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: Scientific Reports

Article Title: Object responses are highly malleable, rather than invariant, with changes in object appearance

doi: 10.1038/s41598-020-61447-8

Figure Lengend Snippet: Methods to generate RDMs and model-free visualizations of the characteristics that were used to differentiate between objects during free sorting, separately for stimuli displayed as 2-D images, AR projections or real-world solids. ( A ) (i) Participants viewed the stimuli and then declared verbally a sorting criterion. The example depicts the 2-D image condition. (ii) Participants sorted the stimuli based on their proposed criterion. In this example, the observer chose to sort the stimuli according to the criterion of whether each item is typically found indoors vs. outdoors. (iii) The physical distance between sorted items reflects the perceived distance relative to the proposed criterion. The distances between items in the arena are transformed in Euclidean distance in the representational dissimilarity matrix (RDM): the father apart the items are in the arena, the higher their dissimilarity value in the RDM. ( B ) Average representational dissimilarity matrices (RDMs) generated based on sorting behavior for real objects (left), AR stimuli (middle), and 2-D images (right). Stimuli positioned closer together during sorting yield smaller numerical values in the RDM (i.e., low dissimilarity, illustrated by cooler colors); items positioned farther apart yield higher numerical values (i.e., high dissimilarity, illustrated by warmer colors). ( C ) Multidimensional scaling (MDS) plots for real objects (left), AR stimuli (middle), and 2-D images (right). For visualization purposes, hue differences in the plots denote typical location: indoor (blue) vs. outdoor (gray); object size represents relative differences in real-world size (larger image = larger real-world size).

Article Snippet: Please see Kriegeskorte and Mur (2012) for the inverse MDS Matlab code which was used to assess dissimilarities for the experiment.

Techniques: Transformation Assay, Generated

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: Frontiers in Neuroscience

Article Title: Brain oscillatory activity during motor preparation: effect of directional uncertainty on beta, but not alpha, frequency band

doi: 10.3389/fnins.2015.00246

Figure Lengend Snippet: Multidimensional scaling analysis of the time-series of alpha and beta relative power from each voxel within the significant brain regions . (A) Projection of each data point in the 3-D MDS space. The data for alpha- (blue) and beta- (red) bands were colored differently to identify them in the MDS space. (B,C) Projection of the alpha-band and the beta-band data points, respectively. The data from the clusters obtained with the cluster analysis are identified by different colors (same as in Figure ). These results illustrate that each cluster was located in a specific region within the MDS space, but data points varied continuously between those regions.

Article Snippet: To this end, we entered the time-series from all voxels with significant alpha- and beta-band activity in the MDS analysis (implemented using MATLAB function mdscale).

Techniques: