Jove
Visualize
联系我们

相关概念视频

Rapid Identification of Pathogens01:25

Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...

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

Strontium and oxygen isotope analysis reveals changing connections to place and group membership in the world's earliest village societies.

Scientific reports·2025
Same author

Evaluation of the sensitivity of a federally endangered freshwater mussel (Venustaconcha trabalis) to selected chemicals.

Environmental toxicology and chemistry·2025
Same author

Active and passive organic carbon fluxes during a bloom in the Southern Ocean (South Georgia).

Scientific data·2024
Same author

Systematic review of the ophthalmic complications of robotic-assisted laparoscopic prostatectomy.

Journal of robotic surgery·2024
Same author

Breaking the menstruation taboo to make fieldwork more inclusive.

Nature·2024
Same author

Occurrence and sources of microplastics on Arctic beaches: Svalbard.

Marine pollution bulletin·2023
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

相关实验视频

Updated: Jun 29, 2026

Multicolor Fluorescence Detection for Droplet Microfluidics Using Optical Fibers
10:21

Multicolor Fluorescence Detection for Droplet Microfluidics Using Optical Fibers

Published on: May 5, 2016

10.5K

使用开源视觉识别模型快速检测环境样本中的微纤维.

Stamatia Galata1, Ian Walkington2, Timothy Lane3

  • 1School of Biological and Environmental Sciences, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, United Kingdom.

Journal of hazardous materials
|October 11, 2024
PubMed
概括

新的人工智能模型YOLOv7和Mask R-CNN有效地检测环境样本中的微纤维. YOLOv7显示了更高的准确性,使得能够快速量化微塑料,并推进污染研究.

关键词:
检测 检测 检测 检测 检测微塑料是一种微塑料.视觉识别模型的模型

更多相关视频

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

683
Separation and Identification of Conventional Microplastics from Farmland Soils
14:10

Separation and Identification of Conventional Microplastics from Farmland Soils

Published on: March 21, 2025

1.5K

相关实验视频

Last Updated: Jun 29, 2026

Multicolor Fluorescence Detection for Droplet Microfluidics Using Optical Fibers
10:21

Multicolor Fluorescence Detection for Droplet Microfluidics Using Optical Fibers

Published on: May 5, 2016

10.5K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

683
Separation and Identification of Conventional Microplastics from Farmland Soils
14:10

Separation and Identification of Conventional Microplastics from Farmland Soils

Published on: March 21, 2025

1.5K

科学领域:

  • 环境科学 环境科学
  • 计算机视觉 计算机视觉
  • 分析化学 分析化学

背景情况:

  • 微塑料,特别是微纤维,是普遍存在的环境污染物.
  • 在复杂样品中精确检测和定量是具有挑战性和劳动密集的.
  • 现有的微塑料分析方法往往缺乏速度和效率.

研究的目的:

  • 引入和评估开源视觉识别模型,以实现高效的微纤维识别.
  • 为了比较YOLOv7和Mask R-CNN在微塑料量化中的性能.
  • 为推进微塑料污染评估提供可访问的工具.

主要方法:

  • 培训和应用两个深度学习模型:YOLOv7和Mask R-CNN.
  • 利用广泛的数据集进行微纤维识别模型培训.
  • 在冰岛Seyðisfjörður的真实水生样本上测试模型性能.

主要成果:

  • 在微纤维检测中,YOLOv7获得了71.4%的准确性,而Mask R-CNN在微纤维检测中获得了49.9%的准确性.
  • 与手动方法相比,YOLOv7显示了显著更快的微纤维识别.
  • 这些模型提供了快速的结果,在几秒钟内处理样本.

结论:

  • 开源视觉识别模型,特别是YOLOv7,为微塑料量化提供了高效和快速的解决方案.
  • 这些用户友好的模型提高了微塑料污染研究的速度和可访问性.
  • 该研究通过提供工具来更好地评估微塑料污染及其影响,从而推动环境科学.