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gaussian (normal) distributions  (Simcyp)

 
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    Simcyp gaussian (normal) distributions
    Gaussian (Normal) Distributions, supplied by Simcyp, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/gaussian (normal) distributions/product/Simcyp
    Average 90 stars, based on 1 article reviews
    gaussian (normal) distributions - by Bioz Stars, 2026-04
    90/100 stars

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    Image Search Results


    Akaike information criterion values of the parametric frailty models

    Journal: Journal of Research in Health Sciences

    Article Title: Modeling Time to Death of Patients with Multidrug-Resistant Tuberculosis at Saint Peter’s Specialized Hospital

    doi: 10.34172/jrhs.2021.50

    Figure Lengend Snippet: Akaike information criterion values of the parametric frailty models

    Article Snippet: The inverse Gaussian (inverse normal) distribution was introduced as a frailty distribution alternative to the gamma distribution by Hougaard and has been used by Klein , and Duchateau & Janssen .

    Techniques:

    Multivariable analysis using the Weibull- inverse Gaussian frailty model

    Journal: Journal of Research in Health Sciences

    Article Title: Modeling Time to Death of Patients with Multidrug-Resistant Tuberculosis at Saint Peter’s Specialized Hospital

    doi: 10.34172/jrhs.2021.50

    Figure Lengend Snippet: Multivariable analysis using the Weibull- inverse Gaussian frailty model

    Article Snippet: The inverse Gaussian (inverse normal) distribution was introduced as a frailty distribution alternative to the gamma distribution by Hougaard and has been used by Klein , and Duchateau & Janssen .

    Techniques:

    ( A ) Zoom-in on pairwise difference distribution for stoichiometry of GFP-labeled Mig1 foci detected during FRAP, ~8 mer intervals (dashed lines) and power spectrum (inset) shown, mean and Gaussian sigma error (arrow). ( B ) GFP-labeled Mig1 cluster size as a function of stoichiometry with power law fit indicated. ( C ) Immuno-gold transmission electron microscopy for negatively stained 90 nm cryosections of (upper panel) two different Mig1-GFP cells and (lower panel) two different Msn2-GFP cells, with zoom in (inset).

    Journal: eLife

    Article Title: Transcription factor clusters regulate genes in eukaryotic cells

    doi: 10.7554/eLife.27451

    Figure Lengend Snippet: ( A ) Zoom-in on pairwise difference distribution for stoichiometry of GFP-labeled Mig1 foci detected during FRAP, ~8 mer intervals (dashed lines) and power spectrum (inset) shown, mean and Gaussian sigma error (arrow). ( B ) GFP-labeled Mig1 cluster size as a function of stoichiometry with power law fit indicated. ( C ) Immuno-gold transmission electron microscopy for negatively stained 90 nm cryosections of (upper panel) two different Mig1-GFP cells and (lower panel) two different Msn2-GFP cells, with zoom in (inset).

    Article Snippet: Stoichiometry distributions were rendered as objective kernel density estimations ( ) using a Gaussian kernel with bandwidth optimized for normally distributed data using standard MATLAB routines.

    Techniques: Labeling, Transmission Assay, Electron Microscopy, Staining

    ( A ) Zoom-in on pairwise difference distribution for stoichiometry of Mig1-GFP foci, 7-mer intervals (dashed) and power spectrum (inset), mean and Gaussian sigma error (arrow). ( B ) Stoichiometry for Mig1-GFP clusters in vitro in PEG absence (blue)/presence (red). n = 1000 foci. Inset shows the full range while outer zooms in on cluster stoichiometry. ( C ) 3C model of chromosomal DNA (blue shaded differently for each chromosome) with overlaid Mig1 promoter binding sites from bioinformatics (red), simulated image based on model with realistic signal and noise added (inset). ( D ) Cluster (red) and monomer (dark blue) model (goodness-of-fit R 2 < 0) for Mig1-GFP stoichiometry (10 replicates) compared against experimental data (cyan, R 2 = 0.75).

    Journal: eLife

    Article Title: Transcription factor clusters regulate genes in eukaryotic cells

    doi: 10.7554/eLife.27451

    Figure Lengend Snippet: ( A ) Zoom-in on pairwise difference distribution for stoichiometry of Mig1-GFP foci, 7-mer intervals (dashed) and power spectrum (inset), mean and Gaussian sigma error (arrow). ( B ) Stoichiometry for Mig1-GFP clusters in vitro in PEG absence (blue)/presence (red). n = 1000 foci. Inset shows the full range while outer zooms in on cluster stoichiometry. ( C ) 3C model of chromosomal DNA (blue shaded differently for each chromosome) with overlaid Mig1 promoter binding sites from bioinformatics (red), simulated image based on model with realistic signal and noise added (inset). ( D ) Cluster (red) and monomer (dark blue) model (goodness-of-fit R 2 < 0) for Mig1-GFP stoichiometry (10 replicates) compared against experimental data (cyan, R 2 = 0.75).

    Article Snippet: Stoichiometry distributions were rendered as objective kernel density estimations ( ) using a Gaussian kernel with bandwidth optimized for normally distributed data using standard MATLAB routines.

    Techniques: In Vitro, Binding Assay