Disparities in air quality downscaler model uncertainty across socioeconomic and demographic indicators in North Carolina.

Studies increasingly use output from the Environmental Protection Agency's Fused Air Quality Surface Downscaler ("downscaler") model, which provides spatial predictions of daily concentrations of fine particulate matter (PM2.5) and ozone (O3) at the census tract level, to study the health and societal impacts of exposure to air pollution. Downscaler outputs have been used to show that lower income and higher minority neighborhoods are exposed to higher levels of PM2.5 and lower levels of O3. However, the uncertainty of the downscaler estimates remains poorly characterized, and it is not known if all subpopulations are benefiting equally from reliable predictions. We examined how the percent errors (PEs) of daily concentrations of PM2.5 and O3 between 2002 and 2016 at the 2010 census tract centroids across North Carolina were associated with measures of racial and educational isolation, neighborhood disadvantage, and urbanicity. Results suggest that there were socioeconomic and demographic disparities in surface concentrations of PM2.5 and O3, as well as their prediction uncertainties. Neighborhoods characterized by less reliable downscaler predictions (i.e., higher PEPM2.5 and PEO3) exhibited greater levels of aerial deprivation as well as educational isolation, and were often non-urban areas (i.e., suburban, or rural). Between 2002 and 2016, predicted PM2.5 and O3 levels decreased and O3 predictions became more reliable. However, the predictive uncertainty for PM2.5 has increased since 2010. Substantial spatial variability was observed in the temporal changes in the predictive uncertainties; educational isolation and neighborhood deprivation levels were associated with smaller increases in predictive uncertainty of PM2.5. In contrast, racial isolation was associated with a greater decline in the reliability of PM2.5 predictions between 2002 and 2016; it was associated with a greater improvement in the predictive reliability of O3 within the same time frame.

Bayesian variable selection for understanding mixtures in environmental exposures.

Social and environmental stressors are crucial factors in child development. However, there exists a multitude of measurable social and environmental factors-the effects of which may be cumulative, interactive, or null. Using a comprehensive cohort of children in North Carolina, we study the impact of social and environmental variables on 4th end-of-grade exam scores in reading and mathematics. To identify the essential factors that predict these educational outcomes, we design new tools for Bayesian linear variable selection using decision analysis. We extract a predictive optimal subset of explanatory variables by coupling a loss function with a novel model-based penalization scheme, which leads to coherent Bayesian decision analysis and empirically improves variable selection, estimation, and prediction on simulated data. The Bayesian linear model propagates uncertainty quantification to all predictive evaluations, which is important for interpretable and robust model comparisons. These predictive comparisons are conducted out-of-sample with a customized approximation algorithm that avoids computationally intensive model refitting. We apply our variable selection techniques to identify the joint collection of social and environmental stressors-and their interactions-that offer clear and quantifiable improvements in prediction of reading and mathematics exam scores.

mTOR-regulated U2af1 tandem exon splicing specifies transcriptome features for translational control.

U2 auxiliary factor 1 (U2AF1) functions in 3'-splice site selection during pre-mRNA processing. Alternative usage of duplicated tandem exons in U2AF1 produces two isoforms, U2AF1a and U2AF1b, but their functional differences are unappreciated due to their homology. Through integrative approaches of genome editing, customized-transcriptome profiling and crosslinking-mediated interactome analyses, we discovered that the expression of U2AF1 isoforms is controlled by mTOR and they exhibit a distinctive molecular profile for the splice site and protein interactomes. Mechanistic dissection of mutually exclusive alternative splicing events revealed that U2AF1 isoforms' inherent differential preferences of nucleotide sequences and their stoichiometry determine the 3'-splice site. Importantly, U2AF1a-driven transcriptomes feature alternative splicing events in the 5'-untranslated region (5'-UTR) that are favorable for translation. These findings unveil distinct roles of duplicated tandem exon-derived U2AF1 isoforms in the regulation of the transcriptome and suggest U2AF1a-driven 5'-UTR alternative splicing as a molecular mechanism of mTOR-regulated translational control.

Effects of Molecular-Level Compositional Variability in Organic Aerosol on Phase State and Thermodynamic Mixing Behavior.

