Search Results (749)

Duchenne muscular dystrophy (DMD) is a severe X-linked neuromuscular disease (NMD) caused by loss-of-function mutations in the DMD gene. RNA-based therapies, especially antisense oligonucleotides (ASO)-mediated exon skipping and adenosine deaminase acting on RNA (ADAR)-guided RNA editing, have emerged as complementary approaches that modulate pre-mRNA splicing or correct transcripts without altering genomic DNA. Current phosphorodiamidate morpholino oligomer (PMO) drugs targeting exons 51, 53, and 45 provide mutation-class-specific benefit. At the same time, next-generation delivery strategies (e.g., peptide-conjugated PMOs (PPMOs), antibody–oligonucleotide conjugates (AOC), and endosomal-escape vehicles) aim to improve skeletal, cardiac, and diaphragm exposure. In parallel, RNA editing strategies offer a route to correct select nonsense or missense variants at the base level and may, in principle, restore near-native dystrophin expression. Meaningful translation of these modalities requires predictive large-animal models. A genome-edited microminipig (MMP) bearing DMD exon-23 mutations faithfully recapitulates hallmark features of human DMD. That includes early locomotor deficits, elevated serum creatine kinase (CK) and cardiac troponin T, progressive myocardial fibrosis, and a decline in left-ventricular ejection fraction (LVEF), while maintaining a manageable lifespan of approximately 30 months suitable for long-term studies. In particular, the MMP model provides a practical platform for addressing the persistent challenge of efficient therapeutic delivery to the heart and diaphragm through longitudinal dosing, imaging, and biopsy. In this review, we synthesize clinical progress in exon skipping, outline the promise of RNA editing, and integrate recent insights from Duchenne muscular dystrophy model for microminipigs (DMD-MMPs) as an advanced surrogate for preclinical development and translational evaluation. Full article

Accurate weed mapping in cereal fields requires pixel-level segmentation from unmanned aerial vehicle (UAV) imagery that remains reliable across fields, seasons, and illumination. Existing multispectral pipelines often depend on thresholded vegetation indices, which are brittle under radiometric drift and mixed crop–weed pixels, or on single-stream convolutional neural network (CNN) and Transformer backbones that ingest stacked bands and indices, where radiance cues and normalized index cues interfere and reduce sensitivity to small weed clusters embedded in crop canopy. We propose VISA (Vegetation Index and Spectral Attention), a two-stream segmentation network that decouples these cues and fuses them at native resolution. The radiance stream learns from calibrated five-band reflectance using local residual convolutions, channel recalibration, spatial gating, and skip-connected decoding, which preserve fine textures, row boundaries, and small weed structures that are often weakened after ratio-based index compression. The index stream operates on vegetation-index maps with windowed self-attention to model local structure efficiently, state-space layers to propagate field-scale context without quadratic attention cost, and Slot Attention to form stable region descriptors that improve discrimination of sparse weeds under canopy mixing. To support supervised training and deployment-oriented evaluation, we introduce BAWSeg, a four-year UAV multispectral dataset collected over commercial barley paddocks in Western Australia, providing radiometrically calibrated blue, green, red, red edge, and near-infrared orthomosaics, derived vegetation indices, and dense crop, weed, and other labels with leakage-free block splits. On BAWSeg, VISA achieves 75.6% mean Intersection over Union (mIoU) and 63.5% weed Intersection over Union (IoU) with 22.8 M parameters, outperforming a multispectral SegFormer-B1 baseline by 1.2 mIoU and 1.9 weed IoU. Under cross-plot and cross-year protocols, VISA maintains 71.2% and 69.2% mIoU, respectively. The full BAWSeg benchmark dataset, VISA code, trained model weights, and protocol files will be released upon publication. Full article

