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

6.9K
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...
6.9K

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

Structure of the Huntingtin F-actin complex reveals its role in cytoskeleton organization.

Science advances·2025
Same author

Inverted PAINT for Material-Specific Super-Resolution Fluorescence Imaging of Semiconductors.

Advanced materials (Deerfield Beach, Fla.)·2025
Same author

Super-Resolution Imaging of Semiconductor Nanopatterns Using the Non-Bridging-Oxygen-Hole-Center-Based Photoluminescence Enhancement Effect.

Journal of the American Chemical Society·2025
Same author

Oxygen-excluded nanoimaging of polymer blend films.

Science advances·2025
Same author

Edge roughness analysis in nanoscale for single-molecule localization microscopy images.

Nanophotonics (Berlin, Germany)·2024
Same author

Nanoscale single-vesicle analysis: High-throughput approaches through AI-enhanced super-resolution image analysis.

Biosensors & bioelectronics·2024

相关实验视频

Updated: Jun 6, 2025

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

8.4K

人工智能支持的光谱单分子定位显微镜

Yoonsuk Hyun1, Doory Kim2

  • 1Department of Mathematics, Inha University, Incheon, 22212, Republic of Korea.

Small methods
|November 27, 2024
PubMed
概括
此摘要是机器生成的。

人工智能 (AI) 通过提高光谱分辨率和定位精度来增强光谱单分子定位显微镜 (SMLM). 这种由人工智能驱动的方法为先进的生物和材料科学应用解锁了对分子行为的新见解.

关键词:
机器学习是机器学习.神经网络的神经网络的神经网络单分子定位显微镜.单分子光谱学单分子光谱学

更多相关视频

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

8.9K
Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM
11:57

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

Published on: December 1, 2016

10.7K

相关实验视频

Last Updated: Jun 6, 2025

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

8.4K
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

8.9K
Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM
11:57

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

Published on: December 1, 2016

10.7K

科学领域:

  • 纳米技术 纳米技术
  • 频谱学是一种光谱学.
  • 生物物理学的生物物理.

背景情况:

  • 光谱单分子定位显微镜 (SMLM) 提供纳米尺度可视化和化学环境洞察力.
  • 由于复杂的数据,挑战包括有限的光谱分辨率和定位精度.

研究的目的:

  • 审查AI整合如何解决光谱SMLM中的局限性.
  • 突出AI在增强多色超分辨率成像和数据分析方面的作用.

主要方法:

  • 对应用到光谱SMLM数据的基于AI的方法的审查.
  • 讨论人工智能驱动的光谱分类和定位方面的进展.
  • 检查AI从点差函数中提取光谱信息的能力.

主要成果:

  • 人工智能显著改善了SMLM中的光谱分类和定位精度.
  • 人工智能可以从未修改的点差函数中提取丰富的光谱数据.
  • 人工智能有助于分析复杂的多维光谱SMLM数据集.

结论:

  • 人工智能赋予了光谱SMLM的权力,克服了关键挑战.
  • 人工智能增强了研究复杂生物系统和材料的能力.
  • 人工智能为纳米级分子相互作用和动态提供了新的见解.