Search Results (6,545)

Deep learning (DL), a hierarchical feature extraction method, has garnered increasing attention in the remote sensing community. Recently, self-supervised learning (SSL) methods in DL have gained wide recognition due to their ability to mitigate the dependence on both the quantity and quality of samples. This advantage is particularly significant when dealing with limited labeled samples in hyperspectral images (HSIs). However, conventional SSL methods face two main challenges. They struggle to construct self-supervised signals based on the unique characteristics of HSI. Moreover, they fail to design network optimization strategies that leverage the intrinsic manifold geometry within HSI. To tackle these issues, we propose a novel self-supervised learning method termed Manifold Geometry-Leveraged Self-supervised Learning (MSSL) for HSI classification. The approach employs a two-stage training strategy. In the initial pre-training stage, it develops self-supervised signals that exploit spatial homogeneity and spectral coherence properties of HSI. Furthermore, it introduces a manifold geometry-guided loss function that enhances feature discrimination by increasing intra-class compactness and inter-class separation. The second stage is a fine-tuning phase utilizing a small set of labeled samples. This stage optimizes the pre-trained model, enabling effective feature extraction from hyperspectral data for classification tasks. Experiments conducted on real-world HSI datasets demonstrate that MSSL achieves superior classification performance compared to several relevant state-of-the-art methods. Full article

Introduction: Identification of the thoracic duct (TD) is essential during esophageal surgery to reduce the risk of complications such as chylothorax. The clinical significance of the white plane, or Morosow’s ligament—a consistent anatomical landmark along the esophagus—remains poorly defined. Methods: A total of 166 patients undergoing robot-assisted minimally invasive esophagectomy (RAMIE) were analyzed. Intraoperative visualization of the white plane was documented. Patient demographics, tumor characteristics, postoperative complications, management strategies, hospital length of stay, and overall survival were assessed. Complication severity was graded using the Clavien–Dindo classification. The Kaplan–Meier and multivariable Cox regression analyses were used to evaluate prognostic factors, including BMI, ASA score, pneumonia, pT status, pN status, neoadjuvant and adjuvant therapy, and white plane visualization. Results: The white plane was visualized in 154 patients (92.8%). Postoperative complications, management strategies, hospital length of stay, and 30-/90-day in-hospital mortality did not differ between groups with visualized and not visualized white planes. Median overall survival was significantly longer in patients with a visible white plane (43.1 vs. 13.1 months; p = 0.0079). The multivariable analysis identified ASA classification, pT stage, pN stage, and adjuvant therapy as independent predictors of overall survival, whereas lymph node stage and adjuvant therapy were independent predictors of recurrence-free survival. Conclusions: The white plane is a distinct intraoperative anatomical structure that can be visualized in most RAMIE procedures. Its identification may assist in TD recognition and provides a framework for describing mediastinal anatomy, but further studies are needed to determine its impact on surgical standardization and patient outcomes. Full article

This paper presents an advanced signal processing framework for multi-channel forward-scatter radar (MC-FSR) systems based on the Multiple Signal Classification (MUSIC) algorithm. The proposed framework addresses the inherent limitations of FFT-based space-domain processing, such as limited angular resolution and the poor detectability of weak or closely spaced targets, which become particularly severe in low-cost FSR systems, which are typically operated with small antenna arrays. The MUSIC algorithm is adapted to operate on real-valued data obtained from the non-coherent, amplitude-based MC-FSR approach by reformulating the steering vectors and adjusting the degrees of freedom (DoFs). While compatible with arbitrary transmitting waveforms, particular emphasis is placed on Orthogonal Frequency Division Multiplexing (OFDM) signals, which are widely used in modern communication systems such as Wi-Fi and cellular networks. An analysis of the OFDM waveform’s autocorrelation properties is provided to assess their impact on target detection, including strategies to mitigate rapid target signature decay using a sub-band approach and to manage signal correlation through spatial smoothing. Simulation results, including multi-target scenarios under constrained array configurations, demonstrate that the proposed MUSIC-based approach significantly enhances angular resolution and enables reliable discrimination of closely spaced targets even with a limited number of receiving channels. Experimental validation using an S-band MC-FSR prototype implemented with software-defined radios (SDRs) and commercial Wi-Fi antennas, involving cooperative targets like people and drones, further confirms the effectiveness and practicality of the proposed method for real-world applications. Overall, the proposed MUSIC-based MC-FSR framework exhibits strong potential for implementation in low-cost, hardware-constrained environments and is particularly suited for emerging Integrated Sensing and Communication (ISAC) systems. Full article

