Pick-and-Place Transform Learning for Fast Multi-View Clustering.

To manipulate large-scale data, anchor-based multi-view clustering methods have grown in popularity owing to their linear complexity in terms of the number of samples. However, these existing approaches pay less attention to two aspects. 1) They target at learning a shared affinity matrix by using the local information from every single view, yet ignoring the global information from all views, which may weaken the ability to capture complementary information. 2) They do not consider the removal of feature redundancy, which may affect the ability to depict the real sample relationships. To this end, we propose a novel fast multi-view clustering method via pick-and-place transform learning named PPTL, which could capture insightful global features to characterize the sample relationships quickly. Specifically, PPTL first concatenates all the views along the feature direction to produce a global matrix. Considering the redundancy of the global matrix, we design a pick-and-place transform with l2,p -norm regularization to abandon the poor features and consequently construct a compact global representation matrix. Thus, by conducting anchor-based subspace clustering on the compact global representation matrix, PPTL can learn a consensus skinny affinity matrix with a discriminative clustering structure. Numerous experiments performed on small-scale to large-scale datasets demonstrate that our method is not only faster but also achieves superior clustering performance over state-of-the-art methods across a majority of the datasets.

Long Non-Coding RNA BCYRN1 Drives Cellular Processes to Promote Asthma Development.

INTRODUCTION: Asthma is a common chronic inflammatory respiratory disease worldwide. The long non-coding RNA (lncRNA) BCYRN1 has been shown to function in the inhibition of smooth muscle cell differentiation and vascular development, but its function and potential molecular mechanisms of lncRNA BCYRN1 in bronchial smooth muscle cells (BSMCs) in asthma remain unknown. METHODS: Quantitative real-time polymerase chain reaction (RT-qPCR) was performed to detect the expression level of lncRNA BCYRN1 in blood and sputum of asthma patients. The effects of lncRNA BCYRN1 on the proliferation and migration of BSMCs were explored by cell counting kit-8, Transwell, colony formation, and flow cytometry analysis. Differentially expressed genes between lncRNA BCYRN1 overexpression and knockdown cells were identified using RNA-seq and verified using RT-qPCR. RESULTS: LncRNA BCYRN1 was upregulated in asthma patients. Overexpression of lncRNA BCYRN1 significantly promoted the proliferation and migration of BSMCs, inhibited cell apoptosis and affected cell cycle arrest, promoting DNA replication. These effects were reversed after lncRNA BCYRN1 inhibition. RNA-seq identified 434 common differentially expressed genes in the lncRNA BCYRN1 overexpression and knockdown groups and verified their expression levels by RT-qPCR. Gene ontology, Kyoto Encyclopedia of Genes and Genomes and Gene set enrichment analysis indicated that these genes were mainly involved in external stimulus, cell cycle, growth factor activity, cytokine interactions, and inflammatory response. CONCLUSION: The identification of the highly expressed lncRNA BCYRN1 in patients with asthma, combined with functional experiments and transcriptional data, suggests that lncRNA BCYRN1 can mediate the development of asthma and can be used as a promising diagnostic and prognostic biomarker for asthma.

Photonic Realization of a Generic Type of Graphene Edge States Exhibiting Topological Flat Band.

Cutting a honeycomb lattice (HCL) ends up with three types of edges (zigzag, bearded, and armchair), as is well known in the study of graphene edge states. Here, we propose and demonstrate a distinctive twig-shaped edge, thereby observing new edge states using a photonic platform. Our main findings are (i) the twig edge is a generic type of HCL edge complementary to the armchair edge, formed by choosing the right primitive cell rather than simple lattice cutting or Klein edge modification; (ii) the twig edge states form a complete flat band across the Brillouin zone with zero-energy degeneracy, characterized by nontrivial topological winding of the lattice Hamiltonian; (iii) the twig edge states can be elongated or compactly localized at the boundary, manifesting both flat band and topological features. Although realized here in a photonic graphene, such twig edge states should exist in other synthetic HCL structures. Moreover, our results may broaden the understanding of graphene edge states, as well as new avenues for realization of robust edge localization and nontrivial topological phases based on Dirac-like materials.

Genome-Wide Identification of the PP2C Gene Family and Analyses with Their Expression Profiling in Response to Cold Stress in Wild Sugarcane.

