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Metallic zinc has emerged as a promising anode material for high‐energy battery systems due to its high theoretical capacity (820 mAh g−1), low redox potential for two‐electron reactions, cost‐effectiveness and inherent safety. However, current zinc metal batteries face challenges in low coulombic efficiency and limited longevity due to uncontrollable dendrite growth, the corrosive hydrogen evolution reaction (HER) and decomposition of the aqueous ZnSO4 electrolyte. Here, we report an interfacial‐engineering approach to mitigate dendrite growth and reduce corrosive reactions through the design of ultrathin selective membranes coated on the zinc anodes. The submicron‐thick membranes derived from polymers of intrinsic microporosity (PIMs), featuring pores with tunable interconnectivity, facilitate regulated transport of Zn2+‐ions, thereby promoting a uniform plating/stripping process. Benefiting from the protection by PIM membranes, zinc symmetric cells deliver a stable cycling performance over 1500 h at 1 mA/cm2 with a capacity of 0.5 mAh while full cells with NaMnO2 cathode operate stably at 1 A g−1 over 300 cycles without capacity decay. Our work represents a new strategy of preparing multi‐functional membranes that can advance the development of safe and stable zinc metal batteries. [ABSTRACT FROM AUTHOR]

Identifying a small set of effective biomarkers from multi-omics data is important for the discrimination of different cell types and helpful for the early detection diagnosis of complex diseases. However, it is challenging to identify optimal biomarkers from the high throughput molecular data. Here, we present a method called protein–protein interaction affinity and co-expression network (PPIA-coExp), a linear programming model designed to discover context-specific biomarkers based on co-expressed networks and protein–protein interaction affinity (PPIA), which was used to estimate the concentrations of protein complexes based on the law of mass action. The performance of PPIA-coExp excelled over the traditional node-based approaches in both the small and large samples. We applied PPIA-coExp to human aging and Alzheimer's disease (AD) and discovered some important biomarkers. In addition, we performed the integrative analysis of transcriptome and epigenomic data, revealing the correlation between the changes in gene expression and different histone modification distributions in human aging and AD. [ABSTRACT FROM AUTHOR]

The feline panleukopenia virus (FPV) is a highly contagious virus that affects cats worldwide, characterized by leukopenia, high temperature and diarrhea. Recently, the continuous prevalence and variation of FPV have attracted widespread concern. The aim of this study was to investigate the isolation, genetic evolution, molecular characterization and epidemiological analysis of FPV strains among cats and dogs in China from 2019 to 2024. The 41 FPV strains, including 38 feline strains and 3 canine strains, were isolated from rectal swab samples by inoculating monolayer FK81 cells and performing a plaque purification assay. The viral and hemagglutination titers of these 41 FPV strains were 104.33~106.33 TCID50/0.1 mL and 7.0 log2~9.7 log2, respectively. Based on the complete VP2 gene, the nucleotide homology of these FPV strains was 98.91~100%, and the homology with 24 reference FPV strains from different countries and hosts was 98.85~100%. The phylogenetic analysis revealed that 41 FPV strains were more closely related to the FPV strains of Asian origin (Asian FPV strain group) than those of European and American origin (European and American FPV strain group). Furthermore, 12 mutation sites of the VP2 protein were found in these FPV strains, of which 91 and 232 amino acid sites were previously reported. Moreover, the 91 amino acid site was found to be a positive selection site with the highest dN/dS value in the selection pressure analysis. Importantly, 35 FPV strains with 91S substitution in the VP2 protein (FPV-VP2-91S strains) had formed obvious evolutionary branches in the Asian FPV strain group. The analysis of all available VP2 protein sequences of Chinese FPV strains in the GenBank database showed that the occurrence rate of FPV-VP2-91S strains had been increasing from 15.63% to 100% during 2017~2024, indicating that the FPV-VP2-91S substitution in the VP2 protein was a noteworthy molecular characteristic of the dominant FPV strains in China. These results contribute to a better understanding of their genetic evolution and renew the knowledge of FPV molecular epidemiology. [ABSTRACT FROM AUTHOR]

Rabbit hemorrhagic disease is a highly contagious and acute fatal disease caused by rabbit hemorrhagic disease virus (RHDV). The first outbreak of RHDV2 in 2020 has posed a serious threat to the rabbit breeding industry in China. An effective and specific detection strategy for RHDV GI.1 (RHDV1) and GI.2 (RHDV2) is urgently needed. In this study, we established a reverse transcription loop‐mediated isothermal amplification (RT‐LAMP)‐CRISPR/Cas12a‐based dual readout portable detection platform. The platform showed excellent specificity to identify RHDV1 and RHDV2 strains and no cross‐reaction with other prevalent pathogens of rabbit. The detection limit for RHDV1 and RHDV2 by RT‐LAMP‐CRISPR/Cas12a could reach 10 copies/μl of the VP60 gene per reaction. Furthermore, 74 clinical samples were detected for both RHDV1 and RHDV2. RT‐LAMP‐CRISPR/Cas12a‐based dual readout portable detection platform showed 25.68% (19/74) RHDV1‐positive samples, 43.24% (32/74) RHDV2‐positive samples, and 8.11% (6/74) RHDV1/RHDV2 double positive samples, respectively. The coincidence rates of detection RHDV1 and RHDV2 between RT‐LAMP‐CRISPR/Cas12a and quantitative real‐time‐polymerase chain reaction (qPCR) were both 97.30%. RT‐LAMP‐CRISPR/Cas12a showed higher sensitivity and detection rate compared with qPCR. Moreover, the results were visible to the naked eye within 1.5 h combined with lateral flow strips (LFSs) and visual fluorescence. The RT‐LAMP‐CRISPR/Cas12a portable platform has the advantages of high sensitivity, specificity, fast, low equipment requirements, which can be used in clinical practice in rural areas and resource‐limited settings. [ABSTRACT FROM AUTHOR]

