Search Results (16,286)

Mercury (Hg) has been extensively studied due to its impact on the environment and health, but its effects on wild mammal populations are still poorly known. Therefore, the use of biomonitors has gained importance. Our objective was to report and compare, for the first time, the amount of mercury in small mammals belonging to three trophic guilds and to provide an initial toxicology perspective in the Mezquital Valley, a critically polluted area of Central Mexico. We quantified total Hg from the hair and liver of a nectarivorous bat (Leptonycteris yerbabuenae), an insectivorous bat (Corynorhinus townsendii) and a granivorous mouse (Peromyscus melanophrys) using atomic absorption spectrometry during the dry and rainy seasons. We compared the mercury concentrations between seasons, species and matrices. In all species, the average mercury content was higher in hair than liver, and there was no correlation between matrices. There was no difference in mercury content among species. Hg concentrations in the livers of P. melanophrys and C. townsendii were lower during the dry season than the rainy season, suggesting a seasonal decline in mercury availability. All of the values detected were below the neurotoxicity threshold reported in small mammals (10 ppm); however, we propose constant monitoring of Hg in their environment and confirm the utility of these species as biomonitors. Full article

A new method based on the Recurrence Quantification Analysis (RQA) of the heart rate (HR) offers an objective, efficient alternative to traditional methods for Aerobic Threshold (AerT) identification that have practical limitations due to the complexity of equipment and interpretation. This study aims to validate the RQA-based method’s applicability across varied demographics, exercise protocols, and health status. Data from 123 cardiopulmonary exercise tests were analyzed, and participants were categorized into four groups: athletes, young athletes, obese individuals, and cardiac patients. Each participant’s AerT was assessed using both traditional ventilatory equivalent methods and the automatic RQA-based method. Ordinary Least Products (OLP) regression analysis revealed strong correlations (r > 0.77) between the RQA-based and traditional methods in both oxygen consumption (VO₂) and HR at the AerT. Mean percentage differences in HR were below 2.5%, and the Technical Error for HR at AerT was under 8%. The study validates the RQA-based method, directly applied to HR time series, as a reliable tool for the automatic detection of the AerT, demonstrating its accuracy across diverse age groups and fitness levels. These findings suggest a versatile, cost-effective, non-invasive, and objective tool for personalized exercise prescription and health risk stratification, thereby fulfilling the study’s goal of broadening the method’s applicability. Full article

Object detection technology holds significant promise in unmanned aerial vehicle (UAV) applications. However, traditional methods face challenges in detecting denser, smaller, and more complex targets within UAV aerial images. To address issues such as target occlusion and dense small objects, this paper proposes a multi-scale object detection algorithm based on YOLOv5s. A novel feature extraction module, DCNCSPELAN4, which combines CSPNet and ELAN, is introduced to enhance the receptive field of feature extraction while maintaining network efficiency. Additionally, a lightweight Vision Transformer module, the CloFormer Block, is integrated to provide the network with a global receptive field. Moreover, the algorithm incorporates a three-scale feature fusion (TFE) module and a scale sequence feature fusion (SSFF) module in the neck network to effectively leverage multi-scale spatial information across different feature maps. To address dense small objects, an additional small object detection head was added to the detection layer. The original large object detection head was removed to reduce computational load. The proposed algorithm has been evaluated through ablation experiments and compared with other state-of-the-art methods on the VisDrone2019 and AU-AIR datasets. The results demonstrate that our algorithm outperforms other baseline methods in terms of both accuracy and speed. Compared to the YOLOv5s baseline model, the enhanced algorithm achieves improvements of 12.4% and 8.4% in AP₅₀ and AP metrics, respectively, with only a marginal parameter increase of 0.3 M. These experiments validate the effectiveness of our algorithm for object detection in drone imagery. Full article

