Ecophysiology of the developing total bacterial and lactobacillus communities in the terminal small intestine of weaning piglets.

Weaning of the pig is generally regarded as a stressful event which could lead to clinical implications because of the changes in the intestinal ecosystem. The functional properties of microbiota inhabiting the pig's small intestine (SI), including lactobacilli which are assumed to exert health-promoting properties, are yet poorly described. Thus, we determined the ecophysiology of bacterial groups and within genus Lactobacillus in the SI of weaning piglets and the impact of dietary changes. The SI contents of 20 piglets, 4 killed at weaning (only sow milk and no creep feed) and 4 killed at 1, 2, 5, and 11 days post weaning (pw; cereal-based diet) were examined for bacterial cell count and bacterial metabolites by fluorescence in situ hybridization (FISH). Lactobacilli were the predominant group in the SI except at 1 day pw because of a marked reduction in their number. On day 11 pw, bifidobacteria and E. coli were not detected, and Enterobacteriaceae and members of the Clostridium coccoides/Eubacterium rectale cluster were only found occasionally. L. sobrius/L. amylovorus became dominant species whereas the abundance of L. salivarius and L. gasseri/johnsonii declined. Concentration of lactic acid increased pw whereas pH, volatile fatty acids, and ammonia decreased. Carbohydrate utilization of 76 Lactobacillus spp. isolates was studied revealing a shift from lactose and galactose to starch, cellobiose, and xylose, suggesting that the bacteria colonizing the SI of piglets adapt to the newly introduced nutrients during the early weaning period. Identification of isolates based on partial 16S rRNA gene sequence data and comparison with fermentation data furthermore suggested adaptation processes below the species level. The results of our study will help to understand intestinal bacterial ecophysiology and to develop nutritional regimes to prevent or counteract complications during the weaning transition.

Multiple test procedures using an upper bound of the number of true hypotheses and their use for evaluating high-dimensional EEG data.

Frequency analyses of EEG data yield large data sets, which are high-dimensional and have to be evaluated statistically without a large number of false positive statements. There exist several methods to deal with this problem in multiple comparisons. Knowing the number of true hypotheses increases the power of some multiple test procedures, however the number of true hypotheses is unknown, in general, and must be estimated. In this paper, we derive two new multiple test procedures by using an upper bound for the number of true hypotheses. Our first procedure controls the generalized family-wise error rate, and thus is an improvement of the step-down procedure of Hommel and Hoffmann [Hommel G., Hoffmann T. Controlled uncertainty. In: Bauer P. Hommel G. Sonnemann E., editors. Multiple Hypotheses Testing, Heidelberg: Springer 1987;ISBN 3540505598:p. 154-61]. The second new procedure controls the false discovery proportion and improves upon the approach of Lehmann and Romano [Lehmann E.L., Romano J.P. Generalizations of the familywise error rate. Ann. Stat. 2005;33:1138-54]. By Monte-Carlo simulations, we show how the gain in power depends upon the accuracy of the estimate of the number of true hypotheses. The gain in power of our procedures is demonstrated in an example using EEG data on the processing of memorized lexical items.

Linear and generalized linear models for the detection of QTL effects on within-subject variability.

Quantitative trait loci (QTLs) may affect not only the mean of a trait but also its variability. A special aspect is the variability between multiple measured traits of genotyped animals, such as the within-litter variance of piglet birth weights. The sample variance of repeated measurements is assigned as an observation for every genotyped individual. It is shown that the conditional distribution of the non-normally distributed trait can be approximated by a gamma distribution. To detect QTL effects in the daughter design, a generalized linear model with the identity link function is applied. Suitable test statistics are constructed to test the null hypothesis H(0): No QTL with effect on the within-litter variance is segregating versus H(A): There is a QTL with effect on the variability of birth weight within litter. Furthermore, estimates of the QTL effect and the QTL position are introduced and discussed. The efficiency of the presented tests is compared with a test based on weighted regression. The error probability of the first type as well as the power of QTL detection are discussed and compared for the different tests.

Power and sample size computations in simultaneous tests for non-inferiority based on relative margins.

In this paper, we address the problem of calculating power and sample sizes associated with simultaneous tests for non-inferiority. We consider the case of comparing several experimental treatments with an active control. The approach is based on the ratio view, where the common non-inferiority margin is chosen to be some percentage of the mean of the control treatment. Two power definitions in multiple hypothesis testing, namely, complete power and minimal power, are used in the computations. The sample sizes associated with the ratio-based inference are also compared with that of a comparable inference based on the difference of means for various scenarios. It is found that the sample size required for ratio-based inferences is smaller than that of difference-based inferences when the relative non-inferiority margin is less than one and when large response values indicate better treatment effects. The results are illustrated with examples.

Multiple quantitative trait loci mapping with cofactors and application of alternative variants of the false discovery rate in an enlarged granddaughter design.

The experimental power of a granddaughter design to detect quantitative trait loci (QTL) in dairy cattle is often limited by the availability of progeny-tested sires, by the ignoring of already identified QTL in the statistical analysis, and by the application of stringent experimentwise significance levels. This study describes an experiment that addressed these points. A large granddaughter design was set up that included sires from two countries (Germany and France), resulting in almost 2000 sires. The animals were genotyped for markers on nine different chromosomes. The QTL analysis was done for six traits separately using a multimarker regression that included putative QTL on other chromosomes as cofactors in the model. Different variants of the false discovery rate (FDR) were applied. Two of them accounted for the proportion of truly null hypotheses, which were estimated to be 0.28 and 0.3, respectively, and were therefore tailored to the experiment. A total of 25 QTL could be mapped when cofactors were included in the model-7 more than without cofactors. Controlling the FDR at 0.05 revealed 31 QTL for the two FDR methods that accounted for the proportion of truly null hypotheses. The relatively high power of this study can be attributed to the size of the experiment, to the QTL analysis with cofactors, and to the application of an appropriate FDR.