Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

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...
2.4K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

[Arthroscopic reconstruction of anterior cruciate ligament with preservation of the remnant bundle].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2013
Same author

[Anterior cruciate ligament reconstruction with tendon graft enveloped by preserved remnants].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2013
Same author

Genetic and molecular biological characterization of two homologous cheR genes from Leptospira interrogans.

Acta biochimica et biophysica Sinica·2013
Same author

Upregulation of glycoprotein nonmetastatic B by colony-stimulating factor-1 and epithelial cell adhesion molecule in hepatocellular carcinoma cells.

Oncology research·2013
Same author

Effect of implantation of biodegradable magnesium alloy on BMP-2 expression in bone of ovariectomized osteoporosis rats.

Materials science & engineering. C, Materials for biological applications·2013
Same author

[Texture variation of CC 5052 aluminum alloy slab from surface to center layer by XRD].

Guang pu xue yu guang pu fen xi = Guang pu·2013

相关实验视频

Updated: Jul 14, 2025

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

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.9K

基于深度学习的监督的语义细分电子的冷子图.

Chang Liu1, Xiangrui Zeng2, Ruogu Lin3

  • 1Electrical and Computer Engineering Department, Carnegie Mellon University, USA.

Proceedings. International Conference on Image Processing
|October 6, 2023
PubMed
概括
此摘要是机器生成的。

细胞电子冷扫描 (CECT) 对宏分子的细分通过一种新的3D深度学习模型得到了改进. 这种方法减少了来自拥挤的细胞环境的偏差,增强了复杂生物样本的结构分析.

关键词:
3D图像语义细分 3D图像语义细分细胞电子冷断层扫描 (Cellular Electron Cryo-Tomography) 是一种细胞电子冷断层扫描.卷积神经网络是一种卷积神经网络.深度学习 (Deep Learning) 是一种深度学习.宏分子复合体 宏分子复合体

更多相关视频

Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data
07:17

Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data

Published on: January 24, 2025

937
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.6K

相关实验视频

Last Updated: Jul 14, 2025

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

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.9K
Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data
07:17

Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data

Published on: January 24, 2025

937
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

24.6K

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:

  • 细胞电子冷扫描 (CECT) 为3D细胞结构可视化提供了亚分子分辨率.
  • 在拥挤的细胞环境中分析宏分子复合体,由于结构复杂性和成像限制,存在重大挑战.
  • 邻近结构的偏差使CECT数据中精确的宏分子恢复变得复杂.

研究的目的:

  • 开发一种新的深度学习方法,用于在CECT子图中对宏分子进行监督细分.
  • 为了减轻细胞环境中分子拥挤引起的细分偏差.
  • 为了提高从CECT图像中恢复宏分子结构的准确性和概括性.

主要方法:

  • 一个新的3D卷积神经网络架构的介绍.
  • 该网络受到完全卷积网络和编码解码器架构的启发.
  • 监督学习用于在子图中对宏分子进行细分.

主要成果:

  • 与基线方法相比,拟议的深度学习模型在模拟的CECT数据上显著改善了细分性能.
  • 该模型显示了概括能力,成功地细分了培训数据中不存在的结构.
  • 由于新的细分方法,观察到宏分子恢复的偏差减少.

结论:

  • 开发的3D卷积神经网络有效地增强了CECT中的宏分子细分.
  • 这种方法解决了分子拥挤的挑战,改善了复杂细胞环境中的结构分析.
  • 该模型的概括能力表明,它可以广泛应用于使用CET的宏分子结构确定.