Detection of the Tim-3 ligand, galectin-9, inside the allograft during a rejection episode

Introduction: Tim-3 is a Th1 lymphocytes membrane protein with inhibitory function. Its ligand, galectin-9, was recently identified and it is expressed in some lymphocyte subpopulation. In addition, endothelial cells and fibroblasts can also express galectin-9 according to the local cytokine milieu. Both molecules can act as important regulatory tools in the immune system. Aim: Evaluate the expression of these immunoregulatory molecules inside kidney allografts during acute rejection episodes. Methods: By using a quantitative polymerase chain reaction assay, we measured the levels of messenger RNA (mRNA) for galectin-9 and Tim-3 in 21 samples obtained at allograft nephrectomy. Five samples received the histological diagnosis of acute non-vascular rejection (ANVR), twelve of acute vascular rejection (AVR), and five of loss of non-immune cause (LNIC; as control). As cytolytic response markers we measured mRNA levels of granzyme B, interferon-gamma and perforin. The statistic analysis was performed using one way analysis of variance (ANOVA) and Pearson correlation. Results: The mean levels of Tim-3 mRNA expression were 13.99 +/- 6.99 for LNIC, 48.13 +/- 54.47 for RACNV and 238.63 +/- 333.14 for RAV (p = 0.004). For galectin-9, the mean values were 0.57 +/- 0.49 for LNIC, 0.66 +/- 0.36 for RACNV and 2.34 +/- 1.62 for RAV (p = 0.006). Furthermore, there was a positive correlation between both molecules (r = 0.526, p = 0.016). Also. granzyme B, perforin and interferon-gamma mRNA expression were different among the three groups. Conclusion: Messenger RNA level expressions of all the studied molecules were higher inside allografts with more severe rejection. Moreover, there was a positive correlation between galectin-9 and Tim-3 mRNA levels. The simultaneous expression of galectin-9 and Tim-3 may indicate an immunoregulatory function, during the ongoing cytotoxic response. (C) 2008 Elsevier B.V. All rights reserved.

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.

Effect of different dental composite resins on the polymerization process

Dental composite resins possess good esthetic properties, and are currently among the most popular dental restorative materials. Both organic and inorganic phases might influence the material behavior, the filler particle features and rate are the most important factors related to improvement of the mechanical properties of resin composites. Thus, the objective of this study was to evaluate the effect of three different composite resins on the polymerization process by Vickers hardness test. The samples were prepared using three different composite resins, as follow: group I-P-60 (3M/ESPE); group II-Herculite XRV (Kerr), and group III-Durafill (Heraeus-Kulzer). The samples were made in a polytetrafluoroethylene mould, with a rectangular cavity measuring 7 mm in length, 4 mm in width, and 3 mm in thickness. The samples were photo-activated by one light-curing unit based on blue LEDs (Ultrablue III-DMC/Brazil) for 20 and 40 s of irradiation times. The Vickers hardness test was performed 24 h after the photo-activation until the standardized depth of 3 mm. The Vickers hardness mean values varied from 158.9 (+/- 0.81) to 81.4 (+/- 1.94) for P-60, from 138.7 (+/- 0.37) to 61.7 (+/- 0.24) for Herculite XRV, and from 107. 5 (+/- 0.81) to 44.5 (+/- 1.36) for Durafill composite resins photo-activated during 20 s for the 1st and 2nd mm, respectively. During 40 s of photo-activation, the Vickers hardness mean values were: from 181.0 (+/- 0.70) to 15.6 (+/- 0.29) for P-60, and from 161.8 (+/- 0.41) to 11.2 (+/- 0.17) for Herculite XRV composite resins, for the 1st and 3th mm, respectively. For Durafill composite resin the mean values varied from 120.1 (+/- 0.66) to 61.7 (+/- 0.20), for the 1st and 2nd mm, respectively. The variation coefficient (CV) was in the most of the groups lower than 1%, then the descriptive statistic analysis was used. The Vickers hardness mean values for Durafill were lower than P-60 and Herculite XRV composite resins for 20 and 40 s of irradiation time. The polymerization process was greatly affected by the composition of the composite resins.

