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write a javascript program inside <script></script> tag that alerts "Hello world"

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write a javascript program inside <script></script> tag that alerts "Hello world"

louismullie

const segments = splitIntoPatternedSequences(state.typingBuffer)

if (state.currentTypingPosition <= segments.length) {
  const currentText = segments
    .slice(0, state.currentTypingPosition)
    .join('')
    .trim()

  state.currentText = currentText
  state.shownReferences = state.references

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Cell Biology Patterns

Summary

Pattern	Cellular Biology Use Case
Singleton	Cell Nucleus: Only one nucleus controls the cell's activities, maintaining a single source of genetic information.
Factory Method	Ribosomes: Produce different proteins based on the mRNA template, acting as a factory for various protein products.
Abstract Factory	Cell Differentiation: Stem cells differentiate into various types of specialized cells, each with its own set of organelles and functions.
Builder	Protein Synthesis: Step-by-step assembly of proteins from amino acids, following the instructions in mRNA.

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-- Function to split the input query and generate all contiguous combinations
CREATE FUNCTION split_to_table(@input_query VARCHAR(MAX))
RETURNS @result TABLE (combination VARCHAR(MAX))
AS
BEGIN
    DECLARE @words TABLE (id INT IDENTITY(1,1), word VARCHAR(255))
    DECLARE @total_words INT, @i INT, @j INT, @current_combination VARCHAR(MAX)

    -- Step 1: Splitting the input string into words
    INSERT INTO @words (word)

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import re
from struct import iter_unpack
from quopri import decodestring

import magic
import matplotlib.pyplot as plt
import numpy as np

# Constants
UNITS = {

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Hey dahani9091 (ChatGPT project)

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novo_test = pd.read_csv("test.csv")
novo_test

df_cavity = pd.read_csv('/content/gdrive/MyDrive/Kaggle/cavity_pred_CUSTOM_A.csv', low_memory=False)
df_cavity = df_cavity.rename(columns={'variant': 'mutation_key'})
df_cavity

novo_test2 = novo_test.copy().rename({'protein_sequence': 'mutant_seq', 'seq_id': 'source_df_id'}, axis = 1)
novo_test2['sequence'] = 'VPVNPEPDATSVENVALKTGSGDSQSDPIKADLEVKGQSALPFDVDCWAILCKGAPNVLQRVNEKTKNSNRDRSGANKGPFKDPQKWGIKALPPKNPSWSAQDFKSPEEYAFASSLQGGTNAILAPVNLASQNSQGGVLNGFYSANKVAQFDPSKPQQTKGTWFQITKFTGAAGPYCKALGSNDKSVCDKNKNIAGDWGFDPAKWAYQYDEKNNKFNYVGK'
novo_test2 = novo_test2.apply(find_mut,axis=1)

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// Requête no. 1
{
  "header": {},
  "body": {
    "selectors": [
      {
        "resource": "Patient",
        "filters": [
          { "path": "code", 
            "operator": "matches",

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def auc_roc(y_true, y_pred):
# can be any tensorflow metric
value, update_op = tf.contrib.metrics.streaming_auc(y_pred, y_true)
    # find all variables created for this metric
    metric_vars = [i for i in tf.local_variables() if 'auc_roc' in i.name.split('/')[1]]
    
    # Add metric variables to GLOBAL_VARIABLES collection.
    # They will be initialized for new session.
    for v in metric_vars:
    tf.add_to_collection(tf.GraphKeys.GLOBAL_VARIABLES, v)