Determinants of depression, problem behavior, and cognitive level of adolescents in China: Findings from a national, population-based cross-sectional study

IntroductionWe aimed to assess the associated factors for adolescent depression, problem behavior and cognitive level in China.MethodsA total of 2,584 adolescents aged from 10 to 15 years old in 2018 were included for analyses. Information on a comprehensive set of potential determinants was collected by the questionnaire, including demographic, health-, school- and family-related factors. Differences in average scores of depression, problem behavior, and cognitive level across subgroups were assessed by two independent sample t-tests and one-way analysis of variance (ANOVA). The clinical relevance among subgroups was assessed by the effect size. Multivariate linear regression models were applied to identify the statistically significant determinants.ResultsSchool-related factors and parental depressive status were strongly associated with depression. Low maternal education, poor/bad health of adolescents, high academic pressure, and parental depression were significantly associated with behavior problems. The socioeconomic factors, poor academic performance and father’s depression were significantly associated with adolescent cognitive level.DiscussionMultiple associated factors were identified for depression, problem behavior, and cognition of Chinese adolescents, which will provide insights into developing more targeted public health policies and interventions to improve their mental health

Infertile human endometrial organoid apical protein secretions are dysregulated and impair trophoblast progenitor cell adhesion

IntroductionEmbryo implantation failure leads to infertility. As an important approach to regulate implantation, endometrial epithelial cells produce and secrete factors apically into the uterine cavity in the receptive phase to prepare the initial blastocyst adhesion and implantation. Organoids were recently developed from human endometrial epithelium with similar apical-basal polarity compared to endometrial gland making it an ideal model to study endometrial epithelial secretions.MethodsEndometrial organoids were established using endometrial biopsies from women with primary infertility and normal fertility. Fertile and infertile organoids were treated with hormones to model receptive phase of the endometrial epithelium and intra-organoid fluid (IOF) was collected to compare the apical protein secretion profile and function on trophoblast cell adhesion.ResultsOur data show that infertile organoids were dysregulated in their response to estrogen and progesterone treatment. Proteomic analysis of organoid apical secretions identified 150 dysregulated proteins between fertile and infertile groups (>1.5-fold change). Trophoblast progenitor spheroids (blastocyst surrogates) treated with infertile organoid apical secretions significantly compromised their adhesion to organoid epithelial cell monolayers compared to fertile group (P < 0.0001).DiscussionThis study revealed that endometrial organoid apical secretions alter trophoblast cell adhesiveness relative to fertility status of women. It paves the way to determine the molecular mechanisms by which endometrial epithelial apical released factors regulate blastocyst initial attachment and implantation

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Agricultural Structures Management Based on Nonpoint Source Pollution Control in Typical Fuel Ethanol Raw Material Planting Area

Increasing the promotion and application of biofuel ethanol has been a national strategy in China, which in turn has affected changes in the raw material planting structure. This study analyzed the effects of agricultural land-use changes on water quality in a typical maize fuel ethanol raw material planting area. The results revealed that an increase in cultivated land and construction land would also increase the load of TN (total nitrogen) and TP (total phosphorus), while an expansion in forest land would reduce the load. As for crop structures, maize might have a remarkable positive effect on TN and TP, while rice and soybean performed in no significant manner. Furthermore, scenarios under the carbon neutralization policy and water pollution control were carried out to forecast the nonpoint source pollutants based on the quantitative relations coefficients. It was proven that maize planting was not suitable for vigorous fuel ethanol development. Reducing maize area in the Hulan River Basin was beneficial to reducing nonpoint source pollution. However, the area of maize should not be less than 187 km2, otherwise, the food security of the population in the basin would be threatened. Under the change in fuel ethanol policy, this study could provide scientific support for local agriculture land-use management in realizing the carbon neutralization vision and set a good example for the development of the fuel ethanol industry in other maize planting countries

A New Endmember Preprocessing Method for the Hyperspectral Unmixing of Imagery Containing Marine Oil Spills

The current methods that use hyperspectral remote sensing imagery to extract and monitor marine oil spills are quite popular. However, the automatic extraction of endmembers from hyperspectral imagery remains a challenge. This paper proposes a data field-spectral preprocessing (DSPP) algorithm for endmember extraction. The method first derives a set of extreme points from the data field of an image. At the same time, it identifies a set of spectrally pure points in the spectral space. Finally, the preprocessing algorithm fuses the data field with the spectral calculation to generate a new subset of endmember candidates for the following endmember extraction. The processing time is greatly shortened by directly using endmember extraction algorithms. The proposed algorithm provides accurate endmember detection, including the detection of anomalous endmembers. Therefore, it has a greater accuracy, stronger noise resistance, and is less time-consuming. Using both synthetic hyperspectral images and real airborne hyperspectral images, we utilized the proposed preprocessing algorithm in combination with several endmember extraction algorithms to compare the proposed algorithm with the existing endmember extraction preprocessing algorithms. The experimental results show that the proposed method can effectively extract marine oil spill data

A robust common-weights WENO scheme based on the flux vector splitting for Euler equations

This paper proposes a common-weights weighted essentially non-oscillatory (Co-WENO) scheme for solving the Euler equations of gas dynamics. Different from the usual component-wise weighting methods, common-weights means that, on one global sten-cil, a set of weights is commonly shared by the split flux vector of Euler equations in one spatial dimension. The common-weights WENO scheme has two significant advantages. First, since only one set of weights is calculated and used for the split flux vector, the method has an improved computational efficiency. Second, for a stencil (or each cell on the stencil), the Co-WENO scheme keeps the same contribution on each component numerical flux in a hyperbolic system of equations. How to calculate the weights is one of the vital issues in developing this kind of Co-WENO schemes.In this paper, based on the flux vector split method, the product of density, pressure, and the split flux of energy equation(Gamma +/- = rho pfE +/-) is proposed to calculate the common weights. This is based on the following considerations: (1) the density jumps at shocks and contact discontinuities; (2) the split energy flux contains the term of the third power of the velocity (for example, u3) and makes the resulting scheme has upwind characteristic; (3) the pressure always jumps at shocks, and it can help improve the stability in high speed flows, in which the kinetic energy is much larger than the internal energy. Numerical experiments also show that the proposed common-weights WENO scheme has good robustness and low numerical dissipation, and it can help suppress phase errors.(c) 2023 Elsevier B.V. All rights reserved

Specific sweat metabolite profile in ocular Behcet's disease

BACKGROUND: Behcet's disease (BD) is an autoimmune disorder with the serious possibility of blindness, calling for further research on its pathogenesis. Our aim was to study the metabolite composition of sweat in BD and to identify possible biomarkers. METHODS: Metabolomics analysis was performed on sweat samples from 20 BD patients and 18 normal controls by liquid chromatography tandem mass spectrometry. RESULTS: A significantly different metabolic profile of sweat was observed when BD patients were compared with healthy controls. The result of the orthogonal partial least squared-discrimination analysis (OPLS-DA) showed that these two comparison groups could be separated with a relatively satisfactory fitting degree (R2Y = 0.995 and Q2 = 0.817 in positive ion mode; R2Y = 0.991 and Q2 = 0.721 in negative ion mode). Based on OPLS-DA, a panel of metabolites was selected as candidate biomarkers, including l-citrulline, l-pyroglutamic acid, urocanic acid, 2-oxoadipic acid, cholesterol 3-sulfate, and pentadecanoic acid. CONCLUSION: This is the first report on the metabolite profile of sweat in BD. Our results demonstrated a significantly different metabolite composition of sweat in BD compared to that of healthy controls

A comparative study on phosphate removal from water using <italic toggle="yes">Phragmites australis</italic> biochars loaded with different metal oxides

Metal oxide-loaded biochars are a promising material to remove phosphate from polluted water to ultra-low concentrations. To facilitate preparing the metal oxide-loaded biochar with the best phosphate adsorption performance, five biochars loaded with Al, Ca, Fe, La and Mg oxides, respectively (Al-BC, Ca-BC, Fe-BC, La-BC and Mg-BC) were produced using Phragmites australis pretreated with 0.1 mol AlCl3, CaCl2, FeCl3, LaCl3 and MgCl2, respectively, characterized, and phosphate adsorption kinetics and isotherms of the biochars were determined. The maximum phosphate adsorption capacities (Qm) of the biochars ranked as Al-BC (219.87 mg g−1) > Mg-BC (112.45 mg g−1) > Ca-BC (81.46 mg g−1) > Fe-BC (46.61 mg g−1) > La-BC (38.93 mg g−1). The time to reach the adsorption equilibrium ranked as La-BC (1 h) < Ca-BC (12 h) < Mg-BC (24 h) = Fe-BC (24 h) <Al-BC (greater than 72 h). Qm of Ca-BC, Fe-BC, La-BC and Mg-BC depend on the molar content of metals in the biochars. The small phosphate adsorption rate of Al-BC is due to the slow intra-particle diffusion of phosphate attributed to the undeveloped porosity and dispersed distribution of AlOOH crystals on the Al-BC surface. Mg-BC is suggested for phosphate removal from water considering adsorption rate and capacity. Al-BC is applicable when a long contact time is allowed, e.g. as a capping material to immobilize phosphate in lake sediments