Silas Bergen

A National Prediction Model for PM2.5 Component Exposures and Measurement Error–Corrected Health Effect Inference

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 accounting for exposure model characteristics in two-stage air pollution studies, we considered a case study based on data from the Multi-Ethnic Study of Atherosclerosis (MESA). Methods: We built national spatial exposure models that used partial least squares and universal kriging to estimate annual average concentrations of four PM2.5 components: elemental carbon (EC), organic carbon (OC), silicon (Si), and sulfur (S). We predicted PM2.5 component exposures for the MESA cohort and estimated cross-sectional associations with carotid intima-media thickness (CIMT), adjusting for subject-specific covariates. We corrected for measurement error using recently developed methods that account for the spatial structure of predicted exposures. Results: Our models performed well, with cross-validated R2 values ranging from 0.62 to 0.95. Naïve analyses that did not account for measurement error indicated statistically significant associations between CIMT and exposure to OC, Si, and S. EC and OC exhibited little spatial correlation, and the corrected inference was unchanged from the naïve analysis. The Si and S exposure surfaces displayed notable spatial correlation, resulting in corrected confidence intervals (CIs) that were 50% wider than the naïve CIs, but that were still statistically significant. Conclusion: The impact of correcting for measurement error on health effect inference is concordant with the degree of spatial correlation in the exposure surfaces. Exposure model characteristics must be considered when performing two-stage air pollution epidemiologic analyses because naïve health effect inference may be inappropriate. Citation: Bergen S, Sheppard L, Sampson PD, Kim SY, Richards M, Vedal S, Kaufman JD, Szpiro AA. 2013. A national prediction model for PM2.5 component exposures and measurement error–corrected health effect inference. Environ Health Perspect 121:1017–1025; http://dx.doi.org/10.1289/ehp.1206010

Prediction of fine particulate matter chemical components with a spatio-temporal model for the Multi-Ethnic Study of Atherosclerosis cohort

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, silicon, and elemental and organic carbon for the Multi-Ethnic Study of Atherosclerosis cohort, and to compare predictions to those from a national spatial model. Using 2-week average measurements from a cohort-focused monitoring campaign, the spatio-temporal model employed selected geographic covariates in a universal kriging framework with the data-driven temporal trend. Relying on long-term means of daily measurements from regulatory monitoring networks, the national spatial model employed dimension-reduced predictors using universal kriging. For the spatio-temporal model, the cross-validated and temporally-adjusted R2 was relatively higher for EC and OC, and in the Los Angeles and Baltimore areas. The cross-validated R2s for both models across the six areas were reasonably high for all components except silicon. Predicted long-term concentrations at participant homes from the two models were generally highly correlated across cities but poorly correlated within cities. The spatio-temporal model may be preferred for city-specific health analyses, whereas both models could be used for multi-city studies.

Quantifying protein function specificity in the gene ontology.

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) proportion of terms; and 4) Information Content (IC). We discuss the relationship between the metrics and the strengths and weaknesses of each.

Chloramine-T Margin-of-Safety Estimates for Fry, Fingerling, and Juvenile Rainbow Trout

Abstract Chloramine-T (CLT) is a candidate for approval for use in U.S. aquaculture to control mortality in freshwater-reared salmonids caused by bacterial gill disease (causative agent, Flavobacterium branchiophilum). The proposed treatment regimen is to administer CLT at 12–20 mg/L in a static or flow-through bath for 60 min/d on three alternate or consecutive days. To estimate a CLT margin of safety, defined as the highest dosing regimen above the proposed maximum therapeutic regimen at which no adverse effects are observed, we conducted seven experiments with fry, fingerling, and juvenile rainbow trout Oncorhynchus mykiss that examined mortality and an eighth experiment that examined mortality, gross pathology, and histopathology after CLT exposure. In each experiment, triplicate groups of fish were exposed to a range of CLT concentrations representing 0, 1, 1.5, 2, 2.5, 3, 3.5, 4, or 5× the highest proposed dose (20 mg/L) for 3× the proposed treatment duration (60 min) on three alternate or consecuti...

Applying morphometrics to early land plant systematics: A new Leclercqia (Lycopsida) species from Washington State, USA

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 compared degrees of variation within the cosmopolitan lycopsid genus Leclercqia with that of living relatives (Lycopodium-Spinulum spp.) Of particular interest was determining whether a new morphotype of Leclercqia from the Middle Devonian Chilliwack flora of Washington State fell within or outside the range of variation of previously described species. METHODS Morphological variation of Leclercqia was assessed across the geographic range of the genus using six vegetative and three reproductive characters. The new morphotype and two previously described species (L. complexa, L. andrewsii) were compared using linear discriminant analysis (LDA). Extant Lycopodium-Spinulum species and variants were similarly analyzed to assess inter- vs. intraspecific variation in living lycopsids. KEY RESULTS The LDA comparisons of Lycopodium-Spinulum yielded notable morphological disparity between species but substantial overlap between intraspecific variants. Among the fossils, LDA separates the new morphotype, Leclercqia complexa, and L. andrewsii to a similar degree as Lycopodium and Spinulum species. Based on these results and further study, we describe a new species of Leclercqia: Leclercqia scolopendra Benca et Strömberg sp. nov. CONCLUSIONS Morphometric analyses can aid in informing taxonomic assignment of fragmentary early land plant fossils using readily preserved features, even in the absence of reproductive structures. Applications of this approach to the Chilliwack flora suggest Leclercqia displayed greater morphological variation, taxonomic diversity, and biogeographic extent than previously thought.