Journal: Frontiers in Pharmacology

Article Title: Targeting Leishmania infantum Mannosyl-oligosaccharide glucosidase with natural products: potential pH-dependent inhibition explored through computer-aided drug design

doi: 10.3389/fphar.2024.1403203

Figure Lengend Snippet: Molecular properties analysis and virtual screening. The chemical space of the generated dataset is represented visually by 3D-PCA (A) . binding affinities of NPs against Mannosyl-Oligosaccharide Glucosidase protein (B) from the dataset; and heatmap generated with Tanimoto scoring matrix of similar structures among compounds (C) .

Article Snippet: To generate a visual representation of the chemical space of the dataset for the auto-scaled properties of pharmaceutical interest, the principal component analysis (PCA), which reduces data dimensions by geometrically projecting them onto lower dimensions called principal components (PCs), calculated by the “PCA” KNIME node.

Techniques: Generated, Binding Assay

Journal: Ultrasonics Sonochemistry

Article Title: Multi-frequency power ultrasound as a novel approach improves intermediate-wave infrared drying process and quality attributes of pineapple slices

doi: 10.1016/j.ultsonch.2022.106083

Figure Lengend Snippet: Effects of ultrasound on the flavor of final products. A-C: Radar plots of different sample groups: US (A), US-OD (B), US-ET (C); D: Discrimination of flavor profile using the principal component analysis test (PCA); MFU, mono-frequency ultrasound; DFU, dual-frequency ultrasound; TFU, tri-frequency ultrasound; US (in distilled water); -OD (in sucrose), -ET (in ethanol).

Article Snippet: Graphs and the principal component analysis (PCA) were performed in OriginPro 9.8 (OriginLab Corporation, MA, USA).

Techniques:

Journal: PLoS ONE

Article Title: Environmental enrichment effects after early stress on behavior and functional brain networks in adult rats

doi: 10.1371/journal.pone.0226377

Figure Lengend Snippet: Loadings for principal components using varimax rotation and percent of variance accounted for by components.

Article Snippet: In addition, in order to identify relationships (connectivity) between metabolic brain regions, a factor analysis was conducted using principal component analysis (PCA) extraction and varimax rotation available in SAS 9.14 PROC FACTOR SAS Institute Inc., USA).

Techniques:

Journal: Environmental Microbiology Reports

Article Title: Microbiological exploration of the Cueva del Viento lava tube system in Tenerife, Canary Islands

doi: 10.1111/1758-2229.13245

Figure Lengend Snippet: Principal component analysis (PCA). (A) loadings of variables (microbial genera); (B) Scores of the four biofilm samples.

Article Snippet: In addition, a Principal Component Analysis (PCA) was performed using Statgraphics Centurion XVIII software for the simultaneous ordination of different microbial communities at the genus taxonomic level (higher than 0.5% abundance) and the identification of the dominant microbial communities of each biofilm sample (Palma et al., ).

Techniques:

Journal: Scientific Reports

Article Title: Multi-omics analysis of multiple missions to space reveal a theme of lipid dysregulation in mouse liver

doi: 10.1038/s41598-019-55869-2

Figure Lengend Snippet: The global transcriptomics differences on livers from mice flown in space compared to ground controls. Clustering of individual biological liver samples from mice for each transcriptomics dataset (STS-135, RR1, and RR3) from GeneLab utilizing t-Distributed Stochastic Neighbor Embedding (t-SNE) (upper plots) and Principal Component Analysis (PCA) (lower plots) comparing Spaceflight samples to ground controls. Lines are drawn in the t-SNA plots to visually separate the flight samples from the ground controls. The axis for t-SNE plots are arbitrary. The variance for the PCA plots is indicated in the axis. The coordinates of each point in each plot represents the two most important gene pattern contributions (e.g. principal component for PCA) of individual biological replicates. Each replicate is obtained from individual mice exposed to the same experimental conditions. Two points in close proximity suggests a very similar gene expression pattern between the two corresponding samples.

Article Snippet: Clustering of individual biological liver samples from mice for each transcriptomics dataset (STS-135, RR1, and RR3) from GeneLab utilizing t-Distributed Stochastic Neighbor Embedding (t-SNE) (upper plots) and Principal Component Analysis (PCA) (lower plots) comparing Spaceflight samples to ground controls.

Techniques: Gene Expression