Search Results (618)

Periprosthetic joint infection (PJI) remains one of the most challenging complications of arthroplasty. Optimal antibiotic strategies and the role of multidisciplinary teams (MDT) are not fully defined. We retrospectively analyzed 86 PJI surgical procedures performed between 2017 and 2023 at a tertiary referral center. Clinical data, microbiology, surgical strategy (debridement, antibiotics, and implant retention -DAIR, one-stage, two-stage) and antibiotic regimens were collected. Outcomes were compared across antibiotic classes and treatment teams: orthopaedics alone, orthopaedics with MDT input, and a dedicated MDT (GRIP). Success was defined as infection-free survival without further surgery. Median patient age was 70 years, with high comorbidity and predominance of Gram-positive, monomicrobial infections. Rifampicin-based regimens were associated with higher cure rates than non-anti-biofilm therapy (OR 4.9, 95% CI 1.4–17.8). Flucloxacillin plus rifampicin achieved outcomes comparable to rifampicin–fluoroquinolone combinations. The strongest predictor of success was MDT involvement: in DAIR procedures, cure reached 100% with MDT versus 48% with orthopaedics alone (p = 0.025). Outcomes were similar between teams in one- and two-stage revisions. In this cohort, rifampicin-based therapy improved outcomes in staphylococcal PJI, and flucloxacillin was a valid alternative partner drug. Crucially, MDT management—particularly in DAIR—was associated with superior results. These findings highlight the value of structured multidisciplinary PJI care pathways alongside optimised antibiotic strategies. Full article

Background: Three-dimensional (3-D) printing paired with tissue-engineering strategies promises to overcome the volume, contour, and donor-site limitations of traditional breast reconstruction. Patient-specific, bioabsorbable constructs could enable one-stage procedures that better restore aesthetics and sensation. Methods: A narrative review was conducted following a targeted PubMed search (inception—April 2025) using combinations of “breast reconstruction,” “tissue engineering,” “3-D printing,” and “scaffold.” Pre-clinical and clinical studies describing polymer-based chambers or scaffolds for breast mound or nipple regeneration were eligible. Data was extracted on scaffold composition, animal/human model, follow-up, and volumetric or histological outcomes. Results: Forty-three publications met inclusion criteria: 35 pre-clinical, six early-phase clinical, and two device reports. The predominant strategy (68% of studies) combined a vascularized fat flap with a custom 3-D-printed chamber to guide adipose expansion. Poly-lactic acid, poly-glyceric acid, poly-lactic-co-glycolic acid, poly-4-hydroxybutyrate, polycarbonate, and polycaprolactone were the principal polymers investigated; only poly-4-hydroxybutyrate and poly-lactic acid have been tested for nipple scaffolds. Bioabsorbable devices supported up to 140% volume gain in large-animal models, but even the best human series (≤18 months) achieved sub-mastectomy volumes and reported high seroma rates. Mechanical testing showed elastic moduli (5–80 MPa) compatible with native breast tissue, yet long-term load-bearing data are scarce. Conclusions: Current evidence demonstrates biocompatibility and incremental adipose regeneration, but clinical translation is constrained by small sample sizes, incomplete resorption profiles, and regulatory uncertainty. Standardized large-animal protocols, head-to-head polymer comparisons, and early human feasibility trials with validated outcome measures are essential next steps. Nevertheless, the convergence of 3-D printing and tissue engineering represents a paradigm shift that could ultimately enable bespoke, single-stage breast reconstruction with superior aesthetic and functional outcomes. Full article

The rapid and accurate identification of pathogenic spores is essential for the early diagnosis of diseases in modern agriculture. Gray mold disease, caused by Botrytis cinerea, is a significant threat to several crops and is traditionally controlled using fungicides or, alternatively, by UV-C radiation. Classically, the determination of conidial germination percentage, a key indicator for assessing pathogen viability, has been a manual, time-consuming, and error-prone process. This study proposes an approach based on deep learning, using one-stage detectors to automate the detection and counting of germinated and non-germinated conidia in microscopy images. We trained and assessed the performance of three models under several metrics: YOLOv8, YOLOv11, and RetinaNET. The results show that these three architectures provide an efficient and accurate solution for the recognition of gray mold conidia viability. Selecting the best model, we performed the task of detecting and counting conidia for determining the germination percentage on samples treated with different UV-C radiation dosages. The results show that these deep-learning models achieved counting accuracies that closely matched those obtained with conventional manual methods, yet they delivered results far more rapidly. Because they operate continuously without fatigue or operator bias, these models begin to open possibilities, after widening field tests and datasets, for efficient and fully automated monitoring pipelines for disease management in the agro-industry. Full article

To address the issues of intense image noise interference and computational intensity faced by traditional vision-based weld tracking systems, we propose WeldLight, a lightweight and noise-resistant convolutional neural network for precise classification and positioning of welding seam feature points using single-line structured light vision. Our approach includes (1) an online data augmentation method to enhance training samples and improve noise adaptability; (2) a one-stage lightweight network for simultaneous positioning and classification; and (3) an attention module to filter features corrupted by intense noise, thereby improving stability. Experiments show that WeldLight achieves an F1-score of 0.9668 for seam classification on an adjusted test set, with mean absolute positioning errors of 1.639 pixels and 1.736 pixels on low-noise and high-noise test sets, respectively. With an inference time of 29.32 ms on a CPU platform, it meets real-time seam tracking requirements. Full article

The chemical composition of Rosa alba L. aromatic products extracted with liquified 1,1,1,2-tetrafluoroethane (freon R134a) has been evaluated in static and dynamic modes of extraction. The yield varies in the range 0.039–0.048% for the different variants. In order to reveal the chemical composition and aroma profile of the extracts, they were analyzed by means of gas chromatography-mass spectrometry (GC-MS) and gas chromatography with flame ionization detection (GC-FID). As a result of the analysis, more than 80 compounds with concentrations higher than 0.01% were identified and quantified in the extracts, representing 92.7, 88.4, and 88.0% of the total content. The study indicated that 2-phenyl ethanol (12.57–14.97%), geraniol (12.09–14.82%), nerol (5.90–6.39%), benzyl alcohol (3.63–5.34%), and citronellol (3.21–4.04%) were the main components of the aroma-bearing fraction. The solid phase consists mainly of nonadecane+nonadecene (15.21–16.85%), heneicosane (11.81–13.78%), and tricosane (2.46–2.96%). In addition, olfactory evaluation of the extracts was performed. The comprehensive assessment of the quantitative and qualitative characteristics of the extracts indicates that the static, one-stage mode is the most appropriate for the subcritical extraction of R. alba blossoms with freon R134a. Full article

One of the most interesting applications of crude glycerol (CG) is its use for biogas production via the anaerobic co-digestion (AcoD) process. The main aim of the current study was to provide a comprehensive review on the performance of the AcoD of CG mixed with various substrates. For this purpose, analyses were performed for studies available in the literature wherein one-stage experiments were conducted. To the best of the authors’ knowledge, the present study is the first one which demonstrates an analysis of the main parameters of CG and substrates (e.g., animal manure, sewage sludge, cattle manure and food waste) used for AcoD. Moreover, a detailed analysis of the impact of selected parameters on AcoD performance was carried out. It is demonstrated that the values of key parameters characterizing the CG used for AcoD were within wide ranges. This can be explained by the fact that the composition of CG depends on many factors; for instance, these include the source of oil used for biodiesel production, processing technology, the ratio of reactants, the type of catalyst and the procedure applied. Moreover, performing a literature review allowed us to demonstrate that adding CG to feedstock caused the enhancement of process performance compared to results obtained for mono-digestion. Additionally, it was shown that, in general, increasing the concentration of CG in feedstock led to improvement of the biogas yield; however, a potential inhibitory effect should be considered. Analysis of data available in the literature allowed us to indicate that for most of the experiments performed, a methane (CH₄) content in biogas higher than 60% was obtained for CG content in feedstock up to 8% v/v. In addition, it is demonstrated that in order to evaluate the performance of AcoD performed under thermophilic conditions, more studies are required. Finally, it should be pointed out that the present study provides considerable insight into the management of CG. Full article

