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相关概念视频

Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

5.4K
Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
5.4K
Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

2.6K
Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...
2.6K

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Barriers and facilitators to developing and implementing artificial intelligence-based clinical decision support in the emergency department: a qualitative study.

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ProT-GFDM: A generative fractional diffusion model for protein generation.

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Conditioned quantum-assisted deep generative surrogate for particle-binary vector indicating thecalorimeter interactions.

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Artificial intelligence-based clinical decision support in the emergency department: A scoping review.

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Can dietary palmitic supplementation and milking frequency modify the cheesemaking properties of milk?

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Decision threshold models in medical decision making: a scoping literature review.

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From Pixels to Patterns: A Multidimensional Framework to Decode Cytoskeletal Organization.

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A Large Concept Model for Mechanistic Simulation of Disease Trajectories: A Hypothesis-Generating Exemplar for Pediatric Acute Lymphoblastic Leukemia.

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Adversarial Sequence Mutations in AlphaFold and ESMFold Reveal Nonphysical Structural Invariance, Confidence Failures, and Concerns for Protein Design.

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High-Throughput Prediction of Protein-Protein Interactions Uncovers Hidden Molecular Networks in Biosynthetic Gene Clusters.

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相关实验视频

Updated: Jan 13, 2026

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy
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Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

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从潜伏动力学到数据几何学:蛋白质结构的非线性扩散建模.

Xiao Liang1, Eric Paquet2,3, Herna Viktor3

  • 1Telfer School of Management, University of Ottawa, Ottawa, K1N 6N5, ON, Canada.

Computational and structural biotechnology journal
|January 8, 2026
PubMed
概括
此摘要是机器生成的。

本研究介绍了ProT-INDM,这是一个用于生成模型的新型非线性扩散框架. 它可以更准确地建模复杂的数据,如蛋白质骨干,优于线性模型.

关键词:
产生性扩散模型的生成性扩散模型基于概率的指标.非线性随机微分方程非线性随机微分方程规范化流量的流动.蛋白质骨干的产生是蛋白质的产生.

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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water
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High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water

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相关实验视频

Last Updated: Jan 13, 2026

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy
08:03

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

Published on: April 13, 2022

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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water
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科学领域:

  • 机器学习 机器学习
  • 计算生物学 计算生物学
  • 生成型模型 生成型模型

背景情况:

  • 生成性扩散模型中的非线性性尚未得到充分探索.
  • 现有的模型通常依赖于线性动态,限制了它们捕捉复杂数据分布的能力.

研究的目的:

  • 介绍ProT-INDM,一个隐性非线性扩散框架.
  • 实现复杂蛋白质骨干分布的原则性和灵活的建模.
  • 桥梁非线性随机微分方程 (SDEs) 与基于分数的生成建模.

主要方法:

  • 使用可逆规范流来诱导非线性数据空间动态.
  • 采用可处理的潜在SDEs来处理潜在的动态.
  • 使用概率流普通微分方程 (ODEs) 进行精确的概率计算.

主要成果:

  • 形成了用于训练非线性扩散模型的第一个可处理的建模方案.
  • 证明了与线性扩散模型基线相比经验上的改进.
  • 提供了对非线性性作用的理论见解.

结论:

  • ProT-INDM为非线性扩散建模提供了一种原则性和灵活的方法.
  • 这一框架推进了复杂生物数据的生成建模.
  • 这项研究为研究非线性生成模型开辟了新的途径.