In recent years, video recognition models have witnessed the rapid development of Deep Neural Networks (DNNs). However, these models remain not robust to adversarial examples that are created by adding imperceptible perturbations to clean samples. Recent studies indicate that generating adversarial examples in the hard-label black-box setting is particularly challenging yet highly practical. Compared to image recognition models, there are few hard-label black-box adversarial example generation algorithms for video recognition models. To this end, we propose a hard-label black-box video adversarial example generation algorithm, referred to as Dynamic Black-box Algorithm (DBA). First, DBA uses the binary search algorithm to find the boundary video between two original videos; then, the sampling-based algorithm is used to estimate the gradient on the boundary video; finally, with a dynamic step size adjustment strategy, DBA moves the boundary video towards the direction of the estimated gradient to generate the adversarial video. Additionally, we designed another strategy to skip invalid samples generated during the adversarial example generation process. Experiments demonstrate that DBA attains a superior trade-off between the magnitude of perturbations and query efficiency. Specifically, DBA outperforms state-of-the-art algorithms, achieving an average reduction in Mean Squared Error (MSE) of over 50%. Full article

Background/Objectives: Food insecurity remains a growing public health concern among university populations, particularly international students who often face financial constraints, limited social support, and cultural adaptation challenges. This study investigated the association between food insecurity and dietary diversity among international students in Hungary, a population for whom empirical evidence remains limited. Methods: A cross-sectional survey was conducted among 380 international university students using a structured questionnaire comprising sociodemographic items, the Food Insecurity Experience Scale (FIES), and a quantitative Food Frequency Questionnaire (FFQ). Dietary diversity was assessed through Food Group Diversity Score (FGDS) and Food Variety Score (FVS). Statistical analyses included chi-square tests, ANOVA, correlation analyses, and multiple regression using IBM SPSS 28.0. Results: Overall, 62% of participants experienced food insecurity, with 25% moderately and 20% severely food insecure, while 17% were classified as mildly food insecure. While 97% achieved high dietary diversity, only 31% exhibited high food variety. Group comparisons indicated differences in FGDS across food security categories (p = 0.006), whereas FVS did not differ significantly (p = 0.411). In multivariable regression models adjusting for socioeconomic and behavioural factors, food security status was not independently associated with FGDS or FVS. However, scholarship status, monthly income, employment, and meal skipping were significant predictors of dietary diversity indicators. Conclusions: These findings suggest that while food insecurity is prevalent among international students, socioeconomic resources and behavioural factors may play a more prominent role in shaping dietary diversity outcomes. Universities and policymakers should prioritise equitable food access, culturally inclusive meal services, and ongoing monitoring of student food security to promote nutrition equity and academic well-being. Full article

Peak-hour congestion in metro systems poses significant challenges to operational reliability and passenger experience. This study investigates a coordinated operational strategy that integrates passenger flow control, reservation-based entry, and skip-stop train operations to alleviate congestion in high-density metro corridors. A mathematical optimization model is formulated to jointly capture passenger demand, station crowding, and train capacity constraints, and is solved using an adaptive large neighborhood search algorithm. Numerical experiments based on a real-world metro line demonstrate that the proposed framework can effectively reduce passenger waiting time and improve the balance of passenger distribution across stations under peak-hour conditions. The results indicate that coordinating multiple operational measures yields better performance than applying individual strategies in isolation, highlighting the practical value of the proposed approach for congested metro systems. Full article

