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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 11, 2026

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake
04:46

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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.

Nature communications
|October 22, 2024
PubMed
概括

这项研究介绍了METRIC,这是一种深度学习工具,用于改进饮食评估. 它使用肠道微生物组数据来纠正自我报告的营养摄入量的错误,提高营养流行病学的准确性.

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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.
  • 营养特征计算中的不准确性来自于这些错误,限制了流行病学研究的可靠性.
  • 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),用于纠正自我报告的饮食评估中的随机错误.
  • 评估METRIC在最小化营养特征错误方面的表现,特别是那些受肠道微生物代谢影响的营养特征.
  • 评估肠道微生物组成对METRIC错误纠正能力的影响.

主要方法:

  • 开发了一个深度学习模型 (METRIC),集成肠道微生物数据以纠正饮食评估错误.
  • 利用24小时的回忆和饮食记录作为对饮食评估纠正的输入.
  • 使用合成数据集和三个现实数据集验证的METRIC.

主要成果:

  • METRIC在最小化营养资料中模拟的随机错误方面表现出色.
  • 该模型对肠道细菌代谢的营养物质特别有效.
  • 即使排除了肠道微生物组成数据,METRIC也保持了显著的错误校正能力.

结论:

  • 在营养流行病学中,METRIC提供了一个有前途的计算解决方案,用于提高自我报告的饮食评估的准确性.
  • 该方法显示了改善营养特征计算的潜力,即使没有直接的微生物群数据.
  • 需要进一步的研究来证实METRIC在纠正饮食评估仪器中实际测量错误方面的有效性.