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Lee, Gwang Jin,Lee, Jeong Hee,Park, Jeong Hill,Kwon, Sung Won,Lim, Johan,Lee, Sungim,Lee, Jeongmi Elsevier 2018 Analytica chimica acta Vol.1037 No.-
<P><B>Abstract</B></P> <P>An intuitive and practical way to control chemical equivalence of secondary metabolites in herbal materials based on chromatographic fingerprints deserves a thorough discussion, yet it is relatively unexplored. For the first time, we propose a mixture of three similarity indices, the congruence coefficient, the average of the peak area ratios, and the larger value between the maximum peak area ratio and the reciprocal of the minimum peak area ratio, to make up for the weak points of some widely used similarity indices and to evaluate the chemical equivalence of two fingerprints from various perspectives. The three similarity values are fed into a three-dimensional kernel density estimation to determine the quality of herbal materials. This estimation enables precise detection of anomalies in the absence of prior quality determination experience. Forty <I>Atractylodes</I> samples similar in appearance and indiscriminately used for medical purposes were used to demonstrate the effectiveness of the developed approach. After a reference sample was postulated, a quality assessment of the 40 samples was performed using the three similarity values and the estimated kernel density. The samples that were judged by the developed approach to be of good quality were compared with those chosen by the most popular approach using decision criterion of a single similarity index. The benefits of the proposed approach were evident in that the qualified samples had the composition ratio and individual concentrations of multi-components closer to those of the reference in general, and their inter-sample deviation was significantly smaller.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A novel method was developed to assess chemical equivalence of herbal materials. </LI> <LI> A mixture of three similarity indices from chromatographic fingerprints was used. </LI> <LI> Three-dimensional kernel density estimation was applied in herbal fingerprints. </LI> <LI> The new method was successfully applied to quality assessment of 40 real samples. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
Lee, Seul Ji,Yi, TacGhee,Ahn, Soo Hyun,Lim, Dong Kyu,Kim, Si-na,Lee, Hyun-Joo,Cho, Yun-Kyoung,Lim, Jae-Yol,Sung, Jong-Hyuk,Yun, Jeong-Ho,Lim, Johan,Song, Sun U.,Kwon, Sung Won Elsevier 2018 Analytica Chimica Acta Vol.1024 No.-
<P><B>Abstract</B></P> <P>Mesenchymal stem cells (MSCs) are a promising therapeutic option for cell-based therapy due to their immunomodulatory and regenerative properties. They can be isolated from various adult tissues, including bone marrow, fat, dental tissue, and glandular tissue. Although they share common characteristics, little is known about the biological differences between MSC populations derived from different tissues. In this study, we used MS to compare the endogenous metabolite level in the human MSCs originating from the bone marrow, adipose tissue, periodontal ligaments, and salivary glands. Using an optimized metabolomics technique, we verified that human MSCs exhibit differences in the endogenous metabolite level depending on their source material, while the multivariate analysis showed that 5 lysophosphatidylcholines and 3 lysophosphatidylethanolamines can serve as markers for the discrimination between MSC sources and may be related to differences in their differentiation capacity. These results may significantly contribute to further mechanistic studies on the MSCs and provide novel insights into the properties and optimal usage of MSCs from different tissues.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Endogenous metabolite level of human mesenchymal stem cells (MSCs) was evaluated. </LI> <LI> MSCs from different tissue sources were compared. </LI> <LI> Metabolic markers to distinguish MSCs by source tissue were identified. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
이요한 ( Johan Lee ),박춘우 ( Chun Woo Park ),김문권 ( Moon Kwon Kim ),최광선 ( Kwang Sun Choi ),김수동 ( Soo Dong Kim ) 한국정보처리학회 2013 한국정보처리학회 학술대회논문집 Vol.20 No.2
IoT(Internet-of-Things)는 사람과 물리적 사물이 언제, 어디서나 소통할 수 있는 컴퓨팅 패러다임으로, IoT 디바이스의 역량이 높아짐에 따라, IoT 디바이스를 이용한 IoT 애플리케이션 개발에 관심이 높아지고 있다. 하지만 IoT 디바이스가 가지는 통신 프로토콜 이질성, API 이질성, 데이터 형식이질성, 프로그래밍 언어 이질성이 IoT 애플리케이션 개발의 어려움으로 작용하고 있다. 본 논문은 이러한 이질성들을 분석하고 각 이질성을 해결하기 위한 설계 기법을 제안하고 실제로 이를 적용하여 IoT 애플리케이션을 설계 및 구현 함으로써 도출한 이질성 해결의 중요성과 제안한 설계 기법의 실효성을 검증한다.
이중 다단계 일반화 선형모형 적합을 위한 SRC-stat의 사용
노맹석,하일도,이영조,임요한,이재용,오희석,신동완,이상구,서진욱,박용태,조성준,박종헌,김유경,유경상,Noh, Maengseok,Ha, Il Do,Lee, Youngjo,Lim, Johan,Lee, Jaeyong,Oh, Heeseok,Shin, Dongwan,Lee, Sanggoo,Seo, Jinuk,Park, Yonhtae,Cho, Sungzoon,Park 한국통계학회 2015 응용통계연구 Vol.28 No.2
We introduce how to fit random effects models via a SRC-Stat statistical package. This package has been developed to fit double hierarchical generalized linear models where mean and dispersion parameters for the variance of random effects and residual variance (overdispersion) can be modeled as random-effect models. The estimates of fixed effects, random effects and variances are calculated by a hierarchical likelihood method. We illustrate the use of our package with practical data-sets. 본 논문에서는 SRC-Stat 통계패키지를 이용하여 변량효과를 적합하는 방법에 대해서 소개하고자 한다. 본 패키지를 통하여 단변량 평균 뿐만 아나리 산포 및 분산에도 변량효과를 고려하는 이중 다단계 일반화 선형모형을 적합할 수 있다. 고정효과 및 변량효과의 추정치는 다단계 우도 방법을 이용하고 있으며, 실제 자료 적합을 통해 패키지의 사용법에 대해서 설명하고자 한다.
하일도,노맹석,이영조,임요한,이재용,오희석,신동완,이상구,서진욱,박용태,조성준,박종헌,김유경,유경상,Ha, Il Do,Noh, Maengseok,Lee, Youngjo,Lim, Johan,Lee, Jaeyong,Oh, Heeseok,Shin, Dongwan,Lee, Sanggoo,Seo, Jinuk,Park, Yonhtae,Cho, Sungzoon,Park 한국통계학회 2015 응용통계연구 Vol.28 No.2
본 논문에서는 SRC-Stat 통계패키지를 이용하여 생존자료를 분석하는 방법을 소개한다. 본 패키지는 단변량 생존 자료 분석을 위한 콕스의 비례위험모형 뿐만아니라, 다변량 생존자료분석을 위한 공통 및 지분 프레일티 모형과 같은 고급 생존분석법을 제공한다. 잘 알려져 있는 실제자료의 사용을 통해 본 패키지의 유용성을 예증한다. In this paper we introduce how to analyze survival data via a SRC-Stat statistical package. This provides classical survival analysis (e.g. Cox's proportional hazards models for univariate survival data) as well as advanced survival analysis such as shared and nested frailty models for multivariate survival data. We illustrate the use of our package with practical data sets.