhB548GHBN2GxMPVJbJM+zai62oBOOuvTUfRMqctgmGnXsc/6NRg80un/M1CBkznKhrLiP9+9/Ycf+TDP/nF7T605uXRoiGZszJZdWC3ykL9kdlsdIrMJi4caMb7Pgnl74rKCggKSmJrl27NtjxT4fOz5ewiWMJmzgWAGNaOjmr1pOzah15azdhKSrm2J9LOfbnUgD0MVG0GzHUMf/SsHPQ+Z0YXdUQZc1t5nJMR446/qWmV00aHTmKvcxc5/5uQYHooyPRhLTDPaoicVSRQNK1C2qQOatqU1O506CKx1WI1ijZWe6uYnSSU6x/IGszjlQmnIQQoqWQhJIQzVDJgeQqpe5q4xEeipu/LxovPRpPT7ReerReejR6z2o/Nfra1lUs9/RAVUePv6ysrIa8xFOScghCiJbAajKx6+E5ZPz6JwChF4wh4a256E4q+VK85VfyFztGJEXc+sVpJYU0nr7oOw/BdHANxj1LcWt/e8NeQBPTeHriOzAB34EJgOOmWuHqDSTd92TtO0mZ01bPsNORUPQfPgTjrr1EP3KPfM7XoOczs8hZvR5jyhH2v/QOvZ59xNUhiTasMPUIa1aswBLoh7rMTP9OcUQNHtDg5/H09KRPnz5s3bqVffv2ERoaip+fX4Of53R5RUfhdX0UMddfhd1qpWjnnsoEU8G2XZhS00lL/YG0L35ApdHg37dXZYLJlJ5Zr7Lm5QVFmNKOVI4sMqalY0pzjDgqO5ZVZ5UJlUaDZ0QY+phIvKKj0MdEoe8QhVdMFProSHQ+3kDTf8dzcnzXm8nhp15GKS+n9HCaS+IQoik4E0axAVVHITrnU0ouyMeuKKgbMZErhKvlLV7JkVfmEf3IPQSOH+HqcMRZkoSSEM2QT9dYgocPIXf1+hrXqzQaQiaMOq1ebC1RZTmEFWsrvzAFjDlPyiEIIZqFkqTDbLnlAQwHD6HSaOj+5AN0uv2GKr16y7OSyfzkJgCCLngIn36XnvZ5vHqNx3RwDYbEJQSMatkJpf9SqVT4Dx9MwOhzKVi1ruZ5G6TMaavnHKHU/vKJ+L/5rIujab50fr7Ev/YsG6+9nZRPvibsgjEEDWn4G/hCnEpW4l42bNuGzc8HjcHEkP79ad+ze6Odr0OHDmRkZHD8+HG2bt3KyJEjm6z0XX2oK+ZTCugfT5eZd2A1GMldt9lRHm/VeoyHUijYspOCLTs5+PoHtR5n+72Pk/7LH5iPZ2NKO4qlqLjO82r0nhUJohOJIuf/e0aEodbpGvpSG1TQhJFYi4pJe+HtEyNVhWhl7IrCoUJHmUlnAsmpg48fbmoNJquFY4YSInwab544IVzJkl9A6tw3sJUYSXnuDXwG9JHOYy2cJJSEaIYKdyRSWNsf1SoVWm8v+rz8VNMG5QLOcghFG7ZiN5UCEP3YPS6OSgghHBNp75j5NDajCfeQdvT/8DWCBvevso29vJT0eZOxlxbj2eVc2k9+4YzO5d1rPDm/PoVx7zIUmxWVpnX9+Val9I2UOW1zLAVFlKWmA+Ad38PF0TR/7UcNo8O1kzjy7S/seOBJRiz/Fa1e7+qwRBtyZP0mtqUcwu6tx62whHPHjMY/OqpRz6lSqejbty///PMPhYWFHDx4kG7dup16RxfRensROn4koeNHAmA6eozc1evIXrmW44uWo1isNe5nN5eTXTG/oJN7++DKJJFXTBT6DpHoK/7fLSiwUUvTNQWf+J4AGHbvQ7HZzqhUuhDNWXpJEWU2Kx5aLRHeVRNGGrWaTv4B7M/PJakwXxJKolVSFIXUuW855m8HbCYpZ94aNJ9uPUIIALKX/8v6STdjLSrGs6bJXBWFPq883WCTtjZ3ztJ3TtbCunvqCdFaZC5cxJI+I8j8fbGrQxEnsZdbSHzyRbbe/hA2o4mgoQMZvuSnaskkgONf34v5yE40Pu2InPE9Ku2Z9RT26NgftVcAdlMRpSmbz/YSmqXK9/oa5g2UMqetm2GXowONR6cOaP3kRkp99HjmYTzCQzGlHWX/C2+5OhzRhiQtWc7WI6nYPdzxzCti9CUXNXoyycnT05P4+HgA9u3bR1FRUZOctyHoI8PocO0kusycUWsy6WS95j7GiBXzueDQJsbvXMm5C7+i7zsv0GXmDCInX0zggATcgxt3nqOm4hkbg9pLj91oojQ51dXhCNHgkgsqyt35B9ZY0u5E2bu8Jo1LiKaSv2QlBcv/PVGJwuYoZ563eKVL4xJnRxJKQjQj6T8tZNMNd2MrLaXdiKGMWPYLoReMqeyppdJoCJ04tkpt7bYgeOIYfCrm2zDsSHRtMEI0AXNuHjsfnoM5J49dD8/BnCtfMJqD0oxjrLviRlI+/QaA2HtuYfAPH+PRPrjatoX/fknhqk9ApSJixrfoAiPO+LwqtQavHmMAMO5ecsbHae4Cx48kYPS5cFIZI//R50qZ01bOOX+Ss5e6ODWdjzcJbzhKA6Z8+i256za5OCLRFuz6dSG7i/NRdFp8cgsZPeUq9E3cwS0qKoqwsDAURWHLli3Y7TWUSW3GfLrGVvlu91/O73odp0/Ft1tcmxh9qNJo8O7tKJdYImXvRCuUVHgioVQT53LndkK0Jo5Sd2/Cf5OpKhWpc9/Ekl/gmsDEWZOEkhDNxKEPPmPHvY+jWK1EXHEh53z5HjpvL/q88jQaL8eXibZS6q4mPgm9ACjZIV80ROumKAq7Zj2LzWgCwGowsuuR51wclchZtY7V46+iYOtOdH6+DPxiHt0fvx+1tnr5ubL03Rz74g4A2l32DN49x571+b17jQfAkNh6E0rO0ncavWflsvAbrnZhRKIpODuKeMf3cnEkLUu7EUPpcN2VAOx84GmsFZ8ZQjQ0u93Opq+/JxkrqNUE5hcz+sZpuHt7NXksztJ3bm5uFBUVceDAgSaP4WyoVKoT3+1quLnWVr/reTvL3knHQdEKOUce/Xf+JKe4AEdiPqOkGJPF0mRxCdFYbEYTJTv2kPXjQvZMuxtbibHGKhQ2o6P0nWiZJKEkhIspdjt75rzK3mdfB6DT7TfQ990XUbs5SiO5BwcR/+ps3NsF0efV2W2m1N1/eSfIFw3RNmQuXMTxv5eh2GwAKDYbx//6h8yFi1wcWduk2O0cfOMDNky5nfL8Avx69+C8xT9UzovwX7bSEo6+dyVKeSlevcYTfMmTDRKHV89xAJQe3ojN1HLK/JwuXWAAMU/NRFWRqCtNPeLiiERjslssGPY4bgg7P+dF/fWc/RCekeGYjhxl39w3XB2OaIXsVitrP/uao3o3AMJKShl+8/Vo3M6shGtD8PDwqCx9t3//fgoLC10Wy5lwDw6izytP13hzrS2VNT+Zj/N7XsWIVdH65S1eyfYxk8lfssrVoTSqknIzx4wGADrXMkLJz92Ddp56FOBwkYxSEi2HYrdTdiSD/GVrOPr+5yQ98DQ7L7qOrcMuZt+N95L2wtuUZxyv/QB2R+k7k5Q7bZFa16zOQrQwdouFHQ88RcYvfwDQ4+kH6TzjpmrbhV9yfpsrc/df3n16gEqF+egxynPzcQuu+Q8yIVoyc24eu2Y96+i1evKNBpWKXbOeJWjowDZ5o8FVzHkFbL/7UXJWrgWgw9TJ9Jr7GBoP9xq3VxSFY5/dSvmxA2gDIoi4/WtU6obpu+PWLga30C6UHz+Icd8KfPtf1iDHbY6CJozEtD+JY599j2FHIu0ubduff62ZaX8yirkcrb8vHtE1zBsp6qT19iL+jWfZcNUtpH7+PWEXjiP43EGuDku0EpbSUlZ99T3FwX6gKHS0quh7w1RXhwVAZGQkGRkZZGZmsnXrVkaNGoW6gT5vm0L4JeeTuWARWUtWothsqDQaQiaMarPf97x7d3d8z8s4RnlOHm7t5G/d1sxRAusNbCVGUp57A58BfVrtXJmHKsrYhXl54+NW8/cHgNiAIHJKTSQV5NMrOKSpwhOi3qwlBkqTDmM6eBhTkuNfaVIK9tKyGrfXtQvCM64T5oxjmNMzwK5U30ijJmDkMPSxMY0bvGgUklASwkWsRhNbbnmAnJVrUWk0xL/xLFFXXerqsJotrY83nrExlCalYNiRSODY4a4OSYgGVaXUXQ29Vi0lBnY98hwDP33LJfG1NYbd+9n1yHOUZR5H7eFBn5efOuV7dMGy9yne+ANotETe9SNa33YNGpNXr/GOhFLikladUIITo1VKtsuo1NbMUFHG1ju+Z6uYXN4V2p03mOgbribtix/Y8cBTjFwxH60LSpGJ1qU0v4BVv/yGKdgPbDa6633pfuEEV4dVSaVSkZCQQG5uLkVFRezfv58ePXq4Oqx6c5a+W752E9bikjZb6s5J4+2FZ1xHSg8exrBzj3zPa8UURSF17lvYTKUA2EyOkldxrz/j2sAaSXJBxfxJAXUnSeP8A1mfmV65vRCuothslKVnUupMHB08hOngYcqPZdW4vcpNh2dsR/RxndB3cfzzjO2ELsAPcCSQd116AzaDqVqHWY1eT8wT9zXFZYlGIAklIVzAnJvPpml3UrgjEY2nJ/0/foOQMee5OqxmzyehF6VJKZRIQkm0QiUHkjn+97LaN7DbOf7XP6T833fE3HBVrRM6i7OjKArZPyzgyGsfoFiteHWKZsDHb+Dbo2ud+5Ue3szxbx8AIOSqV9DHDW3w2Lx7jafgn3mteh4lJ+d8CmWp6VjyC9EF+rs2INEonBOwO9tbnJkeTz1I9vJ/KU3PYO9zr9Pn5addHZJowYozjrFm0WLMQX6ozOUkhEfRccQwV4dVjYeHBwkJCWzatIkDBw4QHh6Ov7+/q8OqN2dZ88QnX6TX84+3+RHoPvG9JKHUBuQvWUnB8n9PLLA5Sl7lLV5J0ISRLoursSRVjFCKraXcnVNsxfxKyYX5KIoinWxEk7AWFWNKSnGMNqpIHJUeSsVeZq5xe7fQ9njGdUTfpbMjeRTXCY8Okai0td+X0AUGEPPkAxx6ZG7VFYpCzJMPtNrRiW2BJJSEaGKm9Aw2XHMbxsNp6AL8GfT1+wT06+PqsFoE74SeZP/0e2WPZiFaE5+usYReMKay/EltEp94nuT3PiV66mQ6XHsFHqHtmzDK1s1mKiXl2TfIX7QcgNCJY0l48zl0vj5172fI5+h7V4LNgs+AKwiccH+jxKfvNhI0WizZhyjPOoRbSOdGOU9zoPP3w6NTB8oOH8Gwcw8Bo5rfzUxxdhRFwSAJpQah9dKT8OZzrJ98M2lf/kjYheNoN3yIq8MSLVDegWTWbliPNcAXtamUQT16EdYv3tVh1SoiIoKIiAgyMjLYsmULo0ePbnGl79pqmbv/8k7oQfZPCymR73mtlqPU3Zs1lvZOnfsmvgPjW9XNZbuikFyRUIoLqDuhFO3rj06txmAp55jRQLh33d89hDgddqsV4+E0ivce4Njm7RUJpMOUZ+XUuL3aw71y1JFnReJI36UT2lN8J65N4PiR5C9eScGqdWCzV5a6a41J5LZEEkpCNKHivQfYcO0dmLNy8IwIY/D3H+Ed29HVYbUYPgm9ADDtT8JWWobG08PFEQnRcP5b/uQ/K9F6exEx6SIyFyyiLPM4B16dx8E3PiD0/NFEX38VwecOarD5etqi0sNpJD30DGWHj4BGTdT9txH/4F2n7CGo2O1kfHQ9ltw0dO07Ez79/xqtV6HG0wd97FBMB1Zj2LOUwFacUALwSehN2eEjlOxIlIRSK1SemYUlJw+VVoN3z7pHAIpTCx52DjE3TSH1s+/YOfNpRqyYj87H29VhiRYkY+sONu/bi93HC22xgWHDziUorpOrw6qTSqUiPj6enJwciouL2bdvHz17SoK6JXJ2LDDtS8JeZkZdy3yVomWqUuquhtLeNmPrK32XYSimzGrFXaMhysevzm21ajWd/AI4UJBHcmG+JJRascyFiypHpoZf3PClZM15BZTsO0jx3oMU7ztA8Z6DlBxMxm4ur3F7t/DQytFG+i6OBJJHZHiDVkNRqVTEPHk/xZu3YysxSqm7VkISSkI0kdx1m9l84z1YSwz4dItj0Lcf4hkmEy6eDrfwUHTtgrDk5GHcexDf/jKyS7Qu7sFB9Hnlabbd8XDVFYpC/GvPEH7J+fSc/TDH/lxK2pc/kr9pG8f+XMqxP5fi1bED0dOuJOrqy3GT8mCnJW/RclLmvI69tAxduyBiX3kKn76965UYyvvrFQw7/0Slcyfq7p/R6Ov+wni2vHqOw3RgNcbEJQSOvqNRz+VqPn17kfPrnxhkHqVWyVnuTt8tTm4cNpDuTzxA9vI1mNKOsnfOa8S/9oyrQxItxOGVa9h5PBNF74F7QTEjJp6Pd1ioq8Oql5NL3x08eJDw8HACAlrPKIe2wj0iDF1wIJbcfIz7DuLTt7erQxINqPRQKimb9mAKCK91m8KNiUQkp6KPjWm6wBpRUsV8SJ39A1HX4ztFbEAgBwrySCrIY3hkdGOHJ1zAnJvHzofnYC0uYdfDcwgaMuCMy53aLRYMh1IdiaO9Byjee5CSfQcpO55d4/YavSe+3bug7Rh1InkU2xFtE3U+cpS+m8mRV+YR/cg9rWo0YlslCSUhmsCxP5ey7a5HsJvLCRw8gHM+fwedn6+rw2pxVCoV3vE9KfhnNYYdiZJQEq1SyLiRVUpBqDQaQiaMqiyJovFwJ3LSRUROuoji/UmkffUTR39aiDHlCHuffZ39L79L2EXjibn+KgIG9pUa3HWwWywcef1Dsr//DQDfc/rS+cXH0QXVXZbCybh/Fdk/PwFA6HXz8IhOaKRIT/DuNZ6cX5/CuHcZis2KStN6/5TzrhiVatx7UHort0KGHY5EoZS7azhaLz3xbzzH+kk3ceSbnwm7aBztR8roPlG3jd/+xI78LHDT4ZVXyIirJuPRwr6nREZGkpGRQUZGBlu3bmXUqFFoZK7JFqXye96yNZTsSJSEUitTEhLBu9c9jVVd++tSa7eREBqOvgnjakzJFQmluFPMn+QU5x8EJFWWyROti6Io7Jr1LDajCQCrwciuR55j4KdvnXJfc24exXsOUFw58ugghoOHsJdbatxeHx2Jb4+u+Pbogm/3Lvj27Iq+QyQqtZqsrKyGvKzTEjRhpJS5a0Va710IIZqJ1C9+YPdjc0FRCL1gDP3efwWN3BQ7Yz4JvSj4ZzUlO6THumidCnckOpJJFUklrbcXfV5+qsZtfbvF0fv5x+n++P1k/PYXaV/8SNHuvWT88gcZv/yBT/c4Yq6/mohJF0npo/8wH8siedZzGHfvAyBs+rVE3nljvYf3WwuPk/H+NaDY8Rt2Pf4jpjdmuJU8OvZH7RWA3VhA6eFN6OOGNsl5XcE98qTeynsP4CPzDbYqhp17AfBJkIRSQwoeOpCO06eS8uk37HzwaUau+O2U88CJtmv5h59yQCkHjQa/vCKGXzcFnaenq8M6IwkJCeTm5lJcXMz+/ful9F0L5B3fg4Jlayo/H0TrUWK21ZlMArCqNRSX2WjfSr6yJBXmAY6RR/Xh3C69uIhSqwVPra7RYhNNL3PhIo7/vazyd8Vm4/hf/5C5cFFlx1F7uYWSpEOVo42K9hygZN9BzDl5NR5T6+2FT/cujsSRM4HULQ6tt1eTXJNo2yShJEQjURSFg6+/z8HXPwAgetqV9H7xyQatRdoWeVfceDLs3Itit8ucMaLVyd+4DQD/fn0oPXKUXs8/fsqh8FovPdFTJxM9dTKFO3aT+uWPZMz/m5J9Sex+bC57n3udiMsvJPr6q/Dv06MpLqNZK1y3mcOPv4C1sBiNjzednn+UgNOYwF6xWTn64bVYi47jHtGTsBveb7KRYCq1Bu8eYyne/BOGxCWtOqGkUqnwTuhJwT9rKNmeKAmlVsRmNGFKOgzICKXG0O3x+8hevsYxcnXOq8S//qyrQxLNjN1u56+33iPd03E7oH2BgaE3X4+6BX9PcXd3JyEhgY0bN3LgwAHCwsIIDKzfjVzRPDjnyzXs3IOiKDLKXrRYRks5mQbHnLix/vUraRbg4UmQp568UhOHCwvoGdy+MUMUTcicm8euWc9WqULitP2+J8hYsAhjShqGpBQUq7X6AVQqvDp2cIw2ciaOenTBMypC3ieFy0hCSYhGoNhs7Hp0Lke+/gmALg/dSZeZM+TNvgHou8ai9vDAVlxCWcoRPDvHuDokIRpU3oYtAERecSEdb772tPf3T+hNQkJves5+mKM//07qlz9iOHiII9/8zJFvfsY/oRfRN1xN+CXno9W3zF7IZ0qx2cj46GsyP/oKFAV99zjiXpuNe0TYaR0nZ/5sTPtWoPbwJvLun1G7N20vMK9e4yne/BPGxKVw+TNNeu6m5pPQ25FQklGprYph9z6w23ELC8GtfbCrw2l1tHo98W/OZd3lN3Dk218JnTiOkDHnuTos0UxYzGZ+e/Ndcv0cn12xNjW9broOdSvopBUREUFkZCRHjx5l69atjB49WkrftSD6brGo3HRYC4ooO3IUz+goV4ckxBk5VFG2LkTvha97/avTxPkHkldqIrkwXxJKrUSVUnf/SSYB2MvMHP/rn8rftb4+1crV+XTtjFbfWopBitai5f/VKEQzYystY8stDziSSWo1vV96iq4P3inJpAai1mnx6tUVgJIde1wcjRANy261UrBlBwBBg/uf1bF0fr50nD6VkSt/Y+j8L4i4fCIqnZbCHYnsfOAplvYdTeKTL1Jy4FADRN78WQqKOHD342T+70tQFNpNvogen79z2smkkp1/kfv7CwCE3fQx7uHdGiPcOnn1GgdA6eGN2IyFTX7+plQ5KnXHHhS73cXRiIZi2On4/Hb2RhcNL2hQPzrdOg2AXQ/NxlJU7OKIRHNQWlTEj6+95Ugm2e3Ee/kx7u7bW0UyySk+Ph53d3dKSkrYt2+fq8MRp0Ht5oZXT8f3PIN8zxMtWFLF/EmxAfUbneTkLHuXVFBziTPR8pQcSOb438tQbLY6t+v1whOM2byU8/evY9j8z+n9/ONEXzeZgL69JZkkmqXW85ejEM2ApaiYDVNu5/ii5ajd3Rjw0evE3HC1q8Nq0RS7DcuhtZi3z8dyaC2K3XaiHIL0WBetTHHiPmymUnR+vvh0i2uQY6pUKoIG96ff+68wbtsyuj85E310JNbiElI+/YaVIy9l7eU3kjH/L2zm8gY5Z3Nj2L2PPVPuoHj9FtQe7nSa+ygdn3wAtbvbaR3HkneEzP85btAGjLkTv8HXNEa4p+QWHI1bWFew2zDuW+GSGJpK5ajUEgOlh9NcHY5oIM75MbzjpQRnY+r6yD14dYqm7Hg2e2a/4upwhIsVH8vih/c+pDjQF5XFypCIaIZef/ojoZs7d3d3+vbtC8DBgwfJz5cJ7lsSn/gT5c1F22MtMbo6hAaRXDFCKc7/9MpuxlWUx0suzEepYTSLaHl8usYSesGYWqe+UGk0hE4cS8ebpqCPDJOO6KLFkISSEA2k9FgWay+7gfyNW9H6eDPo2/8RduE4V4fVopl3/0nhiwMo/t8kDN/NoPh/kyh8cQBe7R29bGWEkmht8jY45k8KPKdvo8wP5h4cROxdNzN63V8M+u5/hJ4/GpVGQ/6GLWy7cxb/9B/LvuffxJiW3uDndgVFUTj+3Xz23XQ/5cezce8QQY+v5hF80em/NyvWco6+dxU2Yz4eHQcQMuWNRoi4/rx7jQfAmLjEpXE0NrVOi3ef7oB0ImgtFJsNw66KhJKMUGpUWr0nCW/NBZWK9B9+I+ufVa4OSbhI9sFD/PTFl5QG+KIuNTO6TwIJl13k6rAaTXh4OFFRjnJpW7duxXaKnuGi+XDOqyef+W1T7oJFrg7hrNkVheTKEUqnl1CK9vVDq1ZTUl5Olql1JNfaOpVKRZ9XnkbjVcMoI5UKrbcXfV5+qukDE+IsSUJJiAZQknSYtRdfR8n+JNxD2jHsty8IHjrQ1WG1aObdf2L46hbsRceqLLcXHce+5WW8Qooxp2dgyZNeh6L1yNuwFYDAQWdX7u5UVGo17UcOY+Bn7zBm02K6PHQnHmEhlOflkzzvU5YPvoANU26nYPm/KNaWeRPGZirl0GPPc+TleShWKwFjh9Pr2w/Qx3U6o+Nlff8wpYc2ovYKIPKun1Dr6l8PvTF4VSSUDK08oQQnyt6VbJebS61B6eE0bAYjar0n+tiOrg6n1Qsc2JdOt18PwM6HnqG8sMjFEYmmlrZlO78tXEC5nzdag4kLR42iy8jWP6fWyaXv9u6V0S4thTOhVHo4DWtxiYujEU0tb/EKzMeyXB3GWck0lGCyWnDXaOjg43da++o0Gjr6+gNS9q41cQ8OovOMG6uvUBT6vPI07sGnVxpRiOZAEkpCnKWCbbtYe+n1lGYcw6tzDOf+/jW+Pbq6OqwWTbHbMC18EqhpmLdjWXDPHEChRMohiFZCsdvJ3+QYoXS28yedDs/wULo+eCdjNi1m4Gfv0G7kMAByVq4laeZsdky8lowPv6Q8K6fJYjpbpYdS2TP1TvIXrUCl1dDhoTuJffVpNN5eZ3S84k0/kb/0HQAibvsSt3YxDRjtmdF3HQEaLZacw5Rnte55sHwSegMyn0Jr4WxH797dUWlrLv8hGla3Wffg1bkj5qwc9jz1kqvDEU1o/z8rWfTvamxenrgXlXD5FVcQ2Tfe1WE1CTc3N/r16wdAUlISeXlyc7Yl0AX6494hApCyd62Jr4cGHXXPham1W/EsKSLjgy+aKKrG4Sx318kvAM0ZVJxwzrvkPI5oHSpL3qlO/B46cSzhl5zvuqCEOAuSUBLiLGQtW8P6ydOxFBTi37c3w377En1UhKvDavGsKRuqjUyqSkGrLcMjwCTlEESrYUg6jKWgELWHB369m35eEbVWS+j5oxn83f8YveFvYu+ejjbAH0t2LhkffsGOideS9MDTFK7bjGKv+wuhK+X9vZw9191FWcoRdO2C6PbxG4ReN+mM61Gbjx8k89PpAARd+Ag+Cc2jRJDG0wd97FCg9Y9S8u7THdRqzBnHKM/OdXU44iyV7KxIKFX0QheNT+PpQd+354JazdGff+f44tY995pw2PrzAlbs243d3Q19fjFX3nwTwZ3b1qjAsLAwOnToAEjpu5bkxDxK0pGktWjv7caTqYs5f80vAAR7aRneyReAYTE+vHtFJ94Z4Ia/oYDc35dgSjrsynDPinNk0emWu3OKq9jPWTZPtA5567cAoHZ3VLmQUneipZOEkhBnKP3HBWy+4W5spaW0GzmMIT99invwmf3RIKqyZSfVazutu5USSSiJVqKy3N2AeNRuOpfG4hUdRfcnHiBh8Xd0fukJfPr3AZudghVrOXjno+y65HqOffY9lvxCl8Z5Mnt5OakvvM2hx57HXlqG76B+9Prhf/j0PfM5WuxmE0ffnYy9rAR91+G0nzS3ASM+e15tZB4ljbdXZalCGaXU8jlvEDpLGYqmEdA/vrLcyq5ZcygvkNJ3rdma//uKTVlHQavFr6CEa+69C5/27Vwdlkv06dMHDw8PDAYDe/bIZ0hL4Jxfr0QSSq2GoijoNm2k2NsfgAFR3ozp4vj/Q3lldA7yoOOA7gSMHQ6KwtF3P3VdsGepcv4k/zMrYxbr77indKSkiDKrtcHiEq5jt1orK5F0fegu3NsF0efV2VLqTrRoklAS4jQpikLy+//HjvueQLHZiJh0Eed8OQ9tTZPsidNiL87CuPBpjAvq11PDatZi2puEvczcyJEJ0fjyNzrnT+rn4khOULu5EXT+aLp/+ia9f/mUkCmXo/H2wnz0GOlvf8yOCdeQ/OjzlGzbhaLUVKKyaZgzs9h38wNk/7gQgPBbr6Pr+y+hCww4q+Me/+puzEd3o/ELIeLO71FptA0RboPxdiaU9i1HsbXuL5zeFYnBku27XRyJOBuWvHzM6ZmgUuHdu7urw2lzuj50F95xnTBn55L45AuuDkc0ArvdzqJ3PiDRbAC1mvZFJq566H7cfbxdHZrLnFz6Ljk5mdxcGena3DlHsBp378duad1/37QV5iMZWHLzSY2MA6B3mBe9w/Ro1SqOl1jILC4HIPKem0GjpnD1Boq37nJlyGfEZLGQYSgGTow0Ol1BnnoCPDyxKwopRQUNGZ5wkaLd+7AZTej8fek840bG71pF+MUTXB2WEGdFEkpCnAbFbmfvM6+y77k3AOg840b6vvMCap1rRxO0dPai4xgXPEnBS4Mo+/cjsFtAU9djqkLlF45NE4litWLce6DJYhWiMSiKUjlCKWjwABdHUzPPzjFEP3I3CUt/pOMzD+PVqxuKxUL+ouXsu/kBEidN5/h387EWG5o0rsK1m0iccjvGxP1ofH3o8u4LRN5104k61WeobPO3FK75DFRqImd8h84/rIEibjgeMf3QeAViLy2m9PAmV4fTqHwqRrPIqNSWzTnvoWdsDNo2fIPbVTQe7iS8/Tyo1WT8+ifH/l7m6pBEA7JZLCx47S1SKv6EjjbbuXzWA2jd3FwbWDMQGhpKdHQ04Ch9Z5Ve/82aZ6cOaHy8sZeVUdqCS5+JE0q27aJM58Gx4EjAkVDy1GnoGerolLs13fH9wTM6ivZXXAhA+tsfubTD2pk4VJiPArTz1OPn7nHGx4mrGKWUJGXvWoW89ZsBCBzUH9UZzKslRHMkz2Qh6slebmH73Y9x+KMvAegx+yF6PP2QfCCcBVvRMYwLnqDg5UGUrf0ErGVoowfic8v3eF/7AY4ZC/8774njd69LnjtRDkFKIIkWrjQ9g7JjWai0Wvz79XF1OHXSeHrQ7rLz6fn1e/T89gPaXXEhag8PSg+nceTleewYfzUpc17DsKdxE72KzcbR9z/n4N2PYysqwatHV3p99yH+5w0662NbM/dgnP84AO2ueBav7qPO+piNQaXW4NVzLND651Hy6dsbANOBQ9iMJhdHI86Us2ShzJ/kOgF9exN7502Ao/SdOU96P7cGZoORn159k+M+nqAodNd6MvH+u1DL95RKvXv3xsPDA6PRKKXvmjmVWo13H8d8olL2rnUo3rqLI2GdUFRqwnzdaOftyHz3i/QCYNtRY+W24bdNQ+3hgXHXPgqW/+uSeM9UcqEjARQXcHalzCrnUSrMO+uYhOvlrXMklIKGDHRxJEI0HPkLU4h6sBlNbLr+LjLm/4lKq6Xvuy/S+Y4bXR1Wi2UrOobxt8cpfHkwZWs/BasZbcw5+Nz6I753LsSty0jce1+E97RPUPuFVtlX7ReG97RPcO99IT7xjoSSQXqsixbOOTrJP74nWr2ni6OpP68eXej49EwSlv5A9GP34hnbEXtZGTnz/2bv1DtJvHYG2b/+ia20tEHPa8kv5MBdj5H50VegKLS/8mK6f/4W7hGhp975FOxlJZR8fStYy/DucwHBFz3WABE3nrYyj5JbSDvcwtqD3Y5h9z5XhyPOkPPz2kcSSi7V5aG78OkaS3luvpS+awWMuXn88PY8CgJ8wGpjQFAII2+90dVhNTsnl747dOiQlL5r5pzz7Mn3vNahZOsuUiNiAegddmKqgP6RjtHKu44ZsdjsALi1CyJ02mQAjr7zCYrV1sTRnrmkAkcCyDkP0plyzr+UVJDf4kZpiarsViv5Gx3zJwUPlYSSaD0koSTEKVjyC9h/60PkrFqHRu/JOV/MI3Lyxa4Oq0WyFWZgmP8ohS8Nomzd/1Ukkgbhe+tP+M5YgFvccFSqEyOS3HtfiP9jW/C+8SucI5N8ZvyGe2/HMPiTJ2xV7PYmvx4hGkpznD/pdGh9vAm5+lJ6/fQx3T97m6ALx6Jy02Hae5DUZ99gx7irSX3xHUzJKWd9rpKde9gz5Q6KN2xF7eFBp+cfJeaJ+1E3QEkfRVEw/vQA9tzDqP0jCL/9q2Y/CtW71zgASg9txGYsdG0wjcwnwTFKySCjUlsku7kc474k4MTnt3ANjbsbCW/NRaXRkPnb32T+sdTVIYkzlJ96hB8+/hRjoC8qczkjunRn4NWTXB1WsyWl71oOZ8cDQ0WpVNFymTOzKD+WRWq4Y/6kPmFeles6Bnng76mh1GJnX9aJDmhhN1yF1t+XsrSj5Pz2d5PHfCbsitJgI5Ri/PzRqFQUl5vJKTWeegfRbBUn7sdqMKL19cG3RxdXhyNEg2ned0mEcDFzxjH23nAfxr0HcAsMYMjP/0f70ee6OqwWx5FIeoTCl4dgXv852MrRdhyM720/4zvjN3Rx51VJJJ1Mpdbg3mMcmqh4x7FSNlSu03eLRe3hjq2ohLLU9Ka4FCEaxYn5k/q7OJKzo1Kp8Onbi87PP0bC4h+Imnk77lER2AxGsn9YQOLkW9h7033k/vkPdnN5ncfKW7yS7WMmk79kFeBI9hz/9lf2T3+A8qwcPGKi6PH1PIIvHNdg8Zet/ZTy3X+ARof31I/Qep/dl8GmoAvqgFtYV1DsGPctd3U4jcrZW7lk+24XRyLOhHFfEorFgjbQH/fI5jcnWVvjn9CL2LunA7D70ecw58o8DS1N5q49/PLjT5j9fdCYSpkwaAg9JoxxdVjNXp8+ffD09JTSd82cV69uoFFTfjwb8/FsV4cjzkLJ9l2Yde4cax8FQO/wEyOU1CoV/SIco5S2Hj0xD6vG24vwW68DION/XzZ4tYPGcNxowGixoFOr6eDrd1bHctNoiPELAGQepZbOOX9S0OD+Zz3HrxDNiSSUhKiFcX8ye2+4F3N6Bm5hIQxb+BUBFXM4iPqxFRzF8OssCl8ejHn9F45EUqch+N7+C34zfkMXe26tiaT/0nUeBoDl0NrKZWqdFq+e3QCZqF20XGXZuRgPp4FKRcDAvq4Op8HoAvwIu/4q+iz4nK4fvkLA2PNAo8awPZHDT7zIjglXc+SN/1F2JKPavpb8AlLnvoElr4CU596g7Ggmhx6Zy5FX3kOx2ggcN4Ke37yPPrZjg8VrSduK6c85AOgvnI0uuuUk97zbSNk7n74VZU537W1R5U+Ew8nl7ur72S8aV9wDd+DTPY7yvHwSn3je1eGI03BozTp+/2cJVh89umIDl0ycSMch57g6rBZBp9NVKX2Xk5Pj4ohETTR6T/RdOgNgkHmUWrSSrbs5EtoJu0pNqI+O9t5Vqwr0j3IklLadlFACaH/lxbiFh2LJySPrm1+bLN4z5Sx318kvAG0DVDiIqyiblywJpRYtb/0WAIKGDHBxJEI0LEkoCVGD4s072H/LTCy5+Xh26USPL97Bu3OMq8NqMWwF6Rh+mUXhK0Mwb/gSbBa0nYY6Ekl3zK9MDp2OmhJKcHJ9bfmiIVomZ7k73+5xuPmfXW+25kilVuM3uD9xrz1Dwt/fEXHXTbiFtsdaWMzxL39k1yXXs/+OWeQvW4PdYkVRFFLnvoXN5OiJaDOaSLzqNvKXrESl1dDh4Tvp/MpTaLz0pzhz/dmN+Ri+uQ1sFtz6XIzHsOkNduym4JxHybB7cauus+7ZOQaNtxf20jJMSYdcHY44Tc4bglLurvlwlL573lH6buFi8havdHVIoh52/b6Ipdu2YPf0wKOgmMlTryW0ezdXh9WihISEEBMTAzhK31ksFtcGJGrkk+CcL1e+57VkJVt3njR/kle19X0rRigl55ZRWHqiDKXazY3Iu24C4NjnP2ApKGqCaM+cs9xd7FmWu3OKDQisclzR8ig224lKJENk/iTRukhCSYj/yF+6igN3PorNYMSnfx+6f/Imbu2DXR1Wi2DLP4Lhl4cpfGUo5o0ViaTYc/G9Yz5+d/x6RokkJ13MIFBrsRccxZafVrncWV9bRiiJlurE/EktZ0TMmXJrH0zErdcR/+fXxL39HH7nDgKViuINW0l+8Bl2TryW5IeeoWD5v1AxMS92O3ZTKRpfH7p9+iahUyc16OgGxW7H8P3d2AszUAd3wmvyGy1u9IRXt5Gg0WHJTcWS3XoTLSqNBu/4HgCUbJf3/JZEURRKKm4Ield8bovmwb9PD+LuuxWAtJfewZJf4OKIRF02fPMDa9OSUdx0+OQXc/WM2/GPjHB1WC1S79690ev1mEwm1q9f7+pwRA2cn/kyj1LLVZ6bT1naUVIjHPMn9Q6vnlAK0GvpFOQBVB+lFHTBaPRdOmMzGMn89JvGD/gsOEcSOUcWna3YiuOkFRdSbpOR+S1RUeJ+rCUGtD7e+PWSjh+idZGEkhAnyfphAcmznkOxWAgYcx5d338Zra+3q8Nq9mz5aRh+frAikfQV2CzoYs/Dd8Zv+N32M7pOQ876HCp3L7RRCUDVUUrOLxrmIxlyE0S0SK1l/qTTodJoCBgxlK7zXiD+j68Jm34t2kB/LDl5FCz7t+ad7HY8osIbPJbSFe9gObActB74TPsEtYdPg5+jsak9vNHHDQXAkLjUxdE0Lp8ER+lZ6a3cspjTM7EWFKLS6fDqEefqcMR/xN13O749u2ItKCL1hbdb9UjHlmzZB5+wvTgfNBqCCg1cPfM+9AH+rg6rxTq59N2ePXs4evSoiyMS/+XsgGA8kNQi5tAR1Rm27casdSOzXQcA+oTVXGGgf2TNZe9UajWR9zs6PWT/sBBzxvFGjPbMlVotpJc4RlA5RxadrWBPPf7uHtgUhcNFcp+jJXKWuwsc1E/mTxKtjiSUhMDRc/bo+5+R9uI7oCi0v/JiYl95CrW726l3bsNseWkYfppJ4SvDMG/6BuxWdHHD8Z2xAN/bfkLXcXCDnu9E2bt1lcu0fr54dooG5AajaHksRcUU7z0ItI0RSjVxjwgl6p7pxC/6zjEBcy1splJSn3+7Qc9tSf6X0iWvAOB1+Qtow3o06PGbklcbmUfJWea0ZPtuuendgjjL3Xn16ILaTf62am7UbjpH6TuthoJ/1pC/eIWrQxInsdts/P76OxzEUZYt3GBm8qyZ6Dw9XBxZy9e+fXs6dnTMx7hy5UrKy8tdHJE4mXtYCG4h7cBmx5h4wNXhiDNQvG0X6WGdsKvVtPfWEeJT898AJ+ZRMmL/z993fkMG4HtOXxSLhaMffN7YIZ+Rw4UFKDiSQAEeng1yTJVKRZyz7F3F/EyiZclbvxmA4KFS7k60PpJQEm2eYrWR+tybZH70NQARM24g+vH7pAdBHRyJpAcofHUo5s3fOhJJXUbie+fv+N76I7qOgxrlvNqKhJL10NoqNxKd8zGUSEJJtDD5m7eDouDVsQMeIe1cHY5LmY9kYEzcX/sGdjsFy9ZgSk5tkPPZi45T8u0doNhxH3ANHgOvbZDjuoq3M6G0bzmKtfXOBeHVqxsqrQZLTh7lmVmuDkfUk7MsrZS7a778enUj/JbrAEh98R3Kc2XOhubAYjbzyytvclSvAyDOruHiB+9FLd9TGkzv3r3x8fGhpKRESt81Q87PDek42DKVbNtFarhj/qQ+NZS7c+oe4omHVk1BqZXUfHOVdSqVisiK0qx5f/6D6UDzK++cVFHuLraByt05OY/nPL5oORSbjfyN2wAIGjLAxdEI0fAkoSTaNHuZmaSHniHn1z9BrSbmyfuJuP36Fjd/RlOx5aZg+PG+ikTSd2C3oesyCt+7/sD3lu/RxTRuzwtd9ADQ6LAXHcOem1K53Nlj3SDzKIkWxvlHZmAbKndXG8/OMQSMPhc0tfxpolETMOY89LExZ30uxWal5Ns7UAy5aEK743XZC2d9TFfziO6LxjsIe2kxpYc3uTqcRqPx9EDfvQsAJTt2uzgaUV/O+S98EiSh1JyFTb8WfddYbEUlpD3/lowCdLHSoiJ+fO0tcv29wG4n3sufsXfdhlotX+EbklarZdSoUQDs3buX9PR0F0ckTlY5d+JOSSi1NNaiYkqTUirnT6qt3B2Am0ZNn3DH+q3phmrrvXt2JXDCSFAU0t/5pFHiPRvJhY4RRHENVO7OKS4gqOL4+fKZ3MIU7z2IpagYrbcXvr26uzocIRpcs/hr1GAwcP/99xMeHo6HhwcJCQl8//339dp3xYoVjBs3jvbt2+Pt7U2fPn145513sMmkdeIUrMUl7J8xi8KV61C56Yh9bTbtJ1/s6rCapfKsZAw/3Evha+di3vKDI5HUdRS+d/2J7y3fORI9TUDlpkfbwXHj/eR5lHwqRigZ9yVhN0upCtFy5G1w1FUOaqPl7k6mUqmIefJ+NHpP+G9SX6VCo9cT88R9DXIu0+KXsKZsQOXujc+0T1C51f4Ft6VQqTV49RwLgKGVl73zqSx7J50IWgJrsYHSQ6nAiRuDonlS67R0em4WKq2WghVryft7uatDahMyFy5iSZ8RZP6+uHJZUeZxfnjvQ4oDfVFZrAyJjGbo9VNcGGXrFhERQa9eju8TUvqueXFWojDs2otit7s4GnE6SrYnUq7RkdHeMX9S77DaRygB9KuYR2nr0eoJJYDIu25GpdVQtHYTxZu3N2ywZ0FRFJILnSOUghr02B39AtCoVBSay8gtNTXosUXjyl3nKHcXOKgfaq3WxdEI0fCaRULpiiuu4IsvvmD27Nn8/fffDBw4kClTpvDtt9/Wud8///zD2LFjsVqtfPzxx/z222+MHDmS++67j5kzZzZR9KIlKs/KYd9N92PYnojG24uuH7xC4OhzXR1Ws2M+nkTGRzeQ/EhXzFt/dCSSuo3B9+6/8J3+Hbropr8JruvsmHj+5ISSe1Q42kB/FIsF416pry1aBquplMKK3pYyQskh06bB+OBM0rr2JK1brxP/uvbE+OADZNrOvsRP+d4llK2cB4DXlW+gadf5rI/ZXHj1HAe0gXmU+vYGpPxNS2HYvRcUBfeocHRBtffcVew2LIfWYt4+H8uhtSh26RzmCvounQm/fRoAaS+9S3mOzNvQGJIzclm39wirNu5lwQc/cDAojIUf/MCqjXtZtGwTn371A6UBvqhLzYyJ70vCpRe5OuRWb/Dgwfj6+mIwGFi3bt2pdxBNQt+lM2oPd2zFJZSlHHF1OOI0lGzbRXpoR+xqDe29dfhoLBQUFNT6r2ew46b7nuMmyizVk4ceHSJoN8nxXpj+1sfNZsROlslASXk5OrWaGD//Bj22m0ZDtK/jmM6klWgZ8tZXdBwdIvMnidbJ5WnSv/76i6VLl/Ltt98yZYqj19WoUaNIS0vj4Ycf5uqrr0ZTS43ozz//HJ1Oxx9//IGXl6O3w9ixYzlw4ACff/45b7/dsJN3i9ah9HAaB+58lPLj2ejaBdH1/ZfQx3VydVjNivnYAXIXzqVo/begOP6Y03Ubi+fYmeg69HNpbLrO51L6zxuOm02KgkqlQqVS4ZPQi4Ll/1KyIxGfipuNQjRnhdt3o1iseIS2R98h0tXhuFxaTjF3zU/DRjsYfVv1DbJBMz+N9y6PJrqd7xmdw5Z/BMMP9wLgMWw67n0uOZuQmx3vXo6EUunhTdiMBWi8AlwcUePwqRjlUpqcgrW4BK2vj4sjEnUxVCTO65o/ybz7T0wLn8RedKxymdovDP0lc3HvfWGjxyiqCrvxGgqW/4tpXxKpc98k7q3npBx0A0rOyGXK/+3A5uzbOXTaiZVLjgOgoSfXlOzj6nGjiOwb74Io2x6dTseoUaNYsGAB+/bto1OnTnTo0MHVYbV5ap0Wr57dKNm6k5Kde/DsHOPqkEQ9lWzdRUqEY/6k7u3cWbp0KfY6RpmpVGrae3Un22hl1zEj53So/vddxG3XkbtwMcY9Byj4ZzWB40Y0Wvz15ZzfKMYvAG0jlCSNDQjkcFEByQX5DAmPavDji4an2O3kb6xIKA2VhJJonVw+Qmn+/Pl4e3tz5ZVXVll+0003kZmZycaNG2vdV6fT4ebmhqenZ5Xl/v7+eHh4NEq8omUr2bmHvTfdR/nxbDyiI+nx+TuSTDqJOXM/GR9ex6HHelC07mtQ7HgnXETH2ZvwvflrlyeTALTR/UHrgWLIwZadVLn8xDxK0mNdtAz5G7cCjtFJcqMO8kpKT9xcq4UNNXklpWd0fMVqpuTrW1FKC9F26If+wtlndJzmTBfUAbewbqDYMe5tvaWqdEGBuHeIAE7MzSOar1PNn2Te/SeGr26pkkwCsBcdx/DVLZh3/9noMYqqHKXvHkGl1VK4aj15f/7j6pBalewiU70+73qdM0SSSU0sPDyc3r0dHdNWrlyJ2Wx2cUQCTvqeJ5/5LYbNaMK4P4m0cEdCqWuQts5kEoCi2OkV4gbAtlrK3umCAgm9/ioA0t/9FLvF2oBRn5nkAme5u4adP8nJedykQhkx3FIU7zuIpbAYjZcev94yf5JonVyeUEpMTKR79+5o/1NTsk+fPpXra3PHHXdQXl7OvffeS2ZmJoWFhXz11VfMnz+fWbNmNWrcouUpXL2BA7c/jK2oBK9e3ej++Tu4R4S6OqxmwZy5j6MfTuXQ4z0oWv+NI5HU9xI6PrOFDg/8jmen5tOrQqV1RxvjmLPJWsM8Soade5rN8Hch6pK3wZFQkvmTmobx99nYju5EpQ/Ae+pHqLRurg6pUXj3Gg+0gXmUKkailmzf7eJIRF0Uqw3DLscNwJpGKCl2G6aFTwI1fW47lpkWPiXl71xAH9uRiBk3AJD28jzKs3NdHFHb49O+vatDaJMGDRqEr68vRqNRSt81otMpc+oT7+w4KHMnthQlO/dQrtKSERIDQPd29fu7u0+IOwBb02tOKAGEXX8l2gB/zEcyyJn/11nHeracpejiAhonoRQX4JiXKbWokHKZK75FyHPOn3SOzJ8kWi+XP7Pz8vLo1Kn6CJHAwMDK9bUZNGgQy5cv58orr+S9994DQKPR8OKLL/Lggw/WeV6z2Vylx1FxcTEAFosFi8Vy2tfRVlitru8Bcibyfl9C2tw3wWbHd+gAOr78FCpPj9O6ntb4vDBn7iX/jxcwbP4JKpIwXgkXE3jxk3hE9wVOXHdzantNzBCsyf9iTlqD9hxHmRC3uI6o3N2wFhZjPJSKR0zDDQdvjW1/OppT2ze1xmp7u8VCwZYdAPgOiG+2z7GmbHu7rX4TLdtt9tOOq3znAszrPwfA88q3UXxC63WM5toudfHoPhqWvoMhcQnl5eVnPPqtub/u9b27w4JFlGzf3SixtsS2bygN+Xia9idjLy1D7aVHFx1Z7djWw+uqjUyqSsFelIk5eS3aTkMbLK66SNuf0G7qJPKXrcG09yCHn32dzm8+26pH1DZV29f3NWazWZtdTK3Vfx/n4cOH88cff7B//36io6OJimq9ZaZc0faWxL8o/WM2SvGJ93+VbxieF81B12tite09enYBoCztKGW5eWj9/RouFnnPbxRFm3eQHhqDTa0h2EtLUD0LCHUP1qJWwdGicjILS2nvrau+kbsbobdcy9FX3yfjf1/if/4oNHrP6tudQkO0vdlq5UhJEQAx3r6N8nzy1+rwdXOjuLyc5Pxc4hpgJJS85zfu6z5n7SYAAgb1a3bvMdL2zas9mpvTeXxcnlAC6vxiUte6rVu3cvnllzNo0CD+97//4eXlxfLly3nyyScpKyvjqaeeqnXfF198kTlz5lRbvmTJEvR6/eldgGi+FAWWrEE9f7Hj10EJFE69hO172nbvJrfiIwTu/xHvo2tRVfQANoQNIr/71Zj9O8GeY45/zZSHJZAowJy0hn2bN4HKMdhSFRWOKjmVxAV/wrABrg1SiLqkpKMuLUPRe7ImaT8cOujqiFzuSLEN8D/ldodTDmPNS6v3cXUlR+mw/CHUQH7XySSV+MKWLWccZ3OnspbSWaXFmpvKPz/9HxbvMFeH1Di0CmqgJHE/W9ZvAF2z+JNW/NeK9agBW3QEW7dtq7baO30D9XmGJu/cgCG/dY4qbPYmX4DqhUMU/7uJrfM+hiGuL3/c0h1JKwBOPTfPjpWryT/cOufCawm8vLwwGo0sXbqU9u3bo26EuVHaIq+M9YRtfBmAk+/02IuPYfz2No4NegRjxJBq+6lC26E6nsOOXxdCHykh1dyp1qwnNdwxsixcV8q+ffvqtV9q0n4iPP1IN2mZv3Yv/YNqubkZHYIqOBBrbj7bX30XLhzdUKGflmyVHbtGwVOBDctXNNp5vNU2itXwx/p1dFXkvahZs9tR/bsBFbDfbmb/X64fRSdEfZlMpnpv6/Jv30FBQTWOQsrPdwwbdY5Uqsldd91FSEgI8+fPR6PRADBq1CjUajXPPPMMU6dOrXH0E8Bjjz3GzJkzK38vLi4mKiqK8ePH4+t7ZpN9twXZ2dmuDqHeFLudo2/+j5yKZFL7aVcScc/NqM7wy0D7VlB2wpyRSP7vL2DY+suJEUn9LiPoosdx75BQ577Nqe0Vax+K1z2LpryEvpHeaMIcE7RnnLuLrORU2hWZiB7QcAml1tD2Z6M5tX1Ta6y2T/nwCw4AIcPOod9FFzXKORpCU7W9xWLh2Ib6Jfo7dexEQsd29dpWKTdheP8R7LYyNB2HEHPda3TU1P9Pn5b62j964ENKD6xiYDsL/qOq9/Stj+b+ulcUhd1vfYa1sIiuXr549+nRoMdvqW3fEBqy7VPmL6UACB8+hLAaPpetgeUYN5/6OLHxg9F2apqOItL21R3PLyFz3v+h+2UR3a+6HLeQ+r0HtzRN1fYrlm+BTaeeDzBh5HAGd2uakTHN/T2/sdXU9larlV9//ZXi4mJ8fHwYMWKECyJrfE3Z9ordRsk/M2oscqqq+G/E/q/wufhOVGpNlfVpg/qTt2ARYcZyIuR7XoNorLa3l5nZmZZJaq/LABjeM5IeIbB69epT7tujRw/O87Ty7fY88nXBDBgQUeu2+Q+UkfrEi2iXraPnvbehC/A/rTgbou3/SEmCpH30Cg1nYnzj/Z2ipCTxU9I+dOEhTIw/++kI5D2/8V73JfsOstZYikbvyfjbp6PW1TDKzoWk7dvue359OKu31YfLE0q9e/fmu+++w2q1VplHafduR038Xr161brvjh07mDJlSmUyyWngwIHY7Xb27dtXa0LJ3d0dd3f3ast1Oh26ZvaCb07+O9dVc2W3WDj89KvkL3JMSh714B2ETbvyrI7Zkp8XZem7yVnwLCWbf65c5jNgEu0ufRqPDn3qdYxm1fZaLbqOg7AcXIk9bSPuUY5r8O3Xm6zPf8C4a2+DxtuS274hNKu2b2KN1faFFeXugocMaNbPr8Zue0VRSEtLY8+ePeQYAWJOuY9ao65XXIqiYFj4OPbsA6h82uN73f9Qu9ez3kaF5tw2dfHpPYHSA6so27cM3fh7zugYLeF1753Qk8KV6yjdvR//fvX7LKuvltr2DaEh2964y9Ej2bdv7xqPq4kdRqlfGPai49Q8j5IKtV8Y7rHDqt1cbCzS9tVF3HgNRavWY9y9j/QX3qbLvBdbZem7xm57S1ExB157n6R1u2Hk9FNur9Fom+z52BLe8xtTTY+zTqdjzJgxzJ8/n6SkJGJjY4mJiWn64BpZU7a95dDGKmXuqlNQijIhfQvazsOqrPHt25u8BYsw7d4n3/MaSGO1ffH+PZgVyAiJBqBvpA8ae/16vWs0GgZG6/l2ex47M02o1Bo06po/b9pdMJrsr3/GtC+J7P/7nuhH7j6tOBui7Q8XFwLQJTCoUZ9LXYPaQdI+DhUVNsh55D2/8dqqaPMOAAIH9sW9GVa/krZvu+/59XE6j4/Lx0pefvnlGAwGfvnllyrLv/jiC8LDwxk0aFCt+4aHh7NlyxZs/5mYbv369QBERkY2fMCi2bMZTRy8+3HyFy1HpdXQ6fnHzjqZ1FKVHdlF+ruTOfxkn8pkks/AyXR6bidR9/xc72RSc6Sr+JJhObS2cplzwtay1HQsBUUuiUuIU1HsdvI3OUo/BQ5uu6UZ8/PzWblyJdu2bcNsNtfYyeNsmDd9Q/m2n0GlxufaD1H7tJ3eSF69xgNg3Lscxdp660T7JDg6HZVs3+3iSERNyrNyKD+WBWo13r261biNSq1Bf8lcak4mASjoL3muyZJJomYqrYZOz85C5aajaO1mchcscnVILYpit3Pk219YPuwiUj75GsUuk6q3FKGhocTHxwOwatWqKnMwi9NnL65fz/iatvNOcHzPM+w5gF3mwGjWSrbu5GhIDDaNliAvLWG+p1eyNi7YEx93DcZyOweyax/NqVKribrvVgCyf/qdsqOZZxX36VIUheQCR2WluICgRj1XR78A1CoVBWWl5JXWvySVaHq56xxD74OGnv1IMiGaM5cnlC644ALGjRvHjBkz+Pjjj1mxYgW33XYbixYt4pVXXqkcfTR9+nS0Wi1paSfmTXjggQdITEzk4osvZsGCBSxdupRHH32UV155hbFjx1b+8SfaDkt+AftufZDijdtQe3oQ987zBF841tVhNbmyIztJf3cSh5+Kp2TLL6BS4XvOVXR6fjdRd//UohNJTs5ea9bD6yu/mGv9fPHo5KhJb9i5x2WxCVGXkgPJWAqL0Xh64lfLTdbWrKysjK1bt7Jy5UoKCgrQarX06tWLc8/pT+03lR002AnyOfWku9aM3RgXPAGA/vzH0HUe2hChtxge0X3ReAdhLyuh9PBGV4fTaLz7OhJKhp17UJS6nzui6Rl27gVA36UTGq/ae2i6974Qt4HX1n4gS1lDhybOgGfHDkTedTMAR177APPxtl0ypb4Ktu1izcQp7HxwNuV5+XjHdmTwQ7ejwV7nfmrstPdrfj2b26JzzjkHf39/TCYT//77r6vDadHUvvXr3FPTdh7RkWj9fVHM5Zj2Jzd0aKIBlWzbTWp4HAC9w7xQqVS4u7ufch4ytVqNu7s7GrWKvhFeAGw7aqhzH7/B/fEd3B/FaiXjvc8bJP76yik1UlxuRqNSEe3r36jn8tBq6eDjB0ByYX6jnkucOcVuJ2+9Y65eSSiJ1s7lCSWAX3/9lWnTpvH0009z/vnns3HjRr777jumTp1auY3NZsNms1W5YXDPPffwyy+/UFJSwi233MLll1/OH3/8wezZs/ntt99ccCXClcqOZrL3hvsw7T2INsCPbh+/jn8bexMvS9tB+jtXcPipBEq2/OpIJA26mk5zdxN51w94RNZeQrKl0Ub0QeXujVJahC3zRPLIJ77iBuOO+s3HIkRTy9uwFYCAgfHNrqZyY7Lb7SQlJbFkyZLKziEdOnRg3LhxdOnShaQiABWeWnhiRBDPjw0mxsPRA69fexXPjw3mvcujiW5X9zyH9tJiSr6+FaxmdN3H4THirka+suZHpVbj1dPRmcKQuMTF0TQer+5xqNzdsBYUUZZ21NXhiP8oqejY4V0xerguiiEHAPdzrsN7ygf43v4LHmMdc50afp2FLedw4wUq6i30ukl49+mBzWAkZc7rksitQ1l2Ljvuf5J/L7yWop170Hp70WP2w4xY/isDLhzFdzcn8PiokIqtFa5d9zW3rv+aSF9HKZox3YKJjQh23QWISlqtllGjRqFSqTh48CApKSmuDqnFsptONTeDCrVfONqOg6uvUakqP08MO6TjYHNlt1gw7NxDangsAH3CHIlxvV7P+PHjK8tGBgcH06GDoyNoaGgoo0aNYvz48egrSoT1i/QGYOspEkoAUffdAkDe38sw7k9q0OupS1LF6KQYvwDcNI0/kjo2ILDivNXnoBfNQ8nBQ1gKCtF4euJfj79/hWjJmkVCydvbm7fffptjx45hNpvZuXMn11xzTZVtPv/8cxRFqVa3+IorrmDNmjXk5ORgMBhITEzkySefxMvLqwmvQLiacX8S+264F3N6Bm7hoXT//O1ay6u0RqVp20l/+zIOP92Xkq3zKxJJ19D5+UQi7/wej8jW92Gm0mjRdnSUxDy57J2zHEKJfNEQzVT+RkdCKWhQ2yl3l52dzbJly9i9ezdWqxV/f39GjBjBgAED8PT0pNRi48vNjt7u0waGcm7XUPp1CuG8KEeJjMOFdvp2bH/KZJKiKBh/uh97XirqgEi8r34H1Sl6Q7ZWlWXvWnFCSe3mhnfPrgAYpOxds+Ps2HGqhJJiLa/8HPcYcgPufS9H13kY+rEPou00FMqNlHxzO4pVSk25mkqjoeOzs1C5u1G8fgs5v/7l6pCaHbvFwqEPv2DFsAtJ/+E3AKKuvoxRa/+k8x03VHYkiY0IJqvIUcopQmemT8YhLp1xNbMv6w3A8gMFpOQaXXINorqTS9+tXr2asjIZOXm6yjZ9i+Hrk+cO+++8OI7f6ypz6vw8KdkpHQebK9O+JMwWO0dDYwDHCCUnvV5PSUkJ4JiewplQKigowN/fvzKZBCcSSgdzSikx110m1Kt7FwLPHw1A+tufNNi1nMqJcneBTXK+OP+gKucVzU9eRbm7gIEJbarjqGib2uZdFtGqFG/azr7pM7HkFeDZpRM9vngHz+goV4fVYBS7DeO+lRSt/w7jvpVV6q6Xpm7jyFuXkvJ0P0q2LXAkkoZcS+cX9hB553e4R/RwYeSNr8Z5lCrm1DDuPYC9vNwlcQlRG0VRKkcoBQ7u5+JoGp/RaGTDhg38+++/lJSU4ObmRt++fRk1ahRBQSdqjf+6K4+CUithvm5c1COgcvmwuHZosVNYriIpp/Ya6k5laz6iPPEv0OjwnvoRan3AKfdprbx7jgOg9PBmbMYCF0fTeLyd8yhJJ4JmxVZahumAoySR83O5Nta0zVBuQuUdjCbsRPJJpdbgM+V9VF6B2DJ3Y/rzuUaNWdSPZ0wUkXdXlL5740PMmVkujqj5yFm1jlWjr2DvnFexGoz4J/Ti3D+/JeGtuXi0rz7aaGNKIQD9on0Zv2sV4RdPoH90ACO6BGNTFN5ZdqiJr0DUZeDAgQQEBEjpu9OkKAqmZW9h/HkmKHbcB1zj+BvNL7TKdiq9P97TPsG994W1HuvkEUoyQrJ5Kt66k6P/z955h8dRXm/7ntmqLeq9WbYk996xwQ0wHUJLMIQkJISWAiEkIb9AEkISSPkooSUhIZDQAphego17N66SuySr964t2j7fH6OVLEurLu1Knvu6dIXsvDNz1rs7M+97znmehHF4VWqiDWpSIjr8k9xuNw0NcjIkISGBmJgYVCoVTqeT5ubO/sdxJg3pUTp8Ehwq771LKfX7tyGo1bTs2kdz21xruMlrkjuFsiJHJqHk71AqbGnC7VW8+EKRdrm7886dwlGFcxcloaQwqqn/fDMnv/dzfDY75vmzmPLPJ9HGDa8h4kjSsu9d8u7PoPjxlZT/9WaKH19J3v0Z1H3yJ0qevJrCX83DevBDEEQizruFzN8fI/Wu19AlTwl26COCP6HkKdyN5PUAoEtPQR0VieRyYzs2ci3vCgp9wV5cirO6FkGjJmrO6PcyC4TX6+XYsWOsX7+eiooKBEEgMzOT1atXM378eAShoyq1we7m7cN1ANy2MB6NquPRJCUxnnSdXKG9Oa9neQd30ZfYP5UXnI1XPYImfewn7HpCE5OGNnkKSD5sxzYGO5xhw5+ssCgdSiGF7dgpJI8XTVwM2qSePTNcpzYDoJm4oktHoRiRiOmrfwHAseMfuI7+b1jiVegfiTdfh2n2dHw2O4WP/PmcX9i1l5Tx5bfvZfdNd2DNL0QbE82sJ37D+Z+8TtTc7u/1Lo+HU03yv9vKacmdtv3wwkxUgsCWU3XsLx67BQGjjTOl7/Ly8jh9WpHi7A3J58X+wS9o/fxxAMJW/hDjjU+im3U1kT/fR/ida9FMkTuq1ZNW9ZhMAjBNm4SgVuGua8ClJLNDEsv+XIpSZP+kmW3+SX5qamqQJAmTyYTRaESlUhEXF9e+7WzmtXUp9eajBKBPTSb+xqsAKP3LP5B8PfvUDRan10NJi5wEy44amfWnBIMRs1aLx+ejqKVpRM6p0HckSWrvUIo9x6w3FM5NlISSwqih/vPNHLzwBhrWbQGg+s33KXjwt0huN1EXLWPSc4+jNpuCHOXQ0bLvXcqeuQFPY2dfCE9jGTVv/RTroY/kRNKSr5P52DFS7noVXfK5I/MHoEqejhAWgeS04inPAWR9bfNsf/WaIoegEFr4u5MiZ09HFaYPcjRDjyRJlJeXs379ek6cOIHP5yM2NpZVq1Yxa9YstFptl33+s68Wp0dicnwY54/vLGmnVquZ0TZH21loCXhen7UO62t3gM+DdtZX0J1325C+r9GKqU32biz7KPllTp0l5bgblIXXUOFMubszF5O6w31yMwDa7OXdbtdOuQj9srvk4751H96m8qELVGFACCoVEx75CaJeR8ueA9Su/TjYIQUFj72VE398lk3Lrqbqsw2yJOB3b2XVjo9JX3Ndj5Kr24+W4JJU6AQvS6eO67RtfKyR6+bKSaYn1+fjO8cTdqFEQkICs2fPBmTpu9bW3runz1UkjxPr63fh2PkSCAKGa36L4bL/a78nCKIKTeZSwpbdCYAnb0uvSQBRr8MwWU5WWJR5Xsgheb1YDx1p90+akWTotN2fNEpISGh/zf/f1dVdE4T+hNL+UlufCheSv/t1RKMB+7FTNKzfMrA30UcKm5vwShKROj0x+rBhPZcfQRDau6HymxTZu1DDeqoAV0Mjol5P5OwZwQ5HQWHYURJKCqMCd0MjRb99And9I4WPPkHxn56n+PFnQJKI/+rVZP3hIURd14XK0Yrk81L16r1A4AcnQWtgwu+PkHLnf9AlTRq54EIIQVS1m7Z6Ovko+SWQlImGQmjR4Z80r8dxPUldhiotLS1s376dPXv2YLfbCQsLY+HChVxwwQVERER0u09xg4N1J+UkwHcXJ3a78Hze+EhEfFTbJUoau3qoSD4v1je/j6+5EjEuC9P1f+51AftcocNH6fMx20GgDjcTljUeUGTvQgnrYfmz6E3uzmetxVshd5dpJq4IOM5w6f+hSpuN1NqE9fW727uSFYKHflwqqT+Q/VBKnvgbzvKqIEc0ckiSRMVHn7PpgqvIe/Kv+JwuYs9fxPINa5n+m5+hiejZ7w9g89EKACZGCWjUXf1i7lw+HqNWxbFKC58fUToxQgm/9F1ra6sifRcAn8NCy0u34Mr5SJYhvvmvhC29vdux6nELQGtEstbhrez9Pt4ue3dYueeHGva80zhane3+STOTO/yTJElqTxrFx3d0Lvv/u76+Ho+n8719WpIBjUqg1uamtKl3KXtNdCRJ3/wqAGXPvoTP7R7U++mJvEZZOSE7KnpE5x3+bqg8xUcp5PDL3UUvmI2o7eyflF9ex85jJQH/8svrghGygsKgUBJKCiGPJEkU/fYpvHa5AsxrtVH92loAUu75FuN+/kMEVffGnaMV+8ltXTqTzkZy2fE2KxNMTdb5ALgLdra/5q9Ytx4+NmYXURVGJx3+SYETSoGkLlv2vTtSYfYLl8tFTk4OGzZsoLa2FlEUmTx5MhdffDGpqak9TrL+uacanwRLx4czNdHQ7ZhxKYmkaOwAbD/d3GV764ancJ/aDJowzLe+iKAfO52qg8U4eTmoNLjrinFV5wc7nGGj/ZqvyN6FBJIkYWlb6PMv/AXCfWorIHcci+a4gOMEtRbzzX9D0JvxFO2ldf2fhy5ghQGTsOZazHNn4LO3ytJ3wywxFAq0nMhj143fYf8dP8ZRUUVYShLzXnySxW/9A/OkrD4f52CFfF9blBHZ7fZoo5ZvLpE7l57ddBqnJ/QLS84VVCoVq1atQhAE8vPzKShQvK7OxGepoeWv1+LJ346gMxH+7dfRzbom4HhBre2Yz53c1OvxzWfM8xRCC8v+HMoTMvCoNESFdfZPstls2O12BEFol7kDMJlMGAwGfD4fdXWdF9X1apHpbfOD/X2QvQNI/PoNaGKicJZWULv2kyF4V92T35bQyY4cWbsFpUMpdKlrk7uLOa+z3F1+eR1rXjrE99fmB/xb89IhJamkMOpQEkoKIU/Dus00btwO3rZJaluCIO66K0i549YxWYnuaaoc0nFjGb+PkrtoD5JXrkIyTslG0GrwNDbhKOk5MaegMFI4qmqwF5WCIBC9YE63YwJLXZZT9swNIZVUkiSJoqIi1q9fT35+PpIkkZSUxMUXX8zUqVNRq9U97n+wzMqXpVZUguydFIiIiAiyjQ4AtuZ3ljRz5W2l9Qt5Ydl47eOoE88N/7i+IuqMGCbKizS2MSx7Z57j70pVqpVDAUdRKd5mC4JOi2FyZo9jXafkxcOeupP8qGLGYbxe/r23bnoaV97WQceqMDgEUWT8r3+CqNfTsvcgNW9/FOyQhg1XUzNHHnqMrRfdQP2OvYh6HRN/fDcrtn5I8pUX92s+Uttso6JVLoa7dO6EgONuWZxGvFlHZbODN/cqz7OhRHx8PHPmyM9yivRdB966QpqfuxJvxREEUyzhd76LJvuCXvfTTloJgOtk756P/kIFe95pvDb74AJWGFIs+3M6yd2deV30dyfFxsZ2miMIgtDepdSt7F1a332UAFSGMJLv/AYA5X/7z7B8RyRJak/oZEVFD/nxeyIzMhoBqG+10+hQrjuhgiRJ1O/yJ5Tmd9pW02zH28vSuxeRmmbleqYwulASSgohjSx19yR0M0lrWL9lzPolqCOThnTcWEaVMBnBEA0uO57SQwCIWi3GabIMoPWgssCoEBrUt8ndhU+bhCbc3GV7z1KX8mtVr90XEvJ3DQ0NbN68mQMHDuB0OjGbzSxdupTzzjsPo9HY6/4+SeIfe+RJ45XTokmJ0AUcKwgCC9IMCEgUN3uoapElL7zNlVhfvxskCd2Cm9HP/9rQvLkxhmnaxcDY9lHyy6rZj5/C2+oIcjQKfhki0/TJiBpNwHGSz4f7lOxxoO1DQglAN+sadItuBUmSpS4ttYOOV2Fw6NNTSLvvuwCUPvV3HGUVQY5oaJG8Xopfe4dNS6+k8J+vIXm9JF5+ESu3fsikB76H2tB/74zPDxQgIRCj9TAhKfBiZJhGxT0r5YTTP7cX02QfPvkmhf4zf/58oqOjcTgcbNu2LdjhBB1P2WGan7sSX0MJYkwGEd/7GHXqzD7tq5koJ5Q8xfvwOQJ7ZgJo42PRJiWAz4c19/ig41YYGiRJwnKgI6F0ptwd0K3cnZ+++CjlVthwefrWBRt37eXo0lLwNDZR+e+3+/4m+khdq50mpwOVIDA+ImrIj98TerWatHBZSlyRvQsdrHmncdU1IOp1RM5R/JMUzg2UhJJCyNJJ6q4b2TKvzU7R754OQmTDj2HSBagjk3sYIaCOTsMwqfeKr7GOIIpoMs8DwF3QoWPuX2C0HlZ8lBRCg4Y9B4DA/km9S11KeBpKKf3LdTRs/Cv2/F14W3uedA81DoeDffv2sXnzZhobG1Gr1cyYMYMLL7ywk8Fub2zMa+Z0vQODRmTN3MAyV34mpCSQ2CZ7t7OoBcnrxvranUi2elRJ0zB+5XcDfk9jHb+Pkv34RiTP2FyM1CYnoomLQfJ4sR09Gexwznn8/oW9yd15q44jWWtBE4Y6Y0GPY8/EeNUjqBImIVlqsP73B+eEzFqoE//VqzHPn4Wv1UHhr8aO9F3DvkNsu3wNOQ/8GldDI6bsCSz+74ss+OdTGNJSBnzcHfmyrM2MhMDFFH6umJHIxAQTVqeHF7cVDvicCkPPmdJ3BQUF5OePXWnZ3nCd2kLzX6+Tn8tSZhJxz0eoYjL6vL8qZhxi7ATwefDk9+5L1THPUwoHQwXH6WIcLXZKE2VfyxlJHQkln89Hba1cANLdfCEuLg5BELBardjtnbs0xkXpiDGocXoljlb1rYND1KhJa/P4q/r3W7jrhzbx4u9OGhceiTYI1gvZ7bJ39SN+boXu8XcnRc+fjWoMebsrKPSEklBSCFlaC4o6S92djc9H44Zt2POLRjSukUAQVWgTswNtBSDxlqcQxLHlHTVQ2mXvCna0v+b31FAkkBRChfrdbUadAfyT+iphaT34IVWv3E3Ro0s4eVc4eQ9kUvr0tdS892ta9r2Lqzp/yBfzfD4feXl5rFu3jpKSEgDS09NZvXo12dnZiGLfHyecHh+vfClXIN40J44Ifc/SeCBXM47XylIX2wqasX/2ezxFexH0Zsy3/gNB0/8K8XMF/bg5qEwx+BxW7AW7gx3OsCAIAua2akDrQaWIINj4fS389+FAuP1yd5lLEdS9L6z7EbQGTF//O2jCcJ/ajGPLcwMPVmFIkKXvHkAM02PZf5iatz4MdkiDwlFTx8Ef/h87rvo6zTnHUJtNTHvkZyzfsJa4ZecN6tg+n49jtbLx/LJJgeVe/ahEgfsukiv+395XTkmDIokTSsTFxTF37lwAtm3b1mUx/FzAeeh9LP/6OrhsaLIuIOLOd3v0xAtEh+xd7z5KpllTAWWeF0pYDuRSnpCOR60hMkxFWmTHonp9fT1erxedTkdERESXfbVaLVFRcqfP2V1KgiAwt61Lqa8+SgBRFy/DOG0SvlYH5X9/dSBvKSB5jXIiZ6Tl7vxkRcW0xaF0KIUK9bvkef7ZcncKCmMZJaGkELKEZWYQtep8UAX4mqpEoi68AENWxojGNRJYc/6H/YQsA6Myd55sqqNTSf3BO4TPvy4YoYUk6raEkqdoH5LHCXRURjsKS3A3NQctNgUFAFdjM5YTcuVqzKK53Y7pq4Rl+OI1mGZehjpKro52157GcuB96t5/hLJnrif/p9mcuCucwt+cR8W/7qRhw/PYT23Hax/Y76C6upoNGzaQm5uLx+MhMjKSFStWMH/+fPR6fb+P935uPXU2D/EmDddM79tETK/XMyNWTqafrG2lcsd/ATDe+BSq2PH9juFcQhBFjG2yd7aj64MczfDRUUSgJJSCibupGUehnHQ2zZja41jXqc0AaCat6Pd51AmTMF4jdybaP38cd/G+fh9DYWjRpyaTdt8dAJQ+9SKOkvIgR9R/fC43BS/8i01Lr6DsbTkplnbTV1i142Mm3HFrjxKOfSWnsBqLV40KHxfO6tv9a/GEaJZkRuPxSTy7sWDQMSgMLfPmzSMmJgaHw8HWrVuRulHWGKu0bn8R6+t3gdeNdtY1mL/9KoLeNKBj+b303Kc29fpv6L/n23KPI3mDLwWtAC37cyhMlgtiZyYZu/VPio+PD+g315Ps3UASSoIgtMux1q79eEjvSf4OpezImCE7Zn/wdygVNjfiGSMdwaMZSZKo3+n3T+p7x72Cwmin97JgBYUgIQgCGQ/dR8uXB/Fa7Z1l7wQBlcFAxi/uDV6Aw4S31ULly3cCEH3Jj0i46U+yFFZTJerIJAyTLlA6k85CFT8RwRSHZK3FU7wfTeYSNJER6Men4ygswXr4KFHLlwQ7TIVzmIYvD4IkYczMQBcX2+0Yw6QLUEel4mksp3sfJQF1dCopd/6n/RrgsdThLMvFUZqDs+Sw/L/lR5CcNloLdtN6VkeKJjYDXdpM9Gkz0afPQpc2E218ZrfXlJaWFnbv3k1FheyHodPpmDZtGuPGjeuX+fiZNLV6+O8hWernmwvi0ar7XteSlRJHfHkrNZ4wDhiXcMXMZHQzrhhQHOcapumradnzJrYj6+C63wQ7nGHBPKdD/kbyehGCIEGiANYcuTtJn5GGJqprFbIfyWXDU7gXAG2bd0Z/0S1Ygzt/G65D72F9/S4i7v0C0RA5oGMpDA3xN15F44ZttOw9SOGv/8TkfzyB0I8O1mDStPNLjv6/v2ErkGXlIufMYPrv/o+oIfZCWH+4GIAMs4TZ0PeijHsvzGL36b18cbyWw2XNzEoN/PtSGFn80ndr166lsLCQ/Px8srMDKU2MDSRJwv6/3+PY9AwA+qW3Y7jqN4P6vWsyl4BKi6+xFF9tAar4rIBjDVkTEA1heK02WguKMEzMHPB5FQaPJElY9h+mePFNQGe5O+hIEvUkj52QkMDx48epra3F5/N1Uj+Yk2pEAIoanDTY3UQb+pbcD18wh4ilC2je8SVlz71E1h8e7uc764rL66WouQkIXodSotGESaPF6nZR3NJEZmRw4lCQsRUU4aytH7R/0rlUjKAwNhgdT/gK5yya6CgyHvpRVw8lSSLjoR+hiR5ZE8SRoObtn+OuL0ETN5746x9FEFUYp6wg4rw1GKesUJJJ3SAIgjwJobPsnbmtes2qyCEoBJmGNrm7QP5JIEtdJn49kC9c91KXanMsxikriVl9L8m3v8SER/Yx+W9WMh87Rso9bxJ71f9hmn0l6ug0ANx1RVgPfkjdh7+l7NkbKfjZJE7cGc7pRxZR8dJ3aVj/DM1HN7Fnx1befPNNKioqEASBzMxMLr74YjIyMgacTAJ4fX8trW4fWbF6VmT1bzEsPiaSjDbZu32xV2C4/KEBx3GuYZwudyi1nv4Sr3VsymMYsjMRw/Tti0sKwcF/v+1V7q5gJ3hdiFGpsm/GABAEAeN1f0SMycDXWIbtnR8rk/EgI4gi43/1AKIhDMuBXKrfeC/YIfWKo6yCU/c9zKl7HsRWUIg2NppZTz7K+R+/NuTJJIB9pbL34by0/nVxZCeYuHqW3Mn85Po85bseYsTGxp4z0neS143t7fvak0mGy36B4epHB508FrRGNBMWA+A61bPsnaBWYZoxBeiQWVUIHs6ySlrrmylNavNPSja0b3M4HDQ3yyoJ8fGBZT6joqLQaDS43W4aGxs7bYvQq8mKkxPw+8ts/Yot7YffBUGg4fPNWIfAZ7OwuRGvJBGh0xEXZuh9h2FAEASy2pJIiuxd8Klr606KmjsTlb7vEs5nc/z4CXxKx5nCKEJJKCmEPNGrV3SWvmuTuou5ZEVQ4xoObCe30bhB9gJIuu1FRJ2xlz0U/HTrozRLrlhXJJAUgk39ngMARAeQu/MTPv86Yi77cZfX+yN1KajU6JKnELHoa8Tf8DvSf/QRE58sYdLzDYz7+RYSv/4XIpffjn7CQgRtGJLLjuP0Xhq3/IPcT//F2vU7OJBzFK/XS5S7hiW600yWChEbi5C8noH9AwBlTU4+OS5Pem5fnIjYz8SUfuczTFDLmuXHVBOxepRHmL6iiU5FlzwVJB+24xuDHc6wIKhVmGYqngrBxr+wZ57Vm3+SLOurmbhyUElqUW/GfPNfQaXBdeQTnLteHvCxFIYGXUoi6ffLnfZlz/wTR3FZkCPqHm9rK2XP/Yvc675N0+adCGoVE+74Bqt2fEL6TdcOS2eVzeGisEX+vl80I63f+9+9YgJ6jUhOWQsbT9QOdXgKg2Tu3LnExsbidDrZsmXLmEz6SS47llduw7nvvyCqMN74JGErfzCo6/iZtMve9clHqU3q9rByzw82lv05lMen41ZridCrSI/sWFSvqakBICIiokepbEEQ2hNO3cnezWuTvTtQ2nfZOwDDpExiLr8QgLKnXxz07/JMubuh+t4PBH93lD8eheBRv6tN7m5J93J38REGVPSeKNpZ2My6devweAY+31ZQGEkUyTuFkKeT9J3FNmal7nyuVir/+R0AIpffjmnahUGOaHThTyh5Sg4guVsRNGEd+tpHT+JzuRC12p4OoaAwLHjsdprbZKD6YtTpsciScOYFNxI+79ohk7pUGaMwTl6GcfKy9tcknxdXTQHVpw6wN6+KWpc8AdQ568ksXktswyEEoH3qptajSpiIOmkqquRpqBOnoEqaimjsXWrhpb3V+CRYmG5iVnL/kuXOA2tx73mFcZOMRKvG0+DVsafEykUTI/t1nHMZ4/TVOCuOYc1dR/iCG4IdzrBgmj2dlj0HsB48QsJXrw52OOccPrcH29ETQMdCXyDa/ZPaFg8HgzptNobLH8b+0S+xffxr1BkLUSf3fH6F4SXu+itpWL+Vlj0HOP2rPzHln0+EjAylJEk0rNtC6ZN/w1UlL3SGL5rLuJ9+j4wli4b13JtyCvEgYlR5mJuV3O/948w6bl2czovbinh6QwHLJsaiCeQ1qzDi+KXv3nnnHYqKisjLy2PixInBDmvI8NkasPzrVjwl+0EThvmWv6GdunpIz6GZtBI++Q3u07uQ3A4ETeAEhKldiUIpHAw2lgM5FKXIEoUzzvJP8ieUepK785OQkEB5eTk1NTVMndrZh3Feqok3D9ZxoNyKT5L6VZiWes9tNKzbQsveg7Ts2k/Ekt7nY4HwdwQFS+7OT7Y/odRYH9Q4znUkSaJ+V5sSSYB5flZKLA+uSuZ3G6tQ4eNP10xAq+5Yit9wqpH3cus55Igl+kQFDsfHXHbZZeh0A+92UlAYCZSEksKoQJa+u5+SPz7LuJ/9YExK3dW+/wiu6jzUkckkfO1PwQ5n1CHGTkAIT0RqqcJTtA9N9gXox6WijorA09iM7XherxXTCgrDQeP+HCSPB31yImGpPS8gST4f1pxPAYhedRfGqauGNTaX28OX+TUcOdaEJOlQqVTMmTOHmZMm4K1aQe3R7Xgqj+OtPIqn6ji47HjLc/CW53Q6jhiRhKotuaROmoYqaQqquEwElaxxfqTSxq4iC6IA31mU2Gtcks+Lp3A3vpYaJK8T27sPAhCfEE9Go4WGVh07C1uUhFI/ME5fTcO6p7AdXYckSUGtqhwu/D5KSldqcLCfzMfncKKKMKPPCNx94W0sxVebD6IKTdb5Q3Ju/fnfxZ2/Dffx9Vheu5PIH36OoHR5Bw1BEBj/qwfIvfF2rIeOUPX6uyTdemOww8Ked5riPzyLZd9hALTJiaQ/cDdRK5eOyDVxy/EqAKbEqDv5g/SHby5J590DFZQ1tvL2vnJuXtT/TieF4SMmJob58+ezd+9etm/fTkpKCkbj6L8WeRvLsPxzDd6aPISwSMzffhXNuIEvygdClTAZMSIJX3Ml7sI9aCcuDzjWNGMKCALOskpcdQ1oYxUfmWBhOZBD0ZxrAZh5htydJEl98k/y4+9QamhowOVyoT2jGHRygoEwjUiLw0tBnYPsuLA+x6dLSST+a1dT/epaSp9+kfDFcwfchervCMoKsm/RhIhoBKC21U6Tw0FkD91fCsOH7XQxzupaRJ2WqLmzAo7LLZFlHCdFwoqZnaWez5uShj4sjzf2lrHZkkRYcSnO99/niiuuwGTqnzyugsJIopQ0KYwaYi5ZwZwN7xC9OvCD5WiltXA/9Z/9GYCkb76AyhgZ3IBGIbKPUmfZO0EQ2qukFR8lhWBxpn9SbwtWjsJ9eFtqEMPCMUwcmoXW7pAkiWPHjvH666+Tm5uLJEmMHz+em266iQULFqALj8Ew8Xz0S27DdP0fifj+J0T/Jp/In+3GdOs/Cbvox2inX44YkwEgT/xPbsSx+Vmsb9xN8xMraHgok6anLqLlzR/yt/W5AFySGUZ6VM/VVs7cT2h6bD4tf7se6xt3Y3vrPvA4UCVOIfWCm8jQyf1S+8ustLq9w/ZvNNYwTl6GoNbirivGVZ0X7HCGBdOMKaAScVVW42zrPFAYOaxtskOmmVN7XKhxn9wMgDptLmJY/7zUAiEIAqavPiUvRNbmY3v/50NyXIWBo0tOIP3+uwAoe/YlWotKgxaLp8VC0ePPcORrd2LZdxhBpyXl7m8y892XiF51/ogl2HOqnAAsyRz4QqRBq+auFbJPyYvbCrE43EMSm8LQMXv27DElfeepOk7L81fhrclDjEgm/J4PhiWZBG3zuXbZu54letVmE2FZGQBYcxQfpWDhqq7FVlFDaWKbf1JSRwK1ubkZp9OJSqUiOrr3657BYMBsNgMdnU1+1KLA7DaFg/1l/ZO9A0j+zi2oTEbsJ/Op/1/vkordUd9qp9HRiigIjI8IboGzQaMhxRwOQH6T0qUULPzdSb35J+1r8/5amtn1eyMIAvdfnM2qyXH4EFhvSSW/xsp7773XxU9MQSGUUBJKCgpBRvK4qXjpO+DzEr7oa5jnKjI9A6U7HyXzLEUOQSG4tPsnLZ7X61jL4Y8BuZtEUA+PRGNVVRVr165ly5YtOBwOoqKiuPLKK7n00ksJDw8PuJ8giqhiMtDNuALD6p9g/sZLRP1sN9G/ySf8ex9jvO6P6M77JuqMhQg6E3hdeCuOsPVULfkOM3qfnSvWX0HDozNp+cdN2D75Dc4D7+CpPIbkcQFyMsn6n9vxNVd2Ob+36gTa4u2kmlWEiy5cXol9/dRRP5cRdUbCsuVrpC13XZCjGR5URgOGiZmAcs0PBv7CjV79k/I2A0Mjd3cmojEG05rnQRBx7n8L5/63h/T4Cv0n7rrLCT9vPpLTReEv/4jkHdkiAMnrpeadj8m5+hvUvPk++HxEXXQBM997mZQ7v4E4CPPs/lJc3UitSw1IXDJnQq/je+Ka2UmMjzXQ3Orhn9uLhyZAhSHDL30niiLFxcWcOnUq2CENGHfhHlpe+Aq+5kpUCRMJ/97HqBMmDes5NZNWyuc+1XcfJeWeHzxa9udQEZ+OW6MjXK/qVDjmTwrFxcWh6qPsqb+T6eyEEsDcNLlbYyAJJU1UBEnf+hogFzn4XK5+H8Mvd5dujkCvDr7YU3Zbl5Q/LoWRp35nm39SD7L2ZbVNVDjk78vl87q//6tEgd9+ZSqz0yJwSSLrLOlUNzt4//33u/UUU1AIBZSEkoJCkKn79I84Sw6jMsWQ+PW/BDucUU27j1LpQSSnXAVimu2XQDo66isEFUYfPpebxv2ytE7Mot4TStZDnwBgnn3lkMdit9vZuHEj7733HrW1tWi1WpYsWcKNN95IWtrAJXMEvQnNuPnoF38D07V/IOKeD4n6TR6RD+5Bf+vLvJ16PwBXOdcR6WtCstTgPrUZx5bnsb75fZqfXEXDw5k0PrES65vfBwL/Tls/+iUJ8XHtXUo7Cy0DjvtcxDRd9jqwHhmbCSUA8xnXfIWRQ5KkdqlB/32323FeD+68bUDHouFQoplwHmEX/RgA63s/w1tbMOTnUOg7svTdj1GZjFhzjlH16toRO7fl0FGOfv17FP32STxNLYRNGMekv/2J7D//Gl1y79JLQ83nBwsBSNJ7SYoJXLzRF9SiyH0XyX4lb+4to6KpddDxKQwtfuk7gO3bt2O1jr4CGNfR/9Hy4teQWptRZywk/O4PUEX23/urv2iyloEg4q0+hbepvMexZkWJIuhYDuRQlNzmn5Ro6ORt1B+5Oz9+2bvq6uouc/d5qXJC6XiVHZur/wUKCbdchyYuBldFFTXvfNzv/f2dQNlB9k/ykx0VA3TI8CmMLLJ/UltCacnCgOM+2X8agES9h3EJgTvb9BoVT3x1JhkxBixeFV/Yx9Fid/Hhhx9SXKwUjyiEHkpCSUEhiDjLj1H3wW8ASLzladTh8UGOaHQjRqcjRqWCz4O7aC8AxqkTETQaPI1NOEt6npQoKAw1TTlH8TmcaKOjME3suSLZ3ViBo/gACAKmmZcNWQxer5dDhw7x+uuvc/LkSQAmT57MmjVrmDVrVp8rBvuDIAioosfxP2k+NR4DMQY1N3/v50Q/mk/49z/FeP2f0S/5NurxixH04eB146s6Du6eFsUkfM0VxNBEhlZOJO0pseDy+oY8/rGKsS2hZD+xCckzNmWS/MkM60GlWnkkcVXW4K6tB5WIcVrg6nVP6UEkRwtCWCTq1MBa84Mh7ML7UGcuBZcdy2t3Irkdw3Iehb6hS4wn/YG7ASh77iVaTw/vooirtp6Chx7n+Ld+iP14HiqTkfSf3MO0//6diEVzh/XcPbH7tLzgNyup774fPXF+VgwLMqJweX08t+n0kBxTYWiZM2cOcXFxuFyuUSd959jzKpZ/fxs8DjRTVhN++5uIhpGR+BINkajT5d+qXyI1EP57vu14Hj5n/ztOFAZPp4RScofcncfjob5eTsD4k0R9ITY2FlEUaW1txWLpXDiWFK4lOVyLV4LcClu/Y1WFhZFy5zcAqHjxVdyW/iV6/Z1AWZEx/T73cOD3cTrd1IjHp8yHRhp7USmOqhpErYaouTMDjtuRL39v5qUYAo7xE2nQ8OzNs4g1aalxqtnmmoDT7eGzzz5rn8crKIQKSkJJQSFISD4vFS/djuRxYZp1BeHn3RzskEY93fkoiTotxqkTAcWoXWHkadi9H4DohXN69WiwHv4UgLAJC4csuVxaWspbb73Frl27cLvdxMfHc91117Fy5UoMht4fageDxeHhzQO1AHxjQTx6tYigNaJJn4t+0dcxfuX3RNz9PlGPnCTy5/vQL7u7T8eNkZpJ0DgxiG5a3T4Olfd/Qnmuok+fjcoci89hxV6wO9jhDAvm2XK1sj3vNF6r8t0YKfz+ScZJ2ajCAhtD+yWMNNkXIIhDn8wGEEQV5pueQzBG4604gv3TR4flPAp9J/aaS4lYuhDJ5eb0L/+I5Bl66Tuf203ly/8l55pvUv/xehAE4q69jJkfvkLiLdcjaoInT+T1+TjRKCcTVkxJGpJjCoLQ3qX02ZFqjlW0DMlxFYYOURTbpe9KSkpGxWKgJEnYv3gC29oHQPKhW3Az5m+8hKAd3mfGs9FM9Mve9eyjpEtNQh0dieR2Yzs2eqUFRyvuhkZsRWWUJslFczPPSCjV1tbi8/kwGAyYTKY+H1OtVhMbGwsEkL1LHbiPEkDcVy5DPy4VT2MzBc//q8/7ub1eilqagNDpUEoymTGoNbh8XkotzcEO55yjbqdcwBw5Z2bAZ1+Hy83JJvm/V89M7dNxkyPDePqmWRi0Kk5bVRwUJuLzSWzcuJGDBw+OquIEhbGNklBSUAgSDV88S2v+LkS9maRvvjBihsBjnXbZuzN8lEyzFTkEheBQv6ctobS4d/Nia5t/kmnWFYM+b0tLC5999hkff/wxTU1NhIWFsXLlSq677rp+yU4MhjcO1mF1+ciI1nFhdmTAcYIgoIpKRTvloj4dVxeVQHR0FBlav+ydsojWVwRRxDjtYgBsY1T2TpsQhy4lCXw+rDnHgx3OOYOlLaHkv98Gwn1qC9CxWDhciBGJmL72DACOHf/EdeSzYT2fQs8IgsD4X96PymTEduQElf95a0iP37RjL0duuJ3Sp/6Oz96KccYUpv7nWcb/6gE00cE1TgfYc6IMh0+FRvCxfMa4ITvulCQzV8xIBODJL/KVRaYQJDo6moULZSmkHTt2hLT0neTzYnv/57Su+yMAYavuw3jD/0NQjXwytt1HKW8bktcTcJwgCB2yd4eVed5IYzmQS0VcOi6NDrNOxbhu/JPi4+P7vc5xpuzd2fhl7/aXDaxoSFCrSP3h7QCc/tu/cVTX9mm/opYmPD4fZq2WeIOx9x1GAFEQyIpSfJSCRf2ufQDELFkQcMyGQ4W4JRGD6GXJ1PQ+H3tKkpk/3jAdlSBwsE6gxDQVgN27d7Nz507lfq8QEigJJQWFIOCqLaTm7f8DIOGmP6GJGbh/iUJn1P6EUtlhfK3yQrPfU0OZaCiMJJLXS8PegwDE9CKz43M5sB79AgDTIPyT3G43e/fu5c0336SoqAhBEJg5cyZr1qxh8uTJI5a4rmxx8dFReWJz++JEVGLv51WPX4wYkQQEGisgRiSjHr+YhISE9oTSrmILXp/yUN1XzgUfJX9Sw3IoN8iRnDv4DdH9Bund4bM34SmVr4maiSuGPSbt5AvbOx+tb/8Ib2PZsJ9TITDahDjSf/o9AMqff4XWgqJBH9NRWsGp+x7m1Pd+jqO4DE1MFON/81OmvvIXTNMnD/r4Q8UXuaUAZEVI6LSaIT32PSsnoFWJ7C9uYmte/ZAeW2FomDVrFvHx8bhcLjZv3hySC4GS24H1tTtx7noZBAHDNb/DcOmDQSt4VKfOQjBEITla8JQe6HGsSZnnBQ3L/jPk7pIG75/kx79PXV0dXm/njtaZyUZUgjzXqGgZmMxh1KrzMc6Ygre1lVNPvNCnffLbEjbZkTEhVQjsl73Lb1Su/yNJJ/+k8wIXjm48VgnAtFgVKrF/y+9LMmN46EpZRnpdsRdH0hwAcnJy2LBhQ5ffhoLCSKMklBQURhhJkqj81x1ILjuGycuJXP7dYIc0plBFpiDGZIDkw1MoSzr5F7haTxfjaVa6GRRGhpYTeXhaLKiMBsJ7Wdiyn9yC5LShjkxGnz673+eSJIn8/HzefPNN9u/fj9frJSUlha9+9assXboUnU7X+0GGkJf3VuPxScxNNbZXEvaGIKowXP1b//87eysAhqsfRRBVJCQkkKSxoxO8tDi8HK2yD13wYxx/h5Kj8Eu81rFZzWie07a4pHSljgheeyv2U7KHi7mHDiV3/laQfKgSJo6IsTuA4dKfo06bg9TahPX1u5G8Y9M7bLQQe9VqIi5YhOR2c/rhgUvfeVtbKX3mn+Re922aNu9EUKtI/MaNzHj/ZeKuvgShn4s2w82BNmnWheMihvzYSRF6blkkF6Y9/UW+4qMRgvil71QqFaWlpZw4cSLYIXXC19pCyz9vxpX7Mai0mG7+K2FLvxPUmARRhSZ7OQDuk5t6HOuf51kOHQ3JZN1YxnIgl6KUbABmJHV07djtdqxWK4Ig9Ms/yU94eDh6vR6v19vuw+THoFUxNVGWYDwwQNk7QRBIu1degyl5bS3WPhQ45DW1+SeFiNydn+wo2c8pv2lsPtOHKvbiUhwV1QgaNVHzAnuCHqpyArBsYuyAznPN7GTuWj4egNeO2AmfdB6iKJKXl8dnn32Gy6V4xykEj9B62lZQOAdo2vYvbEe/QNDoSf72P0Ju0jsWONtHSRMdiX6crFlrPXwsaHEpnFu0+yfNn42o7lkuxHr4EwBMsy7vd9VbfX09H374IevXr8dqtWI2m7nkkku46qqriI4e+UnP8Wo7W0+3IAC3L0rs1766GVdguvUfiBGd9xMjkjDd+g90M2Q5wKioKHRaDePaupR2KLJ3fUYTnYIuZRpIErZjG4IdzrDQXq2ccxyfO7BUjsLQYDtyAnw+tEnxaBPiAo7zm6trsleMTGCAoJYXRwW9GU/xl7Su//OInVuhK4IgMP7h+1GZTdiOnaTy5Tf7tb8kSdT/byO5X7mNyn++juR2E754HtPfepH0++9Cbe67T8dI0WixU2aT/cJWz84YlnN8a+k4Ig0aiurtvH+wcljOoTA4oqKi2qXvdu7cicViCXJEMu6mSlr+ei2e0zsRdCbCv/M6ulnXBDssoEP2znWq54SScUo2gkaDp7EJZ2nFSISmAHharFjyCylJlBe7z/RP8ncnRUdHo9H0vyvzzERUd7J3c9uK1Q6UDlxCMnz+LOIvWobk9XLi8b/0Ot7fAeTvCAoVMiNlWddqu41mpyPI0Zw7+OXuImfPQG0I63ZMbmEVjW41IhKXz88a8Lm+e0EG185JwifBc3uamDB/JWq1mtLSUj788EPsdqWwUiE4KCvZCgojiLuxguo37gcg7rpH0SYM/MaiEJiOhNLO9tf8C4yWNlkeBYXhpn6PLNERvXhej+MkScJySPZPMvdD7s7pdLJ9+3befvttKioqUKlUzJ8/n5tuuokJEyYERY5BkiT+sVue+F00MZLxMd0blPaEbsYVRP58H+F3rsW05gXC71xL5M+/bE8mQcdEM0MnL8jsKGrBp1Sl9hl/l9JYlb0LmzAOldmEz+HAfqog2OGMeSx9kLuTJAl3Xpt/0qQVIxFWO6qYcRhv+H8AtG76C668rSN6foXOaONjGfez7wNQ/td/Y8873af97KcKOHH7/RQ8+Dtc1bVokxPJfuIRJr3wB8ImDJ0v0VCz/uBpfAhEqj1MSe9/pX5fMOvV3LFMXtT965bT2JxKIj0UmTlzJgkJCSEjfeesyqPo0SV4K48imOIIv+s9NFnnBzWmM9G2SaN6yw7js9YFHCfqtBinTgSUed5IYj10hMqYVFxaPSadiozoDjUEfxJoIN1Jfvyyd34vpjPxqx8cqrDhGYTs9ZT/uw8EgcqP19F4ICfguAZHK/WOVgQgM8QSSkaNlhSTGVC6lEaS+p2y3F1sD/5Jnx0sAmCcyUuU2TDgcwmCwM8vn8T5WTE4PD4e21TN3GWXoNfrqa2t5f3336elRSmuVBh5lISSgsIIIUkSVf++B5+9Gf34BcRccl+wQxqz+BNK3soj+OyNQIcMj6KvrTASSJJEw+42o85FPSeUXJUncNcWIqi1GKde2OuxfT4fx44d4/XXXyc3NxdJkpgwYQJr1qxhwYIFqHvphhpOdhZZOFZtR6cS+MaCgU8iBVGFJnMpujnXoslciiCquoxJSEggRWNHK/qot3nIq20dTOjnFMY2HyXbkXVBX9AaDgRRbPdRsh5UFpeGG/991e9X2B3emjx8TeWg1qEZv3ikQmtHN/NqdIu+AZKE9Y3v4bP0zYRbYXiIueIiIlcsQfJ4OP3LP+Jze6j/fDMHL7yBhnVbOo31NLdQ9NhfOHLTXVj25yDqdaTc8y1mvvsSUavODykvi+7Ynid/16bHD6130tlcPzeZ9OgwGmxuXtlZMqznUhgYoiiycuVKVCoVZWVlHD9+PGixtBbuo+i3S3HXFSHGZBDxvY9Rp8wIWjzdIYYnoEqSO6rdvRQC+AsalHneyNGy/3CHf1Jih3+Sz+ejtla+7g3EP8mPPxnV3NxMa2vnZ/zMWD0RehWtbh/HqwfenRE+ZSKpN14NwPHfPhHwmdjvn5QWHoE+iPOsQGRFtsneNSoJpZFAkiTq2hJKMT0klPYUy0mexePCB31OtSjy+PXTmJpkpqnVzSPry1i2+grMZjPNzc2899571NUFTrwrKAwHSkJJIaSx2+00NjYG/BtN7Z0te9/GcuADUGlI/s4/EVSh9zAyVhDDExDjskCS8Jxu81HySyAdOYHPrXgoKAwvtsISnLX1iFoNkXN6nqBbDslyd4YpK7G5JWprawP+nT59mnfffZctW7bgcDiIioriqquu4pJLLsFsNo/EWwuIxyfxr71yReJ1M2OINQ7v4ll8fDxqQSJN7Ze9Cw35mNGAcfIyBLUWd30Jruq8YIczLJiVrtQRQfL52qVkTbOmBhznPrUZAM34xQjagVdpDgbj1Y+gSpyMZK3F+ub3kRSvmaAhCAIZv7gPR3oyZTYrB155gwMvv0a1Uc/+f71KUWUFpZZm8t77mJxrvknNfz8An4/oi5cz491/kXLHrYj6kfUGHChHauRnzvOzA8tBDgUalcgPL5QXd1/dXUJNi3NYz6cwMKKiopg9ezYAO3bsoKioqMuz3nDL4Vlz11H02Aq8llr04+YScc9HqGJCs8vPL3vn7kX2rqNwUJE2Hylk/6S2hNIZcneNjY243W60Wi1RUVHd7iv5vLgLduA8+B7ugh1Ivq5+ejqdjsjISKBrl5IoCMzxy94N0EfJz6SffB9Rp6V+1z5qNm7vdkxem9xddoh1J/nJbvN1ylMSSiNCa2k5jooqBLWaqPnd+yc1WuwUW+WCyMvmZrS/Lvm82I5vpnnXG9iOb+72ux8Ig1bN0zfNIjVKT1mjg199VsylV15NdHQ0drudDz74gPLy8kG9NwWF/qCsaCuELHa7nXXr1uHrYcIviiKrV6/GYAjO4kRf8VjrqfqPLO8Re9X/oU8LrQqwsYgmcwnO2nzcBTvQTr8MfUYa6shwPE0t2E/kY5oxJdghKoxh/N1JkbNnoOpl0ct6WJa7E6dewRtvvIHX2/uDpVarZcGCBUybNg2Vqmv3TjD49FgD5c0uIsNU3DBrYMaj/cFgMGA2m8lwWihwhbOjsIXbFsaHfLV6KCDqjIRNPB/7sY3YctehS5wY7JCGHPOctiKCQ0eQJEn5XgwTraeL8VptiGF6DNmZAcf5FwM1E5ePVGhdEDRhmG75G81/uRR33hYcW54jbOUPghbPuY4t3MhHD9+B1//bnD2+fdtn5bIMnphg5ApRIDYzg3E/+z7hC+cEI9QBc6y4hiaP7J9w8ZwJw36+lZNimZ0WwaHSZp7ffJpfX60864YaFouFQ4cOAeDxePjss8+6jFGpVKxZs2ZYCoWad71O+YvfBK8H47SLSP3Bu9S1hG6BpnbiChybn8V1cjOSzxfQe9hf0NBaUISnxYo6PPT81MYSXnsrlhP5FC+6DYAZSR1rMX65u7i4uG6fvZy5n2D/8CF8zR1+b2JEEoarf9tJ3hrkDqempiaqq6sZN65z0nNeqonN+c3sL7PyzQUD74QypCaRcdsaTv/1FY7/7gniVyxBOGtu5ZeSy46KGfB5hhN/XKebG/D6fKgUj+5hxd+dFDlnOuoA65Cf7svHh0CUxsP0DNkbuGXfu1S9ei+exrL2ceqoVBK//jTh86/r07ljTFqeWTOb2/61n2OVFn7zv0Ieu+Ya1n/+PyorK/n444+56KKLyMwM/EyuoDBUKFcahZDF6XT2mEwCuaXa6Qz9Crzq1+7Da6lFlzKNuKv+L9jhnBN0+CjtAORqWL8cglKxrjDc1O/eD/Tun+S1NWE/JVfDqTIv6FMyKSMjgzVr1jBz5syQSSbZXF5e2y/LW3x9XjwG7cjElZCQQJrWhlqQqGhxUdwY+veDUMHUJns3Vn2UjFMnIajVuOsacJYpJvXDhfWQLC9kmjEFQd39715yO3C3dQtrJq4csdi6Q50wCeNXfgeA/fPHcRd9GdR4zmVsHk9HMikAPq2GqO99i+n//fuoSyYBrD9cBECqcXD+CX1FEAR+dLHcMfDR4UpOVSmdu6GGw+Ho9VnP6/XicDiG/Nz1/3uS8r/eAl4P4YtuIv3+T1CFBbe7vTfUGQtBa0Cy1uKtDCxnp4mJRpeWLMua5ipdSsONNecYlVFJsn+SVmR8dIdnqr+bqDu5O2fuJ1j/c3unZBKAr7kK639ux5n7SafXz/RROluObm6q3BWVX+ug2TE437jsH3wXdbgZy/E8yt7tHIPH56OwWZbQzwrRDqVkk5kwtRqn10upRfHSGW7qd7XJ2i+eH3DM1lOy/NzsRLmwtGXfu5Q9c0OnZBKAp7GcsmduoGXfu30+/7gYA0/eNBOdWmR7Xj1PbCjiiiuuYPz48fh8PtatW8eRI8p6l8LwoySUFBSGGcvhT2ne+SoIIsnfeQlBrQ12SOcEmswlAHirjrcbubZ7ahxS9LUVhpeGPQeAnh80AaxHPgefF23yFDTRKX069vz580OuK/O/B+tocXpJi9Ry6eTu5S2Gg/j4eDSCRJpO1lbfUahMovqK30fJfmITkscV5GiGHlGvazfptipFBMOG36/CX7DRHe6iveBuRQhPRJU4eaRCC4hu/hq0s68Dnxfr63fhszcFOySFHoi9dFXAZGWos7e4GYC5KcZeRg4dM1IiuHhqPBLw1IaCETuvQugiSRLV//0Z1W/cD0D06ntJueu1UTEnFdRaNFkXAB3SqYFQfJRGDsuBHArb/JOmJxlRiXJxgMvloqFB7ubxeyD5kXxe7B8+BHTnUyS/Zv/w4U4SYNHR0ajValwuF01NTZ32iDZoGB+tQwIOltkG9X600ZFk/+B2AE7+4Rm8jo4CteKWJtw+HyaNlkRjaHa+iYJAZluyK7+pPsjRjH3qe/FP8vp8HKmTv8erpiYh+bxUvXovPX33q167r1/yd7NSI3jsummIArx3sIJXdpexevVqpk6VuzW3bdvG3r17x6RXrkLooCSUFBSGEW9rC5Uv3wlA9CX3EZa5MMgRnTuIpjhUCZMAcJ/eBXR4avglkBQUhoPWiirsJWUgigF1lf1Y2/yTzLOvHInQhoUaq4v3j8iTl28vSmyfVI4EsbGxiKJIulpetFMSSn1HnzYLlTkOn8OKPX93sMMZFvzeeZaDSkJpuLD4O5R68k86KcvdaScuDwnpQUEQMF73B8SYDHxN5djeuV95JggCUrcLK2MHp8tNfrP8fb9weuqInvsHqzJRiwK7Tzews0BZXDyXkTxuKl78FvWf/hGA+K8+TsLNTwaUjgtFtBNXAOA6ubHHcWalcHDEsOzPobgtoXSm3J2/O8lsNncpfvMU7u7SmdQZCV9zBZ7CjmdSURSJi5P95/xSemcyr81Haf8gfZQAxn/nFvRJCbSWV1L0yn/bX/f7EmVFRofEM0wg/P5O+YqP0rBiLy2ntawCQa0mekH3ndM7j5XQ6lOhEXxcOHs89pPbunQmdUbC01CK/eS2fsWyYlIcP7lELp57fnMhH+dWs2zZMhYskBNd+/fvZ8uWLb2qPikoDJTR8yShoDAKqXnrQTwNZWjiJhB/3aPBDuec42zZO+PUSQgaDe76RkUCSWHY8HcnRUyfjMYcuJJN8nmx5nwKgGnWFQHHhTqv7K3B7ZWYmWRgUfrIVu6p1WpiY2NJ11oRBShscFLRrMje9QVBFDFOvxgA2xiVvTvTR0lh6HE3NOIslc1/TTN76FBqqyoPttzdmYh6M+Zb/gYqDa4jn+Lc9a9gh3TO4Syt6NM4Rx/HhRpbcotxSyJ60cviKWkjeu7UqDC+tkBOYj31RT5e39hO3il0j89po/Tpa2je8W8QVSTf/i9ir/hZSC+Kd4dm0ioAPEVf4nMElnFs71DKPY7k6Xulv0L/8DldNB85SXGS7NEyM7mjA9Of9OlO7s7XUtO345817kzZu7OZmybPOw6UWQddGKIK0zPpgXsAyHv6b7hb5O+av+MnKyo05e78ZLX5KOU1KQml4cQvdxc5axpqY/eKIetySgGYFAl6rQZPU9/Wnfo67ky+tiCVby1JB+C3H59g1+kG5s+fz7JlyxAEgePHj/P555/j8QxOFlJBoTuUhJKCwjBhO7GFxo0vAJD8nX8g6kJLoupcQJ11PgCetoSSqNNinJINKAuMCsNH/W75QTN6Uc/+Sa2n9+K11iMaIjFkLRmJ0Iac/LpWNubL3UG3L04MyiJFfHw8etHHOIMbgJ1FimdEXxnrPkr+xaXW08W4m5qDHM3Yw18FHpaZEdAA3ddchbfqOAgCmuwLRjK8XlGnzsJwxS8BsH30azwVynPBSKJLS+7TOH0fx4Uam4/LC0OTo4SgGKTffkEGZr2a/BobH+UoRVSjjcF6BHssdRT/4UKsOZ8haMNIu/cDIi/41tAEN8KoYsYhxk4An6d9TtcdYZkZqExGfK0O7PmnRzDCcwvb0RNUhsfj1IVhPMM/SZKk9qTP2XJ3AGJ419e64+xx/mPV19fjdrs7bZuWYECnEmiweygaAh/V1K9egyl7Au7GZvKfewno6PjJbkvYhCp+f6cqmxWLSymuGy7a5e7OCyxrv79clmJfmiV/JurIpD4du6/jzub7qzK5bHoCHp/ET985wvFKC9OmTWP16tWoVCqKior46KOPRoX3vMLoQkkoKSgMAz5XK5UvyTq8kSvuwDgldKpyzyU0E84DQcBbk4fPIj/gtksgKQklhWGiwz+p54SS9dDHAJhmXIKg1gx7XEONJEn8Y7dcibgyK4LsuLCgxOGvXEwVZcNcRfau7xinXgSAo2gfHuvYk0XSREeiz5A7AxRPhaHH4vdPmh24O8mVtwUAVcpMRGPoLcbol96OZspq8LqwvHonknNwPgwKfUdgdHVJ9JfDlfKC0uIJwalqjwjTcPv5GQC8sPk0rS6lY2M0sX79eqqqqga0r6uumKLfnU9rwR5UxmjG/WwD5tmjtxMe+iZ7J4gippmy/Koiezd8WPbnUtQmdzct0dAudW2xWGhtbUUURWJjY7vspx6/GDEiCQJe+wXEiGTU4xd3etVkMmE0GpEkidra2k7btGqRGW0dUvtLBy97J6rVTPm/+wA4/ff/UFVSSm2rHQGYEDFyHrEDwaTVktTm8aTI3g0f/g6lmPO6908qrm6kyqEG4Ip5EwAwTLoAdVTP0rfq6DQMkwZWeCUKAr++egoLMqKwu7zc++ZhKppamTBhAldeeSVarZaqqiree+89rNbB/04UFPwoCSUFhWGg9r1f4arORx2VQsLX/hjscM5ZREMUqiR5octdsBNQ9LUVhhdXQxOWk/kARC+c2+NYy2HZP8k0a3T6J31ZauVwhQ2NSuCbC/pWdTgchIeHo9frSde0IAAnalqps7l73U8BNNEp6FKmgSRhO7oh2OEMCx2yd8o1f6ixHj4GdHSCdYdf7k4bQnJ3ZyIIAqavPoUYkYyvrgDb+w8GOySFMUBlfQuVDhUAl8wZH7Q4vrYgleRIPbUWF6/uLglaHAr9x+Fw8MEHH5Cbm9svKS9HaS5Fjy7BVXkSdXQaGQ9tx5B13jBGOjJoJsn3EPfJTT3+e/j9/CzKPX/YaDmQQ1GKrPhxptydvzspNjYWtVrdZT9BVGG4+rcQ0D9PwnD1owiiqsuWnmTv/D5KB4bARwkg4ZKVRC2Yjc/hYNvrbwOQag7HoAn94r9sRfZuWLGXVmAvKUNQqYhe2L1/0qf75e7IZL2H1LhIQP7uJ3796R6PHbH01m6/+31FoxL5840zyIo3Umd18f3XD9Pc6iY5OZmvfOUrGAwGGhsbee+992hpUYovFYYGJaGkELLodDrEXiQiRFFEp9ONUER9o7VwH/Wf/T8Akr75AipDRJAjOrc520fJX0ndWlCEp0WRxlIYWhr2yt1JpuwJ6GIDVyW760txlhwGQcA081IA9Ho9KlXPD5IqlQq9Xj90AQ8Qr6+jO+ma6dEkmLVBi0UQBOLj4zGIXtLN8iR1V5HyoNxXjG2yd7aj64McyfBgmtXWlXpQ6UodSnwuF7ZjJwEwB0goST4f7rYOJc2kFSMVWr8RjdGYbn4eBBHn/rdp2v7vYId0TmBUq1H3IpOqFgSM3SxMhjqfHzwNCMRpPYxLCF5Vu1Yt8oOVss/JyztLqLMqcjfBpq/Peunp6fh8PrZv384XX3yBy+Xq9di2k9so+v0FeJoq0KVMY/zDO9ElTxmq0IOKJnMJqLT4Gkvx1QWWs/MrUShdycODz+2h5fBRSpLkzosZSX3zT/Kjm3EF6rTABXeqAF0cftk7/znOZF6bj9KRKjsOj6+Xd9A7giAw5Rf3A3CivAwIfbk7P9ltPk/5jWNPdSAUqN8ly91FzJqG2mTsdsyOAlkxY35q5+1hExZ1O17QyeMav3gWZ8WJQcVn1qt5Zs0sEsJ1FNXb+dF/c3B6vMTExHDttdcSERGB1Wpl69at1Ncr3xGFwTP6ntAVzhkMBgOrV6/uVuvz4MGDNDU1ERkZSVhYcGSWukPyuKj4x7dB8hG+eA3mOVcFO6RzHk3mUhzb/taeUNJER6FLT8FZUo718DEiL+j+5q6gMBDqd+8HevdPsuZ8CkBY5nmozbIshNls5qabbuKDDz7AarWycOFC0tPTO+2n1+sxm83DEHn/+PxkI6VNTsJ1Kr42Oy7Y4ZCQkEBJSQkZWgvFhLOj0MJV00bH5C/YmKavpuHzJ7EdWYckSaPOrLs3/B1KtqMn8TldiLrgJT/HErbjeUguN+qoSHTpKd2O8ZbnINkaEHQm1Ok9XxODjWb8YsIufoDWdX+k8t/3EJa5CF3SpGCHNaaJ0um5PWsaf807ggiseuY1LGF69tx+PWrg7kkziNTqiNIFv4iiv+wskKvDZyYGv+ht9bR4XttTypGKFv62pZBfXDE52CGd05jNZtasWYPD4Qg4Rq/XYzKZyM3NZdeuXeTn51NXV8cll1xCdHT3xUot+9+n/IWbkNxOwrKXkv6jj1AZQ1uiqz8IWiOa8Ytw52/DdXITYXGZ3Y4zTZ8MooirshpXdS3ahOA/o44l7CfyqTDE4NAZMGhEMmPk67PX66Wurg7o3j/Jj+S04amUu5uN1/8ZQWtEDI+ndferuA+/h+3Dhwm/+4Muz6JxcXEIgoDNZsNqtWIydfg2pkZoiTNqqLW5OVJpY37a4OdJMYvmknDJSuonyM83fn+iUCcrUp77FDQ14pMkxDH2TB9s/D7JgfyTWp1uTrVZtq6eldZpm2X/uwDoMxeTcONjeJoqUUcmEZa5iOI/rab11HZKn76G8b/cPahrd0K4nmfWzOLbLx/gUGkzD713jD/cMJ3w8HCuvfZaPv30U2pqati+fTsLFy4kKWlgvk0KCqB0KCmEOAaDgaioqC5/CxcuRKVS0dDQQFFRUbDDbKfukz/gLMtFZY4l8Zae21oVRgb1+MUgiPjqTuNtqgDArPgoKQwTDXvkhFJv/kkWv3/SWZr2brcbq9WKSqVixowZxMXFdfoLhWSS3eXl1X2yhvnN8+Iw6Qbenj9U+CeviT55MptbaaPZ4QlmSKMGw6RlCGot7voSXFWngh3OkKNLT0EdHYnkdmM7NvbeX7DwV3+bZk0NmIT0+yeps85HUIW+VEzYqntRZ52P5LRR9vzX8LkCL/gqDA2FNrmbdHJEFItu/hrZeaVEeCU8QIvHPSqTST6fj+P18v1n+ZTEIEcjV9vfd7Hsd/L+wUpO1yo+YcHGbDZ3eb47+1lPEARmzpzJNddcg9FopKmpibVr13LqVNf7WOOmv1P2zPVIbiemOVcz7qfrx1QyyU+H7F1gHyWV0YAhW+6e8cuyKgwdljPk7s70T6qvr8fr9aLX6wkPDw+4v+vEF+BxIEaPQ7fwFnRzrkWTuRTjFQ+DJgxP0V5ch97rsp9GoyEmRk6WnC17JwgC89KGzkfJz8QHf0hDhpxQiivvKrUXiqSaw9Gr1Di8HsosilrDUFO/U+5QCuSftP5gAR5JxKjysnhy5267lr2yfGLEwq9inLKCiPPWYJyyAlEbRtoP1qKJScdVdYqyF9YgeQc3h82KN/HEV2egUQlsOFHL/1uXhyRJhIWFcfXVV5OQkIDX62X37t0UFxcP6lwK5zZKQklhVGIymZg6VdZIzs3NpbW1NcgRgaPsKLUfPApA4i1/QR2uVESFAmJYOKqUGQB4zpK9Uzw1FIYSj81Oc+5xoOcOJZ+rFdsx2a/GfJZ/UkFBAQDp6elotaHZSbE2p57GVg/J4VounxIaCxY6nY7IyEjCVW7SzCI+CfYUK5KWfUHUGTBMlE1gbUfWBTmaoUcQhPYiAqtSRDBk+O+fgeTuQPa6gA4z9VBHEFWYb3oOlTkOZ8lhqt98INghjXkON8pFALOiYom5ZAVzN7zDnGR5ESanbdto40B+BTavGjU+Vs4Mnn/SmcxNj2TlpFi8ksTTG/KDHY5CP0hMTOSGG24gJSUFj8fDhg0b2Lp1K16vF0mSqH3/N1S+fCdIPiKX307aD9YiakNHvWMo0bR58blP70JyB074++d5SuHg0GM5kENRspygPtM/yS9FFx8f32OnuytXLqjTzryq0zhVZDJhq+4FwP7po0iuronvnmTv5rb5KO0vG7qEeWNSLF6tBo2tlYbHn+uXl1mwEAWBzEh5bpbfpEiaDSWt5ZXYi8tAFAP6J206XgXA9DhVJ+sOd1Ml9rztAIQvuL7LfurweNLu/QBBa8CW+znVb/1s0PHOz4jiN1fL66Vv7C3jtT2lgJycPe+880hPT0eSJPbv38/JkydHxfdbIfRQEkoKo5asrCyioqLweDwcOnQoqBdByeel8qXvgNeNafZVhC++KWixKHTlbB8l/+Ki7egJfG6li0FhaGjcdwjJ6yUsNRlDauD2cdvxTUiuVtTRqejSZrS/LklSe0JpwoQJwx7vQKi3uVmbIy/y3bYwAY0qdB4j/JrtWUZ5kWFHoVKZ11f8PkrWMZhQAjDNUbpShxJJkjo6lNrup2fjc1jwFMvSIP5FwNGAGJ5Ayh2yh1Ljhudo2de1UlphaKhutVPVakclCEyL7JAondnmVXG0qQGXzxus8AbMF7nyos34cAmjPnQKQ354YRZqUWBbXj1fFjUGOxyFfmAwGLjyyiuZN08uVjp69Cjvvfcep/91L7Xv/QqA2KsfIum2vyOoxq6jgSpxMkJ4IrhbcRfuCTjO1FbooPgoDS2S10vzwSMUJ8lyg/31T5JcdlzHvwBAN+PKLtvDlt2FGJ2Or7mS1o3PdNnuP3ZtbS0+X2evpNkpJkQBSpuc1Frd/Xxn3ZPX5kMUW1RB45791HyxZUiOO9xktd1D8xobghzJ2KJ+l/xMGzlzKhqzqct2n8/HoSrZ6275pM6yj5Z974IkEZa5CE1Mepd9AfTjZpPy3ZcBaPjfEzRtf2XQMV8yPYH7LpITwE+sz+fzo/LvVBRF5s2bR3a23G149OhRcnJylKSSQr8JnZUgBYV+IggCc+fORRAEKisrKS8vD1osDev+QmvBHsSwcJK++cKY86AY7ZydUNJnpKGKMONzOLGfUCo1FYaGDv+kwGazANbDnwByd9KZ14rGxkaampoQRZGMjIxhi3Mw/GdfDU6PxNSEMJaOD7783pn4J5oJXlmW4kCZDbtr9C1GBgNTW0LJdnwTkqd34+/Rxpkyp5Jv8IbN5zrO8krc9Y0IajXGqRO7HeMp2AE+D2JMBqqYcSMc4eAwzbyUmMt/AkDFP7+Nq06RAxkO/B1I2eZIDOqORfBxRjORWh0un4+TzU1Bim7g7CuRJZeGwsdjKBkXY+C6uckAPLk+H5+ycBQ06lrtFDY3Bvyra7V32UcURRYuXMjll1+OTqejtraWjdY06iOnkXjrs8Rf/+iYn38KgoC2D7J3/nu+/WQ+3lZFunSoaM0vokITjkNvIEwjkhUrS5K2trbS0iIXcfXkn+Q6uRHcrYhRaahSZ3XZLmj0GK78tXzMrS/gre98742MjESr1eLxeGho6JwsMetUTIyTO/MOlA2N7F1+W0ImO1pO0Bz/3VNI3tCfV2S3+T3lKwmlIaV+V5vc3ZLu5e5yi6pp9qgR8XHZvKxO21q+fAcA8/wbejxH+MIbib3mlwBU/usO7Pm7Bxs2ty5O46YFcuf3Lz84xv5iuaBEEARmzJjBjBlycWtBQQFffvlll2StgkJPKAklhVFNREQEkyfL5rKHDx/G6XSOeAyumtPUrP0FAAk3/RlNdPfm1ArBQ5OxCEQVvsZSvA0lCKLYLtOjSCApDBUd/kndG3VCW2V/AP8kf3dSWlpaSMrdFTY4WHeyCYDbFyeG3MJFdHQ0arUak89GkkmFxyfx5RBqqY9ldGkzUZnjkJw27Pm7gh3OkGOYnIWo1+FttuAoKg12OKMev9ydYWo2oq77a5Xr1GYANKNE7u5s4q//HWGZi/DZmyh/YQ2SZ2gqnhU6OOSXu4uO6fS6IAjMbOtYOjzKZO8sdgfFVvneePGs0Euk3rlsPEatihNVFv53pKtslMLwU9dq54HNn/PQ9o0B/x7Y/Hm3SSWA1NgIzqt5B7O1GI/GxJHJ3yM/fME5swjov6e42+4x3aFNikcTF4Pk8WI7enJkAjsHaAngn+T3NIqMjESn0wXc35XzEQDaGVcGnENop12GJusC8Dixf/JIp22CIPQoezevXfZuaJ7985rkhMyCC1eiiQjHcjKfsnc+GpJjDydZUXJCqcJmweoae0ViwcLfoRRzXvfz/M8OFgEw3iwRYezwf/Q0V2M/uRWA8AU9J5QA4r7yK8zzrkXyuCj7y7W4GwZXNC8IAj9enc2qyXG4vRL3v5VLcUNHoj07O5v58+cjCAJlZWXs3LkTt1t55lXoG0pCSWHUM2nSJMLDw3E6neTk5IzouSVJouJf30VytWKYspLI5beP6PkV+oagN6FOnQ10dCn5ZXosh5WEksLg8TpdNB6Qrz89dSg5y4/iri9B0OgxTlnVaZs/oZSZmTl8gQ6Cl/ZUIwEXTAhnSoIh2OF0QRRF4uLiEASYGiUvrOxUZO/6hCCKGKdfDIxNHyVRo8E4TS4+sRxUrvmDxS8jZA4gdwcdi33aUSR3dyaCWkPK3W8gGiJozd9Fzbu/DHZIY4oaRyuVrXZEQWB6ZEyX7bOiYwFZ9s49ihbKNxwuxIuIWeVh5vjA0k/BIsqo5balcqLr2U0FOD2hX20/1rC4nL1+p90+HxZX1yJJd2MFRY8tRzr+GXML/sbE5EgA9u/fzyeffBISnsLDjSZ7GQgi3uqTeJu6X2gVBEGRvRsGLPs7/JNmJHXMA/okd+duxXV8PSD7JwVCEAQMVz8KogrXkU9x523rtN1/jm4TSmlyQulguQ2vb3AdmM1OBzV2GwIwOTWNrB/Kazwn/vhMyHe9mbU6Eo3yv0V+k9KlNBS0VlZjKyxp80/qfp6/t82797yMiE6vt+x/DyQf+vHz0cZl9HouQRRJuePf6FJn4GmuovQvX8HnGty1XSUK/PYrU5mdFoHF4eHhz0qos3UkjdLT01myZAkqlYqamhq2bduGwxHa33OF0EBJKCmMekRRZO5c+cJeWlpKZWXliJ27acs/sR/biKANI/m2F0OuYl+hg7Nl79onGoeOKnqxCoOm+fARfE4X2phoTFmBTbj93UnGqasQdR2TscbGRhobG0NW7u5AmZV9pVbUosC3FobeIpkff+Viqii38+8tseLyjJ7FyGBiGuM+SuY2HyWlK3Xw+L2o/PfRs/HWF+GrLwJR3X7vHY1o48aT/O1/AFD/yeNYc8fmbyMYdMjdRWBUa7psH2c0E6HR4vR5OdUyevx+tp6UK/Wnxqo7GXKHEjcvSiMhXEdVs5M39pYFOxyFPuKsOkXRb5fgLM1BFZHA+J9v5MJr1nDhhReiVqspKyvj7bffpqqqKtihDiuiIQp1ujzvd58K7GnjL3hQEkpDgyRJNB/IpThZLnqbmWxsf93fodRTQsl1chO47IiRKajT5vR4LnXiZPTnfQsA24cPIXk7/I79z/lNTU1dlGkmxoVh0opYnV7y6ga3AO9PxCSbzBg0GsbfdjP65AQcFdUUvfzGoI49EmT5Ze+UhNKQ4O9Oipg+GU14VznbumYbxTYVAJfP67wOYGmTuwtfcGOfzyfqTaTd9wEqUwyOwn1UvHT7oNer9BoVT3x1JhkxBmptbn75WTG2M6ThExISuOCCC9BqtTQ1NbFlyxZsNtugzqkw9gnNJ10FhX4SHR3dbip38ODBEWnTdDeUU/3mjwGIv/63aBNCs6tAQUbdtqjlKdiJJEmYpk1CUKtx1zXgLB+5JKTC2MTvnxSzeF6PiWVLm3+SaVZgubue5CKCgdcn8Y/d8gLFlVOjSA4PPTk+P/7JrNZWQ6xBjcPj40C58jDcF4zT5A4lR9F+PNb6IEcz9JjmdPgoKQwcj8VKa34REDih5D65CQD1uPkI+q7GxaOJ8AU3ELXyLgDK/34rnqaxvVg7Uvil7GZFxXa7XRQEZkaNPtm73Gp5gXNpdvfvKxTQa1R8b8UEAF7aXkSjXZFECnVaC/ZS9NuluOuK0SZkMf6hnYSNkxflJ06cyPXXX09kZCQ2m40PPvhgzJurt8ve9eCj5L8/WQ4fVbwThwBHUSkVQhiteiNhaoGsWNmvqKmpCZfLhVqtJjo6OuD+rhy5oK4nubszCbv4JwiGaLzVJ3Hsfrnj9bAwIiLkDhB/IsuPShSYldImezdIyes8v39S231IFaZn8k9/IG/7y4u4mpoHdfzhJrtN9i6vcew9zweD+p09+yd9uj8fCYEYrYfJaXHtr3taarEdl5+J+yJ3dybauPGkfv8dUKlp2fU69Z/+aYDRdxBp0PDMmllEhakpbHDy2/WluL0d18fo6GiWL1+OwWDAZrOxefNmmpqaBn1ehbHLoBJKJ0+eZMeOHYPOXFqtVu677z6Sk5PR6/XMnj2bN998s8/7f/DBByxfvpzw8HCMRiPTpk3j73//+6BiUhh9TJkyBaPRiMPh4MiR4V0wkiSJylfuxtfagn7CQqJX3zus51MYPJqMBaDS4GuuwFdfhKjXYZgiJyH9fhAKCgOlYc8BoGe5O6+1gda8nUDghNKECROGKcKBszGvicIGJ0atyJq5cb3vEERMJhNGoxGQmBUvV4opsnd9QxOVjC51OkgStqMbgh3OkGOaORUEAWdpBa46pWJzoNhyT4AkoUtNQhvb/eKRK0+uGtdMGp1yd2eTcPMT6FJn4G2pofzvtyqLk4OkztFKud2GCN3K3fnxJ5uONDXgGQX/5gWV9dS71AhIXDI3tIvMLp+ZyKQEE1anlxe3FgU7HIUesOZ+TtEfVuG11KHPmEfGQzvQxnd+VoyOjub6668nMzMTn8/Hjh07WL9+Pa4x6p+imSRLRrvztnbqXjkTw+RMBJ1W9k4sVjrxBsuZcndTE42o2/yT/NJzcXFxAbsyJbcD93G5w7cnubszEQ2RGC75GQCt6/6Ez9aRGOnZR0nunDowSB+lfH9CKbLjOSf1hqswT8rC3dRCwXMvDer4w01W2721oKkB3xhOLo8U9bvkhFJsgITS1lNy4cvsxM5FoZYD78tyd+Pmdrlu9wXjlBUk3vIXAGrefhDLoU/6fYyzSYkK45FL09GrRQ6V23hqS0WnAgSz2czy5cuJiIjA6XSydevWLslbBQU/A0oo/fvf/yY1NZWpU6eybNkyTp6UzQ6/+tWv8uKLL/b7eNdddx2vvPIKv/rVr/jss89YsGABa9as4fXXX+9138cff5zrrruO6dOn89Zbb/Hhhx9yzz33jNkHKIXAqNXqdum7wsJCamtrh+1cLXv+i/XQR6DSkPydlxBE1bCdS2FoELQG1GltEgltsnfm2W3Va0rFusIgkLxeGvbKCaWYxd0bdQJYc/8Hkg9d6nS0sR1m3Y2NjTQ0NCCKIuPHB5bLCwYOj49X9skPkTfNiSNcrw5yRL3jn2hO0MvFLruLLXgGqaV+rmBsk70biz5KarOJsGz596XI3g2c3uTuJK8bT/52ALRtVeSjHVEbRur3/ougNWA7+gX1n/wh2CGNag63VUxnmSMxabrK3fnJMIUTrtHi8Ho51dI0QtENnHVthtzJYV7iIozBDaYXREHgvovlxeF39pdTUm8PckQK3dG041VKnrwSyWnDOO1ixj24CXV4fLdjtVotF198MUuXLkUURQoKCli7di0NDWOvgEKdOgvBEIXkaMFTerDbMaJGg2m67J2oyN4NHsuBHIpS5ELMmckdkt19kbtzn9qC5LQiRiS3z8X7gm7R11ElTUNqbcb+ecd913+umpqaLp14fh+lEzWtWJ0D84jz+nycbpZ/N1lRHUUPgkrFlF/cB8Dpf7xKa0XodiynmcPRqVS0ejxUWC3BDmdU46iqwXa6GAShW/8kt8fLsXq56OWi6SmdtrXsfRvof3fSmURfeLfcKS9JlL+wBmfF8QEfy092XBi/uDgVUYCN+c288mXnhFFYWBjLli0jNjYWj8fDzp07KStTEvMKXel3Quntt9/mW9/6FnPnzuXZZ5/tdBGfO3cub731Vr+O9+mnn7J+/Xqef/557rzzTlauXMmLL77IxRdfzE9+8hO83sA3gv379/OLX/yCxx57jOeee45LL72UCy+8kO9973t8//vf7+9bUxgDxMXFtS/IHjhwAI+n+6qlweCx1FH1qtzyHHf1Q+hTu19UUQg9NFln+Sj59bWVDiWFQdB89CQeqw212UT41IkBx3XI3V3Z6fXTp08DkJKSEnJyd+/l1FNv8xBv0nD1tMBSFqGEf6IZZq8iQq/C4vSSW6nI3vWFM32UxqJcjnlWm+zdQSWhNFD8C3N+f4qz8RTvQ3JaEYzRqJJnjGRow4oueQpJtz4LQM27D2M/tSPIEY1e/P5JM6MDdyeBnPSYETl6ZO92F8peT7OTw4IcSd9YND6a87Ni8Pgk/rKxINjhKJzFlu1rqfj7reD1EH7ezaTf/zGqsK7eHWciCAIzZ87kmmuuwWg00tTUxNq1azl16tQIRT0yCKIKTfYyoI+yd0oRyaCQJInmMzqUZiTJCXO32019vVwg0FNCyZn7EQDaGVcg9MNbThBVGK/5rXyMPa/iqZCfP2JiYlCpVDgcDlpaOqsQxJu0pEVq8UlwuGJgz/6llhacXi9hajXJps6/ufiLlhO9aB4+h5OTf35uQMcfCVSiyIQIRfZuKOjwT5qCJiK8y/btR4tx+FRoBS8rZnQUjHqs9diOy9cn8yASSgCJX38aw6Rl+BwWSp+6Gq9t8N6S89PM3LssGYD/Hqrj42Odiw80Gg1Lly4lOTkZn8/H3r172xVVFBT89Duh9Nhjj3Hbbbfx4Ycfcscdd3TaNmXKFI4dO9av47333nuYTCZuvLGzSdltt91GRUUFe/bsCbjvs88+i06n4wc/+EG/zqkwtpk+fTp6vR6bzcbx44PP4J9N1Wv34rXUoUudQeyVDw758RWGD785uLtgB5IktXcotRYU4WkZXGu8wrlLwx7ZPyl6wRwEVffdipLXgzXnMwDMszvL3fkTSpmZoSWRU2918fZheRHvtoUJaNWjw3YxLi4OQRBotduYnyIv7Cmyd33DMPECBI0OT0Mpvtr8YIcz5Ph9lJQOpYEheb1Yc+XnKtOsqd2OcZ/aDIAme3m/Fo5GAxEXfIuI824Bn5eyF9bgtY69yv/hpt7poNRuRYD2ZFFPzIr2y97Vh7Tsndvj5VSjnIRfNS2ll9Ghw70XZsoVyidqOVqldCmFEuuJ4rO53yX8kvtJueM/COq++1cmJiZy4403kpqaisfjYcOGDWzZsqXHQt3Rhl9S1XVqU8Ax/vuU9XD/1qcUOuOqqKbcraZVb0SnEsiOk5+ta2trkSQJo9HYJjfdFcnjxH3sc6DvcndnoplwHtqZV4Pkw/bBL5AkCZVKRWysfG/oTvZuburgfJTym+QETGZkNOJZfk+CIDDloR8BUPrfD7CcDN1n5aw2H6X8JuVZZTD45e5izutehWR9bjkAk6MFdNqOrmvL/vfB50WXPgtdYvagYhDUWlK//w6a2HG4qvMpe+5rAeU++8PqSVF8fZ4sZ//Cjkp2F3WeL6tUKhYtWtResH/48GGOHj06JosOFQZGv2d6x48f56abbup2W3R0dHuVQl85cuQIU6ZMQa3uLKMzc+bM9u2B2Lp1K1OmTGHt2rVMmjQJlUpFamoqDz74oCJ5dw6j0WiYM0c2Ks3LyxvSVn/LoY9p2fU6CCLJ3/lnvx7uFYKPOn0eqHVIlhp8tfloYqLRpSWDJGHNUbqUFAZG/e62hFIP/kmtBbvx2RpRGaMJy1zc/npzczN1dXUIghBycnd/31pIq9tHdpyeZZldK7JCFY1GQ0yMvFA5ySw/C+wssiga4n1A1BkwTLwAANepLUGOZugxtyWUbCfy8La2Bjma0Yc9rxCfvRWVyUhYZka3Y1z+hNIYkbs7E0EQSPzmC2gTsvA0lFLxz+8ok+p+4u9OyjRHYNb0/gw9wRSOWa2h1eslzxK6Jug7j5fglOQK5aXT0oMdTp/JjDdxzWy5Qvkfu6uU7/MIYNbq0PSSbBd9XpB8HMi6hH+Nv5oWd//XNcLCwrjiiiuYN28eAMeOHeO9997r0tExWtFOlBNK3rLDnfx1zsQ0Uy4cdBSW4G4K3etHqNOy/zCFbXJ305IM7f5JfZK7y9uK5LAghCeiTg8sC94Thit+CZowPIW7ceV82Omc3Xm7tCeUyqwDuqbl+f2ToroveoieP5vEyy4En4/jjz3d7+OPFNlRSofSUFC3U+5Qigngn3SgQp5PnJ/Z+fvS8uU7AIQvuLHLPgNBHR5H2r0ftMkvr6f6zZ8MyXFvnhvHpZMj8Unw+IYyjld3Li4RBIHZs2czZcoUAE6ePMmBAwfwhXCRj8LI0W8zBIPBQHNz9zfk8vJyoqKi+nW8+vr6bk3Io6Oj27cHory8nNraWn74wx/y6KOPMnXqVDZs2MDjjz9OaWkpr732WsB9nU4nTqez/f/7H67cbjdut7tf7+FcYjgk5IaDuLg4UlJSKC8vZ//+/SxfvjygUWRfcTTXUfnyXQBErr4Pddrsc+q7Mlo++x4R1KjS5+E9vRPHqW3oosdjnDkVZ2kFLQdyMS2e1+1u59Ln3B1j4rMfIL199pIktXcoRcwPfE1o3v8BAIbpq2U/H588Li8vD4Dk5GRUKlXIfNeK6u28e0CuuLptfhw+r5fR9NgYGxtLXV0dEa5aDJooGuwejlVamRzfPymiUPk8RhL95FXYjn6B6+QmNIu/FexwhhRVXAyahFjc1XW0HD6Kef7sgGPPxc/eT6BrfsvBXAAM0yfjlSQ4a5zP1oC3PAcAccL5o/be0eNnr9aTcMdrlD12AZYD71O37i9Errpn5IIbZob7MzvUIPubzoiI6vO5pkdEs6u+mkP1NWQbe5b8GiwD/d1vyJW9BbIjQZCkUXX9uH1pGv87UsWJmla25Ddx/vjh/TcOVUbqM4tQa3g43M2/jx4gP2UBk0t3suT4e53GGFxWXDf8mX+7IzjRUMfD2zfyw9kLGRce0e/zzZkzh9jYWDZv3kxtbS3vvPMOK1asIC0trX3MqLxWG2IQE6fgqzqO48QmtLO+0mWIYDaiG5eKs7iMloO5RFywuOtxUO73vdGy7zDFbXJ30xLC2vepqpI9hPw+K93hOCwngDTTLsPr88FAFqHNieiW3Y1zwxPYPn4EMXtVe+FYXV0dDoejU3H61DgdalGgxuqmuKGV1IjAxQvdffb+BMx4c0TA70bWT75H1eebqP58EzU79hK1cE7/39cwk2GUiwHLrRaa7HaMZ3kWjsrf/RDSl9+9o7oWW0EhCALhc2d22aeoupEapxqQuGTOuPbtXmsDtmNfAGCYc82QXWNUSVNJ+M5LVL1wEw3rnkKdPI2I87/Z7+Oc/dnftTieOqubfWU2fv15CX+6Ip3ks3432dnZaLVaDh8+THFxMU6nk7lz53ZpDBkNnMvX/L7Qn3+ffn/6S5cu5dlnn+X666/vsu3ll19mxYoV/T0kwlmtpH3d5vP5sFgsvPHGG+1dUytXrsRms/HUU0/xyCOPkJWV1e2+jz32GI888kiX19etW4fBYOhmD4XRhtfrRRRFLBYLW7ZswWwe3AQp/uALRDSW4zImsU+3COnTT4coUoWRJFo3jhh2Urv/Y6rUUyHSiAhU7thDxcLuPSEUFAJSVYtY34ikVrOrogQ+reh2WPrOt9ABp3zJHDjj2lFbKy+uWa1WPg2ha8qbRXq8koZJ4W6c5cfYVx7siPqHv0u5vraaCQYDR5o1vLs7n9XJzl72VNA26RkHuPJ3cHzvLhA1ve4zmhDSkhGq6zj5yTrg3J5M9xdh0zYEoDk2gn379nXZbirdSpIk4YzIIO9UKVA64jGOFBFTv0F8zj+ofvMB9lf5cEZ2LY5T6IwNiVK1FyTwFJWyr6hvBs9awQcqOFxfy7iaBkQCzw2Dxb4SDaAnRR1a9/K+sjBay5ZqHX/fUYa61sYoUbgdnUheUtbdS8nqPwKwMO9TkpoKOzYDXm04NRYTKwSJ7Sqod7Tym11bWOgTSZMG9uFERETQ2NiI0+nk888/x2QyYTabe1xrCXViwicTXXWc6l1rqXandjtGSIlHKC4j//ONSGGjb+EzFJB27aPoyuUAaJqK2bevEI/Hg90udzKUlJRQVtbN9dznZkLup6iAAk0Wjm6eG/qKYFzEuLA4NM0VFL75EPVTbkKlUuH1etm9ezd6vb7T+DRDGIVWNR/sOs6i2L4vjjqRqFbL0pCn935JWQ/3G2HJXITt+9j9018j/eQOCMHfklEFNgH++8XnJA7w2nFOsy8HEZBSE1m/c3uXzTsrfUAECRonB3Zta3/dXLyBRK8HZ/g4vjhQAAyl95Ce6Ck3EXP8Tar+fTf7C+txxEwe9FEvjoDyegOVrfDgRwV8J8uOUd21wy8qKorGxkaqqqpYv349MTExgy7cVwgt/Nf2vtDvu+ovf/lLzj//fBYuXMjNN9+MIAi8++67/OpXv2Lr1q3s3bu3X8eLiYnptgvJL1Pm71QKtG9VVRWXXHJJp9cvu+wynnrqKQ4cOBAwofTzn/+c+++/v/3/t7S0kJaWxurVqwkPHz3SPiNNd23FoYy/Q8lqtTJv3rwBf7ae0zuxFcr6v+Pv+Q/TJi0byjBHBaPtsw+EJ9aH7fgbhDeeIGXuXBxRsRx/7QNUxeXMmj0boZsqi/j4+CBEGjqMlc9+IPT22Ze+/i5Hgej5s1h0zdXdjnHXF1P0bgkIIktv+Skqk3xfa2lp4a233kIQBK666qouk6FgcaCkiZM5uagEuPeibNIidcEOqd9IksTnn3+Oy+VixcRojnxpodBpZN686f1aPDkXf/uSz0fhl4+DpYbZsaCeMDCJklClpqCcsn05RNQ2kT0/8Hs7Fz97P4Gu+Ud+8xdcwMRLLyJ8fteOXnvRa7gB88xLmd/Dv22o05fPXpIuo/K5KmyHPibz6POkP7wHUT/6OzuG836/taYSKoqZYDJzQfa0Pu/nlST2Hd2PzeMhcmI2E8Mjhy3Ggfzu65pt/CZH7lT+5qXnkZXSuzdUqLHC5eW6F/bQ2ArVxglcMy3w/HusMlLXfPuJLayPGo9LYyDCVkNq3clO2wVA7WphRWY4hsnLucrt4oWc/Rypr2WXykfM+Eyuy5rcxdulL3i9Xvbs2cOxY8ewWq2Eh4ezcuVKLBbLEL27kcUT5cR26j0iGo6QOndut759dWV1lOw8gLmmgYkB7kvK/T4wrtp6Nni12MNM6FQCV54/B41KoLCwkJqaGmJiYli4cGG3+7pPbsTutiGY4pi2+usIYvc+s33FbXwU+xt3EZP/PhlX/YgcjYaSkhIiIyOZPr1zUWixtp7CfXXUqWKYP7/7ZCN0/ewP1VbBwb0kGU18ZemqHuNxzJ3P1mXX4DtdwlyNgYTVKwf+5oaJ0tz97KosJ3piNpdnTuq07Vye30PffvdHtx+kFMhYvYopl1/eZfubL2wGYHFGOJdfvqj99fKn/4YdSFr5TaZ3s99gkS69lKq/ObHuf4+Mg0+S9tBONNFpve/YRqDPfspMDz/5qIRqq5sPamL5/WVp6DVdr6v19fXs3bsXt9uNzWZj8eLFhIX1TwUkmJzL1/y+0B9p3H4nlObPn89nn33GPffcw49//GMAfv/735Odnc2nn37a5WLeGzNmzOCNN97A4/F0apfLzZVlNXo63syZM9tbbc/Er5XaU6ZUp9Oh03VdJNNoNGg0Y6sadygZbS2N6enpVFRUUFlZyeHDh1mxYkW/K7Eklx3Lez8FIGrlXURMv3A4Qg15RttnHwhVxnxsmjAkewNCfQGm7EmozCa8FivOgmJM0yZ12edcvyaMlc9+IPT22Td9eRCA2MXzA461HF0HgCF7KfqoDp3xkpISQJa7G2wH5VDhkySe2VQEwHVzUxgf273J7mggPj6esrIyklQWdCqBKoubkmYPmbF9f+A9V3/7pukX07zrNXynt6Nu81QaK0TMm0UZYMs9jkoQEFTdL3Ccq589dH/Nd9XU4aqoBlEkYvZ0VGeNkSQJT95WAHSTV43q+0ZfP/uU777M6Ydn467Op+71+0i589/DHNnwM5yf25FmuVhwVkxcv86jBmZExrK7roojLY1MjY4dpggH9rvfdKQUCYFojYcpGYnDENXwE6HR8I0FCTy9tYI3DzWwenIMZt3gFn9HGyN2zbfWcmScXJg4rXgbAgE8Xqy1aDQaIjUafrboAt48cYRPTp/i48I8KmxW7p69AEM/Y9ZoNCxfvpykpCS2bNlCRUUF77//PvPnz2+XEBtNqDLPw6Y1IFlrEepOoU7uum4UMXcGALajJxElEDVdrz3K/T4wzTnHKGqTu5uaaCBMJ/9b1dXJfngJCQkBj+E49hkA2hlXoNEOvjhNNfsaXHtewXN6F87Pf0fiil9RUlJCbW1tlxgWjIvg5X115Fba8QkiWlX364Jnf/aFFnkhNTsqptfvhSYthQnf/Tr5z/yDvD88S/IlqxBD7NlnYnQsuyrLOd3S1OX9jObntKGgL7/7xj0HAIhburDLeGurk4IWeV3x0tnj2rd7bU3Y2+TuohbfNGzXl9Q7/k3hb5fiLM2h6vmvkvF/WxF1fVPaCvTZx5nV/Pbycfz4g0Ly6hz8aUslv1ydjkrsvH6akJDAsmXL2LFjBxaLhe3bt7N06dJR05hxLl/z+0J//n361ZvmcrlYt24dmZmZHD9+nLy8PLZv386JEyc4efLkgOTurr32WqxWK2vXru30+iuvvEJycjKLFi0KsCftsnufffZZp9c//fRTRFFkwYLujdMUzh38JnJqtZrGxkby8/P7fQz7uj/iqy9CHZ1K/Nf+MAxRKowkglqLJkOupHIX7EAQRUyz5EpZ66EjwQxNYRTS0PagGRPAfwvAeuhjAEyzr+j0+unTpwHIzMwcpuj6z+dHqjlWacGoVXHn8vHBDmdQ+A17m+qqmZcmG/TuKBqdVbgjjXH6agBcpzYHN5BhwJA9HtFowGezY88r7H0HBQCsOccAMGSNR2Xqmmj2Vp1AslSDJqz9HjvWUZtiSL37DRBVNO/8D03bXwl2SCFLo8tJkc2CAMyM7P/C9ey2JFJuU73s3xVC7MiTF1ZnJAT26egJyefFdnwzzbvewHZ8M5LPO5Th9ZmLJ0YyLkqH1enlvwdrgxLDuYDTnEBB4mwAppdsCzhOHZnU/t+iIHDzlBncPWsBGlHkQE0lv9q5iSrbwJ5pJk6cyPXXX09kZCQ2m42tW7eSn5/fXpQ7WhDUWjSZ5wPgPrmp2zH6jDRUEWYkpwv7yf6vA5zrWPbltCeUZiTJ936fz9cu2e1/1j4byevGdfR/AOhmXDkksQiCgPGa34Eg4sr5iCib/AxnsVi6SDSNj9YRFabG6ZE4VtV3+ab8Nv+k7Ki+dWlmfe/baKIisOadpuytD/p8npEiO0q+3+Y3NuAbZb/vYOOsrcOadxoEgZjFXbsb1x0swCOJmFQeFkxMaX/dcvBD8LrRJU9Flzxl2OIT9SbS7v0AlTkWR9F+Kv75nSG5hqdG6vj1peloVQJ7S6w8u72y2+NGRESwYsUKTCYTra2tbNmypVvlMYWxTb8SSmq1miuvvLLdRDwzM5MlS5YwceLEAQdw2WWXcfHFF3P33Xfz4osvsmnTJu644w7+97//8cc//hFVW+Xod77zHdRqNcXFxe373nbbbcydO5d77rmHv/zlL3zxxRc8+OCDPPfcc9xzzz2MGzduwHEpjB3CwsKYMUOuTvK3+PcVd8kBHNv+DkDSN/+KKmx0ZN0VekaTuRSQE0oA5tn+hNLRoMWkMPqwl1XSWlaBoFIRNX92t2N8Thu24xsBMM3qmFBZLBZqamoQBIHx40MjceP0eHl2k5zk+uaScUQbB7Y4Fir429mbmppYmCpXbO0s7HsL97mMcfrFAHjLc/DZxtbkQFCpMM2cCihFBP3B0vZv5S/AOBv3KXkxTzPhPARNaMh3jgSGiecTd63syVr5yj04K04EOaLQJLdRTrpkmMKJGECleqY5AqNajc3jocDSPNThDRifz8fROtmf44KJ/ZdQadn3Lnn3Z1D8+ErK/3ozxY+vJO/+DFr2vTvUofaKShT4ziJ5cfiDIw1UtbhGPIZzgRxTOj6VhoTGQuJauvMRE1BHp2GY1LU7+PzUdH553gqi9GFUWC08vH0TObVd1Vr6QnR0NNdffz1ZWVlIkkROTk67hNFoQjtJlhlzBUgoCaJ4xj1fmef1l5YDORT7E0rJ8rN0Q0MDHo8HnU5HZGRkt/u5C3Yg2RsRjDGoJ5w3ZPGok6aiW3yrfI5Pf0VUVBTQVcJLEATmpsoJsP1lfVv78UkSBU2NAGT1sfBBExFO9g/vAODkn5/HY2/t034jRZo5Aq2owu5xDzgBfa5Sv0v2/AqfOhFtVESX7VuOy9+5GfGaTspYLV++A4B54Y3DHqM2LoPU768FlZqWPW9S//HjQ3LcKQkGfnZhKgLwvxONvHmwrttxBoOB5cuXExUVhdvtZvv27VRWVg5JDAqjg34llERRJDU1tV+aen3h3Xff5dZbb+WXv/wll156KXv27OGNN97glltuaR/j9Xrxer2dsqMajYb169dz00038fvf/57LL7+c9957j8cff5ynn356SGNUGN1kZGQQFxeH1+vlwIEDfcreSx4XtnfuB8mHds71mM/qLlAYvajbEkqe07uQfD5Ms2WJBMuhI6OuOk8heDTskT0TImZMQW3svsXcdmwjktuJJnYcupSp7a8XFMjmnElJSRgMfWtPH27e3FtGZbODeLOOWxb3XYc5VAkLC2tvvc/Q21EJUNzopKzJGeTIQh9NZBKqxCkgSbjzAldQj1bMczqu+Qp9w3pY7lAyzQ6UUNoMgGZS6HkIDDexVz6IYeoqJJedsue/hs/lCHZIIcfhtqrvWVEDk6tTCQLT2xb4Djd2v7ARDI4UVdPiUSPi46LZE/q1b8u+dyl75gY8jZ2TCp7GcsqeuSEoSaX5aSZmpxjx+CRe+fLc9tcYLnZWyJ/39OKt3WyVZYUSb3kqoN/MhMgoHl26kuzIaOweN3/cu4NPTp8a0PxFq9Vy0UUXMWvWLARBoLy8nE2bNtHcHDpJ297w33M8RXuRHN0nDsx+JYrDSkKpP7gbmylpcGAzmNGqBCbGyZLR1dXVgFy4FchKwJXzEQDa6VcM2jvpbAyrf4YQFom38hgx7qpOMZ2JX53gQJmtT8cts7Tg8HrQq9WkmvteRJzxrZsIS0nCUVlN0Uuv93m/kUAtioyPlJNueY0NQY5mdOFPSH7TlwABAABJREFUKHXXneTz+ThcIxddrJjUUUzitTdjOyL7rocvuGEEogTj5GUk3fosADVrf4Hl4EdDctwlGeHcvVSW8f33vhrWn2rqdpxOp+OCCy4gISEBr9fL7t27KSoqGpIYFEKffiWUQO4Ueu655/B6h64d32Qy8fTTT1NZWYnT6eTw4cPcdNNNnca8/PLLSJJERkZGp9ejo6P561//SlVVFS6Xi5MnT/LAAw/06J+kcO4hCAJz585FpVJRV1fXp4tc66a/4K06gWCMwXj1b4Y/SIURQ506E7RGpNYmvJVHMU6bhKBW4a6tlz0iFBT6QP1uOaEUvagHubvDnwByd9KZk65Qk7trsrv553a5A/ielRMI04wN7wS/FIeloYbZKfLEcmeR0qXUFzQTlwPgztsS5EiGHnNbEYH1oJJQ6gs+hxP7cVmdwNxNQkly2XEX7gFAk718RGMLBQRRRcqdr6Iyx+EszaH6zR8HO6SQosnlpNAqX3dnRg3cp8WfjMptrA8Z6Z71ObIX4jiTj3Bj3zvzJJ+XqlfvhW79c+TXql67b8Tl7wRB4PZFCQjA5oJmTtaEVrX9aKfWbuNkYz1IPqaW7kAM61z1ro5OJfUH7xA+/7oejxOlD+MXi5exPDUDCXj9eC5/PbwP1wDWZwRBIDMzk+XLlxMWFobVamXz5s3tPp+hjiomAzFmPPg8uAu2dztGKRwcGNaDuZ38k/w+RGcmlLpD8npwHZEtKXQzrxryuERjNGGrfwKA+dDLgNyhdPZnO6ftuf90vYNGu6fX4+a1FT5kRkQh9sNzW6XXMemn35eP8cw/cDWGVkI2O1KW7/O/P4W+UbfzSwBilnS1UTlUUEmLR40KH5fO65jLWw99jORxoU2ajC6l+wKs4SBq5Z1EXXgPSBLlf70ZR9nQJM+vmhbDjbPkZ6+nt5QH7PZTq9Wcd955pKenI0kSBw4c4OTJk8r19hyg31kXrVbLyZMnmTJlCg888AD/7//9P5544on2vyeffHI44lRQGDRGo5GpU+UOgdzc3C5au2fiqTpO60a5y814ze8QjaPPqFQhMIJKg2a87M/mLtiBKkyPYXI2oFSsK/Qdf4dSIP8kSZKwtCeUOjocrVZr+2RswoT+VTQPFy9uK8Tq9DAxwcQVM0anqXh3+BNKNTU1LMkwA7CjUJF86AuaiSsAcJ/aMuYmBMYZk0El4qquxVmpFBH0hu3YSSSPB01sNNrkrtcHd+Fu8DgRI1NQxWcHIcLgo4lMIuXO/wDQuOF5Wr5c28se5w65bYtYGUYzkYMwZs82RxCmUmP1uENG9m5fiZwom5dq7td+9pPbunQmdUbC01CK/eTId4hmxoZx4cRIAP6xp2rMXf+DydZjewHIqDlK+sLrmfhcHeMe3ETKXa8z7sFNZP+/wl6TSX40KhXfnTmXb0ydhSgIbC8v4dFdW2hwDCwJGB0dzapVq4iPj8fr9bJv3z4OHjw4pEXEw0W77N2p7mXvjNMmgUqUCwcrlc67vtJyIIeiFL9/kqym4HQ6aWpqAgInlNyndyLZGxCM0UMqd3cm+sXfRJUwCXNdLmo8uN1uGhsbO42JDFOTFSsn+g/0QfYuv0nu4MkaQOFD6vVXEj51Ip4WC/nPvNjv/YeTrDY/KP/7U+gdZ1091lOymkh3haOfHZIT7hPCJcyGjmKSli/fBuTupEDde8NF4s1PYZi8Ap/DSunT1+C1Ds3n/a2F8azIisArwe/Wl5Jf1/09RhRF5s2b126Hc/ToUXJycpRniDFOvxNKP/vZzygvLyc/P58nnniCn/zkJzzwwAOd/hQUQpWsrCyioqLweDwcOnSo2wuc5PNifftH4HWjmXoJ2lnXBCFSheHmbB8lk+KjpNAPnHUNslEnEL1wbvdjSnPwNJQhaA0Yp3TIQIWa3F1Jg52395UDcN9FWajEkX0AHk5iYmJQqVQ4HA6mxchiMqdqW6mxKt4QvaEZvwjUOnzNFXhr8oIdzpCiCgvDOKmtiEDpUuoVS5tMkGn2tG4nyO6TmwE5CTnSE+hQwjTjEmIu/ykAFS99B1dtUXADChH8EnUDlbvzoxJFZrRVWueEQKV1q9NNQYv8fb9wRmq/9vU09c1joK/jhppvzI9HqxI4Umlnd7FShDEUuBrK2VYgX0vnuutI+sZziCo1xikriDhvDcYpK/otDSYIApeMz+LBhedj0mg53dzIw9s3DrgTQafTsXTpUiZPngxAYWEhW7ZswWbrm2RYsGgvgDmxsdu5vSpM337PV2Tv+k7L/pz2DqWZSbIfkd+rKCIigrCwsG73c+V8DIB22uUIKvWwxCao1Biv/i0CPiIbcoEAsnepcpdSX3yU/L8bf0dP/+JRMfn/fgRA4UuvYy8LHR+Z7Da52DJLC/ZR5pEWLPwqJOYp2ehiorps/7JEvi+eN76jy9TbasGa+z8AwhcMv3/S2QhqDanffxtNbAbumgLKnvsqkrf3zrzeEAWBHy1PZlaykVa3j19+VkK1pft5tCAITJ8+nZkzZwLymseXX345KgoTFAZGvxNKhYWFPf75ZXwUFEIRQRCYN28egiBQVVVFWVnX6kDHtr/jLT2EoA/HdO0fzunFkbGMP6HkKdyN5PW0SyBZDiuLiwq907D3AADmSVlooyO7HWM5JE+ojFMvRNR2VC+FmtzdsxsL8PgklmRGs3hC/ydRoYxKpSI2Vl7EdDbXMS1RTuDtKlIWyHpD0IR1dHKOQdk7U5uPklXpSu0Vf6GFaVYg/yS5Kty/qHcuE3/9bwnLXIzP3kz5C2uQPOf24k2LyzUkcnd+ZkXL1/Ocxrqgy95tPFyIRxIxiF4WTEzp177qyKQhHTfUxJk0XDtD/rxe2lONx6dUGA8Gn9PGly9+l1pTEmqfm9U3/RpBrRmy40+LjefR81eSag6nyengt7u3sqW0aEDHEgSBqVOnsmTJErRaLU1NTWzcuDGkjdY1mUtBpcXXWIqvrvu1KH/hoEVJKPUJj8VKSbUFmyEcrQgT47v6J3WHLHcnqzNoZ145rDFqsi9AO/1yopuOdYrtTOa2JZQOllt7vGdYXS4qbXLSyd/R01/iV51PzJIF+JwuTv35uQEdYziI1OuJCzMgAaeblS6lvlDfJncX243cXU2jhVK7nPy/Yl6H0oj10MdIbifahGx0aTNGJtCzUJtjSbvvQwSdEduxDVS/MTQSzFqVyMOr08iI1tHY6uHhz4qxOAInq7KysliwYAGCIFBWVsa2bduora2lsbGx27+elKMUQpt+J5TGjRvX65+CQigTHh7eXnl1+PBhnM4Og3ZvXSH2dX8EwHDFrxAjxo70k0JnVCkzEPThSA4L3orcdn3t1rxCPJbeq5gUzm3a/ZMCyN1Bh3+SeXbHhMpqtVJVJRvIhoLc3eGyZr44XosowL0XZgU7nGGhk+zdeNlkd0eh4qPUF/x+OO5TYy+hZJ7T4amgEBhJktoruv2FF2fibSqXO9gEEU3WBSMdXsghqDWk3P0GoiGC1oLd1Lz7cLBDCiq5TfVIQLrRTJSu7x5Dgcg2RxKmUmHxuNsTVcFi6wn5Xj45Ruy3d69h0gWoo3rualJHJmOYFLzf1I2zY4nQqyhrdvG/442976DQLZLPR/lfv84BtZwMnR0ThzlicN163RFvMPHIkpXMT0jG4/Px95z9/OfoYbw+34COl5iYyKpVq4iKisLtdrNr1y6OHj0akvJFgs7YXgATSPbOXxChFJH0DevhoxQlyYVvUxKNaFUikiS1dyj5n63PxlO4G8lWj2CIai/eHE4MV/6KKKus/NDY0IDL1blzYkpCGGEakaZWL6frHQGP45eDSzSaMA9QmlUQBKY8dD8ApW99QMvxUwM6znDgT5LlNSoJpb5Qv2sfANGLuyaUPt1fgIRArNZDdkrHtbxl3zsAhC+8MagF6fq0GaTcIUswN6z/C41b/jkkxzVqVfzm0nHEGtWUNrl4ZF0pLk/g+0taWhpLlixBFEUaGhrYtm0bmzZt6vZv3bp1SlJplNLvhJKf/Px8/v73v/PYY4/x4osvkp+fP5RxKSgMK5MmTSI8PByXy8Xhw4cB+YHf+s6Pwd2KOut8dAtvDnKUCsOJIKradZ3dBTvQxkajS00CScKaezzI0SmEOu3+Sd3oKgN4WmppLdgNgGnW5e2v+7uTEhMTMRqNwxxlz0iSxJPrZSmzq2clkZ1gCmo8w4W/irKuro5FaXKH0tEqO02tg5cBGOu0y8gU7EDyOHsePMroVETQohQRBMJRUoanqQVBq8EwuWvS2Z9sVKfNQTREjnB0oYk2LoPkb8sT+PpP/oA19/MgRxQ8DrXJ3c0egu4kALUoMr1NvscvpRcscqrkxcklE/r/3gRRhWHy8p4HqTX4HMG7Nhm1Kr4+T75/vrq/BptLkawZCDVvP0jzwQ85Ok5ODl4wYfKwnUuvVnPvvMVclz0FgP8V5fOHvduxuAZ2/zYYDCxbtqy9AOrkyZNs374dhyPwwnywaH9eOdl9Qsnc1qFkP3Uar31gPlPnEpYDue1yd37/pJaWFhwOByqVipiY7q97ztyPANBOuxRBNXRdeIFQRY8javFNhLVWIQE1VRWdtmtUYrtcX08+SvmDkLs7k6g5M0i6cjVIEsd//9SgjjWUZLXdN/OVhFKvOOsbsZyQ58fd+SRvzZO/K3OTO4pkfA4r1sOfAmBecMMIRNkz4fOvJe663wBQ+crd2E/tGJLjxpk0PHrZOIxakaNVdv60qbzHzr+EhATmzu3eGuBMfD5fpyJ/hdFDvxNKkiTx/e9/n8mTJ3PXXXfxi1/8gjvvvJPJkyfzwx/+cDhiVFAYcvymcQBlZWVUVlbi3PsantM7QROG6fr/p0jdnQN09VFSJJAUesdtsdJ85AQA0Yu6f0iy5v4PJAl9+mw00R1VyKEkd7fxRC05ZS3oNSJ3rwh+t9RwYTabCQsLw+fzITqayY7V45NQfCH6gCpxCoIpDtyteIr3BTucIUUbG40uLbmtiOBYsMMJWawH5e4k47RJiJqui0OK3F33hC+4nqhVdwNQ/rdbcQfJCyeYWNwuTluaAZg5SP+kM/EfK6exPmiyd2W1TVQ7ZW+QS+aO7/f+1tx1tOx+AwDR0NmfQR2RhGiIwFNXTOlTV+NzBW/x+9IpUaREaGl2eHn7UHATeKORxs3/oP7TP1ESNxVLWDRGjYZZccOrfiEKAtdPnMp98xajU6k4Wl/LL3dsorTtt9hfVCoVs2fPZsGCBahUKmpra9m4cSN1daH1fdBMWgWAu2AnkrtrwkubEIc2KR58PqVwsA+c6Z80oy0h45eUi42NRaXq6vcl+by4cuVFde3Mq0YoUghb9QNiWosBqMjp2lE/L02Of39p4IRSXluH0kDl7s5k8oM/RFCpqPliK3Vt0mnBJrvtfeU3NYRkl2Eo0bBbnu+YJ2Whi+38fXB5PByvl7tyLpreIXVrOfwpktuBJj4TffrsEYu1J2Kvfkj2cvK6KX3mOtz1JUNy3IxoPQ+vTkctCmwvbOHFXVU9jjebzUNyXoXQpN8JpSeffJLnn3+eO++8kz179lBaWsqePXu46667eP7553nyySeHI04FhSEnKiqK7GzZoPPg/n20fPYHAAyX/hxVjCLdeC7QnlAq3IPkdbdXr/n9IhQUuqPxy0Pg82FITyUsufuFAWubf5Jp1hXtr9lstnYN+mDL3bm9Pp7eIEtE3Lo4nTjzwOQdRgOCILR3KSmyd/1DEEU0E8eu7F27BM5BpYggED3J3Uk+7/9n76zD4zivt33PLKO0YpYsg4ySGQN2mjicNsyctElTSPHXfk0ZUk7TFNO0gaZhahomO2ZmFjPjMs18f4xWtizZFqx2BXtf116XvbMz71nt7sw77znnefAXrwdAU7AqonGNBVJv/D267EKC9mbq/n4rsjSxOjz2tytyd9lGMwlhkLsLUWCNR69S0eX3UREl2bv3dpUDkKYLkJkUd4ZX98bXXEHtX28EWSL+nLuZ9qcmcr+zhsz7niP3O2uY+odq8r77KaIxDtfRddT8+fqwGGsPBbUocPcSRdrq9f2tNDsmtifYYHAc/Jj6Z5SkcsnZXwVgSXoWmn4W4keCRWmZ/Gj5KpINRppcTn64cQ3bG2qHfLzs7GxWrVqFxWLB4/Gwfv16iouLR83itCptOoI1Dfxu/BXb+n1NzzU/5qN0WoJuD1U1bThMcWhEmN7tn3RmubutyI5mBEMcmslnRSxeQWsifYYiedhs9xNo7/09X9Dto3So0Y3b3/c6LMkypaGEUvzwu2nNk/PIuflqAA7//JFR8RvJtcajEUUcfh8NzlhX/ulo3awkARP78U9at78Sr6xCJwQ5Z87x9cKu7S8DYF10zagpShcEgYx7n0SfM5dgVxPVj34OyRseWbmiDBPfWKkk1N440Mbr+0ZXgUGMyDHohNITTzzBl7/8Zf785z+zaNEiMjMzWbRoEX/605944IEH+Mc//jESccaIMSLMnDkTk8mEx+enNG016pwF6FfcHe2wYkQIVdoMBGMC+FwEavYe71Dafxg5MLEWfmIMnNatp/dPkgN+HAcUiSPz3OMJpfJyZQEqNTUVszm68nKv7Kylpt1NoknL7ctzohpLJAjd/DY2NrKiO6G0p9YZk/AZANpuHyXfsbXRDWQEsMxTTHNjPkqnJrTwFlqIO5FA9R5kdweC3oo6a26EIxv9iFo9WV98EUFrxHnoY1re+mW0Q4ooIUm6ojB2J4EiezcrLqF7jNawHnugbClTFh8L0weXKJN8bmoeu4qgsw39pIWk3fonRJUa04yVxC27EdOMlQiiCn1OEdkP/g9Bo8ex53/U/ese5CF64QyXpbkWZqcZ8QVlntnRFJUYxhreusPU/OlqCAYwLLuFfbpkAJZnZEc0jhxrHD896zxmJSbjDQb5w84tvFZ8eMidfVarlVWrVpGVlYUsy+zfv5+tW7fi90c/0SgIAtoe2btP+n1NLKE0MJz7D1OeqnReTk81olWLBAKBnq60UyWUfPuVYjrtrIsQ1NrIBNtN2uLLEeQgXl0CLe/1LnBPt2pJs2gISDL76vouqNc6unAHAuhUKrIt1rDEM+3r96MyGOjYtY+Gdz4KyzGHg1oUmRSndMOGurFi9E+oq6y/hNJHB5Rk5YxEEa1a6VKWvM4euTvromsjFOXAEHUmsr76BipLMp7K3dQ9cWfYEpwrp8T1FJw8vqWRdaVD64KNMbYZdEKprKyMyy67rN9tl112WY+cT4wYYwGVSsUcixOA+tSzcF3wEwQxMpVjMaKPIIpoQj5KJRsw5OeispiR3B5cx0qjHF2M0UrblpB/Uv9yd66STUiuTlSWJAz5i3ueLy1VvlPRlruze/w8vk5Jbt23chJGrTqq8USCUIeS3W4nURskO15HQJLZVhWTvTsTmqnnABCs24/kGF8VaJZ5ShGB88ARJH/MU+tkAp1duMsUGRlz4cw+2/3dSUbN1LMRVEM/j7R7PdQ4Had8tHtHn1/HQNFlTCf9tj8D0Pz6D3Ed2xDliCKDw++npFtiqyghvAmlE4+5r70l4rJ3QUnicJuS3Fk5I33A+8myTP1T9+Gp3I3KkkT2l19F1J46IWUqOJusL70MoorODU/T+MI3o1LpLggC9yxVFo0+PtZBaUvMf+Z0BLqaqfr9ZUiuTgxTltN46U9wBwIkGowUdH9v6zs9HK63n/JR3xm+c55Fq+P/Fp/FhXnK3PPVY4f4464teAJDu+ap1WoWLVpEUVERgiBQV1fHmjVr6OyM/mJiqFP2TD5Kjr2HopagHQucKHcX8h9qaWlBkiQMBkO/RXGyJOHd/zYA2jn9rxWOJBqNhsQ4Ja6muppeXWqCIDC/u0tpZz8+SiFfofy4BFTikC3me6FPTSb/C7cBcPjhR5GG+HsLJ1O6/aFKolSIMRbwtXVgP3xq/6Rddcq5+awpxzvZHPveRfa50CTloc87s19QpNEm5ZL9lddApaFr20u0/O8XYTv21YWJXD5L+V79Zk0t++udYTt2jLHBoM+YcXFxVFZW9rutsrISqzU8Wf0YMSKB5GhB/8FDZDSsA2BfRQuBUXDBjxE51JOXAxAo3Yggij2LZrGK9Rj9EfR46dizH4CEpQv7fU2P3N2ci3sS1C6Xi7o6xSg22nJ3/9pQSac7wKQkI5+dO/DFsLGMVqvFZlMq85qamlgxSdFz3lgeSyidCdGaiip9Jsgy/pLxtRiuz8tGFWdB8nhxHSmJdjijDsc+xWdCn5uFJiG+z/aehNK0ocvdtXs9PHxgJ78/vOeUj4cP7BzTSaW4s24nbvktIAWp+etNBB3jvzp4f4cid5dlNJEYRrm7EAVWGzpRRaffR5UzsufxbUdrcEsqNILEysK8Ae/X/vFf6Nz4DAgiWQ+8hCbxzN3BlrmXkXH3vwBoe/8RWqPU5VaQYuTcyVZk4J9bG0eFhNNoRPJ7qf7jlfiby9AkTyL7q2+wuVHxl1iekY0oCNR3erjyz1u4+Yntp3xc+ectYU0qqUSR22bN5d7CBahFke0Ndfxo0xqaXENb/BMEgcmTJ3PuuediMBhwOBysXbv2lGtEkUIz9RwQRIKNRwl21PXZbpw6GdGgJ+hw9hRLxOhL1659VGQqtgCFGUpC6US5u/4kvQKV25HtjQh6a08hUqRJy1GSpm3xM3D+96FeMrMLspWE0q5+EkrF3QmlqWHwTzqRyV+8E40tHmdpBdXPvx7WYw+FqTYlCRLrUDo1rd3+SeZpk9El9ZY/LK5tocWnRkDmsoXHi0O7tr8CgHXxtaNG7u5kjNPOOl7c9OpD+A6+F5bjCoLAF5alsTzPQkCS+cn7VVS2jd35eozBM+iE0gUXXMBDDz3Ezp07ez2/Z88efvjDH3LhhReGLbgYMUYa538fQna1McV3GINBj9Pp5NChmDn3RKLHR6liO3LA2+MTEfNRitEfHbv3I/n86JITMU3qfzHIvlep0DPPPV6hF+reTUlJiao5ZV2Hm+e31QDw4PlTUIepEm8s0J/s3Y5qO55ArEr1TIQWB/zjTPZOEMWec769O1Ec4zihwor+5O4kdyeB6l0APT5bQ8EZCBA4w+J0QJZxjuFiH0EQSLvtL2hTpxJoq6Y2jJIjo5WRkrsLoRFFZsWHZO8i2zn58X7lGjrZKqPXaga0j+vYRhqeexCA1Ot/jWnGwJOw8WfdRupNioRT0yv/j/Y1jw8u4DBxx6JU1KLA7lpnv1X+Ex1Zlqn/5924izciGuPI+frbePRx7GlWEkorMhW5uw6XH1/w9PMOX1CiwxV+GbmV2Xk8tPQc4nQ6qu1dfH/DJxxsGbqMYUJCAueddx4pKSkEg0F27tzJrl27CAajIycsGm2os+cB/c9XBLUK85wZQOw+71RIfj9VFU3YTXGoBSjo9k9qbGwETiN3t+9/AGhmrkZQR8eXNaRG0GEtwF93CO/2F3q2FWWYEAWo7fTR0OXrtV9Jh9KxE+6EksZiZtrXvgDA0d/9hYArPP41QyX0/qq7OofcoTjead2sJJQSl/UtGn17p3Ivn2UMkmJT7uUlnxt7dyGpdeE1EYpyaNhW3ovt/C8BYH/+AQINh8NyXJUo8O3zspiZasDhk/j+u1W0OqMvgxojMgx6Jenhhx9GrVazePFi5syZw+rVq5kzZw4LFixAFEUefvjhkYgzRoyw4zv4Pr69b4AgEn/1r5g3T2lRLSkpoa0tVrkxUVClFiCYkyDgIVC1C3O3HIJ9z4Fxv+ATY/Ac909a2G8Vkq+pDF/dYRBVmGev7nk+lFCKttzdn9eU4QtKLMqz9WrXnwiEboKbmprIT9CRYtbgDcj9VivG6I0m5EtQ/Om4Oy+GzvmO3bGu1JM5nX+Sv2Q9SEHE5CmobJH1BRmLqAwWMh94EUGtxbH7Tdo+fCzaIY0YzoCfkq4OAApHKKGkHFu5hu1tb43oeSl0zViUGzeg1/s76qn50zUQDGBdfB0JF3190GMmXvggSZd/D4D6p+/rqYiOJGlWLVfMVhYk/7mlkaA0vq4Fw6Xlvz+lc/N/QKUm60uvosuYwbaGGgKSRI4ljmzLwL4vI81UWyI/XXEe+XE2HH4fv9y2gfVNdUP+Del0OlasWMGMGUqipqKigk8//RSnMzrSR8dl707voxRTougf54GjlCXlAop/kk4t4nK5sNuVTtDk5OQ++8iShO+AUkynK7wicsGeRFxcHDqdDkmlpdOSj+u9XyC5FSlGk1bFjFQj0LtLyen3UetQ3tvk+PAmlAByb7seQ3Ym3sZmyv/xbNiPPxhsegOJegMyUBrrUuqX1s2Kf1JSP/5JW8qV79LinOOFoY597yF7nWgSc9Dn991ntJF24+8xzjwPfE7sT92O5AzP90CnFvnhhTlkxmlpdvr5wXtVPT7FOp0O8QwFrKIootNFJxEdY3gMOqGUnZ3Nnj17+Pa3v43JZKK8vByTycR3vvMddu/eTVZW1kjEGSNGWJHcnThe/z8A9Ofejzp7LmlpaWRnK4si0ayuihFZBEFAk6/I3vlLN2KaPR1BrcLf1IKvPmY+HKM3Z/JPcnR3JxmnnY3KFA+MHrm7Q3VdvHtAqTB88Pwpo7Ytf6Sw2WxoNBr8fj8dHR09XUqbyruiHNnoRzNpCah1SJ31BJuKox1OWLHMnQPEighORvIHcB44Chz3nTgR/7FPAXpM0GOcGUPuPFJv+C0ATS9+C3fFrihHNDLsb29FAjINJpL1hhEbZ3qcDa0o0uHzUuWMTGFAp9NDlVORsr2g6MySdXLAR82friXQ2YAucxYZd/9zyNfe5Kt/im3VF0CWqfnrTTgOfDik4wyHG+YlY9apqGj38tGxjoiPP1rp3Pwcza//EID02/6CedZnANhYWw0c704aDCVNDvxn6GQaKokGI99fdi4rMnOQZJnXq8p4qbKEwBB9hQRBYMaMGaxYsQKtVktHRweffPIJ9fX1YY78zIQkWP0l65GDfbswzEWKtLljb0yRpD/su/ZRman4J805Se4uISEBrVbbZ59A1U6kznoEnTlqcnegfA9DxWMdGSuQna24P/p9z/YF/fgohRIrqUYTcSMgz6rSaZn+f18GoOTP/8Lb2h72MQbDlO5CjJJYQqkPvvZOug4dA/p2KNldHkq7lGv3hUXHz+dd218GwLLomjFxXy2oNWQ98BJiQi5SWxX2Z+9FDoanm8iqV/Ozi3OxGdSUtXr4+YfV+IMSRqOR1atXs2rVqlM+Vq9ejdFoDEscMSLLkLRukpKSePjhh9myZQvFxcVs2bKFn//85yQljVwVWowY4cT19k+RuxoQk/IxXvDNnucLCwvR6XR0dXVx9OjRKEYYI5L0yN6VbkRl0GMsUCbSseq1GCciBQK07dgDQEI/Rp1AT9u7uejSnucqKiqQZZnk5OSo+QzKsswjHykeMZfOSWNGevRk96KFKIo9lZWNjY0s7/ZR2lppH7FFm/GCoDGgmbQUULqUxhOmWdMQtBoCbR14q2qjHc6owXWsFMnjQWUxoz9J3lOWZfzHFNNzTSyhNChs538Jy/zPIgd81P7leoLu8efjtq/b8Hsku5MAtKKKmXGRlb37cHcpEgJx6gAzc1LO+PqG57+hSKAZrGR95XVEfV8z+4GiSCf+GeuiayHop/qPV+Iu3XbmHcOIRafipnnK5/rMjiY8/ti101W8ibon7gQg8eJvYlt5LwAtbhdH2loQgGUZg08o/fDNw5z9q3Xc9dRO/vBRCWuONNPq8J15xwGiVam4v2ghN82YgwBsbWnkL0f30+Uf+hipqamcd9552Gw2/H4/mzdv5sCBA0hDTFQNBXX2XARDPLK7k0D17j7bzYVKgYS3uhZ/W3QX90cjXbv2UZHR7Z+UrizwhuTuQpJyJ+Pbr9z7aGZeiKAJf1JmMPTI3qUpc1bPxn8SaFSSBPO7E0p7ap0EujssQ/5JU0agOylE5pWXYJ1VQMDuoOSP/xixcQbC1O73WdIeSyidTNvWnSDLmKdMQpfce/7y/q5SgohY1QHmT8kAQPJ5cOxRpB6ti66NeLxDRW1OxHLHU6A1ESjdiOt/PwzbsdOsWn58UQ56tcjuWiePrlO6X41GIzab7ZSPWDJp7DLohFJzczPHjh3rd9uxY8doaYmsjnWMGIPFX7IB7zal5dh8ze8QNMerJ3U6HUVFRQAcPXqUzs7OqMQYI7KEEkqByp3IfjfmHh+lWEIpxnG6Dhwh6HShtlqwTp/aZ7vkceA6shZQjLRDlJaWAtGVu1tX3MrOyg60KpEvropel1S0OdFHaUaKEZtBjcMnsa8+urrmY4GQT85481EStVpMMwuAWBHBiZwodyecJFUhNZcitdeASotm8rJohDdmEQSBjLv/hTohG19jCQ1P3z+uOuOcAT/H7B0AFCWMfKFhaIy97S0R+TuuP9YMwKxkzRklXDo2PEP7R38CIPO+/6BL6ztvGCyCqCLjC//GNOsCZK+Tqt9fgrcuPD4IA+XSWQmkWTS0uQK8um9i3/f7msqofvRzyAEflvmfI+W6X/Zs29TdnTQ9IZlEg7JYZvcEeHZr1YCObdap8QUl9lR38szmKr7x8n4ueGQDV/xpM99/4xCv7KzlWKNjWNKDgiBwaf407p06C71KRYXTziOH9lDtHHqi22g0cu655/bMeY8dO8bGjRvxeCJj1C6IqhPmK2v6bFdbzRgm5wExH6WTkQNBKksb6DLHoxYUyTtZlns6lPrzT5IlqSehpJtzWZ/tkSaUUOryBJFmXg5SANf/fogsy0xJ0mPVqXD5JY42uYHjnTqhzp2RQBBFZnxPkTqteOp5XNXRK16a0u2jVNwRWanYsUDLJkXuLrEfubs1R5TfQGHK8Wu/88D7SB4H6oQsDPmLIxdoGFCnzcBy419AEPBs+heeLf8O27GnJhv4f+dnIQrwcXEnT2+PKf6MZwadUHrggQf4zW9+0++23/3ud3z5y18edlAxYowUss+J45VvAKBbdjua/L4LIZmZmaSnpyPLMjt37oxoVVWM6CAmT0awpELQR6ByZ4+8T+xGI8aJtG5V5IkSFs9DUKn6bHcc/Ag54EOTnI82XVmgdrvd1NYqNw7RkrsLSBKPdncn3bwkm/S46FYPRpPQzXB7ezvBgJ9leUqXUkz27sz0+CiVbkIOeKMbTJixzIsVEZxMKKFk6cc/ydedVNRMWoKgNUUyrHGBypxA1v3Pg6iic/N/6NzwdLRDChsHO9qQZJl0g5GUEZS7CzHDqsjetfu81LhGXvbuQJPSvXHW1NMny9wVu6h/SjFjT/rcD3sVmQwXUaMj+yuvoc9fTNDRSuVvVuNvHViSIhxoVSJ3Llaupa/sbaXNNTHNt4PODqoeuYygvRl97nwy73sWQTw+N9xYp3wmZ2VmI8kyb+6t56q/bOHd/Y0DOv7fbpnL619cyo+umMGV8zKYnKyca2va3by9v4FfvHOUGx7fxsrfrOOL/9nNX9eWsam0Fbunr8zbmZgeZ+PBGXNJ0Rvo9Pt47Mh+drYOfRFQFEWKiopYtGgRKpWK5uZmPvnkk4gVHvfMV472TSjBCT5Ke2P3eSfiOlZKmS0TgIIUA3q1SHt7O36/H41Gg81m67NPoGYPUkctaE1oClZGOOK+6PV64uPjAXAuug9UWvzH1uA//CEqUWBupvI72lnjQJLlnk6dqbaR61ACSF65nKSzlyL5/Bz9tVJoUPfme3xQeC51/3t/RMc+kTxrPGpRxO7z0eqNTJJ3rNC6eQfQV+5OkiT2NynXuZXTjydVQ16G1oVX9ym8GgtoZ12IYbViAeJ847v4y7eE7diLcix85Wylk+vFPS28fSjWETdeGfQ3f+PGjVx44YX9brvwwgvZsGHDsIOKEWOkcL3/a6S2SsT4TIwXP9TvawRBYO7cuWg0Gjo6OigpKYlwlDEA2r0eapyOUz7awzgJEgShl+xd6EbDVVJO0BEdU9kYo4+2Ld0TzSX9y92F/JMscy/r0VEuLy9HlmWSkpKIi4uOIfMbu+upaHURb9Rwx4rcqMQwWjAajZjNZmRZprm5meXdPkqbK+wxg/EzoEqbgWBOBr+bQMX2aIcTVszdRQT23bGEUohQQYV57sw+20JdaqEq8BiDxzhtBclX/QSA+mcewFNzAOfhtXRufh7n4bXI0tj08QxJzxWNsNxdCK1KxYxu2bs9Iyx7d7S6mXa/GgGZC+adukAk4Gil5rGrkP0ezEWXkPzZH4Q9FlFvJufrb6PNmEGgrYbK36wm0NUc9nFOxdn5VgpSDHgCEs/uiNy4owU54Kfmz9fiqzuM2pZJ9oNvIuqOJ9erujqpsXehFkUsWLnjyZ386M3DtDp9pFkHVtQjCAK5iUauKErn+5dN5+X7lrD2m2fz2I1F3Ht2Hksm2TBqVTh9QbaUtfOP9RV86bm9rPzNOq7721Z+9vYR/re3nqpW14A6EVL0Bh6cUcTMOBsBWeI/5cf4X3U50jC6GLKzs1m1ahUWiwWPx8P69espLi7G6XTS3t5+yofLNbyucW23j1KgZg+Ss7XP9tA1P+aj1JuunXuPy911+yeF5O6Sk5P77cr07XsTAO2MC3qpvkSTUJdSs0tCf/bnAXD+7wfIAS8LshXZu13VDpo9blwBP1pRRbZlZO/RBEFgxv97EICaV9+iecNW9n7rx3ibW9n3rR/jben7PR0JNCoVedZ4ACqG0Yk43vB1dNJ18AgAict6dyhtP1aLI6hGjcTq+UrnpeT3Yt+tfPfHktzdyRjO+yraos+CFMD+zN0E26vDduwLp9u4ZYEiNf/nDfW8vq+FkhZ3v4+mMMq5xogs6sHu0NLSQmJi/y2hNpuN5uaJN6mMMTbwV+3Cs0HRrTVd9WtE/ak9RAwGA3PmzGHXrl0cOnSIjIyMftu8Y4wM7V4PDx/YSeA0NzFqQeC7sxdgC5OBpmbKWfj2vIa/dCPGC/8PbUYavroGHPsOw+SJKxEWQ0GW5eMdSv34J8my3JNQOtE/qaysDIie3J3TG+BvnyoxfP6cSVj0g77sjztSU1NxOBw0NjZSWJSOWSvS7g5wuMnF7LRYt8WpEAQBzbRz8e16BV/xp2imnBXtkMJGqIjAU1GNv60DTUJ8dAOKMt6GJnyNzaASMc2e3mubHPDiL90IHDc/Hw4mtRqVIBA8w/XepB5/566kS/8P16FPcB76mLIfzIcTjJHVtizSbnkU68Krohjh4HAHAhzr6gAil1BSxkpkb3sL+9pbuSwzb8SMsT/YUwFAljFIorX/a4UsBan9y434WyrRpEwm8wvPjljlstqSRO63PqDiZyvw1R+l6ncXk/udNagMI++RKAgC9y5N45tvlvP+0XY+OzuB3ISJ0f0syzINz34Z58GPEHQmsr/2PzQJmb1es6FW6U7SB03c+/ReAIxaFfeencd505O59m/b8J3Gu1GrEok3avo8bzVoWDElkRVTlLWYoCRT2uRgb00ne2s62VfTSU27h5JmJyXNTl7bVQdAvFFDUVYchVlWCrPimJlhxaDp22mvV6m5a8pM3qut5KOGGtY01lLvdnFrfgGGIZ6DrVYrq1atYvfu3VRXV7N//372799/2n1EURyWSbsYl4YqfSbB+kP4i9ejm/u5XttDnbfOQ0eRfLGFzBBdO/dRmb0agDnpvRNK/crdyXKP3J228PIIRXlmUlNTOXbsGE1NTSw476t4d76M1FqBZ/3jzF98HwDHmt0c7ugAID/ehjoCHSbxc2eT8dmLqPvve+y6/9sEnUriNOBwsu//fsqif/5hxGMApRurpKONSoedhYln9gKcCLRt3QWyjGnyJPSpyb22vb9XOZ9PjpMxG3QAOA9+iOTuQh2fgWHK2JV+FgQB87WP0NlcRrBuP/an7iDugTfDpj5w0/xkqtq9rCvr4vEtp+7O1agEnrh+CilmbVjGjRE5Bn3mTE1NPeUkYP/+/adMNsWIEU3kgBfnyw+CLKGdfw3a6Z854z65ubkkJycjSRK7du2K6cxGEGcgcNpkEkBAlnEGBi/rcCp6fJSqdyP7nD2ydzFPjRgAjmNl+Ns7EPV64gv7SkB5KncT6KhH0JkwTleq9j0eT9Tl7p7eVEWb009OgoGr52dEJYbRRqhysampCbUosCRXWXzbWBaTvTsT2pCMzLFPoxtImNHEx6HPzwGOS71NZEJ/A2PBFFSG3hXHgYrt4HcjWFJQpfftXhosNp2eKd2VwUXxSXx9xlwenFFEvEa5qfxMWlZYi0dGE4Kowrr0RuU/wd6yYYH2Wmoeu4auHa9FIbKhcaCjlaAsk6o3kmqInMHyjLgENKJIq9dDrWvkusq3Vii+qvMzT73Q0vTq93Ee/BBBayT7y6+hMvWViAonmoQscr71ASpLEp6KnVT/8XNIvsjIGM1KM7Iiz4Ikw7+2DUzGbTzQ9v4jtK/5OwgCWfc/jyF3Xq/t3kCQjysqADhSpiSNLp2TxutfXMrty3PJTjDy+gNL+c89i075eP2BpQOSJ1aJAtPSLFy7MIuffW4Wb35pOR98bQW/vXYOty7LoSgrDq1KpMPl59NjLTz2SRn3PrObc3+9jlv/uZ3fvH+MDw420uw4fv4RBYFLsvK4Nb8AjShypKudPxzeQ6N76F1DarWahQsX9vgUnwlJkvB6hyete1z27pM+23Q5maht8cg+P87DxcMaZ7wgSxJVxTV0WhJQCTAj1YjP56O9vR3oP6EUrNmj+ClqDGgLhl9gEi4SExNRq9V4vV66vEFMl3wPANfHfyAh2EaeTYcM7G1W3tuU+JGVuzuR6f/3FRBFfC2tyEGlE1kOBml45yPq3nwvIjFMiVfWayudsfueEKeSuwPYUa3MK5ZPiu95rmvbywBYF41NubsTEbRGLHc8hWBOIlh/EMeLXw3buqcgCFxZeOb8gD8o0+UZm535E51Bf/svuugifv7zn3Ps2LFezxcXF/Pwww9zySWXhC24GDHChfuTRwk2HkMwJ2G6/CcD2kcQBObPn49KpaKlpYVDh2Jt8eMZMSEHMT4Tgn78Fdsxz+321IgtLsYAWrfuBMC2oBBR27dq1LFHqdAzz7oAUaNUL1VUVCBJEomJiT163pGkqcvLs1uUqqqvfGYKGtXYnvCGi5Bsh8vlwuFw9MjebaqwxwoHzoBm6jkABGv3ITnGlxm7Ze4cIFZEAMfl7vr3T1I8KbTTzg1LJ0iHz9vT1XJhZjZZJjM5JgvnpioV/4c624jX6oY9zmhEloI0v/6jU20FoOE/D44Z+bt97YpkT9EImpv3h06lYrpVSdzsHSHZO6/PT4mST2LVrMx+X9O14zVa33oYgIy7nkCfUzgisZyMLr2AnG+8h6g34zr0CbV/uwk5GL6Cq9Nx55JUVAJsq3Kwu3bkPayijX3Xf2l84ZsApN7wOyzzendlbC1v45ZnNuCRfAQDApmGBJ68YwE//dxMki3Hz2PpcXpmpFtO+RiO12WSWcd505P52vlTePLOBaz79jk8decCvn7BFD4zPZkks5aAJHOwzs7z22r4zmsHue25Y9z2n2M8/FE1b+xv5WiTmznxSXx5eiE2rY5mr4dHj+zlUMfQfTAEQWDy5MksWNC/bHS40RacByiefyfP7QRBwFykFETE/HIV3GWVlFqUwrOCZAN6jUhzczOyLGOxWPrtFvOGupNmXICgjVwRwZkQRZGkJKVLtrGxEe28a1DnzAefE9e7P2N+liJ7V+dRzlkj7Z90ImqLCUHdtzsQQWDft38SEem70PutcznxBsfG/GKkad2kSHknLe8td1ff2kWNW/m8LlmoFIfKAR/23f8FwLLwmghGOXKo4jOx3PYvUGnw7X8L98ePhO3YanFkusZjjA4Gvbr0ox/9CJVKRWFhIRdffDH33nsvF198MXPmzEGlUvHjH/94JOKMEWPIBOoP4f7kjwCYPvcLRNPAJw0mk4lZs5QFlc2bN+NwjP+bpYnKyT5KoQ4lx75DSGHshIoxNmnboiSUEvuRuwOwh+TuTjDeLi0tBaInd/eXtWV4AhJzs+NYVRA5+aHRjlqt7ummbmpqYkGWGZ1aoMnhp6QlZlB7OsQTulL8JePLM9Myr7uIIOaj1GNUHvKZOBH/0bUAaKauDMtYm5sbkIHJljjSDMc7PxYlpaIRRerdLsod47OK1nV0PYH2mtO8QibQVo3r6PqIxTRU3IEAR7qUau+ihMhfb+Z2j7m3vWVECgM2HKzCJ4vohCDLZmT32e6tO0zdP24HIOHCrxG37Mawx3A6DJMWkP3gmwhqLfadr1P/1H0RKZDIjNNx6UzlvuqfWxqH5bcz2nFX7KLmrzeBLGNbdR8JFz7Ys62uw823Xt7P/c/uwalSzleTLcn8++5FFGVHxz8zhFYtUpgVxy1Lc/jNtXN4/8EVvPXlZfz8czO5bmEm09MsiAI0O/2sK+vi75sbePCNMq558jCPftxMmieDJI0JTzDIP0sO8XF99bC+W1arNYzv7tSo8xaB1ohsbyJY37coNFQwESscVLDv2EtFhnK/Muck/6RQZ/+JyLKMb9//ANAWXtZne7QJdVQ1NjYiiCLGK34GgHfnyxTp6hFFGZ+gdOaFOnZGGlmW2fftn0B/SRxZ7pG+G2kSDUZsegMSUOOKrW35O7vo7PFP6t2h9M7OUkAgRRdgcrryPXEc/AjJ1Yk6Lg3jtBWRDnfE0OQtxnTVrwBwf/BrvAfeiXJEMcYCg04oZWRksGPHDm6++Wb27dvH008/zb59+7jlllvYtm0bGRkxSZ0Yowc5GMDx8tdACqCZdTHaOYPX9508eTIJCQn4/X7WrVsXq2Afx6hDsnelGzFMzkNlNiG5PXQdOnaGPWOMZxT/JKUVPmFJ34RSoLMRT9k2AMxFSpeu1+ulpkZZKIyG3N2xBjv/21sPwNcumDJinhJjldDNcWNjIzq1yKLsbtm78vG5cB1ONFMVSUf/sbXRDSTMhLpSnYeOIXmGJ7Uzlgm4XLiOlgDHvaVCSPYmgvXK4ptm2jnDH0uS2NLcAMCK5PRe24xqNfMTFB37jc31wx5rNBLoGNj7GujrosmhzjaCskyK3kCaPvKV6jPibKgFgRavhzp3+GXv1hxSvGgKEgQ0J1WXB91dVP/xSiSPA+P0laRe/+uwjz8QTDNWkXn/CyCIdKz7J00vfzci4960IBmjRqS01cOa4s6IjBlp/G21VD9yObLPhWn2atJu+SOCIODxB/n7p+Vc/detfHykGZUok5ikFKHdUjQd1SiszBYEgYx4AxfPSeM7Fxfw3L2LeOWO6fzysjxuW5jComwzZp0Kb1DmQL2L1/Z0sH6bhvZmLTLwdm0lv96zn+IW56hOIApq3fFCwX5k70IFE469B2P39oB9134qMqcCUJhhQpZlmpqagFPI3dXuR2qrUuTuBmAlEGlCMbe2thIIBNDkzEe38HoA8jZ+H4sliCBAvNZAvD4ykrr2oyU0vPsx8in800LSd/buOdhIEpL5q3DYR3ys0U7btl0gSZjyc9Gn9U6eri9ROsbmZxyXf7ZvfwUAy8KrEMR+us3GMPpFN6FfcQ8Ajhe+RKD+cJQjijHaGZL+TUZGBv/85z+pra3F5/NRW1vLE088EUsmxRh1eDY8TrBmL4LeivnKXw5pUTUkfSeKIpWVlRQXx7SWxys9Pko1e5H9LsyFSiV++449UYwqRrRx19ThqWtEUKuxLegrYePY9y4A+rwFaOKVRdHy8nIkSSIhIQGbbWQ9FPrjDx+XIgMXzExhTmZ0q2NHI6EbzebmZoLBICt6ZO9iCaUz0eNLUPzpuFqE0WWlo0lKQA4EcB46Gu1wokbH7gMQlNCmJqM76cY65J2lyixENCf3t/ug2Nfeij3gx6rRMqcfD4MVKek9r+vyjz/jdHV8+plfNIjXRZOQ1FyRLSkqBQx6lZrpccq1NiS9F0721LkBWDqp9/dUliTqHr8dX/1R1LZMsh54EUGlDvv4A8W68ErS7/oHAK1v/4qWd3474mPG6dVcP0/pEHt6exPeQP+LpWMVyeOg+g+XE+ioQ5cxk6wHXgKVmo8PN3H1X7fy93XleAMSC3PjeejqPCQkEvUGCqLQqTdUDBoVRRkmbpyfzE8uzuXF2wr4+7VTePCcDFYXxJMdp6eh2kR9lRFZhsZgF48c2seNzx3koXcq+c/OJnbXOHD6Biaf5QiqaQnoTvlwBMPzGwr5+viOrumzzTSzAEGtxt/ajquyOizjjVVkWabqSJXin4TMzFQjDocDl8vVSz7uRLz7u7uTpp+HoD21r1y0MJlMGI1GZFmmubkZAOPF30PQmVFV7yAnXik8MGA43WHCiqVgCmkXfwZB1X8SQlCpSLvkfCwFU0Y8lpDsXcxHCVq65e4Sl/WWu/P6/BxtU+5zLpijSN3KAT9du94AwLro2sgFGUGMl/1IkTj3ubA/dRuSc+RlGGOMXcJiqFBdXc17771Ha2vsyxZj9BBsLsP1vlIlaLz8R4jWvtU1A8VqtbJwodICu3HjRlyuoRuTxhi9qGxZiAm5IAUJlG/pqV5r2747ypHFiCat3XJ3cYUzUfejIW4P+ScVXdrzXFlZGRAdubtNpa1sKWtDLQp8+bzoyO2NduLi4tDpdASDQdra2liUY0YtClR3+Khqn7jdKQNBM2kxqPVInfUEm8ZP96YgCD3nfPsElr0LXe/6k7vzdXelabuTisMl1Hm0LDkNVT+mxllGM3kmC0FZ7ulkGk8YC85GbcsCTp2AUVlTMBacHbmghoAnGOBIZ7fcnS16i+ihscMte9fYbqfOoywAXjgvr9e21rd/hX3XGwhqLVlffhW1ta80VKSxnXMXKdcpsjVNL36LjnVPjviYn52dSIpZQ7PTz38PjJ/1AFkKUvO3m/FU7kZlSSb7629R6VRz/3/28K1XDlDf6SHVquOXV83i77fOo9ypdHQsy8hGHMOd4aIgkGPTceF0G187N5PHr5vCS7cV8LWFU5lCFgRF9MYgqfmdHGpv59mdzfy/dyq57ukjPPBqKY+tr+PjYx3Udfn6/BZbXAFeap/E6x15p3y81D6JFtfw5cY13T5KgcrtyJ7e0l6iTotxptKR07Z9z7DHGst4q2opNijnz6nd/kkhubvExETU6t4JPkXurts/aQjqL5FAEIResnegyDYbPvM1AIwo84+Ozsj9TgVBoPDXP0BlMsLJ5wdBQG02Ufir70ckllCHUqUj5h/bullRITlZ7m7t/kq8sgq9GOSsWbkAOA9/guRsHxNzs6EiqNSYb/47YmIeUns19n/fgxz0RzusGKOUQSeUHnroIb72ta/1/P+jjz5i2rRpXHLJJUybNo2DB2M6tDGijyxJOF79BgQ8aKaeg27h8LXM586dS2JiIh6Ph40bN4YhyhinwhVFz6LePkqKBFLbtlhCaSJzOv8kOeDDeeADACzd/kler5fqaqXaMdJyd0FJ5g8fKVIJ1y/KIssWucq7scTJN5omrYp5mUqFZUz27vQIGgOa/KXA8Y6V8YJl7hwA7HsmbkKpvXth7WS5O1mSemQONdPOHfY4tS4H5Y4uREFgWVLaKV8X6lLa3NxAcJwtegiiirRbHg39r9/XBF0dOA/1lWsaTRzqaCcgyyTr9KQbomfMPis+AZUg0ORx0+AJX+HX+7vLAIEkbYBJacc7lBz7P6Dp1e8BkHbrnzBOXhK2MYdL0qXfJvGSbwFQ96976Nr5xoiOp1OL3L5ISaa9sLuFDvf48B5tfPHbOHa/iaDRYfviG/xxd4Dr/76NbeXtaFUi95yVx2v3L2X1rFRcAT+7m7olPDNzohx5+LHo1SzOsfDAojwemjufDIMJtUZmUoGDxdNFUi0aJBnKWj28c7id366t5e4Xirnp2aP85IMqXtnbwsEGF20uieAZlqCCiNi9w+90UyXmISbmQdCPv7TvvXvIR2miK1HYd+3rkbsryjQDx5Mw/crd1R9Eai0HtR7tjAsiF+ggCclbh6T7APRn3YuQlI/XorzPkpoAHv/AOuvCgS4pkcJf/wBOns/IMoW//gG6pMj4OU2Ks6ESBOwBP22+iVtI5++y07lfkXU7OaH08UFF6nZmotgjddvVLXdnXTD+5O5ORDTasNzxNILOTKBsM843H4p2SDFGKYNOKL366qvMnDmz5/8PPfQQhYWFvPHGG+Tm5vKzn/0srAHGiDEUvFv/TaBsM2gMmK7+TVjkN1QqFatWrUIQBEpKSigvLw9DpDFORpJlPqw/s/SAWhAwqcMvK3I8obQJ05zpoBLx1DXgrh39HgYxRobWrUpCqT//JOfR9UgeO6q4VPR5yvbKykokScJms5GQ0FfGaST53756SpqcWPRq7jk7L6JjjzVO9FECYrJ3g2Dc+ijNU4oIHHsOIkvjS7ZpIMiSRPvOvUDfhFKw/iCysxW0JtS5i/rbfVBs7F54LYxPxKrVnvJ1RbYkzGoNnX4fBzvGT+dDCOvCq8j68iuobZm9nlfbstDlzIWAj+pHLsO+67/RCXAARFvuLoRepWa6VZG929vWErbjbur2UChM0/U852sup/avN4IsE3/uPdhW3hu28cJFynW/Iv6cu0CWqP3rDTgPrx3R8VZOiWNKkh63X+K5Xc0jOlYkaF/zd9re+z0SAnsueZGb3gvy3NZqgrLMymlJvHL/Er64Kh+DVllU3FZfS0CSyLZYybGOb6nhBJ2eL08vZK4tCRmwG1u5bLnI0zdN4XvnZ3PVnESmpxhQiwId7iCbK+z8c2sj33yznJ+uHdhvU6s59XVhMGhCsnfH+vNR6i4cnOBKFF0791GRociszUk3EQwGaWlRPqfQXPlEerqTpp+HoBt9cnchUlJSEAQBh8OB06lI3AlqLe5Lf4JXa0KWZLocArurOiIaV8YVF/WRvjPmZpFxxUURi0GrUpFpVD67CsfEve/p8U+alIMho3dx0+56DwDnTFW69+SAH/vO1wGwLLomsoFGAXVqAeYb/wKCgHfz03g2Pz2k41j1KjSq088NNSoBq378JujGM4Neja2trWXKFOWC09rayvbt23nnnXe48MIL8Xg8fOMb3wh7kDFiDIZgRy2ud34KKFq5qoTcsB07OTmZuXPnsnv3btatW0dGRgY6ne7MO8YYMOsaayl1dKJC4KZJ00jWKx0W9W4nz1cUIwL3TJlFqsGATRd+E81QQilYtx9B9mIsmILr0DHatu8mM3P0+xjECC/e5hacpRUgCCQsntdnu2Pv2wBYCi9B6JZsKi0tBSLfneT2BfnrWkVq756z8ogzaCI6/lgjdJPc2dmJx+NhSa4FUYCSFg+Ndh+plvAsZoxHNNNWwts/xl+2GTngRVCPj+ugcdpkRL2eoN2Bo7gsIjr2owlHcTn+zi5EvR7jtN5ymT3dSZNXIKiH99twBwLsalMqhkMdSKdCLYosSUrl44YaNjbVUxhFSbWRwrrwKizzP4vr6HoCHfWo49MxFpyNHAxQ+7ebse94lerHribz888Qt+ymaIfbC28wyOFuubvCUeAZU5SQxMHONva2t3JR5vDn/5IkcaglAKg5p0Cp1Je8Lmr+eBVBZxv6SYtIu+WxYY8zEgiCQPodfyfoaMO+6w2q/3AFud9diyFv/oiMJwoC9yxN4ztvVfDOoTaumJVAVvzYvDY4DnxI/TMPUKor4D/TfsWxgxbAT16ikW9eOJXlk/t2EWysVYrhxmN3Un/oVCpuzS8go8HEu7WVbGpuoMHt4o7JMzgrXynQ8QUkils8HG50cajRxeFGFx3ugXWD6A3hucfTTluFd9OT+I+uQZblXknvUOGE/UgJ/i47GqslLGOONaoOVdBx0eWIyMxMM9Da2kowGESn0xEX1zs5Kssy3n3d/klzLotGuANGo9GQkJBAa2srjY2NPfdldalzoKIYrd0OcgKby9pY1s9veqQISd99snEbgS47AJ7GFnztnWhtkUtG55osVDkdVDrtLEiMvlxrNDiV3N2R6mZafWoEZC5ZqNwLOI9+StDRisqShGn68Dv1xwLamasxXvT/cL37c5z//R6qlKloJi8f1DFSzFqeuH4KXZ5Tn/utehUp5th991hk0B1KsiwjdVdtbty4EZVKxTnnnANAenp6TzVDjBjRQJZlnK99G9nrQJ27EP3yO8M+xsKFC4mLi8PlcrF58+awH38iU+mw81ZtJQBX5uYzLzGZLJOZLJOZRUmp5JksSEC5s2tEkkkAYlwaYtJkkGUC5Vt65BAmur72RKV16y4ALNOnoI3vO8kPJZRC/kk+n69H7i7S/knPbqmi2e4jI17P9YuyIjr2WESv1/fcKDc1NRFvUDM7TZFr2hSTvTstqrTpCJYU8LsJVGyPdjhhQ9SoMRfOAI7/9icSoSpt0+wCRE3vmrOQqbm2YOWwx9ne2ohPkkjTG8k3W8/4+uXJaQhAsb2TRvf49LAURBWmGSuJW3YjphkrEUQVokZH1hdfIG7FbSAFqf37LbSveTzaofbicGcbAVkiUacn0xD9SvVZcYrsXaPHRUMYvit7SutxBNWokDivaBKyLFP/1H14qvYonjpffhVROzLz0XAgqNRk3v88xukrkTx2qn57Ed6GkfO+K8owsTjHTFCGJ7c1jtg4I4m39hAH/3I3jyd9jR9m/4VjbgsmrYqvnT+FF7+wuN9kUqvbxeE2pStrWUZ2pEOOGoIgcH56NndNmYlOVFHm6OKRw3uodSl+RVq1yKw0I9cUJfGD1Tk8d0sBP1wd2b+PZvIKUGmQ2qqQWnqri2iTEtBlpYMs075rX0TjGi146xop1sQDMDVJj0Gj6pGIS01N7dN1Gmw4gtRSCmodmlEsdxciJNl3ouxdhVNJ4qS3KgWAGw9WRjwuXVIiRb/5IbrkRAxZ6UgeD1X/eSWiMeR1z78qHfaIjjuaaN2k3MMkLuvdef/OTuVckWMKkhSnzG26tr0MgGX+lQiq8KvkjFb0K7+Edu6VIAWwP3sPwbaqQR8jxaxlSpLhlI9YMmnsMuiE0uTJk3nrLaXN9YUXXmDx4sUYDN0dBPX12Gy28EYYI8Yg8O1+Df+Rj0GlxXTN70dE21StVrNy5UoADh8+TE1NTdjHmIi4AwH+XXYESZYpsiX266lwbqoiCbOpuR6fNHJ6xyf6KJljPkoTmuP+SQv7bPM2FONrOAYqDabZyk1VRUUFwWCQ+Pj4iMrdtTi8PLVJmeB9edVktOpBX94nJCcb9i7vlr3bWDFxb64GgiAIaKYqxUS+8SZ7N7e7iGDbxEsohXwkQv6BIWSvk0ClctOtmbpyWGNIstwjd7ciJX1AEmk2nZ5Z3QbSG5snlvysoFKTcc+T2D7zRZBl6p/6Aq3v/T7aYfUwWuTuQhjUagqs8cDx2IbDR/uVApE8i4zZoKP9oz/TuenfIKrIeuBFNImjP3kgavVkP/hf9LnzCdqbqfr1BfjbRu7e5a4lqYgCbKqwc6DeOWLjjASe9kb++uc/8PWUP7HOejEAlxem8doXl3Lrshw0qv7nVpvqlO/JjIQkkqLoIxYtZsUn8OCMIpJ0etp9Xh47so89/chOCoJAkjmy3fOCzoR6kuJv1q/sXchHaYLK3tl376MiQ/FPKsxUOrRCc+L+5e7eBJROdVE/+ju6TvRRChXFhxIo8+K0CLJEuV2ksT3y8/6MKy5i9b5PKfjmAwCU/+s5JL8/YuPnmpTPr9btHNF1ldGK3+44pX/SlopOAJbkKn8jORjokbuzLr42glFGH0EQMF/zO1SZhcjONuxP34HsHVvX9hgjx6BXnL7whS/w6KOPkpiYyIsvvsi99x7XjN64cWMvf6UYMSKJ5GjG+eb3ATCc/3XUqdNGbKyMjAxmzVImoGvXrsUfwYv/eESWZV6uLKHN5yVBq+O63Kn9LkzMtiVi0+pwBgLsbB05ffZQK6+/dCOW7sXFrkNHCThiF8+JxnH/pL4SMaHuJFPBOagMSiKirEyRnJs8eXJEF9f+/mk5bn+Q2RlWVs+amLIFQ+HEykVZllmep3yOhxpctLli5/XToZ22EgB/8broBhJmLHPnANA+AbtS27oTSif7J/lLN0LQj5iQg5g0aVhjlNg7afa60YkqFiQmD3i/FcmKNN72lia8wYm18CGIImm3/onES74NQOPz36D5jZ8gn2zqHWFOlLsrGkVShCFZxHAklHZUKYuMi3IsuI5toOH5rwGQev2vMc1YNezjRwqVwUrON99FmzYNf2sVVb+5kIBjZDzJcm16LpquFJg+saUx6t/TgbL5WAPX/WkNzxhuwK0yMz3FwJN3LuDHn51JsuX00n2buuXulk8Qubv+SDUYeXDGXAqs8fgkiWfKjvBubSXSKPj8tdOU36r/6No+20LXu9D1b6Jh37m/xz+pMMOIx+Ohs1NZTO83obRfKSzXFV4euSCHgc1mQ6vVEggEaG9vxxMMUO9W7ucLl11DfkC5b/vorVejFmPG5y5Bl5yIp76R+rc+jNi4Nq0Oi0aDJMvUOB0RG3e00LZtN3IwiDE3C8MJtgadTg9lduUe/qK5eQC4jq4jaG9GZUrANH1lFKKNLoLWiOX2pxDMyQTrD+F48SsT0ms2Rl8GnVC6//77ee6557j55pt5+umnuf3223u2ud1u7rjjjnDGFyPGgHH+9yFkVxuq9FkYVj4w4uMtXboUs9mM3W5n27ZtIz7eeGZLSyN72lsQBYFb86djUPffRqwSBM5OyQAUr6WRuknt8VGqP4TaLGLIygBJmrByCBOVgN1B18GjACQuWdBnu2OPclMVkrvz+/1UVSldQpH0TyprdvLGbqVq/8ELpoyKKvGxQmJiIiqVCq/XS2dnJ8lmDQXJBmRgS6xL6bSEOpSCtfuQHONH7thcOANEEVdVDZ6GpjPvME7wtrQpfnHQI/sXosc/adrKYZ9fNjTVAbAoKQX9ICRDplrjSdYZ8EpBdrZOnM8lhCAIpFz3S5Kv/hkAza//kKYXvx3Vxfojne34JIkErY4sY/Tl7kLMjk9EFAQa3K5hSSQ63F7KuxeVVuabqPnTtRAMYF1yPQkXfi1c4UYMtTWFnG99gNqWibfuENW/vxTJMzKLiDcvSEavFjna7GZd2eiWkG20+/jpB1U88OIhakjBGuzku2fH8+znl1KUdWY/k6quTqrsnYrfW3pmBCIevRjVau6dOouV3YoSH9ZX82TJYTzBwKCP1dkZvu+NpqA7oVS6Ednv6bUtVDjYvnMf8gQrVgCoOlhGe1yS4p+UauyRhouPj0ev7y3nGWg4QrCpGFRaNDNWRyPcQSMIQk9irLGxkWqnAxklmWKzJvZ4J208VEmgMzoynSqdlrw7bgCg9O9PR+y6LggCeSalkC4kAziRaN3cLXe3vLfc3Xs7S5AQiVMHKJykFB52bVfkCC0LrkRQT0yPYlV8BpbbnwSVFt+Bt3F/PHq65WNEjyFp4txwww388Y9/5JZbbun1/OOPP97nuRgxIoHv4Hv49v4XRBXmax9BUI38iV6r1XLuuYoh3759+2hoaBjxMccj9W4nr1cp1UGXZOaSaz59+/ySpFR0oopGj5sjXR0jEpNoSUbV3eHmL9tMwqJ5wHF/iRgTA8eegyDLGPOy0af1rtILurtwHlU6M8xzFVPayspKgsEgcXFxJCZGztz10Y9LCMoyqwqSmJ8TH7FxxwOiKJKcrHRJHJe9U85BGytG9yJYtBEtKajSlYUYf/H6KEcTPlRmE8apSkJ4Ip3z23fuBcA8bTLquN6+RiFZw1CV95DH8Ho42NEGHO84GiiiILA8RZHC3dBcP2a6HsKJIAgkX/E9Um96BIDWd39Lw9NfjFqV6GiTuwthVKuZZokHYN8wupQ+2VtOEBGzKkDi/+4j0NmALms2GXf/c1S938GgTcol91sfoDIl4C7dSvVjVyMHfGEfJ8Go4dq5yjzoqW2N+IKjr5LZE5D4944mPv9SCZsq7IhykNWdr/PClXFcu3I+4gA/4011SiHR3OQ0TJqYD4QoCFyRPYmbJk1DLQgc7Gzj0cP7aPa4AcV8XaM6/d9WhURr/eB9Ok55vLQZCJZU8LvxV/QuAjVMzkNtNhF0uug6XBy2MccCnqYWjslmAKYk6DBqVaeXu+vuTtJMOxfRcGb/w9HCiQmlyu7ESUjubdXK8wA4oCui/uWHohMgkHvbdYg6LZ17D0ZUZj/0d5iIPkqtm3cAfWXt1x5VkqpFqVpEUUSWgnTtfA0A66JrIhvkKEOTuxDTVb8CwP3hb/F2nxNiTFxiJgsxxjySuxPHa/8HgP7cL6LOKozY2Dk5OUybpiQe1q5dS3ACVjYNB18wyDOlRwnIEtOttp6KttNhUKtZkqRUi6xrrB2x2DSTzwKUaraExd0JpZiP0oTCvns/AIlL+3YnOQ98CEE/2tSp6NIU7fHSUsXcNT8/P2KLTdsr2llf3IpKEPjyeZMjMuZ442TD3pDs3d5aJ13umOzd6dBMU4oq/MVroxtImDHP6/bO2zpxfJRC/kkJC+f2ej7YVonUUgaiCnV39+5Q2dzcgAxMscSROgSfkcWJqWhFkQa3izLHxE34Jl74IOl3PQGCQPuav1H3j9uRh9ABMBx8UpBDnUpysHAUyd2FKEpQkhl724cu67buqLKwOk3bhrdkE6IxjuyvvI6oGz3dWENBlzmT7G+8g6Az4TzwAbWP34Y8Av4ZV81JIsGopsHu562DbWE//lCRZZkNZV184aUSntvVjC8oM9O1m59Xf57/u3wuaUXnDfhYkiz3yN2tmMByd/2xMDGFL00vxKrR0uhx8YfDezja2U6KWcsT10/hsavyez0WZSmJjTmpOq6zleNsrsHlGnqH4YkIgoA21KV0dE3vbSoV8fOVtYP2CSZ717Z1J+WZ3XJ3WRZkWe6ZC4fmxifi2/c/ALRzLotckGEg9F7a29sp6y5GDRWwzs6Kw6QBh8rKnu0bcJfvjEqMuqREsq5W/q5ljz8TsXHzuv8OlU77hCrUCTpddO49CEDisuMdSpIkcaBZuR6eN1MpYnId20CwsxHRZMM08zORD3aUoV90I/qzPg+A44UvE6g7GOWIYkSTWEIpxpjH9daPke2NiEmTMZ7/9YiPv2LFCgwGA+3t7ezcGZ1JyFjl9eoyGj0urBotN06aOuBqwLNTMxCAo10dNLhHxtcotHAWKN2IbdFcADp2TUw5hImKfacicXhy5RIc908KdSedKHc3eXJkEjuSLPPIhyUAXL0gg7yksb3IFS1CN5otLS0EAgGy4nXk2XQEZVhXPH6k3EYCTbePku/Yp+PqRjQkgTOROpRCBROh610I/7FPAVDnLBxWRXJAktjSoizQr0gZXHdSCINazfwEpaNwY1P9kGMZD9jOvZvM+54DlZrOTc9S8+frkPzeiI0fkruzaXXkmMwRG3egzI5PRATq3M6ezojBsq9B+XvOalY8LTK/8B+0qVPCFWJUMU5eQvaXXwOVhq6tL9LwzJfCfg7Xa0RuW6h0Bjy/uwW7J7JJz/6obPPw3bcr+flH1TQ5/CTrJb7S+HO+W/dN5l5wPbZz7x7U8Y62tdDqcWNUa5jb3UEZ4zg5JgtfnzGXPJMFdzDI48UH+bShlmSThilJhl6PO5coc7GDTV7ibAnIstzjSxoOQvMV/7E1fbZN1MLB1i07qez2T5qTYaKzsxOv14tare6jtBBoPEaw8SioNGhnXhiNcIeMwWDAarUic7wTJ9SZo1GJLM5X5hX7DQtoePYrUZvP5n/+NgAa3vsEZ2V1RMbMMpoRBYEuv48OX+TmENHGvvcgcjCIITsTY3ZGz/NbjtTgDKpQCxIXzFPu57u2vQyAZd5nJ6zc3ckYL/0Bmqnngt+N/enbx5X0eYzBEUsoxRjT+IvX493+HADma3+PoDFEPAa9Xs/ZZ58NwO7du2lpiZ1QB8Ku1ma2tjQiADdPmoZlEDIRiTo9s+OVie6njXUjEp8mfxkIAsHGYxjTragtZgIO54STQ5ioSB4vzm7/pIQl83ttkyUJeyih1O2fVFVVRSAQwGq1kpQUmWrt9w40cqTBjkmr4gvnTIrImOMRk8mE0WhElmWam5sBWD5JWThfc6Q5mqGNejR5i0GtR+5qUBYaxgmWeXMA6DxwhIBjZIoWRhOSz0/H3gMAPRKvIXr8kwpWDmuMve0tOAJ+4jTanut3iMrmLnaVNZ7yUdl8vBsplIza19FKly/8Ul1jibilN5D9pVcR1FrsO1+n+tHPInnDU9F/JkKdP4W2xFEp/2ZSa5hqjQeOS/MNhvKGNlp8akBmcds7JH/uR1jmXhreIKOMec5qMr/wbE+nm/uDX4d9jPOnxZOXoMPhDfLC7ujdHzm8Qf62qZ4vvlrK3jonGpXAjTM0/Kr8VhbbP0E3+1JSrvnFoI+7sVYpJFqUlolWpQp32OMCq1bLFwvmsDgpFRn4b005T5UeocLRRY3T0fPQ6APMm6RFqw9wSFYW+MvLywkEwpOI1Ew9BwSRYMMRgh297x1DnbltE6xDqWz3YdrikhGQmZVm7JG7S0pKQhR7LxP2yN1NORvRGB/pUIdNSkoKHrUKtyyhEgSyjMcLIZZNTgBgv3kJ7pJNdG1+LioxWgqmkHzucpAkyp/4T0TG1KpUZBiUgsSJ5KNk36HIPCed5J/0wT4lkTctDgw6jXLPv+NVAKyLr41skKMYQaXGfPPfERMnIbXXYP/3PaeVz233enqd709+tHs9p9w3xugmllCKMWaRfU4cr34DAN3yO9FMWhK1WPLz85k0aRKSJLF27VqkKOnZjxWaPW5erlQ6K85Pz+656R8M56Yq1SQ7W5tw+MMvSyWaElClzQTAVbweW7ccwkSqWJ/IOPYfRg4E0KelYMzN7rXNU7GTYFcTot6CqUBJJofk7iZPnhyRxTWPP8if1ihj3rkiF5sppts/VARB6OlSCt1Mr+hOKG0qbcPti3UlngpBo1eS7xzvZBkPaFOTMWSmgyTRvmtftMMZcTr3H0Ly+tDY4jHl5/Y8Lwf9+EsUfyxtd3X3UAl1FC1LTkN1wjmysrmLB16v5HsftZzy8cDrlT1JpUyjmUlmK5Iss7kl5l1pmX8F2V9/G0FrxLn/fap+exFB98jKAfoliUPdXlhFo1DuLkRIim8oCaX3th0BIE3oJHvmQpI++/2wxjZaiFtyHWm3/QUA98eP4N7wj7AeXyUK3LNE6dx582Ab9V2RTQJLssz7R9q558Vi/nugDUmG5XkW/nZ5CpdvuQutswFVVhHmGx5DEAe3LOIPBtlar0hvr8jMPsOrJzZqUeT63ClcmZ2PAOzvaOWPR/bx+8N7ej08tkYmzbDTGNeM12jF7/dTWVkZlhhEUwLqbKVg4mSZ3vj5hSCKuKtr8TQ0hWW80Y6vvZMDTiUJOtmmxXSCf1K/cnfdCSVt4eWRCzKMpKamYtcq3SVZRjPqE37vy/KVhFKxbiYuwUjji99G8jiiEmeoS6nq+dfwd0UmwROSvauYQD5KXd3J48STEko7qpUismX58QC4ijcS6GxANMZhnnV+JEMc9YjGeCx3PI2gMxMo34Lzv/17kLV7PTx8YGef8/2Jj4cP7IwllcYosYRSjDGL671fIbVVIcZnYrroe1GNRRAEzj77bLRaLc3Nzezduzeq8YxmApLEv8uO4pWC5JutrM4Ymub4JLOVbKOZgCyzsXlkpG803bJ3rsNreuQQ2mMJpQlByD8pYcn8Pgki+x7lpso0ezWCWtvrhjc/Pz8i8b2wrYaGTi+pVh03LYktZAyXkGFvSDt+UoKONIsGb0BiU+nQPTgmAuPVRylhsdKZOBGKCNq27wEgYdHcXue7QNUuZI8dwWhDlTl0f8pal4MKpx1REFia3FsWqtXuJniG25EgIq3247Jly7uPsbm5gWCsgAfzrPPJ/faHiMY4XMfWU/mrzxBwjNx562hnO14pSLxGS063bNBoZE637F2ty0nLIGTvZCnIpkMVAMyggszP/3vQyYaxRMJ595F89c8AcL35fby7Xgnr8Rdkm5mXaSIgyTy1rTGsxz4dR5pcPPhGGX9YV0enJ0h2vI6fX5LLQ59Jx/TGfUjNJYhxGVjveAZBO3hPt73NDbgCfmx6AzMSk0fgHYwvBEHg7NQMrso5syy0KEKNVjnPl5SUhE2CrEf27iQfJY3FjHWG4oc6UbqU2rbtorxb7q4o20ogEKC1VblunJxQCjaXEqw/BKIa7ayLIh5rOEhKSsKuVxJK6Rpdr22ZNgM5CQYkBI6lXUSgo46Wtx6ORpgkr1qBeWo+QaeLqudei8iYIfm/ygnSoRR0uXEdOgZA4rLjsvY1zR3UeZQk62ULlfOUfbtyPVTk7mLFmyejTp2G+aa/giDg3foMns1P9XmNMxAgcIZzeECWcYapGzVGZBm/s+MY4xp/5Q48G5UqOtPVv0XQR1+/3WQysWKFkoDYvn07HR0d0Q1olPJWTQU1LgdGlZpb8gt6VSoPBkEQODc1E4BNTfUERmBRKZRQch5eg23RxNTXnqgMxD/J0u2fVF1dTSAQwGKxkJw88osK7S4f/9pYAcADK/PRa2IyK8MlJSUFQRBwOBw4nU4EQejpUvokJnt3WjRTuxNKZVuQ/eOnuixhSeicvyvKkYw8ISPyU8rdTT0HQRz6eWZDd3dSYXwi1kHI256KIlsSZrWGLr+PA92dMhMd49Tl5P7fJ6jMiXjKd1D58EoCHSPTwRXq+Cm0JQ3Y+zIamDUaJlvigOMSfQOh7qXvURJQupvOX74QlSl+JMIbVSRd/v+Om2y/9FV8hz8M6/HvWZqGAKwr6+JI08jKMra5/Px+bS1fe6Oc4mYPRo3I55em8pdrJjMv04Tztf8jULIBtCYsd/4b0dq3G2MgbKhVpJGWZ2SN6t/BaCN3gEnoPc0yqLQ4nU7q68NTOKgpOA8Af/E65GDvxUtbt+xde3eBxXindcsOKroTSoUZJpqbm5FlGaPRiMnU25PV20vuzhbxWMOBSqXCZVSsEay+vsomyyYrUrzHZtwLQOt7v8PXFD4Pr4EiCEJPl1L5P/+DFIFF9jyzcr9T63LgnwBFOo49B5EDQQxZGRizM3uef3tnGSCQpguQm2pDliS6uhNK1kXXRCna0Y92xgUYL1aK+53/fQh/yYYoRxQjkoQ1obRz507uuuuucB4yRow+yAEvzpe/BrKMbsF1aAtWRTukHgoKCsjKyiIYDLJ27dpxZVIeDg52tLKuSdGtvnHSVOK1ujPscXqKbInEa7TYA352tYV/0VedvxQEEV/DMcz5SQgqFe7aetx1MZmd8YzkD+DYdwjo65/k76jHU7ETAHPhxcBxubv8/PyIyN39Y10FDm+QglQzlxTGTKDDgUajISFBkbw4WfZufXELvsD4v8EaKqq06QiWFPC7CVTuiHY4YSOUXGnfuS8iN/TRQpblni6s0IJaiJ6E0jDk7lyBQM/1+axu/6PhohbFnk6nDSPUoTwWMeTNJ+//rUMdn4635gAVvzgbf2tVWMcISBIHO0e/3F2IogQlxn0DlL3r2v4q6zdvwSur0QpBzjtr2UiGN2oQBAHjZT9CO/8akILY/30v/vKtYTt+fqKe86fFA/DElsYRuT8KSDKv7WvhnhdL+PBYBwAXTIvnH9dP4crCJNSigOfTvyjeu4KI5ea/oc6YNaSxXH4/e7oT5Ssyh6a0EOP0uHwSTcY8QOlSCgfq7LkIhnhkdyeBmj29tk00H6WyXYdpi0/p45+Umpra517Gt+9/AGjnXBbxOMOFNxikq3vlU2zr7LM9JHu3s8uKceb5yH4vjc9/I5Ih9pB19WVoE2y4a+poeO+TER8vQavDrNYQlGVqXdGR+oskXTsVJaETu5MANpYoc5v5mUri0V26hUBHnSJxP3t1ZIMcY+jPfQDtvKtBCmB/9vME28IjVRpj9BPWhFJFRQVPP/10OA8ZIwagyE/4Szfi3f06jle+QbCpGMGcjPHyH0c7tF4IgsC5556LWq2mvr6egwcPRjukUUOHz8vz5cUAnJOSwayTTLmHgkoUWZGieCl92lgb9htU0RCHKmM2AN6KLVhnFgATQwJpIuM6UoLk9qCyWrAUTOm1zbH3HQD0+YtRx6USCAR65O4mTz6zjMdwqWp18cpORbP/wQumxKpiw8jJsncFKQaSzFoc3iDbK9qjGdqoRhAEtN2yd77uBMR4wFIwBbXVQtDpoqtbGmM84q6uxdvUgqBRE190fHFVcrb1LLgNJ6G0vaURvySRbjAyqbsKNhwsS1Y6HkrtnTS4R7bjYSyhy5xJ3v9bjyYpF19jCRU/PxtvQ3HYjn+0qwNPMIhVoyXXPHrl7kLMiU9EAKpdDlrPoM8faDxK3RN3sCtOKVSbEieg6/bcmAgIooj52kfQTD8fAh7sT95KoP5Q2I5/68IUdCqBgw0uNleGV1ppV42DL75Swj+2NOL2S0xLNvDI5ybx9ZWZJBiVz9B74B1c7yrSfsbLf4J2xgVDHm9bQy1+SSLLbCWnuwsuRvjZ1KRBRqClpSUsyh+CqEIz9RwA/Ed7L9SHpM079x8i6B4/3db9EXA42d+p3DPnx2kwaVU9c98+cnct5QTrDoCoQjt7bMrdgXINkAFtIIirpYVgsLc/6sK8eNSiQG2Hh8DlvwNRhX3XGzgOfhTxWFUGPbm3XQdA2d9Hfm1VEISe6/lE8FGy7+hOKJ3gn+Tx+Tnaofx7dWEWAF3bXgbAMu8KRM3wiqDHO4IgYL7mt6iyipBdbdifuh05Sj5kMSJLTPIuxqjHu/9tOh5eSNffr8bx/P34urW9dQuuGZVt11arlaVLlwKwZcsW7Pbxf2E+E0FZ5tmyo7iCAbKMZi7LygvbsZclp6EVRerdLortfSuOhkuPj9KRNSQsngvEfJTGO/bditydZd7sPr4Jjm7/JEvRpYAid+f3+zGbzT0JiZHkj5+UEpBkzpqSyJJJCSM+3kQidBPd1NSEJEmIgsCq6YqE4SdHJoZJ81DRTF0JgP/Yp9ENJIwIKhW2BUXA+Ja9C/knxc2Zicqg73neX7wOZBlV2nRUcUPrLJJkmU3dHUQrUtLD2sFp0+qY3V2YsrEp1qV0ItrUyeR9bwPa9AL8rVVU/OJsPDUHwnLsfT1yd4ljoqDBotH2yN6drktJcndhf/ouJI+DI3rFL2xJ3sRLFAgqDZZbHkedtwTZ00XXEzcQbK0Iy7GTzRquKlR+s//a2khAGn4RWEOXj598UMX33qmkusNHnF7Fg+dk8MjnJjE95bgvUqBmL47nH1DULZbfiX7F3cMad2Ot0vm3PDM7Ip3pExGzTkWzM0CHSekAC1eXkqZb2eRkHyVDdia6lCRkf4COveO7ILRt+x7K0xTP16KcOJxOJw6HA0EQ+kh3+0Jyd5NXIJqGXwwaLSodXQDEByQkSaKlpff1wKhVMzdbOefvdCaQ8JkHAGh89qvIgb4SeSNN3h03IGjUtO/YS/uufSM/Xo+PUteIjxVNgm43zoNHAEg6IaH08Z5y/LKIQQyyYlauIne3o1vubvG1UYl1rCFoDFhufwrBkkKw4Qj2F7+MPAEkFCc6A0ooqVSqAT2uu+66kY43xgTDu/9tHP++B6mz72KB59O/4d3/dhSiOjOzZ88mLS0Nv9/PunXrJrz03Yd1VZQ5utCJKm7NL0AdRnNjo1rNokRlIXhdY23Yjhuifx+lPWEfJ8bowb5rPwCW+b1N6CW/F8dBxVfA3O2fFJK7mzx58ogvKuyp7uCTI82IAnz1MyPfDTXRsNlsaLVaAoEA7e1KR9J53QmlNUdbCIZh8Wu8Eqr4DdbtR3KMH8+pUMXyeC4iCHXcJpxK7q7bI2soFHd10Oz1oFepWJAQ/oT7im4JvR2tTXiC41eWcChoErLI+3/r0GUXEuxspPIX5+IuH54kZUCSONCheBHNHQNydyFC0nx7T5FQkiUJx0tfQWopxZU0i9qg0km3em5epEIcVQhaI5Y7n0GVPhPZ3kTXP65H6moMy7GvKUoi3qCittPHu4eH7n/mCUg8s6OJz79cwuYKO6IAn5udwBPXT+XC6bZeyc5gRy1dT94GfjeaglWYLv/psOZrbR43h1uV69zyjOwhHyfG6TlnsvI73N5pRpaVAi632z3s44Y6bgM1e5Ccx7+DgiAcl7od57J3J/onzckw9cjdJSQkoNH07sr0jgO5O4BKp1Lgm6VX5MxC7/lElk1WCvU2l7aRfOWPUJkT8dYdou2Tv0Yu0G70qclkfu4SAMoef2bExwt5m1WO8w4lx95DyIEg2rQUDCf4J31ySFlrnJWkQiWKuMu2EWirQdSbY3J3g0AVl47ltidBpcV/8F3cH/4m2iHFGGEGnFCaP38+X/ziF0/7uPDCC0c63hgTCFkK4nrzIeDUi3iuN7+PLAVPuT1aCILAypUrEUWRqqoqjh0bv3I5Z6K4q4MP6xXj2mtzp5DcPZELJ+ekZiAAhzrbafKEV/pGM2kpiCr8zeVYpiiLEl2HjhJwxiR2xiOyJGHfHUoozem1zXXkU2SvE3V8OvrceQQCASoqKgDFP2lE45JlHvlQqc787NwMJqeYR3S8iciJlZmhG835OfHEGdR0uPzsruqIYnSjG9GS3CMP6i9eH+VowkfCYsVDrW3b7nFbGBJaOAsVTIByvvEXK91mmmH4VIb8jRYmpqBTqYYe5CmYaokjRW/AKwXZ2Tp+EpnhQm1NIe87azBMXkLQ2UblL8/DeXTov89iewfuYBCLRtNj4j0WmGNTZO+qnA7a+pG9c699DP/B90Cl5fA5jyAjYNMEKMhO7nuwCYJoiMN6zwuIiXlIbZV0PXEDkqtj2Mc1alXcskBJLj+7sxmnb3D3cLIss76sk8+/WMLzu5rxB2XmZpr4yzWT+cLydMy63ucZ2evE/uRtyPZGVGnTMd/8OIJKPaz3sLm2GhkoSEgi2Wga1rFinJqz8+PQqQUqOwLYDWnIskxZWdmwj6uKS0eVNgNkWenEPYGQj+B4lzYv33mIVlsqAjKz04ynlrtrrSRYuw8EEe3si6MRaliQZblHym1at69e6D2fyLJ8pQNrR2U7kj6OlGt+DkDz6z8k0BX5OUb+528DoP6tD3HVjGwndrbJggh0+H10+LwjOlY0CcndWRYW9Sos2NOgvOdzpnWv9WxXupPMcy9H1IZ/7Wo8o8ldgOlqJZHk/vgRfMXjR70iRl8GlFCaPn06U6ZM4bHHHjvt4+67h9c+HiPGiQTKt/TbmXQcGamzjkD5lojFNBhsNhsLFypmfxs3bsTlmngJCLvfx3/KjyEDS5JSmZ84MjfnyXoDM+OUqqJ1jXVhPbagN2OYpHyOUttBDJnpyMFgRNrPY0Qed1klwU47ol6PcfrUXtsce5WOSHPRpQiCQE1NDX6/H5PJ1OcmLNx8dLiZ/bVdGDQq7jt30oiONZEJfY6hhJJGJfbcXHxyJLZgfTo03T5K/nHkoxQ/dzaCWo2noQl3TXivLaMBv91B12HFXydhYVHP88HGo8r8S61HM2nJkI7d5vVwqEOp/l6RfGrJPJtJx+kKhwBUSCRa+t7QC4LA8u5jb2iqH7dJv+GgMieQ860PMU5fieSxU/XbC3Hs/2BIx9rb1i13F580JuTuQlg12h7/rn3trb22+Y5+gvv9XwJg+tzDbG1Wkg2zU7SRDXIUIlpSsN7zYrd8zWHsT92G7Bv+vcxF021kx2vp8gR5ec+pZQhPpqLNw3ffruQXH9XQ7PSTYtbw0AXZ/OKSXHJt+j6vl6Ug9ufuI1h/EMGchOXOfyPqh+/7tbFOkbtbEetOGhImtRr1Gc4fakEg1ajjwgJF2n6/R1noLy8v7+N9MxSOy96d5KO0aC6gSMKN1+tJ0O1hb6sPgCkJeowaoSe5crJ0d0juTp2/DNE8dhPsbT4vjoAflSAwM13pSunq6urT8TYtzUyCSYPLF2RvdSfx596DPmcukquD5te+H/G442ZPJ3HFYuRgkIonnxvRsXQqFendCfLx3KXUtVNJKFkXHFchOVDRQLtfjYjMpQunIMsy9pDc3aJrohLnWEe/8Hr059yn/OetH54x6aAWBEzq4RV7xIgOA0oozZs3j927B1apMZSLr8Ph4MEHHyQjIwO9Xs/cuXN54YUXBn2chx56CEEQmD179qD3jTH6kLoG5lkx0NdFg7lz55KUlITX62XDhg3RDieiSLLM8+XFdPl9pOoNfC57ZDs4zknNAGB7axPOMGsdG6crNx7Ow2smhATSRMa+U0kUmotmImqOT2xkWcbe7Z9k7vZPCsnd5efnj6jcnS8g8dgnSnfSbctySLbEjEFHitDNdHt7Oz6fcsN93nTluU+ONCON0wWGcKDt9lHyHft03CzEqI0G4gpnAtC2dfz5KHXs2geShCE7E33a8YWkHrm7/GUImqFVZm5qbkBG6SJKNRhP+bqSxi5AQIXEN5ZZ+fn5Sfz8/CS+Ok+FQVCu5edPtZCb3H9HzKLEFLSiSKPHRaljfGv/DxWVwULON97BXHgxss9N9R8up2vnG4M6RlCS2N+dICyyjT0fjaLuqvQTfZSCrZU4nvui4quz+Ba0i27kYLPynTt72thdPA0nqsRcrPe8gGCII1CxDfuz9yIHhzfHVokCdy1JA+D1/a00O05/PLs3yN821fPAq6XsrXOiVQncPD+Zv183hRWTrKecf7ne+jH+wx+CWo/ljmdQ2YafAKqxd1HZ1YlKEFiSnjXs401EbDo93529gK/PmNvrceukgp7X3D1lJjadnqsKExEFONQSwKGOw+fzUVVVNewYtNOU+zrfsbW95itxc2Yi6rT42ztwllYMe5zRSPvu/ZSnKoVpi6cm09bWRiAQQKvVYrP19qb27lfk7nSFl0c8znBS0T03yDSaMBsMPe/zZNk7URB6/Gk3l7UhiCpSb/kjAO1rH8dTuSdyQXcT6lKqfPaVEVdHCcneVYxTH6Wg24Nzv+KfZDlB5vndXRUA5JqD2CxGPOU78LdUIuhMmAvHbmdetDFe8n0001ZhcLWg9TkBuDA9u8+5/+sz5vLd2Quw6foWhsQY/QwooXTddddx1llnnfF1ixYt4sknnxx0EFdddRVPP/00P/zhD3n33XdZtGgRN954I889N/BM/J49e/jtb3874lXiMSKHaE2h3FLAzpRzT/kotxQgWsOvyx8uVCoVK1euRBAESktLw9KqP1b4tLGWI13tqAWR2yZPHxG5mxOZYokj02DCL0lsbm4I67FNM44nlOJDcgjbYgml8cip5O589UfxN5chqLWYZ51PMBjskbubPDl8fkb1nR4O19t7Pf60ppSadg/xBjUXzIotco0kRqMRq1VZuA5VbC7Nt2HUqmiyezlUN36r9oaLOm8RqPXI9kaCjUeiHU7YCHkqjEcJnLZuubtQVXaInoTStKH5JwUkia0tynU45HPUH5Ik8fxuJUmxPB3On5PN/PxU5uencuHCAi5IcQDwcYmThi5fv8cwqNUsSFTmgRubRlYSZiwjag1kf/UNLIuuQQ74qPnTNXRu+s+A9y+2d+IOBjCrNeRb4kYw0pGhMF5JglU47bT7vMg+F/Zn7kJ2d6DOnofpcz+ntLGTzoBSpXzBvJhPYQh1+kwsd/4bNAb8Rz7G8dKDwzbaXpJjpiBFjy8o89j6Okpa3H0e9V0+3jvSzj0vFvPfA21IMqyYZOXx66Zwy8IU9OpTL2N4Nj2JZ8PjAJhv+COanPnDijfExlolmTE3JQ2zNtbFNlRsOj1ZJnOvx7zE5J5k9ZYWZaE/1aLlnHzlfHNMVgoHS0pKhl20op60GDQGZHsTwfpDPc+LWg3xRUphcts49VFqO8E/aUGurVd30onJ2WB7NcHqPSAIaGdfEo1Qw0bIPynXpMzvQ8Vj/creneCjBGAqOBvrkutBlml49isRL5hKPf8cTPm5BLrsVL/w+oiOFZKyHa8dSo59h5ADAbSpyeiyjs9Nt1Yq73dJrvL+u7a/DICl6NKY3N0wEEQV5pv/xs7Cm/BoTSR42jgvKZnUhr0kH/uQ1Ia9ZBoMZJnMsWTSGGZAfWWXXnopl1566Rlfl5OTw+233z6oAN555x0+/PBDnnvuOW688UYAVq1aRWVlJd/61re4/vrrUZ1hIToQCHDnnXfyhS98gb1799LSMvD2+Rijl4r42Tx74c8RT5P2lCSIi5/N1FO/JOokJyczd+5cdu/ezfr168nMzESnG98dBpUOO2/XVgJwZU4+6YaR1xgXBIFzUjN4vqKYDU31rEzNRH26L88gME5bASoNgbZqEqcqNzvtO/ciB4MII5woixE5ZFnu6VA6OaEU6k4yTl+JqDdTWVmJz+fDaDSSlpYWlvHrOz1c+ect+IL9L9R0uAPc9PgOXn9gKelxsYnXSJGSkkJXV1dP5aJOreKsKYl8cKiJT440MTtz7HiHRBJBo0eTvwz/sTX4j61DnTYj2iGFhYTF8yj7+9Pjsoigffse4HjSDEDyufGXKVLCIfPywbKnvQVnIEC8Vses+FN3s3y4v4Z6rwY1Ened1VvKUxAELl+Qx44P66jzm3h8cx0/uDCv3+OsSE5nc3MD+zta6fR5idOO7znWUBHUWrLuf546nYnODU9T+/itSF4HtlVfOOO+e7s7ewptiWNK7i5EnFbHJLOVckcX+9pamL/mF4oUmikR863/RFDrWH+0AoAcU5A4U+waeyKavMVYbvkH9qfvwLf7VVxGG8Yrfjrk7uxmp5/SFsWzYnu1g+3Vjj6vETguhpkdr+P+5WnMyzqzf6Tv6Bqcbz4EgOGi76IrvGJIMZ6MJMtsqlM8YVdk5oTlmDF6c0F6DnvbW9nb3kKdy0mG0cTVRYmsLe1kd5PE9EQ92O00NjYOa+4tqHVoppyF//CH+I+tgXnn9WyzLZpL27ZdtG/fQ84NV4bjbY0qynYcpGXGVQjIzMuJ593dyly3j9zdvm65u0lLES2jt3h3IIQSJHlmpQMnNTWVo0eP0tTUhCRJiCesF4R8lI402Glz+kgwaUm9/jfYd7+J69h6ura9RNyS6yMWuyCK5N97K/u/+zPKnniWvDtuGLG1h1CHUo3LQUCSwraOMlqwd8vdWRYU9ly72u0uKhzK+7x4Xh6yLPf4J1kXXxudQMcRPq2JLdMuBUnmrL3P0vXeg3CCdK4Yl47xip+hm3PmXEOM0UnUzxKvv/46ZrOZa6/t/YO98847qaurY+vWrWc8xi9/+Uva2tr4+c9/PlJhxogCLZ7AaZNJAKKovG60s3DhQuLj43G5XGzatCna4Ywo7kCAf5cdQZJlimxJLE2KXNfgvIRkLBoNXX5fz+JHOBB1JgyTFR8JwV+BymQk4HDSdaQ4bGPEiD7e2nr8za0IajWm2b0Xw0P+SZa5lwEjI3fX4fKfMpkUwheU6HCFV9IxRm9O9FEKVSKeN13pDPv4SPO4kXMbCXp8lIrXRjeQMBKSObUfKcbX0RnlaMKHHAzS3n1zbTtB+sN1dD0EPIhx6ahSC06x9+kJdQotS0pDdYrzoyRJvLAn1J0kkGbru1CcmZnJBaluBGQ2VzrZU9t30Rkgw2hiktmKJMth71AebwgqNRl3/wvbZx4AWab+qftoffd3p90nKEkc6FC8h4psSZEIc0QIdT/sqTqIb/erIKqw3PI4qnil82FPveKnMX8ASYuJiHbG+ZivexQAz8YncH/yhyEfq8sTJCCd/loqA3q1wOeXpfGXayYPKJkUaDiM4z+fBymIbsF1GFZ9Zcgxnsyx9lZa3C70ajXzTtN5GWPoZBhNPeeYD+uVbrApSQbmZZqQZChXK7KFJSUlwx4rVDDhO7qm1/PjuStZ8vnZ06Cc5ybHa9EJQdrb2wH6qPyE/JO0cy6LbJBhxicFqXUrcluhhElCQgJqtRqfz9enED3RrKUgVTnXbClT5iiaxGySLv0OAI0vfAvJG1lf7KzrrkATb8VVUU3jh5+O2DhJOj0mtZqALFPrco7YONHCvqM7oXSCZ+i7O0uRELBpAsyZlIanYhf+5nIErSEmdxcGNjTV45JkEoNuZlVt6JVMApA6G3D8+x68+9+OUoQxhsuAEkrf/va3qamp6fWcNMxW9xAHDhxgxowZqE8y4SosLOzZfjoOHTrEz372M/76179iNsduAGKMTtRqNStXrgTgyJEjVFdXRzegEUKWZV6qLKHN5yVBq+O63Ckj6i1zMmpR5Kxug+5PG+vCuvAbkr1zHVuHrdvIMVTdHWN8EOpOMs0qQGU4Xp0cdHbgKlY80MxFlxIMBikvLwfCK3cXY3SQlJSEKIp4PJ6eG+0VUxLRqkSq29yUNo+/m6xw0ZNQKtuC7PdEOZrwoEtKxJSfC9CTgBkP2I+WEHA4UZmMWGcc7/N2HHgfUBbbhnL9rnE6qHTaUQkCS5NPXVDywb5qGrq7k+48K6/f1wiCwNlFU5ih7wDgbxvrCZ5iEfqs7gXeLS2NBMN0jzJeEUSRtFsfI7FngeybNL3+o1POmUodXTgDAUxq9ZiUuwtR2L1QXSka6TIkYLzkB2gmrwDA6w9S4VCqvj8zJ+aNcyp086/GeMXPAHC//ys8m58a0fF+sDqHK+ckohbPfC6S7M3Yn7wV2WNHnb8M09W/Des9SEjubklaJtqYOsGIsTojGwHY295KXfei9rVFym93R7MKj6SiqamJzs7hFXhoC5SupEDFNiTP8WIF2wJlsdlRXIavffwUkQB07j9EebLSXbdoWmrP+p7VasVgOC7tFeyoJVC1EwQB3eyx3TlQ43QgyTJWjRZbd/eyKIo9HVn9rckszVdk70IJJYDES76FJimXQFs1LW//KgKRH0dtNJJ7i1J8X/r3Z0ZsHEEQxq2PkuTx4uj2T7IuOJ5Q+vRoMwBFacp3I9SdZC66FFE38go74xlPMMDahloAzjr4EqLc39xcmXe63vw+shSMYHQxwsWAJO9+97vfcc0115CVpUywg8EgWq2W7du3M3/+8DSJW1tbyc/P7/N8QkJCz/ZTIUkSd911F1dddRWXXDI4bVev14vX6+35f1eXctL0+/34/bHq71MRCESuGygYHNhJJRgMRiSu4X4vkpKSmDlzJocOHWLt2rVcffXVaDSaMEU38gzkb7ylpZG97S2ICNyUOxXNAPcLJ4sTkvmwvpoal4OSznYmmYcvT+X3+9FNPRsA56E1xM37Di3rttCydSeZN1897OOPdiL9GUaLrlBCae6snvfs9/ux730HggE0aQUItuweuTuDwUBiYmLYrhkD/TsHAoGIXacmymd/MomJiTQ3N1NRUYHFYkErwpJJ8awvaePDgw3k2nKjHeKIM5TPXk6cgmBJRbY34i3dgnrKmf03Rysn/sbiF87FWVZJy+YdJJyzLIpRhY/mLTsAiJ83h4AkKRrCgGPfewCIk88e0ndgfaNy8zgnLgGDIPZ7DEmSeHFvO6BheTokWfSnHCslJYWVqUcorQpQ2QH/O9DCZTNtfV43wxyHWd3dodzW3OOZMxQmyn1AwpU/Aa2R1td/QMsbPybg7ERa+Y0+i/C7WxWfidlxCcjBIGP1qmC0N5HVXkqNbTIli+8ha9ldPd+7zUfr8csiBjHIvPzUCfMdOJGB/t41S+9A52jB+8kfcL7xXSSdFe0gZeUCgYHd4xnUA4tL9rtxPnUbUnsNYuIkDDc9ThARBnEOO91nHpAkttYpi+9LUjPG3fdjNM31kjU65sQnsK+jjfdqK7lt0jRmp+rIT9RR1uqlSp3FNKmS4uJi5s6dO/SB4rMRE3KR2irp3P8h5m4FAjHOgnFSDq7yKlq27ST5vLPD88ZGAU0bt1GRoRSQzMuyUFmp+EclJSX1+g549/4PAFXOIiRTEtIo+n4MljK7khTMMZp6rS0lJSVRV1dHZWVlTyF7iMW5cTy9GTaXtuLz+ZRroqAm8Zpf0vC3G2l959eYl9+KJjFy9wJZt15L6d+epm3LDlp27SNuzvBlpfv73WcbzBzqbKfC3sWKxPHjTW/fsx/Z70eTnIgqI5VAIIDH6+VgSxBQsbIgBZ/PR9c2xT/JNO9z4+48fyKROOeva6zFFQyQJMrMPHq6DiQZqbMOb8lG1PnLRzwumDjz/KEymL/PgBJK/VWshbPy/3TVQ6fb9vvf/57i4mLefPPNQY/58MMP8+Mf/7jP8x988AFGo3HQx4sRfso9wACazvYdrUBdVzbi8YQDSZJQqVQ4HA5eeeUV4uLGbqXnyXQg87EqCALMDgo0HT5CX6vLyJAjQpkIbx47zAopPFWEQtBHvqiBznoqEpQFs7p1m6l9552wHD9G9BE270AA6s166nfs6Hk+dccTWIEmywwOvfMOHR0dyusFgffeey9s49e7RODM1VAbN2ygzBirwB9JQgUne/bs6aleTPCoAQNv7igno+tgFKMb3aTaZmK1N1K94UVaO8aJD4lORARKP/iEkjnjoytRePNdBKAlzsg73dcxlbuV/LpDyAgcsJuRTjgPDgQvMru65wEJLR3saOl//wMtEg3eONRILLDa2XGGccxakYXGVjY6U3l6ewPWrlKM/dzB5IhBDonwfnkxvmD5oGKfsAiFxBXeQ8q+J+j48A90Fh+iad4XQFDmThIye7o/U11TCzsa285wwNGJEPSTuf4hpidNpcY2mR3Ji7Ds3Nmz/ZNKAAs5eg/vh/G6Pm6JO5fk/KPEl72L68WvUFLdiCt13pn362ag853Dhw7Rcab5jiyRtu13WGp3E9SYqZj/TfyHSgccy0CoFSScKgm9DGVbt1HB2PMRG0ukI7NPBQc62/hox3biEZhnUlPWamBbq5b8eIGqqiq8Xu8ZvbZPR3L8TOLbKjn8zj9orjsu3COkJSGUV7HjhVeRPfZwvKVRgfO9tTQvuxuApsPbsLcqErEdHR29rsNZW17EANTHFdI5yHnAaGOvGAQRxLZOdrQefy+hBfXGxkbeeuutXj5KAQk0gplWp58nX3ufNEP3OUjWk5k0G2PLAfb/8XYalnw7ou9FmDcTYfs+Nv3k18h3joy/j0eQQAXF7a3saBk/HXrC/z5CAHx5WezsvvZXdAZxSfFohCD++qN8/Px75DaXIolaNjSokGNrPEPGj8wn3XPHWQ3Fp+hO6k3J3i042rQRiC7GmXC5Bi7rOaCE0kiSmJjYbxdSW5tywxLqVDqZqqoqfvCDH/DLX/4SrVbbs8AXCASQJImOjg50Ol2v9t0T+e53v8vXv/71nv93dXWRnZ3N6tWrsVpjhtunoqkpcimCQF0bu5uOnfF1Hzbo2N1mZFmumaW5ZqYm6UdEZu1ks8qhUlNTw3vvvYfT6eS8887ro1k8WjndZ+8LBvnjsQMEvW4KLPHclF8wbNPmquYu2hzeU25PMOvISe7/t5rtcfO7I3upE2UmzZpNom54i5qhz77myHLcRz9lwcJUtv1DRGjrYNX8hejTxrZZ6ZmI5O8+WvhbWtnf3AqCwNxrPovaomSzk5MSKf/gHoLA7M8+gH7a2fznP/8BYOXKlWRkZIQthiMNDh4vObNm+4qzzmJ6WmQkXifCZ98fXV1drF27lkAgwOrVq1Gr1axw+3nrj1to9KgoXLaKLFv/84vxwlA/e5/6KtxVa0iyH2PSwoVhjipynHjNdxTMZMO/X0dVVc/5nzkfUTf2b3g+/cVfcQOLbryWpO6uq84NT9MEqLOKmL/ivNPu3x/rmuoJ1lWSrjdyYcGcfudikiTxt+eVqugV6XDeisVnPK4sy/jXfsrhKg9tQT0HpGy+uLDv3GmKz8uRQ7tpFiBr1kzSDEMrEAvXfG/scAmdGxbT9PQXiKv4gKR4E4arH0FQqSmxd+ItPYxRpebiovmohKjb7w4J93//H762o0yXfXwEtIgC0+YUYtUov+W/HVEk1lfNzOCS8wtPc6Txy2DP+fKCBbhf+jL+ff8lc9uvMd39AuqcBQPat6TFAyWVZ3zdjJkzmZJ0+jm858Pf4K3dCCoN1tufpCh/aF2kp/vd/3nvDmisY2XeZC4rmDWk449mRuNcr6GimL0drdQlWjl/UgHzJJn1r5TT5PBTLaYxSa4nPj6egoKhef0B+E3tuMreJdl+lEUnqN1Ud3g4uHkXiZ1OFg9SBWe0IgeD/OExpfMo36Li/HOW8eqrryKKIsuWLetJzEmd9dhfOwzA5Is/jxgXvvucSCPLMu8e3AUBPysKpvdRLfn4449xOp3Mnj2bvLy8XtvWug+ysbQNTeYsLlma3fO8tzCbqp8sxlK7ien5RozTV0bgnSh0Zuay+fJbEHce4NzHfjXs9Yf+fveeYJB1+7fjEmBqUSFxmrE/3wU49viLOIDcC1aS1H1vsn1tKRBgWjxccfmltLz2fdoBS9HFXHzF+FagGelz/scNtfgaqknR6blg8hTca8+8z5SipajzI3PfOPHm+YMjpN42EKKeUJozZw7PP/88gUCgl4/S/v37AZg9e3a/+5WVleF2u/nqV7/KV7/61T7bbTYbX/3qV/nDH/7Q7/46nQ6dTtfneY1GM6ZkyCLNyV5XI8lAK45EAWo6fby8r42X97WRbNKwLM/C8klWZqcZUQ1Ad3sghOt7MWnSJAoKCjh69Cjr16/n2muvjejfdaicLsZXaspp8rqxarTcnF+Adph/q8rmLr7yv1pFruIUqJD485VqcvtJKmWaLUy32jjS1c6m1kauzBleRXnoszfPPA/30U/xV27FOnMaXQeOYN9zAMvlFw7r+KOdsfD9HC6de5WbJ+O0yeht8T3PB6r3EHS0IBrjsM44l5r6BrxeL3q9nuzs7F4VbcNloH9ntVodsevURPjs+8Nms6HX6/F4PLS0tJCdnU2SRsOCPBvbyttZX9rBbcvGd/HJUD97cfpK3IBUfxDR3Y5oSQ5vYBHixN9YfMEUtAk2fG3tOI8Uk7BwbvQCCwOephbcVTUgCCQtmtfzXj2HPwJAW7Bq0J+/JMtsbm0E4KzUjFOeo97ZVUGjV4NakLj7nMkDHmf2rJksb9vLW505vH+0g8tmJZKf2HuhOUmtZlZ8Ivs7WtnS1sQ1uVMG9R5CTMT7gKRV96IxWqn92y3497wOfg+Wm//GgS7FR26OLRHdGF1Y8mx/Ht/WZ0AQyPrcT8nBTJXTwWF7JytS0mlod9DoUz7zixdOnpCfPwztnK++4THsni78x9bgevp2rPe/gTpt+gDGGtg9nlqtOm1c3p0v4V3zKACmq3+DftrQ5clO9bm7/H72NCudHGdn543L78donOtdmJnLvo5WDnS20+jzkGk0c3VhIn/d1MAeRxy5xnoqKiqYPn36kLuUVNPOwaXS4G8uQ26rRJuqXDOSlyqJ0c49B1EB4jj4zDuPlFCaoCRGFhekUV9fDyjSbyeuibmPKD6K6txFaBNzIh9oGGnzerAH/IiCQK41DrXY+3uSmppKWVkZdXV1TJ06tde25VMS2VjaxrbKTu46+7g9hyZ/Abbz7qP947/Q8sI3yP/JLgRVZH4/SQvnkrB4Pm3bdlHz7CvM+G7fNdDB0N/v3qxWk2YwUu92UetxkTjEwpzRhOT14Tyg3OfHLZnX87531XkANSsmJ6JWq3HufA2A+CXXj8vz/ImM5DnfHQiwrlk5v6zOyMVgS8Abl47U2UDIM6k3AmJcOropKxDEyPgTjvfPd7gM5u8z4JWwo0ePsmvXrp4HwJEjR3o9d+K2gXLllVficDh49dVXez3/9NNPk5GRwZIlS/rdb+7cuaxZs6bPo6ioiLy8PNasWcOXvvSlQcUSY2xy9/Jkvn1eJmdNsqJXizQ7/bx5sI3vvFXBTf8+yu/X1rKlogtvYPRIRC1fvhyDwUBHR0dP2+1YZWdrE9taGhGAWyZNwxyGE3Sr3X3aZBJAEJFWu/uU289NVSqqtrU04Q6TTqxxxioAnIfX9Cwotm07c0dJjNGPfbdSxGCZP6fX8449bwFgnn0hglpDaakio5Kfnx/WZFKM0YUgCD3doyca9p43XUmOfHJk9FXzjhZEczKqDOV35C9eF+VowoMgCNgWKXJO7dvH/jk/9B6sM6aisSoGzLIUxHngQwA001YO+phHuzpo9XrQq1TMT+g/iShJEi/u6wDg7AyB5LiBL1Skp6czPUnLJG0Xkgx/21Tfr/z2WSnpAOxsbcYTHLu+D9Egbsn1WG77F6h1+A++S+dTd7C/vQWAIltSlKMbGoGavThf/w4AhvO/iXbG+T3vZU/3e9twVEmEJmv9ZCfHRyXOsYqg1mK57QnUOQuQ3R10PXEDwbaqiIztL9uM45VvAGBY9RX0C28YkXG2N9TilyQyzBZyreNHqny0k2YwMrf7WvJ+nfKdWl1gw6pT0eKWqSURr9fba442WASdCXWe0iUb8g8EME/NRxNnJeh203Xw6DDexeihdcsOKjOUAssFebaev9vJSim+fcp9j3bOZZENcASodCpyhZkGE9p+FqpPnOefPJ9Ylq+oJO2u6sDt6+35lnzVTxBNNrw1+2lf+4+RCP2U5H/+VgAq//0SAdep10GGQ153J1eFY3zIPTr2H0b2+dEkJaDPyQKgttVOg0dJqly2MB9v9X58jcUIGl2Pn1qMobG+qQ53MECq3sDchCQEUYXxip91bz250F/5v/GKn0YsmRQjvAx4NeyOO+5g0aJFLFq0iKVLlwJw66239jy3aNEiFi5cyKJFiwYVwMUXX8wFF1zA/fffzz/+8Q/WrFnD5z//ed577z1+/etf91Sc3H333ajVaiorlfb4+Ph4Vq5c2ecRHx+PyWRi5cqVTJkytMrEGKODJKM25BF9WjZ3VjMjU8v3LsjmhdsK+OGFOVwwLR6rTkWXN8iHxzr48QfV3PDMUX72YTWfFHfg8A7MDHak0Ov1nHPOOQDs3r2blpaWqMYzVJo9bl6pVBbYL0jPZoo1ProBncA0azxpeiNeKciWloawHNOQvxhBayBobyZuujLRbBsHi4sxwL5rH9BPQmmvYiJpnnsZkiRRXq74ckyeHH4flXij5owdlVqVSLwxVlUTCULt8CcuVqwqUBY39tV00Ww/tSTnREczTbm++YvXRjeQMJK4REkotW4d++f8th17ALCd0GnlqdhF0NmGaLCizpk/6GNubKoDYFFiKrpTVIu/u6eKJp8GjSBx51n5/b7mVAiCwIwZM1hiakaFxP56FxvK+0oyTLHEkao34JWC7GiNJX4Hi3bmaqx3PgtaI2XtjdgDAQwqFVMtY28hXXK2Yn/mLgh40cy4AMNnvgZAYXdCqczeid3vY2eNA4BZSaOvS2MsIGhNWO56FlVqAXJXA11P3IDkaD7tPla9Co3q9PMdjUrAqu//XBJsLlM+26Af7ZzLMFz4nSHHfyY21irJjLMyc0ZEUj3GqVmdno0AHOhoo8blQK8RuXyWcv910JuMLENJScmwvL21BUqxoGP/8YSSIIrYFhQBx6+XY52KbftpSlSKLYsyLdTVKdfsExNKUlcjgYqtAGjnXBr5IMNMKCGSa7b0uz05ORlRFLHb7X0knnITjaTH6fEHZXZWtvfapjYnknLlTwBofvUhgo7IeQumXXQexpws/O2d1Lw8eB/5gZBrUv5eoYTcWMe+Yy8AloVFPefwtYeVtaF0fYCs5Hi6drwCgHnORagM/X9fYpwZdyDAp42K1/jqjJweCwzdnEsx3/oEYlxar9eLcemYb30C3Tg430xUBjRzfvLJJ0c0iNdee43vfe97/OAHP6CtrY3p06fz/PPPc8MNxyuNgsEgwWBwWBOGGGOLqTYLD0wtosXl63e7M+hnU3s1HX4vjx3Zxz1TZzLJbGVproWluRaCkszBBhebKrrYVG6n2elnY3kXG8u7UAlQlGlieZ6VZXkWEqKwQJufn09+fj5lZWWsWbOGq6++ekx1PAQkiX+XHcErBck3W7kgIzxt8Q3tDraWDX9iJggC56Rm8FJlCRua6jknNRPVMG8ERY0O49QVOA9+hNaoxNh14AgBlwu1cey3hE9UAl123MVKoujEhFKwow5P1R4QBMxzLqKurg6Px4Nerw+rd1IIi16NWaui0xPg9mU5rJ7V1yMk3qghPW54nmAxBkYoodTW1obD4cBsNpNs0VGYZWVfTRdrjjZz3cKsKEc5OtFOXYln7Z/xHVuHLMuDXoTzd5birnobQ86laOLCn7wdCid2KA3lPY0m2rfvAXonlBz7FZkb08zPIKgGNydq9Xo43KksuKzo7hA6GUmSeGlfB6AZdHdSiPT0dLITzRR52tjlTuKJLY0syrGgV59gpi4ILE9J5/WqMjY21bMiOX1Mf1bRQDP1bKz3vsSRbUpBRUHdDoRpBWDq39d2NCIHAzj+cx9SRy1i4iTMN/wJoXuOnajTk200U+1ysLethWMdyj5LJsVHLd6xjmi0YbnnBbr+cjlSSxldT9yE9b7XEPX9L8ylmLU8cf0UujynLvCz6lWkmPvKLEqudrqevBXZ1Y46ex7mGx7r+WzDTbvHzaFWJTm2PCP7DK+OEW5SDUbmJSSzq62Z9+uquHvKTC6blcAre1uodsg0aswIXV00NzcP2RNDU3AevPMznIfXIPm9iBpF/s22aC5Nn6ynfcceuOeWML6ryCPLMrurOiAH8swq3J0tBINBTCYTFsvx36jvwNsgy6hz5qOyjf35baVTSRKFEiQno1arSUtLo66ujurqauLijhdOCILAsskJvLarjs1lbZw1tXeXru28+2hf+3e8NQdoev2HpN/62Mi9kRMQVCom3XMzB3/wK8r+8Sy5t14b9vNfXncCrtrpICBJqMfQ+lR/dHUnha0Li3qe21HjBDQszDIhyzJd214GwLLwmihEOH5Y11SHOxgkVW/s09mum3Mp2lkXESjfgtTVhGhNQT1paawzaYwzoITS7bffPqJBmM1mHn30UR599NFTvuapp57iqaeeOuOx1q5dG77AYkSdqTYLU22n3r4sI5Enig9R6bTzt2MHuC2/gFnxiQCoRIHCDBOFGSa+sCyNkhaPklyqsFPV7mVXjZNdNU7+vKGe6akGludZWZ5nISOur7fWSHH22WdTW1tLS0sLe/bsYf78wVcFR4v/1VRQ43JiUqu5Jb9gSMmaQFDiYHUru6vaOdLopsIOnYHwVYguSEzhndpK2n1e9re39EgnDAfjjFU4D36Ev34X+oxUPHWNdOw+QNIAjMVjjE7sew6ALKPPzUKTeHzBzH/kYwAMk5eitiZTuvtTQPFBG4nk71MbK+n0BMhNMPLFVfloVGN7Aj/W0el0pKSk0NTURE1NDdOnK74Q501PYV9NF58ciSWUToU6bxFoDMj2RoKNR1CnzRjwvrIs46n5EMnTjKfmQ9TW/FGREIibMxNRr1N8lEorME+ZFO2QhkTQ46Vj30EAEhbP63necUBJKJnnXEj/ZTynZnNzAzJKZ3CK3tDva945oTvpjrOGliQMdSm1tG/hmDeOJge8ureFmxf0XkhclJjC2zUVNHrclNg7mTqKuqfHCqqcBRxt9YIkU3D0HbqOvIL13pcQrX0LHUYjrvcfxl+yHrRGLLf/C9HQu8Oq0JZEtcvB5oYGXJIONRJLp46N9zZaUcWlY73nJTr/cjnBuv3Yn7oN693PI2j6L4JJMWtJMQ9uDDngw/7M3UgtpYjxmVjueBpB0/85JxxsrqtWzm22RJKNphEbJ8apWZ2Rze62Zg52tFHjdJBlMrO6wMb/DrVxREonTVVMcXHxkBNKqrQZqOPTCXTU4zq2AfOszwCQ0F1EMh6UKBzHyii2KsUei6el9HTeZ2dn95pfeXvk7i6PfJBhxi9J1LqcwHEJt/7Izs6mrq6OqqqqPt7ty/K7E0qlfQtdBZWatJsfpfJXn6H9k79iW/UF9Fn9e7+Hm5wbr+Lob/6Ms7ScpjUbSP3MOWE9fpLOgEGlxh0MUO92kn2KhNxYQPL6cOxX/JMs3V2HHp+fEruSxLiwKBtv7SF89UcUCdd5Y/+7Hy3cgQDrerqTsnu6k05EEFVoJq+IdGgxRpDYalWMMY1JreG+abOZGWfDL0k8WXKYbS2NfV4nCAJTkw3cviiVv187hX9cN4U7F6dQkGJABg43uvnn1kbufrGE+18u4ZkdTZS0uEe8I85oNLJ8+XIAduzYQXt7+xn2GB0c6Ghlfbe8zQ1504jXDiwJ12Z38+G+Kn7/7kHuf3YPV/3rIN/5oIkXj/jZ267uTibJxKv9YYlTI4osT1Zaaz9trAvLMU3dPkquI5/GfJTGCY5d/fsn+Q4rfiL/n73zDI/kKtP2XVWdu9VqtXIapdHknIPDjMc2BhsbJ8A2NphkWMLCLuyykQ3ssuEj7GIwGJbkRHAOONvjMDlnaUY559A5VdX3o7o1mhmFltSK0/d1zQVWd1UdSa2qc877Ps9jW3njpNvdtbsCPLZPW+B9dUdZspg0Qygs1DqSL7S90zquDtX10edLzL1qriHoTehLNXvk8Nl3xnRsxFWF7NMWJLKvmYirKuHjGw+S0YBjpbZZ0LN/bHmhM4n+46dQwxGMmelYol7ysq8ff9UeAKzLrh/T+cKKwr6orewVmUOrk+QBdRJclS+OS50UIycnh4y0VDZYNdXAH4920eG5sARmknSsS9c2F3dFg4GTjI06jwu3omISBEr9HcjtlfQ/9BHk3vHnlUwVwePPE9j5YwBsd/5gyIL2yjSt+aw15EPSKRTbZEyGpJ3sRJEyS7F/9ncIphQiNXtwP/4F1ARlmamqivfpvyJSsxvBaCPl/kcRU8ZXRIiXXc3a531rflKdNF1kmSwDuXyxLKVbV6QjCnCuX6Q7YqS9vR23e3z2XIIgYF32AQC8g2zvHKuXIUgSgZZ2/M2z+znSvfcgdfnlAKwvTaehQfs5xua4AIq7k0jtXmBu2N01+TzIqopNp8c5wj5F7GfQ0tKCLF+omNxQkoYkCNR1+2jtD1xyrHXJNaSsuw0UmbbH/nzKnJR0Nivz7r4dgJqHf5vw84uCMKDqmu05Sp6TFajBEPr0NEzF2u96V2UbEVXEIspsXFSA64CmTrIuux7JMvvsfWcKI6mTksxdkjtWSWY9Rkni/rLFrE/PQgF+V3eON1ovDVccTIHDyEdXZfLDj5TyyD0L+NLWXFbnW5EEqOsN8sThTr7ydA33/+4cP9vdyolWL7IyOZOEhQsXUlhYiCzL7Ny5c8bbOvaGgvyu9hwAV2fnsdQxtAWKoihUNvfwxO4q/uHp43ziV0e554lqvr/XzeuNUOfTE1ZF9IJCqTXM9YXwjc12nrhnPt/cNvSm1HjYmpWLJAjUe93UeS7NWxgr5uJ1CEYrsrcHxxJtQ2IudK9dzrgG8pNWDHxNDfu17mYgZdVNtLa24vf7MRqNk2J395O3awhGFFbPS2XbwuQkbKYQW2g2NTWhREP9Cp0WFmTbkFWVd8/Nzvy7qUBfvg0YW0FJVVV89S8M+opAoOnNGfNcjCl6ZvM9v2eQ3V2sM9l75m1QZAzZ5Rgyx6a8OtrTiTcSwWEwsniY+cBLh+vpjKqTPrl1YsqumEqp1OAmV+8nKKv8395LG4li1nsne7vpCyXzzsbKsV7t3rbMmYnzC08jphWidNfieugjyJ3V0zy64Ym0V+L5w9cAMF31RYwrbxnyfRkmM/kWKwiQkhpmZW7SSjZR6PKXk/LJ34DOSPjUK3if+suE3MMDOx8kePB3IIjY7vkZutz4la/jodntos7VhyQIbMxNqpGnk+vy5iEAp/p7aPS6ybUbuKJEU52cQ5uTV1WNv/nEtuIG4Lz1K4DOYsG+dCEwu5/5APX7jw/kJy1M1w80sBYUnP9ch07+CVQFqXAVkjMxNvbTSX10zV9sSxlR5Z6RkYHJZCIcDtPefuFcIsWkZ1m+9jnbU9095PHZH/9/CHojvtNv4T70TIJGPzoln7kbRJGud/fiOl2Z8PPHbO/qZnmOkvtQND9p7fn8pN01fQAsy5SQRBH3AS0/yb7+zmkZ41xgcHbSB4ZRJyWZmyQLSknmBJIo8vHicq7J0SZGf2qu59nGGpQ4FjAZVj03LXXy7zcW88R9i/jGtnw2F6dglATa3WGePdnDX71Qx/U/eJ9/eeEM753rIhRREjZ2QRC4+uqr0ev1tLW1cerUqYSdO9HIqsqjNZX45AgFFhs35hcPvOYJhHjvTDMPvn6Grz5xlNt/eZKvvdTKb08GOdgl0R3WAwIOXZhVzggfX6Tnvz6QzdOfXsqP71nF1z+4lB3LC3FYE7uoT9EbWBvtbEuESknQ6bEsuBIAo70fgN5Dx1CVxH0mkkwdsj+A7/RZ4EKFUrh6N4T96JwFGAtXUF2tbaCVlJQgDRM4P14q29y8eFzr8P/6teUzwt4riUZWVhZ6vZ5AIEBX1/ni0TWLtHvKW2dGDh6/nNEvuBqAcM0e1PClnZ1DEe4+gRocrNRVZ5RKyblRs6WdzarUWEFpsN2dN5aftPwDYz5fTAG0JTNnSOtbWVF48qT2rJyoOilGTk4OTmcam63tCKi8W+PieIv3gvfkmq2U2uwoaJZ8SeJHUVWO9WqbZyvT0pHSi7B/8TnEzPkofc30P/QRIq1npnmUl6L4Xbh/82kI+dCVbcXywb8b8f2LbZqndkpaiCsXTK7S5XJDX7aFlHseBlEiePD3+F765wkVlYLHX8D38r8BYL3lOxgW7UjUUIdlV4umTlqZmUNKnE4MSSaHLJOZtekXqpRuX6k1X53qN+CWdTQ0NBAMjq95wLr0WhAEgk0nCPc0D3zduX4VgJajNEtRVZXDdZplW5FVxNOjFU2ysrIwmc6vuYMntGYe4/Kbpn6Qk0B9tBAyXH5SDEEQBprHYsqtwWwu0xplhrK9AzBklpD+wW8C0P7EX6KE/OMe81iwFOaT+6FrAaj5+SMJP3/s51afgGbc6cR9MFpQiuYnKYrCqW5tz2bbwiyCLWcINp8CSU/K6punbZyznXfamwnIMjlmCyuS6qTLimRBKcmcQRAEbioo5pZCrfv1vY5WHq2pJDKGjf4Uo8SOBQ7+8fp5/O6Ti/j76wrZUZ6KzSDS6wvz7NFW/vx3x7nme+/x10+d5NWT7XiCE7dySElJYdMmzR5o796945btTzavtTRQ63FhFCWudeTzwsE6/vW5E9z/m6N89Ldn+ff3+nipVuGcW09AkZBQKDCH2Zan8uV1Vn51ZxFPfHoV371jJZ+8agHLizLQDWHtlZ5iRmLk35uEQnpKfL7pV2XnA3C8t4ueYHwbmyNhXXINAErvaSSLmYjLjbtyZmx4Jhkb3hNnUCMyhuxMDHk5A18PVbwBQMrKG1FVddLs7lRV5YdvVKECH1iaNdAJl2RmIEnSQAfnYNu7WEFpb00P3gQ8A+YiUvZCBHsORAKE6/aP+n5FUfA1PD/EKzNHpZQW9V/31tQT7Bq6W3Umo6rqwMZYWtSyVVXVC/KTxkKj102D16N18GcMnT8TUycZBJn7rywd99gHE1MppeuCLDZrmx0/29N2iZL8iqhKaW9X25jmgpc79V43rnAIoyix0K4VXSRHHqlffAYpdymqpxPXT28l0jhzCquqouD5w1fPZ+vc8zMEaeRMzkCPZm9kTYmQl5nMx0k0hqUfwHrH9wEIvPtTAjsfHNd5wg2H8fzuKwCYtn4W05ZPJ2yMw6GqKrubtc3lLUm7uxnBdbmaSul0fy8NXjcLMs2szLOiqHBOyUGW5YG5+ljR2dIxl2pZuLHnIZx/TsYaMWYjvvpGzlq0Oev68gvzk2Ioni4i1buBuZGfBOet2opGyE+KMZS9dYxYQWl/Xe+w84iMm76FzllAuKuO7pe/N94hj5nSB+4DoPnplwh2JtYxYZ41BQHoCQVxh8earDkzUEIh3Me0Rm17tKB0trWP/ogOEYUb1pbhiqqTbMuuQ7I6pmuosxpfJMK70SiMD+TOS6qTLjOSBaUkc46rs/P5RMlCJEHgaG8XPz93isA4/LtNOpGtJXa+sb2AJ+5bxEP3rOLOdflkphjwhWReP93B3zxzih3fe4+vPHGMpw830+Md/wN36dKl5OTkEA6Heeedd2bE5lmMQCjMC2dqeD3arddcq+evX+zg4aN+dreLtAX1qAhYxQhLUsPcVi7xL9ek89T9S/j5vav465uWceOaYnLS4kvfLcq08+Nbi/i3azMu+HfX4pi/vcrXNjkoyoxv8z3PYqU8JRUVeL9j4j7YAzlK594jba2mapnNHeuXM65Dmt2dbfXyAWWQqqqEB+UntbW14fP5MBgM5OfnJ/T6u6t72Ffbi14S+PL2xGczJZk4Qy00yzKtzHOaCckKu6pmX2FhKhAEAUNMpXR256jvDzS+DPJQ3cUzR6VkcKSSskjLIZiN93xvbQOh7h5Eo4HU5UsACLVXEe6sBUmPddG2MZ1vV/R5ujItgxS94ZLXZUXhjyc0ddLVBVLcTSDxkJ2dTVpaGmvNnZgllZruAK9UXJhDudyRTopejzsc5kRf8u80Xo71aBtTSx1OdOL5paJoy8T+wFPo5q1F9ffhevhOwjV7pmuYF+B/+38In3oFJAMp9/4C0TZ6h+ypeh8Bn4QgwGnX7MgwnW2Y1n0My43fBsD38r8R2PfYmI6Xe5tw//qTEAmgX7QDy4f/eTKGeQlne7vp9Psw6XSsyU6cDXeS8ZNpMrM2mo0XUyndGVUpnfRYCSgiNTU1A/bEY8W6XLO98w6yvXOu15S8rlOVRLy+cY99Ounee4i6PG3esq7UOWRBKXTyZc3uLn8FUnrRtIwzkfSFgvSHQ4hAoWX0vYfYz6Krqwuf78Lf85JcO3aTDncgwqmWoRt+RaOV7I/+l3aOF79LuKdpYt9AnDjXrcKxZgVKKEzdb36f0HObdTqyTZqivH6W5ih5T1WiBkPonA5MJZqN4zsVHQAUWWUcNvNAQcm+7o5pG+dsJ6ZOyjVbWB7Np0xy+ZAsKCWZk6xJz+Sz85dgFCXOufv5ceWJCXVX6ESBjaVO/uaDC3n5z7fym0+v5VNb5lHktBCWVXZVdfOdlyq57vvv8+lfH+LRvQ209I1N8iwIAtu2bUOSJBobG6msTLwfbry0dLn4/bsn+dYj7/KR773Gtu+9zet9rSBAb5eBzm4zAirZxjBbshU+u9LMT28t5A+fXs73PraKz21fxPr5ORj147cGK8q0s6Y0+4J/9125gMWpYUDgj8e7x7RouDqqUtrb1TauAuNgTEWrES2pKL5+0pZpi5nZ7q99ueI5cgIA+9rz+UlyeyVKbxPoTFiX7KCmpgZIvN1dRFH44RvaJvnH1xeQn5a4zdYkiSO20GxvbycU0p4jgiCct72rSNreDUe8OUoRfxehjr0jvmemqJRiFjiz8Z4fUyelrliKZNQKQLHNM0v5VkRTfE0fAN5ImMPRwkNMCXQxLx6upyusqZM+dUVi1EkxYiolkyizxqKN47cHOnAHz4dqS6LI5gxNeborAc0klwOKqnI8mp+0agjbEtHiwP65P6Ar24oa9OD6v7sJVb491cO8gFDFm/hf0zbzrLf+B7rC1aMcoVHRI+Pq0xqVjvUkC46ThfnqL2LarimMvE9/k+CJl+I6Tgm4cf/qXlRPJ1LOYlLu/hmCmFjL4eHY3axtuq/Pycc4itItydRxXW4hInCmv5d6j5s1BVZKnEaCMpyLZBAIBGhqGt9mfkyh6zn1Omp0nWjOz8WUl40qy/QdPZmob2NKadh3jPYMbQ1cbFMIBoMYDAayss7bfIZOvAiAccXcUiflWqwY41i3WSwW0tO1jfCLPz+SKLCxJGZ7N/xzwr7p45gXXIEa8tH++78a79DHTOnn7wWg7te/Rw4kNi+yaJbnKLmidnf2QflJh1s1p5o1eSaCbWcJNh4HSUfKmqHzFpOMjDcS5r2oOun6vKQ66XIkWVBKMmdZmJrGny1cjk2np9nn5X8rjtMVmLivrSgILM9P5as75vP0n23kyS9s5M+2lbI4NwUVONrYz/dfr+KmH+3hrof38/C7tZxr98S1EZaWlsa6desA2L179yVdMpNBOCJz8GwTP3rhAJ/76Ztc8x+vcdNDB/nPdzp4rS5Cg08iu8iPTq+iBkVWSzb+9koHf7h3Ab/+5Cr+4Zbl3L6xlKJMO6I4+beUr2wrRkKhwafnhcP1cR+3KDWNLJOZgCyzv+vSAO+xIIgSlgVXAWBK03IbemexHcLlihIO4zl+GrgoPymqTtKXbUEwmAcKSom2u3vhWBvVnV7sJh2fuaI4oedOkjjsdjupqakoikJz83lv/WsWaYvx96u6CUbk4Q6/rNGXXwGA3HoKxT104U1VZXw1T416LiXUB+r0/5ydG2ZvjlJszLGiGIDn5GvA2O3u9ne1E1EV8i3WITMKZEXhyUHqJGcC1UkxYiqlRYYess0qrqDMowc7LnjPpswcRKDG46LF5x36REkGaPC66Yva3S1IdQz5HsFoxf7pR9EvuhbCfty/vi/uIkGikbvr8DzxZ6CqGDfeh2nD3XEdV9/poiesx9OrFZTOuvvwRZL2pZOF5Ya/xbjhHlAVPI9/kXDV+yO+X5UjeB57ALntDEJKFin3P4IwhoL3RIgoCntbtU3lrXlJu7uZxMUqJUEQuCOmUvKnEVEFqqqqxtV8Yi5Zj2hNQ/H24q89MPB154Dt3ex75gMcqtEaBOaZBdxdWmNFfn7+QIOc4u0mXK39PRrmTH6SZoVbbI3fRjwe27vhcpRAa3LJ+cT/giDg2vsEvrMj3+MSRe6N12HKyyHU3UPz0y8m9NzFsYLSLM1RGshPijaN9rj9NPq0BoFti7IH1EnWJTuQbM7pGeQs5932lvPqJEdSnXQ5kiwoJZnTFFptfGXRCpwGI93BAP9bcZwmrydh5xcEgdJMK5+9spjHPruel766hW9+oJx1RQ5EASrbPfz0nVo+9vB+bvnxXn7wRhXHGvtRRpjorlq1ioyMDILBIO+9917Cxhqj2+Xl+b0V/OMT73PnD1/nyv94m8///iy/OurmUKdAX1h70KYbImzMgQ+tFbClRtAJIn+1ZiVfvW4JVy7Ox2a+1N5mKijJdnBNoXbreuyYC18wHNdxoiBwVVYeoD38RvodxEPM9g5fFYgivoYmAu1JpcJswnfmHEogiM5hx1R63uIhVPEmAIbF19HW1obX68VgMAxk6STk2qEID+3UClWfvbIYu1k/yhFJppOhFppL8lLIthvxhWT21STtkoZCtGUi5WnF2vC5d4d8T7D1fRRfE4gGrPM/gW3JF8//W/QZ0GnZJob0FQji9HeKOzdo6of+46eJ+KYmfDlRXJKfFAnhq9DUJdYxFJQUVWV3ZxsAWzNzBzo/B/P8obpJUyfFEASBJUuWIAqw3qh1SL54uoe6nvNZiQ6DccCCY1dnUqU0Gsd7tQ7sJQ4nhhHUIILeTMp9v8Sw4sMgh/E89nmCh5+cqmECoIZ8uH/7aVR/P7p5a7De8q9xH/tuhdZYlCGo5JgtKKrKyaQt4qQhCALW2/4Lw7IbQQ7h+vUniTQeHfb9vhe/TbjyLdCbsX/qN0hpiZt/jcbxzjY84RAOo4mlGVmjH5BkSomplCpcvdR5XFxVlkqWTY8nDNUhB319fXR1jT1PRpB02JZeB4BnkO1dWtT2bjY2Dvpb2qjUaxvl68szaWjQrAIvsLs79SooMlLeMqSMkmkZZ6KpH8hPurTZZThiP5OmpqZLCpKbSrWf4akWFy7/8PsO5qLVOK76LABtj34VVZn8JihRp6P0s/cAUPPwIwlV8hdFC3KNPg/yDHAIGAtKOIwnmp+UEp3zvlPRioqAUx9mfm4a7v1/BMC+/s7pGuasxhsJ8257NDspqU66bEkWlJLMeTJNZr66eCX5ZiueSJgfV57grKtvUq6Vm2rirg2FPHzfGl7/iyv49ocXcVV5BgZJpKnXzyN7Grj/14e44Ye7+LeXKthd3U1YvtC2TRRFtm/fjlfRs/9sM8/uPMDu0w2X/KtqHn2yrCgKp+vb+fmrR/jSL97mA//5Gtf9z17+6fUWXqwKUe2WCKkiOkGhxBbhQ6V6/vHaXF796kZe/+vr+Ys7VtKA9rO6dV4pueaZEVr8+W3l2KQIblnHz3eei/u4delZWCQdPaHghDcOYgWlQM1u7Is15cps7V67XHFH85NSBuUnKb5eInX7AdAvvpbq6moAiouLE2p398ieRro8IQrSTHx03dRtlCQZH0MVlERBYPtCzfbuzYqOIY9LAvpYjtK5nZe8FvG2EGh5CwBL0U3o0xais+ad/5dSjKXoRgCCHQeQg9NfuDMX5mPKyUKNRGaVBU6434W7UrPYjCmUfOd2owQ8SPYsTIUr4z5XRX8v3cEAZklijTPzktdlReGpk1pH67ZJUifFyMrKwul0kqvzsDRNRVHhZ3vaLthU2ZqpWfId6u7An1ShDIuqqhyL2t2tjMMHX9AZsN39U4zrPg6KjOf3XyGw5zeTPUxAG6vnyW8gt55GsGVg+8QvEHTGuI8/0qKp1ZZlGVgZtfaLWf0lmRwEUcJ290/Qz78SQl5cv7wbueMcqiITrt5F8MgzhKt34Xv/5wR2/R8Ato8/GLeFYaLYFbW725xXkNwgm4FkmMysi6qUXmtpRCcK3Lpcu1+dCmWiqFBVNb7MxVhjhffEKwNfiymUeg8dQx1nPtN0oeUnzQdgVZGD9natkH5BQen4C8DcUSdFFIVGn9Y8PJR6ejhyc3PR6XT4fD66uy/cI8hJNVGSYUFRYX/tyPPQrDv+DdGSSqD+CH3v/nLs38A4mHf37UgWM+7KKrreTVyuYZbJjEmSCCsKrbNM4e09Wak1jaalYi7Tmkb312uFxmUZOuSuWgINR0GUknZ34+Sd9haCikye2cqypDrpsmX6Wz2TJJkC7HoDX1q0nF9WnaHK3c/Pz53i7pIFrB5iIyRRpFkM3LIqj1tW5eELRdhd1cPblZ28d66LLk+Ipw638NThFmxGHVeWp7N9YSZb5juxGHT0BeH3PcXIiDzznhu41LtWQuGJT69ifv55j3uPP8juM40cqO7kdJuHOhf4lcGb4NqffKouQqlDYnl+CpsX5LJ6fi4G3YW3A284xI+P7EdWVTblFrApI3syfkzjwmY28PFlKfzimJ836mVu7XQxL3N0WbtBkticmcObbU28297CiiHyAeLFWLgCyepE9vbgXJmF69Q5evYfIe+m68d9ziRTizuan2QbbHdXuVMLps1ZhOgooGa/ZgeVSLu7TneQ3+7RugS/vL0Mgy7Z2zHTyc/PRxRFXC4X/f39pKamAnDNokx+d6CJd892EZYV9FLyd3kxhgXbCOx8kNDZd1BVdaB4qyphfLVPgaqgdyxGn75qyOP1acvQpRwk4q7B3/AnbOX3TOHoL0UQBJwbVtPy/Kv07D9Mxpb10zqeeOmJWn9YS+ZhzNAWfrEubNvS6xDGYFkbU/psyMjGMESh/fmDdXRH1UmfvKJ8okMfkViW0q5du1gh1nFWLOVos5c99W62FGvzgrKUVLJNFtoDPg52d3Bldt6kjmm2UtPfS28oiEEUWZSaFtcxgihhveP7CEYrgV3/h/eZv0YNejBv+9KkjjWw6/8IHX1a2wy652EkR/y/03BEpsalfd43lzkpSrPzaksDla4+vOEQVv30KPAvBwSdkZT7fkX/w7cjNx2j/ye3gKRDdV/alGH54N9hXH7jlI7PFw5zKNpxvTV/3pReO0n8XJtbyMHujgGV0gcWOXjscCfdQZl6gw2xtRWPx4PNNjabRNsyraDkr9lPxNONzpaOfelCJLOZcL8Lz7laUhYm1v56MmnYe5T2jI0A5Br8NKgqqamp2O3as1H29BCu0txQ5kp+UrPPi6yqWHU6MoymuI+TJIm8vDwaGhpobGwkI+PCPYLNZenUdvnYU9PNtUuGVy7q7JlkfuSfaH/863Q8+bfY19+JZHWM99uJC32qnXl33Urt/z1OzcOPkHn1loScVxQEiqwpVLr6qPe6KbBOje1oInAfOm93JwgCEVmhsk9bf2wtSyN04mkArIuvQZcy/v2gyxVvJMx7SXVSEpIKpSSXESZJx+fLl7IyLQNZVXm0pnLgRjjZWAw6rl2Sxb/dupQ3//JKfnTXSm5bk0e61YAnGOHlk+381VMnueb/vc/Xfnec5461Io/y5ykjcqK+g0ffPs5f/Podbvrv19j2/97nWy818FSFnzN9En5FQkShwBLhmnkS37gyk2c+v5q3/+Z6/u+LO/jazRvYuKjwkmKSqqr8/PhhOv0+sixWPrN8zZCWNtPJreuLKTCHiSDyo7dr4z5ua1YuoiBQ43HROIGQSUEUsSzSOu8tmZq9Tm9SoTRrUBUF9xFNXZCyZsXA10Ox/KRF19LT04PX60Wv1yfU7u6n79TiD8ssy7dz3QiLkiQzB71eT05ODnChSmnVvFQcFj39/giHG/qmaXQzG13xetCbUd0dyG0VA18PNL2J4u9A0NkwF98y7DNGEATMRTeCIBLpqyDcd3aqhj4ss9ECZ8Dubv35bn9vND9pLHZ33cEAFf1ah+6WqPJnMBFZ4alTmjppe6FuUtVJMWIqJZsQ4ooczWLm53vaCEW0bnJBENiapf397upsTaglzFxiX6uWEbckdWS7u4sRRBHLzd/BvP2rAPj+9K/4XvuvSfs5h2v24Hvx2wBYbvw2+rKxbZ4dqukgqEoYBZm1pVnkmC1kmyzIqsrh9qQt4mQjmGzYP/M4gj0H1dczZDEJQJwG+62D7S2EFYVcq41iu2PKr58kPjJMZtZFGx1fbWnArJf48BKtCH46ko2qMuAwMBb0znyMBctBVfGeegMAUa/HsXoZMPucKA5Vd6IKIoUmcHdqex6D1Unuw8+BEkHKWYyUOXsKZSNRF81PKrKmjHnvYsQcpdLzOUqjPducO76EIW8xsruLzmf/eUxjGC8ln/0ECAIdb72H++zYP/vDEVN5zbYcJVe0icoeVRgequnAr0gYBJlNC3IIRpV59vV3TNcQZzU725oJKjL5ZivLHMn8qcuZZEEpyWWFThS5t3QhWzNzUYFnGmt4qaluSjcX9JLI1vnp/P2Ni3jla1v55afWcO+mQgrSTIRkhXfPdfHYofiyeP71zTa+/34XOxtlWgI6FAQsoszSNIWPLjHzvZuLee+vrub5v7ye//fJq7l723KKskfvOn2zoZYDbc1IgsCXV2/Aop95+S6iKPJnV+QBKif7dOyqiK846DAYWR1VJr0zwYJizPZOCGkFrf6TFbMuU+NyxV9Vi+z2IFrMWBdqdhCqIhM+q+WJGBZfR3OztrlWXFyMTpcYQW91h4fnjmqfu7+4bv6MK9QmGZ7YQjPmQQ/aM2XbAu1+8lZFMkNtKASdEX3pZgDCZ3cCEHHVEmzfDYCl5COI+pHtVCVzFsZs7Rz+hpdQlfiy8yYL54Y1APQcPIoqT75HfiKIFZRi9j0RVweB+sMA2JbFr6zd3dGKCiy0O8g0XVosei6qTjIKMp+6cnKyky4mplICKAnV4DRLtLnDPH3ivG3NuvQsjKJER8DPOXf/lIxrNqGqKvtbmwAGLODGgiAIWD74t1hu+FsA/G98H9+L/5Tw+bXc34r70c+BImNYdRumKz435nPEgtXL7Aq6qKo0ZvG3P1pUSzK5CGYHqCPZhwn4nv/HKckgGcyuZu35vjV/XnJ+NsO5LrcQURCodPVR63Fx87J09JJAi19HW8RMXV0doVBozOe1RRssPMfP297Fcgdjz9HZQLCrmwq0YsDasoyBIsnggpLroJZ7Z5gj6iQ4n59UbBvdueRiYj+b1tZWwuEL55lrihwYJJE2V5DaLt+I5xF0enLu/iEAPW8+SLDlzJjHMlasxfPIvl7bl6j5+aMJO2/s51g3gSbcqUYJR/AcjeYnrdWaRt8/p80Hy+0KUn8TcvNxEERS1n5kuoY5a/GEw7zfoTXffCAv+ay83EkWlJJcdoiCwG3zSvlQvuan+mZbE7+vr5qWsEFJFFhV6ODr15Xz3Jc287vPb+CBq0rIT43XbkMl2xjhijyBL2908sSnlvPu3+zgkS9fy7du38z2laWYjWMrBjW4+nn0tNbV8fFFyyibwV0Hq0uyWJ+hLUgf3ttBRI7P2/rq7HwAjvZ20RcKjvv6lmhBKdh4EFNeZjRT48S4z5dk6nAfjtrdrVyKoNM6sSMNh1B9vQhmB1LhmoGCUiLt7v7nzWoUVbNKW1XoSNh5k0w+sYVmS0sL8qAiwjWLNOvUtys6UZLKhyHRL9gGQPjcO6hyAG/t04CKIWMtesfCuM5hytuOoE9BCfYQbNs1eYONA/uSBUgWMxGXeyCXaCajRCL0HtYy49Ki+Unek5oa0zRvFbrU+CxtQ4rMvi4tg2Fr1tDqpKdPR9VJ83Q4rPHbzUyUrKws0tPTkZC5Lk/bRPz9kS66vNqmkEnSDeRu7OpIqlAups7VR6ffh34MdndDYb7mq1hu+Q4Agfd+hvepbyasKKBGgnge+Syqpwspdwm2O/57XBsZJzu1z8Tq/POF7JXOaI5SVzu+8PQWrC8HIrV7h1Umaago/S1EavdO2Zh6A35OdWlj2ppfOMq7k0w36UYT66P39FdbGnCYdVy3wAHAqVAWsixTV1c35vNal98AgPfkqwMFcecGTdnbM4sKSt17D1OXrzXMLSuw4Xa7EUWR/HxtDSx7+/BE5wFzJT8JoD5a+BhLflIMh8NBSkoKiqLQ0nJh06lZL7F6nmZ3vbemZ9Rz2ZZfj231zSBHaHvsa1PSvFz2wH0AND35PMHuxGSOzov+HLuDATyz5NnoPV2JEgigc9gxlxUDcLxDG/v6QhuhEy8CYFm8DZ096RQyVna2R9VJFitLZ/A+YZKpIVlQSnJZIggC1+YW8tGi+QjA/q52flV1mtA0dhoLgsCCbBsPXF3C31wbn2/3f3xoHi//1fX872e28+nrV7GwMBNxDDkIFxOIRPjR4X2EFYVVmTncUDK52QeJ4Ms75mMQZDpCep7YHZ/Eu8Bqo9RmR1HVgQ6L8WDMX4qUkoka8pOxRpuQ9OyfXXYIlyuxglLKoPykAbu7hdvpc7nx+/3o9foLuvkmwr7aHt6v6kYnCnz1mrlhLXE5kZGRgclkIhwODwQbA2wocWI1SHR5Qpxonl2WEFOFfoFmDxqu2Yuv7gXUUB+iMQ3zvA/GfQ5BMmIu1DZ6Aq3vogT7JmOocSHqdKStWwnMDgsc1+mzyD4/OnsKKQu0e08sP8k6BnXS0Z4ufHKENIORJamXLiKfO1hHT1Sd9MkrpkadFGOwSinVVcPCTCOBiMIv953/W43Z3p3s655QM8lc5LzdXRrGIXKxxoJ562ex3vkDEESC+x/F87uvoMoT34jyPv8PRBoOIZhTSbnvlwiGkZWNQ9Hj9tMa0BTHVy08X0jNMVnIMpmJKApHkgXHSUdxjVRMGvv7EsHeliZUoNzhJMsye7JCLmdiKqWzrj5q3S5uX5GOANT6TfREDFRXV6Mo8TUbxrAsuALBYCHS10qwUVsrpK3Rnvfe6jqCXaMXE2YCjXuP0JahFY+yRK3IkpOTgz7qOuI+8jzIYaTsheiyF0zbOBNJfyhIbyiIwPlCyFgQBGHA4nxI27syTcm6J46CEkDOXd9H0BnwnnwNz5EXxjyeseLctJbU5UtQAkHqH/lDQs5p0enIjqrR672zY43jPhjLT1qJIIo0dbvpCOkBle1L8wieiNrdrUva3Y0VTZ10PjspqU5KkiwoJbms2ZSZw/3zF6MTRE739/LTsyfxRmZH9wWAzWxM6Pl+e+ooLV43aUYTX1i1blYE7GWlWvjwfE3R9XSlnx53fJZzMZXSns42guMsJAqCgHXRNgCsORFgdmVqXK6oqoo72q0/OD8pfEbzSx9sd1dUVJQQuztFVfmfNzQlwx1r85mXbpnwOZNMLYIgDGl7Z9CJXFmetL0bCSlrAWJqLmRlE+45DghYSm5HkMb2DNM7lyOlFIMSxt/wp0kZa7wM2N7tm/kFpVi+X1p0ca2qKp5TWn6SbQz5STFlz5bMnEvmB4PVSddMsTopRmZmJunp6aiqwnXZXgTg7ap+Trdp9jQ5ZitlKamoaM/+JBqD7e5WjMPubihM6+/Cds9PQdQROvo07kc/hxoOjPt8gQOPE9z7WxAEbHf9BCm9eFznea+yDRUBpz7MvMzzlkiCIAxY/SVt7yYfMc6u8Hjflwh2tZy3u0syO3AaTWwYpFLKSzWytUT7uz4ZzMTv91+iNBkNUW8csDT3nNBs7wxpqdjKtSaJ3kPHEjX8SeXguXZUQSTfqOLq0O5pF9jdHfgjMLfUSTFbtlyzddyNEfHkKB2q6yUYGX3vwJBdhvMDfwFA2xN/gRKe3EYWQRAo/fy9ANT96gnk4NgtH4eiKGZ755kdtnfuQ9E1ftTu7u3T2nwvzxQhXelGbjwKgoB93W3TNcRZy872ZkKKQoHFxtIhGsuSXH4kC0pJLnuWOdL54oJlmCUddV43D1Ycp/cy7Fzd1dzAO031CMCXVm8gxZDYYtVkct+V5aTrwwQUiZ+8HZ/90FKHk3SjCb8c4WD3+DsgY7Z3oqJNPHsPHUMdYzdckqkl2NhCuKsHQa/HtmwRAHJvE3LbGRBEdAu2Jdzu7k8n2qho82AzSnzuquKEnDPJ1DPcQvO87V3HlGbyzRYEQUC38GrEdVcCYMy5Al1K0bjOY5l3EyAS7jtDuP9cgkcaP871UQucWaBQ6rkoPynYeBy5vx3BYMFcvjWuczR43TT6PEiCwMaMnEtef+ZA7bSpk2IMVimFO2q5Zr7WofzT3a0DdpRbMzWrvr2dbUSSz2oA6l39tPu86EVxSOXZeDGuuJmUT/4KdEbCp17B/etPooa8Yz5PpPEo3mf+BgDzdd/EsGjHuMd0sEHbEFuSfulm44pojtKxzjb8s6i5bDaiK9mkNRkwXOOagJiah65k05SMp93vo7a/D0kQ2JRXMCXXTJIYro2plNx91Lj7uWOlVhg+F7DhkXWcO3duzPMyW9T2LlZQgtn1zA/3uzgd0RrX1pY4B4pqsTms7OvHe1JrKpmL+UlFtrGrk2IUFBQgCAJ9fX243RcWUOZnWcmwGQhEFI42xJfFmPHhv0XnyCXcUU3Pqz8Y97jiJe/mGzBmZxLs6KLluZcTcs6YfWD9LMhRUsIR3Ec0ZaE96iRwsEmbd6zKMRA6rtnd6Uo2xW33nEQjqU5KMhTJglKSJEBJip2vLFqOQ2+gPeDnR2eO0eYf+6J3ttLmdfPLE9oE+dbyxSxOz5zmEY0Ng07i/nXaRsDuNoHTjd2jHKFlaV2VlQfAu+3N484+iXWxhduOo7MaCfe7cJ+Nz3ovyfQQUydZly1ENGrqtnCFpk7SFa3HFRLw+XxIkpQQu7tAWOYnb9cA8OmtxaRZ4s1ISzLTiH0eurq68PvPqyG3zk/HqBNp6g1wrt0zXcObsaiqilpcgGA0g9eLKf+acZ9LsmRjzNY2Gf31L6EqkUQNc0ykrVmBIEn4m1rwt8xstUtMORvLgRiwu1u8HVEfX/NITJ202pmBTX9hNmNEVnjmtLbRsKNIR+o0qJNixFRKiqKwKaUHi17kXFeA1yv7AFjucGLXG3BHwhzvHX2ucDkQUyetzMyZsN3dxRgWX4f904+BwUL43Du4fnEXij9+2xzF04X7kc9AJIh+8fWYr/nauMeiKAoVvdpcb0Nx6iWv55mt5FhthBWFox0z+296tiOIEpabvxP7r4tfBcBy878iiIn9PA7H4R5NXbw8M3tWNdQliamUtI3hV1saWJhlZkWuBUWFUwEnvb299PSMzabOGlXu+s6+jxLQ5nSx/MHZ4ETRc+AI9XlaQ1x5tmbVbDabycjQim3uIy+gRkIYchchZceXYzkbiBU8isdhdxfDaDSSna19ni5uHhMEYUClFK/tnWROIeuj/wlA5/PfIdw7NsXcWBENekruvwuAmp8/kpAmt+Joga7B656WzPGx4Ks4h+IPIKWmYJ5fgj8YpsatPUeuLM8YyE8yzqFC6lTxdlsTIUWh0GJjyQSyNpPMLZIFpSRJouSYrXxl8UqyTWb6wiF+VHGCWs/0eMVmpVqQGLlzVkIhK3XitllhWeZHh/cTkCMsdmZwa/niCZ9zOtixvJCF9jAqAg++2xiXZ/aGjGxMkkRnMMCZ/vGFVxpyF6JLzUGNBElfq01AkzlKM5uB/KTVl+YnGRZfO6BOGuw1PhGe2N9ImytIjt3IxzckO19nMxaLhfR0rXg9eKFpNkhsKdMWmW8mbe8uIdR1GEXtR5Vl5F2voHonlkFgyt+OoLehBLsJtu1K0CjHhs5mxb5E24iZyR3L/pY2/M2tIIo4ovc8z8mx5Sd5wmGORDdct0QVPoN5+kAtvRFNnXTf1ulRJ8UQBIElS5YA0NVcx53LHQD8+kA73pCMJIpsztQUVrs6k1k5qqqyr0175m3MnZznk37+Fdg/90cEcyqRuv24Hr4DxTt6MU+VI7gf/wJKXzNiRim2jz+IMIGc0MqWXjyyDgmFzQsuVdkJgsCGHM0OeV+0yJZk8jAuvxHbvb9ATL3wdyGm5mK79xcYl984JeNQVXWgoHRF0u5uVnJtbgGSIHDO3U+1u5/boyqliqCDoCJSVRWfe0UMQ/Z89JmlIIfxnnkbOK/w7Tt2EiU0sxWMjXsO05qh3c/TFW19G1PeALgPPgmAfcOdc0ZlEFEUGr1a8W8iCiVglBylaEGpOv55bOrmezCXbUINeun4w7cmNLZ4KLr3o4gmE66TFXTvOTDh82WbLBhFiZCizPiGa1dUkW9fswJBFHm/spUIIlYpwtLUIJGGQyAIGJZNzfNlruAOhwbmzEl1UpLBJAtKSZIMIs1g5MuLVlBsTcEvR3io8iQn+6a+g3V+fgZPfHoVD94+f9h/T3x6FfPzJ+51/0TFCepcfdj0Bv5s9YZZkZs0HF++uggRhVqvnpePNoz6fqMksSlq3fNu+/g88wVBwLJE67a3FWpdO70zeHMxyXmFUsxbWQ35CFdpm9K6RTsGCkr5+fkTvlavN8Qv368H4EvbyzDpp6bbNsnkMZzt3fYB27tkQWkwcqBnIO9IaGyG/h7C596d0DkFyYS5QOsgDrS+gxLsm+gwx0VM8TOTmwh6Y4vrJQvRWS0oQS/+s+8D8ecn7e9qJ6KqFFisA9YnMcIRmWdOa5s41xbrp1WdFCMzM5OMjAwURWG+2E5BqoE+v8zjh7S/zc0ZWgZUrcdFs+/yVhQ2ul20eT3oRZHV2ZcWWRKFvmgt9geeQrCmIzcfx/XTW1Fc7SMe43vl34lUvQ8GCyn3/RLRbB/x/aOx65z2+59nlbGZhlYKx4pqRzvaCESmR/14OWFcfiOOvzmI/YGnsN31EPYHnsLxNwemrJgEmqqhOxjAJOlYk31pwTzJzMdpNLEh47xKaX2hjeI0IyFF4EzAQXNzM15v/BvhgiBgWxGzvYs2YJQVo09zoARD9J84nfhvIoEcrGhDFUXy9Aruzgvzk2S/a8DKz77ujmkbY6Jp8XuJqAoWSUem0Tyhc8V+Vk1NTZc0qG4sdSIA5zo8dLrji0gQRJGcT/wvAP27H8FXtXdC4xsNg9NB4UdvBqDm4UcmfD5REM7b3s3wHCX3QS3jLCVqd7e7pg+AJWkCkVOaBaCueCOiPWl3NxbebmseUCctTqqTkgwiWVBKkuQirDo9X1iwjCWpaURUhV9VnWHvNIQ3z8/PYMuSecP+S0Qx6VB7C6/WafZsX1i5DqdpYhOw6WZ+bhrb8rWC2CNH+/EHR+8guzIrFxE45+4f98aSdZFme6cTNBn7TO5Wv9wJtXcSbNK69VNWLgUgXPU+RAKIjnw8ply8Xi+SJJGTM/HNtYffq8MbklmUk8IHlycnr3OBwQvNwVYSV5VnoBMFqjq91Hf7pmt4MwpVVfDVPg1KCMlWhM5WDkDo7M4Jn1ufvhLJVgRKGH/jK6MfMAk4N64BoGf/4Wm5fjz0XGR35614BzUSQp9RhCFnwajHK6rK7mhX4tas3Eu6Ep85UEdfRIdJlLl3mtVJg4llKTU11PGptdri97mT3TT2BbEbDKxwaErDXZe5tVnM7m5FZjZm3cQVuSOhy1tG6hefRUzNRW4/S/9DtyD3XtoBDhA8/jyBd34CgO2jP0SXs2jC1z/aGgBgRfbwlmZF9lSyLNak7d0UIogS+rKtGFffir5s65TZ3MU41K0VGtfl5GGUdFN67SSJ49ocTaVU5e6n2uMaUCmdDqUTUQWqq8dmR25bpjVcxIovgiDgjNre9cxg27uI18epoFYwXznPTldXF3B+7uo5+hJqOIghZwHGwuXDnme2UTcoP2mi6omsrCyMRiOhUIiOjgtzltMsBhbnasWVvXHa3gGYS9fjuPJ+ANoe/eqk5y2Xfu5eANpf24mnpn7C54upvupmcI6SGpFxHzkJQMralSiKwulubZ22qSSV0PEXADAsv2naxjgbSaqTkoxEsqCUJMkQGCSJ++cvYUN6Firwh/oqnj1XMafC1rv9Ph4+dgiAD5aUs3qOdOV94ZoFWMUI/REd//fu6BYHaUYTK9K0Rce77ePzNY7lKEW6zyBIKr76JgIdXeM6V5LJJRbUaVlYhmSzAhCK5ifpF183oE7Kzs5Gp5vYxkJ9t4+nDmnn+9q1ZbNa/TdV9AYDNHk9w/7rDQame4jk5uai0+nw+XwX+PLbzXrWFWsb10mVkkawbTeypx5EA5bS2zGWbwMgfPadCT9PBUHAUnQTIBLuPUW4f+qz62Ih3a7TZwm7Z6bSJdbgELPr8ca6rZd9IK5FYUV/Lz2hIGZJx+q0C/MVwxGZZ85omwvXFulJtcyc7JHBKiWLu5EN82zIKjy8pw1VVdmSpc15Dvd04L9MlSiqqrKvVXtGbciZGjtWKasc+xefRXTOQ+muw/WTW5A7q1EVmXD1LoJHniFw4Ak8v/9zAExX/xnGFTdP+Lr+YJh6j1ao2Fo+fE6oIAhszNXUyfvbkrZ3cx1ZUTjaqz2vt+ZPPDMzyfSRZjSxcZBK6eoyOxlWHd6ISFXQTl1dHeFw/FZ1lsXbQdIT7qgm1K6tJ9Oiz9GY8ncm0nvoGLW5WnNHWYZWWMrIyMBi0WzyXQf+CIB9/R1zamM4lp90sYp6PIiiOKLt3aZojtJYCkoAWXf8O6IphUDtAfre/RXeMzvp3/ME3jM7URV5wuMejG1+CVk7rgRVpfYXj074fLNBoeQ9cw7F50eyp2BZUMqZ5l5cUZvbrfkikXrN/s8whQrYucDbbc2EFYV51qQ6KcmlJAtKSZIMgyQIfKy4nB3RRfYfz57it6eOocyBopKsKDx4ZD+ecIjS1DQ+vmjZdA8pYaSYDXx0mQ2AV+siNHWPPvG5OlvbPDjc04krHBrzNfVZpeichSCHSYvmNcxkC6TLmYH8pDVRuztVJXwmWlBaeN7uLraQmAj/+2Y1EUXlivJ0NpQ4J3y+uU5vMMB3Tx7i+2eODvvvuycPTXtRSZIk8vLyAGhouNBa85qo7d1byYISsq+NQLP2t2We9yEkYxq64vWgN6N6OpHbzkz4GpIlB0PWBgD8DS+iKlNbGDDlZGGZVwCKQu+hY1N67XiI+Hy4TlYA5zfCYvlJtjjzk97v0LoSN2ZkY5AuVA48tb+Wvogekyhz3xVlCRp14oiplOrq6rh3lQOdKHCw0cP+Bg9lNjs5JgshReFA98jWa3OVZo+bFq8bnShOqdWX5CzC/sXnkLLKUfpb6PvRDfR+ZxWun92O54kv4v3j1yHsR8xZhOWGv03INfdWtWs5CmKEpYUjP49jxbWjHW0E5cuz2Hi5UOnqwxuJkKLTszQ9a7qHk2SC7MgtRBIEqt391Htd3LpcU6KeDGYQDkeor49fqSGZU7AsuAI4b3sXayLpOXBkxjaZNu45QmuGVhxNjWi2/TF1khLw4Dmu2X7Z1985PQOcJOqjudfFtolZo8YYzt4aBuUo1fSMaV9I58gh45Z/BKD115+n/j+20/zTu6n/j+2c+4tiXAefTsDIz1P6wCcBaPzds4T6+id0rphCqTPoxxuZmRli7ug8PGXNcgRR5N1KTV1WbJMxnNX+hnVF65FS50YT9VTgSqqTkoxCsqCUJMkICILAjQXFfKSwFAF4rb6aB4/sIywntotkqnnq3BnO9nZj0un48uoN6CYQcjwTuWNDCXmmMBFV5MG3akZ9f5EthWJrCrKqsqtj7CHdgiAMqJRSS7QNt2SO0szkfEFJs3mQ286g9LeA3ow/awVerxdRFCdsd3e4oY+3KzsRBfjajvkTHvflgDcSITLKwiyiqnhngJpguIXmtoUZCMDJFhdt/dOvppouVCWCt+ZJUGV0joUYMjRrOEFnRF+2BYBwAmzvAEz51yDorCiBLoLtexJyzrGQFrXAmYn3/L6jp1BlGVNuNuaCXEJd9YRaK0GUsC7ZMerxXQE/lS4t0Htz5oX3xHBE5tkKLZPi+mI9KeahM2mmk8zMTDIzM1FVFXdrDR9Zrm0CPbynjbCisjWqUtrV0TYnmoXGyr6o3d3yjCws+sm1u7sYKTUX+xeeQUwrhIAb1XNpEV5pqyR0+tWEXG9fbR8ACxxa9/lIlKQ6yDRbCMoyxzouz2Lj5cKhHm3DcZUzE2mOrYUuR9IMxoFs3FdbGrhhURpWg0hvWEd9yEZVVdWYCkGxnMGY7Z1j5VIEnY5gRxf+xvFl7042B083o4oiOToZd4c2R401ybmPvYQaDqDPKsM4b+V0DjOhuMIhekJBBGCe1ZaQc8bm+R0dHQSDF2YlrShIxWqQ6POFqWgbm2JHnz5P+z/qhZZ3kd5mmn50R0KLShlXbMS+ZAGy30/DY09N6FxWnX4gm2qmqpRc0fwk+1rts324Vfu9rckzEzrxIgCGFR+ensHNUt5ua4qqk1JYZE+qk5JcSnLmlCRJHFyVnceXVm9AEgT2tTbz3wd24RuDbH4mcbKrg+ertI7lzy5fQ3aCJl4zCVEU+bOtuYDKsV4de8+OXiS6KqpS2t3ZSmgcsvNYQUlv0BanyRylmUe4rx9/VS0AKau1glLozOsA6OdfQXO7ZlOYk5MzIbs7VVX54euaPcZHVudRmmmdyLCTzEBiC83W1tYLLFQybEZWFqYCsLPy8lUpBZrfQvG3I+isWIo/ckFHm37BNkCzvUsEos6MuVDb9Am07EQJTawLc6w4N8RylGbePT9W5EpbtwpBEPCefA0Ac+lGJKtj1ON3d7ahAovsaWRelLH45P5a+qPZSZ/YOvPUSTEGq5Q+sshGmllHiyvEcyd6WJueiVGU6Az6qXJP7edmJrA/ane3MXdq7O4uRrCkocojz6V9z/9DQqyATndpjQhrC0e3QxIEgQ1R27tY0S3J3CMgRzjZp1lWrU0f3gYxyexiR66WpVTtcdEccHPTEq2R4HggHa/XR0tL/PbmtuU3AOA9/RZKOIhkNpG6XHumzMQcJTkQ5KRP295blmfB7/ej0+nIzdWaJ1wHngQ0ddJcUhrEChzZZgumBOWg2Ww20tLSUFWVpqYLnwN6SWR91OJ6T3X8tneqItP+xF8O9yoAbY99LWH2d4IgUBLNUqr95WMoE9y7Ko6qlOpnYI6Slp8UbRpdv5Jut59mv/ZZuLrIQKRuH5C0uxsLrnBoIGf0hqQ6KckwJAtKSZLEyea8Qv5qw1ZMko5T3Z18Z+879AVmVwd6fzDAQ0cPoALbC4vZnDd3/cLXlmWzJl2bkP1sTzvyKOGXy9PSSTMY8UYiHO4e+0awJVpQkvurECSZ/hNniPj8Yx94kknDc/QUAKaSeeidDoDzdneLzucn5efnT+g6r53u4GSLC7Ne4gtXl0zoXElmJg6HA5vNhqIol2xOXO62dxF3PcG29wEwF9+MqL+waUFffhUA4dp9qOHE3CP16SuRbPNACeFvfCUh54wX5wbNAqf30PEJL9YTTU805yEWJB6z7Yl1XY9ESJbZ16WpM2JKnoHXIjLPzXB1UoyMjIwBlVJ99Vnu36hlbDxxpBNfUGV9hmZz9X7H+DIUZyvNbhdNHheSIEyp3d1gIrV7UV1tI7xDRelvIVK7d0LXae520xnSAypXLYpPfRwrsh3paCU0y10JkgzNyb4ewopCptFMoWXuNdddrjguUil9eGkaOlGgPWyiPWKmqmr0fN0YxsIV6FJzUEM+/Od2ARfa3s00+o6epDZbW3cUpWmOGfn5+UiShBL04jn2EqDlJ80l6rxRu7sE5CcNZiTbu01lY89R8lW+R6R3pCYFlUhPI77K98Y0zpHI/8iHMGQ4CbS00/ri6xM6V8z2ri5qLziT8FZWoXh9SDYrlvJSdp5uRUUgXR8mv/VtUFV089YiOSa2xr+ceKu1iYiqUGRNYaHdMd3DSTJDSRaUkiQZA8sysvn7zVdhNxipd/XzT7vfps07M4O4L0ZRVR46eoC+YIACm517l84dqftwfOWaMgyCTFtQz+/3jBzYLgkCV2ZpuSjvtLeM2RvbkFGEPrMEFBl7iR41EqH/2Klxjz1J4nEfPg6ct7tTvN1EGg4CECjcjMfjmbDdXSii8OBb2mftk1vmkWGbOSH1SRKHIAjDLjRjBaXDDX30eseeyTabUeUgvtqnABVDxmoMaUsueY+UtQAxNRciAcK1+xNyXUEQMRfdBAiEe04Sdo1udZooUhaUoU+1I/v9uE5VTtl1R0MdlOvkXL8KVY7gPa0V0K1xFJSO9HbilyM4DcZLQnif3FdDf0SHWZS5dwark2LEVEr19fVsztezMNOMP6zwq/0dbMnUiimn+nqmPZ9tKtnfpjVQLM/IxqqfnoKg4upI6PuG4/1KrTCaY4yQmWqJ65jS1DTSY7Z3nSMVvZLMVg51a5+rNemZyc7rOcaO3AJ0gkCNx0WP7OPaBQ4AjvmddHd309MTXxFAEISB52XM9m7A5jbasDGTaNpziNZMbW6aEtKammJzVc/xl1FDfvSZJZiK10zbGCeDmEIpVvBIFIPn+RfvC2wu1QpKxxr78Qbjs+KO9MVnqx/v++JBMhkp/tTHAah5+LcTyv4qihbsGryeGWcT7I7a3aWsXYEgSexv0D4TyzN1BI+/AIBh+U3TNr7ZhisUYndnUp2UZHSSBaUkScZISWoa/7RlG1kWK51+H/+8eye1/b3TPaxReanmLCe6OjCIEl9ZsxFjgiThM5mcNBsfKtU2Sp4846fPO/Jm0caMbIyiRHvAR6Wrb8zXG8hRmq9dcyZ2r13OXJyfFK7UOpak3KW0ujRlQXZ2NvoJZEn84WATzX0BMmwG7t00b0Lj9Xaepnbnt/F2np7QeZJMDsMVlPIcZhblpKCo8M7ZrukY2rThb3gZJdiLYHBgnvehId8jCAL68quBxOUoAegsuRiyNmjjqH8xYZYhoyGI4sAG00yyvfNU1RLu7Uc0mbAvXYS/Zj+Krx/Rmoa5ZN2Ix6qD8gS3ZOYiDlpIauokH6Cpk2wzWJ0UIyMjg6ysLFRV5WxlJV/YqjUNvHG2jz43zE9JRQX2XEaFg5iVW8zabToQ7VkJfd9wHG7W1HRLM+N/tguCwIYc7WcTswZMMndwhUOcjc7z1ziTdndzDYfByKZo7t8rLQ3cttyJADSEbPRGDGNSKcVs7zzHtYKSc90qAFxnzhF2z6ym0oOnmlBEiSwpgr9bU93G8pNc+/8IaOqkubQ5LCsKjT7t91BstSf03Lm5uUiShMfjoa+v74LXCp0WCtLMRBSVg3Xx7QPpHPGpgeN9X7wUf/JjiEYDfUdPTijvM9dsxSCKBBWZNr8vgSOcOK5ogTdl3UoiskJln/YZ31JgGFA5G1YkC0rx8mabpk4qtqawIKlOSjICyYJSkiTjINtq45+2bKPY7sAVCvKdPe9yonPmBvee6+3mj5WaWua+pSspSEnshGsm86mryknThfErEg+9NfICwqzTsSFDs8N5p33sGwiWRVpByWDuBpIFpZmE7PPjPXMWgJQ1K4Dz+UmGxdcmxO6u3x/mF+/VAfBn20oxG6Rxn0tVVTorniHkaaWz4pkJdZTNFhRm1/dYUFCAIAj09fXhdl/oJ3452t6F+yoIdR0CBKwltyFIpmHfO5CjdC4xOUoxTPk7EHRWlEAnwY6J2WSNhZlogRPrnk5bvQxRr8cTzU+yLbkWQRz53tTg9dDk86IThIFnYow/7q3BJWvqpJmcnXQxg1VKhVZ1oGP9p7va2BLdeNzb1U5kFHvcuUCLx02jW7O7W5udN23j0JVs0tSKDLe5KSCm5qEr2TTua8iKwtl+7fybSscWKL0xWmw7nLS9m3Mc7elEReu4vzgfLsncYEeOplKq9bjwSwE2F2vqiuN+J83Nzfh88W2IW5ddB4JAsOkE4d4WTDlZmAvzQVHoizofzASUcJjjLm0evTjTiKIopKSk4HA4UII+3DG7u3Vzy+6u1e8jrCiYJSnhf8t6vX4gf2oo27vNUdu73XHa3lkWXokurYCRnnk6ZyGWhVeOZ7jDYsxIp+B2rZhS8/Aj4z6PKAjMs868HCVVlvEcOQmAfe1KDlS1E1QljILMKu9urXm0cBVS2tyNekgk/aEgezq1prIPJNVJSUYhWVBKkmScpBpN/N2mq1ianklAjvDfB3axp+XSycZ04w2HePDIfmRVZXNeAdsKi6d7SFOKUS/xqbXahO+9VqhsHnnSd1V2HgJQ6eqjze8d07ViCiXV14Cok+k9eBT1Mticmg14jp8GWcGQm4UxNxtVDg+oIwLF23C73YiiOLBwGA//934drkCE+ZlWPrxyYt1lvs7TBPvrAQj21+O7DFRKB7pmblF+KIxGI1lZWuf8cLZ3+2p7cAfis8KYzShhL77aZwEwZm9GZx85O0xffiUIAnLraRRX4n7vos6MqeA6AALNb6GEpsbn3blBs4/p2X94xhR/Y8WttGg3tTeanxSP3d2uTq2zeZUzE9sgxWYoIvN8pbYJ94ESw6xQJ8VIT08fUClVVlZy/4YszHqRyk4/7Z06UvUGPJEwx3rnvqowprhZmpGFzTB9v0NBlLDc/J3Yf138KgCWm/911ALoSByt6ySgSOgFhQ3zs0c/YBBlDidOk5lAJMKJWfZ8SjIyh6JZqWvSk+qkuUqqwcjmzPNZSrevSAegKmjHE5GoqYnPGldnS8dUsh44/xyNqZR6ZpDtXf/JCmoziwEocGhfKywsRBAEPCdeQQ160afPw1S6ftrGOBnE8pPmWVMuUFMnipFylLZEbe/2VMdpoShK5Hzif2L/NcQ7VLI/9l8TeuYNR8nn7gWg9eU38TWMlOM0MrGcqvoZlKPkq6xG9ni1/KSFZbxXpTX2LkhVUU69CIBx+Yenc4izirfamoioKiU2e1KdlGRUkgWlJEkmgEWv55vrt7IxtwBZVXnwyH5eqT033cMaQFVVfn78MF1+H1kWK59etuay7DK4fuU8ylPCqAj86J2GEd+bbjSxzKEtOt5pH1tIt96ZjyG7HFQFS3aEcJ8Lz7nacY87SeI4n5+kqZMidQdQ/f0IViftSioAWVlZ47a7a+r18/sD2gT9z6+djySO/+9MVVU6K5/l/GJDoLPy2RmzUT0ZnHP1sSsOuykBTUk4UxhuoVmaaaU43UJYVtlVNbc3qFVVxV/3HGrEi2jOwlRw7ajHiNZ0pLyo9eS5dxM6HkPGaiRrASgh/I2vJvTcw+FYuRRBryPY0TWhhXoi6TlwPj9J9vbir9HyqmzLrh/xOFcwyJEe7TO7NfPCwvgf9mjqJIsoc8/W0kkY9eQyWKVkVEPctVrbTP7N/g7WObViQ8zqby6zv037jG7Mmf5wauPyG7Hd+wvE1AuzC8XUXGz3/gLj8hsndP490Y2l0hQZg25sm3TiINu7fUnbuzlDR8BPo8+DCKxKy5ju4SSZRHbkFKITRGo9LiRziGU5FhQETgbSqK2tJRKJr+FnwPYumqPk3BBVJc8gm9vmPYdoydKstq1+bT4dm6O6DjwJQMocs7sDqIvmJxXbJsd9JfYzbGlpueTzsrY4DZ0o0NTrp7EnPsWbfd1tFHzlSXRpFz1/BW1b1leRWOX+wHUXlZNx1WZQFGp+8di4z1MU/TnXzSCFkiuaF2pbvQxBkjjRqf2e1uXqiFTvBpJ2d/HSFwoO2D8n1UlJ4iFZUEqSZILoJYkvr97A9cWa9csjp4/zu4qTM2Lz982GGg60NSMJAl9ZvRHLBLJhZjtfuboIEZVqj55XjtaP+N6roxYwh7o78ITDY7qOJZajVJ7MUZpJDOQnrdY2sUMVWji9fuE1NLdohcOJ2N09+FY1YVllU2kaW6IWCOPlvDopdg9RCfbX07j3+7hbD6FEghM6/0zDFQrxaE0lACsc6Xx98Ur+YvGqC/59snQROkFABfbPoE7x2EKzqakJ5SI14uViexfuPkq47wwIEpaS2xHE+J4zhqjtXSjBtneCIGIuugkQCPccJ+Ka/KK+ZDbhWLkUgJ59hyf9eqMR7O7FW61932lrV+E99QaoCoa8xejTR7b82NlYh6yqFFpsFwRcB8MyL5yNqpNKDdhMs0edFGOwSqmiooJbljvJsxvo9UdobJEQBYE6r5u6/r7pHuqk0eb1UO/qRxQE1uZMn93dYIzLb8TxNwexP/AUtrsewv7AUzj+5sCEi0kAJ9pDAKzMHZ8VUixj6nB7C+Gk7d2c4HB3BwALU9NI0c+++1iS+LEbDEOqlCoCDrxBmfr6kdeDMWwrogWlU6+jKvKA8rfv8HHUGXJfOHCiAUWUyBAjqN4eBEEgPz8fJeTHc/QFAOzr75zmUSaemPVakTVllHeOD6fTicViIRKJ0NZ2YeObzahjRYHWlLg3Tts70IpK5d+vo+hbb5P/hccp+tbbFH79BRAEet/+Kf27x1/wGYmyBz4JQOMTT487/yv2c+4I+PHFWZCdbNwHtYKSfd0q6jtddIX0CKhsVY+BqiDlr0ByFk3zKGcHg9VJ5Smp0z2cJLOAZEEpSZIEIAoC9y1ZyUcXahtKL1RX8vDxQ9PqxV/v6uPR05oq465Fyyl1jM07fq5RnpfGldG9k98c7iMYHn4BUGKzU2ixEVFVdneOrVs5ZntnSukHkgWlmYASCuE5cQaAlLWaQikcLSiFSnfgcrkQBGHcdncnmvt57XQHAvC1HfMn1M2jqiodZ54a8jV/dyUth35G1Wt/QfOBH9PfuBs5NLMCgceKrKo8UlOBOxImx2zh7pIFFFpTKLDaLvi30pnBx4rLAXijtZEz/fEF4E42WVlZGAwGQqEQHR0dF7x2zWKtoPR+VTeBEe43sxkl2IevQfPlN+VtR2eNf4Nav+BqAMJn30l4A4bOmo8hcx0AvoYXUZXJ//mft72b/nt+b6xbc34JBqfjfH7SspHt7hRV5c0GzQZoa9ZF6qS9MXVSZFZlJ13MkiVLAGhoaCAc8PP5zdpm40snXJRbtXnS6/XV0za+ySZmd7ckPZMUg3GaR3MeQZTQl23FuPpW9GVbE2L50+8L0ujXFK1XLsga1znK09JJM5rwRyKc6OoY/YAkMxpVVTnUE7W7cybt7i4HtCwlkTqvm9S0CPPSjIRUkTOBVKqrq+Oaf5hL1iNaHCjeXvw1B0hZNB/JaiHi8eKuHDmfdypQFYXjPdo8p9ypbe9lZ2djNBrxnnwNJeBB5yzAXLphOoeZcNzhEN3BADB5BSVBEEa0vYvlKMVrezdwXlHCungbqZvvwrp4GykrP0TGzX8PQMuvPk+wOfFW55nbt2IrLyXi8dLw+NPjOodNryfDqGWkzoQcJVWWcR+KupCsW8nbZ7SiX745QkrlswAYVyTt7uJhsDrphqQ6KUmcJAtKSZIkCEEQuGX+Ij6/Yi2iIPBuUz0/OLSHoDz13RuBSIQfHd5HWFFYlZXDDSXzp3wMM5Evbp+PRZTpi+j45Ttnh32fIAhcFVUp7epoHVNXaqygRLAZUR+hN1lQmna8p8+hBkPo0hyYiguRe+qR28+CKNFh1rJeYoWBsaKqKj94XVtM3rQyhwU5E1vQeNqPEXIPb5klGe2oShhP+zHajv2aqte/QeOe79Fb+xZh/9gWMzOBl5vrqfa4MIoSnypbhEEafhNxbXoWWzJzUIHHayvpDU2/UksURQoKCoBLF5qLc1LISTUSCCtjXmjOBlRVwVf7NMhBJGshxtwrxnS8rmgdGCyonk7ktjMJH5+p4FoEyYzi7yDYsS/h578Y5/qoBc4MuOfHnjtp61ehqiqeaO6DbZT8pKMdrXT5fVgkHauc562gBquTbigzYjHOXrWz0+kkOzt7QKW0YZ6NdYU2IopKQ5NWfNjd3Ig3HJrmkU4O+1qjdne50293dzHh/mpcJ/6XcH9iCnq7KlpREUjVRZifO76mKlEQWB/9We1vS9rezXbqvW66gwEMojhgb51kbmM3GNiapTUOvNbSyG3LtQLAqYCTfrf3EtXJUAiSDttSLZ/Rc+IVRJ2OtGiD2kx45rvOnKUmQ7O7y0vRmlkH7O72/xEA+7o7EMS5tfUXK2hkm8yTaocdT0HpQF0vYXlijcSZH/k21iU7UEM+Gh+8AyWQ2KZBQRAojWYp1f7fY+NW1xXNoBwl37kaZI8X0WrBunA+h5q0uerKTIFw9S4ADMuTdnfx8GZrE7KqUmqzMz+pTkoSJ3PrqZIkyQzg6sJivr52MwZR4mhHG/++9z3cU7zx+ZtTR2n1ekgzmfnCynXJDoMoqVYTdyyxAPBybYTW3uEnaqvSMkjVG3BHwuxpjT8TQ5eajTFP64A2O314axsIds7tDJWZzvn8pOUIgkDojKZO0hVvoKVDy1cYr93dzsoujjb2Y9KJ/Nm2iWWKKIpC27HfjPAOAZ0pjXlX/gPpCz6M0V4AqoKvu5KOU7+j5s1vUf/ev9F97k8E3TM/B+RUXw9vRbM8PlY8nyyTZdRjPlJYSoHFhjcS4bfVFdOqAo0x3EJTEASuWaR1xc9F27tg+14i7loQDVhKb0cQxqYoEHRG9KWbAQif3Znw8Yk6C6YCbQMo0PIWSnhyOyljFjies9WEevsn9Vqj0XMwlp+0mlBrBZGeRgS9EcvCq0Y87vV6TZ20ISMbwyCFyB/21uCOqpPu2TJ71UkxYllKDQ0NeL1ePr85B0mAwzUhHDoTIUXm3ab4rJBmEx0+D3WuPgRgXfbMKiipqkqg6XWUQCeBptcTolo80KBtdi1yTmwOHMtROtTWMiOeOUnGz+GoOmmZIx3jCA0sSeYW23MK0Isi9V43edkq6RYdXkVHVTCFqqr4FEbWqO2dN9qg4Yw+83sOHJ2MIY+Jlt2Hac7UCkoWrzb/LywsRAkHcR95HgD7+jumbXyTRX00P6lokvKTYsQax7q7u/F6vRe8tignBYdFjzckc6J5YgUWQZTI/+Lj6Bx5hFrO0PrrLyRcwV9wx4fRpznwNzbT+/aucZ0jllc1ExRK7mheaMrq5XgjMrUe7b6+WVcNioyUtwwpo2Q6hzgr6A0F2duVzE5KMnaSBaUkSSaBNdm5/O2mK7Hq9VT19fAvu9+hyx9fWONEeb+pgXeb6hGAL61aP6MsTWYCH91USo4xTFgVefCt4btgJVHkiixNpfRyzbkxTehiOUr2Uq1baiYsNi5nLs5PCkcLSuHyD9Df348gCOTljT1LIiwr/M+b2kL0nk2FZNtNExpnT9XLKGHvCO9QiQR6MdpyyFjwYYqv+kdKtv8bmUvuxOycDwgE+uvpqnyWune+Te3b/0Dnmafx99bOiEy3wfQEAzxeq6kEr8jKZVWc1jM6UeSTZYswSxL1XjcvNtVN4ijjI1ZQ6ujoIBi8sHkglqP07rmuCXcuziRkfzuBptcBMBfegGQaX6e3PpqjFD47OSHEhsy1SJZ8kIP4G1+dlGvEMGY4sZZpi9beg9PXsayEwvQd1e55aetWDaiTLAuuRDQOX7Rt87o53tmOwIV2d4PVSR+c5eqkGBerlAodRm5Zlg4ItLVq398b9TUoM+y+OVH2DbK7sxtn1tww4qpC9mnjk33NRFwTt5Gq6NHuuevnTWyzcaEzA4fRhC8S5mTS9m7WIisKR3u0Bq+16eOzQEwyO7HrDWyJZim92dbELcs0Vclxv5OOjk66ukZv/LMtux4Af81+Ip5u0tZpquTeg0cnZ9Bj4MDxOhRJwimEMSk+jEYjmZmZUbs7NzpHHub5m6d7mAmnLlrQKJ4ku7sYZrOZzExtPn9x85goCGwqidnedU/4Wjp7FgVf+j2IEv17HqNv58MTPudgJLOJ4vs+CkDbI0+O6xyxfM16r3va50muqMWzfe0K3q9oRUYkRYpQVqsp8wxJu7u4eCuqTiqz2Sm3O6Z7OElmEcmCUpIkk0R5Wjr/uHkbTpOZFq+bf9q9k0b35HYtt3rc/PKkFgh+a/liFqcn/cEvRhJFvrBFW1Qc7tZxoGp4q4PNmTkYRJEGdz+nu+NXGMRs78wOTQE1E+wQLldUWcZz9CQAKWuXowa9AxL4TodWYMrMzByX3d3Th1to6PHjtOr51JaJhX162o/TfVbrIkwru4GiK/9u6H9X/B2idH5D12DNxFl6HfO2/BVl1/032cvvxZq5DASJkLednupXaNj1XWre/GvaTzyOt+sMqjK9IaoRReE31RX45QiFFhs3F4ytcyzdaOKukgUAvNvRwrHe6VUApqSk4HA4UFWVpqYL1YwrC1JxWvW4AxEO1s2M3KeJoioRfDVPgRpBl7pgIKtoPOjLozlKtXtRw/5EDXEAQRAxF90ECIS7jxFxT67qJH1j1PZuGnOU+k9VoASC6NNSsc0vwRvNT7KOkp/0RlSdtDIzh3Tj+eL47/ZU45Z1WMUId88BdVKMmEqpsbERj8fD3WszcZgl6poldIJEm9cz54oHsfykDbkF0zySC1FVFX/ja4O+IhBoenNCjRDVbb30RbRg7isW5kxofKIgsC5HazrZPwbFepKZxVl3H55IGJtOz4LkhtllxzVRlVKD1838AgGLXqRPNtIQsnLs2LFRj9c7CzAWLANVwXvqDc3yThDw1TcR6Ji+eaiqqhzt0ixay6IuVQUFBYiiiOuAVjCwr799ztndyapKozemUJrcghKcbx67eJ4P489RGg7LgivIuvO7ALQ9+lX8tYcSct4YxfffhaDX4Tl2Cs+JijEfn2u2YhBFArJMRyDxc/d4URXlfNPoulXsqdX22ZakKcjV7wFgTNrdjcrF6qTJJtH2xkmml7n1ZEmSZIZRkGLnn7ZsI9+WQm/Az7/sfofKnsmZdIZlmQeP7Ccoyyx2ZnJr+eJJuc5cYGN5Diud2qb6T3e3IQ9jYWLR6Vifng3Ay7Xn4j6/ZZG2SSrInUiGCL1JhdK04TtXO+CtbFlQRrjqPZBDiM55tPRpQa7jsbtzByL87N1aAB64qgSrcfze3UF3K61HfgGopM67ksxFt2JKLRryn948fA6EzmjHUXQlBRu/yvzrv0fu6s+SkrsWQTISCfTRV7+Tpr0/oOr1b9J69Fe4246iyFOfE/J8Uy2NPg9mSccnyxahG8cid5kjne1Ry6bf152jcxoXNDC87Z0kCmxbqBX254rtXaBlJ7KvFUEyYyn+yIRsEaSscsTUPIgECddOTs6RzlaAIWMNAL76F1DV8XnGx4Nzg3ad7n2HJ+0aoxF73jjXrUINB/FW7ARGzk8KyhHeadSKbdcVn7fuDITCvHhO+9v64HzTnFAnxbhYpWQ1SHxqfTaqItDbpX2fr9fPncVup89LTX8vArA+Z+yK3Mkk2LkfxT+4uUdF9jUT7o9/3nUx753V7rcF5gip1omphwE2RotwB9uTtnezlUPRxrBVzgykpJ3PZUeK3sDWTE19u7OjiQ8ucQBwzO+kqqrqEiuzobAtP297p7enkLJIyyiezrxcb3UdNWnafDjLrKnkz9vdPQdAyvo7p218k0Wb30tIUTBJEtlxWGZPlMHz/IubHTaVagWlM61uen2JWVelf/Ab2FbfjBoJ0fTjO5G9iWtKM2Vnkv+RDwHQ9tjYVUqSIFBosQFQN405Sr6zNcguN6LFjHlhGad7tN/LelMLKBGk3CVImXOnEWqyeLO1UVMnpaQyf5KbLSbD3jjJ9JIsKCVJMsmkmy384+ZtLEhLxxcJ891973GovSXh13m84gR1rj5SDAa+tHo9YnKxNCJf2V6KTlBoCeh5al/tsO+7MlvbeDnS0UarJz6vYF1KBsZCLazVlOal7/gpZH9g4oNOMmYG8pNWLUWQpIH8pMiiGydkd/fr3fX0+cIUp1v4yOrxb87JIS/NB36MEglgdpaTveyuhPgWS3oL9vwN5K19gPnXf5/89V8mtXArksGGEvbiatpDy8GfUPXaX9B88CH6m/Yih0ZfTE+UIz2dvN+h+bvfU7IAp3H8G30fyi+i1GYnIMv8uvoMIWXyCgWjMdJCM2Z7t7OyC1mZ3RPniKeBYOu7AJiLb0Y0TKwrVBAE9AuiKqVJyFGKYSq4DkEyo/jbCXUcmLTrpK3XFEr9x04iB6Y2OzFGTBGbtm4VvnPvo4b86By5Wmf1MOxpbsQXCZNptrAi87ya43d7a/HE1EmbJ5YRNxMZnKXk8Xi4bqGD8gwTHW2aYvVIeyudvsm/L04F+9s0ddIiZyapE7jvJppQbwWB+peGfM1X80fk0PhCyY+2aDaNy7PHrj4eikXODOwGI95wmNPdc0u5djkQlGVO9ml2VGudSbu7y5XtOfnoRZFGn4flxTp0okB7xEJr0MCpU6dGPd4abczwnHgFVVXP5yhNo+1dy66DNGdpLgkpQa1oWlhYiPf0myi+fnSpOVjKt0zb+CaLuuiafJ4lZUr2PLKzs9Hr9QQCgUssEjNTjMzPsqIC+2sSU/gRBIH8z/0afWYJ4c5aWn5xf0I330s/fx8APa+/Q7Bt7M+0ohmQo+Q+FMtPWsap1j48sg4dCutaonZ3SXXSqPQGA+zragfghilQJ02GvXGS6SVZUEqSZAqwGQx8a+MVrMnKJawo/ODgHt5uGL6IMVYOtjXzWp3WSfuFletIM5kTdu65Sn56CjcUa6qSP5zy0O8bevMvy2RmdZa2wfZKbfwPvZjtnS1fRg1H6Ds2+kIlSeIZkMKvWYGqqoQqtIJSZ8YGQLO7M44xS6KtP8Dj+zQlyp/vKEMvje9RqioyLYcfJuzrQGdOJ2/tFxDE8SudhkOU9NiyV5Cz8pOUXfvfFG7+S9JKdqAzO1HlEJ62I7Qd/SVVr/8ljXt/QG/dTiKBvoSPoyPg4w912t/QjpwCljicEzqfJIrcW7oQm05Pq9/HMw01iRjmuMjLy0MURTweD319fRe8tr44DZtRR7c3xPGmybU9nUxUOaRZ3aGid67A4By+QDEWBmzvJilHCUDUWzEVXAuAv/lNlPD4NqlHw1oyD0OGEyUUpv/41N/zVVUdyHNwrl89EB5uXXb9sIVqVVV5PWp3d21R6cDGTCAU5qWoOulD802Yp0idNJVWGE6nk5wc7fleUVGBKAh8YUsuoaCE16VDBd5M4FxtOjlvdzd2Re5kEWzfh6/qMWCYTTI5gPvEDwn3nhnTeUMRmRq3Fsy9pWx8+W4XIwrCgLIrlkWVZPZwsq+bkKKQYTQxz2qb7uEkmSYGq5R2dzezvVzbFD/md3Lq1CkikZEtoS3lVyAYLET6Wgk2nhhoIplOJ4oDR2uQJR0OIYxNCJGWlobNZsO1X9tUT1l3O4IoTdv4Jov6KbS7A5AkacDRoqGh4ZLXN0efNbtrJp6jNHBNaxoFX/ojgs6A+/BzdL/8vYSdO3XZIlLWrQRZof2JZ8Z8fCy3qj7OZtvJwH0wWlBat5J3z2pFvmJbBF31mwAYk/lJo/JGm5adVJ6SSllK6qReSw704at9dtBXJm5vnGT6SRaUkiSZIoySjq+t3cS2wmJU4BcnDvPMuTMTvol2+X08fFzz1v1QSTmrBoVpJxmZz1w9n1RdBK+i42dvD2+t8sGScgDea67HE4pPyh4rKFkyNGVSMkdp6lFV9bxCac1y5JaTqK42MFhoC2pdy+NRJ/1kZw3BiMLaIgdXLcgY9/g6zzyJr+sMgmQgf/2foTNO/qJIECUs6QvJWvoxSq/5LkVX/h3p5TdiSMkDVcHXdYaOk49T/cZfUf/+fxBofQ85MPHFUUiW+XV1BUFFpsxm54b8iWVOxUg1GPlE6UIEYF9XOweiXVZTjV6vJzdXu/debHunl0SuWqAtNGez7Z2/8RWUYA+C3h7NJUoM+vIrQRCQ286guCbv92fIXIdkyQM5QKDptdEPGAeCIOCMbjD1TMMGk7+phUBbB4JOR+rKpXii+Um2EfKTqvp6qHP1oRdFri4sHvj6E3tqNHWSFOGuKVInTYcVxmCVktvtZkmOhQ8uy6a3U2s02NlYS1iePvVjIuj2+6jq65kxdneqquBv+BP+hhdHf7MSxFv1ON6ap1Ai8VmbHqhqJ6yKmESZVSWJyxKNZU8dbEva3s02YnZ3a5yZCVGBJ5m9bM/JxxBVKa0p0xol6kM22jwKZ8+eHfFY0WAaWN95Tr6Kc/0qAM2JYhpUyaqqcqRTu26xTUYQNHWSGgnhPvwsAPb1d0z5uKaC+qjVWvEUFZRg5BylLVHbu701PQmdu5hL1pJ9z/8A0PHHb+E7+37Czp1zr/bZ6HzqJWTf2KzDY4W89oAP/yiF2MlAVRRc0TW+fe1KjrZqfwerzd0gh5GyFyJllU/5uGYTPcEA+6Pr5snKTlLlAMHOQ3gqfon7xPdQw4MtEtWkSmkOkCwoJUkyhUiiyGeXr+GW+YsAePLsaX596ijKOCcesqLw4yP78YbDlDnS+NiixHSMXy6YDHruW6V1Y7zTrHKudWiZ+pL0TObZUwnKMm/F2a1sWXgVCAIivUjG8LT6a1+uBOqbiPT0IRj0WJcuJHTmdQAiC26gr19Tioy1oFTR6ual41rWw9evnT/ujYn+xl301modVLmr7sdkLxzXeSaCIAiYUovIWHgLJVf/EyXb/pWMRbdhcmibx4G+GgJNr+E+8UNcJ3+Ev+kNIt6WMS+UVFXlqYZq2vw+UnR67i1dlND8ggV2x8BE+MmGalqmyaJquBwlgGsWaRY7b1V0zspOrHDfWUKdmlWcpfQ2RF3iVLCiNR0pX7MIDZ+bPJWSIIiYi24EINR1hIjn0g7TRBDLUerZP/U5SrEiVuqyRaihPoKNx0EQsC67bthjXo+qmzfnFZJi0Ioo/mCYl6q0ZogPlU2dOmk6rDDS0tIuUCkB/PmO+YR9RsIhAXcoNOsVKTG7uwXOjGlXsKtyCG/VEwTb92hfEEexpBO1z164+yjukw/Glau0J2o5VG5XkRIYRL/YmUGKwYAnHKKiZ/Y2B1xuuMMhzrq0z8Ta9KTd3eVOit7A1mjz5WFXKxuLbIDAcX8ax48fH3WOZhtke2cpKsSYmT5tThT+phaqUrTvJUOv2Xxqdndvofj6kOxZWBZeOeXjmmy8kTCdQW2OMs869QWltrY2Qhc1mK6al4pJJ9LpDlHVkdh1SNr2B7BvvhsUmaYff4yIKzG2q44rN2Gcl4/s8dL1/KtjOjZFb8BpMKICDdNge+evqkXudyOaTXjzCmgJaA4fm/teBsCQVCeNyhut59VJpQlUJ6mKTLivEm/17+k/8p/4654l4h5u/yypUprtJAtKSZJMMYIg8NGFS/nk0pUIwBv1Nfzo8D5C4+iAfersac72dmPW6fjy6g3jCre/3Llh1TxKrWEURB7cWT/kewRBGFApvQMu2xUAAQAASURBVFZfHVdnqmRNwzRP61Q3p3npOXgUNdnROqXE1Em25YsRDQbCUbu77jzNYiszMxOTKf4sCVVV+cEbVajAB5dlsyTPPq5x+XuqaT/xGADp5TeRkrt2XOdJNAZbNunzb6Doim9Rdu1/kbXsbnT2MhBEFH8HwdZ38Jx+CNfx7+Nr+BMRVy2qOvpnel9XOwe6OxCAe0sXYjckJtNiMNfmFrLQ7iCsKPymuoKAPPXdcvPmaUWtlpYW5Ivu55vLnJj0Iq39ASraJsdubbJQIj58dc8CYMjahN6e+IBbw4JtAITOvZvwcw9GZ5uHIUMr+PjrX4zr8ztWnBtiCqUjU37Pj9ndpa1fjTeqTjIVr0WXMrSSsj8YYF+02HBd0fnf6xN7avDKOmxShLu3TJ06yd/wp0FfmbpFZkyl1NjYiNvtJstu5NNbS+jt0gpsr9bN7u7JWEFsQ8702t0pITeeiv8j0lcBgg5L2UexL/sqtiVfHPafffmfY1v0OUSjEzXswnv2t/jqnkOVh1cDnOoMA7CmwJrQ8UuiyLrspO3dbONoTxcKMM9qIzNpCT7jmEqb0xjbswswiCJNPg8bF2hF63PBVJq7XUM2BQ3GuvwGAHyV76EGvaRFc5R6pyFHqWXXQZqzNcW/U+1DkiRyc3NxHXgSAPva2+am3V3UZi3LZMaqm5qGF4DU1FTsdjuKotDcfOEzwKiTWFPkADSVUiIRBIG8T/0MQ95iIn0tND90N2oCcmMFUSTn7tsAaHvs6THPWYujOUp101BQcsXs7lYv552qTlQEMg1hss4m85PioScYYH934tRJqqoS8TThq38J17H/xnvuUcI9J0GNIJoy0KevHu7IpEpplpPcfU6SZJq4vng+X169EZ0osr+tmf/avwtfOBz38Se72nm+uhKAzy5fS5Yl6Qk+HkRR5EtXFSKgctat580TQy8kNucWkGo00hvws6/1Uqn7UFhitneZAcK9/Xiq5kYWw2xhcH6S4ukk0qipxNpJAxjwwo6XXVXdHKjrRS8JfGn7+DZZw/4emg89hKpEsOWsJn3BzJzw6kwO0oq3YVv4KeyrvoWl5Hb0aUtA1KOG+gi178FT+UtcR/8TX+0zhPsqUZVL71/NPg9PN2ibBB/ML2K+3TEp4xUFgXtKFuLQG+gM+vlDXdWUdzs5nU4sFguRSITW1tYLXjPrJbaWxWzvZk+gu6qq+OteQA27EU0ZmAuvn5Tr6MuvArQcpckuwpgKrkOQTMi+1gHVVSJJXbYY0WSalnt+zFrVuX4Vnmh+km3Z8L+znY11RBSFMkcapQ7tvugNhPhTVJ10Y7kZk2FqNmuC7XtRAoODrqdukZmWljZgWRlTKd27uRBz2I6qQE1/L7X9iQnanmp6An7O9Wq2pdOZnyT72nCf+RmyrwVBZ8G26H4MzuWIxlR01rxh/4mGVHQp80hZ+iUMWZsACHUexH3yQSKuS/++Ovt9tAW1TuUrF2Yn/PvYOMj2Tk42Cc0KDvVoz9w1zqQ6aaYxHTanADa9niuytOLwGX87KwrsKAicjKqURsKQPR99ZinIYbwVOwds76bD5vbA4SpkSYedMHYxTG5uLjoB3Ie0TBz7hjunfExTQayAUTSF6qQYI7kRxHKU9iQwRymGaLJR+OUnEQwWvKffpPPZf0nIeTNu/gBSio1gYzN97+4d07Ex27vpyFFyH4pa2q9dwYEGrVFvqaUf5BBSVjm6nEVTPqbZxOutjSiqyoIUx4TUSXKwl0DL27hP/g+eMz8j1LEXNeJF0FkxZG/GtuQL2JZ+BcXfAQznTpJUKc1mkgWlJEmmkU15BfzV+q2YdDrO9HTyr3vfoTcwuodtfzDAT44eQAWumVfCpryCyR/sHGZJYTpbc7SH2C8PdhOKXNr1o5ekgQ7ul2vPxfXQG8hRytY6aZM5SlPLQEFp9XJCFW+BqhKat4VelzbxHIvdXURR+OEbWmHkrg2F5DnG3uWqyEGaD/4EOejCkJJP7qr7EYSZ/xgWdWYMGauwzr+L1FXfwjr/bvTpqxEkM2rER6jrMN5zj9J/5D/wVv2OUPdxVDmAPxLh19UVRFSVxalpXJMzufcpm17PfWWLEAWBo71dvN/ZOvpBCUQQBAoKtO9xaNs7LctjNuUohXuOE+49CYKIpfQOBHFyigu6onVgsKB6OpHbzkzKNWKIehum/B0ABJreQAkn1ppENOhJW7McgJ79U3fPj3i8uE5r+Q+ONSvwntIsPq3D5CcpqsqbUQvXweqkn71yFK+iqZM+vqlkkketEfa2EGh8ecjXfNVPosjxZRdOhMEqpd7eXow6ia9dswhXn/aZf3aUbI2Zyv6okqY8LR3nNKkzwv3ncJ/5BWqoH9GUgW3xA+hsY+uIFSQDlqIbsS68H8HgQAn14an8Jf6GP13QzPBuRRsgkGEIk5+e+M3GxemZ2PQGXKEgFT1dox+QZFrpDPhp8HoQgdXO8WdeJpkcpsPmNMa27PyoSsnL9pVaU+aZgIPq+ma6u4cvCAiCcN727vgrpEVzE3sPHp3yDdGjbdqewTxLeCA/yVuxE9nbg5SSodmvz0Fi+UlFU5ifFGOkHKXN0Rylw/X9+MOJz1405i8h7/6HAeh6/l/xnJh4HqhkMZN1u2YH3fbIk2M6tjha0Kv3uscd3zAeVEXBfUhTKJnWrOBcv1ao2ODbDSTVSaPR4fNyoFtrtBiPOkmJ+Al2HMB95he4j3+fQPNbKIFuEPXoncuxlt+LfdU3scz7EDprPgIKSqgPGO4zoqKE+0Gd3XmllyszfycrSZI5ztKMLP5h09WkGo00uPr55907aRtBOqyoKg8dPUB/MEhBip17l6ycwtFOD1Nhh/DF7fMxizI9YT2/eW9oj/5ri0rRiyK1/X1U9o7efWRZeCWIEpLoQWcK0TsN3WuXK8G2DkItbSCJ2FYuIRzNT+op1haBGRkZY7K7e/5oKzVdXlLNOj5zRdGYx6OqKm3HfkuwvwHJYKNg/ZcQdfFff6YgSAb0aYuxlt6GfdVfY114P4asjQh6Oyghwr2n8NX8kb4j/8FjJ9+kOxjAoddzd8kCxCkIwi622flwQTEAzzfWTnnX3Eidi1eUZ6ATBWq7fNR0Tk/O01hQQv34618EwJS7DZ118tQNgs6IvnQLAOGzOyftOjEMWesRzTmocoBA0+sJP79z/Xnbu6mi98gJUBTMBXkIoRZkdxeiKQXL/M1Dvv9weyvdfh82vWFAdeHxB3nmtJYvd9MUqZNkfwfeil8y3EJTlX14Tv0YOZBYC5mLcTgcAyqlgwcPArB9YQY5Oq0QfLC9GW948gtbiSaWn7RxmtRJwY4DeM8+CkoQXUoJtsWfRzI5x30+vb0U+7IvYchcp52/fQ/uUz8m4tHuuYcatXv+knTdxAc/BDpRZF2O1oyyP2l7N+M5HM26WmBPI0WfeLvdJGNHVVUi3mZ8ja/jPff4oFemtkt9sEqp0ttEcbqZkCpREUgdVaVki9reeU68QuryJYgGPaHuHry1k5PNOBSBtg7OWjTVnVPSGuUKCwtx7dcsv1LW3oYgTc59cDpRVJUGr/b9FlvHZz0+EfLz8xFFkf7+flwu1wWvlWRYyLEbCckKh+v7JuX6qVvuIW37A6CqNP/sHsI98bmmjET2Xbci6CTch47hrRg9pzBGntmKThDxyxE642iIThT+mnoifS5Ek4mTBgdBVcIkyCw9+3MADCtunrKxzEaeq6rQ1El2ByUp8f0NqUqEUO9pvOcex3X0P/HXP4/sqQcEdPYyLCW3kbrqr7GWfRS9YwGCcN5qUxB1pIxgbWxb8kVSlnwBQZx796vLgRlRUPJ4PHzta18jLy8Pk8nEqlWr+N3vfjfqcU8//TR33XUX8+fPx2w2U1xczD333MO5c/HfCJMkmQkUpzr4py3byLZY6fT7+Ofd71DTN7S9yks1ZznR1YFBlPjK6o0YpLnnjTyYqbJDcKaYuXWh1r37YlWIjn7fJe9JMRi5Il/r5Hi5ZvT7jGS2YyrW8nHMTl9SoTSFxNRJ1oXliEYd4XPvANCh1zZOx2J35wtF+Ok7Wif/564sIcU09k3WnupXcLccAEEkb+0D6C2zv1NWECX09lIsRTdhX/mX2BY/gDHnSkRTBvvUAk5HrIiqwu2h91DO/ZpA265J3xQGuCorjxVp6ciqym9rKvBG4rcSnSixglJ3dzc+34X3kBSTjo3R7sW3K2e2SklVFXy1z6DKASRrAca8ye9y1UdzlGJ/q5OJIEhYirQOxlDXoYHN6ETh3KDlNPXsP5zQ845E72C7u5Oa3Z11yTUIw+QLvF6vNWhsKywemEf87JWjeGVJUydtTnxW1sXIgW7cFb8CZfg8HAAl2IP71E8I906uei2mUqqqqqKnpwdBEPjWNcsI+CQQVH577PSkXj/R9Ab8nI2qaKY6P0lVFfyNr+Cvfx5QMGSsxrrgPkTdxFVSgmTCUnwL1vJ7EfQpKIFuPGd+jq/xNWr6tQ7XTSWOCV9nOGI/ywPtzVPalZ1kbKiqyqFoF/aa9MxpHs3ljarKhF21Wr7G8e/hOf1TQm3vgjo473LqszS25+RjFCXqXf3sWKWpLU4G0qg4e+6SOdxgLIu3g6Qn3FGN3NdA6sqlwNTmKLXsOURTTjEAOToPVquVtFQ77sNRu7v1d0zZWKaSNr+PoCJjFCVyzJYpv77BYCA7W7NTbWi4sIAoCAKbovP8ROcoDSb77h9iKlqD7O6i6ccfRZ3gOseQnUnatVq+cNujT8V9nCSKFFo1dV/9FOYouaP5SbZVS9ld2wfAAosHKeJDzChDStrdDUuHz8O7TVpm+A2jqJNUVSXirsdX9zyuo/+Fr+oJwn1nQJURzdmYCj6AfeU3sC38FIaM1QiScdhzxWNvnGR2MiMKSrfddhu/+c1v+Pa3v83LL7/M+vXrueuuu3j88cdHPO4///M/8fl8/N3f/R2vvPIK3/nOdzhy5Ahr1qzh1KlTUzT6JEkSQ5bFxre3bKPY7sAVCvKve3byWl01tVHv/tr+Xt5prOP3FScBuL18MQVxdhXMZsI9J6bMDuGuLWVkGcKEVIkfvTn0dW4oKQfgUHsL7dEOqZGI2d6Z0rx4a+oJdiXeVznJpbgPx7yVlxOp248acBNMK6XXq21cjsXu7rd7GujyhChIM3PnurFvynnaj9FV8SwA2cvuwpK+cMznmOkIgojOVoC58Hp6Su7nDUGbzN9gaCOffmRPA4HGV3Cf+AGukz8m0PwWsq9tUgrEgiDwsaJyMowmekNBnqg9O2WbfmazmcxMbeNqNtve9dXtJOKqBlGPpeS2CzrNJgvDAm0xG67dhxoafiMnUehSitCnrwLAX/8iqpq4PJS0dStBEPDVNRLomBpbrFh+Q9q6VXij+UnD2d21etyc7OpAAHYUaXlwmjpJ67a9qdyMUT+5v3Ml2Ien8lcQ8TC8r3oUQQQliLfqcfyNr6FOki2Gw+EYeDYcOnQIgPnZKcy35gDwXlMdIXn2WHIcaGtBBeY7nKRP4cabKofwVf+eYNsuAEz5OzAX35rw7lO9YwEpy76M3rkCUAm1vcfflr9FmbmbTeWJz0+KsTQjC6teT38wSGXS9m7G0uD10BUMYBBFljvSp3s4lx2qEibcV4Gv9hlcR/8Lb+UvtXyNUD8IOpBMDHXv99e/NGUqJatOz5VZmjK1KdJGuk2PV9Fz1mfl9OnhGwgkcwqW8q0AeE68el6VPIU2twcOnEWWdNii+UkFBQX4Kt9Fdnch2dKxLto2ZWOZSmKFi3lW25Q4HwxFXDlK1ZNXUBINJgq+/EdESyr+qj20/+GvJ3zOnE/cDkDPK28T6ox/ryJme1c3hY4Qrmjh1r5uJSc7taL06oi2P2Zc8WGEafpczAaePVeJoqostDsotg29jygHuvE3v4n7xA/wVPyCUOcBVNmPoE/BmLOVlKVfwr7sy5hyr0A0zP29yCQjM+0FpT/96U+8/vrr/OQnP+GBBx5g+/bt/PznP+e6667jm9/8JvIIC7cXXniB5557jvvvv5+rr76aT3ziE7zxxhsEg0F+8IMfTOF3kSRJYkg1mvj7zVexIC2dkKLwm1NH+fv33xr49/DxQwOmME+eO02Xf/I33aYDOdhLoG03rtM/x1fzx0GvTK4dgk4S+fwmzT7gYJfI4Zr2S95TkGJnRWY2KvBq3ejFLevia7T/zQ4C6rSEtl6OeAbnJ0Xt7nrnfwSA9PR0zOb4uqQ73UF+u0frQPvqNWXopbE9NoPuFloO/wJQcRRdjaPo6jEdP9vwhMP8troCBViVlsGOFXdiX/ENzPNuRJdSAogo/jYtwPPUj3Gf+AH+hleIuOsv2dCfiNWlWafjk2WL0AkCp/t7eatt4pYQ8TJSjtLVCzIQBTjT6qalb+rsIcZC0NNK5xmtQ9FccD2SeWo6u8XM+YiOfIgECdfum5Jrmgs/AJIR2ddCqPNQws6rt6dgX6w1H0zFBpOqKPRG/eQdKxfgq9J85G3Lrh/y/W801ACwKiuHLIsVgIdePoJPkbDrJl+dpIRceCp/Fc3USddCe0eywlj25xizNeu+YNt7eCp/jRKenM2LRYu0YnhMpQTwjatWosgCgl7mp3tmj0ppf6t239swhXZ3StiNp/KXhHtPgyBhKb0TU962SdvgEXUWrGV3Yin7OAHFyDxzP/+88A2E7vdRlckp/ulEkbXZSdu7mc7hHk2dtMyRjnGOuznMFNRIgFD3MbxVv9NyNc89RqjrMGrEhyCZNaXi/LuxlH0U5ABDWZ0qwW4Cre9O2ZivzsnHpNPR4O7nuqhK6bjfyYkTJ4lEIsMeZ1sRs717lbR1q4CpVSgdadX2AApMwYH8JPcBLQMnZc1HhlUnz3bqBvKTpm8jO1ZQam5uvmSvcmNJGqIANV1e2voDkzYGQ1Yp+Z/7DQA9r/4A18GnJ3Q+27JF2FYtQ41E6Pj9c3EfF8uxqve6RnlnYlBVFfchrWm0d+ESusN6BFTWV2vZUoYVyfyk4Wj3enivWVMnXZydpIS9BNv34j79M9wnfkiwZSdKsBdEA/r01VgXfAr7ym9gLrwByZIzHcNPMkOZ9oLSM888g81m484777zg6/fffz8tLS3s2zf8pkJWVtYlX8vLy6OgoGDIjZwkSWYDZp2eexavGPV9YUXBHRrZJma2oKoqsq+NQHN0k/n49wk0vozivdiLevLtELYuymO5IwIIPLSrFUW5tGv9g1GV0juN9fjCI8vMLQu2gqRH0vnRmcMDtkRJJo9wbz/+Gm3CZFu9nNCZNwDosGq/t7HY3f30nRoCYYUVBXZ2LB7bxroc8tB84EFUOYjZuYCspR8b0/GzDUVVeay2kr5wiEyjmY8Wz0cQBERjKsbsTdgWfRr76r/GXHIrOsciEHQowV6C7bvwVPwC19H/xlf3HOH+cyhyeMJWl/kWG7fN0zbGX26up8rVl+DveGjmzdMm6Y2NjZeM22k1sHqeA4C3K2deZ7uqRGg78ktUJYzOXoYha8OUXbuhy01FyT2cSr2CA/v2sP+tlziw530OV7dxuKad+s7EL1ZFvQ1z3g4A7fMWSVyTxlTa3rkrq4i4PUgWM6LaCHIEfVYZhuxLC0OBSIR3G7X743VF2usef5DnzmgFmjtXOCdVnaSEPXgqf4US7EE0pmFbeP+INhg6ax6SyYl53oewlH0MRAOyuw73qZ8QcdclfHwOh4OSkhLgfJZSps1MeYqmeNnZVEevb+ZnKfUHA1RMsd2d7G/HffphZG8zgs6CbeH9GNJHn88mAoNzKd+rvZZ9fQVIgkqg+S08Zx5G9ndMyvVimVT725K2dzMRWVU5Ev38r3Em7e4mEyXsJthxAE/lb+g/+h/4ap4k3HsKlBCC3o4haxPWhfdjX/3XWEpuQ+dYRLDlHUZSpgab3yDUNzURBladng8UzwegS+zEYhDplY2cdYlUVQ2/3rRFFcDeM2+RtkprRHBXVhHun/yN9WB3L2eNmrVahk5zy8jPy8N1SCsq2DfcOeyxs52YQqkoqoyZDjIzMzGZTITDYdrbL2w+tZv1LM3Til2TaXsHkLLmFtI/+A0AWn5xP6H2ie2P5Nyr2SS2//F5ZH98xbBYjlWb30dAHr4Amyj81fVEevsRTUb2hrUs4gKDH0egCTG9BCl36aSPYbbybDQ7aUVmNsU2O6oSJtRzAs/ZR3Ed+y/8DS8he5sAEV1qOZbSO7RcpNLb0KeWIQjTXjpIMgOZ9k/FyZMnWbx4MTrdhTYIK1asGHh9LNTU1FBfX8/SpcmbSZLZiyTOfamuqipE3A34G1/BfeKHuE/9mECLZoMFApKtGMGQypB2CA0vT6odwle2l6BDocmv55kDdZe8vjwjiwKbnYAc4e3G2hHPJRqtmEu1TVmz05vMUZoCPEc0dZK5tAgx0o3SVU3QmE5vQPvMxGt3V9cT4LmjrQB8/dryMXVYq4pMy+GHCfu60JvTyVv7wJwPm3yjtZFKVx96UeSTZYswDREGLOosGDPWYCu/h9TV38JS9nHNrkgyoUY8hDoP4j37W1xHv5sQq8uNGdmsT89CBR6pqcQVnvyN4OzsbPR6PYFAgK6uS4tG523vJmejcyJ0n/sTgf56RL0lanU3NdPE+k4XX3qmnn9xf4DvZv4z3wncwrerivnHE2n83Zvd/N0bXXzpmfpJKSoZsjcgmrNRZT+BptcTdt60qAXOVDQRDNjdrV2J77RWQLctH9rubndLI75ImGyLleWZWpHkJ4PUSZ+5buWkjVOJ+PBU/gYl0IWgt2NdeP+YfNMNzmWkLPkCojkLNezBU/ErAq3vJ3w+sH79egCqq6vp7tasXz6/bjkAJluI/3m7MqHXmwwOtDWjAqWpaWRGVWiTSbi/GveZn6OG+hCN6dgWfx5dStGkXzeGJxDijMvKD2u30pt6A4JkQva14D71UPQzkjhLS4BlGdlYdHr6ggHO9SatjGcaZ119eCJhrDodC+2O6R7OnEMO9BBofR/3mZ/jOvrf+Ouf1+ZpqoxoysSYexW2JV/AvvIbWIpuRG8vPW+dq8oooT6GUicNxnfuMcL9U1NU+lBJOSadjmaPi2tWaZkwx31Ojh07NuzzxThvJbrUHNSgF7m3EmuJ1kzUE813mUza9h6mKbsYgFydj8zMTNSGA8iuDkRr2oA7xlzDGwnTEdDU/dNZUBIEYUQ3gliO0p5JLigBZN3x75gXXIHid9H04J0oofG7H6Rt24IxPxe53033S/HNh+0GA2kGIyqazehk446q8W0rl3K4VWusXi5oqnvDipuSdnfD0Ob18H6z1qh9Y5YVX+0z9B/9T3zVfyDSXwmqgmTJw1T4QeyrvoltwX0Y0lciSIZpHnmSmc607251d3dTWlp6ydedTufA6/ESiUT4zGc+g81m4+tf//qI7w0GgwSD59UdLpe2SREOhwmPoji4nBlJ+j3XmcrPRbw/50gkMmXjSsTvXlVkZE8tkb4K5P5K1MigiYcgIaWUoXMsQpe6ENnXQqD6sSHPowQ68be+jyFr84THNBS5aRaunSfySgM8cdLNx652Y7eYLnjPdfNK+NXpY7xaW8WO/CIkcfiNV/PCq/Cf24U5zUvn8dME3B4k0/DBhTON2fZ33x9dzFlXLyNw+jUAuuffDEBaWhoGgyGu7+kXe9tQVNi+MJ0lOZYx/a11nfkjvq4KBMlA1uoHUEXTrHy2xPu7P+fu59UWbaJ6a34xWQZjHMdKiPaFGO0LMQy6N0T6KkD2XvBOf+PrYCke10LhlvwiGr1u2gJ+fltdwefKFiPFeZ7x/s5yc3NpaGigrq4Oh8NxwWtXlqXx38DRhn7a+rykW2fGZD3QV0d31Z8AyFj8MXyiBWWK/vY7+7zIo/Q4yYh09nnJT0t8Foyx4IP4z/2aUOdBJOdqJEvehP9e7Wu0AkT/iTP4+13oLPHZbI6H7qgKKnXNcjzHfwSAafGOS74HVVV5LWrVuq2gCDkSod8f5PkzbkDi9mUOJGFy7vmqHMBf9QiKvw1BZ8M8/z5UKWXs19KnYS7/DMHGl4j0HifQ9Cphdz2mebcg6EyjHx8HTqeT4uJi6urqOHDgADt27CDbZGaeJY0GXy972+s51VTAgmxbQq43Gext0ezu1mXljumzPJ7ffbjrMMHGlwAF0VaEueSjqDrLlM4ddle0IiNikyLkF68HeQnBhueRXVUEml4l1HsaU9FHEKNd/cMxlp/V6sxsdrU2sae5gdKU2R8oPdvmeiNxqEtTDax0pKMqCpEh3AYuZjbO0RLFaL97VVVR/O1E+iuQ+ypQAheqMkRLHrrURegcixFNGQNfHy66wLzwc6jDKIJVRSbU9jaKuwbvuccwlX4cnX3+GL+jsWEUBK6bV8oLNWfxGrqRRD2tEQsV7Z3U19cP62xgXnod7t2P4Dr2J1LXrMBb20D3/kM4r9o0qePdt+8MEf0SrIRJlcLk5+fTt+8RAGyrbiaiAnF+nmfT332dqx+AdIMJkyAkZOzj/bvPy8ujqqqKhoYG1qxZc8FrG4pS+fl7sK+mh0AwNOmNwjmfe4SGf9lAoOEoLY98hez7HorruKF+fpkfu5mm7/+M1kefIu3mDyCMsL8RY57FRm8oSK2rn1LL5M6L+mNNWmtWUufVitTrm38HgG7Jh8b0mbic7vlPnjqIoqqUi33oTz5ErL1S0Keicy5H71yBaNIaHhWYsvXfdHE5/e7Hw1h+PtNeUAJG3CCKd/NIVVU+85nP8N577/HUU08NeJsOx3e/+13++Z//+ZKvv/baa1gsUxdcmyTJUPSixvXXuev9XZweLcx6mhGIYKMTm9CGTehAEs4/oGRVh0fNwqPm4CETtVcHvTJwiiJxFyZguFtAsOk1qhr68DE5VharU1R2iWbcsp6//82b3FB04UAiqBgl6A74+cWrf+L/s3fW4XFc5/7/zCyjmFkyM9txHGZO0zDnJuUUbm97y7e3v9teKLcppE0aaLhJnKQhJw44iWOSZNmSLclippV2pWWc+f0xkmzFtixYkbOf59FjSzNz5szCzDnn+77fN0ceRVDqN5ANGJK9yIeCvPXXv8G8/CnpdwwQPt6LAPTEmdAUb8UItBsVO4pIJDJsYzQaDS4VpW1GRGSW0cwbbzSN+fxxQgsZopIl1RpcTtVHB4Gpj1icKbzIbFdFkAUokARobKGk8ZN2lWMlHRMiOariEX+VfJ3Ul75APwUTanUVMu+ooMHt5O9l+1guTW09BY9HEcTKy8vp6Og4bnumwUiHT8Wftn7A2qSZH9QKRMgXP0InSDilDKrLeoDpy6BqcYaBhFPu19BYT7iveUr6kCFkEid2YK9+nmZpM6PZ8YwVISEOHAO8/Ze/wcLjg6eihfDhLgSgyd9Jvq0eWVCxszmA1PHGiP16kWlVR1DJ4K88whuVNexok/FKVixiiMxwB2+80Rn9/hEmR9yHUXAQljW0BNYQPNQIjJ7hOzrZxAsSqUIlDFTjKG+hXVpDgOgs7A9NphobG3nllVfQaDTkCBItKohLCvLD5/dxV4H/pGOUmcSPTLUqAgK4q47wRlXNFJ1JJkU4QpKo1LkbkLLoGliMfGD660ztaASwkKf3s3//kM3kAuIEA6lCFXhacR/+Ez3yIvrlPKLy/RYkUMHO5kbiG1oQZvl4/NNCGJnywc+/oauXkq5YBtnEkDHgwCJ0YRa60ApHMx9kWcBLIi45HbecRthlABfQ1gQ0ReHcC8kSPVjoxlv3DO3SWjwcX+ogmqiQ0aigy+tmSYaeinYD5b5E3nvvPZKSkk54jDmUQgbQtftF2nS3IgJ1b71H7ZKJjVXHyp6GPlgI6RovggCtLc2Ydj2LGqiScyh9441TtjEXOSRGQASzPzCmudxUMiSW9vb28uqrr6I6pk6bJINONOP0h/nb1rfINkY3Q/ZEGFbeT9bO/8T54d+o8Zhx5Z03sYZyUhH0OgJNrZT+/RlYtvCUhww9C8s72ohri/4YchhZRti7HwH40JCI5BexikGK+ncTMqZR1uGHzpn9XMwmVPixCh1EhD72CKtAEDg7Uk1EVuOSMxiQs/BFEqFDgI5mYGrmVzHmHl7v2C3gZ1xQSkpKOmEW0lAh3KFMpdGQZZn77ruPJ598kscff5xrrrnmlMd873vf45vf/Obw706nk5ycHC6++GKs1pkr8jfb6emZfRY908WJanZNFU3OfrbvOXVB0jO3nEn+NFk5jOe9l8NewgNHlEwkVwPIR0UkQW1CFbcIdfwiVOYC4sTjF3ZlKYz38AfHHnYcggC5qhL0hTehjlswrmsZK3ZNM3854KdkwMz9K5eyICt5xPZwXTX/bKihJzGOL2w866TtSMHzaNjzU9QE0RiDFKgNFF5++ZT0eSqYS9/7iMfLwVZlMLv8yvPxP/RTAhorXpVijbBhw4ZTBg1EJJm//7MZCHD92ixuv2jsBer9jno6ireBDAlFV1A4b+68zyfiVO99RJb4S10VAY+LDL2R+xYsQzOGaLaTIcsyviMPI/kEPmmHkq6qJDs1EV3GuQgnuG+cikRHL08311ElymwqKmJx3KkFjIne9wcGBnj++ecJhUJceOGFaLUjs5Bsu1v50wdN9GozuPzyZRM6RzTprfoHzhYPKl0cyzf/K6u0pmn93gv7do1pvzyzxNp166akD1JoId7KP2CQ+lmZryJryaWTbvPAGx/S9c+3WCBqmTdF9/yArY/3bXYQBNasScbeCMb5m7n0muuP2/fB8lLoamdzdi6fWboKp9fPz/+wB4AbVyZy9SWrgOje82UphL/+GSJuB6h0WObdxQpjRpRaX0/Eewb+xn+gDQ5QoN6DLudyNEmrJ9Xq0Pf+3XffpbGxEbPZzAUXXEBEkjj04TsM4KdHK6ErXM+F46ytNx3saGtCriwn3xrHjZvOGdexY33vZSlEoPllwv2KmKRNP4fM9HPImiGF7Q9VikX6mfOTWbfmWKu99UjBfgLNr4C7iXThMFlmL7q8qxG18ce1M557fkiKUPL+W/giYRZsPoP58aees85m5tJYbzTKHL2Em+tI0uq4eOWqMQenTuc8b7Yx9N7LUpiIq1HJRBo4ghw+JltcUKOyFimZSHELsKiNpE1hn2RpLf6mF2Cgmhz1fvQFN6GOmz8l5xp67+W6al5pqCEhTwXtMo1BMz1eG1ddtfm4bHOAiGsDDSW/QTfQxOYvXcyep19B3drJBRdfjKiemmW2kNPF399U5jnpWh8ajYaLl6TS8YYD0RDHOXd+F0E99sz3ufS9L6uvAtcAa3LzWJecHpU2J/O9f/HFF3E4HCxZsoSiopFzxZ3BSt4/0oeYvpjLt0yH/evl9CVFsL/y/8io+Csbrr4LXdbo84uTvfdt1x2m5+mtxO2rYP7dt53yzKkeNwdqDzGgUbF22dops53zNbZQ5fIg6LS0mjLBD4vUnYiAft11rBu0Kx4rp+M9XwoH8PQcxN2xD19fNSDzMsuQBYFFGj+rF1+PMWU5vX0Osme6szPI6fjeR5Mh97axMOOC0vLly3nmmWcIh8Mj6ihVVCjR3cuWjX4jHBKTHn30Uf72t79x++23j+m8Op0One54yymNRoNGoxnHFXy6+GStq08T0/m5GOvrrFarp61fp+qTFBgg1F9JyFE1WCj76GKwqEtAE78ETcJiVOacMdTlUGNZ8iWksOfEm+UI/vb3CDvr8Dc+h7HwerSJy8d1PWPh6nUFbKspp9mr4X9fO8wT9184YvslBfN5o6mO+gEHTW4n8xNOHMGGRoNh3hl4qz/AkOhhYH/5nLrPzKXvvedwDUgSuqwM1J4aiISw510FKHZ3YwkYeL+mn0Z7AJNW5AvnFo75vQr5+ug+8BDIEcwZa0hZdNWcL2B5qvf+zdZGmjwudKKKu+ctxqCdnH1baKAWyXd8Rs8Q4Z6dSK46TIWfRWUc32RyXUo6LT4PO3s6ebalnn9bsopE3egWWRP9niYnJ2O1WnE6ndhsNvLz80dsv2hpOn/6oImS5n78EbDoZ+5+4LFV4mz5AICMlXejN8UD0/u973OOzfNd8Lumrl/qBKSs8/G3biPY+S7i4vNRaSdXeyZ50zq6/vkWAyUHp+ye33tAWUi3LJxHqHEnAOYVlx53voGAn+Ju5bt1ScE8NBoND71Tgk9SEacOc9/Fq4aPidZrLEthPA0vEHE3gqjFvOAu1ObRHQTGi9qai2bpl/E2vEh4oIZAyz+RvW0Y8q5EECf2mg+9DuvXr6exsZHGxkacTidJSUlcUlDEP44cJiElwAPvN3Lu4jQMmqnNeBwvJT1dAGzMyBn3524s770UcuOpe5qIpxUEFcb8a9Emr5pIV6NCi81JX0iDgMw5izOPvwZ1MppF9xDs2Yev7W0i7ka8VQ9iyL0MbfKaEYtf43m9NGhYm5bBxx2t7Ld1sSRlKpfXp565NNYbjQP9SqDqmqTU8b2fc2hcHk2ksB/JWUXIUUVooAYiR0sCCCo96viFaOIXo4mbP831NNSo592Mt+EfhByV+BufwzTvFjTxp86YGC9D7/0V8xayvaURm9/D+sXpFFcFKPclUllZyTnnHC/OaxIz0Besx9+wD3WwHrXFTNjlxlfbSPyKJVHvJ0DX/kO0DtZPytT4yMrKwn/wZQAsa65BaxjfuGWufO8lWabVq1jlF1jjo9bvyXzvc3NzcTgcdHR0sGjRohHbNs9L5v0jfexrGuBL503PvSXt2h8TqN+D59DbdD14CwU/LkZlOHmtqZO9hhm3fZaeZ1/Gta+MYGMLxvmjZ9jnWqyoBQFvJEx/JEyKfmosnr1lynjXtHIpVQ7lub227y0ADCuvHvdn4nS558uyhLe3GmfbHlxdZcjH3MPdloVUuBXLzls3XEbCYOCLWu2akb7OFk6X936qGM/rM+MrXZ/5zGdwu928+OKLI/7++OOPk5mZycaNG096rCzLfO5zn+PRRx/lL3/5C/fcc89UdzdGjBjHEPH14O/Ygevwn3GW/xJfyxuEXY2AjGhIR595HpalX8Gy/F8x5F6K2pI35gV2UReH2pR54h9zDqb5t6NJXAGyhLf+eQK2/adudJyIosj9Z2UjIHPYIfL6vpG2MfF6PZszlcWxNxvrRm3LtEhJPdcneLAXH4h6EfEYCq795QCYVy8jVK0Up7elKc+RoQKqo+EPS/y9WPGGv2lVCgnGsU2epUiA9uI/EQm60FmzyVh5z5wXk07Fof4+3u9uB+DmgvmTnkDIsoy/7V1GtyISkHxduCofxN/xAbJ8Yn/+k3F1dgE5RjO+SJi/N1SPqa7CRBmy3j1Rwd68JCNFKSbCksyHNTNnxxMJeug6+BgA8XnnYkpdOm3n7veFeeVQH19/qYEHWsYWJycYprZOiS51E6IhFTnspffIy5NuL3G9kinjKD2IfJJaEpPFUXIAgIS1y/FUvgeAedklx+33fksTEVmmKD6RgrgEnB4/r1YrizM3rUpGp43u5EqWI3gbnic8UAOiBvP826MuJg0hqo2Y5t+GPutCQCDYux931V+J+CdXEDspKWk46njIXufcnHzUgojBFKE/7OHxXbPLIsQVDFDZZwNgQ8aJ635MhoivR3ltPa0IKgPmBXfNqJgEsLNGeWZn6MMkWk78HBIEEV3aJixLv4LKlANSAF/Ty3hqn0QKjj0S85NsyFDuXfs625Fi47oZxx0KcWTAAcDapNmXPThbCAdc9LfspG3fA9S9/U289f8gZK+ASABBY0GbsgHTgruwrvoupsLr0SYunZHi7IKowlh4I5qEJSBH8NQ9Q6j/yJSdz6TRcmmBUq9JF+8CZGr9Vg5U1eP3+094jHm58rz1HHqbhLUrAXAM1XmZAkr2VhNWazESIk4VJCc7G2exso5m3XDDlJ13punxe/FHImhFkYxximZTRW5uLgBtbW3HzevPKFQW7g+1O3H5p8faWhBFsr7wJOqELIKdR+h89PMTWm/QZaWTcP4WALqeevEUe4NaFMkerJ3U5J748/RUuEoV+/iuDWfgkdSokVhr+ydiQjaq7FVTdt7Zit/ZSk/l89S/8x3a9v4WZ/se5EgAjTGFpPlXUnDuf7Ev7mxkYHVqOkVzPIs6xuxkxle7LrvsMi666CK+9KUv8dBDD/H+++/z+c9/nm3btvHzn/982I/03nvvRa1W09x8dOL2ta99jb/97W/cc889LF++nD179gz/lJVN3YM8RoypxqLVndI2SiOKWLTHZ9lNJbIsEXa34Wt9G2fF73AdegB/+7tEvB2AgMqchz7nUiwrvol12VfQZ52Pypg+JanPyiTjs2hT1gEyvqaXCHTvifp5luUlsyVLuQ/97v0mgp8oUjg08SjuasfmPUlGFWBcrAhKxiQvIYcDT31T1PsaA1xlSnardc0yglXvENRYcMhKdFZmZuYpj3+5oo9eT5hUs4Zrlo1t4CXLMl0HHifgbEWltZC17iuI6un9bk43fQE/zzQqAutZqZmsTEg+xRFjQI4gBfv5pNXdsQhqI+q4hYNZiu/grnqYiM825lOoRZG7ihZhUKlp8bj5Z9tkariMzmiCEsD5i5TFrveqx97/aNN96BnC/n40plRSFn92ys8XCEvsqBvgx9uaue3JIzy4q4sam2/MlUdUqVNbmFsQVRhyrwSgv/lD/AOTEwusi+ejNpsIuz04q2qj0cXjsA8KStYiPZLfhcqSjD5vpOVbRJJ4t6UBgIvylEjTP75ZNpyddM8F0c3wlWUJb8NWQo5KEFSY5t2K2jq1NSUEQUSfeQ6mhXchqE1EvF24Kv9M0DG5ej7rBi0WGxoa6O3tJU6nZ+OgUJOQHODxXS109I8tw246KOnqQJJl8qxxpJuiWxw75GzAVfUQUsCBqEvEvPhzU/6+joX9bcrYa2nKqUVRlT4J8+L70GdfAoKK8EANrkN/INh3cEILbytS0tCr1PT5fTT0O8Z9fIzoUuawIQE5RjOp+lhN5GMJeftwNLxLy65fUr/9W3SX/x1PTwWyFEbUJaFL34J58eexrvwWxvyr0MTNm5C9cLQ5KiotPUZUqp6y811aMA+jWkNfwMOSQjURRA66LVRWnvhZYl6u2OO6D71NwjpFUBp6Lk8Fpa0DgGJ3JwiQHLER7u9ANFgxLb1oys470zS5lYyKHKMZ1SwpXpieno5arcbj8QyX6xgiM95AfpKRiCyzr3H6ng1qawrZX/kHqNQ49z6L470/T6id9NsV2+S+N94lZD91//PMyly72TM1mS+yLOMqUQSlsjhlflWosaOXfGiXXzllNnuzjZDPQV/dNho/+AnNH/4XjobtRAIDiBoT8XnnkHvmdyg476ckL7yaXox83K7UNP7sgqnJmIwRY1bkuG7dupUf/OAH/Md//Ad2u51FixbxzDPPcPPNNw/vE4lEiEQiIwb7r776KgCPPPIIjzzyyIg28/LyaGpqmpb+x4gRbZINRn557iW4goGT7mPR6kg2TP1kSZbCeO21eJt3EXJUIYeOiTwRVKithWgSlqCJX4Soie7ixakQBBFD3tUIopZA9y58La8jRwLoM8dXM+BU/OAza7n2T3voDar5w+v7+eY1G4a35VnjWZqUwuE+G2831XPbkhUnbMNQtBFBo0eFH40pgL24DPO8mV+IOZ2QgkHcFVUAGHI0BPbY6M28AFDs7kym0SPa+n1h/nGgF4C71qeiVY8t5sJe9wauzhIQRDLXfhGN8STWh6cJIUni8fpqfJEIeSYLV2XnR6VdQTyF1SUgakwIGiuhvoN4W14n4mnDdfhP6LMvQpe2aUxZYYk6PbcVLODhukp29nRSaI5jVWIUBLFPkJWVhSiKDAwM4HQ6j7NbPH9RCg991MTu+j58wQgG7fQu3Dg7SnB17AMEMlb9y5SJoJIsU97h4b3aAXY2OvGFjmaFzU/RszIhjKerkTedp85eaW3vYE1RdHzzT4bGWoAmcTkhewXdFc+Qe+a/TzjbUFCpSFi7EtsHu7AXlxG3bNGpDxoHEX+AgYOHAdBoOvEBpqUXIXwiIGV/Tyd2vw+LVsvGjGwlO+mIG1Bx8+roZifJsoSv6Z+E7OUgiJjm3YwmbmqFwGPRWIuwLP0ynvrniLhb8NY9QyR9C/rsCxGECdReS0xk3rx51NXVUVJSwqWXXsqFeUV83NFKfFKInvYwv3unnv+7fuZroQHs61KyRjdmRNcdP2Dbj6/5FZAlVOZcTPNuRdTMfJR4OCJR51Q+72cUjS0IRBBE9Blb0MTPx9uwlYi3A2/DC2gclYQT7kWtO7lF0CfRqlSsTktnd0cbezvbmJcQiwCeSfYPZuetiWUnIcsyQXcn7q4yXF1lBAZaRmzXxeViSV+NOX01Do84qxdkFVHpBrwNEHIcxlP3LKaim9AkLI76uYaylLbWVhGX4oUGLZX+ePaXH2blypXDwc5DGAo3IBrjkTwO4vOVcZSj+EDU+wUQ9vqoFpT7U4bGp4wrDyvrYZbVVyNqTt9gtiGhIs88e2qdq9VqMjIyaG1tpbW1laSkkfO/M4oSaerzsrvBzgWLp69mi3H+ZtJu/Dndz3yTrqe+gaFgPYbC8dUXMq9cgmnZIjyHqun5x6tkffHOUffPN1n5gA6a3VMjKPmbWgn1ORB0Wg57lTHrCvdeALQrrp6Sc84WpLAfV+d+nG178PYdYSjwUhDVmFJXYM3ehDl1GYI4cmn/5doqZGBNagYFY6gbHCPGRJjxDCUAs9nM7373Ozo7OwkEAhw8eHCEmATw2GOPIcvyiDoETU1NyLJ8wp+YmBRjrpNsMFIQl3DSn6kUk6RIAFdXGZ0HHqVu+7do2/Mbgj17FTFJ1KJJWIax8AbiVn8X84I70aWsm3YxaQhBENDnXIouU8kA8re/g6/17ahayqUmWLhpuWK19HzFAN2OkYOlywqUIrHvtzbiC584rV3U6DDOPxMAQ6IH+xTaIXxa8Rw+ghwMoUlKQHAo1nd9g+JiVtaprX+eKu3BF5KYn6zn3Hljs9ZydR2g98grAKQtuxVj0tQUDJ5NvNLaQJvXjUmt5s6iRahPkU05Hka1ujRlImrjEAQBbfIqrMvuR22dB3IYf+ubuI88OmaLqyXxiZyfriy4PtdUS4/fG7VrGEKr1ZKWptTUOFGW0oI0M1nxevxhiV3102t7F/b3013xJABJ8y/HkDC6P/pEaLL7+dveLu56uobvvd7M9pp+fCGJVLOGm1cn85cbirivyEOC/TDxqiAqTm0/+Ei1ih0f7Yx6Xz+JIedSBJUOf38Dzrbdk2orccMaAOz7om/LOlBRiRQMoU1KJNimZOgO2e8cy/amegDOzSlAq1LxhzfK8Esq4tVh7rnwxEEQE0GWZXwtbxLsLQUEjIU3oImProg2FkStFfPCf0GXthmAQNdO3EceQwpObKFj7dq1ADQ2NtLb28v8hETyrHEgyMQnB9le1UNJ08xnp7iDQQ73KkW2N6RHx+5OliV8be/ga3oJZAlN4nLMC++eFWISwP6GHgKyCq0QYV3h+BbsVIY0zIs/jz7zfBBEQo5Kmj74T1xd4xufbRx8luzrao/ZGc8gvX4fzR4XArA68dMpKMmyhM/RgK3qRRp3/IimD/6T3iOvDIpJAobE+aQuuYnC8/+H/LN+SNL8K9BZMme1mDSEIKowFt2AJmGZkqlU/xwhR9WUnOuygvkY1Rr6Q14KsgWCsor9dg319fXH90ulxrRUqbGrijSCKOJr78TX0RX1ftlKDtKamg9Ahsar2N2VvACAdf31UT/fbGIoQynfPHbBfzoYzY1gyPZud7192p8NiZd8A8u66yASou2PNxBxj88CWBAE0u9QPlPdz72CFAiOuv9QhlKHz0NgCiyeh+zuwhs30BlQBKVNtpcQ47NQ56we7dA5iSyFcXdX0LH/Iere/je6Dj6Gt68akDEkzidt+R0UXfgLstZ9EUv6quPEpA63k10dymfyugXRF95jxBhiVghKMWLEmHkiQQ8DbbtpL/kzdW99k46SP+Ns240U8qLSWtAmr8U0/3biVn8X07yb0CatQFCNXtB+uhAEAUPW+ehzFNuBQNdHSraSHL36KF++fC0pujABWcVPt5aM2LYyNZ0MkxlfOMwHrSe3SBqyvTMkerDvOxC1vsVQcJUO1U9aTqj6HYJqM3a1Ei12KkGprT/AG1XKguB9m9IRxzC5Djjb6Sz7G6DUn4nPO3sy3Z8TlPb1sMvWhQDcWrCQhGm23TwWURuHacGdGPKuBlFLxNWE6/AfCfTsG9PE7bKsPIrMVgJShMfqqwlOwQRotImmIAgzYnsnyzKdBx9HCnnRxeWSNP+KqLVt94Z4sbyXr7xYz5deqOeFg4qFpEkrcumiBH5xVT6P3jKf21Yn0V5dRl2dUntu44qF/OHaXH52YTI/uyCJ/7fcwU/mNfH/ljv42QVJfGutilTRRRA1v6iK45l/vh21Pp8IUWsleYFifWer2kokePKsuVORuEGZ6Nr3RT+IYKh+UuK6hfibSgEwLbt4xD4dbieH+2wIwAW5BQx4/LxWo9ROunl1MtooFbZWaqC9TbBnDyBgLLgObeLMZe0o9oWXYSy6GUTd4P3hT4Sd47e5HMpSAqWWkiAIXJSn1FbKygoDMr94q3ZKa7KNhdLuDiKyTK4ljowoLLrJUghvwwsEOj8AQJdxDsbC6xHE2VPMeFe9skhWZJXQqMefgSaIKvRZ52Fe/AVEQyqRoIuOkj/TWfa3MX/vV6SmoVOp6PV5aRiYeWHx00qpXXmOLrDGY9VMf72fmUKWwnhslXRXPEXDO9+l5eP/xV7/FiFPz2AE+3LSVtxJ0UW/JHfzt0kovGDOZtILggpj0fVoEodEpWcnbWt6IowaDZcXKgFiKZl+QKbCl8D+Aye2xhyyvfNWv4t1yULg6PM5mhTvriSk0WIQwsSrgqRq/ITtbYh6M6YT1E48XfCFw3QPBn7lmWaXoDRUR6mzs5NQaGRQ6dq8BDQqgc4BPy326bXGFQSBzHsfQZNaRKi3mfaH7kIe5xgl8YKz0aanEnb00/fmu6PuG6/VEa/RIgOtU2B75xy0u6tcuQkQSFV5SPc3zlm7O4+tksYdP8ZjO3r/kmUZf38T3Yeepf6d79Be/ACujmJkKYTWlEbywmsoPP+/yd38beLzzkKlPXlgz0u11cjA2rRYdlKMqWVWWN7FiBFjZgj7+3F1HcDdVaak0B4jwKgNSVjSV2FOX40hcR49PTNX52Os6NPPRBC1+JpfJdizF6QghvxrJmRz80k0ahXfvKCQ773Rwq4OieIjbaxfqESlioLApQXzePTQAd5qquPi/KITChKmxedhAwwJXroPNhDoc6BLij3ko4Vrv1I/ybIij8ihP9OXeiYgEB8ff0q7u0f2diPJsDHXworMU0deR4Ju2kv+iBwJYExaSOrSG6NxCbOabp+X55sVAeDCjBwWz4IBqiAI6FLXo46bh7dxKxFXE77mVwk5qjDmX4uoO3mmmUoQuKNwEb+qLKPL5+XFlnpuKVgQ1f7l5OSwb98+2traiEQix1mlnL8olSf2tPJRbS/BsDRmm8XJ0N/8AV7bYQRRTcaqfzkuqm28+EIRdjW6eLe2n4MdHqTBtRa1KLA+x8z58+PZkGsevjafz8fu3bvp7+9HFEXWrVtHdrZyL81PHXy/jrO1S2fTQic/er6UqlAqf+/Kov3pbfzrTRcd95pGi4SCCxho/Zigu4vemn+StuyWCbUTv2Y5gkqFv6MLb1snxuyMqPVxSKSyFopE6mV02cvRxI9s/51mpXbS6tQMUowmfvb8bvySigRNmLujmJ0U6HifQJeSPWbIuwpt8qqotT0ZtIlLURnT8NQ9i+Trxn3kUfTZF6JLP2tcixDr1q2jrq6OxsZGbDYbm7NyeLqqAm84REqyRG2Pm5f2d3DDuuhazY2HvZ1tAGzImHx2khTy4Kl7moi7BQQRQ/416JLXTLrdaHOoJwhoWD2G5/ZoqE2ZWJZ8CVX/Xuz1b+Fs34u39wjpK+/ElDq6MKpTqVmVmsHezjb2dbbHCl/PALIsH7W7+xRkJ0nhAB7bYdxdZbh7KpBCR7OsRbUeU+pyzOmrMacuQ1TPjuC/aCEIKoyF1+NFIGSvwFv/HBTdhDYhujVCLsmfxxsNtbjCPjLSNHR2a9jXHuTszs7jarIOZQb7GvaRuPZ8nIeqsBcfIPPqS6Pap5LmfsiAdI0PlUrE1LQdF2BedRWi9vR6n4+lZVCgSNLpscwysXhojunxeOjs7BwWmAAMWhWrcuIpbnKwu76PvKTpreumMsaRc/8LNP7XJtwHXqPvzV+QfMV3xny8oFaRdvO1tP72r3Q99SLJ11w66rgpz2yl39FLk8fFPGt8FK5AQZbl4aDRw9pk8MGSoCLEaJdfGbXzTBeyLGOrfomguxNb9UtojCm4OvbhbN9L0H00s1GltWDJXE9c9iZ0cXljHrO2u5zsHspOmh+rnRRjaollKMWI8Skj6O6mr24bzTv/h/p3/p2eQ0/j7a0CWUJrySRp/hXknfVDCs//b1KX3oQxacGE60fMBLrU9RgLrgNEgr1leOufR5bCUWn7krXzWJ4oISPwP69XIh0T6bMlKw+TRkOP18P+7o4THm8oWI+gM6HSRtCaA1MSvfZpRQ5HcA3WEjHE9wPQm6lkDJ0qO6mi08PuZheiAP+yMe3U55LCdJT+hZC3F40xmcy1X5j0ovxsJxAZzOKRJOZb4rgkM/fUB00jKl0C5oX3YMi5HAQ1YWcdzsMPEOwtGzVbyarVckfhQgSguK+Hvb3RtShJTk5Gr9cTCoXo7u4+bvvybCvJZi3uQITiabDMCrq7sVUp9ijJi65DZ8k8xREnJiLJlLa6+cV7bdzyxBF+uaOdsnZFTFqSZuArWzJ46vYF/McluWwptA6LSQMDA+zYsYP+/n60Wi1nnXXWsJh0KkxmKz+/4yzOMSmL5u+6c/juEx/g8048e2g0BFFN6qCI1N+0A//A8VlmY0FtNBK3XLGbiKbtnSzLw4W/tZpO4Hi7O384zIdtStbsRfmFOFxeXh/MTrpldUrUspP8nR/h73gfAH3OZehSx+fVP9Wo9MlYFn8eTdIqQMbfth1P3dNI4bFHDCckJDB/vhKxXlJSgk6l5pycPACWzFM+33/a0cCA78S2t1ONJxTk0KDd3cZJCkoRnw131V+JuFsQVHpMC+6alWKSw+2jw698hs9aOPn6FIKoJmXxdeSe+R00pjTCgX7a9v2ervInkML+UY8des33drbFbO9mgDavG1vAh0YUWZ4wN7NvTkUk6GGgdRftxX+k7u1v0lH6IM72vcNOEnG5Z5G14asUXfQrMtd8DmvmutNOTBpCEZU+iyZxBcgS3vrnCNoPR/Ucx2YpZeYEAZlyXyIHD5Yft68mMRtd1lKQJczZytww2tbmUjBElaQIEhkaL2lpafhLngfAuv6GqJ5rttE0VD9plmUngRLYNqrt3WBtv10N47Ocixb6vFWk3/4AAD0v/ABP9QfjOj7luisQDXp8tY04944+hh16f6JdR8nf0kbI1kfEYKDerzhjrO9/CzEuA3Xu2qieazrw2ioJDChj88BAM43v/4DeI68QdHchiBosmRvIWv9Vii78P9KW3Yw+Pn9cAVAv1SnZSevSMsmPi5+ai4gRY5C5s0ocI0aMCaGkzzZjq36Zxh0/pnHHj+it3oq/X7F90ScUkrL4sxSc91MKzvlPkhdegz4ud06mDw+hTV6Fcd5NIKgGi7c+gyxFZ5Hnh9euQoVEk0fN0x8cGv67Xq3m/FylDskbjXUnPFZQazAuOAuI1VGKNt6aeiSPF5XZhOA4SEhtwqFXFqpHE5QkWebhPcpC/6WLEshNOLWFW0/l83j7jiCodGSt+woq7czUD5suZFnmheY6uv1erBottxcuHJMl4HQjCCK69DOwLP0yKlMORAJ4G7fiqXsKKXTyyc08azyXZSkLwy82N9DudUetT6IoDgsmbW1tx28XBM5bOGR71xO1854IWYrQeeAR5EgQY9IiEgrOH9/xskxdr4+/7u7ijqdq+OGbzbxXN0AgLJNp1XL72hT+dvN8fnVNIVcuScSqHylWdHd388EHH+Dz+TCbzZx77rnHFTA+FWq1mu/edgk3p7YjIHMomMbXnjlIT9eJRfzJYkpejCVjLSDTc+jpCS8UJ6xXbO8cUbzne5tbCfbaEbVqQl2DdnefEJQ+bm/BFw6TZjSxLDmNP755YDg76c4LlkelH4HuPfjbFAtCfdaF6NM3R6XdaCOotBgLrsOQf40iOvdX4678M2HP2D87a9euRRAEmpqasNlsXJinPPNtwX7mpesY8IV58IPxW+pFg9LuTiKyTLbZSuYkipaHnY24qx5CCtgRdQmYF38OjTX6NdaiwUfVXcgIJKhDR7Mbo4AhoZD8s39IQsEFAAy0fETTBz/B23vkpMesTElHK6qw+bw0Ofuj1pcYY6N0MDtpWXwietXcCvA5kfXRECGfA0fT+7Tu/jV125U6Gu7ug8hSCI0xmYTCi8jZ/G2KLvoF6SvuwJy6HFE1eywppxJFVLruGFHpH1EXlS7Jn4dJo8Er+UlKDmOP6PioppuBgYHj9jUN2t6pIsozwHmomrA3evU5bfvLaUlVxqoZGi8ZJoFQXwuCzoR5RXQzoWYbQwJF3iyrnzTEUFbSiQSlzYN1lEqaHATDM2OLG3/OfcSdeSdIEdr/dDPh/rEHz6mtZpKvUT5fXU++OOq+Q/Wtmj2uqAZWuIoVu7vWiy8nKKswCGGWOT5Au+wKhCjW8Z0OJEmiq/zvx/3dkLSQ9JV3U3TRL8lccx/mtOUTClZtcznZM5id9Jn5sdpJMaaeufUNjBEjxpiQpQje3iN0H3qWhve+R/POn2Gve4OguxMEEWPKEtKW30bRhT8n78zvklh0CVrT5KM7ZxPahCWY5t8GoobwQA2emieQI4FJtzs/K5nLipSIv4f3dOH2HW3z4vwiVILAEXsvjSfx0TeNqKMUE5SixZDdnXnVYkK1H9KbsAIZgbi4OMzmkws+H9Y7qbH5MGhEbl976u9Af/OH9DcpkfgZq+9FZ41O4fPZzJ7ebkrtNkTgzsKFs85u4pOoDCmYF9+LPvsiEFSE+4/gqniAoL3ipMecn57N4rgEwrLE4/XV+MLRyWqE0esoAcN1lN4/0ktEmrrIdnv9Nvz9jYhqPemr7hpz5qnNHeIfB2x86YV6vrq1gZcq+nD4wlh1Kq5aksivryng4ZvmcdvaVDKtJ/5sNDY2smvXLsLhMMnJyZx77rmjfi9PxV3XXsw3FtjREqEtEsfXX+uguir6dRQAUpbcgKDS4nPU42zfM6E2Ejco2R3RzFAaen4krs4i3N+BoDVgnL9leLssy2xvVgqIX5RXxIDbx+s1SjbXrWuik50UsJXia3kdUOrr6DPPmXSbU4kgCOhS1mFefB+iNh4p4MBd9RABW+mYjk9ISBiupVRcXEy6ycLy5FRkYNNyZRH3hZJ26nqiJ0qPlX1RsLsbaNuNu+Zx5IgPlSkH8+LPozLM3rFhSYuyyLg4Kfq2l6JKR+rSm8jZ9G9oDEmEfH207vkV3YeeRTrBWFKvVrMyVbHq3NvZHvX+xDg5EVmmzD5kdzd7P68n4pPWR7IsE3R30Vf3Js07/5uGd79Dz6FnlGLssoTOkk3S/CvJO/tHFJz3M1KX3IAxcf6ccpKIJiMylRgSlQ6d8rixYtRouHzQCjknN8RQllJFxfHjySFRx9/wEfr0FORIhP4D0RO4incdJqTRoRfCJKiCxPUUA2BZdSWi1hC188w2JFmm2eMEIN808WCJqSQrKwtBEHA4HLjdI5//89LMJJm0+EMSB9uOFyKnA0EQyLjrT+iylhIe6KLtwVuRpbHXjU2/9ToQBAZ27sXXcPJa0dlGMypBwB0O0RcYPat3PAzZ3VXlKfZt8+UWVEhoV1wVtXNMB+GAk5aP/4ew//g1oqSiS4nL2YxKM7nv8ku1VbHspBjTyqdz9BEjxmmIFAnh7j5I58HHqH/n27Tu+RX9Te8R9tkRVFrM6WvIWHUv8y7+NTkbv0F83jmo9fEz3e0pRRM3H/OCO0HUEXY14j7y2Lgsbk7Gt69dh0UVxhlW88uXS4b/nqg3sClDyUZ4s6H2hMcOCUr6BA8D5YeIBIKT7k8McO1XBpvWhQYIeuhN3QSMnp0UjEg8VqxkJ92wMpkE4+gLrN6+WroPPQ1A8sJrsKSvikLPZzdtHjdbW5RF6cuz8im0RC8KfCoRBBX6jLOxLPkiKmMGcsSHt/4feOqfQwofHzEqCgK3FiwgQaujN+DnuebaqEXXDQlKPT09+HzH33/W5MVj1avp94Y40NoflXN+Ev9AM701rwKQuuwWNIbRM4PcgTBvVTv4zquN3PV0DY/u66HZEUCjEjir0MqPL87hydsX8OUtGSxOM540o1WWZSoqKigrU6wHc3Nz2bJlC1rt5EXJi889m//aJGERAvRLBr67M8SHH3886XY/icaQSNL8KwCwVb1IJDT+iOPEDUqGkrOqlpAzOlYgQ5apcYXKa29ceM6IGgo1jj5aXU60ooqzc/J44I0yAvJgdtL5k6+dFOw7iK/pFQB0aZvRZ10w6TanC7UpC/PSL6OOWwhyGF/Ty3gbX0KOnPp5vG7dOgRBoLm5GZvNxkX5RQAccXZz3qIkIrLML96K3v1jLHhDISqG7e7GX8NJlmV6j/yTrgOPghxBk7AU86J7EDWzN/tWkiSqHcprvD5v6hYZjckLyT/nx8TlKtnl/U3v0fThf+Fz1B+375Dt3b6Y7d20UufsxxUOYVKrWRTFuh3TwSetjxre/R6NO/6D3uqX8Pc3AQL6hCJSFl9PwXk/Jf+c/yB54dXorTlz2kkimgiCqIhKSStRRKXnoyoqXZxfhFmjJUCA+MQgHSETH5Q3EAiMFJaN87cgaA2E+ztIXKuM+6KZlVzSqFimZWh8GAx6xP1PAWBdf33UzjEbsfl9+CIRNKJIpmF6axCNFb1eT0qKEhz2yeAxURDYNJiltLu+b9r7NtwPnYns+19A1JvxVr2PbeuPx3ysPjeL+HPOAKDr6a0n3U8timQZlXFDsyc6Y11ZlnEOjnePyMocdLVrJ4IlDXXe7LJXHg2P7TCNH/xk+H4/EgHbkZcnPW5oczmHa2l+dkGsdlKM6SEmKMWIMQc4mR1CJOTF2b6X9tIHqXv7m7QX/xFn6y4iQTeixoQ1ezNZ677MvIt/Tda6L2LN3ohKMzsHY1OF2pKPeeHdCCoDEU8b7upHkEKTix62GPXcu0mptfNGnY+69t7hbZcN+m3v6WzD7j9+8ViftxrRYEWlkVBrnQyUR9ee4dOILMu4ypRoQb2xh5DKgMOsLPKNJii9ethOtytEklHNZ1aMvsAe8vbRUfogyBKWjLUkzrs8ehcwS/GEgjxWX0VEllkal8i56XMvG0tlTMe8+PPoMs8FREL2Q7gqHiDkqDpuX5Naw52Fi1AJAuWOPj7qiY6NmslkIjFRmUyeyPZOoxI5Z0EyAO9V26JyzmORIiE6yx5RvP3T12DN2nTC/UIRiQ9revnOi4e46Nc7+e2HHZR3epGB5RlGvnF2Jk/fvpDvX5jDpnwrGtXoQ8hwOMzevXuprVXE9cWLF7N27VrEKNpTrFixit9clkK66CIgq/m/w1aee2171NofIrHwIjSmNCIBJ32Dwtx40KcmY8zPAVnGUXIwKn2yFx8AQK0+cf2koeykzVk5BP1h3qxVhLDb1qSiUU8uoyNoP4y3YSsgo03ZgD5n9ELNsxFRbcA0/1b0WRcCAsHe/biq/krEP/qCT3x8/HAtpeLiYlanZpBsMOIOBTl7hRGtSqS4yTEl3+WTsb+7k7AkkWm2kG0Zn7giRUJ0HniEvtrXANBlnI2x6EYEcXbbZtV29uOKqBGR2LIwY0rPJar1pK+4g+wNX0Otiyfk6aHl459jq9qKFDlqp7w6NQONKNLt9dDsnJlI9E8jpYPZSasSUlDNQvsjWZaJBN34HI042/fRV/s6nQcfo/njn9NW/IcR+4b9dkDEmLL0GCeJ75BYdPFp5yQRTQRBxFhw3WCdvEFRqe/kWenj4dhaSrl5YUCmzGWlqmrkOFLU6oeDBi2ZSnCCPUq1cqVwmMqwEjCSrvGSkWAk3NuEoDViXnF6z0eGhIkco3lWfr+HGEsdpd31M1NHaQhd5iIy7nkIgN5Xf0aw+t0xH5t+uyJc9r22nVD/yZ9v+YN1lJqiVEcp0NpByNZHT8E8HBEtAjIb+15Ft3xu2N3JUpieyhdo2/s7pODJXhOZwEAz3hPYno6HrYPZSevTs8i1zo0A0Bhzn9n/LYwR41POJ+0QQv4B+ps/pG3v76h7+9/oLPsb7s79yJEAan0C8fnnkbPpm8y76JdkrLobc/oqRNXstqiaatTmbMyL7kVQm5F8Xbir/4YUnNxk//Zzl5NnChNG5KevHBj+e0FcAgsTk4nIMtubjo9gFVRqjAvPBmJ1lKKFv7GFsGMAQacBWyl9iSuRBRGr1YrFcmK/bZc/zLP7lUWIO9enolef/HEohQO0l/yRSNCFzppD+sq759zi6XiRZZm/HizFHgyQqNVxS8GCWVk3aSwIohpD1gWYl3weUZ+CHHbjqXsab+NW5E8UWs8zW7g6uwCAf7Y1UeuITjTh0ETzRIISHLW9e6/aFvXI9t7B54dKZyVtxW0jPruyLHOo3cn/bavhkt9+zDeeK2d7ZQ+BsEROvI6716fy2C3z+flVBVyyKAGzbmxChN/v56OPPqKjowNRFFm3bh2LFy+eku9NVnYOD9y0mIUaGxIij3Vk8utnthGJjN3O41QIopq0ZTcD4Gh6n4DzxO/jaETT9i404MR1pA5BlIj0KUEJxwpK/X4/+wZtty7KKxrOTkrUhLnj/MnVTgr11+BteB6Q0CavxpB3xZy9HwqCiD7zHEwL70ZQm5B83bgq/4yrc/Tn8lAtpebmZnptNi4YrJ9YYmvlzjOUWgq/eacOfyh6n8HR2NulfB43jlP0jwTdtO39La72vSCIpK24E0P2RXPCPmtnrfL8zjFGMBumZ4xrSl1G/jk/HhTlZez122je+TP8gxHHerWalSmK7d2+rpjt3XQQjESoGHxOr0lKmbF+yLJEyNuLp7eK/uYPsVVtpb30QZo+/C/q3vo6dW9/k5aP/4fOsofpPfIKztZd+B11IB9/j8hY+zlyNn590Ekitig4VhRR6TNoklYDEt6GF6ImKg1lKYWEINbEII1BCx/tr0SSRtbEMQ/WURKlJgAcpQeRpcnXzbFXVNOSotRPytR4SXTWKOdbeTmi7vQOFG1yK3Z3eabZWT9piKE6Sm1tbcd9LoYylI50u+lzz6wzSdymm0m44MsAuJ+9n4hjbONZy9oVGBfNQ/IHsL3w2kn3yxuuo+ScfGdhODup9kwlCz5H6MMatqNdfmVU2p9Kgu5uWj7+PxwNSp1RldYCnGy8PLkspVbXwLD18XWx2kkxppHZP2OIEeNTjBT2M9C6a6QdwjvfprviSTy2wyBH0JrTSZx3Gblbvk/hBf9L2rJbMCYvQhCj7yk/l1EZ0zAvvg9BG4fk78Vd9TAR/8QjhURR5LuXL0ZAprxP5O3SuuFtlxcoNRbebWnAf4JaLCPrKB2YcB9iKAzVT4pbmY1kb8aWtBYYPTvpmbJe3EGJgkQdF8yPP+l+sizTefBRAs42VFoLWeu/jKjWRbX/s5E3Gmsp6e5AJQjcVbQIYxTqrcw0alMWlqVfQpe+BSUjoQznoQcIDdSN2G9LagYrE5KRZJkH9u/FFZx87bVjIxdPNFnYWJiIQaOi2xmgsjM6UX0A3t5qHI3vAJC+4k7UWmWi1+7w8dCHjVz3p73c+UgJzxW30e8NkWTScuvGHJ66bz1/uaGIm1ankGYZ32Kt0+lkx44dOBwOtFotW7ZsGZ5oTxVmSxy/vGMLW4zKZGq7K4fvP7ED/wksBieKKWUp5vTVIEt0H3pm3JO+xPWK7V00gggc+ytAlolfZEQOB1An5qDNWDS8/f3WRiKyzPz4RCyCZjg76fZ1k8tOCjnr8dQ9o9iiJS7DkH/tnBAfToXGWohl6ZdRmfMgEqCj9M/0VL6ALJ24ltqxWUolJSWcm5OPWhRpGHBw7jIraVYdHf1+ntzTMuV994ZCVNgU69YN47C7C3p6aP74f/HZaxHVerI3fI343C2nPnCWcLBT+W6vTJ/e57FKayJj9b+QufZLqLQWgq4Omnf+D701ryJL4eEaVjHbu+nh8ICdgBQhUasbjoyfKqRwgICzDVdXGfb6t+mueJrWvb+j4b0fUvPGl2l47/u07fkN3RVPYq/fhrtzPwFnK9Jg4IpaF48hcT7W7M0kLbgajSGZ4xcXBex122KfnQmiiErXok0eEpWeJ9hXPul2DWoNVxQO1lLKCSEDe/t0NDQ0jNjPNBjYEeosQ2XUEHIM4K5rnPT5i3dWENTo0A3WTzJWvgCAdf0Nk257tjOUoTQkVMxWUlNT0Wq1BAIBbLaRGcqJJi2L0hUruD0NM5ulBJB2y6/RF6xD9jpwP/V55PCpRS5BEIazlLqfewUpFDrhfkP34Q6vh2AUArtcg1n91QmKoLrMX4ZgTkFdsHHSbU8Vsiwz0LqLpo9+in+gGVFjImPN51Hu9ye7t8uE/Y6TjjtPxVDtpA2x7KQY08zcXyGKEWMOIcsyciRAOOAkHHASCQwQDriIDP/uJBx0Dv9+Mj9/nTUPS+YazOmr0Jmn1urjdEKlT8Ky6D7cRx5FCthxVz+MeeHdEy46vXFRDpsy6tjdKfPrdxs4b2UBGrWKNWmZpBpN9Hg97Gxv5sK8ohHHGYcEpQQv7SVKbZG5GuE9Gxiun1QgEXYacMQpC6snE5Q6nEFePawM6O/dlI5KPPlr31f7Ou7O/SCoyFz3xVPWnjkdOGLv5dlqxX/+2pxCcmZ5VOB4EEQNhpxL0MQvwtu4FSlgx1PzONqU9RhyLkFQ6RAEgZvy59HhdWPz+/jTgWK+vf7MSWVoZWRkoFKp8Hg82O12kpJGfo70GhVb5iWxvaqH96psLM2cfE2QSMhL54FHAYjLPQvJupgXStt5o6KLA61HMzT1apHzFqVw+fJ0NhYmoB60kOjuHn+toJ6eHvbu3UsoFMJkMnHmmWdiNk9PHRa1Ws0Pbr+ER7a+xQu9WZQH0/na0/v52ZVFpKSlR+UcqUtvxNNzCJ+9Flf7PqzZY5/QDtVRcuyvQAqFEDUTtxQbEqWshQIEwbzs4uFnSESSeLdFWcC6KL+IB944QEBWkaQNc9u5E89OCrua8dQ+BXIYdfwijAXXnxZi0hCi1op54T3427YT6P4YR8Pb+PsbyVzzuRPWm1y3bh21tbU0Nzeztr+fjRnZfNzewocdTXz9giK+/1Ilj3zczFUrM0iz6o8/YZQo6+kkJElkmMzkjNHuzmuvpaP4T0RCHtSGJLI3fBWdJXPK+hht/MEQjS5FGN08L3lG+mDJWI0hcR7dFU/h7tpPX82ruLsPsnTZnWhEkU6Pm1aXM7awM8WU9im1w9YmpU56HK1Y07kIeWwEvT2EvL2D/7cR8tqIBEaPuBdENRpDEhpTChpjKhpjMlpTChpjChpj8gi3CE/PYfpq/nmiXgxbH5lSl07qej6tCIKIIf9ahqxMvQ0vADLapJWTavei/EJeb6jBTZC4xCBH7HHsKi1n3rx5w/to0+ajSSkgZGskeU0i3Tu7cZQcwLKgaJSWT01xfS8kpZGh8ZFoNSJ2liNo9FhWnt52d/5ImC6fMhbNN01drbxoIIoi2dnZNDQ00NraSlpa2ojtZxQmUd3lZndDH1esiM6YdKKIGh3ZX3me+h+tJtyyH+/r/w/TNT895XGJl5xL6+8eImTrw/7WDpKvvOi4feK1OqwaLc5QkFavm6JJ1N6VZRlXaTk+k5m2iAmATY430S6/fNYGTkdCXrornsbVsQ8AQ+ICMlbfi8aQgCGhkMhJbe9ApbUiqsY/N2hxDrB30JXgM7HspBjTTExQihFjksiyjBT2n1QU+uS/snTiiI7xkLLo2thEY4KIunjMi+/DfeQxJF8P7uq/YVpwF2rTxBZTfviZtVz35730BNQ8+OZ+vnrVekRB4JL8eTxReZBtjXWcn1s4YjFan7MS0ZgAXgdCuB1PQzPmovwoXeGnj6H6SVp1G10Jy5EFFVarFav1xJOPx/d1E5Zk1mabWZt98sXuoKMSb50y4U9bfhvGxPnR7/wsYyDg54H9e5FkmTMyc9icMrOTnqlCbcnDsvQr+NreJtizl6CtmLCzDmPBdagt+ehVau4qWszvq8spt3XzSl31pAbparWazMxMWltbaW1tPU5QAjh/cQrbq3p4t7qH+88vnPTiWM/h5wj7HYTVifymegk7XttJKKJExgnAhoIErlieznmLUjDpJj8cbGpqoqxMEciTkpLYtGkTOt30Z/P9y3WXkPneB/ypLoHWSDxff7WN/zyrnwULF5