Correct Matches Research Articles

Frail patients require instrumentation of a more proximal vertebra for a successful outcome after surgery for adult spine deformity.

The aim of this study was to investigate the impact of the level of upper instrumented vertebra (UIV) in frail patients undergoing surgery for adult spine deformity (ASD). Patients with adult spinal deformity who had undergone T9-to-pelvis fusion were stratified using the ASD-Modified Frailty Index into not frail, frail, and severely frail categories. ASD was defined as at least one of: scoliosis ≥ 20°, sagittal vertical axis (SVA) ≥ 5 cm, or pelvic tilt ≥ 25°. Means comparisons tests were used to assess differences between both groups. Logistic regression analyses were used to analyze associations between frailty categories, UIV, and outcomes. A total of 477 patients were included (mean age 60.3 years (SD 14.9), mean BMI 27.5 kg/m2 (SD 5.8), mean Charlson Comorbidity Index (CCI) 1.67 (SD 1.66)). Overall, 74% of patients were female (n = 353), and 49.6% of patients were not frail (237), 35.4% frail (n = 169), and 15% severely frail (n = 71). At baseline, differences in age, BMI, CCI, and deformity were significant (all p = 0.001). Overall, 15.5% of patients (n = 74) had experienced mechanical complications by two years (8.1% not frail (n = 36), 15.1% frail (n = 26), and 16.3% severely frail (n = 12); p = 0.013). Reoperations also differed between groups (20.2% (n = 48) vs 23.3% (n = 39) vs 32.6% (n = 23); p = 0.011). Controlling for osteoporosis, baseline deformity, and degree of correction (by sagittal age-adjusted score (SAAS) matching), frail and severely frail patients were more likely to experience mechanical complications if they had heart failure (odds ratio (OR) 6.6 (95% CI 1.6 to 26.7); p = 0.008), depression (OR 5.1 (95% CI 1.1 to 25.7); p = 0.048), or cancer (OR 1.5 (95% CI 1.1 to 1.4); p = 0.004). Frail and severely frail patients experienced higher rates of mechanical complication than 'not frail' patients at two years (19% (n = 45) vs 11.9% (n = 29); p = 0.003). When controlling for baseline deformity and degree of correction in severely frail and frail patients, severely frail patients were less likely to experience clinically relevant proximal junctional kyphosis or failure or mechanical complications by two years, if they had a more proximal UIV. Frail patients are at risk of a poor outcome after surgery for adult spinal deformity due to their comorbidities. Although a definitively prescriptive upper instrumented vertebra remains elusive, these patients appear to be at greater risk for a poor outcome if the upper instrumented vertebra is sited more distally.

Synthesis of Diaryl Diselenides and Ditellurides via Bromide-Catalyzed C–Se/C–Te Bond Formation Using Se/Te Powder and Boronic Acid

AbstractDiaryl diselenides and diaryl ditellurides are commonly employed as selenyl or telluryl sources, and they have a wide range of applications in organic synthesis. Herein, various diaryl diselenides/ditellurides are furnished in moderate to excellent yields under metal-free conditions. This method features high efficiency, good functional group tolerance, straightforward operation, and easy scale-up (running in 4 mmol-scale for most cases). Mechanistic studies indicate that this reaction may proceed via a radical pathway. Significantly, the correct matching of Br– and dimethyl sulfoxide under an air atmosphere is critical to this transformation.

Multi-line laser 3D reconstruction method based on spatial quadric surface and geometric estimation

