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Association between Obsessive-Compulsive Disorder and Dopamine Transporter Gene Polymorphism
SangWoo Yoo,SeJoo Kim,ChanHyung Kim 대한신경정신의학회 2006 PSYCHIATRY INVESTIGATION Vol.3 No.1
Objective: The definite causes of obsessive-compulsive disorder (OCD) are still unknown. Recently, evidence has been growing that OCD has a specific neurochemical and neuroanatomical basis. The most prevailing biological mechanism of OCD is the serotonin hypothesis. However, in addition to this main hypothesis suggesting serotonin abnormalities, many researchers have proposed that dopamine might also participate in the pathophysiology of OCD. Therefore, the aim of this study was to investigate the association between dopamine transporter (DAT1) polymorphisms and OCD. Methods: 115 OCD patients and 160 normalcontrols participated in this study. Genomic DNA was extracted from their blood, and a comparison of the genotypes and allele frequencies of the DAT1 polymorphism between the OCD group and control group was made. The genotypes of DAT1 are classified into 10/10-repeats and non-10/10 repeats. Results: In this case-control study, there were no statistical differences in the observed genotype distributions or allele frequencies of the DAT1 polymorphism between these two groups. Moreover, there were no significant differences in the total Y-BOCS, CGI-OC, GAF or total HDRS scores between the patients with 10/10-repeat and non-10/10 repeat genotypes. Conclusion: In conclusion, DAT1 polymorphisms do not appear to be associated with the development of OCD, the severity of OC or depressive symptoms, or the general functioning
리튬이온 배터리 수명추정을 위한 용량예측 머신러닝 모델의 성능 비교
유상우(Sangwoo Yoo),신용범(Yongbeom Shin),신동일(Dongil Shin) 한국가스학회 2020 한국가스학회지 Vol.24 No.6
리튬이온 배터리(LIB)는 다른 배터리에 비해 수명이 길고, 에너지 밀도가 높으며, 자체 방전율이 낮아, 에너지 저장장치(ESS)로 선호되고 있다. 하지만, 2017~2019년 기간 동안 국내에서만도 28건의 화재사고가 발생하였으며, LIB의 운영 중 안전성 및 신뢰성을 보장하기 위해 LIB의 정확한 용량추정은 필수요소이다. 본 연구에서는 LIB의 충방전 cycle에 따른 용량변화를 예측하는 기계학습 기반 모델의 설계에 있어 중요한 요소인 최적 머신러닝 모델의 선정을 위해, Decision Tree, 앙상블학습법, Support Vector Regression, Gaussian Process Regression (GPR) 각각을 이용한 예측모델을 구현하고 성능비교를 실시하였다. 학습을 위해 NASA에서 제공하는 시험데이터를 사용하였으며, GPR이 가장 좋은 예측성능을 보였다. 이를 바탕으로 추가 시험데이터 학습을 통해 개선된 LIB 용량예측과 잔여 수명추정 모델을 개발하여, 운영 중 이상 감지 및 모니터링 성능을 높여, 보다 안전하고 안정된 ESS 운용에 활용하고자 한다. Lithium-ion batteries (LIBs) have a longer lifespan, higher energy density, and lower self-discharge rates than other batteries, therefore, they are preferred as an Energy Storage System (ESS). However, during years 2017–2019, 28 ESS fire accidents occurred in Korea, and accurate capacity estimation of LIB is essential to ensure safety and reliability during operations. In this study, data-driven modeling that predicts capacity changes according to the charging cycle of LIB was conducted, and developed models were compared their performance for the selection of the optimal machine learning model, which includes the Decision Tree, Ensemble Learning Method, Support Vector Regression, and Gaussian Process Regression (GPR). For model training, lithium battery test data provided by NASA was used, and GPR showed the best prediction performance. Based on this study, we will develop an enhanced LIB capacity prediction and remaining useful life estimation model through additional data training, and improve the performance of anomaly detection and monitoring during operations, enabling safe and stable ESS operations.
The N-terminal Unnatural Amino Acid Incorporation with Orthogonal Translation System Engineering
Sangwoo LEE,Byeong Sung LEE,Woon Jong CHOI,Tae Hyeon YOO 한국생물공학회 2021 한국생물공학회 학술대회 Vol.2021 No.10
Addition of unnatural amino acids to protein used in various purposes. By orthogonal translation systems, unnatural amino acids are incorporated to target proteins. In modifying N-terminal position of protein, unnatual amino acid can be incorporated without affecting protein structure. But used orthognal translational pair, Methanococcus jannaschii tyrosyl tRNA and aminoacyl-tRNA synthetase only recoginzes amber codons in elogation position. In this study, focusing on differences of initiator tRNA and elongator tRNA, we engineered Mj tRNA to bind formyltransferase and initiation factor 2 which participate in the assembly of aminoacylated initiator tRNA and new tRNA pair system incorporated unnatural amino acid at N-terminal position of target protein in overexpression of formyltrasferase. The engineered Mj tRNA / aminoacyl-tRNA synthetase pair could be used for protein engineering of incorporation of unnatural amino acid to N-terminal position.
The N-terminal Incorporation of Unnatural Amino Acid with Bacterial Translation System Engineering
Sangwoo LEE,Byeong Sung LEE,Woon Jong CHOI,Tae Hyeon YOO 한국생물공학회 2021 한국생물공학회 학술대회 Vol.2021 No.4
Addition of unnatural amino acids to protein used in various purposes. By orthogonal translation systems, unnatural amino acids are incorporated to target proteins. In particular N-terminal position of protein modification, unnatual amino acid can be incorporated without affecting protein structure. But fomal orthognal translational pair, Methanococcus jannaschii tyrosyl tRNA and aminoacyl-tRNA synthetase only recognizes amber codons in elogation position. In this study, focusing on differences of initiator tRNA and elongator tRNA, we engineered Mj tRNA to interact formyltransferase and initiation factor 2 which participate in the assembly of aminoacylated initiator tRNA and new tRNA pair system incorporated unnatural amino acid at N-terminal position of target protein in overexpression of formyltrasferase. The engineered Mj tRNA / aminoacyl-tRNA synthetase pair could be used for protein engineering of incorporation of unnatural amino acid to N-terminal position.