sequence alignment

sequence alignmen

Additional file 1: of Molecular phylogeny of Squaliformes and first occurrence of bioluminescence in sharks

Documentation of conducted analyses. (PDF 1710 kb

Straube_et_al_Squaliform_phylogeny_data

These files contain data supporting results from Straube et al. 2015. Please see also Additional file 1 of the article

Supplementary_Table_1

Supplementary Table 1. Material examined and GenBank Accession numbers

Supplementary Table4

Likelihood (ln) scores for gene trees and AU test P values comparing gene tree topologies estimated with RAxML against the concatenation topology (from Fig. 3, pruned to only include taxa in the subset), calculated under homogeneous (RAxML) and non-homogeneous (nhPhyML) optimization. The nhPhyML model used unlimited base equilibrium frequencies. Values in bold highlight cases in which the concatenation topology resulted in either better or non-significantly worse likelihood scores

Supplementary_Table_3

Supplementary Table 3. Characterization of molecular markers, variation, distribution of present/missing sequences and taxa, and GenBank accession numbers (see also Supplementary Table 1)

FIG_SUP_2_Subset_trees

Supplementary Figure 2. Four representative multi-locus phylogenies based on the subset. Color branches represent flatfish lineages (cyan, Pleuronectoidei; orange, Psettodoidei)

A Novel Algorithm to Enhance P300 in Single Trials: Application to Lie Detection Using F-Score and SVM

<div><p>The investigation of lie detection methods based on P300 potentials has drawn much interest in recent years. We presented a novel algorithm to enhance signal-to-noise ratio (SNR) of P300 and applied it in lie detection to increase the classification accuracy. Thirty-four subjects were divided randomly into guilty and innocent groups, and the EEG signals on 14 electrodes were recorded. A novel spatial denoising algorithm (SDA) was proposed to reconstruct the P300 with a high SNR based on independent component analysis. The differences between the proposed method and our/other early published methods mainly lie in the extraction and feature selection method of P300. Three groups of features were extracted from the denoised waves; then, the optimal features were selected by the F-score method. Selected feature samples were finally fed into three classical classifiers to make a performance comparison. The optimal parameter values in the SDA and the classifiers were tuned using a grid-searching training procedure with cross-validation. The support vector machine (SVM) approach was adopted to combine with an F-score because this approach had the best performance. The presented model F-score_SVM reaches a significantly higher classification accuracy for P300 (specificity of 96.05%) and non-P300 (sensitivity of 96.11%) compared with the results obtained without using SDA and compared with the results obtained by other classification models. Moreover, a higher individual diagnosis rate can be obtained compared with previous methods, and the presented method requires only a small number of stimuli in the real testing application.</p></div

SUBSET_18_GENES

Nexus alignment file for the subset (37 taxa and 18 gene fragments; 17616 sites; no missing data). The alignment file is fully annotated for gene partitions, including the batch command in PAUP* to export individual gene files

Supplementary_Table_2

Supplementary Table 2. Primers used and optimized PCR conditions