Splicing Algorithm Research Articles

Research on hybrid segmentation technologies for postprocessing the lung and trachea CT images

This paper introduces a systematic method for segmenting the main trachea and bronchioles in lung computed tomography scans. It begins with a stack-based three-dimensional region growth algorithm to outline the main trachea, which is then refined using morphological techniques to improve accuracy. The segmentation of bronchioles is achieved through domain labeling, lung tissue segmentation, adaptive binarization, and inner product analysis. The main trachea and bronchioles are integrated using an operating room (OR) operation and a novel splicing algorithm to form a complete tracheal tree. The method’s accuracy is validated against manual labeling, showing a Dice coefficient of about 0.99, on average, in lung parenchyma segmentation and a segmentation overlap with expert results ranging from 79.89% to 93.31% in lung trachea tree segmentation. This robust methodology is thoroughly tested and validated.

A precise modeling method of three‐dimensional discrete fracture network based on rectangular joint model

AbstractAs the weak structure and main seepage channel of rock mass, the discrete fracture network (DFN) has a significant influence on the physical and mechanical properties of rock mass. In this paper, based on the rectangular joint model, two algorithms are proposed to realize the precise modeling of the DFN in any polyhedron. First, the equivalence of DFN modeling with the rectangular joint model and the elliptical joint model is theoretically deduced. On this basis, the chain splicing algorithm suitable for the rectangular joint model is proposed, which can generate the DFN model according to the actual distribution of joint spacing, trace, and bridge length. Aiming at the concave boundary in the model domain, the segmentation‐stitching operation is put forward, and the continuity of the joint when passing through the interface is ensured. Finally, the effectiveness of the above algorithm is verified by several simple cases, and the relationship between the size of the geometrical representative elementary volume (REV) and discontinuity parameters is discussed. The results show that when the mean values of the joint distribution parameters are the same, even if the distribution rules are different, the number of joints generated in the same spatial region is approximately equal. The size of the geometrical REV is independent of the spacing and bridge length and is approximately three to five times the mean of the trace. The relevant algorithms and conclusions in this paper can be used as the basis for subsequent rock mass stability analysis and seepage calculation.

Multiscale method for identifying and marking the multiform fractures from visible-light rock-mass images

Multiform fractures have a direct impact on the mechanical performance of rock masses. To accurately identify multiform fractures, the distribution patterns of grayscale and the differential features of fractures in their neighborhoods are summarized. Based on this, a multiscale processing algorithm is proposed. The multiscale process is as follows. On the neighborhood of pixels, a grayscale continuous function is constructed using bilinear interpolation, the smoothing of the grayscale function is realized by Gaussian local filtering, and the grayscale gradient and Hessian matrix are calculated with high accuracy. On small-scale blocks, the pixels are classified by adaptively setting the grayscale threshold to identify potential line segments and mini-fillings. On the global image, potential line segments and mini-fillings are spliced together by progressing the block frontier layer-by-layer to identify and mark multiform fractures. The accuracy of identifying multiform fractures is improved by constructing a grayscale continuous function and adaptively setting the grayscale thresholds on small-scale blocks. And the layer-by-layer splicing algorithm is performed only on the domain of the 2-layer small-scale blocks, reducing the complexity. By using rock mass images with different fracture types as examples, the identification results show that the proposed algorithm can accurately identify the multiform fractures, which lays the foundation for calculating the mechanical parameters of rock masses.

A super-grid approach for LES combustion closure using the Linear Eddy Model

LES–LEM is a simulation approach for turbulent combustion in which the stochastic Linear Eddy Model (LEM) is used for sub-grid mixing and combustion closure in Large-Eddy Simulation (LES). LEM resolves, along a one-dimensional line, all spatial and temporal scales, provides on-the-fly local turbulent flame statistics, captures finite rate chemistry effects and directly incorporates turbulence-chemistry interaction. However, the approach is computationally expensive as it requires advancing an LEM-line in each LES cell. This paper introduces a novel turbulent combustion closure model for LES using LEM to address this issue. It involves coarse-graining the LES mesh to generate a coarse- level ‘super-grid’ comprised of cell-clusters. Each cell-cluster, instead of each LES cell, then contains a single LEM domain. This domain advances the combined advection–reaction–diffusion solution and also provides suitably conditioned statistics for thermochemical scalars such as species mass fractions. Local LES-filtered thermochemical states are then obtained by probability-density-function (PDF) weighted integration of binned conditionally averaged scalars, akin to standard presumed PDF approaches for reactive LES but with physics-based determination of the full thermochemical state for particular values of the conditioning variables. The proposed method is termed ‘super-grid LEM’ or ‘SG-LEM’. The paper describes LEM reaction–diffusion advancement, the LEM representation of turbulent advection, a novel splicing algorithm (a key feature of LES–LEM) formulated for the super-grid approach, a wall treatment, and a thermochemical LES closure procedure. To validate the proposed model, a pressure-based solver was developed using the OpenFOAM library and tested on a premixed ethylene flame stabilised over a backward facing step, a setup for which some DNS data is available. SG-LEM provides high resolution flame structures, temperature and mass fractions suitable for LES thermochemical closure. Additionally, it provides reaction-rate data at the coarse level, a unique feature compared to other mapping-type closure methods. Quantitative comparisons are made between the proposed model and time-averaged DNS data, focussing on velocity, temperature and species mass fraction. Results show good agreement downstream of the step. Furthermore, comparison with an equivalent Partially-Stirred Reactor (PaSR) simulation demonstrates the superior predictive capability of SG-LEM. Additionally, the paper briefly examines the sensitivity of the model to coarse-graining parameters and finally, explores computational efficiency highlighting the substantial speedup achieved when compared to the standard LES–LEM approach with potentially significant speedup relative to PaSR closure for the intensely turbulent regimes of principal interest.

Advances in alternative splicing identification: deep learning and pantranscriptome.

In plants, alternative splicing is a crucial mechanism for regulating gene expression at the post-transcriptional level, which leads to diverse proteins by generating multiple mature mRNA isoforms and diversify the gene regulation. Due to the complexity and variability of this process, accurate identification of splicing events is a vital step in studying alternative splicing. This article presents the application of alternative splicing algorithms with or without reference genomes in plants, as well as the integration of advanced deep learning techniques for improved detection accuracy. In addition, we also discuss alternative splicing studies in the pan-genomic background and the usefulness of integrated strategies for fully profiling alternative splicing.

From Netlist to Manufacturable Layout: An Auto-Layout Algorithm Optimized for Radio Frequency Integrated Circuits

Layout stitching is a repetitive and tedious task of the radio frequency integrated circuit (RFIC) design process. While academic research on layout splicing algorithms mainly focuses on analog and digital circuits, there is still a lack of well-developed algorithms for RFICs. An RFIC system usually has a symmetrical layout, such as transmitter and receiver components, low-noise amplifier (LNA), an SPDT switch, etc. This paper aims to address this gap by proposing an automated procedure for the layout of RFICs by relying on the basic device/PCell structure based on the interconnection among circuit topologies. This approach makes the in-series generation of layouts and automatic splicing based on circuit logic possible, resulting in superior stitching performance compared with related modules in Advanced Design System. To demonstrate the physical application possibilities, we implemented our algorithm on an LNA and a switch circuit.

