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This study aims to analyze the role of mTOR inhibitor on the expression of miR-211 in rat brain tissue and the biological effect of miR-211 in attenuating seizure. Rats were randomly divided into four groups, and the number of seizures and the duration of single seizure were observed within 24 hours after intervention. The level of miR-211 in brain tissue was detected by RT qPCR, the apoptosis of nerve cells was assessed by TUNEL staining, the level of immune cells was detected by flow cytometry, and the level of serum inflammatory factors was determined by ELISA. The number of seizures and the duration of single seizure in the three groups treated by rapamycin within 24 hours were lower than those in the control group, and the symptom relief in group C was the best. After treatment, the expression level of miR-211 in the brain tissue of epileptic rats increased. TUNEL staining showed that neuronal apoptosis was obvious in epileptic rats. The anti apoptotic ability of group C was the most significant, followed by group D and group B. Compared with group A, the levels of CD3+ cells, CD8+ cells and CD4+/CD25+ cells in brain tissue of group C were decreased, while the levels of IL-2 and IFN-γ were lower in group C than those in control. In group C (n = 5), the levels of CD3+ cells, CD8+ cells and CD4+/CD25+ cells were elevated, and the levels of immune related cytokines IL-2 and IFN-γ were higher than those of rats without miR-211 inhibition. mTOR inhibitors can improve the local immune microenvironment, reduce the release of inflammatory factors, and finally decrease the frequency and duration of seizures by up regulating the level of miR-211 in rat brain tissue.

Information fusion refers to derive an overall precise description of data by using certain fusion technique for utilizing the complementary information from multiple sources of data, which can facilitate effective decision-making, prediction and classification, etc. Multi-source homogeneous data, characterizing the data type of variables in the sample in form of one type (i.e., numerical or categorical) in different information sources, which widely exists in many practical applications. This paper concentrates on efficient fusion of multi-source homogeneous data with a data-level fusion model which involves the consolidation of multiple information sources and unsupervised attribute selection of the fused data. A unified description and modeling method of a multi-source homogeneous information system is introduced. The neighborhood rough sets model is used to construct the neighborhood granular structure, which uses the idea of granular computing to build methods of uncertainty measures. Given the uncertainty of fusing multiple information sources, Sup-Inf fusion functions are developed based on the proposed uncertainty measures, which can fuse the multi-source homogeneous information system into a single-source information system. Finally, an unsupervised attribute selection approach is employed to eliminate redundant attribute of the single-source information system. Theoretical analysis and comprehensive experiments on several datasets demonstrate the feasibility and superiority of our method.

The operating conditions of the transmission lines can be assessed through the information measured by the smart meters in the power supply bureau. Accurate classification of transmission line faults can be helpful to improve the maintenance strategy of smart grids. This paper analyzes the mechanism of the voltage loss and the phase fault of the transmission line by using the operation data collected by the smart meters from three power supply bureaus (named Bureau A, B and C), where the faults are labeled by expert systems. In this work, a novel Particle Swarm Optimization Pattern Recognition Neural Network (PSO-PRNN) classifier is built to accurately categorize the faults and its classification performance is compared with the ones of traditional K-Nearest Neighbor (KNN), Decision Tree (DT), PSO-KNN and PSO-DT classifiers. The results show that the classification accuracy of PSO-PRNN outperforms traditional classifiers when being applied to the data collected from all three bureaus. In the A power supply bureau are 83.0%, 88.7%, 82.0%, 86.9% and 96.1%, and the classification accuracy rates are 55.7%, 68.7%, 56.6%, 68.7% and 82.5%, when used to process the data of the bureau B. The classification accuracy is 57.1%, 66.4%, 57.2%, 69.0% and 82.1%, when processing the data of bureau C. The results show that the PSO-PRNN classifier is superior to the others in terms of accuracy and applicability.

Natural hybridization has been frequently observed in Triticeae; however, few studies have investigated the origin of natural intergeneric Triticeae hybrids. In the present study, we discovered three putative hybrid Triticeae plants in the Western Sichuan Plateau of China. Morphologically, the putative hybrids were intermediate between Kengyilia melanthera (2n = 6x = 42; StStYYPP) and Campeiostachys dahurica var. tangutorum (2n = 6x = 42; StStYYHH) with greater plant height and tiller number. Cytological analyses demonstrated that the hybrids were hexaploid with 42 chromosomes (2n = 6x = 42). At metaphase I, 12.10–12.58 bivalents and 13.81–14.18 univalents per cell were observed in the hybrid plants. Genomic in situ hybridization demonstrated that the hybrids had StStYYHP genomes. Phylogenetic analysis of Acc1 sequences indicated that the hybrids were closely related to K. melanthera and C. dahurica var. tangutorum. Our morphological, cytological, and molecular analyses indicate that these hexaploid natural hybrid plants may be hybrids of K. melanthera and C. dahurica var. tangutorum.

