Bayesian Inference of Species Trees using Diffusion Models

We describe a new and computationally efficient Bayesian methodology for inferring species trees and demographics from unlinked binary markers. Likelihood calculations are carried out using diffusion models of allele frequency dynamics combined with novel numerical algorithms. The diffusion approach allows for analysis of data sets containing hundreds or thousands of individuals. The method, which we call Snapper, has been implemented as part of the BEAST2 package. We conducted simulation experiments to assess numerical error, computational requirements, and accuracy recovering known model parameters. A reanalysis of soybean SNP data demonstrates that the models implemented in Snapp and Snapper can be difficult to distinguish in practice, a characteristic which we tested with further simulations. We demonstrate the scale of analysis possible using a SNP data set sampled from 399 fresh water turtles in 41 populations. [Bayesian inference; diffusion models; multi-species coalescent; SNP data; species trees; spectral methods.]

Nonverbal predictors in the estimates of truthful and deceptive statements

A microstructural analysis of perception of a partner in communication was carried out. Mute video recordings of 15 clips of a structured conversation in which communicants expressed true and false judgments, were subjected to complex coding. In each 40 ms frame 51 nonverbal signs/102 binary markers indicating the state of the facial zones, the nature of the movements of the head, hands and body were considered. Based on expert estimates, the proportion of frames was calculated, in which each of the markers is present at selected time intervals. Looking at the video clips, 35 observers intuitively, by external features, determined the fragments when the communicator is telling truth and when lying. The frequency and time of occurrence of markers were analyzed. Frequency regression models of “true” and “false” response of communicants were built. It is shown that the required estimates are performed by the observer 1.5—2 seconds before the answer. High-frequency features form a stable core of the impression of the reliability of the communicator’s judgments, complemented with changeable low-frequency features explaining the growth of explanatory power of regression models. Markers have been identified that contribute to adequate perception of the reliability of information reported. The style of non-verbal behavior of people implementing alternative communication strategies is described.

Campylobacter jejuni and Campylobacter coli Genotyping by High-Resolution Melting Analysis of a flaA Fragment

ABSTRACT The highly variable flagellin-encoding flaA gene has long been used for genotyping Campylobacter jejuni and Campylobacter coli. High-resolution melting (HRM) analysis is emerging as an efficient and robust method for discriminating DNA sequence variants. The objective of this study was to apply HRM analysis to flaA-based genotyping. The initial aim was to identify a suitable flaA fragment. It was found that the PCR primers commonly used to amplify the flaA short variable repeat (SVR) yielded a mixed PCR product unsuitable for HRM analysis. However, a PCR primer set composed of the upstream primer used to amplify the fragment used for flaA restriction fragment length polymorphism (RFLP) analysis and the downstream primer used for flaA SVR amplification generated a very pure PCR product, and this primer set was used for the remainder of the study. Eighty-seven C. jejuni and 15 C. coli isolates were analyzed by flaA HRM and also partial flaA sequencing. There were 47 flaA sequence variants, and all were resolved by HRM analysis. The isolates used had previously also been genotyped using single-nucleotide polymorphisms (SNPs), binary markers, CRISPR HRM, and flaA RFLP. flaA HRM analysis provided resolving power multiplicative to the SNPs, binary markers, and CRISPR HRM and largely concordant with the flaA RFLP. It was concluded that HRM analysis is a promising approach to genotyping based on highly variable genes.