Search Results (530)

Objectives: This in silico study sought to identify specific biomarkers for mild traumatic brain injury (mTBI) through the analysis of publicly available gene and miRNA databases, hypothesizing their influence on neuronal structure, axonal integrity, and regeneration. Methods: This study implemented a three-step process: (1) data searching for mTBI-related genes in Gene and MalaCard databases and literature review, (2) data analysis involved performing functional annotation through GO and KEGG, identifying hub genes using Cytoscape, mapping protein–protein interactions via DAVID and STRING, and predicting miRNA targets using miRSystem, miRWalk2.0, and mirDIP, and (3) RNA-sequencing analysis applied to the mTBI dataset GSE123336. Results: Eleven candidate hub genes associated with mTBI outcome were identified: APOE, S100B, GFAP, BDNF, AQP4, COMT, MBP, UCHL1, DRD2, ASIC1, and CACNA1A. Enrichment analysis linked these genes to neuron projection regeneration and synaptic plasticity. miRNAs linked to the mTBI candidate genes were hsa-miR-9-5p, hsa-miR-204-5p, hsa-miR-1908-5p, hsa-miR-16-5p, hsa-miR-10a-5p, has-miR-218-5p, has-miR-34a-5p, and has-miR-199b-5p. The RNA sequencing revealed 2664 differentially expressed miRNAs post-mTBI, with 17 showing significant changes at the time of injury and 48 h post-injury. Two miRNAs were positively correlated with direct head hits. Conclusions: Our bioinformatic analysis suggests that specific genes and miRNAs, particularly hsa-miR-10a-5p, may be involved in molecular pathways influencing mTBI outcomes. Our research may guide future mTBI diagnostics, emphasizing the need to measure and track these specific genes and miRNAs in diverse cohorts. Full article

Machine harvesting of cotton in Xinjiang has significantly improved harvesting efficiency; however, it has also resulted in a considerable increase in residual mulch content within the cotton, which has severely affected the quality and market value of cotton textiles. Existing mulch detection algorithms based on machine vision generally suffer from complex parameterization and insufficient real-time performance. To overcome these limitations, this study proposes a novel mulch detection algorithm, Mulch-YOLO, developed on the YOLOv11 framework. Specifically, an improved CBAM (Convolutional Block Attention Module) is incorporated into the BiFPN (Bidirectional Feature Pyramid Network) to achieve more effective fusion of multi-scale mulch features. To enhance the semantic representation of mulch features, a modified Content-Aware ReAssembly of Features module, CARAFE-Mulch (Content-Aware ReAssembly of Features), is designed to reorganize feature maps, resulting in stronger feature expressiveness compared with the original representations. Furthermore, the MobileOne module is optimized by integrating the DECA Dilated Efficient Channel Attention (Dilated Efficient Channel Attention) module, thereby reducing both the parameter count and computational load while improving detection efficiency in real time. To verify the effectiveness of the proposed approach, experiments were conducted on a real-world dataset containing 20,134 images of low-visual-saliency plastic mulch. The results indicate that Mulch-YOLO achieves a lightweight architecture and high detection accuracy. Compared with YOLOv11n, the proposed method improves mAP@0.5 by 4.7% and mAP@0.5:0.95 by 3.3%, with a 24% reduction in model parameters. Full article

Historic floors, including mosaics, stone slabs, and decorated pavements, are fragile elements that can be easily damaged during restoration works. Risks arise from falling tools, concentrated loads of scaffolding or equipment, and the repeated passage of workers. Traditional protection methods, such as plywood sheets, mats, multilayer systems, or modular plastic panels, have been applied in different sites but often present limitations in adaptability to irregular surfaces, in moisture control, and in long-term reversibility. This paper introduces an innovative approach developed within the 3D-EcoCore project. The proposed solution consists of a bio-inspired modular sandwich system manufactured by 3D printing with biodegradable polymers. Each module contains a Voronoi-inspired cellular core, shaped to match the geometry of the floor obtained from digital surveys, and an upper flat skin that provides a safe and resistant surface. The design ensures mechanical protection, adaptability to uneven pavements, and the possibility to integrate ventilation gaps, cable pathways, and monitoring systems. Beyond heritage interventions, the system also supports routine architectural maintenance by enabling safe, reversible protection during inspections and minor repairs. The solution is strictly temporary and non-substitutive, fully aligned with conservation principles of reversibility, recognizability, and minimal intervention. The Ninfeo Ponari in Cassino is presented as a guiding example, showing how multilevel knowledge and thematic mapping become essential inputs for the tailored design of the modules. The paper highlights both the technical innovation of the system and the methodological contribution of a knowledge-based design process, opening future perspectives for durability assessment, pilot installations, and the integration of artificial intelligence to optimise core configurations. Full article

Introduction: Axillary reverse mapping (ARM) aims to reduce the risk of breast cancer-related lymphedema (BCRL) by preserving and limiting dissection of arm-draining lymphatics. The ideal type of dye and the location of injection, which maximize the sparing of lymphatics and improve outcomes of immediate lymphatic reconstruction (ILR), remain under-studied. The current literature reports inconsistent visualization of lymphatics using blue dye alone, whereas indocyanine green (ICG) near-infrared (NIR) lymphography has shown improved rates. However, optimized dual-dye workflows integrating breast–plastics co-surgery are lacking. Methods: A retrospective review of patients who underwent ILR following ALND for breast cancer between June 2021 and June 2023 was conducted. Patients who underwent ARM using our dual-dye technique were included, utilizing intradermal injections of indocyanine green (ICG) into the wrist and isosulfan blue (ISB) into the upper arm. Axillary reverse mapping channels were categorized by the type of dye used to visualize. Dye injection site, number of lymphatic channels visualized, channel diameter (mm), time-to-first channel, coordinates relative to fixed landmarks, ILR configuration, and pathologic findings were reviewed. Mann–Whitney U tests were used to compare channel visualization rates between types of dye. Results: Of 26 patients, 21 underwent dual-dye mapping and were included. A total of 115 ARM channels were identified: 99 (86%) via ICG and 29 (25%) via ISB. A total of 64 lymphaticovenous anastomoses were performed (mean: 2.46 per patient). Both dyes were identified in the axilla in only 11.7% of patients. At the end of the study, the lymphedema rate was 12%. Conclusions: We developed a reproducible dual-dye ARM technique for ALND with planned ILR, reducing lymphedema risk while maintaining oncologic safety. Dual-dye mapping reveals that proximal and distal lymphatics exhibit both overlapping and divergent drainage to axillary nodes. ICG’s higher axillary detection rate may reflect true anatomical differences or dye properties. These findings support the need for individualized lymphatic mapping during breast cancer surgery to guide preservation techniques and reduce the risk of BCRL. Full article

Background and Objectives: Quantifying nasal proportions is central to facial plastic and reconstructive surgery, yet manual measurements are time-consuming and variable. We sought to develop a simple, reproducible deep learning pipeline that localizes the nose in a single frontal photograph and automatically computes the two-dimensional, photograph-derived apparent nasal index (aNI)—width/height × 100—enabling classification into five standard anthropometric categories. Materials and Methods: From CelebA we curated 29,998 high-quality near-frontal images (training 20,998; validation 5999; test 3001). Nose masks were manually annotated with the VGG Image Annotator and rasterized to binary masks. Ground-truth aNI was computed from the mask’s axis-aligned bounding box. A lightweight one-class YOLOv8n detector was trained to localize the nose; predicted aNI was computed from the detected bounding box. Performance was assessed on the held-out test set using detection coverage and mAP, agreement metrics between detector- and mask-based aNI (MAE, RMSE, R²; Bland–Altman), and five-class classification metrics (accuracy, macro-F1). Results: The detector returned at least one accepted nose box in 3000/3001 test images (99.97% coverage). Agreement with ground truth was strong: MAE 3.04 nasal index units (95% CI 2.95–3.14), RMSE 4.05, and R² 0.819. Bland–Altman analysis showed a small negative bias (−0.40, 95% CI −0.54 to −0.26) with limits of agreement −8.30 to 7.50 (95% CIs −8.54 to −8.05 and 7.25 to 7.74). After excluding out-of-range cases (<40.0), five-class classification on n = 2976 images achieved macro-F1 0.705 (95% CI 0.608–0.772) and 80.7% accuracy; errors were predominantly adjacent-class swaps, consistent with the small aNI error. Additional analyses confirmed strong ordinal agreement (weighted κ = 0.71 linear, 0.78 quadratic; Spearman ρ = 0.76) and near-perfect adjacent-class accuracy (0.999); performance remained stable when thresholds were shifted ±2 NI units and across sex and age subgroups. Conclusions: A compact detector can deliver near-universal nose localization and accurate automatic estimation of the nasal index from a single photograph, enabling reliable five-class categorization without manual measurements. The approach is fast, reproducible, and promising as a calibrated decision-support adjunct for surgical planning, outcomes tracking, and large-scale morphometric research. Full article

Computation modeling for large thermoplastic deformation of plastic solids is critical for industrial applications like non-invasive assessment of engineering components. While deep learning-based methods have emerged as promising alternatives to traditional numerical simulations, they often suffer from systematic errors caused by geometric mismatches between predicted and ground truth meshes. To overcome this limitation, we propose a novel boundary geometry-constrained neural framework that establishes direct point-wise mappings between spatial coordinates and full-field physical quantities within the deformed domain. The key contributions of this work are as follows: (1) a two-stage strategy that separates geometric prediction from physics-field resolution by constructing direct, point-wise mappings between coordinates and physical quantities, inherently avoiding errors from mesh misalignment; (2) a boundary-condition-aware encoding mechanism that ensures physical consistency under complex loading conditions; and (3) a fully mesh-free approach that operates on point clouds without structured discretization. Experimental results demonstrate that our method achieves a 36–98% improvement in prediction accuracy over deep learning baselines, offering a efficient alternative for high-fidelity simulation of large thermoplastic deformations. Full article

In engineering practice, soils will inevitably experience some rotation of principal stress directions. Recent experimental evidence has highlighted how principal stress axes rotation significantly impacts clay behavior. However, most existing constitutive models accounting for this effect are essentially designed for sand and may not be applicable to clays. This paper introduces an anisotropic bounding surface model to reproduce the response of clay to principal stress axes rotation. The model’s key innovation lies in its incorporation of a secondary mapping procedure in the deviatoric stress ratio plane, which utilizes a relocatable mapping center. This step is a complement to the conventional radial mapping procedure in the meridional plane, which utilizes a fixed mapping center. This constitutive enhancement facilitates the precise modeling of plastic deformation triggered by the rotation of principal stress axes, without introducing additional loading mechanisms or incremental stress–strain nonlinearity. The performance of the model is first evaluated under various conditions and then verified through comparisons between simulation results and experimental data. The results demonstrate the effectiveness of the model and underscore the necessity of incorporating stress rotation effects into the constitutive modeling of clay. Full article

The Contour Method is a well-established destructive technique for determining cross-sectional maps of residual stress in manufactured metallic components. The validity of the technique is dependent on linear elastic relaxation of residual strains across the plane of interest as the component is progressively cut into two parts across the plane of interest. However, for some welded components, redistribution of the residual stress field during the contour cutting step can result in local plastic deformation of the cut faces, giving errors in the measured residual stress map. This article describes a novel implementation of the Contour Method for welded components that can mitigate the risk of introducing such strain relief errors in the measured residual stress field. An orthogonal cutting sequence is applied where the component is first cut along the weld centreline (in the longitudinal direction) into two mirror-symmetric halves. Each one-half component is then cut transversely towards the weld line at mid-length. The out-of-plane deformation contours of the four cut faces are then measured, and the original residual stresses present on each cut section are determined using the multiple cut contour method. The efficacy of implementing the new contour measurement approach to fusion-line weldments is demonstrated both numerically and experimentally. Full article

