Search Results (52,917)

TDP-43 proteinopathies, encompassing amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration (FTLD), and limbic-predominant age-related TDP-43 encephalopathy (LATE), represent a heterogeneous spectrum of devastating neurodegenerative disorders. For decades, the diverse clinical presentations of these diseases have complicated antemortem diagnosis and hindered the development of disease-modifying therapies. However, recent breakthroughs in basic science are beginning to address these clinical barriers, although substantial hurdles to practical clinical application remain. Structural elucidation via cryo-electron microscopy (Cryo-EM) has shattered the single-protein amyloid dogma by revealing that TDP-43 can form hetero-amyloid filaments with ANXA11, thereby providing a molecular basis for pathological strain diversity. Concurrently, the pathogenic focus has shifted toward nuclear loss of function, which triggers a systemic “RNA crisis” characterized by aberrant alternative polyadenylation (APA) and cryptic exon inclusion (e.g., STMN2, UNC13A). Crucially, this metabolic collapse is profoundly exacerbated by patient-specific genetic risk factors, acting synergistically in a “two-hit” model of neurodegeneration. To translate these findings to the clinic, next-generation diagnostic tools are emerging. Integrating neuron-derived extracellular vesicle (EV) isolation with Seed Amplification Assays (SAAs) holds promise to help overcome the structural camouflage that limits current PET imaging, potentially offering ultra-sensitive, functional strain identification in biofluids. While these structural and diagnostic milestones provide a strong foundation for precision medicine, major challenges in assay standardization and clinical validation must be addressed. Advanced therapeutic strategies—namely, splice-switching antisense oligonucleotides (ASOs) that directly restore RNA metabolism, combined with the targeted suppression of neuronal hyperexcitability—are now entering clinical trials. This review synthesizes how decoding the structural and RNA-metabolic complexities of TDP-43 is paving a promising pathway from bench to bedside, while critically discussing current translational limitations. Full article

The histopathology area is being redefined with the use of artificial intelligence (AI), providing robust tools to help bridge cellular morphology, functional assays, and multi-omics data in stem cell research. The ability of stem cells to undergo self-renewal and differentiation is key in regenerative medicine, but their research requires the careful characterization of morphological and molecular phenotypic traits. Traditional histopathology is invaluable, but its application can be limited by inter-observer variability and restricted scalability. These limitations are circumvented by AI-based techniques, such as machine learning and deep learning, which are capable of classifying cells, performing quantitative morphometry, and forecasting stem cell behavior. Adding AI to genomics, proteomics, and metabolomics will contribute to the further identification of biomarkers and pathways that regulate stem cell fate. This convergence provides new possibilities for precision medicine, personalized therapies, and translational uses like drug discovery and disease modeling. However, its potential has not yet been realized because of the existing difficulties in data quality, variability, regulatory control, and ethical issues, especially in terms of the transparency and justice of AI systems. Emphasized areas for the future include explainable AI, federated learning, and a multimodal framework that integrates imaging, sequencing, and clinical data. Interdisciplinary partnerships and adequate regulatory frameworks will help AI-enabled histopathology reshape stem cell studies and speed up the process of translating regenerative medicine into clinical applications. Full article

Hybrid CNN–Transformer (HCT) synthesis for automated neurodevelopmental diagnostics is an effective approach to constructing intelligent detection systems that are not merely oriented toward feature classification but primarily toward solving spatiotemporal pattern recognition problems in motor disorder assessment. In neurodevelopmental diagnostics, existing automated methods rely on fixed, single-model architectures that process spatial or temporal motion features independently, failing to adapt to the heterogeneous motor irregularities characteristic of developmental coordination disorder and degrading detection sensitivity and generalization across diverse patient populations. There is therefore a pressing need for models capable of simultaneously capturing intra-frame spatial coordination patterns and inter-frame temporal movement dependencies against interrelated diagnostic criteria including accuracy, sensitivity, and motor irregularity specificity. To address this challenge, this paper proposes HCT, a novel framework that integrates ResNet-based spatial feature extraction from optical flow maps and pose estimation skeletons with multi-head self-attention Transformer encoding for modeling long-range temporal dependencies across multi-frame motion sequences. Unlike conventional single-stream approaches, where spatial and temporal processing remain confined to independent architectures, HCT decouples spatiotemporal feature learning through a cross-modal fusion pipeline, constructing a unified discriminative architecture that captures motor coordination dependencies between motion imaging inputs and multiple diagnostic criteria simultaneously. The convolutional encoder generates diverse joint displacement features, which are consolidated through cross-modal attention fusion into a robust, unified embedding with enhanced generalization and resilience to inter-individual motor variability. Integration within neurodevelopmental assessment frameworks facilitates reliable developmental coordination disorder classification, motor irregularity prediction, and interpretable diagnostic decision support, advancing the accuracy, flexibility, and clinical validity of intelligent motor disorder diagnostic systems. Full article

