Search Results (32,531)

Background and Clinical Significance: Cantú syndrome (OMIM #239850) is a rare autosomal dominant disorder caused by gain-of-function variants in ABCC9 or KCNJ8, which encode subunits of the ATP-sensitive potassium (K_ATP) channel. Its characteristic features—generalized hypertrichosis, coarse facial appearance, skeletal abnormalities, and cardiovascular involvement—may be overlooked when other major comorbidities dominate the clinical picture. Case Presentation: A 29-year-old Taiwanese woman, born prematurely and complicated by neonatal hydrocephalus with subdural hemorrhage requiring ventriculoperitoneal shunt placement, had been followed since infancy under a working diagnosis of cerebral palsy with left hemiparesis and borderline-to-mild intellectual disability. Over the ensuing years, additional features gradually emerged, including generalized hypertrichosis with thick scalp and body hair, coarse facial features, bilateral hallux valgus, mild thoracic scoliosis, polycystic ovaries, mild aortic regurgitation, recurrent hemoptysis associated with abnormal pulmonary vasculature, and iron-deficiency anemia. Earlier genetic investigations—including chromosome analysis (46,XX), array comparative genomic hybridization (array-CGH; 2013), and a trio-based next-generation sequencing study performed under a national rare disease research initiative (2019)—were unrevealing. Whole-genome sequencing performed in December 2025 identified a heterozygous ABCC9 variant (NM_020297.4:c.4174A>G, p.(Ile1392Val)), initially classified as a variant of uncertain significance. Parental Sanger sequencing confirmed the variant to be de novo, and reclassification according to ACMG/AMP criteria supported a likely pathogenic interpretation. Re-evaluation of the patient’s phenotype demonstrated findings consistent with Cantú syndrome. Conclusions: This case illustrates how Cantú syndrome may remain unrecognized for years when a prominent neurological comorbidity—perinatally acquired hydrocephalus and presumed cerebral palsy—dominates the clinical narrative. We report a previously undescribed de novo ABCC9 missense variant (c.4174A>G, p.(Ile1392Val)), thereby expanding the mutational spectrum associated with Cantú syndrome. This case also highlights the practical value of resequencing and periodic reanalysis using updated next-generation sequencing platforms in patients with long-standing undiagnosed disease, even after prior negative genetic testing. Full article

Background/Objectives: West Nile virus (WNV) is a persistent mosquito-borne threat in Greece. This study examined whether online information-seeking patterns reflected official WNV surveillance during 2024–2025, with Crete providing descriptive field-level One Health preparedness context. Methods: Official national surveillance data were compared with country-level Google Trends relative search volume and Greek-language Wikipedia pageviews, focusing on weekly locally acquired West Nile neuroinvasive disease/central nervous system (WNND/CNS) cases. The Crete component separately summarized regional One Health preparedness. Results: Greece reported 220 locally acquired WNV cases (157 WNND/CNS) in 2024 and 96 (76 WNND/CNS) in 2025. Wikipedia pageviews showed the strongest full-year associations with WNND/CNS activity when pageviews followed cases by one week (2024: Spearman rho = 0.802; 2025: rho = 0.763; both p < 0.001). Google Trends showed weaker associations at the same lag (2024: rho = 0.402, p = 0.003; 2025: rho = 0.452, p < 0.001). Transmission-period sensitivity analyses attenuated several associations: the 2025 Wikipedia associations and the Google Trends associations in both years were not statistically significant. The first-difference lag analysis identified no leading digital signal. Conclusions: Wikipedia showed more stable language-specific temporal concordance with national surveillance than Google Trends. However, the digital indicators reflected concurrent or lagging public attention and did not demonstrate predictive capacity. The Crete component separately illustrates how regional One Health preparedness complements national surveillance and risk communication. Full article

Hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2–) metastatic breast cancer (MBC) is the most prevalent subtype of advanced breast cancer and is predominantly driven by estrogen receptor (ER) signaling. Endocrine therapy (ET) has become the backbone of first-line treatment; however, both intrinsic and acquired resistance limit long-term disease control. The introduction of cyclin-dependent kinase 4/6 (CDK4/6) inhibitors has fundamentally reshaped the therapeutic landscape even in subsets of patients with aggressive or symptomatic visceral metastatic disease. Advances in molecular profiling have also enabled more precise, adaptive therapy. Circulating tumor DNA (ctDNA)-based liquid biopsy now allows real-time detection of emerging resistance mutations, particularly in ESR1. Additionally, patients with PIK3CA-mutated tumors who had progressed on or within 12 months of completing adjuvant ET and had no prior systemic therapy for metastatic disease had better treatment outcomes when treated with the PI3K inhibitor inavolisib in combination with palbociclib and fulvestrant. Together, these developments mark a shift from fixed treatment sequencing toward a more dynamic, biomarker-driven approach in first-line HR+/HER2– MBC. Integration of CDK4/6 inhibitors with next-generation endocrine agents and liquid biopsy-guided therapy offers the potential to delay resistance, improve survival outcomes, and individualize treatment. Full article

With the large-scale integration of electric vehicles (EVs) into power grids, massive concentrated EV charging accelerates the loss of life (LOL) of distribution transformers. There is a need to reduce transformer LOL while optimizing user experience, thus formulating the EV charge optimization concerning transformer LOL (ECOTL) problem. However, due to its characteristics, such as multi-objective tradeoffs, nonlinearity, uncertainty, and the requirement for time-sensitive online control, the ECOTL problem poses severe challenges in obtaining a high-quality Pareto front, acquiring robust solutions, and improving solving speed. To address these, we propose a multi-objective multi-agent deep reinforcement learning (MOMADRL) method that deploys multiple Critics and Actors for each agent. During training, Critics learn value functions with distinct preferences and guide the corresponding Actors that specialize in learning optimal strategies to update their policies. After sufficient training, each Actor can determine a preference-specific charging strategy in the test phase. Case studies based on real electricity price and load data demonstrate that compared with traditional multi-agent deep reinforcement learning (MADRL) methods, the proposed method achieves better convergence performance on the training set. Moreover, it obtains better Pareto-front approximation than the learning-based baselines while providing fast online inference under the tested simulation conditions. Full article

The acquisition of geophysical data is becoming increasingly important in the context of critical mineral exploration. Geophysical data and inversion products are essential to map many components of the critical mineral system by detecting geophysical anomalies that can be interpreted by expert geologists. However, the inversion of airborne geophysical data acquired along flightlines into subsurface petrophysical properties remains an outstanding challenge. Many inversion techniques rely either on 1D deterministic inversion or on stochastic inversion on a local scale. The outcome of our work is the stochastic inversion along flightlines of 2D panels (flightline direction vs. depth), while at the same time producing plausible spatial variation in the petrophysical properties. Our method relies on a sequential application of Bayesian inversion, where we invert a sequence of 2D panels such that the variation in petrophysical properties avoids generation of artifacts across the panel boundaries. We show that our method can be used in a practical setting in the context of mineral exploration in the Cape Smith Belt of Canada. Full article

Background/Objectives: Lower-limb exoskeletons require reliable movement recognition mechanisms to support adaptive locomotor assistance and rehabilitation. Electromyographic (EMG) signals provide valuable information on muscle activation and user intention, enabling safe and responsive human–exoskeleton interaction. This study aims to develop and experimentally validate an EMG-based platform for intelligent lower-limb movement recognition and locomotor assistance applications. Methods: The proposed platform integrates multichannel EMG acquisition, embedded signal processing, and artificial intelligence for movement classification. EMG signals associated with six movement classes (left/right kneeling, stepping, and dash) were acquired from ten healthy male participants aged 19–24 years. Signal preprocessing, normalization, dataset generation, and model training were performed using a dedicated processing framework. Continuous EMG acquisition without threshold-based segmentation was employed to preserve complete neuromuscular information and improve dataset consistency. Movement classification was implemented using a lightweight one-dimensional convolutional neural network (1D-CNN). Model performance was evaluated using Stratified 5-Fold Cross-Validation and Leave-One-Subject-Out (LOSO) protocols. Results: A dataset containing 608 multichannel EMG recordings was generated for training and validation. The proposed 1D-CNN model achieved an accuracy of 92.43 ± 1.69% and a macro F1-score of 0.9093 ± 0.0247 under Stratified 5-Fold Cross-Validation. LOSO evaluation yielded an accuracy of 62.11 ± 23.26%, highlighting the significant impact of inter-subject variability on classification performance. Conclusions: The developed platform provides an effective framework for EMG-based lower-limb movement recognition in intelligent exoskeleton systems. The results demonstrate the feasibility of integrating multichannel EMG sensing and AI-based inference into adaptive locomotor assistance systems while emphasizing the importance of improving subject-independent generalization. The proposed platform also establishes a foundation for future research on multimodal sensing and real-time adaptive exoskeleton control. Full article

