Search Results (497)

Pine wilt disease (PWD) is a devastating biological forest disturbance, making its large-scale and high-precision remote sensing monitoring crucial for epidemic prevention and control. However, the performance of existing deep learning methods in high-resolution imagery is often limited by the confusion of spectral features among disparate ground objects and the complexity of forest boundaries. To address these challenges, this study proposes an innovative, end-to-end deep learning architecture termed MBA-Former. Built upon the robust Swin Transformer V2 backbone, the model systematically integrates two highly adaptable functional modules: (1) a front-end intelligent fusion module designed to adaptively fuse heterogeneous features, and (2) a back-end boundary refinement module that refines segmentation contours via dual-task learning. To train and evaluate the model, fine-grained manual annotations were first performed on Gaofen-2 satellite imagery acquired from multiple typical epidemic areas across northern and southern China. Information-enhanced datasets were constructed by fusing the original spectral bands, typical vegetation indices, and texture features. A comprehensive performance evaluation was then conducted, specifically targeting typical challenging scenarios characterized by complex ground object boundaries. The experimental results demonstrate that the Multi-modal Boundary-Aware Transformer (MBA-Former) significantly outperforms current state-of-the-art models. It achieved a mean Intersection over Union (mIoU) of 81.74%, an IoU of 77.58% for the most critical infected tree category, and a Boundary F1-Score of 78.62%. Compared to the best-performing baseline model, Swin-Unet, these three metrics exhibited notable improvements of 2.88%, 3.55%, and 4.46%, respectively. These findings convincingly demonstrate that MBA-Former provides a highly accurate and robust solution for the large-scale, automated remote sensing monitoring of forest diseases, offering immense value in preventing significant economic losses and preserving forest ecosystem integrity. Full article

Infrared small-target detection (IRSTD) has benefited from deep learning, yet most existing methods still rely on dense pixel-level annotations, which are costly and often unreliable for tiny and weak targets. Point supervision offers a more practical alternative, but current point-supervised methods usually construct pseudo-labels based on the distance between pixels and annotated points or cluster centers, which introduces spatial bias and may miss genuine target pixels away from these reference points. To address this issue, we propose GMVG-DIR, a point-supervised IRSTD framework that combines Gradient-Guided Minimum Variance Growth (GMVG) with Deep Iterative Refinement (DIR). GMVG first estimates target likelihood from gradient-guided aggregation of contour closure and edge responses and then converts it into structurally coherent pseudo-labels via the Minimum Variance Growth filter, without relying on distance cues. DIR further improves the pseudo-labels by incorporating reliable semantic guidance into an iterative refinement process, thereby reducing error propagation. By emphasizing structural consistency rather than spatial proximity, the proposed framework better preserves irregular target shapes and remains robust to point-label deviation. Extensive experiments on NUDT-SIRST, IRSTD-1k, and NUAA-SIRST show that GMVG-DIR improves pseudo-label fidelity and achieves competitive point-supervised performance across multiple dataset-backbone settings, especially in IoU and $P_d$. Full article

Background: With the increasing demand for non-surgical facial rejuvenation, injectable collagen biostimulators such as poly-L-lactic acid (PLLA), calcium hydroxyapatite (CaHA), polycaprolactone (PCL), poly-D,L-lactic acid (PDLLA) and powdered polydioxanone (PPDO) have become widely used by facial aesthetic practitioners. These agents stimulate neocollagenesis, providing gradual improvement in skin firmness, elasticity and facial contour with long-lasting results. While manufacturers emphasize the efficacy and favorable safety profile of these products, adverse events such as nodules, edema, inflammatory reactions and, in rare cases, granulomas have been reported. To date, no comprehensive systematic review has evaluated the proportion and nature of adverse effects associated with all major collagen biostimulators in facial aesthetic procedures. This study aims to synthesize current evidence on the proportion of adverse events linked to injectable collagen biostimulators. Methods: The systematic review will include clinical studies involving adults undergoing facial aesthetic procedures with PLLA, PDLLA, CaHA, PCL and PPDO that report adverse events during or after treatment. The search will be conducted in six main databases: CENTRAL, EMBASE, LILACS, PubMed, SCOPUS and Web of Science. No restrictions will be applied regarding language or publication date. The screening process will occur in two phases: first, two independent reviewers will assess titles and abstracts against the eligibility criteria; second, the same reviewers will conduct full-text evaluations. Data will be synthesized narratively, with a meta-analysis of proportions performed if appropriate. Additionally, sample characteristics, treatment protocols, study design and main findings will be reported. The risk of bias will be assessed independently by two reviewers using appropriate tools, based on the study design, with the support of artificial intelligence. PROSPERO registration number: CRD420251062785. Full article

