We present a novel unsupervised learning method for human action categories. A video sequence is represented as a collection of spatial-temporal words by extracting space-time interest points. The algorithm automatically learns the probability distributions of the spatial-temporal words and the intermediate topics corresponding to human action categories. This is achieved by using latent topic models such as the probabilistic Latent Semantic Analysis (pLSA) model and Latent Dirichlet Allocation (LDA). Our approach can handle noisy feature points arisen from dynamic background and moving cameras due to the application of the probabilistic models. Given a novel video sequence, the algorithm can categorize and localize the human action(s) contained in the video. We test our algorithm on three challenging datasets: the KTH human motion dataset, the Weizmann human action dataset, and a recent dataset of figure skating actions. Our results reflect the promise of such a simple approach. In addition, our algorithm can recognize and localize multiple actions in long and complex video sequences containing multiple motions.

DESCRIPTIONImagine a video taken on a sunny beach, can a computer automatically tell what is happening in the scene? Can it identify different human activities in the video, such as water surfing, people walking and lying on the beach? To automatically classify or localize different actions in video sequences is very useful for a variety of tasks, such as video surveillance, objectlevel video summarization, video indexing, digital library organization, etc. However, it remains a challenging task for computers to achieve robust action recognition due to cluttered background, camera motion, occlusion, and geometric and photometric variances of objects. For example, in a live video of a skating competition, the skater moves rapidly across the rink, and the camera also moves to follow the skater. With moving camera, non-stationary background, and moving target, few vision algorithms could identify, categorize and localize such motions well. In addition, the challenge is even greater when there are multiple activities in a complex video sequence ( Figure 1).We present a video demo for our novel unsupervised learning method for human action categories [1]. A video sequence is represented as a collection of spatial-temporal words by extracting space-time interest points. The algorithm learns the probability distributions of the spatial-temporal words and intermediate topics corresponding to human action categories automatically using a probabilistic Latent Semantic Analysis (pLSA) model [4]. The learned model is then used for human action categorization and localization in a novel video, by maximizing the posterior of action category (topic) distributions. The contributions of this work are as follows:• Unsupervised learning of actions using 'video words' representation. We deploy a pLSA model with 'bag of video words' representation for video analysis; • Multiple action localization and categorization. Our approach is not only able to classify different actions, but also to localize different actions simultaneously in a novel and complex video sequence. We test our algorithm on two datasets: the KTH human action dataset [3] and a recent dataset of figure skating actions [2]. These datasets contain videos of cluttered background, moving camera, and multiple actions. Our results on action recognition performance are on par or slightly better than the best reported results in the literature. In addition, our algorithm can recognize and localize multiple actions in long and complex video sequences containing multiple motions.

NE could stimulate pancreatic cancer cell proliferation, migration and invasion through a β-AR-dependent activation of P38/MAPK pathway involved.

Tensor approximation is necessary to obtain compact multilinear models for multi-dimensional visual datasets. Traditionally, each multi-dimensional data item is represented as a vector. Such a scheme flattens the data and partially destroys the internal structures established throughout the multiple dimensions. In this paper, we retain the original dimensionality of the data items to more effectively exploit existing spatial redundancy and allow more efficient computation. Since the size of visual datasets can easily exceed the memory capacity of a single machine, we also present an out-of-core algorithm for higher-order tensor approximation. The basic idea is to partition a tensor into smaller blocks and perform tensor-related operations blockwise. We have successfully applied our techniques to three graphics-related data-driven models, including 6D bidirectional texture functions, 7D dynamic BTFs and 4D volume simulation sequences. Experimental results indicate that our techniques can not only process out-of-core data, but also achieve higher compression ratios and quality than previous methods.

Qian et al. shows that ILC2s can be generated from not only thymic multipotent progenitors but also committed T cell precursors. These processes are greatly suppressed by E protein transcription factors. Thymic ILC2s show functional differences from those made elsewhere.

