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There has been a significant advancement in the application of statistical tools in plant pathology during the past four decades. These tools include multivariate analysis of disease dynamics involving principal component analysis, cluster analysis, factor analysis, pattern analysis, discriminant analysis, multivariate analysis of variance, correspondence analysis, canonical correlation analysis, redundancy analysis, genetic diversity analysis, and stability analysis, which involve in joint regression, additive main effects and multiplicative interactions, and genotype-by-environment interaction biplot analysis. The advanced statistical tools, such as non-parametric analysis of disease association, meta-analysis, Bayesian analysis, and decision theory, take an important place in analysis of disease dynamics. Disease forecasting methods by simulation models for plant diseases have a great potentiality in practical disease control strategies. Common mathematical tools such as monomolecular, exponential, logistic, Gompertz and linked differential equations take an important place in growth curve analysis of disease epidemics. The highly informative means of displaying a range of numerical data through construction of box and whisker plots has been suggested. The probable applications of recent advanced tools of linear and non-linear mixed models like the linear mixed model, generalized linear model, and generalized linear mixed models have been presented. The most recent technologies such as micro-array analysis, though cost effective, provide estimates of gene expressions for thousands of genes simultaneously and need attention by the molecular biologists. Some of these advanced tools can be well applied in different branches of rice research, including crop improvement, crop production, crop protection, social sciences as well as agricultural engineering. The rice research scientists should take advantage of these new opportunities adequately in adoption of the new highly potential advanced technologies while planning experimental designs, data collection, analysis and interpretation of their research data sets. [ABSTRACT FROM AUTHOR]

Highlights: [•] We present a review on the statistical tools used to deal with multiple comparisons. [•] We discuss the fundamental theoretical concepts and ideas. [•] We present application to metabolomics and proteomics. [ABSTRACT FROM AUTHOR]

Abstract: Determination of the geographical origin of food and beverages has been a growing issue over the past decade for all countries around the world, mostly because of the concern of consumers about the authenticity of the food that they eat. An increasing number of research articles in the past five years have investigated the elemental composition and the isotope ratios as indicators to determine the origin of food and beverages. The indicators commonly used are stable-isotope ratios of hydrogen, oxygen, nitrogen, carbon and sulfur, combined with isotope ratios of strontium and lead, and elemental concentrations. This article reviews the application of multi-element and multi-isotope methods and the statistical tools used to determine the geographical origin of food and beverages in the European Union from 2008 to early 2012. We review 48 papers, divided between different food commodities. [Copyright &y& Elsevier]

DSCTool is a statistical tool for comparing performance of stochastic optimization algorithms on a single benchmark function (i.e. single-problem analysis) or a set of benchmark functions (i.e., multiple-problem analysis). DSCTool implements a recently proposed approach, called Deep Statistical Comparison (DSC), and its variants. DSC ranks optimization algorithms by comparing distributions of obtained solutions for a problem instead of using a simple descriptive statistic such as the mean or the median. The rankings obtained for an individual problem give the relations between the performance of the applied algorithms. To compare optimization algorithms in the multiple-problem scenario, an appropriate statistical test must be applied to the rankings obtained for a set of problems. The main advantage of DSCTool are its REST web services, which means all its functionalities can be accessed from any programming language. In this paper, we present the DSCTool in detail with examples for its usage. • DSCTool - a statistical tool for comparing stochastic optimization algorithms. • REST web services implementation allows access from any programming language. • Identification of statistical and practical significance. • Understanding of exploitation and exploration powers of single-objective algorithms. • Ranking of multi-objective algorithms using an ensemble of quality indicators. [ABSTRACT FROM AUTHOR]