Accurate hyperspectral image analysis plays a critical role in environmental monitoring, precision agriculture, and urban mapping, yet acquiring large-scale annotated datasets for newly emerging scenes and sensors remains challenging. Cross-domain few-shot hyperspectral image classification addresses this bottleneck by transferring knowledge from a labeled source domain to a sparsely annotated target domain. Existing prototype-based approaches commonly use a single-mean prototype per class, which is often inadequate for target classes with spectral–spatial heterogeneity, intra-class dispersion, and unstable episode-level statistics. Consequently, the resulting alignment reference may fail to accurately characterize class structure, thereby exacerbating negative transfer under domain shift. To address this issue, we propose a plug-and-play prototype-stability module that combines Uncertainty-Guided Clustered Alignment (UGCA) with Center Regularization. UGCA identifies hard classes using a class-level dispersion proxy and dynamically constructs multi-center prototypes to better capture intra-class heterogeneity beyond the single-mean assumption. Meanwhile, Center Regularization adds a lightweight compactness constraint on query embeddings to reduce prototype drift under sparse supervision. Experiments on three cross-domain tasks demonstrate the effectiveness of the proposed method. Compared with the reproduced MLPA baseline over 10 independent runs, the proposed method improves the mean OA from 69.21 ± 2.71%, 81.90 ± 3.45%, and 76.92 ± 1.10% to 71.24 ± 3.39%, 83.73 ± 3.89%, and 78.24 ± 1.55% on the Indian Pines (IP), University of Pavia (UP), and Houston (HT) target domains, respectively. Furthermore, cross-framework insertion into Gia-CFSL further verifies that the module is host-agnostic across prototype-driven CD-FSL frameworks and improves prototype-based hyperspectral image analysis without changing the inference pipeline. These results indicate that improving prototype quality is a critical and complementary dimension for robust cross-domain few-shot hyperspectral classification under sparse supervision. Full article

Magnetic Flux Leakage (MFL) sensors are pivotal for the non-destructive inspection of oil and gas pipelines. However, the accuracy of defect quantification is severely compromised by pervasive noise in field-acquired MFL sensor signals, leading to substantial measurement uncertainty. To address this, we introduce a novel hybrid denoising framework that synergizes Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Wavelet Packet Decomposition (WPD). The key innovation is the employment of the Rime Optimization Algorithm (RIME) to automatically fine-tune the critical parameters of ICEEMDAN—the signal-to-noise ratio (SNR) and the number of noise additions—thereby customizing the decomposition for superior sensor signal enhancement. This optimization effectively suppresses mode aliasing and yields intrinsic mode functions that faithfully represent underlying defect features. The framework’s efficacy is rigorously validated through mathematical modeling, COMSOL Multiphysics 6.3-based finite element simulation, and real-field MFL sensor data. Results demonstrate remarkable improvements in sensor signal quality: a 53.69% increase in the SNR and reductions of 61.03% in MAE and 62.05% in RMSE over conventional methods. Crucially, the method achieved a Feature Preservation Rate (FPR) of 97.18% on simulated defects, underscoring its exceptional capability to retain critical metrological features for defect sizing. This work provides a robust signal-processing framework that significantly advances the measurement fidelity of MFL sensors, enabling more reliable pipeline integrity assessment. Full article

