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Frameworks: Developing CyberInfrastructure for Waterborne Antibiotic Resistance Risk Surveillance (CI4-WARS)

Frameworks: Developing CyberInfrastructure for Waterborne Antibiotic Resistance Risk Surveillance (CI4-WARS)
框架:开发水性抗生素耐药性风险监测网络基础设施 (CI4-WARS)
批准号:
2004751
负责人:
Liqing Zhang
金额:
$129.9万
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
项目状态:
已结题
起止时间:
2020-07-01 至 2024-06-30

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中文摘要
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英文摘要
CI4-WARS is a cyberinfrastructure (CI) being developed as a key step towards establishing an efficient and integrated network of wastewater treatment plants (WWTPs) incorporating CI for antibiotic resistance (AR) surveillance. AR is the ability of some bacteria to survive antibiotic treatment, a capability encoded by antibiotic resistance genes (ARGs). AR rates are increasing globally, with the US Centers for Disease Control estimating 35,000 related deaths in the US per year in 2019, compared to 23,000 deaths per year in 2013. It is a grand challenge that calls for an interdisciplinary approach to combat its spread. Efficient and effective surveillance is needed to pinpoint where ARGs are spreading among bacteria and to inform ways to stop their spread. Combining next generation DNA sequencing with CI for monitoring patterns in ARG detection WWTPs is a promising and novel way to achieve this. WWTPs aggregate antibiotics excreted by all people in a community undergoing antibiotic therapy, as well as any AR bacteria or ARGs that are present on their skin or eliminated by them. Identifying anomalies in ARG patterns in wastewater could help identify potential outbreaks before they occur, better inform clinical use of antibiotics, and improve treatment practices to prevent release of ARGs to rivers and streams. The objectives of this research are to: (1) Develop and demonstrate the CI4-WARS system using DNA sequencing data and associated metadata collected from a local WWTP; (2) Develop new computational tools to identify ARG occurrence patterns in the DNA sequencing data that indicate the risk of AR spreading and develop and apply new algorithms for identifying anomalies in these indicators that are indicative of emergence of new AR bacteria or outbreaks; and (3) Integrate the developed computational tools into CI4-WARS, establishing it as a one-stop service for surveying, evaluating, communicating, and reporting/alerting indicators of AR risk. Broader impact activities include student and professional training, annual workshops, free online videos, tutorials, and making CI4-WARS freely available on the web to maximize the benefit of CI4-WARS and facilitate adoption by the community.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.
期刊论文(27)
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会议论文
Tokenized Incentive for Federated Learning.
联邦学习的代币化激励。
DOI: --
发表时间: 2022
期刊: Verifiable and Auditable Federated Learning (FL-AAAI-22
影响因子: --
作者: [Han, Jingoo, Khan, Ahmad Faraz, Zawad, Syed, Anwar, Ali, Angel, Nathalie Baracaldo, Zhou, Yi, Butt, Ali R.]
通讯作者: Butt, Ali R.
DOI: 10.1145/3452413.3464787
发表时间: 2020-06
期刊: Proceedings of the 1st Workshop on High Performance Serverless Computing
影响因子: --
作者: [S. Yao;Muhammad Ali Gulzar;Liqing Zhang;A. Butt]
通讯作者: S. Yao;Muhammad Ali Gulzar;Liqing Zhang;A. Butt
MetaMLP: A Fast Word Embedding Based Classifier to Profile Target Gene Databases in Metagenomic Samples
MetaMLP:一种基于快速词嵌入的分类器,用于分析宏基因组样本中的目标基因数据库
DOI: 10.1089/cmb.2021.0273
发表时间: 2021
期刊: Journal of Computational Biology
影响因子: 1.7
作者: [Arango-argoty, Gustavo A., Heath, Lenwood S., Pruden, Amy, Vikesland, Peter J., Zhang, Liqing]
通讯作者: Zhang, Liqing
DOI: 10.1109/bigdata55660.2022.10020721
发表时间: 2022-12
期刊: 2022 IEEE International Conference on Big Data (Big Data)
影响因子: --
作者: [Jingoo Han;Ahmad Faraz Khan;Syed Zawad;A. Anwar;Nathalie Baracaldo Angel;Yi Zhou;Feng Yan;A. Butt]
通讯作者: Jingoo Han;Ahmad Faraz Khan;Syed Zawad;A. Anwar;Nathalie Baracaldo Angel;Yi Zhou;Feng Yan;A. Butt
14
    Collaborative Research: URoL:ASC: Using the Rules of Antibiotic Resistance Development to Inform Wastewater Mitigation Strategies
    III-CXT: Collaborative Research: A High-Throughput Approach to the Assignment of Orthologous Genes Based on Genome Rearrangement
    海外基金