364FOgMSSRNP9yeo+8Qk/V85jSVqDSGMZ0rHleAZqEOEKOAQYOVZOweuLijmPQAkSj6UIOjrS729/dicPvw6rVUWRK4Ft1RwAVt61Nm3B2UtjdhrvmCZBCqK3zMBXdNGsn85NBEFUYci8lKXs5XQcfw2evpemjn5K5+nMYkxeO2DcuLo4FCxZw5MgRSkpKuGjzJj5ub2FPRyu3nr+MVTlxHGgd4Hfv1PPf103deG3I2nBDRvaY7hnO9n10HXwMWQqjj8sna8P9qHWze6Huk+yt6yGMiFGMsCx35oI81DoLmWu/gKujmO5DTxMYaKFn9/+yyHQxFW4lSykmKE0d7lCIamc/AGsSx2Z3J0sRpGA/UsCu/PgdSAE7kYCdgWA/cmT0jGRRY0RrTBkUjZSfod/V+vgxieyyLGM78jInj1ZXrI+MKUtiwWYTRBGVrgEYFJVeBJiUqGRQa7iyaAHPVh8iIztItV3Lh61hLu7qIj09ffC8Aubll+J478+YM4N0owSA5N762QmfV5YkKgPKInOGxktyWMlINa+8HFE/PQE7M0WLx40MJGh1WLWz374/JydnWFBat27diG1nFCXy6K5m9jTYkWR5xm3EtSn5mG/6Pa7H7sT/8cOoCzagW3H1qMeIGg1pN11D2x8eoevJF0i64sLj7lGCIJBnslDR30ez2zUpQSnQ1kmw28aRcy5FQiBO8JHvPohu+Y8m3OZU4nM00Fn2MCFvLwgiyQuuInHeZcPPBY0hEY0hMern3Vqr1HPbmBHLToox/cQEpRgxToCSSeRHDrmRwx6kkBuH9zCRoIuwf+AYwUjJLhqvSCSodKh1FlQ6K2qtVflXN/JfldZC5/6HCDhbGTnhiE00JouosWBedC+eI38n4m3HfeRRzPPvQG0Zvy1TRpKV65daeeqQh2fK+7n5bA8pcSbOycnjhZrDdHrcHOzpYnXa0UwyQRQxLToH1/6XMSQodZRigtLECHR0E+zsQdSB3FeJbd59AGRmnlggrOr28mGDEwG4d2PaCfcBiHi7hieg8fnnzykroIkiyTJ/OlCMI+An02ThvuVrGOiLTg2h2Yig0mLMuxJNwpLBbCUH7upH0KWdgT77QjKNJu5etoq/lpfyYk0lCxKSWJo88aLYOTk5tLa20tbWxqpVq47bvmVeEhqVQIvdR4PNQ1HqxBYKZFmm4tBOdG27kWSB/6pcTY1HyUhakGbm8uXpXLo0jVRrdMQeWZY5fPgwNTWKp39OTg5r1qxBpZo5weHS888hPfEAPysO45AMfOdDP//Wt5stm8+YdNsJhRcz0LqbkLeHvtrXSF0yNssZQRRJXL+a7rd3YN+7f8KCkhQO49hfjkoXQva0gSBiWnrh8PbtzUrtvvNy8nnwzfJjspOWTeh8EW8nnpq/gxRAbSnANO8WBPH0nj5YMtags2TRUfoXAq42Wvf8muRF15JYdMmIBeO1a9dSU1NDS0sLa9euJd8aT5Oznw/bmvn2JQu4/eFith3u5oZ1WazOjY96P/3hMAdtXYBiczIasixjr3uD3iOvAGBOX03G6n9BVM09C9e9jQ5AYH68PONF2gVBwJq1AWPSArrKn8DTU0GRq4wKYQV7O5q5fmEs+GuqOOjoRZJlso0m0gxH68jIET8Rv/2oaBRwDIpH9sH6qaPZyQmoDQmKSHSMcKQdzDJSaU2T7rcshQn7HKP046j1kTCBaPUYCkdFJYFgb6kyppdltMmrJtzmRXlFvN5Qg2swS+mwI4GSsnKuvOxo8JV5+SU43vszYqQRiMdefHBS1+GorqMpWZmfZmh8WAbrsVjXXT+pducCzYP1k/Jnud3dEEP21t3d3QQCgRFBVStz4jBoVNg9IWq63CzKmPlr0i65GP259+Pf8Qc8z38TdcZSVCmjZ9OlXH8lHQ8/hbe6DldpOdZ1x4u0+WZFUGqaZB0lV6ny3aldoGT4LwrXojIloS7YNKl2o40sS9jrttFb80+QJTSGJDLW3IchYXKZiWOhxTlAcVc7ArHspBgzw+k9I4xxWhEaqMfX8jqG3CvQxI3/Bj1CJAq5kcJu5JAHKeQa/NeNHHYr/4Y8II/0MD2V+Y8iEh0VhUYIRNqh3y2odVZE9antTzw9hwk4T+S/H7NDiAai2oh50d24a54k4m7GXfMYpnm3Teizdf8Va9hW8z59QTX/vbWY39xzLga1hvNyCnijsZY3G2tHCEqg2N659r+MIdGDo7iM3Js/E6Ur+3ThKlPs7uJXWAgLauwJSwDIzj6+loQsyzy8R4nsu2hhPAVJJ/4eSiGPYu0kBTEmLxrzgvFc56XaKg719qBTqfj62k3o1WoGTn3YnEdjLcS67H58LdsI9pYS6N5FaKAGY8FnOadoLUfsfXzQ1sQfyvbx32ddQIJ+bBkpn2RootnR0UE4HEb9ibpUJp2aTYWJfFTbx3vVtnELSs19Xt6o6OKjyga+mfkyOjW82r0Ih5jFnWekc8XydOanRTeaNRKJUFJSQnu7kiWxaNEiFi9ePCuCHVatWsVvk5r5wVuddEsW/veQirsc27nhiuMtOsaDqNKQuuwm2vc9gKPxXeJyzhyzXdiwoFRcRtEX75rQ+V1VtUQ8XuKLlEAWQ+EGVKYEANpdTg732RCAVQnp/Lq+E1Bxx7qJZSdFfD24jzyOHPGhMuVgmn8bgmr2RwlHA605jdwt36G74hmcbbvorX4Jn6OejJX3DC8qH5eltHIZD5WX8k5zA5eft4BrV2fwUlknv3irhifuXT+qvepEGLK7SzOayBslKlWWwnSVP4GzbTegiKIpi6+bs5aFh21hQDNqhvF0o9bHk7X+fpytHxM8/CKqiESH10dl1XYWL7pgzr7W08V45niyLCGH3JT0tAKwQtWPp/4fw8KRHD7FjE3UIOoSEHWJqHSJw/9PzVqA2pA4Icuh8SCqNOSd9f0psT6KMRJFVLoaBIGgrQRv41aACYtKerWaKwqVLKW0zAA1di3bqm2cfaZz2BXBuPh8UKmRXO2oDUY89Y0E+hzokhImdM6Sj8oJahPRCRFSNCFMLR8gaHSYV105ofbmEk1D9ZNmud3dEBaLhfj4ePr7+2lvb6ewsHB4m0Ylsi4/no9q+9jTYJ8VghKA8ZLvEm4uIdy4B9eTnyPuK68haI0n3V8TH0fSlRdhe+E1up584cSC0uD71ex2TcrW31lykAgC9doUkGGd8320yy5HUM2eJeyQz0HXgUfw9h0BwJK5nrTlt6HSnPw1jCZbaysBJUs9ZxLZYDFiTJTZ822MEWMUZFnG37YdyW/D37YdtVWxA1JEIp8iEIU8nxCLjopDQ78jj7M4oKhD1JgQNBYM5qRjRCLLMdlEcah1lqhGecbsEKYHQaXHvOBOPHXPEHbW4al9AtO8m9HEj88aSafV8PXz8viPt9r5sC3C/tp21szP4uL8IrY11XG4z0azs588a/zwMabBOkr6BC+2faXRvKxPFa5Sxe7OnOGjz7QcWVBjsViwWI4fqO9qclHZ7UWnErhj3YkzTWQpgrf+OaRgP6Iukcw1nz8trZ0+Sbmtm5cGU+bvWbaa7DHW4jhdEFR6jAXXKtlKTS8j+XtxV/0VW+hS7lx8GY0DDlpcA/yhbB/f33jWhKLiExISMJlMeDweOjo6yM0dmRHZOeBncbqFj2r7eKOiiy3zR9YGiTdqyIgbKYI6PEHequzhjfIuDnU4AZlvF+7Eog7ikJM557zb+LeClKgvZAP4/X727NmD3W5HEATWrFlDXl5e1M8zGbJy8njgRis/fKGcmnAyj7Rn0vHsNu6/4aJJZVCZU5djTluJu/sg3YeeJmfTv43pWTxUR8m+b/+EJ9n2Qbs7a77yu+kYu7t3mpUC4WvSMnjyvWqCsopkbZjbzht/NlTE34f7yGPIYQ8qYyamBXcgzMFslskgqnSkr7wLQ2IRPYeewdNdTvPOn5G59gvo45TP+lCWUmtrK1esWY1Jo8Hm83Kwp4uvnFfE9kob1V1uXjnQwXVrRs8iGi97x2B3Fwl6aC/9M76+GhBE0pbdQnzeOVHtx3TS5XDTE1QW2s9aePIs45lAEATicrewKHkx83e+QXXIyM76/Vj6y0lfeRca48zUe5rtnGiOhxxGChy1plMyjhzDGUcOWUOzcDaCLLPQuZMQI2vOCmoT4pBYpB8SjpQfQWM+4fdFa56+z9NUWR/FOB5BEDHkXQVwjKgko01ePaH2Lsor4o2GWpwEiEsMUt6fwMHyCs7aciYAKoMF4/wteKt3kLBYh22/jKP0IOkXnzuh8xXXdEN8IukaLykaL6Icxrz8ClSG2SFITBWyLNPsHhKU5s615uTk0N/fT2tr6whBCeCMoiQ+qu1jV0Mfd585O8bLgkqN5ba/0P/bC4l0VuJ5+fuYb/ztqMek3/ZZbC+8Rv8Hu/G3tKPPHTm2yTKZEAUBVziEIxggUTf+OpKyLOMqOUjr4uX4ZDUaIqy1v4V2xRPjbmuqcHUdoOvg40ghD4JKR9qyW7BmnzFta3NNA/0Ud3UgANfFspNizBAxQSnGnCDQ9TERrzJxjnjbcVX8FlkKI4c94xeJVHpFJFKbETVmBI35uN+Vv5kRxKPRYZ8srjiVxOwQpg9BpcU0/za89f8g1F+Fp+4ZjAWfRZu0YlztXLlhIc/ubaWyX+S/X6vkH1/PIMVoYn16Fns729jWWMcXVh71U9ZlLUU0JYGnj3BvJUF7P9rE+Chf3emPUj9JRiM1Yku6GYCsrKzjBnOhiMQje5XspOtWJpNsOvH3xtf6BmFXI4haTPNvRaWdPRHQU0Wfz8ufDuxDBs7PLeCs7NkxyZkJNPELsCy7H1/z64Ts5djr3sTTXcEXF93C/ztQSbW9l+drDnPzovEv0AuCQE5ODtXV1bS2to4QlDoH/Hzmj3sIRiQAmu0+bnu4eMTxWpXIS1/ZRIJRw4c1vbxe0cXuejthSXlOqASBf1nUzRp9BwhqVp/9FXTWiVv0jYbT6WTXrl14vV40Gg2bNm0iJWVsNSymG4s1gV/esZn/ffY9dvmy2ebMoeuJ9/nxTWeiN0ws2wwgdelNeGyV+PpqcHUUY83acMpj4lYuQ9RpCfba8TS2YC4c/3fNUXwAkNFouyEC5mUXA+ALh/iwvRmADclZfGtbDaDizg3p4xZApUA/7iOPIodciIZUTAvvQlRP/LWaywiCQHzuWejjcuko/Qshby8tH/8fqUtvIS53C3FxcSxcuJDq6mrK95dxdmE+bzbWsr25nn/fkMHnz87n19vr+MP7DVy0JBWLPjpjNn84zMEexe5uY8aJhaqgx0b7vt8T9HQjqvVkrvnCnM9s//CI8hxP1YZIT5idz2eNMYmzF2+hunw/lUI65/R9TOMHPyF16Y3E5Wz51AeCyZHgCFeIsLNhxBxvoOx/IeJjNGu6CpR7Z4HaR1LCymHxSKUfFI0+ZeJ3jNE5KioJBG3FeBtfApiQqKRXq7mycAFPV1eQmumn1m7ltbJWNm4Ioh2s82Nefgne6h2YMwLY0OIoLpuQoCTLMod8aohX7O7iuvYCYPkU2N3ZAn68kTBqQSDLOHmryekiJyeHiooKWltbjwsc2lyoiMgHWgbwBsMYtbNjKVa0pmG59c84H7qRQMmzqAs2ol9/y0n3NxTkErdlIwM799L19Fbyv/vVEdu1ooosg4lWr5smt2tCglKwo4tgVw/VZyl1nQrpQGcwoSncPO62oo0UCWKrfIH+5h0A6OJyyVz9uWkNSgCGA0E3ZWZ/6gJBY8weZsddLEaMUZBlmUD37hF/kwL2Eb8LKv1RIUhtOioIDf07LBaZRohEs5WYHcL0IohqjPNuwtv4EqG+g3gbXkCWQuhS1o6rnR9eu5I7HjtIg1vFP3ZWcvPZy7isYB57O9vY1dHKTQuXEa/XD55TxLTkPFzFL2BI9GAvOTDh6LVPKyG7A39jC7o4H+FwAHu8slCWlXX84tobVQ46nEHiDSquX3HiIt6Bnn0Ee/YBAqaiG1AZZlf081QQliQeKNuHKxgk3xrPHUsmXrD4dEFUGzEV3UAwYQmB1tcIuNqg5NfcknEZj3bCq/U1zE9IYm3a2GzOjuVYQelY+r2hYTHpZAQjEj/fVkNpswN34GggxZIMC5cvT+fCQhX9JVuRI5Cy6Fp01uhmQwxhs9nYs2cPoVAIk8nE5s2bT5gROJvQaDT86I5LePjFbWzty+ZAMIOvP13Kf1+zgKQJ1sXSGJNJnHcZfTX/xFb5Aua0Fae0s1XptMStWIqjuAxHcdmEBCV7cRm6OB9EvIjGOAyFipD1cXsL/nCYDJOZt3a1DGcn3XLO+Gonhf2DYlJwAFGfhHnhPYjq6bHumM3o4/LI2/IDug4+pmSmVTyBz1FH2vJbR2QpnbVsKW+iZH12e9zctD6brfs7aOrz8pcPm/jWxfOj0p+Dti6CUoQUg5H8Y7Kfh/DZ62kv+SORoBu1PoHsDV9FZz3eCnausb/VDahZmjK7p7Dr0rP4W0UZNtmMy7oIi7Oa7vIncHeWkb7yTtT6+JnuYlSRI4FBpwjXoFg0aCUeOsYxIqy4SSAFR28sMmhZJ2oVWzp94tFsI10igjaByvom8PvYmLMSY/LpP1aLMXkUUUmxiRsSlWRkdMlrxt3WBXmFvNZQo2QpJQXZ32+lqqqKlSuVMbR5+aX0PP89RKkVhALsJROrozRQ30xjkmKXnKnxYj3yAYJai2X1VRNqby4xVD8p22hGPcO18sZDZmYmoijicrkYGBggPj5+eFtOooGseD3t/X5Kmvo5e8HsyVrVzNuC4eJ/x/fW/+J56Xuos1eizlhy0v3Tb/8sAzv30vvKNrK/fA9q68gAjzyzhVavm2aPkzVJ4w84cw7WHqsfrOm0wluMdtllM253F3C201H2EEFXBwAJhReRvPDaaV+Taxrop6RbyU66dl4sOynGzDG7R+MxYgBhZx1y6Piifvqcy9EmLFaEpNOwQHTMDmF6EQQVxoLr8IlagrZifE0vQySILn3sRdwX5aRwUYGObY0h/vJxB1dtWMD8hCTmxSdS12/nnZYGrl9wdHBmWjwkKHknHL32acZVdgiAuAVq7AnLkEUNZrN52Md8CE8wwtOlNgDuWJuKUXu81VXY2Yiv5XUA9FkXjNv2cK7ybPUhah19GNUavrZmI9pJ2ICdbmgTl5JRuI7uiqdwd5WR0/Eam3Xr2RVM5MEDJfzsrAtIHWfUZHa2Yk3lcDhwu92YzeOLsP+gpheAjDg9ly9P47Jl6RSmmJBlidZdv0COBDAkzieh8MJxtTtWmpub2b9fsWtLTEzkjDPOGFF0eLZz32cvJfOd93mwIYmWSAJfe7mZn5zjYN78hRNqL7HoEpxtuwh5e+mteY3UJaeOGk7auAZHcRn2ffvJuenacZ3P19mNr62DhCIPAKYlFyKo1MiyzPZBu7tNqdn8cmcnII47OykccNG65zdIATuiLkERkzSzMwtkJlBpTWSu+xL2+rfprX4JZ9tuAgOtZK79AgsWLKC6uprGikOsSEuj3NbNO80N3LZkBd+6ZD73P32QfxS3cd3qTApTJh9tPWR3t/EEdnfOjmK6DjyKLIXRxeWRvf4rp4WAEZEkagaUa91UMLF6JNOFSaNlWXIqB23dtKRfyrlZy+g98jIe2yEaP/hP0pbegiVrw6zNVpJlGaTAsJ34yDqzRy3Fh/6GFBrfCUQNotoEghop0HvcZmPhjWgSl53w9WnzuOnx+1ALIssTThwgFCPGiRiZqbQPX+PLIMuQdvm42tGr1VxZtICnqypISfdT12fltX01LF++HFEU0eWuRB2XTnigC328j/4DFUjBEKJ2fIvOpR8eIKi1ohUiZGq8GP1dmFZdicp4+tdKaR6sn5RvnluZFxqNhoyMDNrb22ltbR0hKAmCwKbCRF7c38HuBvusEpQADOd9jXDTPkJH3sP1xH3Efe0tRP2JA8asG9dgmF+Ar7YR29bXybj7phHb801WdtJJk/vkwdGj4So9SH9KGj0o46XNfa+ivez/JtRWNJBlmf7mD7BVPo8shVDprGSsvGfGsr6HaidtysyJZSfFmFHmjtwf41OJ4qv9LkotoWMRCPUdRNDGnZZiUoyZYWiioUtXfLB9rW/g79ihTKzHyHc+sx6TKsJAWM2vXlEsqy4vVCKS32muJxg5mllgWnw+ALo4L/Z9JdG6jE8NrtJyAIzJTmyJSoThiezunivrxRmIkBOv45JFxy9CRQIOPPXPgiyhSVyOLuPsqe/8LKC4q503G2sB+PzKtaSZYgvHn0Sts5K59otkrLoXUWPkvEAJ2QzgDYf4/f49hCLjs1zV6/XD1nCfzFIaC+ctTOahO1fz6lfP4CvnFQ0vTNvr38bnqEdQ6UhfdU/UC8DLsszhw4cpLS1FlmWys7M566yz5pSYNMTlF57Hf64LYRKC2CUj/77Dx+69eyfUlqjSkLpUsdp0NL5DwNV5ymMS1g/VUSob9/kcpUrEpiVH+dyZB+snVdt7aXM50alUlB7oJSiLpIwzOykS9NC29zcE3Z0IGiumhfcgak//RavxIggiSfMuJWfTN1HprARcbTTv/BlL8rSIokhbWxvrrMpz5oO2JoKRCJuLkjh7fjJhSeZX22vHNaY4EYFImAM9ymdtwzF2d7Is01f3Jp37H0KWwpjTVpJ7xrdOCzEJoKK5F5+kQi1IbJyfPtPdOSUbM5SMsOKuDhKLLibvrB+ii8tDCnnpPPA3OkofJBxQAuY8tkoad/wYj61yyvojyzKRkJeguxtvXy2ujlIcTe/Te+QVusqfwF37FK7Kv+A8+CsG9v8XA/t/hqvit7irH8Zb/yy+5tcIdOwgaCsh3F9NxNOKFHAcFZNEjWI7Z8pBE78Ybcp6dJnnYsi7EmPRzZgX3Ydl+TeIW/ND4tb8CMuKbw5a0x0/xwt0fXzS6yi19wCwLD4R/Swqzh5jbiAIAoa8K9GmbgBkfE2vMNCyc9ztXJhXiFWrQ6OTiEsKsqdPT1NT0/A5TIN2tJbsIJI/wMDh6nGfY98RxdY0Q+Ml2VWNAFjX3zDuduYiQ0JEnnl2Z8CfiJwcJavsROP8M4oUEXxPvf24bTONIIqYb/4DYnwWUm8Dnhe+edLxiiAIpN/2WQC6n30ZKRQesT1/8H1r93kISuMsTwE4Sw5StVqxt0sTBkgVnWjmbRl3O9EgEnTTUfJneg49jSyFMKUsJf/s/5gxMalpoJ/S7s7B2kmfjgDYGLOX2Cgsxqwm7Kwb9tUeiUzE207YWYcmLjr2ITFigDJA0mdfgiDq8He8h7/9XXr1GpIXfWZMkaRxJj33rEvhD3vtvFbr444uO+vSMkk2GOn1efm4vYXzcgsA0GYsRDSngNuGv2kfkUAQlU471Zd42uDaX45KFwK5H3vCie3uetxBXj7UB8C9G9NQiSPfQzkSwFP7FHLYi8qYiTH/2lkbMRxNuj1u/nJQETEvL5jP+vSpsUc7HRAEAWv2RgxJC+guf4LP2g7wV86gcaCfv5fv5d7V4/PzzsnJoaenh9bWVhYvHp9NwX1nFbA4Y+Tk2u9spffIKwCkLb0JbZQLv0ciEUpLS2lrawNg4cKFLFmyZE5/T9asWcuvEhv40Ts2bJKZnx1U8S/2d7jusvFndpnTVmBKXYGnp5yew8+QvfFfR31tEtetAsBd10ig144ueeyZyPZ9ZYjqCGqtA2B4wWooO2l1cjpPlbgZb3ZSJOSjbe/vCDjbUOmsGBfcg0o3uzNAZhpj8kLyz/ohHfsfwmevpb/ycdblL6K4QY+7tp5kk/LM393Ryjk5+Xzz4nnsbuhjd72dD2v7OGcSkcnlPd0EIhGSDUYK45T3SZbCdFc8xUCrshCfUHAhKUuuj7q4PJN8XKc8ywvMEXSa2Z9NuzYtE5WwnxbXAJ1uFxmWTPLO/A59ddvoq30Nd1cZPnsdqctuxV6/jaC7E1v1SxiTF4/5/irLMlLYRyTgJDz4Ezn23+DI32UpfOpGj0XUKnVmNZZhS/GjduImRI3laC3acdYtCg3UjnuOJ8kyZXYlo2kiNkoxYsCgqJR7JSAQ7NlLV/nfkZGJzz1rzG3oVGquKlrAU1UVJKf7qbdb2bangi8XFgKK7d3Ax3/HlB7AVqHUP0xYPb76mxVuEcyD9ZNad4FKg2X11eNqYy4SiETo9CmZ2HmmuSko7dmzh/b2diKRCKpj3B/W5yegEgSa7V46+n1kxs+u+pSiKRHzbX/F+eC1BMtfxV/wNwxn3nfCfZMuu4DW3z9MsKsHx7sfknTp+cPbErQ6LGoNrnCIdo+HgnFk0QTauwh2dlN7uTJHWho8jHbppTNSN9zbe4TOA38j7O8HQUXK4utIKLhgRsdWLw5mJ52RmUPmHMvgi3H6EROUYsxaRmYnnSg6QsDf9i5q67w5vbAVY/YhCAL6rPNApcXfug17/TaksJ/UZTePaQBx94UreLniHdq8an76Uhl/+9IFXJxfxNNVFWxrrOPcnHwEQUAQBMzLLsS55xl0lgEGKiqHFxtjjE7Y5cZb04Alw0Vf/FIkUYvJZCIubmRE/eP7eghFZFZkGNmQOzIDR5YlvI1bkXzdCGozpnm3IqhOf0EvGInwu/178IXDLEhI4qZF46ux8mlFY0gga8NXMbfu5DOH3uFpeQXvdXSSK77FhSsuGvPkIicnZ1igkSQJcRLe8FIkRGfZIyBHMKetxJpz5oTbOhGBQIA9e/bQ19eHIAisXr2a/Pz8qJ5jpsjLL+QP1yfw/a0V1IeTeKg1g47n3uRL1188YvI/FlKX3kRTbyXe3mrcnaVYMteddF9tYjzmBUW4a+pxlBwg/ZgJ+KlwlBzAkOhBQEKbsRBtch4Ov4+SLmVRtqXWq2Qn6cLcPMbsJCkcoL34AfwDTag0JnI2/iv9vtj0YCyo9fHkbPpXbNUv42h4G3OomtWpZg51hNmw4Qze8LWwvbmes7PzyE00ctvGHB7b1cKv3q7ljMJEtOqJfff3dini7oZ0JSM3EvLSUfog3t5qQCB12c0k5J8XxSudHVR0BwANqzJm1wLcyTBrtSxNTqXc1s2+rnaumbcIQVSTvOBKzGkr6DzwKEFXO537/zJ8TGCgGY/tMIb4gpOIQq7BfweUf4OucYtEolqPSmtFrbOg0llR66yodFa8AQFBYxpZe3aKxkQTnePVuQZwhoIYVWoWWWOid4yJo4hKV6CISnvoLn8CZIjPG7uoNFRLaYAA8YlB3m+Tub6nh9TUVEzLLgJBQEUfKl0S9uIyCj9/x5jbdjW305ioZDlmaDzED1RjXnYRKlP8OK907tHicSED8Vod8dq5lwmflJSEwWDA5/PR1dU1ItjRHQhTlGqiptvN1v0dXLB4ZB3PeKOGjLjR63FONZq8tRiv+DHef/4Q72s/QZ2zBk3u8bXGRJ2WtBuvof3Bx+l68kUSLzlv+H4tCAJ5ZguH+u00eZzjEpScpQcJaTS06JSggQ0D29Fe8IXoXNwYkaUwvTWvYq/bBshoTGlkrvkc+rjcae3HJ2kccLB/MDvpM/NjtZNizDyxGWOM2YscQQr2c+KJBoCMFBoAOQJC7KMcI/ro089EELX4ml+lv3kHUiRA+oo7EcTRFxtFUeS7ly3i/hdrKesVeO9gA+ctKWBrTRVtbicVvT2sSFGKCJsWn4dzzzMYEj04istigtIYcR88DJKEJTdMY5JiIfVJu7u6Xh/v1Q0AcN+m9OOE50DHDkKOShBUmObdgqj7dNg7/f3wAZqdA1i0Wu5fvWFOFbudaQRBID73LC5OXkzn7ld43x/PU639JLr+yIq1t6IxnLqeQ1paGlqtlkAggM1mIy1t4gXFe4+8QtDVjkprIW3FHVENrnC5XOzatQuPx4NGo2Hjxo2kpqae+sA5hDU+gd/ccQY/e2YHe/2ZvD6QS9eT7/Gjm8e+oASgNaWQWHQpfbWv0VP5PKbUZYjqky8IJK5fjbumHntx2ZgFpbDXx0BFFUnz3QCYlyl2d++1NBKRZQos8WwvkwCRuzZkjCk7SYqEaC/5Iz57HaLaQPamb6CzZoGve0x9igGCqCZ1yfUYEoroOvgo8bjZkF5NU6sFjSaexoF+6vsdzEtI5N4t+bxW3kWbw8fTe1u5+8y8cZ8vGIlQ1q3YIG3IyCLo7aV93wOKVaFKR+aaz2NOG18U/FzA5QvS6lXG+mctmDv3oQ3pWYqg1KkISkPo43LJ2/J9emtexVG/bcQx7ft+P+7ziGr9UXFIe1QkOv5fC+JJMom6u6fxez/BOV5pn2J3tyoxOTZ2iTFpFFHpcoxGI/1N79Fd8QQgE583NutrJUtpIU9WlpOU7qeh0sKOfeXceOWFqC3J6PPX4W8sxpjkxlFyAFmWxzxOK/3wAAGtAa0QoSDcgibiw7ru1HUaTweG6ifNxewkUD5XOTk51NTU0NraOiwodQ74+cwf9xCMSADsK9/L4r79PN6+hkMuxcZVqxJ56SubZlxU0p95L+GmvQTLX8X95OeI+/p2RNPxGfWpN15FxyNP4zlUjftgJZZVR23g8kxWRVAaZx0lV8lB6lZtIIQKgxBkSaACzbzxjcsnQ9DbS+f+h/H3K9n/cTlnkrr0plHH9dPF1poqADZn5ZI5B+0gY5x+xFbhY8xaBFGNZcmXkMKek+4jakyxGkoxphRd6nriE1PoPPAozrbdSOEAmWvuO+XnbvOSXDbsrGNfN/zy7TpeXZ7POTl5vNVUz5uNtSMEJQB9nA/73n0UfemeKb+m0wHX/goEUUIT56YvXonEPzYCTJZlHt6jLI6cNy+O+SkjI5qD9sP4O94HwJB3FWrLzEYcTRcftTXzfmsTAvCVVRtIMhhnuktzEo0xmXvOu5v2D16jxgtP9Fv53I7/InvZDVizN4+6YCCKIllZWTQ2NtLa2jphQcnbV4OjYTsAaSvuQK2Lnu1BR0cHO3bsIBQKYTQa2bx5M1br6WmroNFo+M87L+IvL2zjZXs2pYFMvvFkMX+5V0dK2tjrtCTOuxRn225Cvj76at8gZfF1J993w2pannoB+779Y25/4OBh5HAIU5oXANPySwhLEu+3NALg6pQIySpSdWFuOvvUvu6yFB7OahFUOrI3fh193PgFjhgKlozV6CyZtBb/CTydzJd3s1R3EQd8Itub65mXkIhJp+Zr5xfxH/+s4uGdTVyxIp0Uy/iir8ttXfgjYZL0BrLkflp2/olI0IVaH0/W+q+ij8uZoiucWT4+0omEgFUVpih97gR/rEvP5JFDZTQ5++n2uEfUKhRVGkxJC48TlIa3qw3HiEEW1Lq4YwSjkdlF4hzLrp7IHC8oRSh3KLaHaxJjdncxooMgCKQuvQlBEHA0vkt3xZMootI5Yzr+gtxCXquvoR8/cYkhXqv2cfm5bsxmM+YVlyqCUoqX7oM9+No6MeZkjqndvVUdYCgiXe0jsbcUVGosa66ZxJXOHZoHBYj8OSooASMEpU2bNgHQ7w0Ni0kgc3NGOdkGJzdnlPNDVxogEIxI9HtDMy4oCYKA6fpfE+44jNTbgPvZ+7Hc8yTCJ4R8TWICSZdfQO/L2+h68oURgtJQHaVmj2tcYqqrtJwj5yj1mRZKTRiWXISgnp5nnLN9H90VTyKF/YhqA2kr7sA6iuvAdNLQ72B/z2B20rxY7aQYs4NYaE+MWY2oi0NtyjzpT6xgdIzpwJq1kcy1X0AQ1bi79tNe/CekSPCUx/3ourVoBYkuv5q/bivj0oJ5CEC5rZs2l1KIWZNahGhOQxBlPEd2TrpY96cF1/4K9AkeHPELkVQ6jEYj8fHxw9uLW90c7PCgUQnctX5kNHPE24m38UUAdGlnoEtZO51dnzHaXE4ePVQGKGnyy1MmnhkTA1Siim9svoR4rRabYOa1SCGdBx+nvfiPitf2KHyyYG+8UYNWNfqQTKsSiTcq/uGRkI+uA48CMtaczVjSV032coapqanh1VdfJRQKkZCQwLnnnnvaiknH8oXrL+XL+TbUSDRFErn14RIqDx8a8/GiSkvK0psAsDdsJ+g+ebR/4gYlq7L/4GEiPv+Y2reXHEBjDKLWBhDUWkyLzqG0uwNHwI9Zo2VPnbLf3ZsyT5mdJEsROvY/jKenAkHUkL3hfgwJhWPqR4yTozWnUXD2D/CqCxEEWOvdB8CejlZcwQAAl69IZ1mWFW8wwgPv1Y/7HPs6FXvDlVYNbXt+TSToQmfNIffM7522YhLAvmYl23hRojApm9DpxqLVsWSw1s++rpH1gmRZxnbkZRTbt2MR0MXlMu+S31J43n+Ru/nbZK39ImnLbiFp/hXE552FOX0VhoRCNMbkOScmDTHeOV5lv52AFCFRqyM/VjciRhQRBIGUJTeSUHABAN0VT9Hf/MGYjtWqVFxVtACA5HQ/VX4Le/ZXAEcziY0pXkDGXlw25j6VK8nIZGi8JPRXYVpyASrz2GsuzlVkWabJo8yR8+ZwBkZ2tmJX2Nvbi9frPW77CksXRSY7AEUmOyssXdPav7Eg6i1Y7ngY1HpCR97Dt+OBE+6XfruSOed4byeB9qPXkWM0IwLOUJD+wTHQqQh0dONr76Q+Xgn0XO3ehXb5lZO7kDEghf10HniMzrKHkcJ+9AlF5J/9o1kjJsHR2klnZuWSMYe/GzFOL+bOiDxGjBgxZhBL+mqy1t+PoNLisR2ibe/vkcKjLwRmJcdx3RIlGvXJMjuqsMDaNCUybVtjLcBgHSVlAqNRd+FpbJnCqzg9kPwBPIePYEpxY0tUPJ2PtbuLSEezk65Zlkia5ehiixTy4K59GqQQamsR+pxLpv8CZgB/OMzvSvcQiERYlpwa812OEnE6Pfev2YQoCJQLWZQJuXh6ymn84Cc42/edVCAeEpS6u7sJBAJkxOl56SubeOq+9Sf9OdYCw1b5D0K+PjSGJFKX3BSVa5FlmeLiYt59910kSSIrK4uzzz4bvX7mLR6mi6suPo//WBPAKATpk4x87qV23nv//TEfb05biSllGcgRug8/e9L335iXgy41GTkUpv/g4TG17Sguw5CkrDAZFmxB1JnY3qTYcYhONSFJRZouzI1blozajixLdB58DHfXfgRRTdb6L2NMWjjma4wxOqJKy7wzv0K1I4802UmGPEBYltleoywkioLAv1+iLD6+Vt5FRfvAmNsORiKU9nQCkNe9HVkKYUpdTu7mb6MxnN71ZKr7lKjudblzbxFlY4aSPb23s23E3722SgIDzRxv+yYTGGjBa6ucng7OEUr7bICSnSTGaufGiDLDolLhRYAiKjmadozp2PNzC4nX6dHoJCxJYV7Y30EoFMJQtBHRGIcoBtHF+XCUHBhTe54uGw3xyn0jRz2A1d2Edf0NE7msWU1ooB5nxe8JDRwNrugL+PGEw6gEgWyjeZSjpx9ZlpDCfsL+foKeHvwDrXjtdXh6DuPq3M9A224cTTuw17+Ft/UdlqfbWZjQQmvxX2gv/hNCzYP8ZP52/m/RG3yr8COGhoiSDDdmlHNyC9CZQ52xBNNn/gcA31v/R6hu53H7GOcVYN20FiSJrme2Dv9dq1KRaTQBR20MT4Wr9CA9+UU40SMisdH9HpoFY8sWnCj+/maaPvopzrZdgEDS/CvJPeNbaIzJU3re8VDfb+dAT9dg7aRYdlKM2UPMKyxGjBgxxogpZQnZG79O+74H8NlraN3za7I3fB2V1nTSY7525Vrernkfe0jN/7xUwn1XLaWku4Od7S3ctGgZFq0O89ILce55eriOkrkwZjs0Gu5D1cihIPqMIH0Jx9vdvXXEQWt/AKtOxU2rjtqiyFIYT/2zyMF+RF0ixqIbEYTR62GdDsiyzMMVpXR4XCToDXxl1YbYYkwUWZyUwo0Ll/Js9SG2CUvIM+lIctfSWfYw7q4yUpffilo7chHUarUSFxfHwMAA7e3tFBYWkhGnH5PFhavrAAOtHwMC6avuQaWZfIH6SCTCjh07qKmpAWD16tXk5+dHtSbTXGH9unX8KqmOH71rp1cy8d2dEb7Ss5W7bjq5hd0QgiCQuuxmmj74T7y2w7i7DmDJWH3C/RI3rKHztbexF5eRtGn0LElZlrGXHCAp/2j9pDaXkyq7Tcl6rVfuY3dvyhw1e0OWZbrLn8TVvhcEkcy1X8CUcmp7vBjjw2KxEJ+7hf21JaxO66ZTiOOd5jrONQdIyDubZVlWrlqZzqsHu/j5thoe/5d1Y7onV/R04g+Hsch+suknPv+8Qaum0zs+sLG7H0dYg4DMWYsyZro742ZtWiaPVJTRONBPj9dDqtH0ieykEy0iCtiOvIwxZcmn8j78STzhEFVOBwBrkuZODa0YcwtBEEhZPJht0bCdnkNPAzIJ+eeNepySpbSQJyoPkpzuo7zSTPnhatauWo5p6UW4il/AmOQec4ZS6Qf7CWj0aIQIRc5SBFE47ezuZFnG37YdyW/D37YdtbUQQRBoGhQeso3mCddJk2UJpCByJIgshZAjAZBCyFLw6P8jQWQpSG+/BikcQIoEkMIB5MjQ/4NIET9yOIAUCSKF/chSaFz9SNEAGsDVh9ul3O0XnEAjEwUoMjm4K7uUf3SsnNA1TyX69bcQbtxLoORZXE9/ifhvvINoHelykX77Z3HuKcX20ptkf/EuVGZlbSTPZKXN66HJ7WLVGKxKnaUHqVqhWATmCL0kLjwDQT0+a+CxIssSjoZ3sFW/BHIEtT6BjNX3YkxaMCXnmwxDtZO2ZOWSPoetIGOcfsQEpRgxYsQYB8bE+eRs+jfa9v4Wf38Trbt/Rfamb5y0foleq+H+c3L4f+90sqMlzO2OIAVxSqHud5ob+Mz8xRgH6yjprD7se/aQc9O103hFcw9XWQUaUwBXahGSSo/BoCchQYnO9gYjPFmiRLHeujYFs+6oYORreYOIqwlEHab5tyGqPx31g95pbmB3RxuiIPDV1Ruw6qZmYP5p5orCBRyx91LW08Xz0jL+tWghvoY3cHWW4u2rIW3FHcfZ0uXm5lJRUUFrayuFhWOzGwsHnHSXPwFAQuFFUZn0+P1+tm3bRmdnJ4IgcPbZZ7NkyZLpLdA+y8gvmMfTnxf54sMf0BBO5Hc1cbQ8+BTf/9zNqFSji9BaUyoJhRdjr3uDnsrnMKUuQVQd/51LXL9aEZTGUEfJU99EqN+BIVGxTTEvv4RXm5WIXn1Qiy+oJk0X5oZRspNkWabn8HMMtO4EBDJW34c5bfYtXJwurF27lurqauSOIIacCAOygZ2H3ma9o560Fbfx1fOLeK/KxuEOF6+Vd3H1ytGFkkjIy/uH3gNMLKabtKU3Ddszne58dKQHgCxDmHjT3MuYjNPpWZKUwuE+G/s627iyaCGyFCbsc3DyiHSZsN+BLIURVJrp7O6s5KC9F0mWyTKaSI/VfowxhRwVlQQcDW/Tc+gZkGUSCs4f9bjzcwt4tf4I/fjRJ0o8u7uONSuXYV52iSIoJbtpL6kh7PagNp88EBFg7+F20OSTrvaR3FeJafH5qC2zJ1siGoSddUS8ig1oxNtOsHc/KmMGjQ5l7JmtChLo3Q+Dwo8cCSoi0aBAJEuho9uO3R4Jghwecz/GZjr8SQQElRZRrUNU6RHVWkSVDkGtQ1QN/qh1eHwhjtQ2IKp1bNh4Jl1u+M17zdySWU66zoX4iViBS1PqOCexkSN7qxFXXMLCwtmTiWK69r8Jtx0k0lWF66kvYP38Cwiqo0vJcZvXoy/Ixd/Ygu3lbaTfrtRAyjNb+NjWOSwUngpXyUFqL/8CAMv9Zeg2XxX9iwGkkIu2fc8MZwKb01eTvuLOUYOEZ4r6fjsHbF2IgsC1MYeRGLOMmKAUI0aMGONEH59HzuZv07rn1wRcbbTs+gU5m/4VjeHE3tbXnrGY54rbODKg4n9eO8zXrl/Inw4Us725nisLF6BNyUc0pSN5unBX7Zjei5mDuErLMaW46Uk6C4CsrOzhCN4Xy/tw+MJkWrVcvvioBVCgZx9BWzEgYCq6AZXh0xHh2tDv4MmqcgBuXrSMhYmn14R0tiAKAl9cuZ4f7nyXHp+XFz2ZfPHM79J18DGCrg46Sv6ENfsMUpfehEqjLIZlZ2cPC0pjKVY7lF0SCbrQWjJJXjj5aNWBgQFef/11BgYG0Gq1XHzxxcN2fJ92klNSeOqbV/H1B15gny+Dl2wZdPzmOX771c+iO4UomzT/Mpztewj77Njrtp3wvRqqo+QoPoAsSccVOj4We3EZhgQvokpCFZeGlL6InZXbAKhtVhab7xklO0mWZXqrt9Lf9B4A6avunlW+8KcjZrOZxYsXc/jwYXIjao6IMiXksrB9D35nC1lrv8R9Z+Xzu3freeDdes5flIJZd+JpWcjbR9O+P1AZWAgCnL3wDBIKzpjmK5o5DnR4AQ3LUudmrSCADRlZiqDU1c6VRQsRVRryzvo+keDJF9lUWitiTEwCoNSuBAqtHUOEe4wYk0URlT6LIAjY69+i5/CzAKOKSlqVimvmLeTxw0qW0r4qAw2NTeQuV6y1dXE+RDGEo6yClLM2jXr+cqcMSUfrJ1kv/5/oXdwsIOzpxNuwdcTffE0vA9DEJhDiSB/Yi29gsoFNAqi0COLgj0oDog5BpUUQlf+bzHGIah3CoAg0JAgp4pAWUa0fKRapB9saQ+ZoJBLh3YOPEA6HWWNaCjo9QamdTP3J7/sGVYTFHIbKw7x3MBVV2mbWrzkPo37ybgSTQdAasdzxMAO/v4Rw4x68b/8fpst+cHS7KJJ+23U0/fS3dD29lbRbrkVQqcgfzKZp97oJS9KoWWeBrh4c/R461Mr8/QzX+2gWfDnq1xLqr8HbuBU57EEQNaQuvZG43LNnbTbwizWK6KVkJ80uG8gYMWKCUowYMWJMAJ0lk9zN/07rnl8T8nQPi0pa04mFih9cvZy7nzhMrVNFR6NiPebw+9jd2cbZ2XmYlpyPq/hp8NYRdAygTYg7YTufduRwBPfBw6Qu99EXvxw4Wvi0zxPixfJeAO7ZkIZGpQxaQ85GfC2vA6DPvhBN/KejXognFOT3+/cQliTWpWVyecH8me7SaY1Zq+Vrazbxk907KOnu4P3EJC7d8gP6al7FXv8WzrbdeHurSF9xF6bUpWRlZSGKIi6Xi4GBAeLj40dt39m2G3f3ARBUZKy6d9ILjZ2dnWzbtg2/34/ZbObyyy8nKSlpUm2ebuh0Ov74rzfz3w89y0u2dPb60rnt16/y4L1nk5x6clFaVOlIXXIjHaUPYq9/C2v2Gcc9G6xLF6IyGAgNOHHXNmBZOO+k7TlKDmBI8gBgXnoxH3e04Y+EUUdUuFwa0vVhrh8lO6mv9nXs9W8BkLb8NuKyPz1ixEyyZs0aqqqqsHTaELKSqReS6dekEu/qoHnnz7h62R28lGigxe7j4Y+a+MaFx38GfP1NtBf/geqAloCgIU6jYeW80RcjTydC4Qj1LuVZvrlo7hakX5+exWOHDlDf78Dm9ZBiNKExJJ40ECnGUewBP41uJwKMyTIpRoxoIAgCyYuuAwTs9dsGRSV51MzQc3MK+GfdERz4USfIPPdRJd+/6wp0WUsJtB/GkOTGUVw2qqDk6+unzqpkrBZIbehD/VjWfibKVzf9SEEnwb5ygn0HkHwnFoqCopkuWXH8yDOaUWusg0KQFoZFocH/DwtFGlDpEETN8fsJ6lOKBGlpaaNunwwqlYqsrCyam5tpa2tDn1bEjRkVSDLHZSeBUkupM2DBLaZSpG4kS9MD9pepfusNujXLWbj8IgpzxuZoMBWoUoow3fBr3E9+Hv/7D6DJW492ycXD25OuuIi2PzxCsKMLx/sfk3jh2STp9JjVGtzhEG1eN/nmEzu6gFI/qXrNGcgIJAhe5ufnIGiil5UsS2H8bW8T6N4NgNaSReaaz6GzZEbtHNGm1tHHQVu3kp00b/ZkrMWIMcTpbbodI0aMGFOI1pRK7uZ/R2NKJezro2XXLwi4Ok6477L8dC7IUxaAH9zZzvnZ+QC82ViLLMtYVl8KoNRRKj0wHd2fk3iqayHkwZefR0RtQK/TDNvdPVHSQyAssyTNwJkFSkRUJODAW/8syBKaxBXo0s+aye5PG5Is8+CBEmw+L6lGE59fuXbWRl6dThTGJ3D74hUAPFt9iLoBJymLryP3zMH7hL+ftn2/o6v8CVRChPT0dABaW1tHbTfk7R2OkE1eeDX6uMllEdXW1vLPf/4Tv99PSkoKn/3sZ2Ni0klQqVT86Iu38fUFA6iRaAgncutDezlSXTnqceb01RhTliBLYXoOP4ssj7S2EjUaEtYqn5W+vaPb3tmLD2BMUuonmZZdwvZBu7uOTg0g8C9nZJ00O8le/xZ9Nf8EIGXJjcTnTW1x4xhHMZvNLFmyBH1EIk1S/laTcQ2GxPlIYT89Bx7iO0urUCHx1J4W3qu2UdXpOvpTuYuWXb8gEnByRKMsIm3IzPlU1cArqe8hKKvQCRFWF8zdzOI4nZ5FgxnCxV3tM9ybucX+weykeZY44rUxy94Y04ciKn2GxCJljtZz+DnsDe+cdH+tSsXVg4u+Sel+dnSCzWbDNJilpNRROjDqOfcfUz9pgasY4+JzUVvnppAqRwIEe8twH3kM58Ff4m9766RiEgh0azKQEbBqtGQtuQvzgjsxzbsZY8F1GPOuxJBzMfrMc9Gnb0aXsg5t0go0CYvRWAtRm3NQGdJQ6RIQNSZFYJoFz8qhrP+WlhbiDALJWu8JxSRQRCaTKsS6875M6paf0mw4l76QGZMqSKFUSujg/7L9pf9k5953CIaC03gVR9GtuBr9mfcB4H7ua0TsLcPbVAY9qdcrFnVdT74IKN+hvMEspWb36LZ3rpKD1BQowaKLwjVoV1wZtX5HfDbcVX85KialbiJvy/dntZgEsLVWqZ10VlYuabHspBizkJigFCNGjBiTQGNIJPeMb6O1ZBEJDNC66xf4+5tPuO93P7MOoxjBEVJzuMKBTqWixTlAZZ8N03AdJT99u3dN5yXMKVxlFRiT3diS1gCQnZOHIAg09vl5+0g/APdtSkcQBORIAE/tk8hhLypjFsaCa2fF5GI6eL2hhv09nWhEka+v2YRJM3etguYaF+YVckZmNhFZ5vdle3EGAhgSisg/+0fE5yt2KQMtH9H0wU/IT1MSxUcTlGRZovPAY0hhP/qEIhKLLplw32RZprS0lHfeeQdJkigoKOCaa67BaIzVpDgVd910Hf+7RYVBCNErmbj3hRY++ODDk+4vCAJpS28BQYWn5xCe7oPH7ZOwfsj27uSFuoOOAXzNNeisitN/a8562t0uBBkcvXoy9GGu23xiT3VH0/vYqpRJffKiz5BYeOGYrzdGdFi9ejWiKJLY2w/Ahx2dpK7/GgmFSlRvonsPP5r/Hla1l8e2badj50/4n+f+yVOvPAH1jynFw+MXcwRlQXFjRvYMXcnMsLvBDsD8OBm1am5PWzcMvnd7O2OC0liRZZnSPqWG1tqkuSsoxpi7DItK8y4DwFb5D+wN20+6/7k5+cRr9Wi0MpE4ga0fVWBerghSxmQ3jlLF5vZk7K5QxoPpah/JA4exrr8hilcz9chyhFB/DZ765xk48H94G7cSdtYDMipzLtrUjSc7kpaAUvso32w5beZrQ4JSV1cXKWY9WWd+D2nxv570J/vM75GZYCElMZmLL7iVjdf8goHsu2kMFyDJArmaDlJs/+Dg69/izW2P0NrZNu3XZLziP1DnrkH29eN68nPI4cDwttSbrkFQq3EfOIT7UDWg1FECTllHyVFaTqNJyc5b59mJduF5k+6rLMsEbKW4Kv9MxNuFoDZimn8bxrwrZr2lbK2jj/Kh7KT5seykGLOTuT0yjxEjRoxZgFofR+4Z30Ifn08k5KF1z6/w2uuO2y/BYuTOtUoWwGvVPlYnKdkJbzbWoknMRjCmIwjgLj959NunHVdpOfpUL30JSmR/VlYWAH/b240MnFVoZXGaEVmW8Da8iOTr+f/t3Xd4VFX6B/DvnZLJzKT3SgIkoaZQQ5FeBKQjKBYERF11RcHesP101XVFXdeCi8rqoqtSRBAERASkQ+iQ0AIkISF9JpNk6vv7Y5gxoYQAyQwz5/08D88DMzeZ98stc+8995wDSekHbfJk+7AIAjhcWozvsg8CAKZ0SEdiYJB7CxKMJEm4N7UzorV+KK+twUd7tsNGBJlchciOtyO+x2wo1KEw15TCt+RHJAXl4Wz+GVit1kv+vvITa1FTlgNJrkJ0xjRI0rWdulmtVvz222/Yvn07ACA9PR1Dhw6FUinGftEUBg4YgM/GxSJUVo1q8sGTG0z47w9LL7u8j18kQloNAWB/stlmrf9EqWMepbLtl29QKt+1B+rzvZN8EzphXbH9BntFqQ9sNgnTe8VdsndS5elN9snEAYQm34LQ8zfDmGs5eikF15qgJaDaYsbWswWIaH8rbK2nwmBRoo1fCf7WZhWmxe1CnFqHvyZsxd1xeyCTgDXFSTgWNhrVFgsCVSrh5sE7UGwGAHSK9fxG725RMZAAHKsoQ2lNtbvL8Qj5NQYU1dZAIUlIDeJetMw9JElCWJuxCEkaAQAoPvT9ZRuVfORy583f0KharDhWBYrrAslHDYWvBTIqhT774mtEh716e2/maEUVgvTHEeABw90RESyGfNSc/hm6Pe/AcPQrmMv2ATYzZKpQ+MYMhH/qLPi1nQFrVR6ASzcW5SEIANBC4++64ptZYGAg/P39YbPZkJ+fj9iIaLRr3e6yf2Ijouv9vFwmR/eMXhg29lkEZr6Mk8reqLSoEaCoRSvLVlTtfBW/LP0/bN21ARaLxSWZJIUP/O6cB0kTDGveXhiWv+x8zyc8FCHD7A1Bjl5Kf/ZQ0l32d5qKipETEIFaKOEDKzKjJEjK65s3ymapQfXx/9nn57KZoQhoBf8OD0MZ5BmNM465k/rEJiBCw72T2I2JG5QYY6wJyH20iO8x2zmUTd6292AovnhIpHuHZiDG1wIzybB3n/1JnaxzhThbpYemjX0oImvZAdhMZpfW7wnIZkPVnv0wJcXZh7tTyhASEoJdeVXYlVcFhUzC1O72sbBrC36DueIwIMmhTboDMh8x5qSqNNbiwyx7A0bv2BYYEN/S3SUJSa1Q4rEuPaGSy7G/5ByWnB+yAAA0YW3Rst9LCGxhH36xhX8xOoUdQP7xnRf9HqMuHyXZSwEAEe0nXnaOtisxGo1Yvnw5srOzIUkS+vTpg169el12mDR2ee07dMTCGV2RqCiHBTL847A/3vh04WWXD02+BQrfYJhrSlF2bFW994K7pAMyGapP56G28Nwlf75se5ZzuDtz6kjsLLIPq1p6zt47aVzPiy+MdfnbUbjvK/tntBqC0JTR15SVNY1OnTpBIZcjvMK+HtecOmEfAjEoFc9lD0VudRAClSbnRN2BSiNsBHyVl4HP87rgcIV9yK9ukbFCDXdXqq9BYa29F2eflOab58JVgn3VSDnfILidh71rlN2l9m2/Q1AI1Aqe+pm5j71RaQxCk28BcL5R6fjqSy7bPz4Rged7KRkCFFizMxvatv0BAOoGhr0z6atwXGs/1iWbcuCf3AOKwBv32GczVqC24HfoD/wTVYc+gbFoC8hSBUmhgU9EJvza3Q//1EfhGzsAct8QgKywmSoA0EW/iwDkwX6tlqjVujRHc5IkydlLKS/v+noTxUREY9jN9yDjlndQGnUHTpnjIJOARMVpBJ/9GjuXP4Ff1nyFopJLn082JXlwHPxu/xAAYNz8BYx7ljrfi7prAgCgfO3vMBaeQwutPyQAFWYTKkzGS/w2QLdzL4606woAaIU8+KWPuK76LPpT0B/8CObyg4Akg2/cEGhT7oHM5/JzON1IcspKsb/kHOSShLHJYsz9zDwT30lgjLEmIlP4Ii5zJrThHUBWE/J3fAh94Z56y8hlMjw5LBkAsLtAjha+9hObVbnHEJg5BgDgG6BD5YHDYPXVnDgNBYpQGpkOAIiJS4CNgPlbCwEAI9sHIybAB6ayAzAWrAcAaBLHQOF3ffPNeAobEf6VtR0VxlrE+QVgesdOXjNkhCeK8w/AtI72HihLjh7G/uI/x42XKXwRlXY3Yrs/AivU0CqNqM75HMVHlsBmNcNQfAgnf5uD/J3/Atks0EakOhugrpZOp8PixYtRUFAApVKJ4cOHo2PHjk2SUVThkVFY+NgIdPUtBCDhh3NReOgf/4XRePGFskyhQkR7+5A1ZcdXwWQodr6n9PdDQPsU+3uXGfaufGeWs4fS7pgesBGhRi+HsVaOey/RO0l/Ngtn93wOgBCY0A/h7W7l44CbOXopRRpqICMgV1eBYxX2nmbnTP6YkzMIOsufw5ISAeeMfvi52H4T4UiF/eZQ9+hY1xfvRhuOnAVBQqjSjPhwz7gJdCXdo+zrcDsPe3dFNiLn/EldQni4O+Z+kiQhNGX0n41Kh39A2fFfLlpOKZdjfMr5XkqRtfh+3zmoOtQZ9m7nnkv+/t3rs1CrUEEp2dBWtxUB3W5tniDXwWapsQ8hdmQ+dPv+gdr8tbDVFgOSAsrgjtAm34mA9KegSRgJhV98vfMPSaaAf/sH4XeJP+akGTBIKsglCXF+3nG8d6g7j1JT8FEq0atrfwwdNweqTs/jhLw7qiwqBCuqkWjciNItL2LVj29h575tsNouPfpBk9TRdhDUAx8DAFT98Dis5+w977Rtk+HfNR1kseLct0uhkssRo7Y3El5uHiX9rn04HpYIAEiv2Q2fNgOvqSYiG2rzf0PVkfkgUwVkqmD4tb0PvtF9r3mEB3dYdPR876Q47p3Ebmyes1cxxpgHkMlViOn6EPyiOoFsFhTs+gS6/O31lumX2hJdz18bH8q290TamHcKSLoJAODjX4vSPza6tG5PUJW1//xwd/YGpbj4eKw7WoGTZUb4+cgwuXM4LNVnUX1yMQBAFdkLPmGd3FmySy3KOYSDpcVQyeWY2TkTvvw0r9v1iUvAgPhEEICP9uxAWW1Nvff9IlIhb30vCg3BkEAoO7YSuRtfx7mD/4PJUAhzdQlkCg2i0qZcU6NAYWEhFi1ahIqKCmi1WowdOxYJCQlNlE5svmo1Pp59G8aEngUAbK2Oxt1zl6G8tOSiZf2iu0AT1hZks+Dcof/Vey+ku30+uLJtuy/6OZvZDEPODihUVthUAdhUY593oazEFzFqC8Ze0Dupqmg/CnbPA8iGgLieiOw4mRuTbhCdOnWCryRDmMF+DFhz6oTzvXZ+JQhQ/DkcoiQBUb5VSPMvhNbfglqrGTKS42CuCXnlNRf9bm+187T9xlP7MLmbK2k6jkbBnPLSi74PWH3H9JXQmU1QyxVoGxjs7nIYA1C3p9JIAEDx4UUovaD3MQD0i0tEgFIFpQ+hWKPEQR/7gzzq4GqU79xxyd+9eZ99Dt5IRTXCdQfh33V8M6W4OmSzwFx+GIZj30K3523U5C6FVZ8LAFD4t4Q6cSwCM56GNuk2KIPaQpJd/pgtUwVCoY256E+ezT6saYxaC58Gft4TxcbGQpIkVFZWQqe7/LBv1yIxNgHDh89A++F/x7nQCThjjoJcIrSUH4f/6fnY+uNTWL3ufyg9/xBLU1MPfRKK1r0BkwH6r+4FmQwAgKi77I2h5xatgLW6xjmP0qnLzKOUe+wMSiR/AIS+IRWQfK5+mFubsQJVRz5HbcE6AARlaDr8OzwEhZ9nzT2ZXVaCA47eSUmeMTwfExc3KDHGWBOTyZWI6Xw/AmJ7AGTD2az5qDhVv4Ho+bGdoJRsOFOqghY+MFqt2KirBnyjIUmAbvdKN1V/49Lt2gdzShQsCg1UcoI2MBgLdtif3L6tUzj85LUwHP3v+XGSk+AbP9TNFbvO3nOFWHrMPvnpjNQuiPX3rqf7PNmUDhlICAiEzmTEP3dvg+WCyZjjE5JwqCwR+0paQqb0g7mqAKaqs873gxL7Q+F79UM2Hjt2DMuWLUNtbS3CwsIwYcIEhIWJNf9Kc5PL5XjpoTvx16RyyGHDMXMobv9kM45mH6m3nCRJiOgwGZBkMBTtQ1XRPud7Id0yAFy6h5Lu4BH4+pcCAHK7TUGF0QiLWYKuQokZvePr9U4ylBxGwa6PAbLCP6YrotLv8ainMb2dVqtF+/btEV1lb0TYdjYPBrMRAGFS9H5YqX7Dn5UkTIreD/9ge0NTaYkcb67MwegPt2D0h1vwxs/ZWHekGPpa7xwe12azIafC/vfMxCB3ltKkQnzVSA62zwW0g3spNWh3qf38Lj04DAoenpXdYMLajHY2KpUcWXxRo5JSLsetbdoDAEIia7HwkAHykBaQZATS58BYfPHDJ1nn50+Kk5UjPLYllEHRFy3jKkQES9UZVJ/6Cbq9f4fh2EL78GFkgcw3HL5xQxCQ9jj82k6HKrwLJIXvdX1ersHe0JLo5z3zJzmoVCpERtqHLjxz5kyzfIZa5Ys+PW/G4HGvQtbxSZyQOqHaqkSYUo+E6l9xdsNzWLnsH9h7OAu2C65Drockk8P/jo8h+UfAWpQNw5JnQEQI6tsDqvhYWPVVKFn2i3MepdxLzKNkOleCPdFJAIAoqRKxaVc/IoOp7CD0B/8Fa9UpQOYDTcsJ0La6FZL8+rZLd1h8fpj0vnEJCNd4z/CPzDvx2RljjDUDSSZHVMZUBCX0A0Ao2v9VvQlcEyKDMaaNFoCEU6fth+Jfco/DJ8l+EmUuzLLPscAA2C9sqvdvR2VcKgAgJiYWS/eXobTagkh/JUa1C4Dh2LcgUyVkqlBoWk+CJHnXE26XU1JTjY/22J92HNyiFXrFijHEn6fwkcvxaOceUCsUyCkvxXfZB+q9r9FoEBYWhpKaINji7oZMUf+pPEPxwas6FhARdu/ejTVr1sBqtSIhIQFjx46F1ovGpL/RTJ88AW/0kkEtmVFs88P073OxaVP9hwhU/tEIbjkYAHDu4LewWe0NAY4eSpUHjsBSZaj3M2U79kATZh/ubkdsTwBAeYkKMb5WjM78c0z16rJjyN9hHx7RLzId0RnTuTHpBtSpUycEWQl+RjMsNhv2lOYjzb8QrbVlkEv193G5RGipLUdwkH0Yxf4JLdCpRSAUMgl55TX4YVc+nvh+Pwa8sxFTv9iJj9efwJ4zFTBbm+5GkTsdyS9HlVUBOWzomRLl7nKaVOb5XkrbeB6lyzLZrNhXYW9M7xoa7uZqGLu0sDajEZoyCoCjUan+w4D94hPhp7D3UjqtkKG4/Z0A7MPeXTiPkrm6Bsd97Q/9tK3dj8DuE5s/wCVYa8tQm78O+v3voerwPJjObQdZqiEp/KCK7AW/9g/Cv+Mj8I3uC5kqqMk+1zEUWoLWOx+Icwx711wNSnUlJyZj+C0PImXI2ygIGoUCcxiUMhtaybLhe/xjbPrxWfz6+xJU1Riu/MsaQeYfAf87PgEkGYy7vodx+0JIMhmi7rT3sCtcuBgJ54dty6uuuujBOv2ufTjWwt4Tp6P5EHzaDm70Z5PVhOrcH1F9/FuQtRZybSz8OzwEn7CMJsnmanV7J43h3knMA/DVJmOMNRNJkiGi4x0IbmXvKVN86HuU5Cx33hyeNborgpQWlJT6QmaTUF5bg5PnhzfwUZei+lTzn3R6ClNBIZSyPJSE2Ie7849qie/32p/um9o1AtaClfankuQqaJPvhEyhdme5LmOx2fDB7m2oMpvQMjAId7VPc3dJ7BIitX54IN0+2eyKE0ex84IbiY4LzeL8g7BZquu9Z6w8heriQ436HKvVivXr12Pbtm0AgNTUVAwbNgxKpfJ6I7ArGDJoID4dE40QWTUM5IPHf6vFN4t+rLdMWMpIKFRBMFeXOOddUMdEQR0XA9hsKN+9r97y5Tu2wzeoBucCW+AYqUAEVJSocF+d3kk1FbnI3/4ByGqCJrwDojvfD0nGw13eiLRaLTp06ODspbSrOA+3Ru+H7TLtxScpGFBIUMuVeLxfKubf0wW/PdEHc29Lw+3d4pAYqoGNgH15Ony2MRfTv9yNge9sxKz/7cP/duThVGm1xz6Ysumofe6cBK0VGpV3Hb8c8yjllJWgnIe9u6RDFeWotVoR7KNCopfNp8K8S1jKKISmjAYAlBxZgtKjPzvfU8hkmNTW3kspOMqIxeYuAAB16MXzKO3ZtBe1chUUsKF9xSb4d3HdcHc2SzWM57ZDf/gz6PfPRW3Bb7AZywCZEsqQNGhTpiAg4wmoWwyHQhvT5EPpmmxW5J9v3PDGHkrAn+f5+fn5TdpDqCFajRYDbhqFAePegLXNozhBHWG0yRGpLEecfiUM+/6O7VsW4mT+iSv/sitQtu4FzbBnAQCGH5+DpeAAwkbfDLm/H4yn86HYsQdahQIWIuRX12/IKt61H6eV9rkAevkVQVI17gE4a/VZ6A99AlPxTgASVFF94Nd2BuS+odedx10W5div9/rFJ3LvJOYRuEGJMcaakSRJCG83AWFtxgAASnOWofjwIhAR1ColHropFkQSzhXaJ+T+XRYMAqDyN6Jk43q31X2j0e3aB0u7SFgUWvhIVqzKtaLGbENKuBo9Ao44Tya1rSZBrhbnadZvDu/H8YoyaBRKzOzcA0q5GL2yPFG3qFgMb5kMAPh07y6cq65yvme/0CSoDbsAXHihLqE4e+kVbwwbjUasWLECR44cgSRJuOmmm3DTTTfVGxaNNa+OqWn4771dkKCogBlyvHPID2/PW+h8X6bwRXh7+7jyZcdWwlxtbxQP6Waf663usHdEhOrsDZBkhN1txwEA9BVKRClsGHW+d1KtLg95296DzVILdWgKYrv+BTK5d9189zadOnVCVK0ZCqsNOnMtqlRqyC5zb+6IZB8iJzU0AvLz+7FWpUC/lDA8NSwFix/qgRUze2HOyLYY2j4CQWolDCYrfs8pwVurcjDuo60Y+c8teG35Yaw5dA6VNZ4zPN7es7UAgLQozxuu5kpC1RokBYWAAOwoLHB3OTek3WX24e46hYRDxvPAsRtcWMpI53VeSfZSlB5d4XyvX3wiNDIfKJSEw3IVyjWJ8NGaULG7fi/mjVknAQCRyhrEhWqhDIlt1pptVjP0Z3fBcHShfV6kUz/BWnUagARFQGtoWk6wz4vUeiKUgcnNOupDnqEKNiL4K5UI9lE12+e4U3h4OFQqFUwmE4qKilz++e2TO2D4qJlIGPAmzvjdjCJzEFQyK1IUhxFc8AUO/PE+du/fiBpj7TV/hm+/h6FsNwSwGKH/agYkyYyICbcAAIq+WuQc9u6Uof6wdzv0BAtk0EomZHTscMXPISIYi7ZCf2gebLXFkJT+0La5B+r4oR79QNXh0mIcLC2GXJIwunWbK/8AYzcAvsvAGGPNTJIkhCbfgvD2kwAA5SdWo+jAQhDZML5XOyT5W1FW4guyASer9MiL6gUAqNy+3J1l31Cqdu+GPrEdAEARGIGVR8oBAA9n1KD2tH2ICd+4oVAGpbitRlfbdjYPq3KPAQAezOiKCH6S6YZ3e9uOSA4ORbXFjPd3bYPJagUAREVFIVxTDT+lAcCFDUd0xV5KOp0OS5YsQX5+PhQKBYYNG4bU1NTmC8IuKzIqGgsfvRmdfYtAkPBtURRmvvtfmEz2uXD8Y7pBE9oGZDPj3MHvAAAhmecblLbvdv6emvxCKGR5MCrU2Bd/fri7YhVm3NQCMpkMxqqzyNs6FzZzNXyDWyGu218hk3vnjRhvotFokNqhAyIN9p4pR0J7w9Zu1kV/LG0fwxFFSwDAgMTEy/6+6EBfjO0UgzcndMTax2/C1zO64pGBrdA1MQhKuYSzlbVYknUWTy86gIHvbMTd83fgw3XHsetU+Q07PF6N0YxTBvvNy5tSvPMBke7nh73bzvMoXcRgMeNwpf0crwsPd8c8RGjyLXUalX50NiopZDJMamfvpRQQacLKxMcAAJai3bDWGp0/v1tnP/dLpAKEdx3VLDUS2VBdehSF+77C8bVPomDXpzBXHAbICrkmCr7xwxCQ/gT82kyFT1gGJBedU+QaHMPd+Td576cbhUwmc+mwd5cT6B+Iwf0n4KYxb+Jc+J04ak6CxSZDnE8JWtWuRnnW29i+9TucKbz6GiWZDH63fQBZcBxspbkwfD8L4beNAeQy6HfuRfT5h1pyzw9vCACm4lIcikgAALSxnYRv+yENfobNbIDh6H9Rc3oFQBYoAlPg3+FhKANaX3W9NxrH3EncO4l5Em5QYowxFwlpNRiRaXcDkFB56ncU7vkSEgjPj+4IskioKLP3UtqZZn+C3ZS/w43V3liMORtREmof7m5DVQxsBNzckhBZ/iMAG5Sh6VBF9XZvkS5UaNBj3r5dAICRrVLQOTLGzRWxxlDIZHikU3f4+/ggV1eBrw7tBWC/0EwOPYfLd0K6fC+loqIiLF68GOXl5dBqtRg7diwSG7gBzZqfWqPFp7Mn4ZZge++DTYZoTHl3KSrKSyFJEiI6TgYkGaqK9sBw7oCzh1L5rn2wWSz2v+/Mgjq0CvsS+8EsU8BYI0OIDRjZPQUmwznkbZkLq0kPVWALxHWfCdl1TojNXKdTp06IqzEDRDiqq0RQZBzatW5X748UHA6D1QqtUon2oRGN+r0ySUL76ABM652IeXd3xvon+uKfk9NxZ2Y8WodrQQAOFujx+R+ncN9/stD/7xsx89u9WLjtDE4UG26Y4fE25xTCChm0cgvaxQa7u5xm4Rj27khZMSqv44lwb7SvvBRWIsSotYhW80015jnsjUpjAdgblUpy7A8GDmjREioooVAS9gS3Qq0iAOqgSlTutz8oZDWacNLHfqxrV52FgK4TmrQuU1Uhio8sxcl1z+PMlr+j8vRG2MzVUPgGQRV1E/w7/BX+HR6Gb1RvyHxcP8SkY/6kRC+dP8khLi4OgHsblBxkMhlSEtuiW697oGg/Gzmy3igx+0EjNyNFvh/+Z+Zh3x8fYs+hrTCaTY3/vZpg+N/1GSD3genACtDRpQgZ0g8AoF23BQBwyvBng1Llzr04HtACANBVVQDJ1++yv9usOw79wX/BUpkNSAqoW9wCbfJdkCk9/3vicGkxDp3vncRzJzFPwg1KjDHmQkEt+iC6072AJIMufysKds9DakIY+rdQoOyc/Ybg4aB4lGsjoJAKYKqodHPF7mcqKYMxXAaL0g/nTEpkFZqgkZtxd8Q6kLUGcm0cNIljvPaptguZrFa8v2sbai0WtAkJw6Q2Vx4egN04QtUaPJTRHRKAdadPYlPeaZDNApXMiMtvwgRLbTnIZqn36vHjx/Hjjz+ipqYGoaGhGD9+PMLD+YnuG4FcLsdrf70LD7YqhRw25JjDMPmjTThxLAcq/xgEtxwEACg68C20SQlQBPjDaqiG7lAOAKBsyzootSZsT7Y/qVxeosL9fRJhNZbjzNa5sBgr4OMfg/jMxyBXatyWk109jUaDrm3bIbjWfpNm7amL5y/YdjYPANA1MgaKaxy2Uu0jR++kUDw+NBnf/yUTqx7tjVdGt8PwjpEI0SpRY7Zi09FSvLP6KG79ZBuGv78Z767Px/pjlaiosVz5A5rJ9lz7eU+bIHjtkJ3hGi1aBQbzsHeXsKvUPtxdZ+6dxDxQaPIIhJ0fprY0ZxlKcn6CQibDbed7KWkjzFgTOx3qEAPKtu8EAOzdvA81Mvv8SZ3V56AMjb/uOixGHcpP/opTG1/HyfVzUHbsZ5hrSiFT+CIgrhfiesxGq0FvQh1/M+SayOv+vGtFRM4GhgQvnT/JwdFD6dy5c6itvXEeJAjyD0T3LsPQsscTKAiZhOOmBFhJQgufIiQaVuDczrexbftiFJYUNur3KeI7QTvqZQBA9c+vIWKIvYHE54efIQEoNxmhO99r/8DBXOjJF3LY0LfdpR+OJJsVNWdWw5C9AGTWQ+YbDv/2D0AV2cNrrv0X5dh7Jw2Ib4kwNZ/TM8/huYNMMsaYhwqI7Q6Z3AcFu+ehqjAL+Ts/wjOjp2DMRztRVamAX6AFO5JHYKjhS5Ru/BXRo1w3MeuNSL9rH6qS2oAI2GmMgQTCyx12QW46P25y0mRIMnHmDfnywB6c1lciwEeFRzp1d86twTxHWngkxia3w5Kjh/H5gd1IDByI8C6z8PNPiyCTyTBmzGgoFPVP0eQ+Ac75cYgIe/bswdatWwEALVq0wJAhQ+Dj4+PyLKxh9905EfGr1+DV7WYU2fwx9dvjeHNAMTK7j4QufxvM1edQcWodQrpm4Ny6jSjfkYWgtPaoyf4NJckdUeEfCasV8KuRY2jHcJzZ8i4sNaVQaiMRnzkLcp/LP83JblwZGRnYcCwH5WoV1p8+iYltOsD3/D5vI8LO840M3aPjmuwzIwJUGJUejVHp0bAR4VhRFbaeLMfWE6XIOl2Jc3oj1uiNWJNTAQBoHeqLTnFadI7zQ4dIDXwUrvmuOVRiAaBE13jvvrmYGR2LE5Xl2H42D4MTWrm7nBtCmbEWJ6p0kAB0DuEGJeaZQpOGA5BQcmQxSnN+AogwKHkkvjl4EFBasCW+H0ac/hCVO1YCuA/rdp8AEIYohQGtMm665s+1WY2oKtwLXf5WGIoPAXR+WFNJBm14BwTEZsIvKv2GGh63wmSEzmyCTJIQr/Hu8xk/Pz8EBwejvLwceXl5SEpKcndJ9chlcrRvnQq0TkVJRSlyjm1GuOkAgpXVaIMs4GQWsrJjoQjrhnZJ6VDIL38rWdVzGswnt8O0dymsW9+Af6fu0GcdRajBiBKtCrkGHdJ8wrCD7NtiC6kYwekXD3dnrS1D9YnvYDXYh4f1Ce8KdfxwSHLvud45VFqMw2XFUMhkGJ3Ecycxz8J3oRhjzA38ojIQ2+2vkOQ+qC4+iOrD/8Y9GX7OXkp7Wg1BrVKD8j+WurfQG4Bh5zqURaThuNEf+UZfTI49gHh5LiApoE2a7JbhGdzl9zO5+D0vFxKAhzt1R7Cv2t0lsWs0PrkdOoZFwGi14v3dW6EJiYXkGwmd0RelVQr4BibU+6NU24dDsdls2LBhg7MxqWPHjhg+fDg3Jt3Ahg0dgk9GRiJYVoMqUmHWumosXr4GEe0mAgBKj65AUE/708tl23fDYqiGZDyGP1LsDxPoylT4S89I5G9/H+bqc1BqwhDfYzYUvoFuy8Suj0ajQZ/WyfA1W1Brs2JzwWnne9llJagw1kKjUKJjWOOGu7taMklCSpQ/pvRsgY/u7ITfnuiDj+7MwK1poWgVaj8POV5aix/2luK5FacwacERvPDzKSzaV4KTZbXNNjxefqkeJWYlAMJNbaKa5TNuFI7GwkOlxdAZjVdYWgxZZcUAgNb+gQjyuXFuejN2tUKThiGsrf07vPToclQcXY4JKfaeGqpwG/6Ivg3mszvsDwjp7PNptrLmIqjb1Q13R2SDoeQIzu75EsfXPImzWf+G4dwBgGzwDUxERIfb0Xrw24jr/sj5BxpvrP3KMX9SjFoLH7nczdU0vxYt7MO73QjD3jUkLCgU3buOQnzmkzgTMBYnTfbvq5Y++YjXLUXe9rexfcdPKKkoveTPS5IEvwnvQBaeBFtlASLangRACNp3BIB9mENzaRlyzg93l67Ig8y3/kMkppI90B/8F6yGfEhyNTStb7ePSOJFjUlEhEU59qEv+8cnIpR7JzEPwz2UGGPMTbTh7RGX+Rjyt3+AmrKjGBpowmpLGow1MkCtwt6WA5FxYKu7y3S7Ct1J1Cg6Yac+DD2CTmNUxEEAgCZxDBR+1z8shKc4ravElwf2AAAmpLRvthuNzDVkkoSHMrrh+Y2/oqBKj88PZKFDXBwOHz6MM2fOICEh4aKfMZlMWL16tfNCtFevXkhLS/OaIR+8WVpGBr4OD8NfFmzHGWsQ3jygxenS45jYPhk1ZUfhk1wFACjbnoXyXVkwxfggNzoVABBosKGtcRlMVWeh8A1GXI/ZzgZG5rk6deqE2KUncDxQgZ+PHsGA+JaQJAnbC+1P4naJuvbh7q6Wr1KOHq1C0FJrxr0AyqstyMqvQla+AbvzqlBWbcGuvCrsyqsCUIRgtQKd47ToFOuHTnFahGiappfwhiP2IXWiVBaEB3r3jZUIjRYtA4NwsrICO4vyMbCF2L2UiAi7Su0NStw7iXmD0KRhkCQZig//gNKjy9E9yYbvrTJAacO6lmPQ/cgiVB3PxUm5/fs8lXLhE3bxud+lGHX50OVvhS5/Gyy1Fc7XlepQBMT1gH9sd6j8opsjVpPKPT9/UoLWu3ukOsTFxWHv3r04c+YMiOiGP39XyBVIbdMFaNMFhSVFOHViM6KtBxGkqEEQtsOWswO7zC2gjuyONq06QC77s1FQ8vWD/92fofKfI4DSvQhLb4WQIyeAnunINehxen8uCiT7sb5/6z8fDiVrLapzf4K5bB8AQO6XAG2riZCpvO8hqkOlxThSVmLvndSaeycxz3ND9FCqqqrCY489hpiYGPj6+iIjIwPffvtto3723LlzmDp1KsLCwqDRaNCzZ0/8+uuvzVwxY4w1DU1IEuJ7Pg65Ugtj5Sm83HELakrth+Y/2owHrGfwS1o/FPz0i5srdY/ipaugi4rEwZoghKqq8GDCNgCAKqo3fMIy3FucC1Wbzfhg91aYbFakhUfyhJ1eIlDli0c6Z0ImSdhccAaF/vYbqJd6clGv12PJkiU4c+YMFAoFhg0bhvT09Bv+YpT9KTo2Dt8+djPSVUUgSPjv2Sh8mRUASDKYzCeh7RiC2sJz2PvIdGzuMAGQJNRWyfBM650w6fMgVwUgvsds+GjC3B2FNQGNRoMBCa0gsxHO1tYgp7wUNiLsOGtvUOoeFeu22oI1CgxMDsLj/WPx9Z0p+OTW1ri/RyS6xvtBJZdQXmPBr0cr8c76fNz5dQ4e/OEYPttaiF1nqlBrsV3z5+7OrwYAdAwXYxjb7lH2p763nV/nIiuoMaCwthpySUJ6MB/jmHcIaT0U4e1uBQBUHPsZtwSbAQBShBzZLW/C6r+9hxrJB3LY0Kd9w8d8S20Fyo6vRu6GV5G74RWUHf8FltoKyJQaBLbog/ieT6LlwNcR1maMRzQmAXDOn5To5fMnOcTExEAul8NgMKC8vNzd5VyVqLBIZHYfh8iuTyFXOxKnTVGQSYTWPqcQU/49cre+g+27VqJC/+f8z4qodvAb/xYAICDyJGJL7POEntFXYs3G3SBICJEMSO42AABgqcqD/uBH5xuTJPjGDIRf2+le2ZhERFh01DF3EvdOYp7phuihNH78eOzYsQNvvvkmUlJSsHDhQkyePBk2mw133HHHZX/OaDRi0KBBqKiowPvvv4+IiAj861//wrBhw7B27Vr069fPhSlYcygrqoKh8vLDQGgDVQiJ9M7xdosLdNCXV1/2ff9gDcJjvHeoL5HWvW9gAuJ7PYkzW+cCxhJMUe3AUnMGatQB+KHTTKh9K7Dj+58RWeMDuVqDxKhgDOyW4u6ym82WA2eQV6ID1RpR8cdOlKb0xmm9DLdG78eR4kD4B4YiI26ou8tsFjm5RThXpq/3GhFhTcUpnK2tQoDCBw9ldIPMSxsRRNrvHdqEhOH2Nh2x8Mh+LC88gziLEkV5lVjyyxaofO1DkxgM1cg7mQ1fmQkajQbDhw9HRIR39VA7mV+GUt3lv/NCAzRoGRviwoqah1qjxb8fn4Q5H32DlRUxWFqWDD+UI963AsYxN+FERCWU/ibsbzkIEoDUmjMotuiRGKZFfOYs+Pi5b/Ls5iLifu+g1YbB72QudMG++GzTZnTUhqHcWAsFJOQfLoCstBYZ7Vu6tUZJkpAQ4ouEEF+MSwuDyWrD4aIa7M6rQlZeFY6V1CK3zIjcMiMW7yuFUi6hQ5QGnWLt8y+1CvW97HfW/kOFOFuoAwAQgJMnauADGfwCFVi9LgfRUQFIbe+dQ9/l5BZBXmYBABwsOYc1O49ALfvz0jwixB8pid63vztceMzPMpYBAKJlahw8Wug1x/xL4Ws8sY75Ia2HApKE4kPfI63yV6yyDACUSixJno7A42fhZ9YjUKrFOkMKNv20rd51ns1SC31hFnR5W1FdcgT2IyUASQ6/iFQExPWANiLVOcfmja7ufm8lwplq+zWPrrAaO8/lefV+DwB5J89BZvRDRVkZfl+1wzkEnkNoZCASk2PcVF3jqJQ+yGifCSATeUV5yD+5GfE4glBlFUJtm2E5tBU7rS0REJOJpBZtoOoyCeaT27D1wBkUpCVCVmuGxVeJ/clp8KvRIwplWLRNg5a+W5Ck2AKQDZJPILStJkLh37gee57gwmv8PKMe2WUlkAOIrlEhJ7fIa7/zRTvmi0Si5hoEu5F+/vln3HLLLc5GJIehQ4fi4MGDOH36NOSXGU/1o48+wsMPP4zNmzejZ8+eAACLxYL09HT4+flh27Ztja5Dp9MhMDAQlZWVCAjw3hO461VUVOSyzyorqsKrdy2BxWS97DIKHznmfD3OJQegyEjXHeCLC3R4dNg8mBvIrvSR4/1V97vsgoPXffMzGc5hw/IP8bkqFZLy8h1IyWzDjNh0lzUquXLdbzlwBt/pT14x/yT/lujZsfmHu3Plfp+TW4SX92xoOLuF8HJ6H5edcIq83wOuW/9ZB0/g7yd2Q5JdvqGQzDb0OleLeyaPhb9/8z/J6cp1fzK/DB+cPnDF/X5mi44uu9HginX/8Vff4atjGiR2qrli9ntDozGoz7VP1n01eL93zX6/59BJvJ2z84rr/qmUri5rVLqWdV9Za8GefAOy8qqwO8+AYoO53vuBvnLn0HidYv0Q7me/8bn/UCE+fvgXyGyXvxS1ySQ8+K+bXdao5Kp136jve7MNL2f09crve1GP+QBf44l8zC87sQa7t/yMz/17Q5I3fL53b1Aw2gWeQlVhFshqcr6nDm5tH9IuuivkPtrrron3e9dd5+UeLcDTY7+C7fKbPmRy4K2ld7ukUakp132NqRaHs3fAR7cbcT4lf36GKRB6bQaqLLH40Vx8xXU/3bAZrRJbQp04GjJF884TfMNd47vwO5+P+d7ZcNdUrqZtxO1D3i1ZsgR+fn6YOHFivdenTZuGgoKCBhuFlixZgjZt2jgbkwBAoVDgrrvuwvbt25Gfz8MHeDJDpbHBAw8AWEzWBlu7PZW+vLrBCw0AMJusDT7d5slEXfc+2gjkqQY0eLIBAJJShtxCz+om31h5JbpG5c8r0bmoItc5V6a/cnaFdFEPJm8h6n4PAGdLKhpsTALs231C67YuaUxytVJddaP2+4Z6MHmiB++ehEERtY3KfqrCM54+vloi7/cFxeWNWvcFxTf2932grwL9WgfisX6xWHBHMj6blIS/9IpCZgt/qJUyVNZasf54Jeb+XoApC3Nw/3fH8Mnms9h6pLTBxiQA9uEACwX9vlfKvPb7XtRjPsDXeCIf80NaDUGhomuDjUmAfdsvO74W+vxtIKsJSk0EQlNGo+WA19Gi99MISujXJI1Jribyfg8ApUWVDTYmAYDNal/O06h9fNE5tQ869n4U5dHTkGNpB6NNjkifSiSZf4f17JrGnetSZ2haT2r2xiRXE/k7X+RjvgjcPuTdgQMH0K5dOygU9UtJS0tzvt+rV6/L/myfPn0uet3xswcPHkRsrPvGH2euYaq1wFhjvvKC16m22nTlhZqIqbZxeUy1ZpfV5Yr/YwdTraXRy3nbujdbfYBG3DcsLz2Hw/v2Nn9BAHQVFS75HADQV5QDjXg4xWSxoLKqptnr8VEbmv0zHAy1jdvOzCYL7/cuqstV/89XurnkYLLaUK5zzTbpiv3LocbYuPVZYzS7rC5X7fsxUZE4gXNXXM7K+72w+31tjQm6Ctdsj1W669+/gmTAoBYaDGqhgdUWhpziGuwtrMa+szU4UVqLvJJq5JVUw6esGnGN+H0Ws9UL133jtntDrUnoY77F6H3rnq/xxD3mA0CNIhFA4RWXM9jUkML7wj+mC1QB8ZAkCQYrYGji4wHv9y68v2Ns3LZfbah1yXd+U3zfX0p4QDTCO4xDda0Bh05kwb9mH6RGzq1YKUVCZ6htlrouJPI1/o14zGeeye1D3qWkpKBVq1ZYtWpVvdfPnj2LmJgYvPHGG3j22Wcv+bM+Pj6YPn06Pvnkk3qvb9myBb169bpoGL26jEYjjMY/W0F1Oh3i4+NRUlLCQ9414Ny5K9/0aCpnckrxjwdXuuzzGLsRSLEqKGc3/1BuzHOZ3z0DyueneLwJ7/fsSni/9z6N3e953XsfPuY3Dm/7zNvwvn9lvN97H97uG0fkbf/xj4cjPiXUJZ/lbfMQNzWdToewsLBGDXnn9h5KgH2y12t573p+9m9/+xteeeWVi15fvXo1NBpNg5/JXKM03zVPJzDGGGOMMcYYY4wxxhhznUOHD6NI5+vuMhiA6urGDzvq9gal0NBQlJaWXvR6WVkZACAk5PIT8l3Pzz777LOYPXu289+OHkpDhw7lHkoNcGkPpYBSrETuFZeb+d5QxCY1/8SN4eHhzf4ZDqeOnMMr9/zvisu9tOA2JLR1TQt7cXGxSz4HAPKPleGDx1ZfcTlvXPcLvl+OLY1YruuZGtw6uHez1wMAJRUXH2eby+rN+7CvdeAVl+t0ugajBzV//rDwsGb/DIcdB0/hP5XZV1zu/leHIrNjYvMXBLH3e8B1+/7arfvxgyXvisuNl8diQGYHF1QElBSXXHmhJrLvWCGWUMEVlxsnxSAtKcoFFblu3//vopXYGi2/4nL9JsdgysSRLqiI9/sbbb+/7fFMDOrZ0QUVAcXnXLfuf9+ci1/e2nHF5YY/3Q39+7R2QUWuW/fbDuTii4rDV1xuSmAbdOuQ4IKKbsxj/u1P9EBGcrQLKnLduudrPHGP+UDjr/N6nLXizgnDm70e3u9dt+73bsvB+zN/ueJyj8wdgrQeyc1ejyu/75et+QO7G7Gcq67vAbGv8W/EY377du24h9INQqdr/Nylbm9QSk1NxTfffAOLxVJvHqX9+/cDADp2vPwFVGpqqnO5uhrzsyqVCiqV6qLXlUollErvnPi4KVw419WN8FlqrQpa/+ZvzfYPdF3PNY1f4/Jo/HxdVld1reueGFBrL943L7ec1637yHgAVz7ZDoxtjfikls1fEACfItfl10ZWAqi44nK+IdEIdMF6CQn2b/bPcNBqfIFGzMPqq/bh/d4F+z3gun1f5asEqq68nFrt47Jt0mxy3aTIao0P0IihzNUaH5fs94Dr9n1tI4/56sh43u9F3e+1KgS5aHs0unC/V/k27npL6av0unXvq/ZpzKkOtBpfoY/5KrX3rXu+xhP3mA80/jpPGxnvkn2f93vXrXu1pnHbvtZf7ZLvfJd+34fH4Ea6vgfEvsa/EY/5CoXCZfea+X5/w67m/0fWjHU0yrhx41BVVYVFixbVe33BggWIiYlBZmZmgz975MgRbNu2zfmaxWLB119/jczMTMTExDRb3Ywx1jwaHubz6pdjjDF24+JjPmOMMSYG/s5njDHmHdzeoDR8+HAMGTIEDz74ID777DP89ttvuP/++7Fq1Sq8/fbbkMvtw4Dce++9UCgUOHXqlPNnp0+fjg4dOmDixIlYuHAh1q5di0mTJiE7OxtvvfWWuyKxJqINVEHh0/AwMAofObSBjWv19iT+wRoor5Bd6SOHf7B3zvcl8rpPiAwAmW0NLkNmGxIivXNozrhQv0bljwv1c1FFrhMR4t+o7BEhrnuiypVE3u9jwoMbte5jwoNdVJFrhQZoGpU/NMD7vvNEP+bzfi/ufh8dFQCbrOGbpjaZhOgo79v2Rf++F/mYz9d44h7zAbG/80Xe7wEgNDIQsiuMcCyT25fzNiJf3wNif+eLfsz3dhIRkbuLqKqqwvPPP4/vvvsOZWVlaNu2LZ599lncfvvtzmWmTp2KBQsW4OTJk0hMTHS+XlRUhKeeegrLly9HdXU1MjIy8Nprr2Hw4MFXVYNOp0NgYCAqKyt5DqUGFBUVufTzyoqqYKg0XvZ9baAKIZGu+eKJjIx0yec4FBfooC+/fFdk/2ANwmNct63yunedXzfvwpmiEtiMtSjZ+BtgswAyJcL69IdM5Yv4yHAM6tXZZfW4et1v3ZeNsyXlgNEI3Y7N5/MrENCtF6BSITosGD3S2rikFlev+5zcIpwr01/2/YgQf6Qkuq4mkfd7wLXrf8+hkygoLr/s+zHhwcho75phLgHXr/uT+WUo1V3+Oy80QIOWsa6ZTwFw7boX/ZjP+724+/3+Q4U4W3j5sdqjowKQ2t4186YBrl33on/fi3zM52s8cY/5wI31nc/7vWvXfe7RApQWXX7ss9DIQCQmu2aUJZGv7wGxr/H5mO/ade9prqZt5IZoULoRcINS47j64HMjEf3Aw+vePQqWrcKBF/6Gjq8/h5hRN7ulBneu+9Jf1uP02x8i4elHEDK0n8s/n/d7cfd7QOz1z+vePete9GP+jYD3e3HxuhcXr3txiXydx+ue93t3cPf1PcDrXmQir/vGuJq2EdfMesUYY+yaxIwehpjRw9xdhtuE3twfoTf3d3cZjDHmEqIf8xljjDFR8Hc+ExFf3zPmHdw+hxJjjDHGGGOMMcYYY4wxxhi7sXGDEmOMMcYYY4wxxhhjjDHGGGsQNygxxhhjjDHGGGOMMcYYY4yxBnGDEmOMMcYYY4wxxhhjjDHGGGsQNygxxhhjjDHGGGOMMcYYY4yxBincXcCNgogAADqdzs2V3Nj0er27S3AbtVrt7hLcite9uHjdi0vkdQ+Ivf553fO6FxWve3HxuhcXr3tx8boXF697cfG6F5fI674xHG0ijjaShnCD0nmOnSo+Pt7NlTDGGGOMMcYYY4wxxhhjjLmOXq9HYGBgg8tI1JhmJwHYbDYUFBTA398fkiS5uxx2CTqdDvHx8Thz5gwCAgLcXY5LiZwdEDu/yNkBsfOLnB0QO7/I2QGx84ucHRA7v8jZAbHzi5wdEDu/yNkBzi9yfpGzA2LnFzk7IHZ+kbN7CiKCXq9HTEwMZLKGZ0niHkrnyWQyxMXFubsM1ggBAQHCHnxEzg6InV/k7IDY+UXODoidX+TsgNj5Rc4OiJ1f5OyA2PlFzg6InV/k7ADnFzm/yNkBsfOLnB0QO7/I2T3BlXomOTTc3MQYY4wxxhhjjDHGGGOMMcaExw1KjDHGGGOMMcYYY4wxxhhjrEHcoMQ8hkqlwksvvQSVSuXuUlxO5OyA2PlFzg6InV/k7IDY+UXODoidX+TsgNj5Rc4OiJ1f5OyA2PlFzg5wfpHzi5wdEDu/yNkBsfOLnN0bSURE7i6CMcYYY4wxxhhjjDHGGGOM3bi4hxJjjDHGGGOMMcYYY4wxxhhrEDcoMcYYY4wxxhhjjDHGGGOMsQZxgxJjjDHGGGOMMcYYY4wxxhhrEDcoMcYYY4wxxhhjjDHGGGOMsQZxgxJjjDHGGGMeiIjcXQJjLif6di96ftGJuP5NJhPy8vLcXYZbibjemR2veyaCqqoq/PLLL+4ug10FblBizMPxCYaY6q53EbcBi8Xi7hLcxmw2o7Ky0t1luI3BYMALL7wAnU7n7lLcTsR932azARAz+6VIkgSA/z9E5tgnRGI2mwGIl91oNKKmpgaSJAm3zxsMBqxZswYmk8ndpbhFdXU13nvvPZw4ccJ53BdFVVUV+vTpg++++w6AeN93jrwGg8HNlbifaMd8Bz7XY96+7dtsNgwdOhSffvopAN7WPYXC3QUw1hQsFgsUCrE2Z5vNBqvVijNnzqBVq1buLsflzGYzioqKcOLECXTp0gW+vr6Qy+XuLsslDAYD/v73v2PEiBHo3r2788aCKBeYOp0OY8eOxTPPPIOhQ4e6uxyXqqqqwh133IH27dvjr3/9K+Li4txdkkvp9Xr07t0bBw8exKhRo5CZmSnMtm80GrF3716cOHEC7dq1Q0pKCtRqtTD5rVYrjEYjSkpK0KJFC2dmm80GmUys56Oqq6vxySef4NixY1Cr1RgzZgwyMzOhUqncXVqzMxqNOHPmDJKSktxdilvU1tZi3bp12L9/P9RqNYYOHYq2bdsKsR9UV1fjs88+w969e6HT6fD444+jZ8+eQmQH7Od+8fHxaN++PX755RdotVphjv86nQ6tWrXC+PHj0bdvX3eX43J6vR79+/eHSqUCEeGxxx4TYr0D9nXfs2dPHD58GMXFxZg0aZJQ574GgwEvv/wydu/ejdOnT+P555/HlClThDjm1dTU4KeffsKJEycQGhqKPn36oG3btu4uy2X4XI/P9UQ615PJZAgJCUFeXh6MRqMQ27k3EOsOPPMqer0eL774It577z0oFAqhGpWqqqrw1FNPYdeuXTh48CD69++PZ555Br179xbiAqOqqgrTp0/Hnj17cOzYMaSnp+PFF1/E+PHj3V1as7NYLLjpppuwd+9enDlzBr6+vkhLSxOmUUmn0yE1NRXJycno1KmTu8txKb1ej27duiE6OhpJSUmIiIhwd0kupdPp0KlTJ0iSBI1Ggy+++AKZmZlev80D9nU/btw4ZGdnIz8/H4GBgZg1axaeffZZKJVKd5fX7KqqqvCXv/wFe/fuRUFBATIzMzF58mSMGTMGAQEBXn2BdSG9Xo/MzEz4+PjAZDKhtrYWc+fOxdSpU/GXv/wF3bt3d3eJzaaqqgpDhw6FVqvFe++9hw4dOri7JJfS6/W45ZZbcPbsWRQUFMBkMkGr1WLt2rXo2rWru8trVnq9HgMGDIDJZILFYkFJSQkWL16M3bt3IyMjw93lucSGDRtQUVGBLVu2YPjw4Vi1ahU0Go3Xn/s5vvszMjIwZ84c4W4yVVdX46abbkJYWBjmzp2Ltm3b1lvf3rz+dTod0tPTkZKSguHDh+Pdd9/Fhg0bcMcdd8BqtXr9g4R6vR49evRAQEAAQkNDER4ejunTpyMkJASjR492d3nNSq/Xo2/fvigtLUVpaSlqamqgUqnw4osvYtKkSV7f0MDnenyuJ9K5nuN7LDk5GXv37oXZbBbuu95jEWMeyGAwULdu3UiSJBoxYoTzdbPZ7MaqXEOv11OHDh3opptuolmzZtHTTz9N0dHR1LlzZzp37py7y2t2Op2O2rRpQwMGDKB//OMf9J///Ifatm1LvXr1cndpLnPzzTdTixYtSJIkuvXWW2nfvn3O92w2mxsra16VlZWUmJhIAwcOpLy8PHeX41Imk4lGjx5NAwcOpNzcXLJYLJdczmq1urgy13Cs+8GDB9P+/ftp6NChFBwcTDt27HB3ac2uqqqKOnbsSMOGDaPvv/+e9u3bR/369aMWLVqQTqdzd3nNTq/XU0pKCmVmZtKsWbNo9uzZFBcXRzKZjCZPnkzFxcVE5L3bfl1ms5nGjx9P/fr1o6NHjxIR0alTp+jtt98mSZKod+/etGbNGjdX2Txqa2vplltuIUmSSKPR0K233koHDhxwd1kuYzAYKCMjg4YPH04bNmwgo9FICxcupPj4eOrTp49XHwuqqqooLS2NBg8eTLt27SKr1UobNmygmJgY+tvf/uZczpvPf4iINm3aRBqNhp599lkKCwuj/v37k8FgICLvPf7pdDpq3bo1DRkyhE6fPn3R+47rPm/NT0T0/vvvU+fOnengwYPOnIWFhVRUVHTJ/xNvUVlZSS1btqQBAwZQWVkZ6XQ6atWqFQ0aNMjdpbmE0WikYcOG0ZAhQ+jo0aNks9koLy+POnfuTHfeeae7y2tWtbW11KtXLxoyZAht3ryZTCYTffvtt9S3b1+Sy+U0bdo02rlzp7vLbDZ8rsfneqKd6znuayxfvpwkSaJNmza5uSLWWNygxDyO2Wymhx9+mOLi4mjMmDEkk8loyJAh9d73VrW1tTRixAgaMmQIHT9+3Pn6ihUrSKFQ0DvvvOPG6ppfdXU19evXjwYOHEgnTpxwvj5//nwKCwujqqoqN1bX/BxfttOnT6dHHnmEvvrqK5IkiSZOnOj1jUpVVVWUnJxMI0aMoLNnzzr38/z8fDp27BitW7eODAbDZRtaPN3JkyepXbt2tHDhQucNha1bt9J7771Hs2fPpvnz53vtjfXKykpKSkqigQMHUn5+PhER/fjjjyRJEr333ntE5J3bPJE91/PPP0/du3ennJwc5/a9efNm0mq1zv8PB29b9zabjWbOnEndu3d3XlQTER0+fJjS09NJkiQaPHiw82EKb8t/oZKSEmrXrl2973pH5u+//57kcjn17t2btmzZ4q4Sm4XNZqN33nmHWrZsSf/85z/p2WefJUmSaMKECULcaLBYLDR79mzq168fHT58uN52/uijj1JoaCgVFBS4scLmYzKZ6LbbbqMBAwZQdna2M7vRaKQuXbrQ/Pnz6fjx43Ty5En3FuoCJpOJevbsSd9++y29++675OvrS/379/faG0w1NTUUFxdHrVq1qnf837p1K7366qt066230owZM+iPP/5wY5XN75577qGbb77Z+e9ly5ZR586dKSgoiHx9fWnq1Kn0+++/u7HCpqfX6ykyMpIGDRrkPLaZTCb6y1/+QpIk0XfffefmCptfVlYWtWzZ8qKsY8aMoZdeeol27NhBO3fu9MqHSbdv307x8fG0YsWKetd1q1atooSEBPLx8aEZM2bUuxfiTfhcj8/1RDjXq6mpoT179pDVanVexx8/fpwkSaLvv/+eiLz3+t6biDE+CPMqO3fuxNq1azFkyBB89NFHePnll7F27VrnXCqO4e+80Q8//IAzZ87gySefRMuWLZ2vZ2RkIDY2Fjk5OW6srvnNmzcPJpMJc+bMQcuWLZ2T9SkUCqSkpOD999/H/fffj/nz56OystLN1TY9x9AON998M3777TdMmDABc+fOxQ8//IDXXnsNR44cAQDs27fPnWU2OZvNhgceeADHjh1D3759ERUVBYVCgZ9++gnjxo1Dly5dMGjQIPTu3Rv//ve/UV1d7e6Sm9zRo0dx8uRJ9OvXDzKZDP/73/8waNAgzJ07F5988glmzJiBPn36IC8vDzKZzGsm7jSbzWjTpg0iIyPx9ddfIyYmBgDQr18/9OrVC++++y7OnDnjtcO9SJKErKwsBAcHIzk52XkM0Ov1iIuLw9tvv40RI0Zg1qxZKCws9Kp1D9jz79+/Hy1btnQOb2K1WtG2bVu8+eab0Gq1WL9+PR577DHo9XqvH/ZOr9ejtLQUVqsVgH3/cGS+9dZb8b///Q+bN2/Ghx9+CJ1O5zUT2kqShIMHD8Jms+G+++7DG2+8geeeew6LFy/GSy+9hIMHDzqX9ZbMdRUUFGDv3r3IyMhAUlISZDIZzGYzAPv5QHV1NU6cOOHmKpuHTqeDRqPBsGHDnNkBoLS0FEVFRXjvvfeQmpqK9PR0zJkzB0VFRW6uuHlVV1fj2LFjeOSRR/D6669jx44dGDt2LCwWi/P811vodDpotVro9Xps3LgRAPDTTz9h6NCh+Pzzz5GVlYWFCxeif//+ztze9P3nYDAYnEP/LFu2DOPGjUO3bt3w5JNP4tFHH8WCBQvwxBNPYMuWLW6utOl8+eWX6N+/P/7zn/8gOjoaAKBUKvHII49Aq9Vi3bp1bq6w+eXk5CA3N7fe8NYVFRXYu3cvvvrqKwwaNAg9evTA9OnTsXnzZjdW2vSOHz+OvLw8ZGRkQC6Xw2QyAbB/33Xv3h1msxlffPEFli1bBsD7vvf5XI/P9bz9XM9kMqFLly7o1q0bMjIyMGzYMLz88stYs2YNwsLCkJWVhaqqqouu773xO97jubM1i7FrcfjwYfrrX/9KJSUlRERUXFxMc+bMIUmS6vVUMplM7iqx2cyfP5/S09NJr9cTUf1W+yFDhtDgwYOJiLy2l8aWLVvo5ZdfptraWudrVVVV1L59e4qNjaVu3bpRq1atSKVS0QMPPOC1PZZ+//13CgsLo7NnzxKRfTgMSZLo9ttvp969e1O7du2orKzMq57qWLNmDWVmZlJERAR99913tG7dOlIoFHTHHXfQu+++S//+97+pXbt25O/vT1988YXX9VQ4dOgQqVQq+v777yknJ4ciIiLo9ddfdz6d9+abb1JERASlp6dTWVmZm6ttWt9///1FPXGIiF5//XWSJIn+85//EJH3HfdsNhtVVlZSZmYmpaamUnZ2NhERlZaWUseOHally5Y0efJkGjx4MPn7+1N6erqzl5o3sFqtpNPpqGPHjjRp0iQiqr+Ot23bRlFRUdSpUyfy8/OjVatWEZH3P83Wo0cP6tatm/PfFoulXub33nuPJEly/n94k4qKinr/fvHFFy/79Ko3bQclJSX06quvUlFRERHVz7Zp0yaSyWS0fv16d5XX7HJzc6mmpsb5b6PRSK1bt6aOHTvSvHnz6IcffqAHH3yQJEmijz/+2I2VNh/HOn/ppZfotttuIyKioqIiev/990mj0VBERAQFBwfT5s2bver8Jy8vj7p06UJxcXE0c+ZM0mq1NGfOHDpy5AgREa1fv57Gjh1LSqXSa4fAmjFjBsXExFBeXh7deeed9PDDD1N1dbXz/R9++IEkSaJHH32UiLzj2FdeXn7RNZzjKfZp06aRQqHwut4ZFzp16hRFRkbS8OHDacWKFbR69Wpq164ddenShVauXEmHDh2iefPmkSRJNGXKFLLZbF6x7omI9u7dSz4+PvTiiy86X3MM7zlz5kx66KGH6O6776bQ0FCv7Z3K53p/4nM97zvXs1gstHDhQlqwYAHdcccd1LNnTwoPDydfX1+SJImio6OpX79+9MILL9CCBQvo6NGjVFlZ6e6y2SVwgxLzKI4Dq+OmkqNhoays7JKNShaLhcrKyuqdeHuy2tpaZ9f2C2+ejho1ytmg5GA2m72mYc2x7h0XylarlSwWC8XGxlKXLl0oKyvLOQzauHHjyN/fnw4dOuS2epuT0Wikjh070tKlS53/Lx999BFJkkRarZb++c9/urnCpuXIuGHDBuratStpNBoKDAyk1157rd5JZ2VlJSUnJ1PPnj296oYKkf2GWlxcHN166600d+5cyszMpPz8fOc2bzQa6dVXXyWFQuGVFxcOdS+Y9Xo9tWnThnr37u3mqpqHI+eCBQtIkiRq06YNjRkzhuLi4qhz5871hgByNK55081UR/7nn3+eJEmiZcuW1Xv/119/pW7dutHhw4cpLi6O7r77bneU6TKOY9q3335LSqWSHn74Yed7jvMBq9VKRUVFlJqaSvfccw+ZzWavuNiue77j+O53cNxoGD9+PB0+fJiI7ENmLF++3KuGAnM0qFz43bZz505SKpW0du1a52t6vZ6ysrJcWZ5LOLK/++67Fw39XFpaSn379qWuXbtSdXW1V2z3l7JgwQKKiIhwPlRXXV1NGRkZJJfLL/vAmadzzBsjSRK9+OKL9a7prFYrrVmzhhQKBT3zzDNelduRZfv27aTRaOiWW26hzMxM+uKLL5zvO67xHnzwQYqMjPS6h8ku5fvvvydJkui5554jIu97mMjBYDDQv/71L4qPjyc/Pz/KyMig1q1bU05OTr3lnnjiCZLL5XTq1Ck3Vdr0CgsLqV+/fhQdHU1z5851vn7kyBHy8/OjTz75hH799VcKCAjwmiEvHfutY58W6VzPUbvRaHS+JtK5Xt3zOsf3m7ef611qez1x4gTt3buX0tLSSJIkZ6OxJEmkVCopOjqaunbtSm+99ZYbKmaX491jgzCvYrFYnN0eS0pKAAAqlQpEhODgYDz22GN48cUXsXbtWtx8883O5Z544gm89dZbHt011jGEn0qlcv7dMfSRo+un1Wqt1y20qqoKr7/+Ov7zn/94dHag/ro/d+4cAEAmk0Eul+Ppp5/GokWLnN3iAeDtt99GVVVVva7Rnqru8I2O7JIkwcfHB+vWrXP+v2zYsAEajQbV1dX4448/vGbYu7rrvnfv3vj000/RoUMHjBkzBvfeey8CAwMBALW1tQgICMCMGTOwbds27N271yu2e4eEhAS88MILWLRoET744AMolUrExMRAoVDAaDTCx8cHjz/+OKxWK7Kzs91YddO53Lbv2B5UKhVGjhyJzZs34/vvv3dLjc2l7vF8ypQpWLlyJfr27YtevXrBz88Pr776KpKSkpzDYTz++ONQKBQoKChwZ9lNpm7+/v37Y/Dgwbjtttvw4YcfYvXq1Vi+fDkmTJiAtLQ0tG3bFpMmTUJhYaFzaAhv4tgPHMOd9OvXD7fddhv+85//YM6cOQDs5wOOIVEiIiIQGBiIyspKKBQKjx4O8sLzHeDP737Htv/qq6/i+eefx5IlS/DCCy/gl19+waxZs3D33XfDaDS6pe6mUvcY6OvrCwAXDeuoUqmgVCqd54I6nQ4PPvggBg4c6NFD/15q6GpH9ttvvx0//PADWrVq5XwvJCQESqUS/v7+UKvVHr3dA5fODwDp6enw9fVFbW0tAGDatGk4ffo0ZsyYgdzcXPTt2xfV1dUenf/C7LGxsVi2bBnuu+8+9O3bF2q1GoD9e0Imk2Hw4MHw9/eHwWDw6NwOjvyOLElJSbjrrruwdu1abN++HcXFxSAiSJIEpVIJwH6MDA4ORlBQkEf/HzRmyPpbb70Vw4cPx8KFC6HT6ep9P3i6uvk1Gg0eeOABHDlyBMeOHcOwYcPQrl07JCUlXTTsU2xsLEJCQlxdbpOqmz0yMhLvvvsuQkJC8NRTT6FTp04YPnw40tPTMWrUKDzwwAMYOHAgTCYTcnNz3Vd0E7DZbDCbzTh58iQAOPfpvn37ev253oXZfXx8nO+JcK7nyF93G3Z8v3nzuV7deztnz551vp6YmIi0tDSMHTsWMTExmDt3Lvbt24djx47h008/xR133IGAgAAMGzbMXaWzS3FrcxZj12DKlCn03HPP1ev26GjlLi0tpZdeeokkSaL+/fvThAkTSKlUes1EfpfK7jB27Fjq168fEdm7Ct93330kSZJX9dJpKH9dX331FUVHR3tldsdwZi+88IJzGKjx48dTaGgorVu3jj788EOSJImmTZtWb2hAT3fXXXfRs88+S0T2oa7q9sKp+5TLU089RQkJCV417Ntdd91Fzz33HB0/fty5X0uSRD/++KNzGbPZXG8SW2/S0H5/5MgRUqvVNH36dDdU1vwc271jGz969Cj5+/vThg0bnMuYzWbasmULxcfH0zfffOOuUpvFtGnT6IUXXqClS5fSuHHjSJIkUigUpFQq6YEHHnAe4yZNmkR9+/b16Cc069LpdM7hi4jI2RPRITs7m4YNG0YBAQE0c+bMeu8VFxfTwIED6cknn6w30a2nuFL2ui58elWpVJK/vz8FBQV57PBXV5OfiGjXrl2kVCrpl19+IbPZTPfddx8FBATQjh07mrnSpne12R1sNhsdOXKEevXqRc8//7xHbvdEjctvMpmodevWNG/ePLrzzjspJCSENmzYQOXl5fTGG29QbGws5ebmurDqptGY7HWHQKs7WsEff/xBcXFx9OWXXzZ7nc3lSvlzcnJo+PDhJEkSde3albZt2+Z8r6SkhMaOHUvjx4+nmpoaj9v2r2a/v3BUhldffdXj8l6oofx1s02aNIn69+9f771z587RuHHjaMKECc4h4TzJ5bI7ch86dIheeeUVGjBgAI0YMYL+/ve/O5ddvnw5xcXF0f79+11ac1PS6/X04IMPUvfu3Umr1dItt9xCGzdudJ7bePO53uWyXyqHN57rXU1+Iu8613O43PX9p59+SpIkXfJcpm4vNnZj4AYldsOr+yXy1ltvUUxMDG3atOmiodwcy5WXl9Ojjz5KkiRRcHCwx3YFJWp8diJ7o0Lv3r1Jr9fTjBkzSKvV0u7du11ZbpNrbP66X75FRUV0++23U79+/ai0tNRltTa1K2X/+uuvKSYmhgYPHkzBwcG0evVq5wX2vHnz6ODBg26pu6lcKv+GDRsuOpGou1xeXh4NHTqUxo8f7xz2xRNdKvsff/xBZrOZ9u/f72xU6tKlCy1dupSI7Bcd999/PyUkJNCZM2fcVXqTaOx+79jeH3jgAVIqlbRp0yaX1tkcLpV948aNzuz79+8ntVpNf//7352NKYWFhfTAAw9Qu3btvG7dR0VF0bZt28hsNpPZbKbffvuNfvnlF/r999+dyx07doz69OlDc+bMcUfJTc5gMFC3bt1IkiQaMWKE8/ULb7QcOXKEJk+eTAqFgvr06UMLFiygBQsW0D333EPBwcHOOUY8yZWyN6SsrIxatGhBISEhHvsQ0bXk37FjB0mSRIsWLaLHH3+cNBqNR577XW32usPBFBcX0/Tp0yk+Pr7eMHiepDH5rVYrmc1mGjp0KEmSRC1atKDVq1c7j5uVlZUeed57teu+7uvFxcU0depUSkpKotOnTzd7rc2hscf8nJwcmjx5Mvn4+FBycjLNmTOH3nnnHRo3bhwFBgZ65HHvWo/5er2eOnbsSN26dfO4G+l1XU3+f/zjH6RQKOipp56i2tpa2rBhA82YMYNCQ0OdQ4B5ksZu90T2Y1/dBuWSkhK6++67qXv37h47d6her6cOHTrQTTfdRLNmzaKnn36aoqOjqXPnzs45dIi881yvoeyOqR0ut197w7neteT3hnO9xl7fb9++nXx9fem7775z/pzj/8OTj/feihuUmMfYuHEjzZ49m95///0GDyb5+fk0cuRICgwM9JoeKg1ld1xU33LLLdS/f3+aOXMmqdVqj/uSaUhD+ev+++TJkzR16lQKDAz06CeW6rpc9qysLEpMTKTk5GT65ZdfvPYLtjHbPpF98tqpU6dScHCwV+73dU/CcnNznXPGSZJEMTEx1KpVK4qMjPToBvQLNfaYv2LFCpIkiV544QWvGUu/oez33HMPqVQquvfee+mhhx6ikSNHUnh4OO3du9dN1Ta9y237F8rNzaV7772XoqKiLppXwBOZzWZ6+OGHKS4ujsaMGUMymazevJAX3mgpKCigzz//nFJTUykwMJBiYmKoR48etGfPHrfUfz0am/1Sjh07RuPHjye1Wu2x3/3Xmn/fvn0UFBRELVu2JLVaTbt27XJVyU3metb9jz/+SHfccQcFBQV57Pff1eZft24ddejQgX7++WfneZCnngNez7pfuXIlTZ48mQIDAz3ymEd09cf8/Px8+uKLL6hLly4UEBBAcXFxNGDAANq3b59b6r8e17ruHecEc+bMIa1W67wB62muNv/x48dpxIgRznP/2NhYSktL88hzv+vZ75ctW0bTp0+noKAgj8xOZJ8Te8SIERfNBbhixQpSKBT0zjvvENGf17nedK7X2OyX4g3netea3xvO9RyudH1fUlJC4eHhzpFp2I2NG5SYR3jiiSfI39+foqKinMM5XeomU2VlJU2bNo0kSfLIL9lLaWz2SZMmOXtlefKXzIUam/+FF16ggQMHUkJCglev+7on2d999x1t3bq10cPCeJrGrvvXXnuN+vfvTzExMR57Q+lCl8p+4RM869evp3feeYf+8pe/0AcffOCxT2ZfSmPXvcM999zjNQ2JV8p+5MgR54MDycnJdMcdd3jk06mX09h1v3jxYpo0aRJFR0d7zX6/ZcsWatOmDU2bNo3y8/Pp1VdfJUmSGrzB6HgtKyuLTp486bHDfV5N9gstXLiQ4uPjPXo7uNb8+/btI0mSyN/f32NvsFxr9q+++ooGDx5MPXr08NjsRFeX33GDsaKiwivO/a513X/22Wd00003UVpamkc2pjhc6zHfYrHQgQMHKC8vjyoqKlxed1O4nmM+EdGBAwc8cnhHh2tZ96dPn6bFixfTSy+9REuWLKG8vDy31H69rnXd63Q6mj17NnXq1Mmjj/lff/01paam0urVq+vt1/n5+ZSQkED3338/EV38oIA3nOs1NvuleMO53rXm94ZzPaIrX+NZrVaqqamhnj170ujRo91VJrsK3KDEPMLatWspMTGRJEmip59+2vl63R4KRPYTjU8++cRju8BeSmOzv/LKK+Tn5+fxQ51dqDH5DQYDffDBB3Tvvfd6xVPqDpfLfqkhD71RY9Z9SUkJ/d///R+NGzfOI7v9X87lsntLD5wraexxz8Gb9onGrvuTJ09SdXU1VVdXu7rEZtXYdb9nzx569dVX6ejRo64usdkcPnyY/vrXv1JJSQkR2YdzcvRGrHujxZu2d4frzX727FmX1NlcrjV/YWEhPfzww5Sdne3SepvStWY/c+YMrVu3jgoLC11ab1Pj/f7qs584cYK+++47j72h7sDr/uqzX+480NNcbX5P7YV4Kdez3et0Oo/tleYwf/58Sk9Pdw7PXnfdDhkyhAYPHkxE9c/7vWX9X0v2ujz9XO9a83vDuR5R46/xJk6cSCkpKWQ0Gr1m2/dW3KDEbjh1Dyh1/75161ZKSEig4OBg+vzzzy+5DJFn33C9nuyFhYUef1F9veu+pqam+YtsJleb3ZO380u5nnVfW1tbb2xtT3O9272nEzn/9ez33nCCfb3r3hue0HdwrE/HOnbMkVVWVnbJGy0Wi4XKysq8okFR5OxE157fMQm7J09SfL3ZPZ3I2/61ZC8tLXWe73n6dyCvezGzE4mdX+TsDrW1tc5GsQuv50eNGuVsVHAwm81e06gscnaia8vv2Ec87f/hWq7xHMeHTz/91OsekvdW3KDEbih1bw6Vl5df9ATKpk2bKCEhgVq3bk1ffPGF83VvuMF4rdm9pWFB5Pwib/dEvO4deN2LlV/k7EScv666/xd1HwxxXFjVvdEydOhQ53LTp0+nl156yaNvrIqcnej68s+ZM6feBbin4XUvbv7rze7J2z0Rr3sH0bITiZ1f5OxE9fMXFBTUe89xbuuYX8dBr9fTyy+/TP/+9789Or/I2YmuL/9nn31GNpvNo/4PrvUaz5seFBQFNygxt3PcFK57kHz00UcpNTWVWrRoQY888gjpdDrn+xs2bKCEhARq1aoVLViwwC01NxWRsxOJnV/k7ERi5xc5O5HY+UXOTsT5r2TKlCn03HPPUWVlpfM1x/9FaWkpvfTSSyRJEvXv358mTJhASqXSa4b4FTk7kdj5Rc5OJHZ+kbMTiZ1f5OxEYucXOTvRpfM7jB07lvr160dE9rny7rvvPpIkyWvmiRU5O5F35+drPDFxgxJzq5qaGurRowf9+9//dr42depUSkhIoJkzZ9Ldd99NarWabr75Zjp69Gi9A1BSUhIFBQXRwoUL3VX+dRE5O5HY+UXOTiR2fpGzE4mdX+TsRJz/Uur2snzrrbcoJiaGNm3adNGwFo7lysvL6dFHHyVJkig4ONijJyYWOTuR2PlFzk4kdn6RsxOJnV/k7ERi5xc5O1Hj8xMRjR8/nnr37k16vZ5mzJhBWq2Wdu/e7cpym5TI2YnEyc/XeOLiBiXmVtnZ2dS9e3fy9/enr7/+mo4ePUoDBw6kNWvWEJG92+PSpUspLCyMBg0aVO8A9Ouvv1JaWprHTsgtcnYisfOLnJ1I7PwiZycSO7/I2Yk4f0M2btxIs2fPpvfff7/BIS3y8/Np5MiRFBgY6DFPLF6JyNmJxM4vcnYisfOLnJ1I7PwiZycSO7/I2Ykazu8Y9uyWW26h/v3708yZM0mtVntMg8KViJydyPvz8zWeuLhBibndnj17aMSIEeTn50fPPPMMjRgxgioqKpzvWywWWr58eb0DkOPA6+kTNIqcnUjs/CJnJxI7v8jZicTOL3J2Is5/KU888QT5+/tTVFQUrVixgoguPT9cZWUlTZs2jSRJoj179ri6zGYhcnYisfOLnJ1I7PwiZycSO7/I2YnEzi9ydqLG5580aZKzZ9auXbtcXWazEDk7kTj5+RpPTNygxNymblfPAwcO0PDhw0mSJEpLS6s3USORveV+xYoVFB0dTV26dKHjx48TETX4dMuNTOTsRGLnFzk7kdj5Rc5OJHZ+kbMTcf6GrF27lhITE0mSJHr66aedrzsushx0Oh198sknXjWPgMjZicTOL3J2IrHzi5ydSOz8ImcnEju/yNmJGp//lVdeIT8/Pzp48KCrS2w2Imcn8v78fI0nNm5QYm5Rt1V+/fr1ZDAYaP/+/TRx4kSSy+X06aefXrI76JIlS6h169Z06tQpV5fcZETOTiR2fpGzE4mdX+TsRGLnFzk7Eeevq+7FY92/b926lRISEig4OJg+//zzSy5DdOknGj2FyNmJxM4vcnYisfOLnJ1I7PwiZycSO7/I2YmuL39hYeFFN+E9icjZicTLz9d4jBuUmMvVPfDce++91KpVK3rhhRfIarXSrl27aMSIEaRWq+m///3vJQ9AVVVVri65yYicnUjs/CJnJxI7v8jZicTOL3J2Is5fl9lsdv69vLyczp07V+/9TZs2UUJCArVu3Zq++OIL5+sX3mjxRCJnJxI7v8jZicTOL3J2IrHzi5ydSOz8Imcnuvb8nt6IRiR2diLx8vM1HiPiBiXmRrfddhslJCTQTz/9RCdOnHC+npWVRcOHD7/sAcgbiJydSOz8ImcnEju/yNmJxM4vcnYicfM7Lrbq5nr00UcpNTWVWrRoQY888gjpdDrn+xs2bKCEhARq1aoVLViwwC01NxWRsxOJnV/k7ERi5xc5O5HY+UXOTiR2fpGzE4mdX+TsRJyfSNxrPGbHDUrMLb744guKi4uj1atXOw8udVu5HQeggIAAmj9/vrvKbBYiZycSO7/I2YnEzi9ydiKx84ucnUjc/DU1NdSjRw/697//7Xxt6tSplJCQQDNnzqS7776b1Go13XzzzXT06NF6F5tJSUkUFBRECxcudFf510Xk7ERi5xc5O5HY+UXOTiR2fpGzE4mdX+TsRGLnFzk7EecnEvcaj/2JG5SYWzz22GPUtm1bKi8vr/d63a6iBw8epB49elB0dDRVVla6uMLmI3J2IrHzi5ydSOz8ImcnEju/yNmJxM2fnZ1N3bt3J39/f/r666/p6NGjNHDgQFqzZg0R2fMvXbqUwsLCaNCgQfUuNn/99VdKS0ujo0ePujPCNRM5O5HY+UXOTiR2fpGzE4mdX+TsRGLnFzk7kdj5Rc5OxPmJxL3GY3/iBiXmFqNGjaL27ds7/33huLn//e9/Sa/XU3Z2ttdN1iZydiKx84ucnUjs/CJnJxI7v8jZicTOv2fPHhoxYgT5+fnRM888QyNGjKCKigrn+xaLhZYvX17vYtPx/1NdXe2uspuEyNmJxM4vcnYisfOLnJ1I7PwiZycSO7/I2YnEzi9ydiLOL/LoUbCkAAAPdklEQVQ1HrPjBiXmFs8++ywplUpatGjRRe+dOHGCunXrRvPmzXNDZc1P5OxEYucXOTuR2PlFzk4kdn6RsxOJmd9kMjn/fuDAARo+fDhJkkRpaWlUWFhYb1mr1UorVqyg6Oho6tKlCx0/fpyI6o/H7klEzk4kdn6RsxOJnV/k7ERi5xc5O5HY+UXOTiR2fpGzE3F+BxGv8Vh93KDE3OLw4cPk7+9PmZmZtHLlSufreXl5NGPGDGrdunW9Sd28icjZicTOL3J2IrHzi5ydSOz8ImcnEi9/3bHD169fTwaDgfbv308TJ04kuVxOn3766UUXkVarlZYsWUKtW7f26Cf4RM5OJHZ+kbMTiZ1f5OxEYucXOTuR2PlFzk4kdn6RsxNx/rpEu8ZjF+MGJeY2K1euJI1GQ2FhYTRp0iSaPn063XTTTRQSEkJ79uxxd3nNSuTsRGLnFzk7kdj5Rc5OJHZ+kbMTiZO/7kXmvffeS61ataIXXniBrFYr7dq1i0aMGEFqtZr++9//XvJis6qqytUlNxmRsxOJnV/k7ERi5xc5O5HY+UXOTiR2fpGzE4mdX+TsRJz/UkS5xmOXxg1KzK2ysrJozJgxlJycTKmpqTRt2jQ6fPiwu8tyCZGzE4mdX+TsRGLnFzk7kdj5Rc5OJFb+2267jRISEuinn36q92ReVlYWDR8+/LIXm95A5OxEYucXOTuR2PlFzk4kdn6RsxOJnV/k7ERi5xc5OxHnv5BI13isPm5QYm5nMpmopqaGTCZTvVZ/EYicnUjs/CJnJxI7v8jZicTOL3J2IjHyf/HFFxQXF0erV692XkjWzeq42AwICKD58+e7q8xmIXJ2IrHzi5ydSOz8ImcnEju/yNmJxM4vcnYisfOLnJ2I81+OCNd47GLcoMQYY4wxxlgTeOyxx6ht27ZUXl5e73Wz2ez8+8GDB6lHjx4UHR1NlZWVLq6w+YicnUjs/CJnJxI7v8jZicTOL3J2IrHzi5ydSOz8Imcn4vyM1SUDY4wxxhhj7LodP34cMpkMQUFBAACbzQYAUCgUAICFCxeiRYsWWLBgAbZu3YqAgAB3ldrkRM4OiJ1f5OyA2PlFzg6InV/k7IDY+UXODoidX+TsAOdnrC5uUGKMMcYYY6wJdOzYEUePHsXixYsBADLZn6faJ0+exHvvvYdvvvkGKSkpaNGihbvKbBYiZwfEzi9ydkDs/CJnB8TOL3J2QOz8ImcHxM4vcnaA8zNWFzcoMcYYY4wx1gSmTJkCX19fvP3221i1apXz9fz8fLzxxhsoKyvD4MGD3Vhh8xE5OyB2fpGzA2LnFzk7IHZ+kbMDYucXOTsgdn6RswOcn7G6JCIidxfBGGOMMcaYN1i1ahUmTJgAjUaDgQMHws/PDzk5OTh06BDWrVuH9PR0d5fYbETODoidX+TsgNj5Rc4OiJ1f5OyA2PlFzg6InV/k7ADnZ8yBeygxxhhjjDHWRIYNG4Y//vgDvXv3RlZWFnbs2IHk5GT88ccfXn+RKXJ2QOz8ImcHxM4vcnZA7PwiZwfEzi9ydkDs/CJnBzg/Yw7cQ4kxxhhjjLEmZjabYbVaIZfLIZPJIJfL3V2Sy4icHRA7v8jZAbHzi5wdEDu/yNkBsfOLnB0QO7/I2QHOzxg3KDHGGGOMMcYYY4wxxhhjjLEG8ZB3jDHGGGOMMcYYY4wxxhhjrEHcoMQYY4wxxhhjjDHGGGOMMcYaxA1KjDHGGGOMMcYYY4wxxhhjrEHcoMQYY4wxxhhjjDHGGGOMMcYaxA1KjDHGGGOMMcYYY4wxxhhjrEHcoMQYY4wxxhhjjDHGGGOMMcYaxA1KjDHGGGOMMcYYY4wxxhhjrEHcoMQYY4wxxhhjjDHGGGOMMcYaxA1KjDHGGGOMMY+0b98+TJs2DS1btoSvry/8/PzQuXNnvP322ygrKwMAJCYmYuTIkW6ulDHGGGOMMcY8n8LdBTDGGGOMMcbY1frss8/w0EMPoU2bNnjyySfRvn17mM1m7Ny5E5988gm2bNmCJUuWuLtMxhhjjDHGGPMa3KDEGGOMMcYY8yhbtmzBgw8+iCFDhmDp0qVQqVTO94YMGYLHH38cq1atuu7Pqampga+vLyRJuu7fxRhjjDHGGGOejoe8Y4wxxhhjjHmUN954A5IkYd68efUakxx8fHwwevToeq+tWrUKnTt3hlqtRtu2bfH555/Xe//LL7+EJElYvXo1pk+fjvDwcGg0GhiNRthsNrz99tto27YtVCoVIiIiMGXKFOTl5dX7Hf3790fHjh2xZcsW9OrVC2q1GomJifjiiy8AACtWrEDnzp2h0WiQmpp6yUavTZs2YdCgQfD394dGo0GvXr2wYsWKestUV1fjiSeecA71FxISgq5du+Kbb76pt9yyZcvQs2dPaDQa+Pv7Y8iQIdiyZUu9ZV5++WVIkoR9+/Zh4sSJCAwMREhICGbPng2LxYLs7GwMGzYM/v7+SExMxNtvv31RzTqdzlmPj48PYmNj8dhjj8FgMFy0LGOMMcYYY8xzcYMSY4wxxhhjzGNYrVasW7cOXbp0QXx8fKN+Zu/evXj88ccxa9Ys/Pjjj0hLS8O9996LDRs2XLTs9OnToVQq8dVXX+GHH36AUqnEgw8+iKeffhpDhgzBsmXL8Nprr2HVqlXo1asXSkpK6v18YWEhpk2bhhkzZuDHH39Eamoqpk+fjldffRXPPvssnnrqKSxatAh+fn4YO3YsCgoKnD/7+++/Y+DAgaisrMT8+fPxzTffwN/fH6NGjcL//vc/53KzZ8/Gxx9/jJkzZ2LVqlX46quvMHHiRJSWljqXWbhwIcaMGYOAgAB88803mD9/PsrLy9G/f39s2rTpotyTJk1Ceno6Fi1ahPvuuw9z587FrFmzMHbsWNxyyy1YsmQJBg4ciKeffhqLFy92/lx1dTX69euHBQsWYObMmVi5ciWefvppfPnllxg9ejSIqFHriDHGGGOMMXbjk4jP8BljjDHGGGMeoqioCFFRUbj99tsv6pFzKYmJiSgqKkJ2djZatGgBAKitrUVsbCwmTpyITz75BIC9h9K0adMwZcoULFiwwPnzR44cQbt27fDQQw/hX//6l/P17du3IzMzE8899xxef/11APYeSr///jt27tyJLl26AADKysoQEREBHx8fHDt2DDExMQDsjVwZGRn44IMP8MgjjwAAevbsiRMnTuD48ePw8/MDYG9Ay8jIQEVFBU6fPg1JkpCamoqkpKTLzhFls9kQHx+P0NBQ7NmzBzKZ/TnCqqoqtG7dGklJSfjjjz8A2HsovfLKK/jHP/6B2bNnO39Hp06dsGfPHixevBjjxo0DAFgsFsTExKBPnz5YtGgRAODNN9/E888/j23btqFr167On1+0aBFuvfVW/Pzzzxg+fPgV1xNjjDHGGGPsxsc9lBhjjDHGGGNeLSMjw9mYBAC+vr5ISUnBqVOnLlp2woQJ9f7922+/AQCmTp1a7/Xu3bujXbt2+PXXX+u9Hh0d7WxMAoCQkBBEREQgIyPD2ZgEAO3atQMAZw0GgwHbtm3Drbfe6mxMAgC5XI67774beXl5yM7Odn72ypUr8cwzz2D9+vWoqampV0N2djYKCgpw9913OxuTAMDPzw8TJkzA1q1bUV1dXe9nRo4cWe/f7dq1gyRJ9RqDFAoFkpKS6v2/LV++HB07dkRGRgYsFovzz8033wxJkrB+/XowxhhjjDHGvAM3KDHGGGOMMcY8RlhYGDQaDU6ePNnonwkNDb3oNZVKdVFDDGBvEKrLMYzcha8DQExMTL1h5gB7A9KFfHx8Lnrdx8cHgL23FACUl5eDiC77OXVr+eCDD/D0009j6dKlGDBgAEJCQjB27FgcPXq0UTXbbDaUl5c3WLePjw80Gg18fX0vet1RM2DvMbZv3z4olcp6f/z9/UFEFw0JyBhjjDHGGPNcCncXwBhjjDHGGGONJZfLMWjQIKxcuRJ5eXmIi4tr0t8vSVK9fzsao86ePXvRZxUUFCAsLKxJPjc4OBgymQxnz5696D3HPEuOz9JqtXjllVfwyiuvoKioyNlbadSoUThy5Ei9mi/1u2QyGYKDg5uk7rCwMKjVanz++eeXfZ8xxhhjjDHmHbiHEmOMMcYYY8yjPPvssyAi3HfffTCZTBe9bzab8dNPPzXJZw0cOBAA8PXXX9d7fceOHTh8+DAGDRrUJJ+j1WqRmZmJxYsX1+s5ZbPZ8PXXXyMuLg4pKSkX/VxkZCSmTp2KyZMnIzs7G9XV1WjTpg1iY2OxcOFC1J0y12AwYNGiRejZsyc0Gk2T1D1y5EgcP34coaGh6Nq160V/EhMTm+RzGGOMMcYYY+7HPZQYY4wxxhhjHqVnz574+OOP8dBDD6FLly548MEH0aFDB5jNZmRlZWHevHno2LEjRo0add2f1aZNG9x///345z//CZlMhuHDhyM3Nxcvvvgi4uPjMWvWrCZIZPe3v/0NQ4YMwYABA/DEE0/Ax8cHH330EQ4cOIBvvvnG2XsqMzMTI0eORFpaGoKDg3H48GF89dVX9RqK3n77bdx5550YOXIkHnjgARiNRvz9739HRUUF3nzzzSar+bHHHsOiRYvQt29fzJo1C2lpabDZbDh9+jRWr16Nxx9/HJmZmU32eYwxxhhjjDH34QYlxhhjjDHGmMe577770L17d8ydOxdvvfUWCgsLoVQqkZKSgjvuuAN//etfm+yzPv74Y7Ru3Rrz58/Hv/71LwQGBmLYsGH429/+dsn5ma5Vv379sG7dOrz00kuYOnUqbDYb0tPTsWzZMowcOdK53MCBA7Fs2TLMnTsX1dXViI2NxZQpU/D88887l7njjjug1Wrxt7/9Dbfddhvkcjl69OiB3377Db169WqymrVaLTZu3Ig333wT8+bNw8mTJ6FWq9GiRQsMHjyYeygxxhhjjDHmRSSqOwYCY4wxxhhjjDHGGGOMMcYYYxfgOZQYY4wxxhhjjDHGGGOMMcZYg7hBiTHGGGOMMcYYY4wxxhhjjDWIG5QYY4wxxhhjjDHGGGOMMcZYg7hBiTHGGGOMMcYYY4wxxhhjjDWIG5QYY4wxxhhjjDHGGGOMMcZYg7hBiTHGGGOMMcYYY4wxxhhjjDWIG5QYY4wxxhhjjDHGGGOMMcZYg7hBiTHGGGOMMcYYY4wxxhhjjDWIG5QYY4wxxhhjjDHGGGOMMcZYg7hBiTHGGGOMMcYYY4wxxhhjjDWIG5QYY4wxxhhjjDHGGGOMMcZYg7hBiTHGGGOMMcYYY4wxxhhjjDXo/wFJoCAUMd7pawAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f1plot('intergenic')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f1plot('missense')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "f1plot('splice site')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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mavedb.scoreDITTO
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mavedb.scoreDITTO
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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc_data.plot(kind='scatter', x='DITTO', y='mavedb.score', color='g')\n" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/analysis/postprocess.job b/src/analysis/postprocess.job new file mode 100644 index 0000000..0fcec4c --- /dev/null +++ b/src/analysis/postprocess.job @@ -0,0 +1,35 @@ +#!/bin/bash +# +#SBATCH --job-name=post_process_gnomad_indel +#SBATCH --output=logs/post_process_gnomad_indel.out +# +# Number of tasks needed for this job. Generally, used with MPI jobs +#SBATCH --ntasks=1 +#SBATCH --partition=amd-hdr100 +# +# Number of CPUs allocated to each task. +#SBATCH --cpus-per-task=1 +#SBATCH --time=06-06:00:00 +# +# Mimimum memory required per allocated CPU in MegaBytes. +#SBATCH --mem=10G +# +# Send mail to the email address when the job fails +#SBATCH --mail-type=FAIL + +#Set your environment here +# module reset +# ml Anaconda3 +conda activate csvkit + +#Modify paths and run the pipeline here +set -euo pipefail + +# Merge all the files after DITTO pipeline +find /data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/processed/snvs -name "*.csv.gz" -print0 | xargs -0 zcat | csvcut -c chrom,pos,ref_base,alt_base,transcript,gene,consequence,DITTO | csvformat -T | awk -v OFS='\t' '{print > "/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/processed/all_snv/DITTO_"$1"_merged.tsv"}' + +echo "merging files successful!" + +sort -t$'\t' -k1,1 -k2,2n -T $USER_SCRATCH /data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/processed/all_snv/DITTO_chrY_merged.tsv >/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/processed/all_snv/DITTO_chrY_merged.tsv_sorted.tsv + +echo "sorting successful!" diff --git a/src/annotation_parsing/get_sample_info.py b/src/annotation_parsing/get_sample_info.py new file mode 100644 index 0000000..e32702f --- /dev/null +++ b/src/annotation_parsing/get_sample_info.py @@ -0,0 +1,36 @@ +# Script to extract sample information from the OpenCRAVAT annotated file + +import csv +import ctypes as ct + +# dealing with large fields in a CSV requires more memory allowed per field +# see https://stackoverflow.com/questions/15063936/csv-error-field-larger-than-field-limit-131072 for discussion +# and this solution +csv.field_size_limit(int(ct.c_ulong(-1).value // 2)) + +input_filename = "data/ciliopathies_exomes_2569.vcf.gz.variant.csv" +output_samples = "data/samples.csv" + +with open(input_filename, "r") as infile, open( + output_samples, "w", newline="" +) as outfile1: + reader = csv.reader(filter(lambda row: row[0] != "#", infile)) + writer1 = csv.writer(outfile1) + + for row in reader: + line = ( + row[1] + + "," + + row[2] + + "," + + row[3] + + "," + + row[4] + + "," + + row[15] + + "," + + row[16] + ) + outfile1.write(line + "\n") + +print("Columns replaced and output written to", output_samples) diff --git a/src/annotation_parsing/opencravat_clinvar_filtering_80-20-20.ipynb b/src/annotation_parsing/opencravat_clinvar_filtering_80-20-20.ipynb new file mode 100644 index 0000000..eb370fc --- /dev/null +++ b/src/annotation_parsing/opencravat_clinvar_filtering_80-20-20.ipynb @@ -0,0 +1,16232 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Filtering annotated variants to extract small variants for ML" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "pd.set_option('display.max_rows', None)\n", + "import numpy as np\n", + "from tqdm import tqdm \n", + "import yaml\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "import os\n", + "os.chdir( '/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Function to open and load config file for filtering columns and rows\n", + "def get_col_configs(config_f):\n", + " with open(config_f) as fh:\n", + " config_dict = yaml.safe_load(fh)\n", + "\n", + " # print(config_dict)\n", + " return config_dict\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the config file as dictionary\n", + "config_f = \"../configs/col_config.yaml\"\n", + "config_dict = get_col_configs(config_f)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['transcript',\n", + " 'gene',\n", + " 'consequence',\n", + " 'protein_hgvs',\n", + " 'cdna_hgvs',\n", + " 'chrom',\n", + " 'pos',\n", + " 'ref_base',\n", + " 'alt_base',\n", + " 'coding',\n", + " 'aloft.tolerant',\n", + " 'aloft.recessive',\n", + " 'aloft.dominant',\n", + " 'aloft.pred',\n", + " 'aloft.conf',\n", + " 'cadd.phred',\n", + " 'cgd.inheritance',\n", + " 'chasmplus.score',\n", + " 'chasmplus.pval',\n", + " 'civic.molecular_profile_score',\n", + " 'cosmic.variant_count',\n", + " 'cosmic_gene.occurrences',\n", + " 'cscape.score',\n", + " 'cgc.class',\n", + " 'cgc.inheritance',\n", + " 'cancer_genome_interpreter.resistant',\n", + " 'cancer_genome_interpreter.responsive',\n", + " 'cancer_genome_interpreter.other',\n", + " 'ccre_screen._group',\n", + " 'ccre_screen.bound',\n", + " 'clingen.disease',\n", + " 'clingen.classification',\n", + " 'clinpred.score',\n", + " 'clinvar.sig',\n", + " 'clinvar.id',\n", + " 'clinvar.rev_stat',\n", + " 'clinvar.sig_conf',\n", + " 'dann.score',\n", + " 'dann_coding.dann_coding_score',\n", + " 'dgi.interaction',\n", + " 'dgi.score',\n", + " 'ensembl_regulatory_build.region',\n", + " 'ess_gene.indispensability_score',\n", + " 'exac_gene.exac_pli',\n", + " 'exac_gene.exac_pnull',\n", + " 'exac_gene.exac_del_score',\n", + " 'exac_gene.exac_dup_score',\n", + " 'exac_gene.exac_cnv_score',\n", + " 'exac_gene.exac_cnv_flag',\n", + " 'fathmm.fathmm_score',\n", + " 'fathmm_xf_coding.fathmm_xf_coding_score',\n", + " 'funseq2.score',\n", + " 'gerp.gerp_rs',\n", + " 'ghis.ghis',\n", + " 'gtex.gtex_tissue',\n", + " 'gwas_catalog.pval',\n", + " 'genehancer.feature_name',\n", + " 'genehancer.score',\n", + " 'linsight.value',\n", + " 'lrt.lrt_score',\n", + " 'lrt.lrt_omega',\n", + " 'loftool.loftool_score',\n", + " 'mavedb.score',\n", + " 'metalr.score',\n", + " 'metasvm.score',\n", + " 'mutpred1.mutpred_general_score',\n", + " 'mutpred_indel.score',\n", + " 'mutation_assessor.score',\n", + " 'mutationtaster.score',\n", + " 'mutationtaster.prediction',\n", + " 'mutationtaster.model',\n", + " 'ncbigene.entrez',\n", + " 'ndex_chd.numhit',\n", + " 'ndex.numhit',\n", + " 'ndex_signor.numhit',\n", + " 'omim.omim_id',\n", + " 'prec.prec',\n", + " 'prec.stat',\n", + " 'provean.score',\n", + " 'pangalodb.sensitivity',\n", + " 'pangalodb.specificity',\n", + " 'phdsnpg.score',\n", + " 'phastcons.phastcons100_vert',\n", + " 'phastcons.phastcons30_mamm',\n", + " 'phastcons.phastcons17way_primate',\n", + " 'phylop.phylop100_vert',\n", + " 'phylop.phylop30_mamm',\n", + " 'phylop.phylop17_primate',\n", + " 'polyphen2.hdiv_rank',\n", + " 'polyphen2.hvar_rank',\n", + " 'revel.score',\n", + " 'rvis.rvis_evs',\n", + " 'repeat.repeatclass',\n", + " 'sift.score',\n", + " 'sift.med',\n", + " 'sift.confidence',\n", + " 'sift.seqs',\n", + " 'segway.mean_score',\n", + " 'siphy.logodds_rank',\n", + " 'spliceai.ds_ag',\n", + " 'spliceai.ds_al',\n", + " 'spliceai.ds_dg',\n", + " 'spliceai.ds_dl',\n", + " 'spliceai.dp_ag',\n", + " 'spliceai.dp_al',\n", + " 'spliceai.dp_dg',\n", + " 'spliceai.dp_dl',\n", + " 'uniprot.acc',\n", + " 'varity_r.varity_r_loo',\n", + " 'varity_r.varity_er_loo',\n", + " 'vest.score',\n", + " 'dbsnp.rsid',\n", + " 'dbscsnv.ada_score',\n", + " 'dbscsnv.rf_score',\n", + " 'gnomad.af',\n", + " 'gnomad_gene.oe_lof',\n", + " 'gnomad_gene.oe_mis',\n", + " 'gnomad_gene.oe_syn',\n", + " 'gnomad_gene.lof_z',\n", + " 'gnomad_gene.mis_z',\n", + " 'gnomad_gene.syn_z',\n", + " 'gnomad_gene.pLI',\n", + " 'gnomad_gene.pRec',\n", + " 'gnomad_gene.pNull',\n", + " 'gnomad3.af',\n", + " 'phi.phi']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config_dict[\"raw_cols\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#print('Loading data...')\n", + "#df = pd.read_csv(\"./interim/clinvar_parsed.csv.gz\", usecols=config_dict[\"display_cols\"], low_memory=False)\n", + "#print('Data Loaded !....')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data...\n", + "Data Loaded !....\n" + ] + } + ], + "source": [ + "print('Loading data...')\n", + "df = pd.read_csv(\"./interim/clinvar_6623_parsed.csv.gz\", low_memory=False) #, usecols=config_dict[\"raw_cols\"]\n", + "print('Data Loaded !....')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['transcript',\n", + " 'gene',\n", + " 'consequence',\n", + " 'protein_hgvs',\n", + " 'cdna_hgvs',\n", + " 'chrom',\n", + " 'pos',\n", + " 'ref_base',\n", + " 'alt_base',\n", + " 'coding',\n", + " 'extra_vcf_info.CLNDN',\n", + " 'extra_vcf_info.CLNREVSTAT',\n", + " 'extra_vcf_info.CLNSIG',\n", + " 'extra_vcf_info.CLNSIGCONF',\n", + " 'aloft.affect',\n", + " 'aloft.tolerant',\n", + " 'aloft.recessive',\n", + " 'aloft.dominant',\n", + " 'aloft.pred',\n", + " 'aloft.conf',\n", + " 'cadd.phred',\n", + " 'cgd.inheritance',\n", + " 'chasmplus.score',\n", + " 'chasmplus.pval',\n", + " 'civic.molecular_profile_score',\n", + " 'cosmic.variant_count',\n", + " 'cosmic_gene.occurrences',\n", + " 'cscape.score',\n", + " 'cgc.class',\n", + " 'cgc.inheritance',\n", + " 'cancer_genome_interpreter.resistant',\n", + " 'cancer_genome_interpreter.responsive',\n", + " 'cancer_genome_interpreter.other',\n", + " 'ccre_screen._group',\n", + " 'ccre_screen.bound',\n", + " 'clingen.disease',\n", + " 'clingen.classification',\n", + " 'clinpred.score',\n", + " 'clinvar.sig',\n", + " 'clinvar.id',\n", + " 'clinvar.rev_stat',\n", + " 'clinvar.sig_conf',\n", + " 'dann.score',\n", + " 'dann_coding.dann_coding_score',\n", + " 'dgi.interaction',\n", + " 'dgi.score',\n", + " 'ensembl_regulatory_build.region',\n", + " 'ess_gene.indispensability_score',\n", + " 'exac_gene.exac_pli',\n", + " 'exac_gene.exac_pnull',\n", + " 'exac_gene.exac_del_score',\n", + " 'exac_gene.exac_dup_score',\n", + " 'exac_gene.exac_cnv_score',\n", + " 'exac_gene.exac_cnv_flag',\n", + " 'fathmm.fathmm_score',\n", + " 'fathmm_xf_coding.fathmm_xf_coding_score',\n", + " 'funseq2.score',\n", + " 'gerp.gerp_rs',\n", + " 'ghis.ghis',\n", + " 'gtex.gtex_tissue',\n", + " 'gwas_catalog.pval',\n", + " 'genehancer.feature_name',\n", + " 'genehancer.score',\n", + " 'linsight.value',\n", + " 'lrt.lrt_score',\n", + " 'lrt.lrt_omega',\n", + " 'loftool.loftool_score',\n", + " 'mavedb.score',\n", + " 'metalr.score',\n", + " 'metasvm.score',\n", + " 'mutpred1.mutpred_general_score',\n", + " 'mutpred_indel.score',\n", + " 'mutation_assessor.score',\n", + " 'mutationtaster.score',\n", + " 'mutationtaster.prediction',\n", + " 'mutationtaster.model',\n", + " 'ncbigene.entrez',\n", + " 'ndex_chd.numhit',\n", + " 'ndex.numhit',\n", + " 'ndex_signor.numhit',\n", + " 'omim.omim_id',\n", + " 'prec.prec',\n", + " 'prec.stat',\n", + " 'provean.score',\n", + " 'pangalodb.sensitivity',\n", + " 'pangalodb.specificity',\n", + " 'phdsnpg.score',\n", + " 'phastcons.phastcons100_vert',\n", + " 'phastcons.phastcons30_mamm',\n", + " 'phastcons.phastcons17way_primate',\n", + " 'phylop.phylop100_vert',\n", + " 'phylop.phylop30_mamm',\n", + " 'phylop.phylop17_primate',\n", + " 'polyphen2.hdiv_rank',\n", + " 'polyphen2.hvar_rank',\n", + " 'revel.score',\n", + " 'rvis.rvis_evs',\n", + " 'repeat.repeatclass',\n", + " 'sift.score',\n", + " 'sift.med',\n", + " 'sift.confidence',\n", + " 'sift.seqs',\n", + " 'segway.mean_score',\n", + " 'siphy.logodds_rank',\n", + " 'spliceai.ds_ag',\n", + " 'spliceai.ds_al',\n", + " 'spliceai.ds_dg',\n", + " 'spliceai.ds_dl',\n", + " 'spliceai.dp_ag',\n", + " 'spliceai.dp_al',\n", + " 'spliceai.dp_dg',\n", + " 'spliceai.dp_dl',\n", + " 'uniprot.acc',\n", + " 'varity_r.varity_r_loo',\n", + " 'varity_r.varity_er_loo',\n", + " 'vest.score',\n", + " 'dbsnp.rsid',\n", + " 'dbscsnv.ada_score',\n", + " 'dbscsnv.rf_score',\n", + " 'gnomad.af',\n", + " 'gnomad_gene.oe_lof',\n", + " 'gnomad_gene.oe_mis',\n", + " 'gnomad_gene.oe_syn',\n", + " 'gnomad_gene.lof_z',\n", + " 'gnomad_gene.mis_z',\n", + " 'gnomad_gene.syn_z',\n", + " 'gnomad_gene.pLI',\n", + " 'gnomad_gene.pRec',\n", + " 'gnomad_gene.pNull',\n", + " 'gnomad3.af',\n", + " 'phi.phi']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns.to_list()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#df = df[config_dict[\"raw_cols\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(12973645, 131)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2177684, 4)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Number of variants that we started with\n", + "df[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "#df = df.replace(['.','-'], np.nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "df['so'] = df['consequence']" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "transcript object\n", + "gene object\n", + "consequence object\n", + "protein_hgvs object\n", + "cdna_hgvs object\n", + "chrom object\n", + "pos int64\n", + "ref_base object\n", + "alt_base object\n", + "coding object\n", + "extra_vcf_info.CLNDN object\n", + "extra_vcf_info.CLNREVSTAT object\n", + "extra_vcf_info.CLNSIG object\n", + "extra_vcf_info.CLNSIGCONF object\n", + "aloft.affect object\n", + "aloft.tolerant float64\n", + "aloft.recessive float64\n", + "aloft.dominant float64\n", + "aloft.pred object\n", + "aloft.conf object\n", + "cadd.phred float64\n", + "cgd.inheritance object\n", + "chasmplus.score float64\n", + "chasmplus.pval float64\n", + "civic.molecular_profile_score float64\n", + "cosmic.variant_count float64\n", + "cosmic_gene.occurrences float64\n", + "cscape.score float64\n", + "cgc.class object\n", + "cgc.inheritance object\n", + "cancer_genome_interpreter.resistant float64\n", + "cancer_genome_interpreter.responsive float64\n", + "cancer_genome_interpreter.other float64\n", + "ccre_screen._group object\n", + "ccre_screen.bound object\n", + "clingen.disease object\n", + "clingen.classification object\n", + "clinpred.score float64\n", + "clinvar.sig object\n", + "clinvar.id float64\n", + "clinvar.rev_stat object\n", + "clinvar.sig_conf object\n", + "dann.score float64\n", + "dann_coding.dann_coding_score float64\n", + "dgi.interaction object\n", + "dgi.score float64\n", + "ensembl_regulatory_build.region object\n", + "ess_gene.indispensability_score float64\n", + "exac_gene.exac_pli float64\n", + "exac_gene.exac_pnull float64\n", + "exac_gene.exac_del_score float64\n", + "exac_gene.exac_dup_score float64\n", + "exac_gene.exac_cnv_score float64\n", + "exac_gene.exac_cnv_flag object\n", + "fathmm.fathmm_score object\n", + "fathmm_xf_coding.fathmm_xf_coding_score float64\n", + "funseq2.score float64\n", + "gerp.gerp_rs float64\n", + "ghis.ghis float64\n", + "gtex.gtex_tissue object\n", + "gwas_catalog.pval float64\n", + "genehancer.feature_name object\n", + "genehancer.score float64\n", + "linsight.value float64\n", + "lrt.lrt_score float64\n", + "lrt.lrt_omega float64\n", + "loftool.loftool_score float64\n", + "mavedb.score float64\n", + "metalr.score float64\n", + "metasvm.score float64\n", + "mutpred1.mutpred_general_score float64\n", + "mutpred_indel.score float64\n", + "mutation_assessor.score float64\n", + "mutationtaster.score float64\n", + "mutationtaster.prediction object\n", + "mutationtaster.model object\n", + "ncbigene.entrez float64\n", + "ndex_chd.numhit float64\n", + "ndex.numhit float64\n", + "ndex_signor.numhit float64\n", + "omim.omim_id object\n", + "prec.prec float64\n", + "prec.stat object\n", + "provean.score float64\n", + "pangalodb.sensitivity float64\n", + "pangalodb.specificity float64\n", + "phdsnpg.score float64\n", + "phastcons.phastcons100_vert float64\n", + "phastcons.phastcons30_mamm float64\n", + "phastcons.phastcons17way_primate float64\n", + "phylop.phylop100_vert float64\n", + "phylop.phylop30_mamm float64\n", + "phylop.phylop17_primate float64\n", + "polyphen2.hdiv_rank float64\n", + "polyphen2.hvar_rank float64\n", + "revel.score float64\n", + "rvis.rvis_evs float64\n", + "repeat.repeatclass object\n", + "sift.score float64\n", + "sift.med float64\n", + "sift.confidence object\n", + "sift.seqs float64\n", + "segway.mean_score float64\n", + "siphy.logodds_rank float64\n", + "spliceai.ds_ag float64\n", + "spliceai.ds_al float64\n", + "spliceai.ds_dg float64\n", + "spliceai.ds_dl float64\n", + "spliceai.dp_ag float64\n", + "spliceai.dp_al float64\n", + "spliceai.dp_dg float64\n", + "spliceai.dp_dl float64\n", + "uniprot.acc object\n", + "varity_r.varity_r_loo float64\n", + "varity_r.varity_er_loo float64\n", + "vest.score float64\n", + "dbsnp.rsid object\n", + "dbscsnv.ada_score float64\n", + "dbscsnv.rf_score float64\n", + "gnomad.af float64\n", + "gnomad_gene.oe_lof float64\n", + "gnomad_gene.oe_mis float64\n", + "gnomad_gene.oe_syn float64\n", + "gnomad_gene.lof_z float64\n", + "gnomad_gene.mis_z float64\n", + "gnomad_gene.syn_z float64\n", + "gnomad_gene.pLI float64\n", + "gnomad_gene.pRec float64\n", + "gnomad_gene.pNull float64\n", + "gnomad3.af float64\n", + "phi.phi float64\n", + "so object\n", + "dtype: object" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "#df['fathmm.fathmm_score'].unique()\n", + "df[\"fathmm.fathmm_score\"] = df[\"fathmm.fathmm_score\"].replace(['.'], np.nan)\n", + "df[\"fathmm.fathmm_score\"] = pd.to_numeric(df[\"fathmm.fathmm_score\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "original = df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "#df = original.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dropping empty columns and rows along with duplicate rows...\n", + "\n", + "Variant-transcript pairs shape = (11153639, 132)\n", + "\n", + "Variants shape = (2137714, 4)\n", + "\n", + "clinvar_CLNSIG:\n", + " Uncertain_significance 5501617\n", + "Likely_benign 2728544\n", + "Benign 817285\n", + "Conflicting_interpretations_of_pathogenicity 670871\n", + "Pathogenic 642776\n", + "Likely_pathogenic 370968\n", + "Benign/Likely_benign 207316\n", + "Pathogenic/Likely_pathogenic 108214\n", + "not_provided 77513\n", + "drug_response 11509\n", + "other 5615\n", + "risk_factor 2433\n", + "association 1330\n", + "Uncertain_significance/Uncertain_risk_allele 1003\n", + "Affects 727\n", + "Likely_risk_allele 512\n", + "Pathogenic|other 348\n", + "Conflicting_interpretations_of_pathogenicity|other 270\n", + "Pathogenic|drug_response 248\n", + "Likely_pathogenic/Likely_risk_allele 199\n", + "Pathogenic/Likely_pathogenic|other 188\n", + "protective 163\n", + "Uncertain_risk_allele 136\n", + "Pathogenic|risk_factor 90\n", + "Benign|other 75\n", + "Conflicting_interpretations_of_pathogenicity|risk_factor 63\n", + "Likely_pathogenic|other 58\n", + "Uncertain_significance|drug_response 58\n", + "Likely_pathogenic|drug_response 58\n", + "Pathogenic/Likely_risk_allele 45\n", + "Pathogenic|association 40\n", + "Likely_benign|other 39\n", + "confers_sensitivity 36\n", + "Pathogenic/Likely_pathogenic|risk_factor 32\n", + "Conflicting_interpretations_of_pathogenicity|association 29\n", + "Benign/Likely_benign|other 28\n", + "Likely_pathogenic|risk_factor 28\n", + "Uncertain_significance|other 27\n", + "Uncertain_significance|risk_factor 27\n", + "Benign/Likely_benign|risk_factor 26\n", + "Uncertain_significance|association 25\n", + "Pathogenic|Affects 25\n", + "Likely_pathogenic,_low_penetrance 24\n", + "Benign|risk_factor 22\n", + "protective|risk_factor 21\n", + "Benign|association 21\n", + "Conflicting_interpretations_of_pathogenicity|other|risk_factor 20\n", + "Likely_benign|drug_response|other 17\n", + "Conflicting_interpretations_of_pathogenicity|drug_response 16\n", + "Pathogenic/Likely_pathogenic/Likely_risk_allele 14\n", + "Benign|drug_response 13\n", + "Likely_pathogenic|association 12\n", + "drug_response|risk_factor 12\n", + "Pathogenic/Likely_pathogenic/Pathogenic,_low_penetrance 9\n", + "Conflicting_interpretations_of_pathogenicity|Affects 9\n", + "Benign/Likely_benign|other|risk_factor 8\n", + "Pathogenic/Likely_pathogenic|drug_response 8\n", + "Pathogenic|protective 8\n", + "Pathogenic|drug_response|other 8\n", + "Pathogenic|confers_sensitivity 6\n", + "Benign|protective 6\n", + "Benign/Likely_benign|drug_response 5\n", + "Conflicting_interpretations_of_pathogenicity|association|risk_factor 5\n", + "Pathogenic|association|protective 5\n", + "Likely_benign|risk_factor 5\n", + "Affects|association 5\n", + "Benign|confers_sensitivity 5\n", + "association_not_found 4\n", + "association|drug_response|risk_factor 3\n", + "Benign/Likely_benign|drug_response|other 3\n", + "Uncertain_risk_allele|protective 3\n", + "Likely_benign|association 3\n", + "Conflicting_interpretations_of_pathogenicity|protective 2\n", + "drug_response|other 2\n", + "Likely_pathogenic/Pathogenic,_low_penetrance 2\n", + "Benign/Likely_benign|association 2\n", + "Likely_pathogenic|Affects 1\n", + "Uncertain_risk_allele|risk_factor 1\n", + "Conflicting_interpretations_of_pathogenicity|drug_response|other 1\n", + "association|drug_response 1\n", + "association|risk_factor 1\n", + "Pathogenic/Likely_pathogenic/Pathogenic,_low_penetrance|other 1\n", + "Affects|risk_factor 1\n", + "Name: extra_vcf_info.CLNSIG, dtype: int64\n", + "\n", + "clinvar_review:\n", + " criteria_provided,_single_submitter 8186660\n", + "criteria_provided,_multiple_submitters,_no_conflicts 1874052\n", + "criteria_provided,_conflicting_interpretations 670116\n", + "no_assertion_criteria_provided 246467\n", + "reviewed_by_expert_panel 95966\n", + "no_assertion_provided 77503\n", + "no_interpretation_for_the_single_variant 2730\n", + "practice_guideline 145\n", + "Name: extra_vcf_info.CLNREVSTAT, dtype: int64\n", + "\n", + "clinvar_confidence:\n", + " Uncertain_significance(1)|Likely_benign(1) 186087\n", + "Uncertain_significance(2)|Likely_benign(1) 63417\n", + "Uncertain_significance(1)|Likely_benign(2) 39661\n", + "Uncertain_significance(3)|Likely_benign(1) 30413\n", + "Likely_pathogenic(1)|Uncertain_significance(1) 28939\n", + "Uncertain_significance(1)|Benign(1) 26494\n", + "Uncertain_significance(4)|Likely_benign(1) 16048\n", + "Pathogenic(1)|Uncertain_significance(1) 14193\n", + "Uncertain_significance(2)|Likely_benign(2) 13719\n", + "Uncertain_significance(1)|Likely_benign(3) 12866\n", + "Uncertain_significance(1)|Benign(1)|Likely_benign(1) 12696\n", + "Uncertain_significance(2)|Benign(1) 9050\n", + "Likely_pathogenic(1)|Uncertain_significance(2) 8988\n", + "Uncertain_significance(5)|Likely_benign(1) 8006\n", + "Uncertain_significance(3)|Likely_benign(2) 7187\n", + "Uncertain_significance(1)|Benign(1)|Likely_benign(2) 7073\n", + "Uncertain_significance(2)|Likely_benign(3) 4860\n", + "Likely_pathogenic(2)|Uncertain_significance(1) 4638\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(1) 4559\n", + "Uncertain_significance(1)|Likely_benign(4) 4295\n", + "Uncertain_significance(2)|Benign(1)|Likely_benign(1) 4109\n", + "Uncertain_significance(6)|Likely_benign(1) 4038\n", + "Uncertain_significance(1)|Benign(1)|Likely_benign(3) 3736\n", + "Uncertain_significance(4)|Likely_benign(2) 3732\n", + "Uncertain_significance(3)|Benign(1) 3719\n", + "Uncertain_significance(1)|Benign(2)|Likely_benign(1) 3568\n", + "Pathogenic(1)|Uncertain_significance(2) 3287\n", + "Likely_pathogenic(1)|Uncertain_significance(3) 3033\n", + "Uncertain_significance(1)|Benign(2) 2845\n", + "Uncertain_significance(2)|Benign(1)|Likely_benign(2) 2659\n", + "Uncertain_significance(5)|Likely_benign(2) 2480\n", + "Uncertain_significance(1)|Benign(2)|Likely_benign(2) 2429\n", + "Uncertain_significance(3)|Likely_benign(3) 2322\n", + "Uncertain_significance(7)|Likely_benign(1) 2273\n", + "Uncertain_significance(3)|Benign(1)|Likely_benign(1) 1973\n", + "Pathogenic(2)|Uncertain_significance(1) 1954\n", + "Uncertain_significance(1)|Benign(1)|Likely_benign(4) 1953\n", + "Uncertain_significance(4)|Benign(1) 1856\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(1) 1850\n", + "Uncertain_significance(1)|Likely_benign(5) 1847\n", + "Likely_pathogenic(2)|Uncertain_significance(2) 1810\n", + "Uncertain_significance(2)|Likely_benign(4) 1726\n", + "Uncertain_significance(4)|Likely_benign(3) 1614\n", + "Uncertain_significance(1)|Benign(2)|Likely_benign(3) 1484\n", + "Likely_pathogenic(3)|Uncertain_significance(1) 1394\n", + "Uncertain_significance(1)|Benign(3)|Likely_benign(1) 1394\n", + "Uncertain_significance(6)|Likely_benign(2) 1392\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(2) 1316\n", + "Uncertain_significance(2)|Benign(1)|Likely_benign(3) 1277\n", + "Uncertain_significance(1)|Benign(3) 1203\n", + "Likely_pathogenic(1)|Uncertain_significance(4) 1197\n", + "Pathogenic(2)|Likely_pathogenic(1)|Uncertain_significance(1) 1178\n", + "Uncertain_significance(2)|Benign(2)|Likely_benign(1) 1048\n", + "Uncertain_significance(4)|Benign(1)|Likely_benign(1) 1036\n", + "Uncertain_significance(1)|Benign(3)|Likely_benign(2) 1006\n", + "Uncertain_significance(8)|Likely_benign(1) 1005\n", + "Uncertain_significance(5)|Likely_benign(3) 976\n", + "Uncertain_significance(3)|Likely_benign(4) 973\n", + "Uncertain_significance(3)|Benign(1)|Likely_benign(2) 956\n", + "Pathogenic(1)|Uncertain_significance(3) 926\n", + "Uncertain_significance(2)|Benign(2)|Likely_benign(2) 860\n", + "Uncertain_significance(1)|Benign(4)|Likely_benign(1) 848\n", + "Uncertain_significance(4)|Benign(2)|Likely_benign(1) 845\n", + "Uncertain_significance(5)|Benign(1) 835\n", + "Uncertain_significance(1)|Benign(2)|Likely_benign(4) 817\n", + "Uncertain_significance(3)|Benign(2) 810\n", + "Uncertain_significance(2)|Benign(1)|Likely_benign(4) 784\n", + "Uncertain_significance(1)|Benign(1)|Likely_benign(5) 762\n", + "Uncertain_significance(4)|Benign(1)|Likely_benign(2) 750\n", + "Pathogenic(1)|Likely_pathogenic(3)|Uncertain_significance(1) 721\n", + "Uncertain_significance(3)|Benign(2)|Likely_benign(1) 711\n", + "Uncertain_significance(2)|Benign(2) 699\n", + "Uncertain_significance(7)|Likely_benign(2) 691\n", + "Likely_pathogenic(1)|Uncertain_significance(5) 689\n", + "Uncertain_significance(2)|Likely_benign(5) 673\n", + "Uncertain_significance(4)|Benign(2) 671\n", + "Pathogenic(2)|Likely_pathogenic(2)|Uncertain_significance(1) 669\n", + "Uncertain_significance(1)|Benign(4) 661\n", + "Uncertain_significance(3)|Benign(2)|Likely_benign(2) 646\n", + "Uncertain_significance(6)|Likely_benign(3) 633\n", + "Uncertain_significance(1)|Likely_benign(6) 619\n", + "Uncertain_significance(3)|Likely_benign(5) 615\n", + "Uncertain_significance(5)|Benign(1)|Likely_benign(1) 606\n", + "Uncertain_significance(3)|Benign(1)|Likely_benign(3) 604\n", + "Pathogenic(3)|Uncertain_significance(1) 588\n", + "Likely_pathogenic(2)|Uncertain_significance(3) 568\n", + "Uncertain_significance(9)|Likely_benign(1) 563\n", + "Uncertain_significance(3)|Benign(2)|Likely_benign(3) 560\n", + "Uncertain_significance(4)|Likely_benign(4) 550\n", + "Uncertain_significance(1)|Benign(3)|Likely_benign(4) 530\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(2) 528\n", + "Uncertain_significance(6)|Benign(1)|Likely_benign(1) 521\n", + "Uncertain_significance(5)|Benign(1)|Likely_benign(2) 508\n", + "Uncertain_significance(1)|Benign(1)|Likely_benign(6) 505\n", + "Uncertain_significance(7)|Benign(2)|Likely_benign(1) 503\n", + "Uncertain_significance(1)|Benign(5)|Likely_benign(1) 502\n", + "Uncertain_significance(4)|Benign(2)|Likely_benign(2) 483\n", + "Pathogenic(3)|Likely_pathogenic(1)|Uncertain_significance(1) 476\n", + "Uncertain_significance(3)|Benign(1)|Likely_benign(4) 473\n", + "Uncertain_significance(1)|Benign(4)|Likely_benign(2) 465\n", + "Uncertain_significance(5)|Benign(2)|Likely_benign(2) 461\n", + "Uncertain_significance(2)|Benign(2)|Likely_benign(3) 455\n", + "Uncertain_significance(8)|Likely_benign(2) 453\n", + "Pathogenic(1)|Uncertain_significance(4) 431\n", + "Uncertain_significance(5)|Benign(2)|Likely_benign(1) 427\n", + "Uncertain_significance(4)|Benign(1)|Likely_benign(3) 426\n", + "Uncertain_significance(1)|Benign(5) 423\n", + "Uncertain_significance(5)|Likely_benign(4) 417\n", + "Pathogenic(2)|Likely_pathogenic(1)|Uncertain_significance(2) 413\n", + "Pathogenic(2)|Uncertain_significance(2) 402\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Likely_benign(1) 398\n", + "Uncertain_significance(1)|Benign(3)|Likely_benign(3) 397\n", + "Uncertain_significance(4)|Benign(2)|Likely_benign(3) 393\n", + "Uncertain_significance(2)|Benign(3)|Likely_benign(1) 379\n", + "Uncertain_significance(7)|Likely_benign(3) 379\n", + "Uncertain_significance(1)|Likely_benign(7) 375\n", + "Uncertain_significance(6)|Likely_benign(4) 372\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(3) 372\n", + "Uncertain_significance(6)|Benign(2) 365\n", + "Likely_pathogenic(3)|Uncertain_significance(2) 361\n", + "Likely_risk_allele(1)|Uncertain_significance(1) 356\n", + "Uncertain_significance(5)|Benign(2) 353\n", + "Uncertain_significance(1)|Benign(5)|Likely_benign(2) 343\n", + "Uncertain_significance(10)|Likely_benign(1) 340\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Likely_benign(1) 333\n", + "Pathogenic(1)|Likely_pathogenic(4)|Uncertain_significance(1) 331\n", + "Uncertain_significance(2)|Benign(1)|Likely_benign(5) 331\n", + "Uncertain_significance(3)|Benign(3)|Likely_benign(3) 330\n", + "Likely_pathogenic(4)|Uncertain_significance(1) 329\n", + "Uncertain_significance(2)|Benign(1)|Likely_benign(6) 326\n", + "Uncertain_significance(1)|Benign(2)|Likely_benign(5) 322\n", + "Uncertain_significance(8)|Benign(2)|Likely_benign(2) 314\n", + "Likely_pathogenic(1)|Likely_benign(1) 304\n", + "Uncertain_significance(1)|Benign(4)|Likely_benign(3) 304\n", + "Likely_pathogenic(1)|Uncertain_significance(6) 302\n", + "Uncertain_significance(6)|Benign(1) 294\n", + "Uncertain_significance(2)|Benign(3)|Likely_benign(2) 293\n", + "Uncertain_significance(6)|Benign(1)|Likely_benign(2) 291\n", + "Uncertain_significance(9)|Benign(2)|Likely_benign(2) 291\n", + "Uncertain_significance(9)|Likely_benign(2) 290\n", + "Uncertain_significance(1)|Benign(6) 289\n", + "Uncertain_significance(1)|Benign(11)|Likely_benign(5) 288\n", + "Uncertain_significance(2)|Benign(3) 285\n", + "Uncertain_significance(2)|Likely_benign(6) 275\n", + "Uncertain_significance(9)|Likely_benign(3) 270\n", + "Uncertain_significance(6)|Benign(2)|Likely_benign(2) 266\n", + "Uncertain_significance(1)|Benign(5)|Likely_benign(3) 265\n", + "Uncertain_significance(4)|Likely_benign(5) 264\n", + "Uncertain_significance(1)|Benign(6)|Likely_benign(1) 263\n", + "Uncertain_significance(7)|Likely_benign(4) 260\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(3) 259\n", + "Uncertain_significance(3)|Benign(5)|Likely_benign(3) 249\n", + "Uncertain_significance(8)|Benign(1)|Likely_benign(1) 240\n", + "Uncertain_significance(3)|Benign(6)|Likely_benign(4) 236\n", + "Uncertain_significance(6)|Benign(2)|Likely_benign(1) 234\n", + "Uncertain_significance(3)|Benign(3)|Likely_benign(1) 230\n", + "Uncertain_significance(2)|Benign(3)|Likely_benign(4) 230\n", + "Uncertain_significance(1)|Benign(2)|Likely_benign(6) 229\n", + "Uncertain_significance(2)|Benign(4)|Likely_benign(1) 228\n", + "Uncertain_significance(7)|Benign(2)|Likely_benign(3) 228\n", + "Uncertain_significance(8)|Benign(2)|Likely_benign(3) 227\n", + "Uncertain_significance(8)|Likely_benign(3) 218\n", + "Uncertain_significance(3)|Benign(2)|Likely_benign(4) 218\n", + "Pathogenic(4)|Uncertain_significance(1) 217\n", + "Uncertain_significance(4)|Benign(1)|Likely_benign(4) 216\n", + "Uncertain_significance(7)|Benign(1)|Likely_benign(1) 212\n", + "Pathogenic(3)|Likely_pathogenic(2)|Uncertain_significance(1) 212\n", + "Uncertain_significance(2)|Benign(2)|Likely_benign(4) 206\n", + "Uncertain_significance(4)|Benign(3)|Likely_benign(2) 205\n", + "Uncertain_significance(3)|Benign(3)|Likely_benign(5) 203\n", + "Uncertain_significance(8)|Benign(2)|Likely_benign(1) 201\n", + "Uncertain_significance(4)|Benign(1)|Likely_benign(5) 201\n", + "Pathogenic(2)|Likely_pathogenic(3)|Uncertain_significance(1) 201\n", + "Uncertain_significance(6)|Benign(1)|Likely_benign(3) 200\n", + "Uncertain_significance(6)|Benign(2)|Likely_benign(4) 198\n", + "Pathogenic(1)|Uncertain_significance(5) 197\n", + "Likely_pathogenic(3)|Uncertain_significance(3) 197\n", + "Likely_pathogenic(2)|Uncertain_significance(4) 190\n", + "Uncertain_significance(3)|Benign(1)|Likely_benign(5) 189\n", + "Uncertain_significance(5)|Likely_benign(5) 187\n", + "Uncertain_significance(7)|Benign(2)|Likely_benign(2) 187\n", + "Uncertain_significance(4)|Benign(2)|Likely_benign(4) 186\n", + "Uncertain_significance(5)|Benign(2)|Likely_benign(3) 186\n", + "Uncertain_significance(1)|Benign(1)|Likely_benign(7) 185\n", + "Uncertain_significance(8)|Benign(1)|Likely_benign(2) 185\n", + "Uncertain_significance(3)|Benign(4)|Likely_benign(2) 185\n", + "Uncertain_significance(3)|Benign(4)|Likely_benign(4) 184\n", + "Uncertain_significance(8)|Benign(2) 184\n", + "Uncertain_significance(7)|Likely_benign(5) 178\n", + "Uncertain_significance(4)|Likely_benign(6) 177\n", + "Uncertain_significance(1)|Benign(3)|Likely_benign(5) 177\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Likely_benign(1) 177\n", + "Uncertain_significance(3)|Benign(2)|Likely_benign(5) 175\n", + "Uncertain_significance(3)|Benign(4)|Likely_benign(1) 174\n", + "Uncertain_significance(5)|Benign(3)|Likely_benign(4) 173\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(4) 