The flat peach, an important commercial crop in the 143rd Regiment of Shihezi, China, is overwintered using plastic film mulching. Flat peaches are cultivated to boost the local temperate rural economy. The development of accurate maps of the spatial distribution of flat peach plantations is crucial for the intelligent management of economic orchards. This study evaluated the performance of pixel-based and object-based random forest algorithms for mapping flat peaches using the GF-1 image acquired during the overwintering period. A total of 45 variables, including spectral bands, vegetation indices, and texture, were used as input features. To assess the importance of different features on classification accuracy, the five different sets of variables (5, 15, 25, and 35 input variables and all 45 variables) were classified using pixel/object-based classification methods. Results of the feature optimization suggested that vegetation indices played a key role in the study, and the mean and variance of Gray-Level Co-occurrence Matrix (GLCM) texture features were important variables for distinguishing flat peach orchards. The object-based classification method was superior to the pixel-based classification method with statistically significant differences. The optimal performance was achieved by the object-based method using 25 input variables, with an overall accuracy of 94.47% and a Kappa coefficient of 0.9273. Furthermore, there were no statistically significant differences between the image-derived flat peach cultivated area and the statistical yearbook data. The result indicated that high-resolution images based on the overwintering period can successfully achieve the mapping of flat peach planting areas, which will provide a useful reference for temperate lands with similar agricultural management. Full article

Coastal debris is a global environmental issue that requires systematic monitoring strategies based on reliable statistical data. Recent advances in remote sensing and deep learning-based object detection have enabled the development of efficient coastal debris monitoring systems. In this study, two state-of-the-art object detection models—RT-DETR and YOLOv10—were applied to UAV-acquired images for coastal debris detection. Their false positive characteristics were analyzed to provide guidance on model selection under different coastal environmental conditions. Quantitative evaluation using mean average precision (mAP@0.5) showed comparable performance between the two models (RT-DETR: 0.945, YOLOv10: 0.957). However, bounding box label accuracy revealed a significant gap, with RT-DETR achieving 80.18% and YOLOv10 only 53.74%. Class-specific analysis indicated that both models failed to detect Metal and Glass and showed low accuracy for fragmented debris, while buoy-type objects with high structural integrity (Styrofoam Buoy, Plastic Buoy) were consistently identified. Error analysis further revealed that RT-DETR tended to overgeneralize by misclassifying untrained objects as similar classes, whereas YOLOv10 exhibited pronounced intra-class confusion in fragment-type objects. These findings demonstrate that mAP alone is insufficient to evaluate model performance in real-world coastal monitoring. Instead, model assessment should account for training data balance, coastal environmental characteristics, and UAV imaging conditions. Future studies should incorporate diverse coastal environments and apply dataset augmentation to establish statistically robust and standardized monitoring protocols for coastal debris. Full article

