Search Results (14,823)

Intra-cardiac kinetic energy (KE) and ventricular flow analysis (VFA), as derived from 4D-flow MRI, can be used to understand the physiological burden placed on the left ventricle (LV) due to bicuspid aortic valve (BAV). Our hypothesis was that the KE of each VFA component would impact the surgical referral outcome depending on LV function decrement, BAV phenotype, and aortic dilation severity. A total of 11 healthy controls and 49 BAV patients were recruited. All subjects underwent cardiac magnetic resonance imaging (MRI) examination. The LV mass was inferior in the controls than in the BAV patients (90 ± 26 g vs. 45 ± 17 g, p = 0.025), as well as the inferior ascending aorta diameter indexed (15.8 ± 2.5 mm/m² vs. 19.3 ± 3.5 mm/m², p = 0.005). The VFA KE was higher in the BAV group; significant increments were found for the maximum KE and mean KE in the VFA components (p < 0.05). A total of 14 BAV subjects underwent surgery after the scans. When comparing BAV nonsurgery vs. surgery-referred cohorts, the maximum KE and mean KE were elevated (p < 0.05). The maximum and mean KE were also associated with surgical referral (r = 0.438, p = 0.002 and r = 0.371, p = 0.009, respectively). In conclusion, the KE from VFA components significantly increased in BAV patients, including in BAV patients undergoing surgery. Full article

The recycling of Carbon Fibre-Reinforced Polymers (CFRPs) is becoming increasingly crucial due to the growing demand for sustainability in high-performance industries such as automotive and aerospace. This study investigates the impact of two chemical recycling techniques, chemically assisted solvolysis and plasma-enhanced solvolysis, on the morphology and properties of carbon fibres (CFs) recovered from end-of-life automotive parts. In addition, the effects of fibre sizing are explored to enhance the performance of the recycled carbon fibres (rCFs). The surface morphology of the fibres was characterised using Scanning Electron Microscopy (SEM), and their structural integrity was assessed through Thermogravimetric Analysis (TGA) and Raman spectroscopy. An automatic analysis method based on optical microscopy images was also developed to quantify filament loss during the recycling process. Mechanical testing of single fibres and yarns showed that although rCFs from both recycling methods exhibited a ~20% reduction in tensile strength compared to reference fibres, the application of sizing significantly mitigated these effects (~10% reduction). X-ray Photoelectron Spectroscopy (XPS) further confirmed the introduction of functional oxygen-containing groups on the fibre surface, which improved fibre-matrix adhesion. Overall, the results demonstrate that plasma-enhanced solvolysis was more effective at fully decomposing the resin, while the subsequent application of sizing enhanced the mechanical performance of rCFs, restoring their properties closer to those of virgin fibres. Full article

Due to the increasing number of people working at computers in professional settings, the incidence of lumbar disc herniation is increasing. Background/Objectives: The early diagnosis and treatment of lumbar disc herniation is much more likely to yield favorable results, allowing the hernia to be treated before it develops further. The aim of this study was to classify lumbar disc herniations in a computer-aided, fully automated manner using magnetic resonance images (MRIs). Methods: This study presents a hybrid method integrating residual network (ResNet50), grey wolf optimization (GWO), and machine learning classifiers such as multi-layer perceptron (MLP) and support vector machine (SVM) to improve classification performance. The proposed approach begins with feature extraction using ResNet50, a deep convolutional neural network known for its robust feature representation capabilities. ResNet50’s residual connections allow for effective training and high-quality feature extraction from input images. Following feature extraction, the GWO algorithm, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to optimize the feature set by selecting the most relevant features. Finally, the optimized feature set is fed into machine learning classifiers (MLP and SVM) for classification. The use of various activation functions (e.g., ReLU, identity, logistic, and tanh) in MLP and various kernel functions (e.g., linear, rbf, sigmoid, and polynomial) in SVM allows for a thorough evaluation of the classifiers’ performance. Results: The proposed methodology demonstrates significant improvements in metrics such as accuracy, precision, recall, and F1 score, outperforming traditional approaches in several cases. These results highlight the effectiveness of combining deep learning-based feature extraction with optimization and machine learning classifiers. Conclusions: Compared to other methods, such as capsule networks (CapsNet), EfficientNetB6, and DenseNet169, the proposed ResNet50-GWO-SVM approach achieved superior performance across all metrics, including accuracy, precision, recall, and F1 score, demonstrating its robustness and effectiveness in classification tasks. Full article

