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NESs contain variability in the spacing between key hydrophobic residues, yet are recognized by the same rigid hydrophobic pockets on Crm1 (Güttler et al., 2010).
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Attempts to provide a functional circuit model have also been made by reference to contextual considerations, such as the anisotropic conduction properties of the transitional area between the atria and the AV node, and variability in the space constant of tissue and poor gap junction connectivity because of differential expression of connexin isoforms in the nodal area.
The results indicate that choosing the plots in a way that ensures ample coverage of both spatial and feature space variability improves the performance of the corresponding models, and that adequate coverage of the variability in the feature space is the most important condition that should be met by the set of plots collected.
The best results were obtained for the cultivar SP87-365 that presented the lowest spectral variability in the discriminant space.
Second, they support an inherent variability in the solution space that is hard to model using a single modeling solution.
The highest variability in the MFCC space can be observed for M otherese speech; this in turn explains why it is difficult to recognise it robustly.
Results showed that Latin hypercube sampling can capture more variability in the sample space than simple random sampling especially when the number of simulations is small.
The first two principal components explained more than 80% of the variance, thus indicating that there are mainly two sources of variability in the descriptor space, namely the isoenzyme type.
It takes into account the big variability in the joint space that exists between individuals.
Increasing the variability in the parameter space affects the Impulse clustering since it makes the clusters more diffused.
Typically the first few principal components capture most of the variability in the covariate space, and in some cases it may be enough to use only the first principal component.
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