@article{truong_rericha_thunga_marvel_wallis_simonich_field_cao_reif_tanguay_2022, title={Systematic developmental toxicity assessment of a structurally diverse library of PFAS in zebrafish}, volume={431}, ISSN={["1873-3336"]}, url={http://dx.doi.org/10.1016/j.jhazmat.2022.128615}, DOI={10.1016/j.jhazmat.2022.128615}, abstractNote={Per- and polyfluoroalkyl substances (PFAS) are a class of widely used chemicals with limited human health effects data relative to the diversity of structures manufactured. To help fill this data gap, an extensive in vivo developmental toxicity screen was performed on 139 PFAS provided by the US EPA. Dechorionated embryonic zebrafish were exposed to 10 nominal water concentrations of PFAS (0.015-100 µM) from 6 to 120 h post-fertilization (hpf). The embryos were assayed for embryonic photomotor response (EPR), larval photomotor response (LPR), and 13 morphological endpoints. A total of 49 PFAS (35%) were bioactive in one or more assays (11 altered EPR, 25 altered LPR, and 31 altered morphology). Perfluorooctanesulfonamide (FOSA) was the only structure that was bioactive in all 3 assays, while Perfluorodecanoic acid (PFDA) was the most potent teratogen. Low PFAS volatility was associated with developmental toxicity (p < 0.01), but no association was detected between bioactivity and five other physicochemical parameters. The bioactive PFAS were enriched for 6 supergroup chemotypes. The results illustrate the power of a multi-dimensional in vivo platform to assess the developmental (neuro)toxicity of diverse PFAS and in the acceleration of PFAS safety research.}, journal={JOURNAL OF HAZARDOUS MATERIALS}, publisher={Elsevier BV}, author={Truong, Lisa and Rericha, Yvonne and Thunga, Preethi and Marvel, Skylar and Wallis, Dylan and Simonich, Michael T. and Field, Jennifer A. and Cao, Dunping and Reif, David M. and Tanguay, Robyn L.}, year={2022}, month={Jun} }
 @article{fleming_marvel_supak_motsinger-reif_reif_2022, title={ToxPi*GIS Toolkit: creating, viewing, and sharing integrative visualizations for geospatial data using ArcGIS}, volume={4}, ISSN={["1559-064X"]}, DOI={10.1038/s41370-022-00433-w}, abstractNote={Abstract
                Background
                Presenting a comprehensive picture of geographic data comprising multiple factors is an inherently integrative undertaking. Visualizing such data in an interactive form is essential for public sharing and geographic information systems (GIS) analysis. The Toxicological Prioritization Index (ToxPi) framework offers a visual analytic integrating data that is compatible with geographic data. ArcGIS is a predominant geospatial software available for presenting and communicating geographic data, yet to our knowledge there is no methodology for integrating ToxPi profiles into ArcGIS maps.
              
                Objective
                We introduce an actively developed suite of software, the ToxPi*GIS Toolkit, for creating, viewing, sharing, and analyzing interactive ToxPi profiles in ArcGIS to allow for new GIS analysis and an avenue for providing geospatial results to the public.
              
                Methods
                The ToxPi*GIS Toolkit is a collection of methods for creating interactive feature layers that contain ToxPi profiles. It currently includes an ArcGIS Toolbox (ToxPiToolbox.tbx) for drawing location-specific ToxPi profiles in a single feature layer, a collection of modular Python scripts that create predesigned layer files containing ToxPi feature layers from the command line, and a collection of Python routines for useful data manipulation and preprocessing. We present workflows documenting ToxPi feature layer creation, sharing, and embedding for both novice and advanced users looking for additional customizability.
              
                Results
                Map visualizations created with the ToxPi*GIS Toolkit can be made freely available on public URLs, allowing users without ArcGIS Pro access or expertise to view and interact with them. Novice users with ArcGIS Pro access can create de novo custom maps, and advanced users can exploit additional customization options. The ArcGIS Toolbox provides a simple means for generating ToxPi feature layers. We illustrate its usage with current COVID-19 data to compare drivers of pandemic vulnerability in counties across the United States.
              
                Significance
                The integration of ToxPi profiles with ArcGIS provides new avenues for geospatial analysis, visualization, and public sharing of multi-factor data. This allows for comparison of data across a region, which can support decisions that help address issues such as disease prevention, environmental health, natural disaster prevention, chemical risk, and many others. Development of new features, which will advance the interests of the scientific community in many fields, is ongoing for the ToxPi*GIS Toolkit, which can be accessed from www.toxpi.org.
              }, journal={JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY}, author={Fleming, Jonathon and Marvel, Skylar W. and Supak, Stacy and Motsinger-Reif, Alison A. and Reif, David M.}, year={2022}, month={Apr} }
 @misc{marvel_house_wheeler_song_zhou_wright_chiu_rusyn_motsinger-reif_reif_2021, title={The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring County-Level Vulnerability Using Visualization, Statistical Modeling, and Machine Learning}, volume={129}, ISSN={["1552-9924"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85099420902&partnerID=MN8TOARS}, DOI={10.1289/EHP8690}, abstractNote={Vol. 129, No. 1 Research LetterOpen AccessThe COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring County-Level Vulnerability Using Visualization, Statistical Modeling, and Machine Learning Skylar W. Marvel, John S. House, Matthew Wheeler, Kuncheng Song, Yi-Hui Zhou, Fred A. Wright, Weihsueh A. Chiu, Ivan Rusyn, Alison Motsinger-Reif, and David M. Reif Skylar W. Marvel Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University (NCSU), Raleigh, North Carolina, USA , John S. House Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA , Matthew Wheeler Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA , Kuncheng Song Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University (NCSU), Raleigh, North Carolina, USA , Yi-Hui Zhou Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University (NCSU), Raleigh, North Carolina, USA , Fred A. Wright Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University (NCSU), Raleigh, North Carolina, USA Department of Statistics, NCSU, Raleigh, North Carolina, USA , Weihsueh A. Chiu Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA , Ivan Rusyn Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA , Alison Motsinger-Reif Address correspondence to Alison Motsinger-Reif, 111 T.W. Alexander Dr., Rall Building, Research Triangle Park, NC 27709 USA. Email: E-mail Address: [email protected], or David M. Reif, Box 7566, 1 Lampe Dr., Raleigh NC 27695 USA. Email: E-mail Address: [email protected] Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA , and David M. Reif Address correspondence to Alison Motsinger-Reif, 111 T.W. Alexander Dr., Rall Building, Research Triangle Park, NC 27709 USA. Email: E-mail Address: [email protected], or David M. Reif, Box 7566, 1 Lampe Dr., Raleigh NC 27695 USA. Email: E-mail Address: [email protected] Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University (NCSU), Raleigh, North Carolina, USA Published:5 January 2021CID: 017701https://doi.org/10.1289/EHP8690AboutSectionsPDF ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InReddit IntroductionExpert groups have coalesced around a roadmap to address the current COVID-19 pandemic centered on social distancing, monitoring case counts and health care capacity, and, eventually, moving to pharmaceutical interventions. However, responsibility for navigating the pandemic response falls largely on state and local officials. To make equitable decisions on allocating resources, caring for vulnerable subpopulations, and implementing local- and state-level interventions, access to current pandemic data and key vulnerabilities at the community level are essential (National Academies of Sciences, Engineering, and Medicine 2020). Although numerous predictive models and interactive monitoring applications have been developed using pandemic-related data sets (Wynants et al. 2020), their capacity to aid in dynamic, community-level decision-making is limited. We developed the interactive COVID-19 Pandemic Vulnerability Index (PVI) Dashboard ( https://covid19pvi.niehs.nih.gov/) to address this need by presenting a visual synthesis of dynamic information at the county level to monitor disease trajectories, communicate local vulnerabilities, forecast key outcomes, and guide informed responses (Figure 1).Figure 1. COVID-19 PVI Dashboard. Dashboard screenshot displaying PVI profiles atop a choropleth map layer indicating overall COVID-19 PVI rank. The PVI Scorecard and associated data for Clarendon County, South Carolina, has been selected. The scorecard summarizes the overall PVI score and rank compared with all 3,142 U.S. counties on each indicator slice. The scrollable score distributions at left compare the selected county PVI to the distributions of overall and slice-wise scores across the United States. The panels below the map are populated with county-specific information on observed trends in cases and deaths, cumulative numbers for the county, historical timelines (for cumulative cases, cumulative deaths, PVI, and PVI rank), daily case and death counts for the most recent 14-d period, and a 14-d forecast of predicted cases and deaths. The information displayed for both observed COVID-19 data and PVI layers is scrollable back through March 2020. Documentation of additional features and usage, including advanced options (accessible via the collapsed menu at the upper left), is provided in a Quick Start Guide (linked at the upper right corner). Note: Pop, population; PVI, Pandemic