Correct Matches Research Articles

Truncated glycosyltransferase coding regions in novel ABO alleles give rise to weak A or B blood group expression and discrepant typing results.

Correct ABO blood-group matching between donor and patient is crucial for safe transfusions. We investigated the underlying reason causing inconclusive ABO serology in samples referred to our laboratory. Flow cytometric analysis, ABO genotyping, and sequencing were used to characterize ABO-discrepant blood samples (n = 13). ABOgene variants were inserted in a GFP-containing bicistronic vector to assess A/B expression following overexpression in HeLa cells. Seven novel alleles with nonsense mutations predicted to truncate the encoded ABO glycosyltransferases were identified. While these variants could represent O alleles, serology showed signs of ABO glycosyltransferase activity. ABO*A1.01-related alleles displayed remarkably characteristic percentages of A-positive cells for samples with the same variant: c.42C>A (p.Cys14*; 10%), c.102C>A (p.Tyr34*; 31%-32%, n = 2), c.106dup (p.Val36Glyfs*21; 16%-17%, n = 3) or c.181_182ins (p.Leu61Argfs*21; 12%-13%, n = 2). Transfection studies confirmed significantly decreased A expression compared to wild type. The remaining variants were found on ABO*B.01 background: c.1_5dup (pGly3Trpfs*20), c.15dup (p.Arg6Alafs*51) or c.496del (p.Thr166Profs*26). Although the absence of plasma anti-B was noted overall, B antigen expression was barely detected on erythrocytes. Overexpression confirmed decreased B in two variants compared to wildtype while c.1_5dup only showed a non-significant downward trend. Samples displaying aberrant ABO serology revealed seven principally interesting alleles. Despite the presence of truncating mutations, normally resulting in null alleles, low levels of ABO antigens were detectable where alterations affected ABO exons 1-4 but not exon 7. This is compatible with the previously proposed concept that alternative start codons in early exons can be used to initiate the translation of functional ABO glycosyltransferase.

TOF-assisted binocular vision accuracy improvement method for underwater fish size inspection.

To address problems such as the lack of accuracy in acquiring depth maps for dynamic fish 3D measurements by usual binocular vision or a time-of-flight (TOF) depth camera, a TOF-assisted binocular vision depth acquisition algorithm is used to obtain high-quality depth maps. The TOF depth energy function is designed to guide the binocular stereo matching process, which improves the correct matching rate of binocular matching in low-texture regions; the TOF and binocular stereo matching confidence weighting functions are designed to achieve the fusion of the two at pixel level to improve the matching quality of fish in the occluded overlapping regions. The experimental results show that the TOF-assisted binocular vision system improves the accuracy of fish size measurement compared to single binocular vision while reducing the measurement error when the fish body has a significant inclination along the depth axis.

Retention Time and Fragmentation Predictors Increase Confidence in Identification of Common Variant Peptides.

Precision medicine focuses on adapting care to the individual profile of patients, for example, accounting for their unique genetic makeup. Being able to account for the effect of genetic variation on the proteome holds great promise toward this goal. However, identifying the protein products of genetic variation using mass spectrometry has proven very challenging. Here we show that the identification of variant peptides can be improved by the integration of retention time and fragmentation predictors into a unified proteogenomic pipeline. By combining these intrinsic peptide characteristics using the search-engine post-processor Percolator, we demonstrate improved discrimination power between correct and incorrect peptide-spectrum matches. Our results demonstrate that the drop in performance that is induced when expanding a protein sequence database can be compensated, hence enabling efficient identification of genetic variation products in proteomics data. We anticipate that this enhancement of proteogenomic pipelines can provide a more refined picture of the unique proteome of patients and thereby contribute to improving patient care.

Improving the matching of deformable objects by learning to detect keypoints

We propose a novel learned keypoint detection method to increase the number of correct matches for the task of non-rigid image correspondence. By leveraging true correspondences acquired by matching annotated image pairs with a specified descriptor extractor, we train an end-to-end convolutional neural network (CNN) to find keypoint locations that are more appropriate to the considered descriptor. Experiments demonstrate that our method enhances the Mean Matching Accuracy of numerous descriptors when used in conjunction with our detection method, while outperforming the state-of-the-art keypoint detectors on real images of non-rigid objects by 20 p.p. We also apply our method on the complex real-world task of object retrieval where our detector performs on par with the finest keypoint detectors currently available for this task. The source code and trained models are publicly available at https://github.com/verlab/LearningToDetect_PRL_2023.

