Search Results (14,132)

Obstructive sleep apnea (OSA) is a chronic disorder associated with serious health consequences, yet many cases remain undiagnosed due to limited access to standard diagnostic tools such as polysomnography. Recent advances in artificial intelligence (AI) have enabled the development of deep convolutional neural networks that analyze craniofacial radiographs, particularly lateral cephalograms, to detect anatomical risk factors for OSA. The goal of this approach is not to replace polysomnography but to identify individuals with a high suspicion of OSA at the primary care or dental level and to guide them toward timely and appropriate diagnostic evaluation. Current studies have demonstrated that AI can recognize patterns of oropharyngeal crowding and anatomical imbalance of the upper airway with high accuracy, often exceeding manual assessment. Furthermore, interpretability analyses suggest that AI focuses on clinically meaningful regions, including the tongue, mandible, and upper airway. Unexpected findings such as predictive signals from outside the airway also suggest AI may detect subtle features associated with age or obesity. Ultimately, integrating AI with cephalometric imaging may support early screening and referral for polysomnography, improving care pathways and reducing delays in OSA treatment. Full article

Background: Autosomal dominant intellectual developmental disorder 51 (MIM #617788) is caused by pathogenic variants in KMT5B, a histone methyltransferase essential for transcriptional repression and central nervous system development. The disorder manifests as a complex neurodevelopmental syndrome with variable neurological and systemic features. Methods: Two adolescents with nonsense KMT5B variants underwent detailed clinical, neuropsychological, and neuroimaging evaluations, including MRI and ¹⁸FDG PET/CT, analyzed with Statistical Parametric Mapping against matched controls. RNA sequencing was performed, and the literature was reviewed to assess genotype–phenotype correlations. Results: Both patients showed global developmental delay, progressing to autism spectrum disorder (ASD) and developmental coordination disorder (DCD), without intellectual disability (ID). The MRI was normal, but neuropsychological testing revealed executive function impairment, expressive language deficits, and behavioral disturbances. PET/CT consistently demonstrated cerebellar and temporal lobe hypometabolism, correlating with symptom severity. RNA sequencing identified shared dysregulated pathways, notably DDIT4 upregulation, linked to synaptic dysfunction and neuronal atrophy in animal models. Conclusions: The findings highlight cerebellar involvement in DCD and ASD, medial temporal lobe contribution to ASD and executive dysfunction, and DDIT4 as a possible molecular signature of KMT5B loss-of-function. An integrative multimodal approach refined genotype–phenotype correlations and revealed novel brain regions and pathways implicated in KMT5B-related disorders. Full article

The background of the article refers to the diagnosis of focal liver lesions (FLLs) through contrast-enhanced ultrasound (CEUS) based on the integration of spatial and temporal information. Traditional computer-aided diagnosis (CAD) systems predominantly rely on static images, which limits the characterization of lesion dynamics. This study aims to assess the effectiveness of Transformer-based architectures in enhancing CAD performance within the realm of liver pathology. The methodology involved a systematic comparison of deep learning models for the analysis of CEUS images and videos. For image-based classification, a Hybrid Transformer Neural Network (HTNN) was employed. It combines Vision Transformer (ViT) modules with lightweight convolutional features. For video-based tasks, we evaluated a custom spatio-temporal Convolutional Neural Network (CNN), a CNN with Long Short-Term Memory (LSTM), and a Video Vision Transformer (ViViT). The experimental results show that the HTNN achieved an outstanding accuracy of 97.77% in classifying various types of FLLs, although it required manual selection of the region of interest (ROI). The video-based models produced accuracies of 83%, 88%, and 88%, respectively, without the need for ROI selection. In conclusion, the findings indicate that Transformer-based models exhibit high accuracy in CEUS-based liver diagnosis. This study highlights the potential of attention mechanisms to identify subtle inter-class differences, thereby reducing the reliance on manual intervention. Full article