Flow lines in aluminum alloy forgings are not merely post-deformation metallographic features; they are integrated indicators of material transport, microstructural evolution, defect susceptibility, and service performance. This review critically examines the mechanisms controlling flow-line evolution, with emphasis on constitutive flow behavior, dynamic recovery and recrystallization, second-phase redistribution, friction, thermal gradients, and die/preform design. It then evaluates how abnormal flow paths promote key defects, including folding/laps, flow-through discontinuities, vortex-like instability, and exposed flow lines, and distinguishes well-established mechanisms from topics that still rely on indirect evidence. Particular attention is given to the effects of flow-line morphology on anisotropy, notch sensitivity, corrosion-assisted damage, and fatigue life in forged aluminum alloys. Current control strategies, including preform optimization, FE-based backward tracing, multiphysics defect indices, frictional heat management, and isothermal forging, are also assessed. The available literature shows that stable contour-following flow lines are essential for the simultaneous control of defect formation, microstructural homogeneity, and durability, while major research needs remain in in situ validation, quantitative defect criteria, and digitally closed-loop process control. This review is therefore framed as a critical narrative synthesis rather than a formal systematic review; emphasis is placed on forging-centered studies that directly relate flow-path evolution to defect formation, anisotropy, fatigue, and process optimization, while evidence transferred from adjacent processes is treated as mechanistic support rather than equivalent proof. Full article

Quantitative microstructure evaluation of sedimentary rock thin sections is essential for revealing reservoir flow mechanisms and assessing reservoir quality. However, traditional manual identification is inefficient and prone to subjectivity. Although current deep learning approaches have improved efficiency, most remain confined to single tasks and lack a pathway to translate image recognition into quantifiable geological parameters. Moreover, these methods struggle with cross-scale feature extraction and accurate grain boundary localization in complex textures. To overcome these limitations, this study proposes a three-stage automated analysis framework integrating intelligent lithology identification, sandstone grain segmentation, and quantitative analysis of fabric parameters. To address scale discrepancies in lithology discrimination, Rock-PLionNet integrates a Partial-to-Whole Context Fusion (PWC-Fusion) module and the Lion optimizer, which mitigates cross-scale feature inconsistencies and enables accurate screening of target sandstone samples. Subsequently, to correct boundary deviations caused by low contrast and grain adhesion, the PetroSAM-CRF strategy integrates polarization-aware enhancement with dense conditional random field (DenseCRF)-based probabilistic refinement to extract precise grain contours. Based on these outputs, the framework automatically calculates key fabric parameters, including grain size and roundness. Experiments on 3290 original multi-source thin-section images show that Rock-PLionNet achieves a classification accuracy of 96.57% on the test set. Furthermore, PetroSAM-CRF reduces segmentation bias observed in general-purpose models under complex texture conditions, enabling accurate parameter estimation with a roundness error of 2.83%. Overall, this study presents an intelligent workflow linking microscopic image recognition with quantitative analysis of geological fabric parameters, providing a practical pathway for digital petrographic evaluation in hydrocarbon exploration. Full article

Background: Age-related macular degeneration (AMD) is the primary cause of severe visual acuity loss in individuals over 60 with increasing prevalence. Currently, no effective treatments exist for geographic atrophy and macular scarring, highlighting the need for visual rehabilitation in these patients. Microperimetry offers functional assessment at any AMD stage and employs fixation training to help patients utilize the most effective retinal areas for vision. Methods: A prospective study involving 25 patients (50 eyes) aged 67 to 90. The MAIA II microperimeter assessed scotoma size and location, retinal sensitivity, macular integrity, fixation parameters (P1, P2, 63%BCEA, 95%BCEA), fixation stability, and preferred retinal locus. Quality of life was evaluated using the National Eye Institute Visual Function Questionnaire (NEI-VFQ-25). A subgroup with inactive AMD-related macular changes, either bilateral geographic atrophy (13 patients, 26 eyes) or bilateral scarring (12 patients, 24 eyes), was identified, all exhibiting bilateral absolute central scotomas of at least 2 degrees. Each patient completed 10 fixation training sessions with a microperimeter, training the eye with better acuity weekly. One-week post-training, a functional assessment was performed on both trained and untrained eyes. Results: Fixation training significantly improved best corrected visual acuity (BCVA) in trained eyes (mean change −0.14 logMAR, p < 0.001, large effect size) and also in fellow untrained eyes (−0.16 logMAR, p < 0.001). BNVA improved from 2.25 to 1.86 in trained eyes (p < 0.001) and from 2.96 to 2.76 in untrained eyes (p = 0.004). Fixation stability parameters improved significantly, including increases in P1 and P2 and reductions in Bivariate Contour Ellipse Area (BCEA). Quality of life measured using the NEI-VFQ-25 questionnaire improved significantly in 9 of 11 domains. Conclusions: Microperimetry may be a valuable tool for assessing visual function in AMD patients. Fixation training with the MAIA II microperimeter is both safe and effective for vision rehabilitation in those with geographic atrophy and macular scarring. Full article

