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Phenolic Compounds from <i>Artemisia iwayomogi</i> and Their Effects on Osteoblastic MC3T3-E1 Cells
Ding, Yan,Liang, Chun,Yang, Seo Young,Ra, Jeong Chan,Choi, Eun Mi,Kim, Jeong-Ah,Kim, Young Ho Pharmaceutical Society of Japan 2010 Biological & pharmaceutical bulletin Vol.33 No.8
<P>One new (4) and twelve known phenolic compounds (1—3, 5—13) were isolated from a 70% MeOH extract of the aerial parts of <I>Artemisia iwayomogi</I>. The new compound was identified as 7,8-dimethoxy-coumarin-9-<I>O</I>-(6′-<I>O</I>-(<I>E</I>)-coumaroyl)-β-<SMALL>D</SMALL>-glucopyranoside (4) and named iwayomin. The effects of compounds 1—13 on the function of osteoblastic MC3T3-E1 cells were examined by evaluating cell viability, alkaline phosphatase (ALP) activity, collagen synthesis, and mineralization in the presence of each compound. Compounds 3, 4, 7, and 9 showed potential in stimulating osteoblastic bone formation and may be useful for the prevention and/or treatment of osteoporosis.</P>
Fam83h is Associated with Intracellular Vesicles and ADHCAI
Ding, Y.,Estrella, M.R.P.,Hu, Y.Y.,Chan, H.L.,Zhang, H.D.,Kim, J.-W.,Simmer, J.P.,Hu, J.C.-C. SAGE Publications 2009 Journal of dental research Vol.88 No.11
<P>Defects in <I>FAM83H</I> on human chromosome 8q24.3 cause autosomal-dominant hypocalcified amelogenesis imperfecta (ADHCAI). <I>FAM83H</I> does not encode a recognizable signal peptide, so we predicted that the Fam83h protein functions within the cell. We tested this hypothesis by constitutively expressing mouse Fam83h with green fluorescent protein (GFP) fused to its C-terminus in HEK293 and HeLa cell lines. Green fluorescent signal from the Fam83h-GFP fusion protein was associated with perinuclear vesicles, usually in the vicinity of the Golgi apparatus. No signal was observed within the nucleus. In addition, we identified <I> FAM83H</I> nonsense mutations in Hispanic (C1330C>T; p.Q444X) and Caucasian (c.1192C>T; p.Q398X) families with ADHCAI. We conclude that Fam83h localizes in the intracellular environment, is associated with vesicles, and plays an important role in dental enamel formation. <I>FAM83H</I> is the first gene involved in the etiology of amelogenesis imperfecta (AI) that does not encode a secreted protein.</P>
Chemical Constituents from Artemisia iwayomogi Increase the Function of Osteoblastic MC3T3-E1 Cells
Yan Ding,Eun Mi Choi,김영호,Chun Liang,Jeong Chan Ra 한국생약학회 2009 Natural Product Sciences Vol.15 No.4
Chemical investigation of the aerial parts of Artemisia iwayomogi has afforded five glycoside compounds. Their chemical structures were characterized by spectroscopic methods to be turpinionoside A (1), (Z)-3-hexenyl O-α-arabinopyranosyl-(1→ 6)-O-β-D-glucopyranoside (2), (Z)-5'-hydroxyjasmone 5'-O-β-Dglucopyranoside (3), (–)-syringaresinol-4-O-β-D-glucopyranoside (4), and methyl 3,5-di-O-caffeoyl quinate (5). All of them were isolated for the first time from Artemisia species. The effect of compounds 1 - 5 on the function of osteoblastic MC3T3-E1 cells was examined by checking the cell viability, alkaline phosphatase (ALP) activity, collagen synthesis, and mineralization. Turpinionoside A (1) significantly increased the function of osteoblastic MC3T3-E1 cells. Cell viability, ALP activity, collagen synthesis, and mineralization were increased up to 117.2% (2 µM), 110.7% (0.4 µM), 156.0% (0.4 µM), and 143.0 % (2 µM), respectively.
