Maebashi, Japan

Maebashi Institute of Technology is a public university at Maebashi, Gunma, Japan. The predecessor of the school was founded in 1952 and was chartered as a university in 1997. Wikipedia.


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Satsu H.,Maebashi Institute of Technology
Bioscience, Biotechnology and Biochemistry | Year: 2017

The intestinal tract comes into direct contact with the external environment despite being inside the body. Intestinal epithelial cells, which line the inner face of the intestinal tract, have various important functions, including absorption of food substances, immune functions such as cytokine secretion, and barrier function against xenobiotics by means of detoxification enzymes. It is likely that the functions of intestinal epithelial cells are regulated or modulated by these components because they are frequently exposed to food components at high concentrations. This review summarizes our research on the interaction between intestinal epithelial cells and food components at cellular and molecular levels. The influence of xenobiotic contamination in foods on the cellular function of intestinal epithelial cells is also described in this review. © 2016 Japan Society for Bioscience, Biotechnology, and Agrochemistry.


Kamal A.H.M.,Japan National Agriculture and Food Research Organization | Rashid H.,Mohammad Ali Jinnah University | Sakata K.,Maebashi Institute of Technology | Komatsu S.,Japan National Agriculture and Food Research Organization
Journal of Proteomics | Year: 2015

Flooding stress causes growth inhibition and ultimately death in most crop species by limiting of energy production. To better understand plant responses to flooding stress, here, flooding-responsive proteins in the cotyledons of soybean were identified using a gel-free quantitative proteomic approach. One hundred forty six proteins were commonly observed in both control and flooding-stressed plants, and 19 were identified under only flooding stress conditions. The main functional categories were protein and development-related proteins. Protein-protein interaction analysis revealed that zincin-like metalloprotease and cupin family proteins were found to highly interact with other proteins under flooding stress. Plant stearoyl acyl-carrier protein, ascorbate peroxidase 1, and secretion-associated RAS superfamily 2 were down-regulated, whereas ferretin 1 was up-regulated at the transcription level. Notably, the levels of all corresponding proteins were decreased, indicating that mRNA translation to proteins is impaired under flooding conditions. Decreased levels of ferritin may lead to a strong deregulation of the expression of several metal transporter genes and over-accumulation of iron, which led to increased levels of reactive oxygen species, resulting to detoxification of these reactive species. Taken together, these results suggest that ferritin might have an essential role in protecting plant cells against oxidative damage under flooding conditions.Biological significance. This study reported the comparative proteomic analysis of cotyledon of soybean plants between non-flooding and flooding conditions using the gel-free quantitative techniques. Mass spectrometry analysis of the proteins from cotyledon resulted in the identification of a total of 165 proteins under flooding stress. These proteins were assigned to different functional categories, such as protein, development, stress, redox, and glycolysis. Therefore, this study provides not only the comparative proteomic analysis but also the molecular mechanism underlying the flooding responsive protein functions in the cotyledon. © 2014 Elsevier B.V.


Zhong N.,Maebashi Institute of Technology | Zhong N.,Beijing University of Technology | Li Y.,Queensland University of Technology | Wu S.-T.,Asia University, Taiwan
IEEE Transactions on Knowledge and Data Engineering | Year: 2012

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance. © 2011 IEEE.


Tao X.,Queensland University of Technology | Li Y.,Queensland University of Technology | Zhong N.,Maebashi Institute of Technology
IEEE Transactions on Knowledge and Data Engineering | Year: 2011

As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or a user local information. In this paper, a personalized ontology model is proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering. The results show that this ontology model is successful. © 2011 IEEE.


Asakawa T.,Maebashi Institute of Technology | Muraki H.,Tohoku University | Watamura S.,Tohoku University
International Journal of Modern Physics A | Year: 2014

The properties of the D-brane fluctuations are investigated using the two types of deformation of the Dirac structure, based on the B-transformation and the β-transformation, respectively. The former gives the standard gauge theory with two-form field strength. The latter gives a nonstandard gauge theory on the Poisson manifold with bivector field strength and the vector field as a gauge potential, where the gauge symmetry is a diffeomorphism generated by the Hamiltonian vector field. The map between the two gauge theories is also constructed with the help of Moser's Lemma and the Magnus expansion. We also investigate the relation to the gauge theory on the noncommutative D-branes. © World Scientific Publishing Company.


