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

Spoken term detection ALBAYZIN 2014 evaluation: overview, systems, results, and discussion

The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1186/s13636-015-0063-8Spoken term detection (STD) aims at retrieving data from a speech repository given a textual representation of the search term. Nowadays, it is receiving much interest due to the large volume of multimedia information. STD differs from automatic speech recognition (ASR) in that ASR is interested in all the terms/words that appear in the speech data, whereas STD focuses on a selected list of search terms that must be detected within the speech data. This paper presents the systems submitted to the STD ALBAYZIN 2014 evaluation, held as a part of the ALBAYZIN 2014 evaluation campaign within the context of the IberSPEECH 2014 conference. This is the first STD evaluation that deals with Spanish language. The evaluation consists of retrieving the speech files that contain the search terms, indicating their start and end times within the appropriate speech file, along with a score value that reflects the confidence given to the detection of the search term. The evaluation is conducted on a Spanish spontaneous speech database, which comprises a set of talks from workshops and amounts to about 7 h of speech. We present the database, the evaluation metrics, the systems submitted to the evaluation, the results, and a detailed discussion. Four different research groups took part in the evaluation. Evaluation results show reasonable performance for moderate out-of-vocabulary term rate. This paper compares the systems submitted to the evaluation and makes a deep analysis based on some search term properties (term length, in-vocabulary/out-of-vocabulary terms, single-word/multi-word terms, and in-language/foreign terms).This work has been partly supported by project CMC-V2 (TEC2012-37585-C02-01) from the Spanish Ministry of Economy and Competitiveness. This research was also funded by the European Regional Development Fund, the Galician Regional Government (GRC2014/024, “Consolidation of Research Units: AtlantTIC Project” CN2012/160)

Approaches in biotechnological applications of natural polymers

Natural polymers, such as gums and mucilage, are biocompatible, cheap, easily available and non-toxic materials of native origin. These polymers are increasingly preferred over synthetic materials for industrial applications due to their intrinsic properties, as well as they are considered alternative sources of raw materials since they present characteristics of sustainability, biodegradability and biosafety. As definition, gums and mucilages are polysaccharides or complex carbohydrates consisting of one or more monosaccharides or their derivatives linked in bewildering variety of linkages and structures. Natural gums are considered polysaccharides naturally occurring in varieties of plant seeds and exudates, tree or shrub exudates, seaweed extracts, fungi, bacteria, and animal sources. Water-soluble gums, also known as hydrocolloids, are considered exudates and are pathological products; therefore, they do not form a part of cell wall. On the other hand, mucilages are part of cell and physiological products. It is important to highlight that gums represent the largest amounts of polymer materials derived from plants. Gums have enormously large and broad applications in both food and non-food industries, being commonly used as thickening, binding, emulsifying, suspending, stabilizing agents and matrices for drug release in pharmaceutical and cosmetic industries. In the food industry, their gelling properties and the ability to mold edible films and coatings are extensively studied. The use of gums depends on the intrinsic properties that they provide, often at costs below those of synthetic polymers. For upgrading the value of gums, they are being processed into various forms, including the most recent nanomaterials, for various biotechnological applications. Thus, the main natural polymers including galactomannans, cellulose, chitin, agar, carrageenan, alginate, cashew gum, pectin and starch, in addition to the current researches about them are reviewed in this article.. }To the Conselho Nacional de Desenvolvimento Cientfíico e Tecnológico (CNPq) for fellowships (LCBBC and MGCC) and the Coordenação de Aperfeiçoamento de Pessoal de Nvíel Superior (CAPES) (PBSA). This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, the Project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and COMPETE 2020 (POCI-01-0145-FEDER-006684) (JAT)

Leveraging epidemiology as a decision support tool during the COVID-19 epidemic in South Africa

By May 2021, South Africa (SA) had experienced two ‘waves’ of COVID-19 infections, with an initial peak of infections reached in July 2020, followed by a larger peak of infections in January 2021. Public health decisions rely on accurate and timely disease surveillance and epidemiological analyses, and accessibility of data at all levels of government is critical to inform stakeholders to respond effectively. In this paper, we describe the adaptation, development and operation of epidemiological surveillance and modelling systems in SA in response to the COVID-19 epidemic, including data systems for monitoring laboratory-confirmed COVID-19 cases, hospitalisations, mortality and recoveries at a national and provincial level, and how these systems were used to inform modelling projections and public health decisions. Detailed descriptions on the characteristics and completeness of individual datasets are not provided in this paper. Rapid development of robust data systems was necessary to support the response to the SA COVID-19 epidemic. These systems produced data streams that were used in decision-making at all levels of government. While much progress was made in producing epidemiological data, challenges remain to be overcome to address gaps to better prepare for future waves of COVID-19 and other health emergencies

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

Full-length human placental sFlt-1-e15a isoform induces distinct maternal phenotypes of preeclampsia in mice

<div><p>Objective</p><p>Most anti-angiogenic preeclampsia models in rodents utilized the overexpression of a truncated soluble fms-like tyrosine kinase-1 (sFlt-1) not expressed in any species. Other limitations of mouse preeclampsia models included stressful blood pressure measurements and the lack of postpartum monitoring. We aimed to 1) develop a mouse model of preeclampsia by administering the most abundant human placental sFlt-1 isoform (hsFlt-1-e15a) in preeclampsia; 2) determine blood pressures in non-stressed conditions; and 3) develop a survival surgery that enables the collection of fetuses and placentas and postpartum (PP) monitoring.</p><p>Methods</p><p>Pregnancy status of CD-1 mice was evaluated with high-frequency ultrasound on gestational days (GD) 6 and 7. Telemetry catheters were implanted in the carotid artery on GD7, and their positions were verified by ultrasound on GD13. Mice were injected through tail-vein with adenoviruses expressing hsFlt-1-e15a (n = 11) or green fluorescent protein (GFP; n = 9) on GD8/GD11. Placentas and pups were delivered by cesarean section on GD18 allowing PP monitoring. Urine samples were collected with cystocentesis on GD6/GD7, GD13, GD18, and PPD8, and albumin/creatinine ratios were determined. GFP and hsFlt-1-e15a expression profiles were determined by qRT-PCR. Aortic ring assays were performed to assess the effect of hsFlt-1-e15a on endothelia.</p><p>Results</p><p>Ultrasound predicted pregnancy on GD7 in 97% of cases. Cesarean section survival rate was 100%. Mean arterial blood pressure was higher in hsFlt-1-e15a-treated than in GFP-treated mice (∆MAP = 13.2 mmHg, p = 0.00107; GD18). Focal glomerular changes were found in hsFlt-1-e15a -treated mice, which had higher urine albumin/creatinine ratios than controls (109.3±51.7μg/mg vs. 19.3±5.6μg/mg, p = 4.4x10^-2; GD18). Aortic ring assays showed a 46% lesser microvessel outgrowth in hsFlt-1-e15a-treated than in GFP-treated mice (p = 1.2x10^-2). Placental and fetal weights did not differ between the groups. One mouse with liver disease developed early-onset preeclampsia-like symptoms with intrauterine growth restriction (IUGR).</p><p>Conclusions</p><p>A mouse model of late-onset preeclampsia was developed with the overexpression of hsFlt-1-e15a, verifying the <i>in vivo</i> pathologic effects of this primate-specific, predominant placental sFlt-1 isoform. HsFlt-1-e15a induced early-onset preeclampsia-like symptoms associated with IUGR in a mouse with a liver disease. Our findings support that hsFlt-1-e15a is central to the terminal pathway of preeclampsia, and it can induce the full spectrum of symptoms in this obstetrical syndrome.</p></div