Alignment Quality Research Articles

Research on Real-Time Fusion Technology of Range Telemetry Data

Abstract Telemetry data is the important data for the ground station to obtain the working status and environmental parameters of the aircraft system. Its fusion processing is the key technology to select the best selection of multiple channels of data and improve the reliability and accuracy of the entire recording. Due to the large amount of telemetry data, there is a delay error in the transmission process, and the phenomenon of frame loss and code error is accompanied by severe challenges for the alignment and optimization of data fusion. The research starts from the application background of fusion technology, introduces and analyzes the characteristics of telemetry data and the difficulties of fusion technology; outlines the current alignment and optimization related research results and development process, according to engineering requirements and technical points, from real-time and after-event From the perspective, the alignment algorithms and quality evaluation algorithms involved are classified and analyzed in detail; finally, the shortcomings of the existing methods are summarized, and the future development direction is looked forward to provide references for related researchers.

Research on Processing Technology of Laser Punching and Positioning on Stainless Steel Plate

The process of sample punching on the stainless steel plate can calibrate the position and effectively improve the development of accurate positioning in the subsequent process. The processing technology of laser punching not only satisfies the efficient work of laser equipment, but also realizes the function of sample punching positioning, which is of great help to sheet metal processing. The laser punching process is affected by many process parameters which will result in slag inclusions in the punching socket after laser punching, in turn preventing the normal development of the subsequent process. In order to solve this problem, this paper sets out to study the influence of different process parameter combinations on the laser punching alignment quality by the orthogonal test method to formulate a scientific test plan for the process parameters involved, and the machining test of the punching alignment on the stainless steel plate with laser. The results show that the laser punching quality is affected by the marking time, laser power, auxiliary gas and air pressure under the condition that the distance from the nozzle remains unchanged, and the marking time has the greatest impact. In the meantime, this paper proposes the optimized combination of process parameters. The above results have instructive significance to the laser punching alignment process.

Refractive geometry for underwater domes

Underwater cameras are typically placed behind glass windows to protect them from the water. Spherical glass, a dome port, is well suited for high water pressures at great depth, allows for a large field of view, and avoids refraction if a pinhole camera is positioned exactly at the sphere’s center. Adjusting a real lens perfectly to the dome center is a challenging task, both in terms of how to actually guide the centering process (e.g. visual servoing) and how to measure the alignment quality, but also, how to mechanically perform the alignment. Consequently, such systems are prone to being decentered by some offset, leading to challenging refraction patterns at the sphere that invalidate the pinhole camera model. We show that the overall camera system becomes an axial camera, even for thick domes as used for deep sea exploration and provide a non-iterative way to compute the center of refraction without requiring knowledge of exact air, glass or water properties. We also analyze the refractive geometry at the sphere, looking at effects such as forward- vs. backward decentering, iso-refraction curves and obtain a 6th-degree polynomial equation for forward projection of 3D points in thin domes. We then propose a pure underwater calibration procedure to estimate the decentering from multiple images. This estimate can either be used during adjustment to guide the mechanical position of the lens, or can be considered in photogrammetric underwater applications.

Matching Cyber Security Ontologies through Genetic Algorithm-Based Ontology Alignment Technique

Security ontology can be used to build a shared knowledge model for an application domain to overcome the data heterogeneity issue, but it suffers from its own heterogeneity issue. Finding identical entities in two ontologies, i.e., ontology alignment, is a solution. It is important to select an effective similarity measure (SM) to distinguish heterogeneous entities. However, due to the complex semantic relationships among concepts, no SM is ensured to be effective in all alignment tasks. The aggregation of SMs so that their advantages and disadvantages complement each other directly affects the quality of alignments. In this work, we formally define this problem, discuss its challenges, and present a problem-specific genetic algorithm (GA) to effectively address it. We experimentally test our approach on bibliographic tracks provided by OAEI and five pairs of security ontologies. The results show that GA can effectively address different heterogeneous ontology-alignment tasks and determine high-quality security ontology alignments.

Guide tree optimization with genetic algorithm to improve multiple protein 3D-structure alignment.

