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Hazelnuts are an economically important nut which is consumed world-wide. It is prone to infection by Aspergillus flavus and contamination with aflatoxins. Taking Tukey's hazelnuts as a research object, the objective of this study were to (a) quantify respiration rates and (b) dry matter losses (DMLs) and (c) aflatoxin B1 (AFB1) contamination of naturally stored shelled hazelnuts at different temperatures and water availabilities. Subsequently, shelled hazelnuts were inoculated with additional A. flavus inoculum prior to storage to examine effects on dry matter losses and on AFB1 contamination. Maximum respiration of hazelnuts and associated mycoflora was under wetter conditions of 0.90-0.95 water activity (aw = 12.5%-18% moisture content). This resulted in between approx. 10% DML at 25 and 30 after 5 days storage. Inoculation and storage of shelled hazelnuts + A. flavus inoculum resulted in similar patterns of respiration with optimal levels at 25-30 and >0.90 aw. Indeed, AFB1 contamination was highest at the maximum water levels tested of 0.90 aw. Indeed the contamination level exceeded the legislative limits set by the EU for AFB1 contamination of these nuts. Correlation between DMLs and all the AFB1 data in both sets of studies showed that very small changes in DML due to poor drying or storage of ≥0.6% resulted in AFB1 contamination levels exceeding the EU legislative limits. Thus, efficient drying and safe monitored storage is necessary to minimise the risk of AFB1 contamination in this economically important commodity and to avoid exposure of consumers to such toxins. [ABSTRACT FROM AUTHOR]

The manuscript introduces a framework for presenting the results of the statistical analysis of experiments with multiple responses and multiple factors. We propose a utilisation of factors scaling to enable a transformation that combines main effects, quadratic effects and interactions into a meaningful summary that allows the scientist/experimenter to immediately recognise the most influential factors for a given response. The framework does not replace the thorough evaluation of the results but provides a clear high‐level summary of the relative importance of findings. The visualisation of such factor importance, using intensity heatmaps, allows the immediate understanding of the results across multiple responses that efficiently guides a following detailed analysis of certain responses and factors and contributes in designing subsequent experiments. The methodology is applied to a real industrial experiment and to a simulated data set with a larger number of responses and factors. [ABSTRACT FROM AUTHOR]

Biochemical mechanism studies often assume statistical models derived from Michaelis–Menten kinetics, which are used to approximate initial reaction rate data given the concentration level of a single substrate. In experiments dealing with industrial applications, however, there are typically a wide range of kinetic profiles where more than one factor is controlled. We focus on optimal design of such experiments requiring the use of multifactor hybrid nonlinear models, which presents a considerable computational challenge. We examine three different candidate models and search for tailor-made D- or weighted-A-optimal designs that can ensure the efficiency of nonlinear least squares estimation. We also study a compound design criterion for discriminating between two candidate models, which we recommend for design of advanced kinetic studies. Supplementary materials accompanying this paper appear on-line [ABSTRACT FROM AUTHOR]

Textbooks on response surface methodology generally stress the importance of lack-of-fit tests and estimation of pure error. For lack-of-fit tests to be possible and other inference to be unbiased, experiments should allow for pure-error estimation. Therefore, they should involve replicated treatments. While most textbooks focus on lack-of-fit testing in the context of completely randomized designs, many response surface experiments are not completely randomized and require block or split-plot structures. The analysis of data from blocked or split-plot experiments is generally based on a mixed regression model with two variance components instead of one. In this article, we present a novel approach to designing blocked and split-plot experiments which ensures that the two variance components can be efficiently estimated from pure error and guarantees a precise estimation of the response surface model. Our novel approach involves a new Bayesian compound D-optimal design criterion which pays attention to both the variance components and the fixed treatment effects. One part of the compound criterion (the part concerned with the treatment effects) is based on the response surface model of interest, while the other part (which is concerned with pure-error estimates of the variance components) is based on the full treatment model. We demonstrate that our new criterion yields split-plot designs that outperform existing designs from the literature both in terms of the precision of the pure-error estimates and the precision of the estimates of the factor effects. [ABSTRACT FROM AUTHOR]

