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    "text": "We are currently looking for a postdoctoral (or post-baccalaureate) fellow to join our group at the forefront of environment, climate, and health utilizing the latest computational workflows and the GeoTox exposomic risk assessment framework. The position is open until filled."
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    "text": "We are currently looking for a postdoctoral (or post-baccalaureate) fellow to join our group at the forefront of environment, climate, and health utilizing the latest computational workflows and the GeoTox exposomic risk assessment framework. The position is open until filled."
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    "section": "Geospatial Exposomic Risk Assessment within the GeoTox Framework",
    "text": "Geospatial Exposomic Risk Assessment within the GeoTox Framework\nThe SET group is looking for a post-baccalaureate or postdoctoral fellow to further developments in the GeoTox exposomic risk assessment framework.\n\nOverview\nGeospatial approaches offer an attractive approach to quantify the exposome because external components such as the social and physical-chemical exposome are more accurately quantified with geospatial models of exposure. Toxic effects are mediated through chemicals that alter critical molecules, cells, and physiological processes inside the body. It follows that phenotypic outcomes (e.g. disease) at the individual or population level can only occur after a series of source, exposure, and biological dynamics (Figure 1). We refer to this as the source-to-outcome-continuum. It follows that if methods and data exist to quantify each step in the sequence, and each step can be integrated into each neighboring step, then individual and population outcomes can be quantified from spatiotemporally resolved environmental sources and exposures.\n\n\n\nFigure 1 - The source-to-outcome-continuum: A schematic showing the necessary sequence of events such that each step must occur for environmental exposures to cause individual and population outcomes\n\n\n\n\nResponsibilities and Expectations\nThe successful candidate will continue methodological and applications of the GeoTox exposomic risk assessment framework outlined in Eccles et al. 2023. Postdoctoral fellows will lead a project that is expected to culminate in first-author peer-reviewed publication in a top environmental health or general scientific journal. Post-baccalaureate are expected to contribute or co-lead projects and peer-reviewed publications. The candidate will also be expected to contribute to the development of the GeoTox package and the broader SET group software ecosystem.\n\n\nQualifications\nGeoTox bridges the fields of geospatial modeling and computational toxicology, thus a successful will have expertise in at least one of these fields and a willingness to learn the other. For expertise in geospatial modeling, the candidate should have experience with GIS, spatial models such as land-use regression, Gaussian processes, and machine learning applications of spatial data. For expertise in computational toxicology, the candidate should have experience with many of these research areas: computational toxicological and bioinformatic methods, toxicokinetic and toxicodynamic models, high-through in-vitro screening, in-vitro to in-vivo extrapolation, concentration-response modeling, and human health risk assessment. The candidate should also have an eagerness to learn new scientific and statistical skills in the environmental health sciences, and demonstrated and on-going ability to contribute to an interdisciplinary and inclusive research group. Most importantly, we are looking for positive, pleasant, and life-long learners.\nPostdoctoral candidates should have a Ph.D. (or doctoral equivalent) in a relevant field such as exposure science, computational toxicology, bioinformatics, geostatistics, statistics, or biostatistics.\nPostbaccaleaurate candidates should have a B.S. or B.A. in a relevant field such as environmental health science, toxicology, statistics, or computer science.\n\n\nSkills\nPreferences will be given to candidates with demonstrated skills in one or more of the following areas:\n\nCoding in R, Python, or Julia languages\nGIS software such as QGIS\nCode version control using Git\nLinux and high-performance computing cluster environments\nExperience with managing and utilizing public and internal databases\nData wrangling\nLaTeX/Overleaf\nStrong writing and communication skills"
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    "text": "Compensation\nNIEHS post-baccalaureate and postdoctoral fellowships are competitive and commensurate with experience. Fellows are eligible for health insurance, paid vacation, sick leave, and up to 12 weeks of paid family leave (i.e. child birth or adoption).\nSalaries are set by the NIH and are based on years of experience and education: https://www.niehs.nih.gov/careers/research/fellows/working/benefits/salary"
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    "text": "Position Location\nThe posistion is located in the Research Triangle, North Carolina (Durham, Raleigh, Chapel Hill). NIH requires that all post-baccalaureate and postdoctoral fellows be located in person, so fully remote is not an option. Teleworking approximately 20-50% of the time is possible."
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    "section": "Additional Information",
    "text": "Additional Information\nPlease visit the SET group website for more information about the group including a syllabus - a document that outlines our general tools, philosophy, and day-to-day activities."
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    "text": "How to Apply\nPlease send the following to Dr. Kyle P Messier at niehs-spatial-apps@nih.gov with the subject line GeoTox Postdoc Application:\n\nResearch Statement or Cover Letter (1-2 pages)\nCurriculum Vitae\nPostdoctoral Required; Post-baccalaureate optional: 1-2 recent peer-review publications\n1-2 code examples\nContact Information for 3 references"
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    "text": "Disclaimers\nThe NIH is dedicated to building a diverse community in its training and employment programs and encourages the application and nomination of qualified women, minorities, and individuals with disabilities."
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    "text": "The Spatiotemporal Exposures and Toxicology group in October 2023. Top Row: Eva Marques, Daniel Zilber, Ranadeep Daw, Mariana Alifa. Bottom Row: Insang Song, Kyle Messier"
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    "section": "Kyle P Messier, PhD",
    "text": "Kyle P Messier, PhD\n\n\n\nKyle P Messier is a Stadtman Tenure-Track Investigator at the National Institute of Environmental Health Sciences (NIEHS) in the Division of Translational Toxicology (DTT). He leads the Spatiotemporal Exposures and Toxicology group, {SET}grp, within the Predictive Toxicology Branch. He also holds a joint appointment with the National Institute of Minority Health and Health Disparities (NIMHD) in Bethesda, Maryland and a secondary appointment in the NIEHS Biostatistics and Computational Biology Branch. Messier received a B.S. in Environmental Studies from the University of North Carolina at Asheville and a M.S. and Ph.D. from the University of North Carolina at Chapel Hill."
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    "section": "Mariana Alifa",
    "text": "Mariana Alifa\n\n\n\nDr. Mariana Alifa research interets lie at the nexus of environment and society. She has experience in air quality modeling, health effect quantification, uncertainty assessment, and space-time geospatial data resources. Currently, Dr. Alifa Kassien is working on novel physics informed neural networks for atmospheric dispersion models of data sparse chemicals. She also has interest in science education and outreach where she is working on developing knowledge and dissemination materials on climate change and health effects."
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    "section": "Eva Marques",
    "text": "Eva Marques\n\n\n\nDr. Eva Marques is an applied geostatistician with a primary focus in urban climatology. She uses Bayesian and machine learning methods to predict climate exposures such as the urban heat island effect. She holds a MS in Applied Math from the National Institute of Applied Sciences (INSA) in Toulouse, France, and a PhD in Urban Climatology from the National Meteorlogical Center in Toulouse, France."
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    "section": "Mitchell Manware",
    "text": "Mitchell Manware\n\n\n\nMr. Mitchell Manware holds a BS in Business and Health Care Management from the University of Connecticut and a MPH from the Yale School of Public Health. He has experience in geospatial data analysis, public health data visualization, and policy-oriented writing. Currently, he is a geospatial analyst in climate change and health funded by the Climate and Health Outcomes Research Data Systems (CHORDS) project where is developing geospatial tools and educational resources focused on environment, climate, and health."
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    "text": "The common theme of the Spatiotemporal Exposures and Toxicology group (SET group) is spatial statistics methods and applications in population-level exposure and risk assessment. As an investigator in the Division of Translational Toxicology, Dr. Kyle P Messier is expanding to include the integration of toxicological methods into geospatial exposure and risk models. The long-term goals are to use integrated geospatial and toxicological data and methods (1) to characterize the large-scale, population-level internal exposome, (2) to model the source-to-outcome continuum for complex mixtures, and (3) utilize these risk assessment tools to improve lives with an emphasis on historically disadvantaged communities and susceptible populations.\n\n\n\nThe Spatiotemporal Exposures and Toxicology group in October 2023. Top Row: Eva Marques, Daniel Zilber, Ranadeep Daw, Mariana Alifa. Bottom Row: Insang Song, Kyle Messier\n\n\n\n\n\n{SET}grp logo. { } is a nod to mathematical set notation. Thanks to the NIEHS Office of Communications and Public Liaison for the design."
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    "text": "Spatiotemporal Exposures and Toxicology group -> {SET} group\n\n\n\n\n\nThis is the professional website for the SET group, maintained by Kyle P Messier. For Kyle and the SET group’s official NIH/NIEHS government website please visit here."
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    "text": "Spatiotemporal Exposures and Toxicology group -> {SET} group\n\n\n\n\n\nThis is the professional website for the SET group, maintained by Kyle P Messier. For Kyle and the SET group’s official NIH/NIEHS government website please visit here."
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    "title": "Long-Term Exposure to Ultrafine Particles and Incidence of Cardiovascular and Cerebrovascular Disease in a Prospective Study of a Dutch Cohort",
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    "text": "Downward GS, van Nunen EJHM, Kerckhoffs J, Vineis P, Brunekreef B, Boer JMA, Messier KP, Roy A, Verschuren WMM, van der Schouw YT, Sluijs I, Gulliver J, Hoek G, Vermeulen R. Long-Term Exposure to Ultrafine Particles and Incidence of Cardiovascular and Cerebrovascular Disease in a Prospective Study of a Dutch Cohort. Environ Health Perspect. 2018 Dec;126(12):127007. doi: 10.1289/EHP3047. PMID: 30566375; PMCID: PMC6371648."
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    "text": "Downward GS, van Nunen EJHM, Kerckhoffs J, Vineis P, Brunekreef B, Boer JMA, Messier KP, Roy A, Verschuren WMM, van der Schouw YT, Sluijs I, Gulliver J, Hoek G, Vermeulen R. Long-Term Exposure to Ultrafine Particles and Incidence of Cardiovascular and Cerebrovascular Disease in a Prospective Study of a Dutch Cohort. Environ Health Perspect. 2018 Dec;126(12):127007. doi: 10.1289/EHP3047. PMID: 30566375; PMCID: PMC6371648."
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    "text": "Abstract\n\nBackground\nThere is growing evidence that exposure to ultrafine particles (UFP; particles smaller than 100 nm) may play an underexplored role inthe etiology of several illnesses, including cardiovascular disease (CVD)\n\n\nObjectives\nWe aimed to investigate the relationship between long-term exposure to ambient UFP and incident cardiovascular and cerebrovasculardisease (CVA). As a secondary objective, we sought to compare effect estimates for UFP with those derived for other air pollutants, including esti-mates from two-pollutant models.\n\n\nMethods\nUsing a prospective cohort of 33,831 Dutch residents, we studied the association between long-term exposure to UFP (predicted via land use regression) and incident disease using Cox proportional hazard models. Hazard ratios (HR) for UFP were compared to HRs for more routinely monitored air pollutants, including particulate matter with aerodynamic diameter≤10 \\(\\mu\\)m (PM10), PM with aerodynamic diameter≤2.5 \\(\\mu\\)m (PM2.5), and NO2.\n\n\nResults\nLong-term UFP exposure was associated with an increased risk for all incident CVD [HR = 1:18 per 10,000 particles= \\(cm^3\\); 95% confidence interval (CI): 1.03, 1.34], myocardial infarction (MI) (HR = 1:34; 95% CI: 1.00, 1.79), and heart failure (HR = 1:76; 95% CI: 1.17, 2.66). Positive associations were also estimated for NO\\(_2\\)(HR for heart failure = 1:22; 95% CI: 1.01, 1.48 per 20\\(\\mu g /m^{3}\\)) and coarse PM (PMcoarse; HR for all CVD = 1:21; 95% CI: 1.01, 1.45 per 10\\(\\mu g /m^{3}\\)). CVD was not positively associated with PM2.5(HR for all CVD = 0:95; 95% CI: 0.75, 1.28 per 5 \\(\\mu g /m^{3}\\)). HRs for UFP and CVAs were positive, but not significant. In two-pollutant models (UFP + NO2 and UFP + PMcoarse), positive associations tended to remain for UFP, while HRs for PMcoarse and NO2 generally attenuated towards the null.\n\n\nConclusions\nThese findings strengthen the evidence that UFP exposure plays an important role in cardiovascular health and that risks of ambient air pollution may have been underestimated based on conventional air pollution metrics."
