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prism 8.4.0 non-linear regression-[inhibitor]-normalized response (y values 100 down to 0) model  (GraphPad Software Inc)


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    GraphPad Software Inc prism 8.4.0 non-linear regression-[inhibitor]-normalized response (y values 100 down to 0) model
    Prism 8.4.0 Non Linear Regression [Inhibitor] Normalized Response (Y Values 100 Down To 0) Model, supplied by GraphPad Software Inc, 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/product/linear+regression+models/prism+software/pmc12280236-159-14-11
    Average 90 stars, based on 1 article reviews
    prism 8.4.0 non-linear regression-[inhibitor]-normalized response (y values 100 down to 0) model - by Bioz Stars, 2026-11
    90/100 stars

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    other:

    Article Title: Enzyme Inhibition Properties of Calendula officinalis, Matricaria chamomilla, and Anthemis pseudocotula: Kinetics and Molecular Docking Studies
    Article Snippet: The samples' IC50 values were calculated using the GraphPad Prism 8.4.0 non-linear regression-[Inhibitor]normalized response (y values 100 down to 0) model. 2.4.1.

    Article Title: Mucosal and Serum Neutralization Immune Responses Elicited by COVID-19 mRNA Vaccination in Vaccinated and Breakthrough-Infection Individuals: A Longitudinal Study from Louisville Cohort.
    Article Snippet: The results were plotted using 5-parameter dose–response curve (non-linear regression curves [log(inhibitor) vs. normalized response—Variable slope] in GraphPad Prism software version 9.1.1.

    Article Title: Additive effects of mirabegron on muscarinic receptor binding and on relaxation of cholinergic detrusor muscle contraction by antimuscarinics.
    Article Snippet: The Competition data of specific 3HNMS binding from experiments on the rat brain were analyzed using the non-linear regression analysis program GraphPad Prism (Version 7.0; GraphPad Software Inc., San Diego, CA).

    Article Title: Isothermal DNA Amplification with Tailed Primers Coupled with Enzyme-Linked Oligonucleotide Assay for the Sensitive Detection of Trichomonas vaginalis .
    Article Snippet: The globally persistent and most common sexually transmitted agent Trichomonas vaginalis continues to be an underestimated public health threat.. Infection with T. vaginalis has been associated with reproductive complications and an increased risk of HIV acquisition.. With the available diagnostic tools for trichomoniasis, there is still an unmet demand for a cost-effective, facile, rapid, and robust diagnostic tool.

    Article Title: Integrating immune library probing with structure-based computational design to develop potent neutralizing nanobodies against emerging SARS-CoV-2 variants
    Article Snippet: The binding relative to that determined with the Wuhan S at the highest hcAb4 concentration (250 nM) is shown, as well as the determined apparent K D values for each S variant.

    Article Title: Metabolic and genomic adaptations of Salmonella Typhimurium grown on itaconate
    Article Snippet: The data was fit to a Gompertz non-linear regression [ ], allowing for calculation of the MIC using Graphpad Prism v9.0.

    Construct:

    Article Title: Simultaneous determination of α-ketoglutarate, ergothioneine, pyrroloquinoline quinone and ascorbic acid in dietary supplements by HPLC-UV: Method validation and quality control application
    Article Snippet: .. Calibration curves were constructed using weighted (1/x2) leastsquares linear regression in GraphPad Prism 8 (GraphPad Software, San Diego, CA, USA). ..



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    Recorded experimental data (A) Experimental setup: human participant face-to-face with Pepper humanoid. Robot performs preprogrammed motor sequences. Human mimics robot motion, mirroring the robot’s movements. Both have reflective markers for motion tracking. (B) Motion tracking data: position in 1 and 2D, and velocity. Human in green, robot in black. Light-to-dark follows beginning to end in an example sequence of movements between the four spatial targets. Note Lag as the time offset from robot to human movement onset. used as a performance measure. (C) ERSP grand average illustrating task-related desynchronization between task epochs of rest, fixation and movement for Theta, mu and alpha bands. Note strong desynchronization in mu and beta during the 80 element movement sequence. Color bar corresponds to min-max ERSP values in decibels (dB). (D) EEG signals mapped onto 10–20 coordinates for the 9 included electrodes that will be used in the MSLR model.

    Journal: iScience

    Article Title: Following the robot’s lead: Predicting human and robot movement from EEG in a motor learning HRI task

    doi: 10.1016/j.isci.2025.112914

    Figure Lengend Snippet: Recorded experimental data (A) Experimental setup: human participant face-to-face with Pepper humanoid. Robot performs preprogrammed motor sequences. Human mimics robot motion, mirroring the robot’s movements. Both have reflective markers for motion tracking. (B) Motion tracking data: position in 1 and 2D, and velocity. Human in green, robot in black. Light-to-dark follows beginning to end in an example sequence of movements between the four spatial targets. Note Lag as the time offset from robot to human movement onset. used as a performance measure. (C) ERSP grand average illustrating task-related desynchronization between task epochs of rest, fixation and movement for Theta, mu and alpha bands. Note strong desynchronization in mu and beta during the 80 element movement sequence. Color bar corresponds to min-max ERSP values in decibels (dB). (D) EEG signals mapped onto 10–20 coordinates for the 9 included electrodes that will be used in the MSLR model.

    Article Snippet: Markov-Switching Linear Regression (MSLR) models, which we ran using Dynamax , are a powerful tool for modeling time series data that exhibit regime-switching behavior, where the underlying dynamics of the system change over time.

    Techniques: Sequencing

    Modeling pipeline and example results for time-resolved predictions (A) The time-resolved model (Markov-switching linear regression, MSLR) learns the linear mapping from EEG inputs to movement readouts. However, this linear relationship varies over time, through different hidden states. After training the model, it will output movement and hidden state predictions from novel EEG inputs. (B) The model is able to predict human velocity (HV), robot X (RX), and human X (HX) positions; ground truth traces are shown in gray, model predictions in light blue. (C) Mapping inferred states as color codes onto the predicted movement readouts, over time.

    Journal: iScience

    Article Title: Following the robot’s lead: Predicting human and robot movement from EEG in a motor learning HRI task

    doi: 10.1016/j.isci.2025.112914

    Figure Lengend Snippet: Modeling pipeline and example results for time-resolved predictions (A) The time-resolved model (Markov-switching linear regression, MSLR) learns the linear mapping from EEG inputs to movement readouts. However, this linear relationship varies over time, through different hidden states. After training the model, it will output movement and hidden state predictions from novel EEG inputs. (B) The model is able to predict human velocity (HV), robot X (RX), and human X (HX) positions; ground truth traces are shown in gray, model predictions in light blue. (C) Mapping inferred states as color codes onto the predicted movement readouts, over time.

    Article Snippet: Markov-Switching Linear Regression (MSLR) models, which we ran using Dynamax , are a powerful tool for modeling time series data that exhibit regime-switching behavior, where the underlying dynamics of the system change over time.

    Techniques: