Search Results (48)

Hereditary predisposition substantially shapes prevention and management across urologic oncology. This narrative review synthesizes contemporary, practice-oriented guidance on whom to test, what to test, how to act on results, and how to implement care equitably for hereditary forms of prostate cancer, renal cell carcinoma (RCC), urothelial carcinoma, pheochromocytoma/paraganglioma (PPGL), and adrenocortical carcinoma (ACC). We delineate between forms of indication-driven germline testing (e.g., universal testing in metastatic prostate cancer; early-onset, bilateral/multifocal, or syndromic RCC; reflex tumor mismatch repair (MMR)/microsatellite instability (MSI) screening in upper-tract urothelial carcinoma (UTUC); universal testing in PPGL; universal TP53 testing in ACC) and pair these strategies with minimum actionable gene sets and syndrome-specific surveillance frameworks. Key points include targeted prostate-specific antigen screening in BRCA2 carriers and the impact of BRCA/ATM variants on reclassification during active surveillance; major hereditary RCC syndromes with genotype-tailored surveillance and pathway-directed therapy (e.g., HIF-2α inhibition for von Hippel–Lindau disease); UTUC/bladder cancer in Lynch syndrome with tumor MMR/MSI screening, annual urinalysis (selective cytology), and immunotherapy opportunities in deficient MMR disease/MSI-H; PPGL management emphasizing universal germline testing, intensified surveillance for SDHB, cortical-sparing adrenalectomy, and emerging HIF-2α inhibition; and ACC care modified by Li–Fraumeni syndrome (minimization of radiation/genotoxic therapy with whole-body imaging surveillance). Testicular germ cell tumor remains largely polygenic, with no routine germline testing in typical presentations. Finally, we provide pre-/post-test genetic-counseling checklists and mainstreamed workflows with equity metrics to operationalize precision care and close real-world access gaps. Full article

Background: Lynch syndrome (LS) is a cancer-susceptibility syndrome associated with autosomal dominant predisposition to a spectrum of cancers, primarily of the colorectum and endometrium, which exhibit impaired DNA mismatch repair (MMR) activity. LS is caused by a hereditary (germline) pathogenic (PV) or likely pathogenic variant (LPV) in one of the mismatch repair (MMR) genes—MLH1, MSH2, MSH6, PMS2, or EPCAM. Although point mutations are the most common genetic changes in MMR genes, >20% are large genomic rearrangements. We hypothesized that a two-tier diagnostic strategy for Lynch syndrome (LS) using next generation sequencing (NGS) and multiplex ligation-dependent probe amplification (MLPA) can increase diagnostic yield of patients with Lynch syndrome. Methods: This study included 60 patients suspected of LS. After genetic counseling, they were referred to genetic testing. Genomic DNA was extracted from peripheral blood and sequenced using NGS multigene panel testing covering 113 cancer susceptibility genes, including MMR genes. Regarding limitations of NGS analysis, which cannot reliably detect genomic alterations larger than 50 base pairs in length, the MLPA method was used for NGS negative DNA samples in order to identify larger deletions and duplications, commonly referred to as copy number variations (CNVs). Results: Different PVs were detected by NGS in 10 patients and CNVs were detected by MLPA in 7 more patients: 3xMLH1 del ex9-15, 2xMSH2 del ex1 and upstream, 1xMSH2 del ex9, and 1xMSH2 del ex1. We did not detect LPVs or variants of uncertain significance (VUS). In our cohort, the addition of MLPA provided an incremental yield of seven pathogenic CNVs, representing an 11.6% absolute increase in diagnostic sensitivity (from 16.7% to 28.3%) over the NGS-alone workflow, with CNVs accounting for 41% of all pathogenic findings. Conclusions: Our results show that MLPA is a very useful method in molecular diagnostics of LS and its implementation in routine genetic testing in combination with NGS using multigene panel testing would benefit both patients and health care providers. Full article

The accurate registration of image pairs is an indispensable key step in the process of disaster assessment, environmental monitoring, and change detection. However, obtaining correct matches from input images is difficult, especially from images with significant resolution and regional variations. The current image-registration algorithms perform poorly in this application scenario. In this article, a spatial point feature-based registration method is proposed for remote sensing images with large regional variations. First, a new edge keypoint extraction method is designed that selects points with gradient magnitude maxima around the neighborhood of the edge line segments as keypoint features. Then, the feature descriptors for each keypoint are constructed based on the geometrical distribution (distance and orientation) of each keypoint. Considering the stability of the distribution of the edge contours, our constructed descriptor vectors can be well used for image pairs with large resolution and regional variations. In addition, all feature descriptors in this method are constructed and matched in the rotated image pyramid. Finally, the fast sampling consensus algorithm is applied to eliminate mismatches. In test images with various scales, rotation angles, and regional variations, the proposed method achieved pixel-level root mean square error, and the average registration precision is nearly 100%. Meanwhile, our proposed method’s rotation and scale invariance are verified by rotating and downsampling the image pairs extensively. In addition, compared with the comparison algorithms, the method proposed in this paper has better registration performance for images with resolution and regional variations. Full article

In order to achieve 0.1 m resolution and fully polarimetric observation capabilities for airborne SAR systems, the adoption of stepped-frequency modulation waveform combined with the polarization time-division transmit/receive (T/R) technique proves to be an effective technical approach. Considering the issue of range resolution degradation and paired echoes caused by multichannel amplitude–phase mismatch in fully polarimetric airborne SAR with 0.1 m resolution, an amplitude–phase error estimation algorithm based on echo data is proposed in this paper. Firstly, the subband amplitude spectrum correction curve is obtained by the statistical average of the subband amplitude spectrum. Secondly, the paired-echo broadening function is obtained by selecting high-quality sample points after single-band imaging and the nonlinear phase error within the subbands is estimated via Sinusoidal Frequency Modulation Fourier Transform (SMFT). Thirdly, based on the minimum entropy criterion of the synthesized compressed pulse image, residual linear phase errors between subbands are quickly acquired. Finally, two-dimensional cross-correlation of the image slice is utilized to estimate the positional deviation between polarization channels. This method only requires high-quality data samples from the echo data, then rapidly estimates both intra-band and inter-band amplitude/phase errors by using SMFT and the minimum entropy criterion, respectively, with the characteristics of low computational complexity and fast convergence speed. The effectiveness of this method is verified by the imaging results of the experimental data. Full article

Three-dimensional reconstruction entails the development of mathematical models of three-dimensional objects that are suitable for computational representation and processing. This technique constructs realistic 3D models of images and has significant practical applications across various fields. This study proposes a rapid and precise multi-view 3D reconstruction method to address the challenges of low reconstruction efficiency and inadequate, poor-quality point cloud generation in incremental structure-from-motion (SFM) algorithms in multi-view geometry. The methodology involves capturing a series of overlapping images of campus. We employed the Scale-invariant feature transform (SIFT) algorithm to extract feature points from each image, applied the KD-Tree algorithm for inter-image matching, and Enhanced autonomous threshold adjustment by utilizing the Random sample consensus (RANSAC) algorithm to eliminate mismatches, thereby enhancing feature matching accuracy and the number of matched point pairs. Additionally, we developed a feature-matching strategy based on similarity, which optimizes the pairwise matching process within the incremental structure from a motion algorithm. This approach decreased the number of matches and enhanced both algorithmic efficiency and model reconstruction accuracy. For dense reconstruction, we utilized the patch-based multi-view stereo (PMVS) algorithm, which is based on facets. The results indicate that our proposed method achieves a higher number of reconstructed feature points and significantly enhances algorithmic efficiency by approximately ten times compared to the original incremental reconstruction algorithm. Consequently, the generated point cloud data are more detailed, and the textures are clearer, demonstrating that our method is an effective solution for three-dimensional reconstruction. Full article

The effective management and conservation of forest resources hinge on accurate monitoring. Nonetheless, individual remote-sensing images captured by low-altitude unmanned aerial vehicles (UAVs) fail to encapsulate the entirety of a forest’s characteristics. The application of image-stitching technology to high-resolution drone imagery facilitates a prompt evaluation of forest resources, encompassing quantity, quality, and spatial distribution. This study introduces an improved SIFT algorithm designed to tackle the challenges of low matching rates and prolonged registration times encountered with forest images characterized by dense textures. By implementing the SIFT-OCT (SIFT omitting the initial scale space) approach, the algorithm bypasses the initial scale space, thereby reducing the number of ineffective feature points and augmenting processing efficiency. To bolster the SIFT algorithm’s resilience against rotation and illumination variations, and to furnish supplementary information for registration even when fewer valid feature points are available, a gradient location and orientation histogram (GLOH) descriptor is integrated. For feature matching, the more computationally efficient Manhattan distance is utilized to filter feature points, which further optimizes efficiency. The fast sample consensus (FSC) algorithm is then applied to remove mismatched point pairs, thus refining registration accuracy. This research also investigates the influence of vegetation coverage and image overlap rates on the algorithm’s efficacy, using five sets of Cyclobalanopsis natural forest images. Experimental outcomes reveal that the proposed method significantly reduces registration time by an average of 3.66 times compared to that of SIFT, 1.71 times compared to that of SIFT-OCT, 5.67 times compared to that of PSO-SIFT, and 3.42 times compared to that of KAZE, demonstrating its superior performance. Full article

Adiabatic logic has been proposed as a method for drastically reducing power consumption in specialized low-power circuits. They often require specialized clock drivers that also function as the main power supply, in contrast to standard CMOS logic, and these power clocks are often a point of difficulty in the design process. A novel, stepwise charging driver circuit for four-phase adiabatic logic is proposed and validated through a simulation study. The proposed circuit consists of two identical driver circuits each driving two opposite adiabatic logic phases. Its performance relative to ideal step-charging and a standard CMOS across mismatched phase loads is analyzed, and new best practices are established. It is compared to a reference circuit consisting of one driver circuit for each phase along with a paired on-chip tank capacitor. The proposed driver uses opposite logic phases to act as the tank capacitor for each other in a “self-tanked” fashion. Each circuit was simulated in 15 nm FinFET across a variety of frequencies for an arbitrary logic operation. Both circuits showed comparable power consumption at all frequencies tested, yet the proposed driver uses fewer transistors and control signals and eliminates the explicit tank capacitors entirely, vastly reducing circuit area, complexity, and development time. Full article

This study presents a fully automated microfluidic electrochemical sensor for the detection of single-stranded DNA (ssDNA) on the ESSENCE platform. The sensor utilizes functionalized single-walled carbon nanotubes (SWCNTs) with short ssDNA strands immobilized through EDC-NHS coupling, placed between non-planar interdigitated electrodes. The detection process involves sequential flow of a background electrolyte and redox probe through the microfluidic channel before introducing the target DNA solution. The same solution is then circulated to enhance selectivity by removing non-specifically bound targets. Electrochemical impedance signals are acquired after the initial and final flow steps, utilizing changes in impedance spectra to quantify target DNA concentration. To streamline complex flow steps and eliminate manual interventions, the system integrates a fully automated fluid control system with syringe pumps, valves, and pressure sensors. Electrochemical impedance spectroscopy (EIS) data is acquired using the Analog Discovery 2 USB oscilloscope, and LabVIEW automation ensures a seamless transition from sample introduction to data acquisition. The transducer material’s flow-through design enables efficient differentiation between different degrees of base pair mismatches, extending applicability to single nucleotide polymorphisms. The system exhibits high sensitivity, detecting single-stranded DNA at concentrations as low as 1 fM within a rapid 15-min detection time. Its compact design and automated data acquisition make it a promising candidate for point-of-care biomolecule sensing, including antigens and toxins. Future applications involve functionalizing SWCNTs with relevant antibodies to enhance the platform’s capabilities for detecting a diverse range of target molecules in clinical settings. Full article

