Multiple pollutants and risk of cardiac and respiratory emergency department visits in Atlanta - air quality data

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A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5_{2.5} concentration

A typical problem in air pollution epidemiology is exposure assessment for individuals for which health data are available. Due to the sparsity of monitoring sites and the limited temporal frequency with which measurements of air pollutants concentrations are collected (for most pollutants, once every 3 or 6 days), epidemiologists have been moving away from characterizing ambient air pollution exposure solely using measurements. In the last few years, substantial research efforts have been placed in developing statistical methods or machine learning techniques to generate estimates of air pollution at finer spatial and temporal scales (daily, usually) with complete coverage. Some of these methods include: geostatistical techniques, such as kriging; spatial statistical models that use the information contained in air quality model outputs (statistical downscaling models); linear regression modeling approaches that leverage the information in GIS covariates (land use regression); or machine learning methods that mine the information contained in relevant variables (neural network and deep learning approaches). Although some of these exposure modeling approaches have been used in several air pollution epidemiological studies, it is not clear how much the predicted exposures generated by these methods differ, and which method generates more reliable estimates. In this paper, we aim to address this gap by evaluating a variety of exposure modeling approaches, comparing their predictive performance and computational difficulty. Using PM$_{2.5}$ in year 2011 over the continental U.S. as case study, we examine the methods' performances across seasons, rural vs urban settings, and levels of PM$_{2.5}$ concentrations (low, medium, high)

Experimental Observation of Total-Internal-Reflection Rainbows

A new class of rainbows is created when a droplet is illuminated from the inside by a point light source. The position of the rainbow depends on both the index of refraction of the droplet and the position of the light source, and the rainbow vanishes when the point source is too close to the center of the droplet. Here we experimentally measure the position of the transmission and one-internal-reflection total-internal-reflection rainbows, and the standard (primary) rainbow, as a function of light-source position. (C) 2003 Optical Society of America

Analysis of the Shadow-Sausage Effect Caustic

We analyze the optical caustic produced by light refracted at the curved meniscus surrounding a cylindrical rod standing partially out of a liquid-filled container. When the rod is tilted from the vertical or when light is diagonally incident, the caustic is a four-cusped astroid with two of its cusps obscured by the rod\u27s shadow. If a portion of the flat end of the rod is raised above the water level, the caustic evolves into a pattern of five interlocking cusps. The five cusps result from symmetry breaking of a three-cusped surface perturbation caustic. (C) 2003 Optical Society of America

Pediatric emergency department visits and ambient Air pollution in the

Estimating the health effects of ambient air pollutant mixtures is necessary to understand the risk of real-life air pollution exposures. Methods: Pediatric Emergency Department (ED) visit records for asthma or wheeze (n = 148,256), bronchitis (n = 84,597), pneumonia (n = 90,063), otitis media (n = 422,268) and upper respiratory tract infection (URI) (n = 744,942) were obtained from Georgia hospitals during 2002-2008. Spatially-contiguous daily concentrations of 11 ambient air pollutants were estimated from CMAQ model simulations that were fused with ground-based measurements. Using a case-crossover study design, odds ratios for 3-day moving average air pollutant concentrations were estimated using conditional logistic regression, matching on ZIP code, day-of-week, month, and year. Results: In multipollutant models, the association of highest magnitude observed for the asthma/wheeze outcome was with "oxidant gases" (O-3, NO2, and SO2)the joint effect estimate for an IQR increase of this mixture was OR: 1.068 (95% CI: 1.040, 1.097). The group of "secondary pollutants" (O-3 and the PM2.5 components SO42 -, NO3-, and NH4+) was strongly associated with bronchitis (OR: 1.090, 95% CI: 1.050, 1.132), pneumonia (OR: 1.085, 95% CI: 1.047, 1.125), and otitis media (OR: 1.059, 95% CI: 1.042, 1.077). ED visits for URI were strongly associated with "oxidant gases," "secondary pollutants," and the " criteria pollutants" (O-3, NO2, CO, SO2, and PM2.5). Conclusions: Short-term exposures to air pollution mixtures were associated with ED visits for several different pediatric respiratory diseases

QM/MM simulations as an assay for carbapenemase activity in class A β-lactamases

Carbapenemases are distinguished from carbapenem-inhibited β-lactamases with a protocol involving QM/MM free energy simulations of acyl–enzyme deacylation, requiring only the enzyme 3D structure as input.</p

Development of a botanical plant protection product from Larix by-products to protect grapevine from Plasmopara viticola

Extracts from European Larch (Larix decidua) were shown to be efficient to control grapevine downy mildew (Plasmopara viticola) under controlled and field conditions. Larixyl acetate and larixol were identified as the active compounds

QM/MM Simulations Reveal the Determinants of Carbapenemase Activity in Class A β-lactamases

[Image: see text] β-lactam antibiotic resistance in Gram-negative bacteria, primarily caused by β-lactamase enzymes that hydrolyze the β-lactam ring, has become a serious clinical problem. Carbapenems were formerly considered “last resort” antibiotics because they escaped breakdown by most β-lactamases, due to slow deacylation of the acyl-enzyme intermediate. However, an increasing number of Gram-negative bacteria now produce β-lactamases with carbapenemase activity: these efficiently hydrolyze the carbapenem β-lactam ring, severely limiting the treatment of some bacterial infections. Here, we use quantum mechanics/molecular mechanics (QM/MM) simulations of the deacylation reactions of acyl-enzyme complexes of eight β-lactamases of class A (the most widely distributed β-lactamase group) with the carbapenem meropenem to investigate differences between those inhibited by carbapenems (TEM-1, SHV-1, BlaC, and CTX-M-16) and those that hydrolyze them (SFC-1, KPC-2, NMC-A, and SME-1). QM/MM molecular dynamics simulations confirm the two enzyme groups to differ in the preferred acyl-enzyme orientation: carbapenem-inhibited enzymes favor hydrogen bonding of the carbapenem hydroxyethyl group to deacylating water (DW). QM/MM simulations of deacylation give activation free energies in good agreement with experimental hydrolysis rates, correctly distinguishing carbapenemases. For the carbapenem-inhibited enzymes, free energies for deacylation are significantly higher than for the carbapenemases, even when the hydroxyethyl group was restrained to prevent interaction with the DW. Analysis of these simulations, and additional simulations of mutant enzymes, shows how factors including the hydroxyethyl orientation, the active site volume, and architecture (conformations of Asn170 and Asn132; organization of the oxyanion hole; and the Cys69-Cys238 disulfide bond) collectively determine catalytic efficiency toward carbapenems