The traditional multi-line laser 3D reconstruction is based on binocular epipolar constraints and the equation of the laser optical plane. Firstly, matching points are identified based on the epipolar constraints. Then, the correct matching points are selected based on the optical plane equation of the multi-line laser. Finally, 3D reconstruction is performed using the selected matching points. In actual production processes involving multi-line lasers, noticeable distortion occurs due to the projection of Diffractive Optical Elements (DOE) at a large angle. This results in curved laser light planes and curved reflections on the measured objects. Under such circumstances, efficiently screening matching points using the traditional method based on the laser plane equation becomes challenging. Additionally, inherent noise in multi-line laser systems introduces errors in extracted laser center coordinates, making it impossible to directly obtain high-precision 3D data. To address these issues, this paper proposes a method that utilizes spatial quadric surfaces and geometric estimation for completing multi-line laser 3D reconstructions. By analyzing the distortion principle of DOE and the positional relationship after optical diffraction, the multi-line laser manifests as a quadric surface on the optical output plane. Consequently, by calibrating the equations of the quadric surface and applying binocular polar constraints, suitable matching points for the multi-line laser can be chosen. Once an accurate matching point is identified, a minimum geometric distance estimation can be established based on the distance constraint between the point and its corresponding epipolar line. This distance represents the separation between the left and right camera’s laser center points and their respective epipolar lines. Utilizing this estimate of the minimum geometric distance, a refined calculation can be performed to better satisfy epipolar constraints and obtain new matching points. Ultimately, utilizing these new matching points enables the completion of 3D reconstruction for multi-line lasers. In comparison with methods relying on spatial plane and epipolar constraints, our algorithm exhibits a superior degree of matchability and accuracy.According to multiple groups of test data, the average error before optimization is 0.3126 mm, and can be increased to 0.0336 mm after optimization.

Correct patient identification and matching of adults in an ambulatory care setting: a best practice implementation project.

Ambulatory care settings are at high risk for errors when identifying patients and matching them to their intended care. The objective of this project was to improve correct and consistent patient identification and matching to their intended care by nurses in ambulatory care settings. The seven-phase JBI Evidence Implementation Framework was used to guide this project. JBI tools were used to audit current practices and implement best practices in four ambulatory care units. The implementation plan included a baseline audit and two follow-up audits. Feedback was obtained through interviews with ambulatory care nursing staff, educational sessions were conducted for nursing staff, and unit guidelines were developed. In the baseline audit, compliance with best practice criteria for patient matching and identification was below 62% for 7/13 criteria. After conducting education sessions and other strategies, 1/3 pre- and post-clinical intervention criteria improved in compliance, while 2 were unchanged. For blood product administration criteria, 2/5 improved, 1 was unchanged, and 2 were lower than baseline. Nurses' education in patient identification procedures improved (1/1) and knowing where to access relevant policies remained unchanged at 100%. Criteria for patients knowing the importance of patient identification (2/2) and the identification band following national standards (1/1) improved from baseline. The results support the use of education sessions and infrastructure changes to promote and sustain change in evidence-based practice in ambulatory care units. Not all criteria improved, and the audit team identified strategies to improve the implementation of evidence-based practice in ambulatory care units. http://links.lww.com/IJEBH/A275.

Hierarchical Graph Neural Network: A Lightweight Image Matching Model with Enhanced Message Passing of Local and Global Information in Hierarchical Graph Neural Networks

Graph Neural Networks (GNNs) have gained popularity in image matching methods, proving useful for various computer vision tasks like Structure from Motion (SfM) and 3D reconstruction. A well-known example is SuperGlue. Lightweight variants, such as LightGlue, have been developed with a focus on stacking fewer GNN layers compared to SuperGlue. This paper proposes the h-GNN, a lightweight image matching model, with improvements in the two processing modules, the GNN and matching modules. After image features are detected and described as keypoint nodes of a base graph, the GNN module, which primarily aims at increasing the h-GNN’s depth, creates successive hierarchies of compressed-size graphs from the base graph through a clustering technique termed SC+PCA. SC+PCA combines Principal Component Analysis (PCA) with Spectral Clustering (SC) to enrich nodes with local and global information during graph clustering. A dual non-contrastive clustering loss is used to optimize graph clustering. Additionally, four message-passing mechanisms have been proposed to only update node representations within a graph cluster at the same hierarchical level or to update node representations across graph clusters at different hierarchical levels. The matching module performs iterative pairwise matching on the enriched node representations to obtain a scoring matrix. This matrix comprises scores indicating potential correct matches between the image keypoint nodes. The score matrix is refined with a ‘dustbin’ to further suppress unmatched features. There is a reprojection loss used to optimize keypoint match positions. The Sinkhorn algorithm generates a final partial assignment from the refined score matrix. Experimental results demonstrate the performance of the proposed h-GNN against competing state-of-the-art (SOTA) GNN-based methods on several image matching tasks under homography, estimation, indoor and outdoor camera pose estimation, and 3D reconstruction on multiple datasets. Experiments also demonstrate improved computational memory and runtime, approximately 38.1% and 26.14% lower than SuperGlue, and an average of about 6.8% and 7.1% lower than LightGlue. Future research will explore the effects of integrating more recent simplicial message-passing mechanisms, which concurrently update both node and edge representations, into our proposed model.

