Membrane-bound β-catenin degradation is enhanced by ETS2-mediated Siah1 induction in Helicobacter pylori-infected gastric cancer cells.

β-catenin has two different cellular functions: intercellular adhesion and transcriptional activity. The E3 ubiquitin ligase Siah1 causes ubiquitin-mediated degradation of the cytosolic β-catenin and therefore, impairs nuclear translocation and oncogenic function of β-catenin. However, the effect of Siah1 on the cell membrane bound β-catenin has not been studied. In this study, we identified that the carcinogenic bacterium H. pylori increased ETS2 transcription factor-mediated Siah1 protein expression in gastric cancer cells (GCCs) MKN45, AGS and Kato III. Siah1 protein level was also noticeably higher in gastric adenocarcinoma biopsy samples as compared to non-cancerous gastric epithelia. Siah1 knockdown significantly decreased invasiveness and migration of H. pylori-infected GCCs. Although, Siah1 could not increase degradation of the cytosolic β-catenin and its nuclear translocation, it enhanced degradation of the membrane-bound β-catenin in the infected GCCs. This loss of membrane-bound pool of β-catenin was not associated with the proteasomal degradation of E-cadherin. Thus, this work delineated the role of Siah1 in increasing invasiveness of H. pylori-infected GCCs

Language independent and unsupervised acoustic models for speech recognition and keyword spotting

Copyright © 2014 ISCA. Developing high-performance speech processing systems for low-resource languages is very challenging. One approach to address the lack of resources is to make use of data from multiple languages. A popular direction in recent years is to train a multi-language bottleneck DNN. Language dependent and/or multi-language (all training languages) Tandem acoustic models (AM) are then trained. This work considers a particular scenario where the target language is unseen in multi-language training and has limited language model training data, a limited lexicon, and acoustic training data without transcriptions. A zero acoustic resources case is first described where a multilanguage AM is directly applied, as a language independent AM (LIAM), to an unseen language. Secondly, in an unsupervised approach a LIAM is used to obtain hypotheses for the target language acoustic data transcriptions which are then used in training a language dependent AM. 3 languages from the IARPA Babel project are used for assessment: Vietnamese, Haitian Creole and Bengali. Performance of the zero acoustic resources system is found to be poor, with keyword spotting at best 60% of language dependent performance. Unsupervised language dependent training yields performance gains. For one language (Haitian Creole) the Babel target is achieved on the in-vocabulary data

Investigation of multilingual deep neural networks for spoken term detection

The development of high-performance speech processing systems for low-resource languages is a challenging area. One approach to address the lack of resources is to make use of data from multiple languages. A popular direction in recent years is to use bottleneck features, or hybrid systems, trained on multilingual data for speech-to-text (STT) systems. This paper presents an investigation into the application of these multilingual approaches to spoken term detection. Experiments were run using the IARPA Babel limited language pack corpora (∼10 hours/language) with 4 languages for initial multilingual system development and an additional held-out target language. STT gains achieved through using multilingual bottleneck features in a Tandem configuration are shown to also apply to keyword search (KWS). Further improvements in both STT and KWS were observed by incorporating language questions into the Tandem GMM-HMM decision trees for the training set languages. Adapted hybrid systems performed slightly worse on average than the adapted Tandem systems. A language independent acoustic model test on the target language showed that retraining or adapting of the acoustic models to the target language is currently minimally needed to achieve reasonable performance. © 2013 IEEE

Combining tandem and hybrid systems for improved speech recognition and keyword spotting on low resource languages

Copyright © 2014 ISCA. In recent years there has been significant interest in Automatic Speech Recognition (ASR) and KeyWord Spotting (KWS) systems for low resource languages. One of the driving forces for this research direction is the IARPA Babel project. This paper examines the performance gains that can be obtained by combining two forms of deep neural network ASR systems, Tandem and Hybrid, for both ASR and KWS using data released under the Babel project. Baseline systems are described for the five option period 1 languages: Assamese; Bengali; Haitian Creole; Lao; and Zulu. All the ASR systems share common attributes, for example deep neural network configurations, and decision trees based on rich phonetic questions and state-position root nodes. The baseline ASR and KWS performance of Hybrid and Tandem systems are compared for both the "full", approximately 80 hours of training data, and limited, approximately 10 hours of training data, language packs. By combining the two systems together consistent performance gains can be obtained for KWS in all configurations

Data augmentation for low resource languages

Recently there has been interest in the approaches for train-ing speech recognition systems for languages with limited re-sources. Under the IARPA Babel program such resources have been provided for a range of languages to support this research area. This paper examines a particular form of approach, data augmentation, that can be applied to these situations. Data aug-mentation schemes aim to increase the quantity of data available to train the system, for example semi-supervised training, multi-lingual processing, acoustic data perturbation and speech syn-thesis. To date the majority of work has considered individual data augmentation schemes, with few consistent performance contrasts or examination of whether the schemes are comple-mentary. In this work two data augmentation schemes, semi-supervised training and vocal tract length perturbation, are ex-amined and combined on the Babel limited language pack con-figuration. Here only about 10 hours of transcribed acoustic data are available. Two languages are examined, Assamese and Zulu, which were found to be the most challenging of the Ba-bel languages released for the 2014 Evaluation. For both lan-guages consistent speech recognition performance gains can be obtained using these augmentation schemes. Furthermore the impact of these performance gains on a down-stream keyword spotting task are also described. Index Terms: data augmentation, speech recognition, babel 1

