Ken Yamane

Ji-Ae Ko3
Koh-Hei Sonoda3
Junko Hirata2
3Ji-Ae Ko
3Koh-Hei Sonoda
2Junko Hirata
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PURPOSE Various protein contents such as enzymes, growth factors, and structural components are responsible for biological activities in organs. We have created a map of vitreous proteins and developed a proteome analysis of human vitreous samples to understand the underlying molecular mechanism and to provide clues to new therapeutic approaches in eyes(More)
Semaphorins not only function in axon guidance during development but also contribute to various other biological processes. We have now examined the expression of semaphorin 3A (Sema3A) and its receptor components neuropilin 1 (Npn1) and plexin A (PlxA) during development of the mouse retina. Immunohistofluorescence analysis revealed that the expression(More)
Development and homeostasis of multicellular organisms require interactions between neighbouring cells. We recently established an in vitro model of cell-cell interaction based on a collagen vitrigel membrane. We have now examined the role of neural cells in retinal homeostasis by coculture of human retinal pigment epithelial (RPE) cells and neural cells on(More)
Age-related macular degeneration (AMD) is a neurodegenerative disease associated with irreversible loss of central vision in the elderly. Disruption of the homeostatic function of the retinal pigment epithelium (RPE) is thought to be fundamental to AMD pathogenesis, and oxidative stress is implicated in the associated RPE damage. We examined the effects of(More)
A key to overcoming the limitations of classical artificial intelligence and to deal well with enormous amounts of information might be brain-like computing in which distributed representations of information are processed by dynamical systems without using symbols. We present a method for such computing. We constructed an inference system using a(More)
It is considered that a key to overcoming the limitations of classical artificial intelligence is to process distributed representations of information without symbolizing them. However, conventional neural networks require local or symbolic representations to perform complicated processing. Here we present a brain-like inference engine consisting of a(More)
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