Comparing the linkage performance of fastLink, Match*Pro and Splink at the Florida Cancer Registry using simulated pseudopeople data

ObjectivesThe objective was to compare the record linkage performance of fastLink, Match*Pro, and Splink using realistic simulated "Rhode Island" data from the Python package pseudopeople. A requirement was no expected wrong matches (false positives, FP) after a limited manual review. ApproachWe incorporated custom noise on (a) Social Security Number (SSN) and (b) address. We first used high-quality data where SSN was available and then low-quality data where SSN was unavailable. Missed matches (false negatives, FN) were acceptable. We used an iterative process of determining the “appropriate” match threshold and the correct matches (true positives, TP). ResultsThe two analysis datasets, df1 and df2, resulted in 660,227 actual matches and 9,987 actual non-matches. The specified match threshold probability was 0.995 for fastLink, 0.999999999 for Splink, and about 0.99 for Match*Pro. When SSN was available, the (rounded) results were: Splink 627,000 TP (4 FP), fastLink 577,000 TP (15 FP), and Match*Pro 436,000 TP (4 FP) in the default frequency-based EpiLink-like option; the EM option crashed. When SSN was unavailable, the results were: Splink 579,000 TP (3 FP), fastLink 582,000 TP (137 FP), and Match*Pro 442,000 TP (3 FP); the EM option had 516,000 TP (442 FP). The fastLink developers are working on adding Active Learning. ConclusionOverall, Splink and fastLink performed better than Match*Pro. A likely explanation is that only Splink and fastLink provide match probability and are open source. Splink (Python) and fastLink (R) require basic user programming skills. The results will be updated once the final report has been approved.

Association between surgical admissions, cognition, and neurodegeneration in older people: a population-based study from the UK Biobank

Previous studies have shown that major surgical and medical hospital admissions are associated with cognitive decline in older people (aged 40-69years at recruitment), which is concerning for patients and caregivers. We aimed to validate these findings in a large cohort and investigate associations with neurodegeneration using MRI. For this population-based study, we analysed data from the UK Biobank collected from March 13, 2006, to July 16, 2023, linked to the National Health Service Hospital Episode Statistics database, excluding participants with dementia diagnoses. We constructed fully adjusted models that included age, time, sex, Lancet Commission dementia risk factors, stroke, and hospital admissions with a participant random effect. Primary outcomes were hippocampal volume and white matter hyperintensities, both of which are established markers of neurodegeneration, and exploratory analyses investigated the cortical thickness of Desikan-Killiany-Tourville atlas regions. The main cognitive outcomes were reaction time, fluid intelligence, and prospective and numeric memory. Surgeries were calculated cumulatively starting from 8years before the baseline evaluation. Of 502 412participants in the UK Biobank study, 492 802participants were eligible for inclusion in this study, of whom 46 706underwent MRI. Small adverse associations with cognition were found per surgery: reaction time increased by 0·273ms, fluid intelligence score decreased by 0·057correct responses, prospective memory (scored as correct at first attempt) decreased (odds ratio 0·96 [95% CI 0·95to 0·97]), and numeric memory maximum correct matches decreased by 0·025in fully adjusted models. Surgeries were associated with smaller hippocampal volume (β=-5·76mm³ [-7·89to-3·64]) and greater white matter hyperintensities volume (β=100·02 mm³ [66·17to 133·87]) in fully adjusted models. Surgeries were also associated with neurodegeneration of the insula and superior temporal cortex. This population-based study corroborates that surgeries are generally safe but cumulatively are associated with cognitive decline and neurodegeneration. Perioperative brain health should be prioritised for older and vulnerable patients, particularly those who have multiple surgical procedures. The Australian and New Zealand College of Anaesthetists (ANZCA) Foundation and the University of Sydney.

