922 resultados para Oral language


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To understand the molecular pathogenesis of oral submucous fibrosis (OSF), which is a chronic inflammatory disease, gene expression profiling was performed in 10 OSF tissues against 8 pooled normal tissues using oligonucleotide arrays. Microarray results revealed differential expression of 5288 genes (P < a parts per thousand currency sign 0.05 and fold change >= a parts per thousand yen 1.5). Among these, 2884 are upregulated and 2404 are downregulated. Validation employing quantitative real-time PCR and immunohistochemistry confirmed upregulation of transforming growth factor-beta beta 1 (TGF-beta beta 1), TGFBIp, THBS1, SPP1, and TIG1 and downregulation of bone morphogenic protein 7 (BMP7) in OSF tissues. Furthermore, activation of TGF-beta beta pathway was evident in OSF as demonstrated by pSMAD2 strong immunoreactivity. Treatment of keratinocytes and oral fibroblasts by TGF-beta beta confirmed the regulation of few genes identified in microarray including upregulation of connective tissue growth factor, TGM2, THBS1, and downregulation of BMP7, which is a known negative modulator of fibrosis. Taken together, these data suggest activation of TGF-beta beta signaling and suppression of BMP7 expression in the manifestation of OSF.

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We present a improved language modeling technique for Lempel-Ziv-Welch (LZW) based LID scheme. The previous approach to LID using LZW algorithm prepares the language pattern table using LZW algorithm. Because of the sequential nature of the LZW algorithm, several language specific patterns of the language were missing in the pattern table. To overcome this, we build a universal pattern table, which contains all patterns of different length. For each language it's corresponding language specific pattern table is constructed by retaining the patterns of the universal table whose frequency of appearance in the training data is above the threshold.This approach reduces the classification score (Compression Ratio [LZW-CR] or the weighted discriminant score[LZW-WDS]) for non native languages and increases the LID performance considerably.

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We present a new approach to spoken language modeling for language identification (LID) using the Lempel-Ziv-Welch (LZW) algorithm. The LZW technique is applicable to any kind of tokenization of the speech signal. Because of the efficiency of LZW algorithm to obtain variable length symbol strings in the training data, the LZW codebook captures the essentials of a language effectively. We develop two new deterministic measures for LID based on the LZW algorithm namely: (i) Compression ratio score (LZW-CR) and (ii) weighted discriminant score (LZW-WDS). To assess these measures, we consider error-free tokenization of speech as well as artificially induced noise in the tokenization. It is shown that for a 6 language LID task of OGI-TS database with clean tokenization, the new model (LZW-WDS) performs slightly better than the conventional bigram model. For noisy tokenization, which is the more realistic case, LZW-WDS significantly outperforms the bigram technique

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Current scientific research is characterized by increasing specialization, accumulating knowledge at a high speed due to parallel advances in a multitude of sub-disciplines. Recent estimates suggest that human knowledge doubles every two to three years – and with the advances in information and communication technologies, this wide body of scientific knowledge is available to anyone, anywhere, anytime. This may also be referred to as ambient intelligence – an environment characterized by plentiful and available knowledge. The bottleneck in utilizing this knowledge for specific applications is not accessing but assimilating the information and transforming it to suit the needs for a specific application. The increasingly specialized areas of scientific research often have the common goal of converting data into insight allowing the identification of solutions to scientific problems. Due to this common goal, there are strong parallels between different areas of applications that can be exploited and used to cross-fertilize different disciplines. For example, the same fundamental statistical methods are used extensively in speech and language processing, in materials science applications, in visual processing and in biomedicine. Each sub-discipline has found its own specialized methodologies making these statistical methods successful to the given application. The unification of specialized areas is possible because many different problems can share strong analogies, making the theories developed for one problem applicable to other areas of research. It is the goal of this paper to demonstrate the utility of merging two disparate areas of applications to advance scientific research. The merging process requires cross-disciplinary collaboration to allow maximal exploitation of advances in one sub-discipline for that of another. We will demonstrate this general concept with the specific example of merging language technologies and computational biology.

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Parallel sub-word recognition (PSWR) is a new model that has been proposed for language identification (LID) which does not need elaborate phonetic labeling of the speech data in a foreign language. The new approach performs a front-end tokenization in terms of sub-word units which are designed by automatic segmentation, segment clustering and segment HMM modeling. We develop PSWR based LID in a framework similar to the parallel phone recognition (PPR) approach in the literature. This includes a front-end tokenizer and a back-end language model, for each language to be identified. Considering various combinations of the statistical evaluation scores, it is found that PSWR can perform as well as PPR, even with broad acoustic sub-word tokenization, thus making it an efficient alternative to the PPR system.

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Genome sequences contain a number of patterns that have biomedical significance. Repetitive sequences of various kinds are a primary component of most of the genomic sequence patterns. We extended the suffix-array based Biological Language Modeling Toolkit to compute n-gram frequencies as well as n-gram language-model based perplexity in windows over the whole genome sequence to find biologically relevant patterns. We present the suite of tools and their application for analysis on whole human genome sequence.

