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相关概念视频

Methods to Assess Microbial Populations01:30

Methods to Assess Microbial Populations

76
Assessing microbial populations is crucial for understanding microbial roles in health, ecology, and industry. Various complementary techniques—both culture-based and molecular—enable detailed analysis of microbial abundance, diversity, and function.Viable Plate CountThe viable plate count is a traditional culture-based method used to estimate the number of living microbes in a sample. After serial dilution, the sample is spread onto nutrient agar plates. Each viable cell forms a...
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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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Microbial Biosensors01:17

Microbial Biosensors

62
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...
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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,...
101
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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Automated Microbial Diagnostics01:24

Automated Microbial Diagnostics

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Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...
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相关实验视频

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A Method to Define the Effects of Environmental Enrichment on Colon Microbiome Biodiversity in a Mouse Colon Tumor Model
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MIDASim:一个快速而简单的模拟器,用于实现真实的微生物组数据.

Mengyu He1, Ni Zhao2, Glen A Satten3

  • 1Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30329, USA.

Microbiome
|July 22, 2024
PubMed
概括

MIDASim是一个新的,高效的工具,用于模拟现实的微生物组数据,捕捉复杂的特征,如相关性和组成性. 这种方法准确地复制数据分布,有助于验证微生物组研究的统计方法.

关键词:
高斯的合器是高斯的合器.微生物组数据模拟税种-税种相关性相关性

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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.
  • 计算生物学 计算生物学

背景情况:

  • 测序技术的进步揭示了微生物群与疾病的关联.
  • 越来越多的微生物组数据需要强大的统计分析方法.
  • 模拟现实的微生物组数据对于方法验证至关重要,但由于数据的复杂性而具有挑战性.

研究的目的:

  • 开发一种快速,简单和可靠的方法来模拟现实的微生物组数据.
  • 为了生成准确复制模板数据集的分布和相关结构的数据.
  • 为验证和评估微生物组研究中的统计方法提供一个工具.

主要方法:

  • 开发了MIDASim (微生物组数据模拟器),一种两步模拟方法.
  • 步骤1:生成相关的二进制指标来确定分类种的存在-缺席.
  • 步骤2:使用高斯偶数来生成相对丰度和计数,可选择非参数或参数 (通用马分布) 边际分布.

主要成果:

  • 与肠道和阴道数据集的现有方法相比,MIDASim表现出更高的性能.
  • 在PERMANOVA,α多样性和β分散度量方面取得了更好的结果.
  • 参数模式有效评估组合模型中的差异丰度检测方法.

结论:

  • 对于大型数据集而言,MIDASim易于实现,灵活,并具有计算效率.
  • 准确地复制在存在-缺席和相对丰度水平上的分布特征.
  • 以最少的假设适应复杂的分布特征,优于竞争方法.