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関連する概念動画

Entropy02:39

Entropy

36.4K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
36.4K
Entropy01:18

Entropy

3.6K
The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
3.6K
Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

25.0K
Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
25.0K
Entropy and Solvation02:05

Entropy and Solvation

8.5K
The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
8.5K
Entropy within the Cell01:22

Entropy within the Cell

13.0K
A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
13.0K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

5.0K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
5.0K

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関連する実験動画

Updated: Feb 13, 2026

Measuring TCR-pMHC Binding In Situ using a FRET-based Microscopy Assay
19:05

Measuring TCR-pMHC Binding In Situ using a FRET-based Microscopy Assay

Published on: October 30, 2015

12.9K

解読器TCR: TCR-pMHC相互作用のための組成的訓練とエントロピー誘導の解読

Boqiao Lai, Melissa Englund, Ramit Bharanikumar

    bioRxiv : the preprint server for biology
    |February 12, 2026
    PubMed
    まとめ
    この要約は機械生成です。

    私たちは,ペプチド-MHC複合体とのT細胞受容体相互作用を予測するためのコンピューティングフレームワークであるDecoderTCRを開発しました. このモデルは,限られたデータであっても,結合と認識を予測する上で強力なパフォーマンスを示しています.

    さらに関連する動画

    Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides
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    Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides

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    A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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    A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

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    関連する実験動画

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    Measuring TCR-pMHC Binding In Situ using a FRET-based Microscopy Assay
    19:05

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    Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides
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    Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides

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    A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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    科学分野:

    • コンピューター免疫学
    • バイオインフォマティックス
    • 免疫学のための機械学習

    背景:

    • T細胞受容体 (TCR) とペプチド-MHC (pMHC) の相互作用をモデリングすることは,限られたペアリングデータがあるため,極めて重要ですが,困難です.
    • ペアリングされていないTCRおよびpMHC配列データは豊富で,新しいモデリングアプローチの機会を提供します.

    研究 の 目的:

    • TCR-pMHC認識モデリングのためのマスクされた言語モデルフレームワークであるDecoderTCRを導入します.
    • ペアリングされたデータとペアリングされていないシーケンスデータの両方を活用することによって,データの散らさに対処します.
    • TCR-pMHC結合とエピトープ特異的認識におけるゼロショット予測能力を向上させる.

    主な方法:

    • クロスチェーンの依存性を精錬する前に,限界データを用いて構成的な継続的な予備訓練カリキュラムを実装しました.
    • Iterative Entropy-Guided Refinement (IEGR) を開発し,効率的な文脈解析のための非自律回帰の解読アルゴリズムである.
    • シーケンスのデータから表現を学習するためにマスクされた言語モデリングを使用しました.

    主要な成果:

    • ゼロショットpMHC結合予測のために0.96AUROCを達成しました.
    • エピトープ特異的なTCR認識で0.76AUROCを達成し,エピトープ特異的なトレーニングなしで監督されたベースライン性能に近づいています.
    • 協調監督なしで構造的接触を回復する学習された表現と,現実的な再組み合わせ統計を持つシーケンスを生成しました.

    結論:

    • DecoderTCRは,TCR-pMHCの相互作用を効果的にモデル化し,稀なデータで高い予測性能を達成しています.
    • 予測生成のギャップが存在し,差別が強い一方で,信頼性の高いシーケンス生成は依然として未解決の課題であることを示しています.
    • このフレームワークは,免疫認識を理解するための計算免疫学の仮面言語モデルの可能性を実証しています.