Coupled-channel effective field theory and proton-7^7Li scattering

We apply the renormalisation group (RG) to analyse scattering by short-range forces in systems with coupled channels. For two S-wave channels, we find three fixed points, corresponding to systems with zero, one or two bound or virtual states at threshold. We use the RG to determine the power countings for the resulting effective field theories. In the case of a single low-energy state, the resulting theory takes the form of an effective-range expansion in the strongly interacting channel. We also extend the analysis to include the effects of the Coulomb interaction between charged particles. The approach is then applied to the coupled $p+{^7}$Li and $n+{^7}$Be channels which couple to a $J^P=2^-$ state of $^8$Be very close to the $n+{^7}$Be threshold. At next-to-leading order, we are able to get a good description of the $p+{^7}$Li phase shift and the ${^7}$Be(n,p)${^7}$Li cross section using four parameters. Fits at one order higher are similarly good but the available data are not sufficient to determine all five parameters uniquely.Comment: 22 pages, 2 figures, RevTeX4, typos corrected, accepted for publication in European Physical Journal

Childhood obesity intervention studies: A narrative review and guide for investigators, authors, editors, reviewers, journalists, and readers to guard against exaggerated effectiveness claims

Being able to draw accurate conclusions from childhood obesity trials is important to make advances in reversing the obesity epidemic. However, obesity research sometimes is not conducted or reported to appropriate scientific standards. To constructively draw attention to this issue, we present 10 errors that are commonly committed, illustrate each error with examples from the childhood obesity literature, and follow with suggestions on how to avoid these errors. These errors are as follows: using self-reported outcomes and teaching to the test; foregoing control groups and risking regression to the mean creating differences over time; changing the goal posts; ignoring clustering in studies that randomize groups of children; following the forking paths, subsetting, p-hacking, and data dredging; basing conclusions on tests for significant differences from baseline; equating “no statistically significant difference” with “equally effective”; ignoring intervention study results in favor of observational analyses; using one-sided testing for statistical significance; and stating that effects are clinically significant even though they are not statistically significant. We hope that compiling these errors in one article will serve as the beginning of a checklist to support fidelity in conducting, analyzing, and reporting childhood obesity research

Conceptual, computational and inferential benefits of the missing data perspective in applied and theoretical statistical problems

Causal inference, EM and extension, multiple imputation, Data Augmentation and extensions, MCMC, posterior predictive model comparisons,

Estimating a Structured Covariance Matrix From Multilab Measurements in High-Throughput Biology

We consider the problem of quantifying the degree of coordination between transcription and translation, in yeast. Several studies have reported a surprising lack of coordination over the years, in organisms as different as yeast and human, using diverse technologies. However, a close look at this literature suggests that the lack of reported correlation may not reflect the biology of regulation. These reports do not control for between-study biases and structure in the measurement errors, ignore key aspects of how the data connect to the estimand, and systematically underestimate the correlation as a consequence. Here, we design a careful meta-analysis of 27 yeast data sets, supported by a multilevel model, full uncertainty quantification, a suite of sensitivity analyses and novel theory, to produce a more accurate estimate of the correlation between mRNA and protein levels—a proxy for coordination. From a statistical perspective, this problem motivates new theory on the impact of noise, model mis-specifications and non-ignorable missing data on estimates of the correlation between high dimensional responses. We find that the correlation between mRNA and protein levels is quite high under the studied conditions, in yeast, suggesting that post-transcriptional regulation plays a less prominent role than previously thought