Search Results (24,723)

Lower-grade gliomas (LGGs) often follow a relatively protracted clinical course; however, a substantial proportion eventually undergo malignant transformation to high-grade, treatment-refractory disease. This process has traditionally been interpreted in the context of stepwise histopathologic progression and recurrent genetic alterations. Increasing evidence, however, suggests that malignant transformation is more accurately understood as an evolutionary process shaped by the interplay among epigenetic constraints, cell-state plasticity, and selective pressures. In this review, we examine current evidence supporting a model in which early LGGs, particularly isocitrate dehydrogenase (IDH)-mutant tumors, are initially maintained in relatively restricted cellular states by metabolically imposed epigenetic programs, but progressively escape these constraints under the cumulative influence of therapy, hypoxia, immune remodeling, and genomic instability. We summarize recent advances demonstrating that progression from lower-grade to high-grade disease is accompanied by cell-state transitions characterized by altered lineage identity, acquisition of stem-like features, increased proliferative capacity, and adaptation to cellular stress. We further discuss how these transitions are reinforced by microenvironmental evolution, including vascular remodeling, extracellular matrix reorganization, and changes in immune composition, thereby creating conditions that favor clonal expansion, invasion, and therapeutic resistance. Particular attention is given to longitudinal, single-cell, and spatially resolved studies, which collectively indicate that malignant transformation is not a discrete event but a continuous process of evolutionary selection and phenotypic reprogramming. Finally, we discuss the translational implications of this framework for early risk stratification, biomarker development, and mechanism-based therapeutic intervention. By reframing malignant transformation in LGGs as a process of cell-state escape under persistent selective pressure, this review aims to provide an integrated view of glioma progression and to highlight new opportunities for precision monitoring and treatment. Full article

Plant disease detection in natural environments is significantly challenged by variations in lesion scales and interference from complicated background clutter. Nevertheless, current models often remain limited in effectively capturing multi-scale features and mitigating background interference simultaneously. To tackle these challenges, we present TGL-YOLO, an improved detection network built on the YOLO11 framework. Methodologically, we introduce the Tri-Scale Dynamic Block (TSDBlock) to adaptively extract fine-grained features across highly variable lesion sizes. Furthermore, a Gated Pyramid Spatial Transformer (GPST) is designed to fuse cross-scale features and suppress background interference, while a Large Separable Pyramid Attention (LSPA) module expands the spatial receptive field to capture global context. Experimental results on two public datasets show that TGL-YOLO demonstrates improved performance over the YOLO11s baseline. On the PlantDoc dataset, it improves mAP50 and mAP50:95 by 4.7% and 3.7%, reaching 0.591 and 0.449, respectively. On the FieldPlant dataset, it reaches 0.793 and 0.608, yielding improvements of 2.3% and 1.9%. The proposed method demonstrates the capability to reduce missed detections and false positives caused by multi-scale lesions and environmental noise, providing a competitive and computationally viable solution for agricultural disease monitoring in natural environments. Full article

Empagliflozin (EMPA), a sodium-glucose cotransporter 2 inhibitor, has garnered attention for its cardiovascular benefits beyond glycemic control. Ferroptosis, a novel form of regulated cell death, contributes to the pathogenesis of diabetic cardiomyopathy (DCM). However, whether EMPA mitigates DCM by suppressing ferroptosis remains unclear. Here, Type 2 diabetic db/db mice were used to establish a DCM model and treated with EMPA (10 mg/kg/day) for 12 weeks. EMPA significantly improved cardiac function, reduced myocardial fibrosis, and attenuated ferroptosis, concomitant with upregulated silent information regulator 3 (SIRT3) expression. In the rat cardiomyocytes (H9c2 cells) exposed to high glucose and palmitic acid, EMPA treatment or SIRT3 overexpression alleviated oxidative stress, mitochondrial dysfunction, and ferroptosis. Mechanistically, molecular docking, molecular dynamics simulation, cellular thermal shift assay and drug affinity responsive target stability assay confirmed that SIRT3 is the drug target of EMPA, stabilizing its protein levels and reducing acetylated p53 expression. Notably, SIRT3 silencing abolished EMPA’s beneficial effects on oxidative stress and ferroptosis. Our findings demonstrate that EMPA exerts cardioprotective effects by inhibiting oxidative stress and ferroptosis in cardiomyocytes, which is mediated by SIRT3. This study provides novel insights into the mechanisms underlying EMPA’s therapeutic effects in DCM. Full article

Bearing fault diagnosis is an important task for condition monitoring and predictive maintenance of rotating machinery. Nevertheless, many existing deep learning-based methods have difficulty in jointly modeling multi-scale fault characteristics, adaptively highlighting informative features, and maintaining robustness under noisy measurement conditions. To address these issues, this study presents MDCAD-Net, a multi-dilated convolution attention denoising network that integrates multi-scale temporal feature extraction, attention-based feature refinement, and explicit noise suppression within an end-to-end learning framework. Parallel dilated convolutions with different dilation rates are employed to capture short-duration transient impulses as well as long-range periodic patterns in vibration signals. Channel-wise feature recalibration using squeeze-and-excitation networks and spatial-temporal attention via a convolutional block attention module are combined to enhance informative representations. In addition, a denoising block with gated attention and residual connections is introduced to reduce noise interference while retaining fault-related signal components. Experiments conducted on the Case Western Reserve University bearing dataset show that the proposed method achieves a classification accuracy of 98.93% and yields competitive performance compared with several commonly used deep learning models. Ablation studies and feature visualization results further illustrate the contributions of the individual components and the separability of the learned feature representations under noisy conditions. The results indicate the potential of the proposed framework for practical bearing fault diagnosis under noisy operating conditions. Full article

Corn processing waste represents an abundant, renewable, and low-cost lignocellulosic resource with considerable potential for environmental remediation applications. Large quantities of residues generated during corn processing, including cobs, husks, bran, and other by-products, are produced annually and can be utilized directly as native biomass or converted through thermochemical processes into hydrochars and biochars. This systematic review provides a comparative analysis of native corn processing biomass, hydrochars produced via hydrothermal carbonization, and biochars obtained through pyrolysis, with a focus on their potential as adsorbents for the removal of organic and inorganic pollutants from soil and water systems. Particular attention is given to the influence of thermochemical conversion processes on the physicochemical properties of the materials, including surface chemistry, porosity, functional groups, and structural characteristics, which govern adsorption mechanisms such as ion exchange, electrostatic interactions, surface complexation, hydrogen bonding, and π–π interactions. Furthermore, the advantages and limitations of each material type are discussed, together with key environmental and techno-economic considerations related to their production and practical application, including indicative production costs (USD per kg of adsorbent) and cost–performance relationships in terms of adsorption capacity. By linking biomass conversion processes, material properties, and adsorption performance, this review aims to provide a comprehensive overview of corn processing waste valorization and to support the development of sustainable adsorbent materials for soil and water remediation. A total of 36 studies were included in the qualitative synthesis following PRISMA guidelines. Full article

Structured radiology reporting can improve clinical decision support by standardizing clinical findings into hierarchical formats. However, thousands of questions in structured report templates about clinical findings are prohibitively time-consuming, which can limit clinical adoption. Furthermore, early medical VQA datasets primarily focused on free-text and independent question–answer pairs while a recent dataset, Rad-ReStruct, introduced a hierarchical VQA, but the accompanying model still relies heavily on flattened embedding representations and single-path text–image fusion mechanisms that inadequately handle complex hierarchical dependencies in responses. In this paper, we propose DPA-HiVQA (Dual-Path Cross-Attention for Hierarchical VQA), addressing these limitations through two key contributions: (1) multi-scale image embedding representing global semantic embeddings with patch-level spatial features from domain-specific BioViL encoder; (2) dual-path cross-attention mechanism enabling simultaneous holistic semantic understanding and fine-grained spatial reasoning. Evaluated on the Rad-ReStruct benchmark, the model substantially outperforms the established benchmark baseline with an overall F1-score and Level 3 F1-score improvement by 21.2% and 31.9%, respectively. The proposed model demonstrates that dual-path cross-attention architectures can effectively connect holistic semantic understanding and fine-grained spatial detail, paving the way for practical AI-assisted structured reporting systems that reduce radiologist burden while maintaining diagnostic accuracy. Full article

