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Ulcerative colitis is a chronic inflammatory bowel disease with unclear pathogenesis and is difficult to cure. The incidence rate is increasing year by year, and there is high drug dependence for the treatment of ulcerative colitis, which leads to obvious the treatment resistance to s side effects. Therefore, seeking natural health products for nutritional therapy has become an attention focus of attention. In this articlestudy, dextran sulfate sodium salt (DSS) was used to establish a mouse model of ulcerative colitis. The mice were intervened with 5-aminosalicylic acid (5-ASA) (130 mg/(kg mb∙d)) or pomegranate juice (30% and 100%, 10 mL/d). Body mass, disease activity index (DAI) and colon length were measured, liver and kidney function and oxidative stress were measured by biochemical kits, the levels of inflammatory factors were measured by enzyme-linked immunosorbent assay (ELISA), colon tissue sections were observed, and the contents of fecal short-chain fatty acids (SCFAs) were determined by gas chromatography-mass spectrometry (GC-MS). Finally, a comprehensive evaluation of the effect and mechanism of pomegranate juice against ulcerative colitis in mice was exploredperformed. Compared with the DSS group, body mass and colon length in the pomegranate juice intervention group was increased, DAI was decreased, liver and kidney functions were improved, and could the expression of proinflammatory factors was significantly decreased (P < 0.01). In addition, anti-inflammatory factor expression was significantly enhanced (P < 0.01), reduce the level of oxidative stress and the activity of inflammatory marker myeloperoxidase (MPO) were significantly reduced (P < 0.01), the level of up-regulate fecal SCFAs was up-regulated, andimprove the colonic epithelial structure was improved. Pomegranate juice can attenuate intestinal mucosal damage and liver and kidney dysfunction, and has a good therapeutic effect on DSS-induced ulcerative colitis in mice.

Alzheimer's disease (AD) is a neurodegenerative disorder with a distinct sex bias. Age‐related vascular alterations, a hallmark of AD onset and progression, are consistently associated with sexual dimorphism. Here, we conducted an integrative meta‐analysis of 335,803 single‐nucleus transcriptomes and 667 bulk transcriptomes from the vascular system in AD and normal aging to address the underlying sex‐dependent vascular aging in AD. All vascular cell types in male AD patients exhibited an activated hypoxia response and downstream signaling pathways including angiogenesis. The female AD vasculature is characterized by increased antigen presentation and decreased angiogenesis. We further confirmed that these sex‐biased alterations in the cerebral vascular emerged and were primarily determined in the early stages of AD. Sex‐stratified analysis of normal vascular aging revealed that angiogenesis and various stress‐response genes were downregulated concurrently with female aging. Conversely, the hypoxia response increased steadily in males upon aging. An investigation of upstream driver transcription factors (TFs) revealed that altered communication between estrogen receptor alpha (ESR1) and hypoxia induced factors during menopause contributes to the inhibition of angiogenesis during normal female vascular aging. Additionally, inhibition of CREB1, a TF that targets estrogen, is also related to female AD. Overall, our study revealed a distinct cerebral vascular profile in females and males, and revealed novel targets for precision medicine therapy for AD. [ABSTRACT FROM AUTHOR]

In order to solve the problems of superficial ecological deterioration and decline of water resources in the ecologically fragile mining area caused by the downward cracks in seam mining, the unconfined compressive strength test and direct shear test were used to determine the changes of physico-mechanical properties of the reinforced sample with microbial solution of different concentrations, and reveal its mechanism. The results show that with the increase of the concentration of the microbial bacteria liquid, the unconfined compressive strength and shear strength of the sample are gradually increased, and the ability of the bacteria liquid to repair the fractured soil becomes enhanced, which can improve the physico-mechanical properties of the soil to a certain extent.

Developing and fabricating a heterogeneous material for efficient dehydrogenation of formic acid (FA) to H2 coupled with hydrogenation of CO2 back to FA is a promising strategy to forming a carbon‐neutral CO2‐mediated hydrogen energy release‐storage system, which remains challenging. Herein, a facile one‐step ultrahigh‐temperature method of arc‐discharge route for synthesizing efficient single‐crystal α‐MoC supported platinum catalysts with high dispersion of Pt single atoms (SAs), nanoclusters (NCs), and nanoparticles (NPs) is reported. These catalysts exhibit remarkable performance for the adsorption properties and chemical conversion of small molecules including room‐temperature FA dehydrogenation, CO2 hydrogenation to FA (formate), and low‐temperature CO oxidation reactions. The enhanced electron transfer from MoC to active Pt species improves catalyst's surface electron density, thus modulating the chemical adsorption of these small molecules. The engineered 0.1Pt/MoC with Pt SAs shows an outstanding FA dehydrogenation efficiency, with ca. 100% FA conversion and an initial TOF of up to 7739 h−1 at 25 °C. The versatile 0.1Pt/MoC also displays the ability for converting CO2 hydrogenation back to the hydrogen storage carrier FA (formate) with 88.3% yield under mild conditions. Moreover, in‐depth insights into the material microstructure, Pt−MoC electronic interactions, reaction‐dependent particle size effect, and adsorption energy are comprehensively investigated. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of deep learning and its outstanding capabilities in target detection, innovative methods have been introduced for infrared dim small target detection. This review comprehensively summarizes public datasets, the latest networks, and evaluation metrics for infrared dim small target detection. This review mainly focuses on deep learning methods from the past three years and categorizes them based on the six key issues in this field: (1) enhancing the representation capability of small targets; (2) improving the accuracy of bounding box regression; (3) resolving the issue of target information loss in the deep network; (4) balancing missed detections and false alarms; (5) adapting for complex backgrounds; (6) lightweight design and deployment issues of the network. Additionally, this review summarizes twelve public datasets for infrared dim small targets and evaluation metrics used for detection and quantitatively compares the performance of the latest networks. Finally, this review provides insights into the future directions of this field. In conclusion, this review aims to assist researchers in gaining a comprehensive understanding of the latest developments in infrared dim small target detection networks. [ABSTRACT FROM AUTHOR]

This paper takes No.4 coal in a mine field in Binchang Mining Area as an example, the main factors affecting the development prospect of coalbed methane are determined through comprehensive analysis, and the evaluation system of the development prospect of coalbed methane is established. Based on that, entropy method is used to determine the weight of each influencing factor, the evaluation results are divided by the K-means clustering method, and MapGIS is used to delineate different types of development prospect, thereby overcoming some limitations of the current evaluation methods of coalbed methane development prospect, and improving the accuracy of prediction of favorable areas for development. The research believes that the main factors affecting the development prospect of the No.4 coalbed methane are resource conditions（gas content, ash content, net thickness of coal seam）, occurrence conditions （tectonic location, roof thickness）, and development conditions （permeability, burial depth）; development prospect are divided into 5 categories: extremely favorable area, favorable area, relatively favorable area, medium area and unfavorable area. The favorable areas are mainly distributed in the northeast of the mine field.

Ionic composition data of coalbed methane (CBM) produced water from five CBM blocks in China were used to investigate the variability in and the geological controls on water quality across geologic basins and the resulting environmental impacts. The assessment tools included the Schoeller diagram, the Na-K-Mg ternary diagram and the Cl-Na-HCO3 ternary diagram. Techniques such as principal component analysis (PCA) and R-mode hierarchical cluster analysis were also applied to the ionic database to reveal the relationships between the ions. The results from this study indicate that three major ions (Na, Cl, and HCO3) and two primary processes (sulfate reduction and mixing of the coal seam water with the fracturing fluid) affect the ionic compositions of CBM produced water. The burial depth of the coal seam, the fresh water influence and the fracturing fluid contamination jointly control the water quality variability of produced water. The Na-K-Mg ternary diagram provides a potential method to evaluate the origin of CBM produced water, such as coal seam water in the partially equilibrated region and exotic water (including the fracturing fluid or shallow cold groundwater) in the immature water region. Most of the CBM produced water in China has high concentrations of Na, HCO3, Cl and TDS compared to the standards for drinking water and irrigation water. The produced waters from deep formations (>1000 m), or those produced during the early drainage periods, are always saline, "Na-Cl" type waters. Appropriate treatment methods and regulations for CBM produced water in China are urgently needed. [ABSTRACT FROM AUTHOR]

Considering the conventional calibration restriction of the complicated calibration procedures, narrow dynamic range, and less correlation in the calibration data, a global optimization radiometric calibration method is proposed in this paper. First, a unified database is generated by integrating different gray-level images, neutral density attenuators, integration times, and target radiations under the deduced infrared physical model. Then, the calibration coefficients are automatically learned through the relative error backward propagation network. Finally, experiments are conducted on a large-aperture ground-based infrared system to evaluate the effectiveness of the proposed method. The results indicate the proposed method can solve the problem of learning imbalance with large fluctuations of infrared radiation, ensure global measurement precision with a simpler calibration procedure, and accurately measure the internal stray radiation of the optical system.