Li Y.,Queensland University of Technology | Algarni A.,Queensland University of Technology | Zhong N.,Maebashi Institute of Technology
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining | Year: 2010

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences, but many experiments do not support this hypothesis. The innovative technique presented in paper makes a breakthrough for this difficulty. This technique discovers both positive and negative patterns in text documents as higher level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the higher level features. Substantial experiments using this technique on Reuters Corpus Volume 1 and TREC topics show that the proposed approach significantly outperforms both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and pattern based methods on precision, recall and F measures. © 2010 ACM.


Sato M.,Maebashi Institute of Technology
Procedia Computer Science | Year: 2012

Promoters are key control regions for the transcription regulation of genes, usually lying upstream of the genes they control. Promoter prediction is worthwhile not only for the detection of orphan genes but also for understanding the mechanisms that regulate gene expression. Promoter prediction therefore remains one of the primary challenges subjects in bioinformatics in the post-genome era. Many methods are used for promoter prediction, such as the presence of the CpG islands, sequence motifs of transcription factor binding sites, and the statistical and chemo-physical properties in the vicinity of transcription start sites. Among these strategies, we have focused on a method which employs wavelet analysis and support vector machine for promoter prediction. The wavelet analysis is based on localized wave packets characterized by both a range of frequency and a location. In our scheme, information from promoter and non-promoter regions is converted to wavelet space as a positive and a negative set, respectively, and the 2 sets are subsequently used to train a support vector machine. Finally, the support vector machine is utilized for promoter prediction. In this study, we improved the coding method of our prediction strategy and analysed a new set of test data. © 2012 Published by Elsevier B.V.


Sato M.,Maebashi Institute of Technology
Procedia Computer Science | Year: 2011

A large number of genomes have been sequenced and the number is growing rapidly. It is crucial to improve sequence annotation, including promoter prediction. Many aspects of DNA sequences have been examined and used in promoter prediction. In particular, the physical instability correlating GC content in the promoter region has been focus of many studies. To extract the GC signals of a promoter region in a genome sequence, we adopt a scheme combining wavelet analysis and a support vector machine (SVM). In this scheme, we take a simplified way to quantize and extract chemo-physical properties of a DNA sequence. Four types of DNA are converted to binary form with respect to G and C or not. The sequences are expanded to two dimensional spaces, frequency and location, by discrete wavelet transformation (DWT). The fixed length of the promoter and randomly selected DNA segments are prepared as the positive and negative training data, respectively. The two types of data are converted by DWT and learned by a SVM. Then, previously unknown DNA segments are classified as promoter or non-promoter by the trained SVM. © 2011 Published by Elsevier Ltd.


Usui H.,Maebashi Institute of Technology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

When we use TeX to edit a document, it is sometimes necessary to place the figure of a preferred shape into a suitable position. In this presentation, we propose a method using KeTCindy for this purpose. KeTCindy is a plug-in to Cinderella that converts the procedure to generate geometric shapes into TeX readable code to generate the corresponding image on TeX final output. One merit of using KeTCindy is its interactive character. On the Cinderella screen, a user can control the shape of the figure as desired. When we place the resulting image at the exterior side of text part, simple conversion to TeX graphical image through KeTCindy is sufficient. However, when it is necessary to place it onto the text part, some extra elaboration is necessary to ensure that both the text part and the generated figure are finely balanced. The key idea is making the screen of Cinderella semi-transparent using software named feewhee. © Springer International Publishing Switzerland 2016.


Saika Y.,Maebashi Institute of Technology
Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016 | Year: 2016

We construct a technique of time-series prediction for power consumption in small-sized systems via the mean-field theory (MFT) which approximates Bayesian inference using the expected a posterior (EAP) estimation. Here, we estimate the power consumption as an expectation of an Ising spin averaged over the Boltzmann factor of the Ising model under random fields. Here, we forecast time evolution of power consumption using multiple time-series of power consumptions similar to the target data due to the correlation coefficients, timetable and environmental data, such as the temperature at the target system. Then, we estimate performance of the MFT for a typical small-sized system. Here, we find that controlling fluctuations around the MAP solution is essential for accurate prediction tuning the parameter corresponding to the absolute temperature in statistical physics, and also that accuracy is improved by tuning parameters which correspond to coupling constant between Ising spins and random fields. Then, we find that the accuracy is further improved by tuning random fields which control effects of the timetable and temperatures at the target system. © 2016 IEEE.

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