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Military-Related Exposures, Social Determinants Of Health, And Dysbiosis: The United States-Veteran Microbiome Project (Us-Vmp), Lisa A. Brenner, Andrew J. Hoisington, Kelly A. Stearns-Yoder, Christopher E. Stamper, Jared A. Heinze, Teodor T. Postolache, Daniel A. Hadidi, Claire A. Hoffmire, Maggie A. Stanislawski
Military-Related Exposures, Social Determinants Of Health, And Dysbiosis: The United States-Veteran Microbiome Project (Us-Vmp), Lisa A. Brenner, Andrew J. Hoisington, Kelly A. Stearns-Yoder, Christopher E. Stamper, Jared A. Heinze, Teodor T. Postolache, Daniel A. Hadidi, Claire A. Hoffmire, Maggie A. Stanislawski
Faculty Publications
Significant effort has been put forth to increase understanding regarding the role of the human microbiome in health- and disease-related processes. In turn, the United States (US) Veteran Microbiome Project (US-VMP) was conceptualized as a means by which to serially collect microbiome and health-related data from those seeking care within the Veterans Health Administration (VHA). In this manuscript, exposures related to military experiences, as well as conditions and health-related factors among patients seen in VHA clinical settings are discussed in relation to common psychological and physical outcomes. Upon enrollment in the study, Veterans complete psychometrically sound (i.e., reliable and valid) …
Cross-Participant Eeg-Based Assessment Of Cognitive Workload Using Multi-Path Convolutional Recurrent Neural Networks, Ryan G. Hefron, Brett J. Borghetti, Christine M. Schubert Kabban, James Christensen, Justin Estep
Cross-Participant Eeg-Based Assessment Of Cognitive Workload Using Multi-Path Convolutional Recurrent Neural Networks, Ryan G. Hefron, Brett J. Borghetti, Christine M. Schubert Kabban, James Christensen, Justin Estep
Faculty Publications
Applying deep learning methods to electroencephalograph (EEG) data for cognitive state assessment has yielded improvements over previous modeling methods. However, research focused on cross-participant cognitive workload modeling using these techniques is underrepresented. We study the problem of cross-participant state estimation in a non-stimulus-locked task environment, where a trained model is used to make workload estimates on a new participant who is not represented in the training set. Using experimental data from the Multi-Attribute Task Battery (MATB) environment, a variety of deep neural network models are evaluated in the trade-space of computational efficiency, model accuracy, variance and temporal specificity yielding three …
Measuring Leak Rates From Abandoned Natural Gas Wells In Western Pennsylvania, John Bradshaw, Jeremy M. Slagley, Nicole Iannacchione, Matthew Lees
Measuring Leak Rates From Abandoned Natural Gas Wells In Western Pennsylvania, John Bradshaw, Jeremy M. Slagley, Nicole Iannacchione, Matthew Lees
Faculty Publications
The proliferation of unconventional natural gas drilling has brought considerable recent attention to the possible impacts that this new technology may have on greenhouse gas emissions. In Pennsylvania, estimates of these possible impacts are very difficult to accurately assess in large part due to the highly uncertain contribution from legacy abandoned and orphaned gas (AOG) wells. This paper outlines our work in establishing a methodology for measuring the methane leak rate from AOG wells in Western Pennsylvania. The theory and methodology of an enclosure method for measuring the methane mass leak rate from one AOG natural gas well is described. …