相关实验视频

Updated: Jan 10, 2026

Author Spotlight: Optimizing Cryo-EM Analysis with CryoSieve for Enhanced Particle Selection Efficiency
06:41

Author Spotlight: Optimizing Cryo-EM Analysis with CryoSieve for Enhanced Particle Selection Efficiency

Published on: May 10, 2024

2.5K

反:一个元数据驱动的神经网络,用于改进冷EM 3D粒子分类.

Raymond F Berkeley1,2, Brian D Cook1, Daniel Ji1

  • 1Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093 USA.

bioRxiv : the preprint server for biology
|November 24, 2025
PubMed
概括

一个新的神经网络,ANTIDOTE,通过自动检测和去除低质量的颗粒来改进冷电子显微镜 (cryoEM). 这提高了3D重建质量,并加速了结构生物学数据处理.

更多相关视频

A Robust Single-Particle Cryo-Electron Microscopy cryo-EM Processing Workflow with cryoSPARC, RELION, and Scipion13:43

A Robust Single-Particle Cryo-Electron Microscopy cryo-EM Processing Workflow with cryoSPARC, RELION, and Scipion

Published on: January 31, 2022

14.9K
Cryo-EM and Single-Particle Analysis with Scipion09:06

Cryo-EM and Single-Particle Analysis with Scipion

Published on: May 29, 2021

4.3K

相关概念视频

Cryo-electron Microscopy01:28

Cryo-electron Microscopy

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...
4.1K