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Abstract Background Hepatic progenitor cells serve not only as the origin of combined hepatocellular cholangiocarcinoma (cHCC-CCA) but are also responsible for malignancy recurrence after surgical resection. Nucleophosmin 1 (NPM1) has been implicated in cancer metastasis and poor prognosis. This study aimed to determine the expression of NPM1 by hepatic progenitor cells in cHCC-CCA and the effects of targeting NPM1 on hepatic progenitor cells and BEL-7402 cells with characteristics of both progenitor cells and cHCC-CCA. Methods First, NPM1 was detected by RT‒PCR, western blotting, and double-immunofluorescence staining in cHCC-CCA tissues. NPM1 expression was subsequently analysed in rat hepatic progenitor cells cultured in vitro and in interleukin 6 (IL6)-treated cells. The effects and mechanism of NPM1 on hepatic progenitor cells were determined by knocking down NPM1 and performing RNA sequencing analysis. Finally, NSC348884, a small-molecule inhibitor that disrupts NPM1 dimer formation, was used to confirm the function of NPM1 in BEL-7402 cells. Results Both human hepatic progenitor cells in cHCC-CCA tissues and rat in vitro cultured hepatic progenitor cells highly expressed NPM1. IL6, a cytokine involved in the malignant transformation of hepatic progenitor cells, dose-dependently increased NPM1 and PCNA expression. Knocking down NPM1 reduced IL6R transcription (P

Abstract Background Reducing production costs while producing high-quality livestock and poultry products is an ongoing concern in the livestock industry. The addition of oil to livestock and poultry diets can enhance feed palatability and improve growth performance. Emulsifiers can be used as potential feed supplements to improve dietary energy utilization and maintain the efficient productivity of broilers. Therefore, further investigation is warranted to evaluate whether dietary emulsifier supplementation can improve the efficiency of fat utilization in the diet of yellow-feathered broilers. In the present study, the effects of adding emulsifier to the diet on lipid metabolism and the performance of yellow-feathered broilers were tested. A total of 240 yellow-feasted broilers (21-day-old) were randomly divided into 4 groups (6 replicates per group, 10 broilers per replicate, half male and half female within each replicate). The groups were as follows: the control group (fed with basal diet), the group fed with basal diet supplemented with 500 mg/kg emulsifier, the group fed with a reduced oil diet (reduced by 1%) supplemented with 500 mg/kg emulsifier, and the group fed with a reduced oil diet supplemented with 500 mg/kg emulsifier. The trial lasted for 42 days, during which the average daily feed intake, average daily gain, and feed-to-gain ratio were measured. Additionally, the expression levels of lipid metabolism-related genes in the liver, abdominal fat and each intestinal segment were assessed. Results The results showed that compared with the basal diet group, (1) The average daily gain of the basal diet + 500 mg/kg emulsifier group significantly increased (P

Abstract Understanding elevation changes on the Tibetan Plateau is crucial to comprehend the changes in topography, landscape, climate, environmental conditions, and water resources. However, some of the current products that track elevation changes only cover specific surface types or limited areas, and others have low spatial resolution. We propose an algorithm to extract ICESat-2 crossover points dataset for the Tibetan Plateau, and form a dataset. The crossover points dataset has a density of 2.015 groups/km², and each group of crossover points indicates the amount of change in elevation before and after a period of time over an area of approximately 17 meters in diameter. Comparing ICESat-2 crossover points data with existing studies on glaciers and lakes, we demonstrated the reliability of the derived elevation changes. The ICESat-2 crossover points provide a refined data source for understanding high-spatial-resolution elevation changes on the Tibetan Plateau. This dataset can provide validation data for various studies that require high-precision or high-resolution elevation change data on the Tibetan Plateau.

Streamflow simulation is crucial for flood mitigation, ecological protection, and water resource planning. Process-based hydrological models and machine learning algorithms are the mainstream tools for streamflow simulation. However, their inherent limitations, such as time-consuming and large data requirements, make achieving high-precision simulations challenging. This study developed a hybrid approach to simultaneously improve the accuracy and computational efficiency of streamflow simulation, which integrates Block-wise use of the TOPMODEL (BTOP) model into the eXtreme Gradient Boosting (XGBoost), i.e., BTOP_XGB. In this approach, BTOP generates simulated streamflow using the Latin hypercube sampling algorithm instead of the time-consuming calibration algorithms to reduce computational costs. Then, XGBoost combines BTOP simulated streamflow with multi-source data to reduce simulation errors. In which, serval input variable selection algorithms are employed to choose relevant inputs and remove redundant information for model. The hybrid approach is validated and compared with a standalone model at three hydrological stations in the Jialing River basin, China. The results show that the performance of BTOP_XGB is significantly better than the BTOP and XGBoost models. The NSE of BTOP_XGB at Beibei, Xiaoheba, and Luoduxi stations increases by 54%, 21%, and 83%, respectively. Meanwhile, the computational time of BTOP_XGB is saved by >90% compared to the original calibrated BTOP. BTOP_XGB is less affected by parameter sample sizes and data amounts, demonstrating the robustness of the hybrid model. This study simplifies the complexity of the hydrological model and enhances the stability of machine learning, jointly improving the reliability of streamflow simulation. The hybrid approach provides a potential shortcut for streamflow simulation over basins with large areas or limited observed data.

IntroductionNeuropathic pain (NP) conditions arising from injuries to the nervous system due to trauma, disease, or neurotoxins are chronic, severe, debilitating, and exceedingly difficult to treat. However, the mechanisms of NP are not yet clear. Here we explored the role of Dock4, an atypical Rac1 GEF, in the development of NP.MethodsMechanical allodynia was assessed as paw withdrawal threshold by a dynamic plantar aesthesiometer. Immunofluorescence staining was conducted to investigate the expression and localization of Dock4, Rac1 and GluN2B. Quantitative analysis of Dock4, Rac1 and GluN2B were determined by qRT-PCR and Western blot assay. Spontaneous excitatory and inhibitory postsynaptic currents in spinal cord slices were examined using whole cell patch clam. Dendritic spine remodeling and synaptogenesis were detected in cultured dorsal spinal neurons.Results and discussionWe found that SNL caused markedly mechanical allodynia accompanied by increase of Dock4, GTP-Rac1and GluN2B, which was prevented by knockdown of Dock4. Electrophysiological tests showed that SNL facilitated excitatory synaptic transmission, however, this was also inhibited by Dock RNAi-LV. Moreover, knockdown of Dock4 prevented dendritic growth and synaptogenesis.ConclusionIn summary, our data indicated that Dock4 facilitated excitatory synaptic transmission by promoting the expression of GluN2B at the synaptic site and synaptogenesis, leading to the occurrence of NP.

