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Elaine A. Cohen Hubal  - - - 
Top co-authors See all
Catherine J. Murphy

298 shared publications

Department of Chemistry, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States

Paul Westerhoff

259 shared publications

Nanosystems Engineering Research Center for Nanotechnology-Enabled Water Treatment, School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, Arizona 85287-5306, United States

Yoram Cohen

237 shared publications

University of California Center for Environmental Implications of Nanotechnology, University of California, Los Angeles, USA

Jorge L. Gardea-Torresdey

236 shared publications

Chemistry Department, The University of Texas at El Paso, 500 West University Avenue, El Paso, Texas 79968, United States

Martin Scheringer

206 shared publications

Institute of Biogeochemistry and Pollutant Dynamics

Publication Record
Distribution of Articles published per year 
(2000 - 2018)
Total number of journals
published in
Publications See all
Article 0 Reads 0 Citations Advancing internal exposure and physiologically-based toxicokinetic modeling for 21st-century risk assessments Elaine A. Cohen Hubal, Barbara A. Wetmore, John F. Wambaugh,... Published: 16 August 2018
Journal of Exposure Science & Environmental Epidemiology, doi: 10.1038/s41370-018-0046-9
DOI See at publisher website ABS Show/hide abstract
Scientifically sound, risk-informed evaluation of chemicals is essential to protecting public health. Systematically leveraging information from exposure, toxicology, and epidemiology studies can provide a holistic understanding of how real-world exposure to chemicals may impact the health of populations, including sensitive and vulnerable individuals and life-stages. Increasingly, public health policy makers are employing toxicokinetic (TK) modeling tools to integrate these data streams and predict potential human health impact. Development of a suite of tools for predicting internal exposure, including physiologically-based toxicokinetic (PBTK) models, is being driven by needs to address large numbers of data-poor chemicals efficiently, translate bioactivity, and mechanistic information from new in vitro test systems, and integrate multiple lines of evidence to enable scientifically sound, risk-informed decisions. New modeling approaches are being designed “fit for purpose” to inform specific decision contexts, with applications ranging from rapid screening of hundreds of chemicals, to improved prediction of risks during sensitive stages of development. New data are being generated experimentally and computationally to support these models. Progress to meet the demand for internal exposure and PBTK modeling tools will require transparent publication of models and data to build credibility in results, as well as opportunities to partner with decision makers to evaluate and build confidence in use of these for improved decisions that promote safe use of chemicals.
Article 1 Read 35 Citations Considerations of Environmentally Relevant Test Conditions for Improved Evaluation of Ecological Hazards of Engineered N... Patricia A. Holden, Jorge L. Gardea-Torresdey, Fred Klaessig... Published: 03 June 2016
Environmental Science & Technology, doi: 10.1021/acs.est.6b00608
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Engineered nanomaterials (ENMs) are increasingly entering the environment with uncertain consequences including potential ecological effects. Various research communities view differently whether ecotoxicological testing of ENMs should be conducted using environmentally relevant concentrations—where observing outcomes is difficult—versus higher ENM doses, where responses are observable. What exposure conditions are typically used in assessing ENM hazards to populations? What conditions are used to test ecosystem-scale hazards? What is known regarding actual ENMs in the environment, via measurements or modeling simulations? How should exposure conditions, ENM transformation, dose, and body burden be used in interpreting biological and computational findings for assessing risks? These questions were addressed in the context of this critical review. As a result, three main recommendations emerged. First, researchers should improve ecotoxicology of ENMs by choosing test endpoints, duration, and study conditions—including ENM test concentrations—that align with realistic exposure scenarios. Second, testing should proceed via tiers with iterative feedback that informs experiments at other levels of biological organization. Finally, environmental realism in ENM hazard assessments should involve greater coordination among ENM quantitative analysts, exposure modelers, and ecotoxicologists, across government, industry, and academia.
Article 0 Reads 4 Citations Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotype... ClarLynda R Williams-DeVane, David M Reif, Elaine Cohen Huba... Published: 04 November 2013
BMC Systems Biology, doi: 10.1186/1752-0509-7-119
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Complex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets. A multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student’s t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups. The multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease.
Article 0 Reads 9 Citations High-Throughput Models for Exposure-Based Chemical Prioritization in the ExpoCast Project John F. Wambaugh, R. Woodrow Setzer, David Reif, Sumit Gangw... Published: 11 July 2013
Environmental Science & Technology, doi: 10.1021/es400482g
DOI See at publisher website PubMed View at PubMed
Article 0 Reads 12 Citations Comparison of modeling approaches to prioritize chemicals based on estimates of exposure and exposure potential Jade Mitchell, Jon A. Arnot, Olivier Jolliet, Panos G. Georg... Published: 22 May 2013
Science of The Total Environment, doi: 10.1016/j.scitotenv.2013.04.051
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
While only limited data are available to characterize the potential toxicity of over 8 million commercially available chemical substances, there is even less information available on the exposure and use-scenarios that are required to link potential toxicity to human and ecological health outcomes. Recent improvements and advances such as high throughput data gathering, high performance computational capabilities, and predictive chemical inherency methodology make this an opportune time to develop an exposure-based prioritization approach that can systematically utilize and link the asymmetrical bodies of knowledge for hazard and exposure. In response to the US EPA’s need to develop novel approaches and tools for rapidly prioritizing chemicals, a “Challenge” was issued to several exposure model developers to aid the understanding of current systems in a broader sense and to assist the US EPA’s effort to develop an approach comparable to other international efforts. A common set of chemicals were prioritized under each current approach. The results are presented herein along with a comparative analysis of the rankings of the chemicals based on metrics of exposure potential or actual exposure estimates. The analysis illustrates the similarities and differences across the domains of information incorporated in each modeling approach. The overall findings indicate a need to reconcile exposures from diffuse, indirect sources (far-field) with exposures from directly, applied chemicals in consumer products or resulting from the presence of a chemical in a microenvironment like a home or vehicle. Additionally, the exposure scenario, including the mode of entry into the environment (i.e. through air, water or sediment) appears to be an important determinant of the level of agreement between modeling approaches.
Article 2 Reads 5 Citations Sustainability, Health and Environmental Metrics: Impact on Ranking and Associations with Socioeconomic Measures for 50 ... Jane E. Gallagher, Elaine Cohen Hubal, Laura Jackson, Jeffer... Published: 22 February 2013
Sustainability, doi: 10.3390/su5020789
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Waste and materials management, land use planning, transportation and infrastructure including water and energy can have indirect or direct beneficial impacts on the environment and public health. The potential for impact, however, is rarely viewed in an integrated fashion. To facilitate such an integrated view in support of community-based policy decision making, we catalogued and evaluated associations between common, publically available, Environmental (e), Health (h), and Sustainability (s) metrics and sociodemographic measurements (n = 10) for 50 populous U.S. cities. E, H, S indices combined from two sources were derived from component (e) (h) (s) metrics for each city. A composite EHS Index was derived to reflect the integration across the E, H, and S indices. Rank order of high performing cities was highly dependent on the E, H and S indices considered. When viewed together with sociodemographic measurements, our analyses further the understanding of the interplay between these broad categories and reveal significant sociodemographic disparities (e.g., race, education, income) associated with low performing cities. Our analyses demonstrate how publically available environmental, health, sustainability and socioeconomic data sets can be used to better understand interconnections between these diverse domains for more holistic community assessments.