Short and long-term consequences of wildfires for Alzheimer's disease and related dementias

野火对阿尔茨海默病和相关痴呆症的短期和长期后果

• 批准号: 10824706
• 负责人: Joan A Casey
• 金额: $ 100.89万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10824706
• 关键词: Short; long; term; consequences; wildfires

PROJECT SUMMARY This application is being submitted in response to the (NOSI) identified as NOT-CA-22-056. Background. The specific aims of the parent grant (RF1AG071024) are to estimate the risk of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) and AD-related dementias (ADRD) associated with wildfire particulate matter (PM2.5) (Aim 1), to identify individual- and area-level susceptibility factors that exacerbate the association between wildfire PM2.5 and MCI and AD/ADRD (Aim 2), and to estimate the risk of MCI and AD/ADRD associated with living near a wildfire disaster and the extent to which specific sub-groups have better or worse outcomes (Aim 3). As part of the work conducted in Aims 1 and 2 of the parent R01, we are modeling daily exposure to wildfire- specific PM2.5 levels using a two-stage machine learning (ML) approach. We have curated and processed a large quantity of data from a range of sources including weather variables, satellite data, and Environmental Protection Agency (EPA) monitor data, in order to model wildfire specific PM2.5 levels. While we have expended considerable effort on the data curation, we have not focused on making the data Artificial Intelligence (AI)/ML ready and publicly available, both for our own researchers and for the broader research community. The data sources required for effective wildfire analysis are disparate, not very accessible, and unfriendly to AI/ML applications. Although the data is rich and publicly available through US agencies, acquiring it and preparing it for analysis presents a significant investment for any researcher. Overall Goals and Aims. With this administrative proposal, we plan to establish a new collaboration with AI/ML and data experts at Harvard University with the goals of improving the vast and wide range of data sources, developing reproducible pipelines, annotating, documenting, and processing the data, ensuring computational scalability, encouraging community engagement, and disseminating these important AI/ML ready datasets for the prediction of wildfire PM2.5 to a wider research community. Our specific aims are to improve the data for AI/ML readiness (Aim 1), make the data publicly available to AI/ML applications (Aim 2), and demonstrate the transformed data in an AI/ML application to predict wildfire PM2.5 exposure for California (Aim 3). Impact. The final datasets will be AI/ML ready, reproducible, and disseminated to a wide user base. We will build a collaborative environment allowing both internal and external researchers to use, contribute, and improve the data inputs. This work will serve as a foundation for our group in the prediction of wildfire PM2.5 exposures for the whole US and for the community and will strengthen the aims of the parent R01.

项目概要 本申请是为了响应标识为 NOT-CA-22-056 的 (NOSI) 而提交的。 背景。家长补助金 (RF1AG071024) 的具体目的是评估轻度认知的风险。 与野火相关的损伤 (MCI) 和阿尔茨海默病 (AD) 以及 AD 相关痴呆 (ADRD) 颗粒物 (PM2.5)（目标 1），以确定加剧颗粒物（PM2.5）的个人和区域水平的易感性因素 野火 PM2.5 与 MCI 和 AD/ADRD 之间的关联（目标 2），并估计 MCI 和 AD/ADRD 的风险 与生活在野火灾害附近以及特定亚群体的好坏程度有关 结果（目标 3）。 作为母版 R01 的目标 1 和 2 中进行的工作的一部分，我们正在对日常暴露于野火的情况进行建模 - 我们使用两阶段机器学习 (ML) 方法策划并处理了一个大型的 PM2.5 级别。 来自各种来源的数据量，包括天气变量、卫星数据和环境保护 机构 (EPA) 监测数据，以便对野火特定的 PM2.5 水平进行建模，同时我们还扩大了范围。 我们在数据管理方面投入了大量精力，但并未专注于使数据人工智能（AI）/ML 为我们自己的研究人员和更广泛的研究界准备并公开可用的数据。 有效的野火分析所需的来源各不相同，不太容易获取，而且对人工智能/机器学习不友好 尽管数据丰富且可通过美国机构公开获取，但仍需获取并准备这些数据。 对于任何研究人员来说，分析都是一项重大投资。 总体目标和目的 通过这项行政提案，我们计划与人工智能/机器学习建立新的合作。 和哈佛大学的数据专家，其目标是改善广泛而广泛的数据源， 开发可重复的管道，注释、记录和处理数据，确保计算 可扩展性、鼓励社区参与以及传播这些重要的 AI/ML 就绪数据集 我们的具体目标是改进数据以供更广泛的研究界使用。 AI/ML 准备就绪（目标 1），将数据公开提供给 AI/ML 应用程序（目标 2），并展示 在 AI/ML 应用程序中转换数据，以预测加利福尼亚州野火 PM2.5 暴露情况（目标 3）。 影响。我们将构建最终的数据集，该数据集将支持人工智能/机器学习、可复制并传播给广泛的用户群。 一个协作环境，允许内部和外部研究人员使用、贡献和改进 这项工作将为我们小组预测野火 PM2.5 暴露奠定基础。 整个美国和社区，并将加强母公司 R01 的目标。