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

Immunofluorescence Microscopy01:12

Immunofluorescence Microscopy

11.4K
A fluorescence microscope uses fluorescent chromophores called fluorochromes, which can absorb energy from a light source and then emit this energy as visible light. Fluorochromes include naturally fluorescent substances (such as chlorophylls) and fluorescent stains that are added to the specimen to create contrast. Dyes such as Texas red and FITC are examples of fluorochromes. Other examples include the nucleic acid dyes 4’,6’-diamidino-2-phenylindole (DAPI), and acridine orange.
11.4K

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

Report on influenza viruses received and tested by the Melbourne WHO Collaborating Centre for Reference and Research on Influenza during 2024.

Communicable diseases intelligence (2018)·2026
Same author

DeBCR: a sparsity-efficient framework for image enhancement through a deep-learning-based solution to inverse problems.

Communications engineering·2026
Same author

Regularized Gradient Statistics Improve Generative Deep Learning Models of Super Resolution Microscopy.

Small methods·2025
Same author

Report on influenza viruses received and tested by the Melbourne WHO Collaborating Centre for Reference and Research on Influenza during 2023.

Communicable diseases intelligence (2018)·2025
Same author

A digital photography dataset for Vaccinia Virus plaque quantification using Deep Learning.

Scientific data·2025
Same author

Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model.

Communications engineering·2024

相关实验视频

Updated: Sep 20, 2025

Vaccinia Reporter Viruses for Quantifying Viral Function at All Stages of Gene Expression
10:48

Vaccinia Reporter Viruses for Quantifying Viral Function at All Stages of Gene Expression

Published on: May 15, 2014

11.6K

病毒感染报告员在光和亮场显微镜中的虚拟染色的基准.

Maria Wyrzykowska1,2,3, Gabriel Della Maggiora1,2,4, Nikita Deshpande1,2

  • 1Center for Advanced Systems Understanding (CASUS), Görlitz, Germany.

Scientific data
|May 28, 2025
PubMed
概括

这项研究引入了用于虚拟染色病毒感染细胞的基准和数据集,使得能够在显微镜中连续检测信号. 这项研究解决了使用先进的机器学习技术准确可视化病毒感染的差距.

更多相关视频

A Luciferase-fluorescent Reporter Influenza Virus for Live Imaging and Quantification of Viral Infection
05:21

A Luciferase-fluorescent Reporter Influenza Virus for Live Imaging and Quantification of Viral Infection

Published on: August 14, 2019

25.4K
Arbovirus Infections As Screening Tools for the Identification of Viral Immunomodulators and Host Antiviral Factors
06:02

Arbovirus Infections As Screening Tools for the Identification of Viral Immunomodulators and Host Antiviral Factors

Published on: September 13, 2018

7.0K

相关实验视频

Last Updated: Sep 20, 2025

Vaccinia Reporter Viruses for Quantifying Viral Function at All Stages of Gene Expression
10:48

Vaccinia Reporter Viruses for Quantifying Viral Function at All Stages of Gene Expression

Published on: May 15, 2014

11.6K
A Luciferase-fluorescent Reporter Influenza Virus for Live Imaging and Quantification of Viral Infection
05:21

A Luciferase-fluorescent Reporter Influenza Virus for Live Imaging and Quantification of Viral Infection

Published on: August 14, 2019

25.4K
Arbovirus Infections As Screening Tools for the Identification of Viral Immunomodulators and Host Antiviral Factors
06:02

Arbovirus Infections As Screening Tools for the Identification of Viral Immunomodulators and Host Antiviral Factors

Published on: September 13, 2018

7.0K

科学领域:

  • 计算生物学 计算生物学
  • 病毒学 病毒学
  • 显微镜成像技术 显微镜成像技术

背景情况:

  • 在光显微镜中检测病毒感染细胞通常依赖于来自免疫组织化学或基因工程的记者信号.
  • 现有的用于检测细胞感染的机器学习方法提供了分类,但缺乏连续信号细微差别.
  • 虚拟染色为连续信号检测提供了机会,但对于感染病毒的细胞来说尚未充分探索.

研究的目的:

  • 为新的病毒感染报告员虚拟染色 (VIRVS) 任务建立一个基准和精选的数据集.
  • 探索深度学习模型的应用,用于在病毒感染细胞中生成连续的虚拟染色信号.
  • 在数据科学和病毒学的交叉点定义一个新的研究挑战.

主要方法:

  • 聚合了各种各样的显微镜数据集,包括各种病毒和成像模式.
  • 实施和评估U-Net和pix2pix架构用于虚拟染色.
  • 探索VIRVS任务的回归式和生成式模型方法.

主要成果:

  • 该研究为VIRVS任务提供了一个全面的基准.
  • 证明了使用U-Net和pix2pix进行病毒感染细胞虚拟染色的可行性.
  • 已建立的数据集和方法论,用于未来的研究在这个领域.

结论:

  • 拟议的基准和数据集有助于推进病毒学虚拟染色技术的发展.
  • 这项工作为使用显微镜对病毒感染进行定量分析开辟了新的途径.
  • 该研究确定了将数据科学应用于病毒学成像的重大挑战.