U.S.- Chile Cooperative Research: Anthropogenic Influences on the Environmental Dissemination of Bacteria and Bacterial Genes

美国-智利合作研究：人为对细菌和细菌基因环境传播的影响

基本信息

批准号：
0405419
负责人：
Randall Singer
金额：
$ 5.85万
依托单位：
University of Minnesota-Twin Cities
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2004
资助国家：
美国
起止时间：
2004-07-01 至 2006-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0405419&HistoricalAwards=false
关键词：
U.S.Chile Cooperative Research Anthropogenic

项目摘要

0405419SingerThis U.S.-Chile award will provide support for Dr. Randall Singer, University of Minnesota-Twin Cities, to collaborate with Dr. Gerardo E. Gonzalez-Rocha, Universidad de Concepcion in Chile. Partial support for this project comes from the Biocomplexity in the Environment (BE) Program.The PIs will work on anthropogenic influences on the environmental dissemination of bacteria and bacterial genes. The focus of this project is on the ecological processes by which E. coli disperse into complex natural environments. They will also look at factors that are hypothesized to increase the diversity, antimicrobial resistance, and virulence of these bacteria. Through an ecosystem-based study in a rural environment, they will assess the relationships among specific anthropogenic changes, other environmental and sociological parameters, and changes in the diversity, resistance and virulence of E. coli. This activity will increase our understanding of how anthropogenic environmental changes facilitate the emergence of novel pathogens in a relevant and important ecological setting. It will aid in the development of methods for linking microbiological, epidemiological and ecological data into a dissemination/dispersal model.

0405419Singer 该美国-智利奖项将为明尼苏达大学双城分校的 Randall Singer 博士与智利康塞普西翁大学的 Gerardo E. Gonzalez-Rocha 博士合作提供支持。该项目的部分支持来自环境中的生物复杂性（BE）计划。PI 将致力于研究人为对细菌和细菌基因的环境传播的影响。该项目的重点是大肠杆菌扩散到复杂自然环境中的生态过程。他们还将研究假设增加这些细菌的多样性、抗菌素耐药性和毒力的因素。通过在农村环境中进行基于生态系统的研究，他们将评估特定的人为变化、其他环境和社会学参数以及大肠杆菌多样性、抵抗力和毒力的变化之间的关系。这项活动将加深我们对人为环境变化如何促进相关且重要的生态环境中新型病原体出现的理解。它将有助于开发将微生物学、流行病学和生态数据连接到传播/扩散模型的方法。