Search Results (7,169)

Traditional diagnostics of power transformers heavily rely on signal transformations, such as Welch’s method, to analyze low-frequency noise signals. This study proposes a novel approach using Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, for direct time-series analysis of raw low-frequency signals without frequency-domain transformation. By training and testing multiple LSTM architectures on transformer vibroacoustic data, the proposed approach achieved approximately 86% accuracy in the fine-grained multi-class benchmark and up to 95.54% in the broader grouped categorization scenario. The model further demonstrated near-perfect classification accuracy in distinguishing transformer types (normal vs. overload) using a simplified RNN architecture. These findings illustrate that RNN-based models can streamline transformer diagnostics and improve accuracy in identifying operational states and types, potentially advancing non-invasive monitoring techniques in power system infrastructure. Full article

Moroccan installations of solar photovoltaic panels experience operational difficulties due to shading and vegetation-related soiling, which reduce energy output by 15–30%. Most monitoring systems depend upon a single vegetation index, which can reduce the accuracy of detecting even moderate anomalies. This paper presents a novel integration of multi-criteria hierarchical labeling with dual-branch deep learning for enhanced vegetation anomaly detection. We combined MODIS (2000–2015) and Sentinel-2 (2015–2025) images and NASA POWER weather records to study a 25-year vegetation record using multi-source satellite data in 5 of Morocco’s ecologically diverse zones. We introduced a three-class hierarchical labeling scheme (normal, moderate, severe) for dynamic vegetation models based on combined vegetation indices (NDVI, EVI, NDWI) and meteorological thresholds. The proposed dual-branch architecture uses independent data streams for unfused data, which include temporal multi-scale CNNs (TMSCNN) for spatiotemporal modeling and bidirectional LSTMs for weather-integrated vegetation data. Systematic ablation studies show improvements from using NDVI (68.98%) to multispectral indices (77.74%), meteorological integration (81.02%), and a final accuracy of 82.34% ± 0.88%. The moderate anomaly class exhibits lower precision (65%), demonstrating the challenge of operationalizing severity-based anomaly classification. This work integrates hierarchical, multi-criteria labeling and hybrid deep learning for solar photovoltaic vegetation monitoring. Full article

Background/Objective: Histone deacetylase inhibitors (HDACi) represent a novel class of antineoplastic agents, yet their comprehensive safety profile warrants further investigation. This study aimed to examine the safety of HDACi using the FDA Adverse Event Reporting System (FAERS) and to explore causal relationships through Mendelian randomization (MR) analysis of drug targets. Methods: Adverse drug event (ADE) reports for Vorinostat, Romidepsin, Belinostat, and Panobinostat submitted to the FAERS from their respective market entry dates through 31 December 2023, were analyzed using disproportionality analyses with four algorithms, supplemented by time-to-onset analysis, logistic regression, and MR analysis. Results: A total of 1360, 1065, 225, and 1234 ADE reports were documented for Vorinostat, Romidepsin, Belinostat, and Panobinostat, respectively. Eight preferred terms, including decreased white blood cell, platelet, and neutrophil counts, hypophosphatemia, hypocalcemia, QT prolongation, increased aspartate aminotransferase, and anemia, exhibited positive signals across all four HDACi. A temporal decline in the risk of most HDACi-related ADEs was observed, and age, gender, and weight were identified as potential confounding factors for important medical events. Notably, MR analysis revealed a positive correlation between HDAC5 expression and serum phosphate levels. Conclusions: This pharmacovigilance study provides hypothesis-generating evidence that hypophosphatemia may represent a potential class effect of HDACi. Full article

Hyphantria cunea (Drury) causes extensive ecological damage primarily during its larval stages, characterized by voracious feeding and rapid dispersal. Given that conventional dsRNA-mediated RNA interference (RNAi) is generally recalcitrant in Lepidoptera, endogenous microRNAs (miRNAs) may represent an additional class of regulatory molecules worthy of systematic investigation. In this study, we utilized high-throughput sequencing to construct nine comprehensive miRNA libraries across three critical developmental milestones (three biological replicates per instar): the 1st, 4th, and 7th instars (L1, L4, and L7). A total of 1667 miRNA entries were catalogued, including 1080 known and 587 bioinformatically predicted, as yet unvalidated novel miRNA candidates. Comparative transcriptomic analysis revealed 52 differentially expressed miRNAs with significant stage-dependent profiles, with the most pronounced divergence observed between the L1 and L7 groups. Bioinformatic prediction identified 16,784 non-redundant target genes. GO and KEGG enrichment analyses indicated that the predicted target genes of these differentially expressed miRNAs were enriched in developmental and metabolic categories, including cellular development, protein digestion, and nutrient absorption, suggesting that these miRNAs may be associated with tissue remodeling and larval developmental transitions. Collectively, our findings expand the currently available miRNA resource for H. cunea and define stage-associated miRNA expression patterns during larval development. Rather than establishing direct functional roles, this work provides a framework and candidate molecules for future design of RNAi-based biopesticides. Full article

