Search Results (12,574)

This study aimed to investigate whether plaque characteristics derived from intravascular optical coherence tomography (IVOCT) could predict a long-term cardiovascular (CV) death. This study was a single-center, retrospective study on 104 patients who had undergone IVOCT-guided percutaneous coronary intervention. Plaque characterization was performed using Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) software developed by our group. A total of 31 plaque features, including lesion length, lumen, calcium, fibrous cap (FC), and vulnerable plaque features (e.g., microchannel), were computed from the baseline IVOCT images. The discriminatory power for predicting CV death was determined using univariate/multivariate logistic regressions. Of 104 patients, CV death was identified in 24 patients (23.1%). Univariate logistic regression revealed that lesion length, calcium angle, calcium thickness, FC angle, FC area, and FC surface area were significantly associated with CV death (p < 0.05). In the multivariate logistic analysis, only the FC surface area (OR 2.38, CI 0.98–5.83, p < 0.05) was identified as a significant determinant for CV death, highlighting the importance of the 3D lesion analysis. The AUC of FC surface area for predicting CV death was 0.851 (95% CI 0.800–0.927, p < 0.05). Patients with CV death had distinct plaque characteristics (i.e., large FC surface area) in IVOCT. Studies such as this one might someday lead to recommendations for pharmaceutical and interventional approaches. Full article

Cholangiopathy has been described in survivors of severe COVID-19, presenting significant clinical parallels to the pre-pandemic condition of secondary sclerosing cholangitis in critically ill patients (SSC-CIP). We aimed to examine the liver histopathology of individuals with persistent cholestasis after severe COVID-19. Methods: We subjected post-COVID-19 cholestasis liver samples to routine staining techniques and cytokeratin 7 immunostaining and semi-quantitatively analyzed the portal and parenchymal changes. Results: All ten patients, five men, had a median age of 56, an interquartile range (IQR) of 51–60, and required intensive care unit and mechanical ventilation. The median and IQR liver enzyme concentrations proximal to biopsy were in IU/L: ALP 645 (390–1256); GGT 925 (664–2169); ALT 100 (86–113); AST 87 (68–106); and bilirubin 4 (1–9) mg/dL. Imaging revealed intrahepatic bile duct anomalies and biliary casts. We performed biopsies at a median of 203 (150–249) days after molecular confirmation of infection. We found portal and periportal fibrosis, moderate-to-severe ductular proliferation, and bile duct dystrophy in all patients, while we observed hepatocyte biliary metaplasia in all tested cases. We observed mild-to-severe parenchymal cholestasis and bile plugs in nine and six cases. We also observed mild swelling of the arteriolar endothelial cells in five patients. We observed a thrombus in a small portal vein branch and mild periductal fibrosis in one case each. One patient developed multiple small biliary infarctions. We did not observe ductopenia in any patient. Conclusions: The alterations were like those observed in SSC-CIP; however, pronounced swelling of endothelial cells, necrosis of the vessel walls, and thrombosis in small vessels were notable. Full article

The aim of this study was to identify the areal surface parameters that correlated with lowering of sliding friction. Different ground surfaces were created on stainless steel and the lubricated sliding friction generated at the contact interface with a flat-faced aluminum pin was studied. The frictional force encountered is an order of magnitude lower for a P1200-finished surface than the other ground surfaces. Using 3D surface profilometry, a unique surface parameter ratio “Spk/Sk” was found to predict the frictional performance of these surfaces. When this surface parameter ratio was less than 1, average sliding friction was close to 0.1. When this ratio was greater than 1, the coefficient was an order of magnitude lower. Using energy dispersive spectrometry, such surfaces after wear showed the presence of a uniform dispersed layer of iron oxide on the surface of the pin. This was absent on the surfaces having high friction, indicating the role of the steel counter surface in building this beneficial transfer layer. Scanning electron microscopy provided topography images to visualize the surface wear. The motivation for the authors was to use a commercially scaled process like precision grinding for the surface modifications on stainless steel. Full article

In this paper, we introduce an invariant by image viewpoint changes by applying an important theorem in conformal geometry stating that every surface of the Minkowski space ${\mathbb{R}}^{3,1}$ leads to an invariant by conformal transformations. For this, we identify the domain of an image to the disjoint union of horospheres ${\coprod }_{\alpha }{H}_{\alpha }$ of ${\mathbb{R}}^{3,1}$ by means of the powerful tools of the conformal Clifford algebras. We explain that every viewpoint change is given by a planar similarity and a perspective distortion encoded by the latitude angle of the camera. We model the perspective distortion by the point at infinity of the conformal model of the Euclidean plane described by D. Hestenesand we clarify the spinor representations of the similarities of the Euclidean plane. This leads us to represent the viewpoint changes by conformal transformations of ${\coprod }_{\alpha }{H}_{\alpha }$ for the Minkowski metric of the ambient space. Full article

The conventional feature point-based image stitching algorithm exhibits inconsistencies in the quality of feature points across diverse scenes. This may result in the deterioration of the alignment effect or even the inability to align two images. To address this issue, this paper presents an unsupervised multigrid image alignment method that integrates the conventional feature point-based image alignment algorithm with deep learning techniques. The method postulates that the feature points are uniformly distributed in the image and employs a deep learning network to predict their displacements, thereby enhancing the robustness of the feature points. Furthermore, the precision of image alignment is enhanced through the parameterization of APAP (As-projective-as-possible image stitching with moving DLT) multigrid deformation. Ultimately, based on the symmetry exhibited by the homography matrix and its inverse matrix throughout the projection process, image chunking inverse warping is introduced to obtain the stitched images for the multigrid deep learning network. Additionally, the mesh shape-preserving loss is introduced to constrain the shape of the multigrid. The experimental results demonstrate that in the real-world UDIS-D dataset, the method achieves notable improvements in feature point matching and homography estimation tasks, and exhibits superior alignment performance on the traditional image stitching dataset. Full article

In cases of distal radius fractures, the fixation of the volar lunate facet fragment is crucial for preventing volar subluxation of the carpal bones. This study aims to clarify the sex differences in the volar morphology of the lunate facet of the distal radius and its relationship with the transverse diameter of the distal radius. Sixty-four CT scans of healthy wrists (30 males and 34 females) were evaluated. Three-dimensional (3D) images of the distal radius were reconstructed from the CT data. We defined reference point 1 as the starting point of the inclination toward the distal volar edge, reference point 2 as the volar edge of the joint on the bone axis, and reference point 3 as the volar edge of the distal radius lunate facet. From the 3D coordinates of reference points 1 to 3, the bone axis distance, volar−dorsal distance, radial−ulnar distance, 3D straight-line distance, and inclination angle were measured. The transverse diameter of the radius was measured, and its correlations with the parameters were evaluated. It was found that in males, compared to females, the transverse diameter of the radius is larger and the protrusion of the volar lunate facet is greater. This suggests that the inclination of the volar surface is steeper in males and that the volar locking plate may not fit properly with the volar cortical bone of the lunate facet, necessitating additional fixation. Full article

Sea ice concentration (SIC) is an important dimension for characterising the geographical features of the pan-Arctic region. Trends in SIC bring new opportunities for human activities in the Arctic region. In this paper, we propose a deep learning technology-based sea ice concentration prediction model, SICFormer, which can realise end-to-end daily sea ice concentration prediction. Specifically, the model uses a 3D-Swin Transformer as an encoder and designs a decoder to reconstruct the predicted image based on PixelShuffle. This is a new model architecture that we have proposed. Single-day SIC data from the National Snow and Ice Data Center (NSIDC) for the years 2006 to 2022 are utilised. The results of 8-day short-term prediction experiments show that the average Mean Absolute Error (MAE) of the SICFormer model on the test set over the 5 years is 1.89%, the Root Mean Squared Error (RMSE) is 5.99%, the Mean Absolute Percentage Error (MAPE) is 4.32%, and the Nash–Sutcliffe Efficiency (NSE) is 0.98. Furthermore, the current popular deep learning models for spatio-temporal prediction are employed as a point of comparison given their proven efficacy on numerous public datasets. The comparison experiments show that the SICFormer model achieves the best overall performance. Full article