Background: The generation of synthetic CT images from MRI scans represents a crucial step toward enabling MRI-only clinical workflows and supporting multi-modal integration in medical imaging, particularly in radiotherapy planning. Despite significant advancements in deep learning models, many current methods still struggle to reconstruct high-density structures, especially bone, and exhibit limited accuracy in density values. This shortcoming is largely attributed to the passage of excessive or noisy features through skip connections in the traditional U-Net architecture, which degrade the quality of information transmitted to the decoder, negatively impacting the clarity of anatomical boundaries and the pixel-wise accuracy of the resulting synthetic image. Methods: In this work, we propose an enhanced 3D U-Net architecture in which the Convolutional Block Attention Module (CBAM) is systematically integrated within each skip connection. The CBAM sequentially applies channel and spatial attention to adaptively reweight encoder feature maps before fusion with the decoder, thereby emphasizing anatomically relevant structures while suppressing irrelevant feature propagation. The model was trained and evaluated on the SynthRAD2023 (Task 1—Brain) MRI–CT dataset. To rigorously assess the contribution of the attention mechanism, a dedicated ablation study was conducted comparing three variants: 3D U-Net with Squeeze-and-Excitation (SE), Coordinate Attention (CA), and the proposed CBAM module. Performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Cross-Correlation (NCC). Results: The ablation study demonstrated that the CBAM-enhanced model consistently outperformed both SE- and CA-based variants across all quantitative metrics. Specifically, the proposed method achieved an MAE of $38.2\pm 5.4$ HU and an RMSE of $51.0\pm 12.0$ HU, representing the lowest reconstruction errors among the evaluated models. In addition, it obtained a PSNR of $29.45\pm 2.10$ dB, SSIM of $0.940\pm 0.031$, and NCC of $0.967\pm 0.015$, indicating superior structural preservation and strong voxel-wise correspondence between synthesized and reference CT volumes. These results confirm that the sequential integration of channel and spatial attention provides a statistically and practically meaningful improvement for high-fidelity MRI-to-CT synthesis. Conclusions: Generating high-resolution brain CT images from brain MRI scans using a 3D U-Net network enhanced with a CBAM module can contribute to supporting the clinical workflow by providing additional diagnostic data without the need for extra radiological examinations, thereby enhancing diagnostic efficiency and reducing radiation exposure. This technique helps reduce patient exposure to radiation and improves accessibility in resource-limited settings. Furthermore, this method is valuable for retrospective studies, surgical planning, and image-guided therapy, where complete multi-modal data may not always be available. Full article

Background/Objectives: The rise of artificial intelligence is injecting intelligence into the healthcare sector, including surgery. Vision-based intelligent systems that assist surgical procedures can significantly increase productivity, safety, and effectiveness during surgery. Surgical instruments are central components of any surgical intervention, yet detecting and locating them during live surgeries remains challenging due to adverse imaging conditions such as blood occlusion, smoke, blur, glare, low-contrast, instrument scale variation, and other artifacts. Methods: To address these challenges, we developed an advanced segmentation architecture termed the frozen-filters-based morphology-aware segmentation network (FFMS-Net). Accurate surgical instrument segmentation strongly depends on edge and morphology information; however, in conventional neural networks, this spatial information is progressively degraded during spatial processing. FFMS-Net introduces a frozen and learnable feature pipeline (FLFP) that simultaneously exploits frozen edge representations and learnable features. Within FLFP, Sobel and Laplacian filters are frozen to preserve edge and orientation information, which is subsequently fused with learnable initial spatial features. Moreover, a tri-atrous blending (TAB) block is employed at the end of the encoder to fuse multi-receptive-field-based contextual information, preserving instrument morphology and improving robustness under challenging conditions such as blur, blood occlusion, and smoke. Datasets focused on surgical instruments often suffer from severe class imbalance and poor instrument visibility. To mitigate these issues, FFMS-Net incorporates a progressively structure-preserving decoder (PSPD) that aggregates dilated and standard spatial information after each upsampling stage to maintain class structure. Multi-scale spatial features from different encoder levels are further fused using light skip paths (LSPs) to project channels with task-relevant patterns. Results/Conclusions: FFMS-Net is extensively evaluated on three challenging datasets: UW-Sinus-surgery-live, UW-Sinus-cadaveric, and CholecSeg8k. The proposed method demonstrates promising performance compared with state-of-the-art approaches while requiring only 1.5 million trainable parameters. In addition, an open-source large language model is integrated for non-clinical summarization of the surgical scene based on the predicted mask and deterministic descriptors derived from it. Full article

