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Phase Contrast and Differential Interference Contrast Microscopy

Phase Contrast and Differential Interference Contrast Microscopy

Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...

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Updated: Jun 24, 2025

A Simple, Robust, and High Throughput Single Molecule Flow Stretching Assay Implementation for Studying Transport of Molecules Along DNA

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HiCDiff:单细胞Hi-C数据与扩散模型进行异化.

Yanli Wang¹, Jianlin Cheng¹

¹Department of Electrical Engineering and Computer Science, NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, United States.

Briefings in bioinformatics

|June 10, 2024

概括

此摘要是机器生成的。

我们开发了HiCDiff,这是一种新的生成扩散模型,可以有效地消除单细胞Hi-C数据. 这种方法增强了来自单个细胞的染色体接触矩阵的分析,提高了基因组研究的准确性.

关键词:

在Hi-C数据中,无声化.深度学习是一种深度学习.扩散模型的扩散模型.一个单细胞的Hi-C.

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Last Updated: Jun 24, 2025

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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
生物技术是生物技术.

背景情况:

单细胞Hi-C (ScHi-C) 是一种强大的技术,用于研究单个细胞中的基因组构造.
ScHi-C数据的特点是稀疏和噪声,阻碍了其应用和分析.
现有的方法很难有效地否定ScHi-C数据.

研究的目的:

开发一种新的计算方法来消除单细胞Hi-C数据.
引入第一个生成扩散模型,HiCDiff,用于处理染色体接触矩阵.
为了评估 HiCDiff 在各种 ScHi-C 数据集上的性能.

主要方法:

开发HiCDiff,一种利用深度残余网络的生成扩散模型.
在无监督和监督学习模式中训练HiCDiff.
将HiCDiff与现有的非扩散和最先进的深度学习方法进行比较.

主要成果:

HiCDiff在单细胞 Hi-C 数据中的噪声大大降低.
无监督的HiCDiff优于大多数监督的非扩散方法.
HiCDiff的性能与最先进的监督方法相提并论.
该模型还证明了在消除批量Hi-C数据方面的有效性.

结论:

像HiCDiff这样的生成扩散模型,代表了一种用于否定ScHi-C数据的有价值的方法.
HiCDiff提高了单细胞Hi-C数据的实用性和可靠性,用于生物研究.
开发的方法显示出对单细胞和批量Hi-C数据分析的前景.