NSF Convergence Accelerator Track D: Deep Monitoring of the Biome Will Converge Life Sciences, Policy, and Engineering

NSF 融合加速器轨道 D：生物群落的深度监测将融合生命科学、政策和工程

• 批准号: 2040688
• 负责人: Janos Sztipanovits
• 金额: $ 92.4万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040688&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Deep

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. Today there is a huge gap between the global need to manage our ecosystems, protect our societies, and discover new therapeutics – and the global capacity to deliver the data and models needed to solve the most pressing challenges of our time. This project is intended to bridge that gap by connecting researchers, policy makers, and industries to the scalable biome monitoring networks of the future – by developing the unified biome datasets, cross-cutting models, and policy paradigms that will empower these disciplines to accelerate, innovate, and converge. If successful, this would lead to a fundamental paradigm shift in how disciplines study and manage the planet. It will contribute to the advent of a new generation of scientists developing predictive AI models of the biome, and to developing science-based methods and tools for shaping policies and delivering policy-aware tools to solve societal-scale challenges. We expect that deep monitoring of biome and the new science and technology ecosystem emerging from it will have wide impact on human health, agriculture, national security, and ecology. The technical goals of this project have been carefully instantiated so that progress towards convergence makes a lasting impact on a range of scientific problems. First, the life sciences, engineering, and policy domains continually face the challenge of managing and unifying disparate biome and ecological datasets. These issues are addressed head on by bringing together uniquely deep and state-of-the-art biome and ecological data sets, identifying the hard unification problems, and providing a reference solution to unification. Second, there is a focus is on new unified agent-based models for predicting mosquito populations, as mosquito-borne diseases already account for over 600 million cases of human disease per year, with a disproportionately large impact on disadvantaged communities in sub-Saharan-Africa. By accelerating the development of new predictive mosquito models – especially by generalizing them to additional species – this project will provide long lasting contributions to human health and pandemic preparedness. Third, as deep biome data exponentially scales, the life sciences will become overwhelmed with genomic information. Convergence must lead to new methods to efficiently harness these data and autonomously derive insights.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.

NSF融合加速器支持以使用为灵感、以团队为基础的多学科努力，以应对国家重要性的挑战，并将在不久的将来产生对社会有价值的成果。今天，管理我们的生态系统、保护我们的社会和发现新疗法的全球需求与提供解决我们时代最紧迫挑战所需的数据和模型的全球能力之间存在着巨大的差距。该项目旨在通过将研究人员、政策制定者和行业与未来可扩展的生物群监测网络连接起来，通过开发统一的生物群数据集、交叉模型和政策范例来弥合这一差距，从而使这些学科能够加速、创新和融合。如果成功，这将导致学科研究和管理地球的方式发生根本性的转变。它将有助于新一代科学家的出现，开发生物群的预测性人工智能模型，并开发基于科学的方法和工具，以制定政策并提供政策感知工具，以解决社会规模的挑战。我们预计，对生物群的深度监测以及由此产生的新的科技生态系统将对人类健康、农业、国家安全和生态产生广泛影响。该项目的技术目标经过了仔细的实例化，以使趋同方面的进展对一系列科学问题产生持久的影响。首先，生命科学、工程和政策领域持续面临着管理和统一不同的生物群和生态数据集的挑战。这些问题是通过汇集独特的深度和最先进的生物群和生态数据集，确定难以统一的问题，并提供统一的参考解决方案来正面解决的。第二，重点是用于预测蚊子数量的新的统一代理模型，因为蚊媒疾病每年已占人类疾病病例的6亿多例，对撒哈拉以南非洲的弱势社区产生了不成比例的巨大影响。通过加快开发新的预测蚊子模型--特别是将其推广到更多物种--该项目将为人类健康和预防大流行作出长期贡献。第三，随着深层生物组数据呈指数级增长，生命科学将被基因组信息淹没。融合必须导致新的方法来有效地利用这些数据并自主地得出见解。该奖项反映了NSF的法定使命，并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，被认为值得支持。