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Millions of hearing impaired people around the world routinely use some variants of sign languages to communicate, thus the automatic translation of a sign language is meaningful and important. Currently, there are two sub-problems in Sign Language Recognition (SLR), i.e., isolated SLR that recognizes word by word and continuous SLR that translates entire sentences. Existing continuous SLR methods typically utilize isolated SLRs as building blocks, with an extra layer of preprocessing (temporal segmentation) and another layer of post-processing (sentence synthesis). Unfortunately, temporal segmentation itself is non-trivial and inevitably propagates errors into subsequent steps. Worse still, isolated SLR methods typically require strenuous labeling of each word separately in a sentence, severely limiting the amount of attainable training data. To address these challenges, we propose a novel continuous sign recognition framework, the Hierarchical Attention Network with Latent Space (LS-HAN), which eliminates the preprocessing of temporal segmentation. The proposed LS-HAN consists of three components: a two-stream Convolutional Neural Network (CNN) for video feature representation generation, a Latent Space (LS) for semantic gap bridging, and a Hierarchical Attention Network (HAN) for latent space based recognition. Experiments are carried out on two large scale datasets. Experimental results demonstrate the effectiveness of the proposed framework.

The present study investigated the sensitization of 5-fluorouracil (5-FU)-resistant colon cancer cells in vitro, using oxymatrine, a Chinese herb, and a quinolizidine alkaloid compound extracted from the root of Sophora flavescens. The HCT-8 colon cancer cell line and its 5-FU-resistant subline HCT-8/5-FU were treated with 5-FU and oxymatrine, alone or in combination, at various doses. The cells were subsequently assessed for changes in cell viability, apoptosis and morphology and analyzed by fluorescence microscopy and western blotting. The data demonstrated that HCT-8/5-FU markedly increased the dose of 5-FU required for the suppression of tumor cell viability (78.77±1.90 µg/ml vs. 9.20±0.96 µg/ml in parental HCT-8 cells), whereas HCT-8/5-FU induced the tumor cell epithelial-mesenchymal transition (EMT). By contrast, oxymatrine alone and in combination with 5-FU altered HCT-8/5-FU cell morphology, apoptosis and EMT phenotypes. The combination of oxymatrine and 5-FU reduced the protein expression of snail family transcriptional repressor 2 and vimentin, phosphorylated p65 and induced the expression of E-cadherin, by inhibiting the nuclear factor κB (NF-κB) signaling pathway. In conclusion, the data from the present study demonstrated that EMT was associated with 5-FU chemoresistance in HCT-8/5-FU colon cancer cells, and that oxymatrine treatment was able to reverse such resistance. Oxymatrine may regulate tumor cell EMT and inactivate the NF-κB signaling pathway, and may therefore serve as a potential therapeutic drug to reverse 5-FU resistance in colon cancer cells.

With the aim of automatic recognition of weak faults in hydraulic systems, this paper proposes an identification method based on multi-scale permutation entropy feature extraction of fault-sensitive intrinsic mode function (IMF) and deep belief network (DBN). In this method, the leakage fault signal is first decomposed by empirical mode decomposition (EMD), and fault-sensitive IMF components are screened by adopting the correlation analysis method. The multi-scale entropy feature of each screened IMF is then extracted and features closely related to the weak fault information are then obtained. Finally, DBN is used for identification of fault diagnosis. Experimental results prove that this identification method has an ideal recognition effect. It can accurately judge whether there is a leakage fault, determine the degree of severity of the fault, and can diagnose and analyze hydraulic weak faults in general.

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