Search Results (5,541)

The expected rise in space operations challenges the European Air Traffic Management (ATM), as traditional static airspace segregation causes operational inefficiencies. To mitigate this, a new function within the European Network Manager Operations Centre (NMOC), supported by the novel Network Real-time Mission Monitoring (N-RMM) tool, and complemented by ad hoc Debris Response Areas (DRAs), are being developed. This paper introduces the safety assessment of this approach using the Expanded Safety Reference Material (E-SRM) methodology. By developing specialised Accident Incident Models (AIMs) for mid-air collisions with space debris, we quantify safety barrier efficiencies and define a Risk Classification Scheme (RCS). The results indicate that by developing dedicated AIMs for the proposed dynamic airspace-management concept, the derived safety criteria, under the stated assumptions, are compatible with the targeted safety thresholds. The potential reduction in segregated airspace volume and duration remains an expected operational benefit to be quantified in subsequent validation work. Full article

Motor imagery (MI)-based brain–computer interfaces (BCIs) have led to great interest as a result of their potential use in neurorehabilitation, assistive robotics, and human–computer interaction. However, decoding electroencephalographic (EEG) signals with high accuracy continues to be a difficult task due to the weak signal-to-noise ratio, differences among subjects, and the complicated temporal–spectral–spatial neural dynamics. Deep learning methods recently developed, such as convolutional neural networks, recurrent architectures, graph neural networks, and adversarial transfer learning, have enhanced MI decoding performance, yet many models are still concentrating on a single representation domain or they need costly adaptation phases in terms of computation. To tackle these shortcomings, we present NeuroCrossNet, a unified tri-modal deep learning model that is able to learn the temporal, spectral, and spatial EEG features jointly for robust and calibration-free MI decoding. The suggested network combines a Temporal HyperMixer Block for capturing long-range temporal dependencies, a wavelet transformer for learning localized time–frequency representation, and a Graph Attention Network for EEG topology-aware spatial reasoning. Additionally, a Dynamic Residual Attention Gate (DRAG) has been developed to adaptively merge heterogeneous feature streams, and a compact subject-aware normalization (SAN) method enhances cross-subject generalization without the use of labeled target-domain calibration data. Our proposed model was tested following the rigorous leave-one-subject-out (LOSO) approach on BCI Competition IV-2a and High-Gamma datasets. NeuroCrossNet reached a classification accuracy of 91.30%, surpassing several strong benchmark methods, including CNN-LSTM, EEGNet, DeepConvNet, spectral CNN, and graph-based EEG decoding frameworks. Furthermore, a large number of ablation studies reveal that the integration of temporally, spectrally, and spatially complementary representations considerably boosts robustness and inter-subject consistency. Full article

Background/Objectives: Endometrial cancer (EC) is the most common gynecologic malignancy in developed countries, with increasing incidence and limited options in advanced disease. Molecular classification has redefined risk stratification and therapeutic decision-making, particularly with the incorporation of immunotherapy. This review provides a clinically oriented overview of immunotherapy in EC across molecular subgroups and treatment settings. Methods: A narrative review was conducted using PubMed/MEDLINE, Embase, and Web of Science, focusing on clinical trials and studies with direct clinical relevance. Results: Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis have demonstrated significant benefit in EC, particularly in mismatch repair-deficient (dMMR)/microsatellite instability–high (MSI-H) tumors, where durable responses are observed. In contrast, mismatch repair-proficient (pMMR) tumors show limited sensitivity to monotherapy and require combination approaches. Recent phase III trials have established chemoimmunotherapy as a first-line standard, with greater benefit in dMMR tumors and clinically meaningful improvements in pMMR disease. In the second-line setting, PD-1 inhibitor monotherapy is standard for dMMR tumors, while lenvatinib plus pembrolizumab is a key option for pMMR disease. However, responses remain heterogeneous and are not fully explained by MMR status alone. Conclusions: Immunotherapy is a cornerstone in advanced EC management, guided by molecular classification. Key challenges include limited efficacy in pMMR tumors, lack of robust predictive biomarkers, and uncertainty in treatment sequencing. Future strategies should focus on biomarker-driven approaches and rational combinations. Full article

