Search Results (14,065)

The top oil temperature of a transformer is a vital sign reflecting its operational condition. The accurate prediction of this parameter is essential for evaluating insulation performance and extending equipment lifespan. At present, the prediction of oil temperature is mainly based on single-feature prediction. However, it overlooks the influence of other features. This has a negative effect on the prediction accuracy. Furthermore, the training dataset is often made up of data from a single transformer. This leads to the poor generalization of the prediction. To tackle these challenges, this paper leverages large-scale data analysis and processing techniques, and presents a transformer top oil temperature prediction model that combines multiple models. The Convolutional Neural Network was applied in this method to extract spatial features from multiple input variables. Subsequently, a Long Short-Term Memory network was employed to capture dynamic patterns in the time series. Meanwhile, a Transformer encoder enhanced feature interaction and global perception. The spatial characteristics extracted by the CNN and the temporal characteristics extracted by LSTM were further integrated to create a more comprehensive representation. The established model was optimized using the Whale Optimization Algorithm to improve prediction accuracy. The results of the experiment indicate that the maximum RMSE and MAPE of this method on the summer and winter datasets were 0.5884 and 0.79%, respectively, demonstrating superior prediction accuracy. Compared with other models, the proposed model improved prediction performance by 13.74%, 36.66%, and 43.36%, respectively, indicating high generalization capability and accuracy. This provides theoretical support for condition monitoring and fault warning of power equipment. Full article

Understanding the biodiversity of aquatic communities and the underlying mechanisms that shape biodiversity patterns and community dynamics is crucial for the effective conservation and management of freshwater ecosystems. However, traditional survey methods often fail to comprehensively capture species diversity, particularly for low-abundance taxa. Moreover, studies integrating both metazoan and fish communities at fine spatial scales remain limited. To address these gaps, we employed a multi-marker eDNA metabarcoding approach, targeting both the 12S and 18S rRNA gene regions, to comprehensively investigate the composition of metazoan and fish communities in the Yujiang River. A total of 12 metazoan orders were detected, encompassing 15 families, 21 genera, and 19 species. For the fish community, 32 species were identified, belonging to 25 genera, 10 families, and 7 orders. Among these, Adula falcatoides and Coptodon zillii were identified as the most prevalent and abundant metazoan and fish species, respectively. Notably, the most prevalent fish species, C. zillii and Oreochromis niloticus, are both recognized as invasive species. The Bray–Curtis distance of metazoa (average: 0.464) was significantly lower than that of fish communities (average: 0.797), suggesting higher community heterogeneity among fish assemblages. Beta-diversity decomposition indicated that variations in the metazoan and fish communities were predominantly driven by species replacement (turnover) (65.4% and 70.9% for metazoa and fish, respectively) rather than nestedness. Mantel tests further revealed that species turnover in metazoan communities was most strongly influenced by water temperature, while fish community turnover was primarily affected by water transparency, likely reflecting the physiological sensitivity of metazoans to thermal gradients and the dependence of fish on visual cues for foraging and habitat selection. In addition, a co-occurrence network of metazoan and fish species was constructed, highlighting potential predator-prey interactions between native species and Corbicula fluminea, which emerged as a potential keystone species. Overall, this study demonstrates the utility of multi-marker eDNA metabarcoding in characterizing aquatic community structures and provides new insights into the spatial dynamics and species interactions within river ecosystems. Full article

Cryptococcus neoformans is an opportunistic fungal pathogen and causative agent of cryptococcosis and cryptococcal meningitis (CM). Cryptococcal disease accounts for up to 19% of AIDS-related mortalities globally, warranting its label as a pathogen of critical priority by the World Health Organization. Standard treatments for CM rely heavily on high doses of antifungal agents for long periods of time, contributing to the growing issue of antifungal resistance. Moreover, mortality rates for CM are still incredibly high (13–78%). Attempts to create new and effective treatments have been slow due to the complex and diverse set of immune-evasive and survival-enhancing virulence factors that C. neoformans employs. To bolster the development of better clinical tools, deeper study into host–Cryptococcus proteomes is needed to identify clinically relevant proteins, pathways, antigens, and beneficial host response mechanisms. Mass spectrometry-based proteomics approaches serve as invaluable tools for investigating these complex questions. Here, we discuss some of the insights into cryptococcal disease and biology learned using proteomics, including target proteins and pathways regulating Cryptococcus virulence factors, metabolism, and host defense responses. By utilizing proteomics to probe deeper into these protein interaction networks, new clinical tools for detecting, diagnosing, and treating C. neoformans can be developed. Full article