Abstract Background Gastrointestinal tract (GIT) nematodes prefer to live in the intestines of wild animals, causing damage and even death, and posing a zoonotic risk. The polyparasitism of GIT nematodes results in the complex dynamics of the nematode communities that occur naturally in wild animals. However, the nematode community in captive wild animals is poorly understood. Methods We combined microscopic examination and amplicon sequencing for community diversity. Results We characterized GIT nematode assemblages to one order, one family, four genera, and ten species, in 512 fecal samples of 121 species from captive wild animals in southern China. The positive rate of GIT nematodes was 20.7% (106/512), including 42.3% (11/26) in reptiles, 26.5% (39/147) in herbivores, 25.0% (25/100) in non-human primates, 20.0% (5/25) in omnivores, 12.2% (9/74) in carnivores, and 12.1% (17/140) in avians. The dominant nematodes were Haemonchus contortus in herbivores and Trichuris species in primates. The nematode communities of arboreal primates differed from their terrestrial counterparts, reflecting both host phylogeny and ecological constraints. Soil-transmitted Strongyloides species were widespread throughout the herbivore, primate, avian, and carnivore communities, and tended to infect omnivorous primates and terrestrial herbivores. In addition, new Trichuris and Heterakis species were found in the nematode communities of captive porcupines and peafowls. Conclusion This study highlights the variation in the composition of the GIT nematode community and strengthens the attention to the harms induced by zoonotic nematodes and co-infective nematodes with low species richness. Graphical Abstract

Dust deposited on the surface of snow and glaciers can significantly reduce the snow and ice albedo and accelerate melting. Manual observations of the dust mass concentration (DMC) on snow and glacier surfaces are routinely performed at many locations worldwide. However, snow and ice surface DMC monitoring methods based on remote sensing data still face challenges. This study presents a new retrieval scheme for estimating dust load on snow-covered surfaces from a moderate-resolution imaging spectroradiometer and visible infrared imaging radiometer suite in Northeast and Northwest China that utilizes a classical snow radiative transfer model. Our results indicate that the coefficient of variation of DMC retrieved from the in situ measurements of snow spectral reflectance is 4%, which is within a +4% difference compared with DMC observed in snow and ice samples. Estimating atmospheric dust pollutants content deposited on snow surfaces based on satellite remote sensing observations is feasible. In Northwest China, the root-mean-square error (RMSE) of the DMC values retrieved from VNP09GA data is 9.78 ppm, while that of the DMC values retrieved from MOD09GA data is 13.74 ppm. In Northeast China, the RMSE of the DMC values retrieved from VNP09GA data is 73.98 ppm, while that of the DMC values retrieved from MOD09GA data is 184.32 ppm. The research results can realize continuous monitoring of the atmospheric dust pollutants deposited on snow surfaces, which is of great practical significance to understanding and studying the pollution process of atmospheric dust on snow.

Abstract Topography is an important factor affecting soil erosion and is measured as a combination of the slope length and slope steepness (LS-factor) in erosion models, like the Chinese Soil Loss Equation. However, global high-resolution LS-factor datasets have rarely been published. Challenges arise when attempting to extract the LS-factor on a global scale. Furthermore, existing LS-factor estimation methods necessitate projecting data from a spherical trapezoidal grid to a planar rectangle, resulting in grid size errors and high time complexity. Here, we present a global 1-arcsec resolution LS-factor dataset (DS-LS-GS1) with an improved method for estimating the LS-factor without projection conversion (LS-WPC), and we integrate it into a software tool (LS-TOOL). Validation of the Himmelblau–Orlandini mathematical surface shows that errors are less than 1%. We assess the LS-WPC method on 20 regions encompassing 5 landform types, and R2 of LS-factor are 0.82, 0.82, 0.83, 0.83, and 0.84. Moreover, the computational efficiency can be enhanced by up to 25.52%. DS-LS-GS1 can be used as high-quality input data for global soil erosion assessment.

In the context of Integrated Energy System (IES), accurate short-term power demand forecasting is crucial for ensuring system reliability, optimizing operational efficiency through resource allocation, and supporting effective real-time decision-making in energy management. However, achieving high forecasting accuracy faces significant challenges due to the inherent complexity and stochastic nature of IES’s short-term load profiles, resulting from diverse consumption patterns among end-users and the intricate coupling within the network of interconnected energy sources. To address this issue, a dedicated Short-Term Power Load Forecasting (STPLF) framework for IES is proposed, which relies on a newly developed hybrid deep learning architecture. The framework seamlessly combines Long Short-Term Memory (LSTM) with Temporal Convolutional Network (TCN), enhanced by an attention mechanism module. By merging these methodologies, the network leverages the parallel processing prowess of TCN alongside LSTM’s ability to retain long-range temporal information, thus enabling it to dynamically concentrate on relevant sections of time series data. This synergy leads to improved prediction accuracy and broader applicability. Furthermore, the integration of residual connections within the network structure serves to deepen its learning capabilities and enhance overall performance. Ultimately, results from a real case study of a user-level IES demonstrate that the Mean Absolute Percentage Error (MAPE) of the proposed framework on the test set is 2.35%. This error rate is lower than the averages of traditional methods (3.43%) and uncombined single submodules (2.80%).

In recent years, analyzing the explanation for the prediction of Graph Neural Networks (GNNs) has attracted increasing attention. Despite this progress, most existing methods do not adequately consider the inherent uncertainties stemming from the randomness of model parameters and graph data, which may lead to overconfidence and misguiding explanations. However, it is challenging for most of GNN explanation methods to quantify these uncertainties since they obtain the prediction explanation in a post-hoc and model-agnostic manner without considering the randomness of graph data and model parameters. To address the above problems, this paper proposes a novel uncertainty quantification framework for GNN explanations. For mitigating the randomness of graph data in the explanation, our framework accounts for two distinct data uncertainties, allowing for a direct assessment of the uncertainty in GNN explanations. For mitigating the randomness of learned model parameters, our method learns the parameter distribution directly from the data, obviating the need for assumptions about specific distributions. Moreover, the explanation uncertainty within model parameters is also quantified based on the learned parameter distributions. This holistic approach can integrate with any post-hoc GNN explanation methods. Empirical results from our study show that our proposed method sets a new standard for GNN explanation performance across diverse real-world graph benchmarks.

Microbiologically influenced corrosion (MIC) poses a threat to various fields, particularly in piping and cooling water systems. As a green corrosion inhibitor, polyaspartic acid (PASP) faces challenges in achieving the intended corrosion inhibition against MIC due to biofilm. Therefore, mitigating biofilm might be the key to improving the corrosion inhibition of PASP. D-Phenylalanine (D-Phe) was selected as an enhancer to promote the inhibition of PASP on MIC caused by Desulfovibrio vulgaris due to its potential role in biofilm formation in this work. The joint application of PASP and D-Phe reduced the corrosion rate by 76.54% and obviously decreased the depth of corrosion pits with the maximum depth at 0.95 µm. Also, fewer cells adhered to the coupon surface due to the combined action of PASP and D-Phe, leading to thin and loose biofilm. Besides, both cathodic and anodic reactions were retarded with PASP and D-Phe, resulting in a low corrosion current at 0.530 × 10−7 A/cm2. The primary synergy mechanism is that D-Phe promoted the formation of PASP protective film via decreasing bacterial adhesion and thus inhibited electrochemical reaction and electron utilization of cells from metal surface. This study introduces a novel strategy to augment the effectiveness of PASP in inhibiting MIC.

Summary: Converging studies showed interstitial fluid (ISF) adjacent to blood vessels flows in adventitia along vasculature into heart and lungs. We aim to reveal circulatory pathways and regulatory mechanism of such adventitial ISF flow in rat model. By MRI, real-time fluorescent imaging, micro-CT, and histological analysis, ISF was found to flow in adventitial matrix surrounded by fascia and along systemic vessels into heart, then flow into lungs via pulmonary arteries and back to heart via pulmonary veins, which was neither perivascular tissues nor blood or lymphatic vessels. Under physiological conditions, speckle-like adventitial ISF flow rate was positively correlated with heart rate, increased when holding breath, became pulsative during heavy breathing. During cardiac or respiratory cycle, each dilation or contraction of heart or lungs can generate to-and-fro adventitial ISF flow along femoral veins. Discovered regulatory mechanisms of adventitial ISF flow along vasculature by heart and lungs will revolutionize understanding of cardiovascular system.

Abstract Water scarcity is a pressing issue in California. We develop ambient noise differential adjoint tomography that improves the sensitivity to fluid-bearing rocks by canceling bias caused by noise sources. Here we image the shallow S-wave velocity structure using this method beneath a linear seismic array (LASSIE) in Los Angeles Basin, which shows significant velocity reduction marking a major regional water producer, the Silverado aquifer, along with other fluid-bearing structures. Based on the S-wave tomography and previous P-wave studies, we derive the porosity in Long Beach and discover that the rock from 1-2 km depth surrounding the Newport-Inglewood Fault contains abundant fluids with pore-fluid fraction ~0.33. The high-porosity rock around the fault coincides with previously observed week-long shallow seismicity south of LASSIE array in Long Beach. The imaged S-wave velocity in the top layer shows a similar trend in the geotechnical layer Vs 30, suggesting additional applications to ground motion prediction.

Exploring the effects of different additives on the improvement of newly cultivated farmland in mountainous areas can provide rational soil fertilization plans for regions lacking means of production. We conducted a paddy planting experiment in Ankang City, Shaanxi Province. Six treatments were set up, including sole chemical fertilizer (CK); fertilizer + bacteria agent (NB); chemical fertilizer + alginate bio-organic fertilizer (NO); fertilizer + fulvic acid biomass nutrient solution (NF); chemical fertilizer + acid soil conditioner (NC); fertilizer + silicon–calcium–magnesium–potassium fertilizer (NSi). We collected topsoil samples after paddy harvest, analyzed their physical, chemical, and biological properties, and selected indicators to construct a Total Data Set (TDS) and a Minimum Data Set (MDS). The Soil Quality Index (SQI) was used to evaluate the soil improvement effects after different fertilization regimes. The SQI calculated by the TDS and the MDS showed that the SQI after NF treatment was higher than that under other treatments. The SQI based on the TDS (SQITDS) and the SQI based on the MDS (SQIMDS) were significantly positively correlated with yield. The SQI calculated based on both the TDS and the MDS can objectively reflect the soil fertility quality. The paddy yield and total dry matter accumulation were the highest under the NF treatment, and the SQI was the largest. Thus, the effect of chemical fertilizer combined with fulvic acid biomass nutrient solution on soil fertility was the most significant.

In recent years, side-channel analysis based on deep learning has garnered significant attention from researchers. A pivotal reason for this lies in the fact that deep learning-based side-channel analysis requires minimal preprocessing of side-channel data. The automatic feature extraction property of deep learning methods drastically reduces the workload for researchers, enabling them to focus more on the core issues of side-channel analysis, namely, extracting sensitive information by attacking devices. However, in prior studies, most scholars have concentrated more on the model construction process, with little research focusing on the choice of optimizers.This paper explores a novel deep learning-based optimization algorithm—CPSGD (combined projection stochastic gradient descent). The algorithm comprises two variants, designed, respectively, for unprotected side-channel analysis (CPSGD1) and desynchronized side-channel analysis (CPSGD2), and their convergence has been theoretically proven. Experimental results demonstrate that, while maintaining the neural network structure unchanged, CPSGD1 exhibits the best performance on unprotected datasets compared to other publicly available optimizers, whereas CPSGD2 performs optimally on desynchronized datasets.

Osteoarthritis (OA), a chronic joint disease affecting over 500 million individuals globally, is characterized by the destruction of articular cartilage and joint inflammation. Conventional treatments are insufficient for repairing damaged joint tissue, necessitating novel therapeutic approaches. Mesenchymal stem cells (MSCs), with their potential for differentiation and self-renewal, hold great promise as a treatment for OA. However, challenges such as MSC viability and apoptosis in the ischemic joint environment hinder their therapeutic effectiveness. Hydrogels with biocompatibility and degradability offer a three-dimensional scaffold that support cell viability and differentiation, making them ideal for MSC delivery in OA treatment. This review discusses the pathological features of OA, the properties of MSCs, the challenges associated with MSC therapy, and methods for hydrogel preparation and functionalization. Furthermore, it highlights the advantages of hydrogel-based MSC delivery systems while providing insights into future research directions and the clinical potential of this approach.

Multi-modal multi-objective problems (MMOPs) have gained much attention during the last decade. These problems have two or more global or local Pareto optimal sets (PSs), some of which map to the same Pareto front (PF). This article presents a new affinity propagation clustering (APC) method based on the Multi-modal multi-objective differential evolution (MMODE) algorithm, called MMODE_AP, for the suit of CEC’2020 benchmark functions. First, two adaptive mutation strategies are adopted to balance exploration and exploitation and improve the diversity in the evolution process. Then, the affinity propagation clustering method is adopted to define the crowding degree in decision space (DS) and objective space (OS). Meanwhile, the non-dominated sorting scheme incorporates a particular crowding distance to truncate the population during the environmental selection process, which can obtain well-distributed solutions in both DS and OS. Moreover, the local PF membership of the solution is defined, and a predefined parameter is introduced to maintain of the local PSs and solutions around the global PS. Finally, the proposed algorithm is implemented on the suit of CEC’2020 benchmark functions for comparison with some MMODE algorithms. According to the experimental study results, the proposed MMODE_AP algorithm has about 20 better performance results on benchmark functions compared to its competitors in terms of reciprocal of Pareto sets proximity (rPSP), inverted generational distances (IGD) in the decision (IGDX) and objective (IGDF). The proposed algorithm can efficiently achieve the two goals, i.e., the convergence to the true local and global Pareto fronts along with better distributed Pareto solutions on the Pareto fronts.

Background: Dexmedetomidine is known for its selective action on α2-adrenoceptor sites and is recognized for its neuroprotective capabilities. It can improve postoperative cognitive function. Commonly used anesthetics, such as sevoflurane and propofol, have been reported to affect postoperative cognitive function. Therefore, it could be valuable to explore dexmedetomidine-led anesthesia strategy. This study was designed to assess the performance, safety, and effective infusion rate in anesthesia maintenance, to explore a feasible dexmedetomidine-led anesthesia maintenance protocol, and to provide a foundation for potential combined anesthesia. Methods: Thirty patients aged 18–60 years, classified as ASA I or II, undergoing abdominal surgery were involved. The anesthesia maintenance was achieved with dexmedetomidine, remifentanil and rocuronium. Dixon up-and-down sequential methodology was utilized to ascertain the ED50 of dexmedetomidine for maintaining Patient State Index (PSI) 25–40 (depth of stage III anesthesia). Intraoperative HR, BP and depth of anesthesia were monitored and controlled. The wake-up time from anesthesia, the incidence of intraoperative awareness and postoperative delirium, and the patients’ satisfaction were assessed. Results: The results indicated that dexmedetomidine-led anesthesia could maintain the depth of stage III anesthesia during abdominal surgery. The ED50 and ED95 of dexmedetomidine infusion rates during anesthesia maintenance were 2.298 μg/kg·h (95%CI: 2.190–2.404 μg/kg·h) and 3.765 μg/kg·h (95%CI: 3.550–4.050 μg/kg·h). Continuous infusion of dexmedetomidine and 0.1–0.3 μg/kg·min remifentanil could maintain PSI 25–40, and provide appropriate anesthesia depth for abdominal surgery. Perioperative bradycardia and hypertension could be rapidly corrected with atropine and nitroglycerin. The median wake-up time after anesthesia was 4.8 min, the perioperative maximum HR had significant correlation with wake-up time and intraoperative dexmedetomidine dose. No intraoperative awareness and postoperative delirium occurred; the patients were satisfied with dexmedetomidine-led anesthesia. Conclusions: dexmedetomidine-led strategy could maintain stable depth of anesthesia throughout surgery, and the ED50 of dexmedetomidine infusion rates was 2.298 μg/kg·h. Intraoperative HR, BP and depth of anesthesia require monitoring, the bradycardia and hypertension could be rapidly corrected.