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Oral submucous fibrosis (OSF) is a chronic inflammatory disease characterized by the accumulation of excess collagen, and areca nut chewing has been proposed as an important etiological factor for disease manifestation. Activation of transforming growth factor-beta signaling has been postulated as the main causative event for increased collagen production in OSF. Oral epithelium plays important roles in OSF, and arecoline has been shown to induce TGF-beta in epithelial cells. In an attempt to understand the role of areca nut constituents in the manifestation of OSF, we studied the global gene expression profile in epithelial cells (HaCaT) following treatment with areca nut water extract or TGF-beta. Interestingly, 64% of the differentially regulated genes by areca nut water extract matches with the TGF-beta induced gene expression profile. Out of these, expression of 57% of genes was compromised in the presence of ALK5 (T beta RI) inhibitor and 7% were independently induced by areca nut, highlighting the importance of TGF-beta in areca nut actions. Areca nut water extract treatment induced p-SMAD2 and TGF-beta downstream targets in HaCaT cells but not in human gingival fibroblast cells (hGF), suggesting epithelial cells could be the source of TGF-beta in promoting OSF. Water extract of areca nut consists of polyphenols and alkaloids. Both polyphenol and alkaloid fractions of areca nut were able to induce TGF-beta signaling and its downstream targets. Also, SMAD-2 was phosphorylated following treatment of HaCaT cells by Catechin, Tannin and alkaloids namely Arecoline, Arecaidine and Guvacine. Moreover, both polyphenols and alkaloids induced TGF-beta 2 and THBS1 (activator of latent TGF-beta) in HaCaT cells suggesting areca nut mediated activation of p-SMAD2 involves up-regulation and activation of TGF-beta. These data suggest a major causative role for TGF-beta that is induced by areca nut in OSF progression.

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The CDC73 gene is mutationally inactivated in hereditary and sporadic parathyroid tumors. It negatively regulates beta-catenin, cyclin D1, and c-MYC. Down-regulation of CDC73 has been reported in breast, renal, and gastric carcinomas. However, the reports regarding the role of CDC73 in oral squamous cell carcinoma (OSCC) are lacking. In this study we show that CDC73 is down-regulated in a majority of OSCC samples. We further show that oncogenic microRNA-155 (miR-155) negatively regulates CDC73 expression. Our experiments show that the dramatic up-regulation of miR-155 is an exclusive mechanism for down-regulation of CDC73 in a panel of human cell lines and a subset of OSCC patient samples in the absence of loss of heterozygosity, mutations, and promoter methylation. Ectopic expression of miR-155 in HEK293 cells dramatically reduced CDC73 levels, enhanced cell viability, and decreased apoptosis. Conversely, the delivery of a miR-155 antagonist (antagomir-155) to KB cells overexpressing miR-155 resulted in increased CDC73 levels, decreased cell viability, increased apoptosis, and marked regression of xenografts in nude mice. Cotransfection of miR-155 with CDC73 in HEK293 cells abrogated its pro-oncogenic effect. Reduced cell proliferation and increased apoptosis of KB cells were dependent on the presence or absence of the 3'-UTR in CDC73. In summary, knockdown of CDC73 expression due to overexpression of miR-155 not only adds a novelty to the list of mechanisms responsible for its down-regulation in different tumors, but the restoration of CDC73 levels by the use of antagomir-155 may also have an important role in therapeutic intervention of cancers, including OSCC.

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Mutations in the MCPH1 (microcephalin 1) gene, located at chromosome 8p23.1, result in two autosomal recessive disorders: primary microcephaly and premature chromosome condensation syndrome. MCPH1 has also been shown to be downregulated in breast, prostate and ovarian cancers, and mutated in 1/10 breast and 5/41 endometrial tumors, suggesting that it could also function as a tumor suppressor (TS) gene. To test the possibility of MCPH1 as a TS gene, we first performed LOH study in a panel of 81 matched normal oral tissues and oral squamous cell carcinoma (OSCC) samples, and observed that 14/71 (19.72%) informative samples showed LOH, a hallmark of TS genes. Three protein truncating mutations were identified in 1/15 OSCC samples and 2/5 cancer cell lines. MCPH1 was downregulated at both the transcript and protein levels in 21/41 (51.22%) and 19/25 (76%) OSCC samples respectively. A low level of MCPH1 promoter methylation was also observed in 4/40 (10%) tumor samples. We further observed that overexpression of MCPH1 decreased cellular proliferation, anchorage-independent growth in soft agar, cell invasion and tumor size in nude mice, indicating its tumor suppressive function. Using bioinformatic approaches and luciferase assay, we showed that the 3'-UTR of MCPH1 harbors two non-overlapping functional seed regions for miR-27a which negatively regulated its level. The expression level of miR-27a negatively correlated with the MCPH1 protein level in OSCC. Our study indicates for the first time that, in addition to its role in brain development, MCPH1 also functions as a tumor suppressor gene and is regulated by miR-27a.