Background/Objectives: Appendiceal neuroendocrine neoplasms (ANENs) are usually found incidentally during histology examination after appendectomy for appendicitis. Due to their rarity in pediatric populations, there is no consensus on treatment or follow-up. The analysis of patients with ANENs of our and other studies will increase the understanding of this tumor. Methods: Pediatric patients with ANENs were uniformly managed at our center between 1998 and 2023. Patients’ presenting symptoms, surgery, tumor histology, post-surgical work-up, follow-up and outcome were analyzed. Results: Our report describes 17 patients with a diagnosis of ANEN after appendectomy. The median age was 14 years (range of 4–17 years). Tumors were located at the tip of the appendix in 58.8% of cases and only one had a diameter >1 cm. All were well-differentiated tumors with free resection margins. The submucosa was invaded in five cases, muscularis propria in eight and subserosa in four. Post-appendectomy work-up included tumor marker measurement, abdominal ultrasound and computed tomography or magnetic resonance imaging, chest X-ray and octreotide scintigraphy. No residual tumors or metastases were detected. Additional surgery was not necessary. Follow-up was carried out for a median duration of 6 years (range of 1–10 years). Only one patient was lost to follow-up and all other patients are alive without tumor recurrence. Conclusions: The tumor characteristics of our patients confirmed data from the literature. With the lack of a sufficient number of large prospective trials, it is important to add more information to confirm the benign nature and excellent outcome of this tumor, even without additional surgery. Consensus guidelines are needed for ANENs in pediatric populations. Full article

Background and Objectives: Breast cancer in patients with prior breast augmentation poses unique challenges for detection, diagnosis, and management. Mastectomy rates are increasing, and patients with prior augmentation often have a lower body mass index, making autologous techniques unsuitable. This study aims to assess the best reconstructive option in patients with a history of subglandular or dual-plane breast augmentation. Materials and methods: A prospective analysis was conducted on patients who underwent breast reconstruction after mastectomy. Patients with subglandular or dual-plane breast augmentation were included. Patients were divided into submuscular breast reconstruction (Group 2) or prepectoral breast reconstruction (Group 1) groups. Demographic and surgical data were collected. Results: A total of 47 patients were included, with 23 in Group 1 and 24 in Group 2. Complications occurred in 11 patients (23.4%), with significant differences between groups. The most common complication was seroma formation. Implant loss occurred in 4.3% of cases in Group 1, while no implant loss was observed in Group 2. Patient-reported satisfaction scores were similar between groups at 12 months postoperatively. Conclusions: Subpectoral breast reconstruction with a tissue expander seems a safer and effective technique for patients with prior breast augmentation. It resulted in fewer complications. This approach should be considered as an option for breast reconstruction after mastectomy in this cohort of patients. Full article

Non-Line of Sight (NLOS) imaging has gained attention for its ability to detect and reconstruct objects beyond the direct line of sight, using scattered light, with applications in surveillance and autonomous navigation. This paper presents a versatile framework for modeling the temporal distribution of photon detections in direct Time of Flight (dToF) Lidar NLOS systems. Our approach accurately accounts for key factors such as material reflectivity, object distance, and occlusion by utilizing a proof-of-principle simulation realized with the Unreal Engine. By generating likelihood distributions for photon detections over time, we propose a mechanism for the simulation of NLOS imaging data, facilitating the optimization of NLOS systems and the development of novel reconstruction algorithms. The framework allows for the analysis of individual components of photon return distributions, yielding results consistent with prior experimental data and providing insights into the effects of extended surfaces and multi-path scattering. We introduce an optimized secondary scattering approach that captures critical multi-path information with reduced computational cost. This work provides a robust tool for the design and improvement of dToF SPAD Lidar-based NLOS imaging systems. Full article

In the intricate and dynamic infrared imaging environment, the detection of infrared dim and small targets becomes notably challenging due to their feeble radiation intensity, intricate background noise, and high interference characteristics. To tackle this issue, this paper introduces a lightweight detection and recognition algorithm, named YOLOv5-IR, and further presents an even more lightweight version, YOLOv5-IRL. Firstly, a lightweight network structure incorporating spatial and channel attention mechanisms is proposed. Secondly, a detection head equipped with an attention mechanism is designed to intensify focus on small target information. Lastly, an adaptive weighted loss function is devised to improve detection performance for low-quality samples. Building upon these advancements, the network size can be further compressed to create the more lightweight YOLOv5-IRL version, which is better suited for deployment on resource-constrained mobile platforms. Experimental results on infrared dim and small target detection datasets with complex backgrounds indicate that, compared to the baseline model YOLOv5, the proposed YOLOv5-IR and YOLOv5-IRL detection algorithms reduce model parameter counts by 42.9% and 45.6%, shorten detection time by 13.6% and 16.9%, and enhance mAP0.5 by 2.4% and 1.8%, respectively. These findings demonstrate that the proposed algorithms effectively elevate detection efficiency, meeting future demands for infrared dim and small target detection. Full article