With the increasing availability of 3D-data, the focus of comparative bioinformatic analysis is shifting from protein sequence alignments toward more content-rich 3D-alignments. This raises the need for new ways to improve the accuracy of 3D-superimposition. We proposed guide tree optimization with genetic algorithm (GA) as a universal tool to improve the alignment quality of multiple protein 3D-structures systematically. As a proof of concept, we implemented the suggested GA-based approach in popular Matt and Caretta multiple protein 3D-structure alignment (M3DSA) algorithms, leading to a statistically significant improvement of the TM-score quality indicator by up to 220-1523% on 'SABmark Superfamilies' (in 49-77% of cases) and 'SABmark Twilight' (in 59-80% of cases) datasets. The observed improvement in collections of distant homologies highlights the potentials of GA to optimize 3D-alignments of diverse protein superfamilies as one plausible tool to study the structure-function relationship. The source codes of patched gaCaretta and gaMatt programs are available open-access at https://github.com/n-canter/gamaps. Supplementary data are available at Bioinformatics online.

Theory of local k-mer selection with applications to long-read alignment.

MotivationSelecting a subset of k-mers in a string in a local manner is a common task in bioinformatics tools for speeding up computation. Arguably the most well-known and common method is the minimizer technique, which selects the ‘lowest-ordered’ k-mer in a sliding window. Recently, it has been shown that minimizers may be a sub-optimal method for selecting subsets of k-mers when mutations are present. There is, however, a lack of understanding behind the theory of why certain methods perform well.ResultsWe first theoretically investigate the conservation metric for k-mer selection methods. We derive an exact expression for calculating the conservation of a k-mer selection method. This turns out to be tractable enough for us to prove closed-form expressions for a variety of methods, including (open and closed) syncmers, (a, b, n)-words, and an upper bound for minimizers. As a demonstration of our results, we modified the minimap2 read aligner to use a more conserved k-mer selection method and demonstrate that there is up to an 8.2% relative increase in number of mapped reads. However, we found that the k-mers selected by more conserved methods are also more repetitive, leading to a runtime increase during alignment. We give new insight into how one might use new k-mer selection methods as a reparameterization to optimize for speed and alignment quality.Availability and implementationSimulations and supplementary methods are available at https://github.com/bluenote-1577/local-kmer-selection-results. os-minimap2 is a modified version of minimap2 and available at https://github.com/bluenote-1577/os-minimap2.Supplementary information Supplementary data are available at Bioinformatics online.

Matching sensor ontologies with unsupervised neural network with competitive learning

Sensor ontologies formally model the core concepts in the sensor domain and their relationships, which facilitates the trusted communication and collaboration of Artificial Intelligence of Things (AIoT). However, due to the subjectivity of the ontology building process, sensor ontologies might be defined by different terms, leading to the problem of heterogeneity. In order to integrate the knowledge of two heterogeneous sensor ontologies, it is necessary to determine the correspondence between two heterogeneous concepts, which is the so-called ontology matching. Recently, more and more neural networks have been considered as an effective approach to address the ontology heterogeneity problem, but they require a large number of manually labelled training samples to train the network, which poses an open challenge. In order to improve the quality of the sensor ontology alignment, an unsupervised neural network model is proposed in this work. It first models the ontology matching problem as a binary classification problem, and then uses a competitive learning strategy to efficiently cluster the ontologies to be matched, which does not require the labelled training samples. The experiment utilizes the benchmark track provided by the Ontology Alignment Evaluation Initiative (OAEI) and multiple real sensor ontology alignment tasks to test our proposal’s performance. The experimental results show that the proposed approach is able to determine higher quality alignment results compared to other matching strategies under different domain knowledge such as bibliographic and real sensor ontologies.

An Approach for Live Motion Correction for TRUS-MR Prostate Fusion Biopsy using Deep Learning.

TRUS-MR fusion guided biopsy highly depends on the quality of alignment between pre-operative Magnetic Resonance (MR) image and live trans-rectal ultrasound (TRUS) image during biopsy. Lot of factors influence the alignment of prostate during the biopsy like rigid motion due to patient movement and deformation of the prostate due to probe pressure. For MR-TRUS alignment during live procedure, the efficiency of the algorithm and accuracy plays an important role. In this paper, we have designed a comprehensive framework for fusion based biopsy using an end-to-end deep learning network for performing both rigid and deformation correction. Both rigid and deformation correction in one single network helps in reducing the computation time required for live TRUS-MR alignment. We have used 6500 images from 34 subjects for conducting this study. Our proposed registration pipeline provides Target Registration Error (TRE) of 2.51 mm after rigid and deformation correction on unseen patient dataset. In addition, with a total computation time of 70ms, we are able to achieve a rendering rate of 14 frames per second (FPS) that makes our network well suited for live procedures.Clinical Relevance- It is shown in the literature that systematic biopsy is the standard method for biopsy sampling in prostate that has high false negative rates. TRUS-MR fusion guided biopsy reduces the false negative rate of the sampling in prostate biopsy. Therefore, a live TRUS-MR fusion framework is helpful for prostate biopsy clinical procedures.