The production of fiber-reinforced polymer composites using 3D printing technology offers significant potential and opportunities for industrial applications. However, current dimensional limitations in 3D printing necessitate the use of joining techniques to obtain larger components. Recently, innovative strategies such as friction stir spot welding (FSSW) have attracted considerable attention for joining polymer composites due to their ability to produce strong joints with relatively low heat input (solid-state welding). Nevertheless, it is important to understand how the fibers present in fiber-reinforced polymer composites influence material flow and welding performance during the FSSW process. In this study, glass fiber-reinforced polylactic acid (PLA-GF) composite samples produced using a 3D printer were joined by means of FSSW. Five different tool rotational speeds (900, 1200, 1500, 1800, and 2100 rpm) and three different plunge rates (10, 20, and 30 mm/min) were employed during the welding process. Mechanical tests were performed on the welded joints to investigate the relationship between the welding parameters and the resulting mechanical properties. In addition, microstructural analyses were conducted to examine the formation of welding defects. The results revealed that three distinct zones were formed in the material after the FSSW process: the stir zone, mixed zone, and shoulder zone. Defects were observed in the mixed zone of the samples exhibiting relatively lower mechanical properties. The highest tensile force was achieved at a plunge rate of 20 mm/min and a rotational speed of 900 rpm. The highest bending force, on the other hand, was obtained at a plunge rate of 30 mm/min and a tool rotational speed of 2100 rpm. Full article

Improving the prediction accuracy of renewable energy power generation units is an important goal of the “source-storage integration” approach. However, the abundance of anomalous data and indistinct features in renewable energy station data seriously affects the health status prediction of these generator sets. To effectively enhance the performance of renewable energy generation prediction, this paper proposes an efficient data cleaning method for renewable energy stations based on anomaly detection and feature enhancement. First, anomaly detection is achieved by calculating a baseline power curve and partitioning data, utilizing the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Subsequently, considering that current models often learn low-frequency features while ignoring high-frequency features when processing time-series data, a data feature enhancement method is proposed. The proposed method integrates high-/low-frequency data decomposition, time–frequency domain conversion, and an improved attention mechanism to effectively enhance the high-frequency features of renewable energy station data, and reduces the RMSE of mainstream forecasting models significantly. Finally, using data from a renewable energy station in a region of China, the effectiveness and superiority of the anomaly detection and feature enhancement methods are analyzed. The results show that for renewable energy generation data, the proposed method reduces the RMSE of LSTM and Transformer models by 15.12%, 16.67% and 16.24%, 18.32% respectively, significantly improving prediction accuracy. Full article

Geopolymers have attracted increasing attention as sustainable binders, but their long-term durability in chloride-rich environments remains a critical concern. To elucidate the mechanistic role of calcined layered double hydroxides (CLDHs) in regulating the mechanical properties and chloride diffusion behavior of geopolymers, geopolymer pastes containing different CLDH contents were prepared. The compressive strength and chloride diffusion coefficient were determined, and the underlying mechanism was analyzed from the perspectives of geopolymerization degree, gel structure development, and pore structure evolution. The results indicate that the incorporation of CLDHs can promote geopolymerization, which may be associated with a nano-seeding effect, increasing the amount and degree of polymerization of the gel phases, refining the pore structure, and reducing pore connectivity. As a result, the compressive strength increases from 38.1 MPa to 49.2 MPa, while the chloride diffusion coefficient decreases by approximately 31.7% when the CLDH content reaches 6 wt.%. However, when the CLDH content exceeds this level, particle agglomeration limits effective gel growth, leading to microstructural deterioration and a weakened regulating effect. Full article

