1,553,237 research outputs found

    A heterotic sigma model with novel target geometry

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    We construct a (1,2) heterotic sigma model whose target space geometry consists of a transitive Lie algebroid with complex structure on a Kaehler manifold. We show that, under certain geometrical and topological conditions, there are two distinguished topological half--twists of the heterotic sigma model leading to A and B type half--topological models. Each of these models is characterized by the usual topological BRST operator, stemming from the heterotic (0,2) supersymmetry, and a second BRST operator anticommuting with the former, originating from the (1,0) supersymmetry. These BRST operators combined in a certain way provide each half--topological model with two inequivalent BRST structures and, correspondingly, two distinct perturbative chiral algebras and chiral rings. The latter are studied in detail and characterized geometrically in terms of Lie algebroid cohomology in the quasiclassical limit.Comment: 83 pages, no figures, 2 references adde

    PPAR-alpha: a novel target in pancreatic cancer

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    Background: Current targeted therapies in pancreatic cancer have been ineffective. The tumor stroma, including intra- and peri-tumoral inflammation and fibrosis, is increasingly implicated in pancreatic cancer. Pancreatic cancer is characterized by a highly fibrotic tumor environment resulting in stromal resistance to chemotherapy. Peroxisome proliferator-activated receptor-alpha (PPARĪ±), a ligand-activated nuclear receptor/transcription factor, is a negative regulator of inflammation. In PPARĪ± deficient mice, stromal processes inhibit tumor growth, resulting in dormant tumors. The presence of PPARĪ± in the tumor cells as well as in the host is necessary for unabated tumor growth. Objective: We hypothesized that blocking the PPARĪ± pathway with a small molecule PPARĪ± antagonist (NXT) may prevent pancreatic cancer progression by targeting tumor cells as well as non-neoplastic cells in the tumor microenvironment. Methods: Growth inhibitory activity of the PPARĪ± antagonist was assessed in murine as well as human pancreatic tumor cell lines (Panc0H7 and BxPC3) and in a murine macrophage cell line (RAW 264.7). Cell viability was determined by trypan blue exclusion assay. AKT, P-AKT, PCNA, BAX, and p27 levels were analyzed by western blot analysis. Cell cycle changes were detected by flow cytometry. Cellular senescence was determined by senescence-associated Ī²-gal (SA-Ī²-gal) staining. Results: The PPARĪ± antagonist inhibited cell growth in macrophages and in pancreatic tumor cells as confirmed by reduced protein level expression of PCNA and activated AKT. Treatment of the PPARĪ± antagonist was non-cytotoxic to tumor cells. Inhibition of PPARĪ± induced cell cycle arrest at G0/G1 in tumor cells and macrophages. The induction of cellular senescence was observed in pancreatic cancer cells. Interestingly, we observed a reduction in protein level expression of BAX, a marker for apoptosis, and p27, an inhibitor of the cell cycle. Conclusion: We now demonstrate that a PPARĪ± antagonist exerts its anti-growth activity by inducing G0/G1 cell cycle arrest, thereby inducing cellular senescence without cell death. These findings provide a mechanism for the anti-tumorigenic activity of PPARĪ± inhibition, and the rationale to use PPARĪ± antagonists as a novel therapeutic approach to pancreatic cancer.2016-11-03T00:00:00

    Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network.

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    Traditional Chinese Medicine (TCM) has received increasing attention as a complementary approach or alternative to modern medicine. However, experimental methods for identifying novel targets of TCM herbs heavily relied on the current available herb-compound-target relationships. In this work, we present an Herb-Target Interaction Network (HTINet) approach, a novel network integration pipeline for herb-target prediction mainly relying on the symptom related associations. HTINet focuses on capturing the low-dimensional feature vectors for both herbs and proteins by network embedding, which incorporate the topological properties of nodes across multi-layered heterogeneous network, and then performs supervised learning based on these low-dimensional feature representations. HTINet obtains performance improvement over a well-established random walk based herb-target prediction method. Furthermore, we have manually validated several predicted herb-target interactions from independent literatures. These results indicate that HTINet can be used to integrate heterogeneous information to predict novel herb-target interactions

    Translation Shift in the Translated Novel of ā€œto Kill a Mockingbirdā€ by Femmy Syahrianni

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    The aim of this study were to find out category shift types used in thetranslation of novel To Kill A Bird and to describe of how category shift is translatedin the novel from English into Indonesian. This study were conducted by usingdescriptive qualitative method. The data of the study were words, phrases, andclauses in the novel To Kill A Mockingbird which is translated into Indonesian byFemmy Syahrianni. It was found that there were 280 data in the novel from Englishinto Indonesian. The data analysis were taken by listing and bolding. Documentarysheets used as the instrument to collect the data. The data were analyzed based onMiles and Huberman (2014) by condensation which consists of selecting, focusing,simplifying, abstracting and transforming and then data display by using table inorder to get easy analyzing the data. The result of this study were (1) there were fourtypes of category shifts found in the novel To Kill a Mockingbird namely; structureshifts (36.78%), class shift (27.14%), unit shift (32.5%) and intra-system shift(3.27%). (2) The process of category shifts in the translation novel by havingmodifier-head in source language changed into head-modifier in target language,adverb in source language changed into verb in target language, one unit in sourcelanguage changed into some units in target language. and plural in source languagechanged into singular in target language

    Unsupervised Domain Adaptation: A Multi-task Learning-based Method

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    This paper presents a novel multi-task learning-based method for unsupervised domain adaptation. Specifically, the source and target domain classifiers are jointly learned by considering the geometry of target domain and the divergence between the source and target domains based on the concept of multi-task learning. Two novel algorithms are proposed upon the method using Regularized Least Squares and Support Vector Machines respectively. Experiments on both synthetic and real world cross domain recognition tasks have shown that the proposed methods outperform several state-of-the-art domain adaptation methods

    Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

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    Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks
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