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Background: Tremendous amounts of omics data accumulated have made it possible to identify cancer driver pathways through computational methods, which is believed to be able to offer critical information in such downstream research as ascertaining cancer pathogenesis, developing anti-cancer drugs, and so on. It is a challenging problem to identify cancer driver pathways by integrating multiple omics data., Results: In this study, a parameter-free identification model SMCMN, incorporating both pathway features and gene associations in Protein-Protein Interaction (PPI) network, is proposed. A novel measurement of mutual exclusivity is devised to exclude some gene sets with "inclusion" relationship. By introducing gene clustering based operators, a partheno-genetic algorithm CPGA is put forward for solving the SMCMN model. Experiments were implemented on three real cancer datasets to compare the identification performance of models and methods. The comparisons of models demonstrate that the SMCMN model does eliminate the "inclusion" relationship, and produces gene sets with better enrichment performance compared with the classical model MWSM in most cases., Conclusions: The gene sets recognized by the proposed CPGA-SMCMN method possess more genes engaging in known cancer related pathways, as well as stronger connectivity in PPI network. All of which have been demonstrated through extensive contrast experiments among the CPGA-SMCMN method and six state-of-the-art ones., (© 2023. The Author(s).)

We present loop structure prediction results of the intracellular and extracellular loops of four G-protein-coupled receptors (GPCRs): bovine rhodopsin (bRh), the turkey β1-adrenergic (β1Ar), the human β2-adrenergic (β2Ar) and the human A2a adenosine receptor (A2Ar) in perturbed environments. We used the protein local optimization program, which builds thousands of loop candidates by sampling rotamer states of the loops' constituent amino acids. The candidate loops are discriminated between with our physics-based, all-atom energy function, which is based on the OPLS force field with implicit solvent and several correction terms. For relevant cases, explicit membrane molecules are included to simulate the effect of the membrane on loop structure. We also discuss a new sampling algorithm that divides phase space into different regions, allowing more thorough sampling of long loops that greatly improves results. In the first half of the paper, loop prediction is done with the GPCRs' transmembrane domains fixed in their crystallographic positions, while the loops are built one-by-one. Side chains near the loops are also in non-native conformations. The second half describes a full homology model of β2Ar using β1Ar as a template. No information about the crystal structure of β2Ar was used to build this homology model. We are able to capture the architecture of short loops and the very long second extracellular loop, which is key for ligand binding. We believe this the first successful example of an RMSD validated, physics-based loop prediction in the context of a GPCR homology model., (Copyright © 2012 Wiley Periodicals, Inc.)

A new method for constructing the model of vascular trees was proposed in this paper. By use of this method, the arterial trees in good agreement with the actual structure could be grown. In this process, all vessels in the vascular tree were divided into two groups: the conveying vessels, and the delivering branches. And different branches could be built by different ways. Firstly, the distributing rules of conveying vessels were ascertained by use of measurement data, and then the conveying vessels were constructed in accordance to the statistical rule and optimization criterion. Lastly, delivering branches were modeled by constrained constructive optimization (CCO) on the conveying vessel-trees which had already been generated. In order to compare the CCO method and stepwise method proposed here, two 3D arterial trees of human tongue were grown with their vascular tree having a special structure. Based on the corrosion casts of real arterial tree of human tongue, the data about the two trees constructed by different methods were compared and analyzed, including the averaged segment diameters at respective levels, the distribution and the diameters of the branches of first level at respective directions. The results show that the vascular tree built by stepwise method is more similar to the true arterial of human tongue when compared against the tree built by CCO method.

The task of segmenting histiocyte is a crucial step in the analysis of histiocytic images which is an important application of computer vision to histopathology. The algorithm presented in this article was composed of two steps: (1) the morph-based preprocessing; (2) the ameliorated watershed method. In the first step, the difference between histiocytes was magnified in order to increase the visibility from the view of the computer vision, and then the ameliorated terrace-flooding-simulated watershed method was used to achieve the segmentation of histiocytic images in the second step. To test the performance of the algorithm, different samples of visual quality were tested and the result figures proved successful.

