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Hu and Yang discuss the study by Jimenez et al, which provide novel evidence concerning the associations between residential greenspace al midlife and cognitive decline laler in life among US women with a mean age of 74.2 y at baseline. They also explored greenspace influence stratified by characteristics--namely, socioeconomic status, population density, and apolipopro-tein E status--that may make individuals particularly sensitive to cognitive decline. They found that greater exposure to residential greenspace al midlife, measured by the normalized difference vegetation index, was associated with better cognitive assessment scores at baseline and a decelerated rate of cognitive decline thereafter, albeit no association was observed between greenspace and verbal memory. Specifically, each interquartile

Background: Greenness, referring to a measurement of the density of vegetated land (e.g., gardens, parks, grasslands), has been linked with many human health outcomes. However, the evidence on greenness exposure and human microbiota remains limited, inconclusive, drawn from specific regions, and based on only modest sample size. Objectives: We aimed to study the association between greenness exposure and human microbial diversity and composition in a large sample across 34 countries and regions. Methods: We explored associations between residential greenness and human microbial alpha-diversity, composition, and genus abundance using data from 34 countries. Greenness exposure was assessed using the normalized difference vegetation index and the enhanced vegetation index mean values in the month before sampling. We used linear regression models to estimate the association between greenness and microbial alpha-diversity and tested the effect modification of age, sex, climate zone, and pet ownership of participants. Differences in microbial composition were tested by permutational multivariate analysis of variance based on Bray-Curtis distance and differential taxa were detected using the DESeq2 R package between two greenness exposure groups split by median values of greenness. Results: We found that higher greenness was significantly associated with greater richness levels in the palm and gut microbiota but decreased evenness in the gut microbiota. Pet ownership and climate zone modified some associations between greenness and alpha-diversity. Palm and gut microbial composition at the genus level also varied by greenness. Higher abundances of the genera Lactobacillus and Bifidobacterium, and lower abundances of the genera Anaerotruncus and Streptococcus, were observed in people with higher greenness levels. Discussion: These findings suggest that residential greenness was associated with microbial richness and composition in the human skin and gut samples, collected across different geographic contexts. Future studies may validate the observed associations and determine whether they correspond to improvements in human health.

A field monitoring system was established in an active river bank landslide in the Three Gorges area, China, and a consecutive monitoring for about 5 years were conducted to understand the displacement characteristics of flexible piles and the surrounding soil. It was found that piles deformed elastically under reservoir operation, and the soil in front of piles was gradually separated from piles. The movement of the pile heads exceeded that of the soil between and behind piles. This phenomenon was further studied by a large-scale physical model test to gain insights into the pile-soil interaction. The displacement relationship between pile heads and the surrounding soil is in good agreement with the field data. The physical model test shows that the deformation process of pile-reinforced landslides can be divided into two stages: firstly, when the piles head movement exceeds soil movement, the soil arching is mainly affected by the deflection of the piles, the arches between and behind piles bent upwards; but when the soil movement exceeds piles head movement, the arches near the upslope and downslope bent downwards and upwards, respectively. Furthermore, the different deformation of two adjacent piles and the pile stiffness influenced the arch’s shape and formation; the flexible piles exhibit great coordinated deformation with the landslide, and caused the soil arch on the downslope.