Aspect-Based Sentiment Analysis explores sentiment trends related to specific opinion aspects and holds significant commercial potential for monitoring brand reputation, understanding customer satisfaction, and personalizing recommendations. However, traditional methods rely exclusively on textual input and often struggle when the target aspect is not mentioned in the sentence. Multimodal Aspect-Based Sentiment Analysis addresses this limitation by incorporating both textual and visual modalities to enable more comprehensive sentiment understanding. Despite advancements in deep learning and transformer-based architectures, existing models often suffer from suboptimal modality fusion and weak aspect grounding, limiting their classification accuracy. To overcome these challenges, we propose an Attention-Driven Feature Fusion (ADFF) approach based on a three-stage hierarchical attention mechanism. First, it only fuses text and image embeddings. Second, it incorporates aspect-level features. Third, a multi-head attention layer further enhances cross-modal dependencies. The resulting representation is passed to a Long Short-Term Memory (LSTM) classifier for sentiment polarity prediction. We evaluate our model on three benchmark datasets, namely Twitter-2015, Twitter-2017, and MASAD. The experimental results demonstrate that the proposed model substantially outperforms state-of-the-art multimodal and unimodal baselines, improves both accuracy and F1-score, achieving 82.55% accuracy and 81.05% F1-score on Twitter-2015, 77.07% accuracy and 77.15% F1-score on Twitter-2017, and up to 99.67% accuracy and F1-score in the Plant domain of MASAD, where we observe consistent improvements across all seven domains. These results highlight the effectiveness and scalability of the hierarchical attention-based fusion strategy for real-world aspect-based sentiment analysis tasks. Full article

Polyethylene (PE) microplastics are increasingly recognized as a critical environmental and food-safety concern; however, routine monitoring remains limited by conventional methods that are labor-intensive, time-consuming, and difficult to translate into rapid, on-site screening. Here, we report a machine learning-guided electrochemical fingerprinting platform for rapid PE microplastic detection using a chitosan–PE interfacial film coupled with electrochemical impedance spectroscopy (EIS) and coulometry. The platform generated concentration-dependent electrical fingerprints in artificial ocean water, captured through Bode, Nyquist, and charge–time responses. Quantification was achieved across 1–256 ng/mL with strong linearity (R² = 0.976) and an ultralow LoD of 0.1 ng/mL, demonstrating high analytical sensitivity. Practical applicability was validated through spike–recovery in ocean water (R² = 0.967) and shrimp-derived matrices with matrix-matched normalization, yielding recoveries of 90–105% across low, mid, and high spike levels. Under the tested particle set, PE produced stronger responses than non-target polypropylene (PP) and polystyrene (PS), supporting empirical polymer discrimination. Machine learning classification using impedance-derived features achieved an AUC = 0.98, with 100% correct identification of Low and 95.24% correct identification of High samples. Overall, this electrochemical–ML framework enables rapid, sensitive, and matrix-tolerant PE microplastic screening in environmental water and seafood-related matrices, offering a promising pathway toward portable microplastic monitoring. Full article

Background/Objectives: Liposarcoma represents a heterogeneous group of mesenchymal malignancies with distinct molecular profiles and clinical behaviors. While localized disease is managed with surgical resection, advanced or metastatic liposarcoma poses a significant therapeutic challenge due to limited response to traditional cytotoxic chemotherapy. This review summarizes current evidence-based systemic therapies and highlights recent advances in subtype-driven treatment strategies. Methods: We review key clinical trials supporting the use of anthracycline regimens, trabectedin, eribulin, and nuclear export inhibition with selinexor, as well as emerging targeted approaches directed at MDM2 and CDK4 amplification. In addition, we discuss the evolving role of immunotherapy, including checkpoint inhibitors and engineered T-cell receptor therapies targeting cancer–testis antigens. Results: Integrating molecular biology with therapeutic development, we emphasize the importance of histologic and genomic classification in guiding treatment selection and clinical trial design. Conclusion: Continued progress in biomarker-driven strategies and rational combination therapies is expected to further refine personalized treatment approaches and improve outcomes for patients with advanced liposarcoma. Full article