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N-gram language models and lexicon-based word-recognition are popular methods in the literature to improve recognition accuracies of online and offline handwritten data. However, there are very few works that deal with application of these techniques on online Tamil handwritten data. In this paper, we explore methods of developing symbol-level language models and a lexicon from a large Tamil text corpus and their application to improving symbol and word recognition accuracies. On a test database of around 2000 words, we find that bigram language models improve symbol (3%) and word recognition (8%) accuracies and while lexicon methods offer much greater improvements (30%) in terms of word recognition, there is a large dependency on choosing the right lexicon. For comparison to lexicon and language model based methods, we have also explored re-evaluation techniques which involve the use of expert classifiers to improve symbol and word recognition accuracies.

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Polyhedral techniques for program transformation are now used in several proprietary and open source compilers. However, most of the research on polyhedral compilation has focused on imperative languages such as C, where the computation is specified in terms of statements with zero or more nested loops and other control structures around them. Graphical dataflow languages, where there is no notion of statements or a schedule specifying their relative execution order, have so far not been studied using a powerful transformation or optimization approach. The execution semantics and referential transparency of dataflow languages impose a different set of challenges. In this paper, we attempt to bridge this gap by presenting techniques that can be used to extract polyhedral representation from dataflow programs and to synthesize them from their equivalent polyhedral representation. We then describe PolyGLoT, a framework for automatic transformation of dataflow programs which we built using our techniques and other popular research tools such as Clan and Pluto. For the purpose of experimental evaluation, we used our tools to compile LabVIEW, one of the most widely used dataflow programming languages. Results show that dataflow programs transformed using our framework are able to outperform those compiled otherwise by up to a factor of seventeen, with a mean speed-up of 2.30x while running on an 8-core Intel system.

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Estrogen-related receptor (ESRRA) functions as a transcription factor and regulates the expression of several genes, such as WNT11 and OPN. Up-regulation of ESRRA has been reported in several cancers. However, the mechanism underlying its up-regulation is unclear. Furthermore, the reports regarding the role and regulation of ESRRA in oral squamous cell carcinoma (OSCC) are completely lacking. Here, we show that tumor suppressor miR-125a directly binds to the 3UTR of ESRRA and represses its expression. Overexpression of miR-125a in OSCC cells drastically reduced the level of ESRRA, decreased cell proliferation, and increased apoptosis. Conversely, the delivery of an miR-125a inhibitor to these cells drastically increased the level of ESRRA, increased cell proliferation, and decreased apoptosis. miR-125a-mediated down-regulation of ESRRA impaired anchorage-independent colony formation and invasion of OSCC cells. Reduced cell proliferation and increased apoptosis of OSCC cells were dependent on the presence of the 3UTR in ESRRA. The delivery of an miR-125a mimic to OSCC cells resulted in marked regression of xenografts in nude mice, whereas the delivery of an miR-125a inhibitor to OSCC cells resulted in a significant increase of xenografts and abrogated the tumor suppressor function of miR-125a. We observed an inverse correlation between the expression levels of miR-125a and ESRRA in OSCC samples. In summary, up-regulation of ESRRA due to down-regulation of miR-125a is not only a novel mechanism for its up-regulation in OSCC, but decreasing the level of ESRRA by using a synthetic miR-125a mimic may have an important role in therapeutic intervention of OSCC and other cancers.

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Lipid coated mesoporous silica nanoparticle (L-MSN) were synthesized for oral delivery of ciprofloxacin for intracellular elimination of Salmonella pathogen. The particle size was found to be between 50-100 nm with a lipid coat of approximately 5 nm thickness. The lipid coating was achieved by sonication of liposomes with the MSN particles and evaluated by CLSMand FTIR studies. The L-MSN particles exhibited lower cytotoxicity compared to bare MSN particles. Ciprofloxacin, a fluoroquinolone antibiotic, loaded into the L-MSN particles showed enhanced antibacterial activity against free drug in in vitro assays. The lipid coat was found to aid in intravacuolar targeting of the drug cargo as observed by confocal microscopy studies. We also observed that a lower dose of antibiotic was sufficient to clear the pathogen from mice and increase their survivability using the L-MSN oral delivery system.

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Tuberculosis is continuing as a problem of mankind. With evolution, MDR and XDR forms of tuberculosis have emerged from drug sensitive strain. MDR and XDR strains are resistant to most of the antibiotics, making the management more difficult. BCG vaccine is not providing complete protection against tuberculosis. Therefore new infections are spreading at a tremendous rate. At the present moment there is experimental evidence to believe that Vitamin A and Vitamin D has anti-mycobacterial property. It is in this context, we have hypothesized a host based approach using the above vitamins that can cause possible prevention and cure of tuberculosis with minimal chance of resistance or toxicity. (C) 2015 Elsevier Ltd. All rights reserved.