Soil pH is one of the critical indicators of soil quality. A fine resolution map of soil pH is urgently required to address practical issues of agricultural production, environmental protection, and ecosystem functioning, which often fall short of meeting the demands for local applications. To fill this gap, we used data from an extensive survey of 13,424 surface soil samples (0–0.2 m) across the cropland of Jiangxi Province in Southern China. Using digital soil mapping techniques with 46 covariates, we produced a 30 m resolution soil map of topsoil pH in cropland of Southern China. We integrate different variable selection algorithms and machine learning methods. Our results indicate the Random Forest with covariates selected by recursive feature selection had the best performance for mapping soil pH with r of 0.583 and RMSE of 0.41. The prediction interval coverage probability for our prediction was 0.92, indicating a low estimated prediction uncertainty. Climate was identified as the most critical variable for predicting soil pH with a contribution of 37.42 %, followed by soil properties (29.09 %), soil management (21.86 %), parent material (6.22 %), and biota (5.39 %) factors. The mean topsoil pH in the cropland of Jiangxi Province was 5.21, indicating a great pressure of soil acidification in this region. The high soil pH values were mainly distributed in the Northern, Western, and Eastern parts of the survey region while low soil pH values were majorly located in the central part. Compared with past surveys in 1980 s, there was no significant soil pH change in the surveyed region. Our soil pH map can provide important implications and guidance decisions on soil heavy metal pollution remediation, precision agriculture, and prevention of soil acidification.

Ulcerative colitis (UC) is an idiopathic, chronic, and progressive inflammatory set of conditions that affects the colonic mucosa. Natural polyphenols are well documented for their excellent antioxidant properties and have obvious advantages in the strategy of anti-oxidation treatment of UC. However, the poor water solubility, low bioavailability, and unstable nature have hindered their clinical application. Therefore, we efficiently encapsulated bioactive polyphenol epigallocatechin gallate (EGCG) with poly(N-vinylpyrrolidone) (PVP) via reliable intermolecular hydrogen-bonded interactions, and conjugated polysaccharide chondroitin sulfate (CS) with excellent gastrointestinal stability and CD44 active targeting capability onto the surface to achieve targeted delivery (EPC). The obtained EPC system showed an average diameter of 54 nm, monodisperse size distribution, and negatively charged surface. In vitro studies demonstrated the obvious reactive oxygen species (ROS)-scavenging and anti-inflammatory abilities of the EPC system. After oral administration, EPC system localized in the inflamed colon and effectively alleviated the symptoms in dextran sulfate sodium salt (DSS)-induced UC mice. Specifically, the EPC system down-regulated the expression of inflammatory cytokines, up-regulated the expression of tight junction-associated proteins to restore intestinal barrier, and modulated the gut microbiota. This oral drug delivery system with a simple self-assembling process may pave new strategy for polyphenol-based therapy in UC.

Study region: The Xiying and Heihe River basins, cold and arid mountainous regions of northwestern China. Study focus: In data-limited areas of Northwestern China, satellite- and reanalysis-based precipitation products provide crucial assistance. However, a comprehensive evaluation of their advantages, limitations, and hydrological performance has yet to be ascertained. This knowledge gap impedes a deeper understanding of hydrological processes within the region. This study evaluates the accuracy of ERA5L, IMERG-F, GSMaP-G, and FY4A-QPE from three aspects: time, space, and extreme precipitation. Comprehensive validation was conducted on four products using discharge, flood events, and peak flood simulated by Geomorphology-Ecology-Hydrology Model. New hydrological insights for the region: ERA5L data can be used to build long-term studies on regional meteorological and hydrological trends. IMERG-F is valuable for analyzing extreme precipitation and hydrological events. GSMaP-G is well-suited for identifying precipitation events in the region. It is essential to be cautious of precipitation underestimation when using satellite precipitation products at high elevations and during the winter.This study highlights the differences in accuracy among various precipitation products and provides recommendations for policymakers in selecting data for high-altitude research. It also offers critical information on hydrometeorology and directions for producing high-accuracy precipitation products.

Events with highly similar waveforms and rupturing of the same fault patch are interpreted as repeating earthquakes, which can be applied in detecting deep fault deformation, characterizing fault behavior, and assess seismic hazards. In this study, we develop a clustering-based repeating earthquakes identification method by using the hierarchical clustering algorithm in machine learning. First, the parallel waveform cross-correlation method is adopted to calculate the cross-correlation coefficient (CC). Then, the S-P differential time is used to measure the inter distance of events. Finally, the hierarchical clustering is applied to obtain repeating clusters. We utilize this method to investigate the seismicity around the Ganzi-Yushu fault (GYF) and the eastern Kunlun fault (EKLF). We identify 6 repeating clusters along the GYF, with an average fault slip rate of 7.4 mm/a. Around the EKLF, we identify 3 repeating clusters, with an average fault slip rate of 6.9 mm/a. Along the EKLF, the fault slip rates gradually decreases from the west to the east along the strike, indicating a complex dynamic process. Our results agree with geology observation and GPS data. Based on real data testing, our results show that the method to identify repeating earthquakes is automatic, efficient and convenient and provides basic information for accurate identification of repeating earthquakes and places constraints on fault activity.

Abstract Cyber-attack activities are complex and ever-changing, posing severe challenges to cybersecurity personnel. Introducing knowledge graphs into the field of cybersecurity helps depict the intricate cybersecurity landscape and provides technical support for threat identification and situational awareness. However, during the process of constructing knowledge graphs, inevitable noise and conflicts may be introduced, leading to misleading inferences and decisions. This paper aims to address the issues of constructing and assessing the quality of cybersecurity knowledge graphs. We manually constructed a dataset of cybersecurity knowledge graphs (CS13K) and expanded the existing cybersecurity ontology, building a more versatile ontology applicable to existing data features. Building upon this, we utilized Neo4j to construct the cybersecurity knowledge graph. Furthermore, we propose an AttTucker model, based on Transformer, for assessing the quality of knowledge graphs. By employing numerous self-attention heads, this model captures latent information among entities and relations. While reducing the dimensionality of knowledge embeddings, the model achieves evaluation results comparable to high-dimensional embeddings. Finally, we incorporate the path-level information between entities in the knowledge graph into the evaluation of knowledge graph quality based on the AttTucker model. Experimental analysis on both generic datasets and cybersecurity datasets demonstrates that our model significantly improves F1 value and accuracy in knowledge graph quality assessment tasks, outperforming other models.

Purpose – This study aims to investigate whether temperature affects the product quality of exporters and whether the effect is non-linear. More specifically, whether the impact of high temperatures differs from the impact of low temperatures, and whether different types of companies or industries are affected differently. Design/methodology/approach – The paper uses detailed data covering all Chinese exporters from 2000 to 2016 to estimate the effects of temperature on the product quality of export firms. To clarify the relationship between them, the authors use a semi-parametric regression method, trying to test whether there is a non-linear relationship between temperature and the export quality of firms. Findings – The increase in the number of high temperature days significantly reduces the quality of exported products, and this negative effect increases as the temperature rises. High temperature has the most significant negative impact on export quality for firms with low technical complexity, private firms and firms with no intermediate imports and located in historical hot cities. Product quality of both labor-intensive and capital-intensive firms will be affected by heat. High temperatures have the greatest negative impact on the export quality of newly entering products, followed by exiting products, with the least negative impact on persisting product. Originality/value – To the best of the authors’ knowledge, this paper is the first to examine the impact of temperature on the quality of economic development. The findings of this paper again show that the potential economic impacts of global warming are huge. In addition to some potentially devastating impacts in the future, global warming is already causing imperceptible impacts in the present. Public and economic agents need to fully understand the possible adverse impacts of climate change and take corresponding adaptation measures to cope with global warming.