Hepatocellular carcinoma (HCC) is the leading cause of cancer-related deaths worldwide. Despite advances in the diagnosis and treatment of HCC, incidence, and mortality continue to rise. For accurate diagnosis and treatment of HCC, there is an urgent need to precisely understand the molecular mechanisms underlying HCC tumorigenesis and progression. Accumulating evidence showed that circRNAs, which are normally produced by scrambling of exons at the splicing process, are recognized as a novel class of endogenous noncoding RNA, which have microRNA sponging properties. In this study, we aim to investigate the circRNA-100338 mediated downstream pathway, and evaluate its association with clinicopathological parameters. Integrated analysis of circRNA-100338, miR-141-3p, and target genes revealed that RHEB, a key regulator in mTOR signaling pathway, was the target of miR-141-3p in hepatitis B-related HCC. CircRNA-100338 regulates the activity of mTOR signaling pathway in vitro . IHC analysis revealed that mTOR signaling pathway was more active in HCC tissues with elevated circRNA-100338 expression. These results indicated that circRNA-100338 could regulate mTOR signaling pathway through circRNA-100338/miR-141-3p/RHEB axis. Finally, correlation analysis of RHEB and EIF5 expression with clinicopathological parameters of HCC patients revealed that the circRNA-100338, RHEB, and EIF5 were indicators of poor prognosis in hepatitis B-related HCC. In conclusion, elevated circRNA-100338 activates mTOR signaling pathway in HCC via circRNA-100338/miR-141-3p/RHEB axis and associates with poor prognosis of hepatitis B-related HCC patients.

The bovine genetic resources in China are diverse, but their value and potential are yet to be discovered. To determine the genetic diversity and population structure of Chinese cattle, we analysed the whole genomes of 46 cattle from six phenotypically and geographically representative Chinese cattle breeds, together with 18 Red Angus cattle (RAN) genomes, 11 Japanese black cattle (JBC) genomes and taurine and indicine genomes available from previous studies. Our results showed that Chinese cattle originated from hybridization between Bos taurus and Bos indicus. Moreover, we found that the level of genetic variation in Chinese cattle depends upon the degree of indicine content. We also discovered many potential selective sweep regions associated with domestication related to breed-specific characteristics, with selective sweep regions including genes associated with coat colour (ERCC2, MC1R, ZBTB17 and MAP2K1), dairy traits (NCAPG, MAPK7, FST, ITFG1, SETMAR, PAG1, CSN3 and RPL37A), and meat production/quality traits (such as BBS2, R3HDM1, IGFBP2, IGFBP5, MYH9, MYH4 and MC5R). These findings substantially expand the catalogue of genetic variants in cattle and reveal new insights into the evolutionary history and domestication traits of Chinese cattle.

BackgroundPancreatic cancer is susceptible to gemcitabine resistance, and patients receive less benefit from gemcitabine chemotherapy. Previous studies report that gambogic acid possesses antineoplastic properties; however, to our knowledge, there have been no specific studies on its effects in pancreatic cancer. Therefore, the purpose of this study was to explore whether increases the sensitivity of pancreatic cancer to gemcitabine, and determine the synergistic effects of gambogic acid and gemcitabine against pancreatic cancer.MethodsThe effects of gambogic acid on cell viability, the cell cycle, and apoptosis were assessed using 4,5-dimethylthiazol-2-yl)-3,5-diphenylformazan (MTT) and flow cytometry in pancreatic cancer cell lines. Protein expression was detected by western blot analysis and mRNA expression was detected using q-PCR. A xenograft tumor model of pancreatic cancer was used to investigate the synergistic effects of gambogic acid and gemcitabine.ResultsGambogic acid effectively inhibited the growth of pancreatic cancer cell lines by inducing S-phase cell cycle arrest and apoptosis. Synergistic activity of gambogic acid combined with gemcitabine was observed in PANC-1 and BxPC-3 cells based on the results of MTT, colony formation, and apoptosis assays. Western blot results demonstrated that gambogic acid sensitized gemcitabine-induced apoptosis by enhancing the expression of cleaved caspase-3, cleaved caspase-9, cleaved-PARP, and Bax, and reducing the expression of Bcl-2. In particular, gambogic acid reduced the expression of the ribonucleotide reductase subunit-M2 (RRM2) protein and mRNA, a trend that correlated with resistance to gemcitabine through inhibition of the extracellular signal-regulated kinase (ERK)/E2F1 signaling pathway. Treatment with gambogic acid and gemcitabine significantly repressed tumor growth in the xenograft pancreatic cancer model. Immunohistochemistry results demonstrated a downregulation of p-ERK, E2F1, and RRM2 in mice receiving gambogic acid treatment and combination treatment.ConclusionsThese results demonstrate that gambogic acid sensitizes pancreatic cancer cells to gemcitabine in vitro and in vivo by inhibiting the activation of the ERK/E2F1/RRM2 signaling pathway. The results also indicate that gambogic acid treatment combined with gemcitabine might be a promising chemotherapy strategy for pancreatic cancer.Electronic supplementary materialThe online version of this article (doi:10.1186/s13046-017-0579-0) contains supplementary material, which is available to authorized users.