Exogenous insulin stimulates glycogen accumulation in Rhipicephalus (Boophilus) microplus embryo cell line BME26 via PI3K/AKT pathway

Ticks are obligatory blood-feeding arthropods and important vectors of both human and animal disease agents. Besides its metabolic role, insulin signaling pathway (ISP) is widely described as crucial for vertebrate and invertebrate embryogenesis, development and cell survival. In such cascade, Phosphatidylinositol 3-OH Kinase (PI3K) is hierarchically located upstream Protein Kinase B (PKB). To study the insulin-triggered pathway and its possible roles during embryogenesis we used a culture of embryonic Rhipicephalus microplus cells (BME26). Exogenous insulin elevated cell glycogen content in the absence of fetal calf serum (FCS) when compared to cells without treatment. Moreover, in the presence of PI3K inhibitors (Wortmannin or LY294002) these effects were blocked. We observed an increase in the relative expression level of PI3K`s regulatory subunit (p85), as determined by qRT-PCR. In the presence of PI3K inhibitors these effects on transcription were also reversed. Additionally, treatment with Wortmannin increased the expression level of the insulin-regulated downstream target glycogen synthase kinase 3 beta (GSK3 beta). The p85 subunit showed elevated transcription levels in ovaries from fully engorged females, but was differentially expressed during tick embryogenesis. These results strongly suggest the presence of an insulin responsive machinery in BME26 cells, and its correlation with carbohydrate/glycogen metabolism also during embryogenesis. (C) 2009 Published by Elsevier Inc.

Hypotheses Testing on a Multivariate Null Intercept Errors-in-Variables Model

Considering the Wald, score, and likelihood ratio asymptotic test statistics, we analyze a multivariate null intercept errors-in-variables regression model, where the explanatory and the response variables are subject to measurement errors, and a possible structure of dependency between the measurements taken within the same individual are incorporated, representing a longitudinal structure. This model was proposed by Aoki et al. (2003b) and analyzed under the bayesian approach. In this article, considering the classical approach, we analyze asymptotic test statistics and present a simulation study to compare the behavior of the three test statistics for different sample sizes, parameter values and nominal levels of the test. Also, closed form expressions for the score function and the Fisher information matrix are presented. We consider two real numerical illustrations, the odontological data set from Hadgu and Koch (1999), and a quality control data set.

Random sums of exchangeable variables and actuarial applications

In this paper we study the accumulated claim in some fixed time period, skipping the classical assumption of mutual independence between the variables involved. Two basic models are considered: Model I assumes that any pair of claims are equally correlated which means that the corresponding square-integrable sequence is exchangeable one. Model 2 states that the correlations between the adjacent claims are the same. Recurrence and explicit expressions for the joint probability generating function are derived and the impact of the dependence parameter (correlation coefficient) in both models is examined. The Markov binomial distribution is obtained as a particular case under assumptions of Model 2. (C) 2007 Elsevier B.V. All rights reserved.

Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.

A multivariate ultrastructural errors-in-variables model with equation error

This paper deals with asymptotic results on a multivariate ultrastructural errors-in-variables regression model with equation errors Sufficient conditions for attaining consistent estimators for model parameters are presented Asymptotic distributions for the line regression estimators are derived Applications to the elliptical class of distributions with two error assumptions are presented The model generalizes previous results aimed at univariate scenarios (C) 2010 Elsevier Inc All rights reserved

Hypothesis testing in an errors-in-variables model with heteroscedastic measurement errors

In many epidemiological studies it is common to resort to regression models relating incidence of a disease and its risk factors. The main goal of this paper is to consider inference on such models with error-prone observations and variances of the measurement errors changing across observations. We suppose that the observations follow a bivariate normal distribution and the measurement errors are normally distributed. Aggregate data allow the estimation of the error variances. Maximum likelihood estimates are computed numerically via the EM algorithm. Consistent estimation of the asymptotic variance of the maximum likelihood estimators is also discussed. Test statistics are proposed for testing hypotheses of interest. Further, we implement a simple graphical device that enables an assessment of the model`s goodness of fit. Results of simulations concerning the properties of the test statistics are reported. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease. Copyright (C) 2008 John Wiley & Sons, Ltd.