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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+model/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, 2026-10
    90/100 stars

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    Related Articles

    Generated:

    Article Title: Role of Percutaneous Transluminal Angioplasty for Wound Healing and Dynamics of the Microbial Community in Patients With Type 2 Diabetes and Diabetic Foot Syndrome
    Article Snippet: All statistical analyses are performed using SPSS for Windows 23.0 (SPSS Inc., Chicago, IL, USA). .. All graphs are generated using GraphPad Prism, version 7.01 (GraphPad Software, Inc., La Jolla, CA, USA). ..

    Software:

    Article Title: USEFULNESS OF MORNING HOME BLOOD PRESSURE MEASUREMENTS IN JAPANESE PATIENTS WITH TYPE 2 DIABETES MELLITUS: RESULTS OF A 20-YEARS, PROSPECTIVE, LONGITUDINAL STUDY
    Article Snippet: .. Analysis was performed using the Prism version software (GraphPad Software, CA, USA) and the Statistical Package for the Bioscience (ComWorks Co,Tokyo,Japan). ..

    Article Title: Personalisation of Non-surgical Treatment in Peripheral Arterial Disease Using a Multicomponent Exercise Approach
    Article Snippet: .. Statistical analysis: Statistical analyses will be carried out using JASP software, version 0.18.1 (JASP Team, 2023) and the figures produced in GraphPad Prism software, version 8.0.1 (GraphPad Software, San Diego, California). ..

    other:

    Article Title: Bromodomain-containing protein 4 knockdown promotes neuronal ferroptosis in a mouse model of subarachnoid hemorrhage
    Article Snippet: GraphPad Prism version 9.4.1 for macOS (GraphPad Software, Boston, MA, USA, www.graphpad.com ) was used for statistical analysis.

    Article Title: Neuroserpin alleviates cerebral ischemia-reperfusion injury by suppressing ischemia-induced endoplasmic reticulum stress
    Article Snippet: Statistical analysis was performed using GraphPad Prism 9.0.0 software (GraphPad Software, San Diego, CA, USA, www.graphpad.com ).

    Article Title: The rise of old villains: the vaccination downfall worldwide.
    Article Snippet: The figures were created using GraphPad Prism version 10.2.3 for Mac, GraphPad Software, Boston, Massachusetts, USA (www.graphpad.com).

    Article Title: Epidemic after pandemic: Dengue surpasses COVID-19 in number of deaths.
    Article Snippet: The figures presented in the editorial were created using the GraphPad prism version 10.2.3 for Mac, GraphPad software, Boston, Massachusetts USA, www. graphpad.com.

    Article Title: Perioperative Evaluation of Cerebellar Tumors: Impact of Cerebellar Functional Topography on Cognition and Motor Ataxia
    Article Snippet: All statistical analysis will be performed by the investigators using Prism 4.0 (GraphPad Software, Inc.) and Stata v10 (StataCorp Lp).

    Produced:

    Article Title: Personalisation of Non-surgical Treatment in Peripheral Arterial Disease Using a Multicomponent Exercise Approach
    Article Snippet: .. Statistical analysis: Statistical analyses will be carried out using JASP software, version 0.18.1 (JASP Team, 2023) and the figures produced in GraphPad Prism software, version 8.0.1 (GraphPad Software, San Diego, California). ..



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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: