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

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

7.7K
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...
7.7K

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

Peripheral biochemical parameters for discrimination between bipolar disorder and major depressive disorder in female patients of reproductive age: a CRP-stratified exploratory study.

BMC psychiatry·2026
Same author

Effects of ciprofol versus propofol sedation on hypoxaemia and hypotension in elderly patients undergoing bidirectional endoscopy: protocol for a randomized controlled trial.

Frontiers in medicine·2026
Same author

ddRAD sequencing of 1076 <i>Camellia</i> accessions reveals the genetic diversity and population introgression of the tea plant in China.

Plant diversity·2026
Same author

Enhancing papaya resistance to ringspot virus through CRISPR/Cas9-mediated gene editing of e<i>IF4E</i>.

Frontiers in plant science·2026
Same author

Associations of single and mixed air pollution exposure with lung function and incident pulmonary fibrosis: The mediation effect of low-grade inflammation.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

Aberration-aware 3D localization microscopy via self-supervised neural-physics learning.

Nature communications·2026

相关实验视频

Updated: Sep 12, 2025

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
10:20

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

8.3K

可扩展和轻量级的深度学习,用于高效的高精度单分子定位显微镜.

Yue Fei1, Shuang Fu1, Wei Shi1,2

  • 1Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China.

Nature communications
|August 5, 2025
PubMed
概括
此摘要是机器生成的。

莱特洛克提供了一个可扩展的解决方案,用于更快地分析单分子局部化显微镜 (SMLM) 数据. 这种深度学习框架提高了高通量生物研究的处理速度和效率.

更多相关视频

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells
11:06

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

8.6K
Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy
12:51

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Published on: December 9, 2013

9.0K

相关实验视频

Last Updated: Sep 12, 2025

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
10:20

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

8.3K
Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells
11:06

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

8.6K
Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy
12:51

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Published on: December 9, 2013

9.0K

科学领域:

  • 生物物理学的生物物理.
  • 显微镜的使用方法
  • 计算生物学 计算生物学

背景情况:

  • 深度学习提高了单分子局部化显微镜 (SMLM) 的性能.
  • 当前的SMLM方法通常是计算密集型的,阻碍了高通量应用程序.

研究的目的:

  • 介绍LiteLoc,这是一个可扩展的框架,用于高通量SMLM数据分析.
  • 在不影响准确性的情况下,提高SMLM中的处理速度和资源效率.

主要方法:

  • 开发了一个轻量级的神经网络架构.
  • 在CPU和GPU资源之间实现并行处理.
  • 优化以减少延迟和能源消耗.

主要成果:

  • LiteLoc显著提高了SMLM数据的处理速度.
  • 在资源效率方面取得了实质性的收益.
  • 保持高定位精度. 保持高定位精度.

结论:

  • 莱特洛克为常规的SMLM工作流提供了一个有效和可扩展的工具.
  • 在生物研究中实现高通量SMLM数据分析.
  • 解决SMLM分析中的计算挑战.