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Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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Cryo-electron Microscopy01:28

Cryo-electron Microscopy

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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...
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Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.4K
Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
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相关实验视频

Updated: Jun 30, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

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基于深度内部学习的冷电子显微镜的多图像超分辨率.

Qinwen Huang1, Ye Zhou1, Hsuan-Fu Liu2

  • 1Department of Computer Science, Duke University, Durham, North Carolina, USA.

Biological imaging
|March 21, 2024
PubMed
概括

我们开发了一个用于冷电子显微镜 (cryo-EM) 的深度学习算法,以增强低信号噪声比 (SNR) 图像. 这种方法提高了3D结构的分辨率,有可能在不影响质量的情况下加速数据收集.

关键词:
低温电子显微镜的使用方法图像超分辨率的超级分辨率一个粒子分析分析.零射击学习的学习

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Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data
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Author Spotlight: Enhancing CryoEM Sample Preparation Using Graphene Monolayer on Microscopy Grids
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相关实验视频

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Author Spotlight: Enhancing CryoEM Sample Preparation Using Graphene Monolayer on Microscopy Grids
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科学领域:

  • 结构生物学 结构生物学
  • 生物物理学的生物物理.
  • 计算成像技术的成像

背景情况:

  • 单粒子冷电子显微镜 (cryo-EM) 提供了近原子分辨率的生物分子.
  • 低电子剂量在冷EM导致低信号噪声比 (SNRs),需要平均许多粒子图像.
  • 由于采样要求,目前的方法在视野和数据收集时间方面面临限制.

研究的目的:

  • 为冷EM数据提供一种新的多图像超分辨率 (SR) 算法.
  • 为了应对低SNR在冷EM成像中的挑战.
  • 为了实现更高分辨率的3D结构确定和更快的数据采集.

主要方法:

  • 开发了一个基于深度内部学习的多图像SR算法,适用于低SNR冷EM数据.
  • 利用来自冷电磁电影的内部图像统计数据,消除了对基础真相训练数据的需求.
  • 将SR算法应用于阿波费里丁和T20S蛋白质组的单颗粒数据集.

主要成果:

  • 在低SNR条件下,SR算法有效地增强了图像.
  • 来自SR微镜的3D结构实现了超越传统成像系统限制的分辨率.
  • 证明了低放大图像的SR处理可以增加每次曝光的粒子产量.

结论:

  • 开发的SR算法显示了提高冷EM分辨率和效率的前景.
  • 结合低放大成像与in silico SR提供了一条加速冷EM数据收集的途径.
  • 这种方法有可能通过实现更快,更详细的分子成像来显著推进结构生物学研究.