The Multi-Granulation Decision-Theoretic Rough Set (MG-DTRS) is an effective method for cost-sensitive decision making from multi-view and multi-level. However, the inherent weak point of MG-DTRS model is to compute three regions with a subjectively given pair of probabilistic parameters (i.e., α and β). To overcome this issue, this paper first proposes a generalized MG-DTRS model called Adaptive Multi-Granulation Decision-Theoretic Rough Sets (AMG-DTRS), which can adaptively obtain a pair of probabilistic thresholds by setting a compensation coefficient ζ. Then, three types of mean AMG-DTRS models are investigated, which provide a novel perspective on multi-granulation method for information fusion. Finally, the connections and differences between the proposed AMG-DTRS and the existing MGRS models are analyzed, which show the advantages and generalization of the AMG-DTRS model. In addition, there are numerous existing MGRS models can be derived explicitly by considering various MG-DTRS, MGRS and VP-MGRS based on our model. These results will be conducive to establishing the framework of information fusion for granular computing.

The set covering problem (SCP) is a fundamental NP-hard problem in computer science and has a broad range of important real-world applications. In practice, SCP instances transformed from real-world applications would be of large scale, so it is of significant importance to design effective heuristic algorithms, especially local search ones. However, there exist only few research works on developing local search algorithms for solving SCP. In this article, we propose a new local search algorithm for solving SCP, dubbed NuSC. In particular, NuSC introduces a new combined scoring function for subset selection, which combines different subset properties in an effective way and helps NuSC find more optimized solutions. Besides, NuSC incorporates a dynamic weighting scheme for elements, a tabu search strategy, and a novelty selection mechanism to further enhance its practical performance. In order to study the effectiveness and robustness of our proposed NuSC algorithm, we conduct extensive experiments to compare NuSC against many state-of-the-art competitors on various types of SCP instances. Our experimental results demonstrate that NuSC significantly outperforms its competitors on the majority of instances, indicating the superiority of NuSC. Also, our empirical evaluations confirm the effectiveness of each algorithmic technique underlying NuSC.

Mining knowledge from imbalanced data is challenging due to the uneven distribution of classes and increasing dimensionality of data accumulated from real-life applications. Selecting informative features from imbalanced data is especially important for building an effective learning method. The global redundancy and the effect of imbalanced distribution need to be considered simultaneously. In this study, a feature selection method that considers the imbalanced distribution of classes in data is investigated by embedding the weighted constraint on the majority class into the global redundancy minimization GRM framework. Global redundancy minimization is acquired through an objective function that contains a feature redundancy matrix and feature scores. A new form of regularization to a within-class scatter matrix is first presented, which emphasizes the minority class and replaces the redundancy measurement approach. Then, after employing this new form of a within-class scatter matrix in GRM and taking the between-class distance as the GRM input score, a GRM-based discriminant feature selection algorithm (GRM-DFS) is proposed. Comparison studies on a within-class scatter matrix with different forms of regularization indicate that the proposed form of a within-class scatter matrix is effective when dealing with imbalanced data. Experiments on public imbalanced datasets are performed. The experimental results indicate that GRM-DFS is effective.

AimThe main purpose of this study was to study the preventive effect of Penicillium sp. CX-1 on Phytophthora cactorum causing Salvia miltiorrhiza blight and its positive effect on plant growth.Methods and ResultsThe endophytic strain CX-1 was isolated from the medicinal plant Corydalis saxicola Bunting and identified as Penicillium oxalicum. The growth inhibitory capacity of CX-1 against Ph. cactorum was 74.4% in the strain co-culture test and 86.2% in filtrate-modified plates. In the pot experiment, the in vivo control of CX-1 against Ph. cactorum in S. miltiorrhiza was 36.0%, which was higher than that of an anti-Phytophthora fungicide (23.4%). In addition, CX-1 had a potent ability to solubilize phosphate and also showed the ability to produce the plant hormone indole-3-acetic acid (IAA) and siderophores, which increase the bioavailability of iron to plants. It was demonstrated through pot experiments that CX-1 could significantly promote plant growth. As determined by real-time quantitative PCR, the expression of some S. miltiorrhiza tanshinone-related biosynthesis genes was significantly upregulated following colonization by CX-1.ConclusionStrain CX-1 could effectively inhibit Ph. cactorum, the causative agent of S. miltiorrhiza blight, and significantly promoted the growth of plants through several different routes.

