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To comprehensively leverage the temporal, static, and dynamic information features of multi-channel surface electromyography (sEMG) signals for gesture recognition, considering the sensitive temporal characteristics of sEMG signals to action amplitude and muscle recruitment patterns, an sEMG-based gesture recognition algorithm is innovatively proposed using Sigimg-GADF-MTF and multi-stream convolutional neural network (MSCNN) by introducing the Sigimg, GADF, and MTF data processing methods and combining them with a multi-stream fusion strategy. Firstly, a sliding window is used to rearrange the multi-channel original sEMG signals through channels to generate a two-dimensional image (named Sigimg method). Meanwhile, each channel signal is respectively transformed into two-dimensional subimages using Gram angular difference field (GADF) and Markov transition field (MTF) methods. Then, the GADF and MTF images are obtained using a horizontal stitching method to splice these subimages, respectively. The Sigimg, GADF, and MTF images are used to construct a training and testing dataset, which is then imported into the constructed MSCNN model for experimental testing. The fully connected layer fusion method is utilized for multi-stream feature fusion, and the gesture recognition results are output. Through comparative experiments, an average accuracy of 88.4% is achieved using the Sigimg-GADF-MTF-MSCNN algorithm on the Ninapro DBl dataset, higher than most mainstream models. At the same time, the effectiveness of the proposed algorithm is fully verified through generalization testing of data obtained from the self-developed sEMG signal acquisition platform with an average accuracy of 82.4%.

Power line segmentation plays a critical role in ensuring the safety of transmission line UAV inspection flights. To address the challenges of small target scale, complex backgrounds, and excessive model parameters in existing deep learning-based power line segmentation algorithms, this paper introduces RGS-UNet, a lightweight segmentation model integrating a residual-like cross-layer feature fusion module. First, ResNet18 is adopted to reconstruct a UNet backbone network as an encoder module to enhance the network’s feature extraction capability for small targets. Second, ordinary convolution in the residual block of ResNet18 is optimized by introducing the Ghost Module, which significantly reduces the computational load of the model’s backbone network. Third, a residual-like addition method is designed to embed the SIMAM attention mechanism module into both encoder and decoder stages, which improves the model’s ability to extract power lines from complex backgrounds. Finally, the Mish activation function is applied in deep convolutional layers to maintain feature extraction accuracy and mitigate overfitting. Experimental results demonstrate that compared with classical UNet, the optimized network achieves 2.05% and 2.58% improvements in F1-Score and IoU, respectively, while reducing the parameter count to 57.25% of the original model. The algorithm achieves better performance improvements in both accuracy and lightweighting, making it suitable for edge-side deployment.

Globally, breast cancer is one of the leading causes of cancer death in women. Metabolic reprogramming and immune escape are two important mechanisms supporting the progression of breast cancer. Lactate in tumors mainly comes from glycolysis and glutaminolysis. Using multiomics data analysis, we found that lactate is mainly derived from glycolysis in breast cancer. Single-cell transcriptome analysis found that breast cancer cells with higher malignancy, especially those in the cell cycle, have higher expression levels of glycolytic metabolic enzymes. Combined with clinical data analysis, it was found that the expression of the lactate transporter SLC16A3 is correlated with breast cancer molecular subtypes and immune infiltration. Among 22 immune cells, macrophages are the most abundant immune cells in breast cancer tissues, and the proportion of M1 macrophages is lower in the high SLC16A3 expression group. Finally, in vitro experiments confirmed that lactate could inhibit the expression of M1 macrophage markers at both RNA and protein levels. In conclusion, we found that lactate produced by glycolysis regulates the polarization of inflammatory macrophages in breast cancer.

A good group reputation often facilitates more efficient synergistic teamwork in production activities. Here we translate this simple motivation into a reputation-based synergy and discounting mechanism in the public goods game. Specifically, the reputation type of a group, either good or bad determined by a reputation threshold, modifies the nonlinear payoff structure described by a unified reputation impact factor. Results show that this reputation-based incentive mechanism could effectively promote cooperation compared with linear payoffs, despite the coexistence of synergy and discounting effects. Notably, the complicated interactions between reputation impact and reputation threshold result in a sharp phase transition from full cooperation to full defection. We also find that the presence of a few discounting groups could increase the average payoffs of cooperators, leading to an interesting phenomenon that when the reputation threshold is raised, the gap between the average payoffs of cooperators and defectors increases while the overall payoff decreases. We further extend our framework to heterogeneous situations and show how the variability of individuals affect the evolutionary outcomes. Our work provides important insights into facilitating cooperation in social groups.

Hyperalgesia has become a major problem restricting the clinical application of tooth bleaching. We hypothesized that transient receptor potential ankyrin 1 (TRPA1), a pain conduction tunnel, plays a role in tooth hyperalgesia and inflammation after bleaching. Dental pulp stem cells were seeded on the dentin side of the disc, which was cut from the premolar buccal tissue, with 15% (90 min) or 40% (3 × 15 min) bleaching gel applied on the enamel side, and treated with or without a TRPA1 inhibitor. The bleaching gel stimulated intracellular reactive oxygen species, Ca 2+ , ATP, and extracellular ATP in a dose-dependent manner, and increased the mRNA and protein levels of hyperalgesia (TRPA1 and PANX1) and inflammation (TNFα and IL6) factors. This increment was adversely affected by TRPA1 inhibitor. In animal study, the protein levels of TRPA1 ( P  =  0.0006 ), PANX1 ( P  <  0.0001 ), and proliferation factors [PCNA ( P  <  0.0001 ) and Caspase 3 ( P  =  0.0066 )] increased significantly after treated rat incisors with 15% and 40% bleaching gels as detected by immunohistochemistry. These results show that TRPA1 plays a critical role in sensitivity and inflammation after tooth bleaching, providing a solid foundation for further research on reducing the complications of tooth bleaching.

Silicon‐based (Si) materials are promising anodes for lithium‐ion batteries (LIBs) because of their ultrahigh theoretical capacity of 4200 mA h g−1. However, commercial applications of Si anodes have been hindered by their drastic volume variation (∼300%) and low electrical conductivity. Here, to tackle the drawbacks, a hierarchical Si anode with double‐layer coatings of a SiOx inner layer and a nitrogen (N), boron (B) co‐doped carbon (C–NB) outer layer is elaborately designed by copyrolysis of Si–OH structures and a H3BO3‐doped polyaniline polymer on the Si surface. Compared with the pristine Si anodes (7 mA h g−1 at 0.5 A g−1 after 340 cycles and 340 mA h g−1 at 5 A g−1), the modified Si‐based materials (Si@SiOx@C–NB nanospheres) present superior cycling stability (reversible 1301 mA h g−1 at 0.5 A g−1 after 340 cycles) as well as excellent rate capability (690 mA h g−1 at 5 A g−1) when used as anodes in LIBs. The unique double‐layer coating structure, in which the inner amorphous SiOx layer acts as a buffer matrix and the outer defect‐rich carbon enhances the electron diffusion of the whole anode, makes it possible to deliver excellent electrochemical properties. These results indicate that our double‐layer coating strategy is a promising approach not only for the development of sustainable Si anodes but also for the design of multielement‐doped carbon nanomaterials. The mechanism illustration about the preparation of Si@SiOx@C–NB materials. Highlights Double‐layer coatings are generated on the surface of Si nanoparticles via the pyrolysis of surficial Si‐OH structures and PANI–H3BO3 precoatings. The amorphous SiOx inner‐layer can effectively promote the wettability of Si anodes with electrolyte and the combination between Si particles and the binder. The N,B co‐doped carbon outer‐layer shows a defect‐rich nature and can enhance the conductivity of the whole Si anode. The Si@SiOx@C–NB anode exhibits excellent rate capability and cycling stability.

Studies have confirmed that olanzapine, the mainstay treatment for schizophrenia, triggers metabolic diseases, including non-alcoholic fatty liver disease (NAFLD). However, the etiology of olanzapine-induced NAFLD is poorly understood. Proprotein convertase subtilisin kexin type 9 (PCSK9) is involved in NAFLD pathogenesis, and metformin can significantly decrease circulating PCSK9. The purpose of this study was to investigate the role of PCSK9 and explore the therapeutic effect of metformin for olanzapine-associated NAFLD. Olanzapine significantly upregulated PCSK9 and promoted lipid accumulation in mouse livers and HepG2 and AML12 cells. Metformin ameliorated these pathological alterations. PCSK9 upstream regulator liver X receptor α (LXRα) was significantly upregulated in olanzapine-induced NAFLD. LXRα antagonist treatment and LXRα overexpression resulted in a decrease and increase of PCSK9, respectively. Hepatic lipogenesis-associated genes FAS and SCD1 were significantly upregulated in olanzapine-induced NAFLD mice and HepG2 cells overexpressing PCSK9, and genes related to lipid β-oxidation (SCAD and PPARα) were downregulated, while metformin reversed these changes. In addition, we found that LXRα overexpression compromised the effect of metformin on PCSK9 levels and intracellular lipid droplet formation. Taken together, our findings suggest that olanzapine enhances hepatic PCSK9 expression by upregulating LXRα, thereby increasing FAS and SCD1 expression as well as decreasing SCAD and PPARα, and promoting lipid accumulation, and, subsequently, NAFLD, which is ameliorated by metformin.

Background Bleaching is widely accepted for improving the appearance of discolored teeth; however, patient compliance is affected by bleaching-related complications, especially bleaching sensitivity. This study aimed to investigate the role of reactive oxygen species (ROS) in cytotoxicity and pain conduction activated by experimental tooth bleaching. Methods Dental pulp stem cells with or without N-acetyl-L-cysteine (NAC), an ROS scavenger, were cultured on the dentin side of the enamel/dentin disc. Subsequently, 15% (90 min) and 40% (30 min) bleaching gels were painted on the enamel surface. Cell viability, intracellular ROS, Ca2+, adenosine triphosphate (ATP), and extracellular ATP levels were evaluated using the Cell Counting Kit-8 assay, 2’,7’-dichlorodihydrofluorescein diacetate, CellROX, fura-3AM fluorescence assay, and ATP measurement kit. The rat incisor model was used to evaluate in vivo effects after 0, 1, 3, 7, and 30 days of bleaching. Changes in gene and protein expression of interleukin 6 (IL-6), tumor necrosis factor-alpha (TNFα), transient receptor potential ankyrin 1 (TRPA1), and Pannexin1 (PANX1) in dental pulp stem cells and pulp tissue were detected through RT-PCR, western blotting, and immunofluorescence. Results The bleaching gel suppressed dental pulp stem cell viability and extracellular ATP levels and increased intracellular ROS, Ca2+, and intracellular ATP levels. The mRNA and protein expression of IL-6, TNFα, TRPA1, and PANX1 were up-regulated in vitro and in vivo. Furthermore, the 40% gel had a stronger effect than the 15% gel, and NAC ameliorated the gel effects. Conclusions Our findings suggest that bleaching gels induce cytotoxicity and pain conduction in dental pulp stem cells via intracellular ROS, which may provide a potential therapeutic target for alleviating tooth bleaching nociception.

Efficient, low-cost photocatalysts with mild synthesis conditions and stable photocatalytic behavior have always been the focus in the field of photocatalysis. This study proves that non-quantum-dot Cs2PbI2Cl2-based materials, created by a simple method, can be successfully employed as new high-efficient photocatalysts. The results demonstrate that two-dimensional Cs2PbI2Cl2 perovskite can achieve over three times higher photocatalytic performance compared to three-dimensional CsPbBr3 perovskite. Moreover, the photocatalytic performance of Cs2PbI2Cl2 can be further improved by constructing a heterojunction structure, such as Cs2PbI2Cl2/CsPbBr3. Cs2PbI2Cl2 can connect well with CsPbBr3 through a simple method, resulting in tight bonding at the interface and efficient carrier transfer. Cs2PbI2Cl2/CsPbBr3 exhibits notable 5-fold and 10-fold improvements in photocatalytic performance and rate compared to CsPbBr3. Additionally, Cs2PbI2Cl2/CsPbBr3 demonstrates superb stable catalytic performance, with nearly no decrease in photocatalytic performance after 7 months (RH = 20% ± 10, T = 25 °C ± 5). This study also reveals that the photocatalytic process based on Cs2PbI2Cl2/CsPbBr3 can directly oxidize organic matter using holes, without relying on the generation of intermediate reactive oxygen species from water or oxygen (such as ·OH or ·O2−), showcasing further potential for achieving high photocatalytic efficiency and selectivity in anhydrous/anaerobic catalytic reactions and treating recalcitrant pollutants.

Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature.