An in situ phase transformation induced mesoporous heterointerface for the alkaline hydrogen evolution reaction.

The phase structure of a catalyst plays a crucial role in determining the catalytic activity. In this study, a facile phosphorization process is employed to achieve the in situ phase transformation from single-phase Co3O4 to CoO/CoP hybrid phases. Characterization techniques, including XRD, BET, SEM, and TEM, confirm the retention of the mesoporous nature during the phase transformation, forming porous CoO/CoP heterointerfaces. Strong charge transfer is observed across the CoO/CoP heterointerface, indicating a robust interaction between the hybrid phases. The CoO/CoP hybrid exhibits significantly enhanced catalytic activity for the alkaline hydrogen evolution reaction (HER) compared to pristine Co3O4. Density Functional Theory (DFT) calculations reveal that the elimination of the band gap in the spin-down band of Co in CoO/CoP contributes to the observed high HER activity. The findings highlight the potential of CoO/CoP hybrids as efficient catalysts for HER, and contribute to the advancement of catalyst design for sustainable energy applications.

A novel hybrid machine learning model for auxiliary diagnosing myocardial ischemia.

Introduction: Accurate identification of the myocardial texture features of fat around the coronary artery on coronary computed tomography angiography (CCTA) images are crucial to improve clinical diagnostic efficiency of myocardial ischemia (MI). However, current coronary CT examination is difficult to recognize and segment the MI characteristics accurately during earlier period of inflammation. Materials and methods: We proposed a random forest model to automatically segment myocardium and extract peripheral fat features. This hybrid machine learning (HML) model is integrated by CCTA images and clinical data. A total of 1,316 radiomics features were extracted from CCTA images. To further obtain the features that contribute the most to the diagnostic model, dimensionality reduction was applied to filter features to three: LNS, GFE, and WLGM. Moreover, statistical hypothesis tests were applied to improve the ability of discriminating and screening clinical features between the ischemic and non-ischemic groups. Results: By comparing the accuracy, recall, specificity and AUC of the three models, it can be found that HML had the best performance, with the value of 0.848, 0.762, 0.704 and 0.729. Conclusion: In sum, this study demonstrates that ML-based radiomics model showed good predictive value in MI, and offer an enhanced tool for predicting prognosis with greater accuracy.

In Situ Construction of Elastic Solid-State Polymer Electrolyte with Fast Ionic Transport for Dendrite-Free Solid-State Lithium Metal Batteries.

Solid-state lithium metal batteries (LMBs) have been extensively investigated owing to their safer and higher energy density. In this work, we prepared a novel elastic solid-state polymer electrolyte based on an in situ-formed elastomer polymer matrix with ion-conductive plasticizer crystal embedded with Li6.5La3Zr1.5Ta0.5O12 (LLZTO) nanoparticles, denoted as LZT/SN-SPE. The unique structure of LZT/SN-SPE shows excellent elasticity and flexibility, good electrochemical oxidation tolerance, high ionic conductivity, and high Li+ transference number. The role of LLZTO filler in suppressing the side reactions between succinonitrile (SN) and the lithium metal anode and propelling the Li+ diffusion kinetics can be affirmed. The Li symmetric cells with LZT/SN-SPE cycled stably over 1100 h under a current density of 5 mA cm-2, and Li||LiFePO4 cells realized an excellent rate (92.40 mAh g-1 at 5 C) and long-term cycling performance (98.6% retention after 420 cycles at 1 C). Hence, it can provide a promising strategy for achieving high energy density solid-state LMBs.

Fast multi-view clustering via correntropy-based orthogonal concept factorization.

Owing to its ability to handle negative data and promising clustering performance, concept factorization (CF), an improved version of non-negative matrix factorization, has been incorporated into multi-view clustering recently. Nevertheless, existing CF-based multi-view clustering methods still have the following issues: (1) they directly conduct factorization in the original data space, which means its efficiency is sensitive to the feature dimension; (2) they ignore the high degree of factorization freedom of standard CF, which may lead to non-uniqueness factorization thereby causing reduced effectiveness; (3) traditional robust norms they used are unable to handle complex noises, significantly challenging their robustness. To address these issues, we establish a fast multi-view clustering via correntropy-based orthogonal concept factorization (FMVCCF). Specifically, FMVCCF executes factorization on a learned consensus anchor graph rather than directly decomposing the original data, lessening the dimensionality sensitivity. Then, a lightweight graph regularization term is incorporated to refine the factorization process with a low computational burden. Moreover, an improved multi-view correntropy-based orthogonal CF model is developed, which can enhance the effectiveness and robustness under the orthogonal constraint and correntropy criterion, respectively. Extensive experiments demonstrate that FMVCCF can achieve promising effectiveness and robustness on various real-world datasets with high efficiency.

Sulfamethoxazole stress endangers the gut health of sea cucumber (Apostichopus japonicus) and affects host metabolism.

Sulfamethoxazole (SMZ) is a frequently detected antibiotic in the environment, and there is a growing concern about its potential toxic effects on aquatic organisms. sea cucumber (Apostichopus japonicas) is a benthic invertebrate whose gut acts as a primary immune defense and serves critical protective barrier. In this study, growth performance, histology, gut microbiota, and metabolomics analyses were performed to investigate the toxic response in the intestine of sea cucumber effects caused by SMZ stress for 56 d by evaluating with different concentrations of SMZ (0, 1.2×10-3, and 1.2 mg/L). The weight gain rate of sea cucumbers under SMZ stress showed significant decrease, indicating that the growth of sea cucumbers was hindered. Analysis of the intestinal morphological features indicated that SMZ stimulation resulted in atrophy of the sea cucumber gut. In the 1.2×10-3 mg/L concentration, the thickness of muscle and mucosal layers was reduced by 12.40% and 21.39%, while in the 1.2 mg/L concentration, the reductions were 35.08% and 26.98%. The abundance and diversity of sea cucumber intestinal bacteria decreased significantly (P < 0.05) under the influence of SMZ. Notably, the intestinal bacteria of sea cucumber became homogenized with the increase in SMZ concentration, and the relative abundance of Ralstonia reached 81.64% under the stress of 1.2 mg/L concentration. The SMZ stress significantly impacted host metabolism and disrupted balance, particularly in L-threonine, L-tyrosine, neuronic acid, piperine, and docosapentaenoic acid. SMZ leads to dysregulation of metabolites, resulting in growth inhibition and potential inflammatory responses that could adversely affect the normal activities of aquatic organisms. Further metabolic pathway enrichment analyses demonstrated that impaired biosynthesis of unsaturated fatty acids and aminoacyl-tRNA biosynthesis metabolic pathway were major reasons for SMZ stress-induced intestinal bacteria dysbiosis. This research aims to provide some theoretical evidence for the ecological hazard assessment of antibiotics in water.

Fast Multiview Anchor-Graph Clustering.

Due to its high computational complexity, graph-based methods have limited applicability in large-scale multiview clustering tasks. To address this issue, many accelerated algorithms, especially anchor graph-based methods and indicator learning-based methods, have been developed and made a great success. Nevertheless, since the restrictions of the optimization strategy, these accelerated methods still need to approximate the discrete graph-cutting problem to a continuous spectral embedding problem and utilize different discretization strategies to obtain discrete sample categories. To avoid the loss of effectiveness and efficiency caused by the approximation and discretization, we establish a discrete fast multiview anchor graph clustering (FMAGC) model that first constructs an anchor graph of each view and then generates a discrete cluster indicator matrix by solving the discrete multiview graph-cutting problem directly. Since the gradient descent-based method makes it hard to solve this discrete model, we propose a fast coordinate descent-based optimization strategy with linear complexity to solve it without approximating it as a continuous one. Extensive experiments on widely used normal and large-scale multiview datasets show that FMAGC can improve clustering effectiveness and efficiency compared to other state-of-the-art baselines.

