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.

An overview of green synthesized silver nanoparticles towards bioactive antibacterial, antimicrobial and antifungal applications.

Present review emphatically introduces the synthesis, biocompatibility, and applications of silver nanoparticles (AgNPs), including their antibacterial, antimicrobial, and antifungal properties. A comprehensive discussion of various synthesis methods for AgNPs, with a particular focus on green chemistry mediated by plant extracts has been made. Recent research has revealed that the optical properties of AgNPs, including surface plasmon resonance (SPR), depend on the particle size, as well as the synthesis methods, preparation synthesis parameters, and used reducing agents. The significant emphasis on the use of synthesized AgNPs as antibacterial, antimicrobial, and antifungal agents in various applications has been reviewed. Furthermore, the application areas have been thoroughly examined, providing a detailed discussion of the underlying mechanisms, which aids in determining the optimal control parameters during the synthesis process of AgNPs. Furthermore, the challenges encountered while utilizing AgNPs and the corresponding advancements to overcome them have also been addressed. This review not only summarizes the achievements and current status of plant-mediated green synthesis of AgNPs but also explores the future prospects of these materials and technology in diverse areas, including bioactive applications.

TCF-Trans: Temporal Context Fusion Transformer for Anomaly Detection in Time Series.

Anomaly detection tasks involving time-series signal processing have been important research topics for decades. In many real-world anomaly detection applications, no specific distributions fit the data, and the characteristics of anomalies are different. Under these circumstances, the detection algorithm requires excellent learning ability of the data features. Transformers, which apply the self-attention mechanism, have shown outstanding performances in modelling long-range dependencies. Although Transformer based models have good prediction performance, they may be influenced by noise and ignore some unusual details, which are significant for anomaly detection. In this paper, a novel temporal context fusion framework: Temporal Context Fusion Transformer (TCF-Trans), is proposed for anomaly detection tasks with applications to time series. The original feature transmitting structure in the decoder of Informer is replaced with the proposed feature fusion decoder to fully utilise the features extracted from shallow and deep decoder layers. This strategy prevents the decoder from missing unusual anomaly details while maintaining robustness from noises inside the data. Besides, we propose the temporal context fusion module to adaptively fuse the generated auxiliary predictions. Extensive experiments on public and collected transportation datasets validate that the proposed framework is effective for anomaly detection in time series. Additionally, the ablation study and a series of parameter sensitivity experiments show that the proposed method maintains high performance under various experimental settings.

Performance of hippocampal radiomics models based on T2-FLAIR images in mesial temporal lobe epilepsy with hippocampal sclerosis.

PURPOSE: Preoperative identification of hippocampal sclerosis (HS) is crucial to successful surgery for mesial temporal lobe epilepsy (MTLE). We aimed to investigate the diagnostic performance of hippocampal radiomics models based on T2 fluid-attenuated inversion recovery (FLAIR) images in MTLE with HS. METHODS: We analysed 210 cases, including 172 HS pathology-confirmed cases (100 magnetic resonance imaging [MRI]-positive cases [MRI + HS], 72 MRI-negative HS cases [MRI - HS]), and 38 healthy controls (HC). The hippocampus was delineated slice by slice on an oblique coronal plane by a T2-FLAIR sequence, perpendicular to the hippocampus's long axis, to obtain a three-dimensional region of interest. Radiomics were processed using Artificial Intelligence Kit software; logistic regression radiomics models were constructed. The model evaluation indexes included the area under the curve (AUC), accuracy, sensitivity, and specificity. RESULTS: The respective AUC, accuracy, sensitivity, and specificity were 0.863, 81.4%, 78.0%, and 84.6% between the MRI - HS and HC groups in the training set and 0.855, 75.0%, 68.2%, and 81.8% in the test set; 0.975, 95.0%, 92.9%, and 98.0% between the MRI + HS and HC groups in the training set and 0.954, 88.7%, 90.0%, and 87.0% in the test set; and 0.912, 84.3%, 83.3%, and 86.5% between the MTLE and HC groups in the training set and 0.854, 79.7%, 80.8%, and 77.3% in the test set. The AUC values of the comparative radiomics models were > 0.85, indicating good diagnostic efficiency. CONCLUSION: The hippocampal radiomics models based on T2-FLAIR images can help diagnose MTLE with HS. They can be used as biological markers for MTLE diagnosis.

