Search Results (2,757)

Surface defects have a serious detrimental effect on the quality of steel. To address the problems of low efficiency and poor accuracy in the manual inspection process, intelligent detection technology based on machine learning has been gradually applied to the detection of steel surface defects. An improved YOLOv8 steel surface defect detection model called YOLOv8-MGVS is designed to address these challenges. The MLCA mechanism in the C2f module is applied to increase the feature extraction ability in the backbone network. The lightweight GSConv and VovGscsp cross-stage fusion modules are added to the neck network to reduce the loss of semantic information and achieve effective information fusion. The self-attention mechanism is exploited into the detection network to improve the detection ability of small targets. Defect detection experiments were carried out on the NEU-DET dataset. Compared with YOLOv8n from experimental results, the average accuracy, recall rate, and frames per second of the improved model were improved by 5.2%, 10.5%, and 6.4%, respectively, while the number of parameters and computational costs were reduced by 5.8% and 14.8%, respectively. Furthermore, the defect detection generalization experiments on the GC-10 dataset and SDD DET dataset confirmed that the YOLOv8-MGVS model has higher detection accuracy, better lightweight, and speed. Full article

Cardiovascular diseases and cancer are leading global causes of morbidity and mortality, necessitating advances in diagnosis and treatment. Doxorubicin (Doxo), a potent chemotherapy drug, causes long-term heart damage due to cardiotoxicity. Small extracellular vesicles (sEVs) carry bioactive molecules—such as proteins, lipids, and nucleic acids—that can modulate gene expression and signaling pathways in recipient cells, including cardiomyocytes. Through the delivery of cytokines, microRNAs, and growth factors, sEVs can influence cell survival, which plays a critical role in the development of cardiotoxicity. This study investigates the role of sEVs derived from breast cancer cells treated or not with Doxo and their potential to induce cardiomyocyte damage, thereby contributing to cardiotoxicity. We isolated sEVs from MCF-7 cells treated or not to Doxo using ultracentrifugation and characterized them through Nanoparticle Tracking Analysis (NTA), Scanning Electron Microscopy (SEM), and Western Blotting (WB) for the markers CD63, CD81, and TSG101. We analyzed cytokine profiles using a Multiplex Assay and Cytokine Membrane Array. We exposed Guinea pig cardiomyocytes to different concentrations of sEVs. We assessed their viability (MTT assay), shortening, reactive oxygen species (ROS–DHE dye) production, mitochondrial membrane potential (JC-1 dye), and calcium dynamics (FLUO-4 dye). We performed statistical analyses, including t-tests, ANOVA, Cohen’s d, and η² to validate the robustness of the results. Treatment of MCF-7 cells with 0.01 μM Doxorubicin resulted in increased sEVs production, particularly after 48 h of exposure (~1.79 × 10⁸ ± 2.77 × 10⁷ vs. ~5.1 × 10⁷ ± 1.28 × 10⁷ particles/mL, n = 3, p = 0.0019). These sEVs exhibited protein profiles in the 130–25 kDa range and 93–123 nm sizes. They carried cytokines including TNF-α, IL-1β, IL-4, IFN-γ, and IL-10. Exposure of cardiomyocytes to sEVs (0.025 μg/mL to 2.5 μg/mL) from both Doxo-treated and untreated cells significantly reduced cardiomyocyte viability, shortened cell length by up to 20%, increased ROS production, and disrupted calcium homeostasis and mitochondrial membrane potential, indicating severe cellular stress and cardiotoxicity. These findings suggest that Doxo enhances sEVs production from breast cancer cells, which plays a key role in cardiotoxicity through their cytokine cargo. The study highlights the potential of these sEVs as biomarkers for early cardiotoxicity detection and as therapeutic targets to mitigate cardiovascular risks in chemotherapy patients. Future research should focus on understanding the mechanisms by which Doxorubicin-induced sEVs contribute to cardiotoxicity and exploring their diagnostic and therapeutic potential to improve patient safety and outcomes in cancer therapy. Full article

In order to solve the problem of detecting, tracking and estimating the size of “low, slow and small” targets (such as UAVs) in the air, this paper designs a single-photon LiDAR imaging system based on Geiger-mode Avalanche Photodiode (Gm-APD). It improves the Mean-Shift algorithm and proposes an automatic tracking method that combines the weighted centroid method to realize target extraction, and the principal component analysis (PCA) method of the adaptive rotating rectangle is realized to fit the flight attitude of the target. This method uses the target intensity and distance information provided by Gm-APD LiDAR. It addresses the problem of automatic calibration and size estimation under multiple flight attitudes. The experimental results show that the improved algorithm can automatically track the targets in different flight attitudes in real time and accurately calculate their sizes. The improved algorithm is stable in the 1250-frame tracking experiment of DJI Elf 4 UAV with a flying speed of 5 m/s and a flying distance of 100 m. Among them, the fitting error of the target is always less than 2 pixels, while the size calculation error of the target is less than 2.5 cm. This shows the remarkable advantages of Gm-APD LiDAR in detecting “low, slow and small” targets. It is of practical significance to comprehensively improve the ability of UAV detection and C-UAS systems. However, the application of this technology in complex backgrounds, especially in occlusion or multi-target tracking, still faces certain challenges. In order to realize long-distance detection, further optimizing the field of view of the Gm-APD single-photon LiDAR is still a future research direction. Full article

Dual-labeled compounds that combine radiolabeling and fluorescence labeling represent a significant advancement in precision oncology. Their clinical implementation enhances patient care and outcomes by leveraging the high sensitivity of radioimaging for tumor detection and taking advantage of fluorescence-based optical visualization for surgical guidance. Non-invasive radioimaging facilitates immediate identification of both primary tumors and metastases, while fluorescence imaging assists in decision-making during surgery by offering a spatial distinction between malignant and non-malignant tissue. These advancements hold promise for enhancing patient outcomes and personalization of cancer treatment. The development of dual-labeled molecular probes targeting various cancer biomarkers is crucial in addressing the heterogeneity inherent in cancer pathology and recent studies had already demonstrated the impact of dual-labeled compounds in surgical decision-making (NCT03699332, NCT03407781). This review focuses on the development and application of small dual-labeled peptides in the imaging and treatment of various cancer types. It summarizes the biomarkers targeted to date, tracing their development from initial discovery to the latest advancements in peptidomimetics. Through comprehensive analysis of recent preclinical and clinical studies, the review demonstrates the potential of these dual-labeled peptides to improve tumor detection, localization, and resection. Additionally, it highlights the evolving landscape of dual-modality imaging, emphasizing its critical role in advancing personalized and effective cancer therapy. This synthesis of current research underscores the promise of dual-labeled peptides in enhancing diagnostic accuracy and therapeutic outcomes in oncology. Full article

Background/Objectives: Identifying treatment failure at earlier time points to could spare cancer patients from ineffective treatment and side effects. In this study, circulating tumor DNA (ctDNA) and [¹⁸F]FDG-PET/CT were investigated during the first cycle of anticancer therapy in patients with advanced non-small cell lung cancer (NSCLC) to explore their potential for early response evaluation. Methods: Patients with advanced NSCLC receiving first-line therapy with immune checkpoint inhibitors and/or chemotherapy were included. CtDNA and [¹⁸F]FDG-PET/CT assessments were conducted before treatment and at weeks 1 and 3 during the first cycle of therapy. ctDNA quantification was performed using a targeted next-generation sequencing (NGS) panel, and the least favorable change in any mutated allele frequency at a given time was used for analysis. [¹⁸F]FDG-PET/CT was quantified using sumSUL_peak and metabolic tumor volume (MTV_4.0). Early changes in ctDNA levels and [¹⁸F]FDG-PET parameters were compared with final treatment response, measured by RECIST after 12 weeks, as well as progression-free survival and overall survival. Results: Of the sixteen included patients, eight were non-responders. ctDNA mutations were detected in baseline blood samples in eight patients. Changes in ctDNA level, MTV_4.0, and sumSUL_peak at week 3 indicated response in 7 out of 8 patients, 13 out of 15 patients, and 9 out of 15 patients, respectively. At week 3, no false increases were seen with ctDNA and MTV_4.0. Conclusions: These results suggest that early changes in ctDNA and [¹⁸F]FDG-PET/CT at 3 weeks of treatment could be used to early assess treatment response. Increased levels of ctDNA and MTV_4.0 at week 3 were only observed in patients with treatment failure. Full article

