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相关概念视频

Diffusion01:12

Diffusion

Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...

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相关实验视频

Updated: May 12, 2026

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy
12:15

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Published on: April 9, 2019

8.7K

从Hi-C学习Micro-C,使用扩散模型.

Tong Liu1, Hao Zhu1, Zheng Wang1

  • 1Department of Computer Science, University of Miami, Coral Gables, Florida, United States of America.

PLoS computational biology
|May 17, 2024
PubMed
概括
此摘要是机器生成的。

HiC2MicroC使用无声扩散概率模型,从Hi-C数据中预测Micro-C染色体相互作用,增强循环检测和基因组特征分析. 这种方法改进了现有的回归技术,为分析染色体组织提供了有价值的工具.

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Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
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Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior
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相关实验视频

Last Updated: May 12, 2026

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy
12:15

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Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
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科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 表观遗传学 在表观遗传学中,表观遗传学是指表观遗传学.

背景情况:

  • 微C提供核细胞分辨率染色体相互作用数据,在信号对噪声和循环检测方面超过传统的Hi-C.
  • 与丰富的Hi-C数据相比,有限的Micro-C数据集阻碍了全面的分析.

研究的目的:

  • 开发一种计算方法 (HiC2MicroC) 来从现有的Hi-C数据集中预测Micro-C数据.
  • 利用无声扩散概率模型 (DDPM) 进行增强的染色体相互作用预测.

主要方法:

  • 经过训练的DDPM和回归模型使用人类前皮纤维细胞 (HFFc6) 细胞系数据.
  • 在5kb和1kb分辨率的6种细胞类型中评估了预测准确性.
  • 将HiC2MicroC性能与回归模型进行比较,并与P五টিCMicro-C和ChIA-PET数据进行验证.

主要成果:

  • HiC2MicroC成功地恢复了Micro-C的循环,包括那些被Hi-C错过的循环.
  • 预测的循环经常将CTCF绑定站点定在一个趋同的位置.
  • 恢复的循环表现出与微C数据一致的基因组和表观遗传特性,将增强剂和促进剂连接起来.

结论:

  • 使用DDPM,HiC2MicroC有效地将Hi-C数据增强到Micro-C分辨率.
  • 该方法准确地预测了生物相关的染色体循环,通过多种实验技术验证.
  • HiC2MicroC提供了一个强大的计算工具,用于从Hi-C数据中深入分析染色质组织.