Type 2C protein phosphatases (PP2Cs) represent a major group of protein phosphatases in plants, some of which have already been confirmed to play important roles in diverse plant processes. In this study, analyses of the phylogenetics, gene structure, protein domain, chromosome localization, and collinearity, as well as an identification of the expression profile, protein-protein interaction, and subcellular location, were carried out on the PP2C family in wild sugarcane (Saccharum spontaneum). The results showed that 145 PP2C proteins were classified into 13 clades. Phylogenetic analysis suggested that SsPP2Cs are evolutionarily closer to those of sorghum, and the number of SsPP2Cs is the highest. There were 124 pairs of SsPP2C genes expanding via segmental duplications. Half of the SsPP2C proteins were predicted to be localized in the chloroplast (73), with the next most common predicted localizations being in the cytoplasm (37) and nucleus (17). Analysis of the promoter revealed that SsPP2Cs might be photosensitive, responsive to abiotic stresses, and hormone-stimulated. A total of 27 SsPP2Cs showed cold-stress-induced expressions, and SsPP2C27 (Sspon.01G0007840-2D) and SsPP2C64 (Sspon.03G0002800-3D) were the potential hubs involved in ABA signal transduction. Our study presents a comprehensive analysis of the SsPP2C gene family, which can play a vital role in the further study of phosphatases in wild sugarcane. The results suggest that the PP2C family is evolutionarily conserved, and that it functions in various developmental processes in wild sugarcane.

Semi-Supervised CT Lesion Segmentation Using Uncertainty-Based Data Pairing and SwapMix.

Semi-supervised learning (SSL) methods show their powerful performance to deal with the issue of data shortage in the field of medical image segmentation. However, existing SSL methods still suffer from the problem of unreliable predictions on unannotated data due to the lack of manual annotations for them. In this paper, we propose an unreliability-diluted consistency training (UDiCT) mechanism to dilute the unreliability in SSL by assembling reliable annotated data into unreliable unannotated data. Specifically, we first propose an uncertainty-based data pairing module to pair annotated data with unannotated data based on a complementary uncertainty pairing rule, which avoids two hard samples being paired off. Secondly, we develop SwapMix, a mixed sample data augmentation method, to integrate annotated data into unannotated data for training our model in a low-unreliability manner. Finally, UDiCT is trained by minimizing a supervised loss and an unreliability-diluted consistency loss, which makes our model robust to diverse backgrounds. Extensive experiments on three chest CT datasets show the effectiveness of our method for semi-supervised CT lesion segmentation.

Coupled Attention Framework of Convolutional Neural Network Based on Computer Intelligence.

Using an attention mechanism based on the convolutional neural networks (CNNs) improves the performance of computer vision tasks by enhancing the representation of the features. The existing attention methods enhance the expression of the features by modeling the internal information of the features. However, due to the limited information flow of the previous features, these methods are difficult to calibrate features more completely. In this paper, we propose a Coupled Attention Framework (CAF) that is a simple attention framework for improving the performance of the existing attention methods. In the CAF, a coupling branch is added to an existing attention method to generate the input attention maps and enhance the input features of the convolution. The input attention is then spread to the output features through coupling between the input attention maps and convolution, the output features. The final result is the experimental results on various visual tasks. The results show that applying CAF to most of the existing attention methods can improve the performance with fewer parameters.

Generalized Pose Decoupled Network for Unsupervised 3D Skeleton Sequence-Based Action Representation Learning.

Human action representation is derived from the description of human shape and motion. The traditional unsupervised 3-dimensional (3D) human action representation learning method uses a recurrent neural network (RNN)-based autoencoder to reconstruct the input pose sequence and then takes the midlevel feature of the autoencoder as representation. Although RNN can implicitly learn a certain amount of motion information, the extracted representation mainly describes the human shape and is insufficient to describe motion information. Therefore, we first present a handcrafted motion feature called pose flow to guide the reconstruction of the autoencoder, whose midlevel feature is expected to describe motion information. The performance is limited as we observe that actions can be distinctive in either motion direction or motion norm. For example, we can distinguish "sitting down" and "standing up" from motion direction yet distinguish "running" and "jogging" from motion norm. In these cases, it is difficult to learn distinctive features from pose flow where direction and norm are mixed. To this end, we present an explicit pose decoupled flow network (PDF-E) to learn from direction and norm in a multi-task learning framework, where 1 encoder is used to generate representation and 2 decoders are used to generating direction and norm, respectively. Further, we use reconstructing the input pose sequence as an additional constraint and present a generalized PDF network (PDF-G) to learn both motion and shape information, which achieves state-of-the-art performances on large-scale and challenging 3D action recognition datasets including the NTU RGB+D 60 dataset and NTU RGB+D 120 dataset.