Reclassification of two germline DICER1 splicing variants leads to DICER1 syndrome diagnosis

DICER1 syndrome is an inherited condition associated with an increased risk of developing hamartomatous and neoplastic lesions in diverse organs, mainly at early ages. Germline pathogenic variants in DICER1 cause this condition. Detecting a variant of uncertain significance in DICER1 or finding uncommon phenotypes complicate the diagnosis and can negatively impact patient care. We present two unrelated patients suspected to have DICER1 syndrome. Both females (aged 13 and 15 years) presented with multinodular goiter (thyroid follicular nodular disease) and ovarian tumours. One was diagnosed with an ovarian Sertoli-Leydig cell tumour (SLCT) and the other, with an ovarian juvenile granulosa cell tumour, later reclassified as a retiform variant of SLCT. Genetic screening showed no germline pathogenic variants in DICER1. However, two potentially splicing variants were found, DICER1 c.5365-4A>G and c.5527+3A>G. Also, typical somatic DICER1 RNase IIIb hotspot mutations were detected in the thyroid and ovarian tissues. In silico splicing algorithms predicted altered splicing for both germline variants and skipping of exon 25 was confirmed by RNA assays for both variants. The reclassification of the ovarian tumour, leading to recognition of the association with DICER1 syndrome and the characterization of the germline intronic variants were all applied to recently described DICER1 variant classification rules. This ultimately resulted in confirmation of DICER1 syndrome in the two teenage girls.

A Task Planning Algorithm for High-Mobility Satellites Based on Trajectory Splicing

Natural disasters are global issues that have led to huge casualties and property losses. Satellite observation is one of the predominant methods of disaster monitoring, and high-mobility satellites can normally provide faster responses. A study of the high-mobility satellite task planning problem for global decentralized multi-target observation is conducted in this paper. First, a “trajectory-target” database composed of candidate trajectories and the corresponding visibilities of targets is established. Second, a splicing algorithm is devised to connect the individual trajectories as a whole so that the task planning problem can be converted to a combinational optimization problem of candidate trajectories. To solve the optimization problem, a heuristic algorithm is designed based on NSGA-II with a double-layer encoding structure and hybrid-heuristic mutation operators. The results presented at the end of the paper verify the effectiveness of the proposed algorithms.

Wear Detection Method for Flexible Polishing Bonnet Tools Based on Improved Iterative Closest Point Splicing Algorithm

Wear Detection Method for Flexible Polishing Bonnet Tools Based on Improved Iterative Closest Point Splicing Algorithm

Fast and accurate reconstruction of large-scale 3D porous media using deep learning

The accurate and efficient reconstruction of the porous media is the fundamental link to revealing its structural features and physical properties. In this work, we propose a deep learning (DL)-based algorithm to reconstruct large-scale three-dimensional (3D) porous media that can be treated as the representative element volumes (REVs), based on generative adversarial networks (GAN) and convolutional neural networks (CNN), named LGCNN. The proposed framework consists of a machine learning based (ML-based) reconstruction method for small-scale porous media and an adjustable splicing algorithm to achieve the REVs reconstruction. On this basis, four special neural networks are established to reconstruct the porous media and ensure the connectivity between the adjacent porous media during the splicing process. Subsequently, the detailed validation of LGCNN against traditional reconstruction methods and other deep learning algorithms is performed. The results show that the reconstruction speed (6003 voxels) of LGCNN (10 min) is much faster than traditional numerical reconstruction methods including QSGS (642 min), CCSIM (5973 min), and SD (33,628 min) with higher accuracy on structural parameters (e.g., porosity and pore size distribution), when compared with real porous media. In particular, the size of constructed porous media is far larger than previous ML-based reconstruction algorithms as much as 3–4 orders of magnitude, indicating the puissant ability of LGCNN to be used for high-resolution or multi-scale reconstruction.

Identification of an elusive spliceogenic MYBPC3 variant in an otherwise genotype-negative hypertrophic cardiomyopathy pedigree

