Stimulus categories and the quality score (QS) of sound exemplars averaged within each category.

<p>Stimulus categories and the quality score (QS) of sound exemplars averaged within each category.</p

Selective brain maps for (a) English and (b) Music categories based on MSVM-RFE classification results.

<p>Panels (a1) – (a4) show the four largest clusters from voxels selected for the English category. Panel (a5) shows the cluster on the left Heschl’s gyrus. Panels (b1) – (b5) show the five largest clusters from voxels selected for the Music category. For each panel, the axial, sagittal and coronal slices are centered at the corresponding cluster. The left side of the brain is on the left side of the figure.</p

Classification accuracies across subjects for all seven sound categories using MSVM-RFE.

<p>Based on within-subject analysis results, the data from subject 4 was not included for the across-subject analysis. “leave sub1 out” means leaving subject 1 out. That means the model is trained on data from the 2nd, 3rd, 5th, and 6th subjects and is tested on data from the 1st subject.</p

Classification accuracies for seven-sound category classifiers built for each subject separately using MSVM-RFE.

<p>Error bars represent one standard deviation of the mean accuracy.</p

Differential Nucleosome Occupancies across Oct4-Sox2 Binding Sites in Murine Embryonic Stem Cells

<div><p>The binding sequence for any transcription factor can be found millions of times within a genome, yet only a small fraction of these sequences encode functional transcription factor binding sites. One of the reasons for this dichotomy is that many other factors, such as nucleosomes, compete for binding. To study how the competition between nucleosomes and transcription factors helps determine a functional transcription factor site from a predicted transcription factor site, we compared experimentally-generated in vitro nucleosome occupancy with in vivo nucleosome occupancy and transcription factor binding in murine embryonic stem cells. Using a solution hybridization enrichment technique, we generated a high-resolution nucleosome map from targeted regions of the genome containing predicted sites and functional sites of Oct4/Sox2 regulation. We found that at Pax6 and Nes, which are bivalently poised in stem cells, functional Oct4 and Sox2 sites show high amounts of in vivo nucleosome displacement compared to in vitro. Oct4 and Sox2, which are active, show no significant displacement of in vivo nucleosomes at functional sites, similar to nonfunctional Oct4/Sox2 binding. This study highlights a complex interplay between Oct4 and Sox2 transcription factors and nucleosomes among different target genes, which may result in distinct patterns of stem cell gene regulation.</p></div

Comparison of seven-sound category classification accuracies obtained by four different MVPA models.

<p>Error bars represent one standard deviation of the mean accuracy.</p

The relationship chart of four different fMRI data analysis cases.

<p>(A) Case WithinSub: Classifiers are trained and tested for each subject separately; (B) Case AcrossSub: Classifiers are built across subjects, that is, leaving out the complete data of one subject for testing and using the data from all other subjects for training; (C) Case AvgItem: fMRI data are averaged over items and classifiers are built across subjects on the averaged data; (D) Case AvgSub: fMRI data are averaged over subjects and classifiers are built for this “averaged subject”. For each fMRI data analysis case, if a red rectangle contains only one subject, it indicates a within-subject analysis. Otherwise, the analysis is done across subjects.</p

Conjunction brain maps for common voxels between Cases WithinSub and each of the other three.

<p>Brain maps of the largest clusters formed by selected voxels that are common between Cases WithinSub and AvgSub, Cases WithinSub and AvgItem, and Cases WithinSub and AcrossSub are shown in parts (a), (b), and (c) respectively. The left side of the brain is on the left side of the figure.</p

In vivo and in vitro nucleosome occupancy tracks at functional and non functional transcription factor binding sites.

<p>(a) A 10kb region containing the Oct4 (Pou5f1) gene locus is shown with tracks for Oct4 occupancy and Sox2 occupancy [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127214#pone.0127214.ref037" target="_blank">37</a>], as well as normalized, GC-adjusted in vitro nucleosome occupancy and in vivo nucleosome occupancy. The TFBS track contains predicted binding sites, generated by the Oct4:Sox2 binding matrix from JASPAR database, with functional sites in black and nonfunctional sites in red. The horizontal scale from Fig 3a is maintained for all subsequent figures. For each gene in our experiment, we chose example regions to examine the patterns of nucleosome occupancy over functional and nonfunctional sites. We highlight the selections for Oct4 to be shown in detail in Fig 3b. (b) Two regions from the Oct4 promoter region are shown. The region in the left panel contains two functional TFBSs. The overlap between the TFBS and the occupancy tracks is displayed with a grey vertical bar across all tracks. In the case of the Oct4 gene, in vivo and in vitro occupancy overlap at both functional and nonfunctional binding sites. (c) Two regions from the Sox2 promoter are shown in detail. Like Oct4, in vivo and in vitro occupancy are colocalized. (d-e) Unlike Oct4 and Sox2, Nes and Pax6 are poised, and show low in vivo occupancy at functional sites, compared to in vitro occupancy. (f-g) Sox1 and Olig2 are not regulated by Sox2 or Oct4, and consequently do not contain any functional TFBS. In vivo and in vitro occupancy seem similar at these nonfunctional sites.</p

Correlations between functional transcription factor binding and nucleosome occupancies.

<p>Using the average transcription factor (TF) occupancy (Oct4 and Sox2, from Whyte <i>et al</i>.) across the 15bp predicted binding sites for Oct4 and Sox2 (n = 58), we identified each site as functional or nonfunctional using a TF occupancy score of > = 20 as a cutoff. We also separated each set of factors by the type of downstream gene regulation (Class 1 Active: functional (n = 10) and nonfunctional (n = 12), Class 2 Poised: functional (n = 4) and nonfunctional (n = 13), Class 3 Repressed: nonfunctional (n = 19)). We calculated the in vitro and in vivo occupancy over the 15bp of each binding site and found the average of each gene type and binding site type. We also calculated the log₂ of the ratio of in vivo to in vitro occupancy at each binding site and found the median for each gene and site type. The standard error of the mean is displayed in the error bars. Paired Z-scores between different classes were calculated and p-values < 0.05 are marked with an asterisk. (a) In vitro occupancy is not significantly different at functional versus nonfunctional sites for both class 1 and class 2 genes. (b) At class 1 genes, in vivo occupancy is not significantly different at functional TFBS. At class 2 genes however, in vivo occupancy is significantly lower at functional TFBS. (c) For class 1 genes’ functional sites, the median fold-change is small and positive, while at class 2 the median fold-change is large and negative. Nonfunctional sites across all gene classes were positive.</p