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Investigating the role of Sirtuins in cell reprogramming
( Jaein Shin ),( Junyeop Kim ),( Hanseul Park ),( Jongpil Kim ) 생화학분자생물학회(구 한국생화학분자생물학회) 2018 BMB Reports Vol.51 No.10
Cell reprogramming has been considered a powerful technique in the regenerative medicine field. In addition to diverse its strengths, cell reprogramming technology also has several drawbacks generated during the process of reprogramming. Telomere shortening caused by the cell reprogramming process impedes the efficiency of cell reprogramming. Transcription factors used for reprogramming alter genomic contents and result in genetic mutations. Additionally, defective mitochondria functioning such as excessive mitochondrial fission leads to the limitation of pluripotency and ultimately reduces the efficiency of reprogramming. These problems including genomic instability and impaired mitochondrial dynamics should be resolved to apply cell reprograming in clinical research and to address efficiency and safety concerns. Sirtuin (NAD+-dependent histone deacetylase) has been known to control the chromatin state of the telomere and influence mitochondria function in cells. Recently, several studies reported that Sirtuins could control for genomic instability in cell reprogramming. Here, we review recent findings regarding the role of Sirtuins in cell reprogramming. And we propose that the manipulation of Sirtuins may improve defects that result from the steps of cell reprogramming. [BMB Reports 2018; 51(10): 500-507]
딥러닝 기반 장애물 인식을 위한 가상환경 및 데이터베이스 구축
이재인(JaeIn Lee),곽기성(Gisung Gwak),김경수(KyongSu Kim),강원율(WonYul Kang),신대영(DaeYoung Shin),황성호(Sung-Ho Hwang) 유공압건설기계학회 2021 드라이브·컨트롤 Vol.18 No.4
This study proposes a method for creating learning datasets to recognize obstacles using deep learning algorithms in automated construction machinery or an autonomous vehicle. Recently, many researchers and engineers have developed various recognition algorithms based on deep learning following an increase in computing power. In particular, the image classification technology and image segmentation technology represent deep learning recognition algorithms. They are used to identify obstacles that interfere with the driving situation of an autonomous vehicle. Therefore, various organizations and companies have started distributing open datasets, but there is a remote possibility that they will perfectly match the user"s desired environment. In this study, we created an interface of the virtual simulator such that users can easily create their desired training dataset. In addition, the customized dataset was further advanced by using the RDBMS system, and the recognition rate was improved.