Novel therapeutic delivery systems and delivery methods to the inner ear are necessary to treat hearing loss and inner ear disorders. However, numerous barriers exist to therapeutic delivery into the bone-encased and immune-privileged environment of the inner ear and cochlea, which makes treating inner ear disorders challenging. Nanoparticles (NPs) are a type of therapeutic delivery system that can be engineered for multiple purposes, and posterior semicircular canal (PSCC) infusion is a method to directly deposit them into the cochlea. We sought to assess PSCC infusion of gold NPs into the cochlea, including the NPs’ distribution and effect on cochlear mechanics. We performed optical coherence tomography (OCT) imaging to monitor PSCC infusion of gold NPs into the cochlear chambers. OCT imaging demonstrated that the infusion specifically targeted the perilymphatic spaces within the cochlea. We assessed cochlear mechanics by using OCT vibrometry to measure sound-evoked movements of the basilar membrane. We found no changes in cochlear mechanics between measurements at baseline, after the PSCC canalostomy, immediately after the infusion, and 1 h after the infusion of gold NPs (p > 0.05, paired t-test). These findings validate the PSCC infusion approach for perfusing the cochlear perilymphatic space with a nanoparticle delivery system. Thus, PSCC infusion of nanoparticles is a feasible therapeutic delivery technique for treating inner ear disorders while preserving residual cochlear function. Full article

This study investigated the impact of reflective cracking on the fatigue performance of asphalt pavements after milling and resurfacing under various conditions. Fatigue life was assessed through four-point flexural fatigue tests, while the crack extension pattern of composite beams was analyzed by digital image correlation (DIC) at both macroscopic and microscopic scales. Evaluation parameters such as stress ratios, immersion time, porosity, and types of viscous oils were assessed. A fatigue life prediction model of composite beams was established, accounting for the combined influence of these factors. To enhance the accuracy of determining composite beam failure, the critical fatigue damage was calculated by defining the damage variable in terms of the dynamic modulus. A nonlinear fatigue damage model was proposed, incorporating this critical damage under the combined influence of various factors. Additionally, a modified logistic function model was developed to describe the relationship between crack extension and failure life under different stress ratios, porosities, and viscous layer oil conditions. It was found that the modulus decay curves and the crack extension curves intersected at different stress levels as the life ratio increased. At the intersection, the modulus ratios were consistently around 0.55, marking the transition of the specimen from a stable to an unstable state. Beyond this point, the crack rapidly propagated, leading to a sharp reduction in the modulus until the specimen ultimately failed. Our results provide a basis for timing and conservation decisions. Full article

Precision depth estimation plays a key role in many applications, including 3D scene reconstruction, virtual reality, autonomous driving and human–computer interaction. Through recent advancements in deep learning technologies, monocular depth estimation, with its simplicity, has surpassed the traditional stereo camera systems, bringing new possibilities in 3D sensing. In this paper, by using a single camera, we propose an end-to-end supervised monocular depth estimation autoencoder, which contains an encoder with a structure with a mixed convolution neural network and vision transformers and an effective adaptive fusion decoder to obtain high-precision depth maps. In the encoder, we construct a multi-scale feature extractor by mixing residual configurations of vision transformers to enhance both local and global information. In the adaptive fusion decoder, we introduce adaptive fusion modules to effectively merge the features of the encoder and the decoder together. Lastly, the model is trained using a loss function that aligns with human perception to enable it to focus on the depth values of foreground objects. The experimental results demonstrate the effective prediction of the depth map from a single-view color image by the proposed autoencoder, which increases the first accuracy rate about 28% and reduces the root mean square error about 27% compared to an existing method in the NYU dataset Full article

Spotlight/sliding spotlight modes can achieve higher resolution than the other imaging modes and are widely used in object detection and recognition applications. This paper reviews the progress of the spaceborne spotlight/sliding spotlight SAR imaging field. The three steps of the current spaceborne spotlight/sliding spotlight SAR imaging algorithm framework are discussed in this paper. These include the following: eliminating the azimuth spectral aliasing by azimuth deramp preprocessing; implementing imaging processing using imaging kernels (RD, CS, RMA, etc.); and degrading the back-folded phenomenon in the final focused image domain by reference function multiplication post-processing. The different imaging kernels, consisting of RD, CS, RMA, BAS, FS, and PFA, are presented. The phase errors in high-resolution spaceborne spotlight/sliding spotlight SAR imaging, especially the stop-and-go error, curved orbit error, and tropospheric delay error, are analyzed in detail. Furthermore, the autofocus methods are described. In addition, some new imaging SAR systems based on spotlight/sliding spotlight SAR mode, which have more advantages than the classic spaceborne spotlight/sliding spotlight SAR imaging, were shown in this paper. These include FMCW-based systems, multichannel systems, varying-PRF systems, and bistatic systems. Full article