Gepirone, a selective 5-hydroxytryptamine (serotonin) 1A (5-HT_1A) receptor agonist, offers a promising strategy for treating mood and anxiety disorders. The therapeutic importance of 5-HT_1A modulation is well established, as these receptors regulate serotonergic neurotransmission both presynaptically, in the somatodendritic regions of raphe neurons, and postsynaptically, in structures including the hippocampus, neocortex, septum, amygdala, and hypothalamus. Gepirone exhibits a distinctive pharmacological profile, acting as a full agonist at presynaptic autoreceptors and a partial agonist at postsynaptic receptors, with high affinity for 5-HT_1A and much lower affinity for 5-HT_2A receptors. Its effects on serotonergic signaling are time-dependent. Acute administration suppresses serotonergic firing through autoreceptor activation, while chronic treatment induces autoreceptor desensitization, leading to enhanced 5-HT release in projection areas. This process is complemented by partial agonism at postsynaptic 5-HT_1A receptors, which further supports long-term neuromodulation. This article provides an integrated overview of gepirone’s mechanism of action, bridging receptor pharmacology, neurophysiological adaptations, and therapeutic implications. Particular emphasis is placed on the compound’s unique dual role in regulating serotonergic tone over time, a feature that differentiates it from other 5-HT_1A-targeting agents. By linking molecular mechanisms to clinical outcomes, we highlight gepirone’s potential advantages in efficacy, safety, and tolerability compared with conventional antidepressants. This comprehensive perspective underscores gepirone as a paradigmatic example of selective 5-HT_1A modulation and offers novel insights into the development of targeted treatments for depression and anxiety. Full article

Multi-modal image segmentation is a key task in various fields such as urban planning, infrastructure monitoring, and environmental analysis. However, it remains challenging due to complex scenes, varying object scales, and the integration of heterogeneous data sources (such as RGB, depth maps, and infrared). To address these challenges, we proposed a novel multi-modal segmentation framework, DyFuseNet, which features dynamic adaptive windows and cross-scale feature fusion capabilities. This framework consists of three key components: (1) Dynamic Window Module (DWM), which uses dynamic partitioning and continuous position bias to adaptively adjust window sizes, thereby improving the representation of irregular and fine-grained objects; (2) Scale Context Attention (SCA), a hierarchical mechanism that associates local details with global semantics in a coarse-to-fine manner, enhancing segmentation accuracy in low-texture or occluded regions; and (3) Hierarchical Adaptive Fusion Architecture (HAFA), which aligns and fuses features from multiple modalities through shallow synchronization and deep channel attention, effectively balancing complementarity and redundancy. Evaluated on benchmark datasets (such as ISPRS Vaihingen and Potsdam), DyFuseNet achieved state-of-the-art performance, with mean Intersection over Union (mIoU) scores of 80.40% and 80.85%, surpassing MFTransNet by 1.91% and 1.77%, respectively. The model also demonstrated strong robustness in challenging scenes (such as building edges and shadowed objects), achieving an average F1 score of 85% while maintaining high efficiency (26.19 GFLOPs, 30.09 FPS), making it suitable for real-time deployment. This work presents a practical, versatile, and computationally efficient solution for multi-modal image analysis, with potential applications beyond remote sensing, including smart monitoring, industrial inspection, and multi-source data fusion tasks. Full article

Not enough attention has been paid to the artistic approach of depicting human figures at the center of double-scroll patterns in Chinese Buddhist art. Originating from Greco-Roman culture, this motif entered China from the overland Silk Road around the late 5th century, evolving into two systems. The Hexi Corridor system, centered on Dunhuang, predominantly features lotus-born beings holding vines. The figural types evolved from lotus-born beings to celestial beings, bodhisattvas, and buddhas, with postures ranging from vine-holding to mudra-forming, lotus-tray-lifting, music-playing, and dancing, demonstrating a clear trajectory of development. The Northern Central Plains system, successively centered in Pingcheng, Qingzhou, and Yecheng, developed a relatively complete sequence only in buddha figures. The motif first spread through the Hexi Corridor before influencing the Northern Central Plains. It was adapted from its original Mediterranean context of mythological themes and funerary or temple use to illustrate Buddhist doctrines in China, absorbing elements of Han, Western Regions, and Central Asian cultures. By clarifying the motif’s origin, spread, evolution, and adaptation through systematic analysis of material evidence, this article reveals an intrinsic connection between Greco-Roman culture and Chinese Buddhist art, enriches the history of Sino-foreign cultural exchange, and reflects how Buddhism absorbed diverse cultural elements to achieve Sinicization. Full article

Single-object vision–language tracking has become an important research topic due to its potential in applications such as intelligent surveillance and autonomous driving. However, existing cross-modal alignment methods typically rely on contrastive learning and struggle to effectively address semantic ambiguity or the presence of multiple similar objects. This study aims to explore how to achieve more robust vision–language alignment under these challenging conditions, thereby achieving accurate object localization. To this end, we propose a text heatmap mapping (THM) module that enhances the spatial guidance of textual cues in tracking. The THM module integrates visual and language features and generates semantically aware heatmaps, enabling the tracker to focus on the most relevant regions while suppressing distractors. This framework, developed based on UVLTrack, combines a visual transformer with a pre-trained language encoder. The proposed method is evaluated on benchmark datasets such as OTB99, LaSOT, and TNL2K. The main contribution of this paper is the introduction of a novel spatial alignment mechanism for multimodal tracking and its effectiveness on various tracking benchmarks. Results demonstrate that the THM-based tracker improves robustness to semantic ambiguity and multi-instance interference, outperforming baseline frameworks. Full article

Schlieren imaging is a powerful optical sensing technique that captures flow-induced refractive index gradients, offering valuable visual data for analyzing complex fluid dynamics. However, the large volume and structural complexity of the data generated by this sensor pose significant challenges for extracting key physical insights and performing efficient reconstruction and temporal prediction. In this study, we propose a Spatial-Frequency-Scale variational autoencoder (SFS-VAE), a deep learning framework designed for the unsupervised feature decomposition of Schlieren sensor data. To address the limitations of traditional $\beta$-variational autoencoder ($\beta$-VAE) in capturing complex flow regions, the Progressive Frequency-enhanced Spatial Multi-scale Module (PFSM) is designed, which enhances the structures of different frequency bands through Fourier transform and multi-scale convolution; the Feature-Spatial Enhancement Module (FSEM) employs a gradient-driven spatial attention mechanism to extract key regional features. Experiments on flat plate film-cooled jet schlieren data show that SFS-VAE can effectively preserve the information of the mainstream region and more accurately capture the high-gradient features of the jet region, reducing the Root Mean Square Error (RMSE) by approximately 16.9% and increasing the Peak Signal-to-Noise Ratio (PSNR) by approximately 1.6 dB. Furthermore, when integrated with a Transformer for temporal prediction, the model exhibits significantly improved stability and accuracy in forecasting flow field evolution. Overall, the model’s physical interpretability and generalization ability make it a powerful new tool for advanced flow diagnostics through the robust analysis of Schlieren sensor data. Full article

Soil erosion is an increasingly severe problem and a global focus. As one of the countries facing relatively serious soil erosion, China encounters significant ecological challenges. This study focuses on the carbon sink benefits of comprehensive soil and water conservation management in the red soil erosion area of southern China, conducting an in-depth analysis using the Ziyang small watershed in Shangyou County, Jiangxi Province, as a typical case. Research methods involved constructing an integrated monitoring approach combining basic data, measured data, and remote sensing data. Changes in soil and vegetation carbon storage in the Ziyang small watershed across different years were determined by establishing a baseline scenario and applying inverse distance spatial interpolation, quadrat calculation, feature extraction, and screening. The results indicate that from 2002 to 2023, after 21 years of continuous implementation of various soil and water conservation measures under comprehensive watershed management, the carbon storage of the Ziyang small watershed increased significantly, yielding a net carbon sink of 54,537.28 tC. Tending and Management of Coniferous and Broad-leaved Mixed Forest, Low-efficiency Forest Improvement, and Thinning and Tending contributed substantially to the carbon sink, accounting for 72.72% collectively. Furthermore, the carbon sink capacity of the small watershed exhibited spatial variation influenced by management measures: areas with high carbon density were primarily concentrated within zones of Tending and Management of Coniferous and Broad-leaved Mixed Forest, while areas with low carbon density were mainly found within zones of Bamboo Forest Tending and Reclamation. The increase in watershed carbon storage was attributed to contributions from both vegetation and soil carbon pools. Comprehensive management of soil erosion demonstrates a significant carbon accumulation effect. The annual growth rate of vegetation carbon storage was higher than that of soil carbon storage, yet the proportion of soil carbon storage increased yearly. This study provides a theoretical basis and data foundation for the comprehensive management of soil and water conservation in small watersheds in the southern red soil erosion region of China and can offer technical and methodological support for other soil and water conservation carbon sink projects in this area. Full article

