MSE FINDR: A Shiny R Application to Estimate Mean Square Error Using Treatment Means and Post Hoc Test Results.

Research synthesis methods such as meta-analysis rely primarily on appropriate summary statistics (i.e., means and variance) of a response of interest for implementation to draw general conclusions from a body of research. A commonly encountered problem arises when a measure of variability of a response across a study is not explicitly provided in the summary statistics of primary studies. Typically, these otherwise credible studies are omitted in research synthesis, leading to potential small-study effects and loss of statistical power. We present MSE FINDR, a user-friendly Shiny R application for estimating the mean square error (i.e., within-study residual variance, [Formula: see text]) for continuous outcomes from analysis of variance (ANOVA)-type studies, with specific experimental designs and treatment structures (Latin square, completely randomized, randomized complete block, two-way factorial, and split-plot designs). MSE FINDR accomplishes this by using commonly reported information on treatment means, significance level (α), number of replicates, and post hoc mean separation tests (Fisher's least significant difference [LSD], Tukey's honest significant difference [HSD], Bonferroni, Sidák, and Scheffé). Users upload a CSV file containing the relevant information reported in the study and specify the experimental design and post hoc test that was applied in the analysis of the underlying data. MSE FINDR then proceeds to recover [Formula: see text] based on user-provided study information. The recovered within-study variance can be downloaded and exported as a CSV file. Simulations of trials with a variable number of treatments and treatment effects showed that the MSE FINDR-recovered [Formula: see text] was an accurate predictor of the actual ANOVA [Formula: see text] for one-way experimental designs when summary statistics (i.e., means, variance, and post hoc results) were available for the single factor. Similarly, [Formula: see text] recovered by the application accurately predicted the actual [Formula: see text] for two-way experimental designs when summary statistics were available for both factors and the sub-plot factor in split-plot designs, irrespective of the post hoc mean separation test. The MSE FINDR Shiny application, documentation, and an accompanying tutorial are hosted at https://garnica.shinyapps.io/MSE_FindR/ and https://github.com/vcgarnica/MSE_FindR/. With this tool, researchers can now easily estimate the within-study variance absent in published reports that nonetheless provide appropriate summary statistics, thus enabling the inclusion of such studies that would have otherwise been excluded in meta-analyses involving estimates of effect sizes based on a continuous response.

A global-temporal analysis on Phytophthora sojae resistance-gene efficacy.

Plant disease resistance genes are widely used in agriculture to reduce disease outbreaks and epidemics and ensure global food security. In soybean, Rps (Resistance to Phytophthora sojae) genes are used to manage Phytophthora sojae, a major oomycete pathogen that causes Phytophthora stem and root rot (PRR) worldwide. This study aims to identify temporal changes in P. sojae pathotype complexity, diversity, and Rps gene efficacy. Pathotype data was collected from 5121 isolates of P. sojae, derived from 29 surveys conducted between 1990 and 2019 across the United States, Argentina, Canada, and China. This systematic review shows a loss of efficacy of specific Rps genes utilized for disease management and a significant increase in the pathotype diversity of isolates over time. This study finds that the most widely deployed Rps genes used to manage PRR globally, Rps1a, Rps1c and Rps1k, are no longer effective for PRR management in the United States, Argentina, and Canada. This systematic review emphasizes the need to widely introduce new sources of resistance to P. sojae, such as Rps3a, Rps6, or Rps11, into commercial cultivars to effectively manage PRR going forward.