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Background Stability of multiple testing procedures, defined as the standard deviation of total number of discoveries, can be used as an indicator of variability of multiple testing procedures. Improving stability of multiple testing procedures can help to increase the consistency of findings from replicated experiments. Benjamini-Hochberg’s and Storey’s q -value procedures are two commonly used multiple testing procedures for controlling false discoveries in genomic studies. Storey’s q -value procedure has higher power and lower stability than Benjamini-Hochberg’s procedure. To improve upon the stability of Storey’s q -value procedure and maintain its high power in genomic data analysis, we propose a new multiple testing procedure, named Bon-EV, to control false discovery rate (FDR) based on Bonferroni’s approach. Results Simulation studies show that our proposed Bon-EV procedure can maintain the high power of the Storey’s q -value procedure and also result in better FDR control and higher stability than Storey’s q -value procedure for samples of large size(30 in each group) and medium size (15 in each group) for either independent, somewhat correlated, or highly correlated test statistics. When sample size is small (5 in each group), our proposed Bon-EV procedure has performance between the Benjamini-Hochberg procedure and the Storey’s q -value procedure. Examples using RNA-Seq data show that the Bon-EV procedure has higher stability than the Storey’s q -value procedure while maintaining equivalent power, and higher power than the Benjamini-Hochberg’s procedure. Conclusions For medium or large sample sizes, the Bon-EV procedure has improved FDR control and stability compared with the Storey’s q -value procedure and improved power compared with the Benjamini-Hochberg procedure. The Bon-EV multiple testing procedure is available as the BonEV package in R for download at https://CRAN.R-project.org/package=BonEV .

We are concerned with a situation in which we would like to test multiple hypotheses with tests whose p-values cannot be computed explicitly but can be approximated using Monte Carlo simulation. This scenario occurs widely in practice. We are interested in obtaining the same rejections and non-rejections as the ones obtained if the p-values for all hypotheses had been available. The present article introduces a framework for this scenario by providing a generic algorithm for a general multiple testing procedure. We establish conditions that guarantee that the rejections and non-rejections obtained through Monte Carlo simulations are identical to the ones obtained with the p-values. Our framework is applicable to a general class of step-up and step-down procedures, which includes many established multiple testing corrections such as the ones of Bonferroni, Holm, Sidak, Hochberg or Benjamini–Hochberg. Moreover, we show how to use our framework to improve algorithms available in the literature in such a way as to yield theoretical guarantees on their results. These modifications can easily be implemented in practice and lead to a particular way of reporting multiple testing results as three sets together with an error bound on their correctness, demonstrated exemplarily using a real biological dataset.

Old buildings, in particular, those coming from the first half of 20\" century often suffer With increased dampness in masonry coming from different sources. Some of them, however, have aesthetically rendered valued facades which are worth saving. For the design of new alternative mortars for renovation interventions, it is essential to obtain properties of original renders. Mechanical properties of mortars are in general assessed on standardized test samples; however, this approach cannot be always well applicable for in situ taken mortars or rendering materials. This paper is devoted to the specification of complex experimental procedure allowing to effectively obtain strength parameters of in situ collected renders which will be comparable with laboratory produced alternative mortars. Throughout this paper the detailed description of in situ collected renders sampling procedure is provided. Further, the adjusted approach of flexural strength testing in three-point arrangement and compressive strength tests for both cut renders samples and laboratory prepared alternative mortars is outlined. The suitable set up of testing procedures of materials coming from old or historical buildings introduces a key area for obtaining experimental relevant data being the bases for their sustainable renovations.