The health monitoring of vegetable and fruit plants, especially during the critical seedling growth stage, is essential to protect them from various environmental stresses and prevent yield loss. Different environmental stresses may cause similar symptoms, making visual inspection alone unreliable and potentially leading to an incorrect diagnosis and delayed corrective actions. This study aimed to address these challenges by proposing a segmented regression model and a Mask R-CNN model for detecting the initiation time and symptoms of nutrient stress in cucumber seedlings within a controlled environment. Nutrient stress was induced by applying two different treatments: an indicative nutrient deficiency with an electrical conductivity (EC) of 0 dSm⁻¹, and excess nutrients with a high-concentration nutrient solution and an EC of 6 dSm⁻¹. Images of the seedlings were collected using an automatic image acquisition system two weeks after germination. The early initiation of nutrient stress was detected using a segmented regression analysis, which analyzed morphological and textural features extracted from the images. For the Mask R-CNN model, 800 seedling images were annotated based on the segmented regression analysis results. Nutrient-stressed seedlings were identified from the initiation day to 4.2 days after treatment application. The Mask R-CNN model, implemented using ResNet-101 for feature extraction, leveraged transfer learning to train the network with a smaller dataset, thereby reducing the processing time. This study identifies the top projected canopy area (TPCA), energy, entropy, and homogeneity as prospective indicators of nutritional deficits in cucumber seedlings. The results from the Mask R-CNN model are promising, with the best-fit image achieving an F1 score of 93.4%, a precision of 93%, and a recall of 94%. These findings demonstrate the effectiveness of the integrated statistical and machine learning (ML) methods for the early and accurate diagnosis of nutrient stress. The use of segmented regression for initial detection, followed by the Mask R-CNN for precise identification, emphasizes the potential of this approach to enhance agricultural practices. By facilitating the early detection and accurate diagnosis of nutrient stress, this approach allows for quicker and more precise treatments, which improve crop health and productivity. Future research could expand this methodology to other crop types and field conditions to enhance image processing techniques, and researchers may also integrate real-time monitoring systems. Full article

Deep cavity configurations are common in various industrial applications, including automotive windows, sunroofs, and many other applications in aerospace engineering. Flows over such a geometry can result in aeroacoustic coupling between the cavity shear layer oscillations and the surrounding acoustic modes. This phenomenon can result in a resonance that can lead to significant noise and may cause damage to mechanical structures. Flow control methods are usually used to reduce or eliminate the aeroacoustic resonance. An experimental set up was developed to study the effectiveness of both a cylinder and a profiled cylinder positioned upstream from the cavity in reducing the flow resonance. The cavity flow and the acoustic signals were obtained using particle image velocimetry (PIV) and unsteady pressure sensors, respectively. A decrease of up to 36 dB was obtained in the sound pressure levels (SPL) using the passive control methods. The profiled cylinder showed a similar efficacy in reducing the resonance despite the absence of a high-frequency forcing. Time-space cross-correlation maps along the cavity shear layer showed the suppression of the feedback mechanism for both control methods. A snapshot proper orthogonal decomposition (POD) showed interesting differences between the cylinder and profiled cylinder control methods in terms of kinetic energy content and the vortex dynamics behavior. Furthermore, the interaction of the wake of the control device with the cavity shear layer and its impact on the aeroacoustic coupling was investigated using the POD analysis. Full article