Modulation of N-methyl-N-nitrosourea induced mammary tumors in Sprague–Dawley rats by combination of lysine, proline, arginine, ascorbic acid and green tea extract

INTRODUCTION: The limited ability of current treatments to control metastasis and the proposed antitumor properties of specific nutrients prompted us to examine the effect of a specific formulation (nutrient supplement [NS]) of lysine, proline, arginine, ascorbic acid, and green tea extract in vivo on the development of N-methyl-N-nitrosourea (MNU)-induced mammary tumors in rats. METHODS: A single intraperitoneal dose of MNU was injected into each of 20 female Sprague–Dawley rats (aged 50 days) to induce tumors. Two weeks after MNU treatment, a time by which the animals had recovered from MNU-induced toxicity, the rats were divided into two groups. Rats in group 1 (n = 10) were fed Purina chow diet, whereas those in group 2 (n = 10) were fed the same diet supplemented with 0.5% NS. After a further 24 weeks, the rats were killed and tumors were excised and processed. RESULTS: NS reduced the incidence of MNU-induced mammary tumors and the number of tumors by 68.4%, and the tumor burden by 60.5%. The inhibitory effect of NS was also reflected by decreased tumor weight; the tumor weights per rat and per group were decreased by 41% and 78%, respectively. In addition, 30% of the control rats developed ulcerated tumors, in contrast to 10% in the nutrient supplemented rats. CONCLUSION: These findings suggest that the specific formulation of lysine, proline, arginine, ascorbic acid, and green tea extract tested significantly reduces the incidence and growth of MNU-induced mammary tumors, and therefore has strong potential as a useful therapeutic regimen for inhibiting breast cancer development

Globular-shaped variable lymphocyte receptors B antibody multimerized by a hydrophobic clustering in hagfish

In hagfish and lampreys, two representative jawless vertebrates, the humoral immunity is directly mediated by variable lymphocyte receptors B (VLRBs). Both monomeric VLRBs are structurally and functionally similar, but their C-terminal tails differ: lamprey VLRB has a Cys-rich tail that forms disulfide-linked pentamers of dimers, contributing to its multivalency, whereas hagfish VLRB has a superhydrophobic tail of unknown structure. Here, we reveal that VLRBs obtained from hagfish plasma have a globular-shaped multimerized form (approximately 0.6 to 1.7 MDa) that is generated by hydrophobic clustering instead of covalent linkage. Electron microscopy (EM) and single-particle analysis showed that the multimerized VLRBs form globular-shaped clusters with an average diameter of 28.7 ± 2.2 nm. The presence of VLRBs in the complex was confirmed by immune-EM analysis using an anti-VLRB antibody. Furthermore, the hydrophobic hagfish C-terminus (HC) was capable of triggering multimerization and directing the cellular surface localization via a glycophosphatidylinositol linkage. Our results strongly suggest that the hagfish VLRB forms a previously unknown globular-shaped antibody. This novel identification of a structurally unusual VLRB complex may suggest that the adaptive immune system of hagfish differs from that of lamprey

Disease surveillance using a hidden Markov model

<p>Abstract</p> <p>Background</p> <p>Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data.</p> <p>Methods</p> <p>A Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS) algorithms and a negative binomial cusum.</p> <p>Results</p> <p>Algorithm performance varied according to the desired false alarm rate for surveillance. At false alarm rates around 0.05, the cusum-based algorithms provided the best overall outbreak detection performance, having similar sensitivity to the HMMs and a shorter average time to detection. At false alarm rates around 0.01, the HMM algorithms provided the best overall outbreak detection performance, having higher sensitivity than the cusum-based Methods and a generally shorter time to detection for larger outbreaks. Overall, the 14-day HMM had a significantly greater area under the receiver operator characteristic curve than the EARS C3 and 7-day negative binomial cusum algorithms.</p> <p>Conclusion</p> <p>Our findings suggest that the HMM provides an effective method for the surveillance of sparse small area notifiable disease data at low false alarm rates. Further investigations are required to evaluation algorithm performance across other diseases and surveillance contexts.</p

Heterogeneous Glycation of Cancellous Bone and Its Association with Bone Quality and Fragility

Non-enzymatic glycation (NEG) and enzymatic biochemical processes create crosslinks that modify the extracellular matrix (ECM) and affect the turnover of bone tissue. Because NEG affects turnover and turnover at the local level affects microarchitecture and formation and removal of microdamage, we hypothesized that NEG in cancellous bone is heterogeneous and accounts partly for the contribution of microarchitecture and microdamage on bone fragility. Human trabecular bone cores from 23 donors were subjected to compression tests. Mechanically tested cores as well as an additional 19 cores were stained with lead-uranyl acetate and imaged to determine microarchitecture and measure microdamage. Post-yield mechanical properties were measured and damaged trabeculae were extracted from a subset of specimens and characterized for the morphology of induced microdamage. Tested specimens and extracted trabeculae were quantified for enzymatic and non-enzymatic crosslink content using a colorimetric assay and Ultra-high Performance Liquid Chromatography (UPLC). Results show that an increase in enzymatic crosslinks was beneficial for bone where they were associated with increased toughness and decreased microdamage. Conversely, bone with increased NEG required less strain to reach failure and were less tough. NEG heterogeneously modified trabecular microarchitecture where high amounts of NEG crosslinks were found in trabecular rods and with the mechanically deleterious form of microdamage (linear microcracks). The extent of NEG in tibial cancellous bone was the dominant predictor of bone fragility and was associated with changes in microarchitecture and microdamage