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    "text": "Lipner EM, French JP, Nelson S, Falkinham Iii JO, Mercaldo RA, Blakney RA, Daida YG, Frankland TB, Messier KP, Honda JR, Honda S, Prevots DR. Vanadium in groundwater aquifers increases the risk of MAC pulmonary infection in O’ahu, Hawai’i. Environ Epidemiol. 2022 Sep 2;6(5):e220. doi: 10.1097/EE9.0000000000000220. PMID: 36249270; PMCID: PMC9555944."
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    "text": "Lipner EM, French JP, Nelson S, Falkinham Iii JO, Mercaldo RA, Blakney RA, Daida YG, Frankland TB, Messier KP, Honda JR, Honda S, Prevots DR. Vanadium in groundwater aquifers increases the risk of MAC pulmonary infection in O’ahu, Hawai’i. Environ Epidemiol. 2022 Sep 2;6(5):e220. doi: 10.1097/EE9.0000000000000220. PMID: 36249270; PMCID: PMC9555944."
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    "title": "Vanadium in groundwater aquifers increases the risk of MAC pulmonary infection in O’ahu, Hawai’i.",
    "section": "Abstract",
    "text": "Abstract\nHawai’i has the highest prevalence of nontuberculous mycobacterial (NTM) pulmonary disease in the United States. Previous studies indicate that certain trace metals in surface water increase the risk of NTM infection. OBJECTIVE: To identify whether trace metals influence the risk of NTM infection in O’ahu, Hawai’i. METHODS: A population-based ecologic cohort study was conducted using NTM infection incidence data from patients enrolled at Kaiser Permanente Hawai’i during 2005-2019. We obtained sociodemographic, microbiologic, and geocoded residential data for all Kaiser Permanente Hawai’i beneficiaries. To estimate the risk of NTM pulmonary infection from exposure to groundwater constituents, we obtained groundwater data from three data sources: (1) Water Quality Portal; (2) the Hawai’i Department of Health; and (3) Brigham Young University, Department of Geological Science faculty. Data were aggregated by an aquifer and were associated with the corresponding beneficiary aquifer of residence. We used Poisson regression models with backward elimination to generate models for NTM infection risk as a function of groundwater constituents. We modeled two outcomes: Mycobacterium avium complex (MAC) species and Mycobacterium abscessus group species. RESULTS: For every 1-unit increase in the log concentration of vanadium in groundwater at the aquifer level, infection risk increased by 22% among MAC patients. We did not observe significant associations between water-quality constituents and infection risk among M. abscessus patients. CONCLUSIONS: Concentrations of vanadium in groundwater were associated with MAC pulmonary infection in O’ahu, Hawai’i. These findings provide evidence that naturally occurring trace metals influence the presence of NTM in water sources that supply municipal water systems."
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    "title": "Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring",
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    "text": "Chambliss SE, Pinon CPR, Messier KP, LaFranchi B, Upperman CR, Lunden MM, Robinson AL, Marshall JD, Apte JS. Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring. Proc Natl Acad Sci U S A. 2021 Sep 14;118(37):e2109249118. doi: 10.1073/pnas.2109249118. PMID: 34493674; PMCID: PMC8449331"
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    "text": "Chambliss SE, Pinon CPR, Messier KP, LaFranchi B, Upperman CR, Lunden MM, Robinson AL, Marshall JD, Apte JS. Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring. Proc Natl Acad Sci U S A. 2021 Sep 14;118(37):e2109249118. doi: 10.1073/pnas.2109249118. PMID: 34493674; PMCID: PMC8449331"
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    "text": "Abstract\nIt is known, to researchers and heavily impacted communities, that people of color face a higher average burden of air pollution. It was unknown whether racial/ethnic disparities were caused by spatial heterogeneities at the level of city blocks, neighborhoods, or urban regions. Our approach leverages a unique set of highly local observations, covering every city block of 13 cities and urban districts that are home to 450,000 people. We find that even for pollutants with steep localized gradients, differences in average outdoor concentrations among racial/ethnic groups are driven by regional variability. However, localized peaks indicate opportunities to reduce extremes within groups. The methods and findings of this study can inform strategies to reduce disparities in urban air pollution exposure."
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    "text": "Zilber, D., & Messier, K. (2024). Reflected generalized concentration addition and Bayesian hierarchical models to improve chemical mixture prediction. Plos one, 19(3), e0298687."
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    "text": "Zilber, D., & Messier, K. (2024). Reflected generalized concentration addition and Bayesian hierarchical models to improve chemical mixture prediction. Plos one, 19(3), e0298687."
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    "text": "Abstract\nEnvironmental toxicants overwhelmingly occur together as mixtures. The variety of possible chemical interactions makes it difficult to predict the danger of the mixture. In this work, we propose the novel Reflected Generalized Concentration Addition (RGCA), a piece-wise, geometric technique for sigmoidal dose-responsed inverse functions that extends the use of generalized concentration addition (GCA) for 3+ parameter models. Since experimental tests of all relevant mixtures is costly and intractable, we rely only on the individual chemical dose responses. Additionally, RGCA enhances the classical two-step model for the cumulative effects of mixtures, which assumes a combination of GCA and independent action (IA). We explore how various clustering methods can dramatically improve predictions. We compare our technique to the IA, CA, and GCA models and show in a simulation study that the two-step approach performs well under a variety of true models. We then apply our method to a challenging data set of individual chemical and mixture responses where the target is an androgen receptor (Tox21 AR-luc). Our results show significantly improved predictions for larger mixtures. Our work complements ongoing efforts to predict environmental exposure to various chemicals and offers a starting point for combining different exposure predictions to quantify a total risk to health."
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    "text": "Messier KP, Serre ML. Lung and stomach cancer associations with groundwater radon in North Carolina, USA. Int J Epidemiol. 2017 Apr 1;46(2):676-685. doi: 10.1093/ije/dyw128. PMID: 27639278; PMCID: PMC5837655."
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    "text": "Messier KP, Serre ML. Lung and stomach cancer associations with groundwater radon in North Carolina, USA. Int J Epidemiol. 2017 Apr 1;46(2):676-685. doi: 10.1093/ije/dyw128. PMID: 27639278; PMCID: PMC5837655."
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    "title": "Lung and stomach cancer associations with groundwater radon in North Carolina, USA",
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    "text": "Abstract\n\nBackground\nThe risk of indoor air radon for lung cancer is well studied, but the risks of groundwater radon for both lung and stomach cancer are much less studied, and with mixed results.\n\n\nMethods\nGeomasked and geocoded stomach and lung cancer cases in North Carolina from 1999 to 2009 were obtained from the North Carolina Central Cancer Registry. Models for the association with groundwater radon and multiple confounders were implemented at two scales: (i) an ecological model estimating cancer incidence rates at the census tract level; and (ii) a case-only logistic model estimating the odds that individual cancer cases are members of local cancer clusters.\n\n\nResults\nFor the lung cancer incidence rate model, groundwater radon is associated with an incidence rate ratio of 1.03 [95% confidence interval (CI) = 1.01, 1.06] for every 100 Bq/l increase in census tract averaged concentration. For the cluster membership models, groundwater radon exposure results in an odds ratio for lung cancer of 1.13 (95% CI = 1.04, 1.23) and for stomach cancer of 1.24 (95% CI = 1.03, 1.49), which means groundwater radon, after controlling for multiple confounders and spatial auto-correlation, increases the odds that lung and stomach cancer cases are members of their respective cancer clusters.\n\n\nConclusion\nOur study provides epidemiological evidence of a positive association between groundwater radon exposure and lung cancer incidence rates. The cluster membership model results find groundwater radon increases the odds that both lung and stomach cancer cases occur within their respective cancer clusters. The results corroborate previous biokinetic and mortality studies that groundwater radon is associated with increased risk for lung and stomach cancer."
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    "text": "Apte JS, Messier KP, Gani S, Brauer M, Kirchstetter TW, Lunden MM, Marshall JD, Portier CJ, Vermeulen RCH, Hamburg SP. High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data. Environ Sci Technol. 2017 Jun 20;51(12):6999-7008. doi: 10.1021/acs.est.7b00891. Epub 2017 Jun 5. PMID: 28578585."
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    "text": "Apte JS, Messier KP, Gani S, Brauer M, Kirchstetter TW, Lunden MM, Marshall JD, Portier CJ, Vermeulen RCH, Hamburg SP. High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data. Environ Sci Technol. 2017 Jun 20;51(12):6999-7008. doi: 10.1021/acs.est.7b00891. Epub 2017 Jun 5. PMID: 28578585."
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    "text": "Abstract\nAir pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world. Urban air pollution concentrations vary sharply over short distances (≪1 km) owing to unevenly distributed emission sources, dilution, and physicochemical transformations. Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize heterogeneous human exposures and localized pollution hotspots. Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4–5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring. We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km2 area of Oakland, CA, developing the largest urban air quality data set of its type. Resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5–8× within individual city blocks. Since local variation in air quality profoundly impacts public health and environmental equity, our results have important implications for how air pollution is measured and managed. If validated elsewhere, this readily scalable measurement approach could address major air quality data gaps worldwide."
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    "section": "",
    "text": "Holcomb DA, Messier KP, Serre ML, Rowny JG, Stewart JR. Geostatistical Prediction of Microbial Water Quality Throughout a Stream Network Using Meteorology, Land Cover, and Spatiotemporal Autocorrelation. Environ Sci Technol. 2018 Jul 17;52(14):7775-7784. doi: 10.1021/acs.est.8b01178. Epub 2018 Jun 25. PMID: 29886747"
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    "text": "Holcomb DA, Messier KP, Serre ML, Rowny JG, Stewart JR. Geostatistical Prediction of Microbial Water Quality Throughout a Stream Network Using Meteorology, Land Cover, and Spatiotemporal Autocorrelation. Environ Sci Technol. 2018 Jul 17;52(14):7775-7784. doi: 10.1021/acs.est.8b01178. Epub 2018 Jun 25. PMID: 29886747"
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    "section": "Abstract",
    "text": "Abstract\nPredictive modeling is promising as an inexpensive tool to assess water quality. We developed geostatistical predictive models of microbial water quality that empirically modeled spatiotemporal autocorrelation in measured fecal coliform (FC) bacteria concentrations to improve prediction. We compared five geostatistical models featuring different autocorrelation structures, fit to 676 observations from 19 locations in North Carolina’s Jordan Lake watershed using meteorological and land cover predictor variables. Though stream distance metrics (with and without flow-weighting) failed to improve prediction over the Euclidean distance metric, incorporating temporal autocorrelation substantially improved prediction over the space-only models. We predicted FC throughout the stream network daily for one year, designating locations “impaired”, “unimpaired”, or “unassessed” if the probability of exceeding the state standard was ≥90%, ≤10%, or >10% but <90%, respectively. We could assign impairment status to more of the stream network on days any FC were measured, suggesting frequent sample-based monitoring remains necessary, though implementing spatiotemporal predictive models may reduce the number of concurrent sampling locations required to adequately assess water quality. Together, these results suggest that prioritizing sampling at different times and conditions using geographically sparse monitoring networks is adequate to build robust and informative geostatistical models of water quality impairment."
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    "text": "Messier KP, Wheeler DC, Flory AR, Jones RR, Patel D, Nolan BT, Ward MH. Modeling groundwater nitrate exposure in private wells of North Carolina for the Agricultural Health Study. Sci Total Environ. 2019 Mar 10;655:512-519. doi: 10.1016/j.scitotenv.2018.11.022. Epub 2018 Nov 5. PMID: 30476830; PMCID: PMC6581064."
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    "text": "Messier KP, Wheeler DC, Flory AR, Jones RR, Patel D, Nolan BT, Ward MH. Modeling groundwater nitrate exposure in private wells of North Carolina for the Agricultural Health Study. Sci Total Environ. 2019 Mar 10;655:512-519. doi: 10.1016/j.scitotenv.2018.11.022. Epub 2018 Nov 5. PMID: 30476830; PMCID: PMC6581064."
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    "text": "Abstract\nUnregulated private wells in the United States are susceptible to many groundwater contaminants. Ingestion of nitrate, the most common anthropogenic private well contaminant in the United States, can lead to the endogenous formation of N-nitroso-compounds, which are known human carcinogens. In this study, we expand upon previous efforts to model private well groundwater nitrate concentration in North Carolina by developing multiple machine learning models and testing against out-of-sample prediction. Our purpose was to develop exposure estimates in unmonitored areas for use in the Agricultural Health Study (AHS) cohort. Using approximately 22,000 private well nitrate measurements in North Carolina, we trained and tested continuous models including a censored maximum likelihood-based linear model, random forest, gradient boosted machine, support vector machine, neural networks, and kriging. Continuous nitrate models had low predictive performance (R2 < 0.33), so multiple random forest classification models were also trained and tested. The final classification approach predicted <1 mg/L, 1-5 mg/L, and ≥5 mg/L using a random forest model with 58 variables and maximizing the Cohen’s kappa statistic. The final model had an overall accuracy of 0.75 and high specificity for the higher two categories and high sensitivity for the lowest category. The results will be used for the categorical prediction of private well nitrate for AHS cohort participants that reside in North Carolina."
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    "text": "Guan Y, Johnson MC, Katzfuss M, Mannshardt E, Messier KP, Reich BJ, Song JJ. Fine-scale spatiotemporal air pollution analysis using mobile monitors on Google Street View vehicles. J Am Stat Assoc. 2020;115(531):1111-1124. doi: 10.1080/01621459.2019.1665526. Epub 2019 Oct 9. PMID: 33716356; PMCID: PMC7953849"
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    "text": "Guan Y, Johnson MC, Katzfuss M, Mannshardt E, Messier KP, Reich BJ, Song JJ. Fine-scale spatiotemporal air pollution analysis using mobile monitors on Google Street View vehicles. J Am Stat Assoc. 2020;115(531):1111-1124. doi: 10.1080/01621459.2019.1665526. Epub 2019 Oct 9. PMID: 33716356; PMCID: PMC7953849"
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    "section": "Abstract",
    "text": "Abstract\nPeople are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. In order to make informed decisions on their day-to-day activities, they are interested in real-time information on a localized scale. Publicly available, fine-scale, high-quality air pollution measurements acquired using mobile monitors represent a paradigm shift in measurement technologies. A methodological framework utilizing these increasingly fine-scale measurements to provide real-time air pollution maps and short-term air quality forecasts on a fine-resolution spatial scale could prove to be instrumental in increasing public awareness and understanding. The Google Street View study provides a unique source of data with spatial and temporal complexities, with the potential to provide information about commuter exposure and hot spots within city streets with high traffic. We develop a computationally efficient spatiotemporal model for these data and use the model to make short-term forecasts and high-resolution maps of current air pollution levels. We also show via an experiment that mobile networks can provide more nuanced information than an equally-sized fixed-location network. This modeling framework has important real-world implications in understanding citizens’ personal environments, as data production and real-time availability continue to be driven by the ongoing development and improvement of mobile measurement technologies."
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    "section": "",
    "text": "Eccles KM, Karmaus AL, Kleinstreuer NC, Parham F, Rider CV, Wambaugh JF, Messier KP. A geospatial modeling approach to quantifying the risk of exposure to environmental chemical mixtures via a common molecular target. Sci Total Environ. 2023 Jan 10;855:158905. doi: 10.1016/j.scitotenv.2022.158905. Epub 2022 Sep 21. PMID: 36152849; PMCID: PMC9979101."
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    "text": "Eccles KM, Karmaus AL, Kleinstreuer NC, Parham F, Rider CV, Wambaugh JF, Messier KP. A geospatial modeling approach to quantifying the risk of exposure to environmental chemical mixtures via a common molecular target. Sci Total Environ. 2023 Jan 10;855:158905. doi: 10.1016/j.scitotenv.2022.158905. Epub 2022 Sep 21. PMID: 36152849; PMCID: PMC9979101."
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    "section": "Abstract",
    "text": "Abstract\nIn the real world, individuals are exposed to chemicals from sources that vary over space and time. However, traditional risk assessments based on in vivo animal studies typically use a chemical-by-chemical approach and apical disease endpoints. New approach methodologies (NAMs) in toxicology, such as in vitro high-throughput (HTS) assays generated in Tox21 and ToxCast, can more readily provide mechanistic chemical hazard information for chemicals with no existing data than in vivo methods. In this paper, we establish a workflow to assess the joint action of 41 modeled ambient chemical exposures in the air from the USA-wide National Air Toxics Assessment by integrating human exposures with hazard data from curated HTS (cHTS) assays to identify counties where exposure to the local chemical mixture may perturb a common biological target. We exemplify this proof-of-concept using CYP1A1 mRNA up-regulation. We first estimate internal exposure and then convert the inhaled concentration to a steady state plasma concentration using physiologically based toxicokinetic modeling parameterized with county-specific information on ages and body weights. We then use the estimated blood plasma concentration and the concentration-response curve from the in vitro cHTS assay to determine the chemical-specific effects of the mixture components. Three mixture modeling methods were used to estimate the joint effect from exposure to the chemical mixture on the activity levels, which were geospatially mapped. Finally, a Monte Carlo uncertainty analysis was performed to quantify the influence of each parameter on the combined effects. This workflow demonstrates how NAMs can be used to predict early-stage biological perturbations that can lead to adverse health outcomes that result from exposure to chemical mixtures. As a result, this work will advance mixture risk assessment and other early events in the effects of chemicals."
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    "section": "",
    "text": "DeFlorio-Barker S, Egorov A, Smith GS, Murphy MS, Stout JE, Ghio AJ, Hudgens EE, Messier KP, Maillard JM, Hilborn ED. Environmental risk factors associated with pulmonary isolation of nontuberculous mycobacteria, a population-based study in the southeastern United States. Sci Total Environ. 2021 Apr 1;763:144552. doi: 10.1016/j.scitotenv.2020.144552. Epub 2020 Dec 18. PMID: 33383509; PMCID: PMC8317204."
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    "title": "Environmental risk factors associated with pulmonary isolation of nontuberculous mycobacteria, a population-based study in the southeastern United States",
    "section": "",
    "text": "DeFlorio-Barker S, Egorov A, Smith GS, Murphy MS, Stout JE, Ghio AJ, Hudgens EE, Messier KP, Maillard JM, Hilborn ED. Environmental risk factors associated with pulmonary isolation of nontuberculous mycobacteria, a population-based study in the southeastern United States. Sci Total Environ. 2021 Apr 1;763:144552. doi: 10.1016/j.scitotenv.2020.144552. Epub 2020 Dec 18. PMID: 33383509; PMCID: PMC8317204."
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    "section": "Abstract",
    "text": "Abstract\nThe prevalence of pulmonary nontuberculous mycobacteria (NTM) disease is increasing in the United States. Associations were evaluated among residents of central North Carolina between pulmonary isolation of NTM and environmental risk factors including: surface water, drinking water source, urbanicity, and exposures to soils favorable to NTM growth. Reports of pulmonary NTM isolation from patients residing in three counties in central North Carolina during 2006 – 2010 were collected from clinical laboratories and from the State Laboratory of Public Health. This analysis was restricted to patients residing in single family homes with a valid residential street address and conducted at the census block level (n=13,495 blocks). Negative binomial regression models with thin-plate spline smoothing function of geographic coordinates were applied to assess effects of census block-level environmental characteristics on pulmonary NTM isolation count. Patients (n = 507) resided in 473 (3.4%) blocks within the study area. Blocks with >20% hydric soils had 26.8% (95% Confidence Interval (CI): 1.8%, 58.0%), p=0.03, higher adjusted mean patient counts compared to blocks with ≤20% hydric soil, while blocks with >50% acidic soil had 24.8% (−2.4%, 59.6%), p=0.08 greater mean patient count compared to blocks with ≤50% acidic soil. Isolation rates varied by county after adjusting for covariates. The effects of using disinfected public water supplies vs. private wells, and of various measures of urbanicity were not significantly associated with NTM. Our results suggest that proximity to certain soil types (hydric and acidic) could be a risk factor for pulmonary NTM isolation in central North Carolina."