To alleviate the performance degradation caused by domain shift, domain adaptive object detection (DAOD) has achieved compelling success in recent years. DAOD aims to improve the model’s detection performance on the target domain by reducing the distribution discrepancy between different domains. However, most existing methods are built on two-stage Faster RCNN, which is not suitable for real applications due to the detection efficiency. In this paper, we propose a novel Hierarchical Multi-scale Domain Adaptive (HMDA) method by integrating a simple but effective one-stage YOLO framework. HMDAYOLO mainly consists of the hierarchical backbone adaptation and the multi-scale head adaptation. The former performs hierarchical adaptation based on the differences in representational information of features at different depths of the backbone network, which promotes comprehensive distribution alignment and suppresses the negative transfer. The latter makes full use of the rich discriminative information in the feature maps to be detected for multi-scale adaptation. Additionally, it can reduce local instance divergence and ensure the model’s multi-scale detection capability. In this way, HMDA can improve the model’s generalization ability while ensuring its discriminating capability. We empirically verify the effectiveness of our method on four cross-domain object detection scenarios, comprising different domain shifts. Experimental results and analyses demonstrate that HMDA-YOLO can achieve competitive performance with real-time detection efficiency. Full article

Instance segmentation stands as a foundational technology in real-world applications such as autonomous driving, where the inherent trade-off between accuracy and computational efficiency remains a key barrier to practical deployment. To tackle this challenge, we propose a dual-path enhanced framework based on YOLO11l. In this framework, two improved models, YOLO-SA and YOLO-SD, are developed to enable high-performance lightweight instance segmentation. The core innovation lies in balancing precision and efficiency through targeted architectural advancements. For YOLO-SA, we embed the parameter-free SimAM attention mechanism into the C3k2 module, yielding a novel C3k2SA structure. This design leverages neural inhibition principles to dynamically enhance focus on critical regions (e.g., object contours and semantic key points) without adding to model complexity. For YOLO-SD, we replace standard backbone convolutions with lightweight SPD-Conv layers (featuring spatial awareness) and adopt DySample in place of nearest-neighbor interpolation in the upsampling path. This dual modification minimizes information loss during feature propagation while accelerating feature extraction, directly optimizing computational efficiency. Experimental validation on the Cityscapes dataset demonstrates the effectiveness of our approach: YOLO-SA increases mAP from 0.401 to 0.410 with negligible overhead; YOLO-SD achieves a slight mAP improvement over the baseline while reducing parameters by approximately 5.7% and computational cost by 1.06%. These results confirm that our dual-path enhancements effectively reconcile accuracy and efficiency, offering a practical, lightweight solution tailored for resource-constrained real-world scenarios. Full article

Radar target detection in sea clutter aims to effectively discern the presence of maritime targets within the current radar echo. The latest detection methods predominantly rely on sophisticated deep neural networks as their underlying design framework. One major obstacle to applying these radar target-detection methods in practical scenarios is the false alarm rate. The existing methods are mostly one-stage, where after feature extraction from radar echoes, a single prediction is made to determine whether or not it contains a sea surface target, resulting in a binary classification result. In this paper, we propose a detection model with the intention of increasing the credibility of the prediction results through a two-stage confirmation process, thereby advancing the practical application of neural-based radar target-detection algorithms. Experimental findings provide compelling evidence supporting the superiority of the proposed method in terms of detection performance and robustness under different conditions, surpassing existing techniques. In light of practical deployment considerations, future efforts should be directed towards investigating the generalization capabilities of the radar detection model specifically under low sea conditions. Full article