Instance segmentation of plant point clouds is challenging due to intricate structures, non-uniform density, and large intra-class variation. Conventional methods often suffer from blurred boundaries, instance adhesion, and insufficient coupling of semantic and instance features. To address these issues, this paper proposes MPRSF-CSA, a novel network integrating recursive slice-based feature extraction with an attention-embedding mechanism. The method first transforms disordered point clouds into ordered sequences via a multi-directional recursive slicing strategy and models inter-slice dependencies using BiLSTM. Parallel decoding branches for semantic and instance segmentation are constructed, and a core attention-embedding module facilitates bidirectional fusion of semantic and instance features. Instance segmentation is achieved via clustering and semantic-aware optimization. Experiments on two public datasets demonstrate that MPRSF-CSA outperforms existing approaches in segmentation accuracy, boundary preservation, and adaptability to complex plant scenes. Full article

Plant pathogens pose a severe threat to global agricultural production, and their pathogenicity is closely linked to the biosynthesis of secondary metabolites. Basidiomycete within the family Ustilaginaceae represent significant plant pathogens, among which Ustilago maydis, as a model species, has been extensively studied for its secondary metabolites. However, the biosynthetic potential of other species within this family remains poorly understood. In this study, we conducted whole-genome bioinformatic analyses of 16 Ustilaginaceae species, including U. maydis, to systematically identify the distribution of biosynthetic gene clusters (BGCs), core gene domain compositions, and interspecies similarities. A total of 181 predicted BGCs were identified, averaging approximately 11 per species. BGCs for mannosylerythritol lipids (MELs), siderophores, and itaconic acid, as well as the melanin-associated genes pks1 and pks2, were widely distributed across most species. Conversely, an additional melanin biosynthetic gene cluster was found exclusively in U. maydis strain 521, indicating species-specific occurrence. Furthermore, this study identified a novel class of polyketide synthase (PKS) gene clusters with uncharacterized functions across 15 species, exhibiting high sequence and structural conservation between species. These findings reveal the rich metabolic diversity and species-specific biosynthetic potential of Ustilaginaceae, and by using U. maydis as a reference model, we highlight several BGCs (e.g., for MELs, siderophores, itaconic acid, and melanin) that are known to contribute to virulence or pathogenicity in plant hosts. This provides new insights into their pathogenic mechanisms. Full article

The detection and identification of unmanned aerial vehicles (UAVs) using radio frequency (RF) signals becomes particularly challenging in congested spectral environments, where conventional approaches relying solely on spectral characteristics often prove limited. This work introduces a novel technique for both UAV detection and classification based on temporal representations derived directly from the envelope of received RF signals. The proposed system follows a two-stage architecture: first, binary detection of UAV presence in a given RF channel, and second, identification of the specific UAV model among several commercial platforms. For the first stage, two signal representation methodologies are employed—Gramian Angular Fields and Hilbert curves—both generated from short-time RF windows and subsequently used as inputs to convolutional neural networks. Experimental evaluation demonstrates that the detection stage achieves accuracy rates exceeding 94% for the non-UAV class and approaching 99% for the UAV class with both approaches. In the identification stage, the system attains an accuracy above 90% for most considered UAV models, reaching up to 100% for certain platforms. These results confirm the effectiveness of the envelope-based approach for analyzing UAV-related RF signals. Full article

Background: Hemagglutinin (HA) is the primary surface protein of the influenza A virus, determining its subtype and antigenic properties. Traditional subtype classification methods rely on DNA or amino acid sequence analysis, which does not account for protein spatial folding. Methods: In this work, we propose EpitopeGNN—a graph neural network (GNN) that constructs a residue interaction network (RIN) from the 3D structure of HA and classifies the virus subtype. The model was trained on 249 structures from the Protein Data Bank (PDB), containing H1N1, H3N2, H5N1, and other subtypes. Results: After rigorous sequence redundancy reduction (92% identity), the model maintained 95–100% accuracy on non-redundant data, significantly outperforming sequence-only baselines (the best baseline achieved 85% for multi-class and 92.3% for binary classification). A significant correlation was found between the obtained structural embeddings and phylogenetic distances (r = 0.38, p < 0.001), confirming their biological relevance and opening opportunities for structural monitoring of virus evolution, as well as rapid analog searching for novel strains. Conclusions: We developed a new graph neural network that classifies influenza A virus subtypes directly from the 3D structure of hemagglutinin using residue interaction networks and physicochemical features, which can serve as a foundation for predicting influenza virus receptor specificity and epitope immunogenicity. Full article