In outdoor unmanned forklift unloading scenarios, pallet detection and localization face challenges posed by uncontrollable lighting conditions. Furthermore, the stacking and close arrangement of pallets also increase the difficulty of positioning a target pallet. To solve these problems, a method for high-precision positioning and rotation angle estimation for a target pallet using the BeiDou Navigation Satellite System (BDS) and vision is proposed. Deep dual-resolution networks (DDRNets) are used to segment the pallet from depth images and RGB images. Then, keypoints for calculating the position and rotation angle are extracted and further combined with the 3D point cloud data to achieve accurate pallet positioning. Constraining the pixel coordinates and depth coordinates of the center point of the pallet and setting the priority of the pallet according to the unloading direction allow the target pallet to be identified from multiple pallets. The positioning of the target pallet in the forklift navigation coordinate system is achieved by integrating BDS positioning data through coordinate transformation. This method is robust in response to lighting influences and can accurately locate the target pallet. The experimental results show that the pallet positioning error is less than 20 mm, and the rotation angle error is less than 0.37°, which meets the accuracy requirements for automated forklift operations. Full article

This article reviews technological advances and global trends in the diagnosis, treatment, and monitoring of cardiovascular diseases. A bibliometric analysis was conducted using the SCOPUS database, following PRISMA-ScR guidelines, to identify relevant publications on technologies applied in the diagnosis and treatment of cardiovascular diseases. An increase in scientific output since 2018 was observed, reflecting a growing interest in the technologies available for the treatment of cardiovascular diseases, with terms such as “telemedicine”, “artificial intelligence”, “image analysis”, and “cardiovascular disease” standing out as some of the most commonly used terms in reference to CVDs. Significant trends were identified, such as the use of artificial intelligence in precision medicine and machine learning algorithms to analyse data and predict cardiovascular risk, as well as advances in image analysis and 3D printing. Highlighting the role of artificial intelligence in the diagnosis and continuous monitoring of cardiovascular diseases, showing its potential to improve prognosis and reduce the incidence of acute cardiovascular events, this study presents the integration of traditional cardiology methods with digital health technologies—through a transdisciplinary approach—as a new direction in cardiovascular health, emphasising individualised care and improved clinical outcomes. These advances have great potential to impact healthcare, and as this field expands, it is crucial to understand the current research landscape and direction in order to take advantage of each technological advancement for improving the diagnosis, treatment, and quality of life of cardiovascular patients. It is concluded that the integration of these technologies into clinical practice has important implications for public health. Early detection and personalised treatment of cardiovascular diseases (CVDs) can significantly reduce the morbidity and mortality associated with these diseases. In addition, the optimisation of public health resources through telemedicine and telecare can improve access to quality care. The implementation of these technologies can be a crucial step towards reducing the global burden of cardiovascular diseases. Full article

The growing expansion of the economy and population has resulted in an increased inclination towards the utilization of groundwater resources. Conducting a geophysical survey is a widely employed method for subsurface mapping and the detection of groundwater. A geophysical study was conducted in Nanshan township, Huarong County, located in the Hunan province of the South-Central region of China. The investigation involved the utilization of a 2D electrical resistivity imaging technique employing forward and inverse pole–dipole electrode arrays. A total of six survey lines were established, each with an electrode distance from the nearest measuring point exceeding 800 m. The maximum current electrode separation was utilized in this setup. The spacing between the electrical resistivity sounding points was established at regular intervals. The findings from the exploration indicate the existence of multiple faults within the surveyed region. The study additionally identified two regions of structural failure, which occurred due to the convergence of faults oriented in different directions. This convergence led to the fracturing of rocks, an increase in water content, and a decrease in resistivity. The findings from the exploration were utilized in the formulation of five verification target boreholes. The results of this study offer significant insights that can inform future investigations into groundwater exploration endeavors within the region. Full article

