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Final Progress Reports: Oregon State University: Data Management and Analysis Core

Superfund Research Program

Data Management and Analysis Core

Project Leader: Katrina M. Waters (Pacific Northwest National Laboratory)
Co-Investigator: Sara Gosline (Pacific Northwest National Laboratory)
Grant Number: P42ES016465
Funding Period: 2009-2025
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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Final Progress Reports

Year:   2019  2012 

The Biostatistics and Modeling Research Support Core for the Oregon State University (OSU) SRP provides a centralized plan for experimental design, data integration and predictive modeling of research data, enabling the center to develop data standards, and modeling approaches that facilitate the use of experimental data in risk assessments and regulatory decisions. In the past year, the Core contributed to a paper investigating the toxicokinetics of benzo[a]pyrene in humans (Madeen et al, 2019), which was a collaboration of several project and core leads. In addition, the team supported the integration of gene expression data with developmental toxicity profiles to characterize polycyclic aromatic hydrocarbon (PAH) hazard (Shankar et al, 2019), as well as a global analysis of chemical exposures across three continents using silicone wristbands (Dixon et al, 2019). The Biostatistics and Modeling Research Support Core also developed a new air liquid interface culture system for human bronchial epithelial cells to study the mechanism of PAH toxicity in the lung (Chang et al, 2019). Work led by the Core to develop and apply a benchmark dose point-of-departure approach to dynamic zebrafish behavioral data has now been published (Thomas et al, 2019). The web interface for the Bioinformatics Resource Manager was also summarized in an application note in BMC Bioinformatics (Brown et al, 2019). The team continues to support the analysis of gene expression data in the zebrafish model to better understand signaling networks associated with PAH mode of action and to identify key hubs and bottlenecks that drive toxicity. In addition, the team provided numerous training opportunities through workshops, externships and one-on-one mentorship for trainees to learn statistical and bioinformatics approaches to design experiments and analyze their data.

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