Xuan-Ze Ding,Young-Chan Lee 대한산업경영학회 2018 산업융합연구 Vol.16 No.4
개인신용평가는 은행이 대출을 승인할 때 수익성 있는 의사결정을 적절히 유도할 수 있는 효과적인 도구이다. 최근 많은 분류 알고리즘 및 모델이 개인신용평가에 사용되고 있다. 개인신용평가 기법은 대체로 통계적 방법과 비 통계 적 방법으로 구분된다. 통계적 방법에는 선형회귀분석, 판별분석, 로지스틱 회귀분석, 의사결정나무 등이 포함된다. 비 통 계적 방법에는 선형계획법, 신경망, 유전자 알고리즘 및 Support Vector Machines 등이 포함된다. 그러나 신용평가모형 개발을 위해 어떠한 방법이 최선인지에 관해서는 일관된 결론을 내리기는 어렵다. 본 논문에서는 중국 금융기관의 개인 신용 데이터를 사용하여 가장 대표적인 신용평가 기법인 로지스틱 회귀분석, 신경망 그리고 Support Vector Machines의 성능을 비교하고자 한다. 구체적으로, 세 가지 모형을 각각 구축하여 고객을 분류하고 분석 결과를 비교하였다. 분석결과 에 따르면, Support Vector Machines이 로지스틱 회귀분석과 신경망보다 더 나은 성능을 가지는 것으로 나타났다. Personal credit scoring is an effective tool for banks to properly guide decision profitably on granting loans. Recently, many classification algorithms and models are used in personal credit scoring. Personal credit scoring technology is usually divided into statistical method and non-statistical method. Statistical method includes linear regression, discriminate analysis, logistic regression, and decision tree, etc. Non-statistical method includes linear programming, neural network, genetic algorithm and support vector machine, etc. But for the development of the credit scoring model, there is no consistent conclusion to be drawn regarding which method is the best. In this paper, we will compare the performance of the most common scoring techniques such as logistic regression, neural network, and support vector machines using personal credit data of the financial institution in China. Specifically, we build three models respectively, classify the customers and compare analysis results. According to the results, support vector machine has better performance than logistic regression and neural networks.
Optimal wind-induced load combinations for structural design of tall buildings
C.M. Chan,F. Ding,K.T. Tse,M.F. Huang,K.M. Shum,K.C.S. Kwok 한국풍공학회 2019 Wind and Structures, An International Journal (WAS Vol.29 No.5
Wind tunnel testing technique has been established as a powerful experimental method for predicting wind-induced loads on high-rise buildings. Accurate assessment of the design wind load combinations for tall buildings on the basis of wind tunnel tests is an extremely important and complicated issue. The traditional design practice for determining wind load combinations relies partly on subjective judgments and lacks a systematic and reliable method of evaluating critical load cases. This paper presents a novel optimization-based framework for determining wind tunnel derived load cases for the structural design of wind sensitive tall buildings. The peak factor is used to predict the expected maximum resultant responses from the correlated three-dimensional wind loads measured at each wind angle. An optimized convex hull is further developed to serve as the design envelope in which the peak values of the resultant responses at any azimuth angle are enclosed to represent the critical wind load cases. Furthermore, the appropriate number of load cases used for design purposes can be predicted based on a set of Pareto solutions. One 30-story building example is used to illustrate the effectiveness and practical application of the proposed optimization-based technique for the evaluation of peak resultant wind-induced load cases.
딩쉬엔저,이영찬,Ding, Xuan-Ze,Lee, Young-Chan DAEHAN Society of Industrial Management 2018 산업융합연구 Vol.16 No.4
Personal credit scoring is an effective tool for banks to properly guide decision profitably on granting loans. Recently, many classification algorithms and models are used in personal credit scoring. Personal credit scoring technology is usually divided into statistical method and non-statistical method. Statistical method includes linear regression, discriminate analysis, logistic regression, and decision tree, etc. Non-statistical method includes linear programming, neural network, genetic algorithm and support vector machine, etc. But for the development of the credit scoring model, there is no consistent conclusion to be drawn regarding which method is the best. In this paper, we will compare the performance of the most common scoring techniques such as logistic regression, neural network, and support vector machines using personal credit data of the financial institution in China. Specifically, we build three models respectively, classify the customers and compare analysis results. According to the results, support vector machine has better performance than logistic regression and neural networks. 개인신용평가는 은행이 대출을 승인할 때 수익성 있는 의사결정을 적절히 유도할 수 있는 효과적인 도구이다. 최근 많은 분류 알고리즘 및 모델이 개인신용평가에 사용되고 있다. 개인신용평가 기법은 대체로 통계적 방법과 비 통계적 방법으로 구분된다. 통계적 방법에는 선형회귀분석, 판별분석, 로지스틱 회귀분석, 의사결정나무 등이 포함된다. 비 통계적 방법에는 선형계획법, 신경망, 유전자 알고리즘 및 Support Vector Machines 등이 포함된다. 그러나 신용평가모형 개발을 위해 어떠한 방법이 최선인지에 관해서는 일관된 결론을 내리기는 어렵다. 본 논문에서는 중국 금융기관의 개인 신용 데이터를 사용하여 가장 대표적인 신용평가 기법인 로지스틱 회귀분석, 신경망 그리고 Support Vector Machines의 성능을 비교하고자 한다. 구체적으로, 세 가지 모형을 각각 구축하여 고객을 분류하고 분석 결과를 비교하였다. 분석결과에 따르면, Support Vector Machines이 로지스틱 회귀분석과 신경망보다 더 나은 성능을 가지는 것으로 나타났다.