The atmospheric epiphyte Tillandsia ionantha is capable of surviving drought stress for 6 months or more without any exogenous water supply via an as of yet to be determined mechanism. When plants were soaked in water for 3 h, leaves absorbed a remarkably large amount of water (30-40% on the basis of fresh weight), exhibiting a bimodal absorption pattern. Radiolabeled water was taken up by the leaves by capillary action of the epidermal trichomes within 1 min (phase 1) and then transported intracellularly to leaf tissues over 3 h (phase 2). The removal of epidermal trichome wings from leaves as well as rinsing leaves with water significantly lowered the extracellular accumulation of water on leaf surfaces. The intracellular transport of water was inhibited by mercuric chloride, implicating the involvement of a water channel aquaporin in second-phase water absorption. Four cDNA clones (TiPIP1a, TiPIP1b, TiPIP1c, and TiPIP2a) homologous to PIP family aquaporins were isolated from the leaves, and RT-PCR showed that soaking plants in water stimulated the expression of TiPIP2a mRNA, suggesting the reinforcement in ability to rapidly absorb a large amount of water. The expression of TiPIP2a complementary RNA in Xenopus oocytes enhanced permeability, and treatment with inhibitors suggested that the water channel activity of TiPIP2a protein was regulated by phosphorylation. Thus, the high water uptake capability of T. ionantha leaves surviving drought is attributable to a bimodal trichome- and aquaporin-aided water uptake system based on rapid physical collection of water and subsequent, sustained chemical absorption.
A major problem in applying a multi-layered neural network to image processing is how to select input components from among a number of features or pixels extracted from a recognized object. This is especially important for achieving both high speed processing and a high recognition rate. This paper proposes a method of optimizing the number of input components, i. e. that of input neurons without lowering a recognition rate. In general, neural networks learn a recognition algorithm from input patterns and their desired output patterns. But if some input components are redundant, namely they are expressed by other input components or they do not contribute to recognition, their effect on the recognition algorithm, or their sensitivity to outputs is considered to be very small. Therefore, by analyzing input-output sensitivity of neural networks, redundant input components can be deleted. Repeatedly applying this method, the useful input components for recognition can be selected. For an experimental neural network for recognition of numerals with 12 feature input components, the input components could be reduced to eight without lowering a recognition rate.
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