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 Growth Measurement: Indirect Methods01:27

Microbial Growth Measurement: Indirect Methods

39
Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...
39
Bacterial Growth Curve01:28

Bacterial Growth Curve

54
The bacterial growth curve is a fundamental concept in microbiology that describes the dynamics of bacterial population growth in a closed system with controlled environmental conditions, such as temperature and nutrient availability. This curve is divided into four distinct phases: lag, log (exponential), stationary, and death phases, each reflecting a unique stage of bacterial adaptation and growth. During the lag phase, bacteria acclimate to their surroundings by synthesizing essential...
54
Microbial Growth Measurement: Direct Methods01:23

Microbial Growth Measurement: Direct Methods

37
Direct methods for measuring microbial populations in a culture are essential tools in microbiology, providing quantitative data for various applications. Among these, microscopic counts, plate counts, and serial dilution are widely used techniques, each with unique principles and applications.Microscopic CountsMicroscopic counting involves the use of a Petroff-Hausser chamber, a specialized microscope slide with a grid and defined depth. By observing a liquid culture under a microscope,...
37

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

Author Correction: Geographics and bacterial networks differently shape the acquired and latent global sewage resistomes.

Nature communications·2026
Same author

Metagenomic peek into a corn mummy.

Scientific reports·2026
Same author

Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings.

Journal of pathology informatics·2026
Same author

Protocol for the assessment of the impact of mycotoxins and glyphosate residues on the gut microbiome and resistome of European fallow deer.

STAR protocols·2026
Same author

Development of Nanopore amplicon sequencing method for culture-free genotyping of <i>Bacillus anthracis</i> strains directly from environmental samples.

Frontiers in microbiology·2026
Same author

A multimodal AI biomarker PATH-ORACLE improves prediction of recurrence in stage I lung adenocarcinoma.

medRxiv : the preprint server for health sciences·2026

相关实验视频

Updated: Jul 12, 2025

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression
12:52

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression

Published on: April 18, 2021

4.9K

通过深度学习估计细菌殖民地规模的增长.

Sára Ágnes Nagy1, László Makrai2, István Csabai3

  • 1Centre for Bioinformatics, University of Veterinary Medicine, 1078, Budapest, Hungary.

BMC microbiology
|October 25, 2023
PubMed
概括

卷积神经网络 (CNN) 准确地检测细菌殖民地,并从图像中预测生长率. 这种人工智能方法为细菌学提供了有效的工具,有助于致病性和食品安全研究.

关键词:
细菌生长速度的增长速度深度学习是一种深度学习.神经网络的神经网络

更多相关视频

ScanLag: High-throughput Quantification of Colony Growth and Lag Time
07:47

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Published on: July 15, 2014

16.2K
Author Spotlight: Unraveling Bacterial Responses to Antibiotics and Immune System in Tissues
08:01

Author Spotlight: Unraveling Bacterial Responses to Antibiotics and Immune System in Tissues

Published on: March 1, 2024

968

相关实验视频

Last Updated: Jul 12, 2025

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression
12:52

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression

Published on: April 18, 2021

4.9K
ScanLag: High-throughput Quantification of Colony Growth and Lag Time
07:47

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Published on: July 15, 2014

16.2K
Author Spotlight: Unraveling Bacterial Responses to Antibiotics and Immune System in Tissues
08:01

Author Spotlight: Unraveling Bacterial Responses to Antibiotics and Immune System in Tissues

Published on: March 1, 2024

968

科学领域:

  • 微生物学 微生物学
  • 计算机科学 计算机科学
  • 生物信息学是一种生物信息学.

背景情况:

  • 细菌生长速度对于病原性和食品安全至关重要.
  • 监测细菌生长提供了重要的医疗和兽医数据.
  • 准确量化细菌生长动态对于研究至关重要.

研究的目的:

  • 开发和验证卷积神经网络 (CNN) 用于细菌殖民地检测和增长率估计.
  • 分析殖民地密度和利芬素预处理对细菌生长动态的影响.
  • 评估AI驱动图像分析在细菌学研究中的有效性.

主要方法:

  • 在固体介质上手动注释细菌培养的图像上培训CNN.
  • 估计细菌殖民地大小和生长速度,使用金黄色葡萄球菌 (Staphylococcus aureus) 的图像序列.
  • 使用线性和混合效应模型来分析增长数据和影响因素.

主要成果:

  • CNNs准确地检测到了细菌殖民地,并预测了生长率.
  • 对照种植物的平均增长率估计在前24小时内为60.3单位/小时.
  • 殖民地生长率因邻近殖民地增加而降低,特别是在对照组,以及因里芬素预处理 (36.5单位/小时).

结论:

  • 基于CNN的细菌殖民地检测是一种准确和有效的方法.
  • 对细菌殖民地生长动态的AI分析为细菌学提供了有价值的工具.
  • 这种方法可以显著推进致病性和食品安全研究.