Perspective Distortion Correction for Multi-Modal Registration between Ultra-Widefield and Narrow-Angle Retinal Images.

Multi-modal retinal image registration between 2D Ultra-Widefield (UWF) and narrow-angle (NA) images has not been well-studied, since most existing methods mainly focus on NA image alignment. The stereographic projection model used in UWF imaging causes strong distortions in peripheral areas, which leads to inferior alignment quality. We propose a distortion correction method that remaps the UWF images based on estimated camera view points of NA images. In addition, we set up a CNN-based registration pipeline for UWF and NA images, which consists of the distortion correction method and three networks for vessel segmentation, feature detection and matching, and outlier rejection. Experimental results on our collected dataset shows the effectiveness of the proposed pipeline and the distortion correction method.

Multiple-Rod Constructs Do Not Reduce Pseudarthrosis and Rod Fracture After Pedicle Subtraction Osteotomy for Adult Spinal Deformity Correction but Improve Quality of Life.

ObjectiveTo compare the radiological and functional outcomes and complications of adult spinal deformity patients who underwent a pedicle subtraction osteotomy (PSO) below L2 but categorized according to their construct where either 2-rod or multiple-rod construct is applied. MethodsSixty-seven patients met the inclusion criteria, and were categorized into 3 groups: 2 rods (2R), multiple rods around the PSO (MRP), multiple rods around the PSO and lumbosacral junction (MRL). Demographic data, operative parameters, spinopelvic parameters, functional outcomes, and complications were collected. ResultsHealth-related quality of life scores showed a better outcome at 6 months and last follow-up visits in the MRP and MRL groups which were noted on different domains of Scoliosis Research Society-22 questionnaire, 36-item Short Form Health Surve, and Oswestry Disability Index scores (p<0.05). The 3 groups showed similar rates of rod-related complications with no significant difference (p=0.95). And inside each group, distribution of complications between pseudarthrosis with revision and rod fracture without revision was also similar (p=0.99). ConclusionThe use of multiple rods across the PSO did not show a better outcome when compared to single rods in terms of incidence and types of mechanical complications. However, better postoperative coronal alignment and health-related quality of life scores in the multiple rods group could be seen demonstrating an improved functional outcome.

Matching heterogeneous ontologies with adaptive evolutionary algorithm

An ontology provides a formal description on the domain concepts and their relationships. Due to the subjectivity of ontology engineers, one concept might be expressed in various ways, yielding the so-called ontology heterogeneity problem, and ontology matching is a ground method to address this problem. Ontology matching technique uses the similarity measure to determine the correspondences between two heterogeneous ontology entities. In order to improve the quality of ontology alignment, it is necessary to combine different kinds of similarity measures, and how to optimize the aggregating weights is called the ontology meta-matching problem. Tin this work, a heuristic evaluating metric on the ontology alignment is presented to evaluate the ontology alignment's quality, and a mathematical model on ontology meta-matching problem is constructed. Then, an Adaptive Evolutionary Algorithm (AEA) is proposed to effectively solve this problem. In particular, when the elite solution remains unchanged, AEA adaptively activates three independent exploring strategies, which, respectively use the adaptive selection, crossover and mutation operators based on the population diversity metric. In the experiment, we compare AEA among EA based matching technique and the state-of-the-art ontology matching technique, and the experimental results show its effectiveness.

PEPATAC: an optimized pipeline for ATAC-seq data analysis with serial alignments.

As chromatin accessibility data from ATAC-seq experiments continues to expand, there is continuing need for standardized analysis pipelines. Here, we present PEPATAC, an ATAC-seq pipeline that is easily applied to ATAC-seq projects of any size, from one-off experiments to large-scale sequencing projects. PEPATAC leverages unique features of ATAC-seq data to optimize for speed and accuracy, and it provides several unique analytical approaches. Output includes convenient quality control plots, summary statistics, and a variety of generally useful data formats to set the groundwork for subsequent project-specific data analysis. Downstream analysis is simplified by a standard definition format, modularity of components, and metadata APIs in R and Python. It is restartable, fault-tolerant, and can be run on local hardware, using any cluster resource manager, or in provided Linux containers. We also demonstrate the advantage of aligning to the mitochondrial genome serially, which improves the accuracy of alignment statistics and quality control metrics. PEPATAC is a robust and portable first step for any ATAC-seq project. BSD2-licensed code and documentation are available at https://pepatac.databio.org.

