Acuna, M., Eaton, L., Ramirez, N. R., Cifuentes, L., & Llop, E. (2003). Genetic variants of serum butyrylcholinesterase in Chilean Mapuche Indians. Am. J. Phys. Anthropol., 121(1), 81–85.
Abstract: We estimated the frequencies of serum butyrylcholinesterase (BChE) alleles in three tribes of Mapuche Indians from southern Chile, using enzymatic methods, and we estimated the frequency of allele BCHE*K in one tribe using primer reduced restriction analysis (PCR-PIRA). The three tribes have different degrees of European admixture, which is reflected in the observed frequencies of the atypical allele BCHE*A: 1.11% in Huilliches, 0.89% in Cuncos, and 0% in Pehuenches. This result is evidence in favor of the hypothesis that BCHE*A is absent in native Amerindians. The frequencies of BCHE*F were higher than in most reported studies (3.89%, 5.78%, and 4.41%, respectively). These results are probably due to an overestimation of the frequency of allele BCHE*F, since none of the 20 BCHE UF individuals (by the enzymatic test) individuals analyzed showed either of the two DNA base substitutions associated with this allele. Although enzymatic methods rarely detect the presence of allele BCHE*K, PCR-PIRA found the allele in an appreciable frequency (5.76%), although lower than that found in other ethnic groups. Since observed frequencies of unusual alleles correspond to estimated percentages of European admixture, it is likely that none of these unusual alleles were present in Mapuche Indians before the arrival of Europeans. (C) 2003 Wiley-Liss, Inc.
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Allende, H., Salas, R., & Moraga, C. (2003). A robust and effective learning algorithm for feedforward neural networks based on the influence function. Lect. Notes Comput. Sc., 2652, 28–36.
Abstract: The learning process of the Feedforward Artificial Neural Networks relies on the data, though a robustness analysis of the parameter estimates of the model must be done due to the presence of outlying observations in the data. In this paper we seek the robust properties in the parameter estimates in the sense that the influence of aberrant observations or outliers in the estimate is bounded so the neural network is able to model the bulk of data. We also seek a trade off between robustness and efficiency under a Gaussian model. An adaptive learning procedure that seeks both aspects is developed. Finally we show some simulations results applied to the RESEX time series.
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de Figueiredo, C. M. H., Meldanis, J., de Mello, C. P., & Ortiz, C. (2003). Decompositions for the edge colouring of reduced indifference graphs. Theor. Comput. Sci., 297(1-3), 145–155.
Abstract: The chromatic index problem-finding the minimum number of colours required for colouring the edges of a graph-is still unsolved for indifference graphs, whose vertices can be linearly ordered so that the vertices contained in the same maximal clique are consecutive in this order. We present new positive evidence for the conjecture: every non neighbourhood-overfull indifference graph can be edge coloured with maximum degree colours. Two adjacent vertices are twins if they belong to the same maximal cliques. A graph is reduced if it contains no pair of twin vertices. A graph is overfull if the total number of edges is greater than the product of the maximum degree by [n/2], where n is the number of vertices. We give a structural characterization for neighbourhood-overfull indifference graphs proving that a reduced indifference graph cannot be neighbourhood-overfull. We show that the chromatic index for all reduced indifference graphs is the maximum degree. We present two decomposition methods for edge colouring reduced indifference graphs with maximum degree colours. (C) 2002 Elsevier Science B.V. All rights reserved.
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