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Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
Methods to Assess Microbial Communities01:19

Methods to Assess Microbial Communities

Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...

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相关实验视频

Updated: May 23, 2026

A Method to Assess Bacteriocin Effects on the Gut Microbiota of Mice
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mbtransfer:使用转移函数和镜像统计数据进行微生物群干预分析.

Kris Sankaran1, Pratheepa Jeganathan2

  • 1Department of Statistics, University of Wisconsin - Madison, Madison, Wisconsin, United States of America.

PLoS computational biology
|June 14, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了微生物社区动态的新型转移函数模型,改善了时间记忆和预测干预效应的准确性. 这种方法有助于识别受环境变化影响的关键微生物种类.

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Last Updated: May 23, 2026

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科学领域:

  • 微生物学 微生物学
  • 计算生物学 计算生物学
  • 系统生物学 系统生物学

背景情况:

  • 微生物组时间序列研究对于理解微生物生态系统结构至关重要.
  • 目前的微生物社区动态模型在时间记忆和表达性方面存在局限性,通常依赖于马尔科夫或线性假设.
  • 这些局限性阻碍了对干预措施微生物反应的准确预测.

研究的目的:

  • 引入基于转移函数的微生物社区动态模型的新类型.
  • 为了捕捉环境变化对微生物群落的延迟影响.
  • 为了能够在假设干预下模拟轨迹,并识别显著扰乱的种类.

主要方法:

  • 开发了一种使用转移函数来学习冲动反应的新型模型类.
  • 模拟了环境变化对微生物社区结构的潜在延迟影响.
  • 实施错误发现率 (FDR) 保障,以选择明显扰乱的种类.
  • 利用模拟来比较新的方法与现有的基线.

主要成果:

  • 与强大的基线相比,转移函数模型显示预测错误减少.
  • 该方法准确地确定了感兴趣的种群,表明有效识别扰乱的微生物.
  • 案例研究展示了从模型中得出的差异反应轨迹的可解释性.

结论:

  • 转移函数模型为微生物社区动态提供了增强的时间记忆和表达能力.
  • 这种新的方法提高了预测干预效应和识别关键微生物种群的准确性.
  • 开发的R包,mbtransfer,促进了这些方法的应用和复制.