The impact of exposure to metals on chronic kidney disease (CKD) has only been investigated in two-way or single metal interactions in previous studies. We investigated the associations between five single metals in blood and their mixed exposure and CKD by using the machine learning approach. Relevant data were extracted from the National Health and Nutrition Examination Survey (NHANES 2011–2020), and the level of five metals in blood detected by inductively coupled plasma mass spectrometry was considered as exposures, namely, cadmium (Cd), lead (Pb), total mercury (Hg), manganese (Mn), and selenium (Se). The correlations between individual metal and metal mixtures and CKD were then evaluated by survey-multivariable logistic regression (SMLR), generalized weighted quantile sum (WQS), and Bayesian kernel machine regression (BKMR). Altogether, our study included 12,412 participants representing 572.6 million non-institutionalized US adults. Several single metals with the high quartile of exposure showed a positive association with the CKD ratio including Cd [(AOR = 1.873, 95% CI: 1.537, 2.284), Q4], Pb [(AOR = 1.559, 95% CI: 1.295, 1.880), Q4], and total Hg [(AOR = 1.169, 95% CI: 1.018, 1.343), Q2], while Mn [(AOR = 0.796, 95% CI: 0.684, 0.927), Q2] and Se [(AOR = 0.805, 95% CI: 0.664, 0.976), Q4] were negatively associated with the CKD ratio. In light of the positive fit of the WQS regression model, a significantly positive correlation was found between mixed metals and CKD (AOR = 1.373, 95% CI: 1.224, 1.539) after full covariate adjustment, and a similar finding was also detected in the BKMR model. Our study revealed that each single metal including Cd, Pb, and total Hg might have a positive association with CKD while this association was negative for both Mn and Se. The five metals might have a positive joint effect on CKD.

Many landslides in reservoir areas continuously deform under cyclic water level fluctuations due to reservoir operations. In this paper, a landslide model, developed for a typical colluvial landslide in the Three Gorges Reservoir area, is used to study the effect of cyclic water level fluctuations on the landslide. Five cyclic water level fluctuations were implemented in the test, and the fluctuation rate in the last two fluctuations doubled over the first three fluctuations. The pore water pressure and lateral landslide profiles were obtained during the test. A measurement of the landslide soil loss was proposed to quantitatively evaluate the influence of water level fluctuations. The test results show that the first water level rising is most negative to the landslide among the five cycles. The fourth drawdown with a higher drawdown rate caused further large landslide deformation. An increase of the water level drawdown rate is much more unfavorable to the landslide than an increase of the water level rising rate. In addition, the landslide was found to have an adaptive ability to resist subsequent water level fluctuations after undergoing large deformation during a water level fluctuation. The landslide deformation and observations in the field were found to support the test results well.

Dydrogesterone (DDG) acts on the reproduction but also affects the functioning of non-reproductive system. So far, the knowledge about other effects of DDG remains limited. Here we investigated the effects of DDG on the transcription of genes in innate immune and coagulation cascade in zebrafish embryos. The zebrafish embryos were exposed to DDG at 49.0, 527 and 5890 ng L− 1 for 144 hour post fertilization (hpf). The results showed that DDG significantly decreased the transcription of marker genes (e.g. tnfa, il8 and cc-chem) involved in the innate immune response at environmental concentrations. Moreover, DDG also down-regulated the transcription of genes in coagulation cascade (e.g. fga, fgb, fgg and f2). These results indicated that DDG had potential effects on the innate immune and coagulation cascade functions in the early life zebrafish, thus further affecting fish growth and health.

Elevated heart rate (HR) can be hypothesized to be involved in the pathways by which ambient air pollution, especially fine particulate matter (PM2.5), causes cardiovascular morbidity and mortality. However, evidence concerning long-term PM2.5 exposure and HR is still limited. Therefore, in this study, we assessed the associations of PM2.5 with HR levels and tachycardia prevalence and explored potential modifiers of the associations. We used baseline data of 1,802,207 adults from the Kashgar Prospective Cohort Study (KPCS). PM2.5 exposure was assessed based on satellite sensing data, meteorological factors, multi-resolution emission inventory, and measurements from ground-based surface monitors measurements. HR was measured using a calibrated electronic sphygmomanometer, and tachycardia was defined as resting heart rate (RHR) equal to or greater than 80 beats per minute. Linear regression and logistic regression models were employed to evaluate the associations of PM2.5 levels with RHR levels and tachycardia prevalence, respectively. Stratified analyses by sex, age, ethnicity, smoking status, alcohol use, and physical activity were also performed. The mean (standard deviation) age of the study participants was 39.4 (15.5) years old. In the adjusted models, an interquartile range (8.8 µg/m3) increase in PM2.5 levels was associated with 0.515 (95% confidence interval: 0.503–0.526) bpm increase in RHR levels and with 1.062-fold (95% confidence interval: 1.059–1.064) increase in the odds of tachycardia. The results were robust against several sensitivity analyses. In addition, we observed the above associations were stronger in participants that were men, of Uyghur ethnicity, smoking cigarettes, drinking alcohol, and having physical inactivity, compared to their counterparts. In summary, our findings indicate that long-term exposure to ambient PM2.5 may be hazardously associated with HR, and women, Uyghur people, and those with unhealthy lifestyles may be more vulnerable to the hazardous effects.

