Search Results (1,640)

Background: Temporal lobe epilepsy (TLE), a prevalent refractory focal epilepsy frequently complicated by comorbid anxiety and depression, poses significant therapeutic challenges due to the inadequate efficacy of current antiepileptic drugs in seizure control. Carbon dots (CDs) demonstrate notable biological activities and represent a promising class of nanomedicines for TLE intervention. Methods: This study established an eco-friendly calcination protocol to synthesize a novel suspension of Crinis Carbonisatus-derived carbon dots (CC-CDs) as a candidate therapeutic for TLE. Results: In a TLE mouse model, the CC-CDs suspension significantly inhibited phosphorylation of the MAPK pathway (p-JNK, p-ERK, p-p38; p < 0.01, p < 0.05), leading to reduced levels of pro-inflammatory cytokines (IL-6, IL-1β, TNF-α; p < 0.01, p < 0.05), upregulation of TGF-β1 (p < 0.01, p < 0.05), and restoration of antioxidant enzyme activities (SOD, GSH, CAT; p < 0.01, p < 0.05). These modifications subsequently regulated the Glu/GABA balance, alleviating excitotoxicity (p < 0.05), attenuating neuronal damage and Nissl body loss in hippocampal CA1/CA3 regions, and improving cognitive function alongside reducing anxiety-like behaviors (p < 0.01, p < 0.05). In vitro, the CC-CDs suspension suppressed LPS-induced apoptosis in BV2 cells. Conclusions: The CC-CDs suspension ameliorates TLE by inhibiting MAPK signaling, thereby reducing neuroinflammation and oxidative stress, rectifying Glu/GABA imbalance, attenuating excitotoxicity, and ultimately improving behavioral deficits. These findings underscore the therapeutic potential of CC-CDs suspension for TLE treatment. Full article

Background/Objectives: Idiopathic intracranial hypertension (IIH) often features dural venous sinus stenosis; venous sinus stenting (VSS) improves venous outflow and intracranial pressure, but most stents are off-label, and few are engineered for intracranial venous anatomy. The aim was to synthesize animal models relevant to IIH/VSS, catalogue stents used clinically for VSS and summarize corresponding animal data, appraise current preclinical VSS research, and propose a pragmatic preclinical evaluation framework. Methods: We performed a targeted search (PubMed, Web of Science, Scopus; through to May 2025), dual-screened the records in Nested Knowledge, and extracted the model/device characteristics and outcomes as per the predefined criteria. Results: We identified 65 clinical VSS studies; most were retrospective and used off-label carotid/peripheral/biliary stents (Precise, Zilver, and Wallstent were the most frequent). Recent dedicated systems (River, BosStent) have limited animal evidence; VIVA has GLP porcine venous peripheral data demonstrating its patency, structural integrity, and benign healing outcomes. Rodent models reproduce obesity/androgen drivers with modest, sustained ICP elevation; large animal models show the technical feasibility of in sinus implantation, but no chronic focal venous stenosis model fully mirrors the IIH condition. Conclusions: Despite broad clinical uptake, the translational underpinnings of VSS in IIH remain incomplete: most devices lack intracranial venous-specific preclinical validation, and there is no existing animal model that recapitulates both IIH biology and focal sinus stenosis. Full article

Automatic detection of clue cells is crucial for rapid diagnosis of bacterial vaginosis (BV), but existing algorithms suffer from low sensitivity. This is because clue cells are highly similar to normal epithelial cells in terms of macroscopic size and shape. The key difference between clue cells and normal epithelial cells lies in the surface texture and edge morphology. To address this specific problem, we propose an clue cell detection algorithm named CA-YOLO. The contributions of our approach lie in two synergistic and custom-designed feature extraction modules: the context-aware module (CAM) extracts and captures bacterial distribution patterns on the surface of clue cells; and the shuffle global attention mechanism (SGAM) enhances cell edge features and suppresses irrelevant information. In addition, we integrate focal loss into the classification loss to alleviate the severe class imbalance problem inherent in clinical samples. Experimental results show that the proposed CA-YOLO achieves a sensitivity of 0.778, which is 9.2% higher than the baseline model, making the automated BV detection more reliable and feasible. Full article

