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Gene-Environment Interactions01:20

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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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Despite the strong genetic influence on traits like intelligence, environmental factors significantly shape outcomes. For example, while over 90% of height variation is due to genetic differences, environmental factors such as nutrition also have a notable impact. Similarly, for intelligence, changes in a child's surroundings can significantly alter their IQ. Research shows that enriched environments boost children's academic success and help them develop key cognitive skills. Children...
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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Updated: Sep 13, 2025

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
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一个基于多种环境暴露的基于代理的评估计算框架.

Oliver Schmitz1, Kees de Hoogh2,3, Nicole Probst-Hensch2,3

  • 1Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands. o.schmitz@uu.nl.

Journal of exposure science & environmental epidemiology
|August 1, 2025
PubMed
概括

这项研究引入了一个基于代理的框架,用于评估大人口中的个人环境暴露,通过考虑个人移动性来提高准确性. 开发的软件为大规模的队列研究提供了可重复使用的开源解决方案.

关键词:
基于活动的建模.空气污染 大气污染噪声 噪声 噪声个人风险 个人风险不确定性估计估计不确定性

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科学领域:

  • 环境健康科学 环境健康科学
  • 暴露科学 暴露科学
  • 计算流行病学计算流行病学

背景情况:

  • 个人长期暴露的评估需要考虑到个人活动路径上的环境因素的时空变化.
  • 全国范围的研究需要整合大量关于环境因素,个人行为和社会经济地位的数据集,包括不确定性传播.

研究的目的:

  • 开发和演示适用于大型队列的基于代理物的个人暴露评估方法.
  • 为这种方法创建一个软件框架,包括不确定性评估.

主要方法:

  • 一种基于代理的方法设计用于处理大型队列中稀疏的个人活动模式数据.
  • 一个基于Python的开源框架被开发和应用,用于评估超过60万个地址的空气污染和噪音暴露.
  • 使用概率分布量化不确定性,并通过蒙特卡洛模拟传播.

主要成果:

  • 该框架根据运动模式 (例如,周日,周末) 汇总时间,并将运动作为具有空间和时间背景的活动序列表示.
  • 根据框架计算的个人暴露与家庭评估相比,显示了较小到中等的差异 (例如,r2=0.79对于噪音,r2=0.98对于乌得勒支的NO2和PM2.5).
  • 基于代理的方法导致与传统方法相比,全人口暴露的差异减少.

结论:

  • 流行病学研究中的基于住宅的暴露评估不充分地捕捉了环境度的个体移动性和空间变化.
  • 需要基于活动的方法,但由于缺乏大型队列的概念框架和软件,面临实施挑战.
  • 提出的可重复使用的开源软件框架解决了这些局限性,使其能够在各种环境因素和队列研究中一致应用.