Multidimensional Signals and Analytic Flexibility: Estimating Degrees of Freedom in Human-Speech Analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis that can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling but also from decisions regarding the quantification of the measured behavior. In this study, we gave the same speech-production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further found little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise, or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system, and calibrate their (un)certainty in their conclusions

Genomic prediction in a multiploid crop: genotype by environment interaction and allele dosage effects on predictive ability in banana

Open Access Journal; Published online: 2 March 2018Improving the efficiency of selection in conventional crossbreeding is a major priority in banana (Musa spp.) breeding. Routine application of classical marker assisted selection (MAS) is lagging in banana due to limitations in MAS tools. Genomic selection (GS) based on genomic prediction models can address some limitations of classical MAS, but the use of GS in banana has not been reported to date. The aim of this study was to evaluate the predictive ability of six genomic prediction models for 15 traits in a multi-ploidy training population. The population consisted of 307 banana genotypes phenotyped under low and high input field management conditions for two crop cycles. The single nucleotide polymorphism (SNP) markers used to fit the models were obtained from genotyping by sequencing (GBS) data. Models that account for additive genetic effects provided better predictions with 12 out of 15 traits. The performance of BayesB model was superior to other models particularly on fruit filling and fruit bunch traits. Models that included averaged environment data were more robust in trait prediction even with a reduced number of markers. Accounting for allele dosage in SNP markers (AD-SNP) reduced predictive ability relative to traditional bi-allelic SNP (BA-SNP), but the prediction trend remained the same across traits. The high predictive values (0.47– 0.75) of fruit filling and fruit bunch traits show the potential of genomic prediction to increase selection efficiency in banana breeding

Lectins offer new perspectives in the development of macrophage-targeted therapies for COPD/emphysema

We have previously shown that the defective ability of alveolar macrophages (AM) to phagocytose apoptotic cells (‘efferocytosis’) in chronic obstructive pulmonary disease/emphysema (COPD) could be therapeutically improved using the C-type lectin, mannose binding lectin (MBL), although the exact mechanisms underlying this effect are unknown. An S-type lectin, galectin-3, is also known to regulate macrophage phenotype and function, via interaction with its receptor CD98. We hypothesized that defective expression of galectin/CD98 would be associated with defective efferocytosis in COPD and that mechanisms would include effects on cytoskeletal remodeling and macrophage phenotype and glutathione (GSH) availability. Galectin-3 was measured by ELISA in BAL from controls, smokers and current/ex-smokers with COPD. CD98 was measured on AM using flow cytometry. We assessed the effects of galectin-3 on efferocytosis, CD98, GSH, actin polymerisation, rac activation, and the involvement of PI3K (using β-actin probing and wortmannin inhibition) in vitro using human AM and/or MH-S macrophage cell line. Significant decreases in BAL galectin-3 and AM CD98 were observed in BAL from both current- and ex-smoker COPD subjects vs controls. Galectin 3 increased efferocytosis via an increase in active GTP bound Rac1. This was confirmed with β-actin probing and the role of PI3K was confirmed using wortmannin inhibition. The increased efferocytosis was associated with increases in available glutathione and expression of CD98. We provide evidence for a role of airway lectins in the failed efferocytosis in COPD, supporting their further investigation as potential macrophage-targeted therapies.Violet R. Mukaro, Johan Bylund, Greg Hodge, Mark Holmes, Hubertus Jersmann, Paul N. Reynolds, Sandra Hodg

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

Multidimensional signals and analytic flexibility: Estimating degrees of freedom in human speech analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting insubstantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions

The Dual-Task Costs of Audiovisual Speech Processing Across Levels of Background Noise

NOTE: Experiments 1a, 1b, 1c, and 1d below are referred to as Experiments 1, 2, 3, and 4, respectively, in the manuscript. I removed the original Experiment 2 (semantic context) and Experiment 3 (pilot study) from the article submission to keep the length of the article reasonable. However, given the number of files that include the original names of the experiments (1a–1d, 2, and 3) not just in the file names but in the files themselves, I opted to keep the original names of the files. So in the manuscript, keep in mind that Experiment 1 corresponds to Experiment 1a on OSF, Experiment 2 corresponds to Experiment 1b on OSF, Experiment 3 corresponds to Experiment 1c on OSF, and Experiment 4 corresponds to Experiment 1d on OSF. Experiments 2 and 3 on OSF are not in the manuscript. Due to space limitations on OSF, I can't upload all versions of all stimuli. Therefore, all mixed stimuli (i.e., the final stimuli) are available for download from Gorilla Open Materials at https://app.gorilla.sc/openmaterials/651808, unmixed stimuli for each experiment are available below, and tone and noise stimuli that were used to create the final mixed stimuli used in the experiments are also available below. Stimuli for Experiment 1b, which didn't have any speech, are in the tone_and_noise/mixed_tone_noise folder (zipped). Note that any references to a pilot study (e.g., the "Pilot" preregistration) refer to Experiment 3, which I had been referring to as a pilot study, but given that it was run last and isn't really a pilot study I changed it to Experiment 3

An introduction to linear mixed effects modeling in R

This tutorial serves as both an approachable theoretical introduction to mixed effects modeling and a practical introduction to how to implement these models in R. The intended audience is any researcher who has some basic statistical knowledge, but little or no experience implementing mixed effects models in R using their own data. In an attempt to increase the accessibility of this paper, I deliberately avoid using mathematical terminology beyond what a student would learn in a standard graduate-level statistics course, but I reference articles and textbooks that provide more detail for interested readers. This tutorial includes snippets of R code throughout, as well as the data and R script used to build the models described in the text so readers can follow along if they wish. The goal of this practical introduction is to provide researchers with the tools they need to begin implementing mixed models in their own research