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    "text": "# Fetch the webpage content\nurl <- \"https://pubmed.ncbi.nlm.nih.gov/36460922/\"\npage <- read_html(url)\n\n# Extract article title\ntitle <- page %>%\n  html_nodes(\".heading-title\") %>%\n  html_text() %>%\n  str_trim()\n\n# Extract article abstract\nabstract <- page %>%\n  html_nodes(\".abstract-content\") %>%\n  html_text() %>%\n  str_trim()\n\n# Extract article authors\nauthors <- page %>%\n  html_nodes(\".authors-list\") %>%\n  html_text() %>%\n  str_trim()\n\n# Extract article journal and publication date\njournal_date <- page %>%\n  html_nodes(\".journal-actions\") %>%\n  html_text() %>%\n  str_trim()\n\n# Print the extracted content\ncat(paste(\"### Title:\\n\\n\", title, \"\\n\\n\"))\n\n### Title:\n\n The skin is no barrier to mixtures: Air pollutant mixtures and reported psoriasis or eczema in the Personalized Environment and Genes Study (PEGS) \n\n ### Title:\n\n The skin is no barrier to mixtures: Air pollutant mixtures and reported psoriasis or eczema in the Personalized Environment and Genes Study (PEGS) \n\ncat(paste(\"### Authors:\\n\\n\", authors, \"\\n\\n\"))\n\n### Authors:\n\n Melissa E Lowe \n                1\n               \n                2\n              , Farida S Akhtari \n                3\n              , Taylor A Potter \n                4\n              , David C Fargo \n                4\n              , Charles P Schmitt \n                5\n              , Shepherd H Schurman \n                6\n               \n                7\n              , Kristin M Eccles \n                4\n              , Alison Motsinger-Reif \n                3\n              , Janet E Hall \n                6\n              , Kyle P Messier \n                4\n               \n                6\n               \n                3\n               \n                8 \n\n ### Authors:\n\n Melissa E Lowe et al. \n\n ### Authors:\n\n Melissa E Lowe \n                1\n               \n                2\n              , Farida S Akhtari \n                3\n              , Taylor A Potter \n                4\n              , David C Fargo \n                4\n              , Charles P Schmitt \n                5\n              , Shepherd H Schurman \n                6\n               \n                7\n              , Kristin M Eccles \n                4\n              , Alison Motsinger-Reif \n                3\n              , Janet E Hall \n                6\n              , Kyle P Messier \n                4\n               \n                6\n               \n                3\n               \n                8 \n\ncat(paste(\"### Journal and Publication Date:\\n\\n\", journal_date, \"\\n\\n\"))\n\n### Journal and Publication Date:\n\n J Expo Sci Environ Epidemiol\n    Actions\n              Search in PubMed\n            \n              Search in NLM Catalog\n            \n              Add to Search \n\n ### Journal and Publication Date:\n\n J Expo Sci Environ Epidemiol\n    Actions\n              Search in PubMed\n            \n              Search in NLM Catalog\n            \n              Add to Search \n\ncat(paste(\"### Abstract:\\n\\n\", abstract, \"\\n\\n\"))\n\n### Abstract:\n\n Background:\n        \n      \n      Autoimmune (AI) diseases appear to be a product of genetic predisposition and environmental triggers. Disruption of the skin barrier causes exacerbation of psoriasis/eczema. Oxidative stress is a mechanistic pathway for pathogenesis of the disease and is also a primary mechanism for the detrimental effects of air pollution.\n    \n  \n\n  \n\n              \n                \n\n\n  \n    \n      \n        \n          Methods:\n        \n      \n      We evaluated the association between autoimmune skin diseases (psoriasis or eczema) and air pollutant mixtures in 9060 subjects from the Personalized Environment and Genes Study (PEGS) cohort. Pollutant exposure data on six criteria air pollutants are publicly available from the Center for Air, Climate, and Energy Solutions and the Atmospheric Composition Analysis Group. For increased spatial resolution, we included spatially cumulative exposure to volatile organic compounds from sites in the United States Environmental Protection Agency Toxic Release Inventory and the density of major roads within a 5 km radius of a participant's address from the United States Geological Survey. We applied logistic regression with quantile g-computation, adjusting for age, sex, diagnosis with an autoimmune disease in family or self, and smoking history to evaluate the relationship between self-reported diagnosis of an AI skin condition and air pollution mixtures.\n    \n  \n\n  \n\n              \n                \n\n\n  \n    \n      \n        \n          Results:\n        \n      \n      Only one air pollution variable, sulfate, was significant individually (OR = 1.06, p = 3.99E-2); however, the conditional odds ratio for the combined mixture components of PM2.5 (black carbon, sulfate, sea salt, and soil), CO, SO2, benzene, toluene, and ethylbenzene is 1.10 (p-value = 5.4E-3).\n    \n  \n\n  \n\n              \n                \n\n\n  \n    \n      \n        \n          Significance:\n        \n      \n      While the etiology of autoimmune skin disorders is not clear, this study provides evidence that air pollutants are associated with an increased prevalence of these disorders. The results provide further evidence of potential health impacts of air pollution exposures on life-altering diseases.\n    \n  \n\n  \n\n              \n                \n\n\n  \n    \n      \n        \n          Significance and impact statement:\n        \n      \n      The impact of air pollution on non-pulmonary and cardiovascular diseases is understudied and under-reported. We find that air pollution significantly increased the odds of psoriasis or eczema in our cohort and the magnitude is comparable to the risk associated with smoking exposure. Autoimmune diseases like psoriasis and eczema are likely impacted by air pollution, particularly complex mixtures and our study underscores the importance of quantifying air pollution-associated risks in autoimmune disease."
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    "section": "",
    "text": "Jahnke JR, Messier KP, Lowe M, Jukic AM. Ambient air pollution exposure assessments in fertility studies: A systematic review and guide for reproductive epidemiologists. Curr Epidemiol Rep. 2022 Jun;9(2):87-107. doi: 10.1007/s40471-022-00290-z. Epub 2022 May 13. PMID: 35754929; PMCID: PMC9229606."
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    "text": "Jahnke JR, Messier KP, Lowe M, Jukic AM. Ambient air pollution exposure assessments in fertility studies: A systematic review and guide for reproductive epidemiologists. Curr Epidemiol Rep. 2022 Jun;9(2):87-107. doi: 10.1007/s40471-022-00290-z. Epub 2022 May 13. PMID: 35754929; PMCID: PMC9229606."
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    "text": "Abstract\nQuantifying the exposome is key to understanding how the environment impacts human health and disease. However, accurately, and cost-effectively quantifying exposure in large population health studies remains a major challenge. Geospatial technologies offer one mechanism to integrate high-dimensional environmental data into epidemiology studies, but can present several challenges. In June 2021, the National Institute of Environmental Health Sciences (NIEHS) held a workshop bringing together experts in exposure science, geospatial technologies, data science and population health to address the need for integrating multiscale geospatial environmental data into large population health studies. The primary objectives of the workshop were to highlight recent applications of geospatial technologies to examine the relationships between environmental exposures and health outcomes; identify research gaps and discuss future directions for exposure modeling, data integration and data analysis strategies; and facilitate communications and collaborations across geospatial and population health experts. This commentary provides a high-level overview of the scientific topics covered by the workshop and themes that emerged as areas for future work, including reducing measurement errors and uncertainty in exposure estimates, and improving data accessibility, data interoperability, and computational approaches for more effective multiscale and multi-source data integration, along with potential solutions."
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    "text": "Cui Y, Eccles KM, Kwok RK, Joubert BR, Messier KP, Balshaw DM. Integrating Multiscale Geospatial Environmental Data into Large Population Health Studies: Challenges and Opportunities. Toxics. 2022 Jul 20;10(7):403. doi: 10.3390/toxics10070403. PMID: 35878308; PMCID: PMC9316943."
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    "text": "Cui Y, Eccles KM, Kwok RK, Joubert BR, Messier KP, Balshaw DM. Integrating Multiscale Geospatial Environmental Data into Large Population Health Studies: Challenges and Opportunities. Toxics. 2022 Jul 20;10(7):403. doi: 10.3390/toxics10070403. PMID: 35878308; PMCID: PMC9316943."
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    "text": "Abstract\nQuantifying the exposome is key to understanding how the environment impacts human health and disease. However, accurately, and cost-effectively quantifying exposure in large population health studies remains a major challenge. Geospatial technologies offer one mechanism to integrate high-dimensional environmental data into epidemiology studies, but can present several challenges. In June 2021, the National Institute of Environmental Health Sciences (NIEHS) held a workshop bringing together experts in exposure science, geospatial technologies, data science and population health to address the need for integrating multiscale geospatial environmental data into large population health studies. The primary objectives of the workshop were to highlight recent applications of geospatial technologies to examine the relationships between environmental exposures and health outcomes; identify research gaps and discuss future directions for exposure modeling, data integration and data analysis strategies; and facilitate communications and collaborations across geospatial and population health experts. This commentary provides a high-level overview of the scientific topics covered by the workshop and themes that emerged as areas for future work, including reducing measurement errors and uncertainty in exposure estimates, and improving data accessibility, data interoperability, and computational approaches for more effective multiscale and multi-source data integration, along with potential solutions."
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    "text": "Lowe ME, Akhtari FS, Potter TA, Fargo DC, Schmitt CP, Schurman SH, Eccles KM, Motsinger-Reif A, Hall JE, Messier KP. The skin is no barrier to mixtures: Air pollutant mixtures and reported psoriasis or eczema in the Personalized Environment and Genes Study (PEGS). J Expo Sci Environ Epidemiol. 2023 May;33(3):474-481. doi: 10.1038/s41370-022-00502-0. Epub 2022 Dec 2. PMID: 36460922; PMCID: PMC10234803."
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    "text": "Lowe ME, Akhtari FS, Potter TA, Fargo DC, Schmitt CP, Schurman SH, Eccles KM, Motsinger-Reif A, Hall JE, Messier KP. The skin is no barrier to mixtures: Air pollutant mixtures and reported psoriasis or eczema in the Personalized Environment and Genes Study (PEGS). J Expo Sci Environ Epidemiol. 2023 May;33(3):474-481. doi: 10.1038/s41370-022-00502-0. Epub 2022 Dec 2. PMID: 36460922; PMCID: PMC10234803."
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    "title": "The skin is no barrier to mixtures: Air pollutant mixtures and reported psoriasis or eczema in the Personalized Environment and Genes Study (PEGS)",
    "section": "Abstract",
    "text": "Abstract\n\nBackground\nAutoimmune (AI) diseases appear to be a product of genetic predisposition and environmental triggers. Disruption of the skin barrier causes exacerbation of psoriasis/eczema. Oxidative stress is a mechanistic pathway for pathogenesis of the disease and is also a primary mechanism for the detrimental effects of air pollution.\n\n\nMethods\nWe evaluated the association between autoimmune skin diseases (psoriasis or eczema) and air pollutant mixtures in 9060 subjects from the Personalized Environment and Genes Study (PEGS) cohort. Pollutant exposure data on six criteria air pollutants are publicly available from the Center for Air, Climate, and Energy Solutions and the Atmospheric Composition Analysis Group. For increased spatial resolution, we included spatially cumulative exposure to volatile organic compounds from sites in the United States Environmental Protection Agency Toxic Release Inventory and the density of major roads within a 5 km radius of a participant’s address from the United States Geological Survey. We applied logistic regression with quantile g-computation, adjusting for age, sex, diagnosis with an autoimmune disease in family or self, and smoking history to evaluate the relationship between self-reported diagnosis of an AI skin condition and air pollution mixtures.\n\n\nResults\nOnly one air pollution variable, sulfate, was significant individually (OR = 1.06, p = 3.99E−2); however, the conditional odds ratio for the combined mixture components of PM2.5 (black carbon, sulfate, sea salt, and soil), CO, SO2, benzene, toluene, and ethylbenzene is 1.10 (p-value = 5.4E−3).\n\n\nSignificance\nWhile the etiology of autoimmune skin disorders is not clear, this study provides evidence that air pollutants are associated with an increased prevalence of these disorders. The results provide further evidence of potential health impacts of air pollution exposures on life-altering diseases."
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    "title": "The skin is no barrier to mixtures: Air pollutant mixtures and reported psoriasis or eczema in the Personalized Environment and Genes Study (PEGS)",
    "section": "Significance and impact statement",
    "text": "Significance and impact statement\nThe impact of air pollution on non-pulmonary and cardiovascular diseases is understudied and under-reported. We find that air pollution significantly increased the odds of psoriasis or eczema in our cohort and the magnitude is comparable to the risk associated with smoking exposure. Autoimmune diseases like psoriasis and eczema are likely impacted by air pollution, particularly complex mixtures and our study underscores the importance of quantifying air pollution-associated risks in autoimmune disease."