Medical image segmentation plays a crucial role in clinical diagnosis and treatment planning. However, existing segmentation frameworks frequently exhibit high computational complexity and often fail to retain fine-grained structural details—especially along intricate anatomical boundaries such as blood vessels and tumor margins. To overcome these limitations, we propose VMMedSAM-X, an efficient and computationally economical medical image segmentation framework that incorporates structured state space modeling into the Medical Segment Anything Model (MedSAM) architecture. The proposed method adopts a state-enhanced encoder that combines extended long short-term memory (xLSTM) with two-dimensional selective scanning (SS2D) and a dual-path cross-attention mechanism to enhance long-range dependency modeling while maintaining linear computational complexity. Experiments conducted on the $1024\times 1024$ ACDC cardiac MRI dataset show that the proposed encoder reduces floating-point operations from 369.44 G to 17.36 G and achieves a $2.4\times$ improvement in inference speed compared with the Vision Transformer (ViT)-based encoder. Additional evaluations on the SegTHOR and MSD-Lung datasets demonstrate consistent improvements in Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) metrics over MedSAM and Vision Mamba U-Net (VM-UNet) baselines. These results indicate that the proposed framework provides an effective and computationally efficient solution for high-resolution medical image segmentation. Full article

In modern intelligent food processing, the unpredictable variability in pomelo orientation on high-speed conveyors poses a significant challenge to automated grading and precision peeling operations. To address this, a deep learning-based method is proposed for the real-time detection of pomelo posture. Firstly, a pomelo posture dataset was constructed to support model training and validation. Secondly, to balance the extraction of posture features from uniform fruits with the low-power constraints of edge deployment, a domain-specific architectural optimization is presented. Building on the YOLOv8n framework, the proposed model synergistically integrates specialized modules. A lightweight GhostHGNetV2 foundation is utilized to significantly reduce computational redundancy while maintaining the resolution required to detect key anatomical landmarks. To overcome spatial confusion and capture multi-scale global appearance information, a multi-path coordinate attention (MPCA) module is introduced. Furthermore, the SlimNeck architecture and VoVGSCSP module streamline multi-scale feature fusion via one-time aggregation, effectively preventing computational bottlenecks. This design optimizes the computational efficiency of the model while maintaining detection accuracy. Experimental results demonstrate that compared with the baseline YOLOv8n model, the proposed method increased the mAP50 accuracy by 3.67% while reducing parameter count and computational load by 17.5% and 23.3%, respectively. Additionally, it achieved a processing speed of 19.3 FPS on the Jetson Orin Nano 6G edge platform. This research provides a critical technical foundation for the recognition of pomelo posture, enabling subsequent orientation rectification and fostering the development of streamlined, automated pomelo processing lines. Full article

Polyester resin biocomposites containing biochar have attracted attention for improving mechanical strength and thermal stability while promoting sustainability. The pyrolysis temperature of biochar and its proportion in the polymer matrix are key factors affecting biocomposite performance. This study examined how biochar pyrolysis temperatures (400, 600, 800 °C) and incorporation levels (10, 20, 30 wt.%) influence the physical, chemical, mechanical, flammability, and morphological properties of polyester-based biocomposites. The samples were analyzed for density, water absorption, FTIR, XRD, flexural and tensile strength, ignition time, structural degradation, volumetric loss, and SEM microstructure. Biocomposites with 30 wt.% biochar produced at 800 °C showed the best mechanical properties, with a flexural strength of 95.3 MPa and an elastic modulus of 4417.4 MPa, representing increases of 14.5% and 45.7%, respectively, over the control. FTIR and XRD results revealed decreased aliphatic groups and increased aromaticity at higher pyrolysis temperatures, improving interactions between the matrix and biochar. These biocomposites also demonstrated enhanced thermal stability, with an ignition time of approximately 963 s, delayed structural degradation, and reduced volumetric loss (~19.3%). Overall, pyrolysis temperature and biochar content significantly influence the structural, mechanical, and thermal properties of polyester biocomposites, showing that biochar serves as a sustainable, performance-enhancing component in thermoset polymer matrices. Full article