Detecting wildfires and smoke is essential for safeguarding forest ecosystems and offers critical information for the early evaluation and prevention of such incidents. The advancement of unmanned aerial vehicle (UAV) remote sensing has further enhanced the detection of wildfires and smoke, which enables rapid and accurate identification. This paper presents an integrated one-stage object detection framework designed for the simultaneous identification of wildfires and smoke in UAV imagery. By leveraging mixed data augmentation techniques, the framework enriches the dataset with small targets to enhance its detection performance for small wildfires and smoke targets. A novel backbone enhancement strategy, integrating region convolution and feature refinement modules, is developed to facilitate the ability to localize smoke features with high transparency within complex backgrounds. By integrating the shape aware loss function, the proposed framework enables the effective capture of irregularly shaped smoke and fire targets with complex edges, facilitating the accurate identification and localization of wildfires and smoke. Experiments conducted on a UAV remote sensing dataset demonstrate that the proposed framework achieves a promising detection performance in terms of both accuracy and speed. The proposed framework attains a mean Average Precision (mAP) of 79.28%, an F1 score of 76.14%, and a processing speed of 8.98 frames per second (FPS). These results reflect increases of 4.27%, 1.96%, and 0.16 FPS compared to the YOLOv10 model. Ablation studies further validate that the incorporation of mixed data augmentation, feature refinement models, and shape aware loss results in substantial improvements over the YOLOv10 model. The findings highlight the framework’s capability to rapidly and effectively identify wildfires and smoke using UAV imagery, thereby providing a valuable foundation for proactive forest fire prevention measures. Full article

Wastewater-based surveillance has gained attention in the four years following the start of the COVID-19 pandemic. Accurate pathogen detection, quantification and characterisation rely on the selection of appropriate methodologies. Here, we explore the impact of viral concentration method on RT-qPCR inhibition and quantification of norovirus genogroups I and II (GI and GII), crAssphage, phi6 and SARS-CoV-2. Additionally, their impact on long amplicon sequencing for typing noroviruses and whole-genome sequencing (WGS) SARS-CoV-2 was explored. RT-qPCR inhibition for each viral concentration method was significantly different apart from the two ultrafiltration methods, InnovaPrep^® concentrating pipette (IP) and Vivaspin^® (VS) centrifugal concentrators. Using an ultrafiltration method reduced inhibition by 62.0% to 96.0% compared to the ammonium sulphate (AS) and polyethylene glycol (PEG) precipitation-based methods. Viral quantification was significantly impacted by concentration method with the highest concentrations (copies/L) observed for VS with 7.2- to 83.2-fold differences from AS depending on the target. Norovirus long amplicon sequencing showed genotype-dependent differences with IP performing best for GI and VS for GII although IP performance gains for GI were relatively small. VS outperformed AS and IP across all metrics during SARS-CoV-2 WGS. Overall, VS performed the best when considering all the areas of investigation. Full article

Background: Lung cancer remains the leading cause of cancer mortality globally with EGFR mutations representing a significant driver in advanced non-small cell lung cancer (aNSCLC). The timely detection of these mutations is critical for initiating targeted therapy, yet tissue biopsy limitations often delay treatment. Methods: This multicenter prospective study evaluated the clinical utility of liquid biopsy (LBx) in real-life settings for the early diagnosis of EGFR mutations in patients with suspected aNSCLC. Circulating tumor DNA (ctDNA) was analyzed using the Cobas EGFR Mutation Test and compared to tissue-based next-generation sequencing (NGS). Results: Among 366 aNSCLC patients tested, LBx demonstrated a significantly shorter median turnaround time (TAT) of 3 days compared to 26 days for tissue NGS (p < 0.001) with 100% specificity and 65% sensitivity for EGFR mutation detection. LBx identified actionable EGFR mutations in cases where tissue biopsy was insufficient or unavailable, enabling 43.7% of patients to commence targeted therapy based on ctDNA results prior to biopsy confirmation. Conclusions: These findings highlight the potential of LBx to reduce diagnostic delays and improve access to personalized therapies in a real-world setting. Integrating LBx into routine diagnostic workflows may address current gaps in molecular testing, ensuring timely and precise treatment for aNSCLC patients. Full article

