RII Track-2 FEC: Marshalling Diverse Big Data Streams to Understand Risk of Tick-Borne Diseases in the Great Plains

RII Track-2 FEC：整理不同的大数据流以了解大平原蜱传疾病的风险

• 批准号: 1920946
• 负责人: Townsend Peterson
• 金额: $ 392.12万
• 依托单位: University of Kansas Center for Research Inc
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1920946&HistoricalAwards=false
• 关键词: RII; Track; FEC; Marshalling; Diverse

Tick-borne diseases, including Lyme disease, Rocky Mountain spotted fever, and others, are increasingly appreciated as a significant public health concern worldwide, and an increasing concern in the Great Plains in particular. However, a detailed understanding of these diseases, how they are acquired, where are the high-risk areas, and how might they best be mitigated, has remained surprisingly opaque. This project, a collaboration between University of Kansas, Kansas State University, Pittsburgh State University, Oklahoma State University, the University of Oklahoma, the University of Oklahoma, Norman campus, and the University of Central Oklahoma, represents a broad-scope, highly interdisciplinary, integrated, and data-intensive effort to illuminate these questions across two states, Kansas and Oklahoma in the Great Plains. Major elements of the project include assembling detailed, large-scale datasets on the occurrences of different tick species, the genomes of the ticks and the pathogens, and environmental variation across the region, as well as marshaling new artificial-intelligence tools to permit rapid and accurate tick identifications by non-experts. Project scientists will use ecological niche modeling and mathematical population modeling approaches to assess and predict transmission of the major tick-borne pathogens, and create and test the automated identification tools. The project will foster what can be termed "big data literacy" via a series of workshops and courses, as well as online data resources. Perhaps most importantly, the project will involve numerous undergraduate and graduate students in many project tasks, giving them opportunities to learn and explore futures in these and related areas of science. Project students will be recruited as broadly as possible, to represent in particular populations that are not well-represented in science, including minorities, women, and those from families without a tradition of university-level education. Project outcomes will include online, interactive maps of tick-borne disease risk, and online facilities for identification of tick photographs taken by the general public. Junior members of the project team (younger faculty, postdocs, and students) will be mentored and guided by more senior individuals, so as to maximize the probability of their successful advancement in this field. The project team will be guided by an advisory board with broad and international expertise, as well as state-level public health policy experience. At the close of the project, we anticipate a much-improved and considerably more detailed understanding of the diversity and risk of tick-borne diseases across Kansas and Oklahoma.Tick-borne diseases are increasingly recognized as an important public health concern across the United States, including Lyme disease, ehrlichiosis, Rocky Mountain spotted fever, southern tick-associated rash illness, human granulocytic anaplasmosis, babesiosis, and viral infections with Heartland and Bourbon viruses. Knowledge of the spatial distributions of ticks and pathogen species, and the associated spatial risk of transmission of tick-borne diseases in the Great Plains is quite limited. This project marshals several "big data" streams (tick occurrence data, tick and pathogen genomic data, remote sensing data to characterize environments) and novel scientific tools to shed light on geographic patterns and temporal dynamics of risk of infection with the pathogens that cause these diseases. Specifically, this project, a collaboration between University of Kansas, Kansas State University, Pittsburgh State University, Oklahoma State University, the University of Oklahoma, the University of Oklahoma, Norman campus, and the University of Central Oklahoma, will involve field collections of ticks and vertebrates hosting ticks from 12 sites in ecologically distinct regions across Kansas and Oklahoma, which will be tested using genomic tools for a diverse suite of pathogens; the resulting data on tick and pathogen distributions will be the basis for detailed modeling of transmission risk using complementary, cutting-edge tools (correlative niche models, mechanistic population models) to achieve new syntheses of population and range dynamics. This project will explore and develop a first automated tick identification system based on deep-learning approaches, which will feed much more information into the other analyses envisioned for this project in the form of detailed distributional data. This project will also have substantial implications for broadening participation in science, via linking senior and junior scientists (including minority faculty members) in a joint, collaborative, and integrative effort designed to mentor and build confidence and stature among junior team members (faculty, postdocs, graduate students, undergraduate students) and among faculty from teaching-focused institutions. This project will offer various educational opportunities in the areas of big data analytics, big data access, and disease risk mapping, which will be made broadly and openly available to the scientific community. Finally, towards mitigating effects of tick-borne diseases in communities across the southern Great Plains, this project will produce and make broadly available detailed risk maps for each tick-borne disease in the region, and create technology for automating tick identifications, to allow citizen scientists and stakeholders in the general public better access to information on ticks and disease risks that are personally and immediately relevant.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

