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Duchenne muscular dystrophy (DMD) is an X-linked recessive progressive degenerative disease of skeletal muscle, characterized by intramuscular inflammation, muscle regeneration disorder and replacement of muscle with fibroadipose tissue. DMD is caused by the absence of normal dystrophy. Impaired self-renew ability and limited differentiation capacity of satellite cells are proved as main reasons for muscle regeneration failure. The deficiency of estrogen impedes the process of muscle regeneration. However, the role of estrogen receptor β (ERβ) in muscle regeneration is still unclear. This study aims to investigate the role and the pharmacological effect of ERβ activation on muscle regeneration in mdx mice. This study showed that mRNA levels of ERβ and myogenic-related genes both witnessed increasing trends in dystrophic context. Our results revealed that treatment with selective ERβ agonist (DPN, diarylpropionitrile) significantly increased myogenic differentiation 1 (MyoD-1) level and promoted muscle regeneration in mdx mice. Similarly, in mdx mice with muscle-specific estrogen receptor α (ERα) ablation, DPN treatment still promoted muscle regeneration. Moreover, we demonstrated that myoblasts differentiation was accompanied by raised nuclear accumulation of ERβ. DPN treatment augmented the nuclear accumulation of ERβ and, thus, contributed to myotubes formation. One important finding was that forkhead box O3A (FOXO3A), as a pivotal transcription factor in Myod-1 transcription, participated in the ERβ-promoted muscle regeneration. Overall, we offered an interesting explanation about the crucial role of ERβ during myogenesis.

Abstract Background Tumor necrosis factor receptor-associated factor 3 (TRAF3) has specific regulatory effects on a wide range of diseases, including tumors. However, the effect and mechanism of TRAF3 on lung adenocarcinoma (LUAD) are still unknown. The aim of the present study was to make clear the role and potential mechanism of TRAF3 in LUAD. Methods TIMER2.0 database and western blot were applied to detect the expression of TRAF3 in lung adenocarcinoma tissue. Kaplan-Meier Plotter database was utilized to explore the effect of TRAF3 on the clinical prognosis of lung adenocarcinoma patients. Specific siRNA was used to inhibit the expression of TRAF3 in LUAD cells (A549 and H1299). CCK-8 and EdU assays were performed for assessing LUAD cells proliferation. Wound healing assay and transwell assay were performed for determining cells migration. CCK-8 assay was used to assess the response of the LUAD cells to paclitaxel. TIMER2.0 bioinformatics and western blot were employed to detect the effects of TRAF3 on pyroptosis in LUAD. Results TRAF3 was highly expressed in lung adenocarcinoma tissues and cell lines. Patients with TRAF3 hyperexpression had a good prognosis compared to those with lower expression. TRAF3 inhibition notably induced proliferation and migration of LUAD cells. Inhibition of TRAF3 also weakened the sensitivity of LUAD cells to paclitaxel. Moreover, bioinformatics results showed that TRAF3 was positively correlated with the expression of pyroptosis-related genes in LUAD. Western blot assays showed that TRAF3 inhibition visibly decreased the expression of apoptosis-associated speck-like protein (ASC), cleaved caspase-1 and matured- IL-1β. Conclusions Inhibition of TRAF3 promotes the proliferation and migration of LUAD cells, and reduces the sensitivity of LUAD cells to paclitaxel. The effects of TRAF3 on LUAD cells were mediated in part by caspase-1-dependent pyroptosis.

Knowledge tracing plays a crucial role in effectively representing learners’ understanding and predicting their future learning progress. However, existing deep knowledge tracing methods, reliant on the forgetting model and Rasch model, often fail to account for the varying rates at which learners forget different knowledge concepts and the variations in question embedding covering the same concept. To address these limitations, this paper introduces an enhanced deep knowledge tracing model that combines the transformer network model with two innovative components. The first component is a multiband attention mechanism, which comprehensively summarizes a learner’s past response history across various temporal scales. By computing attention weights using different decay rates, this mechanism adaptively captures both long-term and short-term interactions for different knowledge concepts. The second component utilizes a quantized question embedding module to effectively capture variations among questions addressing the same knowledge concept. This module represents these differences in a rich embedding space, avoiding overparameterization or overfitting issues. The proposed model is evaluated on popular benchmark datasets, demonstrating its superiority over existing knowledge tracing methods in accuracy. This enhancement holds potential for improving personalized learning systems by providing more precise insights into learners’ progress.

