Print ISSN : 0919-2719
Volume 17
Showing 1-2 articles out of 2 articles from the selected issue
Regular Article
  • Kyoko Yoshioka, Masafumi Hirono, Yuji Sekiguchi, Takafumi Mizuno
    2009 Volume 17 Pages 1-7
    Published: 2009
    Released: October 08, 2009
    This study presents a simple method for evaluating the cortex contractility of Dictyostelium cells. This assay is based on the artificially-induced actomyosin-mediated contraction of the cortex cytoskeleton. Simultaneous application of Triton X-100 and ATP to Dictyostelium cells induced rapid shrinkage of cell mass after cell lysis. This cellular contraction was dependent on ATP in the medium, and mutant cells lacking myosin II did not show significant contraction. Microscopic measurements of artificially-induced contraction such as this require only a small amount of cell suspension and can provide significant information on the contractile properties of the cortex in target cells. As one example analysis, the magnitude and velocity of the contraction in strains that exogenously expressed Tetrahymena actin or a chimeric actin of Dictyostelium and Tetrahymena were measured. The results revealed certain dominant negative effects of these actin variants with respect to the endogenous Dictyostelium actin. This simple method is thus considered to be a useful tool for the rapid assessment of phenotypes in various cytoskeleton-related mutants either in early-stage studies or in genetic manipulations intending to alter the cellular contractility in Dictyostelium.
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  • Makio Akimoto, Michio Miyazaki, Hee-Hyol Lee, Tomonori Nishimura, Muts ...
    2009 Volume 17 Pages 9-18
    Published: 2009
    Released: October 23, 2009
    We herein present a fuzzy reasoning approach for a computer-aided diagnostic scheme using medical imaging. The scheme is utilized in skin color images to identify skin disease and to detect and classify clustered microcalcifications. The fuzzy membership functions are initially generated using various texture-based features obtained from reference images. After optimization, the classifier is used for disease identification. The results of this experiment are very promising and demonstrate that our proposed fuzzy reasoning approach is an effective method for computer-aided diagnosis in disease classification.
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