Search Results (12,390)

Interproximal enamel reduction (IER) is a minimally invasive therapeutic procedure commonly used in orthodontics to address both functional and aesthetic issues. Its mechanical effects on enamel surfaces induce the formation of grooves, furrows, scratches, depressions, and valleys. The aim of this study was to assess the enamel surface roughness resulting after the application of currently available methods for interproximal reduction. Ninety freshly extracted human teeth were divided into six groups and subjected to the stripping procedure, using a different method for each group (diamond burs, abrasive strips of 90 μm, 60 μm, 40 μm, 15 μm, and abrasive discs). A single individual performed stripping according to the manufacturer’s recommendations, involving interproximal reduction on one tooth’s proximal face and leaving the other side untreated. Qualitative and quantitative assessment of the enamel surfaces was carried out using Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM), obtaining 2D and volumetric 3D images of the enamel surface microstructure and nanostructure. The study found that diamond burs and abrasive strips of 60 μm and 90 μm increased enamel roughness due to intense de-structuring effects, while the 40 μm polisher had a gentler effect and 15 μm abrasive strips and polishing discs preserved enamel surface quality and removed natural wear traces. Full article

Convalescent sera, rich in pathogen-specific antibodies, offers passive immunity to patients with infectious diseases. Screening assays using convalescent sera are crucial for evaluating therapeutic efficacy, selecting suitable serum donors, and standardizing assays. They measure antibody levels, neutralizing potential, and specificity against viruses like SARS-CoV-2, ensuring therapeutic serum contains potent antibodies. Standardized procedures enable reliable results and wider adoption of serum therapy for COVID-19. We have developed a high-content image-based assay for screening convalescent sera against SARS-CoV-2 variants. Using various cell lines, we identified optimal candidates, employed immunofluorescence to visualize infected cells, and assessed neutralizing antibody efficacy. Screening convalescent sera for therapeutic potential identified neutralizing activity against SARS-CoV-2 variants. Dose–response analysis showed variable neutralizing activity, with some sera exhibiting broad neutralization. Additionally, we explored the synergy between neutralizing sera and β-d-N4-hydroxycytidine (NHC), an initial metabolite of molnupiravir. These assays enhance serum therapy’s benefits for COVID-19 treatment and aid in understanding neutralizing activity against SARS-CoV-2 variants, addressing viral challenges. Full article

Real-world understanding serves as a medium that bridges the information world and the physical world, enabling the realization of virtual–real mapping and interaction. However, scene understanding based solely on 2D images faces problems such as a lack of geometric information and limited robustness against occlusion. The depth sensor brings new opportunities, but there are still challenges in fusing depth with geometric and semantic priors. To address these concerns, our method considers the repeatability of video stream data and the sparsity of newly generated data. We introduce a sparsely correlated network architecture (SCN) designed explicitly for online RGBD instance segmentation. Additionally, we leverage the power of object-level RGB-D SLAM systems, thereby transcending the limitations of conventional approaches that solely emphasize geometry or semantics. We establish correlation over time and leverage this correlation to develop rules and generate sparse data. We thoroughly evaluate the system’s performance on the NYU Depth V2 and ScanNet V2 datasets, demonstrating that incorporating frame-to-frame correlation leads to significantly improved accuracy and consistency in instance segmentation compared to existing state-of-the-art alternatives. Moreover, using sparse data reduces data complexity while ensuring the real-time requirement of 18 fps. Furthermore, by utilizing prior knowledge of object layout understanding, we showcase a promising application of augmented reality, showcasing its potential and practicality. Full article

Traditional approaches to modular construction progress monitoring and quality control with stringent and tight tolerances for on-site and off-site assembly processes are usually based on 3D laser scanning, but the high equipment costs associated with acquiring point clouds have economic ramifications. This paper provides the details of a new and inexpensive method through the integration of AprilTags and an ordinary phone. By using AprilTags instead of QR codes to label modules, progress management is achieved through the rapid identification and association of multiple modules based on a single image. Moreover, a virtual multi-view vision algorithm based on AprilTags is proposed to generate 3D reverse models of the construction site; the quality result can be acquired by comparing the offset and rotation values of the reverse model and the BIM model. Finally, all the algorithms are validated through comparing the reverse models with the reference models made with 3D printing and 3D laser scanning, which verifies the accuracy and efficiency of the proposed method. Full article