ATP-Dependent Mismatch Recognition in DNA Replication Mismatch Repair

Mismatch repair is a critical step in DNA replication that occurs after base selection and proofreading, significantly increasing fidelity. However, the mechanism of mismatch recognition has not been established for any repair enzyme. Speculations in this area mainly focus on exploiting thermodynamic equilibrium and free energy. Nevertheless, non-equilibrium processes may play a more significant role in enhancing mismatch recognition accuracy by utilizing adenosine triphosphate (ATP). This study aimed to investigate this possibility. Considering our limited knowledge of actual mismatch repair enzymes, we proposed a hypothetical enzyme that operates as a quantum system with three discrete energy levels. When the enzyme is raised to its highest energy level, a quantum transition occurs, leading to one of two low-energy levels representing potential recognition outcomes: a correct match or a mismatch. The probabilities of the two outcomes are exponentially different, determined by the energy gap between the two low energy levels. By flipping the energy gap, discrimination between mismatches and correct matches can be achieved. Within a framework that combines quantum mechanics with thermodynamics, we established a relationship between energy cost and the recognition error.

Robust line segment mismatch removal using point-pair representation and Gaussian-uniform mixture formulation

Line segment matching (LSM) plays an important role in image matching, while it is always a challenging problem due to line fracture and high geometric complexity. In this paper, we propose a line segment mismatch removal to address the sensitivity to segment length and the fracture problem. The mismatch removal removes the massive false matches obtained by the descriptors like LBD. Specifically, we suggest calculating the endpoint error in LSM rather than the direction error in considering the error band and segment length. We propose a point-pair representation, and transform the LSM problem to be an intuitive point-pair alignment problem with a mixture model. The point-pair representation is more robust on the segment length than the linear equation representation used in RANSAC, which significantly preserves short line segments. Then, based on the point-pair representation, we propose a novel Directed Endpoint Drift method to solve the inherent fracture problem of line segments by allowing the endpoints to move along the line to complement the fracture. Compared with the recent learning-based method, the proposed method nearly doubles the number of correct matches on the remote sensing dataset. Especially, the recall is improved by 10% to 30% for both the short segments and the slightly fractured segments, compared with the popular RANSAC and MAGSAC++ methods. The code is available at https://github.com/shenliang16/DEpD.

Unmanned aerial vehicle remote sensing image registration based on an improved oriented FAST and rotated BRIEF- random sample consensus algorithm

Unmanned Aerial Vehicle (UAV) remote sensing image registration is the key step of remote sensing image stitching, image fusion and multi-frames image super-resolution. Its speed and accuracy determines the effect of remote sensing image applications, such as object detection, environment monitoring. To meet the speed and accuracy requirements of UAV remote sensing image registration, an improved Oriented FAST and Rotated BRIEF- Random sample consensus (ORB-RANSAC) algorithm is proposed. Firstly, images to be registered are divided into non-overlapping sub-images, and then a simplified image pyramid is constructed for these sub-images to get scale invariance. Secondly, the traditional FAST corner detection algorithm is improved by setting the adaptive corner detection threshold, and more feature points are detected. Meanwhile, the traditional quadtree algorithm is improved to remove redundant feature points and keep the remaining high-quality feature points. Thirdly, feature points coarse matching is done by bidirectional matching combined with cosine similarity method. Finally, the improved RANSAC algorithm is used for feature points fine matching to eliminate mismatches and calculate the transformation matrix. Experiment results show that, comparing with the traditional ORB algorithm, the number of feature points detected is significantly increased and its distribution is more uniform, and the correct matching rate increases by 58.10% in the case of image scale changing. Comparing with the state-of-the-art UAV remote sensing image registration algorithm, the correct matching rate and mutual information of our method are increased by 0.68% and 1.91% respectively, matching time and root mean square error are reduced by 3.89% and 11.2% respectively.

Guided Local Feature Matching with Transformer

GLFNet is proposed to be utilized for the detection and matching of local features among remote-sensing images, with existing sparse feature points being leveraged as guided points. Local feature matching is a crucial step in remote-sensing applications and 3D reconstruction. However, existing methods that detect feature points in image pairs and match them separately may fail to establish correct matches among images with significant differences in lighting or perspectives. To address this issue, the problem is reformulated as the extraction of corresponding features in the target image, given guided points from the source image as explicit guidance. The approach is designed to encourage the sharing of landmarks by searching for regions in the target image with features similar to the guided points in the source image. For this purpose, GLFNet is developed as a feature extraction and search network. The main challenge lies in efficiently searching for accurate matches, considering the massive number of guided points. To tackle this problem, the search network is divided into a coarse-level match network-based guided point transformer that narrows the search space and a fine-level regression network that produces accurate matches. The experimental results on challenging datasets demonstrate that the proposed method provides robust matching and benefits various applications, including remote-sensing image registration, optical flow estimation, visual localization, and reconstruction registration. Overall, a promising solution is offered by this approach to the problem of local feature matching in remote-sensing applications.

