Magnitude, trends, and impacts of ambient long-term ozone exposure in the United States from 2000 to 2015

Long-term exposure to ambient ozone (O3) is associated with a variety of impacts, including adverse humanhealth effects and reduced yields in commercial crops. Ground-level O3 concentrations for assessments are typically predicted using chemical transport models; however such methods often feature biases that can influence impact estimates. Here, we develop and apply artificial neural networks to empirically model long-term O3 exposure over the continental United States from 2000 to 2015, and we generate a measurement-based assessment of impacts on human-health and crop yields. Notably, we found that two commonly used human-health averaging metrics, based on separate epidemiological studies, differ in their trends over the study period. The population-weighted, April–September average of the daily 1 h maximum concentration peaked in 2002 at 55.9 ppb and decreased by 0.43 [95 % CI: 0.28, 0.57] ppb yr−1 between 2000 and 2015, yielding an ∼ 18 % decrease in normalized human-health impacts. In contrast, there was little change in the population-weighted, annual average of the maximum daily 8 h average concentration between 2000 and 2015, which resulted in a ∼ 5 % increase in normalized human-health impacts. In both cases, an aging population structure played a substantial role in modulating these trends. Trends of all agriculture-weighted crop-loss metrics indicated yield improvements, with reductions in the estimated national relative yield loss ranging from 1.7 % to 1.9 % for maize, 5.1 % to 7.1 % for soybeans, and 2.7 % for wheat. Overall, these results provide a measurement-based estimate of long-term O3 exposure over the United States, quantify the historical trends of such exposure, and illustrate how different conclusions regarding historical impacts can be made through the use of varying metrics

Measurement-based assessment of health burdens from long-term ozone exposure in the United States, Europe, and China

Long-term ozone (O3) exposure estimates from chemical transport models are frequently paired with exposure-response relationships from epidemiological studies to estimate associated health burdens. Impact estimates using such methods can include biases from model-derived exposure estimates. We use data solely from dense ground-based monitoring networks in the United States, Europe, and China for 2015 to estimate long-term O3 exposure and calculate premature respiratory mortality using exposure-response relationships derived from two separate analyses of the American Cancer Society Cancer Prevention Study-II(ACS CPS-II) cohort. Using results from the larger, extended ACS CPS-II study, 34 000 (95% CI: 24, 44 thousand), 32 000 (95% CI: 22, 41 thousand), and 200 000 (95% CI: 140, 253 thousand) premature respiratory mortalities are attributable to long-term O3 exposure in the USA, Europe and China, respectively, in 2015. Results are approximately 32%–50% lower when using an older analysis of the ACS CPS-II cohort. Both sets of results are lower(∼20%–60%) on a region-byregion basis than analogous prior studies based solely on modeled O3, due in large part to the fact that the latter tends to be high biased in estimating exposure. This study highlights the utility of dense observation networks in estimating exposure to long-term O3 exposure and provides an observational constraint on subsequent health burdens for three regions of the world. In addition, these results demonstrate how small biases in modeled results of long-term O3 exposure can amplify estimated health impacts due to nonlinear exposure-response curves

Top-down Modulations in the Visual Form Pathway Revealed with Dynamic Causal Modeling

Perception entails interactions between activated brain visual areas and the records of previous sensations, allowing for processes like figure–ground segregation and object recognition. The aim of this study was to characterize top-down effects that originate in the visual cortex and that are involved in the generation and perception of form. We performed a functional magnetic resonance imaging experiment, where subjects viewed 3 groups of stimuli comprising oriented lines with different levels of recognizable high-order structure (none, collinearity, and meaning). Our results showed that recognizable stimuli cause larger activations in anterior visual and frontal areas. In contrast, when stimuli are random or unrecognizable, activations are greater in posterior visual areas, following a hierarchical organization where areas V1/V2 were less active with “collinearity” and the middle occipital cortex was less active with “meaning.” An effective connectivity analysis using dynamic causal modeling showed that high-order visual form engages higher visual areas that generate top-down signals, from multiple levels of the visual hierarchy. These results are consistent with a model in which if a stimulus has recognizable attributes, such as collinearity and meaning, the areas specialized for processing these attributes send top-down messages to the lower levels to facilitate more efficient encoding of visual form

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder