• Title/Summary/Keyword: sorting

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이동 벡터 모델을 이용한 표층 퇴적물의 이동 경로 분석

  • 김혜진;추용식;성효현
    • Proceedings of the KGS Conference
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    • 2003.11a
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    • pp.19-23
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    • 2003
  • 해안 퇴적 환경의 가장 기본적인 특징은 퇴적물의 입도 특성을 통해 파악할 수 있다. 퇴적물 특성을 정량적으로 표현하는 대표적인 방법은 입자 크기에 대한 값을 이용하여 평균입도(mean size), 분급도(sorting), 왜도(skewness), 첨도(kurtosis) 등의 퇴적물 입도 조직 변수를 구하여 표현하는 것이다. 퇴적 환경에서 입도 분포는 퇴적물의 이동과 퇴적의 동적 상태를 나타내는 기본적인 정보이다. (중략)

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The Unique Mechanism of SNX9 BAR Domain for Inducing Membrane Tubulation

  • Park, Joohyun;Zhao, Haiyan;Chang, Sunghoe
    • Molecules and Cells
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    • v.37 no.10
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    • pp.753-758
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    • 2014
  • Sorting nexin 9 (SNX9) is a member of the sorting nexin family of proteins and plays a critical role in clathrinmediated endocytosis. It has a Bin-Amphiphysin-Rvs (BAR) domain which can form a crescent-shaped homodimer structure that induces deformation of the plasma membrane. While other BAR-domain containing proteins such as amphiphysin and endophilin have an amphiphatic helix in front of the BAR domain which plays a critical role in membrane penetration, SNX9 does not. Thus, whether and how SNX9 BAR domain could induce the deformation of the plasma membrane is not clear. The present study identified the internal putative amphiphatic stretch in the $1^{st}$ ${\alpha}$-helix of the SNX9 BAR domain and proved that together with the N-terminal helix ($H_0$) region, this internal putative amphiphatic stretch is critical for inducing membrane tubulation. Therefore, our study shows that SNX9 uses a unique mechanism to induce the tubulation of the plasma membrane which mediates proper membrane deformation during clathrinmediated endocytosis.

Development of On-line Grading System Using Two Surface Images of Dried Oak Mushrooms (양면영상을 이용한 온라인 검표고 등급판정 시스템 개발)

  • Hwang, H.;Lee, C. H.;Kim, S. C.
    • Journal of Biosystems Engineering
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    • v.24 no.2
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    • pp.153-158
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    • 1999
  • As a basic research for the development of the automatic grading and sorting system for dried oak mushrooms, the device to acquire both cap and gill side images of mushroom has been developed and neural network based side recognition and quality grading has been proposed via inputting both side images. 20 quality grades have been selected considering the requirement of grade classifications imposed by the mushroom company. Developed DC motor driven‘V’type reversing device for the image acquisition of both side images of mushroom showed more than 95% success. Most error was caused by very small size mushrooms with a radius of around 1cm. However, it required a further research to reduce the reversing time. Grading and side recognition were performed via inputting normalized size factors and average gray levels of $8{\times}8$ grids converted from the raw images of both surfaces to the multi-layer back propagation(BP) network. Accuracy of the grading showed about 88.5% and the total grading time including reversing operation was around 2 seconds.

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