In recent years, due to the wide application of 3D vision in the fields of autonomous driving, robot navigation, and the protection of cultural heritage, 3D point cloud registration has received much attention. However, most current methods are time-consuming and are very sensitive to noises and outliers, resulting in low registration accuracy. Therefore, we propose a two-stage framework based on graph neural network and attention—TSGANet, which is effective in registering low-overlapping point cloud pairs and is robust to variable noises as well as outliers. Our method decomposes rigid transformation estimation into two stages: global estimation and fine-tuning. At the global estimation stage, multilayer perceptrons are employed to estimate a seven-dimensional vector representing rigid transformation directly from the fusion of two initial point cloud features. For the fine-tuning stage, we extract contextual information through an attentional graph neural network consisting of attention and feature-enhancing modules. A mismatch-suppression mechanism is also proposed and applied to keep our method robust to partially visible data with noises and outliers. Experiments show that our method yields a state-of-the-art performance on the ModelNet40 dataset. Full article

With the emergence of the Smart City concept, the rapid advancement of urban three-dimensional (3D) reconstruction becomes imperative. While current developments in the field of 3D reconstruction have enabled the generation of 3D products such as Digital Surface Models (DSM), challenges persist in accurately reconstructing shadows, handling occlusions, and addressing low-texture areas in very-high-resolution remote sensing images. These challenges often lead to difficulties in calculating satisfactory disparity maps using existing stereo matching methods, thereby reducing the accuracy of 3D reconstruction. This issue is particularly pronounced in urban scenes, which contain numerous super high-rise and densely distributed buildings, resulting in large disparity values and occluded regions in stereo image pairs, and further leading to a large number of mismatched points in the obtained disparity map. In response to these challenges, this paper proposes a method to refine the disparity in urban scenes based on open-source GIS data. First, we register the GIS data with the epipolar-rectified images since there always exists unignorable geolocation errors between them. Specifically, buildings with different heights present different offsets in GIS data registering; thus, we perform multi-modal matching for each building and merge them into the final building mask. Subsequently, a two-layer optimization process is applied to the initial disparity map based on the building mask, encompassing both global and local optimization. Finally, we perform a post-correction on the building facades to obtain the final refined disparity map that can be employed for high-precision 3D reconstruction. Experimental results on SuperView-1, GaoFen-7, and GeoEye satellite images show that the proposed method has the ability to correct the occluded and mismatched areas in the initial disparity map generated by both hand-crafted and deep-learning stereo matching methods. The DSM generated by the refined disparity reduces the average height error from 2.2 m to 1.6 m, which demonstrates superior performance compared with other disparity refinement methods. Furthermore, the proposed method is able to improve the integrity of the target structure and present steeper building facades and complete roofs, which are conducive to subsequent 3D model generation. Full article

Some traditional robots are based on offline programming reciprocal motion, and with the continuous upgrades in vision technology, more and more tasks are being replaced with machine vision. At present, the main method of target recognition used in palletizers is the traditional SURF algorithm, but this method of grasping leads to low accuracy due to the influence of too many mis-matched points. Due to the accuracy of robot target localization with binocular-based vision being low, an improved random sampling consistency algorithm for performing complete parallel robot target localization and grasping under the guidance of multi-vision is proposed. Firstly, the improved RANSAC algorithm, based on the SURF algorithm, was created based on the SURF algorithm; next, the parallax gradient method was applied to iterate the matched point pairs several times to further optimize the data; then, the 3D reconstruction was completed using the improved algorithm via the program technique; finally, the obtained data were input into the robot arm, and the camera’s internal and external parameters were obtained using the calibration method so that the robot could accurately locate and grasp objects. The experiments show that the improved algorithm shows better recognition accuracy and grasping success with the multi-vision approach. Full article

In scenes where there are lighting changes, localization may fail for visual SLAM due to feature point tracking failure. Thus, a feature point tracking method based on multi-condition constraints is proposed for visual SLAM. The proposed method tracks the feature points of optical flow from aspects such as the overall motion position of feature points, descriptor grayscale information, and spatial geometric constraints. First, to solve the problem of feature point mismatch in complex environments, we propose a feature point mismatch removal method that combines optical flow, descriptor, and RANSAC. We eliminate incorrect feature point matches layer by layer through these constraints. The uniformity of feature point distribution in the image can then affect the accuracy of camera pose estimation, and different scenes can also affect the difficulty of feature point extraction. In order to balance the quality and uniformity of the extracted feature points, we propose an adaptive mask homogenization method that adaptively adjusts the mask radius according to the quality of feature points. Experiments conducted on the EuRoC dataset show that the proposed method which integrates the improved feature point mismatch removal method and mask homogenization method into feature point tracking, exhibits robustness and accuracy under various interferences such as lighting changes, image blurring, and unclear textures. Compared to the RANSAC method, we reduce the location error by about 85% using the EuRoC dataset. Full article

Image stitching is of great importance for multiple fields such as moving object detection and tracking, ground reconnaissance and augmented reality. To ameliorate the stitching effect and alleviate the mismatch rate, an effective image stitching algorithm based on color difference and an improved KAZE with a fast guided filter is proposed. Firstly, the fast guided filter is introduced to reduce the mismatch rate before feature matching. Secondly, the KAZE algorithm based on improved random sample consensus is used for feature matching. Then, the color difference and brightness difference of the overlapping area are calculated to make an overall adjustment to the original images so as to improve the nonuniformity of the splicing result. Finally, the warped images with color difference compensation are fused to obtain the stitched image. The proposed method is evaluated by both visual effect mapping and quantitative values. In addition, the proposed algorithm is compared with other current popular stitching algorithms. The results show that the proposed algorithm is superior to other algorithms in terms of the quantity of feature point pairs, the matching accuracy, the root mean square error and the mean absolute error. Full article

Site-directed RNA editing (SDRE) technologies have great potential in gene therapy. Our group has developed a strategy to redirect exogenous adenosine deaminases acting on RNA (ADARs) to specific sites by making editable structures using antisense RNA oligonucleotides. Improving the editing efficiency of the MS2-ADAR system is important in treating undesirable G-to-A point mutations. This work demonstrates an effective strategy to enhance the editing efficiency of this SDRE system. The strategy involves changing the number of MS2 stem-loops on both sides of the antisense RNA and the mismatch base on the antisense part. The enhanced green fluorescent protein (EGFP) with W58X mutation is used as the reporter gene. Subsequently, we adjusted the amount of plasmids for transfection to tune the expression level of the guide RNA, and finally, we observed the fluorescence signal after transfection. After equalizing number of MS2 stem-loops at both sides of the antisense RNA, high editing efficiency was achieved. In the same level of guide RNA expression, when the paired base position was the target uridine, the editing efficiency was higher than cytidine, adenosine, and guanosine. Full article

The pose estimation can be effectively solved according to the feature point matching relationship in RGB-D. However, the extraction and matching process based on the whole image’s feature point is very computationally intensive and lacks robustness, which is the bottleneck of the traditional ICP algorithm. This paper proposes representing the whole image’s feature points by the salient objects’ robustness SIFT feature points through the salient preprocessing, and further solving the pose estimation. The steps are as follows: (1) salient preprocessing; (2) salient object’s SIFT feature extraction and matching; (3) RANSAC removes mismatching salient feature points; (4) ICP pose estimation. This paper proposes salient preprocessing aided by RANSAC processing based on the SIFT feature for pose estimation for the first time, which is a coarse-to-fine method. The experimental results show that our salient preprocessing algorithm can coarsely reduce the feature points’ extractable range and interfere. Furthermore, the results are processed by RANSAC good optimization, reducing the calculation amount in the feature points’ extraction process and improving the matching quality of the point pairs. Finally, the calculation amount of solving R, t based on all the matching feature points is reduced and provides a new idea for related research. Full article