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Consequently, based on the clear inadequacy of the statistical power used to refute toxic effects (for instance the unquestionable large size effects in this study), knowing also that billions of people and animals can consume these products prior to the performance of appropriate in vivo safety evaluation, we applied an appropriate, experimentally validated statistical analytical methodology [], elements of which are described below.

We first repeated the same statistical analysis as conducted by Monsanto to verify descriptive statistics (sample size, means, and standard deviation) and ANOVA per sex, per variable and for each of the three GMO. For all that, the normality of the residues was tested using the Shapiro test and the homoscedasticity (homogeneity of the variances) using the Bartlett test. In the case where the Shapiro and Bartlett tests were non significant (*p > 0.05 and **p > 0.01, respectively) we performed an ANOVA [, ], and in the case of heteroscedasticity the approximate Welch method was used. In the case where the Shapiro test was significant, we performed the Kruskal-Wallis rank sum test [, ].

We then analyzed the effects of the GM maize varieties on each sex and each diet by pairwise comparisons of the parameters of GM-fed rats versus control groups, and subsequently to the unrelated non-GM maize reference groups. The statistical differences between reference and control groups were calculated in order to study the effects of the different normal diets per se (due to differences in salts, sugars, minerals, vitamins, pesticides, etc composition), and indicated by contrast to Monsanto's work (see legend Table ). In order to select the appropriate two-tailed comparison test [], we again studied first normality (Shapiro test) and variance equality (F test). According to the results, we performed the adapted test; that is, an unpaired t test, a Welch corrected t test or a Mann-Whitney test (which is generally more appropriate with a sample size of 10). To perform multiple pairwise comparisons, we used the False Discovery Rate approach (FDR, []) to calculate adjusted p-values, in order to limit the rate of false positives to 5%. We preferred Benjamini and Yekutieli's method [] rather than that of Benjamini and Hochberg [] as the parameters under investigation are not independent. In addition, after centering and scaling the data, Principal Components Analysis (PCA, []) was performed in order to study the scattering of the different factors (sex, period, diet, dose and group). Finally, we established per group for each rat and by parameter the representations and paired tests corresponding to the temporal changes between the two feeding periods.

We used the R language [] version 2.5 for all statistical computations [] with the appropriate package: pwr package for power studies, the bioconductor's multtest package for FDR [-] and the ADE4 package [, ] for multivariate analysis.

We have previously reported indications of toxicity in rats fed with MON 863 GM maize for 90 days []. However, these signs of toxicity alone do not constitute proof of adverse health effects. We have therefore extended our initial analysis on the MON 863 feeding data by collectively compiling the significant differences observed in the physiological and biochemical parameters measured in feeding trials of rats with each of the three GM maize varieties MON 863, MON 810 and NK 603 (Tables , ; Annex Table ). When we then initially compare all p-values in our calculations with those of Monsanto (significant and non significant differences, Annex Table ), we obtain ratios of 432/452 (NK 603), 435/450 (MON 810) and 442/470 (MON 863). By employing our statistical methods even if we reached a concordance with Monsanto's results (Annex Table ), the level of precision of the main effects and their interpretation are highly different. Therefore, we then progressed to consider only relative differences over 5% (Tables and ).