This study explores the use of conditional Generative Adversarial Networks (cGANs) for simulating urban morphology, a domain where such models remain underutilized but have significant potential to generate realistic and controllable city patterns. To explore this potential, this research includes several contributions: a bespoke model architecture that integrates attention mechanisms with visual reasoning through a generalized conditioning layer. A novel mechanism that enables the steering of urban pattern generation through the use of statistical input distributions, the development of a novel and comprehensive training dataset, meticulously derived from open-source geospatial data of Berlin. Our model is trained using a hybrid loss function, combining adversarial, focal and L1 losses to ensure perceptual realism, address challenging fine-grained features, and enforce pixel-level accuracy. Model performance was assessed through a combination of qualitative visual analysis and quantitative evaluation using metrics such as Kullback–Leibler Divergence (KL Divergence), Structural Similarity Index (SSIM), and Dice Coefficient. The proposed approach has demonstrated effectiveness in generating realistic and spatially coherent urban patterns, with promising potential for controllability. In addition to showcasing its strengths, we also highlight the limitations and outline future directions for advancing future work. Full article

This study presents the development and validation of a high-efficiency optical interface designed for ultra-high-concentration photovoltaic (UHCPV) systems, with a focus on enabling clean and sustainable solar energy conversion. A Fresnel lens serves as the primary optical concentrator in a novel system architecture that integrates advanced optical design with system-level thermal management. The proposed modeling framework combines detailed 3D ray tracing with coupled thermal simulations to accurately predict key performance metrics, including optical concentration ratios, thermal loads, and component temperature distributions. Validation against theoretical and experimental benchmarks demonstrates high predictive accuracies within 1% for optical efficiency and 2.18% for thermal performance. The results identify critical thermal thresholds for long-term operational stability, such as limiting mirror temperatures to below 52 °C and photovoltaic cell temperatures to below 130 °C. The model achieves up to 89.08% optical efficiency, with concentration ratios ranging from 240 to 600 suns and corresponding focal spot temperatures between 37.2 °C and 61.7 °C. Experimental benchmarking confirmed reliable performance, with the measured results closely matching the simulations. These findings highlight the originality of the coupled optical–thermal approach and its applicability to concentrated photovoltaic design and deployment. This integrated design and analysis approach supports the development of scalable, clean photovoltaic technologies and provides actionable insights for real-world deployment of UHCPV systems with minimal environmental impact. Full article

Abnormal shutdown detection in oilfield pumping units presents significant challenges, including degraded image quality under low-light conditions, difficulty in detecting small or obscured targets, and limited capabilities for dynamic state perception. Previous approaches, such as traditional visual inspection and conventional image processing, often struggle with these limitations. To address these challenges, this study proposes an intelligent method integrating multi-scale feature enhancement and low-light image optimization. Specifically, a lightweight low-light enhancement framework is developed based on the Zero-DCE algorithm, improving the deep curve estimation network (DCE-Net) and non-reference loss functions through training on oilfield multi-exposure datasets. This significantly enhances brightness and detail retention in complex lighting conditions. The DAFE-Net detection model incorporates a four-level feature pyramid (P3–P6), channel-spatial attention mechanisms (CBAM), and Focal-EIoU loss to improve localization of small/occluded targets. Inter-frame difference algorithms further analyze motion states for robust “pump-off” determination. Experimental results on 5000 annotated images show the DAFE-Net achieves 93.9% mAP@50%, 96.5% recall, and 35 ms inference time, outperforming YOLOv11 and Faster R-CNN. Field tests confirm 93.9% accuracy under extreme conditions (e.g., strong illumination fluctuations and dust occlusion), demonstrating the method’s effectiveness in enabling intelligent monitoring across seven operational areas in the Changqing Oilfield while offering a scalable solution for real-time dynamic anomaly detection in industrial equipment monitoring. Full article

