Two methods for computing specific airway conductance and resistance.
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The posterior probability of linkage (PPL) is a Bayesian statistic which directly measures the probability of linkage between a trait locus and a marker (in the 2-point case) or a genomic region (in the multipoint case). It has several benefits, including ease of interpretation, the ability to incorporate prior genomic information, and a mathematically rigorous and robust procedure for accumulating linkage information across multiple heterogeneous datasets. To date, the majority of work on the PPL has focused on the development of the 2-point statistic, with only preliminary attempts at the development of an equivalent multipoint version. In this paper we present a new way of computing of the multipoint PPL. This new version imputes to each genomic point an estimate of the 2-point PPL we would have obtained from a fully informative marker giving similar evidence for linkage. This version, which we call the imputed PPL, is shown to be superior to previously developed versions.
This article introduces a new architecture and associated algorithms ideal for implementing the dimensionality reduction of an m-dimensional manifold initially residing in an n-dimensional Euclidean space where n >> m. Motivated by Whitney's embedding theorem, the network is capable of training the identity mapping employing the idea of the graph of a function. In theory, a reduction to a dimension d that retains the differential structure of the original data may be achieved for some d < or = 2m + 1. To implement this network, we propose the idea of a good-projection, which enhances the generalization capabilities of the network, and an adaptive secant basis algorithm to achieve it. The effect of noise on this procedure is also considered. The approach is illustrated with several examples.
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The perception of an unchanging surface color under different illuminations requires the computation of the scene-illuminant color either directly or indirectly. A possible source for the computation is the specular highlight of the surface reflection. Some issues related to color constancy are discussed, and a theory for computing the scene-illuminant chromaticity from specular highlight is described. An interesting result of the theory is that in an ideal situation, two surfaces of different colors will be sufficient for the computation.
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Several methods have been suggested to identify schistosome cercariae. In the present work, a new method is proposed, based on the analysis of the distribution of sensory endings (sensillae) on the body of the cercariae, revealed by an impregnation with silver nitrate. We determined the mutual distances between the sensillae and calculated the mean values, standard deviations, coefficients of asymmetry, and of kurtosis of the distribution of these mutual distances. Applied to two species, Schistosoma mansoni from Brazil and S. intercalatum from Cameroon, these mutual distances had the same mean value and the same standard deviation but quite different coefficients of symmetry (0.34 +/- 0.11 versus 0.73 +/- 0.08; P < 0.0001) and of kurtosis (-0.82 +/- 0.27 versus -0.58 +/- 0.31; P < 0.0001). The latter two indices were therefore very effective for discriminating the two species. The present method can be applied to other species and to hybrids in the field.
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