Nowadays, engine room simulators have become an important tool for maritime training, but programming engine room simulators often involves handling large amounts of data, making the process inefficient. This paper proposes an innovative visual modeling method for the ship pipeline network program in engine room simulators, aimed at addressing the heavy programming tasks associated with traditional text-based design and calculation methods when dealing with complex and large-scale pipeline systems. By creating Scalable Vector Graphics (SVG) images and using Windows Presentation Foundation (WPF) to place controls, an intuitive graphical user interface is built, allowing programmers to easily operate through the graphical interface. Subsequently, You Only Look Once version 5 (YOLOv5) object detection technology is used to identify the completed SVG images and WPF controls, generating corresponding Comma-Separated Values (CSV) files, which are then used as data input via C# (C Sharp). Through automated data processing and equipment recognition, compared to traditional manual design processes (such as using Matlab or C++ for pipeline design), this method reduces human errors and improves programming accuracy. Customization of key pipeline characteristics (such as maximum flow and flow direction) enhances the accuracy and applicability of the pipeline network model. The intuitive user interface design also allows nonprofessional users to easily design and optimize pipeline systems. The results show that this tool not only improves the efficiency of data processing and calculation but also demonstrates excellent performance and broad application prospects in the design and optimization of ship pipeline systems. In the future, this tool is expected to be more widely promoted in ship pipeline network education and practical applications, driving the field towards more efficient and intelligent development. Full article

Background and Objectives: Cerclage cable fixation with 2 mm multiple-braided cables for displaced acetabular fractures has shown good midterm functional and radiographic outcomes. We retrospectively evaluated the clinical and radiographic outcomes of cerclage cable fixations over ten years. Materials and Methods: We extracted data for patients who underwent cerclage cable fixation for acetabular fractures at a single institution from 2007 to 2012. We adopted this procedure for acetabulum fractures with posterior column fractures. Postoperative reduction quality, complications, reoperations, and Japanese Orthopedic Association (JOA) hip objective functional scores were analyzed. Postoperative reduction quality was classified using plain radiography and computed tomography. Results: We evaluated nine patients with a mean follow-up period of 14.1 ± 2.6 years (range: 10.8–18.1 years). The mean age was 47.1 ± 15.5 years old (range: 28–74 years); the mean injury severity score was 13.6 ± 4.7 (range: 9–22). The most frequent type of fracture was a both-column fracture. Anatomical reduction quality was achieved in five cases. Four patients had hip osteoarthritis at the last follow-up; among them, one patient had worsening hip arthritis > 5 years after surgery, and one patient developed osteoarthritis > 10 years after surgery. Their postoperative reduction quality was worse than their anatomical reduction quality, and both engaged in physical labor. None of the patients underwent revision total hip arthroplasty. The mean JOA hip score was 90.9 ± 7.9 (range: 74–100); seven patients scored >90 at the last follow-up. Conclusions: Cerclage cable fixation showed satisfactory postoperative reductions and favorable long-term clinical outcomes. Long-term follow-up might be necessary for patients whose postoperative reduction is not anatomical to detect late occurrence of hip osteoarthritis, even if osteoarthritis is not evident during short-term follow-up periods. Full article