This production study examines how Japanese speakers mark information structure through an Edge-Reinforcing Strategy—a prosodic system that signals focus via boundary-based cues, independently of lexical pitch accent or phrasing constraints. While many Japanese dialects mark focus with F0 expansion and post-focal compression, such strategies are limited in utterances containing unaccented words and in systems without lexical accent or multiword Accentual Phrases. We hypothesize that when pitch cues are constrained, speakers rely on temporal and spectral cues aligned with prosodic edges, such as silence insertion, jaw opening, and duration asymmetry. Nine educated speakers of Japanese standard produced 48 genitive noun-phrases (e.g., umáno hizume ‘horse’s hoof’) under Broad and Narrow Focus. Acoustic measures included word duration, and F1-based estimates of jaw opening and silence insertions. Results showed that silence and duration were the strongest predictors of Narrow Focus, functioning additively and independently of pitch accent. F1-based measurements of jaw opening played a secondary, compensatory role, particularly in unaccented contexts. Cue-profile analysis revealed a functional hierarchy: silence and duration together were most effective, while jaw alone was less informative. These findings broaden current models of focus realization, showing that prosodic restructuring can emerge from gradient, edge-based cue integration. Full article

The cultivation of edible lilies is highly susceptible to weed infestation during its growth period, and the application of herbicides is often impractical, leading to the rampant growth of diverse weed species. Laser weeding, recognized as an efficient and precise method for field weed management, presents a novel solution to the weed challenges in lily fields. The accurate localization of weed regions and the optimal selection of laser targeting points are crucial technologies for successful laser weeding implementation. In this study, we propose a two-stage weed detection and localization method specifically designed for lily fields. In the first stage, we introduce an enhanced detection model named YOLO-Morse, aimed at identifying and removing lily plants. YOLO-Morse is built upon the YOLOv8 architecture and integrates the RCS-MAS backbone, the SPD-Conv spatial enhancement module, and an adaptive focal loss function (ATFL) to enhance detection accuracy in conditions characterized by sample imbalance and complex backgrounds. Experimental results indicate that YOLO-morse achieves a mean Average Precision (mAP) of 86%, reflecting a 3.2% improvement over the original YOLOv8, and facilitates stable identification of lily regions. Subsequently, a ResNet-based segmentation network is employed to conduct semantic segmentation on the detected lily targets. The segmented results are utilized to mask the original lily areas in the image, thereby generating weed-only images for the subsequent stage. In the second stage, the original RGB field images are first converted into weed-only images by removing lily regions; these weed-only images are then analyzed in the HSV color space combined with morphological processing to precisely extract green weed regions. The centroid of the weed coordinate set is automatically determined as the laser targeting point.The proposed system exhibits superior performance in weed detection, achieving a Precision, Recall, and F1-score of 94.97%, 90.00%, and 92.42%, respectively. The proposed two-stage approach significantly enhances multi-weed detection performance in complex environments, improving detection accuracy while maintaining operational efficiency and cost-effectiveness. This method proposes a precise, efficient, and intelligent laser weeding solution for weed management in lily fields. Although certain limitations remain, such as environmental lighting variation, leaf occlusion, and computational resource constraints, the method still exhibits significant potential for broader application in other high-value crops. Full article

Accurately identifying fractures from X-ray images is crucial for timely and appropriate medical treatment. However, existing models suffer from problems of false localization and poor accuracy. Therefore, this research proposes a medical X-ray fracture detection model with precise localization based on the You Only Look Once version 11 nano (YOLOv11n) model. Firstly, a data augmentation technique combining random rotation, translation, flipping and content recognition padding is designed to expand the public dataset, alleviating the overfitting risk due to scarce medical imaging data. Secondly, a Bone-Multi-Scale Convolutional Attention (Bone-MSCA) module, designed by combining multi-directional convolution, deformable convolution, edge enhancement and channel attention, is introduced into the backbone network. It can capture fracture area features, explore multi-scale features and enhance attention to spatial details. Finally, the Focal mechanism is combined with Smoothed Intersection over Union (Focal-SIoU) as the loss function to enhance sensitivity to small fracture areas by adjusting sample weights and optimizing direction perception. Experimental results show that the improved model trained with the expanded dataset outperforms other mainstream single-object detection models. Compared with YOLOv11n, its detection accuracy, recall rate, F1-Score and mean Average Precision 50 increase by 4.33%, 0.92%, 2.52% and 1.24%, respectively, reaching 93.56%, 86.29%, 89.78% and 92.88%. Visualization of the results verifies its high accuracy and positioning ability in medical X-ray fracture detection. Full article

