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相关概念视频

Cryo-electron Microscopy01:28

Cryo-electron Microscopy

4.1K
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
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.8K
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: Jan 11, 2026

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

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使用三重损失嵌入的冷电磁显微镜的少数镜头分类.

Alexander Ho1,2, Brandon Sung3, Sukyeong Lee1,2

  • 1Advanced Technology Core for Macromolecular X-Ray Crystallography, Baylor College of Medicine, Houston, TX 77030.

bioRxiv : the preprint server for biology
|November 19, 2025
PubMed
概括

这项研究引入了一种新的几次射击学习方法,用于评估冷电子显微镜 (cryo-EM) 显微镜的质量. 这种方法有效地用最小的标记数据对微图进行分类,改进了自动选过程.

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Cryo-EM and Single-Particle Analysis with Scipion
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Cryo-EM and Single-Particle Analysis with Scipion

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Single Particle Cryo-Electron Microscopy: From Sample to Structure
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Single Particle Cryo-Electron Microscopy: From Sample to Structure

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相关实验视频

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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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.
  • 计算生物学 计算生物学

背景情况:

  • 低温电子显微镜 (cryo-EM) 显微镜的手动选是耗时且计算成本昂贵的.
  • 自动化质量评估对于高效的冷EM数据处理至关重要.
  • 低射线学习在冷EM中的应用正在出现,但在微图级质量评估方面尚未得到充分探索.

研究的目的:

  • 开发和评估用于自动化冷电磁显微镜质量评估的一些射击学习框架.
  • 提高冷EM数据处理的效率并减少手动标签负担.
  • 探索三重损失嵌入的有效性,用于微图分类.

主要方法:

  • 开发了一个框架,将少数射击学习与冷-EM微图分类结合起来.
  • 使用三重损失嵌入,整合实空间和里埃空间信息.
  • 该模型在多个EMPIAR数据集上进行了训练和验证.

主要成果:

  • 提出的方法在微图分类中取得了竞争性表现,每类仅有1-5个标记的示例.
  • 三倍损失嵌入方法在少数拍摄场景中表现优于传统的交叉训练.
  • 该框架在不同的EMPIAR数据集中表现出强度.

结论:

  • 短期学习为快速适应冷EM中自动化微图查提供了一种实际解决方案.
  • 开发的框架大大减少了大量手动标签的需要.
  • 这种方法提高了冷电磁数据处理管道的效率.