Comparative analysis of alignment tools for application on Nanopore sequencing data

Abstract INTRODUCTION: Long-read sequencing techniques such as Oxford Nanopore sequencing, are representing a promising novel approach in molecular-biological methodology, enabling potential facilitation in mapping and de novo assembly. In comparison to conventional sequencing methods, novel alignment tools are mandated to compensate differing data structures (especially high error rate) to achieve acceptably accurate analysis results. METHODS: In this study, benchmarking for long read aligners BLASR, GraphMap, LAST, minimap2, NGMLR and the short-read aligner BWA MEM on three experimental datasets was conducted. Obtained alignment results were compared for various quality and performance criteria, such as match rate, mismatch rate, error rate, working memory usage and computational time. RESULTS: The comparison yielded differences in alignment quality and performance of tools under test. Tool LAST showed the largest differences among all tools. Minimap2 achieved constant quality with good performance. BLASR, GraphMap, BWA MEM and NGMLR showed slight differences only. CONCLUSION: Differences among the tools could be reasoned with dataset characteristics and algorithm approaches of individual tools. All tools except BLASR seem applicable for Nanopore sequencing data. Therefore, selection of the tool should be done under consideration of the experimental design and the further downstream analysis

Biomedical Ontology Matching Through Attention-Based Bidirectional Long Short-Term Memory Network

Biomedical ontology formally defines the biomedical entities and their relationships. However, the same biomedical entity in different biomedical ontologies might be defined in diverse contexts, resulting in the problem of biomedicine semantic heterogeneity. It is necessary to determine the mappings between heterogeneous biomedical entities to bridge the semantic gap, which is the so-called biomedical ontology matching. Due to the plentiful semantic meaning and flexible representation of biomedical entities, the biomedical ontology matching problem is still an open challenge in terms of the alignment's quality. To face this challenge, in this work, the biomedical ontology matching problem is deemed as a binary classification problem, and an attention-based bidirectional long short-term memory network (At-BLSTM)-based ontology matching technique is presented to address it, which is able to capture the semantic and contextual feature of biomedical entities. In the experiment, the comparisons with state-of-the-art approaches show the effectiveness of the proposal.

The concept of biaxial surface potential and polymer aligning layers in electro-optical cells based on helical nanostructures of ferroelectric liquid crystal

The paper considers the possibility of controlling the alignment quality of helical nanostructures of ferroelectric liquid crystals (FLCs) within the concept of biaxial surface potential due to variation the FLCs helical pitch p0 and polymer aligning layers structures.

Multiple sequence alignment quality comparison in T-Coffee, MUSCLE and M-Coffee based on different benchmarks

Multiple sequence alignment (MSA) is a fundamental process in the studies for determination of evolutionary, structural and functional relationships of biological sequences or organisms. There are various heuristic approaches comparing more than two sequences to generate MSA. However, each tool used for MSA is not suitable for every dataset. Considering the importance of MSA in wide range of relationship studies, we were interested in comparing the performance of different MSA tools for various datasets. In this study, we applied three different MSA tools, T-Coffee, MUSCLE and M-Coffee, on several datasets, BAliBase, SABmark, DIRMBASE, ProteinBali and DNABali. It was aimed to evaluate the differences in the performance of these tools based on the stated benchmarks regarding the % consistency, sum of pairs (SP) and column scores (CS) by using Suite MSA. We also calculated the average values of these scores for each tool to examine the results in comparative perspective. Eventually, we conclude that all three tools performed their best with the datasets from ProteinBali (average % consistency: 29.6, 32.3, 29.7; SP: 0.74, 0.73, 0.74; CS with gaps: 0.27, 0.27, 0.26 for T-Coffee, MUSCLE, M-Coffee, respectively), whereas the lowest performance was obtained in datasets from DIRMBASE (average % consistency: 1.8, 1.1, 4.3; SP: 0.05, 0.04, 0.04 CS with gaps: 0.01, 0, 0.008 for T-Coffee, MUSCLE, M-Coffee, respectively)

Improving the robustness of MOLLI T1 maps with a dedicated motion correction algorithm

Myocardial tissue T1 constitutes a reliable indicator of several heart diseases related to extracellular changes (e.g. edema, fibrosis) as well as fat, iron and amyloid content. Magnetic resonance (MR) T1-mapping is typically achieved by pixel-wise exponential fitting of a series of inversion or saturation recovery measurements. Good anatomical alignment between these measurements is essential for accurate T1 estimation. Motion correction is recommended to improve alignment. However, in the case of inversion recovery sequences, this correction is compromised by the intrinsic contrast variation between frames. A model-based, non-rigid motion correction method for MOLLI series was implemented and validated on a large database of cardiac clinical cases (n = 186). The method relies on a dedicated similarity metric that accounts for the intensity changes caused by T1 magnetization relaxation. The results were compared to uncorrected series and to the standard motion correction included in the scanner. To automate the quantitative analysis of results, a custom data alignment metric was defined. Qualitative evaluation was performed on a subset of cases to confirm the validity of the new metric. Motion correction caused noticeable (i.e. > 5%) performance degradation in 12% of cases with the standard method, compared to 0.3% with the new dedicated method. The average alignment quality was 85% ± 9% with the default correction and 90% ± 7% with the new method. The results of the qualitative evaluation were found to correlate with the quantitative metric. In conclusion, a dedicated motion correction method for T1 mapping MOLLI series has been evaluated on a large database of clinical cardiac MR cases, confirming its increased robustness with respect to the standard method implemented in the scanner.

Focused SANA: Speeding Up Network Alignment

Network Alignment (NA) is a generalization of the graph isomorphism problem for non-isomorphic graphs, where the goal is to find a node mapping as close as possible to isomorphism. Recent successful NA algorithms follow a search-based approach, such as simulated annealing. We propose to speed up search-based NA algorithms by pruning the search-space based on heuristic rules derived from the topological features of the aligned nodes. We define several desirable properties of such pruning rules, analyze them theoretically, and propose a pruning rule based on nodes' degrees. Experimental results show that using the proposed rule yields significant speedup and higher alignment quality compared to the state of the art. In addition, we redefine common NA objective functions in terms of established statistical analysis metrics, opening a wide range of possible objective functions.

PanSVR: Pan-Genome Augmented Short Read Realignment for Sensitive Detection of Structural Variations.

The comprehensive discovery of structure variations (SVs) is fundamental to many genomics studies and high-throughput sequencing has become a common approach to this task. However, due the limited length, it is still non-trivial to state-of-the-art tools to accurately align short reads and produce high-quality SV callsets. Pan-genome provides a novel and promising framework to short read-based SV calling since it enables to comprehensively integrate known variants to reduce the incompleteness and bias of single reference to breakthrough the bottlenecks of short read alignments and provide new evidences to the detection of SVs. However, it is still an open problem to develop effective computational approaches to fully take the advantage of pan-genomes. Herein, we propose Pan-genome augmented Structure Variation calling tool with read Re-alignment (PanSVR), a novel pan-genome-based SV calling approach. PanSVR uses several tailored methods to implement precise re-alignment for SV-spanning reads against well-organized pan-genome reference with plenty of known SVs. PanSVR enables to greatly improve the quality of short read alignments and produce clear and homogenous SV signatures which facilitate SV calling. Benchmark results on real sequencing data suggest that PanSVR is able to largely improve the sensitivity of SV calling than that of state-of-the-art SV callers, especially for the SVs from repeat-rich regions and/or novel insertions which are difficult to existing tools.

Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona.

MotivationSingle-cell multi-omics sequencing data can provide a comprehensive molecular view of cells. However, effective approaches for the integrative analysis of such data are challenging. Existing manifold alignment methods demonstrated the state-of-the-art performance on single-cell multi-omics data integration, but they are often limited by requiring that single-cell datasets be derived from the same underlying cellular structure.ResultsIn this study, we present Pamona, a partial Gromov-Wasserstein distance-based manifold alignment framework that integrates heterogeneous single-cell multi-omics datasets with the aim of delineating and representing the shared and dataset-specific cellular structures across modalities. We formulate this task as a partial manifold alignment problem and develop a partial Gromov-Wasserstein optimal transport framework to solve it. Pamona identifies both shared and dataset-specific cells based on the computed probabilistic couplings of cells across datasets, and it aligns cellular modalities in a common low-dimensional space, while simultaneously preserving both shared and dataset-specific structures. Our framework can easily incorporate prior information, such as cell type annotations or cell-cell correspondence, to further improve alignment quality. We evaluated Pamona on a comprehensive set of publicly available benchmark datasets. We demonstrated that Pamona can accurately identify shared and dataset-specific cells, as well as faithfully recover and align cellular structures of heterogeneous single-cell modalities in a common space, outperforming the comparable existing methods.Availabilityand implementationPamona software is available at https://github.com/caokai1073/Pamona.Supplementary information Supplementary data are available at Bioinformatics online.