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: Virginia Polytechnic Institute and State 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: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2004751&HistoricalAwards=false
• 关键词: Frameworks; Developing; CyberInfrastructure; Waterborne; Antibiotic

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.

CI 4-WARS是一个网络基础设施（CI），是建立一个高效的综合污水处理厂（WWTP）网络的关键一步，该网络将CI用于抗生素耐药性（AR）监测。 AR是某些细菌在抗生素治疗中存活的能力，这种能力由抗生素抗性基因（ARG）编码。 全球AR发病率正在上升，美国疾病控制中心估计2019年美国每年有35，000例相关死亡，而2013年每年有23，000例死亡。 这是一个巨大的挑战，需要采取跨学科的方法来遏制其蔓延。 需要进行高效和有效的监测，以查明ARG在细菌中传播的位置，并提供阻止其传播的方法。将下一代DNA测序与CI相结合用于监测ARG检测WWTP中的模式是实现这一目标的有前途的新方法。 污水处理厂聚集了社区中所有接受抗生素治疗的人排出的抗生素，以及存在于他们皮肤上或被他们消除的任何AR细菌或ARG。 识别废水中ARG模式的异常可以帮助在发生之前识别潜在的爆发，更好地为抗生素的临床使用提供信息，并改善处理实践以防止ARG释放到河流和溪流中。本研究的目的是：（1）利用从当地污水处理厂收集的DNA测序数据和相关元数据开发和演示CI 4-WARS系统;（二）开发新的计算工具，以确定DNA测序数据中表明AR传播风险的ARG发生模式，并开发和应用新的算法，以确定这些指标中表明新AR出现的异常（3）将开发的计算工具整合到CI 4-WARS中，将其建立为调查、评估、交流和报告/警报AR风险指标的一站式服务。更广泛的影响活动包括学生和专业培训，年度研讨会，免费在线视频，教程，并使CI 4-WARS在网络上免费提供，以最大限度地发挥CI 4-WARS的好处，并促进社区的采用。该奖项反映了NSF的法定使命，并已被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Tokenized Incentive for Federated Learning.

联邦学习的代币化激励。

• 发表时间: 2022
• 期刊: Verifiable and Auditable Federated Learning (FL-AAAI-22
• 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.

Towards a Serverless Bioinformatics Cyberinfrastructure Pipeline

• DOI: 10.1145/3452413.3464787
• 发表时间: 2020-06
• 期刊: Proceedings of the 1st Workshop on High Performance Serverless Computing
• 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

Heterogeneity-Aware Adaptive Federated Learning Scheduling

• DOI: 10.1109/bigdata55660.2022.10020721
• 发表时间: 2022-12
• 期刊: 2022 IEEE International Conference on Big Data (Big Data)
• 作者: Jingoo Han;Ahmad Faraz Khan;Syed Zawad;A. Anwar;Nathalie Baracaldo Angel;Yi Zhou;Feng Yan;A. Butt

HT-ARGfinder: A Comprehensive Pipeline for Identifying Horizontally Transferred Antibiotic Resistance Genes and Directionality in Metagenomic Sequencing Data

• DOI: 10.3389/fenvs.2022.901917
• 发表时间: 2022-06-03
• 期刊: FRONTIERS IN ENVIRONMENTAL SCIENCE
• 影响因子: 4.6
• 作者: Das, Badhan;Emon, Muhit Islam;Zhang, Liqing
• 通讯作者: Zhang, Liqing