60 Sustainable impact of an outdoor exercise program on health outcomes of older adults: a quasi-randomized controlled trial with a 3-month follow-up

Abstract Older people benefit in several health-promoting outcomes from needs-oriented exercise programs in their immediate living environment. For a low-threshold, and thus sustainable and cost-effective design of exercise programs a transfer to self-organisation is favourable. The aim of our study is to examine the health-promoting impacts of an outdoor exercise program developed for older persons, as well as its sustainability after 3 months. Ninety-five persons aged 60+ were randomized according to their residence (IG: n = 40, mean age = 72.35 years, 24 female; KG: n = 55, mean age = 75.05 years, 34 female). An age-adjusted exercise program was carried out over 12 weeks (2x/week á 45-60min; adherence: 90.9%) by professional exercise instructors in urban parks. During this instructor-led exercise phase, seven participants were trained as mentors, and thus, enabled to continue the exercise program for further 12 weeks (1-2x/week á 45-60min; adherence: 2-3x/month = 11.5%, 1-2x/week = 88.5%). Before (T1) and after the instructor-led exercise phase (T2) as well as after the mentor-led exercise phase (T3), health outcomes (e.g., walking distance/6MWT, neurocognitive performance/DSST) of IG and CG (no intervention) were assessed and analysed using paired t-Tests. 75.1% of IG participants completed both phases of the exercise program. Neurocognitive performance improved significantly for IG between T1 and T2 (+2.32 correct number-symbol matches; p = .003; d = -.509) and remained stable between T2 and T3 (+0.15 correct matches; p = .863; d = -.028) while for CG the opposite development was observed (T1-T2: -0.6 correct matches; p = .451; d = .102; T2-T3: +2.18 correct matches; p = .004; d = -.423). IG improved their walking distance for T1-T2 by 21.39m (p = .172; d = -.223), which could be maintained for T2-T3 (+5,1m; p = .648; d = -.074). Improvements in CG for walking distance were smaller (T1-T2: +2.8m; p = .690; d = -.055; T2-T3: +14.26m, p = .315; d = -.147). The mentoring of the exercise groups proved to be very successful, as evidenced by the maintained effects in the mentor-led phase, ensuring sustainability of health-promoting interventions. (Funded by the Free State of Saxony, “Demographics” #100535625)

NnU-Net-based Segmentation of Tumor Subcompartments in Pediatric Medulloblastoma Using Multiparametric MRI: A Multi-institutional Study.

Purpose To evaluate nnU-Net-based segmentation models for automated delineation of medulloblastoma tumors on multi-institutional MRI scans. Materials and Methods This retrospective study included 78 pediatric patients (52 male, 26 female), with ages ranging from 2 to 18 years, with medulloblastomas, from three different sites (28 from hospital A, 18 from hospital B, and 32 from hospital C), who had data available from three clinical MRI protocols (gadolinium-enhanced T1-weighted, T2-weighted, and fluid-attenuated inversion recovery). The scans were retrospectively collected from the year 2000 until May 2019. Reference standard annotations of the tumor habitat, including enhancing tumor, edema, and cystic core plus nonenhancing tumor subcompartments, were performed by two experienced neuroradiologists. Preprocessing included registration to age-appropriate atlases, skull stripping, bias correction, and intensity matching. The two models were trained as follows: (a) the transfer learning nnU-Net model was pretrained on an adult glioma cohort (n = 484) and fine-tuned on medulloblastoma studies using Models Genesis and (b) the direct deep learning nnU-Net model was trained directly on the medulloblastoma datasets, across fivefold cross-validation. Model robustness was evaluated on the three datasets when using different combinations of training and test sets, with data from two sites at a time used for training and data from the third site used for testing. Results Analysis on the three test sites yielded Dice scores of 0.81, 0.86, and 0.86 and 0.80, 0.86, and 0.85 for tumor habitat; 0.68, 0.84, and 0.77 and 0.67, 0.83, and 0.76 for enhancing tumor; 0.56, 0.71, and 0.69 and 0.56, 0.71, and 0.70 for edema; and 0.32, 0.48, and 0.43 and 0.29, 0.44, and 0.41 for cystic core plus nonenhancing tumor for the transfer learning and direct nnU-Net models, respectively. The models were largely robust to site-specific variations. Conclusion nnU-Net segmentation models hold promise for accurate, robust automated delineation of medulloblastoma tumor subcompartments, potentially leading to more effective radiation therapy planning in pediatric medulloblastoma. Keywords: Pediatrics, MR Imaging, Segmentation, Transfer Learning, Medulloblastoma, nnU-Net, MRI Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Rudie and Correia de Verdier in this issue.

