Improved two-stream model for human action recognition

This paper addresses the recognitions of human actions in videos. Human action recognition can be seen as the automatic labeling of a video according to the actions occurring in it. It has become one of the most challenging and attractive problems in the pattern recognition and video classification fields. The problem itself is difficult to solve by traditional video processing methods because of several challenges such as the background noise, sizes of subjects in different videos, and the speed of actions. Derived from the progress of deep learning methods, several directions are developed to recognize a human action from a video, such as the long-short-term memory (LSTM)-based model, two-stream convolutional neural network (CNN) model, and the convolutional 3D model.In this paper, we focus on the two-stream structure. The traditional two-stream CNN network solves the problem that CNNs do not have satisfactory performance on temporal features. By training a temporal stream, which uses the optical flow as the input, a CNN can have the ability to extract temporal features. However, the optical flow only contains limited temporal information because it only records the movements of pixels on the x-axis and the y-axis. Therefore, we attempt to design and implement a new two-stream model by using an LSTM-based model in its spatial stream to extract both spatial and temporal features in RGB frames. In addition, we implement a DenseNet in the temporal stream to improve the recognition accuracy. This is in-contrast to traditional approaches which typically utilize the spatial stream for extracting only spatial features. The quantitative evaluation and experiments are conducted on the UCF-101 dataset, which is a well-developed public video dataset. For the temporal stream, we choose the optical flow of UCF-101. Images in the optical flow are provided by the Graz University of Technology. The experimental result shows that the proposed method outperforms the traditional two-stream CNN method with an accuracy of at least 3%. For both spatial and temporal streams, the proposed model also achieves higher recognition accuracies. In addition, compared with the state of the art methods, the new model can still have the best recognition performance

MicroRNA-375 inhibits tumour growth and metastasis in oesophageal squamous cell carcinoma through repressing insulin-like growth factor 1 receptor

Background: To understand the involvement of micro- RNA (miRNA) in the development and progression of oesophageal squamous cell carcinoma (ESCC), miRNA profiles were compared between tumour and corresponding non-tumour tissues. Methods: miRCURY LNA array was used to generate miRNA expressing profile. Real-time quantitative PCR was applied to detectthe expression of miR-375 in ESCC samples and its correlation with insulin-like growth factor 1 receptor (IGF1R). Methylation-specific PCR was used to study the methylation status in the promoter region of miR-375. The tumour-suppressive effect of miR-375 was determined by both in-vitro and in-vivo assays. Results: The downregulation of miR-375 was frequently detected in primary ESCC, which was significantly correlated with advanced stage (p=0.003), distant metastasis (p<0.0001), poor overall survival (p=0.048) and disease-free survival (p=0.0006). Promoter methylation of miR-375 was detected in 26 of 45 (57.8%) ESCC specimens. Functional assays demonstrated that miR-375 could inhibit clonogenicity, cell motility, cell proliferation, tumour formation and metastasis in mice. Further study showed that miR-375 could interact with the 39-untranslated region of IGF1R and downregulate its expression. In clinical specimens, the expression of IGF1R was also negatively correlated with miR-375 expression (p=0.008). Conclusions: This study demonstrates that miR-375 has a strong tumour-suppressive effect through inhibiting the expression of IGF1R. The downregulation of miR-375, which is mainly caused by promoter methylation, is one of the molecular mechanisms involved in the development and progression of ESCC.published_or_final_versio

Design, analysis, tools and applications for programmable high-speed and power-aware 4G processors

Data rate traffic and communication capacity demand have been increased continuously. Therefore, a highly advanced 4G wireless system is required to meet a high demand for modern mobile terminals. For getting a further improvement for 4G communication systems, new paradigms of design, analysis tools and applications for 4G communication processors are necessary. In this paper, some of these new paradigms are discussed. Furthermore, a single-step discrete cosine transform truncation (DCTT) method is proposed for the modeling-simulation in signal integrity verification for high-speed communication processors. ©2011 IEEE.published_or_final_versio

Wnt2 secreted by tumour fibroblasts promotes tumour progression in oesophageal cancer by activation of the Wnt/β-catenin signalling pathway