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    "title": "Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide",
    "section": "",
    "text": "Messier KP, Katzfuss M. Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide. Ann Appl Stat. 2021 Jun;15(2):688-710. doi: 10.1214/20-aoas1422. Epub 2021 Jul 12. PMID: 35069963; PMCID: PMC8774268."
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    "title": "Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide",
    "section": "",
    "text": "Messier KP, Katzfuss M. Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide. Ann Appl Stat. 2021 Jun;15(2):688-710. doi: 10.1214/20-aoas1422. Epub 2021 Jul 12. PMID: 35069963; PMCID: PMC8774268."
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    "title": "Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide",
    "section": "Abstract",
    "text": "Abstract\nNitrogen dioxide (NO2) is a primary constituent of traffic-related air pollution and has well established harmful environmental and human-health impacts. Knowledge of the spatiotemporal distribution of NO2 is critical for exposure and risk assessment. A common approach for assessing air pollution exposure is linear regression involving spatially referenced covariates, known as land-use regression (LUR). We develop a scalable approach for simultaneous variable selection and estimation of LUR models with spatiotemporally correlated errors, by combining a general-Vecchia Gaussian-process approximation with a penalty on the LUR coefficients. In comparisons to existing methods using simulated data, our approach resulted in higher model-selection specificity and sensitivity and in better prediction in terms of calibration and sharpness, for a wide range of relevant settings. In our spatiotemporal analysis of daily, US-wide, ground-level NO2 data, our approach was more accurate, and produced a sparser and more interpretable model. Our daily predictions elucidate spatiotemporal patterns of NO2 concentrations across the United States, including significant variations between cities and intra-urban variation. Thus, our predictions will be useful for epidemiological and risk-assessment studies seeking daily, national-scale predictions, and they can be used in acute-outcome health-risk assessments."
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    "title": "Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression",
    "section": "",
    "text": "Messier KP, Chambliss SE, Gani S, Alvarez R, Brauer M, Choi JJ, Hamburg SP, Kerckhoffs J, LaFranchi B, Lunden MM, Marshall JD, Portier CJ, Roy A, Szpiro AA, Vermeulen RCH, Apte JS. Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression. Environ Sci Technol. 2018 Nov 6;52(21):12563-12572. doi: 10.1021/acs.est.8b03395. Epub 2018 Oct 24. PMID: 30354135."
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    "title": "Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression",
    "section": "",
    "text": "Messier KP, Chambliss SE, Gani S, Alvarez R, Brauer M, Choi JJ, Hamburg SP, Kerckhoffs J, LaFranchi B, Lunden MM, Marshall JD, Portier CJ, Roy A, Szpiro AA, Vermeulen RCH, Apte JS. Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression. Environ Sci Technol. 2018 Nov 6;52(21):12563-12572. doi: 10.1021/acs.est.8b03395. Epub 2018 Oct 24. PMID: 30354135."
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    "title": "Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression",
    "section": "Abstract",
    "text": "Abstract\nAir pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city’s air quality using mobile monitors with “data-only” versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a “data-only” approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation \\(R^2\\) for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1–2) repeated drives but obtained better cross-validation \\(R^2\\) than the LUR-K approach within 4 to 8 repeated drive days per road segment."
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    "objectID": "publications/Kerckhoffs_etal_2016.html",
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    "title": "Comparison of Ultrafine Particle and Black Carbon Concentration Predictions from a Mobile and Short-Term Stationary Land-Use Regression Model",
    "section": "",
    "text": "Kerckhoffs J, Hoek G, Messier KP, Brunekreef B, Meliefste K, Klompmaker JO, Vermeulen R. Comparison of Ultrafine Particle and Black Carbon Concentration Predictions from a Mobile and Short-Term Stationary Land-Use Regression Model. Environ Sci Technol. 2016 Dec 6;50(23):12894-12902. doi: 10.1021/acs.est.6b03476. Epub 2016 Nov 18. PMID: 27809494."
  },
  {
    "objectID": "publications/Kerckhoffs_etal_2016.html#citation",
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    "title": "Comparison of Ultrafine Particle and Black Carbon Concentration Predictions from a Mobile and Short-Term Stationary Land-Use Regression Model",
    "section": "",
    "text": "Kerckhoffs J, Hoek G, Messier KP, Brunekreef B, Meliefste K, Klompmaker JO, Vermeulen R. Comparison of Ultrafine Particle and Black Carbon Concentration Predictions from a Mobile and Short-Term Stationary Land-Use Regression Model. Environ Sci Technol. 2016 Dec 6;50(23):12894-12902. doi: 10.1021/acs.est.6b03476. Epub 2016 Nov 18. PMID: 27809494."
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  {
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    "title": "Comparison of Ultrafine Particle and Black Carbon Concentration Predictions from a Mobile and Short-Term Stationary Land-Use Regression Model",
    "section": "Abstract",
    "text": "Abstract\nMobile and short-term monitoring campaigns are increasingly used to develop land-use regression (LUR) models for ultrafine particles (UFP) and black carbon (BC). It is not yet established whether LUR models based on mobile or short-term stationary measurements result in comparable models and concentration predictions. The goal of this paper is to compare LUR models based on stationary (30 min) and mobile UFP and BC measurements from a single campaign. An electric car collected both repeated stationary and mobile measurements in Amsterdam and Rotterdam, The Netherlands. A total of 2964 road segments and 161 stationary sites were sampled over two seasons. Our main comparison was based on predicted concentrations of the mobile and stationary monitoring LUR models at 12 682 residential addresses in Amsterdam. Predictor variables in the mobile and stationary LUR model were comparable, resulting in highly correlated predictions at external residential addresses (R2 of 0.89 for UFP and 0.88 for BC). Mobile model predictions were, on average, 1.41 and 1.91 times higher than stationary model predictions for UFP and BC, respectively. LUR models based upon mobile and stationary monitoring predicted highly correlated UFP and BC concentration surfaces, but predicted concentrations based on mobile measurements were systematically higher."
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    "title": "Extreme precipitation and emergency room visits for influenza in Massachusetts: a case- crossover analysis",
    "section": "",
    "text": "Smith GS, Messier KP, Crooks JL, Wade TJ, Lin CJ, Hilborn ED. Extreme precipitation and emergency room visits for influenza in Massachusetts: a case- crossover analysis. Environ Health. 2017 Oct 17;16(1):108. doi: 10.1186/s12940-017-0312-7. PMID: 29041975; PMCID: PMC5645981"
  },
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    "title": "Extreme precipitation and emergency room visits for influenza in Massachusetts: a case- crossover analysis",
    "section": "",
    "text": "Smith GS, Messier KP, Crooks JL, Wade TJ, Lin CJ, Hilborn ED. Extreme precipitation and emergency room visits for influenza in Massachusetts: a case- crossover analysis. Environ Health. 2017 Oct 17;16(1):108. doi: 10.1186/s12940-017-0312-7. PMID: 29041975; PMCID: PMC5645981"
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    "title": "Extreme precipitation and emergency room visits for influenza in Massachusetts: a case- crossover analysis",
    "section": "Abstract",
    "text": "Abstract\n\nBackground\nInfluenza peaks during the wintertime in temperate regions and during the annual rainy season in tropical regions – however reasons for the observed differences in disease ecology are poorly understood. We hypothesize that episodes of extreme precipitation also result in increased influenza in the Northeastern United States, but this association is not readily apparent, as no defined ‘rainy season’ occurs. Our objective was to evaluate the association between extreme precipitation (≥ 99th percentile) events and risk of emergency room (ER) visit for influenza in Massachusetts during 2002–2008.\n\n\nMethods\nA case-crossover analysis of extreme precipitation events and influenza ER visits was conducted using hospital administrative data including patient town of residence, date of visit, age, sex, and associated diagnostic codes. Daily precipitation estimates were generated for each town based upon data from the National Oceanic and Atmospheric Administration. Odds ratio (OR) and 95% confidence intervals (CI) for associations between extreme precipitation and ER visits for influenza were estimated using conditional logistic regression.\n\n\nResults\nExtreme precipitation events were associated with an OR = 1.23 (95%CI: 1.16, 1.30) for ER visits for influenza at lag days 0–6. There was significant effect modification by race, with the strongest association observed among Blacks (OR = 1.48 (1.30, 1.68)).\n\n\nConclusions\nWe observed a positive association between extreme precipitation events and ER visits for influenza, particularly among Blacks. Our results suggest that influenza is associated with extreme precipitation in a temperate area; this association could be a result of disease ecology, behavioral changes such as indoor crowding, or both. Extreme precipitation events are expected to increase in the Northeastern United States as climate change progresses. Additional research exploring the basis of this association can inform potential interventions for extreme weather events and influenza transmission."
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    "title": "Application of diagnostic criteria for non- tuberculous mycobacterial disease to a case series of mycobacterial-positive isolates",
    "section": "",
    "text": "Ghio AJ, Smith GS, DeFlorio-Barker S, Messier KP, Hudgens E, Murphy MS, Maillard JM, Stout JE, Hilborn ED. Application of diagnostic criteria for non- tuberculous mycobacterial disease to a case series of mycobacterial-positive isolates. J Clin Tuberc Other Mycobact Dis. 2019 Nov 16;17:100133. doi: 10.1016/j.jctube.2019.100133. PMID: 31867444; PMCID: PMC6904831."
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    "title": "Application of diagnostic criteria for non- tuberculous mycobacterial disease to a case series of mycobacterial-positive isolates",
    "section": "",
    "text": "Ghio AJ, Smith GS, DeFlorio-Barker S, Messier KP, Hudgens E, Murphy MS, Maillard JM, Stout JE, Hilborn ED. Application of diagnostic criteria for non- tuberculous mycobacterial disease to a case series of mycobacterial-positive isolates. J Clin Tuberc Other Mycobact Dis. 2019 Nov 16;17:100133. doi: 10.1016/j.jctube.2019.100133. PMID: 31867444; PMCID: PMC6904831."
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    "title": "Application of diagnostic criteria for non- tuberculous mycobacterial disease to a case series of mycobacterial-positive isolates",
    "section": "Abstract",
    "text": "Abstract\nThe American Thoracic Society (ATS) and Infectious Diseases Society of America (IDSA) have provided guidelines to assist in the accurate diagnosis of lung disease caused by nontuberculous mycobacteria (NTM). These microbiologic, radiographic, and clinical criteria are considered equally important and all must be met to make the diagnosis of NTM lung disease. To assess the significance of the three criteria, each was evaluated for its contribution to the diagnosis of NTM lung disease in a case series. Laboratory reports of any specimen positive for NTM isolation were collected between January 1, 2006 and December 31, 2010 at a university medical center. Medical records were reviewed in detail using a standardized form. The total number of patients with a culture from any site positive for NTM was 297 while the number from respiratory specimens during the same period was 232 (78%). Samples from two of these patients also yielded M. tuberculosis complex and were excluded. While 128 of the remaining 230 patients (55.7%) in the cohort met the microbiologic criterion for diagnosis of NTM lung disease, 151 (65.6%) and 189 (78.3%) met the radiologic and clinical criteria respectively. Only 78 patients (33.9%) met all three criteria provided by the ATS/IDSA for diagnosis of NTM lung disease. This evaluation reaffirms that defining NTM lung disease using either one or two of the criteria provided by the 2007 ATS/IDSA guidelines may significantly overestimate the number of cases of NTM lung disease. Based on the experience of defining NTM lung disease in this case series, recommendations for modification of the ATS/IDSA guidelines are provided which include expansion of both radiologic patterns and the list of symptoms associated with NTM lung disease."