170\n", + "Uncertain_significance(1)|Benign(7)|Likely_benign(1) 170\n", + "Likely_pathogenic(1)|Uncertain_significance(8) 169\n", + "Uncertain_significance(4)|Benign(4)|Likely_benign(3) 168\n", + "Pathogenic(3)|Likely_pathogenic(3)|Uncertain_significance(1) 166\n", + "Pathogenic(5)|Uncertain_significance(1) 162\n", + "Pathogenic(1)|Likely_benign(1) 161\n", + "Pathogenic(2)|Likely_pathogenic(2)|Uncertain_significance(2) 161\n", + "Uncertain_significance(7)|Benign(2) 159\n", + "Uncertain_significance(3)|Benign(6)|Likely_benign(2) 158\n", + "Uncertain_significance(3)|Benign(3)|Likely_benign(4) 158\n", + "Uncertain_significance(3)|Benign(6)|Likely_benign(5) 158\n", + "Uncertain_significance(5)|Benign(1)|Likely_benign(3) 156\n", + "Uncertain_significance(6)|Likely_benign(5) 155\n", + "Uncertain_significance(3)|Benign(3)|Likely_benign(2) 154\n", + "Uncertain_significance(10)|Likely_benign(2) 153\n", + "Uncertain_significance(4)|Benign(5)|Likely_benign(3) 151\n", + "Uncertain_significance(10)|Benign(2)|Likely_benign(3) 150\n", + "Uncertain_significance(4)|Benign(3)|Likely_benign(5) 149\n", + "Uncertain_significance(3)|Benign(7)|Likely_benign(4) 149\n", + "Uncertain_significance(6)|Benign(3)|Likely_benign(4) 149\n", + "Uncertain_significance(7)|Benign(1)|Likely_benign(2) 149\n", + "Likely_pathogenic(1)|Uncertain_significance(7) 147\n", + "Uncertain_significance(4)|Likely_benign(7) 146\n", + "Uncertain_significance(1)|Benign(4)|Likely_benign(4) 146\n", + "Uncertain_significance(1)|Benign(6)|Likely_benign(3) 146\n", + "Uncertain_significance(2)|Benign(4)|Likely_benign(2) 144\n", + "Pathogenic(1)|Benign(1) 143\n", + "Uncertain_significance(6)|Benign(2)|Likely_benign(3) 142\n", + "Uncertain_significance(1)|Benign(7) 140\n", + "Uncertain_significance(8)|Benign(1)|Likely_benign(3) 140\n", + "Uncertain_significance(1)|Benign(7)|Likely_benign(2) 140\n", + "Uncertain_significance(3)|Benign(8)|Likely_benign(2) 139\n", + "Uncertain_significance(9)|Benign(2)|Likely_benign(1) 137\n", + "Uncertain_significance(3)|Benign(5)|Likely_benign(4) 136\n", + "Uncertain_significance(7)|Benign(1) 135\n", + "Pathogenic(4)|Likely_pathogenic(2)|Uncertain_significance(1) 133\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Benign(1) 133\n", + "Uncertain_significance(1)|Benign(5)|Likely_benign(5) 132\n", + "Uncertain_significance(2)|Benign(3)|Likely_benign(3) 131\n", + "Uncertain_significance(1)|Benign(4)|Likely_benign(6) 130\n", + "Uncertain_significance(8)|Benign(1) 129\n", + "Likely_pathogenic(1)|Uncertain_risk_allele(1) 128\n", + "Likely_pathogenic(4)|Uncertain_significance(2) 128\n", + "Uncertain_significance(3)|Benign(9)|Likely_benign(2) 128\n", + "Uncertain_significance(1)|Benign(3)|Likely_benign(6) 123\n", + "Uncertain_significance(5)|Benign(4)|Likely_benign(3) 123\n", + "Uncertain_significance(5)|Benign(6)|Likely_benign(3) 120\n", + "Uncertain_significance(5)|Benign(3)|Likely_benign(2) 119\n", + "Uncertain_significance(3)|Benign(7)|Likely_benign(5) 118\n", + "Uncertain_significance(4)|Benign(2)|Likely_benign(7) 116\n", + "Uncertain_significance(7)|Benign(1)|Likely_benign(3) 115\n", + "Uncertain_significance(3)|Benign(3)|Likely_benign(7) 114\n", + "Uncertain_significance(2)|Benign(2)|Likely_benign(5) 114\n", + "Uncertain_significance(3)|Benign(3) 114\n", + "Pathogenic(1)|Likely_pathogenic(3)|Uncertain_significance(2) 112\n", + "Uncertain_significance(2)|Benign(4)|Likely_benign(3) 112\n", + "Pathogenic(1)|Uncertain_significance(1)|Likely_benign(1) 111\n", + "Uncertain_significance(9)|Benign(2)|Likely_benign(3) 111\n", + "Uncertain_significance(6)|Benign(3)|Likely_benign(3) 110\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(5) 108\n", + "Uncertain_significance(3)|Benign(5) 107\n", + "Uncertain_significance(1)|Benign(8)|Likely_benign(4) 106\n", + "Uncertain_significance(4)|Benign(6)|Likely_benign(5) 106\n", + "Uncertain_significance(2)|Benign(5) 105\n", + "Uncertain_significance(1)|Benign(4)|Likely_benign(5) 104\n", + "Uncertain_significance(9)|Benign(1)|Likely_benign(2) 104\n", + "Uncertain_significance(3)|Benign(2)|Likely_benign(6) 104\n", + "Uncertain_significance(6)|Benign(4)|Likely_benign(3) 103\n", + "Pathogenic(3)|Uncertain_significance(2) 103\n", + "Likely_pathogenic(2)|Uncertain_significance(5) 102\n", + "Uncertain_significance(10)|Benign(2)|Likely_benign(1) 102\n", + "Uncertain_significance(3)|Benign(4)|Likely_benign(5) 102\n", + "Pathogenic(5)|Likely_pathogenic(1)|Uncertain_significance(1) 102\n", + "Uncertain_significance(11)|Benign(2)|Likely_benign(1) 102\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(1) 101\n", + "Uncertain_significance(3)|Benign(5)|Likely_benign(2) 101\n", + "Uncertain_significance(1)|Benign(12)|Likely_benign(5) 101\n", + "Uncertain_significance(1)|Likely_benign(8) 100\n", + "Pathogenic(6)|Likely_pathogenic(1)|Uncertain_significance(1) 100\n", + "Uncertain_significance(6)|Benign(1)|Likely_benign(4) 99\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Likely_benign(2) 97\n", + "Uncertain_significance(2)|Benign(4)|Likely_benign(4) 97\n", + "Uncertain_significance(5)|Likely_benign(6) 97\n", + "Pathogenic(2)|Uncertain_significance(3) 97\n", + "Uncertain_significance(2)|Benign(1)|Likely_benign(7) 97\n", + "Pathogenic(2)|Likely_pathogenic(3)|Uncertain_significance(2) 96\n", + "Uncertain_significance(9)|Benign(2) 96\n", + "Uncertain_significance(3)|Likely_benign(6) 94\n", + "Pathogenic(4)|Likely_pathogenic(1)|Uncertain_significance(1) 94\n", + "Uncertain_significance(4)|Benign(2)|Likely_benign(5) 94\n", + "Uncertain_significance(4)|Benign(3)|Likely_benign(4) 94\n", + "Uncertain_significance(8)|Benign(2)|Likely_benign(5) 94\n", + "Uncertain_significance(5)|Benign(3)|Likely_benign(5) 93\n", + "Uncertain_significance(3)|Likely_benign(7) 92\n", + "Likely_pathogenic(1)|Uncertain_significance(4)|Likely_benign(1) 89\n", + "Uncertain_significance(4)|Benign(4)|Likely_benign(5) 89\n", + "Likely_pathogenic(2)|Uncertain_significance(4)|Likely_benign(3) 89\n", + "Pathogenic(1)|Uncertain_significance(6) 89\n", + "Pathogenic(2)|Likely_pathogenic(1)|Uncertain_significance(3) 89\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(4) 89\n", + "Uncertain_significance(4)|Benign(4)|Likely_benign(4) 88\n", + "Uncertain_significance(2)|Benign(5)|Likely_benign(2) 87\n", + "Uncertain_significance(2)|Benign(5)|Likely_benign(3) 87\n", + "Uncertain_significance(2)|Benign(5)|Likely_benign(5) 87\n", + "Uncertain_significance(8)|Likely_benign(4) 85\n", + "Likely_risk_allele(1)|Uncertain_significance(2) 85\n", + "Uncertain_significance(5)|Benign(4)|Likely_benign(6) 85\n", + "Uncertain_significance(1)|Benign(13)|Likely_benign(2) 85\n", + "Uncertain_significance(1)|Benign(8)|Likely_benign(1) 84\n", + "Uncertain_significance(1)|Benign(8)|Likely_benign(3) 84\n", + "Uncertain_significance(1)|Benign(8) 83\n", + "Uncertain_significance(11)|Likely_benign(2) 82\n", + "Pathogenic(1)|Likely_pathogenic(5)|Uncertain_significance(1) 82\n", + "Uncertain_significance(8)|Benign(3)|Likely_benign(4) 82\n", + "Uncertain_significance(7)|Benign(3)|Likely_benign(5) 81\n", + "Uncertain_significance(2)|Benign(2)|Likely_benign(6) 81\n", + "Uncertain_significance(4)|Benign(5)|Likely_benign(8) 81\n", + "Uncertain_significance(4)|Benign(4)|Likely_benign(8) 80\n", + "Uncertain_significance(1)|Benign(2)|Likely_benign(7) 80\n", + "Uncertain_significance(1)|Benign(11) 80\n", + "Uncertain_significance(3)|Benign(8)|Likely_benign(4) 79\n", + "Uncertain_significance(5)|Benign(1)|Likely_benign(4) 79\n", + "Pathogenic(6)|Uncertain_significance(1) 79\n", + "Pathogenic(1)|Likely_pathogenic(4)|Uncertain_significance(2) 79\n", + "Pathogenic(4)|Likely_pathogenic(3)|Uncertain_significance(1) 79\n", + "Uncertain_significance(9)|Benign(1) 78\n", + "Uncertain_significance(2)|Benign(4)|Likely_benign(5) 76\n", + "Uncertain_significance(1)|Benign(12)|Likely_benign(2) 75\n", + "Pathogenic(5)|Likely_pathogenic(2)|Uncertain_significance(1) 75\n", + "Uncertain_significance(3)|Benign(6)|Likely_benign(3) 75\n", + "Uncertain_significance(1)|Benign(9)|Likely_benign(2) 75\n", + "Uncertain_significance(8)|Benign(1)|Likely_benign(4) 74\n", + "Uncertain_significance(4)|Benign(5)|Likely_benign(2) 74\n", + "Uncertain_significance(3)|Benign(10)|Likely_benign(4) 74\n", + "Uncertain_significance(2)|Likely_benign(8) 74\n", + "Uncertain_significance(1)|Benign(12)|Likely_benign(1) 73\n", + "Uncertain_significance(1)|Benign(9)|Likely_benign(5) 73\n", + "Uncertain_significance(4)|Benign(3)|Likely_benign(6) 73\n", + "Uncertain_significance(11)|Likely_benign(1) 73\n", + "Uncertain_significance(4)|Benign(3)|Likely_benign(3) 72\n", + "Uncertain_significance(7)|Benign(3)|Likely_benign(4) 72\n", + "Uncertain_significance(1)|Benign(9)|Likely_benign(8) 72\n", + "Uncertain_significance(7)|Benign(3)|Likely_benign(3) 71\n", + "Uncertain_significance(9)|Benign(2)|Likely_benign(4) 71\n", + "Uncertain_significance(7)|Benign(4)|Likely_benign(4) 71\n", + "Likely_pathogenic(2)|Uncertain_significance(6) 70\n", + "Uncertain_significance(3)|Benign(4)|Likely_benign(6) 70\n", + "Uncertain_significance(1)|Benign(13)|Likely_benign(3) 70\n", + "Uncertain_significance(3)|Benign(10)|Likely_benign(2) 70\n", + "Pathogenic(8)|Likely_pathogenic(1)|Uncertain_significance(2) 70\n", + "Pathogenic(1)|Uncertain_significance(7) 69\n", + "Uncertain_significance(2)|Benign(7)|Likely_benign(2) 69\n", + "Likely_pathogenic(1)|Uncertain_significance(9) 69\n", + "Uncertain_significance(5)|Benign(2)|Likely_benign(5) 69\n", + "Uncertain_significance(6)|Benign(5)|Likely_benign(1) 68\n", + "Uncertain_significance(5)|Benign(3)|Likely_benign(3) 68\n", + "Uncertain_significance(1)|Benign(5)|Likely_benign(4) 68\n", + "Uncertain_significance(7)|Benign(2)|Likely_benign(5) 68\n", + "Likely_pathogenic(2)|Uncertain_significance(1)|Likely_benign(1) 66\n", + "Uncertain_significance(3)|Benign(3)|Likely_benign(6) 65\n", + "Uncertain_significance(10)|Benign(1)|Likely_benign(2) 64\n", + "Uncertain_significance(7)|Benign(3)|Likely_benign(7) 64\n", + "Uncertain_significance(4)|Benign(3)|Likely_benign(1) 64\n", + "Uncertain_significance(4)|Benign(11)|Likely_benign(3) 64\n", + "Uncertain_significance(9)|Benign(2)|Likely_benign(6) 64\n", + "Likely_pathogenic(1)|Likely_risk_allele(1)|Uncertain_significance(1) 64\n", + "Pathogenic(10)|Likely_pathogenic(1)|Uncertain_significance(1) 63\n", + "Uncertain_significance(1)|Benign(6)|Likely_benign(5) 63\n", + "Pathogenic(3)|Likely_pathogenic(1)|Uncertain_significance(2) 62\n", + "Uncertain_significance(4)|Benign(5)|Likely_benign(4) 62\n", + "Uncertain_significance(3)|Benign(8)|Likely_benign(3) 61\n", + "Uncertain_significance(7)|Benign(2)|Likely_benign(6) 61\n", + "Uncertain_significance(3)|Benign(5)|Likely_benign(1) 61\n", + "Uncertain_significance(4)|Benign(8)|Likely_benign(3) 61\n", + "Uncertain_significance(1)|Benign(10)|Likely_benign(5) 61\n", + "Uncertain_significance(2)|Benign(4) 61\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(5) 61\n", + "Uncertain_significance(1)|Benign(10)|Likely_benign(4) 60\n", + "Pathogenic(8)|Likely_pathogenic(1)|Uncertain_significance(1) 60\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Likely_benign(2) 60\n", + "Pathogenic(1)|Uncertain_significance(1)|Benign(1) 60\n", + "Uncertain_significance(7)|Benign(4)|Likely_benign(3) 60\n", + "Uncertain_significance(1)|Benign(9)|Likely_benign(3) 59\n", + "Uncertain_significance(1)|Benign(6)|Likely_benign(4) 59\n", + "Uncertain_significance(8)|Benign(3)|Likely_benign(3) 59\n", + "Likely_pathogenic(4)|Uncertain_significance(3) 59\n", + "Uncertain_significance(1)|Benign(10)|Likely_benign(2) 58\n", + "Uncertain_significance(6)|Benign(5)|Likely_benign(5) 58\n", + "Uncertain_significance(3)|Benign(6)|Likely_benign(7) 58\n", + "Uncertain_significance(3)|Benign(6)|Likely_benign(6) 58\n", + "Uncertain_significance(12)|Benign(2)|Likely_benign(5) 58\n", + "Uncertain_significance(4)|Benign(3) 58\n", + "Uncertain_significance(6)|Benign(5)|Likely_benign(3) 57\n", + "Uncertain_significance(2)|Benign(12)|Likely_benign(3) 57\n", + "Uncertain_significance(4)|Benign(13)|Likely_benign(3) 56\n", + "Uncertain_significance(1)|Benign(9)|Likely_benign(4) 56\n", + "Uncertain_significance(3)|Benign(5)|Likely_benign(5) 56\n", + "Uncertain_significance(5)|Benign(2)|Likely_benign(6) 56\n", + "Uncertain_significance(2)|Benign(11)|Likely_benign(2) 56\n", + "Pathogenic(1)|Uncertain_risk_allele(1) 56\n", + "Pathogenic(2)|Likely_pathogenic(9)|Uncertain_significance(1) 55\n", + "Uncertain_significance(2)|Likely_benign(7) 55\n", + "Uncertain_significance(7)|Benign(1)|Likely_benign(5) 55\n", + "Pathogenic(1)|Uncertain_significance(2)|Likely_benign(1) 55\n", + "Uncertain_significance(13)|Likely_benign(1) 54\n", + "Uncertain_significance(1)|Benign(1)|Likely_benign(8) 54\n", + "Pathogenic(2)|Likely_pathogenic(3)|Uncertain_significance(3) 53\n", + "Uncertain_significance(5)|Benign(4)|Likely_benign(7) 53\n", + "Pathogenic(2)|Likely_pathogenic(4)|Uncertain_significance(2) 53\n", + "Likely_pathogenic(6)|Uncertain_significance(1) 53\n", + "Pathogenic(6)|Likely_pathogenic(2)|Uncertain_significance(1) 53\n", + "Uncertain_significance(1)|Benign(7)|Likely_benign(6) 53\n", + "Uncertain_significance(3)|Benign(4)|Likely_benign(13) 52\n", + "Uncertain_significance(4)|Benign(9)|Likely_benign(4) 52\n", + "Uncertain_significance(3)|Benign(4)|Likely_benign(3) 52\n", + "Uncertain_significance(1)|Benign(9)|Likely_benign(6) 52\n", + "Uncertain_significance(1)|Benign(6)|Likely_benign(2) 52\n", + "Pathogenic(6)|Likely_pathogenic(3)|Uncertain_significance(1) 52\n", + "Uncertain_significance(1)|Benign(12)|Likely_benign(3) 52\n", + "Uncertain_significance(9)|Benign(1)|Likely_benign(3) 52\n", + "Likely_pathogenic(1)|Benign(2) 52\n", + "Likely_pathogenic(1)|Uncertain_significance(7)|Likely_benign(1) 52\n", + "Uncertain_significance(3)|Benign(7)|Likely_benign(1) 51\n", + "Likely_pathogenic(3)|Uncertain_significance(4) 51\n", + "Uncertain_significance(11)|Benign(2)|Likely_benign(3) 51\n", + "Uncertain_significance(7)|Benign(5)|Likely_benign(3) 51\n", + "Uncertain_significance(4)|Benign(5)|Likely_benign(5) 51\n", + "Uncertain_significance(6)|Benign(5) 51\n", + "Uncertain_significance(3)|Benign(4)|Likely_benign(8) 51\n", + "Uncertain_significance(2)|Benign(10)|Likely_benign(6) 51\n", + "Uncertain_significance(3)|Benign(3)|Likely_benign(9) 50\n", + "Uncertain_significance(2)|Benign(1)|Likely_benign(8) 50\n", + "Uncertain_significance(2)|Benign(3)|Likely_benign(6) 50\n", + "Uncertain_significance(4)|Benign(5)|Likely_benign(1) 50\n", + "Uncertain_significance(8)|Benign(2)|Likely_benign(4) 49\n", + "Uncertain_significance(4)|Benign(6)|Likely_benign(1) 49\n", + "Uncertain_significance(1)|Benign(8)|Likely_benign(2) 49\n", + "Pathogenic(1)|Uncertain_significance(3)|Likely_benign(1) 49\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(7) 49\n", + "Uncertain_significance(1)|Benign(13)|Likely_benign(4) 49\n", + "Uncertain_significance(2)|Benign(10)|Likely_benign(7) 49\n", + "Uncertain_significance(6)|Benign(3)|Likely_benign(8) 49\n", + "Uncertain_significance(7)|Benign(3)|Likely_benign(6) 48\n", + "Pathogenic(1)|Uncertain_significance(8) 48\n", + "Uncertain_significance(5)|Benign(1)|Likely_benign(5) 48\n", + "Uncertain_significance(1)|Benign(13)|Likely_benign(1) 48\n", + "Uncertain_significance(8)|Likely_benign(6) 47\n", + "Uncertain_significance(5)|Benign(3)|Likely_benign(7) 47\n", + "Pathogenic(3)|Likely_pathogenic(4)|Uncertain_significance(1) 47\n", + "Uncertain_significance(10)|Likely_benign(3) 46\n", + "Uncertain_significance(4)|Benign(8)|Likely_benign(5) 46\n", + "Pathogenic(1)|Uncertain_significance(1)|Benign(4) 46\n", + "Likely_pathogenic(1)|Uncertain_significance(10)|Likely_benign(1) 46\n", + "Likely_pathogenic(1)|Uncertain_significance(5)|Likely_benign(1) 46\n", + "Uncertain_significance(5)|Benign(3)|Likely_benign(1) 45\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Benign(1) 45\n", + "Pathogenic(7)|Uncertain_significance(1) 45\n", + "Uncertain_significance(7)|Benign(2)|Likely_benign(4) 45\n", + "Uncertain_significance(3)|Benign(5)|Likely_benign(8) 45\n", + "Uncertain_significance(8)|Likely_benign(5) 45\n", + "Uncertain_significance(1)|Benign(6)|Likely_benign(7) 45\n", + "Uncertain_significance(5)|Benign(5)|Likely_benign(3) 44\n", + "Pathogenic(4)|Uncertain_significance(2) 43\n", + "Uncertain_significance(2)|Benign(6) 43\n", + "Uncertain_significance(3)|Benign(9)|Likely_benign(4) 43\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Likely_benign(2) 43\n", + "Pathogenic(1)|Likely_pathogenic(4)|Uncertain_significance(3) 42\n", + "Uncertain_significance(2)|Benign(6)|Likely_benign(1) 42\n", + "Uncertain_significance(4)|Benign(7) 42\n", + "Uncertain_significance(1)|Benign(5)|Likely_benign(7) 42\n", + "Pathogenic(1)|Likely_pathogenic(1)|Likely_benign(1) 42\n", + "Uncertain_significance(2)|Benign(6)|Likely_benign(4) 42\n", + "Pathogenic(5)|Likely_pathogenic(3)|Uncertain_significance(1) 42\n", + "Uncertain_significance(4)|Benign(4)|Likely_benign(2) 42\n", + "Uncertain_significance(6)|Benign(3)|Likely_benign(1) 41\n", + "Uncertain_significance(1)|Likely_benign(9) 41\n", + "Uncertain_significance(10)|Likely_benign(6) 41\n", + "Pathogenic(3)|Likely_pathogenic(3)|Uncertain_significance(2) 41\n", + "Pathogenic(1)|Likely_pathogenic(10)|Uncertain_significance(1) 41\n", + "Likely_risk_allele(1)|Uncertain_significance(3) 41\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Benign(1)|Likely_benign(2) 41\n", + "Pathogenic(8)|Uncertain_significance(1) 41\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(6) 41\n", + "Uncertain_significance(1)|Benign(4)|Likely_benign(7) 41\n", + "Uncertain_significance(4)|Benign(2)|Likely_benign(6) 40\n", + "Uncertain_significance(4)|Benign(1)|Likely_benign(7) 40\n", + "Pathogenic(1)|Likely_pathogenic(3)|Uncertain_significance(3) 40\n", + "Pathogenic(1)|Likely_benign(2) 40\n", + "Uncertain_significance(3)|Benign(4) 40\n", + "Uncertain_significance(6)|Benign(3) 40\n", + "Likely_pathogenic(1)|Benign(1) 40\n", + "Uncertain_significance(3)|Benign(1)|Likely_benign(7) 40\n", + "Likely_pathogenic(4)|Uncertain_significance(4) 40\n", + "Uncertain_significance(1)|Benign(3)|Likely_benign(7) 40\n", + "Uncertain_significance(6)|Benign(3)|Likely_benign(6) 39\n", + "Uncertain_significance(3)|Benign(7)|Likely_benign(8) 39\n", + "Uncertain_significance(2)|Benign(9)|Likely_benign(2) 39\n", + "Uncertain_significance(14)|Likely_benign(1) 39\n", + "Uncertain_significance(7)|Likely_benign(6) 39\n", + "Pathogenic(2)|Likely_pathogenic(1)|Uncertain_significance(6) 39\n", + "Pathogenic(2)|Likely_pathogenic(5)|Uncertain_significance(1) 39\n", + "Uncertain_significance(8)|Benign(2)|Likely_benign(6) 39\n", + "Pathogenic(1)|Likely_pathogenic(7)|Uncertain_significance(1) 39\n", + "Uncertain_significance(8)|Benign(1)|Likely_benign(5) 38\n", + "Likely_pathogenic(2)|Uncertain_significance(7) 38\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(8) 38\n", + "Uncertain_significance(6)|Benign(6)|Likely_benign(8) 38\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Likely_benign(3) 38\n", + "Likely_pathogenic(1)|Benign(1)|Likely_benign(1) 38\n", + "Pathogenic(4)|Likely_pathogenic(1)|Uncertain_significance(2) 38\n", + "Likely_pathogenic(1)|Likely_benign(2) 38\n", + "Pathogenic(2)|Likely_pathogenic(4)|Uncertain_significance(1) 37\n", + "Uncertain_significance(2)|Benign(2)|Likely_benign(8) 37\n", + "Uncertain_significance(5)|Benign(7)|Likely_benign(4) 37\n", + "Pathogenic(8)|Likely_pathogenic(2)|Uncertain_significance(1) 37\n", + "Uncertain_significance(7)|Benign(4)|Likely_benign(2) 37\n", + "Likely_pathogenic(2)|Uncertain_significance(4)|Benign(2)|Likely_benign(1) 37\n", + "Likely_pathogenic(2)|Uncertain_significance(8) 37\n", + "Uncertain_significance(3)|Benign(5)|Likely_benign(6) 37\n", + "Uncertain_significance(5)|Benign(2)|Likely_benign(4) 37\n", + "Uncertain_significance(10)|Benign(1)|Likely_benign(1) 37\n", + "Uncertain_significance(12)|Benign(2)|Likely_benign(2) 36\n", + "Uncertain_significance(7)|Benign(3)|Likely_benign(1) 36\n", + "Likely_pathogenic(2)|Uncertain_significance(3)|Likely_benign(1) 36\n", + "Uncertain_significance(1)|Benign(7)|Likely_benign(4) 36\n", + "Uncertain_significance(2)|Benign(7)|Likely_benign(7) 36\n", + "Pathogenic(2)|Uncertain_significance(4) 36\n", + "Uncertain_significance(9)|Benign(4)|Likely_benign(5) 35\n", + "Likely_pathogenic(3)|Uncertain_significance(5) 35\n", + "Uncertain_significance(2)|Benign(8)|Likely_benign(5) 35\n", + "Uncertain_significance(12)|Benign(2) 35\n", + "Uncertain_significance(8)|Benign(3)|Likely_benign(5) 35\n", + "Pathogenic(1)|Uncertain_significance(2)|Benign(3)|Likely_benign(2) 35\n", + "Uncertain_significance(10)|Benign(4)|Likely_benign(3) 35\n", + "Uncertain_significance(5)|Benign(5)|Likely_benign(6) 35\n", + "Uncertain_significance(12)|Likely_benign(1) 35\n", + "Uncertain_significance(7)|Benign(1)|Likely_benign(4) 35\n", + "Likely_pathogenic(2)|Likely_benign(1) 34\n", + "Uncertain_significance(4)|Benign(11) 34\n", + "Pathogenic(1)|Likely_pathogenic(3)|Uncertain_significance(5) 34\n", + "Uncertain_significance(8)|Benign(3)|Likely_benign(2) 34\n", + "Uncertain_significance(6)|Benign(6)|Likely_benign(1) 34\n", + "Uncertain_significance(1)|Benign(7)|Likely_benign(5) 34\n", + "Uncertain_significance(5)|Benign(9)|Likely_benign(3) 34\n", + "Uncertain_significance(8)|Benign(3)|Likely_benign(1) 34\n", + "Uncertain_significance(9)|Benign(2)|Likely_benign(5) 34\n", + "Uncertain_significance(2)|Benign(2)|Likely_benign(10) 34\n", + "Uncertain_significance(5)|Benign(4)|Likely_benign(5) 34\n", + "Uncertain_significance(4)|Benign(6)|Likely_benign(4) 34\n", + "Uncertain_significance(3)|Benign(11)|Likely_benign(1) 34\n", + "Uncertain_significance(5)|Benign(5)|Likely_benign(9) 34\n", + "Uncertain_significance(3)|Benign(10)|Likely_benign(6) 34\n", + "Uncertain_significance(6)|Benign(7)|Likely_benign(2) 34\n", + "Pathogenic(2)|Likely_pathogenic(2)|Uncertain_significance(5) 34\n", + "Uncertain_significance(3)|Benign(9)|Likely_benign(5) 34\n", + "Likely_pathogenic(2)|Uncertain_significance(2)|Likely_benign(1) 33\n", + "Uncertain_significance(8)|Benign(4)|Likely_benign(3) 33\n", + "Uncertain_significance(3)|Likely_benign(8) 33\n", + "Uncertain_significance(7)|Benign(3) 33\n", + "Uncertain_significance(2)|Benign(2)|Likely_benign(7) 33\n", + "Pathogenic(9)|Uncertain_significance(1) 33\n", + "Uncertain_significance(2)|Benign(6)|Likely_benign(2) 33\n", + "Uncertain_significance(3)|Benign(10)|Likely_benign(3) 33\n", + "Uncertain_significance(8)|Benign(4)|Likely_benign(1) 32\n", + "Uncertain_significance(1)|Benign(10)|Likely_benign(3) 32\n", + "Uncertain_significance(3)|Benign(1)|Likely_benign(9) 32\n", + "Uncertain_significance(1)|Benign(2)|Likely_benign(8) 32\n", + "Uncertain_significance(5)|Benign(7)|Likely_benign(6) 32\n", + "Likely_pathogenic(1)|Uncertain_significance(9)|Benign(3)|Likely_benign(4) 32\n", + "Uncertain_significance(8)|Benign(4)|Likely_benign(5) 32\n", + "Uncertain_significance(2)|Benign(6)|Likely_benign(3) 32\n", + "Uncertain_significance(11)|Benign(2)|Likely_benign(4) 32\n", + "Likely_pathogenic(5)|Uncertain_significance(4) 32\n", + "Uncertain_significance(4)|Benign(5)|Likely_benign(6) 32\n", + "Uncertain_significance(4)|Benign(6)|Likely_benign(2) 32\n", + "Pathogenic(1)|Uncertain_significance(2)|Benign(4) 32\n", + "Pathogenic(5)|Uncertain_significance(2) 31\n", + "Uncertain_significance(6)|Benign(8)|Likely_benign(2) 31\n", + "Uncertain_significance(2)|Benign(12)|Likely_benign(2) 31\n", + "Likely_pathogenic(5)|Uncertain_significance(1) 31\n", + "Pathogenic(15)|Uncertain_significance(1) 31\n", + "Pathogenic(2)|Likely_pathogenic(2)|Uncertain_significance(3) 31\n", + "Uncertain_significance(5)|Benign(1)|Likely_benign(7) 31\n", + "Uncertain_significance(12)|Likely_benign(4) 31\n", + "Uncertain_significance(9)|Benign(1)|Likely_benign(1) 31\n", + "Pathogenic(3)|Likely_pathogenic(2)|Uncertain_significance(2) 31\n", + "Uncertain_significance(10)|Benign(1) 30\n", + "Uncertain_significance(2)|Benign(9) 30\n", + "Uncertain_significance(9)|Likely_benign(4) 30\n", + "Uncertain_significance(2)|Benign(9)|Likely_benign(1) 30\n", + "Uncertain_significance(1)|Benign(12) 30\n", + "Likely_pathogenic(1)|Likely_benign(3) 30\n", + "Pathogenic(3)|Likely_pathogenic(2)|Uncertain_significance(8) 30\n", + "Likely_pathogenic(1)|Uncertain_significance(6)|Likely_benign(1) 30\n", + "Uncertain_significance(1)|Benign(3)|Likely_benign(8) 30\n", + "Likely_pathogenic(1)|Benign(3)|Likely_benign(2) 30\n", + "Uncertain_significance(1)|Benign(5)|Likely_benign(6) 29\n", + "Uncertain_significance(2)|Uncertain_risk_allele(1)|Likely_benign(1) 29\n", + "Uncertain_significance(11)|Likely_benign(6) 29\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(1)|Likely_benign(1) 29\n", + "Uncertain_significance(5)|Benign(1)|Likely_benign(6) 29\n", + "Uncertain_significance(4)|Benign(1)|Likely_benign(6) 29\n", + "Uncertain_significance(1)|Benign(10)|Likely_benign(1) 29\n", + "Uncertain_significance(2)|Benign(8)|Likely_benign(2) 28\n", + "Uncertain_significance(1)|Benign(7)|Likely_benign(10) 28\n", + "Pathogenic(1)|Likely_pathogenic(4)|Uncertain_significance(8) 28\n", + "Uncertain_significance(6)|Benign(1)|Likely_benign(5) 28\n", + "Likely_pathogenic(4)|Uncertain_significance(5) 28\n", + "Pathogenic(2)|Likely_pathogenic(3)|Uncertain_significance(4) 28\n", + "Uncertain_significance(4)|Benign(4) 28\n", + "Pathogenic(14)|Uncertain_significance(1) 28\n", + "Pathogenic(9)|Likely_pathogenic(1)|Uncertain_significance(1) 28\n", + "Pathogenic(3)|Likely_pathogenic(2)|Uncertain_significance(3) 28\n", + "Pathogenic(18)|Likely_pathogenic(5)|Uncertain_significance(1) 28\n", + "Uncertain_significance(4)|Benign(3)|Likely_benign(7) 27\n", + "Uncertain_significance(5)|Benign(5)|Likely_benign(5) 27\n", + "Uncertain_significance(2)|Benign(8) 27\n", + "Uncertain_significance(4)|Benign(8)|Likely_benign(1) 27\n", + "Pathogenic(1)|Uncertain_significance(3)|Benign(1)|Likely_benign(1) 27\n", + "Uncertain_significance(8)|Benign(2)|Likely_benign(7) 27\n", + "Uncertain_significance(1)|Benign(9)|Likely_benign(1) 27\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Uncertain_risk_allele(1) 27\n", + "Uncertain_significance(2)|Likely_benign(10) 26\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(4)|Likely_benign(4) 26\n", + "Uncertain_significance(2)|Benign(5)|Likely_benign(1) 26\n", + "Uncertain_significance(4)|Benign(4)|Likely_benign(6) 26\n", + "Uncertain_significance(7)|Benign(4)|Likely_benign(6) 26\n", + "Pathogenic(31)|Likely_pathogenic(8)|Uncertain_significance(1) 26\n", + "Likely_pathogenic(10)|Uncertain_significance(2) 26\n", + "Uncertain_significance(9)|Benign(4)|Likely_benign(3) 26\n", + "Uncertain_significance(3)|Benign(1)|Likely_benign(6) 26\n", + "Pathogenic(3)|Likely_pathogenic(1)|Uncertain_significance(4) 26\n", + "Uncertain_significance(15)|Benign(1)|Likely_benign(1) 26\n", + "Pathogenic(18)|Uncertain_significance(1) 26\n", + "Likely_pathogenic(1)|Benign(2)|Likely_benign(1) 26\n", + "Pathogenic(9)|Likely_pathogenic(3)|Benign(1) 26\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(8)|Likely_benign(4) 26\n", + "Pathogenic(2)|Likely_pathogenic(1)|Likely_benign(1) 26\n", + "Uncertain_significance(6)|Benign(2)|Likely_benign(6) 25\n", + "Uncertain_significance(1)|Benign(4)|Likely_benign(9) 25\n", + "Pathogenic(3)|Likely_pathogenic(1)|Uncertain_significance(3) 25\n", + "Pathogenic(10)|Uncertain_significance(1) 25\n", + "Pathogenic(15)|Likely_pathogenic(1)|Uncertain_significance(1) 25\n", + "Likely_pathogenic(13)|Uncertain_significance(1) 25\n", + "Likely_pathogenic(1)|Benign(2)|Likely_benign(3) 25\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(3)|Benign(1) 25\n", + "Uncertain_significance(1)|Benign(8)|Likely_benign(5) 24\n", + "Uncertain_significance(5)|Likely_benign(7) 24\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(1)|Likely_benign(1) 24\n", + "Likely_pathogenic(1)|Uncertain_significance(4)|Likely_benign(2) 24\n", + "Uncertain_significance(3)|Benign(2)|Likely_benign(7) 24\n", + "Uncertain_significance(9)|Benign(1)|Likely_benign(4) 23\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(3)|Likely_benign(1) 23\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Likely_benign(4) 23\n", + "Pathogenic(1)|Likely_pathogenic(3)|Uncertain_significance(13) 23\n", + "Pathogenic(2)|Likely_pathogenic(1)|Uncertain_risk_allele(1) 23\n", + "Uncertain_significance(1)|Benign(12)|Likely_benign(6) 23\n", + "Pathogenic(1)|Likely_pathogenic(1)|Benign(1) 23\n", + "Uncertain_significance(3)|Benign(7)|Likely_benign(2) 22\n", + "Uncertain_significance(3)|Benign(1)|Likely_benign(8) 22\n", + "Uncertain_significance(8)|Likely_benign(7) 22\n", + "Pathogenic(9)|Likely_pathogenic(2)|Uncertain_significance(1) 22\n", + "Pathogenic(3)|Likely_pathogenic(5)|Uncertain_significance(1) 22\n", + "Pathogenic(2)|Uncertain_significance(5) 21\n", + "Uncertain_significance(3)|Benign(7)|Likely_benign(6) 21\n", + "Uncertain_significance(3)|Benign(8)|Likely_benign(5) 21\n", + "Likely_pathogenic(2)|Uncertain_significance(8)|Likely_benign(2) 21\n", + "Uncertain_significance(10)|Benign(2) 21\n", + "Likely_pathogenic(3)|Uncertain_significance(8)|Likely_benign(1) 21\n", + "Uncertain_significance(2)|Benign(11)|Likely_benign(6) 21\n", + "Uncertain_significance(5)|Benign(6)|Likely_benign(5) 21\n", + "Uncertain_significance(5)|Benign(6) 21\n", + "Uncertain_significance(4)|Benign(4)|Likely_benign(7) 21\n", + "Pathogenic(1)|Likely_pathogenic(8)|Uncertain_significance(1) 21\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Benign(1)|Likely_benign(8) 21\n", + "Pathogenic(1)|Likely_pathogenic(3)|Uncertain_significance(7) 21\n", + "Uncertain_significance(2)|Benign(1)|Likely_benign(11) 21\n", + "Likely_pathogenic(1)|Uncertain_significance(7)|Likely_benign(2) 21\n", + "Pathogenic(12)|Likely_pathogenic(1)|Benign(1) 21\n", + "Uncertain_significance(11)|Likely_benign(3) 21\n", + "Pathogenic(2)|Likely_pathogenic(10)|Uncertain_significance(1) 21\n", + "Likely_pathogenic(1)|Benign(2)|Likely_benign(2) 21\n", + "Uncertain_significance(2)|Benign(3)|Likely_benign(5) 21\n", + "Uncertain_significance(1)|Benign(3)|Likely_benign(13) 21\n", + "Likely_pathogenic(1)|Uncertain_significance(10) 21\n", + "Uncertain_significance(7)|Likely_benign(8) 20\n", + "Pathogenic(1)|Likely_risk_allele(1)|Uncertain_significance(1) 20\n", + "Uncertain_significance(9)|Benign(3)|Likely_benign(1) 20\n", + "Likely_pathogenic(1)|Uncertain_significance(6)|Likely_benign(3) 20\n", + "Uncertain_significance(1)|Benign(6)|Likely_benign(9) 20\n", + "Uncertain_risk_allele(1)|Benign(2)|Likely_benign(2) 20\n", + "Pathogenic(1)|Uncertain_significance(10)|Likely_benign(1) 20\n", + "Uncertain_significance(5)|Benign(3)|Likely_benign(6) 20\n", + "Uncertain_significance(2)|Benign(1)|Likely_benign(9) 20\n", + "Pathogenic(2)|Likely_pathogenic(7)|Uncertain_significance(2) 20\n", + "Pathogenic(5)|Uncertain_significance(3) 20\n", + "Pathogenic(11)|Likely_benign(2) 20\n", + "Uncertain_significance(10)|Benign(3)|Likely_benign(6) 20\n", + "Uncertain_significance(6)|Likely_benign(6) 20\n", + "Likely_pathogenic(1)|Likely_benign(4) 20\n", + "Likely_pathogenic(1)|Benign(1)|Likely_benign(4) 20\n", + "Pathogenic(1)|Likely_pathogenic(6)|Uncertain_significance(3) 20\n", + "Likely_pathogenic(5)|Uncertain_significance(2) 20\n", + "Uncertain_significance(3)|Uncertain_risk_allele(1)|Likely_benign(1) 19\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(2)|Benign(1) 19\n", + "Uncertain_significance(3)|Benign(6)|Likely_benign(1) 19\n", + "Uncertain_significance(8)|Benign(4)|Likely_benign(2) 19\n", + "Uncertain_significance(11)|Benign(1)|Likely_benign(2) 19\n", + "Uncertain_significance(11)|Likely_benign(4) 19\n", + "Pathogenic(12)|Uncertain_significance(1) 19\n", + "Uncertain_significance(1)|Benign(1)|Likely_benign(9) 19\n", + "Likely_pathogenic(1)|Benign(10) 19\n", + "Uncertain_significance(2)|Benign(8)|Likely_benign(6) 19\n", + "Likely_risk_allele(1)|Uncertain_significance(1)|Benign(1)|Likely_benign(1) 19\n", + "Pathogenic(2)|Likely_pathogenic(1)|Uncertain_significance(4) 19\n", + "Uncertain_significance(6)|Benign(3)|Likely_benign(2) 19\n", + "Uncertain_significance(4)|Benign(7)|Likely_benign(6) 19\n", + "Uncertain_significance(5)|Benign(5)|Likely_benign(4) 18\n", + "Pathogenic(1)|Uncertain_significance(4)|Likely_benign(1) 18\n", + "Uncertain_significance(2)|Benign(5)|Likely_benign(7) 18\n", + "Likely_pathogenic(2)|Uncertain_risk_allele(1) 18\n", + "Pathogenic(4)|Likely_pathogenic(10)|Uncertain_significance(3)|Likely_benign(1) 18\n", + "Uncertain_significance(1)|Benign(7)|Likely_benign(3) 18\n", + "Likely_pathogenic(1)|Uncertain_significance(9)|Likely_benign(2) 18\n", + "Uncertain_significance(10)|Benign(1)|Likely_benign(4) 18\n", + "Pathogenic(1)|Likely_pathogenic(3)|Uncertain_significance(4) 18\n", + "Uncertain_significance(5)|Benign(3)|Likely_benign(8) 18\n", + "Uncertain_significance(2)|Benign(4)|Likely_benign(6) 18\n", + "Likely_pathogenic(6)|Uncertain_significance(3) 18\n", + "Pathogenic(1)|Uncertain_significance(7)|Likely_benign(6) 18\n", + "Uncertain_significance(7)|Benign(7) 18\n", + "Uncertain_significance(5)|Benign(6)|Likely_benign(9) 18\n", + "Uncertain_significance(1)|Benign(10)|Likely_benign(6) 17\n", + "Uncertain_significance(1)|Benign(16)|Likely_benign(3) 17\n", + "Uncertain_significance(3)|Benign(8)|Likely_benign(6) 17\n", + "Uncertain_significance(2)|Benign(5)|Likely_benign(4) 17\n", + "Pathogenic(1)|Uncertain_significance(1)|Benign(1)|Likely_benign(1) 17\n", + "Uncertain_significance(6)|Benign(2)|Likely_benign(5) 17\n", + "Uncertain_significance(6)|Benign(1)|Likely_benign(6) 17\n", + "Uncertain_significance(1)|Benign(9)|Likely_benign(12) 17\n", + "Pathogenic(3)|Likely_pathogenic(2)|Uncertain_significance(6)|Likely_benign(1) 17\n", + "Likely_risk_allele(1)|Uncertain_significance(4) 17\n", + "Uncertain_significance(8)|Benign(3)|Likely_benign(6) 17\n", + "Pathogenic(2)|Likely_pathogenic(5)|Uncertain_significance(2) 16\n", + "Uncertain_significance(3)|Benign(3)|Likely_benign(11) 16\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(2) 16\n", + "Likely_pathogenic(1)|Uncertain_significance(4)|Likely_benign(3) 16\n", + "Pathogenic(1)|Uncertain_significance(10) 16\n", + "Likely_pathogenic(2)|Uncertain_significance(1)|Benign(1) 16\n", + "Pathogenic(1)|Likely_pathogenic(3)|Uncertain_significance(8) 16\n", + "Uncertain_significance(4)|Benign(4)|Likely_benign(1) 16\n", + "Pathogenic(5)|Likely_benign(1) 16\n", + "Pathogenic(11)|Uncertain_significance(1) 16\n", + "Pathogenic(17)|Likely_pathogenic(1)|Uncertain_significance(1) 16\n", + "Uncertain_significance(1)|Uncertain_risk_allele(1)|Likely_benign(1) 16\n", + "Uncertain_significance(13)|Likely_benign(2) 16\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(7) 16\n", + "Pathogenic(5)|Likely_pathogenic(2)|Uncertain_significance(1)|Benign(1)|Likely_benign(2) 15\n", + "Likely_pathogenic(5)|Uncertain_significance(3) 15\n", + "Likely_pathogenic(1)|Uncertain_significance(12)|Likely_benign(1) 15\n", + "Uncertain_significance(13)|Benign(2)|Likely_benign(1) 15\n", + "Pathogenic(2)|Uncertain_significance(2)|Benign(1) 15\n", + "Pathogenic(4)|Likely_pathogenic(12)|Uncertain_significance(2) 15\n", + "Pathogenic(28)|Likely_pathogenic(2)|Uncertain_significance(2)|Likely_benign(1) 15\n", + "Uncertain_significance(5)|Benign(3) 15\n", + "Pathogenic(1)|Uncertain_significance(3)|Benign(1)|Likely_benign(3) 15\n", + "Pathogenic(1)|Likely_pathogenic(3)|Uncertain_significance(9)|Benign(1)|Likely_benign(2) 15\n", + "Uncertain_significance(3)|Benign(5)|Likely_benign(7) 15\n", + "Pathogenic(1)|Uncertain_significance(5)|Likely_benign(1) 15\n", + "Pathogenic(1)|Uncertain_significance(2)|Likely_benign(2) 15\n", + "Likely_pathogenic(2)|Uncertain_significance(10)|Benign(1)|Likely_benign(1) 15\n", + "Pathogenic(2)|Benign(1) 15\n", + "Uncertain_significance(10)|Benign(3)|Likely_benign(2) 15\n", + "Uncertain_significance(2)|Benign(17)|Likely_benign(1) 15\n", + "Pathogenic(11)|Likely_pathogenic(3)|Uncertain_significance(1) 15\n", + "Pathogenic(10)|Uncertain_significance(2) 15\n", + "Pathogenic(1)|Uncertain_significance(1)|Benign(2) 15\n", + "Likely_pathogenic(2)|Uncertain_significance(9) 15\n", + "Pathogenic(5)|Benign(1) 14\n", + "Likely_pathogenic(1)|Benign(4)|Likely_benign(2) 14\n", + "Uncertain_significance(10)|Benign(1)|Likely_benign(3) 14\n", + "Uncertain_significance(10)|Likely_benign(5) 14\n", + "Pathogenic(1)|Uncertain_significance(6)|Likely_benign(1) 14\n", + "Pathogenic(1)|Likely_pathogenic(8)|Uncertain_significance(4) 14\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(5)|Likely_benign(9) 14\n", + "Likely_pathogenic(1)|Uncertain_significance(6)|Benign(2)|Likely_benign(5) 14\n", + "Uncertain_significance(14)|Likely_benign(4) 14\n", + "Pathogenic(17)|Likely_pathogenic(5)|Pathogenic,_low_penetrance(1)|Established_risk_allele(1)|Uncertain_significance(1) 14\n", + "Pathogenic(2)|Likely_pathogenic(7)|Uncertain_significance(1) 14\n", + "Pathogenic(42)|Uncertain_significance(1) 14\n", + "Likely_pathogenic(1)|Uncertain_significance(12)|Benign(4)|Likely_benign(4) 14\n", + "Likely_pathogenic(8)|Uncertain_significance(5) 14\n", + "Uncertain_significance(1)|Benign(1)|Likely_benign(10) 14\n", + "Pathogenic(5)|Likely_pathogenic(2)|Uncertain_significance(3) 14\n", + "Likely_pathogenic(6)|Uncertain_significance(2) 14\n", + "Pathogenic(13)|Likely_pathogenic(4)|Uncertain_significance(1) 14\n", + "Pathogenic(3)|Likely_pathogenic(13)|Pathogenic,_low_penetrance(1)|Established_risk_allele(1)|Uncertain_significance(8) 14\n", + "Pathogenic(28)|Likely_pathogenic(2)|Uncertain_significance(1) 14\n", + "Uncertain_significance(1)|Benign(1)|Likely_benign(12) 14\n", + "Pathogenic(1)|Uncertain_significance(1)|Likely_benign(4) 14\n", + "Uncertain_significance(6)|Benign(3)|Likely_benign(7) 14\n", + "Likely_pathogenic(1)|Benign(6) 14\n", + "Likely_pathogenic(7)|Uncertain_significance(4) 14\n", + "Likely_pathogenic(7)|Uncertain_significance(13) 14\n", + "Pathogenic(4)|Likely_pathogenic(2)|Uncertain_significance(2) 14\n", + "Likely_pathogenic(9)|Uncertain_significance(4) 14\n", + "Uncertain_significance(7)|Benign(3)|Likely_benign(2) 14\n", + "Uncertain_significance(1)|Benign(11)|Likely_benign(4) 14\n", + "Pathogenic(1)|Likely_pathogenic(2)|Likely_benign(1) 14\n", + "Uncertain_significance(7)|Benign(2)|Likely_benign(8) 14\n", + "Pathogenic(4)|Likely_pathogenic(5)|Uncertain_significance(1) 14\n", + "Uncertain_significance(2)|Benign(4)|Likely_benign(7) 14\n", + "Uncertain_significance(1)|Benign(14)|Likely_benign(4) 14\n", + "Uncertain_significance(9)|Benign(4)|Likely_benign(4) 14\n", + "Uncertain_significance(2)|Benign(6)|Likely_benign(9) 14\n", + "Uncertain_significance(5)|Benign(8)|Likely_benign(5) 14\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Benign(1)|Likely_benign(1) 14\n", + "Uncertain_significance(3)|Benign(11)|Likely_benign(4) 13\n", + "Uncertain_significance(4)|Benign(7)|Likely_benign(2) 13\n", + "Uncertain_significance(6)|Benign(4)|Likely_benign(2) 13\n", + "Uncertain_significance(5)|Benign(5)|Likely_benign(2) 13\n", + "Uncertain_significance(2)|Likely_benign(9) 13\n", + "Uncertain_significance(9)|Benign(3)|Likely_benign(4) 13\n", + "Uncertain_significance(8)|Benign(2)|Likely_benign(9) 13\n", + "Likely_risk_allele(1)|Benign(2)|Likely_benign(6) 13\n", + "Likely_risk_allele(1)|Uncertain_significance(4)|Likely_benign(1) 13\n", + "Pathogenic(5)|Likely_pathogenic(2)|Uncertain_significance(2) 13\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Benign(1)|Likely_benign(3) 13\n", + "Likely_pathogenic(1)|Uncertain_significance(5)|Benign(1) 13\n", + "Likely_pathogenic(8)|Uncertain_significance(1) 13\n", + "Uncertain_significance(1)|Benign(10) 13\n", + "Uncertain_significance(5)|Benign(1)|Likely_benign(8) 13\n", + "Likely_pathogenic(2)|Uncertain_significance(4)|Likely_benign(1) 13\n", + "Uncertain_significance(1)|Benign(16)|Likely_benign(9) 13\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(12) 13\n", + "Pathogenic(7)|Likely_pathogenic(3)|Likely_benign(1) 13\n", + "Pathogenic(5)|Likely_pathogenic(6)|Uncertain_significance(1) 13\n", + "Uncertain_significance(14)|Benign(2) 13\n", + "Pathogenic(5)|Likely_pathogenic(8)|Uncertain_significance(9)|Likely_benign(1) 13\n", + "Likely_pathogenic(1)|Uncertain_significance(8)|Likely_benign(1) 13\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Benign(3)|Likely_benign(2) 13\n", + "Pathogenic(3)|Uncertain_significance(10)|Benign(5)|Likely_benign(5) 13\n", + "Pathogenic(1)|Likely_pathogenic(4)|Uncertain_significance(6) 13\n", + "Likely_pathogenic(2)|Uncertain_significance(10)|Benign(1)|Likely_benign(3) 13\n", + "Pathogenic(3)|Likely_pathogenic(3)|Uncertain_significance(3) 13\n", + "Likely_pathogenic(1)|Uncertain_significance(5)|Benign(1)|Likely_benign(1) 13\n", + "Uncertain_significance(16)|Likely_benign(1) 13\n", + "Pathogenic(4)|Likely_pathogenic(3)|Uncertain_significance(3) 13\n", + "Likely_pathogenic(2)|Uncertain_significance(9)|Benign(1)|Likely_benign(4) 13\n", + "Pathogenic(14)|Likely_pathogenic(5)|Uncertain_significance(5) 13\n", + "Pathogenic(4)|Likely_pathogenic(9)|Uncertain_significance(3) 13\n", + "Pathogenic(11)|Likely_pathogenic(1)|Uncertain_significance(1) 13\n", + "Uncertain_significance(6)|Likely_benign(7) 13\n", + "Uncertain_significance(5)|Uncertain_risk_allele(1)|Likely_benign(1) 13\n", + "Pathogenic(1)|Likely_pathogenic(16)|Established_risk_allele(1)|Uncertain_significance(14) 13\n", + "Pathogenic(3)|Uncertain_significance(3) 13\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(8) 13\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Likely_benign(4) 12\n", + "Pathogenic(1)|Uncertain_significance(5)|Likely_benign(3) 12\n", + "Pathogenic(6)|Uncertain_significance(2) 12\n", + "Pathogenic(7)|Likely_pathogenic(2)|Uncertain_significance(2) 12\n", + "Pathogenic(1)|Benign(3)|Likely_benign(3) 12\n", + "Uncertain_significance(5)|Benign(4)|Likely_benign(4) 12\n", + "Uncertain_significance(11)|Benign(1) 12\n", + "Uncertain_significance(3)|Benign(1)|Likely_benign(10) 12\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(6) 12\n", + "Pathogenic(1)|Likely_pathogenic(3)|Likely_benign(1) 12\n", + "Pathogenic(11)|Likely_pathogenic(2)|Uncertain_significance(1) 12\n", + "Uncertain_significance(2)|Benign(5)|Likely_benign(8) 12\n", + "Pathogenic(3)|Likely_pathogenic(1)|Uncertain_significance(5)|Likely_benign(2) 12\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Likely_benign(3) 12\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(1)|Likely_benign(2) 11\n", + "Pathogenic(3)|Likely_pathogenic(4)|Uncertain_significance(2) 11\n", + "Uncertain_significance(3)|Benign(1)|Likely_benign(13) 11\n", + "Likely_risk_allele(1)|Uncertain_significance(2)|Likely_benign(1) 11\n", + "Pathogenic(16)|Pathogenic,_low_penetrance(1)|Uncertain_significance(1) 11\n", + "Uncertain_significance(3)|Benign(2)|Likely_benign(8) 11\n", + "Uncertain_significance(9)|Likely_benign(5) 11\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Benign(8)|Likely_benign(2) 11\n", + "Pathogenic(30)|Pathogenic,_low_penetrance(1)|Uncertain_significance(1) 11\n", + "Pathogenic(3)|Uncertain_significance(1)|Likely_benign(1) 11\n", + "Likely_pathogenic(3)|Uncertain_significance(10)|Likely_benign(3) 11\n", + "Pathogenic(2)|Likely_pathogenic(6)|Uncertain_significance(1) 11\n", + "Pathogenic(24)|Pathogenic,_low_penetrance(1)|Uncertain_significance(2)|Benign(2) 11\n", + "Likely_risk_allele(1)|Likely_benign(1) 11\n", + "Pathogenic(6)|Likely_pathogenic(5)|Uncertain_significance(1) 11\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(1)|Likely_benign(1) 11\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(1)|Benign(1)|Likely_benign(2) 11\n", + "Pathogenic(1)|Uncertain_significance(1)|Benign(1)|Likely_benign(3) 11\n", + "Pathogenic(12)|Likely_pathogenic(5)|Uncertain_significance(3)|Likely_benign(2) 11\n", + "Pathogenic(1)|Uncertain_significance(9) 11\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(6)|Benign(7) 11\n", + "Uncertain_significance(1)|Benign(5)|Likely_benign(8) 11\n", + "Pathogenic(1)|Benign(1)|Likely_benign(1) 11\n", + "Uncertain_significance(1)|Benign(6)|Likely_benign(15) 11\n", + "Uncertain_significance(2)|Benign(4)|Likely_benign(17) 11\n", + "Pathogenic(3)|Likely_pathogenic(8)|Uncertain_significance(1) 11\n", + "Pathogenic(6)|Likely_pathogenic(2)|Uncertain_significance(1)|Likely_benign(1) 10\n", + "Pathogenic(3)|Likely_pathogenic(9)|Uncertain_significance(2) 10\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(5)|Likely_benign(1) 10\n", + "Likely_pathogenic(2)|Uncertain_significance(1)|Benign(9) 10\n", + "Pathogenic(1)|Uncertain_significance(21)|Likely_benign(1) 10\n", + "Pathogenic(2)|Uncertain_significance(8) 10\n", + "Likely_pathogenic(1)|Uncertain_significance(15)|Benign(3) 10\n", + "Pathogenic(4)|Likely_pathogenic(6)|Uncertain_significance(1) 10\n", + "Pathogenic(3)|Uncertain_significance(8)|Likely_benign(2) 10\n", + "Pathogenic(20)|Likely_pathogenic(3)|Uncertain_significance(1) 10\n", + "Pathogenic(5)|Likely_pathogenic(7)|Uncertain_significance(4) 10\n", + "Likely_pathogenic(1)|Uncertain_significance(9)|Benign(1)|Likely_benign(2) 10\n", + "Pathogenic(2)|Likely_pathogenic(2)|Uncertain_significance(9) 10\n", + "Uncertain_significance(9)|Benign(3)|Likely_benign(7) 10\n", + "Likely_risk_allele(1)|Benign(1) 10\n", + "Likely_pathogenic(9)|Uncertain_significance(3) 10\n", + "Likely_risk_allele(1)|Benign(2)|Likely_benign(1) 10\n", + "Pathogenic(1)|Likely_pathogenic(3)|Uncertain_significance(1)|Likely_benign(1) 10\n", + "Uncertain_significance(1)|Benign(4)|Likely_benign(8) 10\n", + "Pathogenic(27)|Likely_pathogenic(2)|Uncertain_significance(1) 10\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(3)|Benign(1) 10\n", + "Likely_pathogenic(1)|Likely_risk_allele(1)|Uncertain_significance(3) 10\n", + "Pathogenic(4)|Likely_pathogenic(3)|Uncertain_significance(2) 10\n", + "Likely_pathogenic(5)|Uncertain_risk_allele(1) 10\n", + "Likely_pathogenic(2)|Uncertain_significance(4)|Uncertain_risk_allele(1) 10\n", + "Likely_pathogenic(1)|Likely_risk_allele(1)|Uncertain_significance(4) 10\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(2)|Likely_benign(4) 10\n", + "Likely_pathogenic(1)|Uncertain_significance(4)|Benign(1) 10\n", + "Likely_pathogenic(1)|Uncertain_significance(6)|Benign(1)|Likely_benign(4) 10\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Benign(5) 10\n", + "Pathogenic(3)|Likely_pathogenic(5)|Uncertain_significance(2) 10\n", + "Likely_pathogenic(1)|Uncertain_significance(4)|Benign(1)|Likely_benign(2) 10\n", + "Likely_risk_allele(1)|Uncertain_significance(2)|Benign(2)|Likely_benign(5) 10\n", + "Pathogenic(1)|Uncertain_significance(6)|Benign(5)|Likely_benign(4) 10\n", + "Uncertain_risk_allele(1)|Benign(4)|Likely_benign(1) 10\n", + "Likely_pathogenic(1)|Benign(3)|Likely_benign(1) 10\n", + "Uncertain_significance(5)|Benign(4)|Likely_benign(2) 10\n", + "Uncertain_significance(11)|Benign(1)|Likely_benign(5) 10\n", + "Pathogenic(1)|Uncertain_significance(1)|Likely_benign(2) 10\n", + "Uncertain_significance(4)|Uncertain_risk_allele(1)|Likely_benign(4) 10\n", + "Uncertain_significance(3)|Benign(7)|Likely_benign(3) 10\n", + "Likely_pathogenic(1)|Benign(1)|Likely_benign(2) 10\n", + "Uncertain_significance(2)|Benign(3)|Likely_benign(8) 10\n", + "Pathogenic(3)|Likely_pathogenic(2)|Uncertain_significance(5) 9\n", + "Uncertain_significance(16)|Benign(1) 9\n", + "Uncertain_significance(1)|Benign(7)|Likely_benign(8) 9\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Benign(2)|Likely_benign(1) 9\n", + "Uncertain_significance(7)|Benign(4)|Likely_benign(12) 9\n", + "Pathogenic(8)|Likely_pathogenic(4)|Uncertain_significance(1) 9\n", + "Uncertain_significance(6)|Benign(2)|Likely_benign(10) 9\n", + "Uncertain_significance(2)|Benign(9)|Likely_benign(3) 9\n", + "Uncertain_significance(1)|Benign(9) 9\n", + "Pathogenic(31)|Uncertain_significance(1) 9\n", + "Uncertain_significance(2)|Benign(12)|Likely_benign(4) 9\n", + "Uncertain_significance(12)|Benign(1)|Likely_benign(4) 9\n", + "Uncertain_significance(2)|Benign(7)|Likely_benign(1) 9\n", + "Likely_pathogenic(2)|Uncertain_significance(11)|Benign(1) 9\n", + "Uncertain_significance(7)|Benign(9) 9\n", + "Uncertain_significance(11)|Benign(1)|Likely_benign(4) 9\n", + "Pathogenic(7)|Likely_pathogenic(3)|Uncertain_significance(1) 9\n", + "Uncertain_significance(1)|Benign(9)|Likely_benign(9) 9\n", + "Pathogenic(4)|Likely_pathogenic(4)|Uncertain_significance(2) 9\n", + "Pathogenic(2)|Likely_pathogenic(2)|Uncertain_significance(8) 9\n", + "Pathogenic(2)|Likely_pathogenic(3)|Uncertain_significance(2)|Benign(1) 9\n", + "Uncertain_significance(2)|Benign(7) 9\n", + "Pathogenic(2)|Likely_pathogenic(1)|Benign(6)|Likely_benign(2) 9\n", + "Uncertain_significance(1)|Benign(6)|Likely_benign(6) 9\n", + "Likely_pathogenic(3)|Uncertain_significance(9) 9\n", + "Pathogenic(29)|Likely_pathogenic(2)|Uncertain_significance(1) 9\n", + "Uncertain_significance(1)|Benign(2)|Likely_benign(10) 9\n", + "Pathogenic(1)|Uncertain_significance(2)|Benign(1)|Likely_benign(1) 9\n", + "Uncertain_significance(3)|Benign(7)|Likely_benign(7) 9\n", + "Pathogenic(20)|Likely_pathogenic(2)|Uncertain_significance(1) 9\n", + "Uncertain_significance(3)|Benign(6)|Likely_benign(13) 8\n", + "Pathogenic(1)|Likely_pathogenic(7)|Uncertain_significance(2) 8\n", + "Likely_risk_allele(1)|Benign(3)|Likely_benign(1) 8\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(2)|Likely_benign(1) 8\n", + "Pathogenic(1)|Likely_pathogenic(5)|Uncertain_significance(17)|Likely_benign(1) 8\n", + "Pathogenic(3)|Likely_pathogenic(4)|Uncertain_significance(4) 8\n", + "Pathogenic(6)|Likely_pathogenic(5)|Uncertain_significance(2) 8\n", + "Pathogenic(1)|Uncertain_significance(2)|Benign(1) 8\n", + "Pathogenic(1)|Likely_pathogenic(6)|Uncertain_significance(2) 8\n", + "Uncertain_significance(11)|Benign(3)|Likely_benign(6) 8\n", + "Pathogenic(1)|Uncertain_significance(4)|Benign(2)|Likely_benign(3) 8\n", + "Uncertain_significance(4)|Benign(9)|Likely_benign(1) 8\n", + "Pathogenic(1)|Benign(6)|Likely_benign(2) 8\n", + "Pathogenic(2)|Uncertain_significance(1)|Benign(3) 8\n", + "Pathogenic(1)|Benign(3) 8\n", + "Uncertain_significance(12)|Benign(1) 8\n", + "Likely_pathogenic(1)|Uncertain_significance(4)|Likely_benign(4) 8\n", + "Pathogenic(1)|Likely_pathogenic(4)|Uncertain_significance(10)|Likely_benign(2) 8\n", + "Pathogenic(5)|Likely_pathogenic(4)|Uncertain_significance(4)|Benign(1) 8\n", + "Pathogenic(5)|Likely_pathogenic(5)|Uncertain_significance(1) 8\n", + "Pathogenic(26)|Uncertain_significance(1) 8\n", + "Uncertain_significance(1)|Benign(7)|Likely_benign(9) 8\n", + "Uncertain_significance(11)|Benign(2)|Likely_benign(2) 8\n", + "Pathogenic(5)|Likely_pathogenic(1)|Likely_benign(1) 8\n", + "Pathogenic(5)|Likely_pathogenic(4)|Uncertain_significance(1) 8\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Benign(2)|Likely_benign(2) 8\n", + "Pathogenic(7)|Likely_pathogenic(6)|Uncertain_significance(2) 8\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Benign(1)|Likely_benign(3) 8\n", + "Pathogenic(1)|Uncertain_significance(11) 8\n", + "Uncertain_significance(1)|Likely_benign(10) 8\n", + "Pathogenic(2)|Likely_pathogenic(1)|Uncertain_significance(3)|Benign(1) 8\n", + "Pathogenic(7)|Likely_pathogenic(2)|Uncertain_significance(1) 8\n", + "Uncertain_significance(3)|Benign(2)|Likely_benign(10) 8\n", + "Pathogenic(8)|Likely_pathogenic(7)|Uncertain_significance(6) 7\n", + "Uncertain_significance(3)|Benign(7) 7\n", + "Pathogenic(4)|Likely_pathogenic(4)|Uncertain_significance(1) 7\n", + "Pathogenic(1)|Likely_pathogenic(11)|Likely_benign(1) 7\n", + "Pathogenic(2)|Likely_pathogenic(2)|Likely_benign(1) 7\n", + "Pathogenic(2)|Likely_pathogenic(7)|Uncertain_significance(3) 7\n", + "Pathogenic(7)|Likely_pathogenic(3)|Uncertain_significance(3) 7\n", + "Pathogenic(6)|Likely_pathogenic(1)|Uncertain_significance(2) 7\n", + "Pathogenic(14)|Benign(1) 7\n", + "Pathogenic(3)|Uncertain_significance(5)|Benign(6)|Likely_benign(3) 7\n", + "Pathogenic(3)|Likely_pathogenic(3)|Uncertain_significance(2)|Likely_benign(1) 7\n", + "Likely_pathogenic(2)|Uncertain_significance(6)|Likely_benign(3) 7\n", + "Pathogenic(6)|Likely_pathogenic(7)|Uncertain_significance(1) 7\n", + "Uncertain_significance(4)|Benign(6)|Likely_benign(7) 7\n", + "Uncertain_significance(4)|Benign(6)|Likely_benign(3) 7\n", + "Pathogenic(1)|Likely_pathogenic(6)|Uncertain_significance(4) 7\n", + "Uncertain_significance(12)|Benign(2)|Likely_benign(6) 7\n", + "Likely_pathogenic(1)|Uncertain_significance(7)|Benign(1)|Likely_benign(1) 7\n", + "Pathogenic(11)|Likely_pathogenic(4)|Likely_benign(1) 7\n", + "Uncertain_significance(2)|Benign(8)|Likely_benign(10) 7\n", + "Likely_pathogenic(1)|Benign(3)|Likely_benign(3) 7\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Likely_benign(6) 7\n", + "Likely_pathogenic(7)|Uncertain_significance(1) 7\n", + "Likely_pathogenic(1)|Uncertain_significance(13)|Likely_benign(3) 7\n", + "Pathogenic(1)|Benign(2) 7\n", + "Pathogenic(3)|Likely_pathogenic(7)|Uncertain_significance(3) 7\n", + "Pathogenic(2)|Uncertain_significance(2)|Likely_benign(1) 7\n", + "Uncertain_significance(4)|Likely_benign(8) 7\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Benign(3) 7\n", + "Pathogenic(14)|Uncertain_significance(2) 7\n", + "Pathogenic(1)|Uncertain_significance(1)|Benign(2)|Likely_benign(1) 7\n", + "Likely_pathogenic(1)|Uncertain_significance(5)|Benign(2)|Likely_benign(5) 7\n", + "Pathogenic(8)|Likely_pathogenic(6)|Uncertain_significance(3) 7\n", + "Uncertain_significance(3)|Benign(4)|Likely_benign(7) 7\n", + "Likely_pathogenic(1)|Uncertain_significance(8)|Likely_benign(2) 7\n", + "Uncertain_significance(1)|Benign(11)|Likely_benign(2) 7\n", + "Uncertain_significance(3)|Benign(9)|Likely_benign(1) 7\n", + "Likely_pathogenic(2)|Uncertain_significance(10)|Likely_benign(1) 7\n", + "Likely_pathogenic(2)|Likely_risk_allele(1)|Uncertain_significance(3) 7\n", + "Pathogenic(1)|Likely_pathogenic(3)|Pathogenic,_low_penetrance(1)|Uncertain_significance(5) 7\n", + "Pathogenic(6)|Likely_pathogenic(3)|Uncertain_significance(4)|Benign(1)|Likely_benign(2) 7\n", + "Pathogenic(7)|Likely_pathogenic(1)|Benign(1) 7\n", + "Likely_pathogenic(1)|Uncertain_significance(5)|Benign(1)|Likely_benign(2) 7\n", + "Likely_risk_allele(1)|Uncertain_significance(3)|Likely_benign(1) 7\n", + "Pathogenic(1)|Uncertain_significance(2)|Likely_benign(4) 7\n", + "Uncertain_significance(4)|Benign(1)|Likely_benign(11) 7\n", + "Pathogenic(3)|Uncertain_risk_allele(1) 7\n", + "Likely_pathogenic(1)|Uncertain_significance(9)|Likely_benign(3) 7\n", + "Likely_risk_allele(1)|Likely_benign(2) 7\n", + "Pathogenic(1)|Uncertain_significance(4)|Likely_benign(2) 7\n", + "Uncertain_significance(6)|Benign(1)|Likely_benign(8) 7\n", + "Uncertain_significance(2)|Benign(5)|Likely_benign(6) 7\n", + "Uncertain_risk_allele(1)|Benign(1)|Likely_benign(1) 7\n", + "Uncertain_significance(1)|Benign(7)|Likely_benign(7) 7\n", + "Uncertain_significance(2)|Uncertain_risk_allele(1)|Likely_benign(2) 7\n", + "Pathogenic(1)|Uncertain_significance(2)|Benign(5)|Likely_benign(1) 7\n", + "Uncertain_risk_allele(1)|Benign(7)|Likely_benign(2) 7\n", + "Uncertain_risk_allele(1)|Benign(6)|Likely_benign(4) 7\n", + "Likely_pathogenic(1)|Uncertain_significance(4)|Uncertain_risk_allele(1) 7\n", + "Likely_pathogenic(1)|Benign(8) 7\n", + "Uncertain_significance(6)|Benign(4)|Likely_benign(1) 7\n", + "Pathogenic(8)|Likely_pathogenic(2)|Pathogenic,_low_penetrance(1)|Uncertain_significance(1) 7\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(1)|Uncertain_risk_allele(1) 7\n", + "Likely_risk_allele(1)|Uncertain_significance(5) 7\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Benign(6)|Likely_benign(3) 7\n", + "Uncertain_significance(14)|Benign(1) 7\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Likely_benign(4) 7\n", + "Pathogenic(23)|Likely_pathogenic(10)|Uncertain_significance(4) 7\n", + "Pathogenic(1)|Uncertain_significance(4)|Benign(1) 7\n", + "Uncertain_significance(1)|Benign(5)|Likely_benign(11) 7\n", + "Pathogenic(7)|Likely_pathogenic(1)|Uncertain_significance(1) 7\n", + "Pathogenic(3)|Likely_pathogenic(3)|Uncertain_risk_allele(1) 7\n", + "Pathogenic(1)|Likely_pathogenic(3)|Uncertain_significance(14)|Likely_benign(1) 7\n", + "Uncertain_significance(2)|Benign(7)|Likely_benign(3) 7\n", + "Likely_pathogenic(1)|Uncertain_significance(7)|Likely_benign(5) 7\n", + "Likely_pathogenic(2)|Uncertain_significance(16)|Likely_benign(1) 7\n", + "Pathogenic(1)|Likely_pathogenic(2)|Likely_risk_allele(1)|Uncertain_significance(3) 7\n", + "Likely_pathogenic(1)|Benign(1)|Likely_benign(3) 7\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Uncertain_risk_allele(1) 7\n", + "Likely_pathogenic(1)|Uncertain_significance(11)|Likely_benign(1) 6\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Benign(1)|Likely_benign(5) 6\n", + "Pathogenic(13)|Likely_pathogenic(1)|Uncertain_significance(3) 6\n", + "Pathogenic(1)|Uncertain_significance(3)|Benign(3) 6\n", + "Likely_risk_allele(1)|Benign(1)|Likely_benign(3) 6\n", + "Pathogenic(2)|Likely_pathogenic(8)|Uncertain_significance(1) 6\n", + "Pathogenic(5)|Likely_pathogenic(4)|Benign(1) 6\n", + "Pathogenic(1)|Uncertain_significance(6)|Likely_benign(4) 6\n", + "Pathogenic(7)|Likely_pathogenic(6)|Uncertain_significance(1) 6\n", + "Uncertain_significance(6)|Benign(4) 6\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(6)|Likely_benign(1) 6\n", + "Pathogenic(1)|Likely_pathogenic(4)|Uncertain_significance(5)|Likely_benign(1) 6\n", + "Pathogenic(1)|Uncertain_significance(9)|Likely_benign(4) 6\n", + "Pathogenic(1)|Uncertain_significance(7)|Benign(4)|Likely_benign(6) 6\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Benign(6)|Likely_benign(4) 6\n", + "Uncertain_significance(2)|Benign(4)|Likely_benign(8) 6\n", + "Pathogenic(4)|Likely_pathogenic(4)|Uncertain_significance(1)|Likely_benign(1) 6\n", + "Pathogenic(2)|Likely_pathogenic(9)|Uncertain_significance(2)|Likely_benign(1) 6\n", + "Uncertain_significance(11)|Benign(1)|Likely_benign(1) 6\n", + "Pathogenic(12)|Likely_pathogenic(6)|Uncertain_significance(5) 6\n", + "Pathogenic(1)|Uncertain_significance(3)|Benign(2) 6\n", + "Likely_pathogenic(2)|Uncertain_significance(3)|Likely_benign(2) 6\n", + "Uncertain_significance(1)|Benign(13)|Likely_benign(6) 6\n", + "Pathogenic(3)|Likely_pathogenic(3)|Uncertain_significance(1)|Benign(1) 6\n", + "Pathogenic(6)|Likely_pathogenic(1)|Likely_benign(3) 6\n", + "Uncertain_significance(6)|Benign(8) 6\n", + "Uncertain_significance(5)|Benign(3)|Likely_benign(10) 6\n", + "Pathogenic(6)|Likely_pathogenic(5)|Uncertain_significance(4) 6\n", + "Pathogenic(1)|Uncertain_significance(2)|Benign(2)|Likely_benign(1) 6\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(4)|Benign(1) 6\n", + "Pathogenic(12)|Likely_pathogenic(4)|Uncertain_significance(1)|Likely_benign(1) 6\n", + "Pathogenic(2)|Likely_pathogenic(12)|Uncertain_significance(5) 6\n", + "Likely_pathogenic(1)|Uncertain_significance(4)|Benign(1)|Likely_benign(5) 6\n", + "Likely_pathogenic(1)|Uncertain_significance(11) 6\n", + "Likely_pathogenic(1)|Benign(2)|Likely_benign(4) 6\n", + "Pathogenic(1)|Uncertain_significance(8)|Likely_benign(1) 6\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(3)|Likely_benign(3) 6\n", + "Pathogenic(2)|Likely_pathogenic(4)|Uncertain_significance(3) 6\n", + "Pathogenic(1)|Uncertain_significance(2)|Benign(4)|Likely_benign(5) 6\n", + "Uncertain_significance(5)|Likely_benign(9) 6\n", + "Pathogenic(4)|Uncertain_significance(10)|Likely_benign(1) 6\n", + "Likely_risk_allele(1)|Benign(8)|Likely_benign(4) 6\n", + "Pathogenic(3)|Likely_pathogenic(2)|Uncertain_significance(6) 6\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(3)|Likely_benign(1) 6\n", + "Pathogenic(30)|Uncertain_significance(1) 6\n", + "Uncertain_significance(1)|Benign(2)|Likely_benign(9) 6\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(1)|Benign(1) 6\n", + "Likely_pathogenic(2)|Likely_risk_allele(1)|Uncertain_significance(1) 6\n", + "Likely_pathogenic(2)|Uncertain_significance(2)|Benign(1) 6\n", + "Pathogenic(1)|Likely_pathogenic(6)|Uncertain_significance(7) 6\n", + "Uncertain_significance(2)|Benign(4)|Likely_benign(9) 6\n", + "Pathogenic(1)|Uncertain_significance(4)|Benign(3) 5\n", + "Pathogenic(10)|Likely_pathogenic(7)|Uncertain_significance(2)|Benign(1) 5\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(3)|Likely_benign(5) 5\n", + "Pathogenic(8)|Likely_pathogenic(8)|Uncertain_significance(5) 5\n", + "Pathogenic(10)|Likely_pathogenic(2)|Uncertain_significance(2) 5\n", + "Pathogenic(15)|Likely_pathogenic(1)|Uncertain_significance(1)|Likely_benign(1) 5\n", + "Pathogenic(1)|Likely_pathogenic(4)|Uncertain_significance(7) 5\n", + "Uncertain_significance(9)|Benign(1)|Likely_benign(7) 5\n", + "Pathogenic(2)|Uncertain_significance(11)|Benign(3)|Likely_benign(2) 5\n", + "Likely_pathogenic(1)|Uncertain_significance(10)|Benign(2) 5\n", + "Pathogenic(2)|Likely_pathogenic(1)|Uncertain_significance(7) 5\n", + "Pathogenic(1)|Likely_pathogenic(12)|Uncertain_significance(6) 5\n", + "Pathogenic(1)|Uncertain_significance(8)|Benign(2)|Likely_benign(4) 5\n", + "Pathogenic(2)|Likely_pathogenic(2)|Uncertain_significance(6) 5\n", + "Pathogenic(5)|Likely_pathogenic(8)|Uncertain_significance(3) 5\n", + "Uncertain_significance(4)|Benign(9)|Likely_benign(5) 5\n", + "Pathogenic(1)|Likely_pathogenic(3)|Uncertain_significance(12) 5\n", + "Likely_pathogenic(3)|Uncertain_significance(6) 5\n", + "Pathogenic(1)|Likely_pathogenic(5)|Uncertain_significance(2) 5\n", + "Pathogenic(4)|Uncertain_significance(11)|Benign(2)|Likely_benign(1) 5\n", + "Pathogenic(6)|Likely_pathogenic(6)|Uncertain_significance(1) 5\n", + "Pathogenic(2)|Likely_pathogenic(3)|Uncertain_significance(7) 5\n", + "Pathogenic(5)|Likely_pathogenic(4)|Uncertain_significance(5) 5\n", + "Pathogenic(1)|Likely_pathogenic(6)|Uncertain_significance(7)|Likely_benign(1) 5\n", + "Pathogenic(2)|Uncertain_significance(14)|Likely_benign(2) 5\n", + "Pathogenic(2)|Likely_pathogenic(6)|Uncertain_significance(7) 5\n", + "Pathogenic(3)|Uncertain_significance(2)|Likely_benign(1) 5\n", + "Likely_pathogenic(1)|Uncertain_significance(4)|Benign(1)|Likely_benign(1) 5\n", + "Uncertain_significance(7)|Benign(6)|Likely_benign(9) 5\n", + "Pathogenic(1)|Likely_pathogenic(4)|Uncertain_significance(5) 5\n", + "Uncertain_significance(1)|Benign(4)|Likely_benign(10) 5\n", + "Likely_pathogenic(1)|Uncertain_significance(5)|Benign(2)|Likely_benign(3) 5\n", + "Uncertain_significance(2)|Benign(3)|Likely_benign(7) 5\n", + "Likely_pathogenic(1)|Benign(5)|Likely_benign(3) 5\n", + "Uncertain_significance(2)|Benign(7)|Likely_benign(10) 5\n", + "Uncertain_significance(11)|Likely_benign(9) 5\n", + "Uncertain_significance(13)|Likely_benign(5) 5\n", + "Likely_risk_allele(1)|Uncertain_significance(1)|Benign(1) 5\n", + "Pathogenic(6)|Likely_pathogenic(2)|Uncertain_significance(3) 5\n", + "Uncertain_significance(6)|Benign(4)|Likely_benign(10) 5\n", + "Uncertain_significance(2)|Benign(8)|Likely_benign(3) 5\n", + "Pathogenic(5)|Likely_pathogenic(1)|Uncertain_significance(2) 5\n", + "Likely_pathogenic(1)|Uncertain_significance(7)|Benign(2) 5\n", + "Pathogenic(21)|Uncertain_significance(1) 5\n", + "Pathogenic(2)|Likely_pathogenic(2)|Uncertain_significance(5)|Benign(2)|Likely_benign(3) 5\n", + "Pathogenic(23)|Likely_pathogenic(8)|Uncertain_significance(7) 5\n", + "Likely_pathogenic(1)|Uncertain_significance(16)|Benign(1)|Likely_benign(1) 5\n", + "Uncertain_significance(12)|Benign(1)|Likely_benign(3) 5\n", + "Uncertain_significance(3)|Likely_benign(11) 5\n", + "Pathogenic(3)|Likely_pathogenic(1)|Uncertain_significance(5) 5\n", + "Pathogenic(2)|Likely_pathogenic(1)|Uncertain_significance(10)|Benign(3)|Likely_benign(3) 5\n", + "Likely_pathogenic(1)|Uncertain_significance(4)|Benign(6)|Likely_benign(4) 5\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(2)|Likely_benign(3) 5\n", + "Pathogenic(1)|Uncertain_significance(2)|Benign(1)|Likely_benign(3) 5\n", + "Pathogenic(3)|Likely_pathogenic(1)|Benign(1) 5\n", + "Likely_pathogenic(1)|Uncertain_significance(4)|Likely_benign(6) 5\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Benign(1)|Likely_benign(1) 5\n", + "Pathogenic(15)|Likely_pathogenic(4)|Uncertain_significance(1) 5\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Benign(3) 5\n", + "Pathogenic(2)|Likely_pathogenic(2)|Uncertain_significance(4) 5\n", + "Pathogenic(10)|Likely_pathogenic(10)|Uncertain_significance(4)|Likely_benign(2) 5\n", + "Pathogenic(7)|Likely_pathogenic(8)|Uncertain_significance(1)|Likely_benign(1) 5\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Uncertain_risk_allele(1) 5\n", + "Uncertain_significance(6)|Benign(2)|Likely_benign(11) 5\n", + "Pathogenic(13)|Uncertain_significance(1) 5\n", + "Pathogenic(1)|Benign(1)|Likely_benign(2) 5\n", + "Pathogenic(4)|Likely_pathogenic(4)|Uncertain_significance(4)|Likely_benign(1) 5\n", + "Pathogenic(1)|Uncertain_significance(2)|Benign(6)|Likely_benign(12) 5\n", + "Uncertain_significance(1)|Benign(16)|Likely_benign(5) 5\n", + "Pathogenic(9)|Likely_pathogenic(4)|Uncertain_significance(1)|Benign(1) 5\n", + "Pathogenic(3)|Likely_pathogenic(5)|Uncertain_significance(3) 5\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(3)|Likely_benign(1) 5\n", + "Likely_pathogenic(4)|Uncertain_significance(2)|Benign(1) 5\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(8)|Likely_benign(1) 5\n", + "Pathogenic(16)|Likely_pathogenic(2)|Uncertain_significance(1) 4\n", + "Uncertain_significance(5)|Benign(6)|Likely_benign(1) 4\n", + "Pathogenic(19)|Likely_pathogenic(2)|Uncertain_significance(1) 4\n", + "Pathogenic(9)|Likely_pathogenic(1)|Uncertain_significance(3) 4\n", + "Likely_pathogenic(1)|Uncertain_significance(7)|Benign(1) 4\n", + "Pathogenic(19)|Uncertain_significance(1) 4\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Benign(2) 4\n", + "Likely_pathogenic(1)|Benign(1)|Likely_benign(6) 4\n", + "Pathogenic(4)|Likely_pathogenic(7)|Uncertain_significance(1) 4\n", + "Uncertain_significance(1)|Benign(11)|Likely_benign(1) 4\n", + "Pathogenic(1)|Benign(4) 4\n", + "Uncertain_significance(11)|Benign(1)|Likely_benign(3) 4\n", + "Pathogenic(1)|Uncertain_significance(9)|Benign(2)|Likely_benign(1) 4\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(4)|Likely_benign(2) 4\n", + "Pathogenic(2)|Likely_pathogenic(6)|Uncertain_significance(3) 4\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(1)|Likely_benign(8) 4\n", + "Pathogenic(1)|Benign(5)|Likely_benign(3) 4\n", + "Pathogenic(5)|Likely_pathogenic(7)|Uncertain_significance(1) 4\n", + "Pathogenic(1)|Likely_pathogenic(1)|Benign(2) 4\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(3)|Likely_benign(6) 4\n", + "Uncertain_significance(4)|Benign(1)|Likely_benign(9) 4\n", + "Pathogenic(7)|Uncertain_significance(1)|Likely_benign(2) 4\n", + "Pathogenic(3)|Benign(1) 4\n", + "Likely_pathogenic(1)|Benign(6)|Likely_benign(4) 4\n", + "Pathogenic(1)|Uncertain_significance(10)|Benign(1)|Likely_benign(2) 4\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Benign(1)|Likely_benign(8) 4\n", + "Uncertain_significance(16)|Benign(3)|Likely_benign(4) 4\n", + "Likely_pathogenic(2)|Uncertain_significance(7)|Likely_benign(1) 4\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Likely_benign(6) 4\n", + "Likely_pathogenic(1)|Benign(2)|Likely_benign(6) 4\n", + "Pathogenic(1)|Benign(5) 4\n", + "Uncertain_significance(10)|Likely_benign(4) 4\n", + "Pathogenic(2)|Likely_pathogenic(6)|Uncertain_significance(2) 4\n", + "Pathogenic(1)|Likely_pathogenic(5)|Uncertain_significance(4) 4\n", + "Uncertain_significance(1)|Benign(8)|Likely_benign(6) 4\n", + "Likely_pathogenic(1)|Uncertain_significance(8)|Benign(2)|Likely_benign(3) 4\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(5)|Likely_benign(4) 4\n", + "Pathogenic(24)|Likely_pathogenic(10)|Pathogenic,_low_penetrance(1)|Uncertain_significance(1)|Benign(1) 4\n", + "Likely_pathogenic(8)|Uncertain_significance(2) 4\n", + "Uncertain_significance(1)|Benign(5)|Likely_benign(12) 4\n", + "Likely_pathogenic(3)|Uncertain_significance(2)|Likely_benign(3) 4\n", + "Uncertain_significance(5)|Benign(1)|Likely_benign(10) 4\n", + "Likely_pathogenic(2)|Uncertain_significance(11) 4\n", + "Pathogenic(14)|Likely_pathogenic(1)|Uncertain_significance(1) 4\n", + "Pathogenic(2)|Likely_pathogenic(5)|Uncertain_significance(3) 4\n", + "Pathogenic(1)|Likely_pathogenic(4)|Uncertain_significance(9) 4\n", + "Pathogenic(3)|Likely_pathogenic(5)|Uncertain_significance(2)|Likely_benign(1) 4\n", + "Pathogenic(18)|Likely_pathogenic(2)|Uncertain_significance(2) 4\n", + "Pathogenic(1)|Uncertain_significance(3)|Likely_benign(3) 4\n", + "Pathogenic(21)|Benign(1) 4\n", + "Pathogenic(2)|Uncertain_significance(6) 4\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(6)|Benign(2)|Likely_benign(1) 4\n", + "Pathogenic(2)|Likely_pathogenic(4)|Uncertain_significance(9) 4\n", + "Uncertain_significance(3)|Benign(18)|Likely_benign(3) 4\n", + "Likely_pathogenic(1)|Uncertain_significance(6)|Likely_benign(2) 4\n", + "Uncertain_significance(7)|Benign(4)|Likely_benign(11) 4\n", + "Pathogenic(1)|Likely_pathogenic(7)|Uncertain_significance(4) 4\n", + "Likely_pathogenic(1)|Uncertain_significance(7)|Likely_benign(3) 4\n", + "Pathogenic(7)|Likely_pathogenic(4)|Likely_benign(1) 4\n", + "Pathogenic(1)|Benign(2)|Likely_benign(1) 4\n", + "Pathogenic(8)|Likely_pathogenic(2)|Uncertain_significance(5)|Benign(1)|Likely_benign(3) 4\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(1)|Benign(1)|Likely_benign(1) 4\n", + "Likely_pathogenic(1)|Uncertain_significance(14)|Likely_benign(3) 4\n", + "Pathogenic(12)|Likely_pathogenic(2)|Likely_benign(1) 4\n", + "Uncertain_significance(9)|Benign(3)|Likely_benign(6) 4\n", + "Likely_pathogenic(2)|Uncertain_significance(9)|Likely_benign(2) 4\n", + "Pathogenic(5)|Likely_pathogenic(3)|Uncertain_significance(2) 4\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Benign(3)|Likely_benign(2) 4\n", + "Pathogenic(1)|Uncertain_significance(2)|Benign(2) 3\n", + "Pathogenic(3)|Uncertain_significance(7) 3\n", + "Likely_pathogenic(1)|Benign(10)|Likely_benign(5) 3\n", + "Uncertain_significance(7)|Benign(1)|Likely_benign(6) 3\n", + "Likely_pathogenic(1)|Uncertain_significance(5)|Likely_benign(4) 3\n", + "Likely_pathogenic(1)|Benign(10)|Likely_benign(3) 3\n", + "Pathogenic(6)|Likely_pathogenic(2)|Uncertain_significance(2) 3\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Benign(6)|Likely_benign(3) 3\n", + "Likely_pathogenic(1)|Benign(9) 3\n", + "Pathogenic(2)|Likely_pathogenic(1)|Uncertain_significance(1)|Likely_benign(1) 3\n", + "Likely_pathogenic(2)|Benign(1)|Likely_benign(1) 3\n", + "Pathogenic(5)|Likely_pathogenic(3)|Uncertain_significance(1)|Likely_benign(5) 3\n", + "Uncertain_significance(1)|Benign(13) 3\n", + "Pathogenic(3)|Likely_pathogenic(1)|Uncertain_significance(8)|Benign(1)|Likely_benign(3) 3\n", + "Likely_pathogenic(2)|Uncertain_significance(3)|Benign(3)|Likely_benign(10) 3\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(3)|Benign(1)|Likely_benign(2) 3\n", + "Likely_pathogenic(1)|Benign(6)|Likely_benign(2) 3\n", + "Uncertain_significance(6)|Benign(4)|Likely_benign(8) 3\n", + "Likely_pathogenic(2)|Established_risk_allele(1)|Uncertain_significance(4)|Benign(4)|Likely_benign(5) 3\n", + "Likely_pathogenic(1)|Uncertain_significance(9)|Benign(1)|Likely_benign(6) 3\n", + "Likely_pathogenic(2)|Uncertain_significance(4)|Benign(1)|Likely_benign(1) 3\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(4) 3\n", + "Uncertain_significance(7)|Benign(1)|Likely_benign(8) 3\n", + "Pathogenic(3)|Likely_pathogenic(1)|Uncertain_significance(6)|Likely_benign(2) 3\n", + "Established_risk_allele(1)|Benign(5)|Likely_benign(5) 3\n", + "Pathogenic(2)|Likely_pathogenic(5)|Uncertain_significance(4) 3\n", + "Likely_pathogenic(1)|Uncertain_significance(6)|Benign(1) 3\n", + "Likely_pathogenic(2)|Uncertain_significance(8)|Benign(2)|Likely_benign(4) 3\n", + "Pathogenic(4)|Likely_pathogenic(8)|Established_risk_allele(2)|Uncertain_significance(7)|Likely_benign(1) 3\n", + "Uncertain_significance(3)|Benign(5)|Likely_benign(14) 3\n", + "Pathogenic(10)|Likely_pathogenic(6)|Uncertain_significance(3) 3\n", + "Uncertain_significance(7)|Benign(1)|Likely_benign(9) 3\n", + "Pathogenic(9)|Likely_pathogenic(3)|Uncertain_significance(1) 3\n", + "Pathogenic(2)|Benign(1)|Likely_benign(1) 3\n", + "Pathogenic(3)|Likely_pathogenic(4)|Uncertain_significance(3) 3\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(7)|Benign(1)|Likely_benign(1) 3\n", + "Pathogenic(3)|Likely_pathogenic(1)|Uncertain_significance(3)|Benign(1)|Likely_benign(2) 3\n", + "Pathogenic(2)|Likely_pathogenic(6)|Uncertain_significance(4) 3\n", + "Likely_pathogenic(2)|Uncertain_significance(3)|Benign(2) 3\n", + "Likely_pathogenic(2)|Uncertain_significance(3)|Likely_benign(3) 3\n", + "Likely_pathogenic(1)|Benign(5) 3\n", + "Uncertain_significance(1)|Benign(3)|Likely_benign(9) 3\n", + "Pathogenic(4)|Likely_pathogenic(3)|Uncertain_significance(2)|Benign(1) 3\n", + "Pathogenic(1)|Uncertain_significance(1)|Benign(3) 3\n", + "Pathogenic(2)|Uncertain_significance(1)|Benign(1) 3\n", + "Pathogenic(11)|Benign(1) 3\n", + "Pathogenic(10)|Likely_pathogenic(3)|Uncertain_significance(3) 3\n", + "Pathogenic(4)|Uncertain_significance(4) 3\n", + "Pathogenic(8)|Likely_pathogenic(3)|Uncertain_significance(2) 3\n", + "Uncertain_significance(12)|Likely_benign(3) 3\n", + "Pathogenic(2)|Likely_pathogenic(1)|Uncertain_significance(8)|Benign(1)|Likely_benign(4) 3\n", + "Pathogenic(19)|Likely_pathogenic(1)|Uncertain_significance(1) 3\n", + "Uncertain_significance(2)|Benign(6)|Likely_benign(5) 3\n", + "Pathogenic(4)|Benign(1) 3\n", + "Pathogenic(1)|Uncertain_significance(1)|Benign(3)|Likely_benign(8) 3\n", + "Pathogenic(2)|Likely_pathogenic(1)|Likely_benign(2) 3\n", + "Pathogenic(2)|Uncertain_significance(4)|Benign(1)|Likely_benign(1) 3\n", + "Uncertain_significance(11)|Likely_benign(7) 3\n", + "Uncertain_significance(7)|Benign(2)|Likely_benign(9) 3\n", + "Pathogenic(1)|Likely_benign(3) 3\n", + "Pathogenic(1)|Uncertain_significance(1)|Benign(1)|Likely_benign(2) 3\n", + "Pathogenic(1)|Benign(2)|Likely_benign(2) 3\n", + "Pathogenic(3)|Likely_pathogenic(8)|Uncertain_significance(2) 3\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(1)|Benign(1)|Likely_benign(1) 3\n", + "Pathogenic(5)|Likely_pathogenic(2)|Uncertain_significance(1)|Likely_benign(1) 3\n", + "Likely_risk_allele(1)|Benign(4) 3\n", + "Pathogenic(2)|Likely_pathogenic(1)|Uncertain_significance(1)|Uncertain_risk_allele(1) 3\n", + "Pathogenic(4)|Likely_pathogenic(1)|Benign(1) 3\n", + "Pathogenic(4)|Uncertain_significance(1)|Benign(1) 3\n", + "Uncertain_significance(1)|Uncertain_risk_allele(1)|Benign(2)|Likely_benign(4) 3\n", + "Pathogenic(2)|Likely_pathogenic(1)|Uncertain_significance(1)|Benign(1) 3\n", + "Pathogenic(1)|Likely_pathogenic(8)|Uncertain_significance(2) 3\n", + "Likely_pathogenic(1)|Uncertain_significance(6)|Benign(1)|Likely_benign(8) 3\n", + "Pathogenic(1)|Likely_pathogenic(6)|Uncertain_significance(1) 3\n", + "Uncertain_significance(1)|Benign(3)|Likely_benign(15) 3\n", + "Uncertain_significance(3)|Benign(4)|Likely_benign(10) 3\n", + "Likely_pathogenic(1)|Uncertain_significance(7)|Benign(1)|Likely_benign(3) 3\n", + "Likely_pathogenic(1)|Uncertain_significance(8)|Benign(2)|Likely_benign(4) 3\n", + "Uncertain_significance(2)|Benign(4)|Likely_benign(10) 3\n", + "Pathogenic(3)|Uncertain_significance(4)|Benign(1) 3\n", + "Uncertain_significance(3)|Benign(3)|Likely_benign(8) 3\n", + "Uncertain_significance(10)|Benign(2)|Likely_benign(5) 3\n", + "Uncertain_significance(1)|Benign(11)|Likely_benign(6) 3\n", + "Pathogenic(1)|Uncertain_significance(7)|Likely_benign(2) 3\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Benign(4)|Likely_benign(9) 3\n", + "Pathogenic(4)|Likely_pathogenic(3)|Likely_benign(5) 3\n", + "Likely_risk_allele(1)|Benign(8) 3\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(7) 3\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(3)|Likely_benign(6) 3\n", + "Likely_pathogenic(1)|Benign(4)|Likely_benign(4) 3\n", + "Pathogenic(17)|Likely_pathogenic(3)|Pathogenic,_low_penetrance(1)|Uncertain_significance(2) 3\n", + "Uncertain_significance(3)|Uncertain_risk_allele(1)|Likely_benign(2) 3\n", + "Pathogenic(3)|Likely_pathogenic(4)|Uncertain_significance(5) 3\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Benign(3)|Likely_benign(5) 2\n", + "Pathogenic(1)|Benign(3)|Likely_benign(6) 2\n", + "Likely_pathogenic(1)|Uncertain_significance(14) 2\n", + "Uncertain_significance(14)|Likely_benign(9) 2\n", + "Uncertain_significance(2)|Benign(7)|Likely_benign(9) 2\n", + "Uncertain_significance(1)|Benign(1)|Likely_benign(15) 2\n", + "Uncertain_significance(1)|Benign(13)|Likely_benign(5) 2\n", + "Uncertain_significance(16)|Likely_benign(4) 2\n", + "Uncertain_significance(2)|Benign(4)|Likely_benign(13) 2\n", + "Uncertain_significance(3)|Benign(13)|Likely_benign(2) 2\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(2)|Benign(1) 2\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Likely_benign(6) 2\n", + "Pathogenic(1)|Uncertain_significance(1)|Benign(2)|Likely_benign(3) 2\n", + "Likely_pathogenic(2)|Uncertain_significance(2)|Benign(2)|Likely_benign(1) 2\n", + "Pathogenic(2)|Uncertain_significance(6)|Benign(1) 2\n", + "Uncertain_significance(4)|Likely_benign(11) 2\n", + "Uncertain_significance(15)|Likely_benign(5) 2\n", + "Pathogenic(17)|Uncertain_significance(1) 2\n", + "Likely_pathogenic(1)|Uncertain_significance(6)|Benign(1)|Likely_benign(1) 2\n", + "Pathogenic(1)|Uncertain_significance(1)|Benign(3)|Likely_benign(1) 2\n", + "Uncertain_significance(13)|Likely_benign(4) 2\n", + "Uncertain_significance(9)|Benign(1)|Likely_benign(5) 2\n", + "Likely_pathogenic(3)|Uncertain_significance(7) 2\n", + "Pathogenic(2)|Likely_pathogenic(1)|Uncertain_significance(9) 2\n", + "Likely_pathogenic(1)|Uncertain_significance(1)|Benign(2)|Likely_benign(1) 2\n", + "Pathogenic(1)|Uncertain_significance(3)|Benign(1)|Likely_benign(4) 2\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(7)|Benign(1)|Likely_benign(2) 2\n", + "Pathogenic(7)|Likely_pathogenic(4)|Uncertain_significance(1) 2\n", + "Likely_pathogenic(2)|Uncertain_significance(7)|Benign(1) 2\n", + "Likely_pathogenic(3)|Uncertain_significance(10)|Benign(1) 2\n", + "Pathogenic(18)|Uncertain_significance(1)|Benign(1) 2\n", + "Likely_pathogenic(1)|Benign(3)|Likely_benign(9) 2\n", + "Pathogenic(2)|Uncertain_significance(4)|Benign(1) 2\n", + "Likely_pathogenic(1)|Uncertain_significance(7)|Benign(1)|Likely_benign(2) 2\n", + "Pathogenic(8)|Likely_pathogenic(6)|Uncertain_significance(1) 2\n", + "Pathogenic(9)|Pathogenic,_low_penetrance(1)|Uncertain_significance(2) 2\n", + "Pathogenic(1)|Uncertain_significance(3)|Benign(3)|Likely_benign(1) 2\n", + "Pathogenic(2)|Likely_pathogenic(3)|Uncertain_significance(2)|Likely_benign(1) 2\n", + "Pathogenic(1)|Uncertain_significance(1)|Benign(6)|Likely_benign(1) 2\n", + "Pathogenic(1)|Likely_pathogenic(1)|Likely_benign(2) 2\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(3)|Benign(2)|Likely_benign(3) 2\n", + "Pathogenic(12)|Likely_pathogenic(9)|Uncertain_significance(2)|Benign(1) 2\n", + "Uncertain_significance(6)|Likely_benign(8) 2\n", + "Uncertain_significance(3)|Benign(8)|Likely_benign(11) 2\n", + "Uncertain_significance(4)|Benign(5)|Likely_benign(7) 2\n", + "Uncertain_significance(15)|Benign(2)|Likely_benign(2) 2\n", + "Uncertain_significance(10)|Benign(1)|Likely_benign(9) 2\n", + "Uncertain_significance(12)|Benign(3)|Likely_benign(6) 2\n", + "Uncertain_significance(1)|Benign(9)|Likely_benign(10) 2\n", + "Uncertain_significance(1)|Benign(13)|Likely_benign(7) 2\n", + "Pathogenic(2)|Likely_pathogenic(6)|Uncertain_significance(8) 2\n", + "Pathogenic(14)|Likely_pathogenic(1)|Uncertain_significance(2) 2\n", + "Pathogenic(1)|Benign(8) 2\n", + "Uncertain_significance(2)|Benign(2)|Likely_benign(9) 2\n", + "Pathogenic(19)|Likely_pathogenic(2)|Uncertain_significance(1)|Likely_benign(2) 2\n", + "Pathogenic(14)|Likely_pathogenic(3)|Uncertain_significance(1) 2\n", + "Likely_pathogenic(1)|Benign(6)|Likely_benign(3) 2\n", + "Likely_pathogenic(4)|Uncertain_significance(5)|Likely_benign(2) 2\n", + "Likely_pathogenic(3)|Uncertain_significance(6)|Likely_benign(1) 2\n", + "Likely_pathogenic(1)|Uncertain_significance(5)|Likely_benign(5) 2\n", + "Uncertain_significance(3)|Benign(14)|Likely_benign(1) 2\n", + "Pathogenic(8)|Likely_pathogenic(1)|Uncertain_significance(3) 2\n", + "Pathogenic(2)|Uncertain_significance(9)|Benign(1)|Likely_benign(2) 2\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(4)|Likely_benign(1) 2\n", + "Pathogenic(3)|Likely_pathogenic(2)|Uncertain_significance(7)|Benign(1)|Likely_benign(1) 2\n", + "Likely_pathogenic(1)|Uncertain_significance(4)|Likely_benign(5) 2\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(11) 2\n", + "Pathogenic(2)|Likely_pathogenic(6)|Uncertain_significance(6)|Benign(1)|Likely_benign(1) 2\n", + "Pathogenic(13)|Likely_pathogenic(1)|Uncertain_significance(1) 2\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(4)|Benign(3)|Likely_benign(5) 2\n", + "Pathogenic(7)|Uncertain_significance(2) 2\n", + "Pathogenic(4)|Likely_pathogenic(3)|Uncertain_significance(8)|Benign(1) 2\n", + "Pathogenic(1)|Benign(1)|Likely_benign(3) 2\n", + "Uncertain_significance(2)|Likely_benign(13) 2\n", + "Uncertain_significance(2)|Benign(2)|Likely_benign(12) 2\n", + "Pathogenic(6)|Likely_pathogenic(4)|Uncertain_significance(1) 2\n", + "Pathogenic(10)|Likely_pathogenic(3)|Uncertain_significance(1) 2\n", + "Pathogenic(2)|Likely_pathogenic(1)|Benign(1) 2\n", + "Uncertain_significance(6)|Likely_benign(11) 2\n", + "Pathogenic(1)|Likely_pathogenic(2)|Uncertain_significance(2)|Likely_benign(1) 2\n", + "Uncertain_significance(4)|Likely_benign(18) 2\n", + "Uncertain_significance(4)|Likely_benign(10) 2\n", + "Uncertain_significance(7)|Benign(5)|Likely_benign(2) 2\n", + "Pathogenic(4)|Likely_pathogenic(1)|Uncertain_significance(3) 2\n", + "Likely_pathogenic(2)|Uncertain_significance(4)|Benign(1) 2\n", + "Likely_pathogenic(4)|Uncertain_significance(10) 2\n", + "Uncertain_significance(1)|Benign(11)|Likely_benign(7) 2\n", + "Uncertain_significance(3)|Benign(2)|Likely_benign(9) 2\n", + "Uncertain_significance(2)|Benign(7)|Likely_benign(6) 2\n", + "Uncertain_significance(12)|Benign(5)|Likely_benign(2) 2\n", + "Uncertain_significance(6)|Benign(1)|Likely_benign(9) 2\n", + "Pathogenic(3)|Likely_pathogenic(6)|Uncertain_significance(2) 2\n", + "Uncertain_significance(2)|Likely_benign(12) 2\n", + "Uncertain_significance(6)|Likely_benign(9) 2\n", + "Pathogenic(1)|Uncertain_significance(7)|Likely_benign(1) 2\n", + "Likely_pathogenic(1)|Uncertain_significance(2)|Benign(3)|Likely_benign(1) 2\n", + "Pathogenic(11)|Likely_pathogenic(1)|Pathogenic,_low_penetrance(1)|Uncertain_significance(2)|Likely_benign(4) 2\n", + "Pathogenic(4)|Uncertain_significance(3) 2\n", + "Uncertain_significance(5)|Likely_benign(8) 2\n", + "Likely_pathogenic(2)|Uncertain_significance(6)|Benign(1) 2\n", + "Likely_pathogenic(3)|Uncertain_significance(1)|Likely_benign(1) 2\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Benign(8)|Likely_benign(3) 2\n", + "Pathogenic(3)|Likely_pathogenic(2)|Uncertain_significance(1)|Likely_benign(1) 2\n", + "Uncertain_significance(1)|Benign(12)|Likely_benign(4) 2\n", + "Pathogenic(1)|Likely_pathogenic(1)|Benign(1)|Likely_benign(1) 2\n", + "Uncertain_significance(4)|Benign(2)|Likely_benign(11) 2\n", + "Pathogenic(11)|Uncertain_significance(1)|Likely_benign(1) 2\n", + "Uncertain_significance(10)|Benign(3)|Likely_benign(7) 2\n", + "Pathogenic(15)|Likely_pathogenic(4)|Benign(1) 2\n", + "Pathogenic(1)|Likely_pathogenic(5)|Benign(1) 2\n", + "Pathogenic(33)|Benign(1)|Likely_benign(1) 2\n", + "Pathogenic(3)|Likely_pathogenic(11)|Uncertain_significance(2) 2\n", + "Uncertain_significance(1)|Benign(12)|Likely_benign(12) 2\n", + "Pathogenic(10)|Likely_pathogenic(1)|Established_risk_allele(1)|Uncertain_significance(3)|Likely_benign(1) 2\n", + "Uncertain_significance(2)|Benign(9)|Likely_benign(13) 2\n", + "Likely_pathogenic(6)|Uncertain_significance(4) 2\n", + "Likely_pathogenic(1)|Benign(4)|Likely_benign(6) 2\n", + "Likely_pathogenic(1)|Uncertain_significance(4)|Benign(4)|Likely_benign(3) 2\n", + "Pathogenic(18)|Likely_pathogenic(1)|Uncertain_significance(1) 2\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(4)|Benign(1)|Likely_benign(1) 2\n", + "Uncertain_significance(8)|Likely_benign(9) 1\n", + "Uncertain_significance(2)|Benign(8)|Likely_benign(1) 1\n", + "Uncertain_significance(5)|Benign(5)|Likely_benign(7) 1\n", + "Uncertain_significance(2)|Benign(2)|Likely_benign(11) 1\n", + "Pathogenic(2)|Likely_benign(1) 1\n", + "Pathogenic(38)|Likely_pathogenic(4)|Uncertain_significance(1) 1\n", + "Uncertain_significance(5)|Benign(2)|Likely_benign(9) 1\n", + "Pathogenic(8)|Uncertain_significance(1)|Benign(3) 1\n", + "Uncertain_significance(3)|Benign(9)|Likely_benign(3) 1\n", + "Uncertain_significance(2)|Benign(10)|Likely_benign(5) 1\n", + "Pathogenic(2)|Likely_pathogenic(2)|Benign(2) 1\n", + "Likely_pathogenic(1)|Uncertain_significance(13) 1\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Benign(3)|Likely_benign(6) 1\n", + "Pathogenic(8)|Likely_pathogenic(5)|Uncertain_significance(5) 1\n", + "Pathogenic(1)|Uncertain_significance(2)|Benign(1)|Likely_benign(2) 1\n", + "Likely_pathogenic(1)|Benign(7)|Likely_benign(3) 1\n", + "Likely_pathogenic(1)|Uncertain_significance(12) 1\n", + "Uncertain_significance(3)|Likely_benign(10) 1\n", + "Uncertain_significance(3)|Likely_benign(9) 1\n", + "Pathogenic(9)|Likely_pathogenic(4)|Uncertain_significance(1) 1\n", + "Likely_pathogenic(1)|Uncertain_significance(6)|Benign(1)|Likely_benign(2) 1\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Benign(2)|Likely_benign(2) 1\n", + "Likely_pathogenic(1)|Uncertain_significance(13)|Benign(1) 1\n", + "Likely_pathogenic(1)|Uncertain_significance(10)|Likely_benign(9) 1\n", + "Likely_pathogenic(1)|Uncertain_significance(3)|Benign(1)|Likely_benign(10) 1\n", + "Pathogenic(5)|Likely_pathogenic(2)|Likely_benign(1) 1\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(4)|Benign(3) 1\n", + "Uncertain_significance(5)|Benign(4)|Likely_benign(1) 1\n", + "Likely_pathogenic(2)|Uncertain_significance(12)|Likely_benign(5) 1\n", + "Likely_pathogenic(3)|Uncertain_significance(4)|Likely_benign(2) 1\n", + "Uncertain_significance(13)|Benign(1)|Likely_benign(1) 1\n", + "Pathogenic(3)|Likely_pathogenic(2)|Likely_benign(1) 1\n", + "Pathogenic(1)|Likely_pathogenic(1)|Benign(2)|Likely_benign(3) 1\n", + "Pathogenic(1)|Benign(9)|Likely_benign(1) 1\n", + "Pathogenic(1)|Benign(2)|Likely_benign(3) 1\n", + "Pathogenic(2)|Uncertain_significance(1)|Benign(3)|Likely_benign(4) 1\n", + "Pathogenic(1)|Likely_pathogenic(1)|Uncertain_significance(7)|Benign(4) 1\n", + "Pathogenic(4)|Uncertain_significance(3)|Likely_benign(1) 1\n", + "Likely_pathogenic(1)|Uncertain_significance(9)|Likely_benign(5) 1\n", + "Pathogenic(7)|Likely_pathogenic(4)|Uncertain_significance(5)|Likely_benign(1) 1\n", + "Pathogenic(1)|Uncertain_significance(3)|Benign(1) 1\n", + "Name: extra_vcf_info.CLNSIGCONF, dtype: int64\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "clingen_classification:\n", + " Definitive 1637501\n", + "Limited 729915\n", + "Definitive;Definitive 437814\n", + "Disputed 328753\n", + "Moderate 158776\n", + "Definitive;Limited 140907\n", + "Limited;Definitive 138556\n", + "Definitive;Disputed;Definitive 127631\n", + "Disputed;Definitive;Definitive 116768\n", + "No Reported Evidence;No Reported Evidence;Limited;Moderate;Definitive;Limited 53285\n", + "Moderate;Definitive;Limited 49914\n", + "Strong 42827\n", + "Disputed;Limited;Definitive 42505\n", + "Limited;Definitive;Limited 40055\n", + "Disputed;Limited 34099\n", + "Limited;Definitive;No Reported Evidence;Definitive 32912\n", + "No Reported Evidence;Definitive;Definitive 31025\n", + "Definitive;Moderate 22880\n", + "No Reported Evidence 21850\n", + "Moderate;Limited 17291\n", + "No Reported Evidence;Disputed 15253\n", + "Moderate;Definitive;Disputed 11838\n", + "Moderate;Limited;Definitive;Definitive 11559\n", + "Definitive;No Reported Evidence 10657\n", + "Refuted;Definitive;Definitive 9836\n", + "No Reported Evidence;No Reported Evidence;No Reported Evidence;No Reported Evidence;No Reported Evidence;Limited;No Reported Evidence;No Reported Evidence;No Reported Evidence;Strong 9427\n", + "Moderate;Definitive;Disputed;Limited;No Reported Evidence 8593\n", + "Disputed;Disputed 8487\n", + "Definitive;Disputed 6753\n", + "Moderate;Moderate;Definitive;Definitive 6535\n", + "No Reported Evidence;No Reported Evidence;Definitive;No Reported Evidence;Disputed 6153\n", + "Disputed;No Reported Evidence;No Reported Evidence;No Reported Evidence;No Reported Evidence 5531\n", + "No Reported Evidence;No Reported Evidence;Definitive 4638\n", + "Refuted;No Reported Evidence 4117\n", + "Disputed;No Reported Evidence 3931\n", + "Limited;Limited 3634\n", + "Disputed;Limited;Definitive;Disputed;No Reported Evidence 3394\n", + "Definitive;Disputed;Disputed;No Reported Evidence;No Reported Evidence 3199\n", + "Disputed;Definitive 3173\n", + "No Reported Evidence;Moderate 3136\n", + "No Reported Evidence;Definitive;No Reported Evidence;Limited;No Reported Evidence 3063\n", + "No Reported Evidence;No Reported Evidence;No Reported Evidence;No Reported Evidence;Moderate 3033\n", + "Limited;No Reported Evidence 2966\n", + "No Reported Evidence;No Reported Evidence;No Reported Evidence;Disputed;No Reported Evidence 2715\n", + "No Reported Evidence;Limited 2513\n", + "No Reported Evidence;No Reported Evidence 2433\n", + "No Reported Evidence;No Reported Evidence;No Reported Evidence;Limited;No Reported Evidence 2316\n", + "No Reported Evidence;Definitive;Definitive;Disputed;Disputed 2177\n", + "Disputed;Definitive;No Reported Evidence;Strong;No Reported Evidence 1745\n", + "Moderate;Disputed 1641\n", + "Disputed;No Reported Evidence;Disputed;Disputed;Definitive 1600\n", + "Refuted;Definitive 1590\n", + "Limited;Disputed 1497\n", + "Refuted 1491\n", + "No Reported Evidence;Definitive;No Reported Evidence;No Reported Evidence;No Reported Evidence 1132\n", + "Definitive;Refuted 1119\n", + "Limited;Disputed;Limited;Definitive;No Reported Evidence 1069\n", + "Limited;Moderate 863\n", + "No Reported Evidence;No Reported Evidence;No Reported Evidence;Strong;No Reported Evidence 816\n", + "Limited;No Reported Evidence;Limited;Definitive;Limited 764\n", + "Disputed;Strong 691\n", + "Name: clingen.classification, dtype: int64\n" + ] + } + ], + "source": [ + "#Drop variants with leass than 30% of data along with duplicates. Also delete columns with all null values.\n", + "print('Dropping empty columns and rows along with duplicate rows...')\n", + "#df.dropna(axis=1, thresh=(df.shape[0]*0.15), inplace=True) #thresh=(df.shape[0]/4)\n", + "df.dropna(axis=0, thresh=(df.shape[1]*0.3), inplace=True) #thresh=(df.shape[1]*0.3), how='all',\n", + "df.drop_duplicates()\n", + "df.dropna(axis=1, how='all', inplace=True) #thresh=(df.shape[0]/4)\n", + "print('\\nVariant-transcript pairs shape =', df.shape)\n", + "print('\\nVariants shape =', df[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)\n", + "print('\\nclinvar_CLNSIG:\\n', df['extra_vcf_info.CLNSIG'].value_counts())\n", + "print('\\nclinvar_review:\\n', df['extra_vcf_info.CLNREVSTAT'].value_counts())\n", + "print('\\nclinvar_confidence:\\n', df['extra_vcf_info.CLNSIGCONF'].value_counts())\n", + "print('\\nclingen_classification:\\n', df['clingen.classification'].value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Variant-transcript pairs shape = (11047591, 132)\n", + "\n", + "Variants shape = (2122117, 4)\n" + ] + } + ], + "source": [ + "df= df.loc[df['extra_vcf_info.CLNSIG'].isin(config_dict['train_ClinicalSignificance'])]\n", + "print('\\nVariant-transcript pairs shape =', df.shape)\n", + "print('\\nVariants shape =', df[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Likely_benign', 'Uncertain_significance', 'Benign',\n", + " 'Conflicting_interpretations_of_pathogenicity', 'Pathogenic',\n", + " 'Likely_pathogenic', 'Benign/Likely_benign',\n", + " 'Pathogenic/Likely_pathogenic'], dtype=object)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['extra_vcf_info.CLNSIG'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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extra_vcf_info.CLNSIGBenignBenign/Likely_benignConflicting_interpretations_of_pathogenicityLikely_benignLikely_pathogenicPathogenicPathogenic/Likely_pathogenicUncertain_significance
so
intron_variant362725.041009.091923.0756372.017725.037603.05736.0407206.0
synonymous_variant109885.065090.085163.01134299.01065.01364.0426.067647.0
missense_variant80315.033923.0224600.0142639.086905.074138.025632.02769515.0
intron_variant,NMD_transcript_variant42402.05911.017076.099950.05082.07134.01697.092851.0
3_prime_UTR_variant30573.05160.06730.025982.01610.03713.0444.0105404.0
intron_variant,processed_transcript25025.03550.09357.048739.08988.09919.02013.079143.0
2kb_downstream_variant21024.04152.09240.038903.05012.07613.01685.0126609.0
2kb_upstream_variant20854.02925.07068.037942.03687.06160.01438.088889.0
intron_variant,lnc_RNA17970.08461.067259.055714.035200.020851.06446.0330377.0
processed_transcript15543.06827.027208.062927.012610.023769.04577.0284455.0
NMD_transcript_variant,3_prime_UTR_variant14514.07915.033577.079381.014063.030740.05681.0309360.0
2kb_downstream_variant,processed_transcript11849.03346.010288.031757.06545.09972.02070.0114158.0
5_prime_UTR_variant11491.02436.06354.024143.02568.010018.0889.060877.0
2kb_upstream_variant,processed_transcript9914.02335.07063.025995.04235.06601.01355.078705.0
2kb_upstream_variant,lnc_RNA6756.01689.05231.018768.03078.04261.0841.060727.0
2kb_downstream_variant,NMD_transcript_variant5946.01710.07184.017539.02621.06226.01016.054298.0
