Integrating traffic pollution dispersion into spatiotemporal NO2 prediction.

Accurately predicting ambient NO2 concentrations has great public health importance, as traffic-related air pollution is of major concern in urban areas. In this study, we present a novel approach incorporating traffic contribution to NO2 prediction in a fine-scale spatiotemporal model. We used nationally available traffic estimate dataset in a scalable dispersion model, Research LINE source dispersion model (RLINE). RLINE estimates then served as an additional input for a validated spatiotemporal pollution modeling approach. Our analysis uses measurement data collected by the Multi-Ethnic Study of Atherosclerosis and Air Pollution in the greater Los Angeles area between 2006 and 2009. We predicted road-type-specific annual average daily traffic (AADT) on road segments via national-level spatial regression models with nearest-neighbor Gaussian processes (spNNGP); the spNNGP models were trained based on over half a million point-level traffic volume measurements nationwide. AADT estimates on all highways were combined with meteorological data in RLINE models. We evaluated two strategies to integrate RLINE estimates into spatiotemporal NO2 models: 1) incorporating RLINE estimates as a space-only covariate and, 2) as a spatiotemporal covariate. The results showed that integrating the RLINE estimates as a space-only covariate improved overall cross-validation R2 from 0.83 to 0.84, and root mean squared error (RMSE) from 3.58 to 3.48 ppb. Incorporating the estimates as a spatiotemporal covariate resulted in similar model improvement. The improvement of our spatiotemporal model was more profound in roadside monitors alongside highways, with R2 increasing from 0.56 to 0.66 and RMSE decreasing from 3.52 to 3.11 ppb. The observed improvement indicates that the RLINE estimates enhanced the model's predictive capabilities for roadside NO2 concentration gradients even after considering a comprehensive list of geographic covariates including the distance to roads. Our proposed modeling framework can be generalized to improve high-resolution prediction of NO2 exposure - especially near major roads in the U.S.

Characterisation of anhydro-sialic acid transporters from mucosa-associated bacteria.

Sialic acid (Sia) transporters are critical to the capacity of host-associated bacteria to utilise Sia for growth and/or cell surface modification. While N-acetyl-neuraminic acid (Neu5Ac)-specific transporters have been studied extensively, little is known on transporters dedicated to anhydro-Sia forms such as 2,7-anhydro-Neu5Ac (2,7-AN) or 2,3-dehydro-2-deoxy-Neu5Ac (Neu5Ac2en). Here, we used a Sia-transport-null strain of Escherichia coli to investigate the function of members of anhydro-Sia transporter families previously identified by computational studies. First, we showed that the transporter NanG, from the Glycoside-Pentoside-Hexuronide:cation symporter family, is a specific 2,7-AN transporter, and identified by mutagenesis a crucial functional residue within the putative substrate-binding site. We then demonstrated that NanX transporters, of the Major Facilitator Superfamily, also only transport 2,7-AN and not Neu5Ac2en nor Neu5Ac. Finally, we provided evidence that SiaX transporters, of the Sodium-Solute Symporter superfamily, are promiscuous Neu5Ac/Neu5Ac2en transporters able to acquire either substrate equally well. The characterisation of anhydro-Sia transporters expands our current understanding of prokaryotic Sia metabolism within host-associated microbial communities.

Altering Ligand Microenvironment of Atomically Dispersed CrN4 by Axial Ligand Sulfur for Enhanced Oxygen Reduction Reaction in Alkaline and Acidic Medium.

Because of the instability and Fenton reactivity of non-precious metal nitrogen-carbon based catalyst when processing the oxygen reduction reaction (ORR), seeking for electrocatalysts with highly efficient performance becomes very highly desired to speed up the commercialization of fuel cell. Herein, chromium (Cr)-N4  electrocatalyst containing extraterrestrial S formed axial S1 -Cr1 N4  bonds (S1 Cr1 N4 C) is achieved via an assembly polymerization and confined pyrolysis strategy. Benefiting from the adjusting  coordination configuration and electronic structure of the metal center through axial coordination, S1 Cr1 N4 C exhibits enhanced the intrinsic activity (half-wave potential (E1/2 ) is 0.90 V versus reversable hydrogen electrode, RHE) compared with that of CrN4 C and Pt/C catalysts. More notably, the catalyst is almost inert in catalyzing the Fenton reaction, and thus shows the high stability. Density functional theory (DFT) results further reveal that the existence of axial S atoms in S1 Cr1 N4 C moiety has the better ORR activity than Cr1 N4 C moieties. The axial S ligand in S1 Cr1 N4 C moiety can break the electron localization around the planar Cr1 N4  active center, which facilitated the rate-limiting reductive release of OH* and accelerated overall ORR process. The present work opens up a new avenue to modulate the axial ligand type of the single-atoms (SAs) active center to enhance intrinsic SAs performances.

High Prevalence of Diabetes Mellitus and Mental Illness Among Victims of Sudden Death.

OBJECTIVES: Diabetes mellitus (DM) increases the risk of cardiovascular disease and is associated with sudden death. Mental illness among individuals with DM may confound medical care. This study assessed the association of mental illness with DM and poorly controlled DM in sudden death victims. METHODS: We screened out-of-hospital deaths ages 18 to 64 years in Wake County, North Carolina from 2013 to 2015 to adjudicate sudden deaths. We abstracted demographics and clinical characteristics from health records. Mental illness included anxiety, schizophrenia, bipolar disorder, or depression. Poorly controlled DM was defined as a hemoglobin A1c >8 or taking ≥3 medications for glycemic control. Logistic regression assessed the association between DM and mental illness. RESULTS: Among victims with available records, 109 (29.4%) had DM. Of those, 62 (56.9%) had mental illness. Mental illness was present in 53.42% and 63.89% of victims with mild and poorly controlled DM, respectively. Mental illness was associated with DM (adjusted odds ratio 2.46, 95% confidence interval 1.57-3.91). Victims with poorly controlled DM were more likely to have mental illness (adjusted odds ratio 2.66, 95% confidence interval 1.14-6.18). CONCLUSIONS: DM is a common comorbid condition in sudden death victims. Among victims, mental illness is associated with the control of DM. Early management of comorbid mental illnesses may improve the care of patients with DM and reduce the incidence of sudden death.