Vulnerability Index.MethodsThe current PVI model integrates multiple data streams into an overall score derived from 12 key indicators—including well-established, general vulnerability factors for public health, plus emerging factors relevant to the pandemic—distributed across four domains: current infection rates, baseline population concentration, current interventions, and health and environmental vulnerabilities. The PVI profiles translate numerical results into visual representations, with each vulnerability factor represented as a component slice of a radar chart (Figure 2). The PVI profile for each county is calculated using the Toxicological Prioritization Index (ToxPi) framework for data integration within a geospatial context (Marvel et al. 2018; Bhandari et al. 2020). Data sources in the current model (version 11.2.1) include the Social Vulnerability Index (SVI) of the Centers for Disease Control and Prevention (CDC) for emergency response and hazard mitigation planning (Horney et al. 2017), testing rates from the COVID Tracking Project (Atlantic Monthly Group 2020), social distancing metrics from mobile device data ( https://www.unacast.com/covid19/social-distancing-scoreboard), and dynamic measures of disease spread and case numbers ( https://usafacts.org/issues/coronavirus/). Methodological details concerning the integration of data streams—plus the complete, daily time series of all source data since February 2020 and resultant PVI scores—are maintained on the public Github project page (COVID19PVI 2020). Over this period, the PVI has been strongly associated with key vulnerability-related outcome metrics (by rank-correlation), with updates of its performance assessment posted with model updates alongside data at the Github project page (COVID19PVI 2020).Figure 2. Translation of data into COVID-19 PVI profiles. Information from all 3,142 U.S. counties is translated into PVI slices. The illustration shows how air pollution data (average density of fine particulate matterPM2.5 per county) are compared for two example counties. The county with the higher relative measurement (County Y) has a longer air pollution slice than the county with a lower measurement (County X). This procedure is repeated for all slices, resulting in an integrated, overall PVI profile. Note: pop, population; PVI, Pandemic Vulnerability Index.In addition to the PVI itself—which is a summary, human-centric visualization of relative vulnerability drivers—the dashboard is supported by rigorous statistical modeling of the underlying data to enable quantitative analysis and provide short-term, local predictions of cases and deaths [complete methodological details are maintained at the Github project page (COVID19PVI 2020)]. Generalized linear models of cumulative outcome data indicated that, after population size, the most significant predictors were the proportion of Black residents, mean fine particulate matter [particulate matter less than or equal to 2.5 micrometers≤2.5μm in diameter (fine particulate matterPM2.5)], percentage of population with insurance coverage (which was positively associated), and proportion of Hispanic residents. The local predictions of cases and deaths (see the "Predictions" panel in Figure 1) are updated daily using a Bayesian spatiotemporal random-effects model to build forecasts up to 2 weeks out.DiscussionThe PVI Dashboard supports decision-making and dynamic monitoring in several ways. The display can be tailored to add or remove layers of information, filtered by region (e.g., all counties within a state) or clustered by profile shape similarity. The timelines for both PVI models and observed COVID-19 outcomes facilitate tracking the impact of interventions and directing local resource allocations. The "Predictions" panel (Figure 1) connects these historical numbers to local forecasts of cases and deaths. By communicating an integrated concept of vulnerability that considers both dynamic (infection rate and interventions) and static (community population and health care characteristics) drivers, the interactive dashboard can promote buy-in from diverse audiences, which is necessary for effective public health interventions. This messaging can assist in addressing known racial disparities in COVID-19 case and death rates (Tan et al. 2020) or populations, and the PVI Dashboard is part of the "Unique Populations" tab of the CDC's COVID-19 Data Tracker ( https://covid.cdc.gov/covid-data-tracker). By filtering the display to highlight vulnerability drivers within an overall score context, the dashboard can inform targeted interventions for specific localities.Unfortunately, the pandemic endures across the United States, with broad disparities based on the local environment (Tan et al. 2020). We present the PVI Dashboard as a dynamic container for contextualizing these disparities. It is a modular tool that will evolve to incorporate new data sources and analytics as they emerge (e.g., concurrent flu infections, school and business reopening statistics, heterogeneous public health practices). This flexibility