Background/Objectives: This study aimed to evaluate longitudinal changes in the subgingival microbiome over 12 months following non-surgical periodontal treatment, with or without adjunctive systemic antibiotics, in patients with stage III/IV periodontitis and peripheral artery disease. Materials: After randomizing patients to full-mouth mechanical debridement with/without adjunctive systemic antibiotics (PT1/PT2 group) or no subgingival debridement (control group), periodontal probing depths were measured, scores for ‘periodontal inflamed surface area’ (PISA) obtained, and subgingival plaque samples collected at baseline and during the 3-month and 12-month follow-up visits. Next-generation 16S DNA sequencing was used to characterize the microbiota of the samples for alpha/beta diversity and differentially abundant taxa. Results: Complete data was available for 76 patients. At 3 months, shallow (≤3.4 mm) or advanced (≥5.5 mm) pockets were significantly more, or less, prevalent in the PT1 than in the control group (p = 0.013/0.004). Microbiologically, the PT1 group was even more distinct, being associated with statistically significant changes over time (in alpha/beta diversity and differential taxa abundances) not seen in the PT2 and control groups. Conclusions: Although non-surgical treatment can reduce periodontal inflammation with or without antibiotics, subgingival microbial diversity can only be sustainably affected, and periodontitis-associated microbiota reduced, in the presence of adjunctive systemic antibiotics. Full article

Unmanned aerial vehicle (UAV) remote sensing has become an important tool for high-resolution tree species identification in orchards and forests. However, irregular spatial distribution, overlapping canopies, and small crown sizes still limit detection accuracy. To overcome these challenges, we propose YOLOv11-OAM, an enhanced one-stage object detection model based on YOLOv11. The model incorporates three key modules: omni-dimensional dynamic convolution (ODConv), adaptive spatial feature fusion (ASFF), and a multi-point distance IoU (MPDIoU) loss. A class-balanced augmentation strategy is also applied to mitigate category imbalance. We evaluated YOLOv11-OAM on UAV imagery of six fruit tree species—walnut, prune, apricot, pomegranate, saxaul, and cherry. The model achieved a mean Average Precision (mAP@0.5) of 93.1%, an 11.4% improvement over the YOLOv11 baseline. These results demonstrate that YOLOv11-OAM can accurately detect small and overlapping tree crowns in complex orchard environments, offering a reliable solution for precision agriculture and smart forestry applications. Full article

Remote sensing targets typically exhibit characteristics of gradual scale changes and diverse orientations. Most existing remote sensing detectors adapt to these differences by adding multi-level structures for feature fusion. However, this approach leads to incomplete coverage of the overall target by the extracted local features, resulting in the loss of critical directional information and an increase in computational complexity which affect the detector’s performance. To address this issue, this paper proposes a Rotation Target Detection Network based on Multi-kernel Interaction and Hierarchical Expansion (MIHE-Net) as a systematic solution. Specifically, we first refine scale modeling through the Multi-kernel Context Interaction (MCI) module and Hierarchical Expansion Attention (HEA) mechanism, achieving sufficient extraction of local features and global information for targets of different scales. Additionally, the Midpoint Offset Loss Function is employed to mitigate the impact of gradual scale changes on target direction perception, enabling precise regression for targets across various scales. We conducted comparative experiments on three commonly used remote sensing target datasets (DOTA, HRSC2016, and UCAS-AOD), with mean average precision (mAP) as the core evaluation metric. The mAP values of the method in this paper on the three datasets reached 81.72%, 92.43%, and 91.86% respectively, which were 0.65%, 1.93%, and 1.87% higher than those of the optimal method, significantly outperforming existing one-stage and two-stage detectors. Through multi-scale feature interaction and direction-aware optimization, MIHE-Net effectively addresses the challenges posed by scale gradation and direction diversity in remote sensing target detection, providing an efficient and feasible solution for high-precision remote sensing target detection. Full article