Hsa_circ_0007292 promotes chondrocyte injury in osteoarthritis via targeting the miR-1179/HMGB1 axis.

BACKGROUND: Circular RNAs (circRNAs) have been demonstrated to participate in the progression of osteoarthritis (OA). This study aimed to investigate the role and molecular mechanism of hsa_circ_0007292 in OA. METHODS: Hsa_circ_0007292 was identified by analyzing a circRNA microarray from the Gene Expression Omnibus (GEO) database, and its expression was detected by real-time PCR in OA cartilage tissues and interleukin (IL)-1ß-induced two human chondrocytes (CHON-001 and C28/I2), the OA cell models. The effects of hsa_circ_0007292 knockdown and miR-1179 overexpression on IL-1ß-induced chondrocyte injury were examined by CCK-8, BrdU, flow cytometry, ELISA, and western blot. RNA pull-down assay and dual-luciferase reporter gene assay were used to analyze the interaction between hsa_circ_0007292 and miR-1179. Rescue experiments were carried out to determine the correlations among hsa_circ_0007292, miR-1179 and high mobility group box-1 (HMGB1). RESULTS: Hsa_circ_0007292 expression was upregulated in OA tissues and IL-1ß-induced chondrocytes. Both downregulation of hsa_circ_0007292 and miR-1179 overexpression increased the proliferation and Aggrecan expression, suppressed apoptosis, matrix catabolic enzyme MMP13 expression and inflammatory factor (TNF-α, IL-6, and IL-8) levels. There was a negative correlation between hsa_circ_0007292 and miR-1179, and a positive correlation between hsa_circ_0007292 and HMGB1 in OA tissues. The mechanistic study showed that hsa_circ_0007292 prevented HMGB1 downregulation by sponging miR-1179. Upregulation of HMGB1 could reverse the influence of hsa_circ_0007292 downregulation on IL-1ß-induced chondrocyte injury. CONCLUSIONS: Downregulation of hsa_circ_0007292 relieved apoptosis, extracellular matrix degradation and inflammatory response in OA via the miR-1179/HMGB1 axis, suggesting that hsa_circ_0007292 might be a potential therapeutic target for OA treatment.

Event-triggered robust distributed output feedback model predictive control for nonlinear MASs against false data injection attacks.

In this paper, an event-triggered distributed output feedback model predictive control scheme for the nonlinear disturbed multiagent systems with sensor-controller channel false data injection attacks is proposed. To provide valid system states to the controller in the event of cyber attacks, a robust multivariate observer is designed to realize the estimation and separation of uncompromised system states, false data injection attacks, and measurement disturbances, simultaneously. Based on these reconstructed signals and a newly-designed linear robustness constraint, the distributed predictive controller is established to achieve smooth cooperative stabilization among agents. Meanwhile, an event-triggered mechanism is applied to save computing resources, and it restricts the error of predictive states and estimated states to guarantee the feasibility of the optimization control problem. Theoretical analyses on robustness and security for the nonlinear multiagent systems under event-triggered distributed output feedback model predictive control are presented. Finally, a simulation on two pairs of one-link flexible joint manipulator systems verifies the theoretical results.

Bi-Nuclear Tensor Schatten-p Norm Minimization for Multi-View Subspace Clustering.

Multi-view subspace clustering aims to integrate the complementary information contained in different views to facilitate data representation. Currently, low-rank representation (LRR) serves as a benchmark method. However, we observe that these LRR-based methods would suffer from two issues: limited clustering performance and high computational cost since (1) they usually adopt the nuclear norm with biased estimation to explore the low-rank structures; (2) the singular value decomposition of large-scale matrices is inevitably involved. Moreover, LRR may not achieve low-rank properties in both intra-views and inter-views simultaneously. To address the above issues, this paper proposes the Bi-nuclear tensor Schatten- p norm minimization for multi-view subspace clustering (BTMSC). Specifically, BTMSC constructs a third-order tensor from the view dimension to explore the high-order correlation and the subspace structures of multi-view features. The Bi-Nuclear Quasi-Norm (BiN) factorization form of the Schatten- p norm is utilized to factorize the third-order tensor as the product of two small-scale third-order tensors, which not only captures the low-rank property of the third-order tensor but also improves the computational efficiency. Finally, an efficient alternating optimization algorithm is designed to solve the BTMSC model. Extensive experiments with ten datasets of texts and images illustrate the performance superiority of the proposed BTMSC method over state-of-the-art methods.

