• Bulletin of the Korean Chemical Society
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• 제35권1호
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• pp.62-68
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• 2014

Coptis chinensis 3'-hydroxy-N-methylcoclaurine 4'-O-methyltransferase (HOMT), an essential enzyme in the berberine biosynthetic pathway, catalyzes the methylation of 3'-hydroxy-N-methylcoclaurine (HMC) producing reticuline. A 3D model of HOMT was constructed by homology modeling and further subjected to docking with its ligands and molecular dynamics simulations. The 3D structure of HOMT revealed unique structural features which permitted the methylation of HMC. The methylation of HMC was proposed to proceed by deprotonation of the 4'-hydroxyl group via His257 and Asp258 of HOMT, followed by a nucleophilic attack on the SAM-methyl group resulting in reticuline. HOMT showed high substrate specificity for methylation of HMC. The study evidenced that Gly117, Thr312 and Asp258 in HOMT might be the key residues for orienting substrate for specific catalysis.

• 한국정보처리학회논문지
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• 제5권9호
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• pp.2213-2223
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• 1998

하이퍼미디어 응용들은 다양한 미디어들을 동적이고 비 순차적으로 연결하고 동기화 등의 시간성을 지원해야 한다. 최근 이러한 하이퍼미디어 문서를 표현하는 메타 언어로 HyTime(Hypermedia/Time based Structuring language)이 사용되고 있다. 그런데 HyTime을 이용하여 하이퍼미디어 문서를 기술하고 관리하는 일은 비전문가들에게 상당히 어렵다. 본 논문은 하이퍼미디어 문서의 구조를 기술하는 HyTime DTD를 쉽게 설계할 수 있게 하는 다이아그래밍 기법인 HOMT(HyTime support XOMT)를 제안한다. HOMT는 SGML DTD를 설계하기 위해 제안된 XOMT(eXtended OMT)를 확장하는 방법으로 고안되었다.

• International Journal of Internet, Broadcasting and Communication
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• 제15권3호
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• pp.211-218
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• 2023

Due to the implementation of COVID-19 distancing, interest and users in 'home training' are rapidly increasing. Therefore, the purpose of this study is to identify the perception of 'home training' through big data analysis on social media channels and provide basic data to related business sector. Social media channels collected big data from various news and social content provided on Naver and Google sites. Data for three years from March 22, 2020 were collected based on the time when COVID-19 distancing was implemented in Korea. The collected data included 4,000 Naver blogs, 2,673 news, 4,000 cafes, 3,989 knowledge IN, and 953 Google channel news. These data analyzed TF and TF-IDF through text mining, and through this, semantic network analysis was conducted on 70 keywords, big data analysis programs such as Textom and Ucinet were used for social big data analysis, and NetDraw was used for visualization. As a result of text mining analysis, 'home training' was found the most frequently in relation to TF with 4,045 times. The next order is 'exercise', 'Homt', 'house', 'apparatus', 'recommendation', and 'diet'. Regarding TF-IDF, the main keywords are 'exercise', 'apparatus', 'home', 'house', 'diet', 'recommendation', and 'mat'. Based on these results, 70 keywords with high frequency were extracted, and then semantic indicators and centrality analysis were conducted. Finally, through CONCOR analysis, it was clustered into 'purchase cluster', 'equipment cluster', 'diet cluster', and 'execute method cluster'. For the results of these four clusters, basic data on the 'home training' business sector were presented based on consumers' main perception of 'home training' and analysis of the meaning network.