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Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

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Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

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Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

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Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
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Fermi Level Dynamics01:12

Fermi Level Dynamics

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The vacuum level denotes the energy threshold required for an electron to escape from a material surface. It is usually positioned above the conduction band of a semiconductor and acts as a benchmark for comparing electron energies within various materials.
Electron affinity in semiconductors refers to the energy gap between the minimum of its conduction band and the vacuum level and it is a critical parameter in determining how easily a semiconductor can accept additional electrons.
The work...
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BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

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System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
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Operon Model01:23

Operon Model

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The operon model represents a fundamental mechanism of gene regulation in prokaryotes, enabling coordinated expression of genes involved in related metabolic or functional pathways. Operons consist of structural genes, a promoter, and an operator, with transcription regulated by repressors, activators, and small effector molecules.Structure and Function of OperonsAn operon is a cluster of structural genes transcribed together under the control of a single promoter. The promoter region...
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Updated: Mar 17, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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用库普曼运算符对元稳定动态进行数据驱动的表征和加速.

Julien Luzzatto1,2, Feliks Nüske3, Nicolas G Hadjiconstantinou2,4

  • 1Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.

Journal of chemical theory and computation
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PubMed
概括
此摘要是机器生成的。

本研究介绍了一种吸收的库普曼运算符方法来分析元稳定动态. 它准确地从短轨迹数据中量化系统放松和逃逸行为,从而实现更长的模拟.

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科学领域:

  • 计算物理 计算物理
  • 化学物理 化学物理
  • 动态系统理论 动态系统理论

背景情况:

  • 许多物理和生物系统表现出超稳定动态,涉及长期的稳定,随后是快速的过渡.
  • 准确量化局部放松和第一次逃脱时间对于模拟这些系统的长期动态至关重要.

研究的目的:

  • 扩展数据驱动的库普曼运算子方法来分析元稳定动态.
  • 为了提高准确性,将准静止分布 (QSD) 和吸收边界条件纳入.

主要方法:

  • 通过对元稳态强制执行吸收边界条件,开发了一种吸收的库普曼配方.
  • 应用数据驱动的技术,以从短轨迹数据中估计库普曼运算符.

主要成果:

  • 吸收的库普曼配方可靠地恢复了调节放松和逃逸的光谱特性.
  • 仅使用短轨迹数据证明了准确的估计.

结论:

  • 拟议的方法为分析复杂的超稳定系统提供了一种强大的方法.
  • 将光谱估计与时间平行模拟方案相结合,可以显著扩展可访问的模拟时间表.