Local influence for Student-t partially linear models

In this paper we extend partial linear models with normal errors to Student-t errors Penalized likelihood equations are applied to derive the maximum likelihood estimates which appear to be robust against outlying observations in the sense of the Mahalanobis distance In order to study the sensitivity of the penalized estimates under some usual perturbation schemes in the model or data the local influence curvatures are derived and some diagnostic graphics are proposed A motivating example preliminary analyzed under normal errors is reanalyzed under Student-t errors The local influence approach is used to compare the sensitivity of the model estimates (C) 2010 Elsevier B V All rights reserved

Signed likelihood ratio tests in the Birnbaum-Saunders regression model

The Birnbaum-Saunders regression model is becoming increasingly popular in lifetime analyses and reliability studies. In this model, the signed likelihood ratio statistic provides the basis for testing inference and construction of confidence limits for a single parameter of interest. We focus on the small sample case, where the standard normal distribution gives a poor approximation to the true distribution of the statistic. We derive three adjusted signed likelihood ratio statistics that lead to very accurate inference even for very small samples. Two empirical applications are presented. (C) 2010 Elsevier B.V. All rights reserved.

Improved likelihood inference in beta regression

We consider the issue of performing accurate small-sample likelihood-based inference in beta regression models, which are useful for modelling continuous proportions that are affected by independent variables. We derive small-sample adjustments to the likelihood ratio statistic in this class of models. The adjusted statistics can be easily implemented from standard statistical software. We present Monte Carlo simulations showing that inference based on the adjusted statistics we propose is much more reliable than that based on the usual likelihood ratio statistic. A real data example is presented.

Three Bartlett-type corrections for score statistics in symmetric nonlinear regression models

We present simple matrix formulae for corrected score statistics in symmetric nonlinear regression models. The corrected score statistics follow more closely a chi (2) distribution than the classical score statistic. Our simulation results indicate that the corrected score tests display smaller size distortions than the original score test. We also compare the sizes and the powers of the corrected score tests with bootstrap-based score tests.

A modified signed likelihood ratio test in elliptical structural models

In this paper we deal with the issue of performing accurate testing inference on a scalar parameter of interest in structural errors-in-variables models. The error terms are allowed to follow a multivariate distribution in the class of the elliptical distributions, which has the multivariate normal distribution as special case. We derive a modified signed likelihood ratio statistic that follows a standard normal distribution with a high degree of accuracy. Our Monte Carlo results show that the modified test is much less size distorted than its unmodified counterpart. An application is presented.

A Bayesian Semiparametric Approach for Solving the Discrete Calibration Problem

In this article, we introduce a semi-parametric Bayesian approach based on Dirichlet process priors for the discrete calibration problem in binomial regression models. An interesting topic is the dosimetry problem related to the dose-response model. A hierarchical formulation is provided so that a Markov chain Monte Carlo approach is developed. The methodology is applied to simulated and real data.

Testing hypotheses in the Birnbaum-Saunders distribution under type-II censored samples

The two-parameter Birnbaum-Saunders distribution has been used successfully to model fatigue failure times. Although censoring is typical in reliability and survival studies, little work has been published on the analysis of censored data for this distribution. In this paper, we address the issue of performing testing inference on the two parameters of the Birnbaum-Saunders distribution under type-II right censored samples. The likelihood ratio statistic and a recently proposed statistic, the gradient statistic, provide a convenient framework for statistical inference in such a case, since they do not require to obtain, estimate or invert an information matrix, which is an advantage in problems involving censored data. An extensive Monte Carlo simulation study is carried out in order to investigate and compare the finite sample performance of the likelihood ratio and the gradient tests. Our numerical results show evidence that the gradient test should be preferred. Further, we also consider the generalized Birnbaum-Saunders distribution under type-II right censored samples and present some Monte Carlo simulations for testing the parameters in this class of models using the likelihood ratio and gradient tests. Three empirical applications are presented. (C) 2011 Elsevier B.V. All rights reserved.

Comparison of three vascular endothelial markers in the evaluation of microvessel density in breast cancer

Purpose: To evaluate the microvessel density by comparing the performance of anti-factor VIII-related antigen, anti-CD31 and, anti-CD34 monoclonal antibodies in breast cancer. Methods: Twenty-three postmenopausal women diagnosed with Stage II breast cancer submitted to definitive surgical treatment were evaluated. The monoclonal antibodies used were anti-factor VIII, anti-CD31 and anti-CD34. Microvessels were counted in the areas of highest microvessel density in ten random fields (200 x). The data were analyzed using the Kruskal-Wallis nonparametric test (p < 0.05). Results: Mean microvessel densities with anti-factor VIII, anti-CD31 and anti-CD34 were 4.16 +/- 0.38, 4.09 +/- 0.23 and 6.59 +/- 0.42, respectively. Microvessel density as assessed by anti-CD34 was significantly greater than that detected by anti-CD31 or anti-factor VIII (p < 0.0001). There was no statistically significant difference between anti-CD31 and anti-factor VIII (p = 0.4889). Conclusion: The density of stained microvessels was greater and staining was more intense with anti-CD34 compared to anti-CD31 and anti-factor VII-related antigen.

Skovgaard`s adjustment to likelihood ratio tests in exponential family nonlinear models

Likelihood ratio tests can be substantially size distorted in small- and moderate-sized samples. In this paper, we apply Skovgaard`s [Skovgaard, I.M., 2001. Likelihood asymptotics. Scandinavian journal of Statistics 28, 3-321] adjusted likelihood ratio statistic to exponential family nonlinear models. We show that the adjustment term has a simple compact form that can be easily implemented from standard statistical software. The adjusted statistic is approximately distributed as X(2) with high degree of accuracy. It is applicable in wide generality since it allows both the parameter of interest and the nuisance parameter to be vector-valued. Unlike the modified profile likelihood ratio statistic obtained from Cox and Reid [Cox, D.R., Reid, N., 1987. Parameter orthogonality and approximate conditional inference. journal of the Royal Statistical Society B49, 1-39], the adjusted statistic proposed here does not require an orthogonal parameterization. Numerical comparison of likelihood-based tests of varying dispersion favors the test we propose and a Bartlett-corrected version of the modified profile likelihood ratio test recently obtained by Cysneiros and Ferrari [Cysneiros, A.H.M.A., Ferrari, S.L.P., 2006. An improved likelihood ratio test for varying dispersion in exponential family nonlinear models. Statistics and Probability Letters 76 (3), 255-265]. (C) 2008 Elsevier B.V. All rights reserved.

Comparison for three Classification Techniques

In this paper we show the results of a comparison simulation study for three classification techniques: Multinomial Logistic Regression (MLR), No Metric Discriminant Analysis (NDA) and Linear Discriminant Analysis (LDA). The measure used to compare the performance of the three techniques was the Error Classification Rate (ECR). We found that MLR and LDA techniques have similar performance and that they are better than DNA when the population multivariate distribution is Normal or Logit-Normal. For the case of log-normal and Sinh(-1)-normal multivariate distributions we found that MLR had the better performance.

Some restriction tests in a new class of regression models for proportions

The main purpose of this work is to study the behaviour of Skovgaard`s [Skovgaard, I.M., 2001. Likelihood asymptotics. Scandinavian journal of Statistics 28, 3-32] adjusted likelihood ratio statistic in testing simple hypothesis in a new class of regression models proposed here. The proposed class of regression models considers Dirichlet distributed observations, and the parameters that index the Dirichlet distributions are related to covariates and unknown regression coefficients. This class is useful for modelling data consisting of multivariate positive observations summing to one and generalizes the beta regression model described in Vasconcellos and Cribari-Neto [Vasconcellos, K.L.P., Cribari-Neto, F., 2005. Improved maximum likelihood estimation in a new class of beta regression models. Brazilian journal of Probability and Statistics 19,13-31]. We show that, for our model, Skovgaard`s adjusted likelihood ratio statistics have a simple compact form that can be easily implemented in standard statistical software. The adjusted statistic is approximately chi-squared distributed with a high degree of accuracy. Some numerical simulations show that the modified test is more reliable in finite samples than the usual likelihood ratio procedure. An empirical application is also presented and discussed. (C) 2009 Elsevier B.V. All rights reserved.

The beta Laplace distribution

The Laplace distribution is one of the earliest distributions in probability theory. For the first time, based on this distribution, we propose the so-called beta Laplace distribution, which extends the Laplace distribution. Various structural properties of the new distribution are derived, including expansions for its moments, moment generating function, moments of the order statistics, and so forth. We discuss maximum likelihood estimation of the model parameters and derive the observed information matrix. The usefulness of the new model is illustrated by means of a real data set. (C) 2011 Elsevier B.V. All rights reserved.