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相关概念视频

Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Methods to Assess Microbial Populations01:30

Methods to Assess Microbial Populations

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 visible...
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...
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...

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相关实验视频

Updated: Jun 17, 2026

A Method to Assess Bacteriocin Effects on the Gut Microbiota of Mice
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基于微生物组的纠正,用于从自我报告的饮食评估中得出的营养资料中的随机错误.

Tong Wang1, Yuanqing Fu2,3,4, Menglei Shuai2,3,5

  • 1Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.

bioRxiv : the preprint server for biology
|December 4, 2023
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概括

在研究中准确测量饮食是很困难的. 一个新的深度学习工具,METRIC,使用肠道微生物来修复自我报告的饮食数据中的错误,改进营养特征计算.

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科学领域:

  • 营养流行病学 营养流行病学
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 大群体的饮食摄入量评估依赖于自我报告的数据,容易产生重大测量错误.
  • 这些错误导致营养资料计算中的不准确性,限制了流行病学研究的有效性.
  • 现有的计算方法来纠正这些饮食评估错误很少.

研究的目的:

  • 引入一种新的深度学习方法,即基于微生物组的营养特征校正器 (METRIC).
  • 利用肠道微生物组成来纠正自我报告的饮食数据中的随机错误.
  • 评估METRIC在改善营养特征准确性的表现.

主要方法:

  • 开发了一个深度学习模型,METRIC,集成肠道微生物组数据.
  • 应用METRIC来纠正24小时回忆和饮食记录中的自我报告的饮食数据.
  • 使用合成数据集和三个现实世界队列数据集验证的METRIC.

主要成果:

  • METRIC有效地减少了饮食评估中的模拟随机错误.
  • 对于肠道细菌代谢的营养物质来说,修正特别重要.
  • 在合成和真实世界的数据中表现出强大的性能.

结论:

  • METRIC显示出强大的潜力,可以提高从自我报告仪器中获得的饮食摄入数据的准确性.
  • 基于微生物组的方法为营养流行病学的持续挑战提供了一个有希望的计算解决方案.
  • 需要进一步验证,以评估METRIC在纠正实际测量错误方面的有效性.