Cell towers play a key role in providing telecommunications infrastructure, especially in remote mountainous regions. This paper presents an approach to the efficient design of 42-metre-high cell towers intended to install high-power equipment in remote mountainous regions of the Carpathians (750 m above sea level). The region requires rapid deployment of many standardized towers adapted to geographical features. The main design challenges were the limited space available for the base, the impact of extreme weather conditions, and the need for a fast project implementation due to the critical importance of ensuring stable communication. Special methodological attention is given to how the transition between pyramidal and prismatic segments in cell tower shafts influences overall structural performance. The effect of this geometric boundary on structural efficiency and material usage has not been addressed in previous studies. A dedicated investigation shows that positioning the transition at a height of 33 m yields the best compromise between stiffness and weight, minimizing a generalized penalty function that accounts for both the horizontal displacement of the tower top and its total mass. Modal analysis confirms that the chosen configuration maintains a natural frequency of 1.68 Hz, ensuring a safe margin from resonance. For the final analysis of the behavior of towers with elements of different cross-sectional shapes, finite element modeling was used for a detailed numerical study of their structural and performance characteristics. This allowed us to assess the impact of geometric constraints of structures and take into account the most unfavorable combinations of static and dynamic loads. The study yields a concise rule of thumb for towers with compact foundations, namely that the pyramidal-to-prismatic transition should be placed at roughly 78–80% of the total tower height. Full article

Background: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by significant neurological plasticity in early childhood, where timely interventions like behavioral therapy, language training, and social skills development can mitigate symptoms. Contributions: We introduce a novel Multi-Atlas Guided Multi-View Contrast Learning (MAMVCL) framework for ASD classification, leveraging functional connectivity (FC) matrices from multiple brain atlases to enhance diagnostic accuracy. Methodology: The MAMVCL framework integrates imaging and phenotypic data through a population graph, where node features derive from imaging data, edge indices are based on similarity scoring matrices, and edge weights reflect phenotypic similarities. Graph convolution extracts global field-of-view features. Concurrently, a Target-aware attention aggregator processes FC matrices to capture high-order brain region dependencies, yielding local field-of-view features. To ensure consistency in subject characteristics, we employ a graph contrastive learning strategy that aligns global and local feature representations. Results: Experimental results on the ABIDE-I dataset demonstrate that our model achieves an accuracy of 85.71%, outperforming most existing methods and confirming its effectiveness. Implications: The proposed model demonstrates superior performance in ASD classification, highlighting the potential of multi-atlas and multi-view learning for improving diagnostic precision and supporting early intervention strategies. Full article

In recent years, China’s hilly and mountainous areas have faced widespread farmland abandonment. However, research on farmland abandonment and its driving mechanisms in hilly and mountainous regions is limited. This study proposes a transferable methodological framework that integrates Landsat data, Google Earth Engine, a time sliding-window algorithm, and the interpretable XGBoost–Shapley Additive explanation (SHAP) model. The time sliding-window algorithm is used to robustly detect long-term land cover changes across the entire study period. The SHAP quantifies the contributions of key drivers to farmland abandonment, providing transparent insights into the driving mechanisms. Applying this framework, we systematically analyzed the spatiotemporal evolution patterns and driving factors of farmland abandonment in Ji’an City, a typical city located in the hilly and mountainous areas of southern China and ultimately developed a farmland abandonment probability distribution map. The findings demonstrate the following. (1) Methodological validation showed that the random forest classifier achieved a mean overall accuracy (OA) of 91.05% (Kappa = 0.88) and the abandonment maps achieved OA of 91.58% (Kappa = 0.83). (2) Spatiotemporal analysis revealed that farmland area increased by 13.26% over 1990–2023, evolving through three stages: fluctuation (1990–2005), growth (2006–2015), and stability (2016–2023). The abandonment rate showed a long-term decreasing trend, peaking in 1998, whereas the abandoned area reached its minimum in 2007. From a spatial perspective, abandonment was more pronounced in mountainous and hilly regions of the study areas. (3) The XGBoost–SHAP model (R² > 0.85) identified key driving factors, including the potential crop yield, soil properties, mean annual precipitation, population density, and terrain features. By offering an interpretable and transferable monitoring framework, this study not only advances farmland abandonment research in complex terrains but also provides concrete policy implications. The results can guide targeted protection of high-risk abandonment zones, promote sustainable land-use planning, and support adaptive agricultural policies in hilly and mountainous regions. Full article