Object detection in low-light conditions presents significant challenges due to issues such as weak contrast, high noise, and blurred boundaries. Existing methods often use image enhancement to improve detection, which results in a large amount of computational resource consumption. To address these challenges, this paper proposes a detection method, 3L-YOLO, based on YOLOv8n, which eliminates the need for image enhancement modules. First, we introduce switchable atrous convolution (SAConv) into the C2f module of YOLOv8n, improving the model’s ability to efficiently capture global contextual information. Second, we present a multi-scale neck module that aggregates shallow features and incorporates a channel attention mechanism to prioritize the most relevant features. Third, we introduce a dynamic detection head, which employs a cascade of spatial, scale, and channel attention mechanisms to enhance detection accuracy and robustness. Finally, we replace the original loss function with MPDIoU loss, improving bounding box regression and overall reliability. Additionally, we create a synthetic low-light dataset to evaluate the performance of the proposed method. Extensive experiments on the ExDark, ExDark+, and DARK FACE datasets demonstrate that 3L-YOLO outperforms YOLOv8n in low-light object detection, with improvements in [email protected] of 2.7%, 4.3%, and 1.4%, respectively, across the three datasets. In comparison to the LOL-YOLO low-light object detection algorithm, 3L-YOLO requires 16.9 GFLOPs, representing a reduction of 4 GFLOPs. Full article

Background: Inherited retinal diseases (IRDs) are a genetically complex group of disorders, usually resulting in progressive vision loss due to retinal degeneration. Traditional imaging methods help in structural assessments, but limitations exist in early functional cellular-level detection that are crucial for guiding new therapies. Methods: This review includes a systematic search of PubMed and Google Scholar for studies on advanced imaging techniques for IRDs. Results: Key modalities covered are adaptive optics, fluorescence lifetime imaging ophthalmoscopy, polarization-sensitive optical coherence tomography, optoretinography, mitochondrial imaging, flavoprotein fluorescence imaging, and retinal oximetry. Each imaging method covers its principles, acquisition techniques, data from healthy eyes, applications in IRDs with specific examples, and current challenges and future directions. Conclusions: Emerging technologies, including adaptive optics and metabolic imaging, offer promising potential for cellular-level imaging and functional correlation in IRDs, allowing for earlier intervention and improved therapeutic targeting. Their integration into clinical practice may significantly improve IRD management and patient outcomes. Full article

Background: Motor imagery includes visual imagery and kinesthetic imagery, which are two strategies that exist for mental rotation and are currently widely studied. However, different mental rotation tests can lead to different strategic performances. There are also many research results where two different strategies appear simultaneously under the same task. Previous studies on the comparative brain mechanisms of kinesthetic imagery and visual imagery have not adopted consistent stimulus images or mature mental rotation paradigms, making it difficult to effectively compare these types of imagery. Methods: In this study, we utilized functional near-infrared spectroscopy (fNIRS) to investigate the brain activation of sixty-seven young right-handed participants with different strategy preferences during hand lateral judgment tasks (HLJT). Results: The results showed that the accuracy of the kinesthetic imagery group was significantly higher than that of the visual imagery group, and the reaction time of the kinesthetic imagery group was significantly shorter than that of the visual imagery group. The areas significantly activated in the kinesthetic imagery group were wider than those in the visual imagery group, including the dorsolateral prefrontal cortex (BA9, 46), premotor cortex (BA6), supplementary motor area (SMA), primary motor cortex (BA4), and parietal cortex (BA7, 40). It is worth noting that the activation levels in the frontal eye fields (BA8), primary somatosensory cortex (BA1, 2, 3), primary motor cortex (BA4), and parietal cortex (BA40) of the kinesthetic imagery group were significantly higher than those in the visual imagery group. Conclusion: Therefore, we speculate that kinesthetic imagery has more advantages than visual imagery in the mental rotation of egocentric transformations. Full article

Recent advancements in deep learning-based generative models have simplified image generation, increasing the need for improved source tracing and copyright protection, especially with the efficient, high-quality output of latent diffusion models (LDMs) raising concerns about unauthorized use. This paper provides a comprehensive review of watermarking techniques applied to latent diffusion models, focusing on recent trends and the potential utility of these approaches. Watermarking using latent diffusion models offers the potential to overcome these limitations by embedding watermarks in the latent space during the image generation process. This represents a new paradigm of watermarking that leverages a degree of freedom unavailable in traditional watermarking techniques and underscores the need to explore the potential advancements in watermark technology. LDM-based watermarking allows for the natural internalization of watermarks within the content generation process, enabling robust watermarking without compromising image quality. We categorize the methods based on embedding strategies and analyze their effectiveness in achieving key functionalities—source tracing, copyright protection, and AI-generated content identification. The review highlights the strengths and limitations of current techniques and discusses future directions for enhancing the robustness and applicability of watermarking in the evolving landscape of generative AI. Full article

