The effects of pre-exhaustion, exercise order, and rest intervals in a full-body resistance training intervention

Pre-exhaustion (PreEx) training is advocated on the principle that immediately preceding a compound exercise with an isolation exercise can target stronger muscles to pre-exhaust them to obtain greater adaptations in strength and size. However, research considering PreEx training method is limited. The present study looked to examine the effects of a PreEx training programme. Thirty-nine trained participants (male = 9, female = 30) completed 12 weeks of resistance training in 1 of 3 groups: a group that performed PreEx training (n = 14), a group that performed the same exercise order with a rest interval between exercises (n = 17), and a control group (n = 8) that performed the same exercises in a different order (compound exercises prior to isolation). No significant between-group effects were found for strength in chest press, leg press, or pull-down exercises, or for body composition changes. Magnitude of change was examined for outcomes also using effect size (ES). ESs for strength changes were considered large for each group for every exercise (ranging 1.15 to 1.62). In conclusion, PreEx training offers no greater benefit to performing the same exercises with rest between them compared with exercises performed in an order that prioritises compound movements

The wound hormones of plants. I. Traumatin, the active principle of the bean test

An attempt has been made in the present investigation to work out a specific test for wound hormone activity, and to use this test in the purification of the active principle of plant tissue extracts. In this way we have isolated a substance, possessing high wound hormone activity, for which we propose the name “traumatin.” This name seems particularly appropriate in view of the historical background of the subject

Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem

This paper studies the multiplicity-correction effect of standard Bayesian variable-selection priors in linear regression. Our first goal is to clarify when, and how, multiplicity correction happens automatically in Bayesian analysis, and to distinguish this correction from the Bayesian Ockham's-razor effect. Our second goal is to contrast empirical-Bayes and fully Bayesian approaches to variable selection through examples, theoretical results and simulations. Considerable differences between the two approaches are found. In particular, we prove a theorem that characterizes a surprising aymptotic discrepancy between fully Bayes and empirical Bayes. This discrepancy arises from a different source than the failure to account for hyperparameter uncertainty in the empirical-Bayes estimate. Indeed, even at the extreme, when the empirical-Bayes estimate converges asymptotically to the true variable-inclusion probability, the potential for a serious difference remains.Comment: Published in at http://dx.doi.org/10.1214/10-AOS792 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Poisson Latent Feature Calculus for Generalized Indian Buffet Processes

The purpose of this work is to describe a unified, and indeed simple, mechanism for non-parametric Bayesian analysis, construction and generative sampling of a large class of latent feature models which one can describe as generalized notions of Indian Buffet Processes(IBP). This is done via the Poisson Process Calculus as it now relates to latent feature models. The IBP was ingeniously devised by Griffiths and Ghahramani in (2005) and its generative scheme is cast in terms of customers entering sequentially an Indian Buffet restaurant and selecting previously sampled dishes as well as new dishes. In this metaphor dishes corresponds to latent features, attributes, preferences shared by individuals. The IBP, and its generalizations, represent an exciting class of models well suited to handle high dimensional statistical problems now common in this information age. The IBP is based on the usage of conditionally independent Bernoulli random variables, coupled with completely random measures acting as Bayesian priors, that are used to create sparse binary matrices. This Bayesian non-parametric view was a key insight due to Thibaux and Jordan (2007). One way to think of generalizations is to to use more general random variables. Of note in the current literature are models employing Poisson and Negative-Binomial random variables. However, unlike their closely related counterparts, generalized Chinese restaurant processes, the ability to analyze IBP models in a systematic and general manner is not yet available. The limitations are both in terms of knowledge about the effects of different priors and in terms of models based on a wider choice of random variables. This work will not only provide a thorough description of the properties of existing models but also provide a simple template to devise and analyze new models.Comment: This version provides more details for the multivariate extensions in section 5. We highlight the case of a simple multinomial distribution and showcase a multivariate Levy process prior we call a stable-Beta Dirichlet process. Section 4.1.1 expande