Journal: Scientific Reports

Article Title: How deeply does your mutant sleep? Probing arousal to better understand sleep defects in Drosophila

doi: 10.1038/srep08454

Figure Lengend Snippet: (a). Opposing effects of fumin and dumb 2 on dopamine (DA) function and sleep. Released DA impacts DA1 receptors to initiate cAMP signaling pathways (left black arrow) before being recycled back into the cell (red arrow). fumin is mutant for the DA transporter, leading to increased DA levels in the synapse, and consequently increases cAMP signaling in post-synaptic neurons (right thick black arrow). dumb 2 is mutant for the DA1 receptor, leading to decreased cAMP signaling (dashed black arrow). (b). Classical beam-crossing sleep profiles (min sleep/hour (±SEM)) based on a 5 min inactivity criterion, for the w 2202 background strain (blue), fumin (red), dumb 2 (green), and the double mutant fumin ; dumb 2 (black). N = 68 for w 2202 ; N = 67 for fumin ; N = 62 for dumb 2 ; N = 66 for fumin ; dumb 2 . The w 2202 profile is shown in grey for comparison in the three mutant panels. (c). Sleep intensity profiles (% reactive ± SEM) for the same four strains as in b. (d). Multidimensional scaling (MDS) was used to project the data into a two-dimensional space for easier visualization of the multidimensional relationships between different strains. MDS analyses were performed for fumin ; dumb 2 (blue border) compared to its genetic background strain, w 2202 (black border), for daytime (yellow) and nighttime (grey) metrics. Left panel: four beam-crossing metrics (as for b) were used in combination for MDS comparing both strains. The different metrics used are indicated in the green box (bottom of the panel). The daytime effects overlap while the nighttime effects are distinct. Right panel: four arousal-based metrics were combined with four beam-crossing sleep metrics (indicated by the larger magenta box) for MDS analyses, resulting in a complete separation between day and night effects in both strains. a.u., arbitrary units.

Article Snippet: The MDS algorithms used within DART are Classical MDS (using the Matlab function cmdscale ) and Non-Classical Metric/Non-Metric MDS (using the Matlab function mdscale ).

Techniques: Protein-Protein interactions, Mutagenesis, Comparison

Journal: NeuroImage

Article Title: Clarifying the role of higher-level cortices in resolving perceptual ambiguity using Ultra High Field fMRI

doi: 10.1016/j.neuroimage.2020.117654

Figure Lengend Snippet: Dynamic Reconfiguration of Face Network as a function of Task Top Row: Connectivity Matrices for Stimulus Relevant Face Detection Task (Left) and Stimulus Irrelevant Fixation Task (Right). Asterisks indicate correlation coefficients that significantly (p<0.05) difference between tasks and are identical between matrices for visualization purposes. Bottom Row. Classic Multidimensional Scaling for connectivity matrices highlights the higher proximity of the rAIT to the core face areas as a function of increased connectivity during face detection relative to the fixation task. ROIs in grey text indicate those regions with no significant connectivity modulations across tasks.

Article Snippet: For display purposes only, after computing the mean between the Fisher z-normalized connectivity matrices, we computed the inverse of such transformation on the group average connectivity matrices to convert these scores back into meaningful and interpretable Pearson’s r. To better visualize the results of our functional connectivity analysis, we further performed classic multidimensional scaling (MDS - using the function “cmdscale” in MATLAB) on the participants average dissimilarity matrix (i.e. 1- Pearson’s r).

Techniques:

Journal: BMC Bioinformatics

Article Title: Towards an automatic classification of protein structural domains based on structural similarity

doi: 10.1186/1471-2105-9-74

Figure Lengend Snippet: Classical multidimensional scaling (MDS) plot of various automatically generated partitions and the SCOP fold partition. The MDS plot approximately preserves distances, Δ, between partitions (see Methods). The X axis represents the projection of the location of each partition onto the first eigenvector (first principal component), while the Y axis is a projection onto the second eigenvector (second principal component). The X and Y axes of the plot are scaled to represent the distance, Δ, divided by 1000. Automatic partitions are obtained with Ward's method clustering, based on two different similarity score for each of the three methods VAST, SHEBA and DALI. Each partition is designated by an uppercase letter: A, VAST Pcli Level cut; B, VAST Nres Level cut; C, DALI Zscore Level cut; D, DALI Nres Level cut; E, SHEBA Zscore Level cut; F, SHEBA Nres Level cut; G, VAST Pcli Largest Size cut; H, VAST Nres Largest Size cut; I, DALI Zscore Largest Size cut; J, DALI Nres Largest Size cut; K, SHEBA Zscore Largest Size cut; L, SHEBA Nres Largest Size cut, and S, the expert-curated partition SCOP. Automatic partitions resulting from the same tree-cutting strategy, are grouped together within the same dotted area.

Article Snippet: The computation of pairwise Δ-distance between the 13 partitions resulted in a 13 by 13 matrix (see Table ) which is then used as input to the classical MDS procedure in Matlab [ ].

Techniques: Generated

Journal:

Article Title: Odor quality coding and categorization in human posterior piriform cortex

doi: 10.1038/nn.2324

Figure Lengend Snippet: Alignment of fMRI spatial patterns and perceived odor quality. (a) The group-averaged imaging and perceptual data-sets were each projected onto a common three-dimensional space using multidimensional scaling (MDS), revealing that the imaging maps of PPC linear correlations (filled circles) closely overlapped with the perceptual maps of odor quality similarity (empty circles), both for individual odorants and for odor quality categories. Squares labelled “M” (minty), “W” (woody), and “C” (citrus) represent centroids of each category for the imaging (solid squares) and perceptual (empty squares) data. (b) The observed goodness-of-fit in PPC (red line) fell outside the lower bound of the 95% confidence interval (dashed lines) of a randomly permuted distribution of goodness-of-fits, demonstrating a significant alignment between PPC activity and perception in this region. Alignment between imaging and perception was not significant in APC, amygdala, or OFC.

Article Snippet: These distance matrices were each averaged across subjects and entered into classical MDS analyses (Matlab).

Techniques: Imaging, Activity Assay