Background: Solar lentigines are common epidermal hyperpigmented macules associated with chronic ultraviolet exposure and photoaging. Objective: To describe a standardized 532 nm green laser protocol for solar lentigines and to place these observations within a narrative review with a structured PubMed/Medline literature search. Methods: Five patients (two women and three men; age range 42–65 years, mean 53.6 years; Fitzpatrick skin phototypes II–III) with solar lentigines underwent treatment with a 532 nm green laser (QuadroStarPRO GREEN, Asclepion) using a standardized, single-session protocol. Outcomes were assessed at the final available follow-up (day 21) by 2 independent dermatologists using a retrospective categorical response classification (complete response/partial response/no response) based on paired baseline and day 21 image documentation only; patient satisfaction was recorded at day 21 on a 0–10 visual analog scale (VAS). A narrative review with a structured PubMed/Medline literature search was conducted to identify clinical studies evaluating 532 nm KTP/green laser devices for lentigines, freckles, and ephelides. Results: All five target lesions were classified as complete response at day 21 (5/5 complete response), with a mean VAS satisfaction score of 8.6/10 (range, 7–10) and no discordance between dermatologists. Mild transient erythema was observed immediately after treatment and improved within the first day; no persistent adverse events, dyschromia, or scarring were observed during the available 21-day follow-up. Conclusions: In this small case series, a single-session millisecond 532 nm green laser protocol was associated with complete-response classification at day 21 in five target lesions. Published clinical studies indicate that outcomes with 532 nm devices vary with device type, pulse structure, and treatment settings; larger comparative studies with objective pigment measures and longer follow-ups are needed. Full article

Endometriosis is a complex disorder associated with dysregulated immune, hormonal, and microenvironmental signaling. Extracellular vesicles (EVs) are important mediators of intercellular communication and may contribute to disease pathogenesis, biomarker discovery, and therapeutic targeting. Here, we systematically reviewed the literature on EVs in endometriosis, focusing on EV classification, isolation and characterization methods, and the functional relevance of EV-associated cargo. A total of 50 original studies were included and evaluated in the context of current International Society for Extracellular Vesicles (ISEV) recommendations. Our analysis revealed marked heterogeneity in EV nomenclature, biological sources, and methodological approaches. Although most studies used standard EV markers, the assessment of sample purity and inclusion of negative controls was inconsistent. Further studies using standardized workflows and well-characterized cohorts are needed to clarify their biological and clinical significance. Full article

Lithofacies characterization of organic-rich shales constitutes the essential foundation for sweet spot evaluation in lacustrine shale oil systems. This study targets the first member of the Upper Cretaceous Qingshankou Formation (K₂qn¹) in the southern Songliao Basin. Based on systematic core description of 908 m of core from eight cored wells, combined with 123 total organic carbon (TOC) measurements, 47 whole-rock X-ray diffraction (XRD) analyses, 29 major- and trace-element analyses, and six maceral identification datasets (≥500 organic particles counted per sample), together with conventional well log data from 75 wells (measured vitrinite reflectance $R_o$ = 0.34%–1.38%, mean = 0.94%), we establish an integrated lithofacies classification scheme incorporating the TOC as a classification parameter and develop a log-based lithofacies identification workflow. Eight lithofacies are recognized within K₂qn¹ across the study area, of which three are organic-rich. The high-TOC clay-rich mudstone-grade laminated shale deposited in a deep lake setting (LF-A; mean TOC = 3.18%, clay minerals ≥50%, formed under saline and strongly anoxic-euxinic conditions; mean paleosalinity = 8.06‰, V/(V + Ni) = 0.75–0.97) and the high-to-moderate-TOC felsic mudstone-grade laminated shale deposited in a semi-deep lake setting (LF-B; mean TOC = 2.18%, felsic minerals ≥50%, formed under brackish-to-saline anoxic conditions; mean paleosalinity = 5.10‰, V/(V + Ni) = 0.70–0.84) constitute the dominant organic-rich lithofacies. From Y1 to Y3, the cumulative thickness of organic-rich lithofacies expands from approximately 10 m to approximately 25 m. Areally, the mean TOC increases systematically from 1.65% in the southern delta-front zone to 2.74% in the northern deep lake center, reflecting an enrichment pattern governed primarily by paleoproductivity and modulated jointly by preservation conditions and terrigenous dilution. The log-based identification workflow, established by integrating a modified $\Delta$logR method with multiple linear regression, achieves a TOC prediction coefficient of determination of $R^2=0.86$ in the calibration well and lithofacies identification accuracies ranging from 64.6% to 94.0% in validation wells, with the highest performance observed in the delta-front facies zone. These results provide quantitative constraints for the genetic interpretation and log-based identification of organic-rich lacustrine shales. Full article