蜱传疾病，包括莱姆病、落基山斑疹热等，越来越被认为是全世界重大的公共卫生问题，尤其是在大平原地区。然而，对这些疾病、它们是如何获得的、高风险区域在哪里以及如何最好地缓解它们的详细了解仍然令人惊讶地不透明。该项目是堪萨斯大学、堪萨斯州立大学、匹兹堡州立大学、俄克拉荷马州立大学、俄克拉荷马大学、俄克拉荷马大学诺曼校区和中央俄克拉荷马大学之间的合作，代表了一项广泛、高度跨学科、综合性和数据密集型的努力，旨在阐明大平原堪萨斯州和俄克拉荷马州两个州的这些问题。该项目的主要内容包括收集有关不同蜱种的出现情况、蜱虫和病原体的基因组以及整个地区的环境变化的详细的大规模数据集，以及整合新的人工智能工具，以允许非专家快速、准确地识别蜱虫。项目科学家将使用生态位模型和数学种群模型方法来评估和预测主要蜱传病原体的传播，并创建和测试自动识别工具。该项目将通过一系列研讨会和课程以及在线数据资源来培养所谓的“大数据素养”。也许最重要的是，该项目将让众多本科生和研究生参与许多项目任务，为他们提供学习和探索这些及相关科学领域的未来的机会。项目学生将尽可能广泛地招募，以代表在科学领域没有得到充分代表的特定人群，包括少数民族、妇女和来自没有大学教育传统的家庭的学生。项目成果将包括蜱传疾病风险的在线交互式地图，以及用于识别公众拍摄的蜱照片的在线设施。项目团队的初级成员（年轻的教师、博士后和学生）将得到资深人士的指导和指导，以最大限度地提高他们在该领域成功发展的可能性。该项目团队将由具有广泛国际专业知识以及州级公共卫生政策经验的顾问委员会指导。在该项目结束时，我们预计对堪萨斯州和俄克拉荷马州蜱传疾病的多样性和风险会有更大的改进和更详细的了解。蜱传疾病越来越被认为是美国各地的一个重要的公共卫生问题，包括莱姆病、埃利希体病、落基山斑疹热、南方蜱相关皮疹病、人类粒细胞无形体病、 巴贝斯虫病以及哈特兰病毒和波旁病毒的病毒感染。对蜱虫和病原体种类的空间分布以及大平原蜱传疾病传播的相关空间风险的了解相当有限。该项目整合了多个“大数据”流（蜱发生数据、蜱和病原体基因组数据、表征环境的遥感数据）和新颖的科学工具，以揭示引起这些疾病的病原体感染风险的地理模式和时间动态。具体来说，该项目是堪萨斯大学、堪萨斯州立大学、匹兹堡州立大学、俄克拉荷马州立大学、俄克拉荷马大学、俄克拉荷马大学诺曼校区和中央俄克拉荷马大学之间的合作项目，将涉及来自堪萨斯州和俄克拉荷马州生态不同区域的 12 个地点的蜱虫和寄生蜱虫的脊椎动物的现场收集，这些蜱虫和脊椎动物将使用基因组工具进行测试 多种病原体；由此产生的蜱和病原体分布数据将成为使用补充的尖端工具（相关生态位模型、机械种群模型）对传播风险进行详细建模的基础，以实现种群和范围动态的新综合。该项目将探索和开发第一个基于深度学习方法的自动蜱虫识别系统，该系统将以详细的分布数据的形式为该项目设想的其他分析提供更多信息。该项目还将通过将高级和初级科学家（包括少数族裔教职人员）联系起来，共同、协作和综合努力，对扩大科学参与产生重大影响，旨在指导和建立初级团队成员（教职人员、博士后、研究生、本科生）和教学机构教职人员的信心和地位。该项目将在大数据分析、大数据访问和疾病风险图谱领域提供各种教育机会，并将向科学界广泛公开提供。最后，为了减轻大平原南部社区蜱传疾病的影响，该项目将为该地区每种蜱传疾病制作并广泛提供详细的风险地图，并创建自动蜱识别技术，使公民科学家和公众利益相关者能够更好地获取与个人直接相关的蜱和疾病风险信息。该奖项反映了 NSF 的法定使命，并被认为是值得的。 通过使用基金会的智力优势和更广泛的影响审查标准进行评估来获得支持。

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• 发表时间: 2022-04
• 期刊: Methods in Ecology and Evolution
• 影响因子: 6.6
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Predicting the potential distribution of Amblyomma americanum (Acari: Ixodidae) infestation in New Zealand, using maximum entropy-based ecological niche modelling.

• DOI: 10.1007/s10493-019-00460-7
• 发表时间: 2020-02
• 期刊: Experimental & applied acarology
• 影响因子: 2.2
• 作者: Raghavan RK;Heath ACG;Lawrence KE;Ganta RR;Peterson AT;Pomroy WE
• 通讯作者: Pomroy WE

Assessing variability of optimum air temperature for photosynthesis across site-years, sites and biomes and their effects on photosynthesis estimation

• DOI: 10.1016/j.agrformet.2020.108277
• 发表时间: 2021-03
• 期刊: Agricultural and Forest Meteorology
• 影响因子: 6.2
• 作者: Q. Chang;Xiangming Xiao;R. Doughty;Xiaocui Wu;Wenzhe Jiao;Yuanwei Qin

Identification of Rickettsia spp. and Babesia conradae in Dermacentor spp. Collected from Dogs and Cats Across the United States.

• DOI: 10.1089/vbz.2021.0047
• 发表时间: 2021-12
• 期刊: Vector borne and zoonotic diseases (Larchmont, N.Y.)
• 作者: Duncan KT;Grant A;Johnson B;Sundstrom KD;Saleh MN;Little SE
• 通讯作者: Little SE

Flash drought identification from satellite-based land surface water index

• DOI: 10.1016/j.rsase.2022.100770
• 发表时间: 2022-04
• 期刊: Remote Sensing Applications: Society and Environment
• 作者: J. Christian;J. Basara;L. Lowman;Xiangming Xiao;Daniel Mesheske;Yuting Zhou