NMD_transcript_variant,synonymous_variant5552.03430.06154.068777.0266.0429.0128.09444.0
2kb_upstream_variant,NMD_transcript_variant5458.0799.01835.09253.0717.01326.0244.022972.0
missense_variant,NMD_transcript_variant5320.02118.014236.011660.06379.05951.01782.0202928.0
2kb_downstream_variant,lnc_RNA2862.0841.04514.07315.01835.01696.0426.035978.0
lnc_RNA2785.0701.03473.08412.01615.01767.0554.032306.0
NMD_transcript_variant,5_prime_UTR_variant1644.0363.01021.03144.0165.0406.047.07399.0
inframe_deletion923.0707.03254.01912.02970.02826.0664.030306.0
2kb_downstream_variant,miRNA642.0235.0616.01894.0340.0428.083.05548.0
inframe_insertion615.0471.01374.0940.0746.0694.078.012286.0
2kb_upstream_variant,miRNA509.0126.0516.01647.0359.0504.099.04955.0
stop_gained358.082.03688.0933.028980.0120930.014039.021134.0
frameshift_truncation336.023.02172.0602.025262.0101852.08276.015654.0
frameshift_elongation302.059.01229.0508.011999.051298.03517.08452.0
intron_variant,splice_site_variant281.063.03461.0748.049778.038420.09212.015210.0
intron_variant,NSD_transcript241.028.042.0419.033.025.03.0262.0
NSD_transcript193.069.0963.01094.0107.0746.038.01867.0
complex_substitution,missense_variant189.090.0614.0813.0507.0727.090.09417.0
2kb_upstream_variant,misc_RNA189.061.0175.0952.0120.0199.034.02665.0
2kb_downstream_variant,misc_RNA186.047.0158.0698.0104.0171.042.02138.0
intron_variant,polymorphic_pseudogene175.06.036.0352.039.020.07.0249.0
2kb_upstream_variant,snoRNA156.015.036.0147.062.045.04.0765.0
polymorphic_pseudogene126.0101.0188.0480.0144.0250.067.02105.0
2kb_downstream_variant,snoRNA124.021.070.0343.084.0111.010.01112.0
2kb_downstream_variant,snRNA117.027.0127.0463.056.0105.024.01487.0
2kb_downstream_variant,NSD_transcript107.016.01353.0778.072.01121.032.0395.0
missense_variant,start_lost82.021.0532.0157.01153.02142.0527.03721.0
2kb_upstream_variant,snRNA79.017.044.0200.036.055.013.0822.0
2kb_downstream_variant,polymorphic_pseudogene71.06.011.018.0NaN5.0NaN177.0
2kb_upstream_variant,NSD_transcript70.024.059.0253.019.026.04.0606.0
NMD_transcript_variant,stop_gained65.023.0355.0264.01893.09635.01312.01562.0
stop_lost60.012.0106.0137.0227.0284.020.01746.0
2kb_upstream_variant,polymorphic_pseudogene53.05.010.032.014.0NaNNaN168.0
stop_retained_variant52.013.066.0508.0NaN6.0NaN245.0
splice_site_variant50.029.0195.0176.02379.01671.0339.01025.0
intron_variant,NMD_transcript_variant,splice_site_variant49.014.01186.0143.012383.07515.02225.02632.0
inframe_deletion,NMD_transcript_variant48.046.0137.065.074.098.016.01103.0
inframe_insertion,NMD_transcript_variant42.027.098.061.028.029.01.0613.0
polymorphic_pseudogene,3_prime_UTR_variant38.08.027.035.02.0NaNNaN374.0
2kb_upstream_variant,5_prime_UTR_variant31.0NaN3.01.05.043.0NaN37.0
complex_substitution,frameshift_elongation,intron_variant26.05.099.037.01173.0968.0159.0371.0
missense_variant,start_lost,NMD_transcript_variant26.03.0175.047.0445.0691.0234.0944.0
miRNA26.05.06.093.014.014.02.082.0
2kb_upstream_variant,ribozyme24.012.052.0127.04.039.011.089.0
snoRNA22.02.04.012.02.017.02.071.0
frameshift_truncation,stop_gained18.010.0100.021.01203.06366.0558.0677.0
frameshift_elongation,stop_gained14.01.085.021.0559.02633.0193.0370.0
frameshift_truncation,NMD_transcript_variant12.03.0136.019.0471.04176.0383.0281.0
complex_substitution12.02.045.021.089.0140.018.01091.0
complex_substitution,inframe_deletion11.0NaN19.020.083.072.01.0328.0
polymorphic_pseudogene,5_prime_UTR_variant10.06.034.032.039.066.024.0217.0
NMD_transcript_variant,stop_retained_variant9.02.07.033.011.02.03.0118.0
snRNA8.0NaN9.012.08.09.03.0108.0
NMD_transcript_variant7.014.039.055.038.051.020.0250.0
2kb_upstream_variant,scaRNA7.0NaN1.011.09.05.0NaN67.0
NSD_transcript,5_prime_UTR_variant7.03.02.05.0NaNNaNNaN22.0
NMD_transcript_variant,stop_lost6.03.042.085.031.051.013.0353.0
2kb_downstream_variant,3_prime_UTR_variant6.0NaNNaN2.03.035.0NaN12.0
2kb_upstream_variant,rRNA6.01.0NaN10.01.0NaNNaN23.0
misc_RNA6.0NaN9.037.01.0NaNNaN54.0
complex_substitution,inframe_insertion,intron_variant5.016.054.029.0586.0363.038.0203.0
frameshift_truncation,stop_lost5.01.031.03.015.016.03.081.0
frameshift_elongation,NMD_transcript_variant4.02.0117.011.0279.02275.0150.0243.0
exon_loss_variant,frameshift_truncation4.0NaN9.015.0186.0902.04.0131.0
complex_substitution,missense_variant,NMD_transcript_variant4.0NaN31.021.027.045.02.0298.0
complex_substitution,synonymous_variant4.021.016.0237.0NaNNaNNaN48.0
frameshift_truncation,stop_retained_variant3.0NaN13.05.0NaN1.0NaN17.0
frameshift_elongation,stop_retained_variant3.0NaN7.015.0NaNNaNNaN37.0
inframe_insertion,stop_gained2.02.0NaNNaN41.0199.024.045.0
NMD_transcript_variant,splice_site_variant2.0NaN8.03.079.084.010.033.0
frameshift_elongation,stop_lost2.0NaN12.06.013.035.02.081.0
2kb_downstream_variant,NMD_transcript_variant,3_prime_UTR_variant2.0NaNNaN2.03.09.0NaN3.0
2kb_upstream_variant,NMD_transcript_variant,5_prime_UTR_variant2.0NaN3.02.0NaN7.0NaN8.0
2kb_downstream_variant,scaRNA2.01.02.08.01.04.0NaN73.0
ribozyme2.01.011.04.03.04.010.0144.0
inframe_insertion,stop_retained_variant2.0NaNNaNNaNNaNNaNNaN22.0
inframe_deletion,stop_gained1.0NaN2.04.046.0175.011.021.0
start_lost,5_prime_UTR_variant1.0NaN9.0NaN41.0133.011.0137.0
frameshift_truncation,start_lost1.0NaN5.02.036.069.03.057.0
start_retained_variant1.01.0NaNNaNNaN3.01.011.0
frameshift_truncation,stop_lost,stop_retained_variant,3_prime_UTR_variant1.05.010.018.0NaN1.0NaN15.0
2kb_downstream_variant,ribozyme1.0NaNNaN2.0NaNNaNNaN17.0
complex_substitution,intron_variant,synonymous_variant1.0NaNNaNNaN2.0NaNNaNNaN
frameshift_truncation,stop_lost,synonymous_variant,3_prime_UTR_variant1.0NaNNaNNaNNaNNaNNaN3.0
complex_substitution,frameshift_truncationNaNNaN46.010.01728.04168.0179.0465.0
complex_substitution,frameshift_elongationNaNNaN31.01.0801.01764.076.0253.0
complex_substitution,stop_gainedNaNNaN20.02.088.0703.029.085.0
exon_loss_variant,inframe_deletionNaNNaNNaNNaN40.0391.0NaN18.0
complex_substitution,frameshift_truncation,NMD_transcript_variantNaNNaN5.0NaN80.0316.016.019.0
frameshift_truncation,NMD_transcript_variant,stop_gainedNaNNaN5.01.017.0232.039.07.0
exon_loss_variant,intron_variantNaNNaNNaNNaN64.0230.0NaN14.0
frameshift_elongation,NMD_transcript_variant,stop_gainedNaNNaN14.01.03.0120.07.09.0
2kb_upstream_variant,start_lost,transcript_ablation,5_prime_UTR_variantNaNNaNNaNNaN19.0110.0NaN17.0
2kb_downstream_variant,stop_lost,3_prime_UTR_variantNaNNaNNaNNaN21.0105.02.03.0
exon_loss_variant,frameshift_truncation,NMD_transcript_variantNaNNaN1.0NaN5.0103.0NaN5.0
complex_substitution,inframe_insertionNaN3.033.025.085.096.03.0418.0
stop_lost,3_prime_UTR_variantNaNNaN1.0NaN19.095.0NaN12.0
intron_variant,start_lost,5_prime_UTR_variantNaNNaNNaNNaN12.078.0NaN3.0
complex_substitution,inframe_deletion,missense_variantNaNNaN15.06.0153.074.09.0411.0
frameshift_truncation,stop_lost,3_prime_UTR_variantNaNNaNNaN1.09.072.01.091.0
complex_substitution,frameshift_elongation,NMD_transcript_variantNaNNaN1.0NaN18.071.07.05.0
complex_substitution,inframe_insertion,intron_variant,stop_gainedNaNNaN1.0NaN43.050.0NaN16.0
complex_substitution,inframe_deletion,stop_gainedNaNNaNNaNNaN11.043.0NaN11.0
complex_substitution,inframe_insertion,stop_gainedNaNNaNNaNNaN6.039.0NaN4.0
complex_substitution,NMD_transcript_variant,stop_gainedNaNNaNNaNNaN6.037.0NaNNaN
intron_variant,start_lostNaNNaNNaNNaN13.035.0NaN3.0
intron_variant,start_lost,NMD_transcript_variant,5_prime_UTR_variantNaNNaNNaNNaNNaN34.0NaNNaN
complex_substitution,frameshift_elongation,intron_variant,NMD_transcript_variantNaN1.06.0NaN36.033.021.018.0
complex_substitution,frameshift_truncation,intron_variantNaNNaNNaNNaN27.033.0NaN4.0
2kb_downstream_variant,intron_variant,stop_lost,3_prime_UTR_variantNaNNaNNaNNaNNaN27.0NaNNaN
complex_substitution,inframe_insertion,missense_variantNaNNaNNaNNaN9.026.0NaNNaN
start_lost,NMD_transcript_variant,5_prime_UTR_variantNaNNaNNaNNaN10.024.013.023.0
inframe_deletion,stop_lost,3_prime_UTR_variantNaNNaNNaN1.021.023.01.047.0
2kb_upstream_variant,transcript_ablation,5_prime_UTR_variantNaNNaNNaNNaN2.021.0NaN2.0
2kb_downstream_variant,stop_lostNaN2.0NaNNaN2.020.0NaN5.0
NMD_transcript_variant,stop_lost,3_prime_UTR_variantNaNNaNNaNNaN3.017.0NaN5.0
complex_substitution,start_lost,start_retained_variantNaNNaN1.02.01.017.0NaN60.0
inframe_deletion,NMD_transcript_variant,stop_gainedNaNNaNNaN1.01.017.01.0NaN
complex_substitution,inframe_insertion,intron_variant,NMD_transcript_variantNaNNaN4.01.030.015.04.08.0
2kb_upstream_variant,start_lost,NMD_transcript_variant,stop_lost,transcript_ablation,3_prime_UTR_variant,5_prime_UTR_variantNaNNaNNaNNaNNaN13.0NaNNaN
complex_substitution,inframe_deletion,intron_variantNaN1.0NaNNaN2.011.01.09.0
frameshift_elongation,start_lostNaNNaN9.0NaN1.011.03.018.0
start_lost,3_prime_UTR_variant,5_prime_UTR_variantNaNNaNNaNNaN4.011.0NaNNaN
start_lost,NMD_transcript_variantNaNNaNNaNNaN2.011.0NaNNaN
exon_loss_variant,inframe_deletion,NMD_transcript_variantNaNNaNNaNNaNNaN10.0NaNNaN
inframe_deletion,stop_lostNaNNaN1.0NaN5.010.0NaN9.0
2kb_upstream_variant,start_lost,NMD_transcript_variant,transcript_ablation,5_prime_UTR_variantNaNNaNNaNNaN11.09.0NaN3.0
inframe_insertion,NMD_transcript_variant,stop_gainedNaNNaNNaNNaN2.09.01.01.0
exon_loss_variant,intron_variant,NMD_transcript_variantNaNNaNNaNNaN1.08.0NaNNaN
frameshift_truncation,NMD_transcript_variant,stop_lostNaNNaN1.0NaNNaN8.0NaNNaN
frameshift_elongation,NMD_transcript_variant,stop_retained_variantNaNNaNNaNNaNNaN7.0NaNNaN
2kb_downstream_variant,2kb_upstream_variant,start_lost,stop_lost,transcript_ablation,3_prime_UTR_variant,5_prime_UTR_variantNaNNaN1.0NaNNaN6.0NaNNaN
2kb_upstream_variant,intron_variant,start_lost,transcript_ablation,5_prime_UTR_variantNaNNaNNaNNaNNaN6.0NaNNaN
2kb_upstream_variant,start_lost,stop_lost,transcript_ablation,3_prime_UTR_variant,5_prime_UTR_variantNaNNaNNaNNaNNaN6.0NaNNaN
complex_substitution,frameshift_elongation,intron_variant,start_lostNaNNaNNaNNaNNaN6.0NaNNaN
complex_substitution,inframe_insertion,NMD_transcript_variant,stop_gainedNaNNaNNaNNaNNaN6.0NaNNaN
complex_substitution,inframe_insertion,intron_variant,NMD_transcript_variant,stop_gainedNaNNaNNaNNaN3.06.0NaN1.0
frameshift_truncation,start_lost,NMD_transcript_variantNaNNaN2.0NaN19.06.0NaN17.0
2kb_downstream_variant,NMD_transcript_variant,stop_lost,3_prime_UTR_variantNaNNaNNaNNaNNaN5.0NaNNaN
frameshift_truncation,synonymous_variantNaNNaNNaN2.0NaN5.0NaN5.0
intron_variant,stop_lost,3_prime_UTR_variantNaNNaNNaNNaNNaN5.0NaNNaN
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complex_substitution,frameshift_elongation,intron_variant,start_lost,NMD_transcript_variantNaNNaNNaNNaNNaN4.0NaNNaN
complex_substitution,inframe_deletion,missense_variant,NMD_transcript_variantNaNNaNNaN3.06.04.0NaN23.0
complex_substitution,inframe_insertion,intron_variant,missense_variantNaNNaNNaNNaN4.04.02.06.0
exon_loss_variant,splice_site_variantNaNNaNNaNNaN3.04.0NaNNaN
start_lostNaNNaNNaNNaNNaN4.0NaNNaN
2kb_upstream_variant,start_lost,5_prime_UTR_variantNaNNaNNaNNaNNaN3.0NaNNaN
complex_substitution,frameshift_elongation,start_lost,start_retained_variant,synonymous_variantNaNNaNNaNNaNNaN3.0NaNNaN
frameshift_truncation,NMD_transcript_variant,stop_lost,3_prime_UTR_variantNaNNaNNaNNaN1.03.0NaN1.0
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complex_substitution,NMD_transcript_variantNaNNaNNaN1.03.02.0NaN37.0
complex_substitution,frameshift_elongation,intron_variant,stop_lostNaNNaNNaNNaNNaN2.0NaNNaN
complex_substitution,frameshift_truncation,NMD_transcript_variant,stop_lostNaNNaNNaNNaN1.02.0NaNNaN
complex_substitution,inframe_insertion,NMD_transcript_variantNaNNaN1.01.02.02.0NaN20.0
complex_substitution,start_lostNaNNaNNaNNaNNaN2.0NaNNaN
frameshift_elongation,NMD_transcript_variant,stop_lostNaNNaNNaNNaNNaN2.0NaN1.0
frameshift_truncation,stop_gained,stop_lost,3_prime_UTR_variantNaNNaNNaNNaNNaN2.0NaNNaN
2kb_upstream_variant,NMD_transcript_variant,transcript_ablation,5_prime_UTR_variantNaNNaNNaNNaNNaN1.0NaNNaN
complex_substitution,inframe_deletion,NMD_transcript_variantNaNNaN2.0NaNNaN1.0NaN10.0
complex_substitution,inframe_insertion,stop_lostNaNNaNNaNNaNNaN1.0NaNNaN
complex_substitution,start_lost,start_retained_variant,synonymous_variantNaNNaNNaNNaNNaN1.0NaNNaN
frameshift_elongation,start_lost,NMD_transcript_variantNaNNaN5.0NaNNaN1.0NaN1.0
frameshift_truncation,NMD_transcript_variant,stop_lost,stop_retained_variant,3_prime_UTR_variantNaNNaNNaNNaNNaN1.0NaN1.0
frameshift_truncation,NMD_transcript_variant,stop_retained_variantNaNNaN1.0NaNNaN1.0NaNNaN
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inframe_deletion,start_lostNaNNaNNaNNaN2.01.02.012.0
inframe_deletion,stop_lost,stop_retained_variant,3_prime_UTR_variantNaNNaN4.01.0NaN1.0NaN6.0
2kb_downstream_variant,IG_J_geneNaNNaNNaN1.0NaNNaNNaN2.0
2kb_downstream_variant,rRNANaNNaNNaNNaNNaNNaNNaN4.0
IG_C_geneNaNNaNNaN1.0NaNNaNNaN2.0
complex_substitution,NMD_transcript_variant,synonymous_variantNaN4.0NaN6.0NaNNaNNaNNaN
complex_substitution,frameshift_elongation,intron_variant,start_lost,start_retained_variant,synonymous_variantNaNNaNNaNNaNNaNNaN1.0NaN
complex_substitution,frameshift_elongation,stop_lostNaNNaNNaNNaNNaNNaNNaN1.0
complex_substitution,frameshift_truncation,intron_variant,start_lostNaNNaNNaNNaN1.0NaNNaNNaN
complex_substitution,frameshift_truncation,start_lostNaNNaNNaN1.0NaNNaNNaNNaN
complex_substitution,frameshift_truncation,start_lost,start_retained_variantNaNNaNNaNNaN3.0NaNNaN5.0
complex_substitution,frameshift_truncation,stop_lostNaNNaNNaNNaNNaNNaNNaN1.0
complex_substitution,inframe_deletion,NMD_transcript_variant,stop_lostNaNNaNNaNNaNNaNNaNNaN1.0
complex_substitution,inframe_deletion,intron_variant,NMD_transcript_variantNaNNaNNaNNaNNaNNaNNaN1.0
complex_substitution,inframe_deletion,intron_variant,missense_variantNaNNaNNaNNaNNaNNaNNaN3.0
complex_substitution,inframe_deletion,start_lost,start_retained_variantNaNNaNNaNNaNNaNNaNNaN1.0
complex_substitution,inframe_deletion,stop_gained,stop_lost,stop_retained_variantNaNNaNNaNNaN2.0NaNNaNNaN
complex_substitution,inframe_insertion,intron_variant,NMD_transcript_variant,stop_lostNaNNaNNaNNaN2.0NaNNaNNaN
complex_substitution,inframe_insertion,intron_variant,start_lost,start_retained_variant,NMD_transcript_variant,synonymous_variantNaNNaNNaNNaN2.0NaNNaNNaN
complex_substitution,inframe_insertion,intron_variant,start_lost,start_retained_variant,synonymous_variantNaNNaNNaNNaN6.0NaNNaNNaN
complex_substitution,start_lost,start_retained_variant,NMD_transcript_variantNaNNaN1.0NaNNaNNaNNaN7.0
complex_substitution,stop_lostNaNNaNNaNNaN2.0NaNNaN4.0
inframe_deletion,NMD_transcript_variant,stop_lostNaNNaNNaNNaN1.0NaNNaN2.0
inframe_deletion,start_lost,NMD_transcript_variantNaNNaNNaNNaN1.0NaNNaN2.0
inframe_deletion,stop_gained,stop_lost,3_prime_UTR_variantNaNNaNNaNNaNNaNNaNNaN3.0
inframe_insertion,start_retained_variantNaNNaNNaNNaNNaNNaNNaN7.0
inframe_insertion,start_retained_variant,NMD_transcript_variantNaNNaNNaNNaNNaNNaNNaN1.0
\n", + "
" + ], + "text/plain": [ + "extra_vcf_info.CLNSIG Benign \\\n", + "so \n", + "intron_variant 362725.0 \n", + "synonymous_variant 109885.0 \n", + "missense_variant 80315.0 \n", + "intron_variant,NMD_transcript_variant 42402.0 \n", + "3_prime_UTR_variant 30573.0 \n", + "intron_variant,processed_transcript 25025.0 \n", + "2kb_downstream_variant 21024.0 \n", + "2kb_upstream_variant 20854.0 \n", + "intron_variant,lnc_RNA 17970.0 \n", + "processed_transcript 15543.0 \n", + "NMD_transcript_variant,3_prime_UTR_variant 14514.0 \n", + "2kb_downstream_variant,processed_transcript 11849.0 \n", + "5_prime_UTR_variant 11491.0 \n", + "2kb_upstream_variant,processed_transcript 9914.0 \n", + "2kb_upstream_variant,lnc_RNA 6756.0 \n", + "2kb_downstream_variant,NMD_transcript_variant 5946.0 \n", + "NMD_transcript_variant,synonymous_variant 5552.0 \n", + "2kb_upstream_variant,NMD_transcript_variant 5458.0 \n", + "missense_variant,NMD_transcript_variant 5320.0 \n", + "2kb_downstream_variant,lnc_RNA 2862.0 \n", + "lnc_RNA 2785.0 \n", + "NMD_transcript_variant,5_prime_UTR_variant 1644.0 \n", + "inframe_deletion 923.0 \n", + "2kb_downstream_variant,miRNA 642.0 \n", + "inframe_insertion 615.0 \n", + "2kb_upstream_variant,miRNA 509.0 \n", + "stop_gained 358.0 \n", + "frameshift_truncation 336.0 \n", + "frameshift_elongation 302.0 \n", + "intron_variant,splice_site_variant 281.0 \n", + "intron_variant,NSD_transcript 241.0 \n", + "NSD_transcript 193.0 \n", + "complex_substitution,missense_variant 189.0 \n", + "2kb_upstream_variant,misc_RNA 189.0 \n", + "2kb_downstream_variant,misc_RNA 186.0 \n", + "intron_variant,polymorphic_pseudogene 175.0 \n", + "2kb_upstream_variant,snoRNA 156.0 \n", + "polymorphic_pseudogene 126.0 \n", + "2kb_downstream_variant,snoRNA 124.0 \n", + "2kb_downstream_variant,snRNA 117.0 \n", + "2kb_downstream_variant,NSD_transcript 107.0 \n", + "missense_variant,start_lost 82.0 \n", + "2kb_upstream_variant,snRNA 79.0 \n", + "2kb_downstream_variant,polymorphic_pseudogene 71.0 \n", + "2kb_upstream_variant,NSD_transcript 70.0 \n", + "NMD_transcript_variant,stop_gained 65.0 \n", + "stop_lost 60.0 \n", + "2kb_upstream_variant,polymorphic_pseudogene 53.0 \n", + "stop_retained_variant 52.0 \n", + "splice_site_variant 50.0 \n", + "intron_variant,NMD_transcript_variant,splice_si... 49.0 \n", + "inframe_deletion,NMD_transcript_variant 48.0 \n", + "inframe_insertion,NMD_transcript_variant 42.0 \n", + "polymorphic_pseudogene,3_prime_UTR_variant 38.0 \n", + "2kb_upstream_variant,5_prime_UTR_variant 31.0 \n", + "complex_substitution,frameshift_elongation,intr... 26.0 \n", + "missense_variant,start_lost,NMD_transcript_variant 26.0 \n", + "miRNA 26.0 \n", + "2kb_upstream_variant,ribozyme 24.0 \n", + "snoRNA 22.0 \n", + "frameshift_truncation,stop_gained 18.0 \n", + "frameshift_elongation,stop_gained 14.0 \n", + "frameshift_truncation,NMD_transcript_variant 12.0 \n", + "complex_substitution 12.0 \n", + "complex_substitution,inframe_deletion 11.0 \n", + "polymorphic_pseudogene,5_prime_UTR_variant 10.0 \n", + "NMD_transcript_variant,stop_retained_variant 9.0 \n", + "snRNA 8.0 \n", + "NMD_transcript_variant 7.0 \n", + "2kb_upstream_variant,scaRNA 7.0 \n", + "NSD_transcript,5_prime_UTR_variant 7.0 \n", + "NMD_transcript_variant,stop_lost 6.0 \n", + "2kb_downstream_variant,3_prime_UTR_variant 6.0 \n", + "2kb_upstream_variant,rRNA 6.0 \n", + "misc_RNA 6.0 \n", + "complex_substitution,inframe_insertion,intron_v... 5.0 \n", + "frameshift_truncation,stop_lost 5.0 \n", + "frameshift_elongation,NMD_transcript_variant 4.0 \n", + "exon_loss_variant,frameshift_truncation 4.0 \n", + "complex_substitution,missense_variant,NMD_trans... 4.0 \n", + "complex_substitution,synonymous_variant 4.0 \n", + "frameshift_truncation,stop_retained_variant 3.0 \n", + "frameshift_elongation,stop_retained_variant 3.0 \n", + "inframe_insertion,stop_gained 2.0 \n", + "NMD_transcript_variant,splice_site_variant 2.0 \n", + "frameshift_elongation,stop_lost 2.0 \n", + "2kb_downstream_variant,NMD_transcript_variant,3... 2.0 \n", + "2kb_upstream_variant,NMD_transcript_variant,5_p... 2.0 \n", + "2kb_downstream_variant,scaRNA 2.0 \n", + "ribozyme 2.0 \n", + "inframe_insertion,stop_retained_variant 2.0 \n", + "inframe_deletion,stop_gained 1.0 \n", + "start_lost,5_prime_UTR_variant 1.0 \n", + "frameshift_truncation,start_lost 1.0 \n", + "start_retained_variant 1.0 \n", + "frameshift_truncation,stop_lost,stop_retained_v... 1.0 \n", + "2kb_downstream_variant,ribozyme 1.0 \n", + "complex_substitution,intron_variant,synonymous_... 1.0 \n", + "frameshift_truncation,stop_lost,synonymous_vari... 1.0 \n", + "complex_substitution,frameshift_truncation NaN \n", + "complex_substitution,frameshift_elongation NaN \n", + "complex_substitution,stop_gained NaN \n", + "exon_loss_variant,inframe_deletion NaN \n", + "complex_substitution,frameshift_truncation,NMD_... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "exon_loss_variant,intron_variant NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "2kb_upstream_variant,start_lost,transcript_abla... NaN \n", + "2kb_downstream_variant,stop_lost,3_prime_UTR_va... NaN \n", + "exon_loss_variant,frameshift_truncation,NMD_tra... NaN \n", + "complex_substitution,inframe_insertion NaN \n", + "stop_lost,3_prime_UTR_variant NaN \n", + "intron_variant,start_lost,5_prime_UTR_variant NaN \n", + "complex_substitution,inframe_deletion,missense_... NaN \n", + "frameshift_truncation,stop_lost,3_prime_UTR_var... NaN \n", + "complex_substitution,frameshift_elongation,NMD_... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_deletion,stop_gained NaN \n", + "complex_substitution,inframe_insertion,stop_gained NaN \n", + "complex_substitution,NMD_transcript_variant,sto... NaN \n", + "intron_variant,start_lost NaN \n", + "intron_variant,start_lost,NMD_transcript_varian... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_truncation,intr... NaN \n", + "2kb_downstream_variant,intron_variant,stop_lost... NaN \n", + "complex_substitution,inframe_insertion,missense... NaN \n", + "start_lost,NMD_transcript_variant,5_prime_UTR_v... NaN \n", + "inframe_deletion,stop_lost,3_prime_UTR_variant NaN \n", + "2kb_upstream_variant,transcript_ablation,5_prim... NaN \n", + "2kb_downstream_variant,stop_lost NaN \n", + "NMD_transcript_variant,stop_lost,3_prime_UTR_va... NaN \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_ga... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "frameshift_elongation,start_lost NaN \n", + "start_lost,3_prime_UTR_variant,5_prime_UTR_variant NaN \n", + "start_lost,NMD_transcript_variant NaN \n", + "exon_loss_variant,inframe_deletion,NMD_transcri... NaN \n", + "inframe_deletion,stop_lost NaN \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "inframe_insertion,NMD_transcript_variant,stop_g... NaN \n", + "exon_loss_variant,intron_variant,NMD_transcript... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "2kb_downstream_variant,2kb_upstream_variant,sta... NaN \n", + "2kb_upstream_variant,intron_variant,start_lost,... NaN \n", + "2kb_upstream_variant,start_lost,stop_lost,trans... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_insertion,NMD_tran... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "frameshift_truncation,start_lost,NMD_transcript... NaN \n", + "2kb_downstream_variant,NMD_transcript_variant,s... NaN \n", + "frameshift_truncation,synonymous_variant NaN \n", + "intron_variant,stop_lost,3_prime_UTR_variant NaN \n", + "2kb_downstream_variant,2kb_upstream_variant,sta... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_deletion,missense_... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "exon_loss_variant,splice_site_variant NaN \n", + "start_lost NaN \n", + "2kb_upstream_variant,start_lost,5_prime_UTR_var... NaN \n", + "complex_substitution,frameshift_elongation,star... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "2kb_downstream_variant,intron_variant,NMD_trans... NaN \n", + "complex_substitution,NMD_transcript_variant NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_truncation,NMD_... NaN \n", + "complex_substitution,inframe_insertion,NMD_tran... NaN \n", + "complex_substitution,start_lost NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,stop_gained,stop_lost,3_p... NaN \n", + "2kb_upstream_variant,NMD_transcript_variant,tra... NaN \n", + "complex_substitution,inframe_deletion,NMD_trans... NaN \n", + "complex_substitution,inframe_insertion,stop_lost NaN \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "frameshift_elongation,start_lost,NMD_transcript... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,start_lost,stop_lost,3_pr... NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_lo... NaN \n", + "inframe_deletion,start_lost NaN \n", + "inframe_deletion,stop_lost,stop_retained_varian... NaN \n", + "2kb_downstream_variant,IG_J_gene NaN \n", + "2kb_downstream_variant,rRNA NaN \n", + "IG_C_gene NaN \n", + "complex_substitution,NMD_transcript_variant,syn... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_elongation,stop... NaN \n", + "complex_substitution,frameshift_truncation,intr... NaN \n", + "complex_substitution,frameshift_truncation,star... NaN \n", + "complex_substitution,frameshift_truncation,star... NaN \n", + "complex_substitution,frameshift_truncation,stop... NaN \n", + "complex_substitution,inframe_deletion,NMD_trans... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "complex_substitution,inframe_deletion,start_los... NaN \n", + "complex_substitution,inframe_deletion,stop_gain... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "complex_substitution,stop_lost NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_lost NaN \n", + "inframe_deletion,start_lost,NMD_transcript_variant NaN \n", + "inframe_deletion,stop_gained,stop_lost,3_prime_... NaN \n", + "inframe_insertion,start_retained_variant NaN \n", + "inframe_insertion,start_retained_variant,NMD_tr... NaN \n", + "\n", + "extra_vcf_info.CLNSIG Benign/Likely_benign \\\n", + "so \n", + "intron_variant 41009.0 \n", + "synonymous_variant 65090.0 \n", + "missense_variant 33923.0 \n", + "intron_variant,NMD_transcript_variant 5911.0 \n", + "3_prime_UTR_variant 5160.0 \n", + "intron_variant,processed_transcript 3550.0 \n", + "2kb_downstream_variant 4152.0 \n", + "2kb_upstream_variant 2925.0 \n", + "intron_variant,lnc_RNA 8461.0 \n", + "processed_transcript 6827.0 \n", + "NMD_transcript_variant,3_prime_UTR_variant 7915.0 \n", + "2kb_downstream_variant,processed_transcript 3346.0 \n", + "5_prime_UTR_variant 2436.0 \n", + "2kb_upstream_variant,processed_transcript 2335.0 \n", + "2kb_upstream_variant,lnc_RNA 1689.0 \n", + "2kb_downstream_variant,NMD_transcript_variant 1710.0 \n", + "NMD_transcript_variant,synonymous_variant 3430.0 \n", + "2kb_upstream_variant,NMD_transcript_variant 799.0 \n", + "missense_variant,NMD_transcript_variant 2118.0 \n", + "2kb_downstream_variant,lnc_RNA 841.0 \n", + "lnc_RNA 701.0 \n", + "NMD_transcript_variant,5_prime_UTR_variant 363.0 \n", + "inframe_deletion 707.0 \n", + "2kb_downstream_variant,miRNA 235.0 \n", + "inframe_insertion 471.0 \n", + "2kb_upstream_variant,miRNA 126.0 \n", + "stop_gained 82.0 \n", + "frameshift_truncation 23.0 \n", + "frameshift_elongation 59.0 \n", + "intron_variant,splice_site_variant 63.0 \n", + "intron_variant,NSD_transcript 28.0 \n", + "NSD_transcript 69.0 \n", + "complex_substitution,missense_variant 90.0 \n", + "2kb_upstream_variant,misc_RNA 61.0 \n", + "2kb_downstream_variant,misc_RNA 47.0 \n", + "intron_variant,polymorphic_pseudogene 6.0 \n", + "2kb_upstream_variant,snoRNA 15.0 \n", + "polymorphic_pseudogene 101.0 \n", + "2kb_downstream_variant,snoRNA 21.0 \n", + "2kb_downstream_variant,snRNA 27.0 \n", + "2kb_downstream_variant,NSD_transcript 16.0 \n", + "missense_variant,start_lost 21.0 \n", + "2kb_upstream_variant,snRNA 17.0 \n", + "2kb_downstream_variant,polymorphic_pseudogene 6.0 \n", + "2kb_upstream_variant,NSD_transcript 24.0 \n", + "NMD_transcript_variant,stop_gained 23.0 \n", + "stop_lost 12.0 \n", + "2kb_upstream_variant,polymorphic_pseudogene 5.0 \n", + "stop_retained_variant 13.0 \n", + "splice_site_variant 29.0 \n", + "intron_variant,NMD_transcript_variant,splice_si... 14.0 \n", + "inframe_deletion,NMD_transcript_variant 46.0 \n", + "inframe_insertion,NMD_transcript_variant 27.0 \n", + "polymorphic_pseudogene,3_prime_UTR_variant 8.0 \n", + "2kb_upstream_variant,5_prime_UTR_variant NaN \n", + "complex_substitution,frameshift_elongation,intr... 5.0 \n", + "missense_variant,start_lost,NMD_transcript_variant 3.0 \n", + "miRNA 5.0 \n", + "2kb_upstream_variant,ribozyme 12.0 \n", + "snoRNA 2.0 \n", + "frameshift_truncation,stop_gained 10.0 \n", + "frameshift_elongation,stop_gained 1.0 \n", + "frameshift_truncation,NMD_transcript_variant 3.0 \n", + "complex_substitution 2.0 \n", + "complex_substitution,inframe_deletion NaN \n", + "polymorphic_pseudogene,5_prime_UTR_variant 6.0 \n", + "NMD_transcript_variant,stop_retained_variant 2.0 \n", + "snRNA NaN \n", + "NMD_transcript_variant 14.0 \n", + "2kb_upstream_variant,scaRNA NaN \n", + "NSD_transcript,5_prime_UTR_variant 3.0 \n", + "NMD_transcript_variant,stop_lost 3.0 \n", + "2kb_downstream_variant,3_prime_UTR_variant NaN \n", + "2kb_upstream_variant,rRNA 1.0 \n", + "misc_RNA NaN \n", + "complex_substitution,inframe_insertion,intron_v... 16.0 \n", + "frameshift_truncation,stop_lost 1.0 \n", + "frameshift_elongation,NMD_transcript_variant 2.0 \n", + "exon_loss_variant,frameshift_truncation NaN \n", + "complex_substitution,missense_variant,NMD_trans... NaN \n", + "complex_substitution,synonymous_variant 21.0 \n", + "frameshift_truncation,stop_retained_variant NaN \n", + "frameshift_elongation,stop_retained_variant NaN \n", + "inframe_insertion,stop_gained 2.0 \n", + "NMD_transcript_variant,splice_site_variant NaN \n", + "frameshift_elongation,stop_lost NaN \n", + "2kb_downstream_variant,NMD_transcript_variant,3... NaN \n", + "2kb_upstream_variant,NMD_transcript_variant,5_p... NaN \n", + "2kb_downstream_variant,scaRNA 1.0 \n", + "ribozyme 1.0 \n", + "inframe_insertion,stop_retained_variant NaN \n", + "inframe_deletion,stop_gained NaN \n", + "start_lost,5_prime_UTR_variant NaN \n", + "frameshift_truncation,start_lost NaN \n", + "start_retained_variant 1.0 \n", + "frameshift_truncation,stop_lost,stop_retained_v... 5.0 \n", + "2kb_downstream_variant,ribozyme NaN \n", + "complex_substitution,intron_variant,synonymous_... NaN \n", + "frameshift_truncation,stop_lost,synonymous_vari... NaN \n", + "complex_substitution,frameshift_truncation NaN \n", + "complex_substitution,frameshift_elongation NaN \n", + "complex_substitution,stop_gained NaN \n", + "exon_loss_variant,inframe_deletion NaN \n", + "complex_substitution,frameshift_truncation,NMD_... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "exon_loss_variant,intron_variant NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "2kb_upstream_variant,start_lost,transcript_abla... NaN \n", + "2kb_downstream_variant,stop_lost,3_prime_UTR_va... NaN \n", + "exon_loss_variant,frameshift_truncation,NMD_tra... NaN \n", + "complex_substitution,inframe_insertion 3.0 \n", + "stop_lost,3_prime_UTR_variant NaN \n", + "intron_variant,start_lost,5_prime_UTR_variant NaN \n", + "complex_substitution,inframe_deletion,missense_... NaN \n", + "frameshift_truncation,stop_lost,3_prime_UTR_var... NaN \n", + "complex_substitution,frameshift_elongation,NMD_... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_deletion,stop_gained NaN \n", + "complex_substitution,inframe_insertion,stop_gained NaN \n", + "complex_substitution,NMD_transcript_variant,sto... NaN \n", + "intron_variant,start_lost NaN \n", + "intron_variant,start_lost,NMD_transcript_varian... NaN \n", + "complex_substitution,frameshift_elongation,intr... 1.0 \n", + "complex_substitution,frameshift_truncation,intr... NaN \n", + "2kb_downstream_variant,intron_variant,stop_lost... NaN \n", + "complex_substitution,inframe_insertion,missense... NaN \n", + "start_lost,NMD_transcript_variant,5_prime_UTR_v... NaN \n", + "inframe_deletion,stop_lost,3_prime_UTR_variant NaN \n", + "2kb_upstream_variant,transcript_ablation,5_prim... NaN \n", + "2kb_downstream_variant,stop_lost 2.0 \n", + "NMD_transcript_variant,stop_lost,3_prime_UTR_va... NaN \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_ga... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "complex_substitution,inframe_deletion,intron_va... 1.0 \n", + "frameshift_elongation,start_lost NaN \n", + "start_lost,3_prime_UTR_variant,5_prime_UTR_variant NaN \n", + "start_lost,NMD_transcript_variant NaN \n", + "exon_loss_variant,inframe_deletion,NMD_transcri... NaN \n", + "inframe_deletion,stop_lost NaN \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "inframe_insertion,NMD_transcript_variant,stop_g... NaN \n", + "exon_loss_variant,intron_variant,NMD_transcript... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "2kb_downstream_variant,2kb_upstream_variant,sta... NaN \n", + "2kb_upstream_variant,intron_variant,start_lost,... NaN \n", + "2kb_upstream_variant,start_lost,stop_lost,trans... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_insertion,NMD_tran... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "frameshift_truncation,start_lost,NMD_transcript... NaN \n", + "2kb_downstream_variant,NMD_transcript_variant,s... NaN \n", + "frameshift_truncation,synonymous_variant NaN \n", + "intron_variant,stop_lost,3_prime_UTR_variant NaN \n", + "2kb_downstream_variant,2kb_upstream_variant,sta... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_deletion,missense_... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "exon_loss_variant,splice_site_variant NaN \n", + "start_lost NaN \n", + "2kb_upstream_variant,start_lost,5_prime_UTR_var... NaN \n", + "complex_substitution,frameshift_elongation,star... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "2kb_downstream_variant,intron_variant,NMD_trans... NaN \n", + "complex_substitution,NMD_transcript_variant NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_truncation,NMD_... NaN \n", + "complex_substitution,inframe_insertion,NMD_tran... NaN \n", + "complex_substitution,start_lost NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,stop_gained,stop_lost,3_p... NaN \n", + "2kb_upstream_variant,NMD_transcript_variant,tra... NaN \n", + "complex_substitution,inframe_deletion,NMD_trans... NaN \n", + "complex_substitution,inframe_insertion,stop_lost NaN \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "frameshift_elongation,start_lost,NMD_transcript... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,start_lost,stop_lost,3_pr... NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_lo... NaN \n", + "inframe_deletion,start_lost NaN \n", + "inframe_deletion,stop_lost,stop_retained_varian... NaN \n", + "2kb_downstream_variant,IG_J_gene NaN \n", + "2kb_downstream_variant,rRNA NaN \n", + "IG_C_gene NaN \n", + "complex_substitution,NMD_transcript_variant,syn... 4.0 \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_elongation,stop... NaN \n", + "complex_substitution,frameshift_truncation,intr... NaN \n", + "complex_substitution,frameshift_truncation,star... NaN \n", + "complex_substitution,frameshift_truncation,star... NaN \n", + "complex_substitution,frameshift_truncation,stop... NaN \n", + "complex_substitution,inframe_deletion,NMD_trans... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "complex_substitution,inframe_deletion,start_los... NaN \n", + "complex_substitution,inframe_deletion,stop_gain... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "complex_substitution,stop_lost NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_lost NaN \n", + "inframe_deletion,start_lost,NMD_transcript_variant NaN \n", + "inframe_deletion,stop_gained,stop_lost,3_prime_... NaN \n", + "inframe_insertion,start_retained_variant NaN \n", + "inframe_insertion,start_retained_variant,NMD_tr... NaN \n", + "\n", + "extra_vcf_info.CLNSIG Conflicting_interpretations_of_pathogenicity \\\n", + "so \n", + "intron_variant 91923.0 \n", + "synonymous_variant 85163.0 \n", + "missense_variant 224600.0 \n", + "intron_variant,NMD_transcript_variant 17076.0 \n", + "3_prime_UTR_variant 6730.0 \n", + "intron_variant,processed_transcript 9357.0 \n", + "2kb_downstream_variant 9240.0 \n", + "2kb_upstream_variant 7068.0 \n", + "intron_variant,lnc_RNA 67259.0 \n", + "processed_transcript 27208.0 \n", + "NMD_transcript_variant,3_prime_UTR_variant 33577.0 \n", + "2kb_downstream_variant,processed_transcript 10288.0 \n", + "5_prime_UTR_variant 6354.0 \n", + "2kb_upstream_variant,processed_transcript 7063.0 \n", + "2kb_upstream_variant,lnc_RNA 5231.0 \n", + "2kb_downstream_variant,NMD_transcript_variant 7184.0 \n", + "NMD_transcript_variant,synonymous_variant 6154.0 \n", + "2kb_upstream_variant,NMD_transcript_variant 1835.0 \n", + "missense_variant,NMD_transcript_variant 14236.0 \n", + "2kb_downstream_variant,lnc_RNA 4514.0 \n", + "lnc_RNA 3473.0 \n", + "NMD_transcript_variant,5_prime_UTR_variant 1021.0 \n", + "inframe_deletion 3254.0 \n", + "2kb_downstream_variant,miRNA 616.0 \n", + "inframe_insertion 1374.0 \n", + "2kb_upstream_variant,miRNA 516.0 \n", + "stop_gained 3688.0 \n", + "frameshift_truncation 2172.0 \n", + "frameshift_elongation 1229.0 \n", + "intron_variant,splice_site_variant 3461.0 \n", + "intron_variant,NSD_transcript 42.0 \n", + "NSD_transcript 963.0 \n", + "complex_substitution,missense_variant 614.0 \n", + "2kb_upstream_variant,misc_RNA 175.0 \n", + "2kb_downstream_variant,misc_RNA 158.0 \n", + "intron_variant,polymorphic_pseudogene 36.0 \n", + "2kb_upstream_variant,snoRNA 36.0 \n", + "polymorphic_pseudogene 188.0 \n", + "2kb_downstream_variant,snoRNA 70.0 \n", + "2kb_downstream_variant,snRNA 127.0 \n", + "2kb_downstream_variant,NSD_transcript 1353.0 \n", + "missense_variant,start_lost 532.0 \n", + "2kb_upstream_variant,snRNA 44.0 \n", + "2kb_downstream_variant,polymorphic_pseudogene 11.0 \n", + "2kb_upstream_variant,NSD_transcript 59.0 \n", + "NMD_transcript_variant,stop_gained 355.0 \n", + "stop_lost 106.0 \n", + "2kb_upstream_variant,polymorphic_pseudogene 10.0 \n", + "stop_retained_variant 66.0 \n", + "splice_site_variant 195.0 \n", + "intron_variant,NMD_transcript_variant,splice_si... 1186.0 \n", + "inframe_deletion,NMD_transcript_variant 137.0 \n", + "inframe_insertion,NMD_transcript_variant 98.0 \n", + "polymorphic_pseudogene,3_prime_UTR_variant 27.0 \n", + "2kb_upstream_variant,5_prime_UTR_variant 3.0 \n", + "complex_substitution,frameshift_elongation,intr... 99.0 \n", + "missense_variant,start_lost,NMD_transcript_variant 175.0 \n", + "miRNA 6.0 \n", + "2kb_upstream_variant,ribozyme 52.0 \n", + "snoRNA 4.0 \n", + "frameshift_truncation,stop_gained 100.0 \n", + "frameshift_elongation,stop_gained 85.0 \n", + "frameshift_truncation,NMD_transcript_variant 136.0 \n", + "complex_substitution 45.0 \n", + "complex_substitution,inframe_deletion 19.0 \n", + "polymorphic_pseudogene,5_prime_UTR_variant 34.0 \n", + "NMD_transcript_variant,stop_retained_variant 7.0 \n", + "snRNA 9.0 \n", + "NMD_transcript_variant 39.0 \n", + "2kb_upstream_variant,scaRNA 1.0 \n", + "NSD_transcript,5_prime_UTR_variant 2.0 \n", + "NMD_transcript_variant,stop_lost 42.0 \n", + "2kb_downstream_variant,3_prime_UTR_variant NaN \n", + "2kb_upstream_variant,rRNA NaN \n", + "misc_RNA 9.0 \n", + "complex_substitution,inframe_insertion,intron_v... 54.0 \n", + "frameshift_truncation,stop_lost 31.0 \n", + "frameshift_elongation,NMD_transcript_variant 117.0 \n", + "exon_loss_variant,frameshift_truncation 9.0 \n", + "complex_substitution,missense_variant,NMD_trans... 31.0 \n", + "complex_substitution,synonymous_variant 16.0 \n", + "frameshift_truncation,stop_retained_variant 13.0 \n", + "frameshift_elongation,stop_retained_variant 7.0 \n", + "inframe_insertion,stop_gained NaN \n", + "NMD_transcript_variant,splice_site_variant 8.0 \n", + "frameshift_elongation,stop_lost 12.0 \n", + "2kb_downstream_variant,NMD_transcript_variant,3... NaN \n", + "2kb_upstream_variant,NMD_transcript_variant,5_p... 3.0 \n", + "2kb_downstream_variant,scaRNA 2.0 \n", + "ribozyme 11.0 \n", + "inframe_insertion,stop_retained_variant NaN \n", + "inframe_deletion,stop_gained 2.0 \n", + "start_lost,5_prime_UTR_variant 9.0 \n", + "frameshift_truncation,start_lost 5.0 \n", + "start_retained_variant NaN \n", + "frameshift_truncation,stop_lost,stop_retained_v... 10.0 \n", + "2kb_downstream_variant,ribozyme NaN \n", + "complex_substitution,intron_variant,synonymous_... NaN \n", + "frameshift_truncation,stop_lost,synonymous_vari... NaN \n", + "complex_substitution,frameshift_truncation 46.0 \n", + "complex_substitution,frameshift_elongation 31.0 \n", + "complex_substitution,stop_gained 20.0 \n", + "exon_loss_variant,inframe_deletion NaN \n", + "complex_substitution,frameshift_truncation,NMD_... 5.0 \n", + "frameshift_truncation,NMD_transcript_variant,st... 5.0 \n", + "exon_loss_variant,intron_variant NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... 14.0 \n", + "2kb_upstream_variant,start_lost,transcript_abla... NaN \n", + "2kb_downstream_variant,stop_lost,3_prime_UTR_va... NaN \n", + "exon_loss_variant,frameshift_truncation,NMD_tra... 1.0 \n", + "complex_substitution,inframe_insertion 33.0 \n", + "stop_lost,3_prime_UTR_variant 1.0 \n", + "intron_variant,start_lost,5_prime_UTR_variant NaN \n", + "complex_substitution,inframe_deletion,missense_... 15.0 \n", + "frameshift_truncation,stop_lost,3_prime_UTR_var... NaN \n", + "complex_substitution,frameshift_elongation,NMD_... 1.0 \n", + "complex_substitution,inframe_insertion,intron_v... 1.0 \n", + "complex_substitution,inframe_deletion,stop_gained NaN \n", + "complex_substitution,inframe_insertion,stop_gained NaN \n", + "complex_substitution,NMD_transcript_variant,sto... NaN \n", + "intron_variant,start_lost NaN \n", + "intron_variant,start_lost,NMD_transcript_varian... NaN \n", + "complex_substitution,frameshift_elongation,intr... 6.0 \n", + "complex_substitution,frameshift_truncation,intr... NaN \n", + "2kb_downstream_variant,intron_variant,stop_lost... NaN \n", + "complex_substitution,inframe_insertion,missense... NaN \n", + "start_lost,NMD_transcript_variant,5_prime_UTR_v... NaN \n", + "inframe_deletion,stop_lost,3_prime_UTR_variant NaN \n", + "2kb_upstream_variant,transcript_ablation,5_prim... NaN \n", + "2kb_downstream_variant,stop_lost NaN \n", + "NMD_transcript_variant,stop_lost,3_prime_UTR_va... NaN \n", + "complex_substitution,start_lost,start_retained_... 1.0 \n", + "inframe_deletion,NMD_transcript_variant,stop_ga... NaN \n", + "complex_substitution,inframe_insertion,intron_v... 4.0 \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "frameshift_elongation,start_lost 9.0 \n", + "start_lost,3_prime_UTR_variant,5_prime_UTR_variant NaN \n", + "start_lost,NMD_transcript_variant NaN \n", + "exon_loss_variant,inframe_deletion,NMD_transcri... NaN \n", + "inframe_deletion,stop_lost 1.0 \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "inframe_insertion,NMD_transcript_variant,stop_g... NaN \n", + "exon_loss_variant,intron_variant,NMD_transcript... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... 1.0 \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "2kb_downstream_variant,2kb_upstream_variant,sta... 1.0 \n", + "2kb_upstream_variant,intron_variant,start_lost,... NaN \n", + "2kb_upstream_variant,start_lost,stop_lost,trans... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_insertion,NMD_tran... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "frameshift_truncation,start_lost,NMD_transcript... 2.0 \n", + "2kb_downstream_variant,NMD_transcript_variant,s... NaN \n", + "frameshift_truncation,synonymous_variant NaN \n", + "intron_variant,stop_lost,3_prime_UTR_variant NaN \n", + "2kb_downstream_variant,2kb_upstream_variant,sta... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_deletion,missense_... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "exon_loss_variant,splice_site_variant NaN \n", + "start_lost NaN \n", + "2kb_upstream_variant,start_lost,5_prime_UTR_var... NaN \n", + "complex_substitution,frameshift_elongation,star... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "2kb_downstream_variant,intron_variant,NMD_trans... NaN \n", + "complex_substitution,NMD_transcript_variant NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_truncation,NMD_... NaN \n", + "complex_substitution,inframe_insertion,NMD_tran... 1.0 \n", + "complex_substitution,start_lost NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,stop_gained,stop_lost,3_p... NaN \n", + "2kb_upstream_variant,NMD_transcript_variant,tra... NaN \n", + "complex_substitution,inframe_deletion,NMD_trans... 2.0 \n", + "complex_substitution,inframe_insertion,stop_lost NaN \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "frameshift_elongation,start_lost,NMD_transcript... 5.0 \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... 1.0 \n", + "frameshift_truncation,start_lost,stop_lost,3_pr... NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_lo... NaN \n", + "inframe_deletion,start_lost NaN \n", + "inframe_deletion,stop_lost,stop_retained_varian... 4.0 \n", + "2kb_downstream_variant,IG_J_gene NaN \n", + "2kb_downstream_variant,rRNA NaN \n", + "IG_C_gene NaN \n", + "complex_substitution,NMD_transcript_variant,syn... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_elongation,stop... NaN \n", + "complex_substitution,frameshift_truncation,intr... NaN \n", + "complex_substitution,frameshift_truncation,star... NaN \n", + "complex_substitution,frameshift_truncation,star... NaN \n", + "complex_substitution,frameshift_truncation,stop... NaN \n", + "complex_substitution,inframe_deletion,NMD_trans... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "complex_substitution,inframe_deletion,start_los... NaN \n", + "complex_substitution,inframe_deletion,stop_gain... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,start_lost,start_retained_... 1.0 \n", + "complex_substitution,stop_lost NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_lost NaN \n", + "inframe_deletion,start_lost,NMD_transcript_variant NaN \n", + "inframe_deletion,stop_gained,stop_lost,3_prime_... NaN \n", + "inframe_insertion,start_retained_variant NaN \n", + "inframe_insertion,start_retained_variant,NMD_tr... NaN \n", + "\n", + "extra_vcf_info.CLNSIG Likely_benign \\\n", + "so \n", + "intron_variant 756372.0 \n", + "synonymous_variant 1134299.0 \n", + "missense_variant 142639.0 \n", + "intron_variant,NMD_transcript_variant 99950.0 \n", + "3_prime_UTR_variant 25982.0 \n", + "intron_variant,processed_transcript 48739.0 \n", + "2kb_downstream_variant 38903.0 \n", + "2kb_upstream_variant 37942.0 \n", + "intron_variant,lnc_RNA 55714.0 \n", + "processed_transcript 62927.0 \n", + "NMD_transcript_variant,3_prime_UTR_variant 79381.0 \n", + "2kb_downstream_variant,processed_transcript 31757.0 \n", + "5_prime_UTR_variant 24143.0 \n", + "2kb_upstream_variant,processed_transcript 25995.0 \n", + "2kb_upstream_variant,lnc_RNA 18768.0 \n", + "2kb_downstream_variant,NMD_transcript_variant 17539.0 \n", + "NMD_transcript_variant,synonymous_variant 68777.0 \n", + "2kb_upstream_variant,NMD_transcript_variant 9253.0 \n", + "missense_variant,NMD_transcript_variant 11660.0 \n", + "2kb_downstream_variant,lnc_RNA 7315.0 \n", + "lnc_RNA 8412.0 \n", + "NMD_transcript_variant,5_prime_UTR_variant 3144.0 \n", + "inframe_deletion 1912.0 \n", + "2kb_downstream_variant,miRNA 1894.0 \n", + "inframe_insertion 940.0 \n", + "2kb_upstream_variant,miRNA 1647.0 \n", + "stop_gained 933.0 \n", + "frameshift_truncation 602.0 \n", + "frameshift_elongation 508.0 \n", + "intron_variant,splice_site_variant 748.0 \n", + "intron_variant,NSD_transcript 419.0 \n", + "NSD_transcript 1094.0 \n", + "complex_substitution,missense_variant 813.0 \n", + "2kb_upstream_variant,misc_RNA 952.0 \n", + "2kb_downstream_variant,misc_RNA 698.0 \n", + "intron_variant,polymorphic_pseudogene 352.0 \n", + "2kb_upstream_variant,snoRNA 147.0 \n", + "polymorphic_pseudogene 480.0 \n", + "2kb_downstream_variant,snoRNA 343.0 \n", + "2kb_downstream_variant,snRNA 463.0 \n", + "2kb_downstream_variant,NSD_transcript 778.0 \n", + "missense_variant,start_lost 157.0 \n", + "2kb_upstream_variant,snRNA 200.0 \n", + "2kb_downstream_variant,polymorphic_pseudogene 18.0 \n", + "2kb_upstream_variant,NSD_transcript 253.0 \n", + "NMD_transcript_variant,stop_gained 264.0 \n", + "stop_lost 137.0 \n", + "2kb_upstream_variant,polymorphic_pseudogene 32.0 \n", + "stop_retained_variant 508.0 \n", + "splice_site_variant 176.0 \n", + "intron_variant,NMD_transcript_variant,splice_si... 143.0 \n", + "inframe_deletion,NMD_transcript_variant 65.0 \n", + "inframe_insertion,NMD_transcript_variant 61.0 \n", + "polymorphic_pseudogene,3_prime_UTR_variant 35.0 \n", + "2kb_upstream_variant,5_prime_UTR_variant 1.0 \n", + "complex_substitution,frameshift_elongation,intr... 37.0 \n", + "missense_variant,start_lost,NMD_transcript_variant 47.0 \n", + "miRNA 93.0 \n", + "2kb_upstream_variant,ribozyme 127.0 \n", + "snoRNA 12.0 \n", + "frameshift_truncation,stop_gained 21.0 \n", + "frameshift_elongation,stop_gained 21.0 \n", + "frameshift_truncation,NMD_transcript_variant 19.0 \n", + "complex_substitution 21.0 \n", + "complex_substitution,inframe_deletion 20.0 \n", + "polymorphic_pseudogene,5_prime_UTR_variant 32.0 \n", + "NMD_transcript_variant,stop_retained_variant 33.0 \n", + "snRNA 12.0 \n", + "NMD_transcript_variant 55.0 \n", + "2kb_upstream_variant,scaRNA 11.0 \n", + "NSD_transcript,5_prime_UTR_variant 5.0 \n", + "NMD_transcript_variant,stop_lost 85.0 \n", + "2kb_downstream_variant,3_prime_UTR_variant 2.0 \n", + "2kb_upstream_variant,rRNA 10.0 \n", + "misc_RNA 37.0 \n", + "complex_substitution,inframe_insertion,intron_v... 29.0 \n", + "frameshift_truncation,stop_lost 3.0 \n", + "frameshift_elongation,NMD_transcript_variant 11.0 \n", + "exon_loss_variant,frameshift_truncation 15.0 \n", + "complex_substitution,missense_variant,NMD_trans... 21.0 \n", + "complex_substitution,synonymous_variant 237.0 \n", + "frameshift_truncation,stop_retained_variant 5.0 \n", + "frameshift_elongation,stop_retained_variant 15.0 \n", + "inframe_insertion,stop_gained NaN \n", + "NMD_transcript_variant,splice_site_variant 3.0 \n", + "frameshift_elongation,stop_lost 6.0 \n", + "2kb_downstream_variant,NMD_transcript_variant,3... 2.0 \n", + "2kb_upstream_variant,NMD_transcript_variant,5_p... 2.0 \n", + "2kb_downstream_variant,scaRNA 8.0 \n", + "ribozyme 4.0 \n", + "inframe_insertion,stop_retained_variant NaN \n", + "inframe_deletion,stop_gained 4.0 \n", + "start_lost,5_prime_UTR_variant NaN \n", + "frameshift_truncation,start_lost 2.0 \n", + "start_retained_variant NaN \n", + "frameshift_truncation,stop_lost,stop_retained_v... 18.0 \n", + "2kb_downstream_variant,ribozyme 2.0 \n", + "complex_substitution,intron_variant,synonymous_... NaN \n", + "frameshift_truncation,stop_lost,synonymous_vari... NaN \n", + "complex_substitution,frameshift_truncation 10.0 \n", + "complex_substitution,frameshift_elongation 1.0 \n", + "complex_substitution,stop_gained 2.0 \n", + "exon_loss_variant,inframe_deletion NaN \n", + "complex_substitution,frameshift_truncation,NMD_... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... 1.0 \n", + "exon_loss_variant,intron_variant NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... 1.0 \n", + "2kb_upstream_variant,start_lost,transcript_abla... NaN \n", + "2kb_downstream_variant,stop_lost,3_prime_UTR_va... NaN \n", + "exon_loss_variant,frameshift_truncation,NMD_tra... NaN \n", + "complex_substitution,inframe_insertion 25.0 \n", + "stop_lost,3_prime_UTR_variant NaN \n", + "intron_variant,start_lost,5_prime_UTR_variant NaN \n", + "complex_substitution,inframe_deletion,missense_... 6.0 \n", + "frameshift_truncation,stop_lost,3_prime_UTR_var... 1.0 \n", + "complex_substitution,frameshift_elongation,NMD_... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_deletion,stop_gained NaN \n", + "complex_substitution,inframe_insertion,stop_gained NaN \n", + "complex_substitution,NMD_transcript_variant,sto... NaN \n", + "intron_variant,start_lost NaN \n", + "intron_variant,start_lost,NMD_transcript_varian... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_truncation,intr... NaN \n", + "2kb_downstream_variant,intron_variant,stop_lost... NaN \n", + "complex_substitution,inframe_insertion,missense... NaN \n", + "start_lost,NMD_transcript_variant,5_prime_UTR_v... NaN \n", + "inframe_deletion,stop_lost,3_prime_UTR_variant 1.0 \n", + "2kb_upstream_variant,transcript_ablation,5_prim... NaN \n", + "2kb_downstream_variant,stop_lost NaN \n", + "NMD_transcript_variant,stop_lost,3_prime_UTR_va... NaN \n", + "complex_substitution,start_lost,start_retained_... 2.0 \n", + "inframe_deletion,NMD_transcript_variant,stop_ga... 1.0 \n", + "complex_substitution,inframe_insertion,intron_v... 1.0 \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "frameshift_elongation,start_lost NaN \n", + "start_lost,3_prime_UTR_variant,5_prime_UTR_variant NaN \n", + "start_lost,NMD_transcript_variant NaN \n", + "exon_loss_variant,inframe_deletion,NMD_transcri... NaN \n", + "inframe_deletion,stop_lost NaN \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "inframe_insertion,NMD_transcript_variant,stop_g... NaN \n", + "exon_loss_variant,intron_variant,NMD_transcript... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "2kb_downstream_variant,2kb_upstream_variant,sta... NaN \n", + "2kb_upstream_variant,intron_variant,start_lost,... NaN \n", + "2kb_upstream_variant,start_lost,stop_lost,trans... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_insertion,NMD_tran... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "frameshift_truncation,start_lost,NMD_transcript... NaN \n", + "2kb_downstream_variant,NMD_transcript_variant,s... NaN \n", + "frameshift_truncation,synonymous_variant 2.0 \n", + "intron_variant,stop_lost,3_prime_UTR_variant NaN \n", + "2kb_downstream_variant,2kb_upstream_variant,sta... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_deletion,missense_... 3.0 \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "exon_loss_variant,splice_site_variant NaN \n", + "start_lost NaN \n", + "2kb_upstream_variant,start_lost,5_prime_UTR_var... NaN \n", + "complex_substitution,frameshift_elongation,star... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "2kb_downstream_variant,intron_variant,NMD_trans... NaN \n", + "complex_substitution,NMD_transcript_variant 1.0 \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_truncation,NMD_... NaN \n", + "complex_substitution,inframe_insertion,NMD_tran... 1.0 \n", + "complex_substitution,start_lost NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,stop_gained,stop_lost,3_p... NaN \n", + "2kb_upstream_variant,NMD_transcript_variant,tra... NaN \n", + "complex_substitution,inframe_deletion,NMD_trans... NaN \n", + "complex_substitution,inframe_insertion,stop_lost NaN \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "frameshift_elongation,start_lost,NMD_transcript... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,start_lost,stop_lost,3_pr... NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_lo... NaN \n", + "inframe_deletion,start_lost NaN \n", + "inframe_deletion,stop_lost,stop_retained_varian... 1.0 \n", + "2kb_downstream_variant,IG_J_gene 1.0 \n", + "2kb_downstream_variant,rRNA NaN \n", + "IG_C_gene 1.0 \n", + "complex_substitution,NMD_transcript_variant,syn... 6.0 \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_elongation,stop... NaN \n", + "complex_substitution,frameshift_truncation,intr... NaN \n", + "complex_substitution,frameshift_truncation,star... 1.0 \n", + "complex_substitution,frameshift_truncation,star... NaN \n", + "complex_substitution,frameshift_truncation,stop... NaN \n", + "complex_substitution,inframe_deletion,NMD_trans... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "complex_substitution,inframe_deletion,start_los... NaN \n", + "complex_substitution,inframe_deletion,stop_gain... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "complex_substitution,stop_lost NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_lost NaN \n", + "inframe_deletion,start_lost,NMD_transcript_variant NaN \n", + "inframe_deletion,stop_gained,stop_lost,3_prime_... NaN \n", + "inframe_insertion,start_retained_variant NaN \n", + "inframe_insertion,start_retained_variant,NMD_tr... NaN \n", + "\n", + "extra_vcf_info.CLNSIG Likely_pathogenic \\\n", + "so \n", + "intron_variant 17725.0 \n", + "synonymous_variant 1065.0 \n", + "missense_variant 86905.0 \n", + "intron_variant,NMD_transcript_variant 5082.0 \n", + "3_prime_UTR_variant 1610.0 \n", + "intron_variant,processed_transcript 8988.0 \n", + "2kb_downstream_variant 5012.0 \n", + "2kb_upstream_variant 3687.0 \n", + "intron_variant,lnc_RNA 35200.0 \n", + "processed_transcript 12610.0 \n", + "NMD_transcript_variant,3_prime_UTR_variant 14063.0 \n", + "2kb_downstream_variant,processed_transcript 6545.0 \n", + "5_prime_UTR_variant 2568.0 \n", + "2kb_upstream_variant,processed_transcript 4235.0 \n", + "2kb_upstream_variant,lnc_RNA 3078.0 \n", + "2kb_downstream_variant,NMD_transcript_variant 2621.0 \n", + "NMD_transcript_variant,synonymous_variant 266.0 \n", + "2kb_upstream_variant,NMD_transcript_variant 717.0 \n", + "missense_variant,NMD_transcript_variant 6379.0 \n", + "2kb_downstream_variant,lnc_RNA 1835.0 \n", + "lnc_RNA 1615.0 \n", + "NMD_transcript_variant,5_prime_UTR_variant 165.0 \n", + "inframe_deletion 2970.0 \n", + "2kb_downstream_variant,miRNA 340.0 \n", + "inframe_insertion 746.0 \n", + "2kb_upstream_variant,miRNA 359.0 \n", + "stop_gained 28980.0 \n", + "frameshift_truncation 25262.0 \n", + "frameshift_elongation 11999.0 \n", + "intron_variant,splice_site_variant 49778.0 \n", + "intron_variant,NSD_transcript 33.0 \n", + "NSD_transcript 107.0 \n", + "complex_substitution,missense_variant 507.0 \n", + "2kb_upstream_variant,misc_RNA 120.0 \n", + "2kb_downstream_variant,misc_RNA 104.0 \n", + "intron_variant,polymorphic_pseudogene 39.0 \n", + "2kb_upstream_variant,snoRNA 62.0 \n", + "polymorphic_pseudogene 144.0 \n", + "2kb_downstream_variant,snoRNA 84.0 \n", + "2kb_downstream_variant,snRNA 56.0 \n", + "2kb_downstream_variant,NSD_transcript 72.0 \n", + "missense_variant,start_lost 1153.0 \n", + "2kb_upstream_variant,snRNA 36.0 \n", + "2kb_downstream_variant,polymorphic_pseudogene NaN \n", + "2kb_upstream_variant,NSD_transcript 19.0 \n", + "NMD_transcript_variant,stop_gained 1893.0 \n", + "stop_lost 227.0 \n", + "2kb_upstream_variant,polymorphic_pseudogene 14.0 \n", + "stop_retained_variant NaN \n", + "splice_site_variant 2379.0 \n", + "intron_variant,NMD_transcript_variant,splice_si... 12383.0 \n", + "inframe_deletion,NMD_transcript_variant 74.0 \n", + "inframe_insertion,NMD_transcript_variant 28.0 \n", + "polymorphic_pseudogene,3_prime_UTR_variant 2.0 \n", + "2kb_upstream_variant,5_prime_UTR_variant 5.0 \n", + "complex_substitution,frameshift_elongation,intr... 1173.0 \n", + "missense_variant,start_lost,NMD_transcript_variant 445.0 \n", + "miRNA 14.0 \n", + "2kb_upstream_variant,ribozyme 4.0 \n", + "snoRNA 2.0 \n", + "frameshift_truncation,stop_gained 1203.0 \n", + "frameshift_elongation,stop_gained 559.0 \n", + "frameshift_truncation,NMD_transcript_variant 471.0 \n", + "complex_substitution 89.0 \n", + "complex_substitution,inframe_deletion 83.0 \n", + "polymorphic_pseudogene,5_prime_UTR_variant 39.0 \n", + "NMD_transcript_variant,stop_retained_variant 11.0 \n", + "snRNA 8.0 \n", + "NMD_transcript_variant 38.0 \n", + "2kb_upstream_variant,scaRNA 9.0 \n", + "NSD_transcript,5_prime_UTR_variant NaN \n", + "NMD_transcript_variant,stop_lost 31.0 \n", + "2kb_downstream_variant,3_prime_UTR_variant 3.0 \n", + "2kb_upstream_variant,rRNA 1.0 \n", + "misc_RNA 1.0 \n", + "complex_substitution,inframe_insertion,intron_v... 586.0 \n", + "frameshift_truncation,stop_lost 15.0 \n", + "frameshift_elongation,NMD_transcript_variant 279.0 \n", + "exon_loss_variant,frameshift_truncation 186.0 \n", + "complex_substitution,missense_variant,NMD_trans... 27.0 \n", + "complex_substitution,synonymous_variant NaN \n", + "frameshift_truncation,stop_retained_variant NaN \n", + "frameshift_elongation,stop_retained_variant NaN \n", + "inframe_insertion,stop_gained 41.0 \n", + "NMD_transcript_variant,splice_site_variant 79.0 \n", + "frameshift_elongation,stop_lost 13.0 \n", + "2kb_downstream_variant,NMD_transcript_variant,3... 3.0 \n", + "2kb_upstream_variant,NMD_transcript_variant,5_p... NaN \n", + "2kb_downstream_variant,scaRNA 1.0 \n", + "ribozyme 3.0 \n", + "inframe_insertion,stop_retained_variant NaN \n", + "inframe_deletion,stop_gained 46.0 \n", + "start_lost,5_prime_UTR_variant 41.0 \n", + "frameshift_truncation,start_lost 36.0 \n", + "start_retained_variant NaN \n", + "frameshift_truncation,stop_lost,stop_retained_v... NaN \n", + "2kb_downstream_variant,ribozyme NaN \n", + "complex_substitution,intron_variant,synonymous_... 2.0 \n", + "frameshift_truncation,stop_lost,synonymous_vari... NaN \n", + "complex_substitution,frameshift_truncation 1728.0 \n", + "complex_substitution,frameshift_elongation 801.0 \n", + "complex_substitution,stop_gained 88.0 \n", + "exon_loss_variant,inframe_deletion 40.0 \n", + "complex_substitution,frameshift_truncation,NMD_... 80.0 \n", + "frameshift_truncation,NMD_transcript_variant,st... 17.0 \n", + "exon_loss_variant,intron_variant 64.0 \n", + "frameshift_elongation,NMD_transcript_variant,st... 3.0 \n", + "2kb_upstream_variant,start_lost,transcript_abla... 19.0 \n", + "2kb_downstream_variant,stop_lost,3_prime_UTR_va... 21.0 \n", + "exon_loss_variant,frameshift_truncation,NMD_tra... 5.0 \n", + "complex_substitution,inframe_insertion 85.0 \n", + "stop_lost,3_prime_UTR_variant 19.0 \n", + "intron_variant,start_lost,5_prime_UTR_variant 12.0 \n", + "complex_substitution,inframe_deletion,missense_... 153.0 \n", + "frameshift_truncation,stop_lost,3_prime_UTR_var... 9.0 \n", + "complex_substitution,frameshift_elongation,NMD_... 18.0 \n", + "complex_substitution,inframe_insertion,intron_v... 43.0 \n", + "complex_substitution,inframe_deletion,stop_gained 11.0 \n", + "complex_substitution,inframe_insertion,stop_gained 6.0 \n", + "complex_substitution,NMD_transcript_variant,sto... 6.0 \n", + "intron_variant,start_lost 13.0 \n", + "intron_variant,start_lost,NMD_transcript_varian... NaN \n", + "complex_substitution,frameshift_elongation,intr... 36.0 \n", + "complex_substitution,frameshift_truncation,intr... 27.0 \n", + "2kb_downstream_variant,intron_variant,stop_lost... NaN \n", + "complex_substitution,inframe_insertion,missense... 9.0 \n", + "start_lost,NMD_transcript_variant,5_prime_UTR_v... 10.0 \n", + "inframe_deletion,stop_lost,3_prime_UTR_variant 21.0 \n", + "2kb_upstream_variant,transcript_ablation,5_prim... 2.0 \n", + "2kb_downstream_variant,stop_lost 2.0 \n", + "NMD_transcript_variant,stop_lost,3_prime_UTR_va... 3.0 \n", + "complex_substitution,start_lost,start_retained_... 1.0 \n", + "inframe_deletion,NMD_transcript_variant,stop_ga... 1.0 \n", + "complex_substitution,inframe_insertion,intron_v... 30.0 \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "complex_substitution,inframe_deletion,intron_va... 2.0 \n", + "frameshift_elongation,start_lost 1.0 \n", + "start_lost,3_prime_UTR_variant,5_prime_UTR_variant 4.0 \n", + "start_lost,NMD_transcript_variant 2.0 \n", + "exon_loss_variant,inframe_deletion,NMD_transcri... NaN \n", + "inframe_deletion,stop_lost 5.0 \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... 11.0 \n", + "inframe_insertion,NMD_transcript_variant,stop_g... 2.0 \n", + "exon_loss_variant,intron_variant,NMD_transcript... 1.0 \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "2kb_downstream_variant,2kb_upstream_variant,sta... NaN \n", + "2kb_upstream_variant,intron_variant,start_lost,... NaN \n", + "2kb_upstream_variant,start_lost,stop_lost,trans... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_insertion,NMD_tran... NaN \n", + "complex_substitution,inframe_insertion,intron_v... 3.0 \n", + "frameshift_truncation,start_lost,NMD_transcript... 19.0 \n", + "2kb_downstream_variant,NMD_transcript_variant,s... NaN \n", + "frameshift_truncation,synonymous_variant NaN \n", + "intron_variant,stop_lost,3_prime_UTR_variant NaN \n", + "2kb_downstream_variant,2kb_upstream_variant,sta... NaN \n", + "complex_substitution,frameshift_elongation,intr... 3.0 \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_deletion,missense_... 6.0 \n", + "complex_substitution,inframe_insertion,intron_v... 4.0 \n", + "exon_loss_variant,splice_site_variant 3.0 \n", + "start_lost NaN \n", + "2kb_upstream_variant,start_lost,5_prime_UTR_var... NaN \n", + "complex_substitution,frameshift_elongation,star... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... 1.0 \n", + "2kb_downstream_variant,intron_variant,NMD_trans... NaN \n", + "complex_substitution,NMD_transcript_variant 3.0 \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_truncation,NMD_... 1.0 \n", + "complex_substitution,inframe_insertion,NMD_tran... 2.0 \n", + "complex_substitution,start_lost NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,stop_gained,stop_lost,3_p... NaN \n", + "2kb_upstream_variant,NMD_transcript_variant,tra... NaN \n", + "complex_substitution,inframe_deletion,NMD_trans... NaN \n", + "complex_substitution,inframe_insertion,stop_lost NaN \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "frameshift_elongation,start_lost,NMD_transcript... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,start_lost,stop_lost,3_pr... NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_lo... 2.0 \n", + "inframe_deletion,start_lost 2.0 \n", + "inframe_deletion,stop_lost,stop_retained_varian... NaN \n", + "2kb_downstream_variant,IG_J_gene NaN \n", + "2kb_downstream_variant,rRNA NaN \n", + "IG_C_gene NaN \n", + "complex_substitution,NMD_transcript_variant,syn... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_elongation,stop... NaN \n", + "complex_substitution,frameshift_truncation,intr... 1.0 \n", + "complex_substitution,frameshift_truncation,star... NaN \n", + "complex_substitution,frameshift_truncation,star... 3.0 \n", + "complex_substitution,frameshift_truncation,stop... NaN \n", + "complex_substitution,inframe_deletion,NMD_trans... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "complex_substitution,inframe_deletion,start_los... NaN \n", + "complex_substitution,inframe_deletion,stop_gain... 2.0 \n", + "complex_substitution,inframe_insertion,intron_v... 2.0 \n", + "complex_substitution,inframe_insertion,intron_v... 2.0 \n", + "complex_substitution,inframe_insertion,intron_v... 6.0 \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "complex_substitution,stop_lost 2.0 \n", + "inframe_deletion,NMD_transcript_variant,stop_lost 1.0 \n", + "inframe_deletion,start_lost,NMD_transcript_variant 1.0 \n", + "inframe_deletion,stop_gained,stop_lost,3_prime_... NaN \n", + "inframe_insertion,start_retained_variant NaN \n", + "inframe_insertion,start_retained_variant,NMD_tr... NaN \n", + "\n", + "extra_vcf_info.CLNSIG Pathogenic \\\n", + "so \n", + "intron_variant 37603.0 \n", + "synonymous_variant 1364.0 \n", + "missense_variant 74138.0 \n", + "intron_variant,NMD_transcript_variant 7134.0 \n", + "3_prime_UTR_variant 3713.0 \n", + "intron_variant,processed_transcript 9919.0 \n", + "2kb_downstream_variant 7613.0 \n", + "2kb_upstream_variant 6160.0 \n", + "intron_variant,lnc_RNA 20851.0 \n", + "processed_transcript 23769.0 \n", + "NMD_transcript_variant,3_prime_UTR_variant 30740.0 \n", + "2kb_downstream_variant,processed_transcript 9972.0 \n", + "5_prime_UTR_variant 10018.0 \n", + "2kb_upstream_variant,processed_transcript 6601.0 \n", + "2kb_upstream_variant,lnc_RNA 4261.0 \n", + "2kb_downstream_variant,NMD_transcript_variant 6226.0 \n", + "NMD_transcript_variant,synonymous_variant 429.0 \n", + "2kb_upstream_variant,NMD_transcript_variant 1326.0 \n", + "missense_variant,NMD_transcript_variant 5951.0 \n", + "2kb_downstream_variant,lnc_RNA 1696.0 \n", + "lnc_RNA 1767.0 \n", + "NMD_transcript_variant,5_prime_UTR_variant 406.0 \n", + "inframe_deletion 2826.0 \n", + "2kb_downstream_variant,miRNA 428.0 \n", + "inframe_insertion 694.0 \n", + "2kb_upstream_variant,miRNA 504.0 \n", + "stop_gained 120930.0 \n", + "frameshift_truncation 101852.0 \n", + "frameshift_elongation 51298.0 \n", + "intron_variant,splice_site_variant 38420.0 \n", + "intron_variant,NSD_transcript 25.0 \n", + "NSD_transcript 746.0 \n", + "complex_substitution,missense_variant 727.0 \n", + "2kb_upstream_variant,misc_RNA 199.0 \n", + "2kb_downstream_variant,misc_RNA 171.0 \n", + "intron_variant,polymorphic_pseudogene 20.0 \n", + "2kb_upstream_variant,snoRNA 45.0 \n", + "polymorphic_pseudogene 250.0 \n", + "2kb_downstream_variant,snoRNA 111.0 \n", + "2kb_downstream_variant,snRNA 105.0 \n", + "2kb_downstream_variant,NSD_transcript 1121.0 \n", + "missense_variant,start_lost 2142.0 \n", + "2kb_upstream_variant,snRNA 55.0 \n", + "2kb_downstream_variant,polymorphic_pseudogene 5.0 \n", + "2kb_upstream_variant,NSD_transcript 26.0 \n", + "NMD_transcript_variant,stop_gained 9635.0 \n", + "stop_lost 284.0 \n", + "2kb_upstream_variant,polymorphic_pseudogene NaN \n", + "stop_retained_variant 6.0 \n", + "splice_site_variant 1671.0 \n", + "intron_variant,NMD_transcript_variant,splice_si... 7515.0 \n", + "inframe_deletion,NMD_transcript_variant 98.0 \n", + "inframe_insertion,NMD_transcript_variant 29.0 \n", + "polymorphic_pseudogene,3_prime_UTR_variant NaN \n", + "2kb_upstream_variant,5_prime_UTR_variant 43.0 \n", + "complex_substitution,frameshift_elongation,intr... 968.0 \n", + "missense_variant,start_lost,NMD_transcript_variant 691.0 \n", + "miRNA 14.0 \n", + "2kb_upstream_variant,ribozyme 39.0 \n", + "snoRNA 17.0 \n", + "frameshift_truncation,stop_gained 6366.0 \n", + "frameshift_elongation,stop_gained 2633.0 \n", + "frameshift_truncation,NMD_transcript_variant 4176.0 \n", + "complex_substitution 140.0 \n", + "complex_substitution,inframe_deletion 72.0 \n", + "polymorphic_pseudogene,5_prime_UTR_variant 66.0 \n", + "NMD_transcript_variant,stop_retained_variant 2.0 \n", + "snRNA 9.0 \n", + "NMD_transcript_variant 51.0 \n", + "2kb_upstream_variant,scaRNA 5.0 \n", + "NSD_transcript,5_prime_UTR_variant NaN \n", + "NMD_transcript_variant,stop_lost 51.0 \n", + "2kb_downstream_variant,3_prime_UTR_variant 35.0 \n", + "2kb_upstream_variant,rRNA NaN \n", + "misc_RNA NaN \n", + "complex_substitution,inframe_insertion,intron_v... 363.0 \n", + "frameshift_truncation,stop_lost 16.0 \n", + "frameshift_elongation,NMD_transcript_variant 2275.0 \n", + "exon_loss_variant,frameshift_truncation 902.0 \n", + "complex_substitution,missense_variant,NMD_trans... 45.0 \n", + "complex_substitution,synonymous_variant NaN \n", + "frameshift_truncation,stop_retained_variant 1.0 \n", + "frameshift_elongation,stop_retained_variant NaN \n", + "inframe_insertion,stop_gained 199.0 \n", + "NMD_transcript_variant,splice_site_variant 84.0 \n", + "frameshift_elongation,stop_lost 35.0 \n", + "2kb_downstream_variant,NMD_transcript_variant,3... 9.0 \n", + "2kb_upstream_variant,NMD_transcript_variant,5_p... 7.0 \n", + "2kb_downstream_variant,scaRNA 4.0 \n", + "ribozyme 4.0 \n", + "inframe_insertion,stop_retained_variant NaN \n", + "inframe_deletion,stop_gained 175.0 \n", + "start_lost,5_prime_UTR_variant 133.0 \n", + "frameshift_truncation,start_lost 69.0 \n", + "start_retained_variant 3.0 \n", + "frameshift_truncation,stop_lost,stop_retained_v... 1.0 \n", + "2kb_downstream_variant,ribozyme NaN \n", + "complex_substitution,intron_variant,synonymous_... NaN \n", + "frameshift_truncation,stop_lost,synonymous_vari... NaN \n", + "complex_substitution,frameshift_truncation 4168.0 \n", + "complex_substitution,frameshift_elongation 1764.0 \n", + "complex_substitution,stop_gained 703.0 \n", + "exon_loss_variant,inframe_deletion 391.0 \n", + "complex_substitution,frameshift_truncation,NMD_... 316.0 \n", + "frameshift_truncation,NMD_transcript_variant,st... 232.0 \n", + "exon_loss_variant,intron_variant 230.0 \n", + "frameshift_elongation,NMD_transcript_variant,st... 120.0 \n", + "2kb_upstream_variant,start_lost,transcript_abla... 110.0 \n", + "2kb_downstream_variant,stop_lost,3_prime_UTR_va... 105.0 \n", + "exon_loss_variant,frameshift_truncation,NMD_tra... 103.0 \n", + "complex_substitution,inframe_insertion 96.0 \n", + "stop_lost,3_prime_UTR_variant 95.0 \n", + "intron_variant,start_lost,5_prime_UTR_variant 78.0 \n", + "complex_substitution,inframe_deletion,missense_... 74.0 \n", + "frameshift_truncation,stop_lost,3_prime_UTR_var... 72.0 \n", + "complex_substitution,frameshift_elongation,NMD_... 71.0 \n", + "complex_substitution,inframe_insertion,intron_v... 50.0 \n", + "complex_substitution,inframe_deletion,stop_gained 43.0 \n", + "complex_substitution,inframe_insertion,stop_gained 39.0 \n", + "complex_substitution,NMD_transcript_variant,sto... 37.0 \n", + "intron_variant,start_lost 35.0 \n", + "intron_variant,start_lost,NMD_transcript_varian... 34.0 \n", + "complex_substitution,frameshift_elongation,intr... 33.0 \n", + "complex_substitution,frameshift_truncation,intr... 33.0 \n", + "2kb_downstream_variant,intron_variant,stop_lost... 27.0 \n", + "complex_substitution,inframe_insertion,missense... 26.0 \n", + "start_lost,NMD_transcript_variant,5_prime_UTR_v... 24.0 \n", + "inframe_deletion,stop_lost,3_prime_UTR_variant 23.0 \n", + "2kb_upstream_variant,transcript_ablation,5_prim... 21.0 \n", + "2kb_downstream_variant,stop_lost 20.0 \n", + "NMD_transcript_variant,stop_lost,3_prime_UTR_va... 17.0 \n", + "complex_substitution,start_lost,start_retained_... 17.0 \n", + "inframe_deletion,NMD_transcript_variant,stop_ga... 17.0 \n", + "complex_substitution,inframe_insertion,intron_v... 15.0 \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... 13.0 \n", + "complex_substitution,inframe_deletion,intron_va... 11.0 \n", + "frameshift_elongation,start_lost 11.0 \n", + "start_lost,3_prime_UTR_variant,5_prime_UTR_variant 11.0 \n", + "start_lost,NMD_transcript_variant 11.0 \n", + "exon_loss_variant,inframe_deletion,NMD_transcri... 10.0 \n", + "inframe_deletion,stop_lost 10.0 \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... 9.0 \n", + "inframe_insertion,NMD_transcript_variant,stop_g... 9.0 \n", + "exon_loss_variant,intron_variant,NMD_transcript... 8.0 \n", + "frameshift_truncation,NMD_transcript_variant,st... 8.0 \n", + "frameshift_elongation,NMD_transcript_variant,st... 7.0 \n", + "2kb_downstream_variant,2kb_upstream_variant,sta... 6.0 \n", + "2kb_upstream_variant,intron_variant,start_lost,... 6.0 \n", + "2kb_upstream_variant,start_lost,stop_lost,trans... 6.0 \n", + "complex_substitution,frameshift_elongation,intr... 6.0 \n", + "complex_substitution,inframe_insertion,NMD_tran... 6.0 \n", + "complex_substitution,inframe_insertion,intron_v... 6.0 \n", + "frameshift_truncation,start_lost,NMD_transcript... 6.0 \n", + "2kb_downstream_variant,NMD_transcript_variant,s... 5.0 \n", + "frameshift_truncation,synonymous_variant 5.0 \n", + "intron_variant,stop_lost,3_prime_UTR_variant 5.0 \n", + "2kb_downstream_variant,2kb_upstream_variant,sta... 4.0 \n", + "complex_substitution,frameshift_elongation,intr... 4.0 \n", + "complex_substitution,frameshift_elongation,intr... 4.0 \n", + "complex_substitution,inframe_deletion,missense_... 4.0 \n", + "complex_substitution,inframe_insertion,intron_v... 4.0 \n", + "exon_loss_variant,splice_site_variant 4.0 \n", + "start_lost 4.0 \n", + "2kb_upstream_variant,start_lost,5_prime_UTR_var... 3.0 \n", + "complex_substitution,frameshift_elongation,star... 3.0 \n", + "frameshift_truncation,NMD_transcript_variant,st... 3.0 \n", + "2kb_downstream_variant,intron_variant,NMD_trans... 2.0 \n", + "complex_substitution,NMD_transcript_variant 2.0 \n", + "complex_substitution,frameshift_elongation,intr... 2.0 \n", + "complex_substitution,frameshift_truncation,NMD_... 2.0 \n", + "complex_substitution,inframe_insertion,NMD_tran... 2.0 \n", + "complex_substitution,start_lost 2.0 \n", + "frameshift_elongation,NMD_transcript_variant,st... 2.0 \n", + "frameshift_truncation,stop_gained,stop_lost,3_p... 2.0 \n", + "2kb_upstream_variant,NMD_transcript_variant,tra... 1.0 \n", + "complex_substitution,inframe_deletion,NMD_trans... 1.0 \n", + "complex_substitution,inframe_insertion,stop_lost 1.0 \n", + "complex_substitution,start_lost,start_retained_... 1.0 \n", + "frameshift_elongation,start_lost,NMD_transcript... 1.0 \n", + "frameshift_truncation,NMD_transcript_variant,st... 1.0 \n", + "frameshift_truncation,NMD_transcript_variant,st... 1.0 \n", + "frameshift_truncation,start_lost,stop_lost,3_pr... 1.0 \n", + "inframe_deletion,NMD_transcript_variant,stop_lo... 1.0 \n", + "inframe_deletion,start_lost 1.0 \n", + "inframe_deletion,stop_lost,stop_retained_varian... 1.0 \n", + "2kb_downstream_variant,IG_J_gene NaN \n", + "2kb_downstream_variant,rRNA NaN \n", + "IG_C_gene NaN \n", + "complex_substitution,NMD_transcript_variant,syn... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_elongation,stop... NaN \n", + "complex_substitution,frameshift_truncation,intr... NaN \n", + "complex_substitution,frameshift_truncation,star... NaN \n", + "complex_substitution,frameshift_truncation,star... NaN \n", + "complex_substitution,frameshift_truncation,stop... NaN \n", + "complex_substitution,inframe_deletion,NMD_trans... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "complex_substitution,inframe_deletion,start_los... NaN \n", + "complex_substitution,inframe_deletion,stop_gain... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "complex_substitution,stop_lost NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_lost NaN \n", + "inframe_deletion,start_lost,NMD_transcript_variant NaN \n", + "inframe_deletion,stop_gained,stop_lost,3_prime_... NaN \n", + "inframe_insertion,start_retained_variant NaN \n", + "inframe_insertion,start_retained_variant,NMD_tr... NaN \n", + "\n", + "extra_vcf_info.CLNSIG Pathogenic/Likely_pathogenic \\\n", + "so \n", + "intron_variant 5736.0 \n", + "synonymous_variant 426.0 \n", + "missense_variant 25632.0 \n", + "intron_variant,NMD_transcript_variant 1697.0 \n", + "3_prime_UTR_variant 444.0 \n", + "intron_variant,processed_transcript 2013.0 \n", + "2kb_downstream_variant 1685.0 \n", + "2kb_upstream_variant 1438.0 \n", + "intron_variant,lnc_RNA 6446.0 \n", + "processed_transcript 4577.0 \n", + "NMD_transcript_variant,3_prime_UTR_variant 5681.0 \n", + "2kb_downstream_variant,processed_transcript 2070.0 \n", + "5_prime_UTR_variant 889.0 \n", + "2kb_upstream_variant,processed_transcript 1355.0 \n", + "2kb_upstream_variant,lnc_RNA 841.0 \n", + "2kb_downstream_variant,NMD_transcript_variant 1016.0 \n", + "NMD_transcript_variant,synonymous_variant 128.0 \n", + "2kb_upstream_variant,NMD_transcript_variant 244.0 \n", + "missense_variant,NMD_transcript_variant 1782.0 \n", + "2kb_downstream_variant,lnc_RNA 426.0 \n", + "lnc_RNA 554.0 \n", + "NMD_transcript_variant,5_prime_UTR_variant 47.0 \n", + "inframe_deletion 664.0 \n", + "2kb_downstream_variant,miRNA 83.0 \n", + "inframe_insertion 78.0 \n", + "2kb_upstream_variant,miRNA 99.0 \n", + "stop_gained 14039.0 \n", + "frameshift_truncation 8276.0 \n", + "frameshift_elongation 3517.0 \n", + "intron_variant,splice_site_variant 9212.0 \n", + "intron_variant,NSD_transcript 3.0 \n", + "NSD_transcript 38.0 \n", + "complex_substitution,missense_variant 90.0 \n", + "2kb_upstream_variant,misc_RNA 34.0 \n", + "2kb_downstream_variant,misc_RNA 42.0 \n", + "intron_variant,polymorphic_pseudogene 7.0 \n", + "2kb_upstream_variant,snoRNA 4.0 \n", + "polymorphic_pseudogene 67.0 \n", + "2kb_downstream_variant,snoRNA 10.0 \n", + "2kb_downstream_variant,snRNA 24.0 \n", + "2kb_downstream_variant,NSD_transcript 32.0 \n", + "missense_variant,start_lost 527.0 \n", + "2kb_upstream_variant,snRNA 13.0 \n", + "2kb_downstream_variant,polymorphic_pseudogene NaN \n", + "2kb_upstream_variant,NSD_transcript 4.0 \n", + "NMD_transcript_variant,stop_gained 1312.0 \n", + "stop_lost 20.0 \n", + "2kb_upstream_variant,polymorphic_pseudogene NaN \n", + "stop_retained_variant NaN \n", + "splice_site_variant 339.0 \n", + "intron_variant,NMD_transcript_variant,splice_si... 2225.0 \n", + "inframe_deletion,NMD_transcript_variant 16.0 \n", + "inframe_insertion,NMD_transcript_variant 1.0 \n", + "polymorphic_pseudogene,3_prime_UTR_variant NaN \n", + "2kb_upstream_variant,5_prime_UTR_variant NaN \n", + "complex_substitution,frameshift_elongation,intr... 159.0 \n", + "missense_variant,start_lost,NMD_transcript_variant 234.0 \n", + "miRNA 2.0 \n", + "2kb_upstream_variant,ribozyme 11.0 \n", + "snoRNA 2.0 \n", + "frameshift_truncation,stop_gained 558.0 \n", + "frameshift_elongation,stop_gained 193.0 \n", + "frameshift_truncation,NMD_transcript_variant 383.0 \n", + "complex_substitution 18.0 \n", + "complex_substitution,inframe_deletion 1.0 \n", + "polymorphic_pseudogene,5_prime_UTR_variant 24.0 \n", + "NMD_transcript_variant,stop_retained_variant 3.0 \n", + "snRNA 3.0 \n", + "NMD_transcript_variant 20.0 \n", + "2kb_upstream_variant,scaRNA NaN \n", + "NSD_transcript,5_prime_UTR_variant NaN \n", + "NMD_transcript_variant,stop_lost 13.0 \n", + "2kb_downstream_variant,3_prime_UTR_variant NaN \n", + "2kb_upstream_variant,rRNA NaN \n", + "misc_RNA NaN \n", + "complex_substitution,inframe_insertion,intron_v... 38.0 \n", + "frameshift_truncation,stop_lost 3.0 \n", + "frameshift_elongation,NMD_transcript_variant 150.0 \n", + "exon_loss_variant,frameshift_truncation 4.0 \n", + "complex_substitution,missense_variant,NMD_trans... 2.0 \n", + "complex_substitution,synonymous_variant NaN \n", + "frameshift_truncation,stop_retained_variant NaN \n", + "frameshift_elongation,stop_retained_variant NaN \n", + "inframe_insertion,stop_gained 24.0 \n", + "NMD_transcript_variant,splice_site_variant 10.0 \n", + "frameshift_elongation,stop_lost 2.0 \n", + "2kb_downstream_variant,NMD_transcript_variant,3... NaN \n", + "2kb_upstream_variant,NMD_transcript_variant,5_p... NaN \n", + "2kb_downstream_variant,scaRNA NaN \n", + "ribozyme 10.0 \n", + "inframe_insertion,stop_retained_variant NaN \n", + "inframe_deletion,stop_gained 11.0 \n", + "start_lost,5_prime_UTR_variant 11.0 \n", + "frameshift_truncation,start_lost 3.0 \n", + "start_retained_variant 1.0 \n", + "frameshift_truncation,stop_lost,stop_retained_v... NaN \n", + "2kb_downstream_variant,ribozyme NaN \n", + "complex_substitution,intron_variant,synonymous_... NaN \n", + "frameshift_truncation,stop_lost,synonymous_vari... NaN \n", + "complex_substitution,frameshift_truncation 179.0 \n", + "complex_substitution,frameshift_elongation 76.0 \n", + "complex_substitution,stop_gained 29.0 \n", + "exon_loss_variant,inframe_deletion NaN \n", + "complex_substitution,frameshift_truncation,NMD_... 16.0 \n", + "frameshift_truncation,NMD_transcript_variant,st... 39.0 \n", + "exon_loss_variant,intron_variant NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... 7.0 \n", + "2kb_upstream_variant,start_lost,transcript_abla... NaN \n", + "2kb_downstream_variant,stop_lost,3_prime_UTR_va... 2.0 \n", + "exon_loss_variant,frameshift_truncation,NMD_tra... NaN \n", + "complex_substitution,inframe_insertion 3.0 \n", + "stop_lost,3_prime_UTR_variant NaN \n", + "intron_variant,start_lost,5_prime_UTR_variant NaN \n", + "complex_substitution,inframe_deletion,missense_... 9.0 \n", + "frameshift_truncation,stop_lost,3_prime_UTR_var... 1.0 \n", + "complex_substitution,frameshift_elongation,NMD_... 7.0 \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_deletion,stop_gained NaN \n", + "complex_substitution,inframe_insertion,stop_gained NaN \n", + "complex_substitution,NMD_transcript_variant,sto... NaN \n", + "intron_variant,start_lost NaN \n", + "intron_variant,start_lost,NMD_transcript_varian... NaN \n", + "complex_substitution,frameshift_elongation,intr... 21.0 \n", + "complex_substitution,frameshift_truncation,intr... NaN \n", + "2kb_downstream_variant,intron_variant,stop_lost... NaN \n", + "complex_substitution,inframe_insertion,missense... NaN \n", + "start_lost,NMD_transcript_variant,5_prime_UTR_v... 13.0 \n", + "inframe_deletion,stop_lost,3_prime_UTR_variant 1.0 \n", + "2kb_upstream_variant,transcript_ablation,5_prim... NaN \n", + "2kb_downstream_variant,stop_lost NaN \n", + "NMD_transcript_variant,stop_lost,3_prime_UTR_va... NaN \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_ga... 1.0 \n", + "complex_substitution,inframe_insertion,intron_v... 4.0 \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "complex_substitution,inframe_deletion,intron_va... 1.0 \n", + "frameshift_elongation,start_lost 3.0 \n", + "start_lost,3_prime_UTR_variant,5_prime_UTR_variant NaN \n", + "start_lost,NMD_transcript_variant NaN \n", + "exon_loss_variant,inframe_deletion,NMD_transcri... NaN \n", + "inframe_deletion,stop_lost NaN \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "inframe_insertion,NMD_transcript_variant,stop_g... 1.0 \n", + "exon_loss_variant,intron_variant,NMD_transcript... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "2kb_downstream_variant,2kb_upstream_variant,sta... NaN \n", + "2kb_upstream_variant,intron_variant,start_lost,... NaN \n", + "2kb_upstream_variant,start_lost,stop_lost,trans... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_insertion,NMD_tran... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "frameshift_truncation,start_lost,NMD_transcript... NaN \n", + "2kb_downstream_variant,NMD_transcript_variant,s... NaN \n", + "frameshift_truncation,synonymous_variant NaN \n", + "intron_variant,stop_lost,3_prime_UTR_variant NaN \n", + "2kb_downstream_variant,2kb_upstream_variant,sta... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_deletion,missense_... NaN \n", + "complex_substitution,inframe_insertion,intron_v... 2.0 \n", + "exon_loss_variant,splice_site_variant NaN \n", + "start_lost NaN \n", + "2kb_upstream_variant,start_lost,5_prime_UTR_var... NaN \n", + "complex_substitution,frameshift_elongation,star... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "2kb_downstream_variant,intron_variant,NMD_trans... NaN \n", + "complex_substitution,NMD_transcript_variant NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_truncation,NMD_... NaN \n", + "complex_substitution,inframe_insertion,NMD_tran... NaN \n", + "complex_substitution,start_lost NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,stop_gained,stop_lost,3_p... NaN \n", + "2kb_upstream_variant,NMD_transcript_variant,tra... NaN \n", + "complex_substitution,inframe_deletion,NMD_trans... NaN \n", + "complex_substitution,inframe_insertion,stop_lost NaN \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "frameshift_elongation,start_lost,NMD_transcript... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,start_lost,stop_lost,3_pr... NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_lo... NaN \n", + "inframe_deletion,start_lost 2.0 \n", + "inframe_deletion,stop_lost,stop_retained_varian... NaN \n", + "2kb_downstream_variant,IG_J_gene NaN \n", + "2kb_downstream_variant,rRNA NaN \n", + "IG_C_gene NaN \n", + "complex_substitution,NMD_transcript_variant,syn... NaN \n", + "complex_substitution,frameshift_elongation,intr... 1.0 \n", + "complex_substitution,frameshift_elongation,stop... NaN \n", + "complex_substitution,frameshift_truncation,intr... NaN \n", + "complex_substitution,frameshift_truncation,star... NaN \n", + "complex_substitution,frameshift_truncation,star... NaN \n", + "complex_substitution,frameshift_truncation,stop... NaN \n", + "complex_substitution,inframe_deletion,NMD_trans... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "complex_substitution,inframe_deletion,start_los... NaN \n", + "complex_substitution,inframe_deletion,stop_gain... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "complex_substitution,stop_lost NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_lost NaN \n", + "inframe_deletion,start_lost,NMD_transcript_variant NaN \n", + "inframe_deletion,stop_gained,stop_lost,3_prime_... NaN \n", + "inframe_insertion,start_retained_variant NaN \n", + "inframe_insertion,start_retained_variant,NMD_tr... NaN \n", + "\n", + "extra_vcf_info.CLNSIG Uncertain_significance \n", + "so \n", + "intron_variant 407206.0 \n", + "synonymous_variant 67647.0 \n", + "missense_variant 2769515.0 \n", + "intron_variant,NMD_transcript_variant 92851.0 \n", + "3_prime_UTR_variant 105404.0 \n", + "intron_variant,processed_transcript 79143.0 \n", + "2kb_downstream_variant 126609.0 \n", + "2kb_upstream_variant 88889.0 \n", + "intron_variant,lnc_RNA 330377.0 \n", + "processed_transcript 284455.0 \n", + "NMD_transcript_variant,3_prime_UTR_variant 309360.0 \n", + "2kb_downstream_variant,processed_transcript 114158.0 \n", + "5_prime_UTR_variant 60877.0 \n", + "2kb_upstream_variant,processed_transcript 78705.0 \n", + "2kb_upstream_variant,lnc_RNA 60727.0 \n", + "2kb_downstream_variant,NMD_transcript_variant 54298.0 \n", + "NMD_transcript_variant,synonymous_variant 9444.0 \n", + "2kb_upstream_variant,NMD_transcript_variant 22972.0 \n", + "missense_variant,NMD_transcript_variant 202928.0 \n", + "2kb_downstream_variant,lnc_RNA 35978.0 \n", + "lnc_RNA 32306.0 \n", + "NMD_transcript_variant,5_prime_UTR_variant 7399.0 \n", + "inframe_deletion 30306.0 \n", + "2kb_downstream_variant,miRNA 5548.0 \n", + "inframe_insertion 12286.0 \n", + "2kb_upstream_variant,miRNA 4955.0 \n", + "stop_gained 21134.0 \n", + "frameshift_truncation 15654.0 \n", + "frameshift_elongation 8452.0 \n", + "intron_variant,splice_site_variant 15210.0 \n", + "intron_variant,NSD_transcript 262.0 \n", + "NSD_transcript 1867.0 \n", + "complex_substitution,missense_variant 9417.0 \n", + "2kb_upstream_variant,misc_RNA 2665.0 \n", + "2kb_downstream_variant,misc_RNA 2138.0 \n", + "intron_variant,polymorphic_pseudogene 249.0 \n", + "2kb_upstream_variant,snoRNA 765.0 \n", + "polymorphic_pseudogene 2105.0 \n", + "2kb_downstream_variant,snoRNA 1112.0 \n", + "2kb_downstream_variant,snRNA 1487.0 \n", + "2kb_downstream_variant,NSD_transcript 395.0 \n", + "missense_variant,start_lost 3721.0 \n", + "2kb_upstream_variant,snRNA 822.0 \n", + "2kb_downstream_variant,polymorphic_pseudogene 177.0 \n", + "2kb_upstream_variant,NSD_transcript 606.0 \n", + "NMD_transcript_variant,stop_gained 1562.0 \n", + "stop_lost 1746.0 \n", + "2kb_upstream_variant,polymorphic_pseudogene 168.0 \n", + "stop_retained_variant 245.0 \n", + "splice_site_variant 1025.0 \n", + "intron_variant,NMD_transcript_variant,splice_si... 2632.0 \n", + "inframe_deletion,NMD_transcript_variant 1103.0 \n", + "inframe_insertion,NMD_transcript_variant 613.0 \n", + "polymorphic_pseudogene,3_prime_UTR_variant 374.0 \n", + "2kb_upstream_variant,5_prime_UTR_variant 37.0 \n", + "complex_substitution,frameshift_elongation,intr... 371.0 \n", + "missense_variant,start_lost,NMD_transcript_variant 944.0 \n", + "miRNA 82.0 \n", + "2kb_upstream_variant,ribozyme 89.0 \n", + "snoRNA 71.0 \n", + "frameshift_truncation,stop_gained 677.0 \n", + "frameshift_elongation,stop_gained 370.0 \n", + "frameshift_truncation,NMD_transcript_variant 281.0 \n", + "complex_substitution 1091.0 \n", + "complex_substitution,inframe_deletion 328.0 \n", + "polymorphic_pseudogene,5_prime_UTR_variant 217.0 \n", + "NMD_transcript_variant,stop_retained_variant 118.0 \n", + "snRNA 108.0 \n", + "NMD_transcript_variant 250.0 \n", + "2kb_upstream_variant,scaRNA 67.0 \n", + "NSD_transcript,5_prime_UTR_variant 22.0 \n", + "NMD_transcript_variant,stop_lost 353.0 \n", + "2kb_downstream_variant,3_prime_UTR_variant 12.0 \n", + "2kb_upstream_variant,rRNA 23.0 \n", + "misc_RNA 54.0 \n", + "complex_substitution,inframe_insertion,intron_v... 203.0 \n", + "frameshift_truncation,stop_lost 81.0 \n", + "frameshift_elongation,NMD_transcript_variant 243.0 \n", + "exon_loss_variant,frameshift_truncation 131.0 \n", + "complex_substitution,missense_variant,NMD_trans... 298.0 \n", + "complex_substitution,synonymous_variant 48.0 \n", + "frameshift_truncation,stop_retained_variant 17.0 \n", + "frameshift_elongation,stop_retained_variant 37.0 \n", + "inframe_insertion,stop_gained 45.0 \n", + "NMD_transcript_variant,splice_site_variant 33.0 \n", + "frameshift_elongation,stop_lost 81.0 \n", + "2kb_downstream_variant,NMD_transcript_variant,3... 3.0 \n", + "2kb_upstream_variant,NMD_transcript_variant,5_p... 8.0 \n", + "2kb_downstream_variant,scaRNA 73.0 \n", + "ribozyme 144.0 \n", + "inframe_insertion,stop_retained_variant 22.0 \n", + "inframe_deletion,stop_gained 21.0 \n", + "start_lost,5_prime_UTR_variant 137.0 \n", + "frameshift_truncation,start_lost 57.0 \n", + "start_retained_variant 11.0 \n", + "frameshift_truncation,stop_lost,stop_retained_v... 15.0 \n", + "2kb_downstream_variant,ribozyme 17.0 \n", + "complex_substitution,intron_variant,synonymous_... NaN \n", + "frameshift_truncation,stop_lost,synonymous_vari... 3.0 \n", + "complex_substitution,frameshift_truncation 465.0 \n", + "complex_substitution,frameshift_elongation 253.0 \n", + "complex_substitution,stop_gained 85.0 \n", + "exon_loss_variant,inframe_deletion 18.0 \n", + "complex_substitution,frameshift_truncation,NMD_... 19.0 \n", + "frameshift_truncation,NMD_transcript_variant,st... 7.0 \n", + "exon_loss_variant,intron_variant 14.0 \n", + "frameshift_elongation,NMD_transcript_variant,st... 9.0 \n", + "2kb_upstream_variant,start_lost,transcript_abla... 17.0 \n", + "2kb_downstream_variant,stop_lost,3_prime_UTR_va... 3.0 \n", + "exon_loss_variant,frameshift_truncation,NMD_tra... 5.0 \n", + "complex_substitution,inframe_insertion 418.0 \n", + "stop_lost,3_prime_UTR_variant 12.0 \n", + "intron_variant,start_lost,5_prime_UTR_variant 3.0 \n", + "complex_substitution,inframe_deletion,missense_... 411.0 \n", + "frameshift_truncation,stop_lost,3_prime_UTR_var... 91.0 \n", + "complex_substitution,frameshift_elongation,NMD_... 5.0 \n", + "complex_substitution,inframe_insertion,intron_v... 16.0 \n", + "complex_substitution,inframe_deletion,stop_gained 11.0 \n", + "complex_substitution,inframe_insertion,stop_gained 4.0 \n", + "complex_substitution,NMD_transcript_variant,sto... NaN \n", + "intron_variant,start_lost 3.0 \n", + "intron_variant,start_lost,NMD_transcript_varian... NaN \n", + "complex_substitution,frameshift_elongation,intr... 18.0 \n", + "complex_substitution,frameshift_truncation,intr... 4.0 \n", + "2kb_downstream_variant,intron_variant,stop_lost... NaN \n", + "complex_substitution,inframe_insertion,missense... NaN \n", + "start_lost,NMD_transcript_variant,5_prime_UTR_v... 23.0 \n", + "inframe_deletion,stop_lost,3_prime_UTR_variant 47.0 \n", + "2kb_upstream_variant,transcript_ablation,5_prim... 2.0 \n", + "2kb_downstream_variant,stop_lost 5.0 \n", + "NMD_transcript_variant,stop_lost,3_prime_UTR_va... 5.0 \n", + "complex_substitution,start_lost,start_retained_... 60.0 \n", + "inframe_deletion,NMD_transcript_variant,stop_ga... NaN \n", + "complex_substitution,inframe_insertion,intron_v... 8.0 \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "complex_substitution,inframe_deletion,intron_va... 9.0 \n", + "frameshift_elongation,start_lost 18.0 \n", + "start_lost,3_prime_UTR_variant,5_prime_UTR_variant NaN \n", + "start_lost,NMD_transcript_variant NaN \n", + "exon_loss_variant,inframe_deletion,NMD_transcri... NaN \n", + "inframe_deletion,stop_lost 9.0 \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... 3.0 \n", + "inframe_insertion,NMD_transcript_variant,stop_g... 1.0 \n", + "exon_loss_variant,intron_variant,NMD_transcript... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "2kb_downstream_variant,2kb_upstream_variant,sta... NaN \n", + "2kb_upstream_variant,intron_variant,start_lost,... NaN \n", + "2kb_upstream_variant,start_lost,stop_lost,trans... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_insertion,NMD_tran... NaN \n", + "complex_substitution,inframe_insertion,intron_v... 1.0 \n", + "frameshift_truncation,start_lost,NMD_transcript... 17.0 \n", + "2kb_downstream_variant,NMD_transcript_variant,s... NaN \n", + "frameshift_truncation,synonymous_variant 5.0 \n", + "intron_variant,stop_lost,3_prime_UTR_variant NaN \n", + "2kb_downstream_variant,2kb_upstream_variant,sta... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_deletion,missense_... 23.0 \n", + "complex_substitution,inframe_insertion,intron_v... 6.0 \n", + "exon_loss_variant,splice_site_variant NaN \n", + "start_lost NaN \n", + "2kb_upstream_variant,start_lost,5_prime_UTR_var... NaN \n", + "complex_substitution,frameshift_elongation,star... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... 1.0 \n", + "2kb_downstream_variant,intron_variant,NMD_trans... NaN \n", + "complex_substitution,NMD_transcript_variant 37.0 \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_truncation,NMD_... NaN \n", + "complex_substitution,inframe_insertion,NMD_tran... 20.0 \n", + "complex_substitution,start_lost NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... 1.0 \n", + "frameshift_truncation,stop_gained,stop_lost,3_p... NaN \n", + "2kb_upstream_variant,NMD_transcript_variant,tra... NaN \n", + "complex_substitution,inframe_deletion,NMD_trans... 10.0 \n", + "complex_substitution,inframe_insertion,stop_lost NaN \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "frameshift_elongation,start_lost,NMD_transcript... 1.0 \n", + "frameshift_truncation,NMD_transcript_variant,st... 1.0 \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,start_lost,stop_lost,3_pr... NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_lo... NaN \n", + "inframe_deletion,start_lost 12.0 \n", + "inframe_deletion,stop_lost,stop_retained_varian... 6.0 \n", + "2kb_downstream_variant,IG_J_gene 2.0 \n", + "2kb_downstream_variant,rRNA 4.0 \n", + "IG_C_gene 2.0 \n", + "complex_substitution,NMD_transcript_variant,syn... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,frameshift_elongation,stop... 1.0 \n", + "complex_substitution,frameshift_truncation,intr... NaN \n", + "complex_substitution,frameshift_truncation,star... NaN \n", + "complex_substitution,frameshift_truncation,star... 5.0 \n", + "complex_substitution,frameshift_truncation,stop... 1.0 \n", + "complex_substitution,inframe_deletion,NMD_trans... 1.0 \n", + "complex_substitution,inframe_deletion,intron_va... 1.0 \n", + "complex_substitution,inframe_deletion,intron_va... 3.0 \n", + "complex_substitution,inframe_deletion,start_los... 1.0 \n", + "complex_substitution,inframe_deletion,stop_gain... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,start_lost,start_retained_... 7.0 \n", + "complex_substitution,stop_lost 4.0 \n", + "inframe_deletion,NMD_transcript_variant,stop_lost 2.0 \n", + "inframe_deletion,start_lost,NMD_transcript_variant 2.0 \n", + "inframe_deletion,stop_gained,stop_lost,3_prime_... 3.0 \n", + "inframe_insertion,start_retained_variant 7.0 \n", + "inframe_insertion,start_retained_variant,NMD_tr... 1.0 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.pivot_table(df, values='consequence', index='so', columns='extra_vcf_info.CLNSIG',\n", + " aggfunc='count').sort_values(by=['Benign','Pathogenic'], ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#print(len(df[df['clinvar.sig_conf'].str.contains('athogen')==True][['clinvar.sig','clinvar.sig_conf']]))\n", + "#df[df['clinvar.sig_conf'].str.contains('athogen')==True][['clinvar.sig','clinvar.sig_conf']].tail(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "#df[df['clinvar.sig_conf'].str.contains('athogenic, low')==True][['clinvar.sig','clinvar.sig_conf']]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "#print(len(df[df['clinvar.sig'].str.contains('athogenic, low')==True][['clinvar.sig','clinvar.sig_conf']]))\n", + "#df[df['clinvar.sig'].str.contains('athogenic, low')==True][['clinvar.sig','clinvar.sig_conf']].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['sift.confidence',\n", + " 'clinvar.sig_conf',\n", + " 'gene',\n", + " 'protein_hgvs',\n", + " 'alt_base',\n", + " 'clinvar.sig',\n", + " 'dbsnp.rsid',\n", + " 'coding',\n", + " 'ensembl_regulatory_build.region',\n", + " 'exac_gene.exac_cnv_flag',\n", + " 'uniprot.acc',\n", + " 'gtex.gtex_tissue',\n", + " 'extra_vcf_info.CLNSIG',\n", + " 'clingen.disease',\n", + " 'consequence',\n", + " 'ccre_screen._group',\n", + " 'omim.omim_id',\n", + " 'repeat.repeatclass',\n", + " 'dgi.interaction',\n", + " 'aloft.conf',\n", + " 'cgd.inheritance',\n", + " 'aloft.pred',\n", + " 'clingen.classification',\n", + " 'so',\n", + " 'extra_vcf_info.CLNREVSTAT',\n", + " 'chrom',\n", + " 'mutationtaster.model',\n", + " 'prec.stat',\n", + " 'cdna_hgvs',\n", + " 'extra_vcf_info.CLNSIGCONF',\n", + " 'cgc.class',\n", + " 'genehancer.feature_name',\n", + " 'extra_vcf_info.CLNDN',\n", + " 'transcript',\n", + " 'mutationtaster.prediction',\n", + " 'clinvar.rev_stat',\n", + " 'cgc.inheritance',\n", + " 'ccre_screen.bound',\n", + " 'ref_base',\n", + " 'aloft.affect']" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check if there are any categorical columns\n", + "num_cols = df._get_numeric_data().columns\n", + "\n", + "list(set(df.columns) - set(num_cols))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "40" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(list(set(df.columns) - set(num_cols)))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sift.confidenceclinvar.sig_confgeneprotein_hgvsalt_baseclinvar.sigdbsnp.rsidcodingensembl_regulatory_build.regionexac_gene.exac_cnv_flag...cgc.classgenehancer.feature_nameextra_vcf_info.CLNDNtranscriptmutationtaster.predictionclinvar.rev_statcgc.inheritanceccre_screen.boundref_basealoft.affect
