| 项目编号: | 1645121
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| 项目名称: | EAGER: Collaborative Research: Privacy-enhancing CrowdPCR for Early Epidemic Detection |
| 作者: | Danfeng Yao
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| 承担单位: | Virginia Polytechnic Institute and State University
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| 批准年: | 2016
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| 开始日期: | 2016-09-01
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| 结束日期: | 2017-08-31
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| 资助金额: | 50000
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Engineering - Chemical, Bioengineering, Environmental, and Transport Systems
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| 英文关键词: | crowd-sensing
; datum collection
; fundamental research address multiple thrust
; fundamental research
; engineering research
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| 英文摘要: | 1645285/1645121
Ugaz/Danfeng
The 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. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/91180
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| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
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| Recommended Citation: | |
Danfeng Yao. EAGER: Collaborative Research: Privacy-enhancing CrowdPCR for Early Epidemic Detection. 2016-01-01.
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