Summary: Many chemical and biological experiments involve multiple treatment factors and often it is convenient to fit a non‐linear model in these factors. This non‐linear model can be mechanistic, empirical or a hybrid of the two. Motivated by experiments in chemical engineering, we focus on D‐optimal designs for multifactor non‐linear response surfaces in general. To find and study optimal designs, we first implement conventional point and co‐ordinate exchange algorithms. Next, we develop a novel multiphase optimization method to construct D‐optimal designs with improved properties. The benefits of this method are demonstrated by application to two experiments involving non‐linear regression models. The designs obtained are shown to be considerably more informative than designs obtained by using traditional design optimality algorithms. [ABSTRACT FROM AUTHOR]

Many optimal experimental designs depend on one or more unknown model parameters. In such cases, it is common to use Bayesian optimal design procedures to seek designs that perform well over an entire prior distribution of the unknown model parameter(s). Generally, Bayesian optimal design procedures are viewed as computationally intensive. This is because they require numerical integration techniques to approximate the Bayesian optimality criterion at hand. The most common numerical integration technique involves pseudo Monte Carlo draws from the prior distribution(s). For a good approximation of the Bayesian optimality criterion, a large number of pseudo Monte Carlo draws is required. This results in long computation times. As an alternative to the pseudo Monte Carlo approach, we propose using computationally efficient Gaussian quadrature techniques. Since, for normal prior distributions, suitable quadrature techniques have already been used in the context of optimal experimental design, we focus on quadrature techniques for nonnormal prior distributions. Such prior distributions are appropriate for variance components, correlation coefficients, and any other parameters that are strictly positive or have upper and lower bounds. In this article, we demonstrate the added value of the quadrature techniques we advocate by means of the Bayesian D-optimality criterion in the context of split-plot experiments, but we want to stress that the techniques can be applied to other optimality criteria and other types of experimental designs as well. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

A multi-stratum design is a useful tool for industrial experimentation, where factors that have levels which are harder to set than others, due to time or cost constraints, are frequently included. The number of different levels of hardness to set defines the number of strata that should be used. The simplest case is the split-plot design, which includes two strata and two sets of factors defined by their level of hardness-to-set. In this paper, we propose a novel computational algorithm which can be used to construct optimal multi-stratum designs for any number of strata and up to six optimality criteria simultaneously. Our algorithm allows the study of the entire Pareto front of the optimization problem and the selection of the designs representing the desired trade-off between the competing objectives. We apply our algorithm to several real case scenarios and we show that the efficiencies of the designs obtained present experimenters with several good options according to their objectives. [ABSTRACT FROM AUTHOR]

Meta-analysis of rare event studies has recently become a subject of controversy and debate. We will argue and demonstrate in this paper that the occurrence of zero events in clinical trials or cohort studies, even if zeros occur in both arms (the case of a double-zero trial), is less problematic, at least from a statistical perspective, if the available statistical tools are applied in the appropriate way. In particular, it is neither necessary nor advisable to exclude studies with zero events from the meta-analysis. In terms of statistical tools, we will focus here on Mantel-Haenszel techniques, mixed Poisson regression and related regression models. [ABSTRACT FROM AUTHOR]

Cooperation is fundamental to the evolution of human society. We regularly observe cooperative behaviour in everyday life and in controlled experiments with anonymous people, even though standard economic models predict that they should deviate from the collective interest and act so as to maximise their own individual payoff. However, there is typically heterogeneity across subjects: some may cooperate, while others may not. Since individual factors promoting cooperation could be used by institutions to indirectly prime cooperation, this heterogeneity raises the important question of who these cooperators are. We have conducted a series of experiments to study whether benevolence, defined as a unilateral act of paying a cost to increase the welfare of someone else beyond one's own, is related to cooperation in a subsequent one-shot anonymous Prisoner's dilemma. Contrary to the predictions of the widely used inequity aversion models, we find that benevolence does exist and a large majority of people behave this way. We also find benevolence to be correlated with cooperative behaviour. Finally, we show a causal link between benevolence and cooperation: priming people to think positively about benevolent behaviour makes them significantly more cooperative than priming them to think malevolently. Thus benevolent people exist and cooperate more. [ABSTRACT FROM AUTHOR]