A New Algorithm for Eliminating Mismatch

Aiming at the problems of mismatching and low matching efficiency in the process of monocular initialization of visual SLAM, this paper proposes an improved initialization method. Based on the ORB-SLAM2 algorithm, the concept of probability statistics is introduced in its monocular initialization process. By adding a moving grid before the RANSAC algorithm eliminates the mismatching, the region where the feature matching is most likely to be true is found, and the mismatching is eliminated in this region. Finally, a geometric constraint is carried out to retain the correct matching to the greatest extent, thus speeding up the feature matching and retaining more interior points. In this paper, the improved ORB-SLAM2 algorithm is verified in the TUM data set. The experimental results show that the initialization time of the improved algorithm is reduced and the trajectory accuracy is improved. Applying the improved algorithm to the AGV car has good accuracy and robustness in the real factory environment.

An L-Shaped Three-Level and Single Common Element Sparse Sensor Array for 2-D DOA Estimation.

The degree of freedom (DOF) is an important performance metric for evaluating the design of a sparse array structure. Designing novel sparse arrays with higher degrees of freedom, while ensuring that the array structure can be mathematically represented, is a crucial research direction in the field of direction of arrival (DOA) estimation. In this paper, we propose a novel L-shaped sparse sensor array by adjusting the physical placement of the sensors in the sparse array. The proposed L-shaped sparse array consists of two sets of three-level and single-element sparse arrays (TSESAs), which estimate the azimuth and elevation angles, respectively, through one-dimensional (1-D) spatial spectrum search. Each TSESA is composed of a uniform linear subarray and two sparse subarrays, with one single common element in the two sparse subarrays. Compared to existing L-shaped sparse arrays, the proposed array achieves higher degrees of freedom, up to 4Q1Q2+8Q1-5, when estimating DOA using the received signal covariance. To facilitate the correct matching of azimuth and elevation angles, the cross-covariance between the two TSESA arrays is utilized for estimation. By comparing and analyzing performance parameters with commonly used L-shaped and other sparse arrays, it is found that the proposed L-shaped TSESA has higher degrees of freedom and array aperture, leading to improved two-dimensional (2-D) DOA estimation results. Finally, simulation experiments validate the excellent performance of the L-shaped TSESA in 2-D DOA estimation.

SRTPN: Scale and Rotation Transform Prediction Net for Multimodal Remote Sensing Image Registration

How to recover geometric transformations is one of the most challenging issues in image registration. To alleviate the effect of large geometric distortion in multimodal remote sensing image registration, a scale and rotate transform prediction net is proposed in this paper. First, to reduce the scale between the reference and sensed images, the image scale regression module is constructed via CNN feature extraction and FFT correlation, and the scale of sensed image can be recovered roughly. Second, the rotation estimate module is developed for predicting the rotation angles between the reference and the scale-recovered images. Finally, to obtain the accurate registration results, LoFTR is employed to match the geometric-recovered images. The proposed registration network was evaluated on GoogleEarth, HRMS, VIS-NIR and UAV datasets with contrast differences and geometric distortions. The experimental results show that the number of correct matches of our model reached 74.6%, and the RMSE of the registration results achieved 1.236, which is superior to the related methods.

Fitted avatars: automatic skeleton adjustment for self-avatars in virtual reality

In the era of the metaverse, self-avatars are gaining popularity, as they can enhance presence and provide embodiment when a user is immersed in Virtual Reality. They are also very important in collaborative Virtual Reality to improve communication through gestures. Whether we are using a complex motion capture solution or a few trackers with inverse kinematics (IK), it is essential to have a good match in size between the avatar and the user, as otherwise mismatches in self-avatar posture could be noticeable for the user. To achieve such a correct match in dimensions, a manual process is often required, with the need for a second person to take measurements of body limbs and introduce them into the system. This process can be time-consuming, and prone to errors. In this paper, we propose an automatic measuring method that simply requires the user to do a small set of exercises while wearing a Head-Mounted Display (HMD), two hand controllers, and three trackers. Our work provides an affordable and quick method to automatically extract user measurements and adjust the virtual humanoid skeleton to the exact dimensions. Our results show that our method can reduce the misalignment produced by the IK system when compared to other solutions that simply apply a uniform scaling to an avatar based on the height of the HMD, and make assumptions about the locations of joints with respect to the trackers.