Objectives: This study aimed to evaluate the risk of hepatitis B virus reactivation (HBVr) in COVID-19 patients receiving immunosuppressive treatment, which has been insufficiently studied to date. Secondarily, we aimed to evaluate the seroprevalence of HBV infection in COVID-19 patients. Methods: We performed HBV screening on all Romanian adults hospitalized in four COVID-19 wards between October 2021 and September 2022. We enrolled patients with positive hepatitis B core antibody (anti-HBc) without protective hepatitis B surface antibody (anti-HBs), HBV treatment, or baseline immunosuppressive conditions, and we conducted a virological follow-up on these patients at 3 months. Results: We identified 333/835 (39.9%) anti-HBc-positive patients. Follow-up was performed for 13 patients with positive hepatitis B surface antigen (HBsAg) and 19 HBsAg-negative/anti-HBc-positive patients. Among those who received immunosuppressants, 4/23 (17.4%) patients experienced HBVr: 1 out of 8 (12.5%) HBsAg-positive patients (with 1.99 log increase in HBV DNA level) and 3 out of 15 (20%) HBsAg-negative/anti-HBc-positive patients (with a de novo detectable HBV DNA level). Conclusions: Administration of COVID-19 immunosuppressants may result in a significant risk of HBVr in co-infected patients. We recommend performing an HBV triple screen panel (HBsAg, anti-HBs, anti-HBc) for all COVID-19 patients receiving immunosuppressive treatment. HBV prophylaxis may be indicated in certain patients. Larger studies are needed in order to establish appropriate and cost-effective management for these patients. Full article

Background/Objectives: Dermal fibroblasts (DFs) are key participants in skin hypertrophic scarring, and their properties are being studied to identify the molecular and cellular mechanisms underlying the pathogenesis of skin scarring. Methods: In the present work, we performed a comparative analysis of DFs isolated from normal skin (normal dermal fibroblasts, NDFs), and hypertrophic scar skin (hypertrophic scar fibroblasts, HTSFs). The fibroblasts were karyotyped and phenotyped, and experiments on growth rate, wound healing, and single-cell motility were conducted. Results: Comparative analysis revealed a minor karyotype difference between cells. However, HTSFs are characterized by higher proliferation level and motility compared to NDFs. These significant differences may be associated with quantitative and qualitative differences in the cell secretome. A proteomic comparison of NDF and HTSF found that differences were associated with metabolic proteins reflecting physiological differences between the two cells lines. Numerous unique proteins were found only in the vesicular phase of vHTSFs. Some proteins involved in cell proliferation (protein-glutamine gamma-glutamyltransferase K) and cell motility (catenin delta-1), which regulate gene transcription and the activity of Rho family GTPases and downstream cytoskeletal dynamics, were identified. A number of proteins which potentially play a role in fibrosis and inflammation (mucin-5B, CD97, adhesion G protein-coupled receptor E2, antileukoproteinase, protein S100-A8 and S100-A9, protein caspase recruitment domain-containing protein 14) were detected in vHTSFs. Conclusions: A comparative analysis of primary cell cultures revealed their various properties, especially in the cell secretome. These proteins may be considered promising target molecules for developing treatment or prevention strategies for pathological skin scarring. Full article

The integration of thermal imaging data with multimodal large language models (MLLMs) offers promising advancements for enhancing the safety and functionality of autonomous driving systems (ADS) and intelligent transportation systems (ITS). This study investigates the potential of MLLMs, specifically GPT-4 Vision Preview and Gemini 1.0 Pro Vision, for interpreting thermal images for applications in ADS and ITS. Two primary research questions are addressed: the capacity of these models to detect and enumerate objects within thermal images, and to determine whether pairs of image sources represent the same scene. Furthermore, we propose a framework for object detection and classification by integrating infrared (IR) and RGB images of the same scene without requiring localization data. This framework is particularly valuable for enhancing the detection and classification accuracy in environments where both IR and RGB cameras are essential. By employing zero-shot in-context learning for object detection and the chain-of-thought technique for scene discernment, this study demonstrates that MLLMs can recognize objects such as vehicles and individuals with promising results, even in the challenging domain of thermal imaging. The results indicate a high true positive rate for larger objects and moderate success in scene discernment, with a recall of 0.91 and a precision of 0.79 for similar scenes. The integration of IR and RGB images further enhances detection capabilities, achieving an average precision of 0.93 and an average recall of 0.56. This approach leverages the complementary strengths of each modality to compensate for individual limitations. This study highlights the potential of combining advanced AI methodologies with thermal imaging to enhance the accuracy and reliability of ADS, while identifying areas for improvement in model performance. Full article