In general, modeling data from blocked and split-plot response surface experiments requires the use of generalized least squares and the estimation of two variance components. The literature on the optimal design of blocked and split-plot response surface experiments, however, focuses entirely on the precise estimation of the fixed factor effects and completely ignores the necessity to estimate the variance components as well. To overcome this problem, we propose a new Bayesian optimal design criterion which focuses on both the variance components and the fixed effects. A novel feature of the criterion is that it incorporates prior information about the variance components through log-normal or beta prior distributions. The resulting designs allow for a more powerful statistical inference than traditional optimal designs. In our algorithm for generating optimal blocked and split-plot designs, we implement efficient quadrature approaches for the numerical approximation of the new optimal design criterion. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

The problem of variable selection is fundamental to statistical modelling in diverse fields of sciences. In this paper, we study in particular the problem of selecting important variables in regression problems in the case where observations and labels of a real-world dataset are available. At first, we examine the performance of several existing statistical methods for analyzing a real large trauma dataset which consists of 7000 observations and 70 factors, that include demographic, transport and intrahospital data. The statistical methods employed in this work are the nonconcave penalized likelihood methods (SCAD, LASSO, and Hard), the generalized linear logistic regression, and the best subset variable selection (with AIC and BIC), used to detect possible risk factors of death. Supersaturated designs (SSDs) are a large class of factorial designs which can be used for screening out the important factors from a large set of potentially active variables. This paper presents a new variable selection approach inspired by supersaturated designs given a dataset of observations. The merits and the efectiveness of this approach for identifying important variables in observational studies are evaluated by considering several two-levels supersaturated designs, and a variety of different statistical models with respect to the combinations of factors and the number of observations. The derived results are encouraging since the alternative approach using supersaturated designs provided specific information that are logical and consistent with the medical experience, which may also assist as guidelines for trauma management. [ABSTRACT FROM AUTHOR]

In many industrial experiments, complete randomization of the runs is impossible as, often, they involve factors whose levels are hard or costly to change. In such cases, the split-plot design is a cost-efficient alternative that reduces the number of independent settings of the hard-to-change factors. In general, the use of generalized least squares is required for model estimation based on data from split-plot designs. However, the ordinary least squares estimator is equivalent to the generalized least squares estimator for some split-plot designs, including some second-order split-plot response surface designs. These designs are calledequivalent-estimation designs. An important consequence of the equivalence is that basic experimental design software can be used for model estimation. This article introduces two new families of equivalent-estimation split-plot designs, one based on subset designs and another based on supplementary difference set designs. The resulting designs complement existing catalogs of equivalent-estimation designs and allow for a more flexible choice of the number of hard-to-change factors, the number of easy-to-change factors, the number and size of whole plots, and the total sample size. It is shown that many of the newly proposed designs possess good predictive properties when compared to D-optimal split-plot designs. [ABSTRACT FROM AUTHOR]

This study examined the relationship between storage environmental factors (water activity (aw) (0.89–0.97) and temperature (15°C–30°C)), colonisation of wheat and maize by Fusarium graminearum and F. verticillioides respectively and the dry matter losses (DMLs) caused and quantified by contamination with deoxynivalenol (DON), zearalenone (ZEA) and fumonisins (FUMs) during storage. Fungal growth was assessed by the amount of CO2 produced under different interacting conditions of aw and temperature. DMLs were quantified using the cumulative CO2 data, and these were shown to increase as temperature and aw increased. The amount of DON, ZEA (wheat for human consumption) and FUMs (feed maize) produced was significantly affected by the storage conditions. The three toxins however showed different patterns of production. Optimum for DON was at the wettest conditions (0.97aw) and the highest temperature assessed (30°C), whereas for ZEA this shifted to 25°C. FUMs were produced in higher amounts in maize at 30°C and 0.97aw; however, at intermediate aw levels (0.955aw), the highest production occurred at 25°C followed by 20°C. Polynomial models were developed for the effect of the storage factors on DMLs and toxin production. DMLs under different environmental conditions were significantly correlated with DON and FUMs. DON contamination was above the EU limits in at least 80% of the wheat samples with DMLs >1%, whereas at least 70% of the same samples contained ZEA above the respective EU legislative limits. Similarly, at least 75% of the maize samples with DMLs ≥ 0.9% exceeded the EU limits for the sum of FUMs in feed. These results show that it may be possible to use temporal CO2 production during storage of grains as an indicator of the level of contamination of the grain with mycotoxins. [ABSTRACT FROM AUTHOR]

