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BACKGROUND Studies estimating health effects of long-term air pollution exposure often use a two-stage approach: building exposure models to assign individual-level exposures, which are then used in regression analyses. This requires accurate exposure modeling and careful treatment of exposure measurement error. OBJECTIVE To illustrate the importance of(More)
Long-term exposure to outdoor particulate matter with an aerodynamic diameter less than or equal to 2.5 µm (PM2.5) has been associated with cardiovascular morbidity and mortality. The chemical composition of PM2.5 that may be most responsible for producing these associations has not been identified. We assessed cross-sectional associations between long-term(More)
BACKGROUND Exposure to air pollution has been consistently associated with cardiovascular morbidity and mortality, but mechanisms remain uncertain. Associations with blood pressure (BP) may help to explain the cardiovascular effects of air pollution. OBJECTIVE We examined the cross-sectional relationship between long-term (annual average) residential air(More)
Many cohort studies in environmental epidemiology require accurate modeling and prediction of fine scale spatial variation in ambient air quality across the U.S. This modeling requires the use of small spatial scale geographic or "land use" regression covariates and some degree of spatial smoothing. Furthermore, the details of the prediction of air quality(More)
Quantitative or numerical metrics of protein function specificity made possible by the Gene Ontology are useful in that they enable development of distance or similarity measures between protein functions. Here we describe how to calculate four measures of function specificity for GO terms: 1) number of ancestor terms; 2) number of offspring terms; 3)(More)
Air pollution epidemiology studies are trending towards a multi-pollutant approach. In these studies, exposures at subject locations are unobserved and must be predicted using observed exposures at misaligned monitoring locations. This induces measurement error, which can bias the estimated health effects and affect standard error estimates. We characterize(More)
Although cohort studies of the health effects of PM2.5 have developed exposure prediction models to represent spatial variability across participant residences, few models exist for PM2.5 components. We aimed to develop a city-specific spatio-temporal prediction approach to estimate long-term average concentrations of four PM2.5 components including sulfur,(More)
Air pollution epidemiology studies often implement a two-stage approach. Exposure models are built using observed monitoring data to predict exposure at participant locations where the true exposure is unobserved, and the predictions used to estimate the health effect. This induces measurement error which may bias the estimated health effect and affect its(More)
PREMISE OF THE STUDY Early land plant fossils can be challenging to interpret due to their morphological simplicity and often fragmentary nature. Morphometric techniques using commonly preserved characters might increase diagnostic value of such material. To evaluate the utility of morphometrics in assessing morphospecies boundaries in the Devonian, we(More)
Modelling of Gaussian spatio-temporal processes provide ample opportunity for different model formulations, however two principal directions have emerged. The data can be modelled either as a set of spatially varying temporal basis functions or as spatial fields evolving in time. This package provides maximum-likelihood estimation and cross-validation tools(More)
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