NSF Postdoctoral Fellowship in Biology: Identifying and Validating Missing Links in the Global Bat-Virus Network

美国国家科学基金会生物学博士后奖学金：识别和验证全球蝙蝠病毒网络中缺失的环节

• 批准号: 2305782
• 负责人: Briana Betke
• 金额: $ 24万
• 依托单位: Betke, Briana
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2305782&HistoricalAwards=false
• 关键词: NSF; Postdoctoral; Fellowship; Biology; Identifying

This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2023, Broadening Participation of Groups Underrepresented in Biology. The Fellowship supports a research and training plan for the Fellow that will increase the participation of groups underrepresented in biology. There is a pressing need to identify unobserved host-virus associations, as many infectious diseases of concern in humans, domestic animals, and wildlife are caused by viruses. However, predicting these interactions is challenged by a sparsity of information on known associations due to high logistical costs. Developing computer-based models to identify likely associations can cut costs by focusing field research efforts to test predicted interactions. This project will use predictive models to identify associations between bats and viruses, which will then be tested by field research. The research results will provide confirmation (or not) of bat-virus associations that will improve the predictive model. The fellow will broaden participation of underrepresented groups in biology through mentorship, a series of graduate school preparation workshops, and community outreach.The fellow will combine machine learning and systematic validation field and laboratory studies to expand the global bat–virus network and test the importance of bat roosting ecology relative to other bat and virus traits. Bats are of particular interest because they are known to host many viruses of zoonotic potential, leading to increased pathogen surveillance, and show intraspecific variation in roost preference for anthropogenic structures that could increase spillover opportunities to or from humans. The fellow will quantify the probability of interactions between bat species and virus families with link prediction models and then test these predictions as well as assess intraspecific variation in associations across roost structure type (anthropogenic or natural) in bats sampled in Oklahoma and Texas. Creating an iterative loop of model prediction, validation, and improvement is often a neglected step to accurate and adaptive modeling. To address this, the link prediction model will be run again with an updated dataset containing the validated predictions, and changes in model performance will be evaluated. To broaden participation of underrepresented groups in biology, the fellow will mentor students and present a series of workshops about undergraduate research, the graduate student experience, and career exploration at the University of Oklahoma. Additionally, the fellow will engage in community outreach through education of community volunteers on bat roosting ecology, wildlife disease ecology, and/or urban mammals. This project is jointly funded by the Division of Biological Infrastructure in the Directorate for Biological Sciences, and the Established Program to Stimulate Competitive Research (EPSCoR).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.

这项行动资助了 2023 财年 NSF 生物学博士后研究奖学金，扩大生物学领域代表性不足的群体的参与。该奖学金支持一项研究员的研究和培训计划，该计划将增加生物学领域代表性不足的群体的参与。识别未观察到的宿主病毒关联，因为人类、家畜和野生动物的许多传染病都是由病毒引起的。然而，由于已知关联的信息稀少，预测这些相互作用面临挑战。开发基于计算机的模型来识别可能的关联可以通过集中实地研究来测试预测的相互作用来降低成本。该项目将使用预测模型来识别蝙蝠和病毒之间的关联，然后通过现场研究进行测试。研究结果将提供蝙蝠病毒关联的确认（或不确认），从而改善预测模型。该研究员将通过指导、一系列研究生院准备研讨会和社区外展扩大代表性不足的群体对生物学的参与。将机器学习与系统验证现场和实验室研究相结合扩大全球蝙蝠病毒网络并测试蝙蝠栖息生态相对于其他蝙蝠和病毒特征的重要性 蝙蝠特别令人感兴趣，因为已知它们宿主有许多具有人畜共患潜力的病毒，导致病原体监测增加，并显示出种内特征。栖息地对人类结构的偏好变化可能会增加人类之间的溢出机会该研究员将通过链接预测模型量化蝙蝠物种和病毒家族之间相互作用的概率，然后测试这些预测并评估跨栖息地关联的种内差异。结构类型（人为或自然）在俄克拉荷马州和德克萨斯州采样的蝙蝠中创建模型预测、验证和改进的迭代循环通常是准确和自适应建模的一个被忽视的步骤，为了解决这个问题，将使用链接预测模型再次运行。将评估包含经过验证的预测的更新数据集，以及模型性能的变化，以扩大生物学领域代表性不足的群体的参与，该研究员将指导学生并举办一系列关于本科生研究、研究生经历和职业探索的研讨会。俄克拉荷马大学。该研究员将通过对社区志愿者进行有关蝙蝠栖息生态学、野生动物疾病生态学和/或城市哺乳动物的教育来参与社区外展活动。该项目由生物科学局生物基础设施司和既定计划共同资助。刺激竞争性研究 (EPSCoR)。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。