The chest wall represents a complex musculoskeletal structure that provides protection to intrathoracic organs, mechanical support for respiration, and mobility for the upper limbs. Neoplastic diseases of the chest wall encompass a heterogeneous group of benign and malignant lesions, which may be classified as primary—originating from bone, cartilage, muscle, or soft tissue—or secondary, resulting from direct invasion or metastatic spread, most commonly from breast or lung carcinomas. Approximately half of all chest wall tumors are malignant, and their management remains a significant diagnostic and therapeutic challenge. Surgical resection continues to represent the mainstay of curative treatment, with complete en bloc excision and adequate oncologic margins being critical to minimize local recurrence. Advances in reconstructive techniques, including the use of prosthetic materials, biological meshes, and myocutaneous flaps, have markedly improved postoperative stability, respiratory function, and aesthetic outcomes. Optimal management requires a multidisciplinary approach involving thoracic and plastic surgeons, oncologists, and radiotherapists to ensure individualized and comprehensive care. This review summarizes current evidence on the classification, diagnostic evaluation, surgical strategies, and reconstructive options for chest wall tumors, emphasizing recent innovations that have contributed to improved long-term survival and quality of life in affected patients. Full article

Poly (lactic-co-glycolic acid) (PLGA) is a widely used copolymer with applications across medical, pharmaceutical, and other industrial fields. Its biodegradability and biocompatibility make it one of the most versatile polymers for nanoscale drug delivery. The present review addresses current knowledge and recent advances in PLGA-based co-delivery nanoformulations with a special reference to design strategies, functional mechanisms, and translational potential. Conventional and advanced fabrication methods, the structural design of PLGA-based nanocarriers, approaches to scale-up and reproducibility, classification of co-delivery types, mechanisms governing drug release, surface modification and functionalization are all discussed. Special attention is given to PLGA-based co-delivery systems, encompassing drug–drug, drug–gene, gene–gene and multi-modal combinations, supported by recent studies demonstrating synergistic therapeutic outcomes. The review also examines clinical translation efforts and the regulatory landscape for PLGA-based nanocarriers. Unlike most existing reviews that typically focus either on PLGA fundamentals or on co-delivery approaches in isolation, this article bridges these domains by providing an integrated, comparative analysis of PLGA-based co-delivery systems and elucidating a critical gap in linking design strategies with translational requirements. In addition, by emphasising the relevance of PLGA-based co-delivery for combination therapies, particularly in cancer and other complex diseases, the review highlights the strong clinical and translational potential of these platforms. Key challenges, such as reproducibility, large-scale manufacturing, and complex regulatory pathways, are discussed alongside emerging trends and future perspectives. Taken together, this review positions PLGA-based co-delivery strategies as a critical driver for advancing precision therapeutics and shaping the future landscape of nanomedicine. Full article

Background/Objectives: Tissue eosinophilia plays a central role in chronic rhinosinusitis with nasal polyposis (CRSwNP), yet the spectrum of disease, particularly central compartment atopic disease (CCAD), remains underexplored. This study aimed to classify CRSwNP into three distinct phenotypes, eosinophilic CRSwNP (ECRSwNP), non-eosinophilic CRSwNP (NECRSwNP), and CCAD, based on radiologic and endoscopic features. It also proposes a novel severity-based staging system to guide clinical decision-making. Methods: A cross-sectional observational study was conducted in a single private clinic between January 2019 and August 2025. Patients were assessed using clinical history, paranasal sinus computed tomography (CT), and intranasal endoscopy. Key variables included symptom clusters, comorbidities, hematologic and atopy profiles, radiologic and endoscopic findings, histopathology, and pre-treatment SNOT-22 scores. Results: A total of 2060 patients (mean age: 29.8 ± 11 years; 51.8% male) were included. Asthma was the most frequent comorbidity (23.5%). Classification into ECRSwNP, NECRSwNP, and CCAD was achieved using integrated clinical, radiologic, and histopathologic criteria. Conclusions: This study presents a phenotype- and severity-based classification system for CRSwNP that incorporates endoscopic and radiologic features. This framework may enhance diagnostic accuracy and enable more tailored therapeutic strategies. Full article

