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A fluorescence ratiometric method utilising the probe eosin Y is presented for estimating the ATP binding site polarity of P-type ATPases in different conformational states. The method has been calibrated by measurements in a series of alcohols and tested using complexation of eosin Y with methyl-β-cyclodextrin. The results obtained with the Na + ,K +-, H + ,K +-and sarcoplasmic reticulum Ca 2+-ATPases indicate that the ATP binding site, to which eosin is known to bind, is significantly more polar in the case of the Na + ,K +-and H + ,K +-ATPases compared to the Ca 2+-ATPase. This result was found to be consistent with docking calculations of eosin with the E2 conformational state of the Na + ,K +-ATPase and the Ca 2+-ATPase. Fluorescence experiments showed that eosin binds significantly more strongly to the E1 conformation of the Na + ,K +-ATPase than the E2 conformation, but in the case of the Ca 2+-ATPase both fluorescence experiments and docking calculations showed no significant difference in binding affinity between the two conformations. This result could be due to the fact that, in contrast to the Na + ,K +-and H + ,K +-ATPases, the E2-E1 transition of the Ca 2+-ATPase does not involve the movement of a lysine-rich N-terminal tail which may affect the overall enzyme conformation. Consistent with this hypothesis, the eosin affinity of the E1 conformation of the Na + ,K +-ATPase was significantly reduced after N-terminal truncation. It is suggested that changes in conformational entropy of the N-terminal tail of the Na + , K +-and the H + ,K +-ATPases during the E2-E1 transition could affect the thermodynamic stability of the E1 conformation and hence its ATP binding affinity.

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Block layout dimension prediction is an important activity in many very large scale integration computer-aided design tasks, among them structural synthesis, floor planning and physical synthesis. Block layout dimension prediction is harder than block area prediction and has been previously considered to be intractable. The authors present a solution to this problem using a neural network machine learning approach. The method uses a neural network to predict first the number of contacts; then another neural network uses this prediction and other circuit features to predict the width and the height of its layout. The approach has produced much better results than those published-a dimension (aspect ratio) prediction average error of less than 18% with a corresponding area prediction average error of less than 15%. Furthermore, the technique predicts the number of contacts in a circuit with less than 4% error on average.