The presence of dead broilers within a flock can be significant vectors for disease transmission and negatively impact the overall welfare of the remaining broilers. This study introduced a dead broiler detection method that leverages the fact that dead broilers remain stationary within the flock in videos. Dead broilers were identified through the analysis of the historical movement information of each broiler in the video. Firstly, the frame difference method was utilized to capture key frames in the video. An enhanced segmentation network, YOLOv8-SP, was then developed to obtain the mask coordinates of each broiler, and an optical flow estimation method was employed to generate optical flow maps and evaluate their movement. An average optical flow intensity (AOFI) index of broilers was defined and calculated to evaluate the motion level of each broiler in each key frame. With the AOFI threshold, broilers in the key frames were classified into candidate dead broilers and active live broilers. Ultimately, the identification of dead broilers was achieved by analyzing the frequency of each broiler being judged as a candidate death in all key frames within the video. We incorporated the parallelized patch-aware attention (PPA) module into the backbone network and improved the overlaps function with the custom power transform (PT) function. The box and mask segmentation mAP of the YOLOv8-SP model increased by 1.9% and 1.8%, respectively. The model’s target recognition performance for small targets and partially occluded targets was effectively improved. False and missed detections of dead broilers occurred in 4 of the 30 broiler testing videos, and the accuracy of the dead broiler identification algorithm proposed in this study was 86.7%. Full article

For change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additionally, the stability of SAR signals is preserved when calibration accounts for temporal and environmental variations. Although ACD and CCD techniques can detect changes, spatial variability outside the primary target area introduces complexity into the analysis. This study presents a robust change detection methodology designed to identify urban changes using KOMPSAT-5 time-series data. A comprehensive preprocessing framework—including coregistration, radiometric terrain correction, normalization, and speckle filtering—was implemented to ensure data consistency and accuracy. Statistical homogeneous pixels (SHPs) were extracted to identify stable targets, and coherence-based analysis was employed to quantify temporal decorrelation and detect changes. Adaptive thresholding and morphological operations refined the detected changes, while small-segment removal mitigated noise effects. Experimental results demonstrated high reliability, with an overall accuracy of 92%, validated using confusion matrix analysis. The methodology effectively identified urban changes, highlighting the potential of KOMPSAT-5 data for post-disaster monitoring and urban change detection. Future improvements are suggested, focusing on the stability of InSAR orbits to further enhance detection precision. The findings underscore the potential for broader applications of the developed SAR time-series change detection technology, promoting increased utilization of KOMPSAT SAR data for both domestic and international research and monitoring initiatives. Full article

The daily behaviors of dairy cows, including standing, drinking, eating, and lying down, are closely associated with their physical health. Efficient and accurate recognition of dairy cow behaviors is crucial for timely monitoring of their health status and enhancing the economic efficiency of farms. To address the challenges posed by complex scenarios and significant variations in target scales in dairy cow behavior recognition within group farming environments, this study proposes an enhanced recognition method based on YOLOv5. Four Shuffle Attention (SA) modules are integrated into the upsampling and downsampling processes of the YOLOv5 model’s neck network to enhance deep feature extraction of small-scale cow targets and focus on feature information, while maintaining network complexity and real-time performance. The C3 module of the model was enhanced by incorporating Deformable convolution (DCNv3), which improves the accuracy of cow behavior characteristic identification. Finally, the original detection head was replaced with a Dynamic Detection Head (DyHead) to improve the efficiency and accuracy of cow behavior detection across different scales in complex environments. An experimental dataset comprising complex backgrounds, multiple behavior categories, and multi-scale targets was constructed for comprehensive validation. The experimental results demonstrate that the improved YOLOv5 model achieved a mean Average Precision (mAP) of 97.7%, representing a 3.7% improvement over the original YOLOv5 model. Moreover, it outperformed comparison models, including YOLOv4, YOLOv3, and Faster R-CNN, in complex background scenarios, multi-scale behavior detection, and behavior type discrimination. Ablation experiments further validate the effectiveness of the SA, DCNv3, and DyHead modules. The research findings offer a valuable reference for real-time monitoring of cow behavior in complex environments throughout the day. Full article

To address challenges such as the frequent misdetection of targets, missed detections of multiple targets, high computational demands, and poor real-time detection performance in the video surveillance of external breakage obstacles on transmission lines, we propose a lightweight target detection algorithm incorporating the ACmix mechanism. First, the ShuffleNetv2 backbone network is used to reduce the model parameters and improve the detection speed. Next, the ACmix attention mechanism is integrated into the Neck layer to suppress irrelevant information, mitigate the impact of complex backgrounds on feature extraction, and enhance the network’s ability to detect small external breakage targets. Additionally, we introduce the PC-ELAN module to replace the ELAN-W module, reducing redundant feature extraction in the Neck network, lowering the model parameters, and boosting the detection efficiency. Finally, we adopt the SIoU loss function for bounding box regression, which enhances the model stability and convergence speed due to its smoothing characteristics. The experimental results show that the proposed algorithm achieves an mAP of 92.7%, which is 3% higher than the baseline network. The number of model parameters and the computational complexity are reduced by 32.3% and 44.9%, respectively, while the detection speed is improved by 3.5%. These results demonstrate that the proposed method significantly enhances the detection performance. Full article