Background: Familial cold urticarias (FCU) are a group of rare hereditary disorders triggered by exposure to low temperatures. Their pathogenesis is complex, involving mast cell activation, inflammasome dysregulation, and abnormalities of the kallikrein–kinin system. This review aims to summarize the genetic classification, molecular mechanisms, and clinical implications of FCU in diagnosis and management. Methods: Recent literature was reviewed to outline the clinical and molecular characteristics of familial atypical cold urticaria (FACU), familial cold autoinflammatory syndromes (FCAS; including NLRP3-, NLRP12-, NLRC4-, and PLCG2-related subtypes), FXII-associated cold autoinflammatory syndrome (FACAS), and familial predisposed acquired cold urticaria (FP-ACU). Mechanistic clues and diagnostic strategies were analyzed, emphasizing the integration of clinical features with molecular findings. Results: Distinct FCU subtypes exhibit defined genetic bases: gain-of-function mutations in NLRP3, NLRP12, and NLRC4 result in inflammasome hyperactivation; in-frame deletions in PLCG2 lead to temperature-dependent immune signaling dysregulation; and heterozygous F12 variants link contact activation with inflammatory cascades. Combining cold stimulation tests, inflammatory biomarkers, and targeted genetic sequencing enables precise molecular stratification. Conclusions: Molecular subclassification of FCU improves diagnostic accuracy and informs targeted therapy. Future research should focus on the interplay between cold-sensing ion channels, mast cell activation, and inflammasome signaling to advance precision diagnosis and individualized treatment of cold-induced urticarias. Full article

In light of ongoing advancements in smart manufacturing, there is a growing need for intelligent fault diagnosis methods that maintain reliability under noisy, high-variability operating conditions. Conventional feature selection strategies often struggle when data contain outliers or suboptimal feature subsets, limiting their diagnostic utility. This study introduces a density-based feature space optimization (DBFSO) framework that integrates feature selection with localized density estimation to enhance feature space separability and classifier efficiency. Using k-nearest neighbor density estimation, the method identifies and removes low-density feature vectors associated with noise or outlier behavior, thereby sharpening the feature space and improving class discriminability. Experiments using roll-to-roll (R2R) manufacturing data under mechanical disturbances demonstrate that DBFSO improves classification accuracy by up to 36–40% when suboptimal feature subsets are used and reduces training time by 60–71% due to reduced feature space volume. Even with already-optimized feature sets, DBFSO provides consistent performance gains and increased robustness against operational variability. Additional validation using a bearing fault dataset confirms that the framework generalizes across domains, yielding improved accuracy and significantly more compact, noise-resistant feature representations. These findings highlight DBFSO as an effective preprocessing strategy for intelligent fault diagnosis in intelligent manufacturing systems. Full article

Lithofacies, as an integrated representation of key reservoir attributes including mineral composition and organic matter enrichment, provides crucial geological and engineering guidance for identifying “dual sweet spots” and designing fracturing strategies in deep shale gas reservoirs. However, reliable lithofacies characterization remains particularly challenging owing to significant reservoir heterogeneity, scarce core data, and imbalanced facies distribution. Conventional manual log interpretation tends to be cost prohibitive and inaccurate, while existing intelligent algorithms suffer from inadequate robustness and suboptimal efficiency, failing to meet demands for both precision and practicality in such complex reservoirs. To address these limitations, this study developed a super-integrated lithofacies identification model termed SRLCL, leveraging well-logging data and lithofacies classifications. The proposed framework synergistically combines multiple modeling advantages while maintaining a balance between data characteristics and optimization effectiveness. Specifically, SRLCL incorporates three key components: Newton-Weighted Oversampling (NWO) to mitigate data scarcity and class imbalance, the Polar Light Optimizer (PLO) to accelerate convergence and enhance optimization performance, and a Stacking ensemble architecture that integrates five heterogeneous algorithms—Support Vector Machine (SVM), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)—to overcome the representational limitations of single-model or homogeneous ensemble approaches. Experimental results indicated that the NWO-PLO-SRLCL model achieved an overall accuracy of 93% in lithofacies identification, exceeding conventional methods by more than 6% while demonstrating remarkable generalization capability and stability. Furthermore, production simulations of fractured horizontal wells based on the lithofacies-controlled geological model showed only a 6.18% deviation from actual cumulative gas production, underscoring how accurate lithofacies identification facilitates development strategy optimization and provides a reliable foundation for efficient deep shale gas development. Full article