The finding of a genotype-negative hypertrophic cardiomyopathy (HCM) pedigree with several affected members indicating a familial origin of the disease has driven this study to discover causative gene variants. Genetic testing of the proband and subsequent family screening revealed the presence of a rare variant in the MYBPC3 gene, c.3331−26T>G in intron 30, with evidence supporting cosegregation with the disease in the family. An analysis of potential splice-altering activity using several splicing algorithms consistently yielded low scores. Minigene expression analysis at the mRNA and protein levels revealed that c.3331−26T>G is a spliceogenic variant with major splice-altering activity leading to undetectable levels of properly spliced transcripts or the corresponding protein. Minigene and patient mRNA analyses indicated that this variant induces complete and partial retention of intron 30, which was expected to lead to haploinsufficiency in carrier patients. As most spliceogenic MYBPC3 variants, c.3331−26T>G appears to be non-recurrent, since it was identified in only two additional unrelated probands in our large HCM cohort. In fact, the frequency analysis of 46 known splice-altering MYBPC3 intronic nucleotide substitutions in our HCM cohort revealed 9 recurrent and 16 non-recurrent variants present in a few probands (≤ 4), while 21 were not detected. The identification of non-recurrent elusive MYBPC3 spliceogenic variants that escape detection by in silico algorithms represents a challenge for genetic diagnosis of HCM and contributes to solving a fraction of genotype-negative HCM cases.

Analog and Photon Signal Splicing for CO2-DIAL Based on Piecewise Nonlinear Algorithm

In the CO2 differential absorption lidar (DIAL) system, signals are simultaneously collected through analog detection (AD) and photon counting (PC). These two kinds of signals have their own characteristics. Therefore, a combination of AD and PC signals is of great importance to improve the detection capability (detection range and accuracy) of CO2-DIAL. The traditional signal splicing algorithm cannot meet the accuracy requirements of CO2 inversion due to unreasonable data fitting. In this paper, a piecewise least square splicing algorithm is developed to make signal splicing more flexible and efficient. First, the lidar signal is segmented, and according to the characteristics of each signal, the best fitting parameters are obtained by using the least square fitting with different steps. Then, all the segmented and fitted signals are integrated to realize the effective splicing of the near-field AD signal and the far-field PC signal. A weight gradient strategy is also adopted in signal splicing, and the weights of the AD and PC signals in the spliced signal change with the height. The splicing effect of the improved algorithm is evaluated by the measured signal, which are obtained in Wuhan, China, and the splice of the AD and PC signals in the range of 800–1500 m are completed. Compared with the traditional method, the evaluation parameter R2 and the residual sum of squares of the spliced signal are greatly improved. The linear relationship between the AD and PC signals is improved, and the fitting R2 of differential absorption optical depth reaches 0.909, indicating that the improved signal splicing algorithm can well splice the near-field AD signal and the far-field PC signal.

Automatic splicing of Chinese single-sided shreds based on character feature and typesetting characteristics

In this paper we proposed an automatic splicing algorithm for regular cross-cut single-sided shreds of pure Chinese document, which was based on the characteristics of character and typesetting. The algorithm began with extracting the characteristics including character width, character height, line height and line spacing, after which the shreds meeting the typesetting requirement were spliced according to the edge similarity. Afterwards, the horizontal projection vectors of the shreds were corrected and the shreds were clustered by row according to horizontal projection similarity. A greedy algorithm with rejection strategy was then used to complete the intra-cluster horizontal splicing of shreds based on edge similarity. The experiment results revealed that the algorithm proposed in this paper had the characteristics of both high splicing speed and high accuracy.

双维度交叉特征点协同匹配的点云拼接算法

双维度交叉特征点协同匹配的点云拼接算法

LncAS2Cancer: a comprehensive database for alternative splicing of lncRNAs across human cancers.

