Prostaglandin E2 rather than lymphocyte-activating factor produced by activated human mononuclear cells stimulates increases in murine thymocyte cAMP

Culture supernatants (SUPS) of endotoxin (LPS)-activated human mononuclear cells (MNL) stimulated greater production of cAMP by thymocytes than by spleen cells of C3H/HeJ or nude ($\text{nu}\text{nu}$) mice. Similarly, the addition of prostaglandin E₂ (PGE₂) stimulated higher levels of cAMP in thymocytes and progressively lower levels in spleen cells from C3H/HeJ mice and $\text{nu}\text{nu}$ spleen cells, respectively. Partial purification on Bio-Gel P100 of the LPS-induced MNL SUPS yielded peaks of thymocyte proliferative activity characteristic of lymphocyte activation factor (LAF) but these fractions failed to stimulate cAMP levels in thymocytes. Moreover, MNL SUPS induced with LPS in the presence of indomethacin retained their LAF activity but no longer increased thymocyte cAMP levels. Radioimmunoassay of the SUPS for PGE₂ revealed significantly higher levels of PGE₂ in the media of those MNL cultures stimulated by LPS than when stimulated by phorbol myristic acetate, phytohemagglutin, or extracted cell wall fraction of Actinomyces viscosus. Thus, PGE₂ is produced by human MNL and may exert considerable immunoregulatory effects mediated by elevation of lymphocyte cAMP levels.     

Experimental evolution reveals divergence in female genital teeth morphology in response to sexual conflict intensity in a moth

The rapid evolutionary divergence of male genital structures under sexual selection is well documented. However, variation in female genital traits and the potential for sexual conflict to drive the coevolution between male and female traits has only recently received attention. In many lepidopterans, females possess genital teeth (collectively, signa). Comparative studies suggest these teeth, involved in the deflation of spermatophores, may have coevolved with male spermatophore thickness via sexually antagonistic coevolution in a contest over the rate of deflation of spermatophores within the reproductive tract. We tested the hypothesis that sexual conflict should generate coevolution between genital teeth and spermatophore morphology by examining these traits under experimental manipulation of sexual conflict intensity. Using micro‐CT scanning, we examined spermatophore and teeth morphology in populations of the Indian moth, Plodia interpunctella, which had been evolving for 110 generations under different adult sex‐ratio biases. We found divergence in female signa morphology in response to sexual conflict: females from female‐biased populations (reduced sexual conflict) developed wider signa. However, we found no evidence of coevolution between signa traits and spermatophore thickness as reported from comparative studies.     

Inference of a Probabilistic Boolean Network from a Single Observed Temporal Sequence

The inference of gene regulatory networks is a key issue for genomic signal processing. This paper addresses the inference of probabilistic Boolean networks (PBNs) from observed temporal sequences of network states. Since a PBN is composed of a finite number of Boolean networks, a basic observation is that the characteristics of a single Boolean network without perturbation may be determined by its pairwise transitions. Because the network function is fixed and there are no perturbations, a given state will always be followed by a unique state at the succeeding time point. Thus, a transition counting matrix compiled over a data sequence will be sparse and contain only one entry per line. If the network also has perturbations, with small perturbation probability, then the transition counting matrix would have some insignificant nonzero entries replacing some (or all) of the zeros. If a data sequence is sufficiently long to adequately populate the matrix, then determination of the functions and inputs underlying the model is straightforward. The difficulty comes when the transition counting matrix consists of data derived from more than one Boolean network. We address the PBN inference procedure in several steps: (1) separate the data sequence into "pure" subsequences corresponding to constituent Boolean networks; (2) given a subsequence, infer a Boolean network; and (3) infer the probabilities of perturbation, the probability of there being a switch between constituent Boolean networks, and the selection probabilities governing which network is to be selected given a switch. Capturing the full dynamic behavior of probabilistic Boolean networks, be they binary or multivalued, will require the use of temporal data, and a great deal of it. This should not be surprising given the complexity of the model and the number of parameters, both transitional and static, that must be estimated. In addition to providing an inference algorithm, this paper demonstrates that the data requirement is much smaller if one does not wish to infer the switching, perturbation, and selection probabilities, and that constituent-network connectivity can be discovered with decent accuracy for relatively small time-course sequences.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]     

Quantification of the Impact of Feature Selection on the Variance of Cross-Validation Error Estimation

Given the relatively small number of microarrays typically used in gene-expression-based classification, all of the data must be used to train a classifier and therefore the same training data is used for error estimation. The key issue regarding the quality of an error estimator in the context of small samples is its accuracy, and this is most directly analyzed via the deviation distribution of the estimator, this being the distribution of the difference between the estimated and true errors. Past studies indicate that given a prior set of features, cross-validation does not perform as well in this regard as some other training-data-based error estimators. The purpose of this study is to quantify the degree to which feature selection increases the variation of the deviation distribution in addition to the variation in the absence of feature selection. To this end, we propose the coefficient of relative increase in deviation dispersion (CRIDD), which gives the relative increase in the deviation-distribution variance using feature selection as opposed to using an optimal feature set without feature selection. The contribution of feature selection to the variance of the deviation distribution can be significant, contributing to over half of the variance in many of the cases studied. We consider linear-discriminant analysis, 3-nearest-neighbor, and linear support vector machines for classification; sequential forward selection, sequential forward floating selection, and the -test for feature selection; and -fold and leave-one-out cross-validation for error estimation. We apply these to three feature-label models and patient data from a breast cancer study. In sum, the cross-validation deviation distribution is significantly flatter when there is feature selection, compared with the case when cross-validation is performed on a given feature set. This is reflected by the observed positive values of the CRIDD, which is defined to quantify the contribution of feature selection towards the deviation variance.[1,2,3,4,5,6,7,8,9,10,11,12,13]