Nontrivial Topological Surface States in Ru3 Sn7 toward Wide pH-Range Hydrogen Evolution Reaction.

Nontrivial topological surface states (TSSs), which possess extraordinary carrier mobility and are protected by the bulk symmetry, have emerged as an innovative platform to search for efficient electrocatalysts toward hydrogen evolution reaction (HER). Here, a Sn-based nontrivial metal Ru3 Sn7 is prepared using electrical arc melting method. The results indicate that the (001) crystal family of Ru3 Sn7 possesses nontrivial TSSs with linear dispersion relation and large nontrivial energy window. Experimental and theoretical results demonstrate that nontrivial TSSs of Ru3 Sn7 can significantly boost charge transfer kinetics and optimize adsorption of hydrogen intermediates due to bulk symmetry-protected band structures. As expected, nontrivial Ru3 Sn7 exhibits superior HER activity to Ru, Pt/C, and trivial counterparts (e.g., Ru2 Sn3 , IrSn2 , and Rh3 Sn2 ) with higher ratios of noble metals. Furthermore, the wide pH-range activity of topologically nontrivial Ru3 Sn7 implies the robustness of its TSSs against pH variation during the HER. These findings provide a promising approach to the rational design of topologically nontrivial metals as highly efficient electrocatalysts.

Charge Redistribution of Co9S8/MoS2 Heterojunction Microsphere Enhances Electrocatalytic Hydrogen Evolution.

The electrocatalytic hydrogen evolution activity of transition metal sulfide heterojunctions are significantly increased when compared with that of a single component, but the mechanism behind the performance enhancement and the preparation of catalysts with specific morphologies still need to be explored. Here, we prepared a Co9S8/MoS2 heterojunction with microsphere morphology consisting of thin nanosheets using a facile two-step method. There is electron transfer between the Co9S8 and MoS2 of the heterojunction, thus realizing the redistribution of charge. After the formation of the heterojunction, the density of states near the Fermi surface increases, the d-band center of the transition metal moves downward, and the adsorption of both water molecules and hydrogen by the catalyst are optimized. As a result, the overpotential of Co9S8/MoS2 is superior to that of most relevant electrocatalysts reported in the literature. This work provides insight into the synergistic mechanisms of heterojunctions and their morphological regulation.

A hierarchical dynamic Bayesian learning network for EMG-based early prediction of voluntary movement intention.

Decoding human action intention prior to motion onset with surface electromyograms (sEMG) is an emerging neuroengineering topic with interesting clinical applications such as intelligent control of powered prosthesis/exoskeleton devices. Despite extensive prior works in the related fields, it remains a technical challenge due to considerable variability of complex multi-muscle activation patterns in terms of volatile spatio-temporal characteristics. To address this issue, we first hypothesize that the inherent variability of the idle state immediately preceding the motion initiation needs to be addressed explicitly. We therefore design a hierarchical dynamic Bayesian learning network model that integrates an array of Gaussian mixture model - hidden Markov models (GMM-HMMs), where each GMM-HMM learns the multi-sEMG processes either during the idle state, or during the motion initiation phase of a particular motion task. To test the hypothesis and evaluate the new learning network, we design and build a upper-limb sEMG-joystick motion study system, and collect data from 11 healthy volunteers. The data collection protocol adapted from the psychomotor vigilance task includes repeated and randomized binary hand motion tasks (push or pull) starting from either of two designated idle states: relaxed (with minimal muscle tones), or prepared (with muscle tones). We run a series of cross-validation tests to examine the performance of the method in comparison with the conventional techniques. The results suggest that the idle state recognition favors the dynamic Bayesian model over a static classification model. The results also show a statistically significant improvement in motion prediction accuracy by the proposed method (93.83±6.41%) in comparison with the conventional GMM-HMM method (89.71±8.98%) that does not explicitly account for the idle state. Moreover, we examine the progress of prediction accuracy over the course of motion initiation and identify the important hidden states that warrant future research.

Identification and experimental validation of ferroptosis-related gene SLC2A3 is involved in rheumatoid arthritis.

BACKGROUND: Ferroptosis, an iron-dependent manner of lipid peroxidative cell death, has recently been reported to be strongly associated with rheumatoid arthritis (RA). Targeted ferroptosis may be a potential treatment for RA. METHODS: We combined bioinformatics analysis and machine learning algorithm to screen the characteristic gene of RA. Moreover, we used gene set enrichment analysis (GSEA) to investigate the biological function of feature gene and CIBERSORT algorithm to analyze the correlation between selected hub gene and immune cells. The CellMiner database was used to predict potential drugs for RA. Finally, it was further verified by in vitro cell experiment. RESULTS: SLC2A3 was identified as an important potential biomarker based on bioinformatics methods and machine learning algorithms. SLC2A3 encodes the predominantly neuronal glucose transporter 3 (GLUT3). GSEA showed that SLC2A3 high-expression group was correlated with metabolic pathways. Immune cell infiltration analysis showed that SLC2A3 was positively correlated with activated mast cell expression. RSL3 is an activator of ferroptosis that binds to and inactivates GPX4, mediating ferroptosis regulated by GPX4. In our experiment, we treated synovial fibroblast-like cells of RA (RA-FLS) with RSL3 (Ferroptosis inducers) and found that RSL3 can downregulate SLC2A3 expression and induce ferroptosis in RA-FLS. CONCLUSIONS: Our study identifies and validates ferroptosis-related gene SLC2A3 as a potential biomarker for the diagnosis and treatment of RA. It was also found that RSL3 can induce ferroptosis in RA-FLS via lead to the downregulation of SLC2A3.

Phosphorus-modified cobalt single-atom catalysts loaded on crosslinked carbon nanosheets for efficient alkaline hydrogen evolution reaction.

Efficient and low-cost transition metal single-atom catalysts (TMSACs) for hydrogen evolution reaction (HER) have been recognized as research hotspots recently with advances in delivering good catalytic activity without noble metals. However, the high-cost complex preparation of TMSACs and insufficient stability limited their practical applications. Herein, a simple top-down pyrolysis approach to obtain P-modified Co SACs loaded on the crosslinked defect-rich carbon nanosheets was introduced for alkaline hydrogen evolution, where Co atoms are locally confined before pyrolysis to prevent aggregation. Thereby, the abundant defects and the unsaturated coordination formed during the pyrolysis significantly improved the stability of the monatomic structure and reduced the reaction barrier. Furthermore, the synergy between cobalt atoms and phosphorus atoms was established to optimize the decomposition process of water molecules, which delivers the key to promoting the slow reaction kinetics of alkaline HER. As the result, the cobalt SAC exhibited excellent catalytic activity and stability for alkaline HER, with overpotentials of 70 mV and 192 mV at current densities of -10 mA cm-2 and -100 mA cm-2, respectively.

Decoupled neural network training with re-computation and weight prediction.

To break the three lockings during backpropagation (BP) process for neural network training, multiple decoupled learning methods have been investigated recently. These methods either lead to significant drop in accuracy performance or suffer from dramatic increase in memory usage. In this paper, a new form of decoupled learning, named decoupled neural network training scheme with re-computation and weight prediction (DTRP) is proposed. In DTRP, a re-computation scheme is adopted to solve the memory explosion problem, and a weight prediction scheme is proposed to deal with the weight delay caused by re-computation. Additionally, a batch compensation scheme is developed, allowing the proposed DTRP to run faster. Theoretical analysis shows that DTRP is guaranteed to converge to crical points under certain conditions. Experiments are conducted by training various convolutional neural networks on several classification datasets, showing comparable or better results than the state-of-the-art methods and BP. These experiments also reveal that adopting the proposed method, the memory explosion problem is effectively solved, and a significant acceleration is achieved.