Duli Xingyi 獨立性易 (1596–1672) has often been variously interpreted as a Ming loyalist, a marginal Ōbaku monk, a physician, a calligrapher, or a cultural mediator. This article argues that such fragmented interpretations overlook the relatively coherent evaluative framework that connected Duli’s diverse activities. Rooted in the literati culture of Jiangnan, this evaluative framework developed in conjunction with an interconnected cultural repertoire comprising moral character, textual learning, literary accomplishment, artistic cultivation, and practical expertise. Together, they informed how Duli judged persons, texts, practices, and institutions. To explain how this framework informed judgments and choices across changing historical settings—and how those settings conditioned the mobilization and social efficacy of the repertoire—the article proposes the concept of the “Human Road.” Developed in dialogue with studies of Sino-Japanese exchange that have emphasized imported books, trade records, and reception, the Human Road does not simply refer to the movement of people, nor does it merely add human actors to the study of textual circulation. Rather, it examines how historically formed evaluative frameworks and cultural repertoires informed actors’ judgments and choices, and how changing institutional and local environments conditioned the expression and social efficacy of the resulting actions. In Duli’s case, the judgments and choices informed by his Jiangnan evaluative framework contributed to growing tensions within the institutional setting of the Ōbaku community, whereas in Iwakuni his positive evaluation of Yoshikawa Hiroyoshi was reciprocated by recognition and patronage, enabling the cultural repertoire organized by that framework to be mobilized through medicine, poetry, calligraphy, ritual expertise, and local affairs. Drawing on letters, poems, prefaces, local records, and personal writings, this article shows that texts were produced, interpreted, circulated, and preserved through human relationships, embodied knowledge, and changing social environments. Duli’s case demonstrates that the movement of persons did more than carry knowledge across regions: it set in motion processes of judgment, response, and relationship formation through which texts, practices, and cultural projects acquired significance in early modern Japan. Full article

Vision-sensor-based intelligent perception systems are increasingly used in safety-critical scenarios such as autonomous driving, edge surveillance, and Internet-of-Things (IoT) platforms. The vulnerability of deep neural networks to adversarial examples raises security concerns for sensor-acquired visual data in such systems, motivating the study of output-probability-based adversarial example detection methods under controlled benchmark settings. Existing input-level sensitivity detection methods generally rely on static perturbation scales or single-state metrics. When confronted with heterogeneous attacks, such as one-step attacks and iterative attacks, as well as complex tasks with high class density, these methods often suffer from unstable metric directions and insufficient boundary probing capability. To address these issues, this paper proposes a dual-domain adaptive adversarial example detection method based on Multi-scale Input Sensitivity (MSIS). The proposed method introduces a Manifold-Motivated Micro-scale Probing (MMP) mechanism and a Dual-State Sensitivity Fusion (DSF) mechanism. MMP adopts a task-level perturbation scaling strategy motivated by the compressed inter-class manifold structures observed in high-density classification tasks, thereby alleviating perturbation overflow and improving boundary probing effectiveness. DSF employs temperature scaling to extract sensitivity features under both the native state and the smoothed state, and alleviates the directional conflict of heterogeneous attacks under a single metric through dual-state joint modeling. Experimental results demonstrate that, without modifying the parameters of the target model, the proposed method achieves favorable detection performance against representative attacks, including FGSM, PGD, and C&W, on the CIFAR-10 and CIFAR-100 datasets. Taking the CIFAR-10 + ResNet-18 configuration as an example, the detection AUC of the proposed method against the PGD attack reaches 97.75%, an improvement of 24.32 percentage points over the best-performing non-intrusive baseline method, Energy Score (73.43%), with the lowest FPR@95TPR dropping to 7.75%. Under the CIFAR-10 + ResNet-50 configuration, the detection AUC against the PGD attack further reaches 99.14%. Meanwhile, even when compared with PASA (2024), the latest intrusive method requiring access to model gradients, the average AUC of the proposed method on CIFAR-10 + ResNet-18 (97.49%) is still 18.81 percentage points higher, and its inference latency is only 1/11th that of PASA. These results suggest that introducing task-level spatial-domain scaling and temperature-state adaptation can improve output-probability-based adversarial example detection under non-intrusive benchmark settings, providing algorithmic evidence for output-probability-based detection of adversarial perturbations in visual classification tasks. Full article