positions it well as a resource for integrated prioritization of eventual vaccine distribution and monitoring its local impact. The PVI Dashboard can empower local and state officials to take informed action to combat the pandemic by communicating interactive, visual profiles of vulnerability atop an underlying statistical framework that enables the comparison of counties and the evaluation of the PVI's component data.AcknowledgmentsWe thank the information technology and web services staff at the National Institute of Environmental Health Sciences (NIEHS)/National Institutes of Health (NIH) for their help and support, as well as J.K. Cetina and D.J. Reif for their useful technical input and advice. This work was supported by NIEHS/NIH grants (P42 ES027704, P30 ES029067, P42 ES031009, and P30 ES025128) and NIEHS/NIH intramural funds (Z ES103352-01).ReferencesAtlantic Monthly Group.2020. The COVID Tracking Project. https://covidtracking.com/ [accessed 15 November 2020]. Google ScholarBhandari S, Lewis PGT, Craft E, Marvel SW, Reif DM, Chiu WA. 2020. HGBEnviroScreen: enabling community action through data integration in the Houston–Galveston–Brazoria region. Int J Environ Res Public Health 17(4):1130, PMID: 32053902, 10.3390/ijerph17041130. Crossref, Medline, Google ScholarCOVID19PVI.2020. COVID19PVI/data. https://github.com/COVID19PVI/data [accessed 15 November 2020]. Google ScholarHorney J, Nguyen M, Salvesen D, Dwyer C, Cooper J, Berke P. 2017. Assessing the quality of rural hazard mitigation plans in the southeastern United States. J Plan Educ Res 37(1):56–65, 10.1177/0739456X16628605. Crossref, Google ScholarMarvel SW, To K, Grimm FA, Wright FA, Rusyn I, Reif DM. 2018. ToxPi Graphical User Interface 2.0: dynamic exploration, visualization, and sharing of integrated data models. BMC Bioinformatics 19(1):80, PMID: 29506467, 10.1186/s12859-018-2089-2. Crossref, Medline, Google ScholarNational Academies of Sciences, Engineering, and Medicine.2020. Framework for Equitable Allocation of COVID-19 Vaccine. Gayle H, Foege W, Brown L, Kahn B, eds. Washington, DC: National Academies Press. Google ScholarTan TQ, Kullar R, Swartz TH, Mathew TA, Piggott DA, Berthaud V. 2020. Location matters: geographic disparities and impact of coronavirus disease 2019. J Infect Dis 222(12):1951–1954, PMID: 32942299, 10.1093/infdis/jiaa583. Crossref, Medline, Google ScholarWynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al.2020. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 369:m1328, PMID: 32265220, 10.1136/bmj.m1328. Crossref, Medline, Google ScholarThe authors declare they have no actual or potential competing financial interests.FiguresReferencesRelatedDetails Vol. 129, No. 1 January 2021Metrics About Article Metrics Publication History Manuscript received20 November 2020Manuscript revised14 December 2020Manuscript accepted21 December 2020Originally published5 January 2021 Financial disclosuresPDF download License information EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted. Note to readers with disabilities EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact [email protected]. Our staff will work with you to assess and meet your accessibility needs within 3 working days.}, number={1}, journal={ENVIRONMENTAL HEALTH PERSPECTIVES}, author={Marvel, Skylar W. and House, John S. and Wheeler, Matthew and Song, Kuncheng and Zhou, Yi-Hui and Wright, Fred A. and Chiu, Weihsueh A. and Rusyn, Ivan and Motsinger-Reif, Alison and Reif, David M.}, year={2021}, month={Jan} }
 @article{truong_marvel_reif_thomas_pande_dasgupta_simonich_waters_tanguay_2020, title={The multi-dimensional embryonic zebrafish platform predicts flame retardant bioactivity}, volume={96}, ISSN={["0890-6238"]}, DOI={10.1016/j.reprotox.2020.08.007}, abstractNote={Flame retardant chemicals (FRCs) commonly added to many consumer products present a human exposure burden associated with adverse health effects. Under pressure from consumers, FRC manufacturers have adopted some purportedly safer replacements for first-generation brominated diphenyl ethers (BDEs). In contrast, second and third-generation organophosphates and other alternative chemistries have limited bioactivity data available to estimate their hazard potential. In order to evaluate the toxicity of existing and potential replacement FRCs, we need efficient screening methods. We built a 61-FRC library in which we systemically assessed developmental toxicity and potential neurotoxicity effects in the embryonic zebrafish model. Data were compared to publicly available data generated in a battery of cell-based in vitro assays from ToxCast, Tox21, and other alternative models. Of the 61 FRCs, 19 of 45 that were tested in the ToxCast assays were bioactive in our zebrafish model. The zebrafish assays detected bioactivity for 10 of the 12 previously classified developmental neurotoxic FRCs. Developmental zebrafish were sufficiently sensitive at detecting FRC structure-bioactivity impacts that we were able to build a classification model using 13 physicochemical properties and 3 embryonic zebrafish assays that achieved a balanced accuracy of 91.7%. This work illustrates the power of a multi-dimensional in vivo platform to expand our ability to predict the hazard potential of new compounds based on structural relatedness, ultimately leading to reliable toxicity predictions based on chemical structure.}, journal={REPRODUCTIVE TOXICOLOGY}, author={Truong, Lisa and Marvel, Skylar and Reif, David M. and Thomas, Dennis G. and Pande, Paritosh and Dasgupta, Subham and Simonich, Michael T. and Waters, Katrina M. and Tanguay, Robyn L.}, year={2020}, month={Sep}, pages={359–369} }