In traditional time-series image prediction tasks, both accuracy and image quality tend to deteriorate as the prediction horizon extends. To address this challenge in Shiitake mushroom fruiting body growth prediction, this study selected Shiitake mushroom strain No. 509, cultivated by the Shanghai Academy of Agricultural Sciences, as the experimental subject and proposed an enhanced model, VMRNN-DMSA, based on the Vision Mamba RNN Depth architecture. This model integrates a skip-connection mechanism with a Max Feature Map module to effectively filter and fuse features, enhancing feature representation and prediction accuracy. Additionally, a Spatial Attention Mechanism was introduced to strengthen the perception of key regions and improve spatial modeling. Furthermore, an Adaptive Kernel Convolution module with irregular context convolution kernels was incorporated to extract fine-grained local features and enhance visual quality. A weighted loss function was used to balance pixel-level accuracy, structural fidelity, and perceptual quality. This function combines Mean Squared Error Loss, Multi-Scale Structural Similarity, and Perceptual Loss. Experimental results showed that the proposed method achieved an MSE of 39.4255, an SSIM of 0.8579, and a PSNR of 22.0774. Compared with baseline models, MSE decreased by 29.05%, while SSIM and PSNR increased by 19.34% and 14.52%, respectively. These results indicate that VMRNN-DMSA significantly improves both prediction accuracy and image quality in long-term forecasting tasks, providing a reliable reference for the growth prediction of other edible fungi. Full article

Colorectal cancer, as a malignant tumor with a high incidence rate worldwide, relies on the precise segmentation of polyps during colonoscopy for its early diagnosis. However, clinical colonoscopy images often face challenges such as low contrast, blurred boundaries, large differences in morphological scale, and interference from intestinal wall folds, resulting in insufficient accuracy of traditional segmentation methods. To address the above problems, this paper proposes a PCA-TransUNet model based on the parallel cross-attention mechanism, taking TransUNet as the baseline framework and introducing the parallel cross-attention module in its skip connections. This module consists of two branches: channel cross-attention and spatial cross-attention. The channel branch enhances the semantic feature discrimination through cross-scale channel interaction, while the spatial branch optimizes the boundary positioning accuracy by using long-range dependency relationships. The outputs of the two are adaptively integrated through a dynamic weighted fusion mechanism to form multi-scale enhanced features, significantly improving the segmentation robustness in complex scenarios. Experiments on the CVC-ClinicDB and Kvasir-SEG datasets show that the model proposed in this paper outperforms the comparison models in multiple indicators. PCA-TransUNet achieved mIoU of 92.89% and Dice of 95.79% on CVC-ClinicDB, and 90.81% and 95.25%, respectively, on Kvasir-SEG, providing reliable technical support for clinical auxiliary diagnosis. Full article

Ubiquitous aerial sensing with unmanned aerial vehicles (UAVs) is becoming an essential component of AI-native perception systems, motivated by the trend toward edge deployment and potential integration with future sixth-generation (6G)-connected aerial networks. In this work, we focus on improving the perception-side accuracy and computational efficiency of small-object detection in UAV imagery. However, small object detection in high-altitude UAV imagery remains highly challenging due to the extremely low pixel occupancy of targets and the severe multi-scale interference introduced by complex backgrounds. To address these limitations, we propose a Multi-scale Attention Fusion Network (MAF-Net), an AI-native paradigm for real-time small object detection in UAV imagery. The proposed approach enhances small-target representation and robustness through three key designs. First, a density-adaptive anchor optimization strategy is developed by combining K-means++ clustering with an IoU-based distance metric, enabling anchors to better match scale variation under diverse object densities. Second, a multi-scale feature reinforcement module is introduced to strengthen fine-grained detail preservation by integrating shallow feature maps via skip connections and hierarchical aggregation. Third, a dual-path attention mechanism is employed to jointly model channel importance and spatial localization, improving discriminative feature calibration in cluttered aerial scenes. Extensive experiments on three public benchmarks (AI-TOD, DOTA, and RSOD) demonstrate that MAF-Net consistently outperforms the baseline detector, achieving mAP@0.5 gains of 14.1%, 11.28%, and 22.09%, respectively. These results confirm that MAF-Net provides an effective and deployment-friendly solution for robust small object detection, supporting real-time UAV-based inspection and AI-native ubiquitous aerial sensing applications. Full article