Conventional imaging systems often suffer from a coupled limitation of narrow field of view, single modality, and insufficient effective resolution. Wide-angle imaging preserves scene context but compresses distant or small-scale targets into limited pixels, while narrow-field imaging improves details at the cost of global perception. Moreover, single-modal visible imaging is sensitive to illumination and contrast variations, whereas infrared imaging lacks fine spatial texture. To increase information at the imaging source, we propose coupled spectral–spatial fusion-enabled multi-scale panoramic imaging, a dual-field-of-view (FOV) visible–infrared framework for wide-field high-resolution perception. Two imaging units acquire paired visible and infrared images from adjacent overlapping views. For each view, a visible–infrared fusion super-resolution model integrates visible structural details with infrared radiative cues to reconstruct a high-resolution fused image. A multi-scale stitching algorithm then extracts robust features, estimates cross-view correspondences, and merges the two fused images into a large-FOV panoramic result. Outdoor experiments demonstrate that the proposed method improves local contrast, suppresses pixelation artifacts, enhances readable fine details, and expands the observable field of view, providing an effective route toward multimodal panoramic imaging. Full article

The treatment landscape of oncogene-driven non-small-cell lung cancer (NSCLC) has evolved substantially with the development of targeted therapies directed against actionable molecular alterations. Tyrosine kinase inhibitors (TKIs) remain the cornerstone of treatment for many driver-defined subsets; however, acquired resistance, central nervous system progression, and tumor heterogeneity continue to limit long-term disease control. This review examines the mechanistic foundations, clinical evidence, resistance patterns, and emerging therapeutic roles of TKIs, antibody–drug conjugates (ADCs), and bispecific antibodies (bsAbs) in oncogene-driven NSCLC. Relevant preclinical studies, clinical trials, and recent therapeutic advances across major actionable driver alterations were reviewed and compared. TKIs provide potent and selective inhibition of oncogenic signaling and remain the preferred frontline therapy in most molecular subgroups, whereas ADCs offer targeted payload delivery that may overcome diverse resistance mechanisms, and bsAbs provide dual-target blockade and immune-mediated antitumor activity. Emerging evidence supports the expanding role of ADCs and bsAbs in post-TKI settings and selected biomarker-defined populations. Resistance mechanisms differ across therapeutic classes and include secondary target alterations, bypass pathway activation, antigen loss, payload resistance, and receptor adaptation. Collectively, these modalities are increasingly being integrated into biomarker-guided treatment strategies, with future management likely to rely on rational sequencing and combination approaches tailored to resistance mechanisms, target expression, central nervous system involvement, and tumor heterogeneity. Full article

This study aims to evaluate multiple feature sets composed of sensor-based biomarkers acquired during walking for the automated estimation of post-stroke motor impairment levels using Fugl-Meyer Lower Extremity Assessment (FMA-LE)-derived classes. Sensor-based walking data from the open-source ARRA dataset were combined with data collected at the Hospital of Braga. Data from 32 post-stroke individuals (FMA-LE motor score: 24 ± 3) were included. A decision tree classifier was evaluated using stratified six-fold cross-validation across different feature sets, including: correlated with motor impairment levels versus full feature sets; spatiotemporal versus surface electromyographic (sEMG) features; inclusion of demographic variables; and the use of data augmentation. The best performance was achieved using correlated sEMG features combined with age, paretic side, and body mass, along with noise-based data augmentation, yielding a validation Matthews Correlation Coefficient (MCC) of 0.85 ± 0.16 and a test MCC of 0.70. sEMG features provided improved classification performance compared to spatiotemporal features, and comparable results were obtained using a reduced subset of muscles. These results demonstrate the feasibility of using sEMG-based features acquired during walking to classify post-stroke motor impairment levels. Feature reduction and inclusion of demographic variables may support efficient model design, while data augmentation may enhance generalization. Further validation in larger and more diverse datasets is required to assess robustness and clinical applicability. Full article