Abstract Alcoholism is a widespread and damaging behaviour of people throughout the world. Long-term alcohol consumption has resulted in alcoholic liver disease (ALD) being the leading cause of chronic liver disease. Many metabolic enzymes, including alcohol dehydrogenases such as ADH, CYP2E1, and CATacetaldehyde dehydrogenases ALDHsand nonoxidative metabolizing enzymes such as SULT, UGT, and FAEES, are involved in the metabolism of ethanol, the main component in alcoholic beverages. Ethanol consumption changes the functional or expression profiles of various regulatory factors, such as kinases, transcription factors, and microRNAs. Therefore, the underlying mechanisms of ALD are complex, involving inflammation, mitochondrial damage, endoplasmic reticulum stress, nitrification, and oxidative stress. Moreover, recent evidence has demonstrated that the gut-liver axis plays a critical role in ALD pathogenesis. For example, ethanol damages the intestinal barrier, resulting in the release of endotoxins and alterations in intestinal flora content and bile acid metabolism. However, ALD therapies show low effectiveness. Therefore, this review summarizes ethanol metabolism pathways and highly influential pathogenic mechanisms and regulatory factors involved in ALD pathology with the aim of new therapeutic insights.

To develop a kind of food packaging material with good hydrophobicity, agar/konjac glucomannan (AK) composite film incorporated with ethyl cellulose (EC) was developed. The effect of the addition of EC on the microstructure, mechanical properties and water resistance ability (swelling rate, dissolution rate and water vapor transmittance) of the composite film were explored by scanning electron microscopy (SEM), Fourier infrared spectroscopy (FTIR), X-ray diffraction (XRD), and texture analyzer. The results showed that the EC molecular chain in AKE composite film was not completely extended, and the addition of EC inhibited the crystallization of agar. Compared with AK composite film, the AKE-25% composite film had the highest elongation at break (15.08%) and water vapor transmittance (9.88×10−12·g·cm/(cm2·s·Pa)). The dissolution rate decreased from 40.70% to 25.64%. Therefore, the inclusion of EC improved the ductility and water resistance for Agar/KGM/EC composites.

Network traffic anomaly detection is a key step in identifying and preventing network security threats. This study aims to construct a new deep-learning-based traffic anomaly detection model through in-depth research on new feature-engineering methods, significantly improving the efficiency and accuracy of network traffic anomaly detection. The specific research work mainly includes the following two aspects: 1. In order to construct a more comprehensive dataset, this article first starts from the raw data of the classic traffic anomaly detection dataset UNSW-NB15 and combines the feature extraction standards and feature calculation methods of other classic detection datasets to re-extract and design a feature description set for the original traffic data in order to accurately and completely describe the network traffic status. We reconstructed the dataset DNTAD using the feature-processing method designed in this article and conducted evaluation experiments on it. Experiments have shown that by verifying classic machine learning algorithms, such as XGBoost, this method not only does not reduce the training performance of the algorithm but also improves its operational efficiency. 2. This article proposes a detection algorithm model based on LSTM and the recurrent neural network self-attention mechanism for important time-series information contained in the abnormal traffic datasets. With this model, through the memory mechanism of the LSTM, the time dependence of traffic features can be learned. On the basis of LSTM, a self-attention mechanism is introduced, which can weight the features at different positions in the sequence, enabling the model to better learn the direct relationship between traffic features. A series of ablation experiments were also used to demonstrate the effectiveness of each component of the model. The experimental results show that, compared to other comparative models, the model proposed in this article achieves better experimental results on the constructed dataset.

Acid stress is a challenging condition that yeast cells must overcome during fermentation. Enhancing the inherent tolerance of industrial Saccharomyces cerevisiae to organic acid stresses is crucial for increasing fermentation efficiency and reducing economic costs. In a previous study, we constructed a Saccharomyces cerevisiae strain SWY85S with improved tolerance to citric acid stress by modifying the second PEP4-allele. Malic acid is a dominant organic acid in grapefruit, which forms the acidic constituents of wine fermentation mash and finished products. We investigated the malic acid stress tolerance of the strain SWY85S in comparison with that of a strain with one PEP4-allele disrupted and the wild-type strain in this study. Our results revealed that the strain SWY85S demonstrated greater tolerance of malic acid stress, regardless of whether it was cultured with adequate nutrient supplies or under amino acid starvation. Moreover, the strain SWY85S performed remarkably in converting glucose to ethanol during fermentation under malic acid stress. This study provides insights into the role of a vacuolar PEP4-allele coding product in response to environmental stress and the physiological mechanism of yeast to withstand organic acid stress.