Outdoor ecological practice is essential for cultivating ecological literacy; however, there is currently a relative lack of comprehensive outdoor practical teaching case designs for class-based teaching. This study describes the design of an experimental teaching case for ecological education involving UAV-based field data collection. For the scheme, we selected the Xinhui Tangerine Peel Germplasm Resources Conservation Center in Jiangmen City, Guangdong Province as the study area, utilizing the DJI Phantom 4 RTK drone, which serves as the equipment for experimental teaching. The experiment is structured into three phases: indoor preparation, field execution, and data processing. Students from four groups collaboratively conducted aerial surveys across 24 partitioned plots, with flight altitudes stratified between groups to ensure safety and data integrity. (1) In the indoor preparation phase, appropriate single-flight operational units were defined. QGIS software (version 3.26.2) was employed for zonal mission planning, and suitable flight altitudes were estimated using contour data. (2) Field experiment phase. This involved conducting a comprehensive survey of the on-site environment, selecting suitable takeoff and landing points, dividing students into teams to carry out UAV-image-acquisition tasks, and assigning different altitudes for flight routes among the teams. (3) After the fieldwork, students processed imagery using Agisoft Metashape (version 2.0.1) to generate orthomosaics and digital surface models, and engaged in ecological interpretation of the results. The experimental design ensured orderly execution, complete data coverage, and active student participation. The results indicate the approach effectively enhanced students’ UAV operational skills, outdoor problem-solving abilities, and teamwork capabilities, while deepening their ecological understanding through real-world inquiry. This case provides a replicable model for integrating UAV technology into ecological education, contributing to the transformation of ecological awareness into actionable practice. Full article

Long-fiber thermoplastic (LFT) materials are a versatile category of composite materials that can be directly compounded (LFT-D) in twin screw extruders and compression molded. Originating in the automotive sector, the LFT-D process is becoming increasingly attractive for other industries where low cycle times, lightweight performance and recyclability are required. The purpose of this work is to summarize mechanical properties and findings from the investigations into LFT-D process–microstructure–property relationships and present a design of experiments (DoE) study based on the current state of the art. Primary parameters from LFT-D compounding, screw speed, fiber roving amount and polymer throughput m_p are chosen as DoE factors. Polyamide 6 (PA6) is reinforced with a glass fiber (GF) mass fraction w_f between w_f = 20% and w_f = 60%. Tensile, flexural and impact properties are chosen as DoE output parameters, characterized and discussed in relation to the state of the art. The unique microstructure of LFT-D materials, especially the existence of a charge and flow area as well as the fiber migration, is considered in the discussion. All mechanical properties characterized have a linear relation to w_f. This study demonstrates the interactive relationship between the main factors and w_f, which significantly influences the mechanical properties. This dependence of w_f on the DoE factors is accounted for in advanced response contour plots proposed in this work. Parameter recommendations for the screw speed are reported by ranges of w_f and polymer throughput for the goal of maximum mechanical properties or low coefficient of variations. At w_f < 30% a low screw speed is recommended to improve most mechanical properties as well as the coefficient of variation. Full article

Artificial intelligence (AI) is increasingly reshaping esthetic and reconstructive plastic surgery by improving measurement accuracy, treatment planning, and prediction of surgical outcomes. This article provides a scientific overview of current AI applications, including automated image analysis, machine-learning-based outcome forecasting, and generative models for preoperative simulation. AI-driven three-dimensional morphometrics allow precise, reproducible quantification of facial and body structures, supporting more objective assessments of symmetry, proportion, and contour. Predictive algorithms trained on large clinical datasets can estimate postoperative results and complication risks with higher consistency than traditional subjective evaluation. Intraoperative AI tools, such as real-time image guidance and robotic assistance, show potential to increase procedural precision and reduce variability. Despite these advances, important limitations persist. Algorithmic bias, restricted data diversity, opaque model architectures, and unresolved ethical concerns regarding data privacy and esthetic standardization challenge widespread clinical adoption. Overall, AI offers a powerful framework for enhancing precision and reproducibility in esthetic surgery, but its safe and responsible integration will require rigorous validation, transparent methodology, and continued human oversight. Full article