A general framework for regression with mismatched data based on mixture modelling

Abstract The advent of the information age has revolutionized data collection and has led to a rapid expansion of available data sources. Methods of data integration are indispensable when a question of interest cannot be addressed using a single data source. Record linkage (RL) is at the forefront of such data integration efforts. Incentives for sharing linked data for secondary analysis have prompted the need for methodology accounting for possible errors at the RL stage. Mismatch error is a common consequence resulting from the use of nonunique or noisy identifiers at that stage. In this paper, we present a framework to enable valid postlinkage inference in the secondary analysis setting in which only the linked file is given. The proposed framework covers a variety of statistical models and can flexibly incorporate information about the underlying RL process. We propose a mixture model for linked records whose two components reflect distributions conditional on match status, i.e. correct or false match. Regarding inference, we develop a method based on composite likelihood and the expectation-maximization algorithm that is implemented in the R package pldamixture. Extensive simulations and case studies involving contemporary RL applications corroborate the effectiveness of our framework.

Highly robust thermal infrared and visible image registration with canny and phase congruence detection

When a dual-optical vehicle-mounted camera is photographed, visible and thermal infrared (VI-T) images cannot be registered, and realising fine matching of the images to solve for the optimal affine parameters is thus key to this research. Visible and thermal image matching is of great interest owing to the presence of challenging scale transformations, nonlinear radial aberrations, and robustness under illumination conditions. Thus, this paper presents a novel algorithm, namely, Highly Robust Thermal Infrared and Visible Image Registration with Canny and Phase Congruence Detection (HRCP). Firstly, the image is pre-processed, and feature point detection is performed by combining adaptive thresholding GW-Canny edge detection and phase coherence (PC), which increases the edge information. Secondly, SURF and FAST feature detection at the maximum distance of PC maps can obtain stable and reliable feature points, which are robust to scale invariance and rotation invariance. Finally, the optimal affine parameter is solved using the least square method with several iterations. Compared to existing matching methods, we find that VI-T image matching is particularly sensitive to both lighting factors and scale-transformed nonlinear radial distortion and that HRCP is robust in performing matching. For the randomly selected dataset, the average image matching time is reduced by 3.265 s, the number of correct image matches increases by 44 pairs on average, the root mean square error improves to 1.830, and the average error improves to 2.815. The proposed method not only improves the inaccuracy of radiation-insensitive feature transform matching in both low and high light, but also achieves a higher accuracy and an increase in the number of feature matching points, which facilitates robust VI-T image registration.

CCUS source-sink matching model based on sink well placement optimization

Realizing the dual-carbon target is a major strategic plan made by the Party Central Committee and a solemn commitment to address climate change of global significance. Source-sink matching technology in carbon capture, utilization, and storage (CCUS) technology is crucial to achieving the dual-carbon target. However, most previous studies constructed source-sink matching models based on sink-fixed sequestration potential, making it difficult to accurately assess the potential of sequestration sites and thus affect the source-sink matching scheme. Therefore, a dynamic storage potential assessment model has been established which can quickly assess the injection capacity of sinks under different well deployment schemes and then a source-sink matching model based on the overall optimization of sink deployment schemes has been constructed by combining the dynamic storage potential assessment model. The model considers the source matching problem as a mixed-integer linear programming (MILP) problem and obtains the computational results of the dynamic storage potential assessment model to realize the correct matching and simultaneous optimization of the source-sink pipeline network layout and the sink-well deployment scheme. With the proposed model, source-sink matching cases for national-level and project-level have been analyzed. It has been shown that the dynamic storage potential assessment model and the combined method improve the accuracy of the source-sink matching model in the form of MILP while minimizing the total cost and additional computations.

