Search Results (253)

Next-generation sequencing has overturned the dogma of biliary sterility, revealing low-biomass microbiota along the gut–biliary axis with metabolic and immunologic effects. This review synthesizes evidence on composition, function, and routes of colonization across benign and malignant disease. In cholelithiasis, Proteobacteria- and Firmicutes-rich consortia provide β-glucuronidase, phospholipase A₂, and bile salt hydrolase, driving bile supersaturation, nucleation, and recurrence. In primary sclerosing cholangitis, primary biliary cholangitis, and autoimmune hepatitis, intestinal dysbiosis and disturbed bile acid pools modulate pattern recognition receptors and bile acid signaling (FXR, TGR5), promote Th17 skewing, and injure cholangiocytes; bile frequently shows Enterococcus expansion linked to taurolithocholic acid. Distinct oncobiomes characterize cholangiocarcinoma subtypes; colibactin-positive Escherichia coli and intratumoral Gammaproteobacteria contribute to DNA damage and chemoresistance. In hepatocellular carcinoma, intratumoral microbial signatures correlate with tumor biology and prognosis. We critically appraise key methodological constraints—sampling route and post-sphincterotomy contamination, antibiotic prophylaxis, low biomass, and heterogeneous analytical pipelines—and outline a translational agenda: validated microbial/metabolomic biomarkers from bile, tissue, and stent biofilms; targeted modulation with selective antibiotics, engineered probiotics, fecal microbiota transplantation, and bile acid receptor modulators. Standardized protocols and spatial, multi-omic prospective studies are required to enable risk stratification and microbiota-informed therapeutics. Full article

Ultrawideband (UWB) radar has emerged as a compelling solution for noncontact human activity recognition. This study proposes a novel framework that leverages adaptive signal decomposition and video-based deep learning to classify human motions with high accuracy using a single UWB radar. The raw radar signals were processed by empirical wavelet transform (EWT) to isolate the dominant frequency components in a data-driven manner. These components were further analyzed using the Hilbert transform to produce time–frequency spectra that capture motion-specific signatures through subtle phase variations. Instead of treating each spectrum as an isolated image, the resulting sequence was organized into a temporally coherent video, capturing spatial and temporal motion dynamics. The video data were used to train the SlowFast network—a dual-path deep learning model optimized for video-based action recognition. The proposed system achieved an average classification accuracy exceeding 99% across five representative human actions. The experimental results confirmed that the EWT–Hilbert-based preprocessing enhanced feature distinctiveness, while the SlowFast architecture enabled efficient and accurate learning of motion patterns. The proposed framework is intuitive, computationally efficient, and scalable, demonstrating strong potential for deployment in real-world scenarios such as smart healthcare, ambient-assisted living, and privacy-sensitive surveillance environments. Full article

Although SAR systems can provide high-resolution aerial view images all-day, all-weather, the aspect and pose-sensitivity of the SAR target signatures, which defies the Gestalt perceptual principles, sets a frustrating performance upper bound for SAR Automatic Target Recognition (ATR). Therefore, we propose a network to support context-guided ATR by using aligned Electro-Optical (EO)-SAR image pairs. To realize EO-SAR image scene grammar alignment, the stable context features highly correlated to the parking patterns of the vehicle and aircraft targets are extracted from the EO images as prior knowledge, which is used to assist SAR-ATR. The proposed network consists of a Scene Recognition Module (SRM) and an instance-level Cross-modality ATR Module (CATRM). The SRM is based on a novel light-condition-driven adaptive EO-SAR decision weighting scheme, and the Outlier Exposure (OE) approach is employed for SRM training to realize Out-of-Distribution (OOD) scene detection. Once the scene depicted in the cut of interest is identified with the SRM, the image cut is sent to the CATRM for ATR. Considering that the EO-SAR images acquired from diverse observation angles often feature unbalanced quality, a novel class-incremental learning method based on the Context-Guided Re-Identification (ReID)-based Key-view (CGRID-Key) exemplar selection strategy is devised so that the network is capable of continuous learning in the open-world deployment environment. Vehicle ATR experimental results based on the UNICORN dataset, which consists of 360-degree EO-SAR images of an army base, show that the CGRID-Key exemplar strategy offers a classification accuracy 29.3% higher than the baseline model for the incremental vehicle category, SUV. Moreover, aircraft ATR experimental results based on the aligned EO-SAR images collected over several representative airports and the Arizona aircraft boneyard show that the proposed network achieves an F1 score of 0.987, which is 9% higher than YOLOv8. Full article

We previously developed SA-conf, a method designed to quantify backbone structural variability in protein targets. This approach is based on the HMM-SA structural alphabet, which enables efficient and rapid comparison of local backbone conformations across multiple structures of a given target. In this study, SA-conf (version for python2.7) was applied to a dataset of 130 crystallographic chains of Bcl-xL, a protein involved in promoting cell survival. SA-conf quantified and mapped backbone structural variability, revealing the protein’s capacity for conformational rearrangement. Our results showed that while most mutations had minimal impact on backbone conformation, some were associated with long-range structural effects. By jointly analyzing residue flexibility and backbone rearrangements across apo and holo structures, SA-conf identified key regions where the backbone undergoes structural adjustments upon ligand binding. Notably, the $\alpha 2$–$\alpha 3$ region was shown to be a hotspot of structural plasticity, exhibiting ligand-specific conformational signatures. Furthermore, SA-conf enabled the construction of a structural map of the binding site, distinguishing a conserved anchoring core from flexible peripheral regions that contribute to ligand specificity. Overall, this study highlights SA-conf’s capacity to detect conformational changes in protein backbones upon ligand binding and to uncover structural determinants of selective ligand recognition. Full article

With the rapid advancement of UAV-based remote sensing and image recognition techniques, identifying environmental risk factors from aerial imagery has emerged as a focal point in intelligent inspection during the power transmission and distribution projects construction phase. The uneven spatial distribution of risk factors on construction sites, their weak texture signatures, and the inherently multi-scale nature of UAV imagery pose significant detection challenges. To address these issues, we propose a one-stage SRW-YOLO algorithm built upon the YOLOv11 framework. First, a P2-scale shallow feature detection layer is added to capture high-resolution fine details of small targets. Second, we integrate a reparameterized convolution based on channel shuffle (RCS) of a one-shot aggregation (RCS-OSA) module into the backbone and neck’s shallow layers, enhancing feature extraction while significantly reducing inference latency. Finally, a dynamic non-monotonic focusing mechanism WIoU v3 loss function is employed to reweigh low-quality annotations, thereby improving small-object localization accuracy. Experimental results demonstrate that SRW-YOLO achieves an overall precision of 80.6% and mAP of 79.1% on the State Grid dataset, and exhibits similarly superior performance on the VisDrone2019 dataset. Compared with other one-stage detectors, SRW-YOLO delivers markedly higher detection accuracy, offering critical technical support for multi-scale, heterogeneous environmental risk monitoring during the power transmission and distribution projects construction phase, and establishes the theoretical foundation for rapid and accurate inspection using UAV-based intelligent imaging. Full article

Lipedema is a chronic, estrogen-sensitive adipose tissue disorder characterized by disproportionate subcutaneous fat accumulation, fibrosis, inflammation, and resistance to fat mobilization. Despite its high prevalence, lipedema remains poorly understood and frequently misdiagnosed. This narrative review proposes a novel pathophysiological model in which menopause acts as a critical turning point in the progression of lipedema, driven by estrogen receptor imbalance (ERβ predominance over ERα), intracrine estrogen excess, and adipose tissue dysfunction. We demonstrate how menopause amplifies adipose tissue dysfunction by suppressing ERα signaling; enhancing ERβ activity; and disrupting mitochondrial function, insulin sensitivity, and lipid oxidation. Concurrently, the upregulation of aromatase and 17β-HSD1, combined with the suppression of 17β-HSD2, sustains localized estradiol excess, perpetuating inflammation, fibrosis, and immune dysregulation. The molecular signature observed in lipedema closely mirrors that of other estrogen-driven gynecological disorders, such as endometriosis, adenomyosis, and uterine fibroids. Understanding these molecular mechanisms highlights the pivotal role of menopause as a catalyst for disease progression and provides a rationale for targeted therapeutic strategies, including hormonal modulation and metabolic interventions. This review reframes lipedema as an estrogen receptor-driven gynecological disorder, offering a new perspective to improve clinical recognition, diagnosis, and management of this neglected condition. Full article