Background/Objectives: Mesothelioma is a highly aggressive tumor with a poor prognosis that typically develops after a long latency period following asbestos exposure. Although immunotherapy combined with chemotherapy is increasingly used, the efficacy of standard treatments remains limited. This study aimed to explore ferroptosis induction as a potential therapeutic strategy for mesothelioma. Methods: We first screened microbial culture extracts collected from soil and marine environments to identify compounds with selective cytotoxicity against mesothelioma cells. Gene expression profiling was performed to investigate the mechanism of cell death induced by the identified compound. To assess intrinsic ferroptosis susceptibility, patient-derived mesothelioma cell lines and immortalized mesothelial cell lines were treated with RSL3, a GPX4 inhibitor. Results: Screening identified brefeldin A as a compound that selectively induces cell death in mesothelioma cells. Gene expression profiling revealed transcriptional changes consistent with ferroptosis induction. Treatment with RSL3 demonstrated marked variability in ferroptosis sensitivity across cell lines; the subgroup showing high sensitivity to RSL3 did not exhibit significant genetic alterations in NF2 or BAP1, but contained a significantly higher proportion of epithelioid tumors in histological classification. Conclusions: Our findings highlight ferroptosis induction as a promising antitumor mechanism in mesothelioma, particularly in the epithelioid subtype. While GPX4 inhibitors such as RSL3 are effective in vitro, further studies are needed to overcome pharmacological limitations and define molecular determinants of ferroptosis susceptibility, which may inform future personalized therapeutic strategies. Full article

Biological invasions are major threats to global biodiversity, and mapping their distribution is essential to prioritizing management efforts. The Pinaceae family (hereafter pines) includes invasive trees, particularly in Southern Hemisphere regions where they are non-native. These invasions can increase the severity of fires in wildland–urban interfaces (WUIs). We mapped pine invasion in the Bariloche WUI (≈150,000 ha, northwest Patagonia, Argentina) using supervised land cover classification of Sentinel-2 imagery with a Random Forest algorithm on Google Earth Engine, achieving 90% overall accuracy but underestimating the pine invasion area by about 25%. We then assessed in which main vegetation context pine invasions occurred relying on major vegetation units across the precipitation gradient of our study area. Invasions cover 2% of the study area, mainly in forests (61%), steppes (25.4%), and shrublands (13.4%). Most invaded areas (89.1%) are on private land; nearly 70% are on large properties (>10 ha), where state financial incentives could support removal. Another 13.5% occur on many small properties (<1 ha), where awareness campaigns could enable decentralized, low-effort control. Our land cover map can be developed further to integrate invasion dynamics, inform fire risk and behavior models, optimize management actions, and guide territorial planning. Overall, it provides a valuable tool for targeted, scale-appropriate strategies to mitigate ecological and fire-related impacts of invasive pines. Full article

Terraces are the main engineering of soil erosion control on the Loess Plateau, offering measures for sediment reduction and water conservation, as well as the potential for increasing agricultural productivity. Over the years, large-scale terrace construction has been undertaken; however, the management has been inadequate, especially in terms of long-term monitoring and mapping. Moreover, the sediment reduction effect of terrace construction is not yet fully understood. Therefore, this study utilizes Landsat series data, integrating remote sensing imaging principles with machine learning techniques to achieve long–term temporal sequence mapping of terraces at a 30 m spatial resolution on the Loess Plateau. The sediment reduction effect brought about by terrace construction on the Loess Plateau is quantified using a sediment reduction formula. The results show that Elevation (Ele.), red band (R), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Near-infrared Reflectance of Vegetation (NIRv) are key parameters for remote sensing identification of terraces. These five remote sensing variables explain 88% of the terrace recognition variance. Coupling the Random Forest classification model with the LandTrendr algorithm allows for rapid time-series mapping of terrace spatial distribution characteristics on the Loess Plateau. The producer’s accuracy of terrace identification is 93.49%, the user’s accuracy is 93.81%, the overall accuracy is 88.61%, and the Kappa coefficient is 0.87. The LandTrendr algorithm effectively removes terraces affected by human activities. Terraces are mainly distributed in the southeastern Loess areas, including provinces such as Gansu, Shaanxi, and Ningxia. Over the past 30 years, the terrace area on the Loess Plateau has increased from 0.9790 million hectares in 1990 to 9.8981 million hectares in 2020. The sediment reduction effect is particularly notable, with an average reduction of 49.75% in soil erosion across the region. This indicates that terraces are a key measure for soil erosion control in the region and a critical strategy for improving farmland productivity. The data from this study provides scientific evidence for soil erosion control on the Loess Plateau and enhances the precision of terrace management. Full article