Background: Primary hepatocellular carcinoma (HCC) and liver metastases differ in origin, progression, and therapeutic response, yet a direct high-resolution spatial comparison of their tumor microenvironments (TMEs) within the liver has not previously been performed. Methods: We applied high-definition spatial transcriptomics to fresh-frozen specimens of one HCC and one liver metastasis (>16,000 genes per sample, >97% mapping rates) as a proof-of-principle two-specimen study, cross-validated in human proteomics and patients’ survival datasets. Transcriptional clustering revealed spatially distinct compartments, rare cell states, and pathway alterations, which were further compared against an independent systemic dataset. Results: HCC displayed an ordered lineage architecture, with transformed hepatocyte-like tumor cells broadly dispersed across the tissue and more differentiated hepatocyte-derived cells restricted to localized zones. By contrast, liver metastases showed two sharply compartmentalized domains: an invasion zone, where proliferative stem-like tumor cells occupied TAM-rich boundaries adjacent to hypoxia-adapted tumor-core cells, and a plasticity zone, which formed a heterogeneous niche of cancer–testis antigen–positive germline-like cells. Across both tumor types, we detected a conserved metabolic program of “porphyrin overdrive,” defined by reduced cytochrome P450 expression, enhanced oxidative phosphorylation gene expression, and upregulation of FLVCR1 and ALOX5, reflecting coordinated rewiring of heme and lipid metabolism. Conclusions: In this pilot study, HCC and liver metastases demonstrated fundamentally different spatial architectures, with metastases uniquely harboring a germline/neural-like plasticity hub. Despite these organizational contrasts, both tumor types converged on a shared program of metabolic rewiring, highlighting potential therapeutic targets that link local tumor niches to systemic host–tumor interactions. Full article

To address the challenges of multi-scale missed detections, false positives, and incomplete boundary segmentation in cotton field residual plastic film detection, this study proposes the RSE-YOLO-Seg model. First, a PKI module (adaptive receptive field) is integrated into the C3K2 block and combined with the SegNext attention mechanism (multi-scale convolutional kernels) to capture multi-scale residual film features. Second, RFCAConv replaces standard convolutional layers to differentially process regions and receptive fields of different sizes, and an Efficient-Head is designed to reduce parameters. Finally, an NM-IoU loss function is proposed to enhance small residual film detection and boundary segmentation. Experiments on a self-constructed dataset show that RSE-YOLO-Seg improves the object detection average precision (mAP50(B)) by 3% and mask segmentation average precision (mAP50(M)) by 2.7% compared with the baseline, with all module improvements being statistically significant (p < 0.05). Across four complex scenarios, it exhibits stronger robustness than mainstream models (YOLOv5n-seg, YOLOv8n-seg, YOLOv10n-seg, YOLO11n-seg), and achieves 17/38 FPS on Jetson Nano B01/Orin. Additionally, when combined with DeepSORT, compared with random image sampling, the mean error between predicted and actual residual film area decreases from 232.30 cm² to 142.00 cm², and the root mean square error (RMSE) drops from 251.53 cm² to 130.25 cm². This effectively mitigates pose-induced random errors in static images and significantly improves area estimation accuracy. Full article

The European wildcat exhibits considerable plasticity in its habitat requirements across its distribution, with differences increasing along a continental-scale latitudinal gradient. While wildcats often favor deciduous and mixed forests with dense cover and prey, studies show these preferences vary across their expansion. Range-wide conservation efforts will benefit from incorporating knowledge generated by robust regional ecological models. We used data from a large camera trap grid (n = 292 stations), spanning across eight wildcat-associated habitats, within its range in northern Greece, to understand the regional ecological parameters affecting the species’ habitat selection. We analyzed the data using single-season density-induced detection heterogeneity occupancy models (Royle–Nichols), considering 12 environmental and anthropogenic parameters. The global model’s GoF was high (p = 0.9). Elevation and percent forest cover were both significantly negatively related to wildcat occupancy (as derived from the modeled “relative abundance index” N). Likewise, there was a negative, but moderate, relation between distance to freshwater bodies and human settlements with wildcat occupancy. We used the model-average coefficients to generate a predictive map of wildcat relative abundance across northern Greece, which identified 47,930 km² of potential wildcat habitat. Assuming a range of densities between 0.05 and 0.3 ind/km² in areas with predicted low, medium, and high relative abundance, we speculate the putative wildcat population in northern Greece to be between 3535 and 7070 individuals. The findings, which vary from ecological models of the species in northern Europe, show the need for regional models and the importance of Greece, and the Balkan peninsula, for the species. Full article