Prehabilitation represents a proactive, multimodal strategy to enhance patient resilience prior to plastic and reconstructive surgery, building on the success of Enhanced Recovery After Surgery (ERAS) pathways. This narrative review synthesizes the conceptual framework of prehabilitation—encompassing exercise training, nutritional optimization, risk factor modification, and psychological preparation—and examines its current application within plastic surgery. While evidence from selected randomized trials and systematic reviews in orthopedic and colorectal surgery suggests potential reductions in complications (often in the range of 20–40% in higher-risk populations), the results remain heterogeneous and context-dependent. To date, there have been no randomized controlled trials on plastic surgery, despite unique patient populations facing modifiable risks, including smoking, obesity, and malnutrition. This review proposes a risk-stratified prehabilitation framework tailored to key plastic surgery domains: breast reconstruction, head-and-neck microsurgery, post-bariatric body contouring, and major esthetic procedures. Practical implementation strategies address timelines, multidisciplinary teams, and digital delivery tools. By positioning prehabilitation as a structured preoperative component within ERAS pathways, plastic surgeons may support better perioperative readiness, potentially influencing complications, recovery, and patient experience. This review proposes conceptual frameworks intended to guide structured evaluation and future clinical research in plastic surgery. Full article

Background/Objectives: High-voltage, low-current electric shocks inflict superficial second-degree burns on the skin, accompanied by a vicious cycle of excessive oxidative stress and inflammation. As efficient treatment of such electrical burns remains a clinical challenge, we explored the efficacy of an injectable thermosensitive chitosan hydrogel engineered with an antioxidant agent (astaxanthin) and an anti-inflammatory agent (ibuprofen) for the treatment of high-voltage, low-current electrical burn injuries. Methods: The proposed CS/AST/IBU hydrogel was prepared and its thermosensitivity was characterized. Subsequently, the hydrogel was injected into the wounds of male Sprague–Dawley (SD) rats subjected to electrical burn injury (20 kV, 3 mA). Finally, a series of experiments were performed to elucidate the dynamics of wound healing and the mechanisms by which the hydrogel promotes wound repair. Results: The injectable hydrogel, through its thermally responsive gelation effect at 37 °C, adapts to the complex irregularities of the wound surface. This facilitates the release of astaxanthin and ibuprofen throughout the wound, which collectively diminish the formation of reactive oxygen species and MDA. Furthermore, it enhances the synthesis of endogenous antioxidants such as SOD, CAT, and GSH; encourages collagen deposition; stimulates the development of dermal appendages; and fosters neovascularization. It interrupts the deleterious cycle of oxidative stress and inflammation mediated by the NF-κB signaling pathway, thereby suppressing the expression of pro-inflammatory markers such as TNF-α, CD11b, and IL-1β while upregulating CD163, an anti-inflammatory receptor. Conclusions: The use of this multipronged, contour-adaptive hydrogel represents an effective strategy for complex wound management and demonstrates broad therapeutic potential for superficial second-degree electrical burns caused by high-voltage, low-current discharge. Full article

Current image segmentation methods often suffer from issues such as low accuracy, slow processing speed, and inadequate robustness when dealing with images with inhomogeneous noise and intensity. To resolve these issues, we propose a fast image segmentation algorithm based on a grayscale morphological edge differential fitting model. By utilizing morphological erosion and dilation operations, our model matches the differential image intensity inside and outside the contour. The grayscale morphological operator extracts local image information, which can effectively segment images with intensity inhomogeneity. Since the edge differential fitting function is replaced by the image grayscale morphology, it reduces the need for updates during level set evolution, thereby lowering the CPU runtime and complexity. Experimental results indicate that our model demonstrates fair robustness to noise interference and initial contours. Compared with active contour models (ACMs) and deep learning methods, our model exhibits superior segmentation accuracy while remaining robust to initial contours. Full article