The studies of mechanism and calculated model of the earth-fill dam and landslide dam have a significant impact on the prediction and risk assessment for dam failure. The present study selected the event of a multi-stage landslide dam failure in southwestern China as a case study. Based on a mathematic model that can consider the failure mechanism, the key parameter can be obtained by back analysis, the twice failure processes can be displayed as well. It indicates that the second landslide dam cannot break under natural conditions, i.e. the landslide dam failure needs manual intervention, which is consistent with the actual situation. Because the landslide would occur again in different cumulated volumes in the future, the dam failure is predicted using the same model. The result illustrates that the maximal flow comes up to 20,000-60,000 m3/s after the dam failure and the failure would occur in a short day. The analysis and prediction provide an improved insight into the landslide dam failure process and risk control.

The presence of benzothiazoles (BTHs) and organic ultraviolet filters (UV filters) in aquatic ecosystems has emerged as a significant environmental issue, requiring urgent and efficient determination methods. A new, rapid, and sensitive determination method using gas chromatography triple quadrupole mass spectrometer (GC–MS/MS) was developed for the simultaneous extraction and analysis of 10 commonly used BTHs and 10 organic UV filters in surface water, wastewater, sediment, and sludge. For aqueous samples, solid-phase extraction (SPE) method was employed with optimizing of SPE cartridge type, pH, and elution solvent. For solid samples, ultrasonic extraction-solid-phase extraction purification (UE-SPE) and pressurized liquid extraction (PLE) methods were compared. And extraction conditions for ultrasonic extraction method (extraction solvents and extraction times) and PLE method (extraction temperatures and extraction cycles) were optimized. The limits of quantification for the 20 target compounds in surface water and wastewater were 0.01–2.12 ng/L and 0.05–6.14 ng/L, while those for sediment and sludge with UE-SPE method were 0.04–5.88 ng/g and 0.22–6.61 ng/g, respectively. Among the 20 target compounds, the recoveries ranged from 70 to 130% were obtained for 16, 15, 15, and 15 analytes in the matrix-spiked samples of surface water, wastewater, sediment, and sludge with three levels, respectively. And the precision was also acceptable with relative standard deviation (RSD) below 20% for all analytes. The developed methods were applied for the determination and quantification of target compounds in surface water, sediment, wastewater, and sludge samples collected from two wastewater treatment plants (WWTPs) and the Pearl River in Guangzhou, China. BTHs were frequently detected in surface water and wastewater, while UV filters were mainly found in sediment and sludge. Benzotriazole (BT) and 2-hydroxybenzothiazole (2-OH-BTH) were the two major BTHs in influent wastewater and surface water, respectively, with concentrations up to 966 and 189 ng/L. As for sediment and sludge, 2-(2′-hydroxy-5′-octylphenyl)-benzotriazole (UV-329) was a predominant chemical, detected at concentrations of 111 and 151 ng/g, respectively.

This paper presents an integrated approach that predicts the microparameters of the particle flow code in three dimensions (PFC3D) model in triaxial compression simulations. The new approach combines a full factorial design (FFD) with an artificial neural network (ANN). The ANN model maps the input factors (triaxial compressive strength, Poisson’s ratio, and Young’s modulus) onto output variables, which are microparameters that affect the macroscopic responses in a PFC3D model. Emphasis is placed on data collection and optimization of the ANN model using FFD. The data for training and testing the ANN model were obtained from laboratory experiments and numerical simulations of a PFC3D model according to the principles of FFD. Using a backpropagation artificial neural network (BPNN) optimized with FFD principles, the object of the current study (to reliably predict the microparameters for a PFC3D model) has been achieved because the predicting data obtained by the BPNN model were in excellent agreement with the testing data.