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    "title": "Comparison of Mobile and Fixed-Site Black Carbon Measurements for High- Resolution Urban Pollution Mapping",
    "section": "",
    "text": "Chambliss SE, Preble CV, Caubel JJ, Cados T, Messier KP, Alvarez RA, LaFranchi B, Lunden M, Marshall JD, Szpiro AA, Kirchstetter TW, Apte JS. Comparison of Mobile and Fixed-Site Black Carbon Measurements for High- Resolution Urban Pollution Mapping. Environ Sci Technol. 2020 Jul 7;54(13):7848-7857. doi: 10.1021/acs.est.0c01409. Epub 2020 Jun 24. PMID: 32525662."
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    "title": "Comparison of Mobile and Fixed-Site Black Carbon Measurements for High- Resolution Urban Pollution Mapping",
    "section": "",
    "text": "Chambliss SE, Preble CV, Caubel JJ, Cados T, Messier KP, Alvarez RA, LaFranchi B, Lunden M, Marshall JD, Szpiro AA, Kirchstetter TW, Apte JS. Comparison of Mobile and Fixed-Site Black Carbon Measurements for High- Resolution Urban Pollution Mapping. Environ Sci Technol. 2020 Jul 7;54(13):7848-7857. doi: 10.1021/acs.est.0c01409. Epub 2020 Jun 24. PMID: 32525662."
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    "title": "Comparison of Mobile and Fixed-Site Black Carbon Measurements for High- Resolution Urban Pollution Mapping",
    "section": "Abstract",
    "text": "Abstract\nUrban concentrations of black carbon (BC) and other primary pollutants vary on small spatial scales (<100m). Mobile air pollution measurements can provide information on fine-scale spatial variation, thereby informing exposure assessment and mitigation efforts. However, the temporal sparsity of these measurements presents a challenge for estimating representative long-term concentrations. We evaluate the capabilities of mobile monitoring in the represention of time-stable spatial patterns by comparing against a large set of continuous fixed-site measurements from a sampling campaign in West Oakland, California. Custom-built, low-cost aerosol black carbon detectors (ABCDs) provided 100 days of continuous measurements at 97 near-road and 3 background fixed sites during summer 2017; two concurrently operated mobile laboratories collected over 300 h of in-motion measurements using a photoacoustic extinctiometer. The spatial coverage from mobile monitoring reveals patterns missed by the fixed-site network. Time-integrated measurements from mobile lab visits to fixed-site monitors reveal modest correlation (spatial R2 = 0.51) with medians of full daytime fixed-site measurements. Aggregation of mobile monitoring data in space and time can mitigate high levels of uncertainty associated with measurements at precise locations or points in time. However, concentrations estimated by mobile monitoring show a loss of spatial fidelity at spatial aggregations greater than 100 m."
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    "title": "A review of geospatial exposure models and approaches for health data integration",
    "section": "",
    "text": "Background Geospatial methods are common in environmental exposure assessments and increasingly integrated with health data to generate comprehensive models of environmental impacts on public health.\nObjective Our objective is to review geospatial exposure models and approaches for health data integration in environmental health applications.\nMethods We conduct a literature review and synthesis.\nResults First, we discuss key concepts and terminology for geospatial exposure data and models. Second, we provide an overview of workflows in geospatial exposure model development and health data integration. Third, we review modeling approaches, including proximity-based, statistical, and mechanistic approaches, across diverse exposure types, such as air quality, water quality, climate, and socioeconomic factors. For each model type, we provide descriptions, general equations, and example applications for environmental exposure assessment. Fourth, we discuss the approaches used to integrate geospatial exposure data and health data, such as methods to link data sources with disparate spatial and temporal scales. Fifth, we describe the landscape of open-source tools supporting these workflows."
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    "title": "A review of geospatial exposure models and approaches for health data integration",
    "section": "",
    "text": "Background Geospatial methods are common in environmental exposure assessments and increasingly integrated with health data to generate comprehensive models of environmental impacts on public health.\nObjective Our objective is to review geospatial exposure models and approaches for health data integration in environmental health applications.\nMethods We conduct a literature review and synthesis.\nResults First, we discuss key concepts and terminology for geospatial exposure data and models. Second, we provide an overview of workflows in geospatial exposure model development and health data integration. Third, we review modeling approaches, including proximity-based, statistical, and mechanistic approaches, across diverse exposure types, such as air quality, water quality, climate, and socioeconomic factors. For each model type, we provide descriptions, general equations, and example applications for environmental exposure assessment. Fourth, we discuss the approaches used to integrate geospatial exposure data and health data, such as methods to link data sources with disparate spatial and temporal scales. Fifth, we describe the landscape of open-source tools supporting these workflows."
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    "title": "Associations between Flood Risk and United States Census Tract-Level Health Outcomes",
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    "text": "Human-induced climate change has led to more frequent and severe flooding throughout the globe. We examined the association between flood risk and the prevalence of coronary heart disease, high blood pressure, asthma, and poor mental health in the United States, while taking into account different levels of social vulnerability. We aggregated flood risk variables from First Street Foundation by census tract and used principal component analysis to derive a set of five interpretable flood risk factors. The dependent variables were census-tract level disease prevalences generated by the Centers for Disease Control and Prevention. Bayesian spatial conditional autoregressive models were fit on this data to quantify the relationship between flood risk and health outcomes under different stratifications of social vulnerability. We showed that three flood risk principal components had small but significant associations with each of the health outcomes, across the different stratifications of social vulnerability. Our analysis gives the first United States-wide estimates of the associated effects of flood risk on specific health outcomes. We also show that social vulnerability is an important moderator of the relationship between flood risk and health outcomes. Our approach can be extended to other ecological studies that examine the health impacts of climate hazards."
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    "title": "Associations between Flood Risk and United States Census Tract-Level Health Outcomes",
    "section": "",
    "text": "Human-induced climate change has led to more frequent and severe flooding throughout the globe. We examined the association between flood risk and the prevalence of coronary heart disease, high blood pressure, asthma, and poor mental health in the United States, while taking into account different levels of social vulnerability. We aggregated flood risk variables from First Street Foundation by census tract and used principal component analysis to derive a set of five interpretable flood risk factors. The dependent variables were census-tract level disease prevalences generated by the Centers for Disease Control and Prevention. Bayesian spatial conditional autoregressive models were fit on this data to quantify the relationship between flood risk and health outcomes under different stratifications of social vulnerability. We showed that three flood risk principal components had small but significant associations with each of the health outcomes, across the different stratifications of social vulnerability. Our analysis gives the first United States-wide estimates of the associated effects of flood risk on specific health outcomes. We also show that social vulnerability is an important moderator of the relationship between flood risk and health outcomes. Our approach can be extended to other ecological studies that examine the health impacts of climate hazards."
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    "title": "Gene-environment interactions within a precision environmental health framework",
    "section": "",
    "text": "Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies."
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    "title": "Gene-environment interactions within a precision environmental health framework",
    "section": "",
    "text": "Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies."
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    "text": "Home\n    Publications\n  \n\n\n\n    \n      \n      \n    \n\n\n\n\n\n\n  \n    A review of geospatial exposure models and approaches for health data integration\n    Lara P Clark, Daniel Zilber, Charles Schmitt, David C Fargo, David M Reif, Alison A Motsinger-Reif, Kyle P Messier\n    Journal of Exposure Science and Environmental Epidemiology\n    (2024)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n  \n\n  \n    Associations between Flood Risk and United States Census Tract-Level Health Outcomes\n    Alvin Sheng, Brian J Reich, & Kyle P Messier\n    American Journal of Epidemiology\n    (2024)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n  \n\n  \n    Gene-environment interactions within a precision environmental health framework\n    Alison A. Motsinger-Reif, David M. Reif, Farida S. Akhtari, John S. House, C. Ryan Campbell, Kyle P. Messier, David C. Fargo, Tiffany A. Bowen, Srikanth S. Nadadur, Charles P. Schmitt, Kristianna G. Pettibone, David M. Balshaw, Cindy P. Lawler, Shelia A. Newton, Gwen W. Collman, Aubrey K. Miller, B. Alex Merrick, Yuxia Cui, Benedict Anchang, Quaker E. Harmon, Kimberly A. McAllister, Rick Woychik\n    Cell Genomics\n    (2024)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n  \n\n  \n    Reflected generalized concentration addition and Bayesian hierarchical models to improve chemical mixture prediction\n    Daniel Zilber & Kyle P Messier\n    PLOS One\n    (2024)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n  \n\n  \n    A geospatial modeling approach to quantifying the risk of exposure to environmental chemical mixtures via a common molecular target.\n    Kristin M. Eccles, Agnes L. Karmaus, Nicole C. Kleinstreuer, Fred Parham, Cynthia V. Rider, John F. Wambaugh, Kyle P. Messier\n    Science of the Total Environment\n    (2023)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n    \n       PMC\n    \n    \n  \n\n  \n    Integrating Multiscale Geospatial Environmental Data into Large Population Health Studies: Challenges and Opportunities\n    Yuxia Cui, Kristin M Eccles, Richard K Kwok, Bonnie R Joubert, Kyle P Messier, David M Balshaw\n    Toxics\n    (2023)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n    \n       PMC\n    \n    \n  \n\n  \n    The skin is no barrier to mixtures: Air pollutant mixtures and reported psoriasis or eczema in the Personalized Environment and Genes Study (PEGS)\n    Melissa E Lowe, Farida S Akhtari, Taylor A Potter, David C Fargo, Charles P Schmitt, Shepherd H Schurman, Kristin M Eccles, Alison Motsinger-Reif, Janet E Hall, Kyle P Messier\n    Journal of Exposure Science & Environmental Epidemiology\n    (2023)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n    \n       PMC\n    \n    \n  \n\n  \n    Vanadium in groundwater aquifers increases the risk of MAC pulmonary infection in O'ahu, Hawai'i.