RUI: Developing a regression model predicting the toxicity of metal nanoparticles against soil microbial community

RUI：开发预测金属纳米粒子对土壤微生物群落毒性的回归模型

• 批准号: 1559792
• 负责人: Vishal Shah
• 金额: $ 14.69万
• 依托单位: West Chester University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-07 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1559792&HistoricalAwards=false
• 关键词: RUI; Developing; regression; model; predicting

Active and diverse microorganism communities are essential for soil; and hence for agriculture, food security, and ecosystem health. Changes in these communities can significantly influence soil quality, health, and productivity. This research uses experimental and statistical/modeling approaches to investigate and predict the impact of common metal nanoparticles (iron, copper, and silver), which are reported to have anti-microbial properties, on soil microbial communities. The main research goal is to develop models that can help assess the ecotoxicity of common metal nanoparticles without having to resort to carrying out large numbers of experiments for each sample to be tested. This work accomplishes that goal with a combined experimental/modeling approach. Experiments involving the study of the toxicity of ten important soil parameters (pH, temperature, nanoparticle concentration, organic carbon content, soil moisture, and the concentration of various cations and anions like Na, Ca, Mg, nitrates, and phosphates) on the interaction between selected commercially purchased nanoparticles and two model soils, each with their unique microbial community, will be carried out in microcosms. The toxicity of the nanoparticles to microbial communities will be evaluated using three different indicators of microbial health and metabolic activity: PCR-DGGE, Biolog Ecoplates, and total soil FAME analyses from which a single ecotoxicity value can be determined. Results of the experimental matrix will be used to develop a linear regression model that will identify environmental parameters that increase, decrease, or have no influence on toxicity on the soil microbes. A quadratic regression will be developed that build off the results of the linear model to predict the toxic potential of each parameter and identify and assess interactions among the various environmental parameters. Broader impacts of the research include the strong integration of research and education, engaging large numbers of undergraduate students in environmental biology research, and using the research to augment undergraduate course curriculum. Through a summer research experience, the project will also engage students from Southern University, a historically black college that is still recovering from the effects of Hurricane Katrina. Participation of under-represented groups in the sciences will also be increased by recruiting students from the Dowling College Center for Minority Teacher Development and Training Program to participate in the research. Free public lectures tailored for the general public to increase their awareness of their impacts on the environment will also be presented.

活跃和多样化的微生物群落对于土壤至关重要。因此，农业，粮食安全和生态系统健康。 这些社区的变化会极大地影响土壤质量，健康和生产力。 这项研究使用实验和统计/建模方法来研究和预测普通金属纳米颗粒（铁，铜和银）的影响，据报道具有抗微生物特性对土壤微生物群落的影响。 主要的研究目标是开发可以帮助评估普通金属纳米颗粒的生态毒性的模型，而不必诉诸于进行测试的每个样品进行大量实验。 这项工作通过合并的实验/建模方法实现了该目标。 实验涉及研究十种重要土壤参数的毒性（pH，温度，纳米颗粒浓度，有机碳含量，土壤水分以及各种阳离子和阴离子的浓度，例如Na，Ca，Mg，Mg，硝酸盐和磷酸盐）在精选的商业购买的纳米颗粒和两个模型土壤之间，每个纳米粒子都将在微观上进行。 纳米颗粒对微生物群落的毒性将使用微生物健康和代谢活性的三种不同指标进行评估：PCR-DGGE，生物生态层和总土壤名声分析，可以从中确定单个生态毒性值。 实验矩阵的结果将用于开发线性回归模型，该模型将确定会增加，减少或对土壤微生物的毒性没有影响的环境参数。 将开发二次回归，以建立线性模型的结果，以预测每个参数的毒性潜力，并识别和评估各种环境参数之间的相互作用。 这项研究的更广泛的影响包括研究和教育的强烈融合，吸引大量的本科生参与环境生物学研究，并利用研究来扩大本科课程课程。 通过夏季研究经验，该项目还将吸引南方大学的学生，这是一所历史悠久的黑人学院，仍在从卡特里娜飓风的影响中恢复过来。 通过招募Dowling学院少数族裔教师发展与培训计划中心的学生参加研究，还将增加代表性不足的小组的参与。 还将介绍为公众量身定制的免费公开演讲，以提高他们对环境影响的认识。