Accumulating studies demonstrated that the roles of lncRNAs for tumorigenesis were isoform-dependent and their aberrant splicing patterns in cancers contributed to function specificity. However, there is no existing database focusing on cancer-related alternative splicing of lncRNAs. Here, we developed a comprehensive database called LncAS2Cancer, which collected 5335 bulk RNA sequencing and 1826 single-cell RNA sequencing samples, covering over 30 cancer types. By applying six state-of-the-art splicing algorithms, 50859 alternative splicing events for 8 splicing types were identified and deposited in the database. In addition, the database contained the following information: (i) splicing patterns of lncRNAs under seven different conditions, such as gene interference, which facilitated to infer potential regulators; (ii) annotation information derived from eight sources and manual curation, to understand the functional impact of affected sequences; (iii) survival analysis to explore potential biomarkers; as well as (iv) a suite of tools to browse, search, visualize and download interesting information. LncAS2Cancer could not only confirm the known cancer-associated lncRNA isoforms but also indicate novel ones. Using the data deposited in LncAS2Cancer, we compared gene model and transcript overlap between lncRNAs and protein-coding genes and discusses how these factors, along with sequencing depth, affected the interpretation of splicing signals. Based on recurrent signals and potential confounders, we proposed a reliable score to prioritize splicing events for further elucidation. Together, with the broad collection of lncRNA splicing patterns and annotation, LncAS2Cancer will provide important new insights into the diverse functional roles of lncRNA isoforms in human cancers. LncAS2Cancer is freely available at https://lncrna2as.cd120.com/.

Bilayer synchronous measuring method of curved screen based on a line-structured light-scanning sensor.

Curved screens are widely used in smart wearables, optics, aerospace, and other display fields. To overcome the difficulty of overall quality evaluation of large-area curved screens, this paper proposes a novel measuring method to realize the bilayer synchronous measurement based on line-structured light scanning. The integrated shape information of the large bilayer curved screen can be obtained by only a single probe in one scanning step. The two-step image processing in the image and three-dimensional data space is proposed to reduce the noise due to the weak reflection from the bilayer screen. To realize the large-area curved screen measurement, a splicing algorithm based on the optimization principle is also proposed to rectify the position deviation of each splicing step. A curved mobile phone screen was measured, and the experimental results effectively proved the validity of the proposed method. A camera lens was also measured to prove its potential for other multilayer parts.

Research on Four-Point Air Bending Process and Contour Detection Method for JCO Forming Process of LSAW Pipes

Aiming at the forming efficiency and roundness of the longitudinal submerged arc welded (LSAW) pipes in JCO (J-shape to C-shape to O-shape) forming process, this paper proposes a four-point air bending process. Compared with the traditional three-point air bending process, The new process can provide a more uniform bending moment, does not need to crimp the edges of steel sheet, shorten the residual straight segment length, and lengthen the forming length in single pass. The mechanical model is established to analyze the static equilibrium conditions and elastic–plastic deformation. The process is simulated by using the software package ABAQUS, to find the maximum punch spacing, and further determine the formulation principles of other process parameters. In addition, a contour detection method for the LSAW pipes in forming process is proposed based on machine vision (planar-array CCD camera produced by Gray Point Corporation, Vancouver, Canada). This method can not only quickly detect the contour of each pass, but also splice the detected contours together to obtain the overall contour with the given splicing algorithm. According to the measured contour, the bending angle, radius, and roundness can be calculated, to correct the punch reduction in the next pass and improve the forming accuracy of the pipes. Finally, an experimental system is designed to verify the proposed four-point bending JCO forming process and contour detection method. The result shows that the error between the contour detection method and CMM (coordinate measuring machine) is less than 0.5% for the overall contour, the two experimental pipes require 13 and 15 passes respectively, the roundness of pipes are less than 1.1%, which is much better than that of traditional three-point bending JCO forming process.

数字全息中基于优化Harris角点的相位拼接算法

数字全息中基于优化Harris角点的相位拼接算法

Fast Point Cloud Splicing Algorithm Based on Weighted Neighborhood Information of Points

Fast Point Cloud Splicing Algorithm Based on Weighted Neighborhood Information of Points

Survey of gene splicing algorithms based on reads

ABSTRACTGene splicing is the process of assembling a large number of unordered short sequence fragments to the original genome sequence as accurately as possible. Several popular splicing algorithms based on reads are reviewed in this article, including reference genome algorithms and de novo splicing algorithms (Greedy-extension, Overlap-Layout-Consensus graph, De Bruijn graph). We also discuss a new splicing method based on the MapReduce strategy and Hadoop. By comparing these algorithms, some conclusions are drawn and some suggestions on gene splicing research are made.