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The rise of online social networks has fundamentally transformed the traditional way of social interaction and information dissemination, leading to a growing interest in precise community detection and in-depth network structure analysis. However, the complexity of network structures and potential issues like singularity and subjectivity in information extraction affect the accuracy of community detection. To overcome these challenges, we propose a new community detection algorithm, known as the Hierarchical Louvain (H-Louvain) algorithm. It enhances the performance of community detection through a multi-level processing and information fusion strategy. Specifically, the algorithm integrates graph compression techniques with the Hyperlink-Induced Topic Search (HITS) algorithm for initial network hierarchical partitioning, simultaneously filtering out low-quality posts and users while retaining critical information. Furthermore, the proposed method enhances semantic representation by automatically determining an appropriate number of attribute vector dimensions and obtaining attribute weight information through the calculation of self-authority values and the "minimum distance" attribute of posts. Lastly, the method creates an initial user training set through network re-partitioning in hierarchical layers and improves the Louvain algorithm for community partitioning by estimating the comprehensive influence of nodes. Extensive experimentation has demonstrated that the H-Louvain algorithm outperforms state-of-the-art comparative algorithms in terms of accuracy and stability in community detection based on real-world Twitter datasets. [ABSTRACT FROM AUTHOR]

Deep features and texture features each have advantages in image representation. However, exploiting deep textures for image retrieval is challenging because it is difficult to enhance the compatibility of texture features and deep features. To address this problem, we propose a novel image-retrieval method named the deep texture feature histogram (DTFH). The main highlights are: (1) We propose a novel method for identifying effective, limited-effectiveness, or non-valid feature maps via ranking based on Haralick's statistics, which can help understand image content and identify objects, as these statistics have clear physical significance. (2) We use Gabor filtering to mimic the human orientation-selection mechanism, which allows deep texture features to contain a good representation of orientation, thereby enhancing discriminative power. (3) We combine the advantages of classical texture features and deep features to provide a compact representation. This provides a new, yet simple way to exploit deep features via the use of traditional classical texture features. Comparative experiments demonstrate that deep texture features provide highly competitive performance in image retrieval in terms of mean average precision (mAP), and provide new insights into the exploitation of traditional texture features and deep features. [ABSTRACT FROM AUTHOR]

This study examines the effects of pervasive and arbitrary corruption on entrepreneurs' resource acquisition during their new venture creation stages in an emerging economy. It also investigates how legal registration may alter the effects of the two dimensions of government corruption. Our analysis of 160 nascent entrepreneurs in China between 2009 and 2011 shows that pervasive and arbitrary corruption both hamper resource acquisition by nascent ventures. We highlight that the negative effects of corruption vary among nascent ventures, and the effects are dependent on the legal status of nascent ventures. Legal registration helps to reduce the negative effect of arbitrary corruption, but does not reduce the negative effect of pervasive corruption. [ABSTRACT FROM AUTHOR]

Many kinds of real-world multi-way signal, like color images, videos, etc., are represented in tensor form and may often be corrupted by outliers. To recover an unknown signal tensor corrupted by outliers, tensor robust principal component analysis (TRPCA) serves as a robust tensorial modification of the fundamental PCA. Recently, a successful TRPCA model based on the tubal nuclear norm (TNN) (Lu et al. in IEEE Trans Pattern Anal Mach Intell 42:925–938, 2019) has attracted much attention thanks to its superiority in many applications. However, TNN is computationally expensive due to the requirement of full singular value decompositions, seriously limiting its scalability to large tensors. To address this issue, we propose a new TRPCA model which adopts a factorization strategy. Algorithmically, an algorithm based on the non-convex augmented Lagrangian method is developed with convergence guarantee. Theoretically, we rigorously establish the sub-optimality of the proposed algorithm. We also extend the proposed model to the robust tensor completion problem. Both the effectiveness and efficiency of the proposed algorithm is demonstrated through extensive experiments on both synthetic and real data sets. [ABSTRACT FROM AUTHOR]

In recent years, the convolutional neural networks (CNNs) for single image super-resolution (SISR) are becoming more and more complex, and it is more challenging to improve the SISR performance. In contrast, the reference image guided super-resolution (RefSR) is an effective strategy to boost the SR (super-resolution) performance. In RefSR, the introduced high-resolution (HR) references can facilitate the high-frequency residual prediction process. According to the best of our knowledge, the existing CNN-based RefSR methods treat the features from the references and the low-resolution (LR) input equally by simply concatenating them together. However, the HR references and the LR inputs contribute differently to the final SR results. Therefore, we propose a progressive channel attention network (PCANet) for RefSR. There are two technical contributions in this paper. First, we propose a novel channel attention module (CAM), which estimates the channel weighting parameter by weightedly averaging the spatial features instead of using global averaging. Second, considering that the residual prediction process can be improved when the LR input is enriched with more details, we perform super-resolution progressively, which can take advantage of the reference images in multi-scales. Extensive quantitative and qualitative evaluations on three benchmark datasets, which represent three typical scenarios for RefSR, demonstrate that our method is superior to the state-of-the-art SISR and RefSR methods in terms of PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). [ABSTRACT FROM AUTHOR]

A new N-cis-coumaroyltyramine derivative, fissistyramine (1), along with four known coumaroyltyramine derivatives (2–5), was isolated from the stems of Fissistigma oldhamii (Hemsl.) Merr. The structure of the new compound 1 was determined by 1D and 2D NMR as well as by HR-ESI-MS. The inhibitory effect of all the compounds on the proliferation of primary synovial cells was evaluated. Compounds 1, 2, and 5 exhibited inhibitory activity on synoviocytes, with IC50 values of 12.1, 13.8, and 15.6 μM. [ABSTRACT FROM AUTHOR]

Asian firms started to adopt independent directors (InDs) to boards only in the late 1990s after the Asian financial crisis. Despite its relatively short history, Asian firms’ practice of appointing InDs has yielded interesting research findings that may shed new light on the corporate governance literature and management research. However, the literature on InDs in Asian firms has been presented in a piecemeal fashion, which calls for researchers to review and synthesize the disparate findings and identify paths for future research. In this review paper, we identify and systematically analyze 65 articles related to InDs in the Asian context that were published in 21 leading academic journals from 2001 to 2016. Based on this analysis, we developed a framework with four links to map out the Asian InDs-related literature. We also discuss the limitation of the existing research and suggest future research direction on InDs in Asia. [ABSTRACT FROM AUTHOR]

The accurate prediction of whether a protein will crystallize plays a crucial role in improving the success rate of protein crystallization projects. A common critical problem in the development of machine-learning-based protein crystallization predictors is how to effectively utilize protein features extracted from different views. In this study, we aimed to improve the efficiency of fusing multi-view protein features by proposing a new two-layered SVM (2L-SVM) which switches the feature-level fusion problem to a decision-level fusion problem: the SVMs in the 1st layer of the 2L-SVM are trained on each of the multi-view feature sets; then, the outputs of the 1st layer SVMs, which are the 'intermediate' decisions made based on the respective feature sets, are further ensembled by a 2nd layer SVM. Based on the proposed 2L-SVM, we implemented a sequence-based protein crystallization predictor called TargetCrys. Experimental results on several benchmark datasets demonstrated the efficacy of the proposed 2L-SVM for fusing multi-view features. We also compared TargetCrys with existing sequence-based protein crystallization predictors and demonstrated that the proposed TargetCrys outperformed most of the existing predictors and is competitive with the state-of-the-art predictors. The TargetCrys webserver and datasets used in this study are freely available for academic use at: . [ABSTRACT FROM AUTHOR]

Antifreeze proteins (AFPs) are indispensable for living organisms to survive in an extremely cold environment and have a variety of potential biotechnological applications. The accurate prediction of antifreeze proteins has become an important issue and is urgently needed. Although considerable progress has been made, AFP prediction is still a challenging problem due to the diversity of species. In this study, we proposed a new sequence-based AFP predictor, called TargetFreeze. TargetFreeze utilizes an enhanced feature representation method that weightedly combines multiple protein features and takes the powerful support vector machine as the prediction engine. Computer experiments on benchmark datasets demonstrate the superiority of the proposed TargetFreeze over most recently released AFP predictors. We also implemented a user-friendly web server, which is openly accessible for academic use and is available at http://csbio.njust.edu.cn/bioinf/TargetFreeze. TargetFreeze supplements existing AFP predictors and will have potential applications in AFP-related biotechnology fields. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Gabor-based face recognition method. This method fuses multi-resolution Gabor features of face images at the matching score level. The first implementation scheme of this method directly takes the sum of the matching scores of multi-resolution Gabor features of face images as the final matching score. The second implementation scheme first codes the phase of the Gabor feature and then uses a weighted matching score level fusion algorithm to fuse the magnitude and phase of the Gabor feature. A number of experimental results show that the proposed method has a good performance and outperforms conventional Gabor-based face recognition methods that equally treat all the Gabor features and directly fuse them at the feature level. The experimental result also illustrates that in face recognition, the low-resolution representation of the phase of the Gabor feature such as the code of the phase is more discriminative than the phase itself. The codes of our method will be available at http://www.yongxu.org/lunwen.html . [ABSTRACT FROM AUTHOR]

Protein attribute prediction from primary sequences is an important task and how to extract discriminative features is one of the most crucial aspects. Because single-view feature cannot reflect all the information of a protein, fusing multi-view features is considered as a promising route to improve prediction accuracy. In this paper, we propose a novel framework for protein multi-view feature fusion: first, features from different views are parallely combined to form complex feature vectors; Then, we extend the classic principal component analysis to the generalized principle component analysis for further feature extraction from the parallely combined complex features, which lie in a complex space. Finally, the extracted features are used for prediction. Experimental results on different benchmark datasets and machine learning algorithms demonstrate that parallel strategy outperforms the traditional serial approach and is particularly helpful for extracting the core information buried among multi-view feature sets. A web server for protein structural class prediction based on the proposed method (COMSPA) is freely available for academic use at: . [ABSTRACT FROM AUTHOR]

Uridine, a pyrimidine nucleoside, has been proposed to be a potential signaling molecule in the central nervous system. The understanding of uridine release in the brain is therefore of fundamental importance. The present study was performed to determine the characteristics of basal and morphine-induced uridine release in the striatum of freely moving mice by using the microdialysis technique. To ascertain whether extracellular uridine was derived from neuronal release, the following criteria were applied: sensitivity to (a) K depolarization, (b) Na channel blockade and (c) removal of extracellular Ca. Uridine levels were not greatly affected by infusion of tetrodotoxin (TTX) and were unaffected by either Ca-free medium or in the presence of EGTA (a calcium chelator), suggesting that basal extracellular uridine levels were maintained mainly by non-vesicular release mechanisms. In addition, both systemic and local application of morphine increased striatal uridine release. The morphine-induced release was reversed by naloxone pretreatment, but was unaffected by TTX or EGTA infusion. Moreover, co-administration of morphine and nitrobenzylthioinosine (NBTI, an inhibitor of nucleotide transporter) produced increases of uridine levels similar to that produced by NBTI or morphine alone, suggesting a nucleotide transporter mechanism involved. Taken together, these findings suggest that morphine produces a μ-opioid receptor-mediated uridine release via nucleoside transporters in a TTX- and calcium-independent manner. [ABSTRACT FROM AUTHOR]

Though many hierarchical structures have been proposed to analyze the finer or coarser relationships between two granulation spaces, these structures can only be used to compare the single granulation spaces. However, it should be noticed that the concept of multigranulation plays a fundamental role in the development of granular computing. Therefore, the comparison between two multigranulation spaces has become a necessity. To solve such problem, two types of the multigranulation spaces are considered: one is the partition-based multigranulation space, the other is the covering-based multigranulation space. Three different hierarchical structures are then proposed on such two multigranulation spaces, respectively. Not only the properties about these hierarchical structures are discussed, but also the relationships between these hierarchical structures and the multigranulation rough sets are deeply investigated. It is shown that the first hierarchical structure is consistent with the monotonic varieties of optimistic multigranulation rough set, and the second hierarchical structure is consistent to the monotonic varieties of pessimistic multigranulation rough set, the third hierarchical structure is consistent to the monotonic varieties of both optimistic and pessimistic multigranulation rough sets. [ABSTRACT FROM AUTHOR]

Accurately predicting the transmembrane helices (TMH) in a helical membrane protein is an important but challenging task. Recent researches have demonstrated that statistics-based methods are promising routes to improve the TMH prediction accuracy. However, most of existing TMH predictors are parametric models and they have to make assumptions of several or even hundreds of adjustable parameters based on the underlying probability distribution, which is difficult when no a priori knowledge is available. Besides the performances of these parametric predictors significantly depend on the estimated parameters, some of them need to exploit the entire training dataset in the prediction stage, which will lead to low prediction efficiency and this problem will become even worse when dealing with large-scale dataset. In this paper, we propose a novel SOMPNN model for prediction of TMH that features by minimal parameter assumptions requirement and high computational efficiency. In the SOMPNN model, a self-organizing map (SOM) is used to adaptively learn the helices distribution knowledge hidden in the training data, and then a probabilistic neural network (PNN) is adopted to predict TMH segments based on the knowledge learned by SOM. Experimental results on two benchmark datasets show that the proposed SOMPNN outperforms most existing popular TMH predictors and is promising to be extended to deal with other complicated biological problems. The datasets and the source codes of SOMPNN are available at . [ABSTRACT FROM AUTHOR]

Accumulated data have shown the neuroactive properties of oxytocin (OT), a neurohypophyseal neuropeptide, and its capability of reducing the abuse potential of drugs. The present study investigated the effect of OT on methamphetamine (MAP)-induced hyperactivity in mice and its possible mechanism of action. Locomotor activity was measured after administered with MAP using an infrared sensor. High-performance liquid chromatography with electrochemical detection (HPLC-ECD) was used to detect the content of monoamines and their metabolites in the striatum and accumbens and prefrontal cortex in mice after the behavioral test. OT (0.1, 0.5, and 2.5 μg/mouse, i.c.v.) had no effect on locomotor activity in naïve mice, but inhibited, in a dose-dependent manner, the hyperactivity induced by acute administration of MAP. Atosiban (Ato) (2.0 μg/mouse, i.c.v.), the selective inhibitor of OT receptor, attenuated the inhibitory effect of OT on MAP. A marked reduction of the ratios of 3, 4-dihydroxyphenylacetic acid (DOPAC) and homovanillic acid (HVA) to dopamine (DA) was observed in the striatum and accumbens of mice after acute administration of MAP. OT (2.5 μg, i.c.v.) significantly inhibited the reduction of DOPAC/DA and HVA/DA ratios. However, Ato decreased the ratio of DOPAC/DA significantly in mice compared with OT (2.5 μg) in combination with MAP. There was no significant change in serotonin (5-HT) metabolism in mice after a single administration of MAP. These results suggested that OT inhibited the MAP-induced hyperactivity by altering the DA turnover in the mesolimbic region of mice. [ABSTRACT FROM AUTHOR]

In this paper we view the optimal set of discriminant vectors as a global transform, and consider its separability from a global view point. Based on this idea, the concept of a generalised Fisher discriminant criterion and that of a generalised optimal set of discriminant vectors is introduced. After that, a new algorithm is given to calculate the generalised optimal set of discriminant vectors defined in this paper, which is particularly suited to the case of a small number of samples where the scatter matrix is singular. It is then applied to an experiment on human facial recognition, and the results show that the new algorithm is superior to existing methods in terms of correct classification rate. [ABSTRACT FROM AUTHOR]

Studies on the similarities between the energy release on the surface of the shape memory alloy and the strong seismic activities in China lead to the possibilities that the results obtained from the observations and experiments of the energy release phenomenon in the shape memory alloy are promisingly useful in the seismic research area. This method can be realistically significant to the studies of the seismogenic mechanisms (especially for the short-impending characters), the prediction of the strong seismic activity and the seismogenic model. [ABSTRACT FROM AUTHOR]

SL4, a chalcone-based compound, has been shown to retard tumor invasion and angiogenesis by suppressing HIF1 activity and to induce apoptosis by promoting ROS release. Here, we report that SL4 is able to inhibit the proliferation of different types of breast cancer cell in vitro and in vivo by inducing G2/M cell cycle arrest. Our results showed that SL4 exhibited strong anti-proliferative activity in several human breast cancer cell lines, with IC50 values lower than 1.3 μM. Further studies indicated that SL4 induced G2/M arrest in these cell lines. Mechanistically, SL4 reduces the expression of cyclin A2 and cdc25C and decreases the activity of the cdc2/cyclin B1 complex. Notably, SL4 treatment resulted in an obvious increase in p21 mRNA and protein levels through activation of MAPK signaling pathways, but not the TGF-β pathway. SP600125 and PD98059, specific inhibitors of JNK kinase and ERK kinase, significantly blocked the SL4-induced G2/M phase arrest and upregulation of p21. Furthermore, SL4 suppressed the growth of established breast tumors in nude mice through upregulation of p21 and downregulation of cdc25C, and displayed a good safety profile. Taken together, these findings demonstrate the potential value of SL4 as a novel multi-target anti-tumor drug candidate. [ABSTRACT FROM AUTHOR]