0HighNaNOR4F5p.Glu15GlyGNaNrs781394307YesNaNNaN...NaNNaNInborn_genetic_diseasesENST00000335137PolymorphismNaNNaNNaNANaN
1NaNNaNOR4F5p.Glu36GlyGNaNrs781394307YesNaNNaN...NaNNaNInborn_genetic_diseasesENST00000641515PolymorphismNaNNaNNaNANaN
2HighNaNOR4F5p.Pro164ArgGNaNrs1570409925YesNaNNaN...NaNNaNInborn_genetic_diseasesENST00000335137PolymorphismNaNNaNNaNCNaN
3NaNNaNOR4F5p.Pro185ArgGNaNrs1570409925YesNaNNaN...NaNNaNInborn_genetic_diseasesENST00000641515PolymorphismNaNNaNNaNCNaN
4HighNaNOR4F5p.Val198MetANaNrs766444643YesNaNNaN...NaNNaNInborn_genetic_diseasesENST00000335137PolymorphismNaNNaNNaNGNaN
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5 rows ร— 40 columns

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" + ], + "text/plain": [ + " sift.confidence clinvar.sig_conf gene protein_hgvs alt_base clinvar.sig \\\n", + "0 High NaN OR4F5 p.Glu15Gly G NaN \n", + "1 NaN NaN OR4F5 p.Glu36Gly G NaN \n", + "2 High NaN OR4F5 p.Pro164Arg G NaN \n", + "3 NaN NaN OR4F5 p.Pro185Arg G NaN \n", + "4 High NaN OR4F5 p.Val198Met A NaN \n", + "\n", + " dbsnp.rsid coding ensembl_regulatory_build.region \\\n", + "0 rs781394307 Yes NaN \n", + "1 rs781394307 Yes NaN \n", + "2 rs1570409925 Yes NaN \n", + "3 rs1570409925 Yes NaN \n", + "4 rs766444643 Yes NaN \n", + "\n", + " exac_gene.exac_cnv_flag ... cgc.class genehancer.feature_name \\\n", + "0 NaN ... NaN NaN \n", + "1 NaN ... NaN NaN \n", + "2 NaN ... NaN NaN \n", + "3 NaN ... NaN NaN \n", + "4 NaN ... NaN NaN \n", + "\n", + " extra_vcf_info.CLNDN transcript mutationtaster.prediction \\\n", + "0 Inborn_genetic_diseases ENST00000335137 Polymorphism \n", + "1 Inborn_genetic_diseases ENST00000641515 Polymorphism \n", + "2 Inborn_genetic_diseases ENST00000335137 Polymorphism \n", + "3 Inborn_genetic_diseases ENST00000641515 Polymorphism \n", + "4 Inborn_genetic_diseases ENST00000335137 Polymorphism \n", + "\n", + " clinvar.rev_stat cgc.inheritance ccre_screen.bound ref_base aloft.affect \n", + "0 NaN NaN NaN A NaN \n", + "1 NaN NaN NaN A NaN \n", + "2 NaN NaN NaN C NaN \n", + "3 NaN NaN NaN C NaN \n", + "4 NaN NaN NaN G NaN \n", + "\n", + "[5 rows x 40 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[list(set(df.columns) - set(num_cols))].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "#df[df['fathmm.fathmm_score'].str.contains('&') == True]['fathmm.fathmm_score']" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "df = df.drop(['clinvar.sig','clinvar.id','clinvar.rev_stat','clinvar.sig_conf'], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "N 8520543\n", + "Y 802462\n", + "Name: exac_gene.exac_cnv_flag, dtype: int64" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['exac_gene.exac_cnv_flag'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get data for training" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Variant-transcript pairs shape = (1968054, 128)\n", + "\n", + "Variants shape = (329136, 4)\n", + "\n", + "clinvar_CLNSIG:\n", + " Uncertain_significance 917228\n", + "Likely_benign 380955\n", + "Benign/Likely_benign 201484\n", + "Benign 165653\n", + "Pathogenic 158220\n", + "Pathogenic/Likely_pathogenic 103967\n", + "Likely_pathogenic 40547\n", + "Name: extra_vcf_info.CLNSIG, dtype: int64\n", + "\n", + "clinvar_review:\n", + " criteria_provided,_multiple_submitters,_no_conflicts 1872415\n", + "reviewed_by_expert_panel 95535\n", + "practice_guideline 104\n", + "Name: extra_vcf_info.CLNREVSTAT, dtype: int64\n" + ] + } + ], + "source": [ + "#Filter variants for clinvar_review\n", + "df= df.loc[df['extra_vcf_info.CLNREVSTAT'].isin(config_dict['CLNREVSTAT'])]\n", + "#df= df.loc[df['extra_vcf_info.CLNSIG'].isin(config_dict['train_ClinicalSignificance'])]\n", + "print('\\nVariant-transcript pairs shape =', df.shape)\n", + "print('\\nVariants shape =', df[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)\n", + "print('\\nclinvar_CLNSIG:\\n', df['extra_vcf_info.CLNSIG'].value_counts())\n", + "print('\\nclinvar_review:\\n', df['extra_vcf_info.CLNREVSTAT'].value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Variant-transcript pairs shape = (1968054, 128)\n", + "\n", + "Variants shape = (329136, 4)\n", + "\n", + "clinvar_CLNSIG:\n", + " Uncertain_significance 917228\n", + "Likely_benign 380955\n", + "Benign/Likely_benign 201484\n", + "Benign 165653\n", + "Pathogenic 158220\n", + "Pathogenic/Likely_pathogenic 103967\n", + "Likely_pathogenic 40547\n", + "Name: extra_vcf_info.CLNSIG, dtype: int64\n", + "\n", + "clinvar_review:\n", + " criteria_provided,_multiple_submitters,_no_conflicts 1872415\n", + "reviewed_by_expert_panel 95535\n", + "practice_guideline 104\n", + "Name: extra_vcf_info.CLNREVSTAT, dtype: int64\n" + ] + } + ], + "source": [ + "#Filter variants for clinvar_review\n", + "df= df.loc[df['extra_vcf_info.CLNREVSTAT'].isin(config_dict['CLNREVSTAT'])]\n", + "df= df.loc[df['extra_vcf_info.CLNSIG'].isin(config_dict['train_ClinicalSignificance'])]\n", + "print('\\nVariant-transcript pairs shape =', df.shape)\n", + "print('\\nVariants shape =', df[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)\n", + "print('\\nclinvar_CLNSIG:\\n', df['extra_vcf_info.CLNSIG'].value_counts())\n", + "print('\\nclinvar_review:\\n', df['extra_vcf_info.CLNREVSTAT'].value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Genes = (7422, 1)\n", + "transcripts = (32783, 1)\n", + "Genes-transcripts = (32783, 2)\n" + ] + } + ], + "source": [ + "print(f\"Genes = {df[['gene']].drop_duplicates().shape}\")\n", + "print(f\"transcripts = {df[['transcript']].drop_duplicates().shape}\")\n", + "print(f\"Genes-transcripts = {df[['gene','transcript']].drop_duplicates().shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "transcript_counts = df[['gene','transcript']].drop_duplicates().groupby(\"gene\")[\"transcript\"].nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "125" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transcript_counts.values.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Number of Genes')" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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0devWWc9/4mE33XrjeazkZ2Bh5uprVh99PR76ehz083jo6/HQ1ws31RC4TXd/uarOSfLjSfapqj2H0b4Dk1w5bHZlkoOSXFFVeya5W5IvzWjfZuY+M89xcpKTk2Tt2rW9bt26KX2aHdu0aVNmO//x689Y1HG2HHvrY7BrmauvWX309Xjo63HQz+Ohr8dDXy/cNGcHvccwApiqulOSRyb5RJJzkvz8sNlxSd45vN84LGdY//7u7qH9mGH20EOSHJrk3GnVDQAAsJpNcyRw/ySnDjN53i7Jad39rqq6JMmGqnpVko8lefOw/ZuT/E1VbU5ybSYzgqa7L66q05JckuSmJM8eLjMFAABgkaYWArv740l+bJb2yzLL7J7d/Y0kT57jWK9O8uqlrhEAAGBspjo7KAAAALsWIRAAAGBEhEAAAIAREQIBAABGRAgEAAAYESEQAABgRIRAAACAERECAQAARkQIBAAAGBEhEAAAYESEQAAAgBERAgEAAEZECAQAABgRIRAAAGBEhEAAAIAREQIBAABGRAgEAAAYESEQAABgRIRAAACAERECAQAARkQIBAAAGBEhEAAAYESEQAAAgBERAgEAAEZECAQAABgRIRAAAGBEhEAAAIAREQIBAABGRAgEAAAYESEQAABgRIRAAACAERECAQAARkQIBAAAGBEhEAAAYESEQAAAgBERAgEAAEZECAQAABgRIRAAAGBEhEAAAIAREQIBAABGRAgEAAAYESEQAABgRIRAAACAERECAQAARkQIBAAAGBEhEAAAYESEQAAAgBERAgEAAEZECAQAABgRIRAAAGBEhEAAAIAREQIBAABGRAgEAAAYkamFwKo6qKrOqapLquriqnr+0P7yqrqyqi4YXo+dsc8Lq2pzVV1aVY+e0X7U0La5qtZPq2YAAIDVbs8pHvumJCd290er6q5Jzq+qs4Z1f9rdfzxz46q6f5JjkvxwknsmeV9V/cCw+g1JHpnkiiTnVdXG7r5kirUDAACsSlMLgd19VZKrhvdfrapPJDlgnl2ekGRDd9+Y5NNVtTnJEcO6zd19WZJU1YZhWyEQAABgkZblnsCqOjjJjyX58ND0nKr6eFWdUlX7Dm0HJLl8xm5XDG1ztQMAALBI1d3TPUHV3kk+kOTV3f2PVbUmyReTdJJXJtm/u3+pqv48yYe6+2+H/d6c5N3DYY7q7l8e2p+e5CHd/ZztznNCkhOSZM2aNQ/asGHDVD/XfLZu3Zq99977Vu0XXnn9oo5z2AF3W6qSmJK5+prVR1+Ph74eB/08Hvp6PPT1LR155JHnd/fa2dZN857AVNVeSf4hydu6+x+TpLuvnrH+r5K8a1i8MslBM3Y/cGjLPO3f0d0nJzk5SdauXdvr1q1bmg+xEzZt2pTZzn/8+jMWdZwtx976GOxa5uprVh99PR76ehz083jo6/HQ1ws3zdlBK8mbk3yiu183o33/GZv9zyQXDe83Jjmmqu5QVYckOTTJuUnOS3JoVR1SVbfPZPKYjdOqGwAAYDWb5kjgTyZ5epILq+qCoe1FSZ5aVYdncjnoliS/kiTdfXFVnZbJhC83JXl2d387SarqOUnek2SPJKd098VTrBsAAGDVmubsoP+epGZZdeY8+7w6yatnaT9zvv0AAABYmGWZHRQAAIBdgxAIAAAwIkIgAADAiAiBAAAAIyIEAgAAjIgQCAAAMCJCIAAAwIgIgQAAACMiBAIAAIyIEAgAADAiQiAAAMCICIEAAAAjIgQCAACMiBAIAAAwIkIgAADAiAiBAAAAIyIEAgAAjIgQCAAAMCJCIAAAwIgIgQAAACMiBAIAAIyIEAgAADAiQiAAAMCICIEAAAAjIgQCAACMiBAIAAAwIkIgAADAiAiBAAAAIyIEAgAAjIgQCAAAMCJCIAAAwIgIgQAAACMiBAIAAIyIEAgAADAiQiAAAMCICIEAAAAjIgQCAACMiBAIAAAwIkIgAADAiAiBAAAAIyIEAgAAjIgQCAAAMCJCIAAAwIjsMARW1R9V1fdU1V5VdXZVfaGqnrYcxQEAALC0FjIS+Kju/kqSxyXZkuS+SX5nmkUBAAAwHQsJgXsOP49O8vfdff0U6wEAAGCK9tzxJnlXVX0yydeT/FpV3SPJN6ZbFgAAANOww5HA7l6f5CeSrO3ubyW5IckTpl0YAAAAS28hE8PcOcmvJ3nj0HTPJGunWRQAAADTsZB7Av86yTczGQ1MkiuTvGpqFQEAADA1CwmB9+nuP0ryrSTp7huS1FSrAgAAYCoWEgK/WVV3StJJUlX3SXLjVKsCAABgKhYyO+jLkvxrkoOq6m1JfjLJ8dMsCgAAgOlYyOygZyV5UibB7x2ZzBK6aUf7VdVBVXVOVV1SVRdX1fOH9rtX1VlV9anh575De1XVn1XV5qr6eFU9cMaxjhu2/1RVHbdzHxUAAICFXA6aJHdMcl2SryS5f1X9zAL2uSnJid19/yQPTfLsqrp/kvVJzu7uQ5OcPSwnyWOSHDq8TsgwG2lV3T2T0ciHJDkiycu2BUcAAAAWZ4eXg1bVa5L8QpKLk9w8NHeSD863X3dfleSq4f1Xq+oTSQ7I5BmD64bNTk2yKckLhva3dncn+VBV7VNV+w/bntXd1w71nJXkqExGJQEAAFiEmmSueTaoujTJj3T3Tk8GU1UHZxIaH5Dks929z9BeSa7r7n2q6l1JTurufx/WnZ1JOFyX5I7d/aqh/aVJvt7df7zdOU7IZAQxa9asedCGDRt2ttzbbOvWrdl7771v1X7hldcv6jiHHXC3pSqJKZmrr1l99PV46Otx0M/joa/HQ1/f0pFHHnl+d8/6fPeFTAxzWZK9spMzglbV3kn+IclvdPdXJrlvoru7quZPoQvU3ScnOTlJ1q5d2+vWrVuKw+6UTZs2ZbbzH7/+jEUdZ8uxtz4Gu5a5+prVR1+Ph74eB/08Hvp6PPT1wi0kBN6Q5IJhZO47QbC7n7ejHatqr0wC4Nu6+x+H5qurav/uvmq43POaof3KJAfN2P3Aoe3KfPfy0W3tmxZQNwAAANtZyMQwG5O8Msn/S3L+jNe8hks935zkE939uu2Ot22Gz+OSvHNG+zOGWUIfmuT64b7C9yR5VFXtO0wI86ihDQAAgEXa4Uhgd586PCz+Xt196SKO/ZNJnp7kwqq6YGh7UZKTkpxWVc9K8pkkTxnWnZnksUk2ZzL6+Mzh/NdW1SuTnDds94ptk8QAAACwOAuZHfTnkvxxktsnOaSqDs8kiD1+vv2GCV5qjtUPn2X7TvLsOY51SpJTdlQrAAAA81vI5aAvz+T5fF9Oku6+IMn3T60iAAAApmYhIfBb3b39sw1unnVLAAAAdmkLmR304qr6xSR7VNWhSZ6XySQxAAAA7GYWMhL43CQ/nMnjId6R5CtJfmOKNQEAADAlC5kd9IYkLx5eAAAA7MbmHAmsqp+qqmfMWD69qt4/vB62POUBAACwlOYbCfz9TC4F3eZ+SY5PcpdMnvf3/umVBQAAwDTMd0/g93T3JTOWP9Xd53f3B5Pcdcp1AQAAMAXzhcB9Zi5095NmLK6ZSjUAAABM1Xwh8JNVdfT2jVX1uCSXTq8kAAAApmW+ewJ/M8kZVfXzST46tD0oyU8kedy0CwMAAGDpzTkS2N2bk/xIkn9LcvDw+mCSH+nu/1qO4gAAAFha8z4nsLtvTHLKMtUCAADAlM13TyAAAACrjBAIAAAwInOGwKo6e/j5muUrBwAAgGma757A/avqJ5I8vqo2JKmZK7v7o7PvBgAAwK5qvhD4e0lemuTAJK/bbl0nedi0igIAAGA65gyB3X16ktOr6qXd/cplrAkAAIApmfcREUnS3a+sqscn+ZmhaVN3v2u6ZQEAADANO5wdtKr+MMnzk1wyvJ5fVX8w7cIAAABYejscCUxydJLDu/vmJKmqU5N8LMmLplkYAAAAS2+hzwncZ8b7u02hDgAAAJbBQkYC/zDJx6rqnEweE/EzSdZPtSoAAACmYiETw7yjqjYlefDQ9ILu/vxUqwIAAGAqFjISmO6+KsnGKdcCAADAlC30nkAAAABWASEQAABgROYNgVW1R1V9crmKAQAAYLrmDYHd/e0kl1bVvZapHgAAAKZoIRPD7Jvk4qo6N8nXtjV29+OnVhUAAABTsZAQ+NKpVwEAAMCyWMhzAj9QVfdOcmh3v6+q7pxkj+mXBgAAwFLb4eygVfW/kpye5P8OTQck+ecp1gQAAMCULOQREc9O8pNJvpIk3f2pJN83zaIAAACYjoWEwBu7+5vbFqpqzyQ9vZIAAACYloWEwA9U1YuS3KmqHpnk75P8y3TLAgAAYBoWEgLXJ/lCkguT/EqSM5O8ZJpFAQAAMB0LmR305qo6NcmHM7kM9NLudjkoAADAbmiHIbCqjk7yl0n+O0klOaSqfqW73z3t4gAAAFhaC3lY/J8kObK7NydJVd0nyRlJhEAAAIDdzELuCfzqtgA4uCzJV6dUDwAAAFM050hgVT1pePuRqjozyWmZ3BP45CTnLUNtAAAALLH5Lgf9uRnvr07ys8P7LyS509QqAgAAYGrmDIHd/czlLAQAAIDpW8jsoIckeW6Sg2du392Pn15ZAAAATMNCZgf95yRvTvIvSW6eajUAAABM1UJC4De6+8+mXgm3cvD6Mxa1/ZaTjp5SJQAAwGqxkBD4+qp6WZL3JrlxW2N3f3RqVQEAADAVCwmBhyV5epKH5buXg/awDAAAwG5kISHwyUm+v7u/Oe1iAAAAmK7bLWCbi5LsM+U6AAAAWAYLCYH7JPlkVb2nqjZue+1op6o6paquqaqLZrS9vKqurKoLhtdjZ6x7YVVtrqpLq+rRM9qPGto2V9X6RX4+AAAAZljI5aAv28ljvyXJnyd563btf9rdfzyzoarun+SYJD+c5J5J3ldVPzCsfkOSRya5Isl5VbWxuy/ZyZoAAABGbYchsLs/sDMH7u4PVtXBC9z8CUk2dPeNST5dVZuTHDGs29zdlyVJVW0YthUCAQAAdsIOLwetqq9W1VeG1zeq6ttV9ZXbcM7nVNXHh8tF9x3aDkhy+Yxtrhja5moHAABgJyxkJPCu295XVWUyEvfQnTzfG5O8MpNHTLwyyZ8k+aWdPNYtVNUJSU5IkjVr1mTTpk1LcdidsnXr1lnPf+JhN031vCv5mcdqrr5m9dHX46Gvx0E/j4e+Hg99vXALuSfwO7q7k/zz8PD4RU/S0t1Xb3tfVX+V5F3D4pVJDpqx6YFDW+Zp3/7YJyc5OUnWrl3b69atW2x5S2bTpk2Z7fzHrz9jqufdcuytz8l0zdXXrD76ejz09Tjo5/HQ1+OhrxduhyGwqp40Y/F2SdYm+cbOnKyq9u/uq4bF/5nJ4yeSZGOSt1fV6zKZGObQJOcmqSSHVtUhmYS/Y5L84s6cGwAAgIWNBP7cjPc3JdmSySWh86qqdyRZl2S/qroik1lG11XV4ZlcDrolya8kSXdfXFWnZTLhy01Jnt3d3x6O85wk70myR5JTuvviBdQMAADALBZyT+Azd+bA3f3UWZrfPM/2r07y6lnaz0xy5s7UAAAAwC3NGQKr6vfm2a+7+5VTqAcAAIApmm8k8GuztN0lybOSfG8ms3sCAACwG5kzBHb3n2x7X1V3TfL8JM9MsiGTRzsAAACwm5n3nsCqunuS30pybJJTkzywu69bjsIAAABYevPdE/jaJE/K5Nl7h3X31mWrCgAAgKm43TzrTszkmX0vSfK5qvrK8PpqVX1lecoDAABgKc13T+B8AREAAIDdkKAHAAAwIkIgAADAiAiBAAAAIyIEAgAAjIgQCAAAMCJCIAAAwIgIgQAAACMiBAIAAIyIEAgAADAiQiAAAMCICIEAAAAjIgQCAACMiBAIAAAwIkIgAADAiAiBAAAAIyIEAgAAjIgQCAAAMCJCIAAAwIgIgQAAACMiBAIAAIyIEAgAADAiQiAAAMCICIEAAAAjIgQCAACMiBAIAAAwIkIgAADAiAiBAAAAIyIEAgAAjIgQCAAAMCJCIAAAwIgIgQAAACMiBAIAAIyIEAgAADAiQiAAAMCICIEAAAAjIgQCAACMiBAIAAAwIkIgAADAiAiBAAAAIyIEAgAAjIgQCAAAMCJCIAAAwIgIgQAAACMiBAIAAIyIEAgAADAiQiAAAMCITC0EVtUpVXVNVV00o+3uVXVWVX1q+Lnv0F5V9WdVtbmqPl5VD5yxz3HD9p+qquOmVS8AAMAYTHMk8C1JjtqubX2Ss7v70CRnD8tJ8pgkhw6vE5K8MZmExiQvS/KQJEckedm24AgAAMDiTS0EdvcHk1y7XfMTkpw6vD81yRNntL+1Jz6UZJ+q2j/Jo5Oc1d3Xdvd1Sc7KrYMlAAAAC7Tc9wSu6e6rhvefT7JmeH9AkstnbHfF0DZXOwAAADthz5U6cXd3VfVSHa+qTsjkUtKsWbMmmzZtWqpDL9rWrVtnPf+Jh9001fOu5Gceq7n6mtVHX4+Hvh4H/Twe+no89PXCLXcIvLqq9u/uq4bLPa8Z2q9MctCM7Q4c2q5Msm679k2zHbi7T05ycpKsXbu2161bN9tmy2LTpk2Z7fzHrz9jqufdcuytz8l0zdXXrD76ejz09Tjo5/HQ1+OhrxduuUPgxiTHJTlp+PnOGe3PqaoNmUwCc/0QFN+T5A9mTAbzqCQvXOaadxsHLzJkbjnp6ClVAgAA7KqmFgKr6h2ZjOLtV1VXZDLL50lJTquqZyX5TJKnDJufmeSxSTYnuSHJM5Oku6+tqlcmOW/Y7hXdvf1kMwAAACzQ1EJgdz91jlUPn2XbTvLsOY5zSpJTlrA0AACA0Vru2UEBAABYQUIgAADAiAiBAAAAIyIEAgAAjIgQCAAAMCJCIAAAwIgIgQAAACMiBAIAAIyIEAgAADAiQiAAAMCICIEAAAAjIgQCAACMiBAIAAAwIkIgAADAiAiBAAAAIyIEAgAAjIgQCAAAMCJCIAAAwIgIgQAAACMiBAIAAIyIEAgAADAiQiAAAMCICIEAAAAjIgQCAACMiBAIAAAwIkIgAADAiAiBAAAAIyIEAgAAjIgQCAAAMCJCIAAAwIgIgQAAACMiBAIAAIyIEAgAADAiQiAAAMCICIEAAAAjIgQCAACMiBAIAAAwIkIgAADAiAiBAAAAIyIEAgAAjIgQCAAAMCJCIAAAwIgIgQAAACMiBAIAAIyIEAgAADAie650Aaycg9efseBtt5x09BQrAQAAlouRQAAAgBERAgEAAEZECAQAABgRIRAAAGBEhEAAAIAREQIBAABGZEVCYFVtqaoLq+qCqvrI0Hb3qjqrqj41/Nx3aK+q+rOq2lxVH6+qB65EzQAAAKvBSo4EHtndh3f32mF5fZKzu/vQJGcPy0nymCSHDq8Tkrxx2SsFAABYJXaly0GfkOTU4f2pSZ44o/2tPfGhJPtU1f4rUB8AAMBub6VCYCd5b1WdX1UnDG1ruvuq4f3nk6wZ3h+Q5PIZ+14xtAEAALBI1d3Lf9KqA7r7yqr6viRnJXluko3dvc+Mba7r7n2r6l1JTurufx/az07ygu7+yHbHPCGTy0WzZs2aB23YsGGZPs2tbd26NXvvvfet2i+88voVqGZpHHbA3Va6hF3SXH3N6qOvx0Nfj4N+Hg99PR76+paOPPLI82fcencLey53MUnS3VcOP6+pqn9KckSSq6tq/+6+arjc85ph8yuTHDRj9wOHtu2PeXKSk5Nk7dq1vW7duil+gvlt2rQps53/+PVnLH8xS2TLsetWuoRd0lx9zeqjr8dDX4+Dfh4PfT0e+nrhlv1y0Kq6S1Xdddv7JI9KclGSjUmOGzY7Lsk7h/cbkzxjmCX0oUmun3HZKAAAAIuwEiOBa5L8U1VtO//bu/tfq+q8JKdV1bOSfCbJU4btz0zy2CSbk9yQ5JnLXzIAAMDqsOwhsLsvS/Kjs7R/KcnDZ2nvJM9ehtIAAABWvV3pEREAAABMmRAIAAAwIkIgAADAiAiBAAAAIyIEAgAAjIgQCAAAMCJCIAAAwIgIgQAAACMiBAIAAIyIEAgAADAiQiAAAMCICIEAAAAjIgQCAACMiBAIAAAwIkIgAADAiAiBAAAAI7LnShfA7uHg9WcsavstJx09pUoAAIDbwkggAADAiAiBAAAAIyIEAgAAjIgQCAAAMCJCIAAAwIgIgQAAACMiBAIAAIyIEAgAADAiQiAAAMCICIEAAAAjIgQCAACMiBAIAAAwIkIgAADAiAiBAAAAIyIEAgAAjIgQCAAAMCJ7rnQBrE4Hrz9jUdtvOenoKVUCAADMZCQQAABgRIRAAACAERECAQAARkQIBAAAGBEhEAAAYESEQAAAgBERAgEAAEbEcwLZJSzmuYKeKQgAADvPSCAAAMCICIEAAAAjIgQCAACMiBAIAAAwIkIgAADAiAiBAAAAI+IREex2FvM4icQjJQAAYCYjgQAAACMiBAIAAIyIEAgAADAi7glk1XMPIQAAfNduEwKr6qgkr0+yR5I3dfdJK1wSq9RiQuOJh92UddMrBQAAltxuEQKrao8kb0jyyCRXJDmvqjZ29yUrWxksjlFJAABW2m4RApMckWRzd1+WJFW1IckTkgiBrLjFBrtd5dgCJgDAOO0uIfCAJJfPWL4iyUNWqBZYFaYZMHd3AvLsjGQDwOqwu4TAHaqqE5KcMCxurapLV7Cc/ZJ8cQXPzzJ5nr5eleo1szbr60Wa4/e4O9DX46Cfx0Nfj4e+vqV7z7VidwmBVyY5aMbygUPbd3T3yUlOXs6i5lJVH+nutStdB9Onr8dDX4+Hvh4H/Twe+no89PXC7S7PCTwvyaFVdUhV3T7JMUk2rnBNAAAAu53dYiSwu2+qquckeU8mj4g4pbsvXuGyAAAAdju7RQhMku4+M8mZK13HAu0Sl6WyLPT1eOjr8dDX46Cfx0Nfj4e+XqDq7pWuAQAAgGWyu9wTCAAAwBIQApdYVR1VVZdW1eaqWr/S9bB0quqgqjqnqi6pqour6vlD+92r6qyq+tTwc9+VrpXbrqr2qKqPVdW7huVDqurDw3f774ZJqtjNVdU+VXV6VX2yqj5RVT/uO706VdVvDv/tvqiq3lFVd/S9Xh2q6pSquqaqLprRNuv3uCb+bOjzj1fVA1euchZjjn5+7fDf749X1T9V1T4z1r1w6OdLq+rRK1L0LkwIXEJVtUeSNyR5TJL7J3lqVd1/ZatiCd2U5MTuvn+ShyZ59tC/65Oc3d2HJjl7WGb39/wkn5ix/Jokf9rd901yXZJnrUhVLLXXJ/nX7v7BJD+aSZ/7Tq8yVXVAkuclWdvdD8hkkrlj4nu9WrwlyVHbtc31PX5MkkOH1wlJ3rhMNXLbvSW37uezkjygu38kyX8leWGSDH8/OybJDw/7/MXw93QGQuDSOiLJ5u6+rLu/mWRDkiescE0ske6+qrs/Orz/aiZ/WTwgkz4+ddjs1CRPXJECWTJVdWCSo5O8aViuJA9LcvqwiX5eBarqbkl+Jsmbk6S7v9ndX47v9Gq1Z5I7VdWeSe6c5Kr4Xq8K3f3BJNdu1zzX9/gJSd7aEx9Ksk9V7b8shXKbzNbP3f3e7r5pWPxQJs8STyb9vKG7b+zuTyfZnMnf0xkIgUvrgCSXz1i+Ymhjlamqg5P8WJIPJ1nT3VcNqz6fZM1K1cWS+d9JfjfJzcPy9yb58oz/0fhurw6HJPlCkr8eLv19U1XdJb7Tq053X5nkj5N8NpPwd32S8+N7vZrN9T32d7XV65eSvHt4r593QAiERaqqvZP8Q5Lf6O6vzFzXk+l2Tbm7G6uqxyW5prvPX+lamLo9kzwwyRu7+8eSfC3bXfrpO706DPeDPSGT4H/PJHfJrS8rY5XyPV79qurFmdy287aVrmV3IQQurSuTHDRj+cChjVWiqvbKJAC+rbv/cWi+etulJMPPa1aqPpbETyZ5fFVtyeSS7odlct/YPsNlZInv9mpxRZIruvvDw/LpmYRC3+nV5xFJPt3dX+jubyX5x0y+677Xq9d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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "transcript_counts.to_frame().reset_index().transcript.hist(figsize=(15,8), bins=65)\n", + "# Add a title and labels to the axes\n", + "ax.set_title(\"Distribution of Transcripts per Gene\")\n", + "ax.set_xlabel(\"Number of Transcripts\")\n", + "ax.set_ylabel(\"Number of Genes\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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extra_vcf_info.CLNREVSTAT
extra_vcf_info.CLNSIG
Benign165653
Benign/Likely_benign201484
Likely_benign380955
Likely_pathogenic40547
Pathogenic158220
Pathogenic/Likely_pathogenic103967
Uncertain_significance917228
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" + ], + "text/plain": [ + " extra_vcf_info.CLNREVSTAT\n", + "extra_vcf_info.CLNSIG \n", + "Benign 165653\n", + "Benign/Likely_benign 201484\n", + "Likely_benign 380955\n", + "Likely_pathogenic 40547\n", + "Pathogenic 158220\n", + "Pathogenic/Likely_pathogenic 103967\n", + "Uncertain_significance 917228" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.pivot_table(df, values='extra_vcf_info.CLNREVSTAT', index='extra_vcf_info.CLNSIG', #columns='clinvar.sig',\n", + " aggfunc='count')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "#Filter variants for clinvar_review\n", + "#df= df.loc[df['clinvar.rev_stat'].isin(config_dict['CLNREVSTAT'])]\n", + "#df= df.loc[df['clinvar.sig'].isin(config_dict['ClinicalSignificance'])]\n", + "#print('\\nData shape (nsSNV) =', df.shape)\n", + "#print('\\nclinvar_CLNSIG:\\n', df['clinvar.sig'].value_counts())\n", + "#print('\\nclinvar_review:\\n', df['clinvar.rev_stat'].value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "#print(len(df[df['clinvar.sig_conf'].str.contains('athogen')==True][['clinvar.sig','clinvar.sig_conf']]))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "df['class'] = ''" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Variant-transcript pairs shape = (1050826, 129)\n", + "\n", + "Variants shape = (185135, 4)\n", + "\n", + "Classes:\n", + " low_impact 748092\n", + "high_impact 302734\n", + "Name: class, dtype: int64\n" + ] + } + ], + "source": [ + "conditions = [\n", + " #(df['clinvar.sig'] == 'Conflicting interpretations of pathogenicity') & df['clinvar.sig_conf'].str.contains('athogen'),\n", + " (df['extra_vcf_info.CLNSIG'] == 'Likely_benign') | (df['extra_vcf_info.CLNSIG'] == 'Benign') | (df['extra_vcf_info.CLNSIG'] == 'Benign/Likely_benign'),\n", + " (df['extra_vcf_info.CLNSIG'] == 'Likely_pathogenic') | (df['extra_vcf_info.CLNSIG'] == 'Pathogenic') | (df['extra_vcf_info.CLNSIG'] == 'Pathogenic/Likely_pathogenic'),\n", + " \n", + "]\n", + "\n", + "values = ['low_impact', 'high_impact']\n", + "\n", + "df['class'] = np.select(conditions, values)\n", + "df= df.loc[df['class'].isin(['low_impact', 'high_impact'])]\n", + "print('\\nVariant-transcript pairs shape =', df.shape)\n", + "print('\\nVariants shape =', df[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)\n", + "print('\\nClasses:\\n', df['class'].value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['low_impact', 'high_impact'], dtype=object)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['class'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Benign/Likely_benign', 'Benign', 'Likely_benign',\n", + " 'Pathogenic/Likely_pathogenic', 'Pathogenic', 'Likely_pathogenic'],\n", + " dtype=object)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['extra_vcf_info.CLNSIG'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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consequence
so
synonymous_variant298579
intron_variant172463
missense_variant121597
NMD_transcript_variant,3_prime_UTR_variant48963
stop_gained47297
processed_transcript35593
intron_variant,lnc_RNA34800
frameshift_truncation31773
intron_variant,NMD_transcript_variant28629
intron_variant,splice_site_variant24478
NMD_transcript_variant,synonymous_variant19730
intron_variant,processed_transcript17787
2kb_downstream_variant,processed_transcript15962
2kb_downstream_variant15019
frameshift_elongation14845
2kb_upstream_variant14234
5_prime_UTR_variant14018
3_prime_UTR_variant12201
2kb_upstream_variant,processed_transcript11537
2kb_downstream_variant,NMD_transcript_variant10181
missense_variant,NMD_transcript_variant9084
2kb_upstream_variant,lnc_RNA7326
intron_variant,NMD_transcript_variant,splice_site_variant6278
NMD_transcript_variant,stop_gained4140
2kb_downstream_variant,lnc_RNA3598
lnc_RNA3556
2kb_upstream_variant,NMD_transcript_variant3547
inframe_deletion2728
frameshift_truncation,stop_gained2365
frameshift_truncation,NMD_transcript_variant1737
NMD_transcript_variant,5_prime_UTR_variant1377
2kb_downstream_variant,NSD_transcript1344
missense_variant,start_lost1174
NSD_transcript1057
inframe_insertion1033
complex_substitution,frameshift_truncation1022
splice_site_variant943
frameshift_elongation,stop_gained906
2kb_downstream_variant,miRNA875
frameshift_elongation,NMD_transcript_variant786
2kb_upstream_variant,miRNA779
missense_variant,start_lost,NMD_transcript_variant422
complex_substitution,missense_variant409
complex_substitution,frameshift_elongation405
complex_substitution,frameshift_elongation,intron_variant349
2kb_downstream_variant,misc_RNA346
polymorphic_pseudogene331
2kb_upstream_variant,misc_RNA267
2kb_downstream_variant,snRNA217
exon_loss_variant,frameshift_truncation169
complex_substitution,stop_gained147
inframe_deletion,NMD_transcript_variant142
complex_substitution,inframe_insertion,intron_variant138
frameshift_truncation,NMD_transcript_variant,stop_gained135
stop_retained_variant132
2kb_downstream_variant,snoRNA123
intron_variant,NSD_transcript117
2kb_upstream_variant,NSD_transcript116
2kb_upstream_variant,snoRNA87
stop_lost80
2kb_upstream_variant,snRNA77
NMD_transcript_variant73
inframe_insertion,NMD_transcript_variant72
polymorphic_pseudogene,5_prime_UTR_variant69
complex_substitution,frameshift_truncation,NMD_transcript_variant67
2kb_upstream_variant,ribozyme59
NMD_transcript_variant,stop_lost54
frameshift_elongation,NMD_transcript_variant,stop_gained53
complex_substitution51
inframe_deletion,stop_gained47
NMD_transcript_variant,splice_site_variant43
exon_loss_variant,frameshift_truncation,NMD_transcript_variant42
complex_substitution,synonymous_variant42
inframe_insertion,stop_gained40
intron_variant,polymorphic_pseudogene36
exon_loss_variant,inframe_deletion33
complex_substitution,missense_variant,NMD_transcript_variant32
2kb_downstream_variant,polymorphic_pseudogene28
complex_substitution,inframe_insertion26
complex_substitution,frameshift_elongation,intron_variant,NMD_transcript_variant24
miRNA24
start_lost,5_prime_UTR_variant21
polymorphic_pseudogene,3_prime_UTR_variant19
ribozyme19
misc_RNA15
NMD_transcript_variant,stop_retained_variant15
start_lost,NMD_transcript_variant,5_prime_UTR_variant14
complex_substitution,inframe_deletion,missense_variant13
complex_substitution,inframe_deletion13
frameshift_elongation,stop_lost13
frameshift_truncation,start_lost13
complex_substitution,inframe_insertion,stop_gained12
complex_substitution,frameshift_elongation,NMD_transcript_variant12
complex_substitution,NMD_transcript_variant,stop_gained11
snRNA9
frameshift_truncation,stop_lost8
exon_loss_variant,intron_variant8
2kb_upstream_variant,5_prime_UTR_variant7
snoRNA7
intron_variant,start_lost6
inframe_deletion,NMD_transcript_variant,stop_gained6
complex_substitution,inframe_insertion,missense_variant6
complex_substitution,inframe_insertion,intron_variant,NMD_transcript_variant6
complex_substitution,frameshift_truncation,intron_variant5
2kb_upstream_variant,start_lost,NMD_transcript_variant,stop_lost,transcript_ablation,3_prime_UTR_variant,5_prime_UTR_variant5
frameshift_truncation,stop_lost,stop_retained_variant,3_prime_UTR_variant5
2kb_upstream_variant,polymorphic_pseudogene5
2kb_upstream_variant,start_lost,transcript_ablation,5_prime_UTR_variant5
NSD_transcript,5_prime_UTR_variant5
2kb_downstream_variant,stop_lost,3_prime_UTR_variant5
2kb_downstream_variant,3_prime_UTR_variant4
inframe_insertion,NMD_transcript_variant,stop_gained4
complex_substitution,frameshift_elongation,intron_variant,NMD_transcript_variant,stop_lost4
complex_substitution,NMD_transcript_variant,synonymous_variant4
frameshift_elongation,NMD_transcript_variant,stop_retained_variant3
frameshift_elongation,start_lost3
frameshift_elongation,stop_retained_variant3
2kb_upstream_variant,rRNA3
inframe_deletion,stop_lost,3_prime_UTR_variant3
start_retained_variant3
complex_substitution,inframe_insertion,NMD_transcript_variant,stop_gained3
frameshift_truncation,start_lost,NMD_transcript_variant2
2kb_downstream_variant,NMD_transcript_variant,3_prime_UTR_variant2
2kb_upstream_variant,start_lost,NMD_transcript_variant,transcript_ablation,5_prime_UTR_variant2
2kb_upstream_variant,NMD_transcript_variant,5_prime_UTR_variant2
2kb_upstream_variant,scaRNA2
intron_variant,start_lost,5_prime_UTR_variant2
complex_substitution,inframe_deletion,intron_variant2
complex_substitution,inframe_deletion,stop_gained2
frameshift_truncation,stop_lost,3_prime_UTR_variant2
complex_substitution,inframe_insertion,intron_variant,missense_variant2
complex_substitution,start_lost,start_retained_variant2
exon_loss_variant,intron_variant,NMD_transcript_variant2
inframe_deletion,start_lost2
2kb_downstream_variant,stop_lost2
complex_substitution,frameshift_elongation,intron_variant,start_lost,start_retained_variant,synonymous_variant1
NMD_transcript_variant,stop_lost,3_prime_UTR_variant1
2kb_downstream_variant,intron_variant,stop_lost,3_prime_UTR_variant1
frameshift_elongation,NMD_transcript_variant,stop_lost1
inframe_deletion,stop_lost,stop_retained_variant,3_prime_UTR_variant1
2kb_downstream_variant,scaRNA1
frameshift_truncation,NMD_transcript_variant,stop_lost1
frameshift_truncation,NMD_transcript_variant,stop_lost,stop_retained_variant,3_prime_UTR_variant1
\n", + "
" + ], + "text/plain": [ + " consequence\n", + "so \n", + "synonymous_variant 298579\n", + "intron_variant 172463\n", + "missense_variant 121597\n", + "NMD_transcript_variant,3_prime_UTR_variant 48963\n", + "stop_gained 47297\n", + "processed_transcript 35593\n", + "intron_variant,lnc_RNA 34800\n", + "frameshift_truncation 31773\n", + "intron_variant,NMD_transcript_variant 28629\n", + "intron_variant,splice_site_variant 24478\n", + "NMD_transcript_variant,synonymous_variant 19730\n", + "intron_variant,processed_transcript 17787\n", + "2kb_downstream_variant,processed_transcript 15962\n", + "2kb_downstream_variant 15019\n", + "frameshift_elongation 14845\n", + "2kb_upstream_variant 14234\n", + "5_prime_UTR_variant 14018\n", + "3_prime_UTR_variant 12201\n", + "2kb_upstream_variant,processed_transcript 11537\n", + "2kb_downstream_variant,NMD_transcript_variant 10181\n", + "missense_variant,NMD_transcript_variant 9084\n", + "2kb_upstream_variant,lnc_RNA 7326\n", + "intron_variant,NMD_transcript_variant,splice_si... 6278\n", + "NMD_transcript_variant,stop_gained 4140\n", + "2kb_downstream_variant,lnc_RNA 3598\n", + "lnc_RNA 3556\n", + "2kb_upstream_variant,NMD_transcript_variant 3547\n", + "inframe_deletion 2728\n", + "frameshift_truncation,stop_gained 2365\n", + "frameshift_truncation,NMD_transcript_variant 1737\n", + "NMD_transcript_variant,5_prime_UTR_variant 1377\n", + "2kb_downstream_variant,NSD_transcript 1344\n", + "missense_variant,start_lost 1174\n", + "NSD_transcript 1057\n", + "inframe_insertion 1033\n", + "complex_substitution,frameshift_truncation 1022\n", + "splice_site_variant 943\n", + "frameshift_elongation,stop_gained 906\n", + "2kb_downstream_variant,miRNA 875\n", + "frameshift_elongation,NMD_transcript_variant 786\n", + "2kb_upstream_variant,miRNA 779\n", + "missense_variant,start_lost,NMD_transcript_variant 422\n", + "complex_substitution,missense_variant 409\n", + "complex_substitution,frameshift_elongation 405\n", + "complex_substitution,frameshift_elongation,intr... 349\n", + "2kb_downstream_variant,misc_RNA 346\n", + "polymorphic_pseudogene 331\n", + "2kb_upstream_variant,misc_RNA 267\n", + "2kb_downstream_variant,snRNA 217\n", + "exon_loss_variant,frameshift_truncation 169\n", + "complex_substitution,stop_gained 147\n", + "inframe_deletion,NMD_transcript_variant 142\n", + "complex_substitution,inframe_insertion,intron_v... 138\n", + "frameshift_truncation,NMD_transcript_variant,st... 135\n", + "stop_retained_variant 132\n", + "2kb_downstream_variant,snoRNA 123\n", + "intron_variant,NSD_transcript 117\n", + "2kb_upstream_variant,NSD_transcript 116\n", + "2kb_upstream_variant,snoRNA 87\n", + "stop_lost 80\n", + "2kb_upstream_variant,snRNA 77\n", + "NMD_transcript_variant 73\n", + "inframe_insertion,NMD_transcript_variant 72\n", + "polymorphic_pseudogene,5_prime_UTR_variant 69\n", + "complex_substitution,frameshift_truncation,NMD_... 67\n", + "2kb_upstream_variant,ribozyme 59\n", + "NMD_transcript_variant,stop_lost 54\n", + "frameshift_elongation,NMD_transcript_variant,st... 53\n", + "complex_substitution 51\n", + "inframe_deletion,stop_gained 47\n", + "NMD_transcript_variant,splice_site_variant 43\n", + "exon_loss_variant,frameshift_truncation,NMD_tra... 42\n", + "complex_substitution,synonymous_variant 42\n", + "inframe_insertion,stop_gained 40\n", + "intron_variant,polymorphic_pseudogene 36\n", + "exon_loss_variant,inframe_deletion 33\n", + "complex_substitution,missense_variant,NMD_trans... 32\n", + "2kb_downstream_variant,polymorphic_pseudogene 28\n", + "complex_substitution,inframe_insertion 26\n", + "complex_substitution,frameshift_elongation,intr... 24\n", + "miRNA 24\n", + "start_lost,5_prime_UTR_variant 21\n", + "polymorphic_pseudogene,3_prime_UTR_variant 19\n", + "ribozyme 19\n", + "misc_RNA 15\n", + "NMD_transcript_variant,stop_retained_variant 15\n", + "start_lost,NMD_transcript_variant,5_prime_UTR_v... 14\n", + "complex_substitution,inframe_deletion,missense_... 13\n", + "complex_substitution,inframe_deletion 13\n", + "frameshift_elongation,stop_lost 13\n", + "frameshift_truncation,start_lost 13\n", + "complex_substitution,inframe_insertion,stop_gained 12\n", + "complex_substitution,frameshift_elongation,NMD_... 12\n", + "complex_substitution,NMD_transcript_variant,sto... 11\n", + "snRNA 9\n", + "frameshift_truncation,stop_lost 8\n", + "exon_loss_variant,intron_variant 8\n", + "2kb_upstream_variant,5_prime_UTR_variant 7\n", + "snoRNA 7\n", + "intron_variant,start_lost 6\n", + "inframe_deletion,NMD_transcript_variant,stop_ga... 6\n", + "complex_substitution,inframe_insertion,missense... 6\n", + "complex_substitution,inframe_insertion,intron_v... 6\n", + "complex_substitution,frameshift_truncation,intr... 5\n", + "2kb_upstream_variant,start_lost,NMD_transcript_... 5\n", + "frameshift_truncation,stop_lost,stop_retained_v... 5\n", + "2kb_upstream_variant,polymorphic_pseudogene 5\n", + "2kb_upstream_variant,start_lost,transcript_abla... 5\n", + "NSD_transcript,5_prime_UTR_variant 5\n", + "2kb_downstream_variant,stop_lost,3_prime_UTR_va... 5\n", + "2kb_downstream_variant,3_prime_UTR_variant 4\n", + "inframe_insertion,NMD_transcript_variant,stop_g... 4\n", + "complex_substitution,frameshift_elongation,intr... 4\n", + "complex_substitution,NMD_transcript_variant,syn... 4\n", + "frameshift_elongation,NMD_transcript_variant,st... 3\n", + "frameshift_elongation,start_lost 3\n", + "frameshift_elongation,stop_retained_variant 3\n", + "2kb_upstream_variant,rRNA 3\n", + "inframe_deletion,stop_lost,3_prime_UTR_variant 3\n", + "start_retained_variant 3\n", + "complex_substitution,inframe_insertion,NMD_tran... 3\n", + "frameshift_truncation,start_lost,NMD_transcript... 2\n", + "2kb_downstream_variant,NMD_transcript_variant,3... 2\n", + "2kb_upstream_variant,start_lost,NMD_transcript_... 2\n", + "2kb_upstream_variant,NMD_transcript_variant,5_p... 2\n", + "2kb_upstream_variant,scaRNA 2\n", + "intron_variant,start_lost,5_prime_UTR_variant 2\n", + "complex_substitution,inframe_deletion,intron_va... 2\n", + "complex_substitution,inframe_deletion,stop_gained 2\n", + "frameshift_truncation,stop_lost,3_prime_UTR_var... 2\n", + "complex_substitution,inframe_insertion,intron_v... 2\n", + "complex_substitution,start_lost,start_retained_... 2\n", + "exon_loss_variant,intron_variant,NMD_transcript... 2\n", + "inframe_deletion,start_lost 2\n", + "2kb_downstream_variant,stop_lost 2\n", + "complex_substitution,frameshift_elongation,intr... 1\n", + "NMD_transcript_variant,stop_lost,3_prime_UTR_va... 1\n", + "2kb_downstream_variant,intron_variant,stop_lost... 1\n", + "frameshift_elongation,NMD_transcript_variant,st... 1\n", + "inframe_deletion,stop_lost,stop_retained_varian... 1\n", + "2kb_downstream_variant,scaRNA 1\n", + "frameshift_truncation,NMD_transcript_variant,st... 1\n", + "frameshift_truncation,NMD_transcript_variant,st... 1" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.pivot_table(df, values='consequence', index='so', #columns='class',\n", + " aggfunc='count').sort_values(by='consequence', ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Variant-transcript pairs shape = (318356, 129)\n", + "\n", + "Variants shape = (78365, 4)\n", + "\n", + "Classes:\n", + " low_impact 317138\n", + "high_impact 1218\n", + "Name: class, dtype: int64\n" + ] + } + ], + "source": [ + "syn = df[(df['consequence'].str.contains('synonymous_variant'))]\n", + "print('\\nVariant-transcript pairs shape =', syn.shape)\n", + "print('\\nVariants shape =', syn[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)\n", + "print('\\nClasses:\\n', syn['class'].value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Variant-transcript pairs shape = (132739, 129)\n", + "\n", + "Variants shape = (36811, 4)\n", + "\n", + "Classes:\n", + " low_impact 78646\n", + "high_impact 54093\n", + "Name: class, dtype: int64\n" + ] + } + ], + "source": [ + "syn = df[(df['consequence'].str.contains('missense_variant'))]\n", + "print('\\nVariant-transcript pairs shape =', syn.shape)\n", + "print('\\nVariants shape =', syn[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)\n", + "print('\\nClasses:\\n', syn['class'].value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Variant-transcript pairs shape = (285138, 129)\n", + "\n", + "Variants shape = (67977, 4)\n", + "\n", + "Classes:\n", + " low_impact 207443\n", + "high_impact 77695\n", + "Name: class, dtype: int64\n" + ] + } + ], + "source": [ + "syn = df[(df['consequence'].str.contains('intron_variant'))]\n", + "print('\\nVariant-transcript pairs shape =', syn.shape)\n", + "print('\\nVariants shape =', syn[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)\n", + "print('\\nClasses:\\n', syn['class'].value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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transcriptgeneconsequenceprotein_hgvscdna_hgvschromposref_basealt_basecoding...gnomad_gene.lof_zgnomad_gene.mis_zgnomad_gene.syn_zgnomad_gene.pLIgnomad_gene.pRecgnomad_gene.pNullgnomad3.afphi.phisoclass
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32165ENST00000288774PEX10intron_variant,splice_site_variantNaNc.836+1G>Cchr12406719CGNaN...1.7882-0.201140.185590.0003470.954260.045390NaN0.10434intron_variant,splice_site_varianthigh_impact
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5 rows ร— 129 columns

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extra_vcf_info.CLNSIGBenignBenign/Likely_benignLikely_benignLikely_pathogenicPathogenicPathogenic/Likely_pathogenic
so
intron_variant51632.040249.059119.01762.014194.05507.0
synonymous_variant35862.064772.0197011.0182.0335.0417.0
missense_variant23153.033727.016070.09189.015409.024049.0
intron_variant,NMD_transcript_variant7383.05752.011369.0684.01783.01658.0
NMD_transcript_variant,3_prime_UTR_variant5077.07736.019215.01397.010014.05524.0
processed_transcript4949.06717.012762.01173.05611.04381.0
3_prime_UTR_variant4859.03478.02759.0180.0493.0432.0
intron_variant,processed_transcript4132.03445.04840.01185.02240.01945.0
intron_variant,lnc_RNA3805.08003.07356.05957.03651.06028.0
2kb_downstream_variant3679.03389.04359.0518.01486.01588.0
2kb_upstream_variant2971.02756.05216.0493.01398.01400.0
2kb_downstream_variant,processed_transcript2739.03201.04923.0775.02328.01996.0
5_prime_UTR_variant2724.02212.03606.0329.04296.0851.0
NMD_transcript_variant,synonymous_variant1976.03418.014053.046.0111.0126.0
2kb_upstream_variant,processed_transcript1905.02266.04008.0433.01610.01315.0
missense_variant,NMD_transcript_variant1716.02112.01545.0606.01399.01706.0
2kb_upstream_variant,lnc_RNA1461.01597.02305.0368.0773.0822.0
2kb_downstream_variant,NMD_transcript_variant1282.01493.03545.0227.02650.0984.0
2kb_upstream_variant,NMD_transcript_variant898.0747.01206.0108.0368.0220.0
2kb_downstream_variant,lnc_RNA639.0831.01146.0267.0312.0403.0
lnc_RNA616.0690.01281.0166.0301.0502.0
NMD_transcript_variant,5_prime_UTR_variant454.0334.0360.041.0141.047.0
inframe_deletion334.0695.0379.0279.0416.0625.0
inframe_insertion191.0459.0198.064.046.075.0
2kb_downstream_variant,miRNA149.0231.0277.060.083.075.0
2kb_upstream_variant,miRNA131.0120.0236.082.0114.096.0
NSD_transcript102.069.0301.07.0540.038.0
2kb_downstream_variant,NSD_transcript77.016.0294.09.0916.032.0
complex_substitution,missense_variant59.090.090.030.061.079.0
intron_variant,splice_site_variant56.057.043.06678.08601.09043.0
stop_gained46.080.061.01909.031494.013707.0
2kb_upstream_variant,misc_RNA45.058.094.012.026.032.0
2kb_downstream_variant,misc_RNA43.046.0177.011.029.040.0
intron_variant,NSD_transcript41.028.038.04.03.03.0
inframe_deletion,NMD_transcript_variant34.046.022.05.019.016.0
frameshift_truncation33.017.018.01381.022299.08025.0
2kb_upstream_variant,snoRNA33.015.022.02.011.04.0
frameshift_elongation30.049.018.0751.010569.03428.0
2kb_downstream_variant,snRNA30.027.0117.05.014.024.0
missense_variant,start_lost27.021.014.0129.0461.0522.0
2kb_upstream_variant,NSD_transcript27.022.055.02.06.04.0
NMD_transcript_variant,stop_gained24.023.070.074.02657.01292.0
2kb_downstream_variant,snoRNA23.021.034.04.031.010.0
2kb_downstream_variant,polymorphic_pseudogene22.06.0NaNNaNNaNNaN
polymorphic_pseudogene19.0101.036.030.078.067.0
stop_retained_variant19.013.099.0NaN1.0NaN
inframe_insertion,NMD_transcript_variant16.027.020.04.04.01.0
splice_site_variant14.029.014.0275.0276.0335.0
intron_variant,NMD_transcript_variant,splice_site_variant13.014.015.02137.01899.02200.0
2kb_upstream_variant,snRNA13.017.023.02.09.013.0
intron_variant,polymorphic_pseudogene11.06.08.04.0NaN7.0
polymorphic_pseudogene,3_prime_UTR_variant11.08.0NaNNaNNaNNaN
2kb_upstream_variant,ribozyme9.012.011.01.015.011.0
NMD_transcript_variant,stop_retained_variant6.02.04.0NaNNaN3.0
missense_variant,start_lost,NMD_transcript_variant5.03.02.034.0145.0233.0
stop_lost5.012.08.012.024.019.0
miRNA5.05.09.02.01.02.0
complex_substitution,missense_variant,NMD_transcript_variant4.0NaN8.03.015.02.0
frameshift_truncation,stop_gained3.010.03.057.01750.0542.0
complex_substitution,frameshift_elongation,intron_variant3.05.01.0128.058.0154.0
NMD_transcript_variant3.014.07.08.021.020.0
NSD_transcript,5_prime_UTR_variant3.02.0NaNNaNNaNNaN
snoRNA3.02.0NaNNaNNaN2.0
frameshift_elongation,NMD_transcript_variant2.02.0NaN16.0617.0149.0
complex_substitution2.02.02.06.021.018.0
NMD_transcript_variant,splice_site_variant2.0NaNNaN17.014.010.0
ribozyme2.01.01.01.04.010.0
2kb_downstream_variant,3_prime_UTR_variant2.0NaN2.0NaNNaNNaN
2kb_upstream_variant,rRNA2.01.0NaNNaNNaNNaN
frameshift_truncation,NMD_transcript_variant1.03.01.018.01333.0381.0
complex_substitution,inframe_insertion,intron_variant1.016.0NaN33.050.038.0
NMD_transcript_variant,stop_lost1.03.016.01.020.013.0
frameshift_truncation,stop_lost1.01.0NaN1.03.02.0
2kb_downstream_variant,scaRNA1.0NaNNaNNaNNaNNaN
misc_RNA1.0NaN14.0NaNNaNNaN
start_retained_variant1.01.0NaNNaNNaN1.0
complex_substitution,frameshift_truncationNaNNaNNaN65.0781.0176.0
frameshift_elongation,stop_gainedNaN1.0NaN55.0666.0184.0
complex_substitution,frameshift_elongationNaNNaNNaN23.0306.076.0
exon_loss_variant,frameshift_truncationNaNNaNNaNNaN167.02.0
complex_substitution,stop_gainedNaNNaNNaNNaN120.027.0
frameshift_truncation,NMD_transcript_variant,stop_gainedNaNNaNNaNNaN97.038.0
complex_substitution,frameshift_truncation,NMD_transcript_variantNaNNaNNaNNaN51.016.0
frameshift_elongation,NMD_transcript_variant,stop_gainedNaNNaNNaNNaN46.07.0
exon_loss_variant,frameshift_truncation,NMD_transcript_variantNaNNaNNaNNaN42.0NaN
inframe_deletion,stop_gainedNaNNaNNaNNaN36.011.0
exon_loss_variant,inframe_deletionNaNNaNNaNNaN33.0NaN
polymorphic_pseudogene,5_prime_UTR_variantNaN6.05.08.026.024.0
inframe_insertion,stop_gainedNaN2.0NaNNaN20.018.0
complex_substitution,inframe_insertion,stop_gainedNaNNaNNaNNaN12.0NaN
start_lost,5_prime_UTR_variantNaNNaNNaNNaN12.09.0
complex_substitution,NMD_transcript_variant,stop_gainedNaNNaNNaNNaN11.0NaN
frameshift_elongation,stop_lostNaNNaNNaNNaN11.02.0
complex_substitution,inframe_insertionNaN3.010.0NaN10.03.0
frameshift_truncation,start_lostNaNNaNNaNNaN10.03.0
complex_substitution,inframe_deletionNaNNaNNaN3.09.01.0
exon_loss_variant,intron_variantNaNNaNNaNNaN8.0NaN
2kb_upstream_variant,5_prime_UTR_variantNaNNaNNaNNaN7.0NaN
complex_substitution,inframe_insertion,missense_variantNaNNaNNaNNaN6.0NaN
intron_variant,start_lostNaNNaNNaNNaN6.0NaN
2kb_upstream_variant,start_lost,NMD_transcript_variant,stop_lost,transcript_ablation,3_prime_UTR_variant,5_prime_UTR_variantNaNNaNNaNNaN5.0NaN
2kb_upstream_variant,start_lost,transcript_ablation,5_prime_UTR_variantNaNNaNNaNNaN5.0NaN
complex_substitution,frameshift_elongation,NMD_transcript_variantNaNNaNNaNNaN5.07.0
inframe_deletion,NMD_transcript_variant,stop_gainedNaNNaNNaNNaN5.01.0
complex_substitution,frameshift_elongation,intron_variant,NMD_transcript_variant,stop_lostNaNNaNNaNNaN4.0NaN
2kb_downstream_variant,stop_lost,3_prime_UTR_variantNaNNaNNaNNaN3.02.0
complex_substitution,frameshift_truncation,intron_variantNaNNaNNaN2.03.0NaN
complex_substitution,inframe_deletion,missense_variantNaNNaNNaN1.03.09.0
complex_substitution,inframe_insertion,NMD_transcript_variant,stop_gainedNaNNaNNaNNaN3.0NaN
frameshift_elongation,NMD_transcript_variant,stop_retained_variantNaNNaNNaNNaN3.0NaN
inframe_insertion,NMD_transcript_variant,stop_gainedNaNNaNNaNNaN3.01.0
snRNANaNNaNNaN3.03.03.0
2kb_downstream_variant,NMD_transcript_variant,3_prime_UTR_variantNaNNaNNaNNaN2.0NaN
2kb_upstream_variant,NMD_transcript_variant,5_prime_UTR_variantNaNNaNNaNNaN2.0NaN
2kb_upstream_variant,start_lost,NMD_transcript_variant,transcript_ablation,5_prime_UTR_variantNaNNaNNaNNaN2.0NaN
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\n", + "
" + ], + "text/plain": [ + "extra_vcf_info.CLNSIG Benign \\\n", + "so \n", + "intron_variant 51632.0 \n", + "synonymous_variant 35862.0 \n", + "missense_variant 23153.0 \n", + "intron_variant,NMD_transcript_variant 7383.0 \n", + "NMD_transcript_variant,3_prime_UTR_variant 5077.0 \n", + "processed_transcript 4949.0 \n", + "3_prime_UTR_variant 4859.0 \n", + "intron_variant,processed_transcript 4132.0 \n", + "intron_variant,lnc_RNA 3805.0 \n", + "2kb_downstream_variant 3679.0 \n", + "2kb_upstream_variant 2971.0 \n", + "2kb_downstream_variant,processed_transcript 2739.0 \n", + "5_prime_UTR_variant 2724.0 \n", + "NMD_transcript_variant,synonymous_variant 1976.0 \n", + "2kb_upstream_variant,processed_transcript 1905.0 \n", + "missense_variant,NMD_transcript_variant 1716.0 \n", + "2kb_upstream_variant,lnc_RNA 1461.0 \n", + "2kb_downstream_variant,NMD_transcript_variant 1282.0 \n", + "2kb_upstream_variant,NMD_transcript_variant 898.0 \n", + "2kb_downstream_variant,lnc_RNA 639.0 \n", + "lnc_RNA 616.0 \n", + "NMD_transcript_variant,5_prime_UTR_variant 454.0 \n", + "inframe_deletion 334.0 \n", + "inframe_insertion 191.0 \n", + "2kb_downstream_variant,miRNA 149.0 \n", + "2kb_upstream_variant,miRNA 131.0 \n", + "NSD_transcript 102.0 \n", + "2kb_downstream_variant,NSD_transcript 77.0 \n", + "complex_substitution,missense_variant 59.0 \n", + "intron_variant,splice_site_variant 56.0 \n", + "stop_gained 46.0 \n", + "2kb_upstream_variant,misc_RNA 45.0 \n", + "2kb_downstream_variant,misc_RNA 43.0 \n", + "intron_variant,NSD_transcript 41.0 \n", + "inframe_deletion,NMD_transcript_variant 34.0 \n", + "frameshift_truncation 33.0 \n", + "2kb_upstream_variant,snoRNA 33.0 \n", + "frameshift_elongation 30.0 \n", + "2kb_downstream_variant,snRNA 30.0 \n", + "missense_variant,start_lost 27.0 \n", + "2kb_upstream_variant,NSD_transcript 27.0 \n", + "NMD_transcript_variant,stop_gained 24.0 \n", + "2kb_downstream_variant,snoRNA 23.0 \n", + "2kb_downstream_variant,polymorphic_pseudogene 22.0 \n", + "polymorphic_pseudogene 19.0 \n", + "stop_retained_variant 19.0 \n", + "inframe_insertion,NMD_transcript_variant 16.0 \n", + "splice_site_variant 14.0 \n", + "intron_variant,NMD_transcript_variant,splice_si... 13.0 \n", + "2kb_upstream_variant,snRNA 13.0 \n", + "intron_variant,polymorphic_pseudogene 11.0 \n", + "polymorphic_pseudogene,3_prime_UTR_variant 11.0 \n", + "2kb_upstream_variant,ribozyme 9.0 \n", + "NMD_transcript_variant,stop_retained_variant 6.0 \n", + "missense_variant,start_lost,NMD_transcript_variant 5.0 \n", + "stop_lost 5.0 \n", + "miRNA 5.0 \n", + "complex_substitution,missense_variant,NMD_trans... 4.0 \n", + "frameshift_truncation,stop_gained 3.0 \n", + "complex_substitution,frameshift_elongation,intr... 3.0 \n", + "NMD_transcript_variant 3.0 \n", + "NSD_transcript,5_prime_UTR_variant 3.0 \n", + "snoRNA 3.0 \n", + "frameshift_elongation,NMD_transcript_variant 2.0 \n", + "complex_substitution 2.0 \n", + "NMD_transcript_variant,splice_site_variant 2.0 \n", + "ribozyme 2.0 \n", + "2kb_downstream_variant,3_prime_UTR_variant 2.0 \n", + "2kb_upstream_variant,rRNA 2.0 \n", + "frameshift_truncation,NMD_transcript_variant 1.0 \n", + "complex_substitution,inframe_insertion,intron_v... 1.0 \n", + "NMD_transcript_variant,stop_lost 1.0 \n", + "frameshift_truncation,stop_lost 1.0 \n", + "2kb_downstream_variant,scaRNA 1.0 \n", + "misc_RNA 1.0 \n", + "start_retained_variant 1.0 \n", + "complex_substitution,frameshift_truncation NaN \n", + "frameshift_elongation,stop_gained NaN \n", + "complex_substitution,frameshift_elongation NaN \n", + "exon_loss_variant,frameshift_truncation NaN \n", + "complex_substitution,stop_gained NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... 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NaN \n", + "inframe_deletion,start_lost NaN \n", + "inframe_deletion,stop_lost,3_prime_UTR_variant NaN \n", + "\n", + "extra_vcf_info.CLNSIG Benign/Likely_benign \\\n", + "so \n", + "intron_variant 40249.0 \n", + "synonymous_variant 64772.0 \n", + "missense_variant 33727.0 \n", + "intron_variant,NMD_transcript_variant 5752.0 \n", + "NMD_transcript_variant,3_prime_UTR_variant 7736.0 \n", + "processed_transcript 6717.0 \n", + "3_prime_UTR_variant 3478.0 \n", + "intron_variant,processed_transcript 3445.0 \n", + "intron_variant,lnc_RNA 8003.0 \n", + "2kb_downstream_variant 3389.0 \n", + "2kb_upstream_variant 2756.0 \n", + "2kb_downstream_variant,processed_transcript 3201.0 \n", + "5_prime_UTR_variant 2212.0 \n", + "NMD_transcript_variant,synonymous_variant 3418.0 \n", + "2kb_upstream_variant,processed_transcript 2266.0 \n", + "missense_variant,NMD_transcript_variant 2112.0 \n", + "2kb_upstream_variant,lnc_RNA 1597.0 \n", + "2kb_downstream_variant,NMD_transcript_variant 1493.0 \n", + "2kb_upstream_variant,NMD_transcript_variant 747.0 \n", + "2kb_downstream_variant,lnc_RNA 831.0 \n", + "lnc_RNA 690.0 \n", + "NMD_transcript_variant,5_prime_UTR_variant 334.0 \n", + "inframe_deletion 695.0 \n", + "inframe_insertion 459.0 \n", + "2kb_downstream_variant,miRNA 231.0 \n", + "2kb_upstream_variant,miRNA 120.0 \n", + "NSD_transcript 69.0 \n", + "2kb_downstream_variant,NSD_transcript 16.0 \n", + "complex_substitution,missense_variant 90.0 \n", + "intron_variant,splice_site_variant 57.0 \n", + "stop_gained 80.0 \n", + "2kb_upstream_variant,misc_RNA 58.0 \n", + "2kb_downstream_variant,misc_RNA 46.0 \n", + "intron_variant,NSD_transcript 28.0 \n", + "inframe_deletion,NMD_transcript_variant 46.0 \n", + "frameshift_truncation 17.0 \n", + "2kb_upstream_variant,snoRNA 15.0 \n", + "frameshift_elongation 49.0 \n", + "2kb_downstream_variant,snRNA 27.0 \n", + "missense_variant,start_lost 21.0 \n", + "2kb_upstream_variant,NSD_transcript 22.0 \n", + "NMD_transcript_variant,stop_gained 23.0 \n", + "2kb_downstream_variant,snoRNA 21.0 \n", + "2kb_downstream_variant,polymorphic_pseudogene 6.0 \n", + "polymorphic_pseudogene 101.0 \n", + "stop_retained_variant 13.0 \n", + "inframe_insertion,NMD_transcript_variant 27.0 \n", + "splice_site_variant 29.0 \n", + "intron_variant,NMD_transcript_variant,splice_si... 14.0 \n", + "2kb_upstream_variant,snRNA 17.0 \n", + "intron_variant,polymorphic_pseudogene 6.0 \n", + "polymorphic_pseudogene,3_prime_UTR_variant 8.0 \n", + "2kb_upstream_variant,ribozyme 12.0 \n", + "NMD_transcript_variant,stop_retained_variant 2.0 \n", + "missense_variant,start_lost,NMD_transcript_variant 3.0 \n", + "stop_lost 12.0 \n", + "miRNA 5.0 \n", + "complex_substitution,missense_variant,NMD_trans... 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NaN \n", + "complex_substitution,frameshift_truncation,NMD_... NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "exon_loss_variant,frameshift_truncation,NMD_tra... NaN \n", + "inframe_deletion,stop_gained NaN \n", + "exon_loss_variant,inframe_deletion NaN \n", + "polymorphic_pseudogene,5_prime_UTR_variant 6.0 \n", + "inframe_insertion,stop_gained 2.0 \n", + "complex_substitution,inframe_insertion,stop_gained NaN \n", + "start_lost,5_prime_UTR_variant NaN \n", + "complex_substitution,NMD_transcript_variant,sto... NaN \n", + "frameshift_elongation,stop_lost NaN \n", + "complex_substitution,inframe_insertion 3.0 \n", + "frameshift_truncation,start_lost NaN \n", + "complex_substitution,inframe_deletion NaN \n", + "exon_loss_variant,intron_variant NaN \n", + "2kb_upstream_variant,5_prime_UTR_variant NaN \n", + "complex_substitution,inframe_insertion,missense... NaN \n", + "intron_variant,start_lost NaN \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "2kb_upstream_variant,start_lost,transcript_abla... NaN \n", + "complex_substitution,frameshift_elongation,NMD_... NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_ga... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "2kb_downstream_variant,stop_lost,3_prime_UTR_va... NaN \n", + "complex_substitution,frameshift_truncation,intr... NaN \n", + "complex_substitution,inframe_deletion,missense_... NaN \n", + "complex_substitution,inframe_insertion,NMD_tran... NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "inframe_insertion,NMD_transcript_variant,stop_g... NaN \n", + "snRNA NaN \n", + "2kb_downstream_variant,NMD_transcript_variant,3... NaN \n", + "2kb_upstream_variant,NMD_transcript_variant,5_p... NaN \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... 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NaN \n", + "NMD_transcript_variant,stop_lost 16.0 \n", + "frameshift_truncation,stop_lost NaN \n", + "2kb_downstream_variant,scaRNA NaN \n", + "misc_RNA 14.0 \n", + "start_retained_variant NaN \n", + "complex_substitution,frameshift_truncation NaN \n", + "frameshift_elongation,stop_gained NaN \n", + "complex_substitution,frameshift_elongation NaN \n", + "exon_loss_variant,frameshift_truncation NaN \n", + "complex_substitution,stop_gained NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "complex_substitution,frameshift_truncation,NMD_... NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "exon_loss_variant,frameshift_truncation,NMD_tra... NaN \n", + "inframe_deletion,stop_gained NaN \n", + "exon_loss_variant,inframe_deletion NaN \n", + "polymorphic_pseudogene,5_prime_UTR_variant 5.0 \n", + "inframe_insertion,stop_gained NaN \n", + "complex_substitution,inframe_insertion,stop_gained NaN \n", + "start_lost,5_prime_UTR_variant NaN \n", + "complex_substitution,NMD_transcript_variant,sto... NaN \n", + "frameshift_elongation,stop_lost NaN \n", + "complex_substitution,inframe_insertion 10.0 \n", + "frameshift_truncation,start_lost NaN \n", + "complex_substitution,inframe_deletion NaN \n", + "exon_loss_variant,intron_variant NaN \n", + "2kb_upstream_variant,5_prime_UTR_variant NaN \n", + "complex_substitution,inframe_insertion,missense... NaN \n", + "intron_variant,start_lost NaN \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "2kb_upstream_variant,start_lost,transcript_abla... NaN \n", + "complex_substitution,frameshift_elongation,NMD_... NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_ga... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "2kb_downstream_variant,stop_lost,3_prime_UTR_va... NaN \n", + "complex_substitution,frameshift_truncation,intr... NaN \n", + "complex_substitution,inframe_deletion,missense_... NaN \n", + "complex_substitution,inframe_insertion,NMD_tran... NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "inframe_insertion,NMD_transcript_variant,stop_g... NaN \n", + "snRNA NaN \n", + "2kb_downstream_variant,NMD_transcript_variant,3... NaN \n", + "2kb_upstream_variant,NMD_transcript_variant,5_p... NaN \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_deletion,stop_gained NaN \n", + "exon_loss_variant,intron_variant,NMD_transcript... NaN \n", + "frameshift_truncation,start_lost,NMD_transcript... NaN \n", + "intron_variant,start_lost,5_prime_UTR_variant NaN \n", + "2kb_downstream_variant,intron_variant,stop_lost... NaN \n", + "NMD_transcript_variant,stop_lost,3_prime_UTR_va... NaN \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,stop_lost,3_prime_UTR_var... NaN \n", + "inframe_deletion,stop_lost,stop_retained_varian... NaN \n", + "start_lost,NMD_transcript_variant,5_prime_UTR_v... NaN \n", + "2kb_downstream_variant,stop_lost NaN \n", + "2kb_upstream_variant,polymorphic_pseudogene NaN \n", + "2kb_upstream_variant,scaRNA NaN \n", + "complex_substitution,NMD_transcript_variant,syn... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,synonymous_variant 21.0 \n", + "frameshift_elongation,start_lost NaN \n", + "frameshift_elongation,stop_retained_variant 3.0 \n", + "frameshift_truncation,stop_lost,stop_retained_v... NaN \n", + "inframe_deletion,start_lost NaN \n", + "inframe_deletion,stop_lost,3_prime_UTR_variant NaN \n", + "\n", + "extra_vcf_info.CLNSIG Likely_pathogenic \\\n", + "so \n", + "intron_variant 1762.0 \n", + "synonymous_variant 182.0 \n", + "missense_variant 9189.0 \n", + "intron_variant,NMD_transcript_variant 684.0 \n", + "NMD_transcript_variant,3_prime_UTR_variant 1397.0 \n", + "processed_transcript 1173.0 \n", + "3_prime_UTR_variant 180.0 \n", + "intron_variant,processed_transcript 1185.0 \n", + "intron_variant,lnc_RNA 5957.0 \n", + "2kb_downstream_variant 518.0 \n", + "2kb_upstream_variant 493.0 \n", + "2kb_downstream_variant,processed_transcript 775.0 \n", + "5_prime_UTR_variant 329.0 \n", + "NMD_transcript_variant,synonymous_variant 46.0 \n", + "2kb_upstream_variant,processed_transcript 433.0 \n", + "missense_variant,NMD_transcript_variant 606.0 \n", + "2kb_upstream_variant,lnc_RNA 368.0 \n", + "2kb_downstream_variant,NMD_transcript_variant 227.0 \n", + "2kb_upstream_variant,NMD_transcript_variant 108.0 \n", + "2kb_downstream_variant,lnc_RNA 267.0 \n", + "lnc_RNA 166.0 \n", + "NMD_transcript_variant,5_prime_UTR_variant 41.0 \n", + "inframe_deletion 279.0 \n", + "inframe_insertion 64.0 \n", + "2kb_downstream_variant,miRNA 60.0 \n", + "2kb_upstream_variant,miRNA 82.0 \n", + "NSD_transcript 7.0 \n", + "2kb_downstream_variant,NSD_transcript 9.0 \n", + "complex_substitution,missense_variant 30.0 \n", + "intron_variant,splice_site_variant 6678.0 \n", + "stop_gained 1909.0 \n", + "2kb_upstream_variant,misc_RNA 12.0 \n", + "2kb_downstream_variant,misc_RNA 11.0 \n", + "intron_variant,NSD_transcript 4.0 \n", + "inframe_deletion,NMD_transcript_variant 5.0 \n", + "frameshift_truncation 1381.0 \n", + "2kb_upstream_variant,snoRNA 2.0 \n", + "frameshift_elongation 751.0 \n", + "2kb_downstream_variant,snRNA 5.0 \n", + "missense_variant,start_lost 129.0 \n", + "2kb_upstream_variant,NSD_transcript 2.0 \n", + "NMD_transcript_variant,stop_gained 74.0 \n", + "2kb_downstream_variant,snoRNA 4.0 \n", + "2kb_downstream_variant,polymorphic_pseudogene NaN \n", + "polymorphic_pseudogene 30.0 \n", + "stop_retained_variant NaN \n", + "inframe_insertion,NMD_transcript_variant 4.0 \n", + "splice_site_variant 275.0 \n", + "intron_variant,NMD_transcript_variant,splice_si... 2137.0 \n", + "2kb_upstream_variant,snRNA 2.0 \n", + "intron_variant,polymorphic_pseudogene 4.0 \n", + "polymorphic_pseudogene,3_prime_UTR_variant NaN \n", + "2kb_upstream_variant,ribozyme 1.0 \n", + "NMD_transcript_variant,stop_retained_variant NaN \n", + "missense_variant,start_lost,NMD_transcript_variant 34.0 \n", + "stop_lost 12.0 \n", + "miRNA 2.0 \n", + "complex_substitution,missense_variant,NMD_trans... 3.0 \n", + "frameshift_truncation,stop_gained 57.0 \n", + "complex_substitution,frameshift_elongation,intr... 128.0 \n", + "NMD_transcript_variant 8.0 \n", + "NSD_transcript,5_prime_UTR_variant NaN \n", + "snoRNA NaN \n", + "frameshift_elongation,NMD_transcript_variant 16.0 \n", + "complex_substitution 6.0 \n", + "NMD_transcript_variant,splice_site_variant 17.0 \n", + "ribozyme 1.0 \n", + "2kb_downstream_variant,3_prime_UTR_variant NaN \n", + "2kb_upstream_variant,rRNA NaN \n", + "frameshift_truncation,NMD_transcript_variant 18.0 \n", + "complex_substitution,inframe_insertion,intron_v... 33.0 \n", + "NMD_transcript_variant,stop_lost 1.0 \n", + "frameshift_truncation,stop_lost 1.0 \n", + "2kb_downstream_variant,scaRNA NaN \n", + "misc_RNA NaN \n", + "start_retained_variant NaN \n", + "complex_substitution,frameshift_truncation 65.0 \n", + "frameshift_elongation,stop_gained 55.0 \n", + "complex_substitution,frameshift_elongation 23.0 \n", + "exon_loss_variant,frameshift_truncation NaN \n", + "complex_substitution,stop_gained NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "complex_substitution,frameshift_truncation,NMD_... NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "exon_loss_variant,frameshift_truncation,NMD_tra... NaN \n", + "inframe_deletion,stop_gained NaN \n", + "exon_loss_variant,inframe_deletion NaN \n", + "polymorphic_pseudogene,5_prime_UTR_variant 8.0 \n", + "inframe_insertion,stop_gained NaN \n", + "complex_substitution,inframe_insertion,stop_gained NaN \n", + "start_lost,5_prime_UTR_variant NaN \n", + "complex_substitution,NMD_transcript_variant,sto... NaN \n", + "frameshift_elongation,stop_lost NaN \n", + "complex_substitution,inframe_insertion NaN \n", + "frameshift_truncation,start_lost NaN \n", + "complex_substitution,inframe_deletion 3.0 \n", + "exon_loss_variant,intron_variant NaN \n", + "2kb_upstream_variant,5_prime_UTR_variant NaN \n", + "complex_substitution,inframe_insertion,missense... NaN \n", + "intron_variant,start_lost NaN \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "2kb_upstream_variant,start_lost,transcript_abla... NaN \n", + "complex_substitution,frameshift_elongation,NMD_... NaN \n", + "inframe_deletion,NMD_transcript_variant,stop_ga... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "2kb_downstream_variant,stop_lost,3_prime_UTR_va... NaN \n", + "complex_substitution,frameshift_truncation,intr... 2.0 \n", + "complex_substitution,inframe_deletion,missense_... 1.0 \n", + "complex_substitution,inframe_insertion,NMD_tran... NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "inframe_insertion,NMD_transcript_variant,stop_g... NaN \n", + "snRNA 3.0 \n", + "2kb_downstream_variant,NMD_transcript_variant,3... NaN \n", + "2kb_upstream_variant,NMD_transcript_variant,5_p... NaN \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "complex_substitution,frameshift_elongation,intr... 6.0 \n", + "complex_substitution,inframe_deletion,stop_gained NaN \n", + "exon_loss_variant,intron_variant,NMD_transcript... NaN \n", + "frameshift_truncation,start_lost,NMD_transcript... NaN \n", + "intron_variant,start_lost,5_prime_UTR_variant NaN \n", + "2kb_downstream_variant,intron_variant,stop_lost... NaN \n", + "NMD_transcript_variant,stop_lost,3_prime_UTR_va... NaN \n", + "complex_substitution,start_lost,start_retained_... 1.0 \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,stop_lost,3_prime_UTR_var... NaN \n", + "inframe_deletion,stop_lost,stop_retained_varian... NaN \n", + "start_lost,NMD_transcript_variant,5_prime_UTR_v... NaN \n", + "2kb_downstream_variant,stop_lost NaN \n", + "2kb_upstream_variant,polymorphic_pseudogene NaN \n", + "2kb_upstream_variant,scaRNA 2.0 \n", + "complex_substitution,NMD_transcript_variant,syn... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "complex_substitution,inframe_insertion,intron_v... 2.0 \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,synonymous_variant NaN \n", + "frameshift_elongation,start_lost NaN \n", + "frameshift_elongation,stop_retained_variant NaN \n", + "frameshift_truncation,stop_lost,stop_retained_v... NaN \n", + "inframe_deletion,start_lost NaN \n", + "inframe_deletion,stop_lost,3_prime_UTR_variant 2.0 \n", + "\n", + "extra_vcf_info.CLNSIG Pathogenic \\\n", + "so \n", + "intron_variant 14194.0 \n", + "synonymous_variant 335.0 \n", + "missense_variant 15409.0 \n", + "intron_variant,NMD_transcript_variant 1783.0 \n", + "NMD_transcript_variant,3_prime_UTR_variant 10014.0 \n", + "processed_transcript 5611.0 \n", + "3_prime_UTR_variant 493.0 \n", + "intron_variant,processed_transcript 2240.0 \n", + "intron_variant,lnc_RNA 3651.0 \n", + "2kb_downstream_variant 1486.0 \n", + "2kb_upstream_variant 1398.0 \n", + "2kb_downstream_variant,processed_transcript 2328.0 \n", + "5_prime_UTR_variant 4296.0 \n", + "NMD_transcript_variant,synonymous_variant 111.0 \n", + "2kb_upstream_variant,processed_transcript 1610.0 \n", + "missense_variant,NMD_transcript_variant 1399.0 \n", + "2kb_upstream_variant,lnc_RNA 773.0 \n", + "2kb_downstream_variant,NMD_transcript_variant 2650.0 \n", + "2kb_upstream_variant,NMD_transcript_variant 368.0 \n", + "2kb_downstream_variant,lnc_RNA 312.0 \n", + "lnc_RNA 301.0 \n", + "NMD_transcript_variant,5_prime_UTR_variant 141.0 \n", + "inframe_deletion 416.0 \n", + "inframe_insertion 46.0 \n", + "2kb_downstream_variant,miRNA 83.0 \n", + "2kb_upstream_variant,miRNA 114.0 \n", + "NSD_transcript 540.0 \n", + "2kb_downstream_variant,NSD_transcript 916.0 \n", + "complex_substitution,missense_variant 61.0 \n", + "intron_variant,splice_site_variant 8601.0 \n", + "stop_gained 31494.0 \n", + "2kb_upstream_variant,misc_RNA 26.0 \n", + "2kb_downstream_variant,misc_RNA 29.0 \n", + "intron_variant,NSD_transcript 3.0 \n", + "inframe_deletion,NMD_transcript_variant 19.0 \n", + "frameshift_truncation 22299.0 \n", + "2kb_upstream_variant,snoRNA 11.0 \n", + "frameshift_elongation 10569.0 \n", + "2kb_downstream_variant,snRNA 14.0 \n", + "missense_variant,start_lost 461.0 \n", + "2kb_upstream_variant,NSD_transcript 6.0 \n", + "NMD_transcript_variant,stop_gained 2657.0 \n", + "2kb_downstream_variant,snoRNA 31.0 \n", + "2kb_downstream_variant,polymorphic_pseudogene NaN \n", + "polymorphic_pseudogene 78.0 \n", + "stop_retained_variant 1.0 \n", + "inframe_insertion,NMD_transcript_variant 4.0 \n", + "splice_site_variant 276.0 \n", + "intron_variant,NMD_transcript_variant,splice_si... 1899.0 \n", + "2kb_upstream_variant,snRNA 9.0 \n", + "intron_variant,polymorphic_pseudogene NaN \n", + "polymorphic_pseudogene,3_prime_UTR_variant NaN \n", + "2kb_upstream_variant,ribozyme 15.0 \n", + "NMD_transcript_variant,stop_retained_variant NaN \n", + "missense_variant,start_lost,NMD_transcript_variant 145.0 \n", + "stop_lost 24.0 \n", + "miRNA 1.0 \n", + "complex_substitution,missense_variant,NMD_trans... 15.0 \n", + "frameshift_truncation,stop_gained 1750.0 \n", + "complex_substitution,frameshift_elongation,intr... 58.0 \n", + "NMD_transcript_variant 21.0 \n", + "NSD_transcript,5_prime_UTR_variant NaN \n", + "snoRNA NaN \n", + "frameshift_elongation,NMD_transcript_variant 617.0 \n", + "complex_substitution 21.0 \n", + "NMD_transcript_variant,splice_site_variant 14.0 \n", + "ribozyme 4.0 \n", + "2kb_downstream_variant,3_prime_UTR_variant NaN \n", + "2kb_upstream_variant,rRNA NaN \n", + "frameshift_truncation,NMD_transcript_variant 1333.0 \n", + "complex_substitution,inframe_insertion,intron_v... 50.0 \n", + "NMD_transcript_variant,stop_lost 20.0 \n", + "frameshift_truncation,stop_lost 3.0 \n", + "2kb_downstream_variant,scaRNA NaN \n", + "misc_RNA NaN \n", + "start_retained_variant NaN \n", + "complex_substitution,frameshift_truncation 781.0 \n", + "frameshift_elongation,stop_gained 666.0 \n", + "complex_substitution,frameshift_elongation 306.0 \n", + "exon_loss_variant,frameshift_truncation 167.0 \n", + "complex_substitution,stop_gained 120.0 \n", + "frameshift_truncation,NMD_transcript_variant,st... 97.0 \n", + "complex_substitution,frameshift_truncation,NMD_... 51.0 \n", + "frameshift_elongation,NMD_transcript_variant,st... 46.0 \n", + "exon_loss_variant,frameshift_truncation,NMD_tra... 42.0 \n", + "inframe_deletion,stop_gained 36.0 \n", + "exon_loss_variant,inframe_deletion 33.0 \n", + "polymorphic_pseudogene,5_prime_UTR_variant 26.0 \n", + "inframe_insertion,stop_gained 20.0 \n", + "complex_substitution,inframe_insertion,stop_gained 12.0 \n", + "start_lost,5_prime_UTR_variant 12.0 \n", + "complex_substitution,NMD_transcript_variant,sto... 11.0 \n", + "frameshift_elongation,stop_lost 11.0 \n", + "complex_substitution,inframe_insertion 10.0 \n", + "frameshift_truncation,start_lost 10.0 \n", + "complex_substitution,inframe_deletion 9.0 \n", + "exon_loss_variant,intron_variant 8.0 \n", + "2kb_upstream_variant,5_prime_UTR_variant 7.0 \n", + "complex_substitution,inframe_insertion,missense... 6.0 \n", + "intron_variant,start_lost 6.0 \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... 5.0 \n", + "2kb_upstream_variant,start_lost,transcript_abla... 5.0 \n", + "complex_substitution,frameshift_elongation,NMD_... 5.0 \n", + "inframe_deletion,NMD_transcript_variant,stop_ga... 5.0 \n", + "complex_substitution,frameshift_elongation,intr... 4.0 \n", + "2kb_downstream_variant,stop_lost,3_prime_UTR_va... 3.0 \n", + "complex_substitution,frameshift_truncation,intr... 3.0 \n", + "complex_substitution,inframe_deletion,missense_... 3.0 \n", + "complex_substitution,inframe_insertion,NMD_tran... 3.0 \n", + "frameshift_elongation,NMD_transcript_variant,st... 3.0 \n", + "inframe_insertion,NMD_transcript_variant,stop_g... 3.0 \n", + "snRNA 3.0 \n", + "2kb_downstream_variant,NMD_transcript_variant,3... 2.0 \n", + "2kb_upstream_variant,NMD_transcript_variant,5_p... 2.0 \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... 2.0 \n", + "complex_substitution,frameshift_elongation,intr... 2.0 \n", + "complex_substitution,inframe_deletion,stop_gained 2.0 \n", + "exon_loss_variant,intron_variant,NMD_transcript... 2.0 \n", + "frameshift_truncation,start_lost,NMD_transcript... 2.0 \n", + "intron_variant,start_lost,5_prime_UTR_variant 2.0 \n", + "2kb_downstream_variant,intron_variant,stop_lost... 1.0 \n", + "NMD_transcript_variant,stop_lost,3_prime_UTR_va... 1.0 \n", + "complex_substitution,start_lost,start_retained_... 1.0 \n", + "frameshift_elongation,NMD_transcript_variant,st... 1.0 \n", + "frameshift_truncation,NMD_transcript_variant,st... 1.0 \n", + "frameshift_truncation,NMD_transcript_variant,st... 1.0 \n", + "frameshift_truncation,stop_lost,3_prime_UTR_var... 1.0 \n", + "inframe_deletion,stop_lost,stop_retained_varian... 1.0 \n", + "start_lost,NMD_transcript_variant,5_prime_UTR_v... 1.0 \n", + "2kb_downstream_variant,stop_lost NaN \n", + "2kb_upstream_variant,polymorphic_pseudogene NaN \n", + "2kb_upstream_variant,scaRNA NaN \n", + "complex_substitution,NMD_transcript_variant,syn... NaN \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "complex_substitution,inframe_deletion,intron_va... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,inframe_insertion,intron_v... NaN \n", + "complex_substitution,synonymous_variant NaN \n", + "frameshift_elongation,start_lost NaN \n", + "frameshift_elongation,stop_retained_variant NaN \n", + "frameshift_truncation,stop_lost,stop_retained_v... NaN \n", + "inframe_deletion,start_lost NaN \n", + "inframe_deletion,stop_lost,3_prime_UTR_variant NaN \n", + "\n", + "extra_vcf_info.CLNSIG Pathogenic/Likely_pathogenic \n", + "so \n", + "intron_variant 5507.0 \n", + "synonymous_variant 417.0 \n", + "missense_variant 24049.0 \n", + "intron_variant,NMD_transcript_variant 1658.0 \n", + "NMD_transcript_variant,3_prime_UTR_variant 5524.0 \n", + "processed_transcript 4381.0 \n", + "3_prime_UTR_variant 432.0 \n", + "intron_variant,processed_transcript 1945.0 \n", + "intron_variant,lnc_RNA 6028.0 \n", + "2kb_downstream_variant 1588.0 \n", + "2kb_upstream_variant 1400.0 \n", + "2kb_downstream_variant,processed_transcript 1996.0 \n", + "5_prime_UTR_variant 851.0 \n", + "NMD_transcript_variant,synonymous_variant 126.0 \n", + "2kb_upstream_variant,processed_transcript 1315.0 \n", + "missense_variant,NMD_transcript_variant 1706.0 \n", + "2kb_upstream_variant,lnc_RNA 822.0 \n", + "2kb_downstream_variant,NMD_transcript_variant 984.0 \n", + "2kb_upstream_variant,NMD_transcript_variant 220.0 \n", + "2kb_downstream_variant,lnc_RNA 403.0 \n", + "lnc_RNA 502.0 \n", + "NMD_transcript_variant,5_prime_UTR_variant 47.0 \n", + "inframe_deletion 625.0 \n", + "inframe_insertion 75.0 \n", + "2kb_downstream_variant,miRNA 75.0 \n", + "2kb_upstream_variant,miRNA 96.0 \n", + "NSD_transcript 38.0 \n", + "2kb_downstream_variant,NSD_transcript 32.0 \n", + "complex_substitution,missense_variant 79.0 \n", + "intron_variant,splice_site_variant 9043.0 \n", + "stop_gained 13707.0 \n", + "2kb_upstream_variant,misc_RNA 32.0 \n", + "2kb_downstream_variant,misc_RNA 40.0 \n", + "intron_variant,NSD_transcript 3.0 \n", + "inframe_deletion,NMD_transcript_variant 16.0 \n", + "frameshift_truncation 8025.0 \n", + "2kb_upstream_variant,snoRNA 4.0 \n", + "frameshift_elongation 3428.0 \n", + "2kb_downstream_variant,snRNA 24.0 \n", + "missense_variant,start_lost 522.0 \n", + "2kb_upstream_variant,NSD_transcript 4.0 \n", + "NMD_transcript_variant,stop_gained 1292.0 \n", + "2kb_downstream_variant,snoRNA 10.0 \n", + "2kb_downstream_variant,polymorphic_pseudogene NaN \n", + "polymorphic_pseudogene 67.0 \n", + "stop_retained_variant NaN \n", + "inframe_insertion,NMD_transcript_variant 1.0 \n", + "splice_site_variant 335.0 \n", + "intron_variant,NMD_transcript_variant,splice_si... 2200.0 \n", + "2kb_upstream_variant,snRNA 13.0 \n", + "intron_variant,polymorphic_pseudogene 7.0 \n", + "polymorphic_pseudogene,3_prime_UTR_variant NaN \n", + "2kb_upstream_variant,ribozyme 11.0 \n", + "NMD_transcript_variant,stop_retained_variant 3.0 \n", + "missense_variant,start_lost,NMD_transcript_variant 233.0 \n", + "stop_lost 19.0 \n", + "miRNA 2.0 \n", + "complex_substitution,missense_variant,NMD_trans... 2.0 \n", + "frameshift_truncation,stop_gained 542.0 \n", + "complex_substitution,frameshift_elongation,intr... 154.0 \n", + "NMD_transcript_variant 20.0 \n", + "NSD_transcript,5_prime_UTR_variant NaN \n", + "snoRNA 2.0 \n", + "frameshift_elongation,NMD_transcript_variant 149.0 \n", + "complex_substitution 18.0 \n", + "NMD_transcript_variant,splice_site_variant 10.0 \n", + "ribozyme 10.0 \n", + "2kb_downstream_variant,3_prime_UTR_variant NaN \n", + "2kb_upstream_variant,rRNA NaN \n", + "frameshift_truncation,NMD_transcript_variant 381.0 \n", + "complex_substitution,inframe_insertion,intron_v... 38.0 \n", + "NMD_transcript_variant,stop_lost 13.0 \n", + "frameshift_truncation,stop_lost 2.0 \n", + "2kb_downstream_variant,scaRNA NaN \n", + "misc_RNA NaN \n", + "start_retained_variant 1.0 \n", + "complex_substitution,frameshift_truncation 176.0 \n", + "frameshift_elongation,stop_gained 184.0 \n", + "complex_substitution,frameshift_elongation 76.0 \n", + "exon_loss_variant,frameshift_truncation 2.0 \n", + "complex_substitution,stop_gained 27.0 \n", + "frameshift_truncation,NMD_transcript_variant,st... 38.0 \n", + "complex_substitution,frameshift_truncation,NMD_... 16.0 \n", + "frameshift_elongation,NMD_transcript_variant,st... 7.0 \n", + "exon_loss_variant,frameshift_truncation,NMD_tra... NaN \n", + "inframe_deletion,stop_gained 11.0 \n", + "exon_loss_variant,inframe_deletion NaN \n", + "polymorphic_pseudogene,5_prime_UTR_variant 24.0 \n", + "inframe_insertion,stop_gained 18.0 \n", + "complex_substitution,inframe_insertion,stop_gained NaN \n", + "start_lost,5_prime_UTR_variant 9.0 \n", + "complex_substitution,NMD_transcript_variant,sto... NaN \n", + "frameshift_elongation,stop_lost 2.0 \n", + "complex_substitution,inframe_insertion 3.0 \n", + "frameshift_truncation,start_lost 3.0 \n", + "complex_substitution,inframe_deletion 1.0 \n", + "exon_loss_variant,intron_variant NaN \n", + "2kb_upstream_variant,5_prime_UTR_variant NaN \n", + "complex_substitution,inframe_insertion,missense... NaN \n", + "intron_variant,start_lost NaN \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "2kb_upstream_variant,start_lost,transcript_abla... NaN \n", + "complex_substitution,frameshift_elongation,NMD_... 7.0 \n", + "inframe_deletion,NMD_transcript_variant,stop_ga... 1.0 \n", + "complex_substitution,frameshift_elongation,intr... NaN \n", + "2kb_downstream_variant,stop_lost,3_prime_UTR_va... 2.0 \n", + "complex_substitution,frameshift_truncation,intr... NaN \n", + "complex_substitution,inframe_deletion,missense_... 9.0 \n", + "complex_substitution,inframe_insertion,NMD_tran... NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "inframe_insertion,NMD_transcript_variant,stop_g... 1.0 \n", + "snRNA 3.0 \n", + "2kb_downstream_variant,NMD_transcript_variant,3... NaN \n", + "2kb_upstream_variant,NMD_transcript_variant,5_p... NaN \n", + "2kb_upstream_variant,start_lost,NMD_transcript_... NaN \n", + "complex_substitution,frameshift_elongation,intr... 15.0 \n", + "complex_substitution,inframe_deletion,stop_gained NaN \n", + "exon_loss_variant,intron_variant,NMD_transcript... NaN \n", + "frameshift_truncation,start_lost,NMD_transcript... NaN \n", + "intron_variant,start_lost,5_prime_UTR_variant NaN \n", + "2kb_downstream_variant,intron_variant,stop_lost... NaN \n", + "NMD_transcript_variant,stop_lost,3_prime_UTR_va... NaN \n", + "complex_substitution,start_lost,start_retained_... NaN \n", + "frameshift_elongation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,NMD_transcript_variant,st... NaN \n", + "frameshift_truncation,stop_lost,3_prime_UTR_var... 1.0 \n", + "inframe_deletion,stop_lost,stop_retained_varian... NaN \n", + "start_lost,NMD_transcript_variant,5_prime_UTR_v... 13.0 \n", + "2kb_downstream_variant,stop_lost NaN \n", + "2kb_upstream_variant,polymorphic_pseudogene NaN \n", + "2kb_upstream_variant,scaRNA NaN \n", + "complex_substitution,NMD_transcript_variant,syn... NaN \n", + "complex_substitution,frameshift_elongation,intr... 1.0 \n", + "complex_substitution,inframe_deletion,intron_va... 1.0 \n", + "complex_substitution,inframe_insertion,intron_v... 4.0 \n", + "complex_substitution,inframe_insertion,intron_v... 2.0 \n", + "complex_substitution,synonymous_variant NaN \n", + "frameshift_elongation,start_lost 3.0 \n", + "frameshift_elongation,stop_retained_variant NaN \n", + "frameshift_truncation,stop_lost,stop_retained_v... NaN \n", + "inframe_deletion,start_lost 2.0 \n", + "inframe_deletion,stop_lost,3_prime_UTR_variant 1.0 " + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.pivot_table(df, values='consequence', index='so', columns='extra_vcf_info.CLNSIG',\n", + " aggfunc='count').sort_values(by=['Benign','Pathogenic'], ascending=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Variant-transcript pairs shape = (185135, 5)\n", + "\n", + "Variants shape = (185135, 4)\n" + ] + } + ], + "source": [ + "df1 = df[['chrom','pos','ref_base','alt_base', 'class']].drop_duplicates()\n", + "print('\\nVariant-transcript pairs shape =', df1.shape)\n", + "print('\\nVariants shape =', df1[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "y = df1['class']\n", + "X = df1.drop(['class'], axis=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, stratify=y, random_state=41, test_size=0.20\n", + ")\n", + "#X_train, X_val, y_train, y_val = train_test_split(\n", + "# X_train, y_train, stratify=y_train, random_state=41, test_size=0.20\n", + "#)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train data shape = \n", + "Variant-transcripts: (148108,)\n", + "Variants: (148108, 4)\n", + "low_impact 107790\n", + "high_impact 40318\n", + "Name: class, dtype: int64\n", + "\n", + "Test data shape = \n", + "Variant-transcripts: (37027,)\n", + "Variants: (37027, 4)\n", + "low_impact 26947\n", + "high_impact 10080\n", + "Name: class, dtype: int64\n" + ] + } + ], + "source": [ + "print(f\"Train data shape = \\nVariant-transcripts: {y_train.shape}\\nVariants: {X_train[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape}\\n{y_train.value_counts()}\" )\n", + "\n", + "print(f\"\\nTest data shape = \\nVariant-transcripts: {y_test.shape}\\nVariants: {X_test[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape}\\n{y_test.value_counts()}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3596154 low_impact\n", + "5735385 low_impact\n", + "8319921 low_impact\n", + "3077210 low_impact\n", + "9015064 low_impact\n", + "Name: class, dtype: object" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_train.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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chromposref_basealt_base
3596154chr3142458979AG
5735385chr7138722001CA
8319921chr1291151736GA
3077210chr314146199TC
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" + ], + "text/plain": [ + " chrom pos ref_base alt_base\n", + "3596154 chr3 142458979 A G\n", + "5735385 chr7 138722001 C A\n", + "8319921 chr12 91151736 G A\n", + "3077210 chr3 14146199 T C\n", + "9015064 chr15 26567663 A G" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [], + "source": [ + "#y = df1['class']\n", + "#X = df1.drop(['class','aloft.affect','extra_vcf_info.CLNSIGCONF','extra_vcf_info.CLNSIG','extra_vcf_info.CLNREVSTAT','extra_vcf_info.CLNDN'], axis=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "X_train shape = (148108, 4)\n", + "\n", + "Variant-transcript pairs shape = (842659, 129)\n", + "\n", + "Variants shape = (148108, 4)\n", + "\n", + "Class counts shape = class \n", + "low_impact 600283\n", + "high_impact 242376\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "X_train_df = X_train.merge(df, on = ['chrom','pos','ref_base','alt_base'])\n", + "print('\\nX_train shape =', X_train.shape)\n", + "print('\\nVariant-transcript pairs shape =', X_train_df.shape)\n", + "print('\\nVariants shape =', X_train_df[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)\n", + "print('\\nClass counts shape =', X_train_df[['class']].value_counts())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "X_test shape = (37027, 4)\n", + "\n", + "Variant-transcript pairs shape = (208167, 129)\n", + "\n", + "Variants shape = (37027, 4)\n", + "\n", + "Class counts shape = class \n", + "low_impact 147809\n", + "high_impact 60358\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "X_test_df = X_test.merge(df, on = ['chrom','pos','ref_base','alt_base'])\n", + "print('\\nX_test shape =', X_test.shape)\n", + "print('\\nVariant-transcript pairs shape =', X_test_df.shape)\n", + "print('\\nVariants shape =', X_test_df[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)\n", + "print('\\nClass counts shape =', X_test_df[['class']].value_counts())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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chromposref_basealt_basetranscriptCADDCscapeClinpredDANNDANN_coding...ProveanphdsnpgrevelSIFTVESTdbscsnv.ada_scoredbscsnv.rf_scorevarity_rvarity_erclass
0chr633443750TGENST000002937484.4160.183598NaN0.530613NaN...NaNNaNNaN1.0NaNNaNNaNNaNNaNlow_impact
1chr633443750TGENST000004186004.4160.183598NaN0.530613NaN...NaNNaNNaN1.0NaNNaNNaNNaNNaNlow_impact
2chr633443750TGENST000004289824.4160.183598NaN0.530613NaN...NaNNaNNaN1.0NaNNaNNaNNaNNaNlow_impact
3chr633443750TGENST000004493724.4160.183598NaN0.530613NaN...NaNNaNNaN1.0NaNNaNNaNNaNNaNlow_impact
4chr633443750TGENST000006286464.4160.183598NaN0.530613NaN...NaNNaNNaN1.0NaNNaNNaNNaNNaNlow_impact
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5 rows ร— 32 columns

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" + ], + "text/plain": [ + " chrom pos ref_base alt_base transcript CADD Cscape \\\n", + "0 chr6 33443750 T G ENST00000293748 4.416 0.183598 \n", + "1 chr6 33443750 T G ENST00000418600 4.416 0.183598 \n", + "2 chr6 33443750 T G ENST00000428982 4.416 0.183598 \n", + "3 chr6 33443750 T G ENST00000449372 4.416 0.183598 \n", + "4 chr6 33443750 T G ENST00000628646 4.416 0.183598 \n", + "\n", + " Clinpred DANN DANN_coding ... Provean phdsnpg revel SIFT VEST \\\n", + "0 NaN 0.530613 NaN ... NaN NaN NaN 1.0 NaN \n", + "1 NaN 0.530613 NaN ... NaN NaN NaN 1.0 NaN \n", + "2 NaN 0.530613 NaN ... NaN NaN NaN 1.0 NaN \n", + "3 NaN 0.530613 NaN ... NaN NaN NaN 1.0 NaN \n", + "4 NaN 0.530613 NaN ... NaN NaN NaN 1.0 NaN \n", + "\n", + " dbscsnv.ada_score dbscsnv.rf_score varity_r varity_er class \n", + "0 NaN NaN NaN NaN low_impact \n", + "1 NaN NaN NaN NaN low_impact \n", + "2 NaN NaN NaN NaN low_impact \n", + "3 NaN NaN NaN NaN low_impact \n", + "4 NaN NaN NaN NaN low_impact \n", + "\n", + "[5 rows x 32 columns]" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "benchmark_columns = ['chrom','pos','ref_base','alt_base','transcript','cadd.phred','cscape.score','clinpred.score','dann.score','dann_coding.dann_coding_score','dgi.score','fathmm_xf_coding.fathmm_xf_coding_score','funseq2.score','linsight.value','lrt.lrt_score','loftool.loftool_score','metasvm.score','metalr.score','mutpred1.mutpred_general_score','mutpred_indel.score','mutation_assessor.score','mutationtaster.score','provean.score','phdsnpg.score','revel.score','sift.score','vest.score','dbscsnv.ada_score','dbscsnv.rf_score','varity_r.varity_r_loo', 'varity_r.varity_er_loo','class']\n", + "benchmark_df = X_test_df[benchmark_columns]\n", + "benchmark_df.columns = ['chrom','pos','ref_base','alt_base','transcript','CADD','Cscape','Clinpred','DANN','DANN_coding','DGI','fathmm_xf','funseq2','linsight','LRT','loftool','MetaSVM','MetaLR','Mutpred','Mutpred_indel','Mutation_assessor','Mutationtaster','Provean','phdsnpg','revel','SIFT','VEST','dbscsnv.ada_score','dbscsnv.rf_score','varity_r','varity_er','class']\n", + "benchmark_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "benchmark_df.to_csv('./processed/train_data_3_star/benchmark_test_df.csv.gz', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [], + "source": [ + "del X, y, X_train, X_test, y_train, y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "#print(f\"Train data shape = \\nVariant-transcripts: {y_train.shape}\\nVariants: {X_train[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape}\\n{y_train.value_counts()}\" )\n", + "\n", + "#print(f\"\\nTest data shape = \\nVariant-transcripts: {y_test.shape}\\nVariants: {X_train[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape}\\n{y_test.value_counts()}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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classhigh_impactlow_impact
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synonymous_variant710.0239012.0
intron_variant17140.0121139.0
missense_variant39052.058552.0
NMD_transcript_variant,3_prime_UTR_variant13645.025699.0
intron_variant,NMD_transcript_variant3263.019808.0
processed_transcript9027.019614.0
NMD_transcript_variant,synonymous_variant233.015624.0
intron_variant,lnc_RNA12415.014933.0
intron_variant,processed_transcript4344.010027.0
2kb_downstream_variant2832.09293.0
3_prime_UTR_variant878.08930.0
2kb_downstream_variant,processed_transcript4108.08688.0
2kb_upstream_variant2611.08654.0
5_prime_UTR_variant4450.06870.0
2kb_upstream_variant,processed_transcript2636.06493.0
2kb_downstream_variant,NMD_transcript_variant3104.05043.0
2kb_upstream_variant,lnc_RNA1511.04388.0
missense_variant,NMD_transcript_variant2971.04352.0
2kb_upstream_variant,NMD_transcript_variant545.02214.0
2kb_downstream_variant,lnc_RNA770.02156.0
lnc_RNA829.02089.0
inframe_deletion1052.01082.0
NMD_transcript_variant,5_prime_UTR_variant187.0940.0
inframe_insertion165.0680.0
2kb_downstream_variant,miRNA176.0549.0
NSD_transcript471.0391.0
2kb_upstream_variant,miRNA225.0385.0
2kb_downstream_variant,NSD_transcript764.0300.0
2kb_downstream_variant,misc_RNA66.0208.0
complex_substitution,missense_variant131.0203.0
2kb_upstream_variant,misc_RNA50.0148.0
2kb_downstream_variant,snRNA39.0147.0
stop_gained37575.0140.0
polymorphic_pseudogene121.0132.0
intron_variant,splice_site_variant19292.0119.0
stop_retained_variant1.0102.0
NMD_transcript_variant,stop_gained3222.0101.0
intron_variant,NSD_transcript8.090.0
frameshift_elongation11674.087.0
inframe_deletion,NMD_transcript_variant37.083.0
2kb_upstream_variant,NSD_transcript9.080.0
2kb_downstream_variant,snoRNA31.060.0
2kb_upstream_variant,snoRNA14.057.0
inframe_insertion,NMD_transcript_variant6.054.0
frameshift_truncation25753.051.0
missense_variant,start_lost851.048.0
2kb_upstream_variant,snRNA23.048.0
splice_site_variant703.047.0
complex_substitution,synonymous_variantNaN35.0
intron_variant,NMD_transcript_variant,splice_site_variant5034.030.0
2kb_downstream_variant,polymorphic_pseudogeneNaN28.0
2kb_upstream_variant,ribozyme20.027.0
stop_lost48.025.0
intron_variant,polymorphic_pseudogene7.025.0
NMD_transcript_variant35.018.0
NMD_transcript_variant,stop_lost29.018.0
complex_substitution,inframe_insertion,intron_variant97.017.0
polymorphic_pseudogene,3_prime_UTR_variantNaN15.0
complex_substitution,inframe_insertion11.013.0
misc_RNANaN12.0
NMD_transcript_variant,stop_retained_variant2.011.0
complex_substitution,frameshift_elongation,intron_variant282.09.0
miRNA3.09.0
frameshift_truncation,stop_gained1896.08.0
missense_variant,start_lost,NMD_transcript_variant342.07.0
polymorphic_pseudogene,5_prime_UTR_variant42.07.0
complex_substitution,missense_variant,NMD_transcript_variant10.07.0
complex_substitution30.06.0
frameshift_truncation,NMD_transcript_variant1420.05.0
2kb_upstream_variant,polymorphic_pseudogeneNaN5.0
snoRNA2.04.0
2kb_downstream_variant,3_prime_UTR_variantNaN4.0
complex_substitution,NMD_transcript_variant,synonymous_variantNaN4.0
ribozyme14.03.0
NSD_transcript,5_prime_UTR_variantNaN3.0
frameshift_elongation,stop_retained_variantNaN3.0
frameshift_elongation,NMD_transcript_variant612.02.0
inframe_insertion,stop_gained32.02.0
NMD_transcript_variant,splice_site_variant24.02.0
frameshift_truncation,stop_lost4.02.0
2kb_upstream_variant,rRNANaN2.0
frameshift_elongation,stop_gained742.01.0
complex_substitution,frameshift_elongation,intron_variant,NMD_transcript_variant16.01.0
complex_substitution,inframe_deletion,intron_variant1.01.0
start_retained_variant1.01.0
2kb_downstream_variant,scaRNANaN1.0
complex_substitution,frameshift_truncation842.0NaN
complex_substitution,frameshift_elongation331.0NaN
exon_loss_variant,frameshift_truncation122.0NaN
complex_substitution,stop_gained120.0NaN
frameshift_truncation,NMD_transcript_variant,stop_gained87.0NaN
complex_substitution,frameshift_truncation,NMD_transcript_variant52.0NaN
frameshift_elongation,NMD_transcript_variant,stop_gained43.0NaN
inframe_deletion,stop_gained40.0NaN
exon_loss_variant,frameshift_truncation,NMD_transcript_variant36.0NaN
exon_loss_variant,inframe_deletion24.0NaN
start_lost,5_prime_UTR_variant20.0NaN
start_lost,NMD_transcript_variant,5_prime_UTR_variant14.0NaN
frameshift_truncation,start_lost12.0NaN
complex_substitution,NMD_transcript_variant,stop_gained11.0NaN
complex_substitution,inframe_deletion10.0NaN
complex_substitution,inframe_insertion,stop_gained10.0NaN
exon_loss_variant,intron_variant8.0NaN
frameshift_elongation,stop_lost8.0NaN
2kb_upstream_variant,5_prime_UTR_variant7.0NaN
complex_substitution,inframe_deletion,missense_variant6.0NaN
complex_substitution,inframe_insertion,missense_variant6.0NaN
inframe_deletion,NMD_transcript_variant,stop_gained6.0NaN
intron_variant,start_lost6.0NaN
snRNA6.0NaN
2kb_upstream_variant,start_lost,NMD_transcript_variant,stop_lost,transcript_ablation,3_prime_UTR_variant,5_prime_UTR_variant5.0NaN
2kb_upstream_variant,start_lost,transcript_ablation,5_prime_UTR_variant5.0NaN
complex_substitution,frameshift_truncation,intron_variant5.0NaN
2kb_downstream_variant,stop_lost,3_prime_UTR_variant4.0NaN
complex_substitution,frameshift_elongation,NMD_transcript_variant4.0NaN
complex_substitution,frameshift_elongation,intron_variant,NMD_transcript_variant,stop_lost4.0NaN
complex_substitution,inframe_insertion,intron_variant,NMD_transcript_variant4.0NaN
complex_substitution,inframe_insertion,NMD_transcript_variant,stop_gained3.0NaN
frameshift_elongation,NMD_transcript_variant,stop_retained_variant3.0NaN
frameshift_elongation,start_lost3.0NaN
inframe_deletion,stop_lost,3_prime_UTR_variant3.0NaN
inframe_insertion,NMD_transcript_variant,stop_gained3.0NaN
2kb_downstream_variant,NMD_transcript_variant,3_prime_UTR_variant2.0NaN
2kb_upstream_variant,NMD_transcript_variant,5_prime_UTR_variant2.0NaN
2kb_upstream_variant,scaRNA2.0NaN
2kb_upstream_variant,start_lost,NMD_transcript_variant,transcript_ablation,5_prime_UTR_variant2.0NaN
complex_substitution,inframe_deletion,stop_gained2.0NaN
complex_substitution,start_lost,start_retained_variant2.0NaN
exon_loss_variant,intron_variant,NMD_transcript_variant2.0NaN
frameshift_truncation,start_lost,NMD_transcript_variant2.0NaN
frameshift_truncation,stop_lost,3_prime_UTR_variant2.0NaN
inframe_deletion,start_lost2.0NaN
intron_variant,start_lost,5_prime_UTR_variant2.0NaN
complex_substitution,frameshift_elongation,intron_variant,start_lost,start_retained_variant,synonymous_variant1.0NaN
frameshift_elongation,NMD_transcript_variant,stop_lost1.0NaN
frameshift_truncation,NMD_transcript_variant,stop_lost1.0NaN
frameshift_truncation,NMD_transcript_variant,stop_lost,stop_retained_variant,3_prime_UTR_variant1.0NaN
inframe_deletion,stop_lost,stop_retained_variant,3_prime_UTR_variant1.0NaN
\n", + "
" + ], + "text/plain": [ + "class high_impact low_impact\n", + "so \n", + "synonymous_variant 710.0 239012.0\n", + "intron_variant 17140.0 121139.0\n", + "missense_variant 39052.0 58552.0\n", + "NMD_transcript_variant,3_prime_UTR_variant 13645.0 25699.0\n", + "intron_variant,NMD_transcript_variant 3263.0 19808.0\n", + "processed_transcript 9027.0 19614.0\n", + "NMD_transcript_variant,synonymous_variant 233.0 15624.0\n", + "intron_variant,lnc_RNA 12415.0 14933.0\n", + "intron_variant,processed_transcript 4344.0 10027.0\n", + "2kb_downstream_variant 2832.0 9293.0\n", + "3_prime_UTR_variant 878.0 8930.0\n", + "2kb_downstream_variant,processed_transcript 4108.0 8688.0\n", + "2kb_upstream_variant 2611.0 8654.0\n", + "5_prime_UTR_variant 4450.0 6870.0\n", + "2kb_upstream_variant,processed_transcript 2636.0 6493.0\n", + "2kb_downstream_variant,NMD_transcript_variant 3104.0 5043.0\n", + "2kb_upstream_variant,lnc_RNA 1511.0 4388.0\n", + "missense_variant,NMD_transcript_variant 2971.0 4352.0\n", + "2kb_upstream_variant,NMD_transcript_variant 545.0 2214.0\n", + "2kb_downstream_variant,lnc_RNA 770.0 2156.0\n", + "lnc_RNA 829.0 2089.0\n", + "inframe_deletion 1052.0 1082.0\n", + "NMD_transcript_variant,5_prime_UTR_variant 187.0 940.0\n", + "inframe_insertion 165.0 680.0\n", + "2kb_downstream_variant,miRNA 176.0 549.0\n", + "NSD_transcript 471.0 391.0\n", + "2kb_upstream_variant,miRNA 225.0 385.0\n", + "2kb_downstream_variant,NSD_transcript 764.0 300.0\n", + "2kb_downstream_variant,misc_RNA 66.0 208.0\n", + "complex_substitution,missense_variant 131.0 203.0\n", + "2kb_upstream_variant,misc_RNA 50.0 148.0\n", + "2kb_downstream_variant,snRNA 39.0 147.0\n", + "stop_gained 37575.0 140.0\n", + "polymorphic_pseudogene 121.0 132.0\n", + "intron_variant,splice_site_variant 19292.0 119.0\n", + "stop_retained_variant 1.0 102.0\n", + "NMD_transcript_variant,stop_gained 3222.0 101.0\n", + "intron_variant,NSD_transcript 8.0 90.0\n", + "frameshift_elongation 11674.0 87.0\n", + "inframe_deletion,NMD_transcript_variant 37.0 83.0\n", + "2kb_upstream_variant,NSD_transcript 9.0 80.0\n", + "2kb_downstream_variant,snoRNA 31.0 60.0\n", + "2kb_upstream_variant,snoRNA 14.0 57.0\n", + "inframe_insertion,NMD_transcript_variant 6.0 54.0\n", + "frameshift_truncation 25753.0 51.0\n", + "missense_variant,start_lost 851.0 48.0\n", + "2kb_upstream_variant,snRNA 23.0 48.0\n", + "splice_site_variant 703.0 47.0\n", + "complex_substitution,synonymous_variant NaN 35.0\n", + "intron_variant,NMD_transcript_variant,splice_si... 5034.0 30.0\n", + "2kb_downstream_variant,polymorphic_pseudogene NaN 28.0\n", + "2kb_upstream_variant,ribozyme 20.0 27.0\n", + "stop_lost 48.0 25.0\n", + "intron_variant,polymorphic_pseudogene 7.0 25.0\n", + "NMD_transcript_variant 35.0 18.0\n", + "NMD_transcript_variant,stop_lost 29.0 18.0\n", + "complex_substitution,inframe_insertion,intron_v... 97.0 17.0\n", + "polymorphic_pseudogene,3_prime_UTR_variant NaN 15.0\n", + "complex_substitution,inframe_insertion 11.0 13.0\n", + "misc_RNA NaN 12.0\n", + "NMD_transcript_variant,stop_retained_variant 2.0 11.0\n", + "complex_substitution,frameshift_elongation,intr... 282.0 9.0\n", + "miRNA 3.0 9.0\n", + "frameshift_truncation,stop_gained 1896.0 8.0\n", + "missense_variant,start_lost,NMD_transcript_variant 342.0 7.0\n", + "polymorphic_pseudogene,5_prime_UTR_variant 42.0 7.0\n", + "complex_substitution,missense_variant,NMD_trans... 10.0 7.0\n", + "complex_substitution 30.0 6.0\n", + "frameshift_truncation,NMD_transcript_variant 1420.0 5.0\n", + "2kb_upstream_variant,polymorphic_pseudogene NaN 5.0\n", + "snoRNA 2.0 4.0\n", + "2kb_downstream_variant,3_prime_UTR_variant NaN 4.0\n", + "complex_substitution,NMD_transcript_variant,syn... 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classhigh_impactlow_impact
so
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intron_variant4323.029861.0
missense_variant9595.014398.0
NMD_transcript_variant,3_prime_UTR_variant3290.06329.0
processed_transcript2138.04814.0
intron_variant,NMD_transcript_variant862.04696.0
intron_variant,lnc_RNA3221.04231.0
NMD_transcript_variant,synonymous_variant50.03823.0
intron_variant,processed_transcript1026.02390.0
2kb_upstream_variant680.02289.0
2kb_downstream_variant,processed_transcript991.02175.0
3_prime_UTR_variant227.02166.0
2kb_downstream_variant760.02134.0
2kb_upstream_variant,processed_transcript722.01686.0
5_prime_UTR_variant1026.01672.0
2kb_downstream_variant,NMD_transcript_variant757.01277.0
missense_variant,NMD_transcript_variant740.01021.0
2kb_upstream_variant,lnc_RNA452.0975.0
2kb_upstream_variant,NMD_transcript_variant151.0637.0
lnc_RNA140.0498.0
2kb_downstream_variant,lnc_RNA212.0460.0
inframe_deletion268.0326.0
NMD_transcript_variant,5_prime_UTR_variant42.0208.0
inframe_insertion20.0168.0
2kb_downstream_variant,miRNA42.0108.0
2kb_upstream_variant,miRNA67.0102.0
2kb_downstream_variant,NSD_transcript193.087.0
NSD_transcript114.081.0
2kb_downstream_variant,misc_RNA14.058.0
2kb_upstream_variant,misc_RNA20.049.0
stop_gained9535.047.0
intron_variant,splice_site_variant5030.037.0
complex_substitution,missense_variant39.036.0
stop_retained_variantNaN29.0
2kb_downstream_variant,snRNA4.027.0
polymorphic_pseudogene54.024.0
2kb_upstream_variant,NSD_transcript3.024.0
inframe_deletion,NMD_transcript_variant3.019.0
2kb_downstream_variant,snoRNA14.018.0
frameshift_truncation5952.017.0
intron_variant,NSD_transcript2.017.0
NMD_transcript_variant,stop_gained801.016.0
missense_variant,start_lost261.014.0
2kb_upstream_variant,snoRNA3.013.0
intron_variant,NMD_transcript_variant,splice_site_variant1202.012.0
frameshift_elongation3074.010.0
splice_site_variant183.010.0
miRNA2.010.0
inframe_insertion,NMD_transcript_variant3.09.0
frameshift_truncation,stop_gained453.08.0
complex_substitution,synonymous_variantNaN7.0
NMD_transcript_variant14.06.0
complex_substitution,missense_variant,NMD_transcript_variant10.05.0
2kb_upstream_variant,ribozyme7.05.0
2kb_upstream_variant,snRNA1.05.0
frameshift_truncation,stop_lost,stop_retained_variant,3_prime_UTR_variantNaN5.0
polymorphic_pseudogene,5_prime_UTR_variant16.04.0
polymorphic_pseudogene,3_prime_UTR_variantNaN4.0
missense_variant,start_lost,NMD_transcript_variant70.03.0
misc_RNANaN3.0
frameshift_elongation,NMD_transcript_variant170.02.0
NMD_transcript_variant,stop_lost5.02.0
2kb_downstream_variant,stop_lostNaN2.0
NSD_transcript,5_prime_UTR_variantNaN2.0
NMD_transcript_variant,stop_retained_variant1.01.0
ribozyme1.01.0
2kb_upstream_variant,rRNANaN1.0
snoRNANaN1.0
start_retained_variantNaN1.0
frameshift_truncation,NMD_transcript_variant312.0NaN
complex_substitution,frameshift_truncation180.0NaN
frameshift_elongation,stop_gained163.0NaN
complex_substitution,frameshift_elongation74.0NaN
complex_substitution,frameshift_elongation,intron_variant58.0NaN
frameshift_truncation,NMD_transcript_variant,stop_gained48.0NaN
exon_loss_variant,frameshift_truncation47.0NaN
complex_substitution,stop_gained27.0NaN
complex_substitution,inframe_insertion,intron_variant24.0NaN
NMD_transcript_variant,splice_site_variant17.0NaN
complex_substitution15.0NaN
complex_substitution,frameshift_truncation,NMD_transcript_variant15.0NaN
frameshift_elongation,NMD_transcript_variant,stop_gained10.0NaN
exon_loss_variant,inframe_deletion9.0NaN
complex_substitution,frameshift_elongation,NMD_transcript_variant8.0NaN
complex_substitution,frameshift_elongation,intron_variant,NMD_transcript_variant7.0NaN
complex_substitution,inframe_deletion,missense_variant7.0NaN
inframe_deletion,stop_gained7.0NaN
stop_lost7.0NaN
exon_loss_variant,frameshift_truncation,NMD_transcript_variant6.0NaN
inframe_insertion,stop_gained6.0NaN
frameshift_elongation,stop_lost5.0NaN
intron_variant,polymorphic_pseudogene4.0NaN
complex_substitution,inframe_deletion3.0NaN
snRNA3.0NaN
complex_substitution,inframe_insertion2.0NaN
complex_substitution,inframe_insertion,intron_variant,NMD_transcript_variant2.0NaN
complex_substitution,inframe_insertion,intron_variant,missense_variant2.0NaN
complex_substitution,inframe_insertion,stop_gained2.0NaN
frameshift_truncation,stop_lost2.0NaN
2kb_downstream_variant,intron_variant,stop_lost,3_prime_UTR_variant1.0NaN
2kb_downstream_variant,stop_lost,3_prime_UTR_variant1.0NaN
NMD_transcript_variant,stop_lost,3_prime_UTR_variant1.0NaN
frameshift_truncation,start_lost1.0NaN
inframe_insertion,NMD_transcript_variant,stop_gained1.0NaN
start_lost,5_prime_UTR_variant1.0NaN
\n", + "
" + ], + "text/plain": [ + "class high_impact low_impact\n", + "so \n", + "synonymous_variant 224.0 58633.0\n", + "intron_variant 4323.0 29861.0\n", + "missense_variant 9595.0 14398.0\n", + "NMD_transcript_variant,3_prime_UTR_variant 3290.0 6329.0\n", + "processed_transcript 2138.0 4814.0\n", + "intron_variant,NMD_transcript_variant 862.0 4696.0\n", + "intron_variant,lnc_RNA 3221.0 4231.0\n", + "NMD_transcript_variant,synonymous_variant 50.0 3823.0\n", + "intron_variant,processed_transcript 1026.0 2390.0\n", + "2kb_upstream_variant 680.0 2289.0\n", + "2kb_downstream_variant,processed_transcript 991.0 2175.0\n", + "3_prime_UTR_variant 227.0 2166.0\n", + "2kb_downstream_variant 760.0 2134.0\n", + "2kb_upstream_variant,processed_transcript 722.0 1686.0\n", + "5_prime_UTR_variant 1026.0 1672.0\n", + "2kb_downstream_variant,NMD_transcript_variant 757.0 1277.0\n", + "missense_variant,NMD_transcript_variant 740.0 1021.0\n", + "2kb_upstream_variant,lnc_RNA 452.0 975.0\n", + "2kb_upstream_variant,NMD_transcript_variant 151.0 637.0\n", + "lnc_RNA 140.0 498.0\n", + "2kb_downstream_variant,lnc_RNA 212.0 460.0\n", + "inframe_deletion 268.0 326.0\n", + "NMD_transcript_variant,5_prime_UTR_variant 42.0 208.0\n", + "inframe_insertion 20.0 168.0\n", + "2kb_downstream_variant,miRNA 42.0 108.0\n", + "2kb_upstream_variant,miRNA 67.0 102.0\n", + "2kb_downstream_variant,NSD_transcript 193.0 87.0\n", + "NSD_transcript 114.0 81.0\n", + "2kb_downstream_variant,misc_RNA 14.0 58.0\n", + "2kb_upstream_variant,misc_RNA 20.0 49.0\n", + "stop_gained 9535.0 47.0\n", + "intron_variant,splice_site_variant 5030.0 37.0\n", + "complex_substitution,missense_variant 39.0 36.0\n", + "stop_retained_variant NaN 29.0\n", + "2kb_downstream_variant,snRNA 4.0 27.0\n", + "polymorphic_pseudogene 54.0 24.0\n", + "2kb_upstream_variant,NSD_transcript 3.0 24.0\n", + "inframe_deletion,NMD_transcript_variant 3.0 19.0\n", + "2kb_downstream_variant,snoRNA 14.0 18.0\n", + "frameshift_truncation 5952.0 17.0\n", + "intron_variant,NSD_transcript 2.0 17.0\n", + "NMD_transcript_variant,stop_gained 801.0 16.0\n", + "missense_variant,start_lost 261.0 14.0\n", + "2kb_upstream_variant,snoRNA 3.0 13.0\n", + "intron_variant,NMD_transcript_variant,splice_si... 1202.0 12.0\n", + "frameshift_elongation 3074.0 10.0\n", + "splice_site_variant 183.0 10.0\n", + "miRNA 2.0 10.0\n", + "inframe_insertion,NMD_transcript_variant 3.0 9.0\n", + "frameshift_truncation,stop_gained 453.0 8.0\n", + "complex_substitution,synonymous_variant NaN 7.0\n", + "NMD_transcript_variant 14.0 6.0\n", + "complex_substitution,missense_variant,NMD_trans... 10.0 5.0\n", + "2kb_upstream_variant,ribozyme 7.0 5.0\n", + "2kb_upstream_variant,snRNA 1.0 5.0\n", + "frameshift_truncation,stop_lost,stop_retained_v... NaN 5.0\n", + "polymorphic_pseudogene,5_prime_UTR_variant 16.0 4.0\n", + "polymorphic_pseudogene,3_prime_UTR_variant NaN 4.0\n", + "missense_variant,start_lost,NMD_transcript_variant 70.0 3.0\n", + "misc_RNA NaN 3.0\n", + "frameshift_elongation,NMD_transcript_variant 170.0 2.0\n", + "NMD_transcript_variant,stop_lost 5.0 2.0\n", + "2kb_downstream_variant,stop_lost NaN 2.0\n", + "NSD_transcript,5_prime_UTR_variant NaN 2.0\n", + "NMD_transcript_variant,stop_retained_variant 1.0 1.0\n", + "ribozyme 1.0 1.0\n", + "2kb_upstream_variant,rRNA NaN 1.0\n", + "snoRNA NaN 1.0\n", + "start_retained_variant NaN 1.0\n", + "frameshift_truncation,NMD_transcript_variant 312.0 NaN\n", + "complex_substitution,frameshift_truncation 180.0 NaN\n", + "frameshift_elongation,stop_gained 163.0 NaN\n", + "complex_substitution,frameshift_elongation 74.0 NaN\n", + "complex_substitution,frameshift_elongation,intr... 58.0 NaN\n", + "frameshift_truncation,NMD_transcript_variant,st... 48.0 NaN\n", + "exon_loss_variant,frameshift_truncation 47.0 NaN\n", + "complex_substitution,stop_gained 27.0 NaN\n", + "complex_substitution,inframe_insertion,intron_v... 24.0 NaN\n", + "NMD_transcript_variant,splice_site_variant 17.0 NaN\n", + "complex_substitution 15.0 NaN\n", + "complex_substitution,frameshift_truncation,NMD_... 15.0 NaN\n", + "frameshift_elongation,NMD_transcript_variant,st... 10.0 NaN\n", + "exon_loss_variant,inframe_deletion 9.0 NaN\n", + "complex_substitution,frameshift_elongation,NMD_... 8.0 NaN\n", + "complex_substitution,frameshift_elongation,intr... 7.0 NaN\n", + "complex_substitution,inframe_deletion,missense_... 7.0 NaN\n", + "inframe_deletion,stop_gained 7.0 NaN\n", + "stop_lost 7.0 NaN\n", + "exon_loss_variant,frameshift_truncation,NMD_tra... 6.0 NaN\n", + "inframe_insertion,stop_gained 6.0 NaN\n", + "frameshift_elongation,stop_lost 5.0 NaN\n", + "intron_variant,polymorphic_pseudogene 4.0 NaN\n", + "complex_substitution,inframe_deletion 3.0 NaN\n", + "snRNA 3.0 NaN\n", + "complex_substitution,inframe_insertion 2.0 NaN\n", + "complex_substitution,inframe_insertion,intron_v... 2.0 NaN\n", + "complex_substitution,inframe_insertion,intron_v... 2.0 NaN\n", + "complex_substitution,inframe_insertion,stop_gained 2.0 NaN\n", + "frameshift_truncation,stop_lost 2.0 NaN\n", + "2kb_downstream_variant,intron_variant,stop_lost... 1.0 NaN\n", + "2kb_downstream_variant,stop_lost,3_prime_UTR_va... 1.0 NaN\n", + "NMD_transcript_variant,stop_lost,3_prime_UTR_va... 1.0 NaN\n", + "frameshift_truncation,start_lost 1.0 NaN\n", + "inframe_insertion,NMD_transcript_variant,stop_g... 1.0 NaN\n", + "start_lost,5_prime_UTR_variant 1.0 NaN" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.pivot_table(X_test_df, values='consequence', index='so', columns='class',\n", + " aggfunc='count').sort_values(by=['low_impact','high_impact'], ascending=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Variant-transcript pairs shape = (842659, 124)\n" + ] + } + ], + "source": [ + "X_train_df = X_train_df.drop(['aloft.affect','extra_vcf_info.CLNSIGCONF','extra_vcf_info.CLNSIG','extra_vcf_info.CLNREVSTAT','extra_vcf_info.CLNDN'], axis=1)\n", + "\n", + "print('\\nVariant-transcript pairs shape =', X_train_df.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "chrom 0\n", + "pos 0\n", + "ref_base 0\n", + "alt_base 0\n", + "transcript 0\n", + "gene 0\n", + "consequence 0\n", + "protein_hgvs 391489\n", + "cdna_hgvs 108953\n", + "coding 188081\n", + "aloft.tolerant 830706\n", + "aloft.recessive 830706\n", + "aloft.dominant 830706\n", + "aloft.pred 830706\n", + "aloft.conf 830706\n", + "cadd.phred 80466\n", + "cgd.inheritance 666292\n", + "chasmplus.score 816607\n", + "chasmplus.pval 816607\n", + "civic.molecular_profile_score 842164\n", + "cosmic.variant_count 800647\n", + "cosmic_gene.occurrences 3279\n", + "cscape.score 114979\n", + "cgc.class 534204\n", + "cgc.inheritance 531822\n", + "cancer_genome_interpreter.resistant 842527\n", + "cancer_genome_interpreter.responsive 842527\n", + "cancer_genome_interpreter.other 842527\n", + "ccre_screen._group 692123\n", + "ccre_screen.bound 761314\n", + "clingen.disease 351201\n", + "clingen.classification 351201\n", + "clinpred.score 664231\n", + "dann.score 263498\n", + "dann_coding.dann_coding_score 563706\n", + "dgi.interaction 742575\n", + "dgi.score 352867\n", + "ensembl_regulatory_build.region 613684\n", + "ess_gene.indispensability_score 180487\n", + "exac_gene.exac_pli 19741\n", + "exac_gene.exac_pnull 19741\n", + "exac_gene.exac_del_score 114966\n", + "exac_gene.exac_dup_score 114966\n", + "exac_gene.exac_cnv_score 114966\n", + "exac_gene.exac_cnv_flag 114966\n", + "fathmm.fathmm_score 773967\n", + "fathmm_xf_coding.fathmm_xf_coding_score 615028\n", + "funseq2.score 82750\n", + "gerp.gerp_rs 561873\n", + "ghis.ghis 16566\n", + "gtex.gtex_tissue 829517\n", + "gwas_catalog.pval 842491\n", + "genehancer.feature_name 605618\n", + "genehancer.score 605618\n", + "linsight.value 661204\n", + "lrt.lrt_score 631147\n", + "lrt.lrt_omega 631147\n", + "loftool.loftool_score 64734\n", + "mavedb.score 842412\n", + "metalr.score 670163\n", + "metasvm.score 670163\n", + "mutpred1.mutpred_general_score 800895\n", + "mutpred_indel.score 841226\n", + "mutation_assessor.score 779483\n", + "mutationtaster.score 694289\n", + "mutationtaster.prediction 694289\n", + "mutationtaster.model 694289\n", + "ncbigene.entrez 1792\n", + "ndex_chd.numhit 802430\n", + "ndex.numhit 484533\n", + "ndex_signor.numhit 703860\n", + "omim.omim_id 680595\n", + "prec.prec 67635\n", + "prec.stat 691168\n", + "provean.score 777271\n", + "pangalodb.sensitivity 485612\n", + "pangalodb.specificity 485612\n", + "phdsnpg.score 593961\n", + "phastcons.phastcons100_vert 561260\n", + "phastcons.phastcons30_mamm 561260\n", + "phastcons.phastcons17way_primate 561260\n", + "phylop.phylop100_vert 561260\n", + "phylop.phylop30_mamm 561260\n", + "phylop.phylop17_primate 561260\n", + "polyphen2.hdiv_rank 791712\n", + "polyphen2.hvar_rank 791712\n", + "revel.score 777190\n", + "rvis.rvis_evs 16833\n", + "repeat.repeatclass 820299\n", + "sift.score 576039\n", + "sift.med 576039\n", + "sift.confidence 576039\n", + "sift.seqs 576039\n", + "segway.mean_score 687907\n", + "siphy.logodds_rank 562293\n", + "spliceai.ds_ag 762562\n", + "spliceai.ds_al 762562\n", + "spliceai.ds_dg 762562\n", + "spliceai.ds_dl 762562\n", + "spliceai.dp_ag 762562\n", + "spliceai.dp_al 762562\n", + "spliceai.dp_dg 762562\n", + "spliceai.dp_dl 762562\n", + "uniprot.acc 280\n", + "varity_r.varity_r_loo 691527\n", + "varity_r.varity_er_loo 691527\n", + "vest.score 733950\n", + "dbsnp.rsid 64954\n", + "dbscsnv.ada_score 759197\n", + "dbscsnv.rf_score 760175\n", + "gnomad.af 270494\n", + "gnomad_gene.oe_lof 382537\n", + "gnomad_gene.oe_mis 382360\n", + "gnomad_gene.oe_syn 382360\n", + "gnomad_gene.lof_z 382537\n", + "gnomad_gene.mis_z 382360\n", + "gnomad_gene.syn_z 382360\n", + "gnomad_gene.pLI 382537\n", + "gnomad_gene.pRec 382537\n", + "gnomad_gene.pNull 382537\n", + "gnomad3.af 318266\n", + "phi.phi 11467\n", + "so 0\n", + "class 0\n", + "dtype: int64" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check how many columns are null\n", + "X_train_df.isnull().sum(axis = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Variant-transcript pairs shape = (842659, 124)\n", + "\n", + "Variants shape = (148108, 4)\n" + ] + } + ], + "source": [ + "print('\\nVariant-transcript pairs shape =', X_train_df.shape)\n", + "print('\\nVariants shape =', X_train_df[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Variant-transcript pairs shape = (842659, 124)\n", + "\n", + "Variants shape = (148108, 4)\n" + ] + } + ], + "source": [ + "X_train_df.dropna(axis=1, how='all', inplace=True)\n", + "print('\\nVariant-transcript pairs shape =', X_train_df.shape)\n", + "print('\\nVariants shape =', X_train_df[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['transcript',\n", + " 'gene',\n", + " 'consequence',\n", + " 'protein_hgvs',\n", + " 'cdna_hgvs',\n", + " 'chrom',\n", + " 'pos',\n", + " 'ref_base',\n", + " 'alt_base',\n", + " 'clingen.disease',\n", + " 'clingen.classification',\n", + " 'ncbigene.entrez',\n", + " 'omim.omim_id',\n", + " 'uniprot.acc',\n", + " 'dbsnp.rsid']" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config_dict['train_cols']" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [], + "source": [ + "# Drop variant info columns so we can perform one-hot encoding\n", + "var = X_train_df[config_dict['train_cols']]\n", + "X_train_df = X_train_df.drop(config_dict['train_cols'], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "X_train_df.groupby('consequence').apply(lambda x: x.isnull().mean() * 100).to_csv(f\"/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/processed/train_data_3_star/missing.csv\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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/Fm3Peu8X77V9yeHkqTXHe4y2X6TikuL+gOrCiBgCrA3cXP27MaX0Zqo/Lfox8lqKW8b8mTrc3t+LrT2HpDx99wPyKDxJajOnQ0pSuWxF/g/ordVfOoDLgF8BB0bECal96wNFcZ/a2bdW7Z9SmhwRI8hfwh+NiL+Sp0Q8kKquGlf4bHF/SyuOv0Fxf0edY06MiEfIU0jWJF/lsNqDtfuQv3QCrNLMui4fL+7XAuY2JbKyXtAc/+FvrZTSc23c5WxgL/I0lCcj4m/kc1QZBfIOs0flze3YbwK1V5K8KyK+CNxDvlLjIVRN5Sm+IFV/SZoKXB0R95Of/30j4vRUNWUn5Wlxm9UeP2avHfU58v9tbiE/l7uSpyieSR51VHsVyfYaU6fspeJ+QHVh8YXtnXk5WESsQZ7C1RPYp865rvxRs3uxvTKtbXJEfI38mh5CnrZ1+dyOl1L6TU3RM8DxEfEqOdj5BbOnMVb2+V7NPmOAvSLimuK43ydfjKFS/x1ymPnNmsfaZC2qiDiLPJX7B+SRYWcAf4+INVJKH8ztsbRV8RlTG5K3STEN7hZyIPjt1PorQ1afx9+nlE6p2vZ/EdGbPE3xR1R9jkXENuR1ksaQw7ux5KDhCPI0uh0jYotUNUU4pXQJs4OVar8q7r9XTGu7mjyd7tvkYPL3EfFKmvMqki2p936prKFWO9UNZgf5K86l3afIIw33jYhVyOHtPcCYNOcacpeSH9tTEXEF+bPn3pTSW3PvfhNzPJaU0ocR8QZN3/ufIAfIY+qFUUU/D2nDca8jf47sHxHHptlXva2EYsNrdyiWCjiM/N4fyJzf+wYyb394qaetvxfbcg6rvU2+wIYktZkhmCR1Pq+R/4NYO3KiNSp/pR9eXZhSersIlvYgBwPXtKPt5Yv7tn5pICIWJY9yaO3+e5O/6O0HnFyUfVB8of5+ygvuw+xRSq0Z/dS/uG/uP/2V8iXqbHu9TlkluNprLsftM5ftMPuv4Iu2om6HSClNiYhNySOp9iSPEnyX/MXkOPIX6Y/IXzbae4yPIuIicgi2OfXXs6nd56UikNu/2KfFdWtq146KiG+QX6t7p2J9pIhYD/hORJxUJ0htj0l1yioBQ/cOaH+WIgC7k/z+2SeldGML/Xm9NnBJKaWIuIH8RXgjWhGCteAi4Dfk9aT6NvPlvtYF5M+dzVt5jFlrURWjBw8H/pxS+i1ARLxHnnq6HzWjBTuDIgC7gxyCHJVSau1aU9A0LL2uzvbryM9P9YijJckjp6YCX6p6fT8PfLcYkbYbeSTs8Ln0/Qvk0Xc7p5TeiYjvkT8TDixG6N1Bvgrpj5g9krStj6vio1Zsa3GkUkppRjEq7ifkz7DTi03vRsQl5CUBphR1fx0RE8gj344EjgZSRIwCfpBSqhfU1TOpmfKPaPrer/y+eaNO3ZbK60opvV+sy/kN8jm4pWq02VvU/CEo8iL6Z5H/GPBP8hTuqeQ/cOxGHqFZb9TzvGrT78W2nMMai1Fn9JgktYbTISWp86ksbNymRckjYmnyf24BLo+mVxFM5C+iMDsoa6sti/vWLFJda1PyH17eaM10pZTS+ymlYSmlNchTHr5Cfl6+QtMAb1Jx35rAsPJla7lmtg+qqVfdn3qj1yr1di2mYTV3O7nOvrXeLO5bewW5DpFSmpJSOj6ltEZKaZGU0sCU0tfIX476AI+1c9RgtUroWTsdsqP2+R05qDux+LkyhepfVXUeJj+m1dvQh4aLiLXI0wQHAnullP7aTNXKYtyTmtleGWE4xxX32qIYeVUJvlp7Plt9LouRK/sD3yi++K5OnhZXey4BPtnK4y8wETGIfL7WJo8A+21b9i8CrMqIwkl1qtQ7j58jj0CqN1IWZl9Ztt5V9maJiD4UVxNO+crDkN9Lz6Ti6rHF5+AjdKLnPqU0MaV0TEppJfIoo0PIAf4RVF0Upaj7p5TSZ8mfszuS1+zaHLi1+P3ZkSYX982NVmrPKKbaKZE7kh/LZdWf0xHRg3yF6deBT6aU9k4p/SCldFJKaRhtC+BmVrVZa4k6ZW3+vdiWc1j0pVtx7Ddrt0lSaxiCSVLnczF53ZI96q19U61m/aoDyF8YHyb/577e7S3gC8XogFaLiDXJf9lN5KmVbdm3G3nNLdq6L+SRQSmlS8lX4/svsGlEVMKiypXntm9FU48U90Pr9HEJYD3yOiNP125vRuXYc0zPa4fXyOfmE81sr0x96dBRRi2orIfU5vNVR2XKar2rUDZn49bsU712VEqpdlRA9XtjgY2w6ygR8SlyoLIksHtK6YYWqt9PXmR8cJ211wDWKe5fmMc+fYIcuLxLcUXOVmjV+a9di6pmc6c/lxGxInmK3ZrAYW0cAVat8tjXqbOt3nmsPDfNhTiV8pamlkG+QuNiwFE15bWjhTrl8w+QUvpvSun/yOsRTiGPeq5Xb1JK6W8ppW+QR8ctSetHKrbWWPJIpXUjom+d7Zu2tcGU0r3kCx3sWrxfKmFY7bTWgeSQaHRKqcnI5yLs3IDWqwSvK9XZNqRO2Tz9XmzlOfwEeer+o+05hiQZgklSJ1OMlBpGDrRuLha+nUOxOHX1FIjKovffSikdUu9G/pJZWTy/VSJiC/L6P73Iixi3+rLqxdSgK8jB03jyQuZz22fpIgCotTh5dNJHzP5Cdwn5L+6HR8QcX2KKL6YVfyGHi9+JiI/VVP0peYH9v6S8MHNr3AA8B3w7InZo5rFsUqzj06JihMVdwMA6fYPZFxVodiHgiFg9ItZsy2LHEdGvTtk25OlOz5FfL9Xb+hfHGFRTvkERdta2tTWz14H6S822OV7XEdEtIo4jryszgZp1p2rqNlk7qmrTU8X9zlVlO5EvtDBrLa2IGFqMkhzZ3DE6QnPP2Vz2WY88gqcveURFi1PPihFA/0cOKE6NiMr6e5Uw7UDy+6bJNOiiX2vWlK1aPLe1fVqa2Yv/X1G9vlTkCxHM8bqLiHXJa1JBzfmvY9ZaVFVlz5Hf6ztVlVXO65M1xxpXnM/BczlOiyKid/G8tHrR7WIto7vII9cOTild2Ip9BhXH6V+z6Xfk0TfHVo9OKqaUV57L6imt95HP7ecjr8FXfYyVmL3m2u0t9GUz8jTBI1LTC488BXwyIlYr6vUnhxtPztnKgle8Vlers2kAObx7v6rultXviyqVi0p0xDTpWYr1rK4kT4tsciGLiPg07b/wwiXk9/m3yOsgPp5SeqSmzpvkx7NhEXpVjtuTPCV9YBuOV1kPs8lFdYrP9n3r1G/T78W2nMMqlWD9zjrbJGmuXBNMkjqhlNLPi+kHJwEPRcRo8oK8U8jTKDYnTxsYA/kLPflKcP9OKdVbxL3i/8ijsg6KvD7SR1Xb1ovZC9kuUhxnY/LUnpnkq7/9sLmGq/atTFX4JPmv3b3I/5HeP7XuKlwrAI9ExL+Bx8nTg/qRvwgvB/y2shZRSmlCROxH/nJ/Z0TcUuzTD1iX/NfrVYu64yLiaPKXzH8V66u8Rf6L8ybkv9z/qBX9o2jvw4jYnXzp+puLc/Qo+cvHSuSr461GnmbZmi9YfyVPWa2MeKv2DHnds30i4kPgRfKovD+nlF4s6twOrFI83nGtfBhjI+Jx8mP/gDxC4AvkaTS7VqZBVfkSOQi5hBysVPwa+HjxHFSuyLku+UINkK+uObqmrYci4gnyml+vkL8sfp482mUq+fUymebNWjuqpvxScoh8fkRsTA4mtgTOqJkuVgntPmL+au45qysiBpDP5ZLF/SYRsUmdqmelfJW9ihPJnwtHF/vcS34P707+0nx0mnNB/cqox+pwYAvggoi4hzx6621y+LoD+RyNYc7Pge8CO0fE3eT36zTyiKjtyKMX/0ALa5FFzVpUlfJiHarfAcdExN/J74uDimPUjlLsqPO5EfnL9SjqjBptxkjyVTkfJo/GG1anzvCaqeC/II/kOYiqtbpSSg9HxMnktRCfiIgbye/Nbcmf+aPJV9ir1H81In5a1L8lIm5i9sL4u5P/cHBdSqnuxTkiXxn2/4BrU0q1a0WeSQ467oiIa4FtyJ/tp7X0ZCxAnwaujYiHyK/lV8kj33Ylryd2elXd64ApkS+8MY78mt+M/Dn9MLNH4HWkY8mfgT8sPotGk38ffJm89uJutOLCIzX+DJxCPt89qXNxg5TSzIj4bXH8f0deE7AX+XNwSfLre8va/ZpxMfmiFMcV4d1T5P9rbE9+TveortyO34ttOYcVXySPjm5pdKwkNS+l5M2bN2/eOumNvCbLOcAT5BFP08lT524hf2lcpKh3KTkUObIVbf6jqPul4ucDi5+rb++Rw4zbyP/Z/lgL7dXuO408iudh8pff7YBubXjMS5AXyb2DHI5MKx7zSPIXsqizzyfJl5x/pXiO3iB/iT20Tt0vFs/BxKLt/5K/VC5Rp+5IioFaLfR3GfKXwifI/6mfQp6ycg15DbMerXzcvYp+P9DM9s+QQ5F3yF+cEjC0avu4omxwG57rM4B/F6+t98lfnk8DlmymfuW1Mrym/OvATUUfphTP63jySIjNWjj2KPKXng+K524seXTXanPpd+UKll9oZvs6xXP1Pjno/A3Qq6bOUUUbh7Th+Rre3HNMDkwSMKw1z1kLxxjMnO+perd6fehDHi30n+IcTCpe619s6b1bU/ap4nH+mzwC8UNyEHY38J3a57HYZzfgWvJ7qfpzagSwy1webx/y9L6/NLN9EfIC328Vr5M7gLVr6gwgfym+pw3nsrnzVSkf2Ya2WnO+htbsU3ktHdhMm7uTR5dNLh73k+Q/YCzSTP1dyb8X3iIHgZPJocvhQPcW+n5mcZ6XbWb7buTPtunkET4d9X4ZVu95qXnPHFhT3uS8kK8e+XPgXnJwP438e+sWYPuafQ8jhzbPkz9r3iZPkf8h0Lem7kjmfF/Ufb1UbR8HjKtTvgI5qHqL/Hn0KDn83LNo7+jWPp9Vbd5W7PthC+etBzmcfqo47uvkAG2VeueF2Z87w+u09UlyaPcu+fN9JDksr3uein1a9XuxLeewqN+/eDzXt/V58+bNm7fKLVJKSJKkxiumAv4c2CDNOcVFHagY2fIZYPWUpy6pi4qIXcijQnZMzYx4kjqTiPgZ+cq826WUbm10f7qKiPgOeRTwZqm4+q8ktZUhmCRJnUSx7s8z5HVedp5bfbVPsTbQm8DPUkpnNbg7mkcR8Stgq5TS+o3ui1QtIpZPKb1aU/Yp8ii96cAKKV91VXNRTN19jrzg/56N7o+krssQTJKkTiTyAv9bAmemOdfkkiR1ERHxKnma8BPkZQY+Tp7O3Q34akqpI67Au1CIiLWAvZlzfT1JahNDMEmSJEnqYBFxEnldtcHkq71OAu4n/5FjZKP6JUkLM0MwSZIkSZIklV63uVeRJEmSJEmSurYeje7AwmrgwIFp8ODBje6GJEmSJElSaTz88MMTUkpL19tmCNYggwcPZsyYMY3uhiRJkiRJUmlExIvNbXM6pCRJkiRJkkrPEEySJEmSJEmlZwgmSZIkSZKk0jMEkyRJkiRJUukZgkmSJEmSJKn0DMEkSZIkSZJUeoZgkiRJkiRJKj1DMEmSJEmSJJVej0Z3QJIkSZIkNW/atGm8/fbbvPvuu8yYMaPR3ZEWmO7du9O3b1+WXHJJFllkkXluzxBMkiRJkqROatq0aYwfP54BAwYwePBgevbsSUQ0ulvSfJdS4sMPP2Ty5MmMHz+elVdeeZ6DMKdDSpIkSZLUSb399tsMGDCAgQMH0qtXLwMwLTQigl69ejFw4EAGDBjA22+/Pc9tGoJJkiRJktRJvfvuu/Tr16/R3ZAaql+/frz77rvz3I4hmCRJkiRJndSMGTPo2bNno7shNVTPnj07ZD08QzBJkiRJkjoxp0BqYddR7wFDMEmSJEmSJJWeIZgkSZIkSZJKzxBMkiRJkiR1auPGjSMiOPDAA1tVf/jw4UQEw4cPX6DHbYuRI0cSEQwbNqzD21Z9PRrdAUmSJEmS1D4n/fyXvPz6hEZ3o1krLjeQk4//YaO7oU4kIthiiy0YOXLkAj+2IZgkSZIkSV3Uy69PYJ2dD2l0N5r1xIiLGnLcL33pS3z2s59l0KBBDTl+a2y00UY8/fTTDBw4sNFdWWg0dDpkROwVETdGxCsRMSUiHo6IfWvqjIyIVOe2aE29FSLiuoh4NyImRMS5EdG7zjG/ERHPRsQHxfG2rlOnw9qSJEmSJEkLVv/+/VlzzTXp379/o7vSrN69e7Pmmmsagi1AjV4T7LvAFOAYYBfgTuCyiPhOTb07gU1qbtMqGyOiJ3ArsAqwD3AUsBdwYXUjRcB2AfAnYHvgSeCmiFhnfrQlSZIkSZI61rhx49hnn30YOHAgiy66KEOGDOGmm25qUqelNcFuvfVWPv/5z7P44ouz5JJLsttuuzF27FgOPPBAIoJx48a1+7ht0dyaYEOHDiUi+PDDDznllFNYffXVWXTRRfnEJz7BH/7wh1n1LrjgAj71qU+x2GKLseKKK3LSSScxc+bMOfpcWdNs7Nix7Lbbbiy55JIsvvjibLrppvzjH/+Yo1/vvPMOZ5xxBltttRUrrrgivXr1Yumll2aXXXbhvvvua/bxjB07loMPPpjBgwezyCKLsMwyy7DZZptx/vnnA7PPCcCoUaOIiFm3BbUuWqOnQ+6cUqqevHxHRCxPDsfOqSp/O6V0fwvt7AmsBXwspfQCQER8CFwRESenlJ4t6g0DLkkp/bSoMwpYHzgW+Mp8aEuSJEmSJHWQF198kY022ojVVluNr371q7z99ttceeWV7Lrrrtx2221sueWWLe5/xRVXsN9++7Hooovy5S9/mUGDBjF69Gg22WQTPv3pT8+347bHPvvswwMPPMAOO+xAz549ueaaazj00EPp2bMnjz/+OJdccgk77bQTW2+9NTfeeCOnnHIKvXv35kc/+tEcbb3wwgtssskmfOpTn+Kb3/wmr732GldeeSXbb789l112GXvvvfesuk8//TQ//vGP2Xzzzdlxxx0ZMGAA48eP58Ybb+SWW25hxIgRbLfddk3av/nmm9lrr72YNm0a2223Hfvuuy+TJk3iscce45e//CWHH3446623HieddBInn3wyq6yySpOLDQwdOrTDn796GhqC1QRgFY8Ae7Sxqe2BhyqhVeF6YDqwHfBsRKwGrEEe2VU5/syIuLq6rIPbkiRJkiRJHWTkyJEMGzaMk046aVbZfvvtx3bbbccZZ5zRYhj17rvvcvjhh9OzZ0/uu+++JqHXsccey+mnnz5fjtte48eP54knnmCJJZYA4Hvf+x5rrrkmxxxzDEsssQSPP/44K6ywAgDDhg3jYx/7GGeeeSbf+9736NGjadxz11138f3vf58zzjhjVtkRRxzBJptswmGHHcb2229Pv379AFhrrbV49dVX55im+fLLL7PRRhtxzDHHNAnBJkyYwH777cdHH33EHXfcwRZbbDHHfgDrrbce6623HieffDKDBw9uyFUxGz0dsp5NgP/UlH0xIqYWt1sjYt2a7WsCY6sLUkrTgeeKbVTdN6kHPA0sGRFLz4e2JEmSJElSB1lllVU44YQTmpRtu+22rLzyyjz44IMt7nvDDTcwadIk9t9//zlGfZ1wwgmzwqaOPm57nXbaaU36tNpqq7HpppsyadIkTjzxxFkBGMASSyzBzjvvzIQJE3jllVfmaKt///785Cc/aVI2ZMgQ9t9/fyZNmsR1113XpG69dcpWXHFF9txzT8aOHcv48eNnlV9yySVMnjyZww8/fI4ArLJfZ9Ho6ZBNFAvL7wYcXFU8CrgE+C95na4fA3dHxKdTSuOKOgOASXWanFhso+q+tt7Equ1vdXBbC4Wzfv4TJr3xUqO70akssexKHH38KY3uhiRJkiSVynrrrUf37t3nKF9ppZVaXK8K4JFHHgFg0003nWNbnz59WG+99Rg5cmSHH7e9hgwZMkfZ8ssvD8CGG244x7ZKKPbyyy+zyiqrNNm2wQYb0Ldv3zn2GTp0KJdccgmPPPIIBxxwwKzye++9l7PPPpv77ruPN998k+nTpzfZ75VXXmHllVcG4P778+pV22+/fVseXkN0mhAsIgYDlwE3pJSGV8pTSidVVbs7Im4jj8A6urh1GRFxKHAoMOvFUgaT3niJYbut0ehudCrDrq8dzChJkiRJmlfNjdbq0aPHHIvC13rnnXcAWHbZZetub658Xo/bXvWubFmZ5tjStg8//HCObc09tuWWWw6Y/dwAXHfddey5554suuiibLPNNqy++uosvvjidOvWjZEjRzJq1CimTZt1rUImTZoE0GRkWmfVKUKwiFgSuAV4Edi/pboppdcj4l5gg6riiUC9654OAB6rqkNRb1JNnertHdlWbd8vpLjK5JAhQ1K9OpIkSZIkqeNV1rx644036m5vrrwMmntsr7/+OtA0VDvxxBPp1asXY8aMYa211mpS/5vf/CajRo1qUlYJCF955RU+9alPdWCvO17D1wSLiN7ATUAvYKeU0tRW7JaKW8VYZq/TVWm3F7Aas9ftqtw3qVf8/HZK6a2qeh3VliRJkiRJ6gTWX399AO655545tk2ZMoVHH310AfdowfnXv/7Fu+++O0d5Zfpn5bkB+O9//8vaa689RwA2c+bMus/dZz/7WQBuueWWVvWlW7duzJgxo7Vd71ANDcEiogdwNfBxYLuU0put2Gc5YFPg4ariW4DPRET1pNddgEWAvwOklJ4nL7i/V1Vb3Yqfq89UR7YlSZIkSZI6gV133ZX+/ftz6aWX8thjjzXZduqpp86a1ldG77zzDqec0nTd6jFjxnDppZfSv39/vvSlL80qHzx4MM8++yyvvvrqrLKUEsOGDeOpp56ao+0DDjiAfv36cf7553PXXXfNsb1ydciKpZZaipdeasy64o2eDnkesANwFLBURCxVte0R4BPAL8hB2YvAysBxwEzgrKq615AXzL82Ik4kT1P8DXBZSunZqnrDgL9ExDjgXuAAcgC333xqS5IkSZIkdQL9+vXjd7/7HV/96lf53Oc+x5e//GUGDRrE6NGjeeyxx9hiiy0YNWoU3bo1fNJch9t888256KKLeOCBB/j85z/Pa6+9xpVXXsnMmTP5/e9/P2uqKMAxxxzDYYcdxvrrr88ee+xBz549uffee3nqqafYeeedGTFiRJO2Bw4cyGWXXcaee+7Jlltuyfbbb8+6667L5MmTefzxx3nppZd44YUXZtXfeuutueKKK9h5553ZYIMN6NmzJ5tvvjmbb775fH8eGn1mv1jcnw3cV3MbBPwPCHIQdivwa+BJ4HMppVnX40wpfQhsB7wEXAWcC/yVYhH6qnqXA4cBB5JHda1LnoL5xPxoS5IkSZIkdR77778/N998M5/+9Ke58sorOf/88+nfvz/33Xcfffr0AWgSCJXFqquuyujRoxkwYAAXXHABV111FRtssAF/+9vf2HvvvZvU/eY3v8nFF1/MoEGDuOSSS7j00ktZaaWVeOCBB9hggw3qtr/jjjsyZswY9t9/fx555BHOPPNMrr76aiKC4447rknds88+m3333ZcHH3yQU089lRNPPJE77rhjvj32apGS67M3wpAhQ9KYMWMa3Y0OMeyog7w6ZI1h1/+HYWdf3OhuSJIkSerinn766TnWZqp20s9/ycuvT1iAPWqbFZcbyMnH/7DR3ZirGTNmsNpqqzF9+nRee+21Rnenw4wbN45VV12VAw44gOHDhze6O/Nkbu+Fioh4OKU0pN62Rk+HlCRJkiRJ7dQVAqbOZNKkSfTq1YvevXvPKkspceqppzJ+/HgOP/zwBvZO85shmCRJkiRJWijcf//97L333nzxi19k8ODBTJkyhfvvv59HH32UlVZaiWHDhjW6i5qPDMEkSZIkSdJC4ROf+AQ77bQT9957L3/729/46KOPWHHFFTnyyCM5/vjjWWaZZdrd9qOPPsr111/fqrqGbY1hCCZJkiRJkhYKq666Kpdeeul8afvRRx/l5JNPblXdBRWCDR48GNeCn63RV4eUJEmSJEnq8g488EBSSq26qTEMwSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkrPEEySJEmSJEmlZwgmSZIkSZKk0jMEkyRJkiRJUukZgkmSJEmSJKn0DMEkSZIkSZJUeoZgkiRJkiSpUxs3bhwRwYEHHtjorrTK4MGDGTx4cKO7oRo9Gt0BSZIkSZLUPmf9/CdMeuOlRnejWUssuxJHH39Ko7uhBeDAAw/kkksu4YUXXui0AaAhmCRJkiRJXdSkN15i2G5rNLobzRp2/X8a3YWGuP322xvdBdVhCCZJkiRJktSBVl999UZ3QXW4JpgkSZIkSeqSXnvtNb797W8zePBgevXqxdJLL83uu+/Oww8/3KTerbfeSkTw4x//uEn5nXfeSUQQEbz0UtNppXvvvTcRwfPPP9/mftVbE2z48OFEBMOHD+ef//wnm222GX369GHppZfmoIMOYtKkSQA88sgj7LTTTgwYMIA+ffqwyy67MG7cuDmOMXToUCKCadOmccIJJ7DqqquyyCKLsPrqq3PyySczffr0Ofa5/vrr+cpXvsIaa6zB4osvzuKLL86GG27Ib3/7W2bOnFn3sUydOpXTTz+dIUOG0LdvX/r06cNaa63FkUceyRtvvAFARHDJJZcAsOqqq856TjvbtEhHgkmSJEmSpC7nhRdeYNNNN+XVV19lq622Yt999+Wll17i6quv5uabb+avf/0rO+20EwCbbbYZvXr14vbbb+dnP/vZrDaqpy3efvvtsxbeTylx5513MnjwYFZbbbUO7feNN97ITTfdxE477cRhhx3G6NGjGT58OOPGjeMXv/gFW2+9NZttthlf//rX+fe//82IESN4/vnnefzxx+nWbc6xTF/+8pd56KGH2HPPPenZsyc33HADw4YNY8yYMdx4441ExKy6xx57LN26dWPjjTdmhRVW4J133uGOO+7gqKOO4qGHHuLPf/5zk7YnTpzIlltuyWOPPcYnPvEJDj74YHr16sVzzz3HxRdfzO67786yyy7LSSedxPXXX89jjz3GUUcdxRJLLAEw676zMASTJEmSJEldzmGHHcarr77Kqaee2mSE17e+9S0233xzDjjgAF588UX69OlD79692XjjjRk9ejTvvPMO/fv3B3Lwtf766zN+/PgmIdjjjz/OW2+9xc4779zh/b7xxhu5/fbb2WKLLQCYOXMm2267Lbfddhs77LADF154Ifvvv/+s+l//+tf54x//yIgRI9h1113naO/pp5/mySefZMCAAQD87Gc/Y8stt+Smm27iL3/5C1/96ldn1b355pvnmKo5c+ZMDjroIP70pz9xxBFHsPHGG8/a9u1vf5vHHnuMww47jN/97ndNQrgpU6YwY8YMAIYNG8a4ceN47LHHOProozvdCLAKp0NKkiRJkqQu5eWXX+Yf//gHK6+8Mj/84Q+bbPvc5z7Hvvvuy9tvv8211147q3zrrbdmxowZjBo1CoB3332XMWPGsM0227Dllltyxx13zKpbGSG29dZbd3jf991331kBGEC3bt1mBVXrrLNOkwAM4Gtf+xoAjz76aN32TjzxxFkBGMCiiy7KL37xCwD++Mc/Nqlbb62ybt26cdRRRwF52mjFm2++yZVXXsmgQYM488wz5xiF1qdPn1lhYldhCCZJkiRJkrqURx55BMjTHHv27DnH9q222qpJveqySsA1atQoPvroI7beemu22morXn31VZ5++mmAWYFYZZ+ONGTIkDnKll9+eQA23HDDObatsMIKQA7+6qkO1Co23XRTunfv3uTxA/zvf//j2GOPZd1116VPnz6z1u6qHPeVV16ZVfehhx5i5syZbL755iy++OKtfHSdm9MhJUmSJElSl/LOO+8AMGjQoLrbK+WVxeYBPvvZz7L44ovPCsFuv/12evXqxaabbjpr+t7tt9/Oxz/+ce666y7WXnttlltuuQ7ve73RUz169Jjrtg8//LBue8suu2zdfQYOHMibb745q2zSpEl85jOf4YUXXmCjjTbia1/7GksuuSQ9evRg0qRJnH322UybNq1JfZgdwpWBIZgkSZIkSepSKmHR66+/Xnf7a6+91qQeQM+ePdl000259dZbef3117n99tvZZJNN6N27N2ussQYrrrgit912GxtssAHvvvvufBkFNj+88cYbrLzyyk3KPvroIyZMmEC/fv1mlV100UW88MILnHTSSQwbNqxJ/fvuu4+zzz67SVllUfvq0WFdndMhJUmSJElSl7L++usDcM899/DRRx/Nsf3OO+8EYIMNNmhSXlnj6/LLL+eJJ55osubXVlttxciRI/nnP//ZpG5nV1njrNo999zDjBkzZj1PAP/9738B2GOPPVrVxkYbbUS3bt246667eO+99+baj+7duwPMWiy/MzIEkyRJkiRJXcqKK67INttsw7hx4zjrrLOabHvggQe47LLLGDBgAF/60peabKuM7jrttNNIKc0Rgr3zzjucd955dOvWjaFDh87vh9EhfvrTnzJx4sRZP3/wwQccd9xxABx00EGzyitTPkeOHNlk/0ceeWTWQvrVll56afbZZx9ee+01vv/97zNz5swm26dMmTJrWirAUkstBcD48ePn6fHMT06HlCRJkiRJXc4FF1zA5z//eX7wgx/wj3/8gyFDhvDSSy9x9dVX061bNy6++GL69u3bZJ/111+fAQMG8Oabb9K3b1822mijWdsqgdibb77JkCFDZk0H7OzWWmstPvnJT7LnnnvSs2dPbrjhBp577jl23HHHWVedhHyVyTPOOIOjjz6aO++8k49//OM8++yz3HTTTey+++5ceeWVc7R97rnn8sQTT3DBBRcwcuRItt12W3r16sULL7zArbfeyo033jgrLNx6660544wz+MY3vsEee+xB3759WWKJJTjiiCMW1FMxV44EkyRJkiRJXc5qq63GmDFjOOyww3jmmWc488wzueWWW9huu+2499572XXXXefYp1u3bmy55ZZAvrJkZdF5yKPL1lhjDWD+XBVyfrnqqqs4+OCDGTFiBOeeey4zZ85k2LBh/PWvfyUiZtVbfvnlufvuu9lxxx255557OPfcc3nxxRc577zzOO200+q2PWDAAEaPHs2pp55Kz549ufDCCzn//PN58sknOfjgg1l77bVn1d1222351a9+Rc+ePTnrrLM48cQTOfPMM+f742+LSCk1ug8LpSFDhqQxY8Y0uhsdYthRBzFstzUa3Y1OZdj1/2HY2Rc3uhuSJEmSurinn36atdZaq9ntZ/38J0x646UF2KO2WWLZlTj6+FMa3Y1SGjp0KKNGjWJhyXXm9l6oiIiHU0pD6m1zOqQkSZIkSV2UAZPUek6HlCRJkiRJUuk5EkySJEmSJKkFw4YNa1W93XbbjfXWW2++9kXtZwgmSZIkSZLUgpNPPrlV9QYPHrzAQrCRI0cukOOUiSGYJEmSJElSCxaWxefLzjXBJEmSJEmSVHqGYJIkSZIkSSo9QzBJkiRJkiSVniGYJEmSJEmdmOtRaWHXUe8BQzBJkiRJkjqp7t278+GHHza6G1JDffjhh3Tv3n2e2zEEkyRJkiSpk+rbty+TJ09udDekhpo8eTJ9+/ad53YMwSRJkiRJ6qSWXHJJJk6cyIQJE5g+fbpTI7XQSCkxffp0JkyYwMSJE1lyySXnuc0eHdAvSZIkSZI0HyyyyCKsvPLKvP3224wbN44ZM2Y0ukvSAtO9e3f69u3LyiuvzCKLLDLP7RmCSZIkSZLUiS2yyCIMGjSIQYMGNborUpfmdEhJkiRJkiSVniGYJEmSJEmSSs8QTJIkSZIkSaVnCCZJkiRJkqTSMwSTJEmSJElS6RmCSZIkSZIkqfQMwSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkrPEEySJEmSJEmlZwgmSZIkSZKk0jMEkyRJkiRJUukZgkmSJEmSJKn0DMEkSZIkSZJUeoZgkiRJkiRJKj1DMEmSJEmSJJWeIZgkSZIkSZJKzxBMkiRJkiRJpWcIJkmSJEmSpNIzBJMkSZIkSVLpGYJJkiRJkiSp9AzBJEmSJEmSVHqGYJIkSZIkSSo9QzBJkiRJkiSVniGYJEmSJEmSSs8QTJIkSZIkSaVnCCZJkiRJkqTSMwSTJEmSJElS6RmCSZIkSZIkqfQMwSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkrPEEySJEmSJEmlZwgmSZIkSZKk0jMEkyRJkiRJUukZgkmSJEmSJKn0DMEkSZIkSZJUeoZgkiRJkiRJKj1DMEmSJEmSJJWeIZgkSZIkSZJKzxBMkiRJkiRJpWcIJkmSJEmSpNIzBJMkSZIkSVLpGYJJkiRJkiSp9BoagkXEXhFxY0S8EhFTIuLhiNi3Tr1vRMSzEfFBUWfrOnVWiIjrIuLdiJgQEedGRO9GtyVJkiRJkqTGa/RIsO8CU4BjgF2AO4HLIuI7lQpFKHYB8Cdge+BJ4KaIWKeqTk/gVmAVYB/gKGAv4MLqgy3otiRJkiRJktQ59Gjw8XdOKU2o+vmOiFieHI6dU5QNAy5JKf0UICJGAesDxwJfKersCawFfCyl9EJR70Pgiog4OaX0bIPakiRJkiRJUifQ0JFgNQFYxSPA8gARsRqwBnBV1T4zgavJo68qtgceqoRWheuB6cB2DWxLkiRJkiRJnUCjp0PWswnwn+Lfaxb3Y2vqPA0sGRFLV9VrUielNB14rqqNRrQlSZIkSZKkTqBThWDFwvK7Ab8qigYU95Nqqk6s2T6gTp1KvQE1dRdkW5IkSZIkSeoEOk0IFhGDgcuAG1JKwxvbm/kjIg6NiDERMeatt95qdHckSZIkSZIWGp0iBIuIJYFbgBeB/as2VUZW9a/ZZUDN9ol16lTqTaypuyDbaiKldGFKaUhKacjSSztjUpIkSZIkaUFpeAgWEb2Bm4BewE4ppalVmytrbq1Zs9uawNsppbeq6jWpExG9gNWq2mhEW5IkSZIkSeoEGhqCRUQP8hUVPw5sl1J6s3p7Sul58iL5e1Xt0634+ZaqqrcAn4mIVarKdgEWAf7ewLYkSZIkSZLUCfRo8PHPA3YAjgKWioilqrY9klKaBgwD/hIR44B7gQPIodl+VXWvAX4MXBsRJ5KnKf4GuCyl9GxVvQXdliRJkiRJkjqBRodgXyzuz66zbVVgXErp8ojoA/wIOBF4kjxt8olKxZTShxGxHXAucBUwDbgC+EF1gwu6LUmSJEmSJHUODQ3BUkqDW1nvD8Af5lLnZWC3ztaWJEmSJEmSGq/hC+NLkiRJkiRJ85shmCRJkiRJkkrPEEySJEmSJEmlZwgmSZIkSZKk0jMEkyRJkiRJUukZgkmSJEmSJKn0DMEkSZIkSZJUeoZgkiRJkiRJKj1DMEmSJEmSJJWeIZgkSZIkSZJKzxBMkiRJkiRJpWcIJkmSJEmSpNIzBJMkSZIkSVLpGYJJkiRJkiSp9AzBJEmSJEmSVHqGYJIkSZIkSSo9QzBJkiRJkiSVniGYJEmSJEmSSs8QTJIkSZIkSaVnCCZJkiRJkqTSMwSTJEmSJElS6RmCSZIkSZIkqfQMwSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkrPEEySJEmSJEmlZwgmSZIkSZKk0uvR6A5IZfTYo48w7KiDGt2NTmOJZVfi6ONPaXQ3JEmSJEkLMUMwaT5I06cybLc1Gt2NTmPY9f9pdBckSZIkSQs5p0NKkiRJkiSp9AzBJEmSJEmSVHqGYJIkSZIkSSo9QzBJkiRJkiSVniGYJEmSJEmSSs8QTJIkSZIkSaVnCCZJkiRJkqTSMwSTJEmSJElS6RmCSZIkSZIkqfQMwSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkrPEEySJEmSJEmlZwgmSZIkSZKk0jMEkyRJkiRJUukZgkmSJEmSJKn0DMEkSZIkSZJUeoZgkiRJkiRJKj1DMEmSJEmSJJWeIZgkSZIkSZJKzxBMkiRJkiRJpWcIJkmSJEmSpNIzBJMkSZIkSVLpGYJJkiRJkiSp9AzBJEmSJEmSVHqGYJIkSZIkSSo9QzBJkiRJkiSVniGYJEmSJEmSSs8QTJIkSZIkSaVnCCZJkiRJkqTSMwSTJEmSJElS6RmCSZIkSZIkqfQMwSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkrPEEySJEmSJEmlZwgmSZIkSZKk0jMEkyRJkiRJUukZgkmSJEmSJKn0DMEkSZIkSZJUeoZgkiRJkiRJKj1DMEmSJEmSJJWeIZgkSZIkSZJKzxBMkiRJkiRJpWcIJkmSJEmSpNIzBJMkSZIkSVLpGYJJkiRJkiSp9AzBJEmSJEmSVHqGYJIkSZIkSSo9QzBJkiRJkiSVniGYJEmSJEmSSs8QTJIkSZIkSaVnCCZJkiRJkqTSMwSTJEmSJElS6RmCSZIkSZIkqfQMwSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkqv4SFYRHwsIn4fEY9HxIyIGFmnzriISDW31+vUWzsibo+IqRHxakScEhHda+pERBwfES9FxPsRcVdErDc/25IkSZIkSVJj9Wh0B4BPAjsA9wM9W6h3GXBO1c/TqzdGxADgNuApYFdgdeBX5KDvhKqqxwInAj8AxgLfBW6LiHVSSq93dFuSJEmSJElqvM4Qgo1IKd0AEBHXAAObqfdaSun+Fto5DFgM2D2lNBn4Z0T0A4ZFxC9TSpMjYlFycPWLlNK5xTHvA8YBRzA74OrItiRJkiRJktRgDZ8OmVKa2UFNbQ/cWoRWFVeQw6wtip8/B/QDrqo6/nvAiGL/+dGWJEmSJEmSGqzhIVgbfD0ipkfEOxFxTUSsUrN9TfKUxFlSSuOBqcW2Sp0ZwLM1+z5dVaej25IkSZIkSVKDdYbpkK1xA3nNsJeBtYCTgLsj4lMppXeKOgOASXX2nVhsq9SZklKaUadO74jolVKa3sFtSZIkSZIkqcG6RAiWUjqq6se7I2I08ChwEHBWI/rUHhFxKHAowMorr9zg3kiSJEmSJC08utJ0yFlSSk8AzwAbVBVPBPrXqT6g2Fap0yciutepM7Vq5FZHtlXd7wtTSkNSSkOWXnrp+g9OkiRJkiRJHa5LhmCFVNwqxlKzFldErAT0Zvb6XmOB7sDHatqqXQOsI9uSJEmSJElSg7U5BIuI3euMflqgImIdctj0cFXxLcC2EdG3qmxv4H1gVPHzaGAysFdVW72BnYv950dbkiRJkiRJarD2rAl2DfBqRPwRuKi4amK7FcHRDsWPKwD9ImLP4ue/AVsCXwFuAl4lh18nAOOB4VVNXQAcCVwbEacDqwHDgF+nlCYDpJQ+iIjTgBMjYiJ5xNZ3yWHgOfOpLUmSJEmSJDVYe0Kw3wH7k4Oo4yLi78DvgZtTSqnFPetbBri6pqzy86rAS0Wds4AlgP8BfweOrwRSACmliRGxNXAuMIJ8dcffkMOraqeRg6rjgKWAMcA2KaU35kdbkiRJkiRJarw2h2Appe9ExA+AfYBvAjuSR3K9EhEXkUeHvdqG9sYBMZdqW7eyraeAreZSJwE/K24LpC1JkiRJkiQ1VrsWxk8pfZBSGp5S2gRYFzgP6EMeKTUuIq6LiO06rpuSJEmSJElS+83z1SFTSk+klL4DLA8cBLwB7ALcHBEvRMT3I2LxeT2OJEmSJEmS1F7zHIIBFCHX14DvkBe3D+Ax8jpZvwTGRsR6HXEsSZIkSZIkqa3mKQSLiPUj4gLyVRsvIF+58SJgg5TSBuTRYccCA4HfzmNfJUmSJEmSpHZp88L4EdEb2Je8KP6G5FFfT5NDsEtqrtg4BfhlRKwEfL1DeixJkiRJkiS1UZtDMPKor77ADOCvwHkppZFz2ecVYNF2HEuSJEmSJEmaZ+0Jwd4FfgX8IaX0eiv3OQ+4vB3HkiRJkiRJkuZZe0KwVVJKM9uyQzFFcvJcK0qSJEmSJEnzQXsWxr8tIr7WUoWI+EpE3NHOPkmSJEmSJEkdqj0h2FBg8FzqrAJs0Y62JUmSJEmSpA7XnhCsNRYDPppPbUuSJEmSJElt0p41wQBSvcKICGBlYAfgpfZ2SpIkSZIkSepIrRoJFhEzI2JGRMwoioZVfq6+kUd/PQ+sB1wxf7osSZIkSZIktU1rR4LdxezRX5sD44FxderNAP4H3A5cNK+dkyRJkiRJkjpCq0KwlNLQyr8jYiZwcUrplPnVKUmSJEmSJKkjtWdNsFWBSR3cD0mSJEmSJGm+aXMIllJ6cX50RJIkSZIkSZpf5hqCRcRPyOuB/S6l9Hbxc2uklNJP56l3kiRJkiRJUgdozUiwYeQQ7Erg7eLn1kiAIZgkSZIkSZIarjUh2JbF/fianyVJkiRJkqQuYa4hWEppVEs/S5IkSZIkSZ1dt0Z3QJIkSZIkSZrf2hyCRcTgiNghIhavKusRESdHxGMRMToivtSx3ZQkSZIkSZLarzVrgtU6CdgFWLaq7ATgxKqfr4qIzVJK989L5yRJkiRJkqSO0J7pkJsAt6eUPgKIiG7At4CxwMrARsB7wDEd1UlJkiRJkiRpXrQnBFsWeLHq5/WAgcDvUkovp5TGADcAn5n37kmSJEmSJEnzrj0hWE8gVf38+eLnO6rKXgYGzUO/JEmSJEmSpA7TnhDsZWDdqp93ACaklJ6uKlsGmDwvHZMkSZIkSZI6SnsWxr8JOCYizgQ+ALYBLq6pswZNp0xKkiRJkiRJDdOeEOyXwG7Ad4ufXyFfMRKAiFiGvHj+b+e1c5IkSZIkSVJHaHMIllJ6MyI+BWxdFI1KKb1bVWUg8APg1g7onyRJkiRJkjTP2jMSjJTS++RpkfW2PQU8NS+dkiRJkiRJkjpSexbGlyRJkiRJkrqUdo0Ei4glgYOBjYABQPc61VJKaes65ZIkSZIkSdIC1eYQLCLWBEYCSwPRQtXUzj5JkiRJkiRJHao90yHPBJYBTgdWA3qmlLrVudUbHSZJkiRJkiQtcO2ZDrkZcHNK6fiO7owkSZIkSZI0P7RnJFjg1R8lSZIkSZLUhbQnBHsY+ERHd0SSJEmSJEmaX9oTgp0C7BARQzu2K5IkSZIkSdL80Z41wVYCbgD+ERGXk0eGTapXMaX0p/Z3TZIkSZIkSeoY7QnBhgOJvDbYV4tbqqkTRZkhmCRJkiRJkhquPSHYQR3eC0mSJEmSJGk+anMIllK6ZH50RJIkSZIkSZpf2rMwviRJkiRJktSltGc6JAARsTSwB7AWsHhK6ZCq8lWBf6eU3u+QXkqSJEmSJEnzoF0hWER8HfgtsCizF8E/pNi8LHAfcCjwfx3QR0mSJEmSJGmetHk6ZERsA1wI/Af4EnB+9faU0hPAk8BuHdA/SZIkSZIkaZ61ZyTYj4DXgC1SSpMjYv06dR4HNpmnnkmSJEmSJEkdpD0L4w8BbkopTW6hzsvAcu3rkiRJkiRJktSx2hOC9QLem0udJYAZ7WhbkiRJkiRJ6nDtCcHGARvOpc7GwDPtaFuSJEmSJEnqcO0JwW4ANouIveptjIiDgHWBv85LxyRJkiRJkqSO0p6F8X8J7ANcHhF7Av0BIuIIYDNgd+BZ4JyO6qQkSZIkSZI0L9ocgqWUJkbEFsCfgOrRYL8t7u8G9kspzW3dMEmSJEmSJGmBaM9IMFJK44GhEbEusAmwFPAOcH9K6eEO7J8kSZIkSZI0z9oVglWklB4HHu+gvkiSJEmSJEnzRbtDsIhYBVgaSMBbxegwSZIkSZIkqdNp09UhI2JgRPw6Il4DngceAB4EXoiIVyPijIhYcn50VJIkSZIkSWqvVodgEfFxYAxwFLAsMAN4E3ir+PdywHeBMRGxWsd3VZIkSZIkSWqfVoVgEdENuBRYGRgFfAHok1IalFJaDugLfBG4CxgM/GW+9FaSJEmSJElqh9aOBPsiMAS4Ctg6pXRHSml6ZWNKaVpK6TZgK+AaYOOI2KbDeytJkiRJkiS1Q2tDsD2AacB3UkqpuUrFtiOAD4E95717kiRJkiRJ0rxrbQi2AXBvSumtuVVMKb0J3FPsI0mSJEmSJDVca0OwlYAn29Duk8Aqbe+OJEmSJEmS1PFaG4L1Aya1od1J5MXyJUmSJEmSpIZrbQjWC5jRhnZnFvtIkiRJkiRJDdfaEAyg2QXxJUmSJEmSpM6sRxvqDouIYfOrI5IkSZIkSdL80pYQLNrYtiPHJEmSJEmS1Cm0KgRLKbVl2qQkSZIkSZLUqRhuSZIkSZIkqfQMwSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkrPEEySJEmSJEmlZwgmSZIkSZKk0jMEkyRJkiRJUukZgkmSJEmSJKn0DMEkSZIkSZJUeoZgkiRJkiRJKj1DMEmSJEmSJJWeIZgkSZIkSZJKzxBMkiRJkiRJpWcIJkmSJEmSpNIzBJMkSZIkSVLpGYJJkiRJkiSp9AzBJEmSJEmSVHqGYJIkSZIkSSq9hodgEfGxiPh9RDweETMiYmSdOhERx0fESxHxfkTcFRHr1am3dkTcHhFTI+LViDglIro3ui1JkiRJkiQ1VsNDMOCTwA7AM8B/mqlzLHAicDqwMzAFuC0ilqtUiIgBwG1AAnYFTgG+B5zcyLYkSZIkSZLUeJ0hBBuRUloppbQX8GTtxohYlBw2/SKldG5K6TZgL3JAdURV1cOAxYDdU0r/TCldQA6tvhsR/RrYliRJkiRJkhqs4SFYSmnmXKp8DugHXFW1z3vACGD7qnrbA7emlCZXlV1BDrO2aGBbkiRJkiRJarCGh2CtsCYwA3i2pvzpYlt1vbHVFVJK44GpVfUa0ZYkSZIkSZIarCuEYAOAKSmlGTXlE4HeEdGrqt6kOvtPLLY1qi1JkiRJkiQ1WFcIwUojIg6NiDERMeatt95qdHckSZIkSZIWGl0hBJsI9ImI7jXlA4CpKaXpVfX619l/QLGtUW3NklK6MKU0JKU0ZOmll67TvCRJkiRJkuaHrhCCjQW6Ax+rKa9dt2ssNWtxRcRKQO+qeo1oS5IkSZIkSQ3WFUKw0cBkYK9KQUT0BnYGbqmqdwuwbUT0rSrbG3gfGNXAtiRJkiRJktRgPRrdgSI42qH4cQWgX0TsWfz8t5TS1Ig4DTgxIiaSR1l9lxzgnVPV1AXAkcC1EXE6sBowDPh1SmkyQErpgwa0JUmSJEmSpAZreAgGLANcXVNW+XlVYBxwGjlcOg5YChgDbJNSeqOyQ0ppYkRsDZwLjCBf3fE35PCq2gJtS5IkSZIkSY3X8BAspTQOiLnUScDPiltL9Z4CtupsbUmSJEmSJKmxusKaYJIkSZIkSdI8MQSTJEmSJElS6RmCSZIkSZIkqfQMwSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkrPEEySJEmSJEmlZwgmSZIkSZKk0jMEkyRJkiRJUukZgkmSJEmSJKn0DMEkSZIkSZJUeoZgkiRJkiRJKj1DMEmSJEmSJJWeIZgkSZIkSZJKzxBMkiRJkiRJpWcIJkmSJEmSpNIzBJMkSZIkSVLpGYJJkiRJkiSp9AzBJEmSJEmSVHqGYJIkSZIkSSo9QzBJkiRJkiSVniGYJEmSJEmSSs8QTJIkSZIkSaVnCCZJkiRJkqTSMwSTJEmSJElS6RmCSZIkSZIkqfQMwSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkrPEEySJEmSJEmlZwgmSZIkSZKk0jMEkyRJkiRJUukZgkmSJEmSJKn0DMEkSZIkSZJUeoZgkiRJkiRJKr0eje6ApPJ77NFHGHbUQY3uRqexxLIrcfTxpzS6G5IkSZK0UDEEkzTfpelTGbbbGo3uRqcx7Pr/NLoLkiRJkrTQcTqkJEmSJEmSSs8QTJIkSZIkSaVnCCZJkiRJkqTSMwSTJEmSJElS6RmCSZIkSZIkqfQMwSRJkiRJklR6hmCSJEmSJEkqvR6N7oAkSVK1k37+S15+fUKju6EqKy43kJOP/2GjuyFJkjRPDMEkSVKn8vLrE1hn50Ma3Q1VeWLERY3ugiRJ0jxzOqQkSZIkSZJKzxBMkiRJkiRJpWcIJkmSJEmSpNIzBJMkSZIkSVLpGYJJkiRJkiSp9AzBJEmSJEmSVHqGYJIkSZIkSSo9QzBJkiRJkiSVniGYJEmSJEmSSs8QTJIkSZIkSaVnCCZJkiRJkqTSMwSTJEmSJElS6RmCSZIkSZIkqfQMwSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkrPEEySJEmSJEml16PRHZAkqVFO+vkvefn1CY3uhmo8+u8nWGfnRvdCkiRJZWMIJklaaL38+gTW2fmQRndDNUY/eFijuyBJkqQScjqkJEmSJEmSSs8QTJIkSZIkSaVnCCZJkiRJkqTSMwSTJEmSJElS6RmCSZIkSZIkqfQMwSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkrPEEySJEmSJEmlZwgmSZIkSZKk0jMEkyRJkiRJUukZgkmSJEmSJKn0DMEkSZIkSZJUeoZgkiRJkiRJKj1DMEmSJEmSJJWeIZgkSZIkSZJKzxBMkiRJkiRJpWcIJkmSJEmSpNIzBJMkSZIkSVLpGYJJkiRJkiSp9AzBJEmSJEmSVHpdIgSLiAMjItW5HVZVJyLi+Ih4KSLej4i7ImK9Om2tHRG3R8TUiHg1Ik6JiO41dTqsLUmSJEmSJDVej0Z3oI22At6v+vn5qn8fC5wI/AAYC3wXuC0i1kkpvQ4QEQOA24CngF2B1YFfkcPAE+ZTW5IkSZIkSWqwrhaCPZRSmlJbGBGLkoOrX6SUzi3K7gPGAUcwO5Q6DFgM2D2lNBn4Z0T0A4ZFxC9TSpM7sq2Of/iSJEmSJElqjy4xHbIVPgf0A66qFKSU3gNGANtX1dseuLUmoLqCHGZtMR/akiRJkiRJUifQ1UKw5yLio4h4JiK+WVW+JjADeLam/tPFtup6Y6srpJTGA1Or6nVkW5IkSZIkSeoEusp0yNfIa3Q9CHQH9gEuiIjeKaXfAAOAKSmlGTX7TQR6R0SvlNL0ot6kOu1PLLbRwW1JkiRJkiSpE+gSIVhK6Vbg1qqiW4q1u06IiLMb1K02i4hDgUMBVl555Qb3RpIkSZIkaeHR1aZDVrsGWBIYTB591SciutfUGQBMLUZuUdTrX6etAcW2Sp2OaquJlNKFKaUhKaUhSy+9dLMPTJIkSZIkSR2rK4dgqep+LHma5Mdq6tSu2zWWmvW6ImIloHdVvY5sS5IkSZIkSZ1AVw7B9gQmAC8Co4HJwF6VjRHRG9gZuKVqn1uAbSOib1XZ3sD7wKji545sS5IkSZIkSZ1Al1gTLCL+Sl4U/3HyKK29i9uRKaWZwAcRcRpwYkRMJI/E+i455DunqqkLgCOBayPidGA1YBjw65TSZICUUoe1JUmSJEmSpM6hS4RgwDPAwcBKQABPAV9LKf25qs5p5KDqOGApYAywTUrpjUqFlNLEiNgaOBcYQb6642/I4RXzqS1JkiRJkiQ1WJcIwVJKxwPHz6VOAn5W3Fqq9xSw1YJqS5IkSZIkSY3XldcEkyRJkiRJklrFEEySJEmSJEmlZwgmSZIkSZKk0jMEkyRJkiRJUukZgkmSJEmSJKn0DMEkSZIkSZJUeoZgkiRJkiRJKj1DMEmSJEmSJJWeIZgkSZIkSZJKzxBMkiRJkiRJpWcIJkmSJEmSpNIzBJMkSZIkSVLpGYJJkiRJkiSp9AzBJEmSJEmSVHqGYJIkSZIkSSo9QzBJkiRJkiSVniGYJEmSJEmSSs8QTJIkSZIkSaVnCCZJkiRJkqTSMwSTJEmSJElS6RmCSZIkSZIkqfQMwSRJkiRJklR6PRrdAUmSzvr5T5j0xksL/LjjH3qYya8/t8CPOzfd+w1i472PbHQ3JEmSpFIxBJMkNdykN15i2G5rLPDj3rDoiyyzxqoL/Lhz8+tbX2h0FyRJkqTScTqkJEmSJEmSSs8QTJIkSZIkSaVnCCZJkiRJkqTSMwSTJEmSJElS6RmCSZIkSZIkqfQMwSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkqvR6M7IEkLm8cefYRhRx3U6G50Kk/9+1HYbY1Gd0OSJElSiRmCSdIClqZPZZiBTxO7PXhvo7sgSZIkqeScDilJkiRJkqTSMwSTJEmSJElS6RmCSZIkSZIkqfQMwSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkrPEEySJEmSJEmlZwgmSZIkSZKk0jMEkyRJkiRJUukZgkmSJEmSJKn0DMEkSZIkSZJUeoZgkiRJkiRJKj1DMEmSJEmSJJWeIZgkSZIkSZJKzxBMkiRJkiRJpWcIJkmSJEmSpNIzBJMkSZIkSVLpGYJJkiRJkiSp9AzBJEmSJEmSVHqGYJIkSZIkSSo9QzBJkiRJkiSVniGYJEmSJEmSSs8QTJIkSZIkSaVnCCZJkiRJkqTSMwSTJEmSJElS6RmCSZIkSZIkqfQMwSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkrPEEySJEmSJEmlZwgmSZIkSZKk0jMEkyRJkiRJUukZgkmSJEmSJKn0ejS6A5IkSercHnn0Ub5+5A8b3Q3VWHG5gZx8vOdFkqTWMgSTpAXkzrvvZfJ7U3ljwv+44e//bHR3OpVGPSevv/Emy6yxwA8rdTnvT/+IdXY+pNHdUI0nRlzU6C5IktSlGIJJ0gIy+b2pLLPGBvRa/HmWWWODRnenU2nUc/LSKzct8GNKkiRJagzXBJMkSZIkSVLpGYJJkiRJkiSp9AzBJEmSJEmSVHqGYJIkSZIkSSo9QzBJkiRJkiSVniGYJEmSJEmSSs8QTJIkSZIkSaVnCCZJkiRJkqTSMwSTJEmSJElS6RmCSZIkSZIkqfQMwSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkrPEEySJEmSJEml16PRHZAkSU29+txTjP7DcY3uRsP0+t/YOR5/936D2HjvIxvUI0lqnZN+/ktefn1Co7uhGisuN5CTj/9ho7shqRMwBJMkqZPpMeN9vrvtqo3uRsM83OtJNtyy6eP/9a0vNKg3ktR6L78+gXV2PqTR3VCNJ0Zc1OguSOokDMEkSZIkSaX1yKOP8vUjHQnWmTg6T41iCDaPImJt4BxgE2AScBFwckppRiP7JUmSpHLzi33n8+i/n2CdnRvdC9V6f/pHjtDrZBydp0YxBJsHETEAuA14CtgVWB34FfmCAyc0sGuSJEkqOb/Ydz6jHzys0V2QJLXAEGzeHAYsBuyeUpoM/DMi+gHDIuKXRZnUEHfefS+T35va6G4A8MaE/3HD3//Z6G403OtvvMkyazS6F5IkSZK0cDIEmzfbA7fWhF1XAKcDWwAjGtKrBeyRfz/BDYu+2OhudCqdIfR5/Y03WXez7Rrah4peiz/PMmts0OhuNNxLr9zU6C5IkiRJ0kLLEGzerAncUV2QUhofEVOLbQtFCDb1/Wkss8YWje5Gp9IZQh8DF0mSJEmSZjMEmzcDyIvh15pYbJMkSR3g1eeeYvQfjmt0NzqN7v0GsfHeRza6G5IktYsX9uicFoardkZKqdF96LIi4kPgBymls2rKXwb+lFI6vqb8UODQ4sdPAM8siH4uAAOBCY3uhBrCc7/w8twvnDzvCy/P/cLLc7/w8twvvDz3C6+ynPtVUkpL19vgSLB5MxHoX6d8QLGtiZTShcCF87tTC1pEjEkpDWl0P7Tgee4XXp77hZPnfeHluV94ee4XXp77hZfnfuG1MJz7bo3uQBc3lrz21ywRsRLQu9gmSZIkSZKkTsAQbN7cAmwbEX2ryvYG3gdGNaZLkiRJkiRJqmUINm8uAKYB10bEF4o1v4YBv04pTW5ozxas0k3xVKt57hdenvuFk+d94eW5X3h57hdenvuFl+d+4VX6c+/C+PMoItYGzgU2IV8p8iJgWEppRiP7JUmSJEmSpNkMwSRJkiRJklR6TodUu0TE2hFxe0RMjYhXI+KUiOje6H6pY0XExyLi9xHxeETMiIiRdepERBwfES9FxPsRcVdErLfge6uOEhF7RcSNEfFKREyJiIcjYt869b4REc9GxAdFna0b0V91nIjYMyJGR8T/ivP6TEScEBG9qur4nl8IRMQKxfs/RUSfqnLPf8lExIHFea69HVZVx/NeQhHRIyKOLX6XT4uIlyPiNzV1PPclFBEjm3nfp4jYpKjjuS+hiNgnIv5V/I5/JSL+FBHL19Qp9bk3BFObRcQA4DYgAbsCpwDfA05uZL80X3wS2AF4BvhPM3WOBU4ETgd2BqYAt0XEcgukh5ofvks+j8cAuwB3ApdFxHcqFYpQ7ALgT8D2wJPATRGxzoLvrjrQUsAdwCHk8/pH4MfAr6vq+J5fOJxBPre1PP/ltRV5eY/K7dqqbZ73choOHAmcCXyRfJ7fr6njuS+nb9H0/b4J8E9gAvBQUcdzXzIRsQtwOTCa/D3+R8DmwM0RUZ0NlfrcOx1SbRYRxwE/BFapXAAgIn5IvijAcgvZRQFKLSK6pZRmFv++BhiYUhpatX1R4A3gVymlU4qyxYFxwO9TSics8E5rnkXEwJTShJqyy4BNUkqrFj8/A9ybUjq4+Lkb8BjwWErpKwu6z5p/IuJnwLeBAcAi+J4vvYjYHLge+Dk5DOubUpriZ345RcSBwMUU57nOds97CUXEdsAI4NMppaeaqeO5X0gUI75fB65MKR3uuS+niLgC+HhKacOqsl2AG4C1U0pPLwzn3pFgao/tgVtrwq4rgMWALRrTJc0PlQCsBZ8D+gFXVe3zHvk/VdvPx65pPqoNwAqPAMsDRMRqwBo0Pe8zgavxvJfR/4DKdEjf8yUXeWmDc8ijvGs/Czz/CyfPezkdDNzRXABW8NwvPLYj/7Hr8uJnz3059QTeqSmbVNxHcV/6c28IpvZYExhbXZBSGg9MLbZp4bEmMAN4tqb8aXwtlM0mzJ4SWzm3Y2vqPA0sGRFLL7Beab6IiO4R0TsiNiVPlTk/5aHjvufL7zDyiL/f1dnm+S+35yLio8hrAX6zqtzzXk4bA/+JiHMjYnLkdX6vrVkbyHO/8NgHeBm4u/jZc19OfwQ2i4ivRUS/iFgDOJWmgXjpz70hmNpjALMT42oTi21aeAwApqSUZtSUTwR6R9Vi2uq6Ii94vxvwq6Ko8j6fVFN1Ys12dV3vFbe7gVHAD4py3/MlFhFLAT8FvptS+rBOFc9/Ob1GXvvlq+S1X+4HLoiIY4rtnvdyWg44EFiPHIAcBGwIXBcRlREhnvuFQET0Jq8Be1WavVaS576EUko3k9/3F5JHhD0DdAf2qKpW+nPfo9EdkCR1XhExGLgMuCGlNLyxvdEC9DmgN7AR8BPgXPIiuiq3nwH3p5T+1uiOaMFJKd0K3FpVdEuxJswJEXF2g7ql+S+K264ppf8BRMRr5D98bAXc3sC+acHaGVic2VMhVVIRsSX5wlZnA7cAy5LX9b4uIr5QJ/gqJUMwtcdEoH+d8gHMHgmihcNEoE9EdK/50BwATE0pTW9Qv9QBImJJ8i/IF4H9qzZV3uf9aToabEDNdnVRKaV/Ff+8JyImAJdExK/wPV9aEfFJ8hpBm0fEEkVx7+K+f0TMwPO/MLkG+DIwGM97WU0Enq8EYIV7gOnA2uQQzHO/cNgH+G9KaUxVmee+nH4F3JhS+lGlICIeJS9xsiv5qsClP/dOh1R7jKVmPnBErET+z3LtGkEqt7HkIbQfqymfY904dS3F0PibyAui75RSmlq1uXJua9cFWBN4O6X01gLoohacSiC2Kr7ny+zj5AVz7yP/B3gis9cFe5m8WL7nf+GRqu497+X0NLMXwq4WQOXCSJ77kouI/uTFzmtHgXnuy2lN4NHqgpTSM8D7wOpFUenPvSGY2uMWYNuI6FtVtjf5zTOqMV1Sg4wGJgN7VQqK8GRn8utEXVBE9CBf6fHjwHYppTert6eUnicvkl993rsVP3vey+fzxf0L+J4vs3uALWtupxfbdgDOwPO/MNmTfHXQF/G8l9VNwKciYmBV2ebkMPyx4mfPffl9iXwxlNoQzHNfTi8CG1QXRMRawGLAuKKo9Ofe6ZBqjwvIVwu7NiJOB1YjzyX+dUppciM7po5VfODtUPy4AtAvIvYsfv5bSmlqRJwGnBgRE8l/HfguOWA/Z4F3WB3lPPJ5PwpYqlgsu+KRlNI08nv+LxExDrgXOIAcmu23YLuqjhQRfwduA54kXxno88D3gCtTSs8VdXzPl1BKaQIwsrqsWBMQ4O6U0pSizPNfMhHxV+BB4HHyX//3Lm5HppRmAh943kvpQvL/50dExM+BvuTg+7aU0j0AKSXPffntAzyWUnq6utBzX1oXAL+JiFeZvSbYT8gB2N9g4Tj3hmBqs5TSxOJqcecCI8hrAv2G/KVY5bIMeURQtcrPq5I/ME8jfygeBywFjAG2SSm9sYD6qI73xeK+3oLIqwLjUkqXR0Qf4Efkq4o9SZ42+cQC6qPmj4fIVw0aDHwEPE9+b19QVcf3/MLN818+z5DXg1uJPBXuKeBrKaU/V9XxvJdMSmlyRGwF/Ba4grwW2A3AMTVVPfclVYwC3Jr8/7h6PPfl81vye/1w4DDy9/h7gONSSu9V1Sv1uY/ZV0GVJEmSJEmSysk1wSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkrPEEySJEmSJEmlZwgmSZIkSZKk0jMEkyRJkiRJUukZgkmSpC4lItaMiHMi4omIeCcipkfEqxFxc0R8PSIWaWHfH0dEKm6faKHegVX1KrcpEfFyRNwWEadExMdb2L9232kR8VZE/CsiLoqI7SOiexsf99A67U4v+nR1RGzSTP2RLbQ5uKgzrpny6ttHEfFmRPw9InZtQx/nuNXsM7xm+4zivD4XEddHxBERsVRbnitJkqR6ejS6A5IkSa0VET8BTiL/Ie8+4BJgCrAsMBS4CDgcGFJn3wAOARIQwDeA78/lkI8B1xf/XgxYBtgYOBH4cUScA3w/pfRRM/ufXNx3B5YAPgl8Ffg6MCYi9k8p/Wcufaj1IjC8+PfiwGeBPYHdI2LPlNJ1bWyvJe8AZxX/XoTc/52AbSPiBymlM1vRx9a6AXi0+HdfYCVgM2BX4GcRcVRKqa1tSpIkzWIIJkmSuoSIOJ4cKr0E7JVSeqBOnZ2A7zXTxBeBweRwZjvggIg4PqU0vYXDPppSGlbnOFsW7RwFLAocVm/nZvZdFjgH2Au4LSKGpJTebKEPtcbVthsRJwM/AX4FdGQINqnOsfYBLgdOjojzUkpTW9PHVri+NuSKiB7AwcDZwMURMS2ldHkb25UkSQKcDilJkrqAiBgMDAM+BHaoF4ABpJRuIgdc9XyjuP8DcCkwEPhSe/qTUroT2BaYDhwaEeu3Yd83gH2AkeTRTse3pw81flfcrxoRS3dAey25EngP6A2sPT8PlFL6KKV0IfCtoujXEbFYa/aNiM0iYkQxXXRaRLweEfdHxEl16vaOiB9FxJiIeLeY+vp0RPy2CC2r6w6KiN9FxLhiOupbEXFtRGxYp93KtNoDI2K7iBhZTPVMVXV6RMS3ir5NjoipEfFIMQ3U/6tLktSB/MUqSZK6goOAnsBfU0pPtFQxpTSttqwIMnYB/pNSGs3sqXqHtrdDKaWxwFXkqZX7tXHfmcCpxY/7FlM150X1/qnZWh3vwwV0nEvIUyyXA7aaW+WI2I4cMm4K3E4eIXc9MI3ZgVql7gBgNHAa0Af4I3A+8DT5dbdWVd1VgTFFG88V7d4K7AiMLkYi1rMncBPwLnABOUgkInoW5b8jT5e9DLiQ/H/0c4rHLUmSOojTISVJUlewaXF/ezv3r4RowwFSSk9ExMPAlhHxsZTSf9vZ7kjgK8BG7dj3HuAj8jpjg4EX2tkHgG8X98+nlCbUbBscEcOa2W+Jdhxrf/JaZG8BzzRTp6Vjjk0pXdGWA6aUZkbE3cAq5Of65rns8g1ykDQ0pfRY9YaIGFhT93fAp8nh1LeLgLJStw95PbeKC4DlgRNSSj+rqncecBdwSUSsklKaUnOMHcgjGP9eU/5j8ojCc4GjU0oziva6k8OwgyPimpTSDXN5vJIkqRUMwSRJUlcwqLh/ua07Vi2IPxP4U9Wm4cCG5MDkR+3s1yvFfZunIKaUpkXE/8iL+i9N60Ow6oBpcfJC/ZuRH1+9hf5XIV9MoD2WqDrWIsA65FFP04FvppQ+aGa/lo55A9CmEKzQnuf6/dqC6pAwIpYB9gZeI1/gYGZN3SlVdVckrys3HvhlTb3REXE5ORDdnaavM4AbagOwYqrjd4DXgWMqAVjR3oyI+B45vN2f/JxJkqR5ZAgmSZLKbitgdeDWlNIrVeWXkaezHRgRJ6SU2jO1rzINsb1TENuzf3XA9BF5RNa1wK+KqZ61RqWUhtY9eF5rraXwrT9zhlnTgF1TSre2sF+zx5wHbXmuLiWHUQ9ExJXAncC9KaXaEPUz5BFjd6WU3ptLm5V13+5u5rVyBzkEW585Q7AH69RfA1gSeBY4oZkZse9TNR1TkiTNG0MwSZLUFbxGDgNWaMe+lXW/hlcXppTejogRwB7ArsA17Wh7+eL+rbbuGBGLkkOQtu4/PwKm5ryYUhoMEBH9gG2Ai4CrImKTlNJTC6gf0IbnOqV0bdWVQg8GvglQTIE9LqX0z6LqEsX9K3M0Mqf+xf1rzWyvlC9RZ9vrdcqWKu4/Tssj9frMtWeSJKlVXBhfkiR1BfcU91u3ZafiSom7FT9eXlypb9aNHIBB+xfI37K4r3u1yrnYlPwHyTdSSuPaefwFJqU0OaX0V/Jop37AnzpgQf9WKaYObl782KrnOqV0c0ppK2AA+XXzG+CTwE0RUbmq5aTivjXh6jvF/XLNbB9UU69Jd1po77qUUrRwW7UVfZMkSa3gSDBJktQVXAwcB+wREWu3NAIpIhapukLkAUAv4GHg0WZ22QX4QkSsmlJq9eL0EbEmsBc54ListfsV+3YjL4pOW/dttJTSzRHxd2A78lUxL10Ahz0QWJk82urOtuxYTHO8A7gjIiYCpwDbA0+RpynOBDaPiMXnMiXykeJ+04jokVL6qGZ7JRD9Vyu7NpYcwn02Inq2czquJElqA0eCSZKkTq8YKTWMHGjdHBFD6tWLiO2AW6qKvlHcfyuldEi9G/B78npTh7S2PxGxBfD3oj/n116BcC77LkNeGH4oeZH1n7d2307kxOL+5IiYb39UjYgeEfEN8hUcE3kB+eYW46/eb/Nm+rVscT8VIKX0FvlcDALOLMLJ6nb6RET/ou7LwD/JV/I8uqbexuRAcCJwXWseWxGinVMc+7cRsVidxzGoatSaJEmaR44EkyRJXUJK6edFsHES8FBEjAbGAFPI4cbm5PWVxgBExFDy4uP/TinVW5i84v/Io7IOioiTakb4rFdzdcRlyVdjXJs8gujXwA+ba7hq327ktaI+SZ4G2Ys8Cmn/6qsVdhUppTERcQN5LbWvk4PEatVXsKznrJTSpJqy3YqF+iFf9XJl8lUvB5GnDh6aUrqylV38LbBCRNwLjCNfzXJD8kUSXqTp1SmPIF/18jBgaETcWtRfFdiWPFJwZFH3MOBe4IyI+CL5tbYSeUTgTOCglNK7rewjwE+BTxft7hwRd5DXJ1uG/Fr+PPm1uSDXXpMkqbQMwSRJUpeRUjolIq4GvkWefnYQsCjwP/J0x9OBvxTVK6PALppLm+Mi4jbyou8703Qkz6eLG+TRQxPJ09iuAf6cUvrvXLpcWfB8OvAuOYD5E/BX4B8ppZlz2b8zO4kcEJ0YEZfUjNCqvoJlPcOZvR5Xxa7FbSbwHnkB/AeB24DLUkpvt6FvPwe+BAwBvlC0WRl1d1ZKaWKlYkppYkR8jjy6a2/y+nAzgJeAP1IVQKWUni9GIZ4A7EAezTeZPCrwZymlh9rQR1JKH0bEbuR11g4EdiIvhP8W+aqdJ7JgpptKkrRQiJTae0VvSZIkSZIkqWtwTTBJkiRJkiSVniGYJEmSJEmSSs8QTJIkSZIkSaVnCCZJkiRJkqTSMwSTJEmSJElS6RmCSZIkSZIkqfQMwSRJkiRJklR6hmCSJEmSJEkqPUMwSZIkSZIklZ4hmCRJkiRJkkrv/wHNmUkqLZawPgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "X_train_df.groupby('class')['cadd.phred'].plot(kind='hist', edgecolor='black',figsize=(20,10), alpha=0.5)\n", + "fsize = 20\n", + "#add legend\n", + "plt.legend(fontsize=fsize)\n", + "#plt.legend(X_train_df.groupby('class')['cadd.phred'].apply(lambda x: x.isnull().mean() * 100).to_frame(), fontsize=fsize)\n", + "perct = X_train_df['cadd.phred'].isnull().sum(axis = 0)/X_train_df.shape[0]\n", + "gperct = X_train_df.groupby('class')['cadd.phred'].apply(lambda x: round(x.isnull().mean() * 100,2)).to_frame()\n", + "\n", + "#add x-axis label\n", + "plt.xlabel('CADD PHRED score', fontsize=fsize)\n", + "plt.ylabel('Density', fontsize=fsize)\n", + "plt.title(f\"CADD score (t: {round(perct*100,2)}%; h: {gperct.iloc[0]['cadd.phred']}%; l: {gperct.iloc[1]['cadd.phred']}% missing values)\", fontsize=fsize)\n", + "plt.tick_params(axis='both', labelsize=fsize-5) #which='major',\n", + "plt.savefig(\n", + " f\"/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/processed/train_data_3_star/distribution/CADD.pdf\",\n", + " format=\"pdf\",\n", + " dpi=1000,\n", + " bbox_inches=\"tight\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 114/114 [00:56<00:00, 2.02it/s]\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fsize = 20\n", + "for key in tqdm(X_train_df.columns):\n", + " try:\n", + " plt.clf()\n", + " perct = X_train_df[key].isnull().sum(axis = 0)/X_train_df.shape[0]\n", + " gperct = X_train_df.groupby('class')[key].apply(lambda x: round(x.isnull().mean() * 100,2)).to_frame()\n", + "\n", + " X_train_df.groupby('class')[key].plot(kind='hist', edgecolor='black', alpha=0.5,figsize=(20,10))\n", + " \n", + " #add legend\n", + " plt.legend(fontsize=fsize)\n", + "\n", + " #add x-axis label\n", + " plt.xlabel(key, fontsize=fsize)\n", + " plt.ylabel('Frequency', fontsize=fsize)\n", + " plt.title(f\"{key} (t: {round(perct*100,2)}%; h: {gperct.iloc[0][key]}%; l: {gperct.iloc[1][key]}% missing values)\", fontsize=fsize)\n", + " plt.tick_params(axis='both', labelsize=fsize-5)\n", + " plt.savefig(\n", + " f\"/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/processed/train_data_3_star/distribution/{key}_hist.pdf\",\n", + " format=\"pdf\",\n", + " dpi=1000,\n", + " bbox_inches=\"tight\",\n", + " )\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Variant-transcript pairs shape = (842659, 109)\n" + ] + } + ], + "source": [ + "print('\\nVariant-transcript pairs shape =', X_train_df.shape)\n", + "#print('\\nVariants shape =', X_train[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['cgc.class',\n", + " 'mutationtaster.prediction',\n", + " 'ccre_screen.bound',\n", + " 'cgc.inheritance',\n", + " 'so',\n", + " 'repeat.repeatclass',\n", + " 'cgd.inheritance',\n", + " 'prec.stat',\n", + " 'class',\n", + " 'aloft.pred',\n", + " 'mutationtaster.model',\n", + " 'coding',\n", + " 'gtex.gtex_tissue',\n", + " 'sift.confidence',\n", + " 'ensembl_regulatory_build.region',\n", + " 'genehancer.feature_name',\n", + " 'aloft.conf',\n", + " 'exac_gene.exac_cnv_flag',\n", + " 'dgi.interaction',\n", + " 'ccre_screen._group']" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check if there are any categorical columns\n", + "num_cols = X_train_df._get_numeric_data().columns\n", + "\n", + "list(set(X_train_df.columns) - set(num_cols))" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " cgc.class mutationtaster.prediction ccre_screen.bound cgc.inheritance \\\n", + "0 TSG NaN NaN somatic/germline \n", + "1 TSG NaN NaN somatic/germline \n", + "2 NaN Polymorphism NaN NaN \n", + "3 NaN Polymorphism NaN NaN \n", + "4 NaN Polymorphism NaN NaN \n", + "\n", + " so repeat.repeatclass cgd.inheritance prec.stat \\\n", + "0 synonymous_variant NaN NaN recessive \n", + "1 synonymous_variant NaN NaN recessive \n", + "2 missense_variant NaN NaN NaN \n", + "3 missense_variant NaN NaN NaN \n", + "4 missense_variant NaN NaN NaN \n", + "\n", + " class aloft.pred mutationtaster.model coding gtex.gtex_tissue \\\n", + "0 low_impact NaN NaN Yes NaN \n", + "1 low_impact NaN NaN Yes NaN \n", + "2 low_impact NaN simple_aae Yes NaN \n", + "3 low_impact NaN simple_aae Yes NaN \n", + "4 low_impact NaN simple_aae Yes NaN \n", + "\n", + " sift.confidence ensembl_regulatory_build.region genehancer.feature_name \\\n", + "0 High NaN NaN \n", + "1 NaN NaN NaN \n", + "2 High NaN NaN \n", + "3 High NaN NaN \n", + "4 High NaN NaN \n", + "\n", + " aloft.conf exac_gene.exac_cnv_flag dgi.interaction ccre_screen._group \n", + "0 NaN N inhibitor NaN \n", + "1 NaN N inhibitor NaN \n", + "2 NaN N NaN NaN \n", + "3 NaN N NaN NaN \n", + "4 NaN N NaN NaN " + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train_df[list(set(X_train_df.columns) - set(num_cols))].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "TSG 245127\n", + "Oncogene, TSG, fusion 14750\n", + "Oncogene, fusion 13170\n", + "Oncogene 11814\n", + "fusion 11071\n", + "TSG, fusion 7589\n", + "Oncogene, TSG 4934\n", + "Name: cgc.class, dtype: int64" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train_df['cgc.class'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, [ax_var, ax_fts] = plt.subplots(1, 2, figsize=(15, 10))\n", + "fig.suptitle(\"Distribution of null values (842659 variants, 108 features)\", fontsize=20)\n", + "\n", + "#fig, ax = plt.subplots(figsize=(9, 8))\n", + "ax_var.set_title(\"Distribution of variants with null values\")\n", + "#ax.plot(50, y, '--')\n", + "ax_var.set_xlabel('# features')\n", + "ax_var.set_ylabel('# variants')\n", + "ax_var.grid(linestyle=\"--\")\n", + "ax_var.hist(X_train_df.isnull().sum(axis = 1))\n", + "\n", + "#fig, ax = plt.subplots(figsize=(9, 8))\n", + "ax_fts.set_title(\"Distribution of features with null values\")\n", + "#ax.plot(50, y, '--')\n", + "ax_fts.set_xlabel('# null values')\n", + "ax_fts.set_ylabel('# features')\n", + "ax_fts.grid(linestyle=\"--\")\n", + "ax_fts.hist(X_train_df.isnull().sum(axis = 0))\n", + "\n", + "#fig.tight_layout()\n", + "#for container in ax_var.containers:\n", + "# ax_var.bar_label(container)\n", + "plt.show()\n", + "\n", + "\n", + "#fig.tight_layout()\n", + "#for container in ax_fts.containers:\n", + "# ax_fts.bar_label(container)\n", + "#plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/scratch/local/ipykernel_114543/1806320573.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", + " median_scores = X_train_df.median().to_dict()\n" + ] + }, + { + "data": { + "text/plain": [ + "{'extra_vcf_info.CLNSIGCONF': nan,\n", + " 'aloft.tolerant': 0.03175,\n", + " 'aloft.recessive': 0.5456,\n", + " 'aloft.dominant': 0.38385,\n", + " 'cadd.phred': 9.129,\n", + " 'chasmplus.score': 0.073,\n", + " 'chasmplus.pval': 0.277,\n", + " 'civic.molecular_profile_score': 7.5,\n", + " 'cosmic.variant_count': 1.0,\n", + " 'cosmic_gene.occurrences': 1725.0,\n", + " 'cscape.score': 0.436793,\n", + " 'cancer_genome_interpreter.resistant': 0.0,\n", + " 'cancer_genome_interpreter.responsive': 2.0,\n", + " 'cancer_genome_interpreter.other': 0.0,\n", + " 'clinpred.score': 0.109,\n", + " 'dann.score': 0.6540942459117577,\n", + " 'dann_coding.dann_coding_score': 0.994319113925808,\n", + " 'dgi.score': 3.55,\n", + " 'ess_gene.indispensability_score': 0.863945523287189,\n", + " 'exac_gene.exac_pli': 0.0614137585139882,\n", + " 'exac_gene.exac_pnull': 1.01047322872054e-05,\n", + " 'exac_gene.exac_del_score': 0.2455437788049,\n", + " 'exac_gene.exac_dup_score': 0.194157677715669,\n", + " 'exac_gene.exac_cnv_score': 0.034861123316654,\n", + " 'fathmm.fathmm_score': -1.59,\n", + " 'fathmm_xf_coding.fathmm_xf_coding_score': 0.346275,\n", + " 'funseq2.score': 0.498146534129084,\n", + " 'gerp.gerp_rs': 4.66,\n", + " 'ghis.ghis': 0.561316286,\n", + " 'gwas_catalog.pval': 2e-09,\n", + " 'genehancer.score': 1.09,\n", + " 'linsight.value': 0.142985,\n", + " 'lrt.lrt_score': 4.2e-05,\n", + " 'lrt.lrt_omega': 0.099477,\n", + " 'loftool.loftool_score': 0.101,\n", + " 'mavedb.score': 0.8677136380949829,\n", + " 'metalr.score': 0.4031,\n", + " 'metasvm.score': -0.3355,\n", + " 'mutpred1.mutpred_general_score': 0.7979999999999999,\n", + " 'mutpred_indel.score': 0.581,\n", + " 'mutation_assessor.score': 2.0,\n", + " 'mutationtaster.score': 1.0,\n", + " 'ndex_chd.numhit': 2.0,\n", + " 'ndex.numhit': 4.0,\n", + " 'ndex_signor.numhit': 1.0,\n", + " 'prec.prec': 0.36264,\n", + " 'provean.score': -2.3,\n", + " 'pangalodb.sensitivity': 0.0,\n", + " 'pangalodb.specificity': 0.0112782,\n", + " 'phdsnpg.score': 0.93,\n", + " 'phastcons.phastcons100_vert': 1.0,\n", + " 'phastcons.phastcons30_mamm': 0.987,\n", + " 'phastcons.phastcons17way_primate': 0.961,\n", + " 'phylop.phylop100_vert': 4.239,\n", + " 'phylop.phylop30_mamm': 1.026,\n", + " 'phylop.phylop17_primate': 0.599,\n", + " 'polyphen2.hdiv_rank': 0.55554,\n", + " 'polyphen2.hvar_rank': 0.53365,\n", + " 'revel.score': 0.38,\n", + " 'rvis.rvis_evs': -0.41,\n", + " 'sift.score': 1.0,\n", + " 'sift.med': 2.74,\n", + " 'sift.seqs': 46.0,\n", + " 'segway.mean_score': 0.0858073982609,\n", + " 'siphy.logodds_rank': 0.62632,\n", + " 'spliceai.ds_ag': 0.0004,\n", + " 'spliceai.ds_al': 0.0,\n", + " 'spliceai.ds_dg': 0.0004,\n", + " 'spliceai.ds_dl': 0.0,\n", + " 'spliceai.dp_ag': -1.0,\n", + " 'spliceai.dp_al': 0.0,\n", + " 'spliceai.dp_dg': 0.0,\n", + " 'spliceai.dp_dl': 0.0,\n", + " 'varity_r.varity_r_loo': 0.3558284342288971,\n", + " 'varity_r.varity_er_loo': 0.29223165,\n", + " 'vest.score': 0.754,\n", + " 'dbscsnv.ada_score': 0.5035390098589105,\n", + " 'dbscsnv.rf_score': 0.516,\n", + " 'gnomad.af': 0.000116777782496,\n", + " 'gnomad_gene.oe_lof': 0.34946,\n", + " 'gnomad_gene.oe_mis': 0.88424,\n", + " 'gnomad_gene.oe_syn': 1.0119,\n", + " 'gnomad_gene.lof_z': 3.7638,\n", + " 'gnomad_gene.mis_z': 0.92308,\n", + " 'gnomad_gene.syn_z': -0.1187,\n", + " 'gnomad_gene.pLI': 7.8292e-05,\n", + " 'gnomad_gene.pRec': 0.30551,\n", + " 'gnomad_gene.pNull': 1.1292e-05,\n", + " 'gnomad3.af': 0.000167535,\n", + " 'phi.phi': 0.48298}" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "median_scores = X_train_df.median().to_dict()\n", + "median_scores" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 114/114 [00:32<00:00, 3.54it/s]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fsize = 20\n", + "for key in tqdm(X_train_df.columns):\n", + " try:\n", + " X_train_df[key] = X_train_df[key].fillna(median_scores[key])\n", + " plt.clf()\n", + " X_train_df.groupby('class')[key].plot(kind='hist', edgecolor='black', alpha=0.5,figsize=(20,10))\n", + " \n", + " #add legend\n", + " plt.legend(fontsize=fsize)\n", + "\n", + " #add x-axis label\n", + " plt.xlabel(key, fontsize=fsize)\n", + " plt.ylabel('Frequency', fontsize=fsize)\n", + " plt.title(f\"{key} (median value: {round(median_scores[key],2)})\", fontsize=fsize)\n", + " plt.tick_params(axis='both', labelsize=fsize-5)\n", + " plt.savefig(\n", + " f\"/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/processed/train_data_3_star/distribution/{key}_hist_after.pdf\",\n", + " format=\"pdf\",\n", + " dpi=1000,\n", + " bbox_inches=\"tight\",\n", + " )\n", + " except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/scratch/local/ipykernel_221266/1617137900.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", + " X_train_df = X_train_df.fillna(X_train_df.median())\n" + ] + } + ], + "source": [ + "#Fill NAs in dataframe\n", + "X_train_df['gnomad3.af'] = X_train_df['gnomad3.af'].fillna(0)\n", + "X_train_df = X_train_df.fillna(X_train_df.median())" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['gtex.gtex_tissue',\n", + " 'dgi.interaction',\n", + " 'cgd.inheritance',\n", + " 'so',\n", + " 'repeat.repeatclass',\n", + " 'genehancer.feature_name',\n", + " 'cgc.inheritance',\n", + " 'cgc.class']" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(config_dict[\"dummies_sep\"].keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:31<00:00, 3.88s/it]\n" + ] + }, + { + "data": { + "text/plain": [ + "(842659, 229)" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Perform one-hot encoding using sep\n", + "for key in tqdm(config_dict[\"dummies_sep\"]):\n", + " X_train_df = pd.concat((X_train_df , X_train_df[key].str.get_dummies(sep = config_dict[\"dummies_sep\"][key])), axis =1)\n", + "\n", + "X_train_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "coding 188081\n", + "aloft.tolerant 0\n", + "aloft.recessive 0\n", + "aloft.dominant 0\n", + "aloft.pred 830706\n", + "aloft.conf 830706\n", + "cadd.phred 0\n", + "cgd.inheritance 666292\n", + "chasmplus.score 0\n", + "chasmplus.pval 0\n", + "civic.molecular_profile_score 0\n", + "cosmic.variant_count 0\n", + "cosmic_gene.occurrences 0\n", + "cscape.score 0\n", + "cgc.class 534204\n", + "cgc.inheritance 531822\n", + "cancer_genome_interpreter.resistant 0\n", + "cancer_genome_interpreter.responsive 0\n", + "cancer_genome_interpreter.other 0\n", + "ccre_screen._group 692123\n", + "ccre_screen.bound 761314\n", + "clinpred.score 0\n", + "dann.score 0\n", + "dann_coding.dann_coding_score 0\n", + "dgi.interaction 742575\n", + "dgi.score 0\n", + "ensembl_regulatory_build.region 613684\n", + "ess_gene.indispensability_score 0\n", + "exac_gene.exac_pli 0\n", + "exac_gene.exac_pnull 0\n", + "exac_gene.exac_del_score 0\n", + "exac_gene.exac_dup_score 0\n", + "exac_gene.exac_cnv_score 0\n", + "exac_gene.exac_cnv_flag 114966\n", + "fathmm.fathmm_score 0\n", + "fathmm_xf_coding.fathmm_xf_coding_score 0\n", + "funseq2.score 0\n", + "gerp.gerp_rs 0\n", + "ghis.ghis 0\n", + "gtex.gtex_tissue 829517\n", + "gwas_catalog.pval 0\n", + "genehancer.feature_name 605618\n", + "genehancer.score 0\n", + "linsight.value 0\n", + "lrt.lrt_score 0\n", + "lrt.lrt_omega 0\n", + "loftool.loftool_score 0\n", + "mavedb.score 0\n", + "metalr.score 0\n", + "metasvm.score 0\n", + "mutpred1.mutpred_general_score 0\n", + "mutpred_indel.score 0\n", + "mutation_assessor.score 0\n", + "mutationtaster.score 0\n", + "mutationtaster.prediction 694289\n", + "mutationtaster.model 694289\n", + "ndex_chd.numhit 0\n", + "ndex.numhit 0\n", + "ndex_signor.numhit 0\n", + "prec.prec 0\n", + "prec.stat 691168\n", + "provean.score 0\n", + "pangalodb.sensitivity 0\n", + "pangalodb.specificity 0\n", + "phdsnpg.score 0\n", + "phastcons.phastcons100_vert 0\n", + "phastcons.phastcons30_mamm 0\n", + "phastcons.phastcons17way_primate 0\n", + "phylop.phylop100_vert 0\n", + "phylop.phylop30_mamm 0\n", + "phylop.phylop17_primate 0\n", + "polyphen2.hdiv_rank 0\n", + "polyphen2.hvar_rank 0\n", + "revel.score 0\n", + "rvis.rvis_evs 0\n", + "repeat.repeatclass 820299\n", + "sift.score 0\n", + "sift.med 0\n", + "sift.confidence 576039\n", + "sift.seqs 0\n", + "segway.mean_score 0\n", + "siphy.logodds_rank 0\n", + "spliceai.ds_ag 0\n", + "spliceai.ds_al 0\n", + "spliceai.ds_dg 0\n", + "spliceai.ds_dl 0\n", + "spliceai.dp_ag 0\n", + "spliceai.dp_al 0\n", + "spliceai.dp_dg 0\n", + "spliceai.dp_dl 0\n", + "varity_r.varity_r_loo 0\n", + "varity_r.varity_er_loo 0\n", + "vest.score 0\n", + "dbscsnv.ada_score 0\n", + "dbscsnv.rf_score 0\n", + "gnomad.af 0\n", + "gnomad_gene.oe_lof 0\n", + "gnomad_gene.oe_mis 0\n", + "gnomad_gene.oe_syn 0\n", + "gnomad_gene.lof_z 0\n", + "gnomad_gene.mis_z 0\n", + "gnomad_gene.syn_z 0\n", + "gnomad_gene.pLI 0\n", + "gnomad_gene.pRec 0\n", + "gnomad_gene.pNull 0\n", + "gnomad3.af 0\n", + "phi.phi 0\n", + "so 0\n", + "class 0\n", + "Adipose_Subcutaneous 0\n", + "Adipose_Visceral_Omentum 0\n", + "Adrenal_Gland 0\n", + "Artery_Aorta 0\n", + "Artery_Coronary 0\n", + "Artery_Tibial 0\n", + "Brain_Amygdala 0\n", + "Brain_Anterior_cingulate_cortex_BA24 0\n", + "Brain_Caudate_basal_ganglia 0\n", + "Brain_Cerebellar_Hemisphere 0\n", + "Brain_Cerebellum 0\n", + "Brain_Cortex 0\n", + "Brain_Frontal_Cortex_BA9 0\n", + "Brain_Hippocampus 0\n", + "Brain_Hypothalamus 0\n", + "Brain_Nucleus_accumbens_basal_ganglia 0\n", + "Brain_Putamen_basal_ganglia 0\n", + "Brain_Spinal_cord_cervical_c-1 0\n", + "Brain_Substantia_nigra 0\n", + "Breast_Mammary_Tissue 0\n", + "Cells_EBV-transformed_lymphocytes 0\n", + "Cells_Transformed_fibroblasts 0\n", + "Colon_Sigmoid 0\n", + "Colon_Transverse 0\n", + "Esophagus_Gastroesophageal_Junction 0\n", + "Esophagus_Mucosa 0\n", + "Esophagus_Muscularis 0\n", + "Heart_Atrial_Appendage 0\n", + "Heart_Left_Ventricle 0\n", + "Liver 0\n", + "Lung 0\n", + "Minor_Salivary_Gland 0\n", + "Muscle_Skeletal 0\n", + "Nerve_Tibial 0\n", + "Ovary 0\n", + "Pancreas 0\n", + "Pituitary 0\n", + "Prostate 0\n", + "Skin_Not_Sun_Exposed_Suprapubic 0\n", + "Skin_Sun_Exposed_Lower_leg 0\n", + "Small_Intestine_Terminal_Ileum 0\n", + "Spleen 0\n", + "Stomach 0\n", + "Testis 0\n", + "Thyroid 0\n", + "Uterus 0\n", + "Vagina 0\n", + "Whole_Blood 0\n", + "activator 0\n", + "adduct 0\n", + "agonist 0\n", + "allosteric modulator 0\n", + "antagonist 0\n", + "antibody 0\n", + "binder 0\n", + "blocker 0\n", + "chaperone 0\n", + "cofactor 0\n", + "inducer 0\n", + "inhibitor 0\n", + "ligand 0\n", + "modulator 0\n", + "negative modulator 0\n", + "positive modulator 0\n", + "potentiator 0\n", + "product of 0\n", + "stimulator 0\n", + "substrate 0\n", + "vaccine 0\n", + "AD 0\n", + "AR 0\n", + "AR 0\n", + "BG 0\n", + "Digenic 0\n", + "XL 0\n", + "2kb_downstream_variant 0\n", + "2kb_upstream_variant 0\n", + "3_prime_UTR_variant 0\n", + "5_prime_UTR_variant 0\n", + "NMD_transcript_variant 0\n", + "NSD_transcript 0\n", + "complex_substitution 0\n", + "exon_loss_variant 0\n", + "frameshift_elongation 0\n", + "frameshift_truncation 0\n", + "inframe_deletion 0\n", + "inframe_insertion 0\n", + "intron_variant 0\n", + "lnc_RNA 0\n", + "miRNA 0\n", + "misc_RNA 0\n", + "missense_variant 0\n", + "polymorphic_pseudogene 0\n", + "processed_transcript 0\n", + "rRNA 0\n", + "ribozyme 0\n", + "scaRNA 0\n", + "snRNA 0\n", + "snoRNA 0\n", + "splice_site_variant 0\n", + "start_lost 0\n", + "start_retained_variant 0\n", + "stop_gained 0\n", + "stop_lost 0\n", + "stop_retained_variant 0\n", + "synonymous_variant 0\n", + "transcript_ablation 0\n", + "LINE 0\n", + "LTR 0\n", + "Low_complexity 0\n", + "SINE 0\n", + "Satellite 0\n", + "Simple_repeat 0\n", + "Enhancer 0\n", + "Promoter 0\n", + "germline 0\n", + "somatic 0\n", + "Oncogene 0\n", + "TSG 0\n", + "fusion 0\n", + "dtype: int64" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check how many columns are null\n", + "X_train_df.isnull().sum(axis = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Variant-transcript pairs shape = (842659, 221)\n" + ] + } + ], + "source": [ + "X_train_df = X_train_df.drop(list(config_dict[\"dummies_sep\"].keys()), axis=1)\n", + "print('\\nVariant-transcript pairs shape =', X_train_df.shape)\n", + "#print('\\nVariants shape =', X_train[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Variant-transcript pairs shape = (842659, 240)\n" + ] + } + ], + "source": [ + "#Perform one-hot encoding\n", + "y = X_train_df['class']\n", + "X_train_df = pd.get_dummies(X_train_df.drop('class', axis=1), prefix_sep='_')\n", + "print('\\nVariant-transcript pairs shape =', X_train_df.shape)\n", + "#print('\\nVariants shape =', X_train_df[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "X_train_df = pd.concat([y.reset_index(drop=True), X_train_df.reset_index(drop=True)], axis=1)\n", + "del y" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "class 0\n", + "aloft.tolerant 0\n", + "aloft.recessive 0\n", + "aloft.dominant 0\n", + "cadd.phred 0\n", + "chasmplus.score 0\n", + "chasmplus.pval 0\n", + "civic.molecular_profile_score 0\n", + "cosmic.variant_count 0\n", + "cosmic_gene.occurrences 0\n", + "cscape.score 0\n", + "cancer_genome_interpreter.resistant 0\n", + "cancer_genome_interpreter.responsive 0\n", + "cancer_genome_interpreter.other 0\n", + "clinpred.score 0\n", + "dann.score 0\n", + "dann_coding.dann_coding_score 0\n", + "dgi.score 0\n", + "ess_gene.indispensability_score 0\n", + "exac_gene.exac_pli 0\n", + "exac_gene.exac_pnull 0\n", + "exac_gene.exac_del_score 0\n", + "exac_gene.exac_dup_score 0\n", + "exac_gene.exac_cnv_score 0\n", + "fathmm.fathmm_score 0\n", + "fathmm_xf_coding.fathmm_xf_coding_score 0\n", + "funseq2.score 0\n", + "gerp.gerp_rs 0\n", + "ghis.ghis 0\n", + "gwas_catalog.pval 0\n", + "genehancer.score 0\n", + "linsight.value 0\n", + "lrt.lrt_score 0\n", + "lrt.lrt_omega 0\n", + "loftool.loftool_score 0\n", + "mavedb.score 0\n", + "metalr.score 0\n", + "metasvm.score 0\n", + "mutpred1.mutpred_general_score 0\n", + "mutpred_indel.score 0\n", + "mutation_assessor.score 0\n", + "mutationtaster.score 0\n", + "ndex_chd.numhit 0\n", + "ndex.numhit 0\n", + "ndex_signor.numhit 0\n", + "prec.prec 0\n", + "provean.score 0\n", + "pangalodb.sensitivity 0\n", + "pangalodb.specificity 0\n", + "phdsnpg.score 0\n", + "phastcons.phastcons100_vert 0\n", + "phastcons.phastcons30_mamm 0\n", + "phastcons.phastcons17way_primate 0\n", + "phylop.phylop100_vert 0\n", + "phylop.phylop30_mamm 0\n", + "phylop.phylop17_primate 0\n", + "polyphen2.hdiv_rank 0\n", + "polyphen2.hvar_rank 0\n", + "revel.score 0\n", + "rvis.rvis_evs 0\n", + "sift.score 0\n", + "sift.med 0\n", + "sift.seqs 0\n", + "segway.mean_score 0\n", + "siphy.logodds_rank 0\n", + "spliceai.ds_ag 0\n", + "spliceai.ds_al 0\n", + "spliceai.ds_dg 0\n", + "spliceai.ds_dl 0\n", + "spliceai.dp_ag 0\n", + "spliceai.dp_al 0\n", + "spliceai.dp_dg 0\n", + "spliceai.dp_dl 0\n", + "varity_r.varity_r_loo 0\n", + "varity_r.varity_er_loo 0\n", + "vest.score 0\n", + "dbscsnv.ada_score 0\n", + "dbscsnv.rf_score 0\n", + "gnomad.af 0\n", + "gnomad_gene.oe_lof 0\n", + "gnomad_gene.oe_mis 0\n", + "gnomad_gene.oe_syn 0\n", + "gnomad_gene.lof_z 0\n", + "gnomad_gene.mis_z 0\n", + "gnomad_gene.syn_z 0\n", + "gnomad_gene.pLI 0\n", + "gnomad_gene.pRec 0\n", + "gnomad_gene.pNull 0\n", + "gnomad3.af 0\n", + "phi.phi 0\n", + "Adipose_Subcutaneous 0\n", + "Adipose_Visceral_Omentum 0\n", + "Adrenal_Gland 0\n", + "Artery_Aorta 0\n", + "Artery_Coronary 0\n", + "Artery_Tibial 0\n", + "Brain_Amygdala 0\n", + "Brain_Anterior_cingulate_cortex_BA24 0\n", + "Brain_Caudate_basal_ganglia 0\n", + "Brain_Cerebellar_Hemisphere 0\n", + "Brain_Cerebellum 0\n", + "Brain_Cortex 0\n", + "Brain_Frontal_Cortex_BA9 0\n", + "Brain_Hippocampus 0\n", + "Brain_Hypothalamus 0\n", + "Brain_Nucleus_accumbens_basal_ganglia 0\n", + "Brain_Putamen_basal_ganglia 0\n", + "Brain_Spinal_cord_cervical_c-1 0\n", + "Brain_Substantia_nigra 0\n", + "Breast_Mammary_Tissue 0\n", + "Cells_EBV-transformed_lymphocytes 0\n", + "Cells_Transformed_fibroblasts 0\n", + "Colon_Sigmoid 0\n", + "Colon_Transverse 0\n", + "Esophagus_Gastroesophageal_Junction 0\n", + "Esophagus_Mucosa 0\n", + "Esophagus_Muscularis 0\n", + "Heart_Atrial_Appendage 0\n", + "Heart_Left_Ventricle 0\n", + "Liver 0\n", + "Lung 0\n", + "Minor_Salivary_Gland 0\n", + "Muscle_Skeletal 0\n", + "Nerve_Tibial 0\n", + "Ovary 0\n", + "Pancreas 0\n", + "Pituitary 0\n", + "Prostate 0\n", + "Skin_Not_Sun_Exposed_Suprapubic 0\n", + "Skin_Sun_Exposed_Lower_leg 0\n", + "Small_Intestine_Terminal_Ileum 0\n", + "Spleen 0\n", + "Stomach 0\n", + "Testis 0\n", + "Thyroid 0\n", + "Uterus 0\n", + "Vagina 0\n", + "Whole_Blood 0\n", + "activator 0\n", + "adduct 0\n", + "agonist 0\n", + "allosteric modulator 0\n", + "antagonist 0\n", + "antibody 0\n", + "binder 0\n", + "blocker 0\n", + "chaperone 0\n", + "cofactor 0\n", + "inducer 0\n", + "inhibitor 0\n", + "ligand 0\n", + "modulator 0\n", + "negative modulator 0\n", + "positive modulator 0\n", + "potentiator 0\n", + "product of 0\n", + "stimulator 0\n", + "substrate 0\n", + "vaccine 0\n", + "AD 0\n", + "AR 0\n", + "AR 0\n", + "BG 0\n", + "Digenic 0\n", + "XL 0\n", + "2kb_downstream_variant 0\n", + "2kb_upstream_variant 0\n", + "3_prime_UTR_variant 0\n", + "5_prime_UTR_variant 0\n", + "NMD_transcript_variant 0\n", + "NSD_transcript 0\n", + "complex_substitution 0\n", + "exon_loss_variant 0\n", + "frameshift_elongation 0\n", + "frameshift_truncation 0\n", + "inframe_deletion 0\n", + "inframe_insertion 0\n", + "intron_variant 0\n", + "lnc_RNA 0\n", + "miRNA 0\n", + "misc_RNA 0\n", + "missense_variant 0\n", + "polymorphic_pseudogene 0\n", + "processed_transcript 0\n", + "rRNA 0\n", + "ribozyme 0\n", + "scaRNA 0\n", + "snRNA 0\n", + "snoRNA 0\n", + "splice_site_variant 0\n", + "start_lost 0\n", + "start_retained_variant 0\n", + "stop_gained 0\n", + "stop_lost 0\n", + "stop_retained_variant 0\n", + "synonymous_variant 0\n", + "transcript_ablation 0\n", + "LINE 0\n", + "LTR 0\n", + "Low_complexity 0\n", + "SINE 0\n", + "Satellite 0\n", + "Simple_repeat 0\n", + "Enhancer 0\n", + "Promoter 0\n", + "germline 0\n", + "somatic 0\n", + "Oncogene 0\n", + "TSG 0\n", + "fusion 0\n", + "coding_Yes 0\n", + "aloft.pred_Dominant 0\n", + "aloft.pred_Recessive 0\n", + "aloft.pred_Tolerant 0\n", + "aloft.conf_High 0\n", + "aloft.conf_Low 0\n", + "ccre_screen._group_CTCF-only 0\n", + "ccre_screen._group_DNase-H3K4me3 0\n", + "ccre_screen._group_PLS 0\n", + "ccre_screen._group_dELS 0\n", + "ccre_screen._group_pELS 0\n", + "ccre_screen.bound_Yes 0\n", + "ensembl_regulatory_build.region_CTCF_binding_site 0\n", + "ensembl_regulatory_build.region_TF_binding_site 0\n", + "ensembl_regulatory_build.region_enhancer 0\n", + "ensembl_regulatory_build.region_open_chromatin_region 0\n", + "ensembl_regulatory_build.region_promoter 0\n", + "ensembl_regulatory_build.region_promoter_flanking_region 0\n", + "exac_gene.exac_cnv_flag_N 0\n", + "exac_gene.exac_cnv_flag_Y 0\n", + "mutationtaster.prediction_Automatic Disease Causing 0\n", + "mutationtaster.prediction_Automatic Polymorphism 0\n", + "mutationtaster.prediction_Damaging 0\n", + "mutationtaster.prediction_Polymorphism 0\n", + "mutationtaster.model_complex_aae 0\n", + "mutationtaster.model_simple_aae 0\n", + "mutationtaster.model_without_aae 0\n", + "prec.stat_lof-tolerant 0\n", + "prec.stat_recessive 0\n", + "sift.confidence_High 0\n", + "sift.confidence_Low 0\n", + "dtype: int64" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check how many columns are null\n", + "X_train_df.isnull().sum(axis = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 1, ..., 1, 1, 1])" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "from sklearn.preprocessing import label_binarize\n", + "Y_train = label_binarize(\n", + " X_train_df['class'].values, classes=list(np.unique(X_train_df['class']))\n", + " ).ravel()\n", + "Y_train.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 600283\n", + "0 242376\n", + "Name: class, dtype: int64" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = X_train_df['class']\n", + "X_train_df['class'] = Y_train\n", + "X_train_df['class'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "class int64\n", + "aloft.tolerant float64\n", + "aloft.recessive float64\n", + "aloft.dominant float64\n", + "cadd.phred float64\n", + "chasmplus.score float64\n", + "chasmplus.pval float64\n", + "civic.molecular_profile_score float64\n", + "cosmic.variant_count float64\n", + "cosmic_gene.occurrences float64\n", + "cscape.score float64\n", + "cancer_genome_interpreter.resistant float64\n", + "cancer_genome_interpreter.responsive float64\n", + "cancer_genome_interpreter.other float64\n", + "clinpred.score float64\n", + "dann.score float64\n", + "dann_coding.dann_coding_score float64\n", + "dgi.score float64\n", + "ess_gene.indispensability_score float64\n", + "exac_gene.exac_pli float64\n", + "exac_gene.exac_pnull float64\n", + "exac_gene.exac_del_score float64\n", + "exac_gene.exac_dup_score float64\n", + "exac_gene.exac_cnv_score float64\n", + "fathmm.fathmm_score float64\n", + "fathmm_xf_coding.fathmm_xf_coding_score float64\n", + "funseq2.score float64\n", + "gerp.gerp_rs float64\n", + "ghis.ghis float64\n", + "gwas_catalog.pval float64\n", + "genehancer.score float64\n", + "linsight.value float64\n", + "lrt.lrt_score float64\n", + "lrt.lrt_omega float64\n", + "loftool.loftool_score float64\n", + "mavedb.score float64\n", + "metalr.score float64\n", + "metasvm.score float64\n", + "mutpred1.mutpred_general_score float64\n", + "mutpred_indel.score float64\n", + "mutation_assessor.score float64\n", + "mutationtaster.score float64\n", + "ndex_chd.numhit float64\n", + "ndex.numhit float64\n", + "ndex_signor.numhit float64\n", + "prec.prec float64\n", + "provean.score float64\n", + "pangalodb.sensitivity float64\n", + "pangalodb.specificity float64\n", + "phdsnpg.score float64\n", + "phastcons.phastcons100_vert float64\n", + "phastcons.phastcons30_mamm float64\n", + "phastcons.phastcons17way_primate float64\n", + "phylop.phylop100_vert float64\n", + "phylop.phylop30_mamm float64\n", + "phylop.phylop17_primate float64\n", + "polyphen2.hdiv_rank float64\n", + "polyphen2.hvar_rank float64\n", + "revel.score float64\n", + "rvis.rvis_evs float64\n", + "sift.score float64\n", + "sift.med float64\n", + "sift.seqs float64\n", + "segway.mean_score float64\n", + "siphy.logodds_rank float64\n", + "spliceai.ds_ag float64\n", + "spliceai.ds_al float64\n", + "spliceai.ds_dg float64\n", + "spliceai.ds_dl float64\n", + "spliceai.dp_ag float64\n", + "spliceai.dp_al float64\n", + "spliceai.dp_dg float64\n", + "spliceai.dp_dl float64\n", + "varity_r.varity_r_loo float64\n", + "varity_r.varity_er_loo float64\n", + "vest.score float64\n", + "dbscsnv.ada_score float64\n", + "dbscsnv.rf_score float64\n", + "gnomad.af float64\n", + "gnomad_gene.oe_lof float64\n", + "gnomad_gene.oe_mis float64\n", + "gnomad_gene.oe_syn float64\n", + "gnomad_gene.lof_z float64\n", + "gnomad_gene.mis_z float64\n", + "gnomad_gene.syn_z float64\n", + "gnomad_gene.pLI float64\n", + "gnomad_gene.pRec float64\n", + "gnomad_gene.pNull float64\n", + "gnomad3.af float64\n", + "phi.phi float64\n", + "Adipose_Subcutaneous int64\n", + "Adipose_Visceral_Omentum int64\n", + "Adrenal_Gland int64\n", + "Artery_Aorta int64\n", + "Artery_Coronary int64\n", + "Artery_Tibial int64\n", + "Brain_Amygdala int64\n", + "Brain_Anterior_cingulate_cortex_BA24 int64\n", + "Brain_Caudate_basal_ganglia int64\n", + "Brain_Cerebellar_Hemisphere int64\n", + "Brain_Cerebellum int64\n", + "Brain_Cortex int64\n", + "Brain_Frontal_Cortex_BA9 int64\n", + "Brain_Hippocampus int64\n", + "Brain_Hypothalamus int64\n", + "Brain_Nucleus_accumbens_basal_ganglia int64\n", + "Brain_Putamen_basal_ganglia int64\n", + "Brain_Spinal_cord_cervical_c-1 int64\n", + "Brain_Substantia_nigra int64\n", + "Breast_Mammary_Tissue int64\n", + "Cells_EBV-transformed_lymphocytes int64\n", + "Cells_Transformed_fibroblasts int64\n", + "Colon_Sigmoid int64\n", + "Colon_Transverse int64\n", + "Esophagus_Gastroesophageal_Junction int64\n", + "Esophagus_Mucosa int64\n", + "Esophagus_Muscularis int64\n", + "Heart_Atrial_Appendage int64\n", + "Heart_Left_Ventricle int64\n", + "Liver int64\n", + "Lung int64\n", + "Minor_Salivary_Gland int64\n", + "Muscle_Skeletal int64\n", + "Nerve_Tibial int64\n", + "Ovary int64\n", + "Pancreas int64\n", + "Pituitary int64\n", + "Prostate int64\n", + "Skin_Not_Sun_Exposed_Suprapubic int64\n", + "Skin_Sun_Exposed_Lower_leg int64\n", + "Small_Intestine_Terminal_Ileum int64\n", + "Spleen int64\n", + "Stomach int64\n", + "Testis int64\n", + "Thyroid int64\n", + "Uterus int64\n", + "Vagina int64\n", + "Whole_Blood int64\n", + "activator int64\n", + "adduct int64\n", + "agonist int64\n", + "allosteric modulator int64\n", + "antagonist int64\n", + "antibody int64\n", + "binder int64\n", + "blocker int64\n", + "chaperone int64\n", + "cofactor int64\n", + "inducer int64\n", + "inhibitor int64\n", + "ligand int64\n", + "modulator int64\n", + "negative modulator int64\n", + "positive modulator int64\n", + "potentiator int64\n", + "product of int64\n", + "stimulator int64\n", + "substrate int64\n", + "vaccine int64\n", + "AD int64\n", + "AR int64\n", + "AR int64\n", + "BG int64\n", + "Digenic int64\n", + "XL int64\n", + "2kb_downstream_variant int64\n", + "2kb_upstream_variant int64\n", + "3_prime_UTR_variant int64\n", + "5_prime_UTR_variant int64\n", + "NMD_transcript_variant int64\n", + "NSD_transcript int64\n", + "complex_substitution int64\n", + "exon_loss_variant int64\n", + "frameshift_elongation int64\n", + "frameshift_truncation int64\n", + "inframe_deletion int64\n", + "inframe_insertion int64\n", + "intron_variant int64\n", + "lnc_RNA int64\n", + "miRNA int64\n", + "misc_RNA int64\n", + "missense_variant int64\n", + "polymorphic_pseudogene int64\n", + "processed_transcript int64\n", + "rRNA int64\n", + "ribozyme int64\n", + "scaRNA int64\n", + "snRNA int64\n", + "snoRNA int64\n", + "splice_site_variant int64\n", + "start_lost int64\n", + "start_retained_variant int64\n", + "stop_gained int64\n", + "stop_lost int64\n", + "stop_retained_variant int64\n", + "synonymous_variant int64\n", + "transcript_ablation int64\n", + "LINE int64\n", + "LTR int64\n", + "Low_complexity int64\n", + "SINE int64\n", + "Satellite int64\n", + "Simple_repeat int64\n", + "Enhancer int64\n", + "Promoter int64\n", + "germline int64\n", + "somatic int64\n", + "Oncogene int64\n", + "TSG int64\n", + "fusion int64\n", + "coding_Yes uint8\n", + "aloft.pred_Dominant uint8\n", + "aloft.pred_Recessive uint8\n", + "aloft.pred_Tolerant uint8\n", + "aloft.conf_High uint8\n", + "aloft.conf_Low uint8\n", + "ccre_screen._group_CTCF-only uint8\n", + "ccre_screen._group_DNase-H3K4me3 uint8\n", + "ccre_screen._group_PLS uint8\n", + "ccre_screen._group_dELS uint8\n", + "ccre_screen._group_pELS uint8\n", + "ccre_screen.bound_Yes uint8\n", + "ensembl_regulatory_build.region_CTCF_binding_site uint8\n", + "ensembl_regulatory_build.region_TF_binding_site uint8\n", + "ensembl_regulatory_build.region_enhancer uint8\n", + "ensembl_regulatory_build.region_open_chromatin_region uint8\n", + "ensembl_regulatory_build.region_promoter uint8\n", + "ensembl_regulatory_build.region_promoter_flanking_region uint8\n", + "exac_gene.exac_cnv_flag_N uint8\n", + "exac_gene.exac_cnv_flag_Y uint8\n", + "mutationtaster.prediction_Automatic Disease Causing uint8\n", + "mutationtaster.prediction_Automatic Polymorphism uint8\n", + "mutationtaster.prediction_Damaging uint8\n", + "mutationtaster.prediction_Polymorphism uint8\n", + "mutationtaster.model_complex_aae uint8\n", + "mutationtaster.model_simple_aae uint8\n", + "mutationtaster.model_without_aae uint8\n", + "prec.stat_lof-tolerant uint8\n", + "prec.stat_recessive uint8\n", + "sift.confidence_High uint8\n", + "sift.confidence_Low uint8\n", + "dtype: object" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train_df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0 842659\n", + "Name: cancer_genome_interpreter.other, dtype: int64" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train_df['cancer_genome_interpreter.other'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [], + "source": [ + "X_train_df = X_train_df.drop(['cancer_genome_interpreter.other'],axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(35, 25))\n", + "corr_matrix = X_train_df.corr()\n", + "sns.heatmap(corr_matrix, fmt=\".2g\", cmap=\"coolwarm\")" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/scratch/local/ipykernel_197585/1278552867.py:6: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " np.triu(np.ones(corr_matrix_abs.shape), k=1).astype(np.bool)\n" + ] + } + ], + "source": [ + "# Create correlation matrix\n", + "corr_matrix_abs = corr_matrix.abs()\n", + "\n", + "# Select upper triangle of correlation matrix\n", + "upper = corr_matrix_abs.where(\n", + " np.triu(np.ones(corr_matrix_abs.shape), k=1).astype(np.bool)\n", + " )\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6\n", + "Correlated columns to be dropped: ['aloft.dominant', 'metasvm.score', 'polyphen2.hvar_rank', 'varity_r.varity_er_loo', 'dbscsnv.rf_score', 'gnomad3.af']\n" + ] + } + ], + "source": [ + "# Find features with correlation greater than 0.9\n", + "to_drop = [column for column in upper.columns if any(upper[column] > 0.95)]\n", + "print(len(to_drop))\n", + "print(\n", + " f\"Correlated columns to be dropped: {to_drop}\"\n", + " )\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(15, 15))\n", + "sns.heatmap(X_train_df[to_drop].corr(), fmt=\".2g\", cmap=\"coolwarm\")" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "# Drop features\n", + "#df.drop(to_drop, axis=1, inplace=True)\n", + "X_train_df = X_train_df.reset_index(drop=True)\n", + "del corr_matrix, to_drop, upper, corr_matrix_abs" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(842659, 240)" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "#fig = plt.figure(figsize=(30,20))\n", + "#sns.heatmap(X_train.corr(), fmt=\".2g\", cmap=\"coolwarm\")" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "#Give variant IDs and add the variant info columns\n", + "#df = df.reset_index(drop=True)\n", + "#df['ID'] = [f'var_{num}' for num in range(len(df))]\n", + "#print('NAs filled!')