 @article{kosnik_strickland_marvel_wallis_wallace_richard_reif_shafer_2020, title={Concentration-response evaluation of ToxCast compounds for multivariate activity patterns of neural network function}, volume={94}, ISSN={["1432-0738"]}, DOI={10.1007/s00204-019-02636-x}, abstractNote={The US Environmental Protection Agency's ToxCast program has generated toxicity data for thousands of chemicals but does not adequately assess potential neurotoxicity. Networks of neurons grown on microelectrode arrays (MEAs) offer an efficient approach to screen compounds for neuroactivity and distinguish between compound effects on firing, bursting, and connectivity patterns. Previously, single concentrations of the ToxCast Phase II library were screened for effects on mean firing rate (MFR) in rat primary cortical networks. Here, we expand this approach by retesting 384 of those compounds (including 222 active in the previous screen) in concentration–response across 43 network activity parameters to evaluate neural network function. Using hierarchical clustering and machine learning methods on the full suite of chemical-parameter response data, we identified 15 network activity parameters crucial in characterizing activity of 237 compounds that were response actives ("hits"). Recognized neurotoxic compounds in this network function assay were often more potent compared to other ToxCast assays. Of these chemical-parameter responses, we identified three k-means clusters of chemical-parameter activity (i.e., multivariate MEA response patterns). Next, we evaluated the MEA clusters for enrichment of chemical features using a subset of ToxPrint chemotypes, revealing chemical structural features that distinguished the MEA clusters. Finally, we assessed distribution of neurotoxicants with known pharmacology within the clusters and found that compounds segregated differentially. Collectively, these results demonstrate that multivariate MEA activity patterns can efficiently screen for diverse chemical activities relevant to neurotoxicity, and that response patterns may have predictive value related to chemical structural features.}, number={2}, journal={ARCHIVES OF TOXICOLOGY}, author={Kosnik, Marissa B. and Strickland, Jenna D. and Marvel, Skylar W. and Wallis, Dylan J. and Wallace, Kathleen and Richard, Ann M. and Reif, David M. and Shafer, Timothy J.}, year={2020}, month={Feb}, pages={469–484} }
 @article{rotroff_yee_zhou_marvel_shah_jack_havener_hedderson_kubo_herman_et al._2018, title={Genetic variants in CPA6 and PRPF31 are associated with variation in response to metformin in individuals with type 2 diabetes}, volume={67}, number={7}, journal={Diabetes}, author={Rotroff, D. M. and Yee, S. W. and Zhou, K. X. and Marvel, S. W. and Shah, H. S. and Jack, J. R. and Havener, T. M. and Hedderson, M. M. and Kubo, M. and Herman, M. A. and et al.}, year={2018}, pages={1428–1440} }
 @article{marvel_to_grimm_wright_rusyn_reif_2018, title={ToxPi Graphical User Interface 2.0: Dynamic exploration, visualization, and sharing of integrated data models}, volume={19}, journal={BMC Bioinformatics}, author={Marvel, S. W. and To, K. and Grimm, F. A. and Wright, F. A. and Rusyn, I. and Reif, D. M.}, year={2018} }
 @article{marvel_rotroff_wagner_buse_havener_mcleod_motsinger-reif_2017, title={Common and rare genetic markers of lipid variation in subjects with type 2 diabetes from the ACCORD clinical trial}, volume={5}, journal={PeerJ}, author={Marvel, S. W. and Rotroff, D. M. and Wagner, M. J. and Buse, J. B. and Havener, T. M. and McLeod, H. L. and Motsinger-Reif, A. A.}, year={2017} }
 @article{rotroff_marvel_jack_havener_doria_shah_mychaleckyi_mcleod_buse_wagner_et al._2017, title={Common genetic variants in neurobeachin (nbea) are associated with metformin drug response in individuals with type 2 diabetes in the accord clinical trial}, volume={101}, number={S1}, journal={Clinical Pharmacology & Therapeutics}, author={Rotroff, D. M. and Marvel, S. W. and Jack, J. R. and Havener, T. M. and Doria, A. and Shah, H. S. and Mychaleckyi, J. C. and McLeod, H. L. and Buse, J. B. and Wagner, M. J. and et al.}, year={2017}, pages={S9–9} }
