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関連する概念動画

Ogive Graph01:07

Ogive Graph

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Graphing Antiderivatives01:30

Graphing Antiderivatives

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The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
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Graphs of Functions01:30

Graphs of Functions

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Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
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Bar Graph01:07

Bar Graph

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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Network Covalent Solids02:18

Network Covalent Solids

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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
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Updated: Feb 13, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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マイクロバイオームのメタボロミクスに関する検索可能なメタデータネットワークグラフ.

Vincent Charron-Lamoureux, Shipei Xing, Abubaker Patan

    bioRxiv : the preprint server for biology
    |February 12, 2026
    PubMed
    まとめ

    MicrobiomeMASSTは,多様なデータセットの微生物代謝物をマッピングし,質量スペクトロメトリデータを生物学的文脈にリンクします. このフレームワークは,腸内細菌がエナラプリルなどの薬剤をデプロイレーションによって無効化する方法を明らかにしています.

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    A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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    Last Updated: Feb 13, 2026

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    Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia
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    科学分野:

    • マイクロバイオームの研究
    • メタボロミクスとは
    • バイオインフォマティックス

    背景:

    • 微生物の代謝産物とその生物学的役割を特定することは困難です.
    • 既存のデータは,多数の研究とサンプルタイプに分割されています.

    研究 の 目的:

    • 微生物の代謝物質をマッピングするためのメタデータ主導のネットワークグラフ (microbiomeMASST) を開発する.
    • 多様な質量スペクトロメトリーデータセットを統合して,クロススタディ分析と生物学的文脈化を行う.

    主な方法:

    • 人間,動物,微生物からの144,424の質量スペクトロメトリーファイルを含む467のデータセットをまとめました.
    • モノカルチャー,合成コミュニティ,宿主関連サンプルからの統合データ.
    • ホスト,条件,介入の間でメタボライトの発生を追跡するために,検索可能なネットワークグラフを開発しました.

    主要な成果:

    • 微生物と結合した胆酸を文脈化し,微生物の媒介による薬物の代謝を調査した.
    • 特定された腸内細菌が,ACE阻害剤前薬エナラプリルを脱プロリエートしている.
    • ヒト,微生物,環境,ゴリラサンプルで代謝物を追跡し,ACE阻害の喪失を示した.

    結論:

    • MicrobiomeMASSTは,質量スペクトロメトリー/質量スペクトロメトリースペクトルと生物学的文脈を効果的に結びつけています.
    • このフレームワークは,孤立した観察を包括的な微生物群マップに解釈することを可能にします.
    • エナラプリルの微生物による脱塩化は,その治療効果を無効化し,薬物代謝相互作用を強調する.