Journal: bioRxiv

Article Title: Human skeletal muscle possesses both reversible proteomic signatures and a retained proteomic memory after repeated resistance training

doi: 10.1101/2024.11.19.624068

Figure Lengend Snippet: A , Venn diagram analysis of the statistically differentially regulated proteins: 1) RT1 vs. baseline, 2) DT vs. baseline, 3) RT2 vs. baseline, 4) DT vs. RT1 (pre-DT) and 5) RT2 vs. DT (pre-RT2) ( FDR < 0.05, FC >|1.2|). The 6th comparison: RT2 vs. RT1 is not shown as it did not produce any statistically significantly different proteins. The figure illustrates the number of common overlapping and unique significant proteins across each comparison. There were 285 proteins that were significantly different in at least one of the pairwise comparisons highlighted in the Venn diagram. B , SOM profiling of 285 proteins identified in the Venn diagram above in the PRT group over the 30 weeks time-course of baseline, RT1, DT and RT2.

Article Snippet: To plot temporal changes in proteins across the intervention, we undertook Self Organising Map (SOM) profiling of the change in standardised mean protein content within each time-point using Partek Genomics Suite as previously described earlier ( Turner et al ., 2020 ).

Techniques: Comparison

Journal: bioRxiv

Article Title: Human skeletal muscle possesses both reversible proteomic signatures and a retained proteomic memory after repeated resistance training

doi: 10.1101/2024.11.19.624068

Figure Lengend Snippet: A and B , Reactome pathway analysis together with Sankey-diagram illustrates that the retained proteins are enriched in cytokine signalling, biosynthesis, and folding, sorting and degradation pathways and functions. C , The 29 and 81 retained proteins (SOM) in GO have as a molecular function especially in calcium or calcium-dependent protein binding, actin filament binding and phospholipase inhibitor activity. D , CAPN2, CLIC1, MYH9 and CAPNS1 proteins increasing after RT1 and maintaining their levels after DT still above the baseline. N=17 in PRT and n=11 in CRT. * <0.05, ** <0.01 and *** <0.001 or exact FDR-value ( FDR < 0.10) vs. baseline. Data is presented as mean ± SEM.

Article Snippet: To plot temporal changes in proteins across the intervention, we undertook Self Organising Map (SOM) profiling of the change in standardised mean protein content within each time-point using Partek Genomics Suite as previously described earlier ( Turner et al ., 2020 ).

Techniques: Protein Binding, Binding Assay, Activity Assay

Journal: Combinatorial Chemistry & High Throughput Screening

Article Title: Generating ‘Omic Knowledge’: The Role of Informatics in High Content Screening

doi: 10.2174/138620709789383259

Figure Lengend Snippet: Useful Statistical and Data Mining Tools for HCS Data. This Table Shows Technique Name and Abbreviation, a Brief Description as Well as Key References where the Technique has been Employed for HCS

Article Snippet: In an internal study at Thermo Fisher Scientific, we employed T-tests, Z’ measurements, Self Organizing maps (SOM) and K-nearest neighbor (K-NN) analyses to determine the optimal set of morphological parameters.

Techniques: Sequencing, Membrane, Permeability, Control, Plasmid Preparation

Journal: Frontiers in Plant Science

Article Title: In vivo spectroscopy and NMR metabolite fingerprinting approaches to connect the dynamics of photosynthetic and metabolic phenotypes in resurrection plant Haberlea rhodopensis during desiccation and recovery

doi: 10.3389/fpls.2015.00564

Figure Lengend Snippet: Evaluation and quantification of plant water status during desiccation and recovery of Haberlea rhodopen sis. (A) Drying and recovery of different plants (upper left, Control plants; upper right 120 h of desiccation; lower left, 192 h of desiccation; lower right 36 h of recovery). (B) Box plots of changes in water content during desiccation (blue) and recovery (green). (C) Self organizing map (SOM) visualization of the classified samples according to the four pre-defined neurons for desiccation and recovery. The positions of the scores (leaf samples) derived after Principal Component Analysis (PCA) transformation are clustered in different colors with the neurons according to their weights defined with the two weight vectors, for desiccation (left) and for recovery (right), respectively.

Article Snippet: To classify the stress states according to the similarity of JIP parameters, we clustered the scores from the first tree components of PCA under a typical self organizing map (SOM) scheme (neural network toolbox MatLab).

Techniques: Control, Derivative Assay, Transformation Assay