Non-negative Matrix Factorization (NMF) is a fundamental technique in unsupervised learning for data representation and clustering tasks. Although deep NMF methods have been developed to uncover hierarchical latent features, many existing approaches primarily rely on coefficient-matrix-based decomposition or single-centroid representations. This often limits the integration of intra-class structural features during deep decomposition, resulting in ambiguous and incomplete local feature representations. Moreover, these frameworks often exhibit feature blurring and inadequate discriminability across hierarchical levels. This paper introduces a novel Deep Basis Non-negative Matrix Factorization with Multi-Centroid Contrastive Learning (DBMCNMF) algorithm that addresses these limitations through innovative architectural design. The proposed method integrates multi-centroid representation learning with contrastive regularization constraints within a deep basis matrix factorization framework. The algorithm uses Gaussian similarity measures to establish attractive and repulsive regularization terms that preserve manifold topology while promoting discriminative clustering. DBMCNMF uses multiple centroids instead of single-centroid methods to comprehensively cover complex data distributions and capture local geometric structures that are typically inaccessible to conventional methods. The proposed model is evaluated on several benchmark image datasets. The results indicate that DBMCNMF consistently outperforms traditional single-centroid methods by achieving higher clustering accuracy and preserving the underlying manifold structure more effectively. Full article

Protective behavior in dairy cattle is one of the important potential indicators of their health and welfare status, and the precise detection of this behavior is of great significance for improving pasture management. However, existing methods face challenges, including capturing rapid motions, excessive background interference, and sample imbalance in complex agricultural environments. In response to these challenges, we proposed a Multi-Stage Attention SlowFast (MSA-SlowFast) model based on the improved SlowFast network to explore the model’s ability to distinguish between normal and protective behavior of dairy cattle. It achieves performance improvement through three core modules: the Multi-Path Balanced Head (MPBHead) for alleviating category imbalance, the Spatio-Temporal Convolutional Block Attention Module (ST-CBAM) for enhancing key feature extraction, and the 7 (BAF) for promoting multi-path feature complementarity. Additionally, we proposed novel timing-aware oversampling methods and dynamic loss adjustment mechanisms to further improve the detection performance of minority-class protective behaviors. Finally, a spatio-temporal-oriented dairy cattle protective behaviors dataset is constructed. Experimental results demonstrate that the proposed MSA-SlowFast model achieves 79.41% mAP, surpassing the standard SlowFast (70.58%) and Slow-only (68.21%). Further validation shows that the model exhibits high detection confidence in four specific actions labeled as protective behavior: 0.97 for tail swaying, 0.90 for head shaking, 0.92 for ear flapping, and 0.90 for leg kicking. These preliminary results show that the method proposed in this study has certain feasibility and reference value for the detection of protective behavior of dairy cattle under our constructed dataset. Full article

Background/Objectives: KRAS G12C mutations define a clinically actionable subset of solid tumors, particularly non–small cell lung cancer. Clinical responses to approved covalent inhibitors remain limited by intrinsic and acquired resistance, highlighting the need for structurally distinct inhibitor scaffolds to expand therapeutic options. The objective of this study was to identify novel covalent binders targeting the KRAS G12C switch-II pocket through large-scale in silico screening and experimental validation. Methods: More than 1.9 million small molecules from diverse commercial libraries were screened using covalent docking, followed by multi-stage refinement incorporating molecular dynamics simulations, MM/GBSA free-energy estimation, and cancer-focused QSAR modeling. Results: This integrated workflow yielded 50 prioritized compounds spanning several chemically distinct scaffold classes. These candidates displayed favorable predicted binding energetics, stable ligand-protein interactions over extended simulation timescales, and low structural similarity to clinically approved KRAS G12C inhibitors sotorasib and adagrasib. Benchmarking against these clinical agents, using identical computational parameters, yielded comparable predicted binding energies for several candidate molecules. In cellular NanoBRET target-engagement assays, selected scaffolds, including K788-7251 and AN-989/14669131, exhibited sub-micromolar engagement of KRAS G12C with minimal endothelial cytotoxicity. Conclusions: Collectively, these findings identify structurally distinct, KRAS G12C inhibitor chemotypes and provide tractable starting points for the development of next-generation targeted therapies. Full article

Feature selection is a core step in data analysis and is referred to as attribute reduction in rough set theory. Granular ball computing has emerged as a novel data analysis paradigm characterized by high computational efficiency, robustness, and scalability. However, in previous attribute reduction methods for interval numbers, the construction of tolerance classes and the reduction iteration process suffer from inefficiency. To address these limitations, this paper proposes an efficient attribute reduction method based on fuzzy interval-valued granular balls. This method integrates fuzzy interval-valued granular balls with an acceleration strategy based on the positive region. Specifically, we first construct tolerance classes efficiently using fuzzy interval-valued granular balls, thereby enabling a reasonable partition of the universe. We then remove redundant objects in the positive region during the reduction iteration to avoid unnecessary computations. On this basis, we further propose a conditional entropy-based algorithm for attribute reduction. Experimental results show that this algorithm substantially improves computational efficiency while maintaining high classification accuracy. Full article

Urban agriculture plays a critical yet increasingly complex role in sustainable urban development, especially in high-density regions undergoing rapid transformation. Accurate mapping of its spatial distribution and functional composition remains a methodological challenge due to its fragmented landscape, small plot sizes, and multifunctional nature. This study addresses this gap by developing and applying a novel hierarchical classification framework that integrates agricultural land cover types with key socio-economic functions to map urban agriculture in the Pearl River Delta (PRD), China. This framework is structured around agricultural land categories (i.e., cropland, garden, forest, grass, and water body) and further delineated by two primary production functions, planting and breeding, with a third functional dimension, leisure activities, proposed as a conceptual extension for future research. Using unmanned aerial vehicle (UAV) imagery and high-resolution satellite data, we constructed a spatial sample database for urban agriculture. The random forest algorithm was applied to classify urban agriculture with Gaofen-2 imagery, generating detailed spatial distribution maps across the study area, with consistently reliable overall accuracy (79.07–81.82%), though this may be slightly optimistic due to potential spatial autocorrelation between training and testing samples. While the framework performed exceptionally well for spectrally and spatially distinct classes such as water bodies and perennial plantations, challenges remained in discriminating among annual field crops due to spectral similarity. These findings underscore the potential of integrating multi-temporal remote sensing data to capture phenological variations for improved classification. This study provides a replicable, functionally informed mapping approach that not only advances the methodological toolkit for urban agriculture characterization but also offers a valuable evidence base for land use planning, agricultural policy, and sustainable urban development in rapidly urbanizing regions. Full article

Empowered by nanotechnology, messenger RNA (mRNA) therapeutics have shown a rapid evolution post COVID-19 from a conceptual platform to a clinically validated modality, and they diversified into oncology, cardiovascular diseases, and rare disorders. As a template for in situ protein production, it offers several advantages over traditional proteins and DNA drugs. The intrinsic stability of mRNA and its sensitivity to innate immune sensing hinder its capacity for immediate cellular entry, necessitating its need for a delivery system to obtain optimal therapeutic potential. This review explores the innovations in nanocarrier engineering, design principles for lipid nanoparticles-mRNA (LNPs) platforms, and their clinical translation across the prominent indications. It also addresses their safety, immunogenicity, and scalability while addressing the key limitations and manufacturing scalability through comparative platform analysis. Although LNPs usually dominate their delivery through encapsulation and manufacturability, their limitations, like repeat dose reactogenicity and liver tropism, require next-generation designs like SORT lipids, stimuli-responsive hybrids for extrahepatic targeting. In oncology, LNP-mRNA drives the neoantigen vaccines, and rare diseases leverage the transient enzyme replacement. While the safety profiles highlight the innate immune tuning through nucleoside mods and lipid biodegradability, chronic administration risks are still persistent. While there are novel scalability options like microfluidic mixing to support the production gaps in organ selectivity and durability, their adoption is hindered. We outline the future directions to perceive mRNA’s full potential as a broader therapeutic class. Full article

This study introduces a novel inertial-type iteration algorithm based on the Normal S iteration for the class of almost contraction mappings in Banach spaces. Traditional fixed point iterations often suffer from slow convergence and high computational cost; to address these limitations, the proposed framework incorporates an adaptive inertial-type parameter. We establish strong convergence of the algorithm and derive explicit a posteriori error estimates under weak contractive conditions. In addition, we demonstrate the asymptotic equivalence of the NS inertial-type trajectories with the classical Normal S iteration, provide a comprehensive weak $w^2-$stability analysis, and obtain sharp upper bounds for the data dependence problem. The practical performance of the algorithm is evaluated in two distinct computational domains: image deblurring via wavelet-based ${\ell }_1$ regularization and the generation of complex fractal patterns, including Julia and Mandelbrot sets. Numerical results show that the proposed inertial-type iteration algorithm significantly outperforms existing methods—such as Picard, Mann, Ishikawa, and standard Normal S iterations—achieving faster convergence, higher PSNR values in image restoration, and more stable basins of attraction in fractal visualizations. These findings highlight the effectiveness and versatility of the NS inertial-type iteration algorithm approach for both theoretical analysis and real-world applications. Full article