Significant effects of exposure to relatively low level ozone on daily mortality in 17 cities from three Eastern Asian Countries.

BACKGROUND: Studies have examined the variation in mortality risk associated with ambient ozone. We conducted an international cooperation study for investigating the non-linear ozone-mortality association accounting for lag effect and for examining the ozone level with significant health effect. METHODS: Daily counts of total non-accidental death and ambient air pollutant concentration were obtained in 17 cities from 3 Eastern Asian countries or regions (Taiwan, Korea, and Japan). The total study period was from 1979 to 2010 and differed by city based on data availability. The ozone-mortality association in each city was estimated by running a time-series quasi-Poisson regression model, allowing for overdispersion. The city-specific estimates were then pooled by country by using multivariate random effects meta-analysis. RESULTS: The non-linear ozone exposure-mortality response curves were generated in 17 cities from Taiwan, Korea, and Japan. The association curves in the three countries all showed increased mortality with elevated ozone, and the significant mortality effects of ozone exposure were observed at level higher than 40, 50, and 40 ppb for Taiwan, Korea, and Japan, respectively. These associations are unaffected by co-pollutant of particulate matter in Taiwan and Japan. But the potential confounding effect of co-pollutant could not be ignorable in Korea. CONCLUSIONS: Our study provides evidence that exposure to a relative low level of ambient ozone is associated with an increased risk of mortality. Our results emphasize the continual need to examine the existing standard by documenting potential human adverse effects.

A split-and-merge deep learning approach for phenotype prediction.

BACKGROUND: Phenotype prediction with genome-wide markers is a critical but difficult problem in biomedical research due to many issues such as nonlinearity of the underlying genetic mapping and high-dimensionality of marker data. When using the deep learning method in the small-n-large-p data, some serious issues occur such as over-fitting, over-parameterization, and biased prediction. METHODS: In this study, we propose a split-and-merge deep learning method, named SM-DL method, to learn a neural network on the dimension reduce data by using the split-and-merge technique. CONCLUSIONS: Numerically, the proposed method has significant performance in phenotype prediction for a simulated example. A real example is used to demonstrate how the proposed method can be applied in practice.

A general strategy for the synthesis of α-trifluoromethyl- and α-perfluoroalkyl-ß-lactams via palladium-catalyzed carbonylation.

ß-Lactam compounds play a key role in medicinal chemistry, specifically as the most important class of antibiotics. Here, we report a novel one-step approach for the synthesis of α-(trifluoromethyl)-ß-lactams and related products from fluorinated olefins, anilines and CO. Utilization of an advanced palladium catalyst system with the Ruphos ligand allows for selective cycloaminocarbonylations to give diverse fluorinated ß-lactams in high yields.

Multivariate multiple regression models of poly(ethylene-terephthalate) film degradation under outdoor and multi-stressor accelerated weathering exposures.

Developing materials for use in photovoltaic (PV) systems requires knowledge of their performance over the warranted lifetime of the PV system. Poly(ethylene-terephthalate) (PET) is a critical component of PV module backsheets due to its dielectric properties and low cost. However, PET is susceptible to environmental stressors and degrades over time. Changes in the physical properties of nine PET grades were modeled after outdoor and accelerated weathering exposures to characterize the degradation process of PET and assess the influence of stabilizing additives and weathering factors. Multivariate multiple regression (MMR) models were developed to quantify changes in color, gloss, and haze of the materials. Natural splines were used to capture the non-linear relationship between predictors and responses. Model performance was evaluated via adjusted-R2 and root mean squared error values from leave-one-out cross validation analysis. All models described over 85% of the variation in the data with low relative error. Model coefficients were used to assess the influence of weathering stressors and material additives on the property changes of films. Photodose was found to be the primary degradation stressor and moisture was found to increase the degradation rate of PET. Direct moisture contact was found to impose more stress on the material than airbone moisture (humidity). Increasing the concentration of TiO2 was found to generally decrease the degradation rate of PET and mitigate hydrolytic degradation. MMR models were compared to physics-based models and agreement was found between the two modeling approaches. Cross-correlation of accelerated exposures to outdoor exposures was achieved via determination of cross-correlation scale factors. Cross-correlation revealed that direct moisture contact is a key factor for reliable accelerated weathering testing and provided a quantitative method to determine when accelerated exposure results can be made more aggressive to better approximate outdoor exposure conditions.