Image segmentation plays a significant role in image processing and scientific research. In this paper, we develop a novel approach, which provides effective and robust performances for image segmentation based on the region-based (block-based) graph instead of pixel-based graph. The modified Discrete Cosine Transform (DCT) is applied to obtain the Square Block Structures (DCT-SBS) of the image in the compressed domain together with the coefficients, due to its low memory requirement and high processing efficiency on extracting the block feature. A novel weight computation approach focusing on multi-feature fusion from the location, texture and RGB-color information is employed to efficiently obtain weights between the DCT-SBS. The energy function is redesigned to meet the region-based requirement and can be easily transformed into the traditional Normalized cuts (Ncuts). The proposed image segmentation algorithm is applied to the salient region detection database and Corel1000 database. The performance results are compared with the state-of-the-art segmentation algorithms. Experimental results clearly show that our method outperforms other algorithms, and demonstrate good segmentation precision and high efficiency.

This study assesses the effect of exosomes derived from bone marrow mesenchymal stem cells (MSCs) on cardiomyocytes by inhibiting the apoptotic factors Caspase-3 and Caspase-9. Cell purity was evaluated under a microscope and exosomes were obtained by ultracentrifugation from the culture supernatant of BMSCs. Tunable resistive pulse sensing (TRPS) method analyzed the concentration distribution of exosomes particle size, and specific surface antigens were examined by flow cytometry. Exosomes were used to process cardiomyocytes to detect cardiomyocyte repair. After plasmid interference technology, the effect of exosomes on caspase-3 and caspase-9 expression was detected by western blot. The activity of cardiomyocytes was analyzed by CCK-8. Exosomes can promote the viability of cardiomyocytes. The mRNA and protein levels of GLUT3 in cardiomyocytes were significantly increased. Exosomes can inhibit cardiomyocyte apoptosis by down-regulating the expression of apoptosis-related proteins. Exosomes can improve the function and promote the repair of myocardium by inhibiting the expression of apoptotic factors Caspase-3 and Caspase-9.

Unsupervised attribute reduction becomes very challenging due to a lack of decision information, which is to select a subset of attributes that can maintain learning ability without decision information. However, most of the existing unsupervised attribute reduction methods are proposed for numerical or nominal attributes, and little research has been done on unsupervised mixed attribute reduction methods. In view of this, this paper proposes a generalized unsupervised mixed attribute reduction model based on fuzzy rough sets . First, based on all single attribute subsets , the significance is defined to indicate the importance of a candidate attribute. Then, a specific fuzzy rough-based unsupervised attribute reduction (FRUAR) algorithm is designed. Finally, the proposed algorithm is compared with the existing algorithms by using thirty public data sets. Experimental results show that the algorithm FRUAR can select fewer attributes to maintain or improve the performance of learning algorithms, and it is suitable for mixed attribute data.

In a lidar system, replacing moving components with solid-state devices is highly anticipated to make a reliable and compact lidar system, provided that a substantially large beam area with a large angular extent as well as high angular resolution is assured for the lidar transmitter and receiver. A new quasi-solid-state lidar optical architecture employs a transmitter with a two-dimensional MEMS mirror for fine beam steering at a fraction of the degree of the angular resolution and is combined with a digital micromirror device for wide FOV scanning over 37 degree while sustaining a large aperture area of 140 mm squared. In the receiver, a second digital micromirror device is synchronized to the transmitter DMD, which enables a large FOV receiver. An angular resolution of 0.57°(H) by 0.23° (V) was achieved with 0.588 fps for scanning 1344 points within the field of view.

Feature selection aims to remove irrelevant or redundant features and thereby remain relevant or informative features so that it is often preferred for alleviating the dimensionality curse, enhancing learning performance, providing better readability and interpretability, and so on. Data that contain numerical and categorical representations are called heterogeneous data, and they exist widely in many real-world applications. Neighborhood rough set (NRS) can effectively deal with heterogeneous data by using neighborhood binary relation, which has been successfully applied to heterogeneous feature selection. In this article, the NRS model as a unified framework is used to design a feature selection method to handle categorical, numerical, and heterogeneous data. First, the concept of neighborhood combination entropy (NCE) is presented. It can reflect the probability of pairs of the neighborhood granules that are probably distinguishable from each other. Then, the conditional neighborhood combination entropy (cNCE) based on NCE is proposed under the condition of considering decision attributes. Moreover, some properties and relationships between cNCE and NCE are derived. Finally, the functions of inner and outer significances are constructed to design a feature selection algorithm based on cNCE (FScNCE). The experimental results show the effectiveness and superiority of the proposed algorithm.

In order to more accurately simulate the injury of riders and the motion characteristics of two-wheelers, the modeling method and the simulation model of two-wheeler traffic accidents are studied i...

Positive-unlabeled learning (PU learning) is an important case of binary classification where the training data only contains positive and unlabeled samples. The current state-of-the-art approach for PU learning is the cost-sensitive approach, which casts PU learning as a cost-sensitive classification problem and relies on unbiased risk estimator for correcting the bias introduced by the unlabeled samples. However, this approach requires the knowledge of class prior and is subject to the potential label noise. In this paper, we propose a novel PU learning approach dubbed PULNS, equipped with an effective negative sample selector, which is optimized by reinforcement learning. Our PULNS approach employs an effective negative sample selector as the agent responsible for selecting negative samples from the unlabeled data. While the selected, likely negative samples can be used to improve the classifier, the performance of classifier is also used as the reward to improve the selector through the REINFORCE algorithm. By alternating the updates of the selector and the classifier, the performance of both is improved. Extensive experimental studies on 7 real-world application benchmarks demonstrate that PULNS consistently outperforms the current state-of-the-art methods in PU learning, and our experimental results also confirm the effectiveness of the negative sample selector underlying PULNS.

Multi-Source Information Fusion (MSIF) is a comprehensive and interdisciplinary subject, and is referred to as, multi-sensor information fusion which was originated in the 1970s. Nowadays, the types and updates of data are becoming more multifarious and frequent, which bring new challenges for information fusion to deal with the multi-source data. Consequently, the construction of MSIF models suitable for different scenarios and the application of different fusion technologies are the core problems that need to be solved urgently. Rough set theory (RST) provides a computing paradigm for uncertain data modeling and reasoning, especially for classification issues with noisy, inaccurate or incomplete data. Furthermore, due to the rapid development of MSIF in recent years, the methodologies of learning under RST are becoming increasingly mature and systematic, unveiling a framework which has not been mentioned in the literature. In order to better clarify the approaches and application of MSIF in RST research community, this paper reviews the existing models and technologies from the perspectives of MSIF model (i.e., homogeneous and heterogeneous MSIF model), multi-view rough sets information fusion model (i.e., multi-granulation, multi-scale and multi-view decisions information fusion models), parallel computing information fusion model, incremental learning fusion technology and cluster ensembles fusion technology. Finally, RST based MSIF related research directions and challenges are also covered and discussed. By providing state-of-the-art understanding in specialized literature, this survey will directly help researchers understand the research developments of MSIF under RST.

A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019. Computed Tomography (CT) images have been used as a crucial alternative to the time-consuming RT-PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspected cases, resulting in urgent requirements for accurate and automatic segmentation of COVID-19 infections. Unfortunately, since the imaging characteristics of the COVID-19 infection are diverse and similar to the backgrounds, existing medical image segmentation methods cannot achieve satisfactory performance. In this article, we try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections. We first maintain a large and new chest CT image dataset consisting of 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases. We fuse features at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to handle the sophisticated infection areas with diverse appearance and shapes. The proposed method achieves state-of-the-art performance. Dice similarity coefficients are 0.987 and 0.726 for lung and COVID-19 segmentation, respectively. We conducted experiments on the data collected in China and Germany and show that the proposed deep CNN can produce impressive performance effectively. The proposed network enhances the segmentation ability of the COVID-19 infection, makes the connection with other techniques and contributes to the development of remedying COVID-19 infection.

A comprehensive understanding of the effects of mountain roads on plant diversity is critical to finding the most effective solutions for managing this particular driver. Little is known, however, about the simultaneous effects that road have on the multiple facets of biodiversity, although roads are considered to be one of the major disturbances in the Qionglai mountain range. In this study, we analyzed the impact of roads on the multiple facets of plant diversity (taxonomic, functional and phylogenetic diversity) in the study area using Hill numbers by comparing plant diversity between roadside and interior plots at the landscape scale, then, we used linear mixed models to analyze the effect of mountain roads on the multiple facets of plant diversity along an elevational gradient. The results showed that the roadside plots lacked 29.45% of the total number of species with particular functional traits (such as a relatively high specific leaf area (SLA), a relatively low leaf dry matter content (LDMC) and relatively old clades) and exclusively contained 14.62% of the total number of species. Compared with the interior community, the taxonomic, functional and phylogenetic diversity of roadside community decreased by no more than 26.78%, 24.90% and 16.62%, respectively. Taxonomic and functional diversity of dominant and common species showed greater changes to road disturbances, while rare species showed the greatest change in phylogenetic diversity. Taxonomic homogenization of roadside communities was accompanied by functional and phylogenetic homogenization. Additionally, the impact of roads on these three facets of plant diversity showed the characteristics of peak clipping along the elevation gradient. Our findings highlight the negative impact of roads on the taxonomic, functional and phylogenetic diversity of the Qionglai mountain range, as roads promote communities that are more similar in taxonomic, functional, and phylogenetic composition, and to a greater extent contributed to compositional evenness. These effects tend to be functionally and phylogenetically non-random, and species in some clades or with some functional traits are at higher risk of loss. Our results are important for the conservation and management of nature reserves, especially for local governments aiming to create new infrastructure to connect natural mountainous areas.