This study pioneers a fully remote sensing-based framework for mapping heatwave susceptibility, integrating the TabTransformer deep learning model with Particle Swarm Optimization (PSO) for robust hyperparameter tuning. The central question addressed is whether a fully remote sensing-driven, PSO-optimized TabTransformer can achieve accurate, scalable, and spatially detailed heatwave susceptibility mapping in data-scarce regions such as Central Asia. Utilizing ERA5-derived heatwave evidence and thirteen environmental and socio-economic predictors, the workflow produces high-resolution susceptibility maps spanning five Central Asian countries. Comparative analysis evidences that the PSO-optimized TabTransformer model outperforms the baseline across multiple metrics. On the test set, the optimized model achieved an RMSE of 0.123, MAE of 0.034, and R² of 0.938, outperforming the standalone TabTransformer (RMSE = 0.132, MAE = 0.038, R² = 0.93). Discriminative capacity also improved, with AUROC increasing from 0.933 to 0.940. The PSO-tuned model delivered faster convergence, lower final loss, and more stable accuracy during training and validation. Spatial outputs reveal heightened susceptibility in southern and southwestern sectors—Turkmenistan, Uzbekistan, southern Kazakhstan, and adjacent lowlands—with statistically significant improvements in spatial precision and class delineation confirmed by Chi-squared, Friedman, and Wilcoxon tests, all with congruent p-values of <0.0001. Feature importance analysis consistently identifies maximum temperature, frequency of hot days, and rainfall as dominant predictors. These advancements validate the potential of data-driven, deep learning approaches for reliable, scalable environmental hazard assessment, crucial for climate adaptation planning in vulnerable regions. Full article

To address the challenges of poor model generalization and suboptimal recognition accuracy stemming from limited and imbalanced sample sizes in tomato leaf disease identification, this study proposes a novel recognition strategy. This approach synergistically combines an enhanced image augmentation method based on generative adversarial networks with a lightweight deep learning model. Initially, an Atrous Spatial Pyramid Pooling (ASPP) module is integrated into the CycleGAN framework. This integration enhances the generator’s capacity to model multi-scale pathological lesion features, thereby significantly improving the diversity and realism of synthesized images. Subsequently, the Convolutional Block Attention Module (CBAM), incorporating both channel and spatial attention mechanisms, is embedded into the MobileNetV4 architecture. This enhancement boosts the model’s ability to focus on critical disease regions. Experimental results demonstrate that the proposed ASPP-CycleGAN significantly outperforms the original CycleGAN across multiple disease image generation tasks. Furthermore, the developed CBAM-MobileNetV4 model achieves a remarkable average recognition accuracy exceeding 97% for common tomato diseases, including early blight, late blight, and mosaic disease, representing a 1.86% improvement over the baseline MobileNetV4. The findings indicate that the proposed method offers exceptional data augmentation capabilities and classification performance under small-sample learning conditions, providing an effective technical foundation for the intelligent identification and control of tomato leaf diseases. Full article

Bolivian hemorrhagic fever (BHF) is a zoonotic disease caused by Mammarenavirus machupoense (MACV) featuring severe neurological and hemorrhagic symptoms and a high mortality rate. BHF is usually diagnosed by serological tests or real-time polymerase chain reaction (RT-PCR); these methods are often inaccessible in endemic regions due to a lack of laboratory infrastructure, creating a demand for sensitive and rapid equipment-free alternatives. Here, we present an isothermal method for MACV nucleic acid detection based on the Cas12a-based DETECTR system combined with recombinase polymerase amplification (RPA) in a single tube: the RT-RPA/DETECTR assay. We demonstrate the possibility of using more than one primer set for the simultaneous detection of MACV genetic variants containing multiple point mutations. The method was optimized and tested using specially developed virus-like armored particles containing the target sequence. The multiplex RT-RPA/DETECTR method achieved a limit of detection of approximately 5 × 10⁴ copies/ mL (80 aM) of armored particles. The method was validated using clinical samples spiked with virus-like particles. The assay proved to be selective and reliable in detecting certain nucleotide substitutions simultaneously. Full article