Maurizio Manuguerra

An evolutionary paradigm for carcinogenesis

Mutations seem to be only one of the mechanisms involved in carcinogenesis; selection of mutated clones is a second crucial mechanism. An evolutionary (darwinian) theory of carcinogenesis can be useful to explain some contradictory observations of epidemiology, and to provide a common theoretical framework for carcinogenesis. In both the selection of species and in carcinogenesis (selection of mutated cells), mutation and selection can be interpreted as necessary and insufficient causes. Selection presupposes competition among clones—that is, survival advantage of the mutated species; without selective forces a mutation is mute, while the lack of mutations makes selective advantage impossible. The identification of carcinogen related fingerprints is ambiguous: it can suggest both a genuine mutational hotspot left by the carcinogenic stimulus (like in tobacco related p53 mutations), and selective advantage of clones whose mutations seem to be not exposure specific (like in the case of aflatoxin). We present several examples of exposures that can increase the risk of cancer in humans not via mutations but through a putative mechanism of clone selection.

How to analyze the Visual Analogue Scale: Myths, truths and clinical relevance

Abstract Background and aims The Visual Analogue Scale (VAS) is a popular tool for the measurement of pain. A variety of statistical methods are employed for its analysis as an outcome measure, not all of them optimal or appropriate. An issue which has attracted much discussion in the literature is whether VAS is at a ratio or ordinal level of measurement. This decision has an influence on the appropriate method of analysis. The aim of this article is to provide an overview of current practice in the analysis of VAS scores, to propose a method of analysis which avoids the shortcomings of more traditional approaches, and to provide best practice recommendations for the analysis of VAS scores. Methods We report on the current usage of statistical methods, which fall broadly into two categories: those that assume a probability distribution for VAS, and those that do not. We give an overview of these methods, and propose continuous ordinal regression, an extension of current ordinal regression methodology, which is appropriate for VAS at an ordinal level of measurement. We demonstrate the analysis of a published data set using a variety of methods, and use simulation to compare the power of the various methods to detect treatment differences, in differing pain situations. Results We demonstrate that continuous ordinal regression provides the most powerful statistical analysis under a variety of conditions. Conclusions and Implications We recommend that in the situation in which no covariates besides treatment group are included in the analysis, distribution-free methods (Wilcoxon, Mann–Whitney) be used, as their power is indistinguishable from that of the proposed method. In the situation in which there are covariates which affect VAS, the proposed method is optimal. However, in this case, if the VAS scores are not concentrated around either extreme of the scale, normal-distribution methods (t-test, linear regression) are almost as powerful, and are recommended as a pragmatic choice. In the case of small sample size and VAS skewed to either extreme of the scale, the proposed method has vastly superior power to other methods.

A Decision Support Model and Tool to Assist Financial Decision-Making in Universities.

In this paper, a model and tool is proposed to assist universities and other mission-based organisations to ascertain systematically the optimal portfolio of projects, in any year, meeting the organisations risk tolerances and available funds. The model and tool presented build on previous work on university operations and decision support systems with an added robust process element to the task of reviewing projects that form part of a budget. In addition, the model and tool enable review of risk and return and assists universities and other organisations to determine the optimal mix of projects within a portfolio that provides the greatest financial return and the greatest contribution to the organisations mission. While the paper utilises key academic concepts, it has been created for practical use either inside or outside the budget cycle to assist decision-makers at universities and mission-based organisations. The aim has been to present in a manner to appeal to those with broad finance and accounting skills and to those with a more theoretical understanding of statistics.

Monte Carlo methods in spatio-temporal regression modeling of migration in the EU

Spatio-temporal regression models are well developed in disciplines such as, for example, climate and geostatistics, but have had little application in the modelling of economic phenomena. In this study we have modelled migrations of skilled workers and firms across the European Union during the period 1998-2010. The data set has been extracted from Eurostats Labour Force Survey (LFS) and contains information stratified by European region. We investigate whether the spatial component in the migration patterns is based either on neighbourhood or on some other metric (such as the existence of a flight connection). The complete spatio-temporal model has been implemented using conditional autoregressive (CAR) random effects in the Bayesian framework. In recent years, Bayesian methods have been widely applied to spatio-temporal modelling since they enable the use of Markov chain Monte Carlo (MCMC) samplers to estimate model parameters. In this paper, we consider the Bayesian Adaptive Independence Sampler (BAIS) for estimation, and compare different computing schemes. The results suggest that the regions with a stronger increase of skilled workers are more likely to have similarities with other advanced regions which they are connected to by flight connections, than with the regions at their border. The conclusion of this study is that graphical proximity is not a sufficient condition to reduce differences in skill endowments between regions.