The molecular-level composition and structure of organic aerosol (OA) affect its chemical/physical properties, transformations, and impacts. Here, we use the molecular-level chemical composition of functionalized OA from three diverse field sites to evaluate the effect of molecular-level compositional variability on OA phase state and thermodynamic mixing favorability. For these ambient sites, modeled aerosol phase state ranges from liquid to semisolid. The observed variability in OA composition has some effect on resulting phase state, but other factors like the presence of inorganic ions, aerosol liquid water, and internal versus external mixing with water are determining factors in whether these particles exist as liquids, semisolids, or solids. Organic molecular composition plays a more important role in determining phase state for phase-separated (verus well-mixed) systems. Similarly, despite the observed OA compositional differences, the thermodynamic mixing favorability for OA samples with aerosol liquid water, isoprene oxidation products, or monoterpene oxidation products remains fairly consistent within each campaign. Mixing of filter-sampled OA and isoprene or monoterpene oxidation products is often favorable in both seasons, while mixing with water is generally unfavorable.

Bouncier Particles at Night: Biogenic Secondary Organic Aerosol Chemistry and Sulfate Drive Diel Variations in the Aerosol Phase in a Mixed Forest.

Aerosol phase state is critical for quantifying aerosol effects on climate and air quality. However, significant challenges remain in our ability to predict and quantify phase state during its evolution in the atmosphere. Herein, we demonstrate that aerosol phase (liquid, semisolid, solid) exhibits a diel cycle in a mixed forest environment, oscillating between a viscous, semisolid phase state at night and liquid phase state with phase separation during the day. The viscous nighttime particles existed despite higher relative humidity and were independently confirmed by bounce factor measurements and atomic force microscopy. High-resolution mass spectrometry shows the more viscous phase state at night is impacted by the formation of terpene-derived and higher molecular weight secondary organic aerosol (SOA) and smaller inorganic sulfate mass fractions. Larger daytime particulate sulfate mass fractions, as well as a predominance of lower molecular weight isoprene-derived SOA, lead to the liquid state of the daytime particles and phase separation after greater uptake of liquid water, despite the lower daytime relative humidity. The observed diel cycle of aerosol phase should provoke rethinking of the SOA atmospheric lifecycle, as it suggests diurnal variability in gas-particle partitioning and mixing time scales, which influence aerosol multiphase chemistry, lifetime, and climate impacts.

Altered leaching composition of maize starch granules by irradiative depolymerization: The key role of degraded molecular structure.

The molecular composition of starch leachates from starch-based foods has been recently recognised as a crucial determinant of food properties. However, there is limited knowledge on the regulation of this composition through irradiative depolymerization of starch. This research investigates the leaching behaviour of maize starch depolymerized by electron beam irradiation, and the relationship between the composition of leached starch and structures of modified starch granules. The analysis using 1H NMR spectroscopy confirmed a decrease in the degree of branching (from 4.4 % to 2.8 %), while size-exclusion chromatography identified a newly-derived amylopectin fraction of a smaller hydrodynamic radius (approximately 60-80 nm). The structural properties of the starch granules were also analysed, revealing an increased BET-area of granules and reduced total crystallinity after depolymerization. In the leachates of swollen granules, the bimodal distribution of starch molecules evolves into unimodal with the increase of the irradiative dosage, while modified starch leached more starch molecules with Rh < 10 nm. The results of principal component analysis and Pearson correlation analysis indicate that the degree of branching of degraded starch molecules, as well as the newly-derived amylopectin fraction, significantly correlates (p < 0.01) with the molecular size of leached starch molecules (Rh < 10 nm). It is thus proposed that the cleavage of α-1,6 linkage may be a critical factor in controlling the leaching process of irradiated starch granules. This study highlights the potential of irradiative degradation to control the molecular composition and structure of starch leachates, thereby optimizing the properties of starch-based foods.

Traffic, transport, and vegetation drive VOC concentrations in a major urban area in Texas.