The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially large chemical domain. This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback. The framework employs curriculum-based training that progressively increases molecular complexity, enabling robust detection of degradation markers linking polymer architectures to enzymatic breakdown kinetics. Experimental validation through automated synthesis and in situ characterization of 847 novel polymers demonstrates the framework’s sensing capabilities, achieving a 73.2% synthesis success rate and identifying 42 structures with precisely monitored degradation profiles spanning 6 to 24 months. Learned molecular patterns reveal previously undetected correlations between specific spectroscopic signatures and degradation susceptibility, validated through accelerated aging studies with continuous sensor monitoring. Our results establish that physics-informed constraints significantly improve both the validity (94.7%) and diversity (0.82 Tanimoto distance) of generated molecular structures compared with unconstrained baselines. This work advances the convergence of intelligent sensing technologies and materials science, demonstrating how physics-informed machine learning can enhance real-time monitoring capabilities for next-generation sustainable materials. Full article

Proper nutrition is a fundamental aspect to maintaining overall health and well-being, influencing both physical and social aspects of human life; an unbalanced or inadequate diet can lead to various nutritional deficiencies and chronic health conditions. In today’s fast-paced world, monitoring nutritional intake has become increasingly important, particularly for those with specific dietary needs. While smartphone-based applications using image recognition have simplified food tracking, they still rely heavily on user interaction and raise concerns about practicality and privacy. To address these limitations, this paper proposes a novel, compact spectroscopic sensing platform for automatic beverage recognition. The system utilizes the AS7265x commercial sensor to capture the spectral signature of beverages, combined with a K-Nearest Neighbors (KNN) machine learning algorithm for classification. The approach is designed for integration into everyday objects, such as smart glasses or cups, offering a noninvasive and user-friendly alternative to manual tracking. Through optimization of both the sensor configuration and KNN parameters, we identified a reduced set of four wavelengths that achieves over 96% classification accuracy across a diverse range of common beverages. This demonstrates the potential for embedding accurate, low-power, and cost-efficient sensors into Internet of Things (IoT) devices for real-time nutritional monitoring, reducing the need for user input while enhancing accessibility and usability. Full article

Hyperspectral imaging (HSI) plays a pivotal role in modern agriculture by capturing fine-grained spectral signatures that support crop classification, health assessment, and land-use monitoring. However, the transition from raw spectral data to reliable semantic understanding remains challenging—particularly under fragmented planting patterns, spectral ambiguity, and spatial heterogeneity. To address these limitations, we propose UniHSFormer-X, a unified transformer-based framework that reconstructs agricultural semantics through prototype-guided token routing and hierarchical context modeling. Unlike conventional models that treat spectral–spatial features uniformly, UniHSFormer-X dynamically modulates information flow based on class-aware affinities, enabling precise delineation of field boundaries and robust recognition of spectrally entangled crop types. Evaluated on three UAV-based benchmarks—WHU-Hi-LongKou, HanChuan, and HongHu—the model achieves up to 99.80% overall accuracy and 99.28% average accuracy, outperforming state-of-the-art CNN, ViT, and hybrid architectures across both structured and heterogeneous agricultural scenarios. Ablation studies further reveal the critical role of semantic routing and prototype projection in stabilizing model behavior, while parameter surface analysis demonstrates consistent generalization across diverse configurations. Beyond high performance, UniHSFormer-X offers a semantically interpretable architecture that adapts to the spatial logic and compositional nuance of agricultural imagery, representing a forward step toward robust and scalable crop classification. Full article