Preoperative prediction of cytokeratin-19 expression for hepatocellular carcinoma using T1 mapping on gadoxetic acid-enhanced MRI combined with diffusion-weighted imaging and clinical indicators.

Objectives: To explore the value of T1 mapping on gadoxetic acid-enhanced magnetic resonance imaging (MRI) in preoperative predicting cytokeratin 19 (CK19) expression for hepatocellular carcinoma (HCC). Methods: This retrospective study included 158 patients from two institutions with surgically resected treatment-native solitary HCC who underwent preoperative T1 mapping on gadoxetic acid-enhanced MRI. Patients from institution I (n = 102) and institution II (n = 56) were assigned to training and test sets, respectively. univariable and multivariable logistic regression analyses were performed to investigate the association of clinicoradiological variables with CK19. The receiver operating characteristic (ROC) curve and precision-recall (PR) curve were used to evaluate the performance for CK19 prediction. Then, a prediction nomogram was developed for CK19 expression. The performance of the prediction nomogram was evaluated by its discrimination, calibration, and clinical utility. Results: Multivariable logistic regression analysis showed that AFP>400ng/ml (OR=4.607, 95%CI: 1.098-19.326; p=0.037), relative apparent diffusion coefficient (rADC)≤0.71 (OR=3.450, 95%CI: 1.126-10.567; p=0.030), T1 relaxation time in the 20-minute hepatobiliary phase (T1rt-HBP)>797msec (OR=4.509, 95%CI: 1.301-15.626; p=0.018) were significant independent predictors of CK19 expression. The clinical-quantitative model (CQ-Model) constructed based on these significant variables had the best predictive performance with an area under the ROC curve of 0.844, an area under the PR curve of 0.785 and an F1 score of 0.778. The nomogram constructed based on CQ-Model demonstrated satisfactory performance with C index of 0.844 (95%CI: 0.759-0.908) and 0.818 (95%CI: 0.693-0.902) in the training and test sets, respectively. Conclusions: T1 mapping on gadoxetic acid-enhanced MRI has good predictive efficacy for preoperative prediction of CK19 expression in HCC, which can promote the individualized risk stratification and further treatment decision of HCC patients.

Multi-Task Classification and Segmentation for Explicable Capsule Endoscopy Diagnostics.

Capsule endoscopy is a leading diagnostic tool for small bowel lesions which faces certain challenges such as time-consuming interpretation and harsh optical environment inside the small intestine. Specialists unavoidably waste lots of time on searching for a high clearness degree image for accurate diagnostics. However, current clearness degree classification methods are based on either traditional attributes or an unexplainable deep neural network. In this paper, we propose a multi-task framework, called the multi-task classification and segmentation network (MTCSN), to achieve joint learning of clearness degree (CD) and tissue semantic segmentation (TSS) for the first time. In the MTCSN, the CD helps to generate better refined TSS, while TSS provides an explicable semantic map to better classify the CD. In addition, we present a new benchmark, named the Capsule-Endoscopy Crohn's Disease dataset, which introduces the challenges faced in the real world including motion blur, excreta occlusion, reflection, and various complex alimentary scenes that are widely acknowledged in endoscopy examination. Extensive experiments and ablation studies report the significant performance gains of the MTCSN over state-of-the-art methods.

Joint segmentation and detection of COVID-19 via a sequential region generation network.