Water supply systems that use rivers with high sedimentation levels may experience issues such as reservoir siltation. The suspended sediment concentration (SSC) of rivers experiences interannual variation and high nonlinearity due to its close relationship with meteorological factors, which increase the mismatch between the river water source and urban water demand. The raw water system scheduling problem is expressed as a reservoir and pump station control problem that involves real-time SSC changes. To lower the SSC of the water intake and lower the pumping station’s energy consumption, a deep reinforcement learning (DRL) model based on SSC prediction was developed. The framework consists of a DRL model, a hydraulic model for simulating the raw water system, and a neural network for predicting river SSC. The framework was tested using data from a Yellow River water withdrawal pumping station in China with an average capacity of 400,000 m3/d. The strategy created in this study can reduce the system energy consumption per unit of water withdrawal by 8.33% and the average annual water withdrawal SSC by 37.01%, when compared to manual strategy. Meanwhile, the deep reinforcement learning algorithm had good response robustness to uncertain imperfect predictive data.

Abstract The genetic paradox of biological invasions is complex and multifaceted. In particular, the relative role of disparate propagule sources and genetic adaptation through postintroduction hybridization has remained largely unexplored. To add resolution to this paradox, we investigate the genetic architecture responsible for the invasion of two invasive Asian carp species, bighead carp (Hypophthalmichthys nobilis) and silver carp (H. molitrix) (bigheaded carps) that experience extensive hybridization in the Mississippi River Basin (MRB). We sequenced the genomes of bighead and silver carps (~1.08G bp and ~1.15G bp, respectively) and their hybrids collected from the MRB. We found moderate‐to‐high heterozygosity in bighead (0.0021) and silver (0.0036) carps, detected significantly higher dN/dS ratios of single‐copy orthologous genes in bigheaded carps versus 10 other species of fish, and identified genes in both species potentially associated with environmental adaptation and other invasion‐related traits. Additionally, we observed a high genomic similarity (96.3% in all syntenic blocks) between bighead and silver carps and over 90% embryonic viability in their experimentally induced hybrids. Our results suggest intrinsic genomic features of bigheaded carps, likely associated with life history traits that presumably evolved within their native ranges, might have facilitated their initial establishment of invasion, whereas ex-situ interspecific hybridization between the carps might have promoted their range expansion. This study reveals an alternative mechanism that could resolve one of the genetic paradoxes in biological invasions and provides invaluable genomic resources for applied research involving bigheaded carps.

Federated learning (FL) has received considerable attention with the development of mobile internet technology, which is an emerging framework to train a deep learning model from decentralized data. Modern mobile devices often have access to rich but privacy-sensitive data, and computational abilities are often limited because of the hardware restriction. In previous works based on federated averaging (FedAvg) algorithm, mobile devices need to perform lots of calculations, and it is time-consuming in the process of global communication. Inspired by edge computing, we proposed edge federated learning (EdgeFed), which separates the process of updating the local model that is supposed to be completed independently by mobile devices. The outputs of mobile devices are aggregated in the edge server to improve the learning efficiency and decrease the global communication frequency. Empirical experiments demonstrate that our proposed EdgeFed has advantages in different bandwidth scenarios. Especially, by offloading part of the calculations from mobile clients to the edge server, the computational cost of the mobile devices and the global communication expense can be simultaneously reduced as compared to FedAvg.

The digestive tract is replete with complex and diverse microbial communities that are important for the regulation of multiple pathophysiological processes in humans and animals, particularly those involved in the maintenance of intestinal homeostasis, immunity, inflammation, and tumorigenesis. The diversity of bile acids is a result of the joint efforts of host and intestinal microflora. There is a bidirectional relationship between the microbial community of the intestinal tract and bile acids in that, while the microbial flora tightly modulates the metabolism and synthesis of bile acids, the bile acid pool and composition affect the diversity and the homeostasis of the intestinal flora. Homeostatic imbalances of bile acid and intestinal flora systems may lead to the development of a variety of diseases, such as inflammatory bowel disease (IBD), colorectal cancer (CRC), hepatocellular carcinoma (HCC), type 2 diabetes (T2DM), and polycystic ovary syndrome (PCOS). The interactions between bile acids and intestinal flora may be (in)directly involved in the pathogenesis of these diseases.