When treating anaerobic brain abscesses, healthcare professionals often face the difficulty of identifying the causal pathogens, necessitating empiric therapies with uncertain efficacy. We present the case of a 57-year-old woman who was admitted to our hospital with a fever and headache. Brain magnetic resonance imaging revealed a hemorrhagic lesion with wall enhancement at the left hemisphere on contrast-enhanced T1-weighted imaging. Cerebrospinal fluid examination showed pleocytosis (23 cells/μL), an elevated protein level (125 mg/dL), and decreased glucose level (51 mg/dL; blood glucose was 128 mg/dL). Intracerebral hemorrhage accompanied by a brain abscess was clinically suspected. The patient received empirical treatment with intravenous meropenem and vancomycin for 2 weeks. However, conventional bacterial culture tests failed to identify the pathogen. We then performed shotgun sequencing and ribosomal multilocus sequence typing, which identified Paraclostridium tenue. Based on this finding, we de-escalated to benzylpenicillin potassium for 4 weeks, leading to a 2.5-year remission of the anaerobic brain abscess. Therefore, Paraclostridium can be a causative pathogen for brain abscesses. Furthermore, whole-metagenome sequencing is a promising method for detecting rare pathogens that are not identifiable by conventional bacterial culture tests. This approach enables more targeted treatment and contributes to achieving long-term remission in clinical settings. Full article

In the context of distribution networks, constructing 3D point cloud maps is crucial, particularly for UAV navigation and path planning tasks. Methods that utilize reflections from surfaces, such as laser and structured light, to obtain depth point clouds for surface modeling and environmental depth estimation are already quite mature in some professional scenarios. However, acquiring dense and accurate depth information typically requires very high costs. In contrast, monocular image-based depth estimation methods do not require relatively expensive equipment and specialized personnel, making them available for a wider range of applications. To achieve high precision and efficiency in UAV distribution networks, inspired by knowledge distillation, we employ a teacher–student architecture to enable efficient inference. This approach maintains high-quality depth estimation while optimizing the point cloud to obtain more precise results. In this paper, we propose KD-MonoRec, which integrates knowledge distillation into the semi-supervised MonoRec framework for UAV distribution networks. Our method demonstrates excellent performance on the KITTI dataset and performs well in collected distribution network environments. Full article

Hybrid positron emission tomography/magnetic resonance imaging (PET/MR) opens new possibilities in multimodal multiparametric (m2p) image analyses. But even the simultaneous acquisition of positron emission tomography (PET) and magnetic resonance imaging (MRI) does not guarantee perfect voxel-by-voxel co-registration due to organs and distortions, especially in diffusion-weighted imaging (DWI), which would be, however, crucial to derive biologically meaningful information. Thus, our aim was to optimize fusion and voxel-wise analyses of DWI and standardized uptake values (SUVs) using a novel software for m2p analyses. Using research software, we evaluated the precision of image co-registration and voxel-wise analyses including the rigid and elastic 3D registration of DWI and [18F]-Fluorodeoxyglucose (FDG)-PET from an integrated PET/MR system. We analyzed DWI distortions with a volume-preserving constraint in three different 3D-printed phantom models. A total of 12 PET/MR-DWI clinical datasets (bronchial carcinoma patients) were referenced to the T1 weighted-DIXON sequence. Back mapping of scatterplots and voxel-wise registration was performed and compared to the non-optimized datasets. Fusion was rated using a 5-point Likert scale. Using the 3D-elastic co-registration algorithm, geometric shapes were restored in phantom measurements; the measured ADC values did not change significantly (F = 1.12, p = 0.34). Reader assessment showed a significant improvement in fusion precision for DWI and morphological landmarks in the 3D-registered datasets (4.3 ± 0.2 vs. 4.6 ± 0.2, p = 0.009). Most pronounced differences were noted for the chest wall (p = 0.006), tumor (p = 0.007), and skin contour (p = 0.014). Co-registration increased the number of plausible ADC and SUV combinations by 25%. The volume-preserving elastic 3D registration of DWI significantly improved the precision of fusion with anatomical sequences in phantom and clinical datasets. The research software allowed for a voxel-wise analysis and visualization of [18F]FDG-PET/MR data as a “combined diffusivity–metabolic index” (cDMI). The clinical value of the optimized PET/MR biomarker can thus be tested in future PET/MR studies. Full article