With the advancement of UAV trajectory planning and sensing technologies, unmanned aerial vehicles (UAVs) are now capable of performing high-performance ground detection and search tasks. Mountainous regions, due to their complex terrain, have long been a focal point in the field of remote sensing. Effective UAV search tasks in such areas must consider not only horizontal coverage but also variations in detection range and angle caused by changes in elevation. Conventional algorithms typically require complete prior knowledge of the environment for trajectory optimization and often depend on scenario-specific policy models, limiting their generalizability. To address these challenges, this paper proposes a Feature-Aware Elite Imitation Multi-Agent Reinforcement Learning (FA-EIMARL) algorithm that leverages partial terrain information to construct a feature extraction network. This approach enables batch training across diverse terrains without the need for full environmental maps. In addition, an elite imitation mechanism has been proposed for convergence acceleration and task performance enhancement. Simulation results demonstrate that the proposed method achieves superior reward performance, convergence rate, and computational efficiency while maintaining strong adaptability to varying terrains. Full article

This paper proposes IFEM-YOLOv13, a high-precision underwater target detection method designed to address challenges such as image degradation, low contrast, and small target obscurity caused by light attenuation, scattering, and biofouling. Its core innovation is an end-to-end degradation-aware system featuring: (1) an Intelligent Feature Enhancement Module (IFEM) that employs learnable sharpening and pixel-level filtering for adaptive optical compensation, incorporating principles of symmetry in its multi-branch enhancement to balance color and structural recovery; (2) a degradation-aware Focal Loss incorporating dynamic gradient remapping and class balancing to mitigate sample imbalance through symmetry-preserving optimization; and (3) a cross-layer feature association mechanism for multi-scale contextual modeling that respects the inherent scale symmetry of natural objects. Evaluated on the J-EDI dataset, IFEM-YOLOv13 achieves 98.6% mAP@0.5 and 82.1% mAP@0.5:0.95, outperforming the baseline YOLOv13 by 0.7% and 3.0%, respectively. With only 2.5 M parameters and operating at 217 FPS, it surpasses methods including Faster R-CNN, YOLO variants, and RE-DETR. These results demonstrate its robust real-time detection capability for diverse underwater targets such as plastic debris, biofouled objects, and artificial structures, while effectively handling the symmetry-breaking distortions introduced by the underwater environment. Full article

Low-grade-serous ovarian carcinomas (LGSOC) are rare tumors characterized by a high recurrence rate and limited treatment options. Most LGSOC are estrogen receptor (ER)-positive and demonstrate alterations in the RAS/MAPK pathway. Avutometinib is a dual RAF/MEK clamp, whereas defactinib and VS-4718 are focal adhesion kinase (FAK) inhibitors. Fulvestrant is an ER antagonist/degrader. We assessed the preclinical efficacy of fulvestrant, avutometinib + VS-4718 (FAKi), and the triple combination in a chemotherapy/aromatase inhibitor-resistant LGSOC patient-derived tumor xenograft (PDX) model. Tissue obtained from a LGSOC patient wild-type for KRAS/NRAS/BRAF mutations in progression after chemotherapy/anastrozole was transplanted into female CB17/lcrHsd-Prkdc/SCID mice (PDX-OVA(K)250). The animals were treated with either saline/control, fulvestrant, avutometinib/FAKi, or the triple combination of avutometinib/FAKi/fulvestrant. Avutometinib and FAKi were given five-days on and two-days off through oral gavage. Fulvestrant was administered subcutaneously weekly. Mechanistic studies were performed ex vivo using Western blot assays. Animals treated with the triple combination demonstrated stronger tumor growth inhibition compared to all the other experimental groups including control/saline (p < 0.001), single-agent fulvestrant (p = 0.04 from day eight and onwards), and avutometinib/FAKi (p = 0.02 from day 18). Median survival for mice treated with saline/control was 29 days while mice in all other experimental groups were alive at day 60 (p < 0.0001). Treatment was well tolerated across all experimental treatments. By Western blot, exposure of OVA(K)250 to the triple combination demonstrated a decrease in phosphorylated MEK (p-MEK) and p-ERK levels. The addition of fulvestrant to avutometinib/FAKi is well tolerated in vivo and enhances the antitumor activity of avutometinib/FAKi in a LGSOC-PDX model with acquired resistance to chemotherapy/aromatase inhibitors. These results support the clinical evaluation of avutometinib/defactinib in combination with fulvestrant or an aromatase inhibitor in patients with recurrent LGSOC. Full article