Background/Objectives: The stimulator of interferon genes (STING) pathway plays a central role in innate immune signaling and represents an attractive therapeutic target for cancer immunotherapy. Amidobenzimidazole (ABZI) derivatives have emerged as promising non-nucleotide STING agonists with improved drug-like properties compared to cyclic dinucleotides. However, current ABZI compounds still exhibit limited oral bioavailability and cross-species potency discrepancies. In addition, potential systemic toxicity remains a concern, indicating the need for further structural optimization. Methods: In this study, a comprehensive computer-aided drug design strategy was employed to systematically investigate ABZI derivatives and identify novel STING agonists with enhanced activity and favorable pharmacokinetic profiles. A 3D quantitative structure–activity relationship (3D-QSAR) model was constructed using the Topomer CoMFA approach based on a dataset of 109 reported ABZI compounds. Guided by the contour map analysis, new chemical groups were introduced through a fragment growth method, generating a large virtual library. The library was subsequently filtered via molecular docking, molecular dynamics simulations, and MM-PBSA binding free energy calculations. Results: Among the newly designed ABZI compounds, five compounds displayed lower binding free energies than D59, with M13 and M44 showing reductions exceeding 6.7 kcal/mol. This work demonstrates the effectiveness of an integrated in silico design strategy for the discovery of novel STING agonists. Conclusions: The identified compounds represent promising candidates for subsequent experimental validation and may support the development of nitrogen heterocycle-based STING agonists for antitumor applications. Full article

In remote sensing object detection, current models typically employ feature extraction modules and attention mechanisms to tackle issues such as significant scale variations among targets, cluttered backgrounds, and the subtle characteristics of small objects. Nevertheless, existing feature extraction approaches often depend on convolution kernels with fixed sizes, which can blur the contours of large objects and provide inadequate feature representation for small objects. Moreover, many attention mechanisms simply combine spatial and channel attention, without fully considering the deep integration between spatial and channel features, consequently leading to high-dimensional features and considerable computational overhead. To overcome these shortcomings, this paper introduces a multi-scale object detection network with integrated spatial-channel collaborative attention for remote sensing images. This approach enhances feature perception and representation for multi-scale targets, particularly small targets, through the design of the cross-channel multi-scale feature extraction module (CC-MSFE). Furthermore, a new channel-spatial cross-attention mechanism (CSCA) is introduced, comprising the channel attention mechanism (CA), the spatial attention mechanism (SA), and the cross-attention fusion module (CAFM). This design fosters dynamic interaction and joint optimization across channel and spatial dimensions, thereby improving detection accuracy while effectively reducing computational cost. The efficacy of the proposed model is evaluated on three publicly available remote sensing datasets. Experimental results show that the model achieves a mAP of 78.1% on the DIOR dataset and of 90.6% on the HRRSD dataset, outperforming YOLOv11 by 0.7% and 1.4%, respectively. On the RSOD dataset, it attains a mAP of 96.5%, surpassing YOLOv8 by 2.1%. In addition, the proposed method maintains a notably lower parameter count and computational complexity compared to existing approaches, achieving an effective balance between detection accuracy and computational efficiency. Full article

Background: Current sepsis guidelines recommend the best supportive treatment for severe sepsis, but they are limited on the effectiveness of immunomodulatory therapies. Recent data suggest that IgM-enriched immunoglobulin preparations may decrease mortality, but the exact pathomechanism remains unknown. The present experimental study aims to test the hypothesis that IgM-enriched immunoglobulin may improve hemodynamics in E-coli-induced severe sepsis. Subjects and methods: Sepsis was induced in the E. coli bacteriemia (n = 8), E. coli-parallel Pentaglobin treatment (PR-PG; n = 8), and E. coli-delayed Pentaglobin treatment (D-PG; n = 8). Sepsis was induced in the sepsis, PR-PG, and D-PG groups by infusing 38 mL of an E. coli suspension (2.5 × 10⁵/mL) over 3 h. The PR-PG group received a 0.75 g/kg Pentaglobin bolus over 20 min concurrently with the start of E. coli infusion. The D-PG group was given a 0.67 g/kg Pentaglobin bolus one hour after starting E. coli, followed by a continuous infusion at 0.02 g/kg/h for 240 min. Hemodynamic parameters were monitored every 2 h using a pulse contour cardiac output monitoring technique (PiCCo™). Results: Heart rate increased in all groups to varying extents. Mean arterial pressure (MAP) remained stable in controls but declined in untreated sepsis. Both Pentaglobin-treated groups showed higher MAP than untreated septic animals. Mild cardiac index increases occurred in controls and untreated sepsis, whereas the treated groups maintained a consistently elevated CI after Pentaglobin administration. Systemic vascular resistance index (SVRI) transiently increased in controls before normalizing, while untreated septic animals experienced continuous SVRI decline. Treated animals showed an initial transient SVRI rise followed by a decline; yet, SVRI remained higher than in untreated sepsis. Conclusions: IgM-enriched immunoglobulin led to a slight stabilization of some hemodynamic parameters, probably due to the reduced extpnfiravasation of fluids into the interstitium and, hence, had an effect on preload. Full article