Ongoing improvements in the sensitivity of sub-mm- and mm-range interferometers and single-dish radio telescopes allow for the increasingly detailed study of AGB and post-AGB objects in molecular species other than ${}^{12}\mathrm{CO}$ and ${}^{13}\mathrm{CO}$. With a new update introduced in the modelling tool SHAPE + shapemol, we can now create morpho-kinematical models to reproduce observations of these AGB and post-AGB circumstellar shells in different molecular species, allowing for an accurate description of their physical features as well as their molecular abundances and isotopic ratios. The pre-planetary nebula M1-92 (Minkowski’s Footprint) is one of the most complex objects of this kind, with a wide range of physical conditions and more than 20 molecular species detected. We model this nebula, reproducing the observational data from IRAM-30m and HSO/HiFi spectra and NOEMA interferometric maps, trying to understand the unusual evolution of its central star in the last phases of its life. The results show interesting features that tell us the story of its death. According to standard evolution models, a ${}^{17}\mathrm{O}$/${}^{18}\mathrm{O}$ isotopic ratio of 1.6 implies a stellar initial mass of ∼1.7$M_{\odot }$. Such a star should have turned C-rich by the end of the AGB phase, in striking contrast to the O-rich nature of the nebula. The most plausible way of reconciling this discrepancy is that M1-92 resulted from a sudden massive ejection event, interrupting the AGB evolution of the central source and preventing its transformation into a C-rich star. We also detect a changing ${}^{12}\mathrm{C}$/${}^{13}\mathrm{C}$ ratio across the nebula, which is particularly relevant in the inner equatorial region traced by ${\mathrm{HCO}}^{+}$ and ${\mathrm{H}}^{13}$${\mathrm{CO}}^{+}$, indicating an isotopic ratio variation taking place at some point during the last 1200 yr. Full article

In the field of object detection, enhancing algorithm performance in complex scenarios represents a fundamental technological challenge. To address this issue, this paper presents an efficient optimized YOLOv8 model with extended vision (YOLO-EV), which optimizes the performance of the YOLOv8 model through a series of innovative improvement measures and strategies. First, we propose a multi-branch group-enhanced fusion attention (MGEFA) module and integrate it into YOLO-EV, which significantly boosts the model’s feature extraction capabilities. Second, we enhance the existing spatial pyramid pooling fast (SPPF) layer by integrating large scale kernel attention (LSKA), improving the model’s efficiency in processing spatial information. Additionally, we replace the traditional IOU loss function with the Wise-IOU loss function, thereby enhancing localization accuracy across various target sizes. We also introduce a P6 layer to augment the model’s detection capabilities for multi-scale targets. Through network structure optimization, we achieve higher computational efficiency, ensuring that YOLO-EV consumes fewer computational resources than YOLOv8s. In the validation section, preliminary tests on the VOC12 dataset demonstrate YOLO-EV’s effectiveness in standard object detection tasks. Moreover, YOLO-EV has been applied to the CottonWeedDet12 and CropWeed datasets, which are characterized by complex scenes, diverse weed morphologies, significant occlusions, and numerous small targets. Experimental results indicate that YOLO-EV exhibits superior detection accuracy in these complex agricultural environments compared to the original YOLOv8s and other state-of-the-art models, effectively identifying and locating various types of weeds, thus demonstrating its significant practical application potential. Full article

Detecting tiny objects in aerial imagery presents a major challenge regarding their limited resolution and size. Existing research predominantly focuses on evaluating average precision (AP) across various detection methods, often neglecting computational efficiency. Furthermore, state-of-the-art techniques can be complex and difficult to understand. This paper introduces a comprehensive benchmarking analysis specifically tailored for enhancing small object detection within the DOTA dataset, focusing on one-stage detection methods. We propose a novel data-processing approach to enhance the overall AP for all classes in the DOTA-v1.5 dataset using the YOLOv8 framework. Our approach utilizes the YOLOv8’s darknet architecture, a proven effective backbone for object detection tasks. To optimize performance, we introduce innovative pre-processing techniques, including data formatting, noise handling, and normalization, in order to improve the representation of small objects and improve their detectability. Extensive experiments on the DOTA-v1.5 dataset demonstrate the superiority of our proposed approach in terms of overall class mean average precision (mAP), achieving 66.7%. Additionally, our method establishes a new benchmark regarding computational efficiency and speed. This advancement not only enhances the performance of small object detection but also sets a foundation for future research and applications in aerial imagery analysis, paving the way for more efficient and effective detection techniques. Full article