Our understanding of phylogenetic relationships among bony fishes has been transformed by analysis of a small number of genes, but uncertainty remains around critical nodes. Genome-scale inferences so far have sampled a limited number of taxa and genes. Here we leveraged 144 genomes and 159 transcriptomes to investigate fish evolution with an unparalleled scale of data: >0.5 Mb from 1,105 orthologous exon sequences from 303 species, representing 66 out of 72 ray-finned fish orders. We apply phylogenetic tests designed to trace the effect of whole-genome duplication events on gene trees and find paralogy-free loci using a bioinformatics approach. Genome-wide data support the structure of the fish phylogeny, and hypothesis-testing procedures appropriate for phylogenomic datasets using explicit gene genealogy interrogation settle some long-standing uncertainties, such as the branching order at the base of the teleosts and among early euteleosts, and the sister lineage to the acanthomorph and percomorph radiations. Comprehensive fossil calibrations date the origin of all major fish lineages before the end of the Cretaceous.

Closed testing procedures are classically used for familywise error rate control, but they can also be used to obtain simultaneous confidence bounds for the false discovery proportion in all subsets of the hypotheses, allowing for inference robust to post hoc selection of subsets. In this paper we investigate the special case of closed testing with Simes local tests. We construct a novel fast and exact shortcut and use it to investigate the power of this approach when the number of hypotheses goes to infinity. We show that if a minimal level of signal is present, the average power to detect false hypotheses at any desired false discovery proportion does not vanish. Additionally, we show that the confidence bounds for false discovery proportion are consistent estimators for the true false discovery proportion for every nonvanishing subset. We also show close connections between Simes-based closed testing and the procedure of Benjamini and Hochberg.

Although gait speed is a widely used measure in older people, testing methods are highly variable. We conducted a systematic review to investigate the influence of testing procedures on resulting gait speed. We followed the PRISMA checklist for this systematic review. Two independent reviewers screened Pubmed and Embase for publications on pairwise comparisons of testing procedures of usual gait speed. Descriptives were abstracted from the included publications using a predefined extraction tool by two independent reviewers. We defined the cut-off for the minimal clinically imporant diffence in gait speed as 0.1 m/sec. Of a total of 2109 records identified for screening, 29 reports on 53 pairwise comparisons were analyzed. The median (range) difference in gait speed for dynamic versus static start was 0.06 (-0.02 to 0.35) m/sec (14 reports); for longer versus shorter test distance 0.04 (-0.05 to 0.23) m/sec (14 reports); for automatic versus manual timing 0.00 (-0.05 to 0.07) m/sec (12 reports), for hard versus soft surfaces -0.11 (-0.18 to 0.08) m/sec (six reports), and electronic walkways versus usual walk test 0.04 (-0.08 to 0.14) m/sec (seven reports), respectively. No report compared the effect of finishing procedures. The type of starting procedure, the length of the test distance, and the surface of the walkway may have a clinically relevant impact on measured gait speed. Manual timing resulted in statistically significant differences of measured gait speed as compared to automatic timing, but was below the level of clinical importance. These results emphasize that it is key to use a strictly standardized method for obtaining a reliable and valid measurement of gait speed.

The study of modularity is paramount for understanding trends of phenotypic evolution, and for determining the extent to which covariation patterns are conserved across taxa and levels of biological organization. However, biologists currently lack quantitative methods for statistically comparing the strength of modular signal across datasets, and a robust approach for evaluating alternative modular hypotheses for the same dataset. As a solution to these challenges, we propose an effect size measure (ZCR ) derived from the covariance ratio, and develop hypothesis-testing procedures for their comparison. Computer simulations demonstrate that ZCR displays appropriate statistical properties and low levels of mis-specification, implying that it correctly identifies modular signal, when present. By contrast, alternative methods based on likelihood (EMMLi) and goodness of fit (MINT) suffer from high false positive rates and high model mis-specification rates. An empirical example in sigmodontine rodent mandibles is provided to illustrate the utility of ZCR for comparing modular hypotheses. Overall, we find that covariance ratio effect sizes are useful for comparing patterns of modular signal across datasets or for evaluating alternative modular hypotheses for the same dataset. Finally, the statistical philosophy for pairwise model comparisons using effect sizes should accommodate any future analytical developments for characterizing modular signal.