The fast pandemics of coronavirus disease (COVID-19) has led to a devastating influence on global public health. In order to treat the disease, medical imaging emerges as a useful tool for diagnosis. However, the computed tomography (CT) diagnosis of COVID-19 requires experts' extensive clinical experience. Therefore, it is essential to achieve rapid and accurate segmentation and detection of COVID-19. This paper proposes a simple yet efficient and general-purpose network, called Sequential Region Generation Network (SRGNet), to jointly detect and segment the lesion areas of COVID-19. SRGNet can make full use of the supervised segmentation information and then outputs multi-scale segmentation predictions. Through this, high-quality lesion-areas suggestions can be generated on the predicted segmentation maps, reducing the diagnosis cost. Simultaneously, the detection results conversely refine the segmentation map by a post-processing procedure, which significantly improves the segmentation accuracy. The superiorities of our SRGNet over the state-of-the-art methods are validated through extensive experiments on the built COVID-19 database.

A Hybrid-Attention Nested UNet for Nuclear Segmentation in Histopathological Images.

Nuclear segmentation of histopathological images is a crucial step in computer-aided image analysis. There are complex, diverse, dense, and even overlapping nuclei in these histopathological images, leading to a challenging task of nuclear segmentation. To overcome this challenge, this paper proposes a hybrid-attention nested UNet (Han-Net), which consists of two modules: a hybrid nested U-shaped network (H-part) and a hybrid attention block (A-part). H-part combines a nested multi-depth U-shaped network and a dense network with full resolution to capture more effective features. A-part is used to explore attention information and build correlations between different pixels. With these two modules, Han-Net extracts discriminative features, which effectively segment the boundaries of not only complex and diverse nuclei but also small and dense nuclei. The comparison in a publicly available multi-organ dataset shows that the proposed model achieves the state-of-the-art performance compared to other models.

Integration of Transcriptional and Post-transcriptional Analysis Revealed the Early Response Mechanism of Sugarcane to Cold Stress.

Cold stress causes major losses to sugarcane production, yet the precise molecular mechanisms that cause losses due to cold stress are not well-understood. To survey miRNAs and genes involved in cold tolerance, RNA-seq, miRNA-seq, and integration analyses were performed on Saccharum spontaneum. Results showed that a total of 118,015 genes and 6,034 of these differentially expressed genes (DEGs) were screened. Protein-protein interaction (PPI) analyses revealed that ABA signaling via protein phosphatase 2Cs was the most important signal transduction pathway and late embryogenesis abundant protein was the hub protein associated with adaptation to cold stress. Furthermore, a total of 856 miRNAs were identified in this study and 109 of them were differentially expressed in sugarcane responding to cold stress. Most importantly, the miRNA-gene regulatory networks suggested the complex post-transcriptional regulation in sugarcane under cold stress, including 10 miRNAs-42 genes, 16 miRNAs-70 genes, and three miRNAs-18 genes in CT vs. LT0.5, CT vs. LT1, and CT0.5 vs. LT1, respectively. Specifically, key regulators from 16 genes encoding laccase were targeted by novel-Chr4C_47059 and Novel-Chr4A_40498, while five LRR-RLK genes were targeted by Novel-Chr6B_65233 and Novel-Chr5D_60023, 19 PPR repeat proteins by Novel-Chr5C_57213 and Novel-Chr5D_58065. Our findings suggested that these miRNAs and cell wall-related genes played vital regulatory roles in the responses of sugarcane to cold stress. Overall, the results of this study provide insights into the transcriptional and post-transcriptional regulatory network underlying the responses of sugarcane to cold stress.

Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction With Archive.

As a typical model-based evolutionary algorithm, estimation of distribution algorithm (EDA) possesses unique characteristics and has been widely applied in global optimization. However, the commonly used Gaussian EDA (GEDA) usually suffers from premature convergence, which severely limits its search efficiency. This paper first systematically analyzes the reasons for the deficiency of traditional GEDA, then tries to enhance its performance by exploiting the evolution direction, and finally develops a new GEDA variant named EDA2. Instead of only utilizing some good solutions produced in the current generation to estimate the Gaussian model, EDA2 preserves a certain number of high-quality solutions generated in the previous generations into an archive and employs these historical solutions to assist estimating the covariance matrix of Gaussian model. By this means, the evolution direction information hidden in the archive is naturally integrated into the estimated model, which in turn can guide EDA2 toward more promising solution regions. Moreover, the new estimation method significantly reduces the population size of EDA2 since it needs fewer individuals in the current population for model estimation. As a result, a fast convergence can be achieved. To verify the efficiency of EDA2, we tested it on a variety of benchmark functions and compared it with several state-of-the-art EAs. The experimental results demonstrate that EDA2 is efficient and competitive.

