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Texas A&M University

Superfund Research Program

Data Management and Analysis Core

Project Leader: Efstratios N. Pistikopoulos
Grant Number: P42ES027704
Funding Period: 2022-2027
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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Project Summary (2022-2027)

The Texas A&M University Superfund Research Center aims to develop descriptive models and tools that can predict the possible hazardous outcomes of chemical exposure during environmental emergencies while providing powerful solutions that can mitigate their negative effects on human health. The Data Management & Analysis Core (DMAC) is one of the key components of the Center that supports all projects and cores in their data management, analysis, quality control needs. The DMAC provides a number of essential services to the Center’s researchers by assisting them in achieving key environmental and biomedical outcomes under four specific aims: (i) providing a new platform for data management and sharing across the Center, (ii) applying best-practice analysis methods to Center data, (iii) developing new methods that are urgently needed to solve the problems posed in the Projects, and (iv) maintaining research and data quality control protocols for the Center. The DMAC establishes a data universe (“dataverse”) for data sharing, integration, and collaboration. The “dataverse” is used to manage Center datasets where each component securely deposits and accesses data through a web-based platform and ensures Center generated data comply with Findable, Accessible, Interoperable, and Reusable (FAIR) principles.

The DMAC also provides additional assistance in developing and utilizing advanced data science methodologies for translating raw experimental data into actionable insights and predictive models for all projects. Novel Analytical and Computational Strategies for Exposure Assessment of Complex Mixtures performs and optimizes ion mobility spectrometry and mass spectrometry analyses of complex environmental samples; DMAC provides guidance on geospatial sampling, feature selection, and classification analysis. The project RAPiD: Responding to Air Pollution in Disasters develops an in vitro pediatric lung model to characterize respiratory risks from VOCs; DMAC performs concentration-response modeling, nonlinear, and spatial modeling techniques to evaluate the respiratory risks from ambient VOCs. Feto-Maternal Interface Tissue Chip Models for Rapid Assessment of Preterm Birth Risks of Hazardous Substances addresses pregnancy risk implications of exposures to hazardous substances by developing a feto-maternal interface organ-on-a-chip model; DMAC provides expertise in hypothesis testing, regression analysis, and ANOVA testing for analyzing proinflammatory cytokine measures. Inter-Tissue and -Individual Variability in Responses to Mixtures utilizes in vitro cultures and reverse toxicokinetic analysis to characterize hazards of environmental mixtures; DMAC provides service in analyzing high-content screening data, high-throughput transcriptomics data, and performs population variability analyses. The project Experimental and Computational Engineering of Novel Optimized Multicomponent Sorbents for Toxic Mixtures studies the mitigation of adverse health effects of chemicals through broad-acting sorption materials; DMAC provides services for experimental design and statistical testing. The DMAC, working in concert with the Research Experience & Training Coordination Core, provides data science training workshops for center personnel. Finally, DMAC develops Quality Assurance Project Plans to cover all aspects of quality assurance and control for all center components.

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