Ovarian cancer remains a formidable global health burden, characterized by frequent late-stage diagnosis and elevated mortality rates attributable to its elusive pathogenesis and the critical lack of reliable early-detection biomarkers. Emerging investigations into the gut–vaginal microbiome axis have unveiled novel pathogenic mechanisms and potential diagnostic targets in ovarian carcinogenesis. This comprehensive review systematically examines the compositional alterations in and functional interplay between vaginal and intestinal microbial communities in ovarian cancer patients. We elucidate three principal mechanistic pathways through which microbial dysbiosis may drive oncogenesis: (1) estrogen-mediated metabolic reprogramming via β-glucuronidase activity; (2) chronic activation of pro-inflammatory cascades (particularly NF-κB and STAT3 signaling); (3) epigenetic silencing of tumor suppressor genes through DNA methyltransferase modulation. We propose an integrative diagnostic framework synthesizing multi-omics data—incorporating microbial profiles, metabolic signatures, pathway-specific molecular alterations, established clinical biomarkers, and imaging findings—within a multifactorial etiological paradigm. This innovative approach aims to enhance early-detection accuracy through machine learning-enabled multidimensional pattern recognition. By bridging microbial ecology with tumor biology, this review provides novel perspectives for understanding ovarian cancer etiology and advancing precision oncology strategies through microbiome-targeted diagnostic innovations. Full article

Reliable fault diagnosis in aero-engine bearing systems is essential for maintaining process stability and safety. However, acquiring fault samples in aerospace applications is costly and difficult, resulting in severely limited data for model training. Traditional methods often perform poorly under such constraints, lacking the ability to extract discriminative features or effectively correlate observed signal changes with underlying process faults. To address this challenge, this study presents a process-oriented framework—WSET-CNN-OOA-LSSVM—designed for effective fault recognition in small-sample scenarios. The framework begins with Wavelet Synchroextracting Transform (WSET), enhancing time–frequency resolution and capturing energy-concentrated fault signatures that reflect degradation along the process timeline. A tailored CNN with asymmetric pooling and progressive dropout preserves temporal dynamics while preventing overfitting. To compensate for limited labels, confidence-based pseudo-labeling is employed, guided by Mahalanobis distance and adaptive thresholds to ensure reliability. Classification is finalized using an Osprey Optimization Algorithm (OOA)-enhanced Least Squares SVM, which adapts decision boundaries to reflect subtle process state transitions. Validated on both test bench and real aero-engine data, the framework achieves 93.4% accuracy with only five fault samples per class and 100% in full-scale scenarios, outperforming eight existing methods. Therefore, the experimental results confirm that the proposed framework can effectively overcome the data scarcity challenge in aerospace bearing fault diagnosis, demonstrating its practical viability for few-shot learning applications in industrial condition monitoring. Full article

This article addresses the issue of the elevator cabin door drive system failure diagnosis. The analyzed component is one of the most critical and the most vulnerable part of the entire elevator. Existing solutions in the literature include methods such as spectral analysis of system vibrations, motor current signature analysis, fishbone diagrams, fault trees, multi-agent systems, image recognition, and machine learning techniques. However, there is a noticeable gap in comprehensive studies that specifically address classification of the multiple types of system components failures, class imbalance in the dataset, and the need to reduce data transmitted over the elevator’s internal bus. The developed diagnostic system measures the drive system’s parameters, processes them to reduce data, and classifies 11 device failures. This was achieved by constructing a test bench with a prototype cabin door drive system, identifying the most frequent system faults, developing a data preprocessing method that aggregates every driving cycle to one sample, reducing the transmitted data by 300 times, and using machine learning for modeling. A comparative analysis of the fault detection performance of seven different machine learning algorithms was conducted. An optimal cross-validation method and hyperparameter optimization techniques were employed to fine-tune each model, achieving a recall of over 97% and an F1 score approximately 97%. Finally, the developed data preparation method was implemented in the cabin door drive controller. Full article

