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相关概念视频

Methods to Assess Microbial Communities01:19

Methods to Assess Microbial Communities

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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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Introduction to the Human Microbiota01:22

Introduction to the Human Microbiota

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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,...
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Development of Human Microbiota01:30

Development of Human Microbiota

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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...
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The Oral Microbiota01:27

The Oral Microbiota

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The oral microbiome includes a complex ecosystem comprising over 700 microbial species, identified through genomic sequencing and culture-based analyses to date. This community includes a core microbiome, found universally among individuals, and a variable component influenced by environmental factors such as diet, lifestyle, and host genetics. Site-specific conditions, including oxygen gradients, pH levels, and nutrient availability, determine the spatial distribution of these microorganisms...
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Microbiota of the Large Intestine01:27

Microbiota of the Large Intestine

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The large intestine hosts the most densely populated microbial ecosystem in the human body. This complex community primarily consists of anaerobic bacteria, with Bacillota (formerly Firmicutes) and Bacteroidota (formerly Bacteroidetes) as the predominant groups. The distribution of these microbes varies along different sections of the large intestine, influenced by local environmental factors such as oxygen availability and nutrient composition.The cecum, located at the beginning of the large...
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Functions of the Gut Microbiota01:18

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The gut microbiota includes trillions of microorganisms that colonize the human gastrointestinal tract, including bacteria, archaea, viruses, and fungi. This complex ecosystem plays a critical role in maintaining intestinal and systemic health. Most of these microbes inhabit the large intestine, establishing a relatively stable and diverse community that contributes to gut homeostasis through various metabolic, immunological, and protective mechanisms.Dominant bacterial phyla, such as...
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相关实验视频

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构成数据和微生物群分析:想象力和现实

Tatsuki Itagaki1,2, Hirokazu Kobayashi1,2, Ken-Ichiro Sakata1

  • 1Oral Diagnosis and Medicine, Faculty of Dental Medicine, Graduate School of Dental Medicine, Hokkaido University, Kita-13 Nishi-7, Kita-ku, Sapporo 060-8586, Japan.

Microorganisms
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概括

比率分析和主要成分分析 (PCA) 有效地分析肠道微生物群组成数据. 这些方法为细菌菌群提供了强大的洞察力,克服了传统统计方法的局限性.

关键词:
组合数据是指组合数据的组成数据.微生物组是一个微生物组.分析比率分析比率分析

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科学领域:

  • 微生物学 微生物学
  • 生物信息学是一种生物信息学.
  • 统计分析 统计分析

背景情况:

  • 肠道微生物群在健康和疾病中的作用已被证实.
  • 细菌菌群的组成受饮食和疾病状态的影响.
  • 来自16S rRNA基因测序的操作分类学单位 (OTU) 代表组成数据.

研究的目的:

  • 评估分析肠道微生物群研究中的组成数据的统计方法.
  • 为了比较比率分析和主要成分分析 (PCA) 与传统方法的有效性.
  • 解决微生物社区数据的单变量分析中固有的偏见.

主要方法:

  • 使用Aitchison的比率分析来处理组成数据.
  • 采用多变量分析,包括非参数多维缩放 (NMDS) 和PCA.
  • 基于绝对和相对丰度假设进行的模拟.

主要成果:

  • PCA有效地降低了维度,以更低的维度表示堆叠条形图数据.
  • NMDS在复制相对多样性方面表现出局限性.
  • 比率分析和PCA对于解释复杂的肠道微生物群组成数据非常有价值.

结论:

  • 建议进行比率分析和PCA来对肠道微生物群组成数据进行可靠的分析.
  • 多变量方法减轻了在单变量分析中发现的偏差.
  • 需要进一步的研究来验证相对数据对绝对丰度的假设.