A crucial challenge faced by noninvasive imaging through strongly scattering media is overcoming background light interference. Polarization-based anti-scattering methods can eliminate background light interference, but fail to utilize speckle images that do not contain unscattered object light for object reconstruction. Although speckle correlation imaging (SCI) methods can utilize speckle images for object reconstruction, it is difficult to achieve stable high-quality reconstruction and overcome background light interference using these methods. In this study, we propose a polarized-speckle deviation imaging (PSDI) method to overcome background light interference and achieve high-quality imaging through strongly scattering media. PSDI utilizes the bispectrum and autocorrelation of polarized speckle image deviations to reconstruct the Fourier phase and amplitude spectra of the object image, respectively. Experimental results show that when the background light is polarized and unpolarized, PSDI can achieve stable high-fidelity reconstruction of a polarized object when the signal-to-background ratio (SBR) is lower than −7 dB and −9 dB, respectively. PSDI bridges the gap between imaging with strongly scattered light and overcoming strong background light interference, and is expected to find widespread applications in fields such as biomedical imaging, astronomical observation, underwater imaging, and remote sensing. Full article

The incorporation of automatic segmentation methodologies into dental X-ray images refined the paradigms of clinical diagnostics and therapeutic planning by facilitating meticulous, pixel-level articulation of both dental structures and proximate tissues. This underpins the pillars of early pathological detection and meticulous disease progression monitoring. Nonetheless, conventional segmentation frameworks often encounter significant setbacks attributable to the intrinsic limitations of X-ray imaging, including compromised image fidelity, obscured delineation of structural boundaries, and the intricate anatomical structures of dental constituents such as pulp, enamel, and dentin. To surmount these impediments, we propose the Deformable Convolution and Mamba Integration Network, an innovative 2D dental X-ray image segmentation architecture, which amalgamates a Coalescent Structural Deformable Encoder, a Cognitively-Optimized Semantic Enhance Module, and a Hierarchical Convergence Decoder. Collectively, these components bolster the management of multi-scale global features, fortify the stability of feature representation, and refine the amalgamation of feature vectors. A comparative assessment against 14 baselines underscores its efficacy, registering a 0.95% enhancement in the Dice Coefficient and a diminution of the 95th percentile Hausdorff Distance to 7.494. Full article

Background: This work aims to establish the relationship between depression and epidemiological or imaging variables, frailty, and cognitive status in patients who suffered hospital admission for COVID-19. Methods: A longitudinal observational study investigated 72 patients admitted for COVID-19 to a hospital in Spain. Patients were evaluated at discharge and six months later. Clinical, analytical, and imaging variables were collected. A neurocognitive, nutritional, and frailty (FRAIL scale) assessment of the included patients was carried out. The risk of depression was considered for a result above 5 points on the PHQ-9 scale. Results: The variables that were significantly related to the risk of depression 6 months after admission for COVID-19 were frailty (p = 0.006 for pre-frail and p = 0.001 for frail), small-vessel vascular disease in imaging tests (p = 0.033), vitamin D level (p = 0.006), and taking antidepressants (p = 0.011). Factors that were negatively associated with the presence of depression 6 months after discharge were a higher score on the CAMCOG cognitive scale (p = 0.041) and older age (p = 0.006). Conclusions: Frailty worsened the score on the PHQ-9 depression scale in patients who required hospital admission for SARS-CoV-2 infection. It is important to implement prevention measures both for frailty and depression in these patients. Full article

In order to realize automatic surface defect detection for large aperture precision spherical optical elements, an automatic surface defect detection instrument has been designed. The instrument consists of a microscopic imaging system, illumination system, motion scanning system, and a software algorithm system. Firstly, a multi-angle channel illumination source and a coaxial illumination source were designed. Bright and dark field images of surface defects were captured by cooperating with an automatic zoom microscope. Then, algorithms for scanning trajectory planning, image stitching, and intelligent defect recognition were designed to achieve full-aperture surface image acquisition and defect quantification detection. The automated defect detection process of the instrument is summarized and introduced. Finally, the experimental platform was constructed, which can work well for the optical elements with a maximum diameter of 400 mm and a relative aperture R/D value of 1. It takes about 15 min to detect an optical element with a diameter of 200 mm in dark-field imaging mode. As a result, the minimum line width of scratch detectable is 2 μm and the minimum diameter of pitting detectable is 4 μm. Clearly, the instrument can realize the automatic detection of surface defects of spherical optical elements, and has the advantages of a high efficiency, stability, reliability, quantification, and data traceability. Full article