Traditional simultaneous localization and mapping (SLAM) performs well in a static environment; however, with the abrupt increase of dynamic points in dynamic environments, the algorithm is influenced by a lot of meaningless information, leading to low precision and poor robustness in pose estimation. To tackle this problem, a new visual SLAM algorithm of dynamic scenes named YG-SLAM is proposed, which creates an independent dynamic-object-detection thread and adds a dynamic-feature-point elimination step in the tracking thread. The YOLOv5 algorithm is introduced in the dynamic-object-detection thread for target recognition and deployed on the GPU to speed up image frame identification. The optic-flow approach employs an optic flow to monitor feature points and helps to remove the dynamic points in different dynamic objects based on the varying speeds of pixel movement. While combined with the antecedent information of object detection, the system can eliminate dynamic feature points under various conditions. Validation is conducted in both TUM and KITTI datasets, and the results illustrate that YG-SLAM can achieve a higher accuracy in dynamic indoor environments, with the maximum accuracy augmented from 0.277 m to 0.014 m. Meanwhile, YG-SLAM requires less processing time than other dynamic-scene SLAM algorithms, indicating its positioning priority in dynamic situations. [ABSTRACT FROM AUTHOR]

Background: Patients with esophageal squamous cell carcinoma (ESCC) with lymph node metastasis may be misclassified as pN0 due to an insufficient number of lymph nodes examined (LNE). The purpose of this study was to confirm that patients with ESCC are indeed pN0 and to propose an adequate number for the correct nodal stage using the nodal staging score (NSS) developed by the beta‐binomial model. Methods: A total of 1249 patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2017, and 1404 patients diagnosed with ESCC in our database between 2005 and 2018 were included. The NSS was developed to assess the probability of pN0 status based on both databases. The effectiveness of NSS was verified using survival analysis, including Kaplan–Meier curves and Cox models. Results: Many patients were misclassified as pN0 based on our algorithm due to insufficient LNE. As the number of LNE increased, false‐negative findings dropped; accordingly, the NSS increased. In addition, NSS was an independent prognostic indicator for pN0 in patients with ESCC in the SEER database (hazard ratio [HR] 0.182, 95% confidence interval [CI] 0.046–0.730, p = 0.016) and our database (HR 0.215, 95% CI 0.055–0.842, p = 0.027). A certain number of nodes must be examined to achieve 90% of the NSS. Conclusions: NSS could determine the probability of true pN0 status for patients, and it was sufficient in predicting survival and obtaining adequate numbers for lymphadenectomy. [ABSTRACT FROM AUTHOR]

Recently, the tumor microenvironment (TME) has been reported to be closely related to the tumor initiation, progression, and prognosis. Bladder urothelial carcinoma (BLCA), one of the most common subtypes of bladder cancer worldwide, has been associated with increased morbidity and mortality in the past decade. However, whether the TME status of BLCA contributes to the prediction of BLCA prognosis still remains uncertain. In this study, the ESTIMATE algorithms were used to estimate the division of immune and stromal components in 406 BLCA samples downloaded from The Cancer Genome Atlas database (TCGA). Based on the comparison between ESTIMATE scores, the differentially expressed genes (DEGs) were selected. Using the univariate Cox regression analysis, prognosis-related DEGs were further identified (p < 0.05). The LASSO regression analysis was then used to screen 11 genes that were highly related to the TME of BLCA to generate a novel prognostic gene signature. The following survival analyses showed that this signature could effectively predict the prognosis of BLCA. The clinical value of this signature was further verified in an external cohort obtained from the First Affiliated Hospital of Wenzhou Medical University (n = 120). Based on the stage-correlation analysis and differential expression analysis, IGF1 and MMP9 were identified as the hub genes in the signature. Additionally, using CIBERSORT algorithms, we found that both IGF1 and MMP9 were significantly associated with immune infiltration. Collectively, a novel TME-related prognostic signature contributes to accurately predict the prognosis of BLCA. [ABSTRACT FROM AUTHOR]

It is generally acknowledged that driver pathway plays a decisive role in the occurrence and progress of tumors, and the identification of driver pathways has become imperative for precision medicine or personalized medicine. Due to the inevitable sequencing error, the noise contained in single omics cancer data usually plays a negative effect on identification. It is a feasible approach to take advantage of multi-omics cancer data rather than a single one now that large amounts of multi-omics cancer data have become available. The identification of driver pathways by integrating multi-omics cancer data has attracted attention of researchers in bioinformatics recently. In this paper, a weighted non-binary mutation matrix is constructed by integrating copy number variations, somatic mutations and gene expressions. Based on the weighted non-binary mutation matrix, a new identification model is proposed through defining new measurements of coverage and exclusivity. Then, a cooperative coevolutionary algorithm CGA-MWS is put forward for solving the presented model. Both real cancer data and simulated one were used to conduct comparisons among methods Dendrix, GA, iMCMC, MOGA, PGA-MWS and CGA-MWS. Compared with the pathways identified by the other five methods, more genes, belonging to the pathway identified by the CGA-MWS method, are enriched in a known signaling pathway in most cases. Simultaneously, the high efficiency of method CGA-MWS makes it practical in realistic applications. All of which have been verified through a number of experiments. [ABSTRACT FROM AUTHOR]

In recent years, image super-resolution (SR) based on deep learning technology has made significant progress. However, most methods are difficult to apply in real life because of their large parameters and heavy computation. Recently, residual learning has been widely applied to the problem of super-resolution. It can make the shallow features extracted from the input image act on each middle layer through long and short connection. Therefore, residual learning can be focused on processing high-frequency feature information, which significantly improves the SR performance of the network. However, with the improvement of network depth, the features that can be effectively utilized are still the shallow ones extracted from the input image. In this paper, we propose the feature separation and fusion network(FSFN). We further enrich the high-frequency feature information by separating and fusing the extracted and unextracted features in the internal shallow layer of each feature separation and fusion module. As the depth of the network increases, the shallow features extracted from the input image can be updated in a direction closer to those extracted from the real high-resolution image. A large number of experimental results show that this method has a strong performance compared with the existing SR algorithm with similar parameters and computation. [ABSTRACT FROM AUTHOR]

Research on rice (Oryza sativa) roots demands the automatic analysis of root architecture during image processing. It is challenging for a digital filter to identify the roots from the obscure and cluttered background. The original Frangi algorithm, presented by Alejandro F. Frangi in 1998, is a successful low-pass filter dedicated to blood vessel image enhancement. Considering the similarity between vessels and roots, the Frangi filter algorithm is applied to outline the roots. However, the original Frangi only enhances the tube-like primary roots but erases the lateral roots during filtering. In this paper, an improved Frangi filtering algorithm (IFFA), designed for plant roots, is proposed. Firstly, an automatic root phenotyping system is designed to fulfill the high-throughput root image acquisition. Secondly, multilevel image thresholding, connected components labeling, and width correction are used to optimize the output binary image. Thirdly, to enhance the local structure, the Gaussian filtering operator in the original Frangi is redesigned with a truncated Gaussian kernel, resulting in more discernible lateral roots. Compared to the original Frangi filter and commercially available software, IFFA is faster and more accurate, achieving a pixel accuracy of 97.48%. IFFA is an effective morphological filtering approach to enhance the roots of rice for segmentation and further biological research. It is convincing that IFFA is suitable for different 2-D plant root image processing and morphological analysis. [ABSTRACT FROM AUTHOR]

Prediction models are essential in dam crack behavior identification. Prototype monitoring data arrive sequentially in dam safety monitoring. Given such characteristic, sequential learning algorithms are preferred over batch learning algorithms as they do not require retraining whenever new data are received. A new methodology using the genetic optimized online sequential extreme learning machine and bootstrap confidence intervals is proposed as a practical tool for identifying concrete dam crack behavior. First, online sequential extreme learning machine is adopted to build an online prediction model of crack behavior. The characteristic vector of crack behavior, which is taken as the online sequential extreme learning machine input, is extracted by the statistical model. A genetic algorithm is introduced to optimize the input weights and biases of online sequential extreme learning machine. Second, the BCa method is proposed to produce confidence intervals based on the improved online sequential extreme learning machine prediction. The improved online sequential extreme learning machine for identifying crack behavior is then built. Third, the crack behavior of an actual concrete dam is taken as an example. The capability of the built model for predicting dam crack opening is evaluated. The comparative results demonstrate that the improved online sequential extreme learning machine can provide highly accurate forecasts and reasonably identify crack behavior. [ABSTRACT FROM AUTHOR]

Driver pathways have been acknowledged to play critical roles in the initiation and progression of cancers►hence it is essential for precision medicine related studies to develop accurate and efficient methods to identify them. Although previous approaches have shown promising results by integrating multi-omics data, their preset artificial parameters may decrease the convenience to use and limit the application scalability. In this paper, a novel integration approach is proposed to incorporate four omics data, i.e., construct a weighted non-binary mutation matrix without presetting artificial parameters. A parameter-free identification model is put forward based on it. It takes advantage of the association between genes in the PPI network as well as balances the contribution of coverage and mutual exclusivity with the harmonic mean. Furthermore, a cooperative co-evolution algorithm is proposed for solving this model. In the algorithm, a particle swarm optimization algorithm suitable for solving combinatorial problems is presented. Three cooperative operators are devised to construct the cooperation among the populations and the swarm to increase the population diversity. Both real biological datasets and simulated ones were exerted to perform experimental comparisons among the proposed method and six other state-of-the-art ones. The gene sets identified by the presented method generally contain more genes involved in known signaling pathways than those obtained by the other methods. Simultaneously, both high accuracy and high efficiency of the proposed method were verified in experiments, making it practical in realistic applications and an effective supplementary tool to identify driver pathways. • A non-binary mutation matrix is constructed without artificial adjustable parameters. • The model incorporates gene association and balances coverage and mutual exclusivity. • A PSO algorithm suitable for solving combinatorial problems is presented. • A co-operative co-evolution algorithm is put forward for solving the presented model. • The performance is compared among the proposed method and seven state of the art ones. [ABSTRACT FROM AUTHOR]

The identification of driver pathways has attracted considerable attention in recent years due to the high significance of driver pathways in cancer related studies. In most identification methods, the linear measurement is adopted for evaluating exclusivity, and some preset parameters are introduced to balance the contributions of different measurements. However, both the linear measurement and the preset parameters may exert negative effects on the applications of these methods. In this study, a nonlinear function is devised to measure exclusivity, and a parameter free nonlinear maximum weight submatrix (NMWS) identification model is proposed by considering coverage, exclusivity and connectivity. A competitive co-evolution algorithm (CCA) is also put forward for solving the presented NMWS model. Experimental results on both real biological data and simulated one were used compare the identification performance of the presented method with that of seven state of the art ones. The pathway detected by the presented method not only contains more genes enriched in a known signaling pathway, but also has a stronger connectivity in Protein–Protein Interaction (PPI) network. Simultaneously, the high efficiency of the presented method makes it practical in realistic applications. All of which have been confirmed through a large number of experiments. • A nonlinear function is devised to measure exclusivity. • A parameter free nonlinear identification model is proposed. • A competitive co-evolution algorithm is put forward for solving the presented model. • Experiments were conducted on both real biological data and simulated one. • Comparisons were performed among the presented method and state-of-the-art ones. [ABSTRACT FROM AUTHOR]