Recent studies have witnessed significant advances in unified multi-weather image restoration, which aims to handle diverse weather degradations within a single model. In this work, we observe that rain, haze, and snow restoration exhibit substantial differences in both degradation characteristics and learning dynamics, making straightforward joint optimization prone to performance imbalance and ineffective knowledge transfer. To this end, we propose UMWIR-Net, a unified multi-weather image restoration network equipped with an Asymmetric Task Collaborative Learning strategy. ATCL consists of Intra-Task Difficulty Optimization and Inter-Task Contribution Scheduling. Specifically, Intra-Task Difficulty Optimization jointly models the remaining restoration error and recent learning progress to dynamically estimate the optimization difficulty of each weather task, thereby assigning larger weights to slowly converging and under-optimized tasks. Inter-Task Contribution Scheduling measures the directional influence of a source-task update on the validation objective of a target task, constructs an asymmetric task-contribution matrix, and accordingly promotes tasks that provide stronger transferable knowledge while compensating those that benefit less from collaborative learning. In this manner, different weather restoration tasks collaborate selectively and asymmetrically, allowing the model to exploit complementary knowledge across tasks and improve overall restoration performance. Furthermore, UMWIR-Net adopts a wavelet-based Transformer backbone to capture low- and high-frequency information, enabling effective modeling of both global structures and local details for diverse weather restoration. Extensive experiments on multi-weather image restoration datasets show that UMWIR-Net achieves state-of-the-art performance and delivers more balanced restoration quality across rain, haze, and snow removal. Full article

Accurate and non-destructive assessment of drought stress is important for improving lettuce production and supporting timely crop management. This study presents a detection-guided deep learning framework for plant-level drought-stress assessment in hydroponically grown lettuce using bird’s-eye-view RGB images. The study further investigates whether canopy segmentation can improve classification performance by reducing irrelevant background information. The framework was evaluated using 2190 images collected across three independent cultivation cycles in which drought stress was induced by isolating the plant root zones from the nutrient solution. In the first stage, YOLO-based object detection was used to localize individual plants, with YOLO26m achieving the highest detection performance of 99.4% mAP@0.5. The detected regions were subsequently used as spatial prompts for zero-shot canopy segmentation using the Segment Anything Model (SAM), with SAM ViT-B achieving a mean IoU of 0.9864. Six convolutional, transformer-based, and hybrid classification architectures were then evaluated independently using YOLO-cropped and SAM-segmented plant images. Segmented inputs consistently improved classification performance, with MaxViT-S achieving the highest binary test accuracy of 96.3%. The framework further distinguished time-defined pre-stress, early-stress, and late-stress periods with an accuracy of 92.4%. Plant-level generalization was further assessed using six-fold leave-one-plant-out cross-validation, resulting in a mean test accuracy of 90.25 ± 1.78% on unseen plants. These findings demonstrate that RGB-based plant-level analysis can support non-destructive drought-stress assessment and that canopy segmentation improves classification by reducing background influence. Full article

Vibration signals of planetary gearboxes under variable-speed conditions exhibit strong non-stationarity and modulation, so a single feature representation cannot comprehensively describe intricate fault patterns, and distribution discrepancies across rotating speeds degrade the cross-condition generalization of diagnostic models. To address these limitations, this paper proposes an adaptive domain-aligned multi-modal feature fusion network (ADAMFFN). Three parallel branches extract complementary features from dual-channel vibration signals: spatial coupling features from orbit images, time–frequency energy features from continuous wavelet transform (CWT) representations, and frequency-domain statistical (FreqStat) features from power and envelope spectra. Heterogeneous features are mapped into a shared latent subspace through a unified projection layer, deep cross-modal interaction is realized by a progressive fusion network, and a domain alignment mechanism based on a domain-adversarial neural network (DANN) is introduced to eliminate source–target distribution gaps via adversarial training. On eight leave-one-speed-out (LOSO) cross-speed tasks constructed on the public WT-Planetary Gearbox dataset, ADAMFFN achieves an average accuracy of 99.25%, outperforming the best single-branch and dual-branch schemes by 2.63 and 0.40 percentage points, respectively; ablation experiments verify the complementarity of the three modalities and the effectiveness of domain alignment. Cross-condition external validation on the Southeast University (SEU) gearbox dataset further demonstrates its generalization capability under a different test rig and acquisition conditions. Full article

Wastewater treatment plants (WWTPs) are essential infrastructure for urban water management. Their stable operation is critical to effluent quality and public safety. However, wastewater treatment involves complex biochemical processes and extensive electromechanical equipment. Hazards such as sludge flotation in secondary clarifiers, fire, electric shock, and toxic gas poisoning may occur. These hazards can threaten worker safety and reduce treatment efficiency. Traditional inspection mainly relies on manual patrols, fixed-camera monitoring, and experience-based judgment. These methods often have low efficiency, limited coverage, and delayed responses. To address these limitations, this paper investigates robot-based hazard detection for WWTPs. A multisource hazard detection dataset is constructed for secondary clarifiers and confined spaces, including images collected by an inspection robot. Object detection models are then applied to identify typical hazards. Comparative experiments are conducted using Faster R-CNN and several YOLO-series models. YOLOv12 achieves mAP@0.5 values of 0.917 and 0.819 for sludge flotation detection and confined space hazard detection, respectively. It also provides a good balance between detection performance and inference efficiency. The results demonstrate that robot vision combined with object detection can support intelligent inspection in WWTPs. Full article

Social media has become an important tool for understanding visitor experiences in museums and cultural heritage sites. This literature review identifies, organizes, and thematically synthesizes existing studies on museum visitor experience based on social media data. It examines 41 studies retrieved from Scopus and Web of Science and published between 2017 and 2026. Furthermore, the review examines the selected studies across six dimensions: social media platforms, types of user-generated data, the role of digital interactions, the museums and cultural heritage sites studied, the analytical methodologies applied, and the main findings on visitor experience. The findings indicate that TripAdvisor is the most frequently used platform for collecting textual reviews and star ratings, whereas Instagram and Flickr are mainly used for visual and spatial data. Most studies rely on computational methods, often combined with quantitative techniques, while qualitative approaches are used less frequently. The identified methods include content analysis, statistical analysis, sentiment analysis, topic modeling, machine learning, image analysis, and spatial analysis. Across the reviewed studies, visitor experience is examined as a multidimensional phenomenon encompassing emotions, service quality, authenticity, historical connection, aesthetics, education, and social participation. Finally, the review identifies recurring themes across the dimensions and synthesizes them into broader research streams. These are brought together in an integrative synthesis framework that organizes existing research, highlights research gaps, and outlines directions for future studies. Full article

Background: The rotator cable is thought to contribute to load transmission and preservation of shoulder function in rotator cuff tears; however, its detectability on arthroscopy and magnetic resonance imaging (MRI), agreement between these modalities, and clinical relevance remain incompletely defined. This study evaluated rotator cable visibility on arthroscopy and MRI, factors associated with arthroscopic cable visibility, and its relationship with preoperative active shoulder motion. Methods: This retrospective cross-sectional study included 128 patients who underwent shoulder arthroscopy for supraspinatus tears between January 2019 and February 2023. Arthroscopic video recordings were reviewed for rotator cable visibility. Standardized preoperative MRI review was available in 58 patients. Agreement between MRI and arthroscopic visualization was assessed using Cohen’s kappa and percentage agreement measures. Multivariable binary logistic regression was performed to identify factors independently associated with arthroscopic cable non-visibility. Results: The rotator cable was visible arthroscopically in 79 of 128 patients (61.7%) and on MRI in 38 of 58 patients (65.5%). Overall agreement between MRI and arthroscopy was 67.2% (95% CI, 53.7–79.0%), with a Cohen’s kappa of 0.315 (95% CI, 0.090–0.540; p = 0.011), indicating fair agreement. Positive and negative percent agreement were 78.8% and 52.0%, respectively. Although increasing age was associated with cable non-visibility in univariable analysis, this association did not remain statistically significant after multivariable adjustment. Higher Lafosse grade was independently associated with cable non-visibility in the overall cohort (adjusted OR, 1.43 per grade; 95% CI, 1.04–1.97; p = 0.028), whereas increasing tear size was independently associated with cable non-visibility among patients with full-thickness tears (adjusted OR, 2.78 per category; 95% CI, 1.19–6.52; p = 0.019). The unadjusted association between massive tear size and reduced MRI cable visibility did not remain significant after false discovery rate adjustment (q = 0.276). No statistically significant associations were detected between cable visibility or MRI-measured cable dimensions and preoperative active abduction or forward elevation. Conclusions: MRI and arthroscopy demonstrated fair agreement in the assessment of rotator cable visibility. After multivariable adjustment, cable non-visibility was more closely associated with tear-related characteristics than with patient age. No statistically significant associations were detected between cable characteristics and the assessed preoperative range-of-motion measures; however, smaller or moderate associations cannot be excluded, particularly within the MRI subgroup. Rotator cable visibility should primarily be interpreted as a marker of detectability and tear morphology rather than as a direct surrogate for structural integrity or shoulder function. Full article