The Nearctic aphid genus Drepanaphis Del Guercio, 1909, the largest within the subfamily Drepanosiphinae (Hemiptera: Aphididae), is characterised by distinctive dorsal abdominal tubercles. This study presents a comprehensive taxonomic revision of the genus, expanding the recognised species to 18, including the newly described Drepanaphis robinsoni Malik sp. nov. Detailed descriptions and figures for 44 morphs, encompassing alate viviparous females, oviparous females and males, are provided, with new identification keys for all known species and morphs. The sexual morphs of 15 species, particularly oviparous females, are documented for the first time. Morphometric and principal component analyses (PCA) are employed to distinguish the studied taxa. This study identifies and corrects numerous misidentifications in museum collections, previously labelled as D. acerifoliae, D. choanotricha, D. kanzensis, D. knowltoni, D. parva, D. sabrinae or D. tissoti. Furthermore, it revalidates the distinct status of D. nigricans and D. tissoti, which had been synonymised in earlier works. Current range maps for all species and images of key morphological features obtained through light and scanning electron microscopy are also presented, providing a more complete understanding of this understudied genus. Full article

The quality of underwater bridge piers significantly impacts bridge safety and long-term usability. To address limitations in conventional inspection methods, this paper presents a sonar-based technique for the three-dimensional (3D) reconstruction and visualization of underwater bridge piers. Advanced MS1000 scanning sonar is employed to detect and image bridge piers. Automated image preprocessing, including filtering, denoising, binarization, filling, and morphological operations, introduces an enhanced wavelet denoising method to accurately extract the foundation contour coordinates of bridge piers from sonar images. Using these coordinates, along with undamaged pier dimensions and sonar distances, a model-driven approach for a 3D pier reconstruction algorithm is developed. This algorithm leverages multiple sonar data points to reconstruct damaged piers through multiplication. The Visualization Toolkit (VTK) and surface contour methodology are utilized for 3D visualization, enabling interactive manipulation for enhanced observation and analysis. Experimental results indicate a relative error of 13.56% for the hole volume and 10.65% for the spalling volume, demonstrating accurate replication of bridge pier defect volumes by the reconstructed models. Experimental validation confirms the method’s accuracy and effectiveness in reconstructing underwater bridge piers in three dimensions, providing robust support for safety assessments and contributing significantly to bridge stability and long-term safety assurance. Full article

This work compares photoacoustic images of a tooth by analyzing the signals generated with wavelengths 532 and 355 nm. This comparison addresses the differences in the optical properties of dental tissue for these wavelengths that affect the resulting photoacoustic images. A pulsed Nd:YAG laser was used to illuminate a complete extracted tooth sample, and 2D photoacoustic images (PAIs) were reconstructed using the single-sensor scanning synthetic aperture focusing technique (SSC-SAFT), which is a suitable method for our experimental system with forward detection mode. Signal comparison was conducted using sinogram, signal-to-noise ratio (SNR), root mean square (RMS), arrival time, maximum amplitude, and fast Fourier transform (FFT). PAI comparison utilized intensity profile, edge correlation, and image composition tools. The signal analysis revealed that at 532 nm, the signals exhibited longer decay time and a wider distribution of vibration frequencies due to higher laser pulse energy and greater optical penetration depth. Conversely, at 355 nm, the signals had shorter decay times and a lower frequency distribution, which was attributed to lower energy but improved optical absorption, resulting in reconstructed images with better sharpness and contour definition. This study contributes to the advancement of photoacoustic imaging technology in dentistry by providing insights that could optimize signal generation and image reconstruction for dental tissue. Full article