Nimotuzumab is a humanized anti-EGFR monoclonal antibody with a unique pharmacodynamic profile characterized by intermediate affinity and bivalent binding dependence, enabling density-selective tumor targeting while sparing normal tissues from the severe skin rash and other toxicities common to EGFR inhibitors. Since its first approval in 2002, nimotuzumab has been registered for eight cancer indications. Unlike conventional fixed-dose schedules, emerging evidence supports prolonged administration beyond initial combination therapy. This review summarizes clinical data from pancreatic cancer, esophageal cancer, high-grade glioma, pediatric diffuse intrinsic pontine glioma, head and neck squamous cell carcinoma, nasopharyngeal cancer and other solid tumors, showing that extended nimotuzumab exposure, often as maintenance monotherapy, may prolong overall survival, progression-free survival, and disease control compared to limited cycles. Despite heterogeneity in tumor types and treatment regimens, maintenance nimotuzumab was consistently associated with better results, particularly in terms of overall survival. Notably, significant survival benefits were observed in locally advanced SCCHN (24.9 vs. 12.5 months) and esophageal cancer (15.9 vs. 8.1 months) across independent clinical trials. Mechanistically, nimotuzumab exerts direct cytostatic effects via G1 arrest, potent anti-angiogenic activity through VEGF downregulation, indirect pro-apoptotic effects, and broad immunomodulation including ADCC, NK-DC cross-talk, EGFR-specific CD8+ T cell priming, upregulation of HLA class I, and favorable regulation of regulatory T cells. Its density-selective binding reduces selective pressure for acquired resistance. Future research priorities should include prospective randomized trials specifically evaluating maintenance strategies, biomarker-driven patient selection, the molecular characterization of resistance mechanisms, integration with immunotherapy and modern combination regimens, and the development of next-generation platforms, including antibody–drug conjugates and multi-specific constructs. Full article

Background: Congenital and acquired venous abnormalities in children represent a heterogeneous group of conditions associated with significant clinical morbidity, including systemic desaturation, hepatic dysfunction, and life-threatening hemodynamic compromise. Evidence regarding transcatheter management across this diverse spectrum remains limited and largely confined to small, lesion-specific series. This study aimed to evaluate the feasibility, procedural, and clinical outcomes of transcatheter venous interventions in a heterogeneous pediatric population. Methods: This retrospective single-center study included all consecutive pediatric patients (median age: 9 years; range: 2 days–17 years) who underwent a total of 28 transcatheter procedures for congenital and acquired venous abnormalities between January 2022 and September 2025. Lesions included congenital portosystemic shunts (n = 4), systemic venous stenoses (n = 2), obstructed pulmonary venous pathways (n = 3), and veno-venous collaterals or azygos/hemiazygos continuation associated with Glenn circulation (n = 9). Procedural strategies were tailored according to anatomical characteristics, and outcomes were assessed in terms of technical success, procedural complications, and lesion-specific clinical and hemodynamic improvement. Results: A total of 28 transcatheter procedures were performed in 18 patients. The median procedure duration was 120 min (range, 35–235 min), and the median follow-up duration was 13.5 months (range, 0.6–35 months). During follow-up, two patients required reintervention for restenosis of previously treated lesions, whereas five additional procedures were performed because of newly developed veno-venous collaterals. Transcatheter interventions resulted in lesion-specific clinical and hemodynamic improvements, including reductions in serum ammonia levels, relief of venous obstruction, and improvements in oxygen saturation. One patient with complex pulmonary venous obstruction died following subsequent surgery despite initial hemodynamic improvement. Conclusions: In this single-center experience, transcatheter venous interventions were feasible and were associated with favorable immediate and mid-term clinical and hemodynamic outcomes in children with diverse venous abnormalities. Given the small sample size, heterogeneous patient population, and retrospective design, these findings should be considered preliminary. Larger prospective multicenter studies are warranted to better define optimal patient selection, timing of intervention, and long-term outcomes. Full article