Emotional eating (EE) is associated with poor dietary quality and weight gain among university students. Therefore, exploring innovative, non-invasive interventions to support healthier food-related responses is essential. While multisensory environments show promise in influencing food-related responses, their effectiveness in managing stress-induced EE remains understudied. Grounded in the Environment–Organism–Health (EOH) model, this study used an immersive virtual reality (VR) platform and recruited 49 Chinese university students. After emotional induction tasks, participants were randomly assigned to VR campus environments with varying wall colors and environmental sounds. The study combined physiological indicators and subjective questionnaires to evaluate the effects of color–sound combinations on emotional responses, physiological recovery, food preferences, and food cravings. Food preference was assessed via an image-selection task, and food healthiness was rated by an expert panel (n = 7). A mixed-design analysis of variance (ANOVA) revealed a significant interaction between wall color and sound. Natural sounds were associated with reduced negative emotions and higher healthy food preference scores under several color conditions, while both color and sound influenced food craving. Independent t-tests revealed significant differences according to gender and emotional eating tendency on multiple outcomes. This study suggests that multisensory environments may offer a novel approach to influencing stress-related emotional responses, food preferences, and food cravings. Full article

Degraded tax forms and other legal–administrative documents require restoration methods that impose strict constraints on content fidelity. This paper presents a structure-aware restoration framework for degraded tax documents that combines geometric normalization, canonical template guidance, and supervised image restoration within a common alignment space. Documents are first mapped to a canonical layout through homography estimation based on Scale-Invariant Feature Transform (SIFT) and Random Sample Consensus (RANSAC), using the canonical visual template as the reference. Restoration is then performed using template priors and masked constraints designed to preserve the fixed document structure while recovering variable content. Under a fixed-budget comparative protocol, U-Net with template priors achieved the highest visual and structural quality, whereas the proposed hybrid model obtained the best functional OCR performance on synthetic data, reaching a Character Error Rate (CER) of 0.1091 and a Word Error Rate (WER) of 0.3495. As a complementary evaluation on 37 real-world documents with human-generated textual ground truth, restoration increased full-page word coverage across all three OCR engines evaluated, yielding absolute improvements ranging from 4.15 to 18.05 percentage points. Full article

Alzheimer’s disease (AD) classification from structural magnetic resonance imaging (MRI) may benefit from weak supervision that uses clinically meaningful but imperfect supervisory signals. We evaluated a weakly supervised framework in which a multilayer perceptron (MLP) trained on age, sex, and Mini-Mental State Examination (MMSE) scores generated clinical pseudo-labels to initialize a patch-based fully convolutional network (FCN). For 260 Alzheimer’s Disease Neuroimaging Initiative (ADNI) training participants, subsequent refinement combined 80% of the preceding MRI-model probability with 20% of the participant’s ground-truth diagnostic label. This design preserves a dominant pseudo-label/self-training component while using partial diagnostic guidance to stabilize refinement. The FCN generated whole-brain probability maps, and selected voxel probabilities were classified by a second MLP. The framework was developed using ADNI (n = 417). Using ADNI validation data only, iteration 3 and a classification threshold of 0.5 were selected and then applied unchanged to the held-out ADNI test set and the external AIBL (n = 182), FHS (n = 102), and NACC (n = 265) cohorts. The selected model achieved F1 scores of 0.853 in ADNI, 0.707 in AIBL, 0.765 in FHS, and 0.807 in NACC. These results support the feasibility and cross-cohort transferability of clinical pseudo-label-based weak supervision for MRI classification. The framework is not intended to be label-free; rather, it provides a transparent strategy for integrating imperfect clinical pseudo-labels with partially weighted diagnostic guidance during training. Full article