Objectives: Interaction between neoplastic and stromal cells plays an important role in tumour progression. It was recently found that WNT2 was frequently overexpressed in fibroblasts isolated from tumour tissue tumour fibroblasts (TF) compared with fibroblasts from non-tumour tissue normal fibroblasts in oesophageal squamous cell carcinoma (OSCC). This study aimed to investigate the effect of TF-secreted Wnt2 in OSCC development via the tumour - stroma interaction. Methods: Quantitative PCR, western blotting, immunohistochemistry and immunofluorescence were used to study the expression pattern of Wnt2 and its effect on the Wnt/β-catenin pathway. A Wnt2-secreting system was established in Chinese hamster ovary cells and its conditioned medium was used to study the role of Wnt2 in cell proliferation and invasion. Results: Expression of Wnt2 could only be detected in TF but not in OSCC cancer cell lines. In OSCC tissues, Wnt2 (+) cells were mainly detected in the boundary between stroma and tumour tissue or scattered within tumour tissue. In this study, Wnt2-positive OSCC was defined when five or more Wnt2(+) cells were observed in 2003X microscopy field. Interestingly, Wnt2-positive OSCC (22/51 cases) was significantly associated with lymph node metastases (p=0.001), advanced TNM stage (p=0.001) and disease-specific survival (p<0.0001). Functional study demonstrated that secreted Wnt2 could promote oesophageal cancer cell growth by activating the Wnt/β-catenin signalling pathway and subsequently upregulated cyclin D1 and c-myc expression. Further study found that Wnt2 could enhance cell motility and invasiveness by inducing epithelial-mesenchymal transition. Conclusions: TF-secreted Wnt2 acts as a growth and invasion-promoting factor through activating the canonical Wnt/β-catenin signalling pathway in oesophageal cancer cells.published_or_final_versio

Suppression of Raf-1 kinase activity and MAP kinase signalling by RKIP

Raf-1 phosphorylates and activates MEK-1, a kinase that activates the extracellular signal regulated kinases (ERK). This kinase cascade controls the proliferation and differentiation of different cell types. Here we describe a Raf-1-interacting protein, isolated using a yeast two-hybrid screen. This protein inhibits the phosphorylation and activation of MEK by Raf-1 and is designated RKIP (Raf kinase inhibitor protein). In vitro, RKIP binds to Raf-1, MEK and ERK, but not to Ras. RKIP co-immunoprecipitates with Raf-1 and MEK from cell lysates and colocalizes with Raf-1 when examined by confocal microscopy. RKIP is not a substrate for Raf-1 or MEK, but competitively disrupts the interaction between these kinases. RKIP overexpression interferes with the activation of MEK and ERK, induction of AP-1-dependent reporter genes and transformation elicited by an oncogenically activated Raf-1 kinase. Downregulation of endogenous RKIP by expression of antisense RNA or antibody microinjection induces the activation of MEK-, ERK- and AP-1-dependent transcription. RKIP represents a new class of protein-kinase-inhibitor protein that regulates the activity of the Raf/MEK/ERK modul

RBMS3 at 3p24 inhibits nasopharyngeal carcinoma development via inhibiting cell proliferation, angiogenesis, and inducing apoptosis.

Deletion of the short arm of chromosome 3 is one of the most frequent genetic alterations in many solid tumors including nasopharyngeal carcinoma (NPC), suggesting the existence of one or more tumor suppressor genes (TSGs) within the frequently deleted region. A putative TSG RBMS3 (RNA binding motif, single stranded interacting protein 3), located at 3p24-p23, has been identified in our previous study. Here, we reported that downregulation of RBMS3 was detected in 3/3 NPC cell lines and 13/15 (86.7%) primary NPC tissues. Functional studies using both overexpression and suppression systems demonstrated that RBMS3 has a strong tumor suppressive role in NPC. The tumor suppressive mechanism of RBMS3 was associated with its role in cell cycle arrest at the G1/S checkpoint by upregulating p53 and p21, downregulating cyclin E and CDK2, and the subsequent inhibition of Rb-ser780. Further analysis demonstrated that RBMS3 had a pro-apoptotic role in a mitochondrial-dependent manner via activation of caspase-9 and PARP. Finally, RBMS3 inhibited microvessel formation, which may be mediated by down-regulation of MMP2 and β-catenin and inactivation of its downstream targets, including cyclin-D1, c-Myc, MMP7, and MMP9. Taken together, our findings define a function for RBMS3 as an important tumor suppressor gene in NPC.published_or_final_versio

Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction

Large-scale protein signalling networks are useful for exploring complex biochemical pathways but do not reveal how pathways respond to specific stimuli. Such specificity is critical for understanding disease and designing drugs. Here we describe a computational approach—implemented in the free CNO software—for turning signalling networks into logical models and calibrating the models against experimental data. When a literature-derived network of 82 proteins covering the immediate-early responses of human cells to seven cytokines was modelled, we found that training against experimental data dramatically increased predictive power, despite the crudeness of Boolean approximations, while significantly reducing the number of interactions. Thus, many interactions in literature-derived networks do not appear to be functional in the liver cells from which we collected our data. At the same time, CNO identified several new interactions that improved the match of model to data. Although missing from the starting network, these interactions have literature support. Our approach, therefore, represents a means to generate predictive, cell-type-specific models of mammalian signalling from generic protein signalling networks

Revealing the molecular signatures of host-pathogen interactions.

Advances in sequencing technology and genome-wide association studies are now revealing the complex interactions between hosts and pathogen through genomic variation signatures, which arise from evolutionary co-existence