The population of Texas has increased rapidly in the past decade. The San Antonio Field Study (SAFS) was designed to investigate ozone (O3) production and precursors in this rapidly changing, sprawling metropolitan area. There are still many questions regarding the sources and chemistry of volatile organic compounds (VOCs) in urban areas like San Antonio which are affected by a complex mixture of industry, traffic, biogenic sources and transported pollutants. The goal of the SAFS campaign in May 2017 was to measure inorganic trace gases, VOCs, methane (CH4), and ethane (C2H6). The SAFS field design included two sites to better assess air quality across the metro area: an urban site (Traveler's World; TW) and a downwind/suburban site (University of Texas at San Antonio; UTSA). The results indicated that acetone (2.52 ± 1.17 and 2.39 ± 1.27 ppbv), acetaldehyde (1.45 ± 1.02 and 0.93 ± 0.45 ppbv) and isoprene (0.64 ± 0.49 and 1.21 ± 0.85 ppbv; TW and UTSA, respectively) were the VOCs with the highest concentrations. Additionally, positive matrix factorization showed three dominant factors of VOC emissions: biogenic, aged urban mixed source, and acetone. Methyl vinyl ketone and methacrolein (MVK + MACR) exhibited contributions from both secondary photooxidation of isoprene and direct emissions from traffic. The C2H6:CH4 demonstrated potential influence of oil and gas activities in San Antonio. Moreover, the high O3 days during the campaign were in the NOx-limited O3 formation regime and were preceded by evening peaks in select VOCs, NOx and CO. Overall, quantification of the concentration and trends of VOCs and trace gases in a major city in Texas offers vital information for general air quality management and supports strategies for reducing O3 pollution. The SAFS campaign VOC results will also add to the growing body of literature on urban sources and concentrations of VOCs in major urban areas.

High amylose wheat starch structures display unique fermentability characteristics, microbial community shifts and enzyme degradation profiles.

A slower rate of starch digestion in the small intestine increases the amount of resistant starch (RS) entering the large intestine, which is associated with health benefits. Although increasing the amylose (AM) content of dietary starch intake is one way to increase RS, the processes involved in gut microbial hydrolysis and fermentation of high AM-RS substrates are poorly understood. In this study, five high AM wheat (HAW) starches ranging from 47% AM to 93% AM and a wild type (37% AM), in both native granular and cooked forms, were subjected to in vitro fermentation with a porcine faecal inoculum. Fermentation kinetics, temporal microbial changes, amylolytic enzyme activities and residual starch were determined. All granular starches showed similar fermentation characteristics, independent of AM level, whereas cooking accelerated fermentation of lower AM but slowed fermentation of high AM starches. HAW starches with a very high AM content (>85%) all had similar fermentation kinetics and short-chain fatty acid end-product profiles. Microbial α-amylase, ß-amylase, pullulanase and amyloglucosidase enzymatic activities were all detected and followed fermentation kinetics. HAW starch promoted shifts in the microbial community, with increases of the family Lachnospiraceae and the genus Treponema observed, while the genera Prevotella and Streptococcus were reduced in comparison to 37% AM. Overall, these findings suggest that any HAW starch incorporated into high RS food products would be expected to have beneficial microbiota-mediated effects in terms of fermentation kinetics and end products.

Integrating light-sheet imaging with virtual reality to recapitulate developmental cardiac mechanics.

Currently, there is a limited ability to interactively study developmental cardiac mechanics and physiology. We therefore combined light-sheet fluorescence microscopy (LSFM) with virtual reality (VR) to provide a hybrid platform for 3D architecture and time-dependent cardiac contractile function characterization. By taking advantage of the rapid acquisition, high axial resolution, low phototoxicity, and high fidelity in 3D and 4D (3D spatial + 1D time or spectra), this VR-LSFM hybrid methodology enables interactive visualization and quantification otherwise not available by conventional methods, such as routine optical microscopes. We hereby demonstrate multiscale applicability of VR-LSFM to (a) interrogate skin fibroblasts interacting with a hyaluronic acid-based hydrogel, (b) navigate through the endocardial trabecular network during zebrafish development, and (c) localize gene therapy-mediated potassium channel expression in adult murine hearts. We further combined our batch intensity normalized segmentation algorithm with deformable image registration to interface a VR environment with imaging computation for the analysis of cardiac contraction. Thus, the VR-LSFM hybrid platform demonstrates an efficient and robust framework for creating a user-directed microenvironment in which we uncovered developmental cardiac mechanics and physiology with high spatiotemporal resolution.

Designing a patient education framework via use case analysis.

One of the challenges of developing patient education content in electronic form is to determine the information and interaction needs that motivate a patient to learn in the first place. We have found that use case analysis of patient education helps in clarifying the types of information and interaction required to educate a patient effectively. This paper presents a use case model for patient education as well as a Java-based framework that facilitates both the extension and updating of individually tailored, electronic patient education content. The framework defines an abstract interface that represents a particular panel of information, and provides a content manager that dynamically discovers and refreshes new panels as they are added or modified.