Variable selection is fundamental to high-dimensional statistical modeling in diverse fields of sciences. In our health study, different statistical methods are applied to analyze trauma annual data, collected by 30 General Hospitals in Greece. The dataset consists of 6334 observations and 111 factors that include demographic, transport, and clinical data. The statistical methods employed in this work are the nonconcave penalized likelihood methods, Smoothly Clipped Absolute Deviation, Least Absolute Shrinkage and Selection Operator, and Hard, the maximum partial likelihood estimation method, and the best subset variable selection, adjusted to Cox's proportional hazards model and used to detect possible risk factors, which affect the length of stay in a hospital. A variety of different statistical models are considered, with respect to the combinations of factors while censored observations are present. A comparative survey reveals several differences between results and execution times of each method. Finally, we provide useful biological justification of our results. [ABSTRACT FROM AUTHOR]

The quality of harvested wheat grain can deteriorate markedly during the post-harvest management stages. Biotic factors, such as grain type and ripeness, coupled with the prevailing abiotic factors, such as water content and temperature, and also preservative concentration will influence the safe storage life and the level of contamination with mycotoxins. These mycotoxins include deoxynivalenol (DON) produced pre-harvest and zearalenone (ZEA) produced post-harvest by Fusarium graminearum and Fusarium poae, respectively, ochratoxin (OTA) produced by Penicillium verrucosum post-harvest in cool damp northern European climates, and perhaps T-2 and HT-2 toxins produced by Fusarium langsethiae. This review presents recent data on the relationship between dry matter losses caused by F. graminearum under different environmental regimes (water activities, temperatures) and the level of contamination with DON. This is important as poor post-harvest drying and storage management may exacerbate DON contamination already present pre-harvest. It is thus critical to relate the environmental factors in stored wheat grain during storage, especially of intergranular relative humidity (RH) and temperature, to safe storage periods without spoilage or risk from increased DON contamination. The growth/no growth and DON/no DON (F. graminearum) and OTA/no toxin production (P. verrucosum) have been used to build a model with a simple interface to link temperature and RH values to the potential risk level which may allow growth or toxin production. This paper also considers the use of modified atmospheres, preservatives and biocontrol to minimise DON and OTA in moist wheat grain. These approaches together with clear monitoring criteria and hygiene could contribute to better post-harvest management of stored temperate cereals and ensure that mycotoxin contamination is minimised during this key phase in the food/feed chain. [ABSTRACT FROM AUTHOR]

Two garlic-derived compounds, Propyl Propane Thiosulfonate (PTS) and Propyl Propane Thiosulfinate (PTSO), were examined for their efficacy against mycotoxigenic Fusarium species (F. graminearum, F. langsethiae, F. verticillioides). The objectives were to assess the inhibitory effect of these compounds on growth and mycotoxin production in vitro, and in situ in artificially inoculated wheat, oats and maize with one isolate of each respectively, at different water activity (aw) conditions when stored for up to 20 days at 25 °C. In vitro, 200 ppm of either PTS or PTSO reduced fungal growth by 50–100% and mycotoxin production by >90% depending on species, mycotoxin and aw conditions on milled wheat, oats and maize respectively. PTS was generally more effective than PTSO. Deoxynivalenol (DON) and zearalenone (ZEN) were decreased by 50% with 80 ppm PTSO. One-hundred ppm of PTS reduced DON and ZEN production in wheat stored at 0.93 aw for 20 days, although contamination was still above the legislative limits. Contrasting effects on T-2/HT-2 toxin contamination of oats was found depending on aw, with PTS stimulating production under marginal conditions (0.93 aw), but at 0.95 aw effective control was achieved with 100 ppm. Treatment of stored maize inoculated with F. verticilliodies resulted in a stimulation of total fumonsins in most treatments. The potential use of such compounds for mycotoxin control in stored commodities is discussed. [ABSTRACT FROM AUTHOR]