Abstract Objective The purpose of this study was to develop an individual survival prediction model based on multiple machine learning (ML) algorithms to predict survival probability for remnant gastric cancer (RGC). Methods Clinicopathologic data of 286 patients with RGC undergoing operation (radical resection and palliative resection) from a multi-institution database were enrolled and analyzed retrospectively. These individuals were split into training (80%) and test cohort (20%) by using random allocation. Nine commonly used ML methods were employed to construct survival prediction models. Algorithm performance was estimated by analyzing accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC), confusion matrices, five-fold cross-validation, decision curve analysis (DCA), and calibration curve. The best model was selected through appropriate verification and validation and was suitably explained by the SHapley Additive exPlanations (SHAP) approach. Results Compared with the traditional methods, the RGC survival prediction models employing ML exhibited good performance. Except for the decision tree model, all other models performed well, with a mean ROC AUC above 0.7. The DCA findings suggest that the developed models have the potential to enhance clinical decision-making processes, thereby improving patient outcomes. The calibration curve reveals that all models except the decision tree model displayed commendable predictive performance. Through CatBoost-based modeling and SHAP analysis, the five-year survival probability is significantly influenced by several factors: the lymph node ratio (LNR), T stage, tumor size, resection margins, perineural invasion, and distant metastasis. Conclusions This study established predictive models for survival probability at five years in RGC patients based on ML algorithms which showed high accuracy and applicative value.

Abstract We aimed to explore the association between FFP transfusion and outcomes of DC patients with significant coagulopathy. A total of 693 DC patients with significant coagulopathy were analyzed with 233 patients per group after propensity score matching (PSM). Patients who received FFP transfusion were matched with those receiving conventional therapy via PSM. Regression analysis showed FFP transfusion had no benefit in 30-day (HR: 1.08, 95% CI 0.83–1.4), 90-day (HR: 1.03, 95% CI 0.80–1.31) and in-hospital(HR: 1.30, 95% CI 0.90–1.89) mortality, associated with increased risk of liver failure (OR: 3.00, 95% CI 1.78–5.07), kidney failure (OR: 1.90, 95% CI 1.13–3.18), coagulation failure (OR: 2.55, 95% CI 1.52–4.27), respiratory failure (OR: 1.76, 95% CI 1.15–2.69), and circulatory failure (OR: 2.15, 95% CI 1.27–3.64), and even associated with prolonged the LOS ICU (β: 2.61, 95% CI 1.59–3.62) and LOS hospital (β: 6.59, 95% CI 2.62–10.57). In sensitivity analysis, multivariate analysis (HR: 1.09, 95%CI 0.86, 1.38), IPTW (HR: 1.11, 95%CI 0.95–1.29) and CAPS (HR: 1.09, 95% CI 0.86–1.38) showed FFP transfusion had no beneficial effect on the 30-day mortality. Smooth curve fitting demonstrated the risk of liver failure, kidney failure and circulatory failure increased by 3%, 2% and 2% respectively, for each 1 ml/kg increase in FFP transfusion. We found there was no significant difference of CLIF-SOFA and MELD score between the two group on day 0, 3, 7, 14. Compared with the conventional group, INR, APTT, and TBIL in the FFP transfusion group significantly increased, while PaO2/FiO2 significantly decreased within 14 days. In conclusion, FFP transfusion had no beneficial effect on the 30-day, 90-day, in-hospital mortality, was associated with prolonged the LOS ICU and LOS hospital, and the increased risk of liver failure, kidney failure, coagulation failure, respiratory failure and circulatory failure events. However, large, multi-center, randomized controlled trials, prospective cohort studies and external validation are still needed to verify the efficacy of FFP transfusion in the future.

In this study, a rapid, inexpensive, and accurate colorimetric sensor for detecting psychrophilic bacteria was designed, comprising gold (Au) nanoparticles (NPs) modified by d-amino acid (D-AA) as color-metric probes. Based on the aggregation of Au NPs induced by psychrophilic bacteria, a noticeable color shift occurred within 6 h. Depending on the various metabolic behaviors of bacteria to different D-AA, four primary psychrophilic bacteria in raw milk were successfully distinguished by learning the response patterns. Furthermore, the quantification of single bacteria and the practical application in milk samples could be realized. Notably, a rapid colorimetric method was constructed by combining Au/D-AA with antibiotics for the minimum inhibitory concentration of psychrophilic bacteria, which relied on differences in bacteria metabolic activity in response to diverse antibiotic treatments. Therefore, the method enables the rapid detection and susceptibility evaluation of psychrophilic bacteria, promoting clinical practicability and antibiotic management.

