• 제목/요약/키워드: Research laboratory

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Antibacterial Compounds from Korean Marine Sponges

  • Nam, Sang-Jip;Kim, Hee-Young;Kim, Young-Shin;Choi, Hyuk-Jae;Kim, Soo-Whan;Oh, Ki-Bong;Rhee, Joon-Haeng;Kang, Heon-Joong
    • 한국생물공학회:학술대회논문집
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    • 한국생물공학회 2003년도 생물공학의 동향(XIII)
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    • pp.67-67
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    • 2003

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정상흐름하 천해역 수로에서의 저밀도수 표층방출 모델링 (Modeling buoyant surface discharges in a shallow channel with steady flow)

  • Jung, Kyung-Tae;Jin, Jae-Youll;Park, Jin-Soon;Yum, Ki-Dai;Park, Chang-Wook;Kim, Sung-Dae;Suk Yoon
    • 한국해안해양공학회:학술대회논문집
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    • 한국해안해양공학회 2002년도 한국해안해양공학발표논문집 Proceedings of Coastal and Ocean Engineering in Korea
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    • pp.191-197
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    • 2002

  • The prediction of the dynamic behaviors of buoyant water discharges into a large volume of water bodies, the flows of water accompanying the density differences due to temperature differences and sometimes also to salinity differences, have attracted great concern over several decades. Heated water surface discharges from power plants and freshwater discharges in estuaries are typical examples of the buoyant flows. (omitted)

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Increase of Membrane Potential by Ginsenosides in Prostate Cancer and Glioma cells

  • Lee, Yun-Kyung;Im, Young-Jin;Kim, Yu-Lee;Sacket Santosh J.;Lim, Sung-Mee;Kim, Kye-Ok;Kim, Hyo-Lim;Ko, Sung-Ryong;Lm, Dong-Soon
    • Journal of Ginseng Research
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    • 제30권2호
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    • pp.70-77
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    • 2006

  • Ginseng has an anti-cancer effect in several cancer models. As a mechanism study of ginsenoside-induced growth inhibition in cancer cells, we measured change of membrane potential in prostate cancer and glioma cells by ginsenosides, active constituents of ginseng. Membrane potential was estimated by measuring fluorescence change of DiBAC-Ioaded cells. Among 11 ginsenosides tested, ginsenosides $Rb_2$, $Rg_3$, and $Rh_2$ increased significantly and robustly the membrane potential in a concentration-dependent manner in prostate cancer and glioma cells. Ginsenosides Rc, Ro, and $Rb_1$ slightly increased membrane potential. The ginsenoside-induced membrane potential increase was not affected by treatment with pertussis toxin or U73122. The ginsenoside-induced membrane potential increase was not diminished in $Na^+$-free or $HCO_3^-$-free media. Furthermore, the ginsenoside-induced increase of membrane potential was not changed by EIPA (5-(N-ethyl-N-isopropyl)-amiloride), SITS (4-acetoamido-4'-isothiocyanostilbene-2,2'-disulfonic acid), and omeprazole. In summary, ginsenosides $Rb_2$, $Rg_3$, and $Rh_2$ increased membrane potential in prostate cancer and glioma cells in a GPCR-independent and $Na^+$ independent manner.

  • https://doi.org/10.5142/JGR.2006.30.2.070 인용 PDF KSCI

Tokamak plasma disruption precursor onset time study based on semi-supervised anomaly detection

  • X.K. Ai;W. Zheng;M. Zhang;D.L. Chen;C.S. Shen;B.H. Guo;B.J. Xiao;Y. Zhong;N.C. Wang;Z.J. Yang;Z.P. Chen;Z.Y. Chen;Y.H. Ding;Y. Pan
    • Nuclear Engineering and Technology
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    • 제56권4호
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    • pp.1501-1512
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    • 2024

  • Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.

  • https://doi.org/10.1016/j.net.2023.12.004 인용 PDF