As crucial transportation hubs and economic nodes, the underwater security and infrastructure maintenance of harbors are of paramount importance. Harbors are characterized by high vessel traffic and complex underwater environments, where traditional underwater inspection methods, such as diver operations, face challenges of low efficiency, high risk, and limited operational range. This paper introduces a collaborative survey and disposal system that integrates a deformable unmanned surface vehicle (USV) with a lightweight remotely operated vehicle (ROV). The USV is equipped with a side-scan sonar (SSS) and a multibeam echo sounder (MBES), enabling rapid, large-area searches and seabed topographic mapping. The ROV, equipped with an optical camera system, forward-looking sonar (FLS), and a manipulator, is tasked with conducting close-range, detailed observations to confirm and dispose of abnormal objects identified by the USV. Field trials were conducted at an island harbor in the South China Sea, where simulated underwater objects, including an iron drum, a plastic drum, and a rubber tire, were deployed. The results demonstrate that the USV-ROV collaborative system effectively meets the demands for underwater environmental measurement, object localization, identification, and disposal in complex harbor environments. The USV acquired high-resolution (0.5 m × 0.5 m) three-dimensional topographic data of the harbor, effectively revealing its topographical features. The SSS accurately localized and preliminarily identified all deployed simulated objects, revealing their acoustic characteristics. Repeated surveys revealed a maximum positioning deviation of 2.2 m. The lightweight ROV confirmed the status and location of the simulated objects using an optical camera and an underwater positioning system, with a maximum deviation of 3.2 m when compared to the SSS locations. The study highlights the limitations of using either vehicle alone. The USV survey could not precisely confirm the attributes of the objects, whereas a full-area search of 0.36 km² by the ROV alone would take approximately 20 h. In contrast, the USV-ROV collaborative model reduced the total time to detect all objects to 9 h, improving efficiency by 55%. This research offers an efficient, reliable, and economical practical solution for applications such as underwater security, topographic mapping, infrastructure inspection, and channel dredging in harbor environments. Full article

Bioplastics are gaining attention as eco-friendly alternatives to conventional plastics, with Polybutylene Adipate Terephthalate (PBAT) emerging as a promising biodegradable substitute for polyethylene (PE) in food packaging. Commercial PBAT is often blended with other plastics or bio-based fillers to improve mechanical properties and reduce costs, though these additives can influence its environmental footprint. Therefore, this study quantifies the environmental impacts of producing PBAT resin blends reinforced with common inorganic fillers and compares end-of-life (EoL) performance against PE. While prior studies have largely assessed virgin PBAT or PBAT/Polylactic Acid (PLA) systems, systematic LCA of commercial-style PBAT blends with inorganic fillers and screening LCA level for comparisons of composting vs. landfill remain limited. The contributions of this study are to: (i) map gate-to-gate environmental hotspots for PBAT-blend conversion, (ii) provide a screening gate-to-grave comparison of PBAT composting vs. PE landfill using ReCiPe 2016 and IPCC GWP100 methods, and (iii) discuss theoretical implications for material substitution in the context of EoL strategies. The results indicated that producing 1 kg of PBAT blend generated a single score impact of 921 mPt with Human Health and Resource categories contributing similarly, and a GWP of 8.64 kg CO₂-eq, dominated by mixing and drying processes. EoL screening showed PBAT composting offered clear advantages over landfilling PE, yielding −53.9 mPt and 11.35 kg CO₂-eq savings, effectively offsetting production emissions. In contrast, landfilling PE resulted in 288.8 mPt and 2.2 kg CO₂-eq emissions. Sensitivity analysis further demonstrated that a 30% reduction in electricity use could decrease impacts by up to 10%, underscoring the importance of energy efficiency improvements and renewable energy adoption for sustainable PBAT development. Full article