Background: Perianal involvement is a well-recognized manifestation of Crohn’s disease (CD). However, non-fistulizing perianal phenotypes remain underrecognized despite their significance in clinical practice and impact on patients’ quality of life. Methods: A narrative review of the literature up to September 2025 was conducted, with an emphasis on studies that differentiated between non-fistulizing and fistulizing lesions. Results: During the CD course, approximately 45% of patients with CD develop non-fistulizing perianal manifestations, including fissures, ulcers, strictures, and skin tags. These lesions may resolve spontaneously with the ongoing CD therapy or additional conservative measures, but some evolve into more complex conditions, with challenging management. Deep ulcers and strictures appear to be associated with a less favorable disease course. While biologic therapy has altered the overall course of CD, its role in treating non-fistulizing perianal Crohn’s disease (PCD) requires further understanding. Surgical intervention, which carries an increased risk of complications, is typically reserved for individuals who are refractory to other treatments. The potential association between non-fistulizing PCD and anal cancer remains uncertain. Conclusions: Non-fistulizing PCD is a clinically significant condition that requires early recognition and individualized management. Prospective studies with standardized lesion classification, careful monitoring of disease course, and evaluation of biologic therapies and biomarkers are needed to develop evidence-based strategies and improve patient outcomes on non-fistulizing PCD. Full article

This review examines scientific and engineering strategies for adapting bituminous and asphalt concrete materials to the highly diverse climates of Central Asia. The region’s sharp gradients—from arid lowlands to cold mountainous zones—expose pavements to thermal fatigue, photo-oxidative aging, freeze–thaw cycles, and wind abrasion. Existing climatic classifications and principles for designing thermally and radiatively resilient pavements are summarized. Special emphasis is placed on linking binder morphology, rheology, and climate-induced transformations in composite bituminous systems. Advanced characterization methods—including dynamic shear rheometry (DSR), multiple stress creep recovery (MSCR), bending beam rheometry (BBR), and linear amplitude sweep (LAS), supported by FTIR, SEM, and AFM—enable quantitative correlations between phase composition, oxidative chemistry, and mechanical performance. The influence of polymeric, nanostructured, and biopolymeric modifiers on stability and durability is critically assessed. The review promotes region-specific material design and the use of integrated accelerated aging protocols (RTFOT, PAV, UV, freeze–thaw) that replicate local climatic stresses. A climatic rheological profile is proposed as a unified framework combining climate mapping with microstructural and rheological data to guide the development of sustainable and durable pavements for Central Asia. Key rheological indicators—complex modulus (G*), non-recoverable creep compliance (Jnr), and the BBR m-value—are incorporated into this profile. Full article

Fetal ventriculomegaly (VM) is a condition characterized by abnormal enlargement of the cerebral ventricles of the fetus brain that often causes developmental disorders in children. Manual segmentation and classification of ventricular structures from brain MRI scans are time-consuming and require clinical expertise. To address this challenge, we develop an automated pipeline for ventricle segmentation, ventricular width estimation, and VM severity classification using a publicly available dataset. An adaptive slice selection strategy converts 3D MRI volumes into the most informative 2D slices, which are then segmented to isolate the lateral ventricles and deep gray matter. Ventricular width is automatically estimated to assign severity levels based on clinical thresholds, generating labeled data for training a deep learning classifier. Finally, an explainability module using a large language model integrates the MRI slices, segmentation masks, and predicted severity to provide interpretable clinical reasoning. Experimental results demonstrate that the proposed decision support system delivers robust performance, achieving dice scores of 89% and 87.5% for the 2D and 3D segmentation models, respectively. Also, the classification network attains an accuracy of 86% and an F1-score of 0.84 in VM analysis. Full article