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

这项行动资助了2023财年的NSF生物学博士后研究奖学金，扩大了生物学中代表性不足的群体的参与。该研究金支持研究员的一项研究和培训计划，该计划将增加在生物学领域代表性不足的群体的参与。迫切需要确定未观察到的宿主-病毒关联，因为人类、家畜和野生动物中许多令人关注的传染病都是由病毒引起的。然而，预测这些相互作用的挑战是由于高物流成本的已知协会的信息稀疏。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确定蝙蝠和病毒之间的联系，然后通过实地研究进行测试。研究结果将提供蝙蝠病毒关联的确认（或不确认），这将改善预测模型。该研究员将通过指导、一系列研究生院准备讲习班和社区外展来扩大生物学中代表性不足的群体的参与。该研究员将联合收割机结合机器学习和系统验证实地和实验室研究，以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。蝙蝠是特别感兴趣的，因为它们是已知的宿主许多病毒的人畜共患病的潜力，导致增加病原体监测，并显示种内变异栖息地偏好人为结构，可能会增加溢出的机会，或从人类。该研究员将量化蝙蝠物种和病毒家族之间的相互作用与链接预测模型的概率，然后测试这些预测，以及评估种内变化的协会在俄克拉荷马州和得克萨斯州的蝙蝠采样栖息结构类型（人为或自然）。创建模型预测、验证和改进的迭代循环通常是精确和自适应建模的一个被忽视的步骤。为了解决这个问题，链接预测模型将使用包含验证预测的更新数据集再次运行，并评估模型性能的变化。为了扩大生物学中代表性不足的群体的参与，该研究员将指导学生，并在俄克拉荷马州大学举办一系列关于本科研究，研究生经验和职业探索的研讨会。此外，该研究员将通过对社区志愿者进行蝙蝠栖息生态学、野生动物疾病生态学和/或城市哺乳动物教育，参与社区外联活动。该项目由生物科学理事会生物基础设施部和刺激竞争性研究的既定计划（EPSCoR）共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.