Restoration practitioners specify goals that describe how the focal ecosystem will look or function upon reaching recovery goals. Goals may be influenced by the level of degradation, surrounding landscape conditions, societal choice, and a changing climate regime. The Society for Ecological Restoration’s International Principles and Standards for the Practice of Ecological Restoration recommend that goals should be informed by reference models of site conditions, which include the biotic composition, the environmental setting, and dynamic processes—had anthropogenic degradation not occurred—while accounting for anticipated changes. The SER principles address many aspects of ecological restoration, and practical steps include conceptualizing the structure and function of the natural system, measuring ecological integrity, and assessing potential climate change effects and adaptations. Models optimally reflect a variety of information sources and are based, where possible, on multiple reference sites of similar native ecological conditions. Using a project site from the Colorado National Monument in the USA, we illustrate a stepwise process to address these principles and standards by compiling and synthesizing map, text, and tabular information from reference materials and sites. By addressing these principles and systematically utilizing existing frameworks and locally available data, practitioners can streamline the establishment of reference models for ecological restoration. Full article

Peony flowers in Luoyang are renowned for their diverse varieties and substantial economic value. However, recognizing multiple peony varieties in natural field conditions remains challenging due to limited datasets and the shortcomings of existing detection models. High intra-class similarity among peony varieties, frequent occlusions, and imbalanced sample quality pose significant challenges to conventional approaches. To address these issues, we propose BCP-YOLOv5, an enhanced YOLOv5-based model designed for peony variety detection. The proposed model incorporates the Vision Transformer with Bi-Level Routing Attention (Biformer) to improve the detection accuracy of occluded targets. Inspired by Focal-EIoU, we redesign the loss function as Focal-CIoU to reduce the impact of low-quality samples and enhance bounding box localization. Additionally, Content-Aware Reassembly of Features (CARAFE) is employed to replace traditional upsampling, further improving performance. The experiments show that BCP-YOLOv5 improves precision by 3.4%, recall by 4.4%, and mAP@0.5 by 4.5% over baseline YOLOv5s. This work fills the gap in multi-variety peony detection and offers a practical, reproducible solution for intelligent agriculture. Full article

The anatomical and physiological similarities between human and canine eyes suggest that dogs may serve as a valuable model for studying retinopathies and developing future gene therapies. This study aims to evaluate the similarities and differences between humans with GUCY2D gene variants causing Leber’s congenital amaurosis (LCA) and a group of German Spitz dogs with hereditary retinopathy due to variants in the same gene, to assess their potential as an animal model for gene therapy research. A review of medical records, genetic testing, and ophthalmological examinations was conducted, including data such as age, genotyping, fundus photography, visual acuity (VA), fundus autofluorescence, optical coherence tomography (OCT), and electroretinography (ERG). Both groups presented subtle fundus abnormalities and severely reduced or absent ERG responses. In humans, OCT scans revealed decreased retinal thickness and structural alterations in the outer retinal layers. Similarly, the affected dogs exhibited focal neurosensory retinal detachments. The German Spitz model with GUCY2D variants shows significant parallels in retinal structure and functional impairment and may represent a promising candidate for preclinical gene therapy studies for LCA. Full article