 @article{knecht_truong_marvel_reif_garcia_lu_simbnich_teeguarden_tanguay_2017, title={Transgenerational inheritance of neurobehavioral and physiological deficits from developmental exposure to benzo[a]pyrene in zebrafish}, volume={329}, ISSN={["1096-0333"]}, url={http://europepmc.org/abstract/med/28583304}, DOI={10.1016/j.taap.2017.05.033}, abstractNote={Benzo[a]pyrene (B[a]P) is a well-known genotoxic polycylic aromatic compound whose toxicity is dependent on signaling via the aryl hydrocarbon receptor (AHR). It is unclear to what extent detrimental effects of B[a]P exposures might impact future generations and whether transgenerational effects might be AHR-dependent. This study examined the effects of developmental B[a]P exposure on 3 generations of zebrafish. Zebrafish embryos were exposed from 6 to 120 h post fertilization (hpf) to 5 and 10 μM B[a]P and raised in chemical-free water until adulthood (F0). Two generations were raised from F0 fish to evaluate transgenerational inheritance. Morphological, physiological and neurobehavioral parameters were measured at two life stages. Juveniles of the F0 and F2 exhibited hyper locomotor activity, decreased heartbeat and mitochondrial function. B[a]P exposure during development resulted in decreased global DNA methylation levels and generally reduced expression of DNA methyltransferases in wild type zebrafish, with the latter effect largely reversed in an AHR2-null background. Adults from the F0 B[a]P exposed lineage displayed social anxiety-like behavior. Adults in the F2 transgeneration manifested gender-specific increased body mass index (BMI), increased oxygen consumption and hyper-avoidance behavior. Exposure to benzo[a]pyrene during development resulted in transgenerational inheritance of neurobehavioral and physiological deficiencies. Indirect evidence suggested the potential for an AHR2-dependent epigenetic route.}, journal={TOXICOLOGY AND APPLIED PHARMACOLOGY}, author={Knecht, Andrea L. and Truong, Lisa and Marvel, Skylar W. and Reif, David M. and Garcia, Abraham and Lu, Catherine and Simbnich, Michael T. and Teeguarden, Justin G. and Tanguay, Robert L.}, year={2017}, month={Aug}, pages={148–157} }
 @article{irvin_rotroff_aslibekyan_zhi_hidalgo_motsinger-reif_marvel_srinivasasainagendra_claas_buse_et al._2016, title={A genome-wide study of lipid response to fenofibrate in Caucasians: A combined analysis of the GOLDN and ACCORD studies}, volume={26}, number={7}, journal={Pharmacogenetics and Genomics}, author={Irvin, M. R. and Rotroff, D. M. and Aslibekyan, S. and Zhi, D. G. and Hidalgo, B. and Motsinger-Reif, A. and Marvel, S. and Srinivasasainagendra, V. and Claas, S. A. and Buse, J. B. and et al.}, year={2016}, pages={324–333} }
 @article{zhang_marvel_truong_tanguay_reif_2016, title={Aggregate entropy scoring for quantifying activity across endpoints with irregular correlation structure}, volume={62}, ISSN={["0890-6238"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84965025830&partnerID=MN8TOARS}, DOI={10.1016/j.reprotox.2016.04.012}, abstractNote={Robust computational approaches are needed to characterize systems-level responses to chemical perturbations in environmental and clinical toxicology applications. Appropriate characterization of response presents a methodological challenge when dealing with diverse phenotypic endpoints measured using in vivo systems. In this article, we propose an information-theoretic method named Aggregate Entropy (AggE) and apply it to scoring multiplexed, phenotypic endpoints measured in developing zebrafish (Danio rerio) across a broad concentration-response profile for a diverse set of 1060 chemicals. AggE accurately identified chemicals with significant morphological effects, including single-endpoint effects and multi-endpoint responses that would have been missed by univariate methods, while avoiding putative false-positives that confound traditional methods due to irregular correlation structure. By testing AggE in a variety of high-dimensional real and simulated datasets, we have characterized its performance and suggested implementation parameters that can guide its application across a wide range of experimental scenarios.}, journal={REPRODUCTIVE TOXICOLOGY}, author={Zhang, Guozhu and Marvel, Skylar and Truong, Lisa and Tanguay, Robert L. and Reif, David M.}, year={2016}, month={Jul}, pages={92–99} }
 @article{shah_gao_morieri_skupien_marvel_pare_mannino_buranasupkajorn_mendonca_hastings_et al._2016, title={Genetic predictors of cardiovascular mortality during intensive glycemic control in type 2 diabetes: Findings from the ACCORD clinical trial}, volume={39}, number={11}, journal={Diabetes Care}, author={Shah, H. S. and Gao, H. and Morieri, M. L. and Skupien, J. and Marvel, S. and Pare, G. and Mannino, G. C. and Buranasupkajorn, P. and Mendonca, C. and Hastings, T. and et al.}, year={2016}, pages={1915–1924} }
 @article{graham_rotroff_marvel_buse_havener_wilson_wagner_motsinger-reif_2016, title={Incorporating concomitant medications into genome-wide analyses for the study of complex disease and drug response}, volume={7}, journal={Frontiers in Genetics}, author={Graham, H. T. and Rotroff, D. M. and Marvel, S. W. and Buse, J. B. and Havener, T. M. and Wilson, A. G. and Wagner, M. J. and Motsinger-Reif, A. A.}, year={2016} }
 @article{rotroff_joubert_marvel_haberg_wu_nilsen_ueland_nystad_london_motsinger-reif_2016, title={Maternal smoking impacts key biological pathways in newborns through epigenetic modification in Utero}, volume={17}, journal={BMC Genomics}, author={Rotroff, D. M. and Joubert, B. R. and Marvel, S. W. and Haberg, S. E. and Wu, M. C. and Nilsen, R. M. and Ueland, P. M. and Nystad, W. and London, S. J. and Motsinger-Reif, A.}, year={2016} }
 @article{reif_truong_mandrell_marvel_zhang_tanguay_2016, title={High-throughput characterization of chemical-associated embryonic behavioral changes predicts teratogenic outcomes}, volume={90}, ISSN={["1432-0738"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84934783723&partnerID=MN8TOARS}, DOI={10.1007/s00204-015-1554-1}, abstractNote={New strategies are needed to address the data gap between the bioactivity of chemicals in the environment versus existing hazard information. We address whether a high-throughput screening (HTS) system using a vertebrate organism (embryonic zebrafish) can characterize chemical-elicited behavioral responses at an early, 24 hours post-fertilization (hpf) stage that predict teratogenic consequences at a later developmental stage. The system was used to generate full concentration-response behavioral profiles at 24 hpf across 1060 ToxCast™ chemicals. Detailed, morphological evaluation of all individuals was performed as experimental follow-up at 5 days post-fertilization (dpf). Chemicals eliciting behavioral responses were also mapped against external HTS in vitro results to identify specific molecular targets and neurosignalling pathways. We found that, as an integrative measure of normal development, significant alterations in movement highlighted active chemicals representing several modes of action. These early behavioral responses were predictive for 17 specific developmental abnormalities and mortality measured at 5 dpf, often at lower (i.e., more potent) concentrations than those at which morphological effects were observed. Therefore, this system can provide rapid characterization of chemical-elicited behavioral responses at an early developmental stage that are predictive of observable adverse effects later in life.}, number={6}, journal={ARCHIVES OF TOXICOLOGY}, publisher={Springer Science and Business Media LLC}, author={Reif, David M. and Truong, Lisa and Mandrell, David and Marvel, Skylar and Zhang, Guozhu and Tanguay, Robert L.}, year={2016}, month={Jun}, pages={1459–1470} }
 @article{marvel_williams_2012, title={Set membership experimental design for biological systems}, volume={6}, ISSN={["1752-0509"]}, DOI={10.1186/1752-0509-6-21}, abstractNote={Experimental design approaches for biological systems are needed to help conserve the limited resources that are allocated for performing experiments. The assumptions used when assigning probability density functions to characterize uncertainty in biological systems are unwarranted when only a small number of measurements can be obtained. In these situations, the uncertainty in biological systems is more appropriately characterized in a bounded-error context. Additionally, effort must be made to improve the connection between modelers and experimentalists by relating design metrics to biologically relevant information. Bounded-error experimental design approaches that can assess the impact of additional measurements on model uncertainty are needed to identify the most appropriate balance between the collection of data and the availability of resources. In this work we develop a bounded-error experimental design framework for nonlinear continuous-time systems when few data measurements are available. This approach leverages many of the recent advances in bounded-error parameter and state estimation methods that use interval analysis to generate parameter sets and state bounds consistent with uncertain data measurements. We devise a novel approach using set-based uncertainty propagation to estimate measurement ranges at candidate time points. We then use these estimated measurements at the candidate time points to evaluate which candidate measurements furthest reduce model uncertainty. A method for quickly combining multiple candidate time points is presented and allows for determining the effect of adding multiple measurements. Biologically relevant metrics are developed and used to predict when new data measurements should be acquired, which system components should be measured and how many additional measurements should be obtained. The practicability of our approach is illustrated with a case study. This study shows that our approach is able to 1) identify candidate measurement time points that maximize information corresponding to biologically relevant metrics and 2) determine the number at which additional measurements begin to provide insignificant information. This framework can be used to balance the availability of resources with the addition of one or more measurement time points to improve the predictability of resulting models.}, journal={BMC SYSTEMS BIOLOGY}, author={Marvel, Skylar W. and Williams, Cranos M.}, year={2012}, month={Mar} }
 @inproceedings{marvel_williams_2012, title={Set membership state and parameter estimation for nonlinear differential equations with sparse discrete measurements}, DOI={10.1109/icsmc.2012.6377679}, abstractNote={This paper presents a method to perform parameter and state estimation in a bounded-error context for nonlinear continuous-time systems with sparse, discrete measurements. Direct application of a guaranteed parameter estimation method can be fruitless when few data measurements are available. This lack of measurements results in what we term “phantom” sets of parameter values that cannot be correctly discarded due to instability in the estimation method caused by the lack of information. Preprocessing the measurements through the addition of application specific stabilizing bounds vastly improves bounded parameter and state estimations. Comparisons between applying guaranteed estimation methods to raw and preprocessed data measurements are illustrated with an example application.}, booktitle={Ieee international conference on systems man and cybernetics conference}, author={Marvel, S. W. and Williams, Cranos}, year={2012}, pages={72–77} }
 @article{marvel_okrasinski_bernacki_loboa_dayton_2010, title={The Development and Validation of a LIPUS System With Preliminary Observations of Ultrasonic Effects on Human Adult Stem Cells}, volume={57}, ISSN={["1525-8955"]}, DOI={10.1109/tuffc.2010.1645}, abstractNote={To study the potential effects of low-intensity pulsed ultrasound (LIPUS) on cell response in vitro, the ability to alter LIPUS parameters is required. However, commercial LIPUS systems have very little control over parameter selection. In this study, a custom LIPUS system was designed and validated by exploring the effects of using different pulse repetition frequency (PRF) parameters on human adipose derived adult stem cells (hASCs) and bone marrow derived mesenchymal stem cells (hMSCs), two common stem cell sources for creating bone constructs in vitro. Changing the PRF was found to affect cellular response to LIPUS stimulation for both cell types. Proliferation of LIPUS-stimulated cells was found to decrease for hASCs by d 7 for all three groups compared with unstimulated control cells (P = 0.008, 0.011, 0.014 for 1 Hz, 100 Hz and 1 kHz PRF, respectively) and for hMSCs by d 14 (donor 1: P = 0.0005, 0.0002, 0.0003; donor 2: P = 0.0003, 0.0002, 0.0001; for PRFs of 1 Hz, 100 Hz, and 1 kHz, respectively). Additionally, LIPUS was shown to strongly accelerate osteogenic differentiation of hASCs based on amount of calcium accretion normalized by total DNA (P = 0.003, 0.001, 0.003, and 0.032 between control/100 Hz, control/1 kHz, 1 Hz/1 kHz, and 100 Hz/1 kHz pulse repetition frequencies, respectively). These findings promote the study of using LIPUS to induce osteogenic differentiation and further encourage the exploration of LIPUS parameter optimization. The custom LIPUS system was successfully designed to allow extreme parameter variation, specifically PRF, and encourages further studies.}, number={9}, journal={IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL}, author={Marvel, Skylar and Okrasinski, Stan and Bernacki, Susan H. and Loboa, Elizabeth and Dayton, Paul A.}, year={2010}, month={Sep}, pages={1977–1984} }
 @article{hanson_marvel_bernacki_banes_aalst_loboa_2009, title={Osteogenic Effects of Rest Inserted and Continuous Cyclic Tensile Strain on hASC Lines with Disparate Osteodifferentiation Capabilities}, volume={37}, ISSN={["1573-9686"]}, DOI={10.1007/s10439-009-9648-7}, abstractNote={We investigated the effects of two types of cyclic tensile strain, continuous and rest inserted, on osteogenic differentiation of human adipose-derived adult stem cells (hASCs). The influence of these mechanical strains was tested on two hASC lines having different mineral deposition potential, with one cell line depositing approximately nine times as much calcium as the other hASC line after 14 days of culture in osteogenic medium on tissue culture plastic. Results showed that both continuous (10% strain, 1 Hz) and rest inserted cyclic tensile strain (10% strain, 1 Hz, 10 s rest after each cycle) regimens increased the amount and rate of calcium deposition for both high and low calcium depositing hASC lines as compared to unstrained controls. The response was similar for both types of tensile strain for a given cell line, however, cyclic tensile strain had a much stronger osteogenic effect on the high calcium depositing hASC line, suggesting that mechanical loading has a greater effect on cell lines that already have an innate ability to produce bone as compared to cell lines that do not. This is the first study to investigate the osteodifferentiation effects of cyclic tensile strain on hASCs and the first to show that both continuous (10%, 1 Hz) and rest inserted (10%, 1 Hz, 10 s rest) cyclic tensile strain accelerate hASC osteodifferentiation and increase calcium accretion.}, number={5}, journal={ANNALS OF BIOMEDICAL ENGINEERING}, author={Hanson, Ariel D. and Marvel, Skylar W. and Bernacki, Susan H. and Banes, Albert J. and Aalst, John and Loboa, Elizabeth G.}, year={2009}, month={May}, pages={955–965} }