Optimizing the nipple-areola sparing mastectomy with double concentric periareolar incision and biodimensional expander-implant reconstruction: Aesthetic and technical refinements

Although the biodimensional anatomical expander-implant system (BEIS) is a reliable technique, little information has been available regarding outcome following nipple-areola sparing mastectomy (NSM). To perform the resection of glandular tissue, while improving the surgical access and maintaining the nipple-areola vascularization we have developed a new approach for NSM based on the double concentric periareolar incision (DCPI). The purpose of this study is to analyze the feasibility, surgical planning and its outcome following NSM. 18 patients underwent NSM reconstructions. Mean time of follow-up was 29 months. The technique was indicated in patients with small/moderate volume breasts. Flap complications were evaluated and information on aesthetic results and patient satisfaction were collected. 83.3% had tumors measuring 2 cm or less (T1) and 72.1% were stage 0 and 1. All patients presented peripherally tumors located (at least 5 cm from the nipple). Skin complications occurred in 11.1%. One patient (5.5%) presented small skin necrosis and a wound dehiscence was observed in one patient (5.5%). The aesthetic result was good/very good in 94.4 percent and the majority of patients were very satisfied/satisfied. No local recurrences were observed. All complications except one were treated by a conservative approach. DCPI-BEIS is a simple and reliable technique for NSM reconstruction. The success depends on patient selection, coordinated planning with the oncologic surgeon and careful intra-operative and post-operative management. (C) 2009 Elsevier Ltd. All rights reserved.

Technical variation in a sample of high level judo players

Technical actions performed by two groups of judokas who won medals at World Championships and Olympic Games during the period 1995-2001 were analyzed. In the Super Elite group (n = 17) were the best athletes in each weight category. The Elite group (n = 16) were medal winners who were not champions and did not win more than three medals. Super Elite judokas used a greater number of throwing techniques which resulted in scores, even when expressed relative to the total number of matches performed, and these techniques were applied in more directions than those of Elite judokas. Further, the number of different throwing techniques and the variability of directions in which techniques were applied were significantly correlated with number of wins and the number of points and ippon scored. Thus, a greater number of throwing techniques and use of directions for attack seem to be important in increasing unpredictability during judo matches.

Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: Estimation and case influence diagnostics

The purpose of this paper is to develop a Bayesian analysis for nonlinear regression models under scale mixtures of skew-normal distributions. This novel class of models provides a useful generalization of the symmetrical nonlinear regression models since the error distributions cover both skewness and heavy-tailed distributions such as the skew-t, skew-slash and the skew-contaminated normal distributions. The main advantage of these class of distributions is that they have a nice hierarchical representation that allows the implementation of Markov chain Monte Carlo (MCMC) methods to simulate samples from the joint posterior distribution. In order to examine the robust aspects of this flexible class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. Further, some discussions on the model selection criteria are given. The newly developed procedures are illustrated considering two simulations study, and a real data previously analyzed under normal and skew-normal nonlinear regression models. (C) 2010 Elsevier B.V. All rights reserved.

On estimation and influence diagnostics for log-Birnbaum-Saunders Student-t regression models: Full Bayesian analysis

The purpose of this paper is to develop a Bayesian approach for log-Birnbaum-Saunders Student-t regression models under right-censored survival data. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the considered model. In order to attenuate the influence of the outlying observations on the parameter estimates, we present in this paper Birnbaum-Saunders models in which a Student-t distribution is assumed to explain the cumulative damage. Also, some discussions on the model selection to compare the fitted models are given and case deletion influence diagnostics are developed for the joint posterior distribution based on the Kullback-Leibler divergence. The developed procedures are illustrated with a real data set. (C) 2010 Elsevier B.V. All rights reserved.

Influence diagnostics in nonlinear mixed-effects elliptical models

In this work we propose and analyze nonlinear elliptical models for longitudinal data, which represent an alternative to gaussian models in the cases of heavy tails, for instance. The elliptical distributions may help to control the influence of the observations in the parameter estimates by naturally attributing different weights for each case. We consider random effects to introduce the within-group correlation and work with the marginal model without requiring numerical integration. An iterative algorithm to obtain maximum likelihood estimates for the parameters is presented, as well as diagnostic results based on residual distances and local influence [Cook, D., 1986. Assessment of local influence. journal of the Royal Statistical Society - Series B 48 (2), 133-169; Cook D., 1987. Influence assessment. journal of Applied Statistics 14 (2),117-131; Escobar, L.A., Meeker, W.Q., 1992, Assessing influence in regression analysis with censored data, Biometrics 48, 507-528]. As numerical illustration, we apply the obtained results to a kinetics longitudinal data set presented in [Vonesh, E.F., Carter, R.L., 1992. Mixed-effects nonlinear regression for unbalanced repeated measures. Biometrics 48, 1-17], which was analyzed under the assumption of normality. (C) 2009 Elsevier B.V. All rights reserved.

Results of Joint Experiments and other IAEA activities on research using small tokamaks

This paper presents an overview of the results obtained during the Joint Experiments organized in the framework of the IAEA Coordinated Research Project on `Joint Research Using Small Tokamaks` that have been carried out on the tokamaks CASTOR at IPP Prague, Czech Republic (2005), T-10 at RRC `Kurchatov Institute`, Moscow, Russia (2006), and the most recent one at ISTTOK at IST, Lisbon, Portugal, in 2007. Experimental programmes were aimed at diagnosing and characterizing the core and the edge plasma turbulence in a tokamak in order to investigate correlations between the occurrence of transport barriers, improved confinement, electric fields and electrostatic turbulence using advanced diagnostics with high spatial and temporal resolution. On CASTOR and ISTTOK, electric fields were generated by biasing an electrode inserted into the edge plasma and an improvement of the global particle confinement induced by the electrode positive biasing has been observed. Geodesic acoustic modes were studied using heavy ion beam diagnostics on T-10 and ISTTOK and correlation reflectometry on T-10. ISTTOK is equipped with a gallium jet injector and the technical feasibility of gallium jets interacting with plasmas has been investigated in pulsed and ac operation. The first Joint Experiments have clearly demonstrated that small tokamaks are suitable for broad international cooperation to conduct dedicated joint research programmes. Other activities within the IAEA Coordinated Research Project on Joint Research Using Small Tokamaks are also overviewed.

Femtosecond light distribution at skin and liver of rats: analysis for use in optical diagnostics

In this study we investigated the light distribution under femtosecond laser illumination and its correlation with the collected diffuse scattering at the surface of ex-vivo rat skin and liver. The reduced scattering coefficients mu`s for liver and skin due to different scatterers have been determined with Mie-scattering theory for each wavelength (800, 630, and 490 nm). Absorption coefficients mu(a) were determined by diffusion approximation equation in correlation with measured diffused reflectance experimentally for each wavelength (800, 630, and 490 nm). The total attenuation coefficient for each wavelength and type of tissue were determined by linearly fitting the log based normalized intensity. Both tissues are strongly scattering thick tissues. Our results may be relevant when considering the use of femtosecond laser illumination as an optical diagnostic tool. [GRAPHICS] A typical sample of skin exposed to 630 nm laser light (C) 2010 by Astro Ltd. Published exclusively by WILEY-VCH Verlag GmbH & Co. KGaA

On estimation and influence diagnostics for the Grubbs` model under heavy-tailed distributions

The Grubbs` measurement model is frequently used to compare several measuring devices. It is common to assume that the random terms have a normal distribution. However, such assumption makes the inference vulnerable to outlying observations, whereas scale mixtures of normal distributions have been an interesting alternative to produce robust estimates, keeping the elegancy and simplicity of the maximum likelihood theory. The aim of this paper is to develop an EM-type algorithm for the parameter estimation, and to use the local influence method to assess the robustness aspects of these parameter estimates under some usual perturbation schemes, In order to identify outliers and to criticize the model building we use the local influence procedure in a Study to compare the precision of several thermocouples. (C) 2008 Elsevier B.V. All rights reserved.