Aberrant DNA methylation plays a crucial role in breast cancer progression by regulating gene expression. However, the regulatory pattern of DNA methylation in long noncoding RNAs (lncRNAs) for breast cancer remains unclear. In this study, we integrated gene expression, DNA methylation, and clinical data from breast cancer patients included in The Cancer Genome Atlas (TCGA) database. We examined DNA methylation distribution across various lncRNA categories, revealing distinct methylation characteristics. Through genome-wide correlation analysis, we identified the CpG sites located in lncRNAs and the distally associated CpG sites of lncRNAs. Functional genome enrichment analysis, conducted through the integration of ENCODE ChIP-seq data, revealed that differentially methylated CpG sites (DMCs) in lncRNAs were mostly located in promoter regions, while distally associated DMCs primarily acted on enhancer regions. By integrating Hi-C data, we found that DMCs in enhancer and promoter regions were closely associated with the changes in three-dimensional chromatin structures by affecting the formation of enhancer–promoter loops. Furthermore, through Cox regression analysis and three machine learning models, we identified 11 key methylation-driven lncRNAs (DIO3OS, ELOVL2-AS1, MIAT, LINC00536, C9orf163, AC105398.1, LINC02178, MILIP, HID1-AS1, KCNH1-IT1, and TMEM220-AS1) that were associated with the survival of breast cancer patients and constructed a prognostic risk scoring model, which demonstrated strong prognostic performance. These findings enhance our understanding of DNA methylation's role in lncRNA regulation in breast cancer and provide potential biomarkers for diagnosis. [ABSTRACT FROM AUTHOR]

The aerospace industry has made extensive use of titanium alloy material due to its exceptional qualities, which include high strength, low weight, and resistance to corrosion. However, these qualities also pose challenges for the material's processing. This article examined the coated end mills for Ti6Al4V milling. First, an analysis was conducted on the solubility of Ti and Si elements. It was discovered that W and Co elements were far more soluble in Ti than Si and Zr elements, which could effectively stop element diffusion. Next, the base's composition was planned. It was discovered that when the amount of Al increased, the base's surface roughness increased, while its hardness and elastic modulus decreased. The binding force between the substrate and the base was greater at a 50:50 Ti:Al ratio. The H3/E2 was about 0.23 and the surface roughness was about 0.15 μm. TiSiN and TiSiN/ZrN functional layer properties were also examined. When Zr was added to TiSiN/ZrN coating, it improved the coating's hardness and elastic modulus, increased density, and decreased surface roughness and friction coefficient when compared to TiSiN coating. There was an increase in hardness by 8.09% and an increase in elastic modulus by 9.65%. The average coefficient of friction decreased from 0.315 to 0.299. Lastly, an analysis of the initial and intermediate tool wear was done using the Ti6Al4V milling experiment. It was discovered that adding Zr element could successfully extend the tool's cutting life by preventing adhesive wear. [ABSTRACT FROM AUTHOR]

To address the challenge of power absorption in grids with high renewable energy integration, electric vehicle battery swapping stations (EVBSSs) serve as critically important flexible resources. Current research on load forecasting for EVBSSs primarily employs Transformer models, which have increasingly shown a lack of adaptability to the rapid growth in scale and complexity. This paper proposes a novel data-driven forecasting model that combines the geographical feature extraction capability of graph convolutional networks (GCNs) with the multitask learning capability of Transformers. The GCN-Transformer model first leverages Spearman's rank correlation to create a multinode feature set encompassing date, weather, and historical load data. It then employs data-adaptive graph generation for dynamic spatio-temporal graph construction and graph convolutional layers for spatial aggregation tailored to each node. Unique swapping patterns are identified through node-adaptive parameter learning, while the temporal dynamics of multidimensional features are managed by the Transformer's components. Numerical results demonstrate enhanced accuracy and efficiency in load forecasting for multiple and widely distributed EVBSSs. [ABSTRACT FROM AUTHOR]

This paper focuses on the scattering of SH waves by two circular cavities in an elastic plate. In the course of this study, wave function unfolding combined with the complex function, the mirror method, and the multi-polar coordinate translation technique are used to derive the incident and scattered conductive waves that fit the free conditions of stress on the upper and lower surface of the band domain. Then according to the stress free conditions of the edge of cylindrical hole, the infinite linear algebraic equations system is obtained. The unknown coefficients are finally calculated by intercepting the finite terms. The results show that when two cylindrical cavities of the same size are present within the plate, the presence of cavity 2, in most cases, leads to an increase in the concentration of kinetic stress along the edges of cavity 1; When two cylindrical cavity sizes are different, at medium and high frequency incidence, the enlargement of cavity 2 will lead to an increase in the kinetic stress concentration of cavity 1. [ABSTRACT FROM AUTHOR]