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

Microbial Biosensors01:17

Microbial Biosensors

Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...

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

Characterization of <i>Pseudomonas aeruginosa</i> and <i>Acinetobacter calcoaceticus-baumannii</i> complex traumatic wound isolates.

Microbiology spectrum·2026
Same author

The Genetic and Environmental Architecture of the Human Functional Connectome.

ArXiv·2026
Same author

Impact of Selenium and Vitamin E Deficiency on Zika Virus Pathogenesis and Immune Response in Mice.

Viruses·2026
Same author

Challenges and opportunities: computational biology and the future of agriculture.

Bioinformatics advances·2026
Same author

Beyond microbial abundance: metadata integration enhances disease prediction in human microbiome studies.

Frontiers in microbiology·2026
Same author

Microbiome dynamics in the congregate environment of U.S. Army Infantry training.

Microbiology spectrum·2025
Same journal

A Metagenomic Biosurveillance Network for Emerging Infectious Diseases: A Simulation-Based Model.

Health security·2026
Same journal

Reimagining Global One Health Governance: How the International Mental Health Organization and the International Health Tribunal Bridge Psychosocial and Environmental Frontlines.

Health security·2026
Same journal

Mitigating Cross-Border Biological Threats: An Animal Health and Law Enforcement Perspective on One Health.

Health security·2026
Same journal

Showing that We Care<sup>®</sup>: One Health in Practice in the Swine Industry.

Health security·2026
Same journal

Scientific Advocacy and Dissent in China and Russia: Role of Scientists in Shaping Communication and Governance Around Human Germline Genome Editing.

Health security·2026
Same journal

Recognizing Bioterrorism-Related Acute Illness in Clinical Practice: An Urgent Research Priority.

Health security·2026
查看所有相关文章

相关实验视频

Updated: May 10, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K

利用机器学习来实现不可知生物检测

Sarah H Sandholtz1, Camilo Valdes1, Nisha Mulakken1

  • 1Sarah H. Sandholtz, PhD, is a Staff Scientist; Camilo Valdes, PhD, is a Postdoctoral Researcher; Jeffrey A. Drocco, PhD, is Group Leader, Advanced Biotechnologies Integration Group; Crystal Jaing, PhD, is Group Leader, Genomics Group; and Nicholas A. Be, PhD, is Group Leader, Microbiology/Immunology Group; all in the Biosciences and Biotechnology Division, Physical and Life Sciences Directorate. Nisha Mulakken, MA, is Deputy Division Leader; Marisa W. Torres, MS, is Bioinformatics Lead; Aram Avila-Herrera, PhD, is Group Leader, Biomolecular Design and Development Group; Jose Manuel Martí, PhD, is a Staff Scientist; and Jonathan E. Allen, PhD, is a Senior Technical Staff Member; all in the Global Security Computing Applications Division, Computing Directorate. Uttara Tipnis, PhD, is a Staff Scientist, Computational Engineering Division, Engineering Directorate. All of the authors are at Lawrence Livermore National Laboratory, Livermore, CA.

Health security
|May 30, 2025
PubMed
概括
此摘要是机器生成的。

美国的生物防御战略需要一种无代理的方法来检测新的威胁. 机器学习 (ML) 为可适应的环境生物检测系统提供了一个有希望的解决方案.

关键词:
代理不可知论者是一个不可知论者.生物检测是一种生物检测.生物监控 生物监控机器学习是机器学习.

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

699
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K

相关实验视频

Last Updated: May 10, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K
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

699
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K

科学领域:

  • 生物防御和环境监测
  • 计算生物学和机器学习

背景情况:

  • 目前美国的生物防御依赖于识别已知的生物制剂,限制其对新威胁的有效性.
  • 使用签名的无代理方法为不断变化的生物威胁提供了更大的适应性.
  • 机器学习 (ML) 在不同数据的模式识别方面表现出色,显示出生物检测的潜力.

研究的目的:

  • 审查当前用于环境生物检测的机器学习 (ML) 平台.
  • 为了确定开发需要的ML-enabled,无源生物检测.
  • 支持从基于列表的生物防御战略过渡到基于签名的生物防御战略.

主要方法:

  • 对现有的适用于生物检测的ML平台进行系统的文献审查.
  • 分析机器学习能力,以从多式联络数据中识别复杂模式.
  • 对环境生物检测中的ML技术要求的讨论.

主要成果:

  • 确定了当前的ML平台及其生物检测应用的潜力.
  • 强调了现有ML系统的关键技术能力和局限性.
  • 概述了有效的ML驱动的不可知生物检测所需的进展.

结论:

  • 过渡到支持ML的无毒生物检测对于加强国家安全至关重要.
  • 需要进一步开发,以充分利用ML用于适应性环境威胁检测.
  • 对机器学习能力的系统理解对于未来的生物防御创新至关重要.