Accurate crack segmentation in civil infrastructure imagery remains challenging because of the prevalence of thin, low-contrast, and spatially discontinuous defects that often appear amid textured surfaces, shadows, and acquisition noise. Although Transformer-based models improve global context modeling, many existing solutions incur substantial computational and memory overhead, which limits their use in practical, resource-constrained inspection settings. In this work, we introduce an efficient hybrid segmentation architecture that combines a convolutional encoder for high-fidelity local representation with a lightweight Transformer bottleneck for global dependency modeling, followed by a progressive decoder that restores spatial resolution through multi-level skip-feature fusion. To better accommodate severe foreground sparsity and preserve fine crack structures, the framework is trained with a composite Dice–Binary Cross-Entropy objective and employs a tokenization strategy designed to preserve fine spatial details while enabling efficient global context modeling. We validate the proposed approach on four public benchmarks, demonstrating consistent improvements over representative convolutional, Transformer-based, and hybrid baselines, while ablation studies confirm the contribution of each design component. Finally, runtime profiling shows favorable latency and memory characteristics, supporting real-time or near real-time deployment on embedded and edge inspection platforms. Full article

With the acceleration of urbanization, public transit systems face prominent challenges, including insufficient passenger flow prediction accuracy and low scheduling efficiency. This study analyzes passenger flow variation patterns from both spatial and temporal dimensions, constructs spatiotemporal matrices, and employs matrix dimensionality reduction methods to extract key features. We propose a passenger flow prediction model based on GCN-AM-LSTM and a dynamic real-time intelligent scheduling strategy. For passenger flow prediction, the model first utilizes Graph Convolutional Networks (GCNs) to extract spatial features of the transit network, then employs Attention Mechanism-enhanced Long Short-Term Memory networks (AM-LSTM) to perform weighted extraction of temporal features, and finally integrates external factors such as weather conditions to generate prediction outputs. For scheduling optimization, a dynamic real-time scheduling mode is adopted: the foundational framework optimizes dynamic departure timetables using a multi-objective particle swarm optimization algorithm, which is then combined with real-time passenger flow data to adjust departure intervals at the route level and implement stop-skipping strategies at the station level. Validation was conducted using Xiamen BRT Line 1 as a case study. Experimental results demonstrate that the proposed GCN-AM-LSTM prediction model reduces Mean Absolute Error (MAE) by 14% and 22% compared to CNN and LSTM models, respectively, achieving significantly improved prediction accuracy. Regarding scheduling optimization, the number of departures decreased by 15.24%, passenger waiting time costs were reduced by 3.7%, and transit operating costs decreased by 3.19%, effectively balancing service quality and operational efficiency. Full article

Semantic segmentation of remote sensing imagery is crucial for applications such as land resource management and urban planning, yet it remains challenging due to low intra-class variation, ambiguous boundaries, and the coexistence of multi-scale geospatial features. To tackle these issues, we propose GS-USTNet, a novel architecture that enhances both feature representation and boundary recovery. First, we introduce a Global–Local Adaptive Convolution (GLAConv) module that dynamically fuses global contextual cues with local responses to generate content-aware convolutional weights, thereby improving feature discriminability. Second, we design a Skip-Guided Attention (SGA) mechanism that leverages spatial–channel joint attention to guide the decoder, effectively mitigating attention dispersion in complex scenes or under class imbalance and significantly sharpening object boundaries. Built upon the efficient USTNet framework, our model achieves substantial performance gains without compromising computational efficiency. Extensive experiments on benchmark datasets demonstrate that GS-USTNet achieves consistent improvements over the original USTNet, with gains of approximately 3.5% in overall accuracy and 6.0% in F1-score across datasets. Ablation studies further confirm the effectiveness of the proposed GLAConv and SGA modules. This work provides an efficient and robust approach for fine-grained semantic segmentation of high-resolution remote sensing imagery. Full article

