Notes

Effect regarding mother's hypertensive problems of pregnancy about human brain volumes in term-equivalent grow older in preterm newborns: A new voxel-based morphometry study.
The green infrastructure (GI) is identified as a passive exposure control measure of air pollution. This work examines particulate matter (PM) reduction by a roadside hedge and its deposition on leaves. The objectives of this study are to (i) quantify the relative difference in PM concentration in the presence of GI and at an adjacent clear area; (ii) estimate the total mass and number density of PM deposited on leaves of a hedge; (iii) ascertain variations in PM deposition at adult (1.5m) and child (0.6 m) breathing levels on either side of a hedge; (iv) illustrate the relationship between PM deposition to leaves and ambient PM concentration reductions; and (v) quantify the elemental composition of collected particles of the leaves on different heights and sides of hedge. PM reduction of 2-9% was observed behind hedge compared to a clear area and followed a trend of ΔPM1 >ΔPM10 >ΔPM2.5. Counting of particles was found to be an effective method to quantify deposition than weighting methods. Sub-micron particles (PM1) dominated particle deposition on leaves at all sampling points on both sides of the hedge. PM mass deposition and number concentration to the leaves on traffic-facing side was up to 36% and 58% higher at 0.6m compared with 1.5m height, respectively. Such a difference was absent on the backside of the hedge. The SEM-EDS analysis showed up to 12% higher traffic-originated particles deposited to leaves on the traffic-facing side compared to the backside. The naturally occurring particles dominated in identified particles on leaf samples from all collection points on the hedge. These new evidence expand our understanding of PM reduction of GI in the near-road environment and its variations in particle deposition, depending on height and sides of GI, which could allow a better parameterisation of dispersion-deposition models for GI assessment at micro-scale.Nitrate is one of the most common pollution sources in groundwater, particularly in highly vulnerable karst aquifers. The potential for nitrification and denitrification within karst aquifers varies in different settings depending on the extent of anthropogenic inputs, so that accurate identification of nitrate sources can be difficult. Geochemical data and dual nitrate isotopes were measured in this study, incorporating a Bayesian isotopic mixing model, and used to identify nitrate sources, nitrification and denitrification, and quantitatively determine nitrate sources under different extents of anthropogenic inputs in three karst catchments within Chongqing Municipality, SW China Laolongdong (an urbanized area), Qingmuguan (a suburban village), and Shuifang Spring (a protected natural area). At the Laolongdong catchment, the groundwater was in a reducing condition and enriched in δ15NNO3 (averaging 18.9 ± 6.9‰) and δ18ONO3 (averaging 8.5 ± 4.6‰). Manure and sewage waste were the main contributing nitrate soG Anthropogenic activities can change biogeochemical nitrogen dynamics of vulnerable karst aquifers, such that the groundwater overlain by an urban settlement has undergone denitrification, while suburban and pristine areas have been dominated by nitrification.This study investigates the influence of meteorology, land use, built environment, and traffic characteristics on near-road ultrafine particle (UFP) concentrations. To achieve this objective, minute-level UFP concentrations were measured at various locations along a major arterial road in the Greater Toronto Area (GTA) between February and May 2019. Each location was visited five times, at least once in the morning, mid-day, and afternoon. Each visit lasted for 30 min, resulting in 2.5 h of minute-level data collected at each location. Local traffic information, including vehicle class and turning movements, were processed using computer vision techniques. VB124 The number of fast-food restaurants, cafes, trees, traffic signals, and building footprint, were found to have positive impacts on the mean UFP, while distance to the closest major road was negatively associated with UFP. We employed the Extreme Gradient Boosting (XGBoost) method to develop prediction models for UFP concentrations. The Shapley additive explanation (SHAP) measures were used to capture the influence of each feature on model output. The model results demonstrated that minute-level counts of local traffic from different directions had significant impacts on near-road UFP concentrations, model performance was robust under random cross-validation as coefficients of determination (R2) ranged from 0.63 to 0.69, but it revealed weaknesses when data at specific locations were eliminated from the training dataset. This result indicates that proper cross-validation techniques should be developed to better evaluate machine learning models for air quality predictions.Animal studies have suggested that phthalate exposure alters the fatty acid composition of blood plasma. Therefore, we conducted an epidemiological study to examine whether urinary concentrations of phthalates are correlated with circulating fatty acids in the general US population. The 2003-2004 and 2011-2012 National Health and Nutrition Examination Survey were used in this study. Ten urinary phthalate metabolites and 23 fatty acids were measured. Fatty acid patterns were identified using principal component analysis (PCA) with an eigenvalue greater than 1. A two-step analysis was performed. We first performed multivariable linear regressions to evaluate whether urinary phthalate metabolites were related to the PCA-derived components of blood fatty acid levels. Then we performed multivariable linear regressions to investigate each of the fatty acids that were suggestively correlated with some of the phthalates in PCA. There were 994 participants (51.91% women). As for men, after adjustments for potential confounding factors, MECPP, MEHHP, and ∑DEHP were all positively correlated with gamma-linolenic, myristoleic, and myristic acids; both MEHHP and ∑DEHP were positively correlated with stearic acid; MMP was positively correlated with docosahexaenoic acid. As for women, MMP was negatively correlated with docosanoic, lignoceric, and arachidic acids; MBzP was negatively correlated with docosahexaenoic acid; both MEHP and MCPP were negatively correlated with docosatetraenoic acid; MEHP was negatively correlated with arachidonic acid, and MCPP was negatively correlated with docosapentaenoic-6 acid. Our findings support that phthalates may be correlated with circulating fatty acids.
My Website: https://www.selleckchem.com/products/vb124.html