Abstract Background The occurrence of orofacial Clefts (OFCs) is a congenital disease caused by many factors. According to recent studies, air pollution has a strong correlation with the occurrence of OFCs. However, there are still some controversies about the current research results, and there is no relevant research to review the latest results in recent years. Objective In this paper, the authors conducted a systematic review and meta-analysis to explore the correlation between ambient air pollution and the occurrence of neonatal OFCs deformity. Methods We searched Pubmed, Web of science, and Embase databases from the establishment of the database to May 2023. We included observational studies on the relationship between prenatal exposure to fine particulate matter 2.5 (PM2.5), fine particulate matter 10 (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO) and the risk of cleft lip (CL), cleft palate (CP), cleft lip with or without palate (CL/P). the Newcastle-Ottawa quality assessment scale (NOS) was used to evaluate the quality of the literature. Funnel plot and Egger’s regression were used to verify the publication bias. Random effect model or fixed effect model was used to estimate the combined relative risk (RR) and 95% confidence interval (95%CI). Results A total of eleven studies were included in this study, including four cohort studies and seven case-control studies, including 22,453 cases of OFCs. Ten studies had low risk of bias and only one study had high risk of bias. Three studies reported that PM2.5 was positively correlated with CL and CP, with a combined RR and 95%CI of 1.287(1.174,1.411) and 1.267 (1.105,1.454). Two studies reported a positive correlation between O3 and CL, with a combined RR and 95%CI of 1.132(1.047,1.225). Two studies reported a positive correlation between PM10 and CL, with a combined RR and 95%CI of 1.108 (1.017,1.206). No association was found between SO2, CO, NO2 exposure during pregnancy and the risk of OFCs. Conclusion The results of this study showed that there was a significant statistical correlation between exposure to PM10, PM2.5, O3 and the risk of OFCs in the second month of pregnancy. Exposure assessment, research methods and mechanisms need to be further explored.

To improve the delightful flavor of mulberry wine through semi-artificial inoculation fermentation with Saccharomyces cerevisiae, we studied the dynamics change of microbiota, along with the physicochemical properties and metabolite profiles and their interaction relationship during the fermentation process. The abundance of lactic acid bacteria (Weissella, Lactobacillus, Fructobacillus, and Pediococcus) increased significantly during fermentation, while yeasts gradually established dominance. The inter-kingdom network of the dominant genera analysis further identified the following as core microbiota: Alternaria, Botrytis, Kazachstania, Acremonium, Mycosphaerella, Pediococcus, Gardnerella, and Schizothecium. Additionally, pH, alcohol, and total acid were significantly affected by microbiota variation. Fourteen of all identified volatile compounds with key different aromas were screened using PCA, OPLS-DA, and rOAV. The network of interconnected core microbiota with key different aromas revealed that Kazachstania and Pediococcus had stronger correlations with 1-butanol, 3-methyl-, propanoic acid, and 2-methyl-ethyl ester.

During the flood season, Francis turbines often operate under low-head and full-load conditions, frequently experiencing significant pressure pulsations, posing potential threats to the safe and stable operation of the units. However, the factors contributing to substantial pressure pulsations in Francis turbines are multifaceted. This paper focuses on a mixed-flow hydroelectric generating unit at a specific hydropower station. Field tests were conducted to investigate abnormal vibrations and hydraulic pressure pulsations under low-head and full-load conditions. Utilizing the Navier–Stokes equations and the RNG k-ε turbulence model, the unsteady flow field within the turbine under these conditions was calculated. The results indicate that the abnormal pressure pulsations detected in the bladeless zone between the wicket gates and the turbine inlet are due to operational points deviating from the normal operating range of the turbine. When water flows at a large inflow angle, striking the turbine blade heads, it leads to significant flow separation and vortex formation at the back of the blade inlet edges, causing severe vibrations in the hydroelectric generating unit. These findings provide a basis and assurance for the safe and stable operation of the power station.

By efficiently and accurately predicting the adoptability of pets, shelters and rescuers can be positively guided on improving attraction of pet profiles, reducing animal suffering and euthanization. Previous prediction methods usually only used a single type of content for training. However, many pets contain not only textual content, but also images. To make full use of textual and visual information, this paper proposed a novel method to process pets that contain multimodal information. We employed several CNN (Convolutional Neural Network) based models and other methods to extract features from images and texts to obtain the initial multimodal representation, then reduce the dimensions and fuse them. Finally, we trained the fused features with two GBDT (Gradient Boosting Decision Tree) based models and a Neural Network (NN) and compare the performance of them and their ensemble. The evaluation result demonstrates that the proposed ensemble learning can improve the accuracy of prediction. [ABSTRACT FROM AUTHOR]