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Busan, South Korea

Kyungsung University is a private university in Busan, South Korea. It is located in the district of Nam-gu, southwest of the famous Haeundae beach. The campus is located near Kyungsung University-Pukyong National University Station on Line 2.The university was established by the late Reverend Dr. Kim Gil-Chang, a pioneer of Christianity in Korea. The university was originally called Kyungnam Teacher's College and established in 1955 under the ideals of Christian love and service, In 1979 the school was re-organized and renamed Pusan Industrial University. At the time of the founding of Pusan Industrial University, the General Construction Committee began work on expanding and improving the quality of the university's facilities. As a result, the university received official sanctioning as a general university in September,1983. The name of the university was changed to Kyungsung University in 1988 during the process of becoming an international university which can fulfill the needs of today's society.The university has 10 undergraduate colleges encompassing 7 different faculties and 54 departments at the time of 2011.There are seven graduate schools . In addition, seven affiliated organizations and seven affiliated research institutes have been established to aid teaching and research.. Kyungsung University's 700 employees and over 13,000 students are all working together to achieve the school's educational goals. Since 2002, the university has held a regional conference between South Korea and China regarding sustainable development of the Northeast Asia region.The Kyungsung University Museum has been involved in the excavation of important regional archaeological sites. In the 1990s, the university museum was responsible for excavating the Daeseong-dong site, a protohistoric cemetery Gimhae with high-status burials from the Proto–Three Kingdoms Period. Wikipedia.

Kim M.-S.,Pusan National University | Baek I.-H.,Kyungsung University
International journal of nanomedicine | Year: 2014

The aim of this study was to fabricate valsartan composite nanoparticles by using the supercritical antisolvent (SAS) process, and to evaluate the correlation between in vitro dissolution and in vivo pharmacokinetic parameters for the poorly water-soluble drug valsartan. Spherical composite nanoparticles with a mean size smaller than 400 nm, which contained valsartan, were successfully fabricated by using the SAS process. X-ray diffraction and thermal analyses indicated that valsartan was present in an amorphous form within the composite nanoparticles. The in vitro dissolution and oral bioavailability of valsartan were dramatically enhanced by the composite nanoparticles. Valsartan-hydroxypropyl methylcellulose-poloxamer 407 nanoparticles exhibited faster drug release (up to 90% within 10 minutes under all dissolution conditions) and higher oral bioavailability than the raw material, with an approximately 7.2-fold higher maximum plasma concentration. In addition, there was a positive linear correlation between the pharmacokinetic parameters and the in vitro dissolution efficiency. Therefore, the preparation of composite nanoparticles with valsartan-hydroxypropyl methylcellulose and poloxamer 407 by using the SAS process could be an effective formulation strategy for the development of a new dosage form of valsartan with high oral bioavailability. Source

We present an accurate and fast web server, WegoLoc for predicting subcellular localization of proteins based on sequence similarity and weighted Gene Ontology (GO) information. A term weighting method in the text categorization process is applied to GO terms for a support vector machine classifier. As a result, WegoLoc surpasses the state-of-the-art methods for previously used test datasets. WegoLoc supports three eukaryotic kingdoms (animals, fungi and plants) and provides human-specific analysis, and covers several sets of cellular locations. In addition, WegoLoc provides (i) multiple possible localizations of input protein(s) as well as their corresponding probability scores, (ii) weights of GO terms representing the contribution of each GO term in the prediction, and (iii) a BLAST E-value for the best hit with GO terms. If the similarity score does not meet a given threshold, an amino acid composition-based prediction is applied as a backup method. © The Author 2012. Published by Oxford University Press. All rights reserved. Source

In an earlier work, we proposed the chromatic dispersion monitoring technique of non-return to zero (NRZ) signal based on clock-frequency component (CFC) through numerical simulations. However, we have not yet shown any experimental demonstration or analytic derivation of it. In this paper, we show an experimental demonstration and analytic derivation of the proposed chromatic dispersion monitoring technique. We confirm that the experimental results and the analytic results correspond with the simulation results. We also demonstrate that monitoring range and accuracy can be improved by using a simple clock-extraction method. Source

Lee S.H.,Kyungsung University
Bulletin of the Korean Chemical Society | Year: 2013

In this study, molecular dynamics simulations of SPC/E (extended simple point charge) model have been carried out in the canonical NVT ensemble over the range of temperatures 300 to 550 K with and without Ewald summation. The quaternion method was used for the rotational motion of the rigid water molecule. Radial distribution functions gOO(r), gOH(r), and gHH(r) and self-diffusion coefficients D for SPC/E water were determined at 300-550 K and compared to experimental data. The temperature dependence on the structural and diffusion properties of SPC/E water was discussed. Source

Chi S.-M.,Kyungsung University
Biochemical and Biophysical Research Communications | Year: 2010

We develop a new weighting approach of gene ontology (GO) terms for predicting protein subcellular localization. The weights of individual GO terms, corresponding to their contribution to the prediction algorithm, are determined by the term-weighting methods used in text categorization. We evaluate several term-weighting methods, which are based on inverse document frequency, information gain, gain ratio, odds ratio, and chi-square and its variants. Additionally, we propose a new term-weighting method based on the logarithmic transformation of chi-square. The proposed term-weighting method performs better than other term-weighting methods, and also outperforms state-of-the-art subcellular prediction methods. Our proposed method achieves 98.1%, 99.3%, 98.1%, 98.1%, and 95.9% overall accuracies for the animal BaCelLo independent dataset (IDS), fungal BaCelLo IDS, animal Höglund IDS, fungal Höglund IDS, and PLOC dataset, respectively. Furthermore, the close correlation between high-weighted GO terms and subcellular localizations suggests that our proposed method appropriately weights GO terms according to their relevance to the localizations. © 2010 Elsevier Inc. Source

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