Background Threshold regression models are a diverse set of non-regular regression models that all depend on change points or thresholds. They provide a simple but elegant and interpretable way to model certain kinds of nonlinear relationships between the outcome and a predictor. Results The R package chngpt provides both estimation and hypothesis testing functionalities for four common variants of threshold regression models. All allow for adjustment of additional covariates not subjected to thresholding. We demonstrate the consistency of the estimating procedures and the type 1 error rates of the testing procedures by Monte Carlo studies, and illustrate their practical uses using an example from the study of immune response biomarkers in the context of Mother-To-Child-Transmission of HIV-1 viruses. Conclusion chngpt makes several unique contributions to the software for threshold regression models and will make these models more accessible to practitioners interested in modeling threshold effects.

Phylogenetic signal is the tendency for closely related species to display similar trait values due to their common ancestry. Several methods have been developed for quantifying phylogenetic signal in univariate traits and for sets of traits treated simultaneously, and the statistical properties of these approaches have been extensively studied. However, methods for assessing phylogenetic signal in high-dimensional multivariate traits like shape are less well developed, and their statistical performance is not well characterized. In this article, I describe a generalization of the statistic of Blomberg et al. that is useful for quantifying and evaluating phylogenetic signal in highly dimensional multivariate data. The method (Kmult) is found from the equivalency between statistical methods based on covariance matrices and those based on distance matrices. Using computer simulations based on Brownian motion, I demonstrate that the expected value of Kmult remains at 1.0 as trait variation among species is increased or decreased, and as the number of trait dimensions is increased. By contrast, estimates of phylogenetic signal found with a squared-change parsimony procedure for multivariate data change with increasing trait variation among species and with increasing numbers of trait dimensions, confounding biological interpretations. I also evaluate the statistical performance of hypothesis testing procedures based on and find that the method displays appropriate Type I error and high statistical power for detecting phylogenetic signal in highdimensional data. Statistical properties of Kmult were consistent for simulations using bifurcating and random phylogenies, for simulations using different numbers of species, for simulations that varied the number of trait dimensions, and for different underlying models of trait covariance structure. Overall these findings demonstrate that provides a useful means of evaluating phylogenetic signal in high-dimensional multivariate traits. Finally, I illustrate the utility of the new approach by evaluating the strength of phylogenetic signal for head shape in a lineage of Plethodon salamanders.

Bioelectrical impedance vector analysis (BIVA) is a general concept that includes all methodologies used in the analysis of the bioelectrical vector, whereas the \"classic\" BIVA is a patented methodology included among these methods of analysis. Once this was clarified, the systematic review of the literature provides a deeper insight into the scope and range of application of BIVA in sport and exercise. The main goal of this work was to systematically review the sources on the applications of BIVA in sport and exercise and to examine its usefulness and suitability as a technique for the evaluation of body composition, hydration status, and other physiological and clinical relevant characteristics, ultimately to trace future perspectives in this growing area, including a proposal for a research agenda. Systematic literature searches in PubMed, SPORTDiscus and Scopus databases up to July, 2017 were conducted on any empirical investigations using phase-sensitive bioimpedance instruments to perform BIVA within exercise and sport contexts. The search included healthy sedentary individuals, physically active subjects and athletes. Nineteen eligible papers were included and classified as sixteen original articles and three scientific conference communications. Three studies analysed short-term variations in the hydration status evoked by exercise/training through whole-body measurements, eleven assessed whole-body body composition changes induced by long-term exercise, four compared athletic groups or populations using the whole-body assessment, and two analysed bioelectrical patterns of athletic injuries or muscle damage through localised bioimpedance measurements. BIVA is a relatively new technique that has potential in sport and exercise, especially for the assessment of soft-tissue injury. On the other hand, the current tolerance ellipses of \"classic\" BIVA are not a valid method to identify dehydration in individual athletes and a new approach is needed. \"Specific\" BIVA, a method which proposes a correction of bioelectrical values for body geometry, emerges as the key to overcome \"classic\" BIVA limitations regarding the body composition assessment. Further research establishing standardised testing procedures and investigating the relationship between physiology and the bioelectrical signal in sport and exercise is needed.