Ship target detection is a prerequisite for achieving automated monitoring in ship detection systems. To address the challenge of accurately detecting ship targets in complex water environments, this study proposes a ship target detection method based on an improved YOLOv11 framework. To enhance the model’s ability to perceive and fuse features across multiple scales and in complex backgrounds, an Iterative Attention Feature Fusion (iAFF) module and a Biformer module are integrated at the end of the backbone network. The iAFF module iteratively optimizes multi-scale features through a two-stage attention mechanism, effectively focusing on key target regions, thereby improving the model’s detection capability for small, medium-sized, and occluded ships. The Biformer module leverages its innovative Bi-level Routing Attention (BRA) mechanism to enhance the modeling of global semantic information while reducing computational complexity, mitigating false detections caused by occlusions among ship targets, and consequently improving detection precision. This study employs the Minimum Point Distance Intersection over Union (MPDIoU) loss function, which more comprehensively measures the similarity between predicted and ground-truth bounding boxes by optimizing the distances of their key geometric points, effectively enhancing the accuracy of bounding box regression. Experimental results show that the proposed model achieved 93.96% mAP, 92.93% recall, and 94.97% precision on a self-built ship dataset, surpassing mainstream detection algorithms including YOLOv11 in multiple metrics. The model has only 2.90 M parameters, achieving a good balance between accuracy and efficiency. This provides an accurate and efficient solution for intelligent ship supervision. Full article

Against the background of a warming and humidifying climate on the Qinghai–Tibet Plateau, increasing attention has been paid to the sustainability of water resources and ecosystems in the Qinghai Lake Basin. Investigating the characteristics of precipitation stable isotopes and moisture sources provides critical insights into the driving mechanisms of the regional hydrological cycle. In this study, precipitation samples collected at the Qinghai Lake Wetland Ecosystem National Observation and Research Station from June 2023 to October 2024 were analyzed for hydrogen (δ²H) and oxygen (δ¹⁸O) stable isotopes. The temporal variations of δ²H, δ¹⁸O, and deuterium excess (d-excess) were characterized, and their relationships with air temperature and precipitation amount were examined. In addition, a backward trajectory model was employed to identify the moisture sources of precipitation during the observation period. The results indicate that: (1) precipitation stable isotopes and d-excess exhibit pronounced seasonal variability, characterized by enrichment in summer and depletion in spring and autumn; (2) the Local Meteoric Water Line (LMWL) for the basin is defined as δ²H = 8.15δ¹⁸O + 38.71 (R² = 0.93), with both slope and intercept exceeding those of the Global Meteoric Water Line (GMWL); (3) precipitation isotopes show a discernible temperature effect but are jointly controlled by multiple moisture sources and meteorological factors; and (4) backward trajectory analysis combined with d-excess values reveals that precipitation moisture is primarily derived from westerly transport, while locally recycled moisture and continental air masses also exert significant influences. Overall, these findings reveal the multi-source driving mechanisms of the regional hydrological cycle and provide critical scientific support for understanding hydrological processes in alpine inland basins and their responses to future climate change, thereby contributing to the sustainable management of regional water resources. Full article

Artificial composite stones have recently attracted attention as multifunctional materials for construction and defense-related applications. In this study, a novel composite stone was developed using waste limestone as the primary mineral filler, combined with an unsaturated polyester resin matrix and reinforced with glass powder and chopped glass fibers. The influence of binder content and reinforcement type on physico-mechanical and microstructural behavior was investigated. Experimental characterization included water absorption, compressive strength, abrasion resistance, acid resistance, and optical microscopy. The results demonstrated that fine fillers improved matrix densification and reduced porosity, while short glass fiber reinforcement enhanced load-bearing capacity. Abrasion resistance and durability were found to depend on binder content and particle packing characteristics. Overall, the developed composite material exhibits promising mechanical performance, low water absorption, and improved durability, suggesting its potential as a candidate material for applications requiring environmental resistance, including potential use in defense-related camouflage applications. Full article