Patient-centered precision oncology strives to deliver individualized cancer care. In lung cancer, preclinical models and technological innovations have become critical in advancing this approach. Preclinical models enable deeper insights into tumor biology and enhance the selection of appropriate systemic therapies across chemotherapy, targeted therapies, immunotherapies, antibody–drug conjugates, and emerging investigational treatments. While traditional human lung cancer cell lines offer a basic framework for cancer research, they often lack the tumor heterogeneity and intricate tumor–stromal interactions necessary to accurately predict patient-specific clinical outcomes. Patient-derived xenografts (PDXs), however, retain the original tumor’s histopathology and genetic features, providing a more reliable model for predicting responses to systemic therapeutics, especially molecularly targeted therapies. For studying immunotherapies and antibody–drug conjugates, humanized PDX mouse models, syngeneic mouse models, and genetically engineered mouse models (GEMMs) are increasingly utilized. Despite their value, these in vivo models are costly, labor-intensive, and time-consuming. Recently, patient-derived lung cancer organoids (LCOs) have emerged as a promising in vitro tool for functional precision oncology studies. These LCOs demonstrate high success rates in growth and maintenance, accurately represent the histology and genomics of the original tumors and exhibit strong correlations with clinical treatment responses. Further supported by advancements in imaging, spatial and single-cell transcriptomics, proteomics, and artificial intelligence, these preclinical models are reshaping the landscape of drug development and functional precision lung cancer research. This integrated approach holds the potential to deliver increasingly accurate, personalized treatment strategies, ultimately enhancing patient outcomes in lung cancer. Full article

In plants, lipids function as structural elements and signaling molecules. Understanding lipid composition and dynamics is essential for unraveling their biological functions and metabolism. Mapping the spatiotemporal distribution of lipids in plants holds great potential for elucidating lipid biosynthetic pathways and gaining insights to guide crop genetic engineering. Recent progress in fluorescence microscopy and imaging has opened new opportunities for researchers to visualize plant lipids in vivo at high spatiotemporal resolution. In this review, we provide an up-to-date overview of the methods used to image plant lipids with fluorescence microscopy. We highlight caveats and potential limitations of these approaches and provide suggestions for optimizing their utilization. This review synthesizes current knowledge and highlights the potential of these methods to provide new insights into lipid biology. Full article

Pansharpening is a traditional image fusion problem where the reference image (or ground truth) is not accessible. Machine-learning-based algorithms designed for this task require an extensive optimization phase of network parameters, which must be performed using unsupervised learning techniques. The learning phase can either rely on a companion problem where ground truth is available, such as by reproducing the task at a lower scale or using a pretext task, or it can use a reference-free cost function. This study focuses on the latter approach, where performance depends not only on the accuracy of the quality measure but also on the mathematical properties of these measures, which may introduce challenges related to computational complexity and optimization. The evaluation of the most recognized no-reference image quality measures led to the proposal of a novel criterion, the Regression-based QNR (RQNR), which has not been previously used. To mitigate computational challenges, an approximate version of the relevant indices was employed, simplifying the optimization of the cost functions. The effectiveness of the proposed cost functions was validated through the reduced-resolution assessment protocol applied to a public dataset (PairMax) containing images of diverse regions of the Earth’s surface. Full article

Background: Angelicae Dahuricae Radix (ADR) is used as both a traditional Chinese medicine and a food ingredient in China and East Asian countries. ADR is generally sun-dried post-harvest but is sometimes sulfur-fumigated to prevent decay and rot. Although there are some studies on the effect of sulfur fumigation on ADR, they are not comprehensive. Methods: This study used HPLC fingerprinting, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), in vitro anti-inflammatory assays, and metabolite analysis in blood based on UPLC-MS/MS to assess the impact of sulfur fumigation on the active ingredients of ADR. Results: There were significant decreases in specific coumarins and amino acids, particularly byakangelicol, oxypeucedanin, L-proline, and L-arginine, following sulfur fumigation. Among the 185 metabolites in blood, there were 30 different compounds, and oxypeucedanin was the most obvious component to decrease after sulfur fumigation. ADR showed anti-inflammatory activity regardless of sulfur fumigation. However, the effects on the production of cytokines in LPS-induced RAW264.7 cells were different. Conclusions: Chemometric analysis and in vitro anti-inflammatory studies suggested that byakangelicol and oxypeucedanin could serve as potential quality markers for identifying sulfur-fumigated ADR. These findings provide a chemical basis for comprehensive safety and functional evaluations of sulfur-fumigated ADR, supporting further research in this field. Full article