Abstract Despite the abundance of water’s crystalline polymorphs, the growth mechanisms of most ice forms remain poorly understood. This study applies extensive molecular dynamics (MD) simulations to examine the growth of ice VII, revealing a fast growth rate comparable to pure metals while maintaining robust hydrogen-bond networks. The results from an unsupervised machine learning applied to identify local structure suggest that the surface of ice VII consistently exhibits a body-centered cubic (bcc) plastic ice layer, indicating the decoupling of translational and rotational orderings. The study also uncovers the ultrafast growth rate of pure plastic ice, indicating that orientational disorder in the crystal structure may be associated with faster kinetics. Additionally, we discuss the impacts of interfacial plastic layer width and surface roughness on growth mode.

Snow is a crucial component of the cryosphere and plays a vital role in the hydrological cycle, energy balance, and ecosystem function of mountainous regions [...]

The paradigm shift from a coal-based power system to a renewable-energy-based power system brings more challenges to the supply-demand balance of the grid. Distributed energy resources (DERs), which can provide operating reserve to the grid, are regarded as a promising solution to compensate for the power fluctuation of the renewable energy resources. Small-scale DERs can be aggregated as a virtual power plant (VPP), which is eligible to bid in the operating reserve market. Since the DERs usually belong to different entities, it is important to investigate the VPP operation framework that coordinates the DERs in a trusted manner. In this paper, we propose a blockchain-assisted operating reserve framework for VPPs that aggregates various DERs. Considering the heterogeneity of various DERs, we propose a unified reserve capacity evaluation method to facilitate the aggregation of DERs. By considering the mismatch between actual available reserve capacity and the estimated value, the performance of VPP in the operating reserve market is improved. A hardware-based experimental system is developed, and numerical results are presented to demonstrate the effectiveness of the proposed framework.

Abstract Objective The aim of this study was to construct a model used for the accurate diagnosis of Atopic dermatitis (AD) using pyroptosis related biological markers (PRBMs) through the methods of machine learning. Method The pyroptosis related genes (PRGs) were acquired from molecular signatures database (MSigDB). The chip data of GSE120721, GSE6012, GSE32924, and GSE153007 were downloaded from gene expression omnibus (GEO) database. The data of GSE120721 and GSE6012 were combined as the training group, while the others were served as the testing groups. Subsequently, the expression of PRGs was extracted from the training group and differentially expressed analysis was conducted. CIBERSORT algorithm calculated the immune cells infiltration and differentially expressed analysis was conducted. Consistent cluster analysis divided AD patients into different modules according to the expression levels of PRGs. Then, weighted correlation network analysis (WGCNA) screened the key module. For the key module, we used Random forest (RF), support vector machines (SVM), Extreme Gradient Boosting (XGB), and generalized linear model (GLM) to construct diagnostic models. For the five PRBMs with the highest model importance, we built a nomogram. Finally, the results of the model were validated using GSE32924, and GSE153007 datasets. Results Nine PRGs were significant differences in normal humans and AD patients. Immune cells infiltration showed that the activated CD4+ memory T cells and Dendritic cells (DCs) were significantly higher in AD patients than normal humans, while the activated natural killer (NK) cells and the resting mast cells were significantly lower in AD patients than normal humans. Consistent cluster analysis divided the expressing matrix into 2 modules. Subsequently, WGCNA analysis showed that the turquoise module had a significant difference and high correlation coefficient. Then, the machine model was constructed and the results showed that the XGB model was the optimal model. The nomogram was constructed by using HDAC1, GPALPP1, LGALS3, SLC29A1, and RWDD3 five PRBMs. Finally, the datasets GSE32924 and GSE153007 verified the reliability of this result. Conclusions The XGB model based on five PRBMs can be used for the accurate diagnosis of AD patients.

This paper aims to study the effect of the normal stress on the shear performance of splicing joints for precast ultrahigh-performance concrete (UHPC) segmental bridges (PUSBs), an oblique-shear joint is designed to decompose the vertical force into a normal force and a shear force for the joint. The effect of the number, height and inclination angle of the keys on the bearing capacity of the joint is studied by 19 oblique-shear joints. Then, the bearing capacity and failure modes are analyzed significantly. The results show that the failure modes of the joints are shear failure, compression-shear failure and splitting failure, all showing primarily brittle failure. Moreover, the proportion of shear stress in the specimens increases with the increase of the inclination angle of the joint, and the shear capacity of the specimens decreases. Additionally, the shear capacity of the specimens increases with the increase of the height of the keys. After that, we derive a new formula for calculating the shear capacity utilizing Mohr’s stress circle theory and Mohr-Coulomb failure criterion, i.e., the interfacial frictional coefficient is 0.94 and the shear capacity of keys is 0.06. One may find the present formula shows a high guarantee by comparing with those from references. The formula established in this paper may be used to calculate the shear capacity of splicing joint of precast ultrahigh-performance concrete segmental bridges.

Microbiologically influenced corrosion (MIC) caused by sulfate reducing bacteria (SRB) is a serious challenge in many industries, but biofilm greatly decreases the toxicity of bactericides to cell inside. d-amino acids are potential enhancers for bactericides due to their excellent performance on biofilm inhibition. However, the mechanism of d-amino acid cooperating with bactericides for MIC inhibition is still unknown. In this study, d-tyrosine（D-Tyr）and disoctyl dimethyl ammonium chloride (DDAC) were selected as the typical d-amino acid and bactericide, respectively, to evaluate their synergetic inhibition on the corrosion caused by Desulfovibrio vulgaris. D-Tyr obviously enhanced the role of DDAC in inhibiting corrosion with high corrosion inhibition efficiency at 77.23 %. The attachment of EPS and live cells on the coupon surface decreased in the presence of D-Try, leading to more cells directly exposed to DDAC. Besides, D-Try decreased the amount of live cells on the surface and thus reduced the utilization of Fe by SRB and corrosion current. Moreover, dead cells settling to the coupon surface may form a protective lay to retard the contact between live SRB and Fe, leading to slow cathode reaction and less corrosion. Therefore, D-Tyr can reduce the coverage of biofilm, thereby reducing its protective effect on SRB and achieving better corrosion inhibition effect. This work provides a new strategy for improving bactericides and inhibiting MIC.

