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相关概念视频

Cryo-electron Microscopy01:28

Cryo-electron Microscopy

3.3K
Conventional electron microscopy (EM) involves dehydration, fixation, and staining of biological samples, which distorts the native state of biological molecules and results in several artifacts. Also, the high-energy electron beam damages the sample and makes it difficult to obtain high-resolution images. These issues can be addressed using cryo-EM, which uses frozen samples and gentler electron beams. The technique was developed by Jacques Dubochet, Joachim Frank, and Richard Henderson, for...
3.3K

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Using <i>spIsoNet</i> to address the preferred-orientation problem in cryoEM reconstructions.

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相关实验视频

Updated: Jun 28, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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通过自我监督的深度学习来克服冷EM中首选的导向问题.

Yun-Tao Liu1,2, Hongcheng Fan1,2, Jason J Hu1,2,3

  • 1Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, CA, USA.

bioRxiv : the preprint server for biology
|April 22, 2024
PubMed
概括

spIsoNet是一个新的深度学习软件,通过计算解决了冷电子显微镜中首选的定向问题. 它可以提高3D重建的准确性和同位素性,而无需复杂的样本准备.

科学领域:

  • 结构生物学是结构生物学.
  • 生物物理学的生物物理.
  • 计算生物学是一种计算生物学.

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Single-Particle Cryo-EM Data Collection with Stage Tilt using Leginon
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Single-Particle Cryo-EM Data Collection with Stage Tilt using Leginon

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Optimizing Sample Preparation for Cryogenic Electron Microscopy
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Optimizing Sample Preparation for Cryogenic Electron Microscopy

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相关实验视频

Last Updated: Jun 28, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

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Single-Particle Cryo-EM Data Collection with Stage Tilt using Leginon
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Single-Particle Cryo-EM Data Collection with Stage Tilt using Leginon

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背景情况:

  • 单粒子冷电子显微镜 (cryo-EM) 实现了宏分子复合体的原子分辨率.
  • 粒子定向偏差,或首选的定向问题,使冷EM中的3D重建变得复杂.
  • 目前的解决方案涉及复杂的生化和物理样本操纵.

结论:

  • spIsoNet为冷EM中首选方向问题提供了一个通用的计算解决方案.
  • 消除了需要额外的,复杂的样本准备程序.
  • 增强地图同变性和粒子对齐,以偏向导向的分子.