Collaborative Research: High-precision monitoring of foodborne pathogens in food manufacturing facilities

合作研究：食品生产设施中食源性病原体的高精度监测

• 批准号: 2130643
• 负责人: Jian Liu
• 金额: $ 8万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2130643&HistoricalAwards=false
• 关键词: Collaborative; Research; precision; monitoring; foodborne

Disease-causing bacterial microorganisms associated with food can cause various infectious diseases with significant health impacts. Food manufacturing and processing facilities are a rich place for these foodborne pathogens to spread; thus, it is important to have efficient detection systems that can be used to quickly monitor foodborne bacterial pathogens in food manufacturing facilities. Current technologies that are available for detecting pathogens can be expensive and/or take a long time to get results, so they are not suitable for routine application in fast-paced food manufacturing sites where products are developed continuously. New and improved technologies are needed for rapid detection of foodborne pathogens in order to avoid health impacts and economic losses due to food product recalls. This project will develop a novel technology that will accurately detect foodborne bacterial pathogens using a unique artificial molecule called ‘aptamer’ that is designed to act as a true test for the desired pathogen with the result detected in multiple ways to enhance precision. The technology will use computational methods and mobile devices, such as smartphones, to enable rapid detection and wireless data transmission for remote access to the results. The technology can be developed to detect various types of foodborne bacterial pathogens in real-time, a significant step towards improving food safety in food manufacturing and processing facilities. The project will provide students with hands-on training in the development of the technology to build the related skillset in the United States workforce. The project will also facilitate K-12 outreach programs on food safety as well as promote STEM (Science, Technology, Engineering, and Math) education amongst women and underrepresented minorities.The food industry needs “fit-for-purpose” pathogen detection technologies that can be easily integrated into manufacturing process systems to rapidly deliver precise identification and quantification of bacterial pathogens in real-time. Leveraging an interdisciplinary research team that will exploit structural bioinformatics, data analytics, and aptamer-based biosensing, the proposed project will develop a wireless multimodal aptasensor with mobile integration to precisely detect foodborne bacterial pathogens via an integrated electrochemical and optical monitoring. The technology will deliver the following performance characteristics: (i) a high-precision method for flow-through monitoring; (ii) a portable device that can be moved to different process locations; (iii) an ability to detect target pathogens in complex food micro environments; (iv) capacity for real-time remote monitoring and integration into SCADA (supervisory control and data acquisition) systems; and (v) multimodal sensing capability to enhance precision. These represent significant improvements over existing foodborne pathogen detection systems used in food manufacturing facilities.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.

与食物相关的引起疾病的细菌微生物会引起各种感染性疾病，具有重大的健康影响。食品制造和加工设施是这些食源性病原体传播的丰富地方。因此，重要的是要拥有有效的检测系统，该检测系统可用于快速监测食品制造设施中的食源性细菌病原体。可用于检测病原体的当前技术可能很昂贵和/或花费很长时间才能获得结果，因此它们不适合在不断开发产品的快节奏的食品制造地点进行常规应用。需要新的和改进的技术来快速检测食源性病原体，以避免由于食品产品召回而造成的健康影响和经济损失。该项目将开发一种新型技术，该技术将使用称为“ Aptamer”的独特人造分子准确检测出食源性细菌病原体，该分子旨在作为对所需病原体的真实测试，并以多种方式检测到可提高精度的结果。该技术将使用计算方法和移动设备（例如智能手机）启用快速检测和无线数据传输，以远程访问结果。可以开发该技术来实时检测各种类型的食源性细菌病原体，这是朝着改善食品制造和加工设施中食品安全迈出的重要一步。该项目将为学生提供技术开发的动手培训，以在美国劳动力中建立相关技能。该项目还将促进有关食品安全的K-12外展计划，并促进女性和代表性不足的少数族裔的STEM（科学，技术，工程和数学）教育。食品行业需要“适合使用”病原体检测技术，这些技术可以轻松地集成到制造过程中，以便快速地实现细菌patateria Pathogenogenogensememene-Pathogenogensenogengecensementia patheriperia pathotime-Pathotime-Paterime-Paterime-Paterime-Paterime-Paterime-Paterime-Paterime-Paterime-Patherime-Paterime-Paterime-Paterime-Paterime。利用跨学科研究团队将利用结构性生物信息学，数据分析和基于ATAMER的生物传感，该项目将开发具有移动整合的无线多模态适率器，以精确检测到通过集成的电化学和光学监测来检测食品生存的细菌病原体。该技术将提供以下性能特征：（i）一种用于流通监测的高精度方法； （ii）可以移动到不同过程位置的便携式设备； （iii）在复杂食品微环境中检测靶病原体的能力； （iv）实时远程监视和集成到SCADA（监管控制和数据采集）系统的能力； （v）增强精度的多模式传感能力。这些代表了食品制造设施中使用的现有食源性病原体检测系统的重大改进。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响标准，通过评估诚实地表示支持。

项目成果

DroneChase: A Mobile and Automated Cross-Modality System for Continuous Drone Tracking

• DOI: 10.1145/3597060.3597237
• 发表时间: 2023-06
• 期刊: Proceedings of the Ninth Workshop on Micro Aerial Vehicle Networks, Systems, and Applications
• 作者: Neel Vora;Yi Wu;Jian Liu;Phuc Nguyen

RecUP-FL: Reconciling Utility and Privacy in Federated learning via User-configurable Privacy Defense

• DOI: 10.1145/3579856.3582819
• 发表时间: 2023-04
• 期刊: Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security
• 作者: Yue-li Cui;Syed Imran Ali Meerza;Zhuohang Li;Luyang Liu;Jiaxin Zhang;Jian Liu