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相关概念视频

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...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Introduction to the Human Microbiota01:22

Introduction to the Human Microbiota

Microorganisms colonize various regions of the human body, including the mouth, nasal passages, throat, stomach, intestines, urogenital tract, and skin. The total number of microbial cells is estimated to range from 10¹³ to 10¹⁴—comparable to, or exceeding, the number of human somatic cells. This host–microbiome relationship has led to the conceptualization of humans as supraorganisms, wherein microbial communities perform vital roles in development, immunity, and disease...
Development of Human Microbiota01:30

Development of Human Microbiota

The human microbiota begins developing at birth and undergoes continual change as we age. Infancy marks a critical period of microbial sensitivity, offering a “window of opportunity” during which beneficial microbes help mature the immune system. By age three, children typically develop a more stable and diverse microbial community. Newborns acquire microbes from their immediate environment; vaginal delivery favors maternal vaginal microbes, while cesarean births favor microbes from the skin...
Dysbiosis of the Gut Microbiota01:18

Dysbiosis of the Gut Microbiota

The human gut microbiome includes a diverse array of microbial species, including beneficial commensals and opportunistic pathogens, which interact to support host health. These microbes contribute to essential functions such as nutrient metabolism, immune system modulation, and maintenance of intestinal barrier integrity. However, disruptions to this equilibrium—referred to as dysbiosis—can have widespread physiological consequences.Dysbiosis is often characterized by reduced microbial...
Microbiota Modulation by Antibiotics01:21

Microbiota Modulation by Antibiotics

Antibiotics have revolutionized modern medicine by saving countless lives from bacterial infections. However, their widespread use has inadvertently harmed the delicate balance of the human gut microbiota. The gut microbiota, a complex community of bacteria, archaea, viruses, and fungi, plays a vital role in regulating metabolism, immune responses, and maintaining intestinal health. Antibiotics, especially broad-spectrum types, disrupt this ecosystem by eradicating both harmful and beneficial...

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多媒体:微生物组数据的多式调解分析.

Hanying Jiang1, Xinran Miao1, Margaret W Thairu2

  • 1Statistics Department, University of Wisconsin-Madison, Madison, Wisconsin, USA.

Microbiology spectrum
|December 17, 2024
PubMed
概括

多媒体R包通过提供先进的调解分析来增强微生物组研究. 这种工具有助于揭示因果路径,揭示治疗如何通过各种媒介影响微生物群.

关键词:
生物统计学 生物统计学计算生物学是计算生物学.人类微生物组的人类微生物组统计 统计 统计 统计 统计

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科学领域:

  • 微生物组研究的研究.
  • 统计建模 统计建模
  • 生物信息学是一种生物信息学.

背景情况:

  • 调解分析对于理解微生物组研究中的因果关系途径至关重要.
  • 现有的软件往往具有严格的假设,限制了可访问性和可解释性.
  • 微生物组研究中的补充数据需要灵活的分析来理解治疗效应.

研究的目的:

  • 引入多媒体R包,用于微生物组研究中的可访问和可适应的调解分析.
  • 为因果推理中先进的统计技术提供一个用户友好的界面.
  • 通过灵活的建模,实现精确和因果相关的微生物组工程.

主要方法:

  • 在R.多媒体R包的开发在R.
  • 实现各种建模技术的模块 (规则化的线性,组成,随机森林,层次,障碍).
  • 整合直接/间接效应估计,合成零假设测试,启动置信区间和灵敏度分析.
  • 该包适用于涉及炎症性肠病和正念实践的案例研究.

主要成果:

  • 多媒体包为复杂的调解分析提供了一个统一的界面.
  • 案例研究表明,该包能够在微生物组数据中发现新的机械相互作用.
  • 分析显示,与抑郁症相关的微生物组变化不仅仅是由饮食或睡眠解释的.

结论:

  • 多媒体R包为微生物组研究人员民主化了先进的调解分析.
  • 它有助于更深入地了解微生物组,治疗方法和结果之间的因果关系.
  • 该方案支持更精确,更有效的微生物组工程策略.