EAGER: Collaborative Research: Privacy-enhancing CrowdPCR for Early Epidemic Detection

1645285/1645121 UGAZ/Danfeng PIs提出了一项基础研究，旨在建立一个新的平台，利用人群(即，大量不懂技术的参与者)作为资源，极大地扩展分布式检测细菌和病毒病原体的能力。一个廉价的基于智能手机的移动实验室平台可以进行基于核酸的黄金标准分析，它将与最先进的人群传感范式相结合，允许以较低的基础设施支持进行大规模传感数据收集。这些新功能将使非专家参与者能够通过智能手机连接执行快速分析，消除样本收集和分析之间的延迟，以便在几分钟内提供测试结果。这项基础研究解决了公众参与工程研究的多重推动力，重点关注公民科学和众包，包括：(1)分布式数据收集方法，以及(2)改进数据收集的新技术。拟议的人群感知方法将提供一个新的平台，以支持一系列需要安全和保护隐私的网络基础设施的多学科公民科学项目。安全的人群感知鼓励参与，这反过来又提高了数据和发现的质量。PI设想，他们方法的效率和可扩展性将有助于增加开发人员、科学家和工程师在其人群感知应用程序中实际采用群签名。他们的生物分析仪器的超低成本也将使一次部署数千人成为可能，以实现有针对性的诊断和监测。通过首次以一台专用实验室分析机器的相同成本部署数千台仪器的组合，他们的平台有望弥合当前一代快速诊断测试与聚合酶链式反应黄金标准之间的差距。《美国临床实验室改进修正案》根据所进行测试的性质，将临床诊断测试分为高复杂性、中等复杂性或免除复杂性。基于聚合酶链式反应的诊断目前被归类为高复杂性，因为必要的操作培训和复杂的仪器，从而使其昂贵和不切实际地在便携式应用中大规模分发。拟议的多功能平台提供了将聚合酶链式反应归类为中等或免除复杂性类别的潜力，为新一代快速、准确和负担得起的诊断工具打开了大门，影响了许多需要快速现场部署但尚未广泛获得的新场景(例如，公民科学)。多学科的人群感知和公民科学项目需要安全和保护隐私的网络基础设施。安全的人群感知鼓励参与，这反过来又提高了数据和发现的质量。PI设想，具有后向不可链接性的次线性撤销的效率和可扩展性有助于增加开发人员、科学家和工程师在他们的人群感知应用中对群签名的真实世界采用。

1645285/1645121Ugaz/DanfengThe PIs propose fundamental research aimed at establishing a new platform to leverage crowds (i.e., large numbers of non-technical savvy participants) as a resource to greatly expand capabilities for distributed detection of bacterial and viral pathogens. An inexpensive smartphone-based mobile laboratory platform enabling gold-standard nucleic acid-based analysis will be merged with a state-of-the-art crowd-sensing paradigm that permits large scale sensory data collection with low infrastructure support. These new capabilities will empower non-expert participants to perform rapid assays with smartphone connectivity, eliminating delays between sample collection and analysis so that test results can be delivered in minutes.This fundamental research addresses multiple thrusts in Public Participation in Engineering Research, focusing on Citizen Science and Crowdsourcing, including: (1) methodologies for distributed data collection, and, (2) new technologies for improved data collection. The proposed crowd-sensing approach will deliver a new platform to support a host of multidisciplinary citizen-science projects that require secure and privacy-preserving cyberinfrastructures. Secure crowd-sensing encourages participation, which in turn boosts the quality of data and discovery. The PIs envision that the efficiency and scalability of their methodology will help increase the real-world adoption of group signatures by developers, scientists and engineers in their crowd-sensing applications. The ultra-low cost of their bioanalytical instrumentation will also make it possible to deploy thousands at once to enable targeted diagnostics and monitoring. By making it feasible, for the first time, to deploy ensembles of thousands instruments for the same cost of a single dedicated laboratory analysis machine, their platform promises to bridge the gap between current-generation rapid diagnostic tests and the polymerase chain reaction gold standard. The United States clinical laboratory improvement amendments classify clinical diagnostic tests as either high, moderate, or waived complexity based upon the nature of the test performed. Polymerase chain reaction-based diagnostics are currently classified as high complexity due to prerequisite operational training and sophisticated instrumentation, thereby making them expensive and impractical for mass distribution in portable applications. The versatile platform proposed offers potential to enable polymerase chain reaction to be classified in the moderate or waived complexity categories, opening the door for a new generation of fast, accurate, and affordable diagnostic tools impacting a host of new scenarios where rapid field-deployable analysis is needed but not yet widely available (e.g., citizen science). Multi-disciplinary crowd-sensing and citizen-science projects require secure and privacy-preserving cyberinfrastructures. Secure crowd-sensing encourages participation, which in turn boosts the quality of data and discovery. The PIs envision that the efficiency and scalability of sublinear revocation with backward unlinkability helps increase the real-world adoption of group signatures by developers, scientists and engineers in their crowd-sensing applications.