\n", + "X_train_df = pd.concat([var.reset_index(drop=True), X_train_df.reset_index(drop=True)], axis=1)\n", + "del var" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Variant-transcript pairs shape = (842659, 255)\n", + "\n", + "Variants shape = (148108, 4)\n" + ] + } + ], + "source": [ + "print('\\nVariant-transcript pairs shape =', X_train_df.shape)\n", + "print('\\nVariants shape =', X_train_df[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['transcript', 'gene', 'consequence', 'protein_hgvs', 'cdna_hgvs', 'chrom', 'pos', 'ref_base', 'alt_base', 'clingen.disease', 'clingen.classification', 'ncbigene.entrez', 'omim.omim_id', 'uniprot.acc', 'dbsnp.rsid', 'class', 'aloft.tolerant', 'aloft.recessive', 'aloft.dominant', 'cadd.phred', 'chasmplus.score', 'chasmplus.pval', 'civic.molecular_profile_score', 'cosmic.variant_count', 'cosmic_gene.occurrences', 'cscape.score', 'cancer_genome_interpreter.resistant', 'cancer_genome_interpreter.responsive', 'clinpred.score', 'dann.score', 'dann_coding.dann_coding_score', 'dgi.score', 'ess_gene.indispensability_score', 'exac_gene.exac_pli', 'exac_gene.exac_pnull', 'exac_gene.exac_del_score', 'exac_gene.exac_dup_score', 'exac_gene.exac_cnv_score', 'fathmm.fathmm_score', 'fathmm_xf_coding.fathmm_xf_coding_score', 'funseq2.score', 'gerp.gerp_rs', 'ghis.ghis', 'gwas_catalog.pval', 'genehancer.score', 'linsight.value', 'lrt.lrt_score', 'lrt.lrt_omega', 'loftool.loftool_score', 'mavedb.score', 'metalr.score', 'metasvm.score', 'mutpred1.mutpred_general_score', 'mutpred_indel.score', 'mutation_assessor.score', 'mutationtaster.score', 'ndex_chd.numhit', 'ndex.numhit', 'ndex_signor.numhit', 'prec.prec', 'provean.score', 'pangalodb.sensitivity', 'pangalodb.specificity', 'phdsnpg.score', 'phastcons.phastcons100_vert', 'phastcons.phastcons30_mamm', 'phastcons.phastcons17way_primate', 'phylop.phylop100_vert', 'phylop.phylop30_mamm', 'phylop.phylop17_primate', 'polyphen2.hdiv_rank', 'polyphen2.hvar_rank', 'revel.score', 'rvis.rvis_evs', 'sift.score', 'sift.med', 'sift.seqs', 'segway.mean_score', 'siphy.logodds_rank', 'spliceai.ds_ag', 'spliceai.ds_al', 'spliceai.ds_dg', 'spliceai.ds_dl', 'spliceai.dp_ag', 'spliceai.dp_al', 'spliceai.dp_dg', 'spliceai.dp_dl', 'varity_r.varity_r_loo', 'varity_r.varity_er_loo', 'vest.score', 'dbscsnv.ada_score', 'dbscsnv.rf_score', 'gnomad.af', 'gnomad_gene.oe_lof', 'gnomad_gene.oe_mis', 'gnomad_gene.oe_syn', 'gnomad_gene.lof_z', 'gnomad_gene.mis_z', 'gnomad_gene.syn_z', 'gnomad_gene.pLI', 'gnomad_gene.pRec', 'gnomad_gene.pNull', 'gnomad3.af', 'phi.phi', 'Adipose_Subcutaneous', 'Adipose_Visceral_Omentum', 'Adrenal_Gland', 'Artery_Aorta', 'Artery_Coronary', 'Artery_Tibial', 'Brain_Amygdala', 'Brain_Anterior_cingulate_cortex_BA24', 'Brain_Caudate_basal_ganglia', 'Brain_Cerebellar_Hemisphere', 'Brain_Cerebellum', 'Brain_Cortex', 'Brain_Frontal_Cortex_BA9', 'Brain_Hippocampus', 'Brain_Hypothalamus', 'Brain_Nucleus_accumbens_basal_ganglia', 'Brain_Putamen_basal_ganglia', 'Brain_Spinal_cord_cervical_c-1', 'Brain_Substantia_nigra', 'Breast_Mammary_Tissue', 'Cells_EBV-transformed_lymphocytes', 'Cells_Transformed_fibroblasts', 'Colon_Sigmoid', 'Colon_Transverse', 'Esophagus_Gastroesophageal_Junction', 'Esophagus_Mucosa', 'Esophagus_Muscularis', 'Heart_Atrial_Appendage', 'Heart_Left_Ventricle', 'Liver', 'Lung', 'Minor_Salivary_Gland', 'Muscle_Skeletal', 'Nerve_Tibial', 'Ovary', 'Pancreas', 'Pituitary', 'Prostate', 'Skin_Not_Sun_Exposed_Suprapubic', 'Skin_Sun_Exposed_Lower_leg', 'Small_Intestine_Terminal_Ileum', 'Spleen', 'Stomach', 'Testis', 'Thyroid', 'Uterus', 'Vagina', 'Whole_Blood', 'activator', 'adduct', 'agonist', 'allosteric modulator', 'antagonist', 'antibody', 'binder', 'blocker', 'chaperone', 'cofactor', 'inducer', 'inhibitor', 'ligand', 'modulator', 'negative modulator', 'positive modulator', 'potentiator', 'product of', 'stimulator', 'substrate', 'vaccine', 'AD', 'AR', 'AR ', 'BG', 'Digenic', 'XL', '2kb_downstream_variant', '2kb_upstream_variant', '3_prime_UTR_variant', '5_prime_UTR_variant', 'NMD_transcript_variant', 'NSD_transcript', 'complex_substitution', 'exon_loss_variant', 'frameshift_elongation', 'frameshift_truncation', 'inframe_deletion', 'inframe_insertion', 'intron_variant', 'lnc_RNA', 'miRNA', 'misc_RNA', 'missense_variant', 'polymorphic_pseudogene', 'processed_transcript', 'rRNA', 'ribozyme', 'scaRNA', 'snRNA', 'snoRNA', 'splice_site_variant', 'start_lost', 'start_retained_variant', 'stop_gained', 'stop_lost', 'stop_retained_variant', 'synonymous_variant', 'transcript_ablation', 'LINE', 'LTR', 'Low_complexity', 'SINE', 'Satellite', 'Simple_repeat', 'Enhancer', 'Promoter', 'germline', 'somatic', 'Oncogene', 'TSG', 'fusion', 'coding_Yes', 'aloft.pred_Dominant', 'aloft.pred_Recessive', 'aloft.pred_Tolerant', 'aloft.conf_High', 'aloft.conf_Low', 'ccre_screen._group_CTCF-only', 'ccre_screen._group_DNase-H3K4me3', 'ccre_screen._group_PLS', 'ccre_screen._group_dELS', 'ccre_screen._group_pELS', 'ccre_screen.bound_Yes', 'ensembl_regulatory_build.region_CTCF_binding_site', 'ensembl_regulatory_build.region_TF_binding_site', 'ensembl_regulatory_build.region_enhancer', 'ensembl_regulatory_build.region_open_chromatin_region', 'ensembl_regulatory_build.region_promoter', 'ensembl_regulatory_build.region_promoter_flanking_region', 'exac_gene.exac_cnv_flag_N', 'exac_gene.exac_cnv_flag_Y', 'mutationtaster.prediction_Automatic Disease Causing', 'mutationtaster.prediction_Automatic Polymorphism', 'mutationtaster.prediction_Damaging', 'mutationtaster.prediction_Polymorphism', 'mutationtaster.model_complex_aae', 'mutationtaster.model_simple_aae', 'mutationtaster.model_without_aae', 'prec.stat_lof-tolerant', 'prec.stat_recessive', 'sift.confidence_High', 'sift.confidence_Low']\n" + ] + } + ], + "source": [ + "train_columns = X_train_df.columns.values.tolist()\n", + "print(train_columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Data shape (After filtering) = (842659, 255)\n", + "Class shape= (842659, 255)\n" + ] + } + ], + "source": [ + "print('\\nData shape (After filtering) =', X_train_df.shape)\n", + "print('Class shape=', X_train_df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [], + "source": [ + "# Write it to a file\n", + "X_train_df.to_csv('./processed/train_data_3_star/train_class_data_80.csv.gz', index=False)\n", + "#y_train.to_csv('./processed/train_data_3_star/train_data-y_80.csv.gz', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preparing Testing data" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Variant-transcript pairs shape = (208167, 123)\n" + ] + } + ], + "source": [ + "X_test_df = X_test_df.drop(['aloft.affect','cancer_genome_interpreter.other','extra_vcf_info.CLNSIGCONF','extra_vcf_info.CLNSIG','extra_vcf_info.CLNREVSTAT','extra_vcf_info.CLNDN'], axis=1)\n", + "\n", + "print('\\nVariant-transcript pairs shape =', X_test_df.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Impact (Class):\n", + " low_impact 147809\n", + "high_impact 60358\n", + "Name: class, dtype: int64\n" + ] + } + ], + "source": [ + "print('\\nImpact (Class):\\n', X_test_df['class'].value_counts())" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Drop variant info columns so we can perform one-hot encoding\n", + "var = X_test_df[config_dict['train_cols']]\n", + "X_test_df = X_test_df.drop(config_dict['train_cols'], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['genehancer.feature_name',\n", + " 'ccre_screen._group',\n", + " 'mutationtaster.prediction',\n", + " 'exac_gene.exac_cnv_flag',\n", + " 'coding',\n", + " 'class',\n", + " 'cgc.class',\n", + " 'ccre_screen.bound',\n", + " 'mutationtaster.model',\n", + " 'sift.confidence',\n", + " 'so',\n", + " 'aloft.conf',\n", + " 'gtex.gtex_tissue',\n", + " 'cgc.inheritance',\n", + " 'ensembl_regulatory_build.region',\n", + " 'prec.stat',\n", + " 'cgd.inheritance',\n", + " 'aloft.pred',\n", + " 'repeat.repeatclass',\n", + " 'dgi.interaction']" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Double check if there are any columns with weird formatting as categorical before performing one-hot encoding\n", + "num_cols = X_test_df._get_numeric_data().columns\n", + "\n", + "list(set(X_test_df.columns) - set(num_cols))" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(208167, 108)" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:06<00:00, 1.28it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "(208167, 237)" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Perform one-hot encoding\n", + "for key in tqdm(config_dict[\"dummies_sep\"]):\n", + " X_test_df = pd.concat((X_test_df , X_test_df[key].str.get_dummies(sep = config_dict[\"dummies_sep\"][key])), axis =1)\n", + "X_test_df = X_test_df.drop(list(config_dict[\"dummies_sep\"].keys()), axis=1)\n", + "y = X_test_df['class']\n", + "X_test_df = pd.get_dummies(X_test_df.drop('class',axis=1), prefix_sep='_')\n", + "X_test_df.shape\n" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "X_test_df['gnomad3.af'] = X_test_df['gnomad3.af'].fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 89/89 [00:00<00:00, 632.02it/s]\n" + ] + } + ], + "source": [ + "for key in tqdm(median_scores.keys()):\n", + " if key in X_test_df.columns:\n", + " X_test_df[key] = (\n", + " X_test_df[key]\n", + " .fillna(median_scores[key])\n", + " .astype(\"float64\")\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['aloft.tolerant', 'aloft.recessive', 'aloft.dominant', 'cadd.phred',\n", + " 'chasmplus.score', 'chasmplus.pval', 'civic.molecular_profile_score',\n", + " 'cosmic.variant_count', 'cosmic_gene.occurrences', 'cscape.score',\n", + " ...\n", + " 'mutationtaster.prediction_Automatic Polymorphism',\n", + " 'mutationtaster.prediction_Damaging',\n", + " 'mutationtaster.prediction_Polymorphism',\n", + " 'mutationtaster.model_complex_aae', 'mutationtaster.model_simple_aae',\n", + " 'mutationtaster.model_without_aae', 'prec.stat_lof-tolerant',\n", + " 'prec.stat_recessive', 'sift.confidence_High', 'sift.confidence_Low'],\n", + " dtype='object', length=237)" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test_df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 40%|โ–ˆโ–ˆโ–ˆโ–ˆ | 102/255 [00:00<00:00, 1013.96it/s]/scratch/local/ipykernel_197585/3124834107.py:4: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df2[key] = X_test_df[key]\n", + "/scratch/local/ipykernel_197585/3124834107.py:6: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + " df2[key] = 0\n", + "100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 255/255 [00:00<00:00, 1161.91it/s]\n" + ] + } + ], + "source": [ + "df2 = pd.DataFrame()\n", + "for key in tqdm(train_columns):\n", + " if key in X_test_df.columns:\n", + " df2[key] = X_test_df[key]\n", + " else:\n", + " df2[key] = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Data shape = (208167, 237)\n", + "\n", + "Data shape = (208167, 255)\n" + ] + } + ], + "source": [ + "print('\\nData shape =', X_test_df.shape)\n", + "print('\\nData shape =', df2.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "X_test_df = df2.copy()\n", + "del df2" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "transcript 208167\n", + "gene 208167\n", + "consequence 208167\n", + "protein_hgvs 208167\n", + "cdna_hgvs 208167\n", + "chrom 208167\n", + "pos 208167\n", + "ref_base 208167\n", + "alt_base 208167\n", + "clingen.disease 208167\n", + "clingen.classification 208167\n", + "ncbigene.entrez 208167\n", + "omim.omim_id 208167\n", + "uniprot.acc 208167\n", + "dbsnp.rsid 208167\n", + "class 208167\n", + "aloft.tolerant 0\n", + "aloft.recessive 0\n", + "aloft.dominant 0\n", + "cadd.phred 0\n", + "chasmplus.score 0\n", + "chasmplus.pval 0\n", + "civic.molecular_profile_score 0\n", + "cosmic.variant_count 0\n", + "cosmic_gene.occurrences 0\n", + "cscape.score 0\n", + "cancer_genome_interpreter.resistant 0\n", + "cancer_genome_interpreter.responsive 0\n", + "clinpred.score 0\n", + "dann.score 0\n", + "dann_coding.dann_coding_score 0\n", + "dgi.score 0\n", + "ess_gene.indispensability_score 0\n", + "exac_gene.exac_pli 0\n", + "exac_gene.exac_pnull 0\n", + "exac_gene.exac_del_score 0\n", + "exac_gene.exac_dup_score 0\n", + "exac_gene.exac_cnv_score 0\n", + "fathmm.fathmm_score 0\n", + "fathmm_xf_coding.fathmm_xf_coding_score 0\n", + "funseq2.score 0\n", + "gerp.gerp_rs 0\n", + "ghis.ghis 0\n", + "gwas_catalog.pval 0\n", + "genehancer.score 0\n", + "linsight.value 0\n", + "lrt.lrt_score 0\n", + "lrt.lrt_omega 0\n", + "loftool.loftool_score 0\n", + "mavedb.score 0\n", + "metalr.score 0\n", + "metasvm.score 0\n", + "mutpred1.mutpred_general_score 0\n", + "mutpred_indel.score 0\n", + "mutation_assessor.score 0\n", + "mutationtaster.score 0\n", + "ndex_chd.numhit 0\n", + "ndex.numhit 0\n", + "ndex_signor.numhit 0\n", + "prec.prec 0\n", + "provean.score 0\n", + "pangalodb.sensitivity 0\n", + "pangalodb.specificity 0\n", + "phdsnpg.score 0\n", + "phastcons.phastcons100_vert 0\n", + "phastcons.phastcons30_mamm 0\n", + "phastcons.phastcons17way_primate 0\n", + "phylop.phylop100_vert 0\n", + "phylop.phylop30_mamm 0\n", + "phylop.phylop17_primate 0\n", + "polyphen2.hdiv_rank 0\n", + "polyphen2.hvar_rank 0\n", + "revel.score 0\n", + "rvis.rvis_evs 0\n", + "sift.score 0\n", + "sift.med 0\n", + "sift.seqs 0\n", + "segway.mean_score 0\n", + "siphy.logodds_rank 0\n", + "spliceai.ds_ag 0\n", + "spliceai.ds_al 0\n", + "spliceai.ds_dg 0\n", + "spliceai.ds_dl 0\n", + "spliceai.dp_ag 0\n", + "spliceai.dp_al 0\n", + "spliceai.dp_dg 0\n", + "spliceai.dp_dl 0\n", + "varity_r.varity_r_loo 0\n", + "varity_r.varity_er_loo 0\n", + "vest.score 0\n", + "dbscsnv.ada_score 0\n", + "dbscsnv.rf_score 0\n", + "gnomad.af 0\n", + "gnomad_gene.oe_lof 0\n", + "gnomad_gene.oe_mis 0\n", + "gnomad_gene.oe_syn 0\n", + "gnomad_gene.lof_z 0\n", + "gnomad_gene.mis_z 0\n", + "gnomad_gene.syn_z 0\n", + "gnomad_gene.pLI 0\n", + "gnomad_gene.pRec 0\n", + "gnomad_gene.pNull 0\n", + "gnomad3.af 0\n", + "phi.phi 0\n", + "Adipose_Subcutaneous 0\n", + "Adipose_Visceral_Omentum 0\n", + "Adrenal_Gland 0\n", + "Artery_Aorta 0\n", + "Artery_Coronary 0\n", + "Artery_Tibial 0\n", + "Brain_Amygdala 0\n", + "Brain_Anterior_cingulate_cortex_BA24 0\n", + "Brain_Caudate_basal_ganglia 0\n", + "Brain_Cerebellar_Hemisphere 0\n", + "Brain_Cerebellum 0\n", + "Brain_Cortex 0\n", + "Brain_Frontal_Cortex_BA9 0\n", + "Brain_Hippocampus 0\n", + "Brain_Hypothalamus 0\n", + "Brain_Nucleus_accumbens_basal_ganglia 0\n", + "Brain_Putamen_basal_ganglia 0\n", + "Brain_Spinal_cord_cervical_c-1 0\n", + "Brain_Substantia_nigra 0\n", + "Breast_Mammary_Tissue 0\n", + "Cells_EBV-transformed_lymphocytes 0\n", + "Cells_Transformed_fibroblasts 0\n", + "Colon_Sigmoid 0\n", + "Colon_Transverse 0\n", + "Esophagus_Gastroesophageal_Junction 0\n", + "Esophagus_Mucosa 0\n", + "Esophagus_Muscularis 0\n", + "Heart_Atrial_Appendage 0\n", + "Heart_Left_Ventricle 0\n", + "Liver 0\n", + "Lung 0\n", + "Minor_Salivary_Gland 0\n", + "Muscle_Skeletal 0\n", + "Nerve_Tibial 0\n", + "Ovary 0\n", + "Pancreas 0\n", + "Pituitary 0\n", + "Prostate 0\n", + "Skin_Not_Sun_Exposed_Suprapubic 0\n", + "Skin_Sun_Exposed_Lower_leg 0\n", + "Small_Intestine_Terminal_Ileum 0\n", + "Spleen 0\n", + "Stomach 0\n", + "Testis 0\n", + "Thyroid 0\n", + "Uterus 0\n", + "Vagina 0\n", + "Whole_Blood 0\n", + "activator 0\n", + "adduct 0\n", + "agonist 0\n", + "allosteric modulator 0\n", + "antagonist 0\n", + "antibody 0\n", + "binder 0\n", + "blocker 0\n", + "chaperone 0\n", + "cofactor 0\n", + "inducer 0\n", + "inhibitor 0\n", + "ligand 0\n", + "modulator 0\n", + "negative modulator 0\n", + "positive modulator 0\n", + "potentiator 0\n", + "product of 0\n", + "stimulator 0\n", + "substrate 0\n", + "vaccine 0\n", + "AD 0\n", + "AR 0\n", + "AR 0\n", + "BG 0\n", + "Digenic 0\n", + "XL 0\n", + "2kb_downstream_variant 0\n", + "2kb_upstream_variant 0\n", + "3_prime_UTR_variant 0\n", + "5_prime_UTR_variant 0\n", + "NMD_transcript_variant 0\n", + "NSD_transcript 0\n", + "complex_substitution 0\n", + "exon_loss_variant 0\n", + "frameshift_elongation 0\n", + "frameshift_truncation 0\n", + "inframe_deletion 0\n", + "inframe_insertion 0\n", + "intron_variant 0\n", + "lnc_RNA 0\n", + "miRNA 0\n", + "misc_RNA 0\n", + "missense_variant 0\n", + "polymorphic_pseudogene 0\n", + "processed_transcript 0\n", + "rRNA 0\n", + "ribozyme 0\n", + "scaRNA 0\n", + "snRNA 0\n", + "snoRNA 0\n", + "splice_site_variant 0\n", + "start_lost 0\n", + "start_retained_variant 0\n", + "stop_gained 0\n", + "stop_lost 0\n", + "stop_retained_variant 0\n", + "synonymous_variant 0\n", + "transcript_ablation 0\n", + "LINE 0\n", + "LTR 0\n", + "Low_complexity 0\n", + "SINE 0\n", + "Satellite 0\n", + "Simple_repeat 0\n", + "Enhancer 0\n", + "Promoter 0\n", + "germline 0\n", + "somatic 0\n", + "Oncogene 0\n", + "TSG 0\n", + "fusion 0\n", + "coding_Yes 0\n", + "aloft.pred_Dominant 0\n", + "aloft.pred_Recessive 0\n", + "aloft.pred_Tolerant 0\n", + "aloft.conf_High 0\n", + "aloft.conf_Low 0\n", + "ccre_screen._group_CTCF-only 0\n", + "ccre_screen._group_DNase-H3K4me3 0\n", + "ccre_screen._group_PLS 0\n", + "ccre_screen._group_dELS 0\n", + "ccre_screen._group_pELS 0\n", + "ccre_screen.bound_Yes 0\n", + "ensembl_regulatory_build.region_CTCF_binding_site 0\n", + "ensembl_regulatory_build.region_TF_binding_site 0\n", + "ensembl_regulatory_build.region_enhancer 0\n", + "ensembl_regulatory_build.region_open_chromatin_region 0\n", + "ensembl_regulatory_build.region_promoter 0\n", + "ensembl_regulatory_build.region_promoter_flanking_region 0\n", + "exac_gene.exac_cnv_flag_N 0\n", + "exac_gene.exac_cnv_flag_Y 0\n", + "mutationtaster.prediction_Automatic Disease Causing 0\n", + "mutationtaster.prediction_Automatic Polymorphism 0\n", + "mutationtaster.prediction_Damaging 0\n", + "mutationtaster.prediction_Polymorphism 0\n", + "mutationtaster.model_complex_aae 0\n", + "mutationtaster.model_simple_aae 0\n", + "mutationtaster.model_without_aae 0\n", + "prec.stat_lof-tolerant 0\n", + "prec.stat_recessive 0\n", + "sift.confidence_High 0\n", + "sift.confidence_Low 0\n", + "dtype: int64" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check how many columns are null\n", + "X_test_df.isnull().sum(axis = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [], + "source": [ + "X_test_df = X_test_df.drop(config_dict['train_cols'], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Data shape = (208167, 240)\n" + ] + } + ], + "source": [ + "print('\\nData shape =', X_test_df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_test_df.shape[0] == var.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [], + "source": [ + "X_test_df = pd.concat([var.reset_index(drop=True), X_test_df.reset_index(drop=True)], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [], + "source": [ + "X_test_df['class'] = y" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "transcript 0\n", + "gene 0\n", + "consequence 0\n", + "protein_hgvs 97243\n", + "cdna_hgvs 27364\n", + "chrom 0\n", + "pos 0\n", + "ref_base 0\n", + "alt_base 0\n", + "clingen.disease 86112\n", + "clingen.classification 86112\n", + "ncbigene.entrez 410\n", + "omim.omim_id 168174\n", + "uniprot.acc 80\n", + "dbsnp.rsid 16196\n", + "class 0\n", + "aloft.tolerant 0\n", + "aloft.recessive 0\n", + "aloft.dominant 0\n", + "cadd.phred 0\n", + "chasmplus.score 0\n", + "chasmplus.pval 0\n", + "civic.molecular_profile_score 0\n", + "cosmic.variant_count 0\n", + "cosmic_gene.occurrences 0\n", + "cscape.score 0\n", + "cancer_genome_interpreter.resistant 0\n", + "cancer_genome_interpreter.responsive 0\n", + "clinpred.score 0\n", + "dann.score 0\n", + "dann_coding.dann_coding_score 0\n", + "dgi.score 0\n", + "ess_gene.indispensability_score 0\n", + "exac_gene.exac_pli 0\n", + "exac_gene.exac_pnull 0\n", + "exac_gene.exac_del_score 0\n", + "exac_gene.exac_dup_score 0\n", + "exac_gene.exac_cnv_score 0\n", + "fathmm.fathmm_score 0\n", + "fathmm_xf_coding.fathmm_xf_coding_score 0\n", + "funseq2.score 0\n", + "gerp.gerp_rs 0\n", + "ghis.ghis 0\n", + "gwas_catalog.pval 0\n", + "genehancer.score 0\n", + "linsight.value 0\n", + "lrt.lrt_score 0\n", + "lrt.lrt_omega 0\n", + "loftool.loftool_score 0\n", + "mavedb.score 0\n", + "metalr.score 0\n", + "metasvm.score 0\n", + "mutpred1.mutpred_general_score 0\n", + "mutpred_indel.score 0\n", + "mutation_assessor.score 0\n", + "mutationtaster.score 0\n", + "ndex_chd.numhit 0\n", + "ndex.numhit 0\n", + "ndex_signor.numhit 0\n", + "prec.prec 0\n", + "provean.score 0\n", + "pangalodb.sensitivity 0\n", + "pangalodb.specificity 0\n", + "phdsnpg.score 0\n", + "phastcons.phastcons100_vert 0\n", + "phastcons.phastcons30_mamm 0\n", + "phastcons.phastcons17way_primate 0\n", + "phylop.phylop100_vert 0\n", + "phylop.phylop30_mamm 0\n", + "phylop.phylop17_primate 0\n", + "polyphen2.hdiv_rank 0\n", + "polyphen2.hvar_rank 0\n", + "revel.score 0\n", + "rvis.rvis_evs 0\n", + "sift.score 0\n", + "sift.med 0\n", + "sift.seqs 0\n", + "segway.mean_score 0\n", + "siphy.logodds_rank 0\n", + "spliceai.ds_ag 0\n", + "spliceai.ds_al 0\n", + "spliceai.ds_dg 0\n", + "spliceai.ds_dl 0\n", + "spliceai.dp_ag 0\n", + "spliceai.dp_al 0\n", + "spliceai.dp_dg 0\n", + "spliceai.dp_dl 0\n", + "varity_r.varity_r_loo 0\n", + "varity_r.varity_er_loo 0\n", + "vest.score 0\n", + "dbscsnv.ada_score 0\n", + "dbscsnv.rf_score 0\n", + "gnomad.af 0\n", + "gnomad_gene.oe_lof 0\n", + "gnomad_gene.oe_mis 0\n", + "gnomad_gene.oe_syn 0\n", + "gnomad_gene.lof_z 0\n", + "gnomad_gene.mis_z 0\n", + "gnomad_gene.syn_z 0\n", + "gnomad_gene.pLI 0\n", + "gnomad_gene.pRec 0\n", + "gnomad_gene.pNull 0\n", + "gnomad3.af 0\n", + "phi.phi 0\n", + "Adipose_Subcutaneous 0\n", + "Adipose_Visceral_Omentum 0\n", + "Adrenal_Gland 0\n", + "Artery_Aorta 0\n", + "Artery_Coronary 0\n", + "Artery_Tibial 0\n", + "Brain_Amygdala 0\n", + "Brain_Anterior_cingulate_cortex_BA24 0\n", + "Brain_Caudate_basal_ganglia 0\n", + "Brain_Cerebellar_Hemisphere 0\n", + "Brain_Cerebellum 0\n", + "Brain_Cortex 0\n", + "Brain_Frontal_Cortex_BA9 0\n", + "Brain_Hippocampus 0\n", + "Brain_Hypothalamus 0\n", + "Brain_Nucleus_accumbens_basal_ganglia 0\n", + "Brain_Putamen_basal_ganglia 0\n", + "Brain_Spinal_cord_cervical_c-1 0\n", + "Brain_Substantia_nigra 0\n", + "Breast_Mammary_Tissue 0\n", + "Cells_EBV-transformed_lymphocytes 0\n", + "Cells_Transformed_fibroblasts 0\n", + "Colon_Sigmoid 0\n", + "Colon_Transverse 0\n", + "Esophagus_Gastroesophageal_Junction 0\n", + "Esophagus_Mucosa 0\n", + "Esophagus_Muscularis 0\n", + "Heart_Atrial_Appendage 0\n", + "Heart_Left_Ventricle 0\n", + "Liver 0\n", + "Lung 0\n", + "Minor_Salivary_Gland 0\n", + "Muscle_Skeletal 0\n", + "Nerve_Tibial 0\n", + "Ovary 0\n", + "Pancreas 0\n", + "Pituitary 0\n", + "Prostate 0\n", + "Skin_Not_Sun_Exposed_Suprapubic 0\n", + "Skin_Sun_Exposed_Lower_leg 0\n", + "Small_Intestine_Terminal_Ileum 0\n", + "Spleen 0\n", + "Stomach 0\n", + "Testis 0\n", + "Thyroid 0\n", + "Uterus 0\n", + "Vagina 0\n", + "Whole_Blood 0\n", + "activator 0\n", + "adduct 0\n", + "agonist 0\n", + "allosteric modulator 0\n", + "antagonist 0\n", + "antibody 0\n", + "binder 0\n", + "blocker 0\n", + "chaperone 0\n", + "cofactor 0\n", + "inducer 0\n", + "inhibitor 0\n", + "ligand 0\n", + "modulator 0\n", + "negative modulator 0\n", + "positive modulator 0\n", + "potentiator 0\n", + "product of 0\n", + "stimulator 0\n", + "substrate 0\n", + "vaccine 0\n", + "AD 0\n", + "AR 0\n", + "AR 0\n", + "BG 0\n", + "Digenic 0\n", + "XL 0\n", + "2kb_downstream_variant 0\n", + "2kb_upstream_variant 0\n", + "3_prime_UTR_variant 0\n", + "5_prime_UTR_variant 0\n", + "NMD_transcript_variant 0\n", + "NSD_transcript 0\n", + "complex_substitution 0\n", + "exon_loss_variant 0\n", + "frameshift_elongation 0\n", + "frameshift_truncation 0\n", + "inframe_deletion 0\n", + "inframe_insertion 0\n", + "intron_variant 0\n", + "lnc_RNA 0\n", + "miRNA 0\n", + "misc_RNA 0\n", + "missense_variant 0\n", + "polymorphic_pseudogene 0\n", + "processed_transcript 0\n", + "rRNA 0\n", + "ribozyme 0\n", + "scaRNA 0\n", + "snRNA 0\n", + "snoRNA 0\n", + "splice_site_variant 0\n", + "start_lost 0\n", + "start_retained_variant 0\n", + "stop_gained 0\n", + "stop_lost 0\n", + "stop_retained_variant 0\n", + "synonymous_variant 0\n", + "transcript_ablation 0\n", + "LINE 0\n", + "LTR 0\n", + "Low_complexity 0\n", + "SINE 0\n", + "Satellite 0\n", + "Simple_repeat 0\n", + "Enhancer 0\n", + "Promoter 0\n", + "germline 0\n", + "somatic 0\n", + "Oncogene 0\n", + "TSG 0\n", + "fusion 0\n", + "coding_Yes 0\n", + "aloft.pred_Dominant 0\n", + "aloft.pred_Recessive 0\n", + "aloft.pred_Tolerant 0\n", + "aloft.conf_High 0\n", + "aloft.conf_Low 0\n", + "ccre_screen._group_CTCF-only 0\n", + "ccre_screen._group_DNase-H3K4me3 0\n", + "ccre_screen._group_PLS 0\n", + "ccre_screen._group_dELS 0\n", + "ccre_screen._group_pELS 0\n", + "ccre_screen.bound_Yes 0\n", + "ensembl_regulatory_build.region_CTCF_binding_site 0\n", + "ensembl_regulatory_build.region_TF_binding_site 0\n", + "ensembl_regulatory_build.region_enhancer 0\n", + "ensembl_regulatory_build.region_open_chromatin_region 0\n", + "ensembl_regulatory_build.region_promoter 0\n", + "ensembl_regulatory_build.region_promoter_flanking_region 0\n", + "exac_gene.exac_cnv_flag_N 0\n", + "exac_gene.exac_cnv_flag_Y 0\n", + "mutationtaster.prediction_Automatic Disease Causing 0\n", + "mutationtaster.prediction_Automatic Polymorphism 0\n", + "mutationtaster.prediction_Damaging 0\n", + "mutationtaster.prediction_Polymorphism 0\n", + "mutationtaster.model_complex_aae 0\n", + "mutationtaster.model_simple_aae 0\n", + "mutationtaster.model_without_aae 0\n", + "prec.stat_lof-tolerant 0\n", + "prec.stat_recessive 0\n", + "sift.confidence_High 0\n", + "sift.confidence_Low 0\n", + "dtype: int64" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check how many columns are null\n", + "X_test_df.isnull().sum(axis = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Variant-transcript pairs shape = (208167, 255)\n", + "\n", + "Variants shape = (37027, 4)\n" + ] + } + ], + "source": [ + "print('\\nVariant-transcript pairs shape =', X_test_df.shape)\n", + "print('\\nVariants shape =', X_test_df[['chrom','pos','ref_base','alt_base']].drop_duplicates().shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Data shape = (208167, 255)\n", + "\n", + "Class shape = (208167,)\n" + ] + } + ], + "source": [ + "print('\\nData shape =', X_test_df.shape)\n", + "print('\\nClass shape =', y.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [], + "source": [ + "# Write it to a file\n", + "X_test_df.to_csv('./processed/train_data_3_star/test_class_data_20.csv.gz', index=False)\n", + "#y_test.to_csv('./processed/train_data_3_star/test_data-y_20.csv.gz', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [], + "source": [ + "del X_train, X_test, df" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "df = original.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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chromposref_basealt_basetranscriptCADDCscapeClinpredDANNDANN_coding...ProveanphdsnpgrevelSIFTVESTdbscsnv.ada_scoredbscsnv.rf_scorevarity_rvarity_erClinvar
0chr169134AGENST0000033513716.910.4800420.0040.9577710.957771...-3.560.1330.0750.1070.107NaNNaN0.1303390.187185Likely_benign
1chr169134AGENST0000064151516.910.4800420.0040.9577710.957771...NaN0.133NaNNaNNaNNaNNaN0.1303390.187185Likely_benign
2chr169581CGENST0000033513723.400.2798630.9980.9964600.996460...-8.330.6980.0790.0000.431NaNNaN0.9201560.857305Uncertain_significance
3chr169581CGENST0000064151523.400.2798630.9980.9964600.996460...NaN0.698NaNNaNNaNNaNNaN0.9201560.857305Uncertain_significance
4chr169682GAENST0000033513720.800.3294020.1610.9961490.996149...0.130.0250.1120.1860.073NaNNaN0.0751640.069712Uncertain_significance
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5 rows ร— 32 columns

\n", + "
" + ], + "text/plain": [ + " chrom pos ref_base alt_base transcript CADD Cscape Clinpred \\\n", + "0 chr1 69134 A G ENST00000335137 16.91 0.480042 0.004 \n", + "1 chr1 69134 A G ENST00000641515 16.91 0.480042 0.004 \n", + "2 chr1 69581 C G ENST00000335137 23.40 0.279863 0.998 \n", + "3 chr1 69581 C G ENST00000641515 23.40 0.279863 0.998 \n", + "4 chr1 69682 G A ENST00000335137 20.80 0.329402 0.161 \n", + "\n", + " DANN DANN_coding ... Provean phdsnpg revel SIFT VEST \\\n", + "0 0.957771 0.957771 ... -3.56 0.133 0.075 0.107 0.107 \n", + "1 0.957771 0.957771 ... NaN 0.133 NaN NaN NaN \n", + "2 0.996460 0.996460 ... -8.33 0.698 0.079 0.000 0.431 \n", + "3 0.996460 0.996460 ... NaN 0.698 NaN NaN NaN \n", + "4 0.996149 0.996149 ... 0.13 0.025 0.112 0.186 0.073 \n", + "\n", + " dbscsnv.ada_score dbscsnv.rf_score varity_r varity_er \\\n", + "0 NaN NaN 0.130339 0.187185 \n", + "1 NaN NaN 0.130339 0.187185 \n", + "2 NaN NaN 0.920156 0.857305 \n", + "3 NaN NaN 0.920156 0.857305 \n", + "4 NaN NaN 0.075164 0.069712 \n", + "\n", + " Clinvar \n", + "0 Likely_benign \n", + "1 Likely_benign \n", + "2 Uncertain_significance \n", + "3 Uncertain_significance \n", + "4 Uncertain_significance \n", + "\n", + "[5 rows x 32 columns]" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "benchmark_columns = ['chrom','pos','ref_base','alt_base','transcript','cadd.phred','cscape.score','clinpred.score','dann.score','dann_coding.dann_coding_score','dgi.score','fathmm_xf_coding.fathmm_xf_coding_score','funseq2.score','linsight.value','lrt.lrt_score','loftool.loftool_score','metasvm.score','metalr.score','mutpred1.mutpred_general_score','mutpred_indel.score','mutation_assessor.score','mutationtaster.score','provean.score','phdsnpg.score','revel.score','sift.score','vest.score','dbscsnv.ada_score','dbscsnv.rf_score','varity_r.varity_r_loo', 'varity_r.varity_er_loo','extra_vcf_info.CLNSIG']\n", + "benchmark_df = df[benchmark_columns]\n", + "benchmark_df.columns = ['chrom','pos','ref_base','alt_base','transcript','CADD','Cscape','Clinpred','DANN','DANN_coding','DGI','fathmm_xf','funseq2','linsight','LRT','loftool','MetaSVM','MetaLR','Mutpred','Mutpred_indel','Mutation_assessor','Mutationtaster','Provean','phdsnpg','revel','SIFT','VEST','dbscsnv.ada_score','dbscsnv.rf_score','varity_r','varity_er','Clinvar']\n", + "benchmark_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(12973645, 32)" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "benchmark_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [], + "source": [ + "# Write it to a file\n", + "benchmark_df.to_csv('./processed/train_data_3_star/benchmark_class_data.csv.gz', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 missense_variant\n", + "2 missense_variant\n", + "4 missense_variant\n", + "6 missense_variant\n", + "8 missense_variant\n", + "Name: so, dtype: object" + ] + }, + "execution_count": 165, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "original.so.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [], + "source": [ + "def test_parsing(dataframe, config_dict):\n", + " # Drop variant info columns so we can perform one-hot encoding\n", + " var = dataframe[config_dict['id_cols']]\n", + " dataframe = dataframe.drop(config_dict['id_cols'], axis=1)\n", + " #dataframe = dataframe.replace(['.','-'], np.nan)\n", + " \n", + " #Perform one-hot encoding\n", + " for key in tqdm(config_dict[\"dummies_sep\"]):\n", + " dataframe = pd.concat((dataframe , dataframe[key].str.get_dummies(sep = config_dict[\"dummies_sep\"][key])), axis =1)\n", + " dataframe = dataframe.drop(list(config_dict[\"dummies_sep\"].keys()), axis=1)\n", + " dataframe = pd.get_dummies(dataframe, prefix_sep='_')\n", + " \n", + " for key in tqdm(list(config_dict['median_scores'].keys())):\n", + " if key in dataframe.columns:\n", + " dataframe[key] = (\n", + " dataframe[key]\n", + " .fillna(config_dict['median_scores'][key])\n", + " .astype(\"float64\")\n", + " )\n", + " \n", + " df2 = pd.DataFrame()\n", + " for key in tqdm(config_dict[\"filtered_cols\"]):\n", + " if key in dataframe.columns:\n", + " df2[key] = dataframe[key]\n", + " else:\n", + " df2[key] = 0\n", + " del dataframe\n", + " \n", + " df2 = df2.drop(config_dict['id_cols'], axis=1)\n", + " df2 = pd.concat([var.reset_index(drop=True), df2.reset_index(drop=True)], axis=1)\n", + " return df2\n" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "294" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(config_dict[\"filtered_cols\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [18:39<00:00, 139.89s/it]\n", + "100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 117/117 [00:16<00:00, 6.96it/s]\n", + "100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 294/294 [00:53<00:00, 5.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Data shape = (10695155, 293)\n" + ] + } + ], + "source": [ + "df2 = test_parsing(original, config_dict)\n", + "print('\\nData shape =', df2.shape)\n", + "# Write it to a file\n", + "df2.to_csv('./processed/clinvar_filtered.csv.gz', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 186, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(set(df2.columns) - set(config_dict[\"filtered_cols\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/annotation_parsing/parse_multi_sample.py b/src/annotation_parsing/parse_multi_sample.py new file mode 100644 index 0000000..270e3a8 --- /dev/null +++ b/src/annotation_parsing/parse_multi_sample.py @@ -0,0 +1,339 @@ +from pathlib import Path +import argparse +import os +import json +import csv +import ctypes as ct +import gzip + +# dealing with large fields in a CSV requires more memory allowed per field +# see https://stackoverflow.com/questions/15063936/csv-error-field-larger-than-field-limit-131072 for discussion +# and this solution +csv.field_size_limit(int(ct.c_ulong(-1).value // 2)) + +ALL_MAPPINGS_COLUMN_ID = "all_mappings" +NUMBER_OF_SAMPLES_COLUMN_ID = "numsample" +SAMPLES_COLUMN_ID = "samples" + + +def create_data_config(annot_csv, outfile=None): + # Column description. Column 0 uid=UID + # Column description. Column 1 chrom=Chrom + # Column description. Column 2 pos=Position + # Column description. Column 3 ref_base=Ref Base + # Column description. Column 4 alt_base=Alt Base + columns = list() + with open(annot_csv) as csvfp: + cntr = 0 + for line in csvfp: + if line.startswith("#Column description. Column"): + line = line.replace("#Column description. Column ", "").strip() + info = line.replace("=", " ").split(" ") + columns.append( + { + "col_id": info[1], + "parse_type": { + "none": "none", + "list": { + "trx_index_col": "fathmm.ens_tid", + "column_list": [ + "fathmm.fathmm_score", + "fathmm.fathmm_pred", + ], + "separator": ";", + }, + "list-o-dicts": { + "dict_index": {0: "column_name", 1: "column_name1"}, + "trx_mapping_col_index": 0, + }, + }, + } + ) + if cntr > 2000: + break + else: + cntr += 1 + + with open(outfile, "wt") as ofp: + json.dump(columns, ofp) + + +def parse_list_of_dicts(data_value, column_config): + dict_of_dicts = dict() + if data_value == "": + return dict_of_dicts + + for sublist in json.loads(data_value): + sublist_dict = dict() + trx_id = sublist[column_config["trx_mapping_col_index"]].split(".")[0] + dict_of_dicts[trx_id] = sublist_dict + for index, value in enumerate(sublist): + # look up column name by index value, assign column name as key in return dict + if str(index) not in column_config["dict_index"]: + continue + + sublist_dict[column_config["dict_index"][str(index)]] = value + + return dict_of_dicts + + +def parse_multicolumn_list_of_dicts(index_column, multi_column_config, data_cols_dict): + # data_cols_n_configs => list of tuples where tuple[0] is column value, tuple[1] is column config + index_mapping = dict() + dict_of_dicts = dict() + + if index_column == "": + return dict_of_dicts + + for index, index_value in enumerate( + index_column.split(multi_column_config["separator"]) + ): + index_mapping[index] = index_value.split(".")[0] + dict_of_dicts[index_mapping[index]] = dict() + + for column, value in data_cols_dict.items(): + sublist = value.split(multi_column_config["separator"]) + for index, data_value in enumerate(sublist): + dict_of_dicts[index_mapping[index]][column] = data_value + + return dict_of_dicts + + +def parse_annotations(annot_csv, data_config_file, outdir): + # reading data config for determination of parsing + data_config = list() + with open(data_config_file, "rt") as dcfp: + # parse and filter for column configs that needing parsing + data_config = json.load(dcfp) + + # reading the number of samples to parse + # situations where the GT column for sample info is 0/0 or 0|0 will cause OpenCravat to not print + # the variant information to the output (it put the variant in the error output file instead) but + # this issue can't be accounted for in the parsing logic here + samples = set() + with open(annot_csv, "r", newline="") as csvfile: + reader = csv.DictReader(filter(lambda row: row[0] != "#", csvfile)) + for row in reader: + if int(row[NUMBER_OF_SAMPLES_COLUMN_ID]) == 0: + samples.add( + "_" + ) # use underscore to represent single sample VCF as a convention + break + + samples.update(row[SAMPLES_COLUMN_ID].split(";")) + + samples = list(samples) + + # create chunks of samples to process to limit number of open file handles and avoid OS open file handle errors + sample_groups = [] + for group_index_start in range(0, len(samples), 2): + end = ( + group_index_start + 2 + if (group_index_start + 2) > len(samples) + else len(samples) + ) + sample_groups.append(samples[group_index_start:end]) + + # the column "all_mappings" is the key split-by column to separate results on a per variant + transcript + hardcoded_fieldnames = [ + "transcript", + "gene", + "consequence", + "protein_hgvs", + "cdna_hgvs", + ] + parsed_fieldnames = list() + for colconf in data_config: + if "list" in colconf["parse_type"]: + parsed_fieldnames += colconf["parse_type"]["list"]["column_list"] + elif "list-o-dicts" in colconf["parse_type"]: + parsed_fieldnames += colconf["parse_type"]["list-o-dicts"][ + "dict_index" + ].values() + else: + parsed_fieldnames.append(colconf["col_id"]) + + predefined_keys = hardcoded_fieldnames + parsed_fieldnames + + for sample_group in sample_groups: + # create dict of output writers for all samples in the group + sample_output_writers = dict() + sample_group_fh = [] + for sample in sample_group: + # with open(outdir, "w", newline="") as paserdcsv: + samplepath = ( + outdir / f"{sample}_parsed.csv.gz" + if sample != "_" + else outdir / f"{Path(annot_csv).stem}_parsed.csv.gz" + ) + sample_fh = gzip.open(samplepath, "wt", newline="") + sample_group_fh.append(sample_fh) + csvwriter = csv.DictWriter(sample_fh, fieldnames=predefined_keys) + sample_output_writers[sample] = csvwriter + csvwriter.writeheader() + + with open(annot_csv, "r", newline="") as csvfile: + reader = csv.DictReader(filter(lambda row: row[0] != "#", csvfile)) + for row in reader: + # parse list of dict columns first since this only needs to be done once per row and cached + cached_dicts_o_dicts = dict() + + for column in filter( + lambda colconf: "list-o-dicts" in colconf["parse_type"], data_config + ): + cached_dicts_o_dicts[column["col_id"]] = parse_list_of_dicts( + row[column["col_id"]], column["parse_type"]["list-o-dicts"] + ) + + # parse list which is a list of dicts spread across multiple columns + for column in filter( + lambda colconf: "list" in colconf["parse_type"], data_config + ): + col_data_dict = { + subcolumn: row[subcolumn] + for subcolumn in column["parse_type"]["list"]["column_list"] + } + cached_dicts_o_dicts[ + column["col_id"] + ] = parse_multicolumn_list_of_dicts( + row[column["col_id"]], + column["parse_type"]["list"], + col_data_dict, + ) + + for variant_trx in row[ALL_MAPPINGS_COLUMN_ID].split(";"): + vtrx_cols = variant_trx.split(":") + trx = vtrx_cols[0].split(".")[0].strip() + annot_variant = {key: None for key in predefined_keys} + if len(vtrx_cols) < 6: + # parse intergenic variant + annot_variant["transcript"] = "" + annot_variant["gene"] = "" + annot_variant["consequence"] = "" + annot_variant["protein_hgvs"] = "" + annot_variant["cdna_hgvs"] = "" + for column in data_config: + if "none" in column["parse_type"]: + annot_variant[column["col_id"]] = row[column["col_id"]] + elif "list-o-dicts" in column["parse_type"]: + for subcol in column["parse_type"]["list-o-dicts"][ + "dict_index" + ].values(): + annot_variant[subcol] = row[subcol] + else: + for subcol in column["parse_type"]["list"][ + "column_list" + ]: + annot_variant[subcol] = row[subcol] + else: + # parse variant with transcript info + annot_variant["transcript"] = trx + annot_variant["gene"] = vtrx_cols[1] + annot_variant["consequence"] = vtrx_cols[3] + annot_variant["protein_hgvs"] = vtrx_cols[4] + annot_variant["cdna_hgvs"] = vtrx_cols[5] + for column in data_config: + if "none" in column["parse_type"]: + annot_variant[column["col_id"]] = row[column["col_id"]] + elif trx in cached_dicts_o_dicts[column["col_id"]]: + annot_variant.update( + cached_dicts_o_dicts[column["col_id"]][trx] + ) + else: + continue + + # print parsed variant + transcript annotations to csv file output + samples = row[SAMPLES_COLUMN_ID].split(";") + if len(samples) == 1 and samples[0] == "": + sample_output_writers["_"].writerow(annot_variant) + else: + for sample in samples: + if sample in sample_output_writers: + sample_output_writers[sample].writerow(annot_variant) + + # don't forget to close all the open writer file handles for the group ;) + for sample_fh in sample_group_fh: + sample_fh.close() + + +def is_valid_output_dir(p, arg): + if os.access(Path(os.path.expandvars(arg)), os.W_OK): + return os.path.expandvars(arg) + else: + p.error(f"Output directory {arg} can't be accessed or is invalid!") + + +def is_valid_file(p, arg): + if not Path(os.path.expandvars(arg)).is_file(): + p.error(f"The file {arg} does not exist!") + else: + return os.path.expandvars(arg) + + +if __name__ == "__main__": + EXECUTIONS = [ + "config", + "parse", + ] + + PARSER = argparse.ArgumentParser( + description="Simple parser for creating data model, data parsing config, and data parsing of annotations from OpenCravat", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + + PARSER.add_argument( + "-i", + "--input_csv", + help="File path to the CSV file of annotated variants from OpenCravat", + required=True, + type=lambda x: is_valid_file(PARSER, x), + metavar="\b", + ) + + PARSER.add_argument( + "-e", + "--exec", + help="Determine what should be done: create a new data config file or parse the annotations from the OpenCravat CSV file", + required=True, + choices=EXECUTIONS, + metavar="\b", + ) + + OPTIONAL_ARGS = PARSER.add_argument_group("Override Args") + PARSER.add_argument( + "-o", + "--output", + help="Output directory for parsing", + type=lambda x: is_valid_output_dir(PARSER, x), + metavar="\b", + ) + + PARSER.add_argument( + "-v", + "--version", + help="Verison of OpenCravat used to generate the config file (only required during config parsing)", + type=str, + metavar="\b", + ) + + PARSER.add_argument( + "-c", + "--config", + help="File path to the data config JSON file that determines how to parse annotated variants from OpenCravat", + type=lambda x: is_valid_file(PARSER, x), + metavar="\b", + ) + + ARGS = PARSER.parse_args() + + if ARGS.exec == "config" and not ARGS.version: + print( + "Version of OpenCravat must be specified when creating a config from their data for tracking purposes" + ) + raise SystemExit(1) + + if ARGS.exec == "config": + create_data_config(ARGS.input_csv, f"opencravat_{ARGS.version}_config.json") + else: + outdir = Path(ARGS.output) if ARGS.output else Path(ARGS.input_csv).parent + parse_annotations(ARGS.input_csv, ARGS.config, outdir) diff --git a/src/annotation_parsing/parse_annotated_vars.py b/src/annotation_parsing/parse_single_sample.py similarity index 94% rename from src/annotation_parsing/parse_annotated_vars.py rename to src/annotation_parsing/parse_single_sample.py index e4925ba..56ea457 100644 --- a/src/annotation_parsing/parse_annotated_vars.py +++ b/src/annotation_parsing/parse_single_sample.py @@ -4,6 +4,7 @@ import json import csv import ctypes as ct +import gzip # dealing with large fields in a CSV requires more memory allowed per field # see https://stackoverflow.com/questions/15063936/csv-error-field-larger-than-field-limit-131072 for discussion @@ -104,9 +105,11 @@ def parse_annotations(annot_csv, data_config_file, outfile): data_config = json.load(dcfp) # the column "all_mappings" is the key split-by column to separate results on a per variant + transcript - with open(outfile, "w", newline="") as paserdcsv: + with gzip.open(outfile, "wt", newline="") if outfile.endswith(".gz") else open( + outfile, "w", newline="" + ) as paserdcsv: hardcoded_fieldnames = [ - "trx", + "transcript", "gene", "consequence", "protein_hgvs", @@ -123,12 +126,13 @@ def parse_annotations(annot_csv, data_config_file, outfile): else: parsed_fieldnames.append(colconf["col_id"]) - csvwriter = csv.DictWriter( - paserdcsv, fieldnames=hardcoded_fieldnames + parsed_fieldnames - ) + predefined_keys = hardcoded_fieldnames + parsed_fieldnames + csvwriter = csv.DictWriter(paserdcsv, fieldnames=predefined_keys) csvwriter.writeheader() - with open(annot_csv, "r", newline="") as csvfile: + with gzip.open(annot_csv, "rt", newline="") if annot_csv.endswith( + ".gz" + ) else open(annot_csv, "r", newline="") as csvfile: reader = csv.DictReader(filter(lambda row: row[0] != "#", csvfile)) for row in reader: # parse list of dict columns first since this only needs to be done once per row and cached @@ -160,10 +164,10 @@ def parse_annotations(annot_csv, data_config_file, outfile): for variant_trx in row[ALL_MAPPINGS_COLUMN_ID].split(";"): vtrx_cols = variant_trx.split(":") trx = vtrx_cols[0].split(".")[0].strip() - annot_variant = dict() + annot_variant = {key: None for key in predefined_keys} if len(vtrx_cols) < 6: # parse intergenic variant - annot_variant["trx"] = "" + annot_variant["transcript"] = "" annot_variant["gene"] = "" annot_variant["consequence"] = "" annot_variant["protein_hgvs"] = "" @@ -183,7 +187,7 @@ def parse_annotations(annot_csv, data_config_file, outfile): annot_variant[subcol] = row[subcol] else: # parse variant with transcript info - annot_variant["trx"] = trx + annot_variant["transcript"] = trx annot_variant["gene"] = vtrx_cols[1] annot_variant["consequence"] = vtrx_cols[3] annot_variant["protein_hgvs"] = vtrx_cols[4] diff --git a/src/model.job b/src/model.job deleted file mode 100644 index 16d9dd1..0000000 --- a/src/model.job +++ /dev/null @@ -1,36 +0,0 @@ -#!/bin/bash -# -#SBATCH --job-name=dbNSFP_variants_parsed -#SBATCH --output=dbNSFP_variants_parsed.out -# -# Number of tasks needed for this job. Generally, used with MPI jobs -#SBATCH --ntasks=1 -#SBATCH --partition=short -# -# Number of CPUs allocated to each task. -#SBATCH --cpus-per-task=1 -# -# Mimimum memory required per allocated CPU in MegaBytes. -#SBATCH --mem=1G -# -# Send mail to the email address when the job fails -#SBATCH --mail-type=FAIL -#SBATCH --mail-user=tmamidi@uab.edu - -#Set your environment here -module load BCFtools -module load tabix -#module load Anaconda3/2020.02 -#source activate testing - -#Run your commands here -#python training/data-prep/extract_class.py -#python training/data-prep/parse_dbNSFP.py -i ../data/interim/dbNSFP_clinvar_variants.tsv.gz -o ../data/interim/dbNSFP_clinvar_variants_parsed.tsv.gz -#zcat /data/project/worthey_lab/temp_datasets_central/tarun/dbNSFP/v4.3_20220319/dbNSFP4.3a_variant.complete.parsed.tsv.gz | grep -v ^"#" | sort -T /data/project/worthey_lab/temp_datasets_central/tarun/dbNSFP/v4.3_20220319/tmp -k1,1 -k2,2n | bgzip -c > /data/project/worthey_lab/temp_datasets_central/tarun/dbNSFP/v4.3_20220319/dbNSFP4.3a_variant.complete.parsed.sorted.tsv.gz - -zcat /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/Ditto/dbnsfp_only_ditto_predictions.tsv.gz | grep -v ^"#" | sort -T /data/project/worthey_lab/temp_datasets_central/tarun/dbNSFP/v4.3_20220319/tmp -k1,1 -k2,2n | bgzip -c > /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/Ditto/dbnsfp_only_ditto_predictions.tsv.bgz - -##chr pos(1-based) ref alt cds_strand genename Ensembl_geneid Ensembl_transcriptid Ensembl_proteinid Uniprot_acc clinvar_clnsig clinvar_review Interpro_domain Ditto_Benign Ditto_Deleterious - -#1 65565 A C + OR4F5 ENSG00000186092 ENST00000641515 ENSP00000493376 A0A2U3U0J3 . . . 0.9490702580176865 0.05092974198231349 -#tabix -s 1 -b 2 -e 2 /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/Ditto/dbnsfp_only_ditto_predictions.tsv.gz diff --git a/src/predict/predict.py b/src/predict/predict.py index 712e728..22fd471 100644 --- a/src/predict/predict.py +++ b/src/predict/predict.py @@ -1,112 +1,178 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- -#python src/Ditto/predict.py -i /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/annotated_vcf/train/CAGI6_RGP_TRAIN_34_PROBAND/data.csv --sample CAGI6_RGP_TRAIN_34_PROBAND -o /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/annotated_vcf/train/CAGI6_RGP_TRAIN_34_PROBAND/ditto_predictions.csv -o100 /data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/debugged/annotated_vcf/train/CAGI6_RGP_TRAIN_34_PROBAND/ditto_predictions_100.csv +# python src/predict/predict.py -i data/external/test_parse.csv -o data/interim -c configs/col_config.yaml -d model/ import pandas as pd import yaml -import warnings - -warnings.simplefilter("ignore") -from joblib import load import argparse import os -import glob +from pathlib import Path +from tensorflow import keras +import numpy as np + + +def parse_and_predict(dataframe, config_dict, clf): + # Drop variant info columns so we can perform one-hot encoding + dataframe["so"] = dataframe["consequence"] + var = dataframe[config_dict["id_cols"]] + var["gnomad3.af"] = dataframe["gnomad3.af"].copy() + dataframe = dataframe.drop(config_dict["id_cols"], axis=1) + dataframe = dataframe.replace([".", "-", ""], np.nan) + for key in dataframe.columns: + try: + dataframe[key] = dataframe[key].astype("float64") + except: + pass + temp_df = dataframe.copy() + # Perform one-hot encoding + for key in config_dict["dummies_sep"]: + if not dataframe[key].isnull().all(): + dataframe = pd.concat( + ( + dataframe, + dataframe[key].str.get_dummies(sep=config_dict["dummies_sep"][key]), + ), + axis=1, + ) + + dataframe = dataframe.drop(list(config_dict["dummies_sep"].keys()), axis=1) + dataframe = pd.get_dummies(dataframe, prefix_sep="_") + + dataframe = dataframe * 1 + df2 = pd.DataFrame(columns=config_dict["filtered_cols"]) + for key in config_dict["filtered_cols"]: + if key in dataframe.columns: + df2[key] = dataframe[key] + else: + df2[key] = 0 + del dataframe + + df2 = df2.drop(config_dict["train_cols"], axis=1) + for key in list(config_dict["median_scores"].keys()): + if key in df2.columns: + df2[key] = ( + df2[key].fillna(config_dict["median_scores"][key]).astype("float64") + ) + + y_score = 1 - clf.predict(df2, verbose=0) + y_score = pd.DataFrame(y_score, columns=["DITTO"]) + + var = pd.concat( + [var.reset_index(drop=True), y_score.reset_index(drop=True)], axis=1 + ) + dataframe = pd.concat( + [var.reset_index(drop=True), temp_df.reset_index(drop=True)], axis=1 + ) + del df2, temp_df + return dataframe, var + + +def is_valid_output_file(p, arg): + if os.access(Path(os.path.expandvars(arg)).parent, os.W_OK): + return os.path.expandvars(arg) + else: + p.error(f"Output file {arg} can't be accessed or is invalid!") + + +def is_valid_dir(p, arg): + if not Path(os.path.expandvars(arg)).is_dir(): + p.error(f"The folder {arg} does not exist!") + else: + return os.path.expandvars(arg) + + +def is_valid_file(p, arg): + if not Path(os.path.expandvars(arg)).is_file(): + p.error(f"The file {arg} does not exist!") + else: + return os.path.expandvars(arg) + if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--input", "-i", - type=str, required=True, help="Input csv file with path for predictions", + type=lambda x: is_valid_file(parser, x), + metavar="\b", ) parser.add_argument( - "--sample", - type=str, - # required=True, - help="Input sample name to showup in results", + "--outdir", + "-o", + default="./", + help="Output directory for DITTO output files", + type=lambda x: is_valid_dir(parser, x), + metavar="\b", ) parser.add_argument( - "--output", - "-o", - type=str, - default="ditto_predictions.csv", - help="Output csv file with path", + "--config", + "-c", + required=True, + default="/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/configs/col_config.yaml", + help="config file to use for predictions", + type=lambda x: is_valid_file(parser, x), + metavar="\b", ) parser.add_argument( - "--output100", - "-o100", + "--DITTO", + "-d", type=str, - default="ditto_predictions_100.csv", - help="Output csv file with path for Top 100 variants", + # required=True, + default="/data/project/worthey_lab/projects/experimental_pipelines/tarun/DITTO/data/processed/train_data_3_star/", + help="DITTO model for predictions", + # type=lambda x: is_valid_file(parser, x), + # metavar="\b", ) - # parser.add_argument( - # "--variant", - # type=str, - # help="Check index/rank of variant of interest. Format: chrX,101412604,C,T") args = parser.parse_args() # print("Loading data....") - with open( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/configs/testing.yaml" - ) as fh: + with open(args.config) as fh: config_dict = yaml.safe_load(fh) - # with open('SL212589_genes.yaml') as fh: - # config_dict = yaml.safe_load(fh) - - X = pd.read_csv(args.input) - X_test = X - # print('Data Loaded!') - var = X_test[config_dict["ML_VAR"]] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - X_test = X_test.values - - with open( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/models/F_3_0_1_nssnv/StackingClassifier_F_3_0_1_nssnv.joblib", - "rb", - ) as f: - clf = load(f) - - # print('Ditto Loaded!\nRunning predictions.....') - - y_score = clf.predict_proba(X_test) - del X_test - # print('Predictions finished!\nSorting ....') - pred = pd.DataFrame(y_score, columns=["Ditto_Benign", "Ditto_Deleterious"]) - - overall = pd.concat([var, pred], axis=1) - - # overall = overall.merge(X,on='Gene') - del X, pred, y_score, clf - overall.drop_duplicates(inplace=True) - overall.insert(0, "PROBANDID", args.sample) - overall["SD"] = 0 - overall["C"] = "*" - overall = overall.sort_values("Ditto_Deleterious", ascending=False) - # print('writing to database...') - overall.to_csv(args.output, index=False) - # print('Database storage complete!') - - overall = overall.drop_duplicates( - subset=["Chromosome", "Position", "Alternate Allele", "Reference Allele"], - keep="first", - ).reset_index(drop=True) - overall = overall[ - [ - "PROBANDID", - "Chromosome", - "Position", - "Reference Allele", - "Alternate Allele", - "Ditto_Deleterious", - "SD", - "C", - ] - ] - overall.columns = ["PROBANDID", "CHROM", "POS", "REF", "ALT", "P", "SD", "C"] - overall.head(100).to_csv(args.output100, index=False, sep=":") - del overall + # X = pd.read_csv(args.input,low_memory=False) + # + clf = keras.models.load_model(f"{args.DITTO}/Neural_network") + clf.load_weights(f"{args.DITTO}/weights.h5") + # + # var = test_parsing(X, config_dict, clf) + # var.to_csv(args.output, index=False) + basename = str(args.input).split(".")[0] + + df = pd.read_csv(args.input, chunksize=100000) + + for i, df_chunk in enumerate(df): + df2, ditto_scores = parse_and_predict(df_chunk, config_dict, clf) + # Set writing mode to append after first chunk + mode = "w" if i == 0 else "a" + + # Add header if it is the first chunk + header = i == 0 + # print('\nData shape (nsSNV) =', df2.shape) + # Write it to a file + # df2.to_csv(args.outdir + f"/{basename}_filtered_annots.csv.gz", index=False, + # header=header, + # mode=mode, + # compression='gzip') + ditto_scores[ + [ + "transcript", + "gene", + "consequence", + "chrom", + "pos", + "ref_base", + "alt_base", + "DITTO", + ] + ].to_csv( + args.outdir + f"/{basename}_DITTO_scores.csv.gz", + index=False, + header=header, + mode=mode, + compression="gzip", + ) + del df2, ditto_scores diff --git a/src/slurm-launch.py b/src/slurm-launch.py deleted file mode 100644 index 099309e..0000000 --- a/src/slurm-launch.py +++ /dev/null @@ -1,128 +0,0 @@ -# slurm-launch.py -# Usage: -# for i in {0..4}; do python slurm-launch.py --exp-name Ditto_tuning --command "python optuna-tpe-stacking_training.ipy --vtype snv_protein_coding" sleep 60 ; done - -import argparse -import subprocess -import sys -import time -import os - -# template_file = "slurm-template.sh" #Path(__file__) / -JOB_NAME = "${JOB_NAME}" -NUM_NODES = "${NUM_NODES}" -NUM_GPUS_PER_NODE = "${NUM_GPUS_PER_NODE}" -NUM_CPUS_PER_NODE = "${NUM_CPUS_PER_NODE}" -TOT_MEM = "${TOT_MEM}" -PARTITION_OPTION = "${PARTITION_OPTION}" -COMMAND_PLACEHOLDER = "${COMMAND_PLACEHOLDER}" -GIVEN_NODE = "${GIVEN_NODE}" -LOAD_ENV = "${LOAD_ENV}" - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--exp-name", - type=str, - required=True, - help="The job name and path to logging file (exp_name.log).", - ) - parser.add_argument( - "--slurm-template", - "-temp", - type=str, - default="./", - help="Path to slurm template. Default: ./ (current location)", - ) - parser.add_argument( - "--num-nodes", "-n", type=int, default=1, help="Number of nodes to use." - ) - parser.add_argument( - "--node", - "-w", - type=str, - help="The specified nodes to use. Same format as the " - "return of 'sinfo'. Default: ''.", - ) - parser.add_argument( - "--num-gpus", - type=int, - default=0, - help="Number of GPUs to use in each node. (Default: 0)", - ) - parser.add_argument( - "--num-cpus", - type=int, - default=10, - help="Number of CPUs to use in each node. (Default: 10)", - ) - parser.add_argument( - "--mem", type=str, default="150G", help="Total Memory to use. (Default: 150G)" - ) - parser.add_argument( - "--partition", type=str, default="short", help="Default partition: short" - ) - parser.add_argument( - "--load-env", - type=str, - default="training", - help="Environment name to load before running script. (Default: 'training')", - ) - parser.add_argument( - "--command", - type=str, - required=True, - help="The command you wish to execute. For example: " - " --command 'python ML_models.py'. " - "Note that the command must be a string.", - ) - args = parser.parse_args() - - if args.node: - # assert args.num_nodes == 1 - node_info = "#SBATCH -w {}".format(args.node) - else: - node_info = "" - - job_name = "{}_{}".format( - args.exp_name, time.strftime("%m%d-%H%M%S", time.localtime()) - ) - - partition_option = ( - "#SBATCH --partition={}".format(args.partition) if args.partition else "" - ) - - # ===== Modified the template script ===== - with open(f"{args.slurm_template}slurm-template.sh", "r") as f: - text = f.read() - text = text.replace(JOB_NAME, job_name) - text = text.replace(NUM_NODES, str(args.num_nodes)) - text = text.replace(NUM_GPUS_PER_NODE, str(args.num_gpus)) - text = text.replace(NUM_CPUS_PER_NODE, str(args.num_cpus)) - text = text.replace(TOT_MEM, str(args.mem)) - text = text.replace(PARTITION_OPTION, str(args.partition)) - text = text.replace(COMMAND_PLACEHOLDER, str(args.command)) - text = text.replace(LOAD_ENV, str(args.load_env)) - text = text.replace(GIVEN_NODE, node_info) - text = text.replace( - "# THIS FILE IS A TEMPLATE AND IT SHOULD NOT BE DEPLOYED TO " "PRODUCTION!", - "# THIS FILE IS MODIFIED AUTOMATICALLY FROM TEMPLATE AND SHOULD BE " - "RUNNABLE!", - ) - - # ===== Save the script ===== - if not os.path.exists("./logs/"): - os.makedirs("./logs/") - script_file = "./logs/{}.sh".format(job_name) - with open(script_file, "w") as f: - f.write(text) - - # ===== Submit the job ===== - print("Starting to submit job!") - subprocess.Popen(["sbatch", script_file]) - print( - "Job submitted! Script file is at: <{}>. Log file is at: <{}>".format( - script_file, "./logs/{}.log".format(job_name) - ) - ) - sys.exit(0) diff --git a/src/slurm-template.sh b/src/slurm-template.sh deleted file mode 100644 index cb08db8..0000000 --- a/src/slurm-template.sh +++ /dev/null @@ -1,70 +0,0 @@ -#!/bin/bash -# shellcheck disable=SC2206 -# THIS FILE IS GENERATED BY AUTOMATION SCRIPT! PLEASE REFER TO ORIGINAL SCRIPT! -# THIS FILE IS A TEMPLATE AND IT SHOULD NOT BE DEPLOYED TO PRODUCTION! -#SBATCH --job-name=${JOB_NAME} -#SBATCH --output=./logs/${JOB_NAME}.log -${GIVEN_NODE} -### This script works for any number of nodes, Ray will find and manage all resources -#SBATCH --nodes=${NUM_NODES} -#SBATCH --exclusive -#SBATCH --partition=${PARTITION_OPTION} -### Give all resources to a single Ray task, ray can manage the resources internally -#SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=${NUM_CPUS_PER_NODE} -#SBATCH --mem=${TOT_MEM} -#SBATCH --gpus-per-task=${NUM_GPUS_PER_NODE} -# Send mail to the email address when the job fails -#SBATCH --mail-type=FAIL -#SBATCH --mail-user=tmamidi@uab.edu -# Load modules or your own conda environment here -module load Anaconda3/2020.02 -# conda activate ${CONDA_ENV} -conda activate ${LOAD_ENV} -#module load CUDA/10.1.243 -#module load cuDNN/7.6.2.24-CUDA-10.1.243 - -## ===== DO NOT CHANGE THINGS HERE UNLESS YOU KNOW WHAT YOU ARE DOING ===== -## This script is a modification to the implementation suggest by gregSchwartz18 here: -## https://github.com/ray-project/ray/issues/826#issuecomment-522116599 -redis_password=$(uuidgen) -export redis_password - -nodes=$(scontrol show hostnames "$SLURM_JOB_NODELIST") # Getting the node names -nodes_array=($nodes) - -node_1=${nodes_array[0]} -ip=$(srun --nodes=1 --ntasks=1 -w "$node_1" hostname --ip-address) # making redis-address - -# if we detect a space character in the head node IP, we'll -# convert it to an ipv4 address. This step is optional. -if [[ "$ip" == *" "* ]]; then - IFS=' ' read -ra ADDR <<< "$ip" - if [[ ${#ADDR[0]} -gt 16 ]]; then - ip=${ADDR[1]} - else - ip=${ADDR[0]} - fi - echo "IPV6 address detected. We split the IPV4 address as $ip" -fi - -port=6379 -ip_head=$ip:$port -export ip_head -echo "IP Head: $ip_head" - -echo "STARTING HEAD at $node_1" -srun --nodes=1 --ntasks=1 -w "$node_1" \ - ray start --head --node-ip-address="$ip" --port=$port --redis-password="$redis_password" --temp-dir=$USER_SCRATCH --block & -sleep 30 - -worker_num=$((SLURM_JOB_NUM_NODES - 1)) #number of nodes other than the head node -for ((i = 1; i <= worker_num; i++)); do - node_i=${nodes_array[$i]} - echo "STARTING WORKER $i at $node_i" - srun --nodes=1 --ntasks=1 -w "$node_i" ray start --address "$ip_head" --redis-password="$redis_password" --block & - sleep 5 -done - -# ===== Call your code below ===== -${COMMAND_PLACEHOLDER} \ No newline at end of file diff --git a/src/training/training/NN.py b/src/training/NN.py similarity index 66% rename from src/training/training/NN.py rename to src/training/NN.py index 2ffe41b..c30a246 100644 --- a/src/training/training/NN.py +++ b/src/training/NN.py @@ -1,40 +1,42 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ -Created on Thu Oct 1 01:11:09 2020 +Started working on Thu Oct 1 01:11:09 2020 @author: tarunmamidi + +This script is used to train and tune a neural network using Optuna. It can tune itself and save the best model, +parameters in the models folder along with testing metrics, and SHAP explanation plot. + +python src/training/NN.py --train_x ./data/processed/train_data_3_star/train_class_data_80.csv.gz --test_x ./data/processed/train_data_3_star/test_class_data_20.csv.gz -c ./configs/col_config.yaml -o ./data/processed/train_data_3_star + """ import time -import os import numpy as np np.random.seed(5) +from pathlib import Path import optuna from optuna.integration import TFKerasPruningCallback from optuna.integration.tensorboard import TensorBoardCallback from optuna.samplers import TPESampler import tensorflow as tf -import tensorflow.keras as keras import argparse +import os +from sklearn.preprocessing import label_binarize +from sklearn.utils import class_weight -# import ray -# Start Ray. -# ray.init(ignore_reinit_error=True) try: tf.get_logger().setLevel("INFO") except Exception as exc: print(exc) +import pickle import warnings warnings.simplefilter("ignore") -# import ray from tensorflow.keras.models import Sequential -from tensorflow.keras.layers import Dense, Dropout, Activation -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import label_binarize +from tensorflow.keras.layers import Dense, Dropout -# from sklearn.preprocessing import StandardScaler from sklearn.metrics import ( precision_score, roc_auc_score, @@ -52,18 +54,12 @@ # EPOCHS = 150 class Objective(object): - def __init__(self, train_x, val_x, test_x, train_y, val_y, test_y): - + def __init__(self, train_x, test_x, train_y, test_y, class_weights): self.train_x = train_x self.test_x = test_x self.train_y = train_y self.test_y = test_y - self.val_x = val_x - self.val_y = val_y - # self.var = var - # self.x = x - # self.n_columns = 112 - # self.CLASS = 2 + self.class_weights = class_weights def __call__(self, config): # Clear clutter from previous TensorFlow graphs. @@ -173,20 +169,21 @@ def __call__(self, config): ] # Train the model - model.fit( + history = model.fit( self.train_x, self.train_y, - validation_data=(self.val_x, self.val_y), + validation_split=0.2, verbose=0, shuffle=True, callbacks=callbacks, - batch_size=config.suggest_int("batch_size", 100, 1000), - epochs=150, + batch_size=config.suggest_int("batch_size", 10, 1000), + epochs=500, + class_weight=self.class_weights, ) - + return history.history["val_accuracy"][-1] # Evaluate the model accuracy on the validation set. - score = model.evaluate(self.val_x, self.val_y, verbose=0) - return score[1] + # score = model.evaluate(self.val_x, self.val_y, verbose=0) + # return score[1] def tuned_run(self, config): # Clear clutter from previous TensorFlow graphs. @@ -231,12 +228,13 @@ def tuned_run(self, config): verbose=2, batch_size=config["batch_size"], epochs=500, + class_weight=self.class_weights, ) # Evaluate the model accuracy on the validation set. # score = model.evaluate(test_x, test_y, verbose=0) return model - def show_result(self, study, var, output, feature_names): + def show_result(self, study, out_dir, output, feature_names): pruned_trials = [ t for t in study.trials if t.state == optuna.trial.TrialState.PRUNED ] @@ -254,8 +252,8 @@ def show_result(self, study, var, output, feature_names): for key, value in trial.params.items(): print(" {}: {}".format(key, value)) print( - f"NeuralNetwork_{var}: {trial.params}", - file=open(f"../tuning/tuned_parameters.csv", "a"), + f"NeuralNetwork: {trial.params}", + file=open(out_dir + "/tuned_parameters.csv", "a"), ) model = self.tuned_run(trial.params) @@ -267,9 +265,8 @@ def show_result(self, study, var, output, feature_names): roc_auc = roc_auc_score(self.test_y, y_score.round()) accuracy = accuracy_score(self.test_y, y_score.round()) # prc_micro = average_precision_score(self.test_y, y_score, average='micro') - matrix = confusion_matrix( - np.argmax(self.test_y.values, axis=1), np.argmax(y_score, axis=1) - ) + # matrix = confusion_matrix(np.argmax(self.test_y.values, axis=1), np.argmax(y_score, axis=1)) + matrix = confusion_matrix(self.test_y, y_score.round()) print( f"Model\tTest_loss\tTest_accuracy\tPrecision\tRecall\troc_auc\tAccuracy\tConfusion_matrix[low_impact, high_impact]", file=open(output, "a"), @@ -279,11 +276,17 @@ def show_result(self, study, var, output, feature_names): file=open(output, "a"), ) # results:\nstorage ="sqlite:///../tuning/{var}/Neural_network_{var}.db" # Calling `save('my_model')` creates a SavedModel folder `my_model`. - model.save(f"../tuning/{var}/Neural_network/Neural_network_{var}") - model.save_weights(f"../tuning/{var}/Neural_network/weights.h5") + model.save(out_dir + "/Neural_network") + model.save_weights(out_dir + "/weights.h5") - # explain all the predictions in the test set + # explain predictions background = shap.kmeans(self.train_x, 10) + + output = open(out_dir + "background.pkl", "wb") + # Pickle dictionary using HIGHEST_PROTOCOL. + pickle.dump(background, output, protocol=pickle.HIGHEST_PROTOCOL) + output.close() + explainer = shap.KernelExplainer(model.predict, background) background = self.test_x[ np.random.choice(self.test_x.shape[0], 10000, replace=False) @@ -294,7 +297,7 @@ def show_result(self, study, var, output, feature_names): # shap.plots.beeswarm(shap_vals, feature_names) # shap.plots.waterfall(shap_values[1], max_display=10) plt.savefig( - f"../tuning/{var}/Neural_network_{var}_features.pdf", + out_dir + "/Neural_network_features.pdf", format="pdf", dpi=1000, bbox_inches="tight", @@ -303,101 +306,140 @@ def show_result(self, study, var, output, feature_names): return None -def data_parsing(var, config_dict, output): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) +def data_parsing(train_x, test_x, config_dict): # Load data # print(f'\nUsing merged_data-train_{var}..', file=open(output, 'a')) - X_train = pd.read_csv(f"train_{var}/merged_data-train_{var}.csv") + X_train = pd.read_csv(train_x) # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["ML_VAR"], axis=1) + Y_train = X_train["class"] + X_train = X_train.drop(config_dict["train_cols"], axis=1) + X_train = X_train.drop("class", axis=1) X_train.replace([np.inf, -np.inf], np.nan, inplace=True) - X_train.fillna(0, inplace=True) + X_train.fillna(X_train.mean(), inplace=True) feature_names = X_train.columns.tolist() X_train = X_train.values - Y_train = pd.read_csv(f"train_{var}/merged_data-y-train_{var}.csv") - Y_train = pd.get_dummies(Y_train) - # Y_train = label_binarize(Y_train.values, classes=['low_impact', 'high_impact']).ravel() - X_train, X_val, Y_train, Y_val = train_test_split( - X_train, Y_train, test_size=0.20, random_state=42 + + Y_train = label_binarize(Y_train.values, classes=list(np.unique(Y_train))).ravel() + class_weights = class_weight.compute_class_weight( + class_weight="balanced", classes=np.unique(Y_train), y=Y_train ) + class_weights = {i: w for i, w in enumerate(class_weights)} + Y_train = Y_train.reshape(-1, 1) + # Y_train = pd.get_dummies(Y_train) - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() + X_test = pd.read_csv(test_x) + # var = X_train[config_dict['ML_VAR']] + Y_test = X_test["class"] + X_test = X_test.drop(config_dict["train_cols"], axis=1) + X_test = X_test.drop("class", axis=1) + X_test.replace([np.inf, -np.inf], np.nan, inplace=True) + X_test.fillna(0, inplace=True) X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - Y_test = pd.get_dummies(Y_test) - # Y_test = label_binarize(Y_test.values, classes=['low_impact', 'high_impact']).ravel() + + # Y_test = pd.get_dummies(Y_test) + Y_test = ( + label_binarize(Y_test.values, classes=list(np.unique(Y_test))) + .ravel() + .reshape(-1, 1) + ) + print(f"Shape: {Y_train.shape}") print("Data Loaded!") # scaler = StandardScaler().fit(X_train) # X_train = scaler.transform(X_train) # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - return X_train, X_val, X_test, Y_train, Y_val, Y_test, feature_names + return X_train, X_test, Y_train, Y_test, feature_names, class_weights + + +def is_valid_file(p, arg): + if not Path(os.path.expandvars(arg)).is_file(): + p.error(f"The file {arg} does not exist!") + else: + return os.path.expandvars(arg) + + +def is_valid_dir(p, arg): + if not Path(os.path.expandvars(arg)).is_dir(): + p.error(f"The directory {arg} does not exist!") + else: + return os.path.expandvars(arg) if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--var-tag", - "-v", - type=str, - default="nssnv", - help="Var tag used while filtering or untuned models. (Default: nssnv)", + PARSER = argparse.ArgumentParser( + description="Script to train and tune DITTO using processed annotations from OpenCravat", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) - args = parser.parse_args() + PARSER.add_argument( + "--train_x", + help="File path to the CSV file of X_train data", + required=True, + type=lambda x: is_valid_file(PARSER, x), + metavar="\b", + ) + PARSER.add_argument( + "--test_x", + help="File path to the CSV file of X_test data", + required=True, + type=lambda x: is_valid_file(PARSER, x), + metavar="\b", + ) + PARSER.add_argument( + "-c", + "--config", + help="File path to the data config JSON file that determines how to process annotated variants from OpenCravat", + required=True, + type=lambda x: is_valid_file(PARSER, x), + metavar="\b", + ) + OPTIONAL_ARGS = PARSER.add_argument_group("Override Args") + PARSER.add_argument( + "-o", + "--outdir", + help="Output directory to save files from training/tuning DITTO", + type=lambda x: is_valid_dir(PARSER, x), + metavar="\b", + ) - var = args.var_tag + ARGS = PARSER.parse_args() + out_dir = ARGS.outdir if ARGS.outdir else f"{Path().resolve()}" - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test" - ) - with open("../../../configs/columns_config.yaml") as fh: + with open(ARGS.config) as fh: config_dict = yaml.safe_load(fh) start = time.perf_counter() - if not os.path.exists("../tuning/" + var): - os.makedirs("../tuning/" + var) - output = "../tuning/" + var + "/ML_results_" + var + ".csv" + + output = out_dir + "/ML_results.csv" # print('Working with '+var+' dataset...', file=open(output, "w")) - print("Working with " + var + " dataset...") - # X_train, X_test, Y_train, Y_test, feature_names = ray.get(data_parsing.remote(var,config_dict,output)) - X_train, X_val, X_test, Y_train, Y_val, Y_test, feature_names = data_parsing( - var, config_dict, output + print("Working with dataset...") + + X_train, X_test, Y_train, Y_test, feature_names, class_weights = data_parsing( + ARGS.train_x, ARGS.test_x, config_dict ) - # print('Model\tCross_validate(avg_train_score)\tCross_validate(avg_test_score)\tPrecision(test_data)\tRecall\troc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]', file=open(output, "a")) #\tConfusion_matrix[low_impact, high_impact] - # list1 = ray.get(classifier.remote(classifiers,var, X_train, X_test, Y_train, Y_test,background,feature_names)) - # print(f'{list1[0]}\t{list1[1]}\t{list1[2]}\t{list1[3]}\t{list1[4]}\t{list1[5]}\t{list1[6]}\t{list1[7]}\n{list1[8]}', file=open(output, "a")) - # print(f'training and testing done!') print("Starting Objective...") - objective = Objective(X_train, X_val, X_test, Y_train, Y_val, Y_test) + objective = Objective(X_train, X_test, Y_train, Y_test, class_weights) tensorboard_callback = TensorBoardCallback( - f"../tuning/{var}/Neural_network_{var}_logs/", metric_name="accuracy" + out_dir + "/Neural_network_logs/", metric_name="val_accuracy" ) study = optuna.create_study( sampler=TPESampler(**TPESampler.hyperopt_parameters()), - study_name=f"Neural_network_{var}", - storage=f"sqlite:///../tuning/{var}/Neural_network_{var}.db", # study_name= "Ditto3", + study_name=f"DITTO_NN", + storage=f"sqlite:///{out_dir}/Neural_network.db", # study_name= "Ditto3", direction="maximize", - pruner=optuna.pruners.MedianPruner(n_startup_trials=100), + pruner=optuna.pruners.MedianPruner(n_startup_trials=200), load_if_exists=True, # , pruner=optuna.pruners.MedianPruner(n_startup_trials=150) ) # study = optuna.load_study(study_name= "Ditto_all", sampler=TPESampler(**TPESampler.hyperopt_parameters()),storage ="sqlite:///Ditto_all.db") # study_name= "Ditto3", study.optimize( objective, - n_trials=1000, + n_trials=500, callbacks=[tensorboard_callback], - n_jobs=-1, + n_jobs=2, gc_after_trial=True, - ) # , n_jobs = -1 timeout=600, + ) finish = (time.perf_counter() - start) / 120 - # ttime = (finish- start)/120 print(f"Total time in hrs: {finish}") - objective.show_result(study, var, output, feature_names) + objective.show_result(study, out_dir, output, feature_names) del X_train, X_test, Y_train, Y_test, feature_names diff --git a/src/training/benchmark_consequence.py b/src/training/benchmark_consequence.py new file mode 100644 index 0000000..d7e1d4b --- /dev/null +++ b/src/training/benchmark_consequence.py @@ -0,0 +1,268 @@ +# This script is used to benchmark DITTO. This script outputs roc/prc plots, confusion matrices, and SHAP plots for each +# consequence. This is replacement for the script `src/analysis/opencravat_latest_benchmarking-Consequence_80_20.ipynb`. + +import pandas as pd + +pd.set_option("display.max_rows", None) +import warnings + +warnings.simplefilter("ignore") +import yaml +import argparse +import shap +import numpy as np +import matplotlib.pyplot as plt +import functools + +print = functools.partial(print, flush=True) +from sklearn.metrics import ( + roc_curve, + roc_auc_score, + precision_recall_curve, + average_precision_score, + confusion_matrix, + ConfusionMatrixDisplay, + f1_score, +) +from sklearn.preprocessing import label_binarize, MinMaxScaler +from sklearn.utils import class_weight +import os +from pathlib import Path +import tensorflow as tf +from tensorflow import keras + +tf.config.run_functions_eagerly(True) + + +def get_roc_plot(so, X_test, Y_test, outdir, weights): + + # prepare plots + fig, [ax_roc, ax_prc] = plt.subplots(1, 2, figsize=(50, 20)) + fig.suptitle(f"DITTO Benchmarking for {so}", fontsize=40) + fsize = 30 + ax_roc.tick_params(axis="both", which="major", labelsize=fsize) + ax_prc.tick_params(axis="both", which="major", labelsize=fsize) + ax_roc.set_xlabel("False Positive Rate", fontsize=fsize) + ax_roc.set_ylabel("True Positive Rate", fontsize=fsize) + ax_roc.set_title("Receiver Operating Characteristic (ROC) curves", fontsize=fsize) + ax_roc.grid(linestyle="--") + ax_prc.set_xlabel("Recall", fontsize=fsize) + ax_prc.set_ylabel("Precision", fontsize=fsize) + ax_prc.set_title("Precision Recall (PRC) curves", fontsize=fsize) + ax_prc.grid(linestyle="--") + + roc_scores_so = {} + prc_scores_so = {} + f1_scores_so = {} + for name in list(X_test.columns): + fpr, tpr, _ = roc_curve(Y_test, X_test[name].round(), sample_weight=weights) + auc = roc_auc_score( + Y_test, X_test[name].round(), sample_weight=weights, average="weighted" + ) + auc = "{:.2f}".format(auc) + roc_scores_so[name] = auc + ax_roc.plot(fpr, tpr, label=str(name) + " = " + str(auc)) + precision, recall, _ = precision_recall_curve( + Y_test, X_test[name].round(), sample_weight=weights + ) + prc = average_precision_score( + Y_test, X_test[name].round(), sample_weight=weights, average="weighted" + ) + prc = "{:.2f}".format(prc) + prc_scores_so[name] = prc + f1 = f1_score( + Y_test, X_test[name].round(), sample_weight=weights, average="weighted" + ) + f1_scores_so[name] = "{:.2f}".format(f1) + ax_prc.plot(recall, precision, label=str(name) + " = " + str(prc)) + ax_prc.legend(bbox_to_anchor=(1, 0.5), loc="center left", fontsize=fsize) + ax_roc.legend(bbox_to_anchor=(1, 0.5), loc="center left", fontsize=fsize) + fig.tight_layout() + + fig.savefig( + f"{outdir}/{so}_ROC_PRC_benchmarking.pdf", + format="pdf", + dpi=1000, + bbox_inches="tight", + ) + return roc_scores_so, prc_scores_so, f1_scores_so + + +def get_prediction(clf, X_test): + y_score = clf.predict(X_test.values) + return y_score + + +def get_matrix(y_score, Y_test, so, outdir): + cm = confusion_matrix(Y_test, y_score.round()) + disp = ConfusionMatrixDisplay( + confusion_matrix=cm, display_labels=["Path", "Benign"] + ) + disp.plot() + plt.title(f"Confusion matrix for {so}", fontsize=15) + plt.savefig( + f"{outdir}/{so}_matrix.pdf", + format="pdf", + dpi=1000, + bbox_inches="tight", + ) + return None + + +def get_SHAP(test_x, clf, so, outdir, feature_names): + if test_x.shape[0] > 500: + sample_size = 500 + else: + sample_size = test_x.shape[0] + background_x = test_x.sample(n=sample_size) + explainer = shap.KernelExplainer( + model=clf, data=background_x + ) # (background_so, background_x)) + shap_values = explainer.shap_values(background_x) + + plt.clf() + plt.xlabel("mean SHAP value") + plt.ylabel("Features") + plt.title(f"SHAP plot for {so}") + shap.summary_plot(shap_values, background_x, feature_names, show=False) + # plt.show() + plt.savefig( + f"{outdir}/{so}_SHAP.pdf", + format="pdf", + dpi=1000, + bbox_inches="tight", + ) + return None + + +def run_test(X_test, outdir, clf, config_dict, feature_names, class_weights): + consequence_list = list(set(config_dict["consequence_cols"]) & set(X_test.columns)) + roc_scores = {} + prc_scores = {} + f1_scores = {} + for so in consequence_list: + roc_scores[so] = {} + prc_scores[so] = {} + f1_scores[so] = {} + test_x = X_test[X_test[so] == 1] + Y_test = test_x["class"] + test_x = test_x.drop(["class"], axis=1) + if not test_x.empty and len(Y_test.unique()) == 2: + benchmark_df = test_x[config_dict["Benchmark_cols"].keys()] + benchmark_df.columns = benchmark_df.columns.to_series().map( + config_dict["Benchmark_cols"] + ) + benchmark_df_names = benchmark_df.columns.tolist() + min_max_scaler = MinMaxScaler() + benchmark_df = min_max_scaler.fit_transform(benchmark_df) + benchmark_df = pd.DataFrame(benchmark_df) + benchmark_df.columns = benchmark_df_names + # test_x.rename(columns=config_dict['Benchmark_cols'], inplace=True) + class_weights = np.array([class_weights[i] for i in Y_test]) + print(f"{so} class shape: {Y_test.value_counts()}") + y_score = get_prediction(clf, test_x) + get_matrix(y_score, Y_test, so, outdir) + benchmark_df["DITTO"] = y_score + roc_scores_so, prc_scores_so, f1_scores_so = get_roc_plot( + so, benchmark_df, Y_test, outdir, class_weights + ) + roc_scores[so].update(roc_scores_so) + prc_scores[so].update(prc_scores_so) + f1_scores[so].update(f1_scores_so) + get_SHAP(test_x, clf, so, outdir, feature_names) + + pd.DataFrame(roc_scores).to_csv(f"{outdir}/NN_roc_scores.csv") + pd.DataFrame(prc_scores).to_csv(f"{outdir}/NN_prc_scores.csv") + pd.DataFrame(f1_scores).to_csv(f"{outdir}/NN_f1_scores.csv") + return None + + +def data_parsing(test_x, config_dict): + # Load data + X_test = pd.read_csv(test_x) + Y_test = X_test["class"] + X_test = X_test.drop(config_dict["train_cols"], axis=1) + X_test = X_test.drop("class", axis=1) + X_test.replace([np.inf, -np.inf], np.nan, inplace=True) + X_test.fillna(X_test.mean(), inplace=True) + feature_names = X_test.columns.tolist() + + class_weights = class_weight.compute_class_weight( + class_weight="balanced", classes=np.unique(Y_test), y=Y_test + ) + class_weights = {i: w for i, w in enumerate(class_weights)} + Y_test = label_binarize(Y_test.values, classes=list(np.unique(Y_test))).ravel() + X_test["class"] = Y_test + print("Data Loaded!") + + return X_test, feature_names, class_weights + + +def load_model(model_path): + clf = keras.models.load_model(model_path + "/Neural_network") + clf.load_weights(model_path + "/weights.h5") + return clf + + +def is_valid_file(p, arg): + if not Path(os.path.expandvars(arg)).is_file(): + p.error(f"The file {arg} does not exist!") + else: + return os.path.expandvars(arg) + + +def is_valid_dir(p, arg): + if not Path(os.path.expandvars(arg)).is_dir(): + p.error(f"The directory {arg} does not exist!") + else: + return os.path.expandvars(arg) + + +if __name__ == "__main__": + PARSER = argparse.ArgumentParser( + description="Script to benchmark DITTO using processed annotations from OpenCravat", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + + PARSER.add_argument( + "--test_x", + help="File path to the CSV file of X_test data", + required=True, + type=lambda x: is_valid_file(PARSER, x), + metavar="\b", + ) + PARSER.add_argument( + "-c", + "--config", + help="File path to the data config JSON file that determines how to process annotated variants from OpenCravat", + required=True, + type=lambda x: is_valid_file(PARSER, x), + metavar="\b", + ) + PARSER.add_argument( + "-d", + "--ditto", + help="Folder path to the DITTO model", + required=True, + type=lambda x: is_valid_dir(PARSER, x), + metavar="\b", + ) + OPTIONAL_ARGS = PARSER.add_argument_group("Override Args") + PARSER.add_argument( + "-o", + "--outdir", + help="Output directory to save files from DITTO", + type=lambda x: is_valid_dir(PARSER, x), + metavar="\b", + ) + + ARGS = PARSER.parse_args() + out_dir = ARGS.outdir if ARGS.outdir else f"{Path().resolve()}" + + with open(ARGS.config) as fh: + config_dict = yaml.safe_load(fh) + + clf = load_model(ARGS.ditto) + X_test, feature_names, class_weights = data_parsing(ARGS.test_x, config_dict) + + run_test(X_test, out_dir, clf, config_dict, feature_names, class_weights) diff --git a/src/training/training/ML_models.py b/src/training/training/ML_models.py deleted file mode 100644 index a855e43..0000000 --- a/src/training/training/ML_models.py +++ /dev/null @@ -1,237 +0,0 @@ -# python slurm-launch.py --exp-name Training --command "python training/training/ML_models.py" --partition largemem --mem 500G - -import numpy as np -import pandas as pd -import time -import argparse -import ray - -# Start Ray. -ray.init(ignore_reinit_error=True) -import warnings - -warnings.simplefilter("ignore") -from joblib import dump, load -import shap - -from sklearn.preprocessing import StandardScaler -# from sklearn.preprocessing import MinMaxScaler -from sklearn.model_selection import train_test_split, cross_validate -from sklearn.preprocessing import label_binarize -from sklearn.metrics import ( - precision_score, - roc_auc_score, - accuracy_score, - confusion_matrix, - average_precision_score, - recall_score, - plot_roc_curve, - plot_precision_recall_curve, -) -from sklearn.tree import DecisionTreeClassifier -from sklearn.ensemble import ( - RandomForestClassifier, - AdaBoostClassifier, - GradientBoostingClassifier, - ExtraTreesClassifier, -) -from sklearn.naive_bayes import GaussianNB -from imblearn.ensemble import BalancedRandomForestClassifier -from sklearn.neural_network import MLPClassifier -from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -import matplotlib.pyplot as plt -import yaml -import gc -import os - -os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" -) - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - -# @ray.remote(num_returns=6) -def data_parsing(var, config_dict): - # Load data - # print(f'\nUsing merged_data-train_{var}..', file=open(output, "a")) - X_train = pd.read_csv(f"train_custom_data-{var}.csv") - # var = X_train[config_dict['ML_VAR']] - X_train = X_train.drop(config_dict["var"], axis=1) - feature_names = X_train.columns.tolist() - #X_train = X_train.sample(frac=1).reset_index(drop=True) - X_train = X_train.values - Y_train = pd.read_csv(f"train_custom_data-y-{var}.csv") - Y_train = label_binarize( - Y_train.values, classes=["low_impact", "high_impact"] - ).ravel() - - X_test = pd.read_csv(f"test_custom_data-{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["var"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_custom_data-y-{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - #scaler = StandardScaler().fit(X_train) - #X_train = scaler.transform(X_train) - #X_test = scaler.transform(X_test) - # explain all the predictions in the test set - background = shap.kmeans(X_train, 10) - return X_train, X_test, Y_train, Y_test, background, feature_names - - -@ray.remote # (num_cpus=9) -def classifier( - name, clf, var, X_train, X_test, Y_train, Y_test, background, feature_names, output -): - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/" - ) - start = time.perf_counter() - score = cross_validate( - clf, - X_train, - Y_train, - cv=10, - return_train_score=True, - return_estimator=True, - n_jobs=-1, - verbose=0, - scoring=("roc_auc", "neg_log_loss"), - ) - clf = score["estimator"][np.argmin(score["test_neg_log_loss"])] - # y_score = cross_val_predict(clf, X_train, Y_train, cv=5, n_jobs=-1, verbose=0) - # class_weights = class_weight.compute_class_weight('balanced', np.unique(Y_train), Y_train) - # clf.fit(X_train, Y_train) #, class_weight=class_weights) - # name = str(type(clf)).split("'")[1] #.split(".")[3] - with open(f"./models_custom/{var}/{name}_{var}.joblib", "wb") as f: - dump(clf, f, compress="lz4") - # del clf - # with open(f"./models_custom/{var}/{name}_{var}.joblib", 'rb') as f: - # clf = load(f) - y_score = clf.predict(X_test) - prc = precision_score(Y_test, y_score, average="weighted") - recall = recall_score(Y_test, y_score, average="weighted") - roc_auc = roc_auc_score(Y_test, y_score) - prc_auc = average_precision_score(Y_test, y_score, average="weighted") - # roc_auc = roc_auc_score(Y_test, np.argmax(y_score, axis=1)) - accuracy = accuracy_score(Y_test, y_score) - # score = clf.score(X_train, Y_train) - # matrix = confusion_matrix(np.argmax(Y_test, axis=1), np.argmax(y_score, axis=1)) - matrix = confusion_matrix(Y_test, y_score) - - finish = (time.perf_counter() - start) / 60 - with open(output, "a") as f: - f.write( - #f"{name}\t{np.mean(score['train_roc_auc'])}\t{np.mean(score['test_roc_auc'])}\t{np.mean(score['train_neg_log_loss'])}\t{np.mean(score['test_neg_log_loss'])}\t{prc}\t{recall}\t{roc_auc}\t{prc_auc}\t{accuracy}\t{finish}\n{matrix}\n" - f"{name}\t{np.mean(score['train_roc_auc'])}\t{np.mean(score['test_roc_auc'])}\t{np.mean(score['train_neg_log_loss'])}\t{np.mean(score['test_neg_log_loss'])}\t{prc}\t{recall}\t{roc_auc}\t{prc_auc}\t{accuracy}\t{finish}\n" - ) - # explain all the predictions in the test set - # background = shap.kmeans(X_train, 6) - explainer = shap.KernelExplainer(clf.predict, background) - del clf, X_train - background1 = X_test[np.random.choice(X_test.shape[0], 5000, replace=False)] - shap_values = explainer.shap_values(background1) - plt.figure() - shap.summary_plot(shap_values, background1, feature_names, max_display = 30, show=False) - # shap.plots.waterfall(shap_values[0], max_display=15) - plt.savefig( - f"./models_custom/{var}/{name}_{var}_features.pdf", - format="pdf", - dpi=1000, - bbox_inches="tight", - ) - del shap_values, background1, explainer - return None - - -if __name__ == "__main__": - - parser = argparse.ArgumentParser() - parser.add_argument( - "--var-tag", - "-v", - type=str, - #required=True, - default="dbnsfp", - help="The tag used when generating train/test data. Default:'dbnsfp'", - ) - - args = parser.parse_args() - - # Classifiers I wish to use - classifiers = { - "DecisionTree": DecisionTreeClassifier(class_weight="balanced"), - "RandomForest": RandomForestClassifier(class_weight="balanced", n_jobs=5), - "BalancedRF": BalancedRandomForestClassifier(), - "AdaBoost": AdaBoostClassifier(), - "ExtraTrees": ExtraTreesClassifier(class_weight="balanced", n_jobs=5), - "GaussianNB": GaussianNB(), - "LDA": LinearDiscriminantAnalysis(), - "GradientBoost": GradientBoostingClassifier(), - "MLP": MLPClassifier(), - } - - with open("../../configs/col_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - var = args.var_tag - if not os.path.exists("./models_custom/" + var): - os.makedirs("./models_custom/" + var) - output = "./models_custom/" + var + "/ML_results_" + var + ".csv" - # print('Working with '+var+' dataset...', file=open(output, "a")) - print("Working with " + var + " dataset...") - X_train, X_test, Y_train, Y_test, background, feature_names = data_parsing( - var, config_dict - ) - with open(output, "w") as f: - f.write( - #"Model\tCross_validate(avg_train_roc_auc)\tCross_validate(avg_test_roc_auc)\tCross_validate(avg_train_neg_log_loss)\tCross_validate(avg_test_neg_log_loss)\tPrecision(test_data)\tRecall\troc_auc\tprc_auc\tAccuracy\tTime(min)\tConfusion_matrix[low_impact, high_impact]\n" - "Model\tCross_validate(avg_train_roc_auc)\tCross_validate(avg_test_roc_auc)\tCross_validate(avg_train_neg_log_loss)\tCross_validate(avg_test_neg_log_loss)\tPrecision(test_data)\tRecall\troc_auc\tprc_auc\tAccuracy\tTime(min)\n" - ) - remote_ml = [ - classifier.remote( - name, - clf, - var, - X_train, - X_test, - Y_train, - Y_test, - background, - feature_names, - output, - ) - for name, clf in classifiers.items() - ] - ray.get(remote_ml) - gc.collect() - - # prepare plots - fig, [ax_roc, ax_prc] = plt.subplots(1, 2, figsize=(20, 10)) - fig.suptitle(f"Model performances on Testing data with filters", fontsize=20) - - for name, clf in classifiers.items(): - - with open(f"./models_custom/{var}/{name}_{var}.joblib", "rb") as f: - clf = load(f) - - plot_precision_recall_curve(clf, X_test, Y_test, ax=ax_prc, name=name) - plot_roc_curve(clf, X_test, Y_test, ax=ax_roc, name=name) - - ax_roc.set_title("Receiver Operating Characteristic (ROC) curves") - ax_prc.set_title("Precision Recall (PRC) curves") - - ax_roc.grid(linestyle="--") - ax_prc.grid(linestyle="--") - - plt.legend() - plt.savefig( - f"./models_custom/{var}/roc_{var}.pdf", format="pdf", dpi=1000, bbox_inches="tight" - ) - gc.collect() diff --git a/src/training/training/plot_roc.py b/src/training/training/plot_roc.py deleted file mode 100644 index c1439f4..0000000 --- a/src/training/training/plot_roc.py +++ /dev/null @@ -1,123 +0,0 @@ -# from numpy import mean -import numpy as np -import pandas as pd -import time -import warnings -import argparse - -warnings.simplefilter("ignore") -from joblib import dump, load - -# from sklearn.preprocessing import StandardScaler -# from sklearn.feature_selection import RFE -# from sklearn.preprocessing import MinMaxScaler -from sklearn.preprocessing import label_binarize -from sklearn.metrics import plot_roc_curve, plot_precision_recall_curve - -# from sklearn.multiclass import OneVsRestClassifier -from sklearn.tree import DecisionTreeClassifier -from sklearn.ensemble import ( - RandomForestClassifier, - AdaBoostClassifier, - GradientBoostingClassifier, - ExtraTreesClassifier, -) -from sklearn.naive_bayes import GaussianNB -from imblearn.ensemble import BalancedRandomForestClassifier -from sklearn.neural_network import MLPClassifier -from sklearn.discriminant_analysis import LinearDiscriminantAnalysis -import matplotlib.pyplot as plt -import yaml -import gc -import os - - -TUNE_STATE_REFRESH_PERIOD = 10 # Refresh resources every 10 s - - -def data_parsing(var, config_dict): - # Load data - X_test = pd.read_csv(f"test_{var}/merged_data-test_{var}.csv") - # var = X_test[config_dict['ML_VAR']] - X_test = X_test.drop(config_dict["ML_VAR"], axis=1) - # feature_names = X_test.columns.tolist() - X_test = X_test.values - Y_test = pd.read_csv(f"test_{var}/merged_data-y-test_{var}.csv") - print("Data Loaded!") - # Y = pd.get_dummies(y) - Y_test = label_binarize( - Y_test.values, classes=["low_impact", "high_impact"] - ).ravel() - - # scaler = StandardScaler().fit(X_train) - # X_train = scaler.transform(X_train) - # X_test = scaler.transform(X_test) - # explain all the predictions in the test set - # background = shap.kmeans(X_train, 10) - return X_test, Y_test - - -if __name__ == "__main__": - os.chdir( - "/data/project/worthey_lab/projects/experimental_pipelines/tarun/ditto/data/processed/train_test/" - ) - - parser = argparse.ArgumentParser() - parser.add_argument( - "--var-tag", - "-v", - type=str, - required=True, - default="nssnv", - help="The tag used when generating train/test data. Default:'nssnv'", - ) - - args = parser.parse_args() - var = args.var_tag - - # Classifiers I wish to use - classifiers = [ - "DecisionTree", - "RandomForest", - "BalancedRF", - "AdaBoost", - "ExtraTrees", - "GaussianNB", - "LDA", - "GradientBoost", - "MLP", - "StackingClassifier", - ] - - with open("../../../configs/columns_config.yaml") as fh: - config_dict = yaml.safe_load(fh) - - if not os.path.exists("../models/" + var): - os.makedirs("../models/" + var) - - print("Working with " + var + " dataset...") - X_test, Y_test = data_parsing(var, config_dict) - - # prepare plots - fig, [ax_roc, ax_prc] = plt.subplots(1, 2, figsize=(20, 10)) - fig.suptitle(f"Model performances on Testing data", fontsize=20) - - for name in classifiers: - - with open(f"../models/{var}/{name}_{var}.joblib", "rb") as f: - clf = load(f) - - plot_precision_recall_curve(clf, X_test, Y_test, ax=ax_prc, name=name) - plot_roc_curve(clf, X_test, Y_test, ax=ax_roc, name=name) - - ax_roc.set_title("Receiver Operating Characteristic (ROC) curves") - ax_prc.set_title("Precision Recall (PRC) curves") - - ax_roc.grid(linestyle="--") - ax_prc.grid(linestyle="--") - - plt.legend() - plt.savefig( - f"../models/{var}/roc_{var}.pdf", format="pdf", dpi=1000, bbox_inches="tight" - ) - gc.collect()