Sample Fusion Network: An End-to-End Data Augmentation Network for Skeleton-Based Human Action Recognition.

Data augmentation is a widely used technique for enhancing the generalization ability of deep neural networks for skeleton-based human action recognition (HAR) tasks. Most existing data augmentation methods generate new samples by means of handcrafted transforms. However, these methods often cannot be trained and then are discarded during testing because of the lack of learnable parameters. To solve those problems, a novel type of data augmentation network called a sample fusion network (SFN) is proposed. Instead of using handcrafted transforms, an SFN generates new samples via a long short-term memory (LSTM) autoencoder (AE) network. Therefore, an SFN and HAR network can be cascaded together to form a combined network that can be trained in an end-to-end manner. Moreover, an adaptive weighting strategy is employed to improve the complementarity between a sample and the new sample generated from it by an SFN, thus allowing the SFN to more efficiently improve the performance of the HAR network during testing. The experimental results on various datasets verify that the proposed method outperforms state-of-the-art data augmentation methods. More importantly, the proposed SFN architecture is a general framework that can be integrated with various types of networks for HAR. For example, when a baseline HAR model with three LSTM layers and one fully connected (FC) layer was used, the classification accuracy was increased from 79.53% to 90.75% on the NTU RGB+D dataset using a cross-view protocol, thus outperforming most other methods.

An efficient estimation of distribution algorithm with rank-one modification and population reduction.

As a type of model-based metaheuristic, estimation of distribution algorithms (EDAs) show certain advantages over other metaheuristics by using statistical learning method to estimate the distribution of promising solutions. However, the commonly-used Gaussian EDAs (GEDAs) usually suffer from premature convergence that severely limits their efficiency. In this paper, we first attempt to enhance the performance of GEDA by improving its model estimation method. The new estimation method shifts the weighted mean of high-quality solutions towards the fitness improvement direction and estimates the covariance matrix by taking the shifted mean as the center. Theoretical analyses show that the new covariance matrix is essentially a rank-one modification (R1M) of the original one. It could effectively adjust both the search scope and the search direction of GEDA, and thus improving the search efficiency. Furthermore, considering the importance of the population size tuning in GEDA, we develop a population reduction (PR) strategy which linearly reduces the population size throughout the evolution. By this means, the exploration and exploitation ability of GEDA could be balanced better in different search stages and a more proper utilization of limited computation resource can be achieved. Combining GEDA with the R1M and PR strategies, a novel EDA variant named EDA-R1M-PR is developed. The performance of EDA-R1M-PR was comprehensively evaluated and compared with that of several state-of-the-art evolutionary algorithms. Experimental results indicate that the R1M and PR strategies effectively enhance the global optimization ability of GEDA and the resultant EDA-R1M-PR significantly outperforms its competitors on a set of benchmark functions.

Genome-Wide Analysis of the DREB Subfamily in Saccharum spontaneum Reveals Their Functional Divergence During Cold and Drought Stresses.

Drought and cold stresses are the main environmental factors that affect the yield of sugarcane, and DREB genes play very important roles in tolerance to drought, cold, and other environmental stresses. In this study, bioinformatics analysis was performed to characterize Saccharum spontaneum SsDREB genes. RNA sequencing (RNA-seq) was used to detect the expression profiles of SsDREBs induced by cold and drought stresses. According to our results, there are 110 SsDREB subfamily proteins in S. spontaneum, which can be classified into six groups; 106 of these genes are distributed among 29 chromosomes. Inter- and intraspecies synteny analyses suggested that all DREB groups have undergone gene duplication, highlighting the polyploid events that played an important role in the expansion of the DREB subfamily. Furthermore, RNA-seq results showed that 45 SsDREBs were up- or downregulated under cold stress; 35 of them were found to be involved in responding to drought stress. According to protein-protein interaction analysis, SsDREB100, SsDREB102, and SsDREB105 play key roles during the response to cold stress. These results reveal that functional divergence exists between collinear homologous genes or among common origin genes in the DREB subfamily of S. spontaneum. This study presents a comprehensive analysis and systematic understanding of the precise mechanism of SsDREBs in response to abiotic stress and will lead to improvements in sugarcane.

Boosting Cooperative Coevolution for Large Scale Optimization With a Fine-Grained Computation Resource Allocation Strategy.

Cooperative coevolution (CC) has shown great potential for solving large-scale optimization problems (LSOPs). However, traditional CC algorithms often waste part of the computation resource (CR) as they equally allocate CR among all subproblems. The recently developed contribution-based CC algorithms improve the traditional ones to a certain extent by adaptively allocating CR according to some heuristic rules. Different from existing works, this paper explicitly constructs a mathematical model for the CR allocation (CRA) problem in CC and proposes a novel fine-grained CRA (FCRA) strategy by fully considering both the theoretically optimal solution of the CRA model and the evolution characteristics of CC. FCRA takes a single iteration as a basic CRA unit and always selects the subproblem which is most likely to make the largest contribution to the total fitness improvement to undergo a new iteration, where the contribution of a subproblem at a new iteration is estimated according to its current contribution, current evolution status, as well as the estimation for its current contribution. We verified the efficiency of FCRA by combining it with the success-history-based adaptive differential evolution which is an excellent DE variant but has never been employed in the CC framework. Experimental results on two benchmark suites for LSOPs demonstrate that FCRA significantly outperforms existing CRA strategies and the resulting CC algorithm is highly competitive in solving LSOPs.

Self-learning variable structure control for a class of sensor-actuator systems.

Variable structure strategy is widely used for the control of sensor-actuator systems modeled by Euler-Lagrange equations. However, accurate knowledge on the model structure and model parameters are often required for the control design. In this paper, we consider model-free variable structure control of a class of sensor-actuator systems, where only the online input and output of the system are available while the mathematic model of the system is unknown. The problem is formulated from an optimal control perspective and the implicit form of the control law are analytically obtained by using the principle of optimality. The control law and the optimal cost function are explicitly solved iteratively. Simulations demonstrate the effectiveness and the efficiency of the proposed method.

Intelligent control of a sensor-actuator system via kernelized least-squares policy iteration.

In this paper a new framework, called Compressive Kernelized Reinforcement Learning (CKRL), for computing near-optimal policies in sequential decision making with uncertainty is proposed via incorporating the non-adaptive data-independent Random Projections and nonparametric Kernelized Least-squares Policy Iteration (KLSPI). Random Projections are a fast, non-adaptive dimensionality reduction framework in which high-dimensionality data is projected onto a random lower-dimension subspace via spherically random rotation and coordination sampling. KLSPI introduce kernel trick into the LSPI framework for Reinforcement Learning, often achieving faster convergence and providing automatic feature selection via various kernel sparsification approaches. In this approach, policies are computed in a low-dimensional subspace generated by projecting the high-dimensional features onto a set of random basis. We first show how Random Projections constitute an efficient sparsification technique and how our method often converges faster than regular LSPI, while at lower computational costs. Theoretical foundation underlying this approach is a fast approximation of Singular Value Decomposition (SVD). Finally, simulation results are exhibited on benchmark MDP domains, which confirm gains both in computation time and in performance in large feature spaces.

A real-time platform for monitoring schistosomiasis transmission supported by Google Earth and a web-based geographical information system.

A basic framework for the rapid assessment of the risk for schistosomiasis was developed by combining spatial data from Google Earth® with a geographical information system (GIS) package, bundling the modules together with an Internet connection into a WebGIS platform. It operates through functions such as "search", "evaluation", "risk analysis" and "prediction" and is primarily aimed to be a dynamic, early-warning system (EWS) providing user-friendly, evidence-based, near real-time awareness of the status of an important endemic disease. It contributes to rapid information-sharing at all levels of decision-making, facilitating "point-of-care" response, i.e. treatment provided at newly discovered transmission sites. The experience using the platform is encouraging and it has the potential to improve support systems and strengthen schistosomiasis control activities, in particular with regard to surveillance and EWS. It can quickly and intuitively locate early, high-risk areas, retrieve all important data needed as well as provide detailed, up-to-date information on the performance of the control programme. This WebGIS, the first of its kind in the People's Republic of China, is not only applicable for schistosomiasis but can easily be adapted for improving control of any endemic disease in any geographical area.