ObjectiveHyperthyroidism in Chinese children is relatively high and has been increasing in recent years, which has a significant impact on their healthy development. Hyperthyroidism is a polygenic disorder that presents greater challenges in terms of prediction and treatment than monogenic diseases. This study aims to elucidate the associated functions and gene sets of mutated genes in children with hyperthyroidism in terms of the gene ontology through GO enrichment analysis and in terms of biological signaling pathways through KEGG enrichment analysis, thereby enhancing our understanding of the expected effects of multiple mutated genes on hyperthyroidism in children.MethodsWhole-exome sequencing was performed on the DNA samples of children with hyperthyroidism. Screening for pathogenic genes related to hyperthyroidism in affected children was performed using the publicly available disease databases Malacards, MutationView, and Clinvar, and the functions and influences of the identified pathogenic genes were analyzed using statistical analysis and the gene enrichment approach.ResultsThrough GO enrichment analysis, it was found that the most significant gene ontology enrichment was the function “hormone activity” in terms of gene ontology molecular function. The corresponding mutated genes set that has common effects on hyperthyroidism in children included TG, CALCA, POMC, CGA, PTH, GHRL, FBN1, TRH, PRL, LEP, ADIPOQ, INS, GH1. The second most significant gene ontology enrichment was the function “response to peptide hormone” in terms of biological process. The corresponding mutated genes set that has common effects on hyperthyroidism in children included LRP6, TSC2, KANK1, COL1A1, CDKN1B, POMC, STAT1, MEN1, APC, GHRL, TSHR, GJB2, FBN1, GPT, LEP, ADIPOQ, INS, GH1. Through KEGG enrichment analysis, it was found that the most significant biological signaling pathway enrichment was the pathway “Thyroid hormone signaling pathway” function. The corresponding mutated genes set that has common effects on hyperthyroidism in children included NOTCH3, MYH7, TSC2, STAT1, MED13L, MAP2K2, SLCO1C1, SLC16A2, and THRB. The second most significant biological signaling pathway enrichment was the pathway “Hypertrophic cardiomyopathy” in terms of biological process. The corresponding mutated genes set that has common effects on hyperthyroidism in children included IGF1, CACNA1S, MYH7, IL6, TTN, CACNB2, LAMA2, and DMD.ConclusionThe mutated genes in children with hyperthyroidism were closely linked to function involved in “hormone activity” and “response to peptide hormone” in terms of the biological signaling pathway, and to the functional pathways involved in “Thyroid hormone signaling pathway” and “Hypertrophic cardiomyopathy” in terms of the biological signaling pathway.

Abstract Studies in recent years have highlighted an elaborate crosstalk between T cells and bone cells, suggesting that T cells may be alternative therapeutic targets for the maintenance of bone homeostasis. Here, it is reported that systemic administration of low‐dose staphylococcal enterotoxin C2 (SEC2) 2M‐118, a form of mutant superantigen, dramatically alleviates ovariectomy (OVX)‐induced bone loss via modulating T cells. Specially, SEC2 2M‐118 treatment increases trabecular bone mass significantly via promoting bone formation in OVX mice. These beneficial effects are largely diminished in T‐cell‐deficient nude mice and can be rescued by T‐cell reconstruction. Neutralizing assays determine interferon gamma (IFN‐γ) as the key factor that mediates the beneficial effects of SEC2 2M‐118 on bone. Mechanistic studies demonstrate that IFN‐γ stimulates Janus kinase/signal transducer and activator of transcription (JAK–STAT) signaling, leading to enhanced production of nitric oxide, which further activates p38 mitogen‐activated protein kinase (MAPK) and Runt‐related transcription factor 2 (Runx2) signaling and promotes osteogenic differentiation. IFN‐γ also directly inhibits osteoclast differentiation, but this effect is counteracted by proabsorptive factors tumor necrosis factor alpha (TNF‐α) and interleukin 1 beta (IL‐1β) secreted from IFN‐γ‐stimulated macrophages. Taken together, this work provides clues for developing innovative approaches which target T cells for the prevention and treatment of osteoporosis.

Abstract We reprove a result by Bartsch, Weth, and Willem (Calc. Var. Partial Differ. Equ. 18(3):253–268, 2003) concerning the nondegeneracy of bubble solutions for a critical semilinear elliptic equation involving the polyharmonic operator. The merit of our proof is that it does not rely on the comparison theorem. The argument of our proof mainly uses the stereographic projection with the Funk–Hecke formula, which works for general critical semilinear elliptic equations.

Although a growing number of studies have suggested the relevance of greenspace exposure to respiratory health, most have explored single greenfield indicators or selected disease outcomes. This systematic review aimed to comprehensively assess the evidence that greenspace exposure is associated with chronic obstructive pulmonary disease (COPD) incidence, prevalence, mortality, and related lung function indicators and to explore the impact of greenspace exposure on COPD-related disease outcomes. We searched the literature published before 28 September 2023, using six electronic databases and snowball searches. After screening 3689 studies, 16 eligible studies were included. These studies examined various greenspace indicators such as the Normalized Difference Vegetation Index (NDVI), canopy coverage, greenspace coverage, and land cover conditions. Most studies have reported the positive effects of greenspace exposure. However, the findings had significant heterogeneity, with large differences in the greenness assessment, COPD identification methods, and population characteristics. The mechanisms of this impact have not been conclusively determined, but we propose several hypotheses to explain the pathways by which greenspace exposure affects COPD. Overall, our findings contribute valuable knowledge for understanding the relationship between greenspace exposure and COPD, informing future research in this field.

The digital transformation of banks has led to a paradigm shift, promoting the open sharing of data and services with third-party providers through APIs, SDKs, and other technological means. While data sharing brings personalized, convenient, and enriched services to users, it also introduces security risks, including sensitive data leakage and misuse, highlighting the importance of data classification and grading as the foundational pillar of security. This paper presents a cloud-edge collaborative banking data open application scenario, focusing on the critical need for an accurate and automated sensitive data classification and categorization method. The regulatory outpost module addresses this requirement, aiming to enhance the precision and efficiency of data classification. Firstly, regulatory policies impose strict requirements concerning data protection. Secondly, the sheer volume of business and the complexity of the work situation make it impractical to rely on manual experts, as they incur high labor costs and are unable to guarantee significant accuracy. Therefore, we propose a scheme UP-SDCG for automatically classifying and grading financially sensitive structured data. We developed a financial data hierarchical classification library. Additionally, we employed library augmentation technology and implemented a synonym discrimination model. We conducted an experimental analysis using simulation datasets, where UP-SDCG achieved precision surpassing 95%, outperforming the other three comparison models. Moreover, we performed real-world testing in financial institutions, achieving good detection results in customer data, supervision, and additional in personally sensitive information, aligning with application goals. Our ongoing work will extend the model’s capabilities to encompass unstructured data classification and grading, broadening the scope of application.

The northeastern margin of the Tibetan Plateau (NE Tibetan Plateau) exhibits active geological structures and has experienced multiple strong earthquakes, with M ≥ 7, throughout history. Particularly noteworthy is the 1920 M81/2 earthquake in the Haiyuan region that occurred a century ago and is documented as one of the deadliest earthquakes. Consequently, analyzing seismic risks in the northeastern margin of the Tibetan Plateau holds significant importance. The b value, a crucial parameter for seismic activity, plays a pivotal role in seismic hazard analyses. This study calculates the spatial b values in this region based on earthquake catalogs since 1970. The study area encompasses several major active faults, and due to variations in b values across different fault types, traditional grid-search methods may introduce significant errors in calculating the spatial b value within complex fault systems. To address this, we employed the hierarchical space–time point–process (HIST-PPM) method proposed by Ogata. This method avoids partitioning earthquake samples, optimizes parameters using Akaike’s Bayesian Information Criterion (ABIC) with entropy maximization, and theoretically allows for a higher spatial resolution and more accurate b value calculations. The results indicate a high spatial heterogeneity in b values within the study area. The northwestern and southeastern regions exhibit higher b values. Along the Haiyuan fault zone, the central rupture zone of the Haiyuan earthquake has relatively higher b values than other regions of this fault zone, which is possibly related to the sufficient release of stress during the main rupture of the Haiyuan earthquake. The b values vary from high in the west to low in the east along the Zhongwei fault. On the West Qinling fault zone, the epicenter of the recent Minxian–Zhangxian earthquake is associated with a low b value. In general, regions with low b values correspond well to areas with moderate–strong seismic events in the past 50 years. The spatial differences in b values may reflect variances in seismic hazards among fault zones and regions within the same fault zone.

Improving urban land green use efficiency (ULGUE) is essential for achieving the sustainable use of land resources and the high-quality economic development of cities. The digital economy has overcome spatial and locational constraints, blurred the boundaries between industries, and created a collaborative and open economic model, inevitably affecting urban land utilization. However, whether the digital economy will affect ULGUE and how such an effect would take place are still unclear. This study evaluates the digital economy and ULGUE of Chinese cities from 2011 to 2019 and systematically analyzes both the direct and indirect impact mechanisms through which the digital economy affects ULGUE. The results show that the digital economy can significantly enhance ULGUE, and these results prove to be reliable, as shown by various endogeneity treatments and robustness tests. The digital economy enhances ULGUE by optimizing the industrial structure (structural effect), increasing green technology innovation (technical effect), and agglomerating digital talent (scale effect). Moreover, land finance dependency plays an adverse moderating role in the relationship between the digital economy and ULGUE. Further heterogeneity analysis shows that the promoting effect of the digital economy on ULGUE takes full effect in the eastern cities, larger cities, and cities with high levels of digital economy development and land marketization. This paper presents recommendations for supporting the balanced and integrated development of the digital economy across regions and provides differentiated development strategies to enhance ULGUE in the context of digitization.

Submerged Reefs (SRs) are a kind of artificial fish habitat that can protect coasts and maintain ecological biodiversity. In this study, the flow field of the SR is simulated by solving a Reynolds-averaged Navier–Stokes equation closed with the Realizable k-ε model based on the finite volume method. The turbulent characteristics of SRs under different inflow velocities and submergences in the vicinity of the SR are analyzed. The wake vorticities are the primary turbulent pattern within and around the SR. The back wake and vorticity are chosen as critical indicators to quantitatively assess the hydrodynamic characteristics induced by the SR. The results show: (1) as the main flow passes through the SR, the upwelling is produced in front of the SR and a large-scale wake region is formed behind the SR which contains a clockwise vortex; (2) the length of the wake region formed behind the SR is positively and linearly correlated with both the inflow velocity and submergence; (3) the dipole-type vorticity patterns are induced within the compartment of the SR, where the area and average value of high vorticity have a positive correlation with the flow velocity and a negative correlation with the submergence, respectively.

Psychological anxiety among college students has attracted research interest. Previous studies have shown that greenspaces play a positive role in the recovery of student health. However, limited studies have explored the benefits of restorative environmental greenspace components. Therefore, this study used virtual reality to conduct control variable experiments. Considering the terrain scene, pavement material, and green vision rate as research elements, we monitored the skin conductance level and heart rate variability of 36 college students, as well as the positive and negative affect schedule and perceptual recovery scales, and we found that terrain elements have a significant impact on perceptual recovery, while pavement material has a significant impact on physiological recovery. Significant differences in perceptual recovery scores and changes in negative emotions among the different green vision levels were also observed. According to the regression relationship, the scene’s attractiveness rating was the highest when the scene’s green vision rate was 50%, while at 48%, the positive emotional improvement was the highest, and at 40%, the negative emotional improvement was the greatest.

To reduce platinum usage, ultrathin MXene sheets with little restacking effect were prepared. The ultrathin MXene was prepared by a two-step etching process, which showed high specific surface area with low charge transfer resistance. The sample showed a double layer capacity of 64.98 mF cm−2, which is 14 times as large as that of ordinary HF prepared MXene, indicating a larger electrochemically active surface area. It showed a much better HER performance of ∼190 mV at 10 mA cm−2. The better performance attributes to 0.4 wt% Pt loaded. The Pt loaded MXene exhibited a better HER performance of ∼75 mV at 10 mA cm−2 and a Tafel slope of 61.7 mV·dec−1 close to 40 wt% commercial Pt/C. The sample performed better than Pt/C in a 3 h chronopotentiometry test and hardly changed in ECSA after the cyclic experiment. With more Pt loading, the sample delivered better HER performance than Pt/C in the LSV test (∼51 mV at 10 mA cm−2). This work provides an effective route for the preparation of ultrathin MXene sheets with larger electrochemically active area and more active sites for Pt loading, leading to superior HER performance.

BackgroundSepsis is a clinical syndrome with high mortality. Subtype identification in sepsis is meaningful for improving the diagnosis and treatment of patients. The purpose of this research was to identify subtypes of sepsis using RNA-seq datasets and further explore key genes that were deregulated during the development of sepsis.MethodsThe datasets GSE95233 and GSE13904 were obtained from the Gene Expression Omnibus database. Differential analysis of the gene expression matrix was performed between sepsis patients and healthy controls. Intersection analysis of differentially expressed genes was applied to identify common differentially expressed genes for enrichment analysis and gene set variation analysis. Obvious differential pathways between sepsis patients and healthy controls were identified, as were developmental stages during sepsis. Then, key dysregulated genes were revealed by short time-series analysis and the least absolute shrinkage and selection operator model. In addition, the MCPcounter package was used to assess infiltrating immunocytes. Finally, the dysregulated genes identified were verified using 69 clinical samples.ResultsA total of 898 common differentially expressed genes were obtained, which were chiefly related to increased metabolic responses and decreased immune responses. The two differential pathways (angiogenesis and myc targets v2) were screened on the basis of gene set variation analysis scores. Four subgroups were identified according to median expression of angiogenesis and myc target v2 genes: normal, myc target v2, mixed-quiescent, and angiogenesis. The genes CHPT1, CPEB4, DNAJC3, MAFG, NARF, SNX3, S100A9, S100A12, and METTL9 were recognized as being progressively dysregulated in sepsis. Furthermore, most types of immune cells showed low infiltration in sepsis patients and had a significant correlation with the key genes. Importantly, all nine key genes were highly expressed in sepsis patients.ConclusionThis study revealed novel insight into sepsis subtypes and identified nine dysregulated genes associated with immune status in the development of sepsis. This study provides potential molecular targets for the diagnosis and treatment of sepsis.

Alzheimer’s disease (AD), one of the leading diseases of the nervous system, is accompanied by symptoms such as loss of memory, thinking and language skills. Both mild cognitive impairment (MCI) and very mild cognitive impairment (VMCI) are the transitional pathological stages between normal aging and AD. While the changes in whole-brain structural and functional information have been extensively investigated in AD, The impaired structure–function coupling remains unknown. The current study employed the OASIS-3 dataset, which includes 53 MCI, 90 VMCI, and 100 Age-, gender-, and education-matched normal controls (NC). Several structural and functional parameters, such as the amplitude of low-frequency fluctuations (ALFF), voxel-based morphometry (VBM), and The ALFF/VBM ratio, were used To estimate The whole-brain neuroimaging changes In MCI, VMCI, and NC. As disease symptoms became more severe, these regions, distributed in the frontal-inf-orb, putamen, and paracentral lobule in the white matter (WM), exhibited progressively increasing ALFF (ALFFNC

Rabdosia serra (R. serra), an important component of Chinese herbal tea, has traditionally been used to treat hepatitis, jaundice, cholecystitis, and colitis. However, the chemical composition of R. serra and its effect against colitis remain unclear. In this study, the chemical composition of the water extract of R. serra was analyzed using ultra performance liquid chromatography coupled with a hybrid linear ion trap quadrupole-orbitrap mass spectrometer (UPLC-LTQ-Orbitrap-MS). A total of 46 compounds, comprising ent-kaurane diterpenoids, flavonoids, phenolic acids, and steroids, were identified in the water extract of R. serra, and the extract could significantly alleviate dextran sulfate sodium salt-induced colitis by improving colon length, upregulating anti-inflammatory factors, downregulating proinflammatory factors, and restoring the balance of T helper 17/T regulatory cells. R. serra also preserved intestinal barrier function by increasing the level of tight junction proteins (zonula occludens 1 and occludin) in mouse colonic tissue. In addition, R. serra modulated the gut microbiota composition by increasing bacterial richness and diversity, increasing the abundance of beneficial bacteria (Muribaculaceae, Bacteroides, Lactobacillus, and Prevotellaceae_UCG-001), and decreasing the abundance of pathogenic bacteria (Turicibacter, Eubacterium_fissicatena_group, and Eubacterium_xylanophilum_group). Gut microbiota depletion by antibiotics further confirmed that R. serra alleviated colitis in a microbiota-dependent manner. Overall, our findings provide chemical and biological evidence for the potential application of R. serra in the management of colitis.