Phase unwrapping in interferometric synthetic aperture radar (InSAR) aims to recover a continuous phase field from wrapped observations, which enable accurate topographic reconstruction and surface deformation measurements. With the recent advances in deep learning (DL), several DL-based unwrapping approaches have shown promising performance. However, deep learning networks suffer from inconsistent feature representations between encoder and decoder stages. This leads to incompatible skip connections that provide limited benefits and even degrade reconstruction quality. To overcome this limitation, we propose ResUCTransNet that integrates residual learning with transformer-based feature modeling. The network employs a multi-scale residual backbone derived from Res_UNet to extract stable deep features. Then, to replace conventional skip connections, a channel transformer (CTrans) module is introduced that composed of channel-wise cross fusion transformer (CCT) and channel-wise cross attention (CCA). This design effectively reduces the semantic gap in different network stages, which allows adaptive integration of local CNN features and global transformer representations. Experiments on the public InSAR-DLPU dataset demonstrate that ResUCTransNet effectively reduces model complexity and achieves substantial improvements over existing deep learning models and classical unwrapping algorithms. Specifically, the proposed method attains the best performance in terms of RMSE and SSIM (RMSE = 1.6247, SSIM = 0.7741). Compared with the second-best model, Res_Unet (RMSE = 2.8409, SSIM = 0.7733), ResUCTransNet achieves an approximately 42.8% reduction in RMSE while maintaining nearly identical structural similarity. The proposed method provides higher reconstruction accuracy and better structural fidelity, while maintaining strong robustness and generalization in complex terrain or severe noise conditions. Full article

Background and objective: Depression is the most common mental health problem in women during pregnancy, associated with psychological, social, and medical factors characteristic of this stage. However, a lack of knowledge and limited attention to this condition can aggravate its consequences and restrict access to appropriate treatment. This research seeks to fill a gap in the scientific literature by exploring the association between eating habits and dietary diversity with depressive symptomatology in a group with high psychosocial vulnerability: pregnant adolescents. Material and methods: A cross-sectional analytical study was conducted with a sample of 344 pregnant adolescents attending prenatal care at the National Institute of Perinatology (INPer), a tertiary care center. Non-probabilistic sampling was used for recruitment. Relevant information was collected using a pre-validated structured questionnaire administered via interview. Depressive symptomatology was assessed using the Edinburgh Postnatal Depression Scale (EPDS), with a score of ≥12 considered indicative of a higher risk of depression. Eating habits were evaluated based on meal omission, activities performed during meals, and dietary diversity, comparing them with national recommendations. Food group consumption was assessed using a semi-quantitative Food Frequency Questionnaire (FFQ). Robust variance Poisson regression models were employed to evaluate the independent association between undesirable eating habits, inadequate food group intake, and the presence of depressive symptomatology. Results: A significant association was observed between the presence of depressive symptoms (EPDS ≥ 12) and the omission of main meals. Specifically, skipping breakfast was associated with a higher prevalence of EPDS scores ≥ 12 (aPR = 1.55; 95% CI: 1.10–2.19; p = 0.013). Similarly, adolescents who skipped lunch showed a higher prevalence of depressive symptomatology compared to those who did not (aPR = 2.02; 95% CI: 1.11–3.68; p = 0.022). Regarding food groups, only insufficient intake of milk and dairy products was significantly associated with the presence of depressive symptoms (aPR: 1.78; 95% CI: 1.16–2.73; p = 0.008). Conclusions: This cross-sectional study found a significant association between breakfast skipping, distraction while eating, and inadequate dairy intake with a higher prevalence of depressive symptoms in socially vulnerable pregnant adolescents treated at a tertiary care center. However, due to the study’s design, causality or the direction of the relationship cannot be established (it could be bidirectional), and it cannot be affirmed that modifying the diet will necessarily reduce depression. Furthermore, the results are not generalizable to all pregnant adolescents, and future research (longitudinal or interventional) is needed to better understand these associations before developing specific dietary interventions. Full article