Background/Objectives: Phase II oncology trials often rely on single-arm designs to test $H_0:\pi ={\pi }_0$ versus $H_a:\pi >{\pi }_0$, especially when randomized trials are infeasible due to cost or disease rarity. Traditional approaches, such as the exact binomial test and Simon’s two-stage design, tend to be conservative, with actual Type I error rates falling below the nominal $\alpha$ due to the discreteness of the underlying binomial distribution. This study aims to develop a more efficient and flexible method that maintains accurate Type I error control in such settings. Methods: We propose a convolution-based method that combines the binomial distribution with a simulated normal variable to construct an unbiased estimator of $\pi$. This method is designed to precisely control the Type I error rate while enabling more efficient trial designs. We derive its theoretical properties and assess its performance against traditional exact tests in both one-stage and two-stage trial designs. Results: The proposed method results in more efficient designs with reduced sample sizes compared to standard approaches, without compromising the control of Type I error rates. We introduce a new two-stage design incorporating interim futility analysis and compare it with Simon’s design. Simulations and real-world examples demonstrate that the proposed approach can significantly lower trial cost and duration. Conclusions: This convolution-based approach offers a flexible and efficient alternative to traditional methods for early-phase oncology trial design. It addresses the conservativeness of existing designs and provides practical benefits in terms of resource use and study timelines. Full article

Object detection in aerial images can provide great support in traffic planning, national defense reconnaissance, hydrographic surveys, infrastructure construction, and other fields. Objects in aerial images are characterized by small pixel–area ratios, dense arrangements between objects, and arbitrary inclination angles. In response to these characteristics and problems, we improved the feature extraction network Inception-ResNet using the Fast Architecture Search (FAS) module and proposed a one-stage anchor-free rotation object detector. The structure of the object detector is simple and only consists of convolution layers, which reduces the number of model parameters. At the same time, the label sampling strategy in the training process is optimized to resolve the problem of insufficient sampling. Finally, a decoupled object detection head is used to separate the bounding box regression task from the object classification task. The experimental results show that the proposed method achieves mean average precision (mAP) of 82.6%, 79.5%, and 89.1% on the DOTA1.0, DOTA1.5, and HRSC2016 datasets, respectively, and the detection speed reaches 24.4 FPS, which can meet the needs of real-time detection. Full article

With the rapid advancement of UAV-based remote sensing and image recognition techniques, identifying environmental risk factors from aerial imagery has emerged as a focal point in intelligent inspection during the power transmission and distribution projects construction phase. The uneven spatial distribution of risk factors on construction sites, their weak texture signatures, and the inherently multi-scale nature of UAV imagery pose significant detection challenges. To address these issues, we propose a one-stage SRW-YOLO algorithm built upon the YOLOv11 framework. First, a P2-scale shallow feature detection layer is added to capture high-resolution fine details of small targets. Second, we integrate a reparameterized convolution based on channel shuffle (RCS) of a one-shot aggregation (RCS-OSA) module into the backbone and neck’s shallow layers, enhancing feature extraction while significantly reducing inference latency. Finally, a dynamic non-monotonic focusing mechanism WIoU v3 loss function is employed to reweigh low-quality annotations, thereby improving small-object localization accuracy. Experimental results demonstrate that SRW-YOLO achieves an overall precision of 80.6% and mAP of 79.1% on the State Grid dataset, and exhibits similarly superior performance on the VisDrone2019 dataset. Compared with other one-stage detectors, SRW-YOLO delivers markedly higher detection accuracy, offering critical technical support for multi-scale, heterogeneous environmental risk monitoring during the power transmission and distribution projects construction phase, and establishes the theoretical foundation for rapid and accurate inspection using UAV-based intelligent imaging. Full article