Cohabitation with a B16F10 melanoma-bearer cage mate influences behavior and dendritic cell phenotype in mice

This study evaluated the effects of cohabitation with a B16F10 melanoma-bearer cage mate on behavior and immune functions in mice. Five different experiments were conducted. In each of them, the female mice were divided into two groups: control and experimental. One mouse of each control pair was kept undisturbed and called ""companion of health partner"" (CHP). One mouse of each experimental pair was inoculated with B16FI0 cells and the other, the subject of this study, was called ""companion sick partner"" (CSP). On Day 20 of cohabitation, behavior and immune parameters from CHP and CSP mice were analyzed. In comparison to the CHP, the CSP mice: (1) presented an increased general locomotion in the open field and a decreased exploration time and number of entries in the plus-maze open arms; (2) had an enhanced expression of the CD80 costimulatory molecule on Iab(+)CD11c(+) spleen cells, but no differences were found on lymph nodes cells; (3) presented an altered differentiation of bone marrow cells in the presence of GM-CSF, IL-4, and LPS in vitro, resulting in a lower percentage of Iab(+)CD80(+) cells; (4) had a deficit in the establishment of a Delayed Type of Hypersensitivity to ovalbumin, which was associated to an in vitro proliferation of an IL-10-producing lymphocyte subpopulation after ovalbumin stimulation. Corticosterone levels detected on Day 20 of cohabitation were similar in CHP and CSP mice. It is shown here that DCs phenotype in mice is affected by conditions associated with behavioral alterations indicative of an anxiety-like state induced by the cohabitation with a tumor-bearer conspecific. This phenomenon occurred probably through a nondependent corticosterone mechanism. (C) 2009 Elsevier Inc. All rights reserved.

On the influence of imputation in classification: practical issues

The substitution of missing values, also called imputation, is an important data preparation task for many domains. Ideally, the substitution of missing values should not insert biases into the dataset. This aspect has been usually assessed by some measures of the prediction capability of imputation methods. Such measures assume the simulation of missing entries for some attributes whose values are actually known. These artificially missing values are imputed and then compared with the original values. Although this evaluation is useful, it does not allow the influence of imputed values in the ultimate modelling task (e.g. in classification) to be inferred. We argue that imputation cannot be properly evaluated apart from the modelling task. Thus, alternative approaches are needed. This article elaborates on the influence of imputed values in classification. In particular, a practical procedure for estimating the inserted bias is described. As an additional contribution, we have used such a procedure to empirically illustrate the performance of three imputation methods (majority, naive Bayes and Bayesian networks) in three datasets. Three classifiers (decision tree, naive Bayes and nearest neighbours) have been used as modelling tools in our experiments. The achieved results illustrate a variety of situations that can take place in the data preparation practice.

Investigating relationships within and between category networks in Wikipedia

This work maps and analyses cross-citations in the areas of Biology, Mathematics, Physics and Medicine in the English version of Wikipedia, which are represented as an undirected complex network where the entries correspond to nodes and the citations among the entries are mapped as edges. We found a high value of clustering coefficient for the areas of Biology and Medicine, and a small value for Mathematics and Physics. The topological organization is also different for each network, including a modular structure for Biology and Medicine, a sparse structure for Mathematics and a dense core for Physics. The networks have degree distributions that can be approximated by a power-law with a cut-off. The assortativity of the isolated networks has also been investigated and the results indicate distinct patterns for each subject. We estimated the betweenness centrality of each node considering the full Wikipedia network, which contains the nodes of the four subjects and the edges between them. In addition, the average shortest path length between the subjects revealed a close relationship between the subjects of Biology and Physics, and also between Medicine and Physics. Our results indicate that the analysis of the full Wikipedia network cannot predict the behavior of the isolated categories since their properties can be very different from those observed in the full network. (C) 2011 Elsevier Ltd. All rights reserved.

A criterion for unitary similarity of upper triangular matrices in general position

Each square complex matrix is unitarily similar to an upper triangular matrix with diagonal entries in any prescribed order. Let A = [a(ij)] and B = [b(ij)] be upper triangular n x n matrices that are not similar to direct sums of square matrices of smaller sizes, or are in general position and have the same main diagonal. We prove that A and B are unitarily similar if and only if parallel to h(A(k))parallel to = parallel to h(B(k))parallel to for all h is an element of C vertical bar x vertical bar and k = 1, ..., n, where A(k) := [a(ij)](i.j=1)(k) and B(k) := [b(ij)](i.j=1)(k) are the leading principal k x k submatrices of A and B, and parallel to . parallel to is the Frobenius norm. (C) 2011 Elsevier Inc. All rights reserved.