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相关概念视频

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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相关实验视频

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

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解码器TCR:用于TCR-pMHC相互作用的组合预训练和引解码.

Boqiao Lai, Melissa Englund, Ramit Bharanikumar

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    |February 12, 2026
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    概括
    此摘要是机器生成的。

    我们开发了DecoderTCR,这是一个用于预测T细胞受体与-MHC复合体相互作用的计算框架. 该模型在预测绑定和识别方面表现强,即使数据有限.

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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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    相关实验视频

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    Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides
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    科学领域:

    • 计算免疫学计算免疫学
    • 生物信息学是一种生物信息学.
    • 机器学习用于免疫学.

    背景情况:

    • 建模T细胞受体 (TCR) 和-MHC (pMHC) 相互作用至关重要,但由于对联数据有限,具有挑战性.
    • 不配对的TCR和pMHC序列数据丰富,为新的建模方法提供了机会.

    研究的目的:

    • 引入 DecoderTCR,这是一个面具语言模型框架,用于 TCR-pMHC 识别建模.
    • 通过利用配对和不配对的序列数据来解决数据稀疏性.
    • 改进TCR-pMHC结合和表位特异性识别的零射击预测能力.

    主要方法:

    • 在完善跨链依赖之前,使用边际数据实施了组成的持续预培训课程.
    • 开发了一种以代透为导向的精制 (Iterative Entropy-Guided Refinement,IEGR),是一种非自行回归的解码算法,用于高效的上下文解析.
    • 利用掩盖语言建模来从序列数据中学习表示.

    主要成果:

    • 实现了0.96 AUROC,用于零射击的pMHC结合预测.
    • 在表位特异性TCR识别方面达到0.76 AUROC,接近监督基线性能,没有表位特异性培训.
    • 学习的表示可以在没有协调监督的情况下恢复结构联系,并生成具有现实的重组统计数据的序列.

    结论:

    • 解码器TCR有效地模拟了TCR-pMHC相互作用,并通过稀疏的数据实现了高预测性能.
    • 存在预测生成差距,表明虽然歧视很强,但可靠的序列生成仍然是一个公开的挑战.
    • 该框架展示了掩面语言模型在计算免疫学中的潜力,以了解免疫识别.