Time series classification is a popular and important topic in machine learning, and it suffers from the class imbalance problem in many real-world applications. In this paper, to address the class imbalance problem, we propose a novel and practical oversampling method named T-SMOTE, which can make full use of the temporal information of time-series data. In particular, for each sample of minority class, T-SMOTE generates multiple samples that are close to class border. Then, based on those samples near class border, T-SMOTE synthesizes more samples. Finally, a weighted sampling method is called on both generated samples near class border and synthetic samples. Extensive experiments on a diverse set of both univariate and multivariate time-series datasets demonstrate that T-SMOTE consistently outperforms the current state-of-the-art methods on imbalanced time series classification. More encouragingly, our empirical evaluations show that T-SMOTE performs better in the scenario of early prediction, an important application scenario in industry, which indicates that T-SMOTE could bring benefits in practice.

Real-world software product lines (SPLs) often encompass enormous valid configurations that are impossible to enumerate. To understand properties of the space formed by all valid configurations, a feasible way is to select a small, valid and representative sample set. Even though a number of sampling strategies have been proposed, they either fail to produce diverse samples with respect to the number of selected features (an important property to characterize behaviors of configurations), or achieve diverse sampling but with limited scalability (the handleable configuration space size is limited to $10^{13}$). To resolve this dilemma, we propose a scalable diverse sampling strategy, which uses a distance metric in combination with the novelty search algorithm to produce diverse samples in an incremental way. The distance metric is carefully designed to measure similarities between configurations, and further diversity of a sample set. The novelty search incrementally improves diversity of samples through the search for novel configurations. We evaluate our sampling algorithm on 39 real-world SPLs. It is able to generate the required number of samples for all the SPLs, including those which can not be counted by sharpSAT, a state-of-the-art model counting solver. Moreover, it performs better than or at least competitively to some state-of-the-art samplers with respect to the diversity of the sample sets. Our results suggest that only the proposed sampler (among all tested ones) achieves scalable diverse sampling.

The recent development of the Micro Electromechanical System (MEMS) Phase Light Modulator (PLM) enables fast laser beam steering for lidar applications by displaying a Computer-Generated Hologram (CGH) without employing an iterative CGH calculation algorithm. We discuss the application of MEMS PLM (Texas Instruments PLM) for quasi-continuous laser beam steering by deterministically calculated CGHs. The effect on the diffraction efficiency of PLM non-equally spaced phase levels was quantified. We also address the CGH calculation algorithm and an experimental demonstration that steered and scanned the beam into multiple regions of interest points, enabling beam steering for lidar without sequential raster scanning.

The selection of prominent features for building more compact and efficient models is an important data preprocessing task in the field of data mining. The rough hypercuboid approach is an emerging technique that can be applied to eliminate irrelevant and redundant features, especially for the inexactness problem in approximate numerical classification. By integrating the meta-heuristic-based evolutionary search technique, a novel global search method for numerical feature selection is proposed in this article based on the hybridization of the rough hypercuboid approach and binary particle swarm optimization (BPSO) algorithm, namely RH-BPSO. To further alleviate the issue of high computational cost when processing large-scale datasets, parallelization approaches for calculating the hybrid feature evaluation criteria are presented by decomposing and recombining hypercuboid equivalence partition matrix via horizontal data partitioning. A distributed meta-heuristic optimized rough hypercuboid feature selection (DiRH-BPSO) algorithm is thus developed and embedded in the Apache Spark cloud computing model. Extensive experimental results indicate that RH-BPSO is promising and can significantly outperform the other representative feature selection algorithms in terms of classification accuracy, the cardinality of the selected feature subset, and execution efficiency. Moreover, experiments on distributed-memory multicore clusters show that DiRH-BPSO is significantly faster than its sequential counterpart and is perfectly capable of completing large-scale feature selection tasks that fail on a single node due to memory constraints. Parallel scalability and extensibility analysis also demonstrate that DiRH-BPSO could scale out and extend well with the growth of computational nodes and the volume of data.