Chikungunya virus (CHIKV), a mosquito-borne alphavirus, has re-emerged, causing widespread outbreaks and a significant clinical burden. Despite advances in virology, the molecular mechanisms governing CHIKV’s interaction with host cells remain poorly understood. In this study, we aimed to identify novel host protein interactors of the CHIKV nonstructural protein 3 (nsP3), a critical component of the viral replication complex, using mass spectrometry-based proteomic profiling in liver-derived Huh7 cells. Co-immunoprecipitation followed by LC-MS/MS identified a wide array of host proteins associated with nsP3, revealing 52 proteins classified as high-confidence (FDR of 1%, and unique peptides > 2) CHIKV-specific interactors. A bioinformatic analysis using STRING and Cytoscape uncovered interaction networks enriched in metabolic processes, RNA processing, translation regulation, cellular detoxification, stress responses, and immune signaling pathways. A subcellular localization analysis showed that many interactors reside in the cytosol, while others localize to the nucleus, nucleolus, and mitochondria. Selected novel host protein interactions were validated through co-immunoprecipitation and immunofluorescence assays. Our findings provide new insights into the host cellular pathways hijacked by CHIKV and highlight potential targets for therapeutic intervention. This is the first report mapping direct nsP3–host protein interactions in Huh7 cells during CHIKV infection. Full article

Background and Objectives: VPS26A, a core component of the retromer complex, is pivotal to endosomal trafficking and membrane protein recycling. However, its expression profile, prognostic significance, and clinical relevance in liver hepatocellular carcinoma (LIHC) remain unexplored. This study investigates the prognostic potential of VPS26A by extensively analyzing publicly available LIHC-related databases. Materials and Methods: Multiple databases, including TIMER, UALCAN, HPA, GSCA, KM Plotter, OSlihc, MethSurv, miRNet, OncomiR, LinkedOmics, GeneMANIA, and STRING, were used to evaluate VPS26A expression patterns, prognostic implications, correlations with tumor-infiltrating immune cells (TIICs), epigenetic modifications, drug sensitivity, co-expression networks, and protein–protein interactions in LIHC. Results: VPS26A was significantly overexpressed at both the mRNA and protein levels in LIHC tissues compared to that in normal tissues. This upregulation was strongly associated with a poor prognosis. Furthermore, VPS26A expression was both positively and negatively correlated with various TIICs. Epigenetic analysis indicated that VPS26A is regulated by promoter and regional DNA methylation. Additionally, VPS26A influences the sensitivity of LIHC cells to a broad range of anticancer agents. Functional enrichment and co-expression analyses revealed that VPS26A serves as a central regulator of the LIHC transcriptomic landscape, with positively correlated gene sets linked to poor prognosis. Additionally, VPS26A contributes to the molecular architecture governing vesicular trafficking, with potential relevance to diseases involving defects in endosomal transport and autophagy. Notably, miRNAs targeting VPS26A-associated gene networks have emerged as potential prognostic biomarkers for LIHC. VPS26A was found to be integrated into a highly interconnected signaling network comprising proteins in cancer progression, immune regulation, and cellular metabolism. Conclusions: Overall, VPS26A may serve as a potential prognostic biomarker and therapeutic target in LIHC. This study provides novel insights into the molecular mechanisms underlying LIHC progression, and highlights the multifaceted role of VPS26A in tumor biology. Full article

Compared to other multi-source image fusion tasks, visible and SAR image fusion faces a lack of training data in deep learning-based methods. Introducing structural priors to design fusion networks is a viable solution. We incorporated the feature hierarchy concept from computer vision, dividing deep features into low-, mid-, and high-level tiers. Based on the complementary modal characteristics of SAR and visible, we designed a fusion architecture that fully analyze and utilize the difference of hierarchical features. Specifically, our framework has two stages. In the cross-modal enhancement stage, a CycleGAN generator-based method for cross-modal interaction and input data enhancement is employed to generate pseudo-modal images. In the fusion stage, we have three innovations: (1) We designed feature extraction branches and fusion strategies differently for each level based on the features of different levels and the complementary modal features of SAR and visible to fully utilize cross-modal complementary features. (2) We proposed the Layered Strictly Nested Framework (LSNF), which emphasizes hierarchical differences and uses hierarchical characteristics, to reduce feature redundancy. (3) Based on visual saliency theory, we proposed a Gradient-weighted Pixel Loss (GWPL), which dynamically assigns higher weights to regions with significant gradient magnitudes, emphasizing high-frequency detail preservation during fusion. Experiments on the YYX-OPT-SAR and WHU-OPT-SAR datasets show that our method outperforms 11 state-of-the-art methods. Ablation studies confirm each component’s contribution. This framework effectively meets remote sensing applications’ high-precision image fusion needs. Full article

Background/Objectives: Gastric cancer (GC) remains a significant global health burden due to its high mortality rate and frequent diagnosis at advanced stages. This study aimed to identify reliable diagnostic biomarkers and elucidate molecular mechanisms underlying GC by integrating transcriptomic data from independent platforms and applying machine learning techniques. Methods: Two transcriptomic datasets from the Gene Expression Omnibus were analyzed: GSE26899 (microarray, n = 108) as the discovery dataset and GSE248612 (RNA-seq, n = 12) for validation. Differential expression analysis was conducted using limma and DESeq2, selecting genes with |log2FC| > 1 and adjusted p < 0.05. The top 200 differentially expressed genes (DEGs) were used to develop machine learning models (random forest, logistic regression, neural networks). Functional enrichment analyses (GO, KEGG, Hallmark) were applied to explore relevant biological pathways. Results: In GSE26899, 627 DEGs were identified (201 upregulated, 426 downregulated), with key genes including CST1, KIAA1199, TIMP1, MSLN, and ATP4A. The random forest model demonstrated excellent classification performance (AUC = 0.952). GSE248612 validation yielded 738 DEGs. Cross-platform comparison confirmed 55.6% concordance among core genes, highlighting CST1, TIMP1, KRT17, ATP4A, CHIA, KRT16, and CRABP2. Enrichment analyses revealed involvement in ECM–receptor interaction, PI3K-Akt signaling, EMT, and cell cycle. Conclusions: This integrative transcriptomic and machine learning framework effectively identified high-confidence biomarkers for GC. Notably, CST1, TIMP1, KRT16, and ATP4A emerged as consistent, biologically relevant candidates with strong diagnostic performance and potential clinical utility. These findings may aid early detection strategies and guide future therapeutic developments in gastric cancer. Full article

This article examines how religious freedom is enacted and redefined through everyday practices in pluralistic urban settings. Moving beyond the classical notion of negative liberty as non-interference, it explores the social conditions that enable or constrain the practical expression of religious life. Drawing on forty-three qualitative interviews with religious leaders and civic actors in Florence, Italy, the study analyses how religious freedom is experienced across institutional contexts such as hospitals, schools, prisons, workplaces, and sport facilities. The findings reveal a persistent tension between formal legal rights and their uneven implementation in daily life. While legal guarantees are generally upheld, structural barriers and discretionary practices often hinder access to religious expression. At the same time, informal interactions, local networks, and dialogical engagement play a key role in supporting the concrete exercise of religious freedom. The article argues that freedom is not simply a legal status but a social process, realized through relational and institutional arrangements. By foregrounding the role of everyday interaction in shaping the conditions of freedom, this study contributes to broader sociological debates on pluralism, normativity, and the social foundations of institutional life. Full article

This study investigated the effects of curdlan (CUR) with different helical conformations on the gelling behavior and mechanisms of heat-induced soy protein isolate (SPI) gels. The results demonstrated that CUR significantly improved the functional properties of SPI gels, including water-holding capacity (0.31–5.06% increase), gel strength (7.01–240.51% enhancement), textural properties, viscoelasticity, and thermal stability. The incorporation of CUR facilitated the unfolding and cross-linking of SPI molecules, leading to enhanced network formation. Notably, SPI composite gels containing CUR with an ordered triple-helix bundled structure exhibited superior gelling performance compared to other helical conformations, characterized by a more compact and uniform microstructure. This improvement was attributed to stronger hydrogen bonding interactions between the triple-helix CUR and SPI molecules. Furthermore, the entanglement of triple-helix CUR with SPI promoted the formation of a denser and more homogeneous interpenetrating polymer network. These findings indicate that triple-helix CUR is highly effective in optimizing the gelling characteristics of heat-induced SPI gels. This study provides new insights into the structure–function relationship of CUR in SPI-based gel systems, offering potential strategies for designing high-performance protein–polysaccharide composite gels. The findings establish a theoretical foundation for applications in the food industry. Full article

Sphingolipids (SLs) are a class of bioactive lipids characterized by sphingoid bases (SBs) as their backbone structure. These molecules exhibit distinct cellular functions, including cell growth, apoptosis, senescence, migration, and inflammatory responses, by interacting with esterases, amidases, kinases, phosphatases, and membrane receptors. These interactions result in a highly interconnected network of enzymes and pathways, known as the sphingolipidome. Dysregulation within this network is implicated in the onset and progression of cardiovascular diseases, metabolic disorders, neurodegenerative disorders, autoimmune diseases, and various cancers. This review highlights the pharmacologically significant sphingoid-based medicinal agents in preclinical and clinical studies. These include myriocin, fingolimod, fenretinide, safingol, spisulosine (ES-285), jaspine B, D-e-MAPP, B13, and α-galactosylceramide. It covers enantioselective syntheses, drug development efforts, and advances in molecular modeling to facilitate an understanding of the binding interactions of these compounds with their biological targets. This review provides a comprehensive evaluation of chiral pool synthetic strategies, translational studies, and the pharmacological relevance of sphingolipid-based drug candidates, offering a pathway for future research in sphingolipid-based therapeutic development. Full article

The agricultural products trade network is essentially a complex adaptive system formed by nonlinear interactions between countries. Based on the complex system theory, this study reveals the dynamic self-organization law of the RCEP regional agricultural products trade network by using the panel data of RCEP agricultural products export trade from 2000 to 2023, combining social network analysis (SNA) and the temporal exponential random graph model (TERGM). The results show the following: (1) The RCEP agricultural products trade network presents a “core-edge” hierarchical structure, with China as the core hub to drive regional resource integration and ASEAN countries developing into secondary core nodes to deepen collaborative dependence. (2) The “China-ASEAN-Japan-Korea “riangle trade structure is formed under the RCEP framework, and the network has the characteristics of a “small world”. The leading mode of South–South trade promotes the regional economic order to shift from the traditional vertical division of labor to multiple coordination. (3) The evolution of trade network system is driven by multiple factors: endogenous reciprocity and network expansion are the core structural driving forces; synergistic optimization of supply and demand matching between economic and financial development to promote system upgrading; geographical proximity and cultural convergence effectively reduce transaction costs and enhance system connectivity, but geographical distance is still the key system constraint that restricts the integration of marginal countries. This study provides a systematic and scientific analytical framework for understanding the resilience mechanism and structural evolution of regional agricultural trade networks under global shocks. Full article

Bifidobacterium is a predominant probiotic in animals that is associated with host intestinal health. The protective mechanisms of the Bifidobacterium animalis (B. animalis) strain, specifically those related to functional gene–host interactions in intestinal homeostasis, remain poorly elucidated. This study reports the complete genome sequence and characterization of a B. animalis strain isolated from wild pig feces, which comprised a single circular chromosome (1,944,022 bp; GC content 60.49%) with 1567 protein-coding genes, and the B. animalis strain had certain acid resistance, bile salt resistance, gastrointestinal fluid tolerance, and antibacterial characteristics. Genomic annotation revealed three putative genomic islands and two CRISPR-Cas systems. Functional characterization identified genes encoding carbohydrate-active enzymes (CAZymes) and associated metabolic pathways, indicating that this strain can degrade complex dietary carbohydrates and synthesize bioactive metabolites for gut homeostasis. Although the antibiotic resistance genes were predicted, phenotypic assays demonstrated discordant resistance patterns, indicating complex regulatory networks. This study indicated the genomic basis of Bifidobacterium–host crosstalk in intestinal protection, providing a framework for developing novel probiotic interventions. Full article

The remote sensing monitoring of land use changes and future scenario simulation hold crucial significance for accurately characterizing urban expansion patterns, optimizing urban land use configurations, and thereby promoting coordinated regional development. Through the integration of multi-source data, this study systematically analyzes the spatiotemporal trajectories and driving forces of land use changes in the Pearl River Delta urban agglomeration (PRD) from 1990 to 2020 and further simulates the spatial patterns of urban land use under diverse development scenarios from 2025 to 2035. The results indicate the following: (1) During 1990–2020, urban expansion in the Pearl River Delta urban agglomeration exhibited a “stepwise growth” pattern, with an annual expansion rate of 3.7%. Regional land use remained dominated by forest (accounting for over 50%), while construction land surged from 6.5% to 21.8% of total land cover. The gravity center trajectory shifted southeastward. Concurrently, cropland fragmentation has intensified, accompanied by deteriorating connectivity of ecological lands. (2) Urban expansion in the PRD arises from synergistic interactions between natural and socioeconomic drivers. The Geographically and Temporally Weighted Regression (GTWR) model revealed that natural constraints—elevation (regression coefficients ranging −0.35 to −0.05) and river network density (−0.47 to −0.15)—exhibited significant spatial heterogeneity. Socioeconomic drivers dominated by year-end paved road area (0.26–0.28) and foreign direct investment (0.03–0.11) emerged as core expansion catalysts. Geographic detector analysis demonstrated pronounced interaction effects: all factor pairs exhibited either two-factor enhancement or nonlinear enhancement effects, with interaction explanatory power surpassing individual factors. (3) Validation of the Patch-generating Land Use Simulation (PLUS) model showed high reliability (Kappa coefficient = 0.9205, overall accuracy = 95.9%). Under the Natural Development Scenario, construction land would exceed the ecological security baseline, causing 408.60 km² of ecological space loss; Under the Ecological Protection Scenario, mandatory control boundaries could reduce cropland and forest loss by 3.04%, albeit with unused land development intensity rising to 24.09%; Under the Economic Development Scenario, cross-city contiguous development zones along the Pearl River Estuary would emerge, with land development intensity peaking in Guangzhou–Foshan and Shenzhen–Dongguan border areas. This study deciphers the spatiotemporal dynamics, driving mechanisms, and scenario outcomes of urban agglomeration expansion, providing critical insights for formulating regionally differentiated policies. Full article

Amid growing global concerns about environmental sustainability and food security, plant-based meat substitutes have emerged as a promising alternative to conventional meat. However, current formulations, especially those based on soy protein isolate (SPI) often fail to replicate the desired texture and structural integrity. To address this limitation, this study aimed to evaluate the use of whole yeast powder (WYP) combined with SPI for producing plant-based meat analogues via high-moisture extrusion. Seven groups were designed: a control group with 0% WYP, five treatment groups with 5%, 10%, 20%, 30%, and 40% WYP, and one reference group containing 20% yeast protein powder (YPP). Although lower in protein content than yeast protein powder (YPP), whole yeast powder exhibits superior water-binding capacity and network-forming ability owing to its complex matrix and fiber content. At a 20% inclusion level, whole yeast powder demonstrated a higher fibrous degree (1.84 ± 0.02 vs. 1.81 ± 0.04), greater hardness (574.93 ± 5.84 N vs. 531.18 ± 17.34 N), and increased disulfide bonding (95.33 ± 0.92 mg/mL vs. 78.41 ± 0.78 mg/mL) compared to 20% YPP. Scanning electron microscopy (SEM) and low-field nuclear magnetic resonance (LF-NMR) revealed that whole yeast powder facilitated the formation of aligned fibrous networks and enhanced water binding. Fourier transform infrared spectroscopy (FTIR) confirmed an increase in β-sheet content (0.267 ± 0.003 vs. 0.260 ± 0.003), which contributed to improved protein aggregation. Increasing the WYP content to 30–40% led to a decline in these parameters, including a reduced fibrous degree (1.69 ± 0.06 at 40% WYP) and weakened molecular interactions (p < 0.05). The findings highlight 20% WYP as the optimal substitution level, offering superior textural enhancement and fibrous structure formation compared to YPP. These results suggest that WYP is not only a cost-effective and processing-friendly alternative to YPP but also holds great promise for scalable industrial application in the plant-based meat sector. Its compatibility with extrusion processes and ability to improve sensory and structural attributes supports its relevance for sustainable meat analogue production. Full article

Urban plots are pivotal links between individual buildings and the city fabric, yet conventional plot classification methods often overlook how buildings interact within each plot. This oversight is particularly problematic in the irregular fabrics typical of many Global South cities. This study aims to create a plot classification method that jointly captures metric and configurational characteristics. Our approach converts each cadastral plot into a graph whose nodes are building centroids and whose edges reflect Delaunay-based proximity. The model then learns unsupervised graph embeddings with a two-layer Graph Attention Network guided by a triple loss that couples building morphology with spatial topology. We then cluster the embeddings together with normalized plot metrics. Applying the model to 8973 plots in Nanjing’s historic walled city yields seven distinct plot morphological types. The framework separates plots that share identical FAR–GSI values but differ in internal organization. The baseline and ablation experiments confirm the indispensability of both configurational and metric information. Each type aligns with specific renewal strategies, from incremental upgrades of courtyard slabs to skyline management of high-rise complexes. By integrating quantitative graph learning with classical typo-morphology theory, this study not only advances urban form research but also offers planners a tool for context-sensitive urban regeneration and land-use management. Full article