Accurate cloud segmentation from ground-based fisheye camera imagery is essential for solar irradiance forecasting and photovoltaic system optimization. Traditional computer vision approaches, such as HSV thresholding and K-means clustering, face significant limitations when applied globally to sky images due to the spectral similarity between cloud regions and sky areas under varying atmospheric conditions. This study presents a hybrid methodology that leverages YOLOv8 instance segmentation to provide contextual cloud regions followed by refined HSV thresholding within these detected areas. The approach incorporates solar trajectory modeling using pvlib for accurate sun disk detection and exclusion, preventing false cloud classification. The methodology was developed and validated at the CIESOL using Mobotix Q71 fisheye cameras, and later tested in Antofagasta (Chile) and Huelva (Spain). The YOLOv8l-seg model achieved a mask precision of 0.821 and box mAP@0.5 of 0.680 on validation data. The results show a promising correlation with radiometric measurements such as clearness index $k_t$ and diffuse fraction $k_d$ in preliminary validation cases. While YOLOv8 demonstrates good cross-site generalization, HSV thresholding requires camera-specific calibration for optimal performance. The method addresses the context-dependency limitations of traditional algorithms, though computational performance and broader validation remain areas for future work. Full article

Accurate identification of multiphase flow patterns in vertical pipelines is critical for operational safety and efficiency in the oil and gas industry. Yet, conventional methods struggle with subjectivity and transitional regimes. This study develops and integrates three neural network architectures: a convolutional neural network (CNN) for spatial features, a transformer neural network (TNN) for long-range dependencies, and a physics-encoding network (PEN) for embedding physical constraints. These are combined into a hybrid framework trained on an experimental dataset of 2131 images from a wire mesh sensor, annotated using a semi-automated pipeline. Results show the hybrid model achieved 95.91% test accuracy with a macro F1-score of 0.96, the highest of the four models evaluated, with its main advantage in transitional regimes. A multi-seed ablation shows that the convolutional branch provides the dominant discriminative signal, while the transformer and physics-inspired branches added complementary improvements that are consistent across runs. This hybridisation mitigates individual model weaknesses, with the physics-inspired branch acting as a spatial regulariser that improves interpretability, providing a robust and objective tool for reliable pipeline monitoring. Full article

Background/Objectives: Nonsurgical treatment for pancreatolithiasis is widely performed. However, treatment success remains difficult to predict before treatment initiation, complicating the selection of an appropriate treatment strategy. This study aimed to predict pancreatic stone clearance after nonsurgical treatment for pancreatolithiasis associated with chronic pancreatitis by integrating pretreatment computed tomography (CT) images and clinical information using deep learning and machine learning models. Methods: Of 195 patients with pancreatolithiasis associated with chronic pancreatitis who underwent nonsurgical treatment, including extracorporeal shock wave lithotripsy, at our institution between 1992 and 2024, only 91 (47%) had extractable pretreatment noncontrast abdominal CT images and were included in the AI analysis. Multiple deep learning models (VGG16/19, InceptionV3, ResNet50, DenseNet121/169/201, Vision Transformer, and Swin Transformer) were trained using CT images, and their predictive performance was compared. Imaging-derived and clinical predictors selected using only the training data in each patient-level cross-validation fold were combined and used as inputs for conventional machine learning models, including random forest, support vector machine, naïve Bayes, neural network, and gradient boosting. Results: Successful pancreatic stone clearance was achieved in 54 of 91 patients (59%). Compared with the 104 patients without extractable CT data, the analyzed cohort had a higher proportion of asymptomatic pancreatolithiasis (43% vs. 15%) and a markedly lower pancreatic stone clearance rate (59% vs. 88%), indicating potential selection bias. Asymptomatic pancreatolithiasis and a pancreatic stone size of ≥15 mm, defined using a data-derived exploratory cutoff, were significantly associated with unsuccessful stone clearance. Among the deep learning models, ResNet50 achieved the highest performance (area under the receiver operating characteristic curve [AUC], 0.718), followed by Vision Transformer (Large model, 16 × 16 patches) (AUC, 0.700). When image-derived features were combined with clinical features, the neural network achieved the best performance, with a mean AUC of 0.757, a median sensitivity of 0.568, a median specificity of 0.704, and a median accuracy of 0.659. Conclusions: A neural network integrating CT-derived image features with clinical information showed moderate internal predictive performance for pancreatic stone clearance. Because this was a single-center retrospective study without external validation, the present model should be regarded as a preliminary predictive model requiring validation in independent cohorts before clinical application. Full article