The optical image sub-pixel correlation (SPC) technique is an important method for monitoring large-scale surface deformation. RapidEye images, distinguished by their short revisit period and high spatial resolution, are crucial data sources for monitoring surface deformation. However, few studies have comprehensively analyzed the error sources and correction methods of the deformation field obtained from RapidEye images. We used RapidEye images without surface deformation to analyze potential errors in the offset fields. We found that the errors in RapidEye offset fields primarily consist of decorrelation noise, orbit error, and attitude jitter distortions. To mitigate decorrelation noise, the careful selection of offset pairs coupled with spatial filtering is essential. Orbit error can be effectively mitigated by the polynomial fitting method. To address attitude jitter distortions, we introduced a linear fitting approach that incorporated the coherence of attitude jitter. To demonstrate the performance of the proposed methods, we utilized RapidEye images to extract the coseismic displacement field of the 2019 Ridgecrest earthquake sequence. The two-dimensional (2D) offset field contained deformation signals extracted from two earthquakes, with a maximum offset of 2.8 m in the E-W direction and 2.4 m in the N-S direction. A comparison with GNSS observations indicates that, after error correction, the mean relative precision of the offset field improved by 92% in the E-W direction and by 89% in the N-S direction. This robust enhancement underscores the effectiveness of the proposed error correction methods for RapidEye data. This study sheds light on large-scale surface deformation monitoring using RapidEye images. Full article

3D object detection is a challenging and promising task for autonomous driving and robotics, benefiting significantly from multi-sensor fusion, such as LiDAR and cameras. Conventional methods for sensor fusion rely on a projection matrix to align the features from LiDAR and cameras. However, these methods often suffer from inadequate flexibility and robustness, leading to lower alignment accuracy under complex environmental conditions. Addressing these challenges, in this paper, we propose a novel Bidirectional Attention Fusion module, named BAFusion, which effectively fuses the information from LiDAR and cameras using cross-attention. Unlike the conventional methods, our BAFusion module can adaptively learn the cross-modal attention weights, making the approach more flexible and robust. Moreover, drawing inspiration from advanced attention optimization techniques in 2D vision, we developed the Cross Focused Linear Attention Fusion Layer (CFLAF Layer) and integrated it into our BAFusion pipeline. This layer optimizes the computational complexity of attention mechanisms and facilitates advanced interactions between image and point cloud data, showcasing a novel approach to addressing the challenges of cross-modal attention calculations. We evaluated our method on the KITTI dataset using various baseline networks, such as PointPillars, SECOND, and Part-A², and demonstrated consistent improvements in 3D object detection performance over these baselines, especially for smaller objects like cyclists and pedestrians. Our approach achieves competitive results on the KITTI benchmark. Full article

The liver is a vital organ in the human body, and CT images can intuitively display its morphology. Physicians rely on liver CT images to observe its anatomical structure and areas of pathology, providing evidence for clinical diagnosis and treatment planning. To assist physicians in making accurate judgments, artificial intelligence techniques are adopted. Addressing the limitations of existing methods in liver CT image segmentation, such as weak contextual analysis and semantic information loss, we propose a novel Dual Attention-Based 3D U-Net liver segmentation algorithm on CT images. The innovations of our approach are summarized as follows: (1) We improve the 3D U-Net network by introducing residual connections to better capture multi-scale information and alleviate semantic information loss. (2) We propose the DA-Block encoder structure to enhance feature extraction capability. (3) We introduce the CBAM module into skip connections to optimize feature transmission in the encoder, reducing semantic gaps and achieving accurate liver segmentation. To validate the effectiveness of the algorithm, experiments were conducted on the LiTS dataset. The results showed that the Dice coefficient and HD95 index for liver images were 92.56% and 28.09 mm, respectively, representing an improvement of 0.84% and a reduction of 2.45 mm compared to 3D Res-UNet. Full article

Poland syndrome (PS) is a rare congenital musculoskeletal entity occurring in approximately 1 in 30,000 newborns that manifests with variable symbrachydactyly, ipsilateral costochondral deformities, an absence of pectoral muscles, and breast underdevelopment. These have potential impacts on social, somatic, and psychological functionality, often leading affected individuals to seek expert opinions on corrective surgery. Due to phenotypic variability, strict management guidelines are lacking, with treatment decisions often based on the specialist’s personal experience rather than published evidence. Comprehensive imaging with CT and MRI with 3D reconstruction is crucial for providing a descriptive assessment of musculoskeletal defects. Management is multidisciplinary, involving thoracic, plastic, and pediatric surgeons and hand surgery specialists, as well as psychologists and developmental growth specialists. Surgery should achieve both structural and cosmetic correction to reverse the psychological and social impact and achieve patient satisfaction. We aim to provide thoracic surgeons the essential answers for sharing with affected adult individuals during consultations focusing on chest surgical correction. Full article