Carbon dots (CDs), including carbon quantum dots (CQDs), are ultra-small carbon-based nanomaterials, typically below 10 nm, with tunable photoluminescence, high aqueous dispersibility, favorable biocompatibility, low toxicity, and abundant surface functional groups. These properties make CDs promising multifunctional platforms for nanomedicine, particularly in bioimaging, biosensing, targeted drug/gene delivery, photodynamic therapy (PDT), photothermal therapy (PTT), antimicrobial treatment, and theranostic applications. This review critically examines recent advances in CD fabrication, including top-down, bottom-up, green biomass-derived, microwave-assisted, hydrothermal, and emerging hybrid strategies, with emphasis on how precursor selection, heteroatom doping, surface passivation, and polymer/ligand functionalization regulate optical performance, biological interaction, and therapeutic efficiency. The review discusses structural classification, including CQDs, graphene quantum dots (GQDs), carbon nanodots, and carbonized polymer dots (CPDs), together with major characterization approaches such as ultraviolet–visible (UV–Vis) spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and high-resolution transmission electron microscopy (HRTEM). Particular attention is given to red/near-infrared (NIR) emission, renal clearance, drug-loading behavior, reactive oxygen species (ROS) generation, toxicity mechanisms, biodistribution, and long-term biosafety. This review also highlights key translational barriers, including batch-to-batch variability, limited standardization, scalable manufacturing, regulatory uncertainty, and incomplete pharmacokinetic evaluation. It considers artificial intelligence (AI) and machine learning (ML) as emerging tools for reproducible CD design. CDs represent versatile and clinically promising nanoplatforms, but their translation requires standardized synthesis, rigorous safety assessment, and application-specific regulatory validation. Full article

Background: Parabens are frequently utilized as preservatives in processed foods; nevertheless, the primary dietary factors contributing to exposure in northern Thailand remain undetermined. Methods: A cross-sectional study was conducted among 130 adults in Northern Thailand. Dietary intake was assessed using self-reported food consumption data combined with previously measured paraben concentrations. Due to the skewed distribution of intake, participants were classified into lower and higher exposure groups. LASSO regression was applied for variable selection, followed by multivariable logistic regression to identify dietary predictors of exposure. Results: Several processed food items were significantly associated with higher paraben exposure, including soft drinks, potato chips, and canned fish. No demographic factors were significantly associated with exposure. The final model demonstrated good explanatory power and classification performance. Conclusions: These findings suggest that routine consumption of certain processed foods and beverages may play a larger role in exposure than individual characteristics, and they highlight practical targets, particularly soft drinks, potato chips, and canned fish, for community-based health-promotion strategies aimed at reducing unnecessary preservative intake. Full article

As a critical component of ice-snow tourism, land cover classification for ski resorts is crucial to ice-snow resource management. However, there is currently a scarcity of datasets and methods capable of high-precision mapping for such fine-grained scenarios. Although Transformers with long-sequence interactions and convolutional neural networks (CNNs) have emerged as mainstream solutions, their performance remains limited on high-resolution remote sensing data characterized by small datasets and high heterogeneity. Targeting land cover classification in ski resort areas, this study proposes a triple-branch segmentation framework integrating CNNs and Transformers to extract global, detail and boundary features (GDBNet), and constructs the first high-resolution ski resort land cover dataset with a resolution of 0.75 m using JiLin-1 satellite constellation (LULC_SKI). The framework employs a backbone combining SegFormer with dual CNN branches. SegFormer captures global semantic context, while dual ResNet-18 branches extract local semantics and edge details respectively. The neck integrates two specialized feature interaction modules, the proposed Pixel-Guided Feature Attention (PG-AFM) and Boundary-Guided Feature Attention (BG-AFM), which synergistically fuse these heterogeneous feature representations for enhanced multi-scale modeling. For the segmentation head, a multi-task learning approach supervises both semantic and edge outputs. LULC_SKI covers seven representative ski resorts in Jilin Province, China, comprising 10,000 multi-seasonal images annotated with six land cover classes, including roads, vegetation, built-up areas, ski runs, water bodies, and cropland. Experiments demonstrate GDBNet achieves 85.44% mIoU and 91.84% mF1 on LULC_SKI, outperforming other advanced models with particularly significant improvements for linear objects like roads and ski runs. Extensive experimental comparisons show that GDBNet delivers consistently excellent performance on both the iSAID and LoveDA datasets, underscoring the superiority of our proposed method. Ablation studies validate the effectiveness of the triple-branch architecture, attention modules, and multi-task supervision. This work proposes a modular framework for land cover classification in complex ski resort scenarios. Full article