Accurate safflower recognition is a critical research challenge in the field of automated safflower harvesting. The growing environment of safflowers, including factors such as variable weather conditions in unstructured environments, shooting distances, and diverse morphological characteristics, presents significant difficulties for detection. To address these challenges and enable precise safflower target recognition in complex environments, this study proposes an improved safflower detection model, WED-YOLO, based on YOLOv8n. Firstly, the original bounding box loss function is replaced with the dynamic non-monotonic focusing mechanism Wise Intersection over Union (WIoU), which enhances the model’s bounding box fitting ability and accelerates network convergence. Then, the upsampling module in the network’s neck is substituted with the more efficient and versatile dynamic upsampling module, DySample, to improve the precision of feature map upsampling. Meanwhile, the EMA attention mechanism is integrated into the C2f module of the backbone network to strengthen the model’s feature extraction capabilities. Finally, a small-target detection layer is incorporated into the detection head, enabling the model to focus on small safflower targets. The model is trained and validated using a custom-built safflower dataset. The experimental results demonstrate that the improved model achieves Precision (P), Recall (R), mean Average Precision (mAP), and F₁ score values of 93.15%, 86.71%, 95.03%, and 89.64%, respectively. These results represent improvements of 2.9%, 6.69%, 4.5%, and 6.22% over the baseline model. Compared with Faster R-CNN, YOLOv5, YOLOv7, and YOLOv10, the WED-YOLO achieved the highest mAP value. It outperforms the module mentioned by 13.06%, 4.85%, 4.86%, and 4.82%, respectively. The enhanced model exhibits superior precision and lower miss detection rates in safflower recognition tasks, providing a robust algorithmic foundation for the intelligent harvesting of safflowers. Full article

In 2001, two enzyme-encoding genes were recognized in the fruit fly Drosophila melanogaster. The genetic material, labeled Dicer-1 and Dicer-2, encodes ribonuclease-type enzymes with slightly diverse target substrates. The human orthologue is DICER1. It is a gene, which has been positioned on chromosome 14q32.13. It contains 27 exons, which are linking the two enzyme domains. DICER1 is found in all organ systems. It has been proved that it is paramount in human development. The protein determined by DICER1 is a ribonuclease (RNase). This RNase belongs to the RNase III superfamily, formally known as ’endoribonuclease’. It has been determined that the function of RNase III proteins is set to identify and degrade double-stranded molecules of RNA. DICER1 is a vital “housekeeping” gene. The multi-domain enzyme is key for small RNA processing. This enzyme functions in numerous pathways, including RNA interference paths, DNA damage renovation, and response to viruses. At the protein level, DICER is also involved in several human diseases, of which the pleuro-pulmonary blastoma is probably the most egregious entity. Numerous studies have determined the full range of DICER1 functions and the corresponding relationship to tumorigenic and non-neoplastic diseases. In fact, genetic mutations (somatic and germline) have been detected in DICER1 and are genetically associated with at least two clinical syndromes: DICER1 syndrome and GLOW syndrome. The ubiquity of this enzyme in the human body makes it an exquisite target for nanotechnology-supported therapies and repurposing drug approaches. Full article

Accurate and efficient object detection in UAV images is a challenging task due to the diversity of target scales and the massive number of small targets. This study investigates the enhancement in the detection head using sparse convolution, demonstrating its effectiveness in achieving an optimal balance between accuracy and efficiency. Nevertheless, the sparse convolution method encounters challenges related to the inadequate incorporation of global contextual information and exhibits network inflexibility attributable to its fixed mask ratios. To address the above issues, the MFFCESSC-SSD, a novel single-shot detector (SSD) with multi-scale feature fusion and context-enhanced spatial sparse convolution, is proposed in this paper. First, a global context-enhanced group normalization (CE-GN) layer is developed to address the issue of information loss resulting from the convolution process applied exclusively to the masked region. Subsequently, a dynamic masking strategy is designed to determine the optimal mask ratios, thereby ensuring compact foreground coverage that enhances both accuracy and efficiency. Experiments on two datasets (i.e., VisDrone and ARH2000; the latter dataset was created by the researchers) demonstrate that the MFFCESSC-SSD remarkably outperforms the performance of the SSD and numerous conventional object detection algorithms in terms of accuracy and efficiency. Full article