We present a thorough machine-learning framework based on real-time state-of-polarization (SOP) monitoring for robust anomaly identification in optical fiber networks. We exploit SOP data under three different threat scenarios: (i) malicious or critical vibration events, (ii) overlapping mechanical disturbances, and (iii) malicious fiber tapping (eavesdropping). We used various supervised machine learning techniques like k-Nearest Neighbor (k-NN), random forest, extreme gradient boosting (XGBoost), and decision trees to classify different vibration events. We also assessed the framework’s resilience to background interference by superimposing sinusoidal noise at different frequencies and examining its effects on the polarization signatures. This analysis provides insight into how subsurface installations, subject to ambient vibrations, affect detection fidelity. This highlights the sensitivity to which external interference affects polarization fingerprints. Crucially, it demonstrates the system’s capacity to discern and alert on malicious vibration events even in the presence of environmental noise. However, we focus on the necessity of noise-mitigation techniques in real-world implementations while providing a potent, real-time mechanism for multi-threat recognition in the fiber networks. Full article

This study explored the biosynthetic mechanisms and structural diversity of pyoverdines (PVDs) produced by Pseudomonas fulva. Genomic analysis using antiSMASH identified the PVD biosynthetic gene cluster, although the C-terminal peptide sequence could not be predicted. Subsequent liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis revealed the full peptide structure, including modified residues, such as N-acetylhydroxyornithine and cyclohydroxyornithine, and confirmed the presence of several PVD isoforms with different chromophore side chains. Comparative LC-MS analysis across Pseudomonas species demonstrated that P. fulva produces unique PVD molecular mass patterns. The bioinformatic and structural modeling of non-ribosomal peptide synthetase PvdL open reading frame 3 revealed that the A2 and A3 adenylation domains are lysine selective. Although their sequences differ from known lysine-specific signatures, AlphaFold3-based structural prediction revealed conserved substrate-binding configurations, suggesting that similar substrate-binding features may have arisen independently. Notably, Thr²⁹⁷, a unique residue in the non-ribosomal code, likely plays a key role in lysine recognition. The high degree of sequence similarity between the A2 and A3 domains may reflect domain duplication and could be involved in the diversification of the PVD structure. Further functional and ecological studies are required to assess the physiological significance of P. fulva PVDs in microbial iron acquisition. Full article

KRAS is a pivotal oncoprotein that regulates cell proliferation and survival through interactions with downstream effectors such as RAF1. Despite significant advances in understanding KRAS biology, the structural and dynamic mechanisms of KRAS allostery remain poorly understood. In this study, we employ microsecond molecular dynamics simulations, mutational scanning, and binding free energy calculations together with dynamic network modeling to dissect how engineered DARPin proteins K27, K55, K13, and K19 engage KRAS through diverse molecular mechanisms ranging from effector mimicry to conformational restriction and allosteric modulation. Mutational scanning across all four DARPin systems identifies a core set of evolutionarily constrained residues that function as universal hotspots in KRAS recognition. KRAS residues I36, Y40, M67, and H95 consistently emerge as critical contributors to binding stability. Binding free energy computations show that, despite similar binding modes, K27 relies heavily on electrostatic contributions from major binding hotspots while K55 exploits a dense hydrophobic cluster enhancing its effector-mimetic signature. The allosteric binders K13 and K19, by contrast, stabilize a KRAS-specific pocket in the α3–loop–α4 motif, introducing new hinges and bottlenecks that rewire the communication architecture of KRAS without full immobilization. Network-based analysis reveals a strikingly consistent theme: despite their distinct mechanisms of recognition, all systems engage a unifying allosteric architecture that spans multiple functional motifs. This architecture is not only preserved across complexes but also mirrors the intrinsic communication framework of KRAS itself, where specific residues function as central hubs transmitting conformational changes across the protein. By integrating dynamic profiling, energetic mapping, and network modeling, our study provides a multi-scale mechanistic roadmap for targeting KRAS, revealing how engineered proteins can exploit both conserved motifs and isoform-specific features to enable precision modulation of KRAS signaling in oncogenic contexts. Full article