Acute respiratory failure (ARF) often leads to ICU admission, ventilatory support, illness, and death. The usual classification into hypoxemic and hypercapnic types does not capture its full complexity. Precision medicine uses the concept of “treatable traits” to guide care based on traits that are clinically relevant, identifiable, measurable, and possibly changeable. Arterial carbon dioxide pressure (PaCO₂) reflects factors like alveolar ventilation, dead space, respiratory mechanics, and how patients respond to ventilatory support. This makes it clinically relevant in selected situations. We carried out a scoping review using PRISMA-ScR and JBI guidelines to summarize evidence on hypocapnia and hypercapnia as prognostic, stratification, or clinically relevant variables during respiratory support. We searched PubMed/MEDLINE, ScienceDirect, and Web of Science (1994–2025), and checked references by hand. Thirty-four studies met our criteria and were grouped into four areas: pre-intubation or early acute presentation, non-invasive support (NIV/HFNC), invasive mechanical ventilation (IMV), and weaning or post-extubation. In summary, hypocapnia was linked to worse outcomes or failure of support in hypoxemic or cardiogenic cases. Hypercapnia helped identify patients who benefited from NIV, such as those with chronic obstructive pulmonary disease or obesity hypoventilation. For IMV, the effects depended on the presence and severity of acidosis and on its duration. Overall, PaCO₂ showed context-dependent clinical relevance, acting mainly as a prognostic or stratification marker and, in narrower settings, as a variable that may inform monitoring or support decisions. This review provides a pragmatic framework for interpreting PaCO₂ across respiratory support contexts and highlights the need for safe and clinically meaningful targets. Full article

Background: Iodine nutrition requires continued surveillance after changes in salt iodization policy. This study evaluated iodine status and associated factors among children and adolescents in Zhejiang Province, ten years after the national salt iodization standard was lowered. Methods: A cross-sectional survey employing a stratified, multistage cluster sampling design was conducted in 2022. A total of 688 participants aged 6–17 years with complete data on urinary iodine concentration, household salt iodine concentration, geographic classification, and key questionnaire variables were included in the analysis. Multivariate logistic regression analysis was performed to identify factors independently associated with iodine sufficiency. Results: Among 688 participants, the median household salt iodine concentration was 21.50 mg/kg, and iodized salt coverage was 64.68%. The median urinary iodine concentration (UIC) was 191.4 μg/L; however, 15.26% of participants had UIC < 100 μg/L. Participants in coastal areas had lower UIC levels and lower household iodized salt coverage than those in inland areas. Multivariate logistic regression analysis identified age, geographic region, and household use of iodized salt as factors significantly associated with iodine sufficiency. Conclusions: The overall iodine nutritional status among children and adolescents aged 6–17 years in Zhejiang Province is adequate. However, a certain proportion of iodine deficiency persists. Continued, targeted monitoring and health education on the appropriate use of qualified iodized salt are warranted, particularly in coastal areas and among younger children. Full article

Osteoarthritis (OA) severity grading from hand distal interphalangeal (DIP) joint radiographs using the Kellgren–Lawrence (KL) scale is challenged by severe class imbalance, with higher grades (KL3 and KL4) markedly underrepresented in clinical datasets. To address this limitation, we propose a VGG19-based classification framework that systematically evaluates six training strategies targeting imbalance at the data level, algorithmic level, or in combination. Synthetic images for minority classes were generated using CycleGAN and subsequently filtered through rheumatologist validation. The evaluated strategies include baseline training, rheumatologist-validated synthetic augmentation (SD), oversampling (OS), focal loss (FL) optimization, and multiple combinations of these approaches. The results show that strategies incorporating oversampling demonstrated the most consistent and statistically robust improvements in minority-class performance. Specifically, the combination of synthetic data and oversampling (SD + OS) achieved the highest binary OA sensitivity (96.12%) and significantly improved OA F1 score compared to baseline (0.613 vs. 0.416, p = 0.029). The full combined strategy (SD + OS + FL) yielded the highest KL3 F1 score (0.527 vs. 0.280 baseline, p = 0.048) and significantly improved KL4 F1 score (0.730 vs. 0.570 baseline, p = 0.150). Importantly, all strategies maintained higher or similar overall performance with no significant change in majority-class performance (p > 0.10), indicating that improvements in minority classes were not achieved at the expense of sacrificing majority classes or overall model reliability. These findings suggest that the proposed imbalance-mitigation strategies may improve minority class OA detection, particularly when oversampling and validated synthetic augmentation are combined. It is worth noting that the above results are derived from a held-out test set comprising 1626 samples, among which only 43 are OA-positive due to data imbalance. The results should be treated as preliminary findings subject to change upon validation in larger cohorts of OA patients. Full article