\n    Ettie M Lipner, Joshua P French, Stephend Nelson, Joseph O Falkinham III, Rachel A Mercaldo, Rebekah A Blakney, Yihe G Daida, Timothy B Frankland, Kyle P Messier, Jennifer R Honda, Stacey Honda, D. Rebecca Prevots\n    Environmental Epidemiology\n    (2023)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n    \n       PMC\n    \n    \n  \n\n  \n    Ambient air pollution exposure assessments in fertility studies: A systematic review and guide for reproductive epidemiologists\n    Johanna R Jahnke, Kyle P Messier, Melissa Lowe, Anne Marie Jukic\n    Current Epidemiology Reports\n    (2022)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n    \n       PMC\n    \n    \n  \n\n  \n    Environmental risk factors associated with pulmonary isolation of nontuberculous mycobacteria, a population-based study in the southeastern United States\n    Stephanie DeFlorio-Barker, Andrey Egorov, Genee S. Smith, Mark S. Murphy, Jason E. Stout, Andrew J. Ghio, Edward E. Hudgens, Kyle P. Messier, Jean-Marie Maillard, and Elizabeth D. Hilborn\n    Science of The Total Environment\n    (2021)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n    \n       PMC\n    \n    \n  \n\n  \n    Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring\n    Sarah E. Chambliss, Carlos P.R. Pinon, Kyle P. Messier, Brian LaFranchi, Crystal Romeo Upperman, Melissa M. Lunden, Allen L. Robinson, Julian D. Marshall, and Joshua S. Apte\n    Proceedings of the National Academy of Sciences\n    (2021)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n    \n       PMC\n    \n    \n  \n\n  \n    Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide\n    Kyle P Messier & Matthias Katzfuss\n    Annals of Applied Statistics\n    (2021)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n    \n       PMC\n    \n    \n  \n\n  \n    Comparison of Mobile and Fixed-Site Black Carbon Measurements for High- Resolution Urban Pollution Mapping\n    Sarah E. Chambliss, Chelsea V. Preble, Julien J. Caubel, Troy Cados, Kyle P. Messier, Ramón A. Alvarez, Brian LaFranchi, Melissa Lunden, Julian D. Marshall, Adam A. Szpiro, Thomas W. Kirchstetter and Joshua S. Apte\n    Environmental Science & Technology\n    (2020)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n  \n\n  \n    Fine-scale spatiotemporal air pollution analysis using mobile monitors on Google Street View vehicles\n    Yawen Guan, Margaret C Johnson, Matthias Katzfuss, Elizabeth Mannshardt, Kyle P Messier, Brian J Reich, and Joon Jin Song\n    Journal of the American Statistical Association: Applications and Case Studies\n    (2020)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n    \n       PMC\n    \n    \n  \n\n  \n    Application of diagnostic criteria for non- tuberculous mycobacterial disease to a case series of mycobacterial-positive isolates\n    Andrew J. Ghio, Genee S. Smith, Stephanie DeFlorio-Barker, Kyle P. Messier, Edward Hudgens, Mark S. Murphy, Jean-Marie Maillard, Jason E. Stout, Elizabeth D. Hilborn\n    Journal of Clincal Tuberculosis and Other Mycobacterial Diseases\n    (2019)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n    \n       PMC\n    \n    \n  \n\n  \n    Modeling groundwater nitrate exposure in private wells of North Carolina for the Agricultural Health Study\n    Kyle P Messier, David C Wheeler, Abigail R Flory, Rena R Jone, Deven Patel, Bernard T Nolan, & Mary M Ward\n    Science of the Total Environment\n    (2019)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n    \n       PMC\n    \n    \n  \n\n  \n    Geostatistical Prediction of Microbial Water Quality Throughout a Stream Network Using Meteorology, Land Cover, and Spatiotemporal Autocorrelation\n    David A. Holcomb, Kyle P. Messier, Marc L. Serre, Jakob G. Rowny, and Jill R. Stewart\n    Environmental Science & Technology\n    (2018)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n  \n\n  \n    Long-Term Exposure to Ultrafine Particles and Incidence of Cardiovascular and Cerebrovascular Disease in a Prospective Study of a Dutch Cohort\n    George S. Downward, Erik J.H.M. van Nunen, Jules Kerckhoffs, Paolo Vineis, Bert Brunekreef, Jolanda M.A. Boer, Kyle P. Messier, Ananya Roy, W. Monique M. Verschuren, Yvonne T. van der Schouw, Ivonne Sluijs, John Gulliver, Gerard Hoek, and Roel Vermeulen\n    Environmental Health Perspectives\n    (2018)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n    \n       PMC\n    \n    \n  \n\n  \n    Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression\n    Kyle P. Messier, Sarah E. Chambliss, Shahzad Gani, Ramon Alvarez, Michael Brauer, Jonathan J. Choi, Steven P. Hamburg, Jules Kerckhoffs, Brian LaFranchi, Melissa M. Lunden, Julian D. Marshall, Christopher J. Portier, Ananya Roy, Adam A. Szpiro, Roel C. H. Vermeulen and Joshua S. Apte\n    Environmental Science & Technology\n    (2018)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n  \n\n  \n    Extreme precipitation and emergency room visits for influenza in Massachusetts: a case- crossover analysis\n    Genee S. Smith, Kyle P. Messier, James L. Crooks, Timothy J. Wade, Cynthia J. Lin, & Elizabeth D. Hilborn\n    Environmental Health\n    (2017)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n    \n       PMC\n    \n    \n  \n\n  \n    High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data\n    Joshua S. Apte, Kyle P. Messier, Shahzad Gani, Michael Brauer, Thomas W. Kirchstetter, Melissa M. Lunden, Julian D. Marshall, Christopher J. Portier, Roel C.H. Vermeulen and Steven P. Hamburg\n    Environmental Science & Technology\n    (2017)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n  \n\n  \n    Lung and stomach cancer associations with groundwater radon in North Carolina, USA\n    Kyle P Messier & Marc L Serre\n    International Journal of Epidemiology\n    (2017)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n    \n       PMC\n    \n    \n  \n\n  \n    Comparison of Ultrafine Particle and Black Carbon Concentration Predictions from a Mobile and Short-Term Stationary Land-Use Regression Model\n    Jules Kerckhoffs, Gerard Hoek, Kyle P. Messier, Bert Brunekreef, Kees Meliefste, Jochem O. Klompmaker and Roel Vermeulen\n    Environmental Science & Technology\n    (2016)\n    \n      Details\n    \n    \n    \n      DOI\n    \n    \n    \n    \n    \n  \n\n\n\n\nNo matching items"
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    "text": "The following is a list of software and tools that we have developed or contributed to.\n\n\n\nNo.\nPackage Name\nDescription\nStatus\n\n\n\n\n1.\namadeus\nA Machine for Data, Environments, and User Setup for common environmental and climate health datasets is an R package developed to improve and expedite users’ access to large, publicly available geospatial datasets.\n\n\n\n2.\nbeethoven\nBuilding an Extensible, Reproducible, Test-driven, Harmonized, Open-source, Versioned, Ensemble model for air quality is an R package developed to facilitate the development of ensemble models for air quality.\n\n\n\n3.\nchopin\nComputation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing.\n\n\n\n4.\nGeoTox\nGeoTox, or source-to-outcome, modeling framework with an S3 object-oriented approach. Facilitates the calculation and visualization of single and multiple chemical risk at individual and group levels.\n\n\n\n\n\n\n\n\n\n5.\nRGCA\nImplements Reflected Generalized Concentration Addition: A geometric, piecewise inverse function for 3+ parameter sigmoidal models used in chemical mixture concentration-response modeling.\n\n\n\n6.\nPrestoGP\nScalable penalized regression on spatio-temporal outcomes using Gaussian processes. Designed for big data, large-scale geospatial exposure assessment, and geophysical modeling."
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    "text": "The following is a list of software and tools that we have developed or contributed to.\n\n\n\nNo.\nPackage Name\nDescription\nStatus\n\n\n\n\n1.\namadeus\nA Machine for Data, Environments, and User Setup for common environmental and climate health datasets is an R package developed to improve and expedite users’ access to large, publicly available geospatial datasets.\n\n\n\n2.\nbeethoven\nBuilding an Extensible, Reproducible, Test-driven, Harmonized, Open-source, Versioned, Ensemble model for air quality is an R package developed to facilitate the development of ensemble models for air quality.\n\n\n\n3.\nchopin\nComputation of Spatial Data by Hierarchical and Objective Partitioning of Inputs for Parallel Processing.\n\n\n\n4.\nGeoTox\nGeoTox, or source-to-outcome, modeling framework with an S3 object-oriented approach. Facilitates the calculation and visualization of single and multiple chemical risk at individual and group levels.\n\n\n\n\n\n\n\n\n\n5.\nRGCA\nImplements Reflected Generalized Concentration Addition: A geometric, piecewise inverse function for 3+ parameter sigmoidal models used in chemical mixture concentration-response modeling.\n\n\n\n6.\nPrestoGP\nScalable penalized regression on spatio-temporal outcomes using Gaussian processes. Designed for big data, large-scale geospatial exposure assessment, and geophysical modeling."
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    "text": "Software Development Practices\nWe are focused on developing and promoting software and computational best-practices such as test-driven-development (TDD) and open-source code for the environmental health sciences. To this end, we have protocols in place to ensure that our code is well-documented, tested, and reproducible. Below are some of the key practices we follow:\n\nUnit and Integration Testing\nWe will utilize various testing approaches to ensure functionality and quality of code.\n\n\nGit + GitHub\nVersion control of software is essential for reproducibility and collaboration. We use Git and the NIEHS Enterprise GitHub for version control and collaboration.\n\nCI/CD Workflows\nWithin GitHub, we will utilize continuous integration and continuous deployment workflows to ensure that our code is always functional and up-to-date. Multiple ** branch protection rules** within GitHub aresetup and enforced for our GitHub repositories:\n\nRequire pull request and 1 review before merging to main\nTest pass: Linting: Code shall adhere to the style/linting rules defined in the repository.\nTest pass: Tets Coverage: A given push should not decrease overall test coverage of the repository.\nTest pass: Build Checks: The code should build without errors or warnings.\n\nThe CI/CD workflows in GitHub are setup to run on every push to the main branch and on every pull request. The workflows are setup using yaml files in the .github/workflows directory of the repository.\n\n\n\nProcesses to test or check\n\ndata type\ndata name\ndata size\nrelative paths\noutput of one module is the expectation of the input of the next module\n\n\n\nTest Drive Development\nStarting from the end product, we work backwards while articulating the tests needed at each stage.\n\nKey Points of Unit and Integration Testing\n\nFile Type\n\nNetCDF\nNumeric, double precision\nNA\nVariable Names Exist\nNaming Convention\n\n\n\nStats\n\nNon-negative variance (\\(\\sigma^2\\))\nMean is reasonable (\\(\\mu\\))\nSI Units\n\n\n\nDomain\n\nIn the geographic domain (eg. US + buffer)\nIn Time range (e.g. 2018-2022)\n\n\n\nGeographic\n\nProjections\nCoordinate names (e.g. lat/lon)\nTime in acceptable format\n\n\n\n\n\nTest Driven Development (TDD)- Key Steps\n\nWrite a Test: Before you start writing any code, you write a test case for the functionality you want to implement. This test should fail initially because you haven’t written the code to make it pass yet. The test defines the expected behavior of your code.\nRun the Test: Run the test to ensure it fails. This step confirms that your test is correctly assessing the functionality you want to implement.\nWrite the Minimum Code: Write the minimum amount of code required to make the test pass. Don’t worry about writing perfect or complete code at this stage; the goal is just to make the test pass.\nRun the Test Again: After writing the code, run the test again. If it passes, it means your code now meets the specified requirements.\nRefactor (if necessary): If your code is working and the test passes, you can refactor your code to improve its quality, readability, or performance. The key here is that you should have test coverage to ensure you don’t introduce new bugs while refactoring.\nRepeat: Continue this cycle of writing a test, making it fail, writing the code to make it pass, and refactoring as needed. Each cycle should be very short and focused on a small piece of functionality.\nComplete the Feature: Keep repeating the process until your code meets all the requirements for the feature you’re working on.\n\nTDD helps ensure that your code is reliable and that it remains functional as you make changes and updates. It also encourages a clear understanding of the requirements and promotes better code design.\n\n\n_targets and/or snakemake pipelines\nWe will utilize the targets and/or snakemake packages in R and Python respectively to create reproducible workflows for our data analysis. These packages allow us to define the dependencies between the steps in our analysis and ensure that our analysis is reproducible. Additionally, they keep track of pipeline objects and skip steps that have already been run, saving time and resources.\n\nSome Benefits of _targets and/or snakemake pipelines\n\nReproducibility: By defining the dependencies between the steps in our analysis, we ensure that our analysis is reproducible. This is essential for scientific research and data analysis.\nHigh-Level Abstract: _targets and snakemake allow us to define our analysis at a high level of abstraction, making it easier to understand and maintain.\nTesting: Creating pipelines and unit/integration testing go hand-in-hand together. As we write the pipeline, the tests to write become obvious."
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    "text": "Welcome to the Spatiotemporal Exposures and Toxicology group (a.k.a. SET group). This is a document intended for current and prospective members of the SET group at the National Institute of Environmental Health Sciences (NIEHS). As an investigator, it is my job to ensure you have the resources and support you need to succeed in your research. This syllabus is a living document that outlines the expectations and resources available to you as a member of the group. Please make suggestions for improvements and additions to this document through GitHub pull requests.\nSincerely,\nKyle P Messier, PhD"
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    "text": "Welcome to the Spatiotemporal Exposures and Toxicology group (a.k.a. SET group). This is a document intended for current and prospective members of the SET group at the National Institute of Environmental Health Sciences (NIEHS). As an investigator, it is my job to ensure you have the resources and support you need to succeed in your research. This syllabus is a living document that outlines the expectations and resources available to you as a member of the group. Please make suggestions for improvements and additions to this document through GitHub pull requests.\nSincerely,\nKyle P Messier, PhD"
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    "text": "General On-Boarding Steps\n\nGet NIH Badge (Background Check, Fingerprints, VISA if applicable)\nGet NIH Email\nGet NIH Computer\nSetup HPC account with NIEHS OSC\nSetup GitHub account with NIEHS Organization\nDevelop Individual Development Plan (eIDP)"
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    "text": "Projects\n\nFellows in the group are expected to have a primary project with the goal of a first author publication\nFellows are encouraged to collaborate on secondary projects with each other\nThe group has an on-going group project that everyone is expected to contribute to\n\n\nProjects should be within the SET group’s research themes of (1) geospatial analysis, (2) mixtures and exposomic predictions, and (3) source-to-outcome-continuum or GeoTox modeling. However, I encourage everyone to provide their own unique perspective and expertise to these projects. Ultimately, a project will be successful if it is of great interest to you and you are passionate about it.\n\n\nProject Management\nWe utilize GitHub Projects to manage our projects. Currently, each project has a project board with the following columns:\n\nNew Issues: General ideas and tasks that need to be fleshed out\nBacklog: Fleshed out ideas and tasks that need to be done. Priority is set by tasks at the top of the backlog.\nIn Progress: Tasks that are currently being worked on. A best-practice is to minimize the number of tasks in this column.\nReview: Tasks that are ready for review by a collaborator\nDone: Completed tasks\n\nGitHub Project tasks can be directly linked to issues on the corresponding GitHub repository. This allows for a seamless integration of project management and issue tracking."
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    "text": "Software Installations\n\nR, Rstudio\nPython, VS Code\nGit\nQuarto\nQGIS\nAdobe Illustrator (optional)\nmySQL (optional)\nPostgresSQL (optional)"
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    "text": "eIDP (Individual Development Plans)\nIn the first few weeks to months as a fellow, we will develop an eIDP. This will be a living document that outlines your career goals and the steps you will take to achieve them. We will review this document quarterly and update it as needed."
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    "text": "Software Best Practices\nWe are focused on developing and promoting software and computational best-practices such as test-driven-development (TDD) and open-source code for the environmental health sciences. To this end, we have protocols in place to ensure that our code is well-documented, tested, and reproducible. Below are some of the key practices we follow:\n\nUnit and Integration Testing\nWe will utilize various testing approaches to ensure functionality and quality of code.\n\n\nGit + GitHub\nVersion control of software is essential for reproducibility and collaboration. We use Git and the NIEHS Enterprise GitHub for version control and collaboration.\n\nCI/CD Workflows\nWithin GitHub, we will utilize continuous integration and continuous deployment workflows to ensure that our code is always functional and up-to-date. Multiple ** branch protection rules** within GitHub aresetup and enforced for our GitHub repositories:\n\nRequire pull request and 1 review before merging to main\nTest pass: Linting: Code shall adhere to the style/linting rules defined in the repository.\nTest pass: Tets Coverage: A given push should not decrease overall test coverage of the repository.\nTest pass: Build Checks: The code should build without errors or warnings.\n\nThe CI/CD workflows in GitHub are setup to run on every push to the main branch and on every pull request. The workflows are setup using yaml files in the .github/workflows directory of the repository.\n\n\n\nProcesses to test or check\n\ndata type\ndata name\ndata size\nrelative paths\noutput of one module is the expectation of the input of the next module\n\n\n\nTest Drive Development\nStarting from the end product, we work backwards while articulating the tests needed at each stage.\n\nKey Points of Unit and Integration Testing\n\nFile Type\n\nNetCDF\nNumeric, double precision\nNA\nVariable Names Exist\nNaming Convention\n\n\n\nStats\n\nNon-negative variance (\\(\\sigma^2\\))\nMean is reasonable (\\(\\mu\\))\nSI Units\n\n\n\nDomain\n\nIn the geographic domain (eg. US + buffer)\nIn Time range (e.g. 2018-2022)\n\n\n\nGeographic\n\nProjections\nCoordinate names (e.g. lat/lon)\nTime in acceptable format\n\n\n\n\n\nTest Driven Development (TDD)- Key Steps\n\nWrite a Test: Before you start writing any code, you write a test case for the functionality you want to implement. This test should fail initially because you haven’t written the code to make it pass yet. The test defines the expected behavior of your code.\nRun the Test: Run the test to ensure it fails. This step confirms that your test is correctly assessing the functionality you want to implement.\nWrite the Minimum Code: Write the minimum amount of code required to make the test pass. Don’t worry about writing perfect or complete code at this stage; the goal is just to make the test pass.\nRun the Test Again: After writing the code, run the test again. If it passes, it means your code now meets the specified requirements.\nRefactor (if necessary): If your code is working and the test passes, you can refactor your code to improve its quality, readability, or performance. The key here is that you should have test coverage to ensure you don’t introduce new bugs while refactoring.\nRepeat: Continue this cycle of writing a test, making it fail, writing the code to make it pass, and refactoring as needed. Each cycle should be very short and focused on a small piece of functionality.\nComplete the Feature: Keep repeating the process until your code meets all the requirements for the feature you’re working on.\n\nTDD helps ensure that your code is reliable and that it remains functional as you make changes and updates. It also encourages a clear understanding of the requirements and promotes better code design.\n\n\n_targets and/or snakemake pipelines\nWe will utilize the targets and/or snakemake packages in R and Python respectively to create reproducible workflows for our data analysis. These packages allow us to define the dependencies between the steps in our analysis and ensure that our analysis is reproducible. Additionally, they keep track of pipeline objects and skip steps that have already been run, saving time and resources.\n\nSome Benefits of _targets and/or snakemake pipelines\n\nReproducibility: By defining the dependencies between the steps in our analysis, we ensure that our analysis is reproducible. This is essential for scientific research and data analysis.\nHigh-Level Abstract: _targets and snakemake allow us to define our analysis at a high level of abstraction, making it easier to understand and maintain.\nTesting: Creating pipelines and unit/integration testing go hand-in-hand together. As we write the pipeline, the tests to write become obvious."
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    "text": "Writing and Presentations\n\nOverleaf. I have a template for manuscripts and presentations. This is a good option for writing when there are a decent amount of equations.\nQuarto. I have a template for manuscripts and presentations. Quarto RevealJS and Beamer make pretty presentations that can be easily converted to HTML or PDF and shareable on our websites.\n\nWe will use these tools to write manuscripts and presentations."
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    "text": "Conferences\nWe attend one to three major research society conferences per year. These conferences are a great opportunity to present our research, network with other researchers, and learn about the latest developments in our field.\nThe conferences can vary year-to-year depending on the research of each person, but some of the conferences we typically attend include:\n\nAmerican Geophysical Union (AGU)\nSociety of Toxicology (SOT)\n\nNorth Carolina Chapter of the Society of Toxicology (NCSOT)\n\nInternational Society for Environmental Epidemiology (ISEE)\nInternational Society for Exposure Science (ISES)"
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    "text": "Group Meetings\nWe have 1 group meetings per week\nThursdays from 10-11:30am\nThe general format of the meetings is as follows:\n\nCheck-in: Each member of the group will give a brief update on their progress and any issues they are facing\nRubber Ducking: We’ll spend 10 - 20 minutes looking at someone’s code, so that everyone can help and also learn new techniques, tricks, packages, etc. This could include code in progress, code that is not working, efficiency improvements, or unit/integreation testing review.\nLogistics: We’ll discuss any upcoming deadlines, meetings, etc."
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    "text": "Attendance and Participation\nAttendance and engagement in your own research and the research of others is critical to the success of the group. I expect you to attend all group meetings and participate in group activities. If you are unable to attend a meeting, please let me know in advance.\nIn general, we work in the office but we are flexible for teleworking days.\n\nTime Management\nI expect you to manage your time effectively and meet deadlines. If you are having trouble meeting a deadline, please let me know as soon as possible so we can work together to find a solution. There is no set number of hours per week that you are expected to work, but I expect you to be productive and engaged in your work."
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    "text": "Time Off\nWe follow the NIH guidelines for time off. However, in general, my philosophy is to take time off when you need it. I trust that you will get your work done and that you will communicate with me if you need time off. If you are getting work done, participating and engaging with group activities, and meeting deadlines, I am happy to be flexible with time off.\n\nPaid family leave\nNIH provides paid family leave for the birth or adoption of a child. This is a great benefit and I encourage you to take advantage of it if you need it."
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    "text": "Required Traininings\n\nNIH Ethics Training: All members of the group are required to complete the NIH Ethics Training. This training covers topics such as conflict of interest, research misconduct, and ethical conduct in research. The training must be completed annually.\nNIH Information Security : All members of the group are required to complete the NIH Information Security Training. This training covers topics such as data security, privacy, and information handling. The training must be completed annually."
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    "text": "Resources\n\nNIH Office of Intramural Training and Education (OITE) https://www.training.nih.gov/\nTrainee and Fellow Policies. Includes health insurance, family leave, outside activities, etc. https://www.training.nih.gov/policies/trainee-fellow-policies/\nNIH Library https://www.nihlibrary.nih.gov/\nNIH Data Science Interest Group https://datascience.nih.gov/\nNIH Data Science Training https://datascience.nih.gov/data-science-training\nBecoming a Resilient Scientist https://www.training.nih.gov/becoming_a_resilient_scientist\nNIH Library Training https://www.nihlibrary.nih.gov/training"
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