Abstract Maojian is one of China’s traditional famous teas. There are many Maojian-producing areas in China. Because of different producing areas and production processes, different Maojian have different market prices. Many merchants will mix Maojian in different regions for profit, seriously disrupting the healthy tea market. Due to the similar appearance of Maojian produced in different regions, it is impossible to make a quick and objective distinction. It often requires experienced experts to identify them through multiple steps. Therefore, it is of great significance to develop a rapid and accurate method to identify different regions of Maojian to promote the standardization of the Maojian market and the development of detection technology. In this study, we propose a new method based on Near infra-red (NIR) with deep learning algorithms to distinguish different origins of Maojian. In this experiment, the NIR spectral data of Maojian from different origins are combined with the back propagation neural network (BPNN), improved AlexNet, and improved RepSet models for classification. Among them, improved RepSet has the highest accuracy of 99.30%, which is 8.67% and 0.70% higher than BPNN and improved AlexNet, respectively. The overall results show that it is feasible to use NIR and deep learning methods to quickly and accurately identify Maojian from different origins and prove an effective alternative method to discriminate different origins of Maojian.

In order to realize short-time load forecasting, an Adaboost-BP method with a weight update mechanism is proposed based on ensemble learning theory. Firstly, the original historical load power is decomposed into a set of sub-series with diverse characteristics via using ensemble empirical mode decomposition. Then, BP neural network is performed as a weak learner to predict the load power of test samples. At the same time, the prediction results are used to update the weight of the weak learner and test sample and then construct a strong learner to obtain the final prediction results. According to the analysis results of the characteristics of each sub-series, the load forecasting model is established. The result of analysing the calculation example shows that the proposed prediction model outperforms all other algorithms in accuracy, which has high engineering application value.

Abstract Purpose This study aims to examine the use of 3D printing technology to treat clavicular fractures by skilled and inexperienced surgeons. Methods A total of 80 patients with clavicle fractures (from February 2017 to May 2021) were enrolled in this study. Patients were divided randomly into four groups: group A: Patients underwent low-dose CT scans, and 3D models were printed before inexperienced surgeons performed surgeries; group B: Standard-dose CT were taken, and 3D models were printed before experienced surgeons performed surgeries; group C and D: Standard-dose CT scans were taken in both groups, and the operations were performed differently by inexperienced (group C) and experienced (group D) surgeons. This study documented the operation time, blood loss, incision length, and the number of intraoperative fluoroscopies. Results No statistically significant differences were found in age, gender, fracture site, and fracture type (P value: 0.23–0.88). Group A showed shorter incision length and fewer intraoperative fluoroscopy times than groups C and D (P

Abstract Background To address the clinical features and potential risk factors of ovarian pregnancy (OP). Methods In this retrospective case–control study performed in West China Second University Hospital from March 17, 2005 to December 8, 2018, 146 OP patients were selected as a case group, 292 patients with tubal pregnancy (TP) and 292 women with intrauterine pregnancy (IUP) were matched as controls at a ratio of 1:2:2. Results When compared with TP patients, OP patients tend to have worse clinical complications (hemorrhagic shock (7.41% vs 2.89%), rupture of pregnancy sac (54.07% vs 37.78%), hemoperitoneum (363.1 ± 35.46 ml vs 239.3 ± 27.61 ml) and increased need for emergency laparotomy (9.60% vs 3.97%) at an early gestational age. Assisted reproductive technology (ART) (adjusted OR1 2.08, 95%CI 1.04 to 4.18; adjusted OR2 2.59, 95%CI 1.25 to 5.37) and intrauterine contraceptive device (IUD) use (adjusted OR1 2.19, 95%CI 1.10 to 4.36; adjusted OR2 2.77, 95%CI 1.74 to 5.71) may be risk factors for ovarian ectopic pregnancy as compared to the control groups of TP and IUP patients. Conclusions OP patients tend to have more severe clinical complications and this study has identified ART and IUD use as potential risk factors for OP. Results of this study may contribute to improve the understanding of OP and promote early surgical intervention.

Abstract Objective To explore the feasibility of the three-dimensional printing (3DP) knee model using the ultra-low-dose computed tomography (CT) scan for preoperative planning and simulated surgery. Methods Thirty-six patients were divided into the standard-dose protocol group (A) and ultra-low-dose protocol group (B). The anteroposterior diameter, left and right diameter of femur, anteroposterior diameter of tibial plateau (APTP), left and right diameter, distance from the intercondylar ridge to tibial tuberosity, lower femur angle, and upper tibial angle were measured on CT images. On the 3D printed knee joint model, Vernier calipers were used to measure: anteroposterior diameter, left and right diameter of the internal and external condyles of femur; left and right diameters, anteroposterior diameters of tibial plateau; upper and lower meridian, left and right diameters of patella. Results With group A as reference, the effective radiation dose in group B was significantly reduced to 97.0% (36.4 ± 3.7 uSv and 1.1 ± 0.2 uSv, respectively). There was no difference in objective parameters for 3DP model (p = 0.31–0.84). None of the quantitative parameters of image quality showed significant difference (p = 0.11–0.96). Despite lower score of image quality and 3DP model in group B (3.0 ± 0.0 vs. 2.1 ± 0.2, 2.9 ± 0.3 vs. 2.2 ± 0.4; p

The July 2019 MW6.4 Ridgecrest, California earthquake and its distinct foreshocks were well recorded by local and regional stations, providing a great opportunity to characterize its foreshocks and investigate the nucleation mechanisms of the mainshock. In this study, we utilized the match-and-locate (M&L) method to build a high-precision foreshock catalog for this MW6.4 earthquake. Compared with the sequential location methods (matched-filter + cross-correlation-based hypoDD), our new catalog contains more events with higher location accuracy. The MW6.4 mainshock was preceded by 40 foreshocks within ∼2 h (on July 4, 2019 from 15:35:29 to 17:32:52, UTC). Their spatiotemporal distribution revealed a complex seismogenic structure consisting of multiple fault strands, which were connected as a throughgoing fault by later foreshocks and eventually accommodated the 2019 MW6.4 mainshock. To better understand the nucleation mechanism, we determined the rupture dimension of the largest ML4.0 foreshock by calculating its initial rupture and centroid points using the M&L method. By estimating Coulomb stress change we suggested that the majority of foreshocks following the ML4.0 event and MW6.4 mainshock occurred within regions of increasing Coulomb stress, indicating that they were triggered by stress transfer. The nucleation process before the ML4.0 event remains unclear due to the insufficient sampling rate of waveforms and small magnitude of events. Thus, our study demonstrates that the M&L method has superior detection and location ability, showing potential for studies that require high-precision location (e.g., earthquake nucleation).