Highly adaptive multi-modal image matching based on tuning-free filtering and enhanced sketch features

The generalizability and adaptiveness of multi-modal image matching (MMIM) techniques are hampered by the nonlinear radiometric differences that vary in a highly non-uniform manner across different modal combinations. A major challenge is the extensive filter tuning required by many filter-based matching methods to mitigate the impact of radiometric aberrations. Additionally, the low correct matching rate resulting from the non-repeatability and low similarity of local or detailed features presents another significant obstacle. In this paper, we introduce a novel MMIM framework, called Adaptive Matching with enhanced Edge Sketches (AMES), for reducing manual interference in filter parameter configuration and improving the correct matching rate of extracted feature points. Specifically, AMES trains a support vector regression model to adaptively predict the optimized filtering parameters for each test image using its internal statistical factors as input. We also propose a sketch feature enhancement method, based on fusing multi-scale moment maps through principal component analysis, for cross-modal adaptive feature point extraction. We generate matching results by applying feature descriptors within the Log-Polar window and performing outlier removal following a coarse-to-fine strategy. In an evaluation involving four datasets comprising 1055 image pairs with 26 different cross-modal combinations, the assessment of 10,599 manually measured checkpoints demonstrates that AMES surpasses the state-of-the-art in terms of success rate, correct matching rate, point coverage, average matching accuracy, and spatial distribution uniformity. Our source code and multi-modal image datasets are publicly available at https://dpcv.whu.edu.cn/zm1/gksjj.htm.

PDE-Constrained Scale Optimization Selection for Feature Detection in Remote Sensing Image Matching

Feature detection and matching is the key technique for remote sensing image processing and related applications. In this paper, a PDE-constrained optimization model is proposed to determine the scale levels advantageous for feature detection. A variance estimation technique is introduced to treat the observation optical images polluted by additive zero-mean Gaussian noise and determine the parameter of a nonlinear scale space governed by the partial differential equation. Additive Operator Splitting is applied to efficiently solve the PDE constraint, and an iterative algorithm is proposed to approximate the optimal subset of the original scale level set. The selected levels are distributed more uniformly in the total variation sense and helpful for generating more accurate and robust feature points. The experimental results show that the proposed method can achieve about a 30% improvement in the number of correct matches with only a small increase in time cost.

Raman-Activated Cell Ejection for Validating the Reliability of the Raman Fingerprint Database of Foodborne Pathogens

Raman spectroscopy for rapid identification of foodborne pathogens based on phenotype has attracted increasing attention, and the reliability of the Raman fingerprint database through genotypic determination is crucial. In the research, the classification model of four foodborne pathogens was established based on t-distributed stochastic neighbor embedding (t-SNE) and support vector machine (SVM); the recognition accuracy was 97.04%. The target bacteria named by the model were ejected through Raman-activated cell ejection (RACE), and then single-cell genomic DNA was amplified for species analysis. The accuracy of correct matches between the predicted phenotype and the actual genotype of the target cells was at least 83.3%. Furthermore, all anticipant sequencing results brought into correspondence with the species were predicted through the model. In sum, the Raman fingerprint database based on Raman spectroscopy combined with machine learning was reliable and promising in the field of rapid detection of foodborne pathogens.

Grounding rod hanging and removing robot with hand-eye self-calibration capability in substation

In this paper, a robot system for hanging and removing grounding rods is designed by integrating 3D recognition technology, hand-eye self-calibration technology, and automatic operation technology. Specifically, a novel hand-eye self-calibration algorithm is proposed that only uses common objects in the actual scene, which differs from traditional robot hand-eye calibration in that it requires a dedicated calibration board to assist with offline completion. Addressing the problem that existing self-calibration methods cannot be optimized as a whole, resulting in low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Experimental verifications are conducted using both public dataset and practical robot system. In the dataset experiment, our method demonstrates superior accuracy and robustness compared to existing self-calibration methods. Furthermore, the practical robot platform experiment confirms the feasibility and efficacy of our approach across various wind speeds and lighting conditions.

An exclusionary screening method based on 3D morphometric features to sort commingled atlases and axes

In forensic commingled contexts, when the disarticulation occurs uniquely at the atlantoaxial joint, the correct match of atlas and axis may lead to the desirable assembly of the entire body. Notwithstanding the importance of this joint in such scenarios, no study has so far explored three-dimensional (3D) methodologies to match these two adjoining bones. In the present study, we investigated the potential of re-associating atlas and axis through 3D–3D superimposition by testing their articular surfaces congruency in terms of point-to-point distance (Root Mean Square, RMS). We analysed vertebrae either from the same individual (match) and from different individuals (mismatch). The RMS distance values were assessed for both groups (matches and mismatches) and a threshold value was determined to discriminate matches with a sensitivity of 100%. The atlas and the corresponding axis from 41 documented skeletons (18 males and 23 females), in addition to unpaired elements (the atlas or the axis) from 5 individuals, were superimposed, resulting in 41 matches and 1851 mismatches (joining and non-joining elements). No sex-related significant differences were found in matches and mismatches (p = 0.270 and p = 0.210, respectively), allowing to pool together the two sexes in each group. RMS values ranged between 0.41 to 0.77 mm for matches and between 0.37 and 2.18 mm for mismatches. Significant differences were found comparing the two groups (p < 0.001) and the highest RMS of matches (0.77 mm) was used as the discriminative value that provided a sensitivity of 100% and a specificity of 41%. In conclusion, the 3D–3D superimposition of the atlanto-axial articular facets cannot be considered as a re-association method per se, but rather as a screening one. However, further research on the validation of the 3D approach and on its application to other joints might provide clues to the complex topic of the reassociation of crucial adjoining bones.

A Deep-Based Approach for Multi-Descriptor Feature Extraction: Applications on SAR Image Registration

Synthetic aperture radar (SAR) images have found increasing attention in various applications, such as remote sensing, surveillance, and disaster management. Unlike optical sensors, SAR can capture images in any weather conditions at any time, making it particularly valuable. However, it poses unique challenges for image processing tasks, like complex speckle noise, texture, and geometric distortions. For many image processing tasks such as image registration; feature detection and extraction are critical initial steps. In this work, we propose a deep learning-based method for detecting and describing keypoints in SAR images, which can be specifically applied to SAR image registration tasks. To address the challenges of SAR images, we utilize the R2D2 network and propose a training scheme specifically tailored for these images. Furthermore, our approach involves multiple descriptors utilization for outlier removal between image keypoints and a two-stage pipeline consisting of coarse and fine stages for robust feature point matching of SAR images. By leveraging the coarse registration stage, which maintains robustness, and also a fine stage that employs multiple descriptors along with a local search technique, our approach enhances the keypoint matching accuracy. Evaluation results show that the proposed method outperforms most of the traditional and deep learning-based methods in terms of the percentage of correct matches, while achieving comparable or even better root mean squared error (RMSE) results.

Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification

Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.

Crater Triangle Matching Algorithm Based on Fused Geometric and Regional Features

Craters are regarded as significant navigation landmarks during the descent and landing process in small body exploration missions for their universality. Recognizing and matching craters is a crucial prerequisite for visual and LIDAR-based navigation tasks. Compared to traditional algorithms, deep learning-based crater detection algorithms can achieve a higher recognition rate. However, matching crater detection results under various image transformations still poses challenges. To address the problem, a composite feature-matching algorithm that combines geometric descriptors and region descriptors (extracting normalized region pixel gradient features as feature vectors) is proposed. First, the geometric configuration map is constructed based on the crater detection results. Then, geometric descriptors and region descriptors are established within each feature primitive of the map. Subsequently, taking the salience of geometric features into consideration, composite feature descriptors with scale, rotation, and illumination invariance are generated through fusion geometric and region descriptors. Finally, descriptor matching is accomplished by computing the relative distances between descriptors and adhering to the nearest neighbor principle. Experimental results show that the composite feature descriptor proposed in this paper has better matching performance than only using shape descriptors or region descriptors, and can achieve a more than 90% correct matching rate, which can provide technical support for the small body visual navigation task.