Evaluation of renal function in chronic kidney disease using histogram analysis based on multiple diffusion models.

OBJECTIVES: To compare the diagnostic value of histogram features of multiple diffusion metrics in predicting early renal impairment in chronic kidney disease (CKD). METHODS: A total of 77 patients with CKD (mild group, estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2) and 30 healthy controls (HCs) were enrolled. Diffusion-weighted imaging was performed by using single-shot echo planar sequence with 13 b values (0, 20, 50, 80, 100, 150, 200, 500, 800, 1000, 1500, 2000, and 2500 s/mm2). Diffusion models including mono-exponential (Mono), intravoxel incoherent motion (IVIM), stretched-exponential (SEM), and kurtosis (DKI) were calculated, and their histogram features were analysed. All diffusion models for predicting early renal impairment in CKD were established using logistic regression analysis, and diagnostic efficiency was compared among the models. RESULTS: All diffusion models had high differential diagnosis efficiency between the mild group and HCs. The areas under the curve (AUCs) of Mono, IVIM, SEM, DKI, and the combined diffusion model for predicting early renal impairment in CKD were 0.829, 0.809, 0.760, 0.825, and 0.861, respectively. There were no significant differences in AUCs except SEM and combined model, SEM, and DKI model. There were significant correlations between eGFR/serum creatinine and some of histogram features. CONCLUSIONS: Histogram analysis based on multiple diffusion metrics was practicable for the non-invasive assessment of early renal impairment in CKD. ADVANCES IN KNOWLEDGE: Advanced diffusion models provided microstructural information. Histogram analysis further reflected histological characteristics and heterogeneity. Histogram analysis based on multiple diffusion models could provide an accurate and non-invasive method to evaluate the early renal damage of CKD.

Cathepsin B serves as a potential prognostic biomarker and correlates with ferroptosis in rheumatoid arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is a long-term, systemic, and progressive autoimmune disorder. It has been established that ferroptosis, a type of iron-dependent lipid peroxidation cell death, is closely associated with RA. Fibroblast-like synoviocytes (FLS) are the main drivers of RA joint destruction, and they possess a high concentration of endoplasmic reticulum structure. Therefore, targeting ferroptosis and RA-FLS may be a potential treatment for RA. METHODS: Four machine learning algorithms were utilized to detect the essential genes linked to RA, and an XGBoost model was created based on the identified genes. SHAP values were then used to visualize the factors that affect the development and progression of RA, and to analyze the importance of individual features in predicting the outcomes. Moreover, WGCNA and PPI were employed to identify the key genes related to RA, and CIBERSORT was used to analyze the correlation between the chosen genes and immune cells. Finally, the findings were validated through in vitro cell experiments, such as CCK-8 assay, lipid peroxidation assay, iron assay, GSH assay, and Western blot. RESULTS: Bioinformatics and machine learning were employed to identify cathepsin B (CTSB) as a potential biomarker for RA. CTSB is highly expressed in RA patients and has been found to have a positive correlation with macrophages M2, neutrophils, and T cell follicular helper cells, and a negative correlation with CD8 T cells, monocytes, Tregs, and CD4 memory T cells. To investigate the effect of CTSB on RA-FLS from RA patients, the CTSB inhibitor CA-074Me was used and it was observed to reduce the proliferation and migration of RA-FLS, as indicated by the accumulation of lipid ROS and ferrous ions, and induce ferroptosis in RA-FLS. CONCLUSIONS: This study identified CTSB, a gene associated with ferroptosis, as a potential biomarker for diagnosing and managing RA. Moreover, CA-074Me, a CTSB inhibitor, was observed to cause ferroptosis and reduce the migratory capacity of RA-FLS.