Learners’ report and perceptions concerning undergraduate paediatric endocrinology curriculum content and delivery across Southern Nigeria

Introduction: This study aimed to compare the paediatric endocrinology curriculum across Southern Nigeria medical schools, using reports from learners. It also checked the learners’ perceptions about different learning patterns and competency in some expected core skills. Methods: This mixed (quantitative and qualitative) study was conducted with 7 medical schools in Southern Nigeria. A multi-staged randomized selection of schools and respondents, was adopted for a focus group discussion (FGD), and the information derived was used to develop a semi-structured questionnaire, which 314 doctors submitted. The FGD discussed rotation patterns, completion rates of topics and perceptions for some skills. These themes were included in the forms for general survey, and Likert scale was used to assess competency in skills. Data generated was analysed using statistical package for social sciences, SPSS 24, and p values < 0.05 were considered significant Results: Lectures and topics had various completion rates, 42.6% - 98%, highest being “diabetes mellitus”. Endocrinology rotation was completed by 58.6% of respondents, and 58 – 78 % perceived competency in growth measurement and charting. Significantly more learners, 46.6% who had staggered posting got correct matching of Tanner staging, versus learners who had block posting, 33.3%, p = 0.018. Conclusion: Respondents reported high variability in the implementation of the recommended guidelines for paediatric endocrinology curriculum between schools in Southern Nigeria. Variabilities were in the courses’ completion, learners’ skills exposure and how much hands-on were allowed in various skills acquisitions. This variability will hamper the core objectives of human capital development should the trend continue.

Dragon detectives: citizen science confirms photo-ID as an effective tool for monitoring an endangered reptile

Context Among amphibians and reptiles, traditional methods of capture–mark–recapture (CMR) have relied on artificial marking techniques (in particular, toe clipping), which has raised concerns because it may impact climbing ability, survival, and behaviour. A potential alternative involves the exploitation of natural biometric identifiers that are already present, including scale configuration or colour patterns. These natural markers can be applied in photo-based CMR, which has several advantages over artificial markers, including reduced costs, the reduction of harm or stress, and the potential for public participation in conservation and research. Aim Our aim was to test the feasibility of applying citizen science in the manual visual identification of the endangered Monaro grassland earless dragon (Tympanocryptis osbornei) using dorsal pattern as a natural marker. Methods We collected photographs of dorsal patterns of wild T. osbornei individuals using a smartphone device under field conditions. We subsequently recruited participants anonymously from the public using social media to complete an online survey, in which they were asked to correctly match these field-captured images of individuals from small image pools, mimicking the process of detecting recapture events. Key results Participants were able to successfully detect recapture events from small image pools based solely on a comparison of dorsal patterns. High consensus was reached on all matches included in the online survey, with the majority vote among participants representing the correct matching of individuals on all occasions. Conclusion Our results indicate that there is sufficient intra-specific variability and temporal stability in dorsal patterning for it to be used as a reliable natural marker for identifying T. osbornei at the individual level. Implications Our findings suggest that photo-CMR could be applied to other agamids with similar dorsal patterns, making it a potentially valuable tool and an alternative to artificial marking for monitoring wild populations of Australian lizards in the future.

Homography Matrix-Based Local Motion Consistent Matching for Remote Sensing Images

Feature matching is a fundamental task in the field of image processing, aimed at ensuring correct correspondence between two sets of features. Putative matches constructed based on the similarity of descriptors always contain a large number of false matches. To eliminate these false matches, we propose a remote sensing image feature matching method called LMC (local motion consistency), where local motion consistency refers to the property that adjacent correct matches have the same motion. The core idea of LMC is to find neighborhoods with correct motion trends and retain matches with the same motion. To achieve this, we design a local geometric constraint using a homography matrix to represent local motion consistency. This constraint has projective invariance and is applicable to various types of transformations. To avoid outliers affecting the search for neighborhoods with correct motion, we introduce a resampling method to construct neighborhoods. Moreover, we design a jump-out mechanism to exit the loop without searching all possible cases, thereby reducing runtime. LMC can process over 1000 putative matches within 100 ms. Experimental evaluations on diverse image datasets, including SUIRD, RS, and DTU, demonstrate that LMC achieves a higher F-score and superior overall matching performance compared to state-of-the-art methods.

Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data

The manual processing and analysis of videos from camera traps is time-consuming and includes several steps, ranging from the filtering of falsely triggered footage to identifying and re-identifying individuals. In this study, we developed a pipeline to automatically analyze videos from camera traps to identify individuals without requiring manual interaction. This pipeline applies to animal species with uniquely identifiable fur patterns and solitary behavior, such as leopards (Panthera pardus). We assumed that the same individual was seen throughout one triggered video sequence. With this assumption, multiple images could be assigned to an individual for the initial database filling without pre-labeling. The pipeline was based on well-established components from computer vision and deep learning, particularly convolutional neural networks (CNNs) and scale-invariant feature transform (SIFT) features. We augmented this basis by implementing additional components to substitute otherwise required human interactions. Based on the similarity between frames from the video material, clusters were formed that represented individuals bypassing the open set problem of the unknown total population. The pipeline was tested on a dataset of leopard videos collected by the Pan African Programme: The Cultured Chimpanzee (PanAf) and achieved a success rate of over 83% for correct matches between previously unknown individuals. The proposed pipeline can become a valuable tool for future conservation projects based on camera trap data, reducing the work of manual analysis for individual identification, when labeled data is unavailable.

SLIPMAT: A pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data

1H Magnetic Resonance Spectroscopy (MRS) is an important non-invasive tool for measuring brain metabolism, with numerous applications in the neuroscientific and clinical domains. In this work we present a new analysis pipeline (SLIPMAT), designed to extract high-quality, tissue-specific, spectral profiles from MR spectroscopic imaging data (MRSI). Spectral decomposition is combined with spatially dependant frequency and phase correction to yield high SNR white and grey matter spectra without partial-volume contamination. A subsequent series of spectral processing steps are applied to reduce unwanted spectral variation, such as baseline correction and linewidth matching, before direct spectral analysis with machine learning and traditional statistical methods. The method is validated using a 2D semi-LASER MRSI sequence, with a 5-minute duration, from data acquired in triplicate across 8 healthy participants. Reliable spectral profiles are confirmed with principal component analysis, revealing the importance of total-choline and scyllo-inositol levels in distinguishing between individuals – in good agreement with our previous work. Furthermore, since the method allows the simultaneous measurement of metabolites in grey and white matter, we show the strong discriminative value of these metabolites in both tissue types for the first time. In conclusion, we present a novel and time efficient MRSI acquisition and processing pipeline, capable of detecting reliable neuro-metabolic differences between healthy individuals, and suitable for the sensitive neurometabolic profiling of in-vivo brain tissue.

MMNet: a medical image-to-image translation network based on manifold-value correction and manifold matching

MMNet: a medical image-to-image translation network based on manifold-value correction and manifold matching

Wheeled Robot Visual Odometer Based on Two-dimensional Iterative Closest Point Algorithm

According to the two-dimensional motion characteristics of planar motion wheeled robot, the visual odometer was dimensionally reduced in this study. In the feature point matching part of visual odometer, the contour constraint was used to filter out the mismatched feature point pairs (abbreviated as FPP). This method could also filter out the matched FPP, and the feature of FPP was correct color image matches, however, their depth image error was large. This offered higher quality matched FPP for the subsequent interframe motion estimation. Dimension reduction was performed in the interframe motion estimation part, and the two-dimensional Iterative Closest Point (ICP) algorithm was used for camera motion estimation. The experiments indicated that the proposed algorithm effectively improved the computational speed and precision of planar motion wheeled robot visual odometer. This research indicates that the dimension reduction processing of ICP algorithm can effectively improve the operation speed and calculation accuracy of planar motion wheeled robot visual odometry, which provides a good reference and data support for the subsequent research of wheeled robot visual odometry in the future.

Optical and SAR Image Registration Based on Multi-Scale Orientated Map of Phase Congruency

Optical and Synthetic Aperture Radar (SAR) images are highly complementary, and their registrations are a fundamental task for other remote sensing applications. Traditional feature-matching algorithms fail to solve the significant nonlinear radiation difference (NRD) caused by different sensors. To address this problem, a robust registration algorithm with the multi-scale orientated map of phase congruency (MSPCO) is proposed. First, a nonlinear diffusion scale space is established to obtain the scale invariance of feature points. Compared with the linear Gaussian scale space, the nonlinear diffusion scale space can better preserve the edge and texture information. Second, to ensure the quantity and repeatability of features, corner points and edge points are detected on the moment map of phase congruency, respectively, which is the foundation to the next feature matching. Third, the MSPCO descriptor is constructed via the orientation of phase congruency (PCO). PCO is highly robust to NRD, and the different scales of PCOs enhance the robustness of the descriptor. Finally, a feature-matching strategy based on an effective scale ratio is proposed, which reduces the number of comparisons among features and improves computational efficiency. The experimental results show that the proposed method is better than the existing feature-based methods in terms of the number of correct matches and registration accuracy. The registration accuracy is only inferior to that of the most advanced template matching method, and the accuracy difference is within 0.3 pixels, which fully demonstrates the robustness and accuracy of the proposed method in optical and SAR image registration.