Influence diagnostics for linear models with first-order autoregressive elliptical errors

We introduce in this paper the class of linear models with first-order autoregressive elliptical errors. The score functions and the Fisher information matrices are derived for the parameters of interest and an iterative process is proposed for the parameter estimation. Some robustness aspects of the maximum likelihood estimates are discussed. The normal curvatures of local influence are also derived for some usual perturbation schemes whereas diagnostic graphics to assess the sensitivity of the maximum likelihood estimates are proposed. The methodology is applied to analyse the daily log excess return on the Microsoft whose empirical distributions appear to have AR(1) and heavy-tailed errors. (C) 2008 Elsevier B.V. All rights reserved.

Influence Diagnostics for a Skew Extension of the Grubbs Measurement Error Model

Influence diagnostics methods are extended in this article to the Grubbs model when the unknown quantity x (latent variable) follows a skew-normal distribution. Diagnostic measures are derived from the case-deletion approach and the local influence approach under several perturbation schemes. The observed information matrix to the postulated model and Delta matrices to the corresponding perturbed models are derived. Results obtained for one real data set are reported, illustrating the usefulness of the proposed methodology.

A new class of survival regression models with heavy-tailed errors: robustness and diagnostics

Birnbaum-Saunders models have largely been applied in material fatigue studies and reliability analyses to relate the total time until failure with some type of cumulative damage. In many problems related to the medical field, such as chronic cardiac diseases and different types of cancer, a cumulative damage caused by several risk factors might cause some degradation that leads to a fatigue process. In these cases, BS models can be suitable for describing the propagation lifetime. However, since the cumulative damage is assumed to be normally distributed in the BS distribution, the parameter estimates from this model can be sensitive to outlying observations. In order to attenuate this influence, we present in this paper BS models, in which a Student-t distribution is assumed to explain the cumulative damage. In particular, we show that the maximum likelihood estimates of the Student-t log-BS models attribute smaller weights to outlying observations, which produce robust parameter estimates. Also, some inferential results are presented. In addition, based on local influence and deviance component and martingale-type residuals, a diagnostics analysis is derived. Finally, a motivating example from the medical field is analyzed using log-BS regression models. Since the parameter estimates appear to be very sensitive to outlying and influential observations, the Student-t log-BS regression model should attenuate such influences. The model checking methodologies developed in this paper are used to compare the fitted models.

Influence diagnostics in beta regression

We consider the issue of assessing influence of observations in the class of beta regression models, which is useful for modelling random variables that assume values in the standard unit interval and are affected by independent variables. We propose a Cook-like distance and also measures of local influence under different perturbation schemes. Applications using real data are presented. (c) 2008 Elsevier B.V.. All rights reserved.

Influence diagnostics in Birnbaum-Saunders nonlinear regression models

We consider the issue of assessing influence of observations in the class of Birnbaum-Saunders nonlinear regression models, which is useful in lifetime data analysis. Our results generalize those in Galea et al. [8] which are confined to Birnbaum-Saunders linear regression models. Some influence methods, such as the local influence, total local influence of an individual and generalized leverage are discussed. Additionally, the normal curvatures for studying local influence are derived under some perturbation schemes. We also give an application to a real fatigue data set.

A note on influence diagnostics in nonlinear mixed-effects elliptical models

This paper provides general matrix formulas for computing the score function, the (expected and observed) Fisher information and the A matrices (required for the assessment of local influence) for a quite general model which includes the one proposed by Russo et al. (2009). Additionally, we also present an expression for the generalized leverage on fixed and random effects. The matrix formulation has notational advantages, since despite the complexity of the postulated model, all general formulas are compact, clear and have nice forms. (C) 2010 Elsevier B.V. All rights reserved.

Diagnostics for the Developing World: Microfluidic Paper-Based Analytical Devices

Microfluidic paper-based analytical devices (mu PADs) are a new class of point-of-care diagnostic devices that are inexpensive, easy to use, and designed specifically for use in developing countries. (To listen to a podcast about this feature, please go to the Analytical Chemistry multimedia page at pubs.acs.org/page/ancham/audio/index.html.)