Deformation of flattening is one of the difficulties in ship reconstruction, which requires inversion of spline curves to accurately calculate the knots of end points of flattening line segment. To reduce the error of spline knot in the flattening algorithm caused by unstable inversion, the bisection feedback search (BFS) algorithm is proposed to stably achieve the global optimum. The feedback object of feedback operation, which is recorded by a bisection table, is judged using convergence criteria. Considering the strong convex hull property of the spline curve, the IR-FBS algorithm is proposed, which is the BFS algorithm accelerated by the proposed interval reformation (IR) method by reducing the feedback time and the number of iterations. Finally, the flattening algorithm of the spline curve is improved based on the IR-BFS algorithm, considering both stability and accuracy. To verify the effectiveness of the algorithms, comparative experiments are designed. First, the BFS algorithm and Newton–Raphson (NR) method are compared to verify the stability of solutions. Then, the IR-FBS algorithm is compared with FBS and the best existing three compound algorithms to check the acceleration effects and computational efficiency. Next, the improved flattening algorithm is used to process spline curves, and the flattening effect is investigated through the change of curve curvature. The experiments demonstrate that the BFS algorithm can ensure both accuracy and stability of solutions. In addition, the IR-BFS has the optimal performance among compound algorithms by the accelerated effects of IR method, and the improved flattening algorithm can ensure the high smoothness and meets the requirements of practical engineering. • A new global spline inversion algorithm (BFS) provides accurate and stable results. • The interval reformation method reduces the computation time of the BFS algorithm. • The spline curve flattening algorithm is improved by BFS and interval reformation. • The proposed methods can be used for the deformation of ship reconstruction. • The results can be applied to ship dynamic damage stability. [ABSTRACT FROM AUTHOR]

• Analyze concrete crack shapes in dataset and adapt YOLO-based model accordingly. • Propose two multiple branch-shaped blocks to optimize the model's performance. • Employ depthwise separable convolutions to reduce the model size. • Resultant software supported by multi-threading, enables real-time detection of actual concrete cracks. Deep learning algorithms have been employed for real-time concrete crack detection. However, many algorithms are not specifically tailored for this purpose. Moreover, their lightweight iterations are generally optimized at the macro-model level, leaving room for further lightweight enhancements at the block level. Therefore, this study developed an enhanced YOLOv3 (You Only Look Once Network v3) model, named YOLO-Crack. The structural optimization of the model takes into consideration the shapes of concrete cracks in the dataset. Meanwhile, two multiple branch-shaped blocks based on dilated convolutions, convolutions and pooling operations were proposed. The two blocks, incorporating depthwise separable convolutions and attention mechanisms, were used to rebuild the model at the block level. These enhancements significantly reduce the size and improve the detection performance of YOLO-Crack. Furthermore, YOLO-Crack was softwareized for real-time detection of concrete cracks. The software was designed to support parallel computing, allowing for real-time detection of concrete cracks even on laptops with limited computing power. It was utilized to detect cracks on concrete roads at a university in Nanjing, China, enabling real-time detection at a frame rate of 30 frames per second with satisfactory accuracy. [ABSTRACT FROM AUTHOR]

With the rapid development of high-throughput sequencing technologies, huge volumes of generated cancer genomics data make it into reality to understand the carcinogenic pathogenesis from the molecular level. It is believed that the study of commonalities among different cancers is one of the significant problems for understanding cancers, and will be beneficial for personalized therapy and precision medicine in cancer treatment. The ComMDP method is a useful one for solving this problem. However, when there is a substantially difference among the number of samples, the method of accumulating the absolute weight value of every cancer, employed by the ComMDP method, may give rise to missing some driver pathways. In this paper, two mathematical models CDP-V and CDP-H, replacing the absolute weight values with relative ones, are presented by using variance and harmonic mean , respectively. By devising a sort of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm is proposed for solving these two models. Extensive experiments were performed on both simulated and real cancer data. The experimental results show that given several types of cancer, the gene sets identified based on the presented models and algorithm not only mutate in a large proportion of samples of these cancers, but have close proportion of mutated samples in each cancer. In addition, some biologically meaningful gene sets, which are missed by the ComMDP one, are indeed detected. Hence the identified methods based on the presented models and algorithm may become useful complementary tools for identifying cancer pathways. [ABSTRACT FROM AUTHOR]