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Matlab r2016

Manufactured by MathWorks
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MATLAB R2016 is a high-performance technical computing software that provides a programming environment for algorithm development, data analysis, and visualization. It is a numerical computing environment that allows for matrix manipulations, plotting of functions and data, implementation of algorithms, and creation of user interfaces.

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23 protocols using matlab r2016

1

Robotic vs Treadmill Gait Training

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Baseline characteristics and training parameters were compared between two groups using t tests, and Wilcoxon rank sum tests, as appropriate, and data normality was checked using Shapiro-Wilk test. All parametric measures were analyzed using repeated measures (pre, post, and follow up tests) ANOVAs for within group comparison. If a significant difference was detected, Tukey-Kramer post-hoc tests were conducted to determine which conditions were different from each other. Modified Ashworth Scale (MAS) scores were analyzed using Friedman tests with post-hoc Wilcoxon tests. Changes in primary outcomes were calculated by subtracting the baseline value from the value obtained at post and follow up tests, and analyzed using repeated-measures ANOVA with main factor of group (robotic vs. treadmill only), and repeated for time (post training and follow up tests). Data were analyzed using Matlab_R2016 (The MathWorks, Natick, Massachusetts). Statistical significance for all tests was set at p < 0.05. Effect sizes were calculated and expressed as Cohen d. Effect sizes of 0.20–0.49 were considered small, 0.50–0.79 were considered moderate, and ≥ 0.80 were considered large.
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2

Robotic Treadmill Training Efficacy Study

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Only data from all participants who completed all training and evaluation sessions were analyzed. Independent sample t tests, and Wilcoxon rank sum tests, as appropriate, were used to compare baseline characteristics of robotic treadmill training and treadmill only training groups. Data normality was checked using Shapiro-Wilk test. All parametric measures were analyzed using repeated measure (i.e., pre, post, and follow up tests) ANOVAs for within group comparison. If a significant difference was detected, Tukey-Kramer post-hoc tests were conducted to determine which conditions were different from each other. Non-parametric measures were analyzed using Friedman tests with post-hoc Wilcoxon tests. Changes in primary outcomes, i.e., walking speed, and 6-minute walking distance, were calculated by subtracting the baseline value from the value obtained at post and follow up tests, and analyzed using repeated-measures ANOVA with main factor of group (robotic vs. treadmill only), and repeated for time (post training and follow up tests). Data were analyzed using Matlab_R2016 (The MathWorks, Natick, Massachusetts). Significance was noted at p < 0.05 (0.05<= p <0.1 was defined as a trend) for all analysis.
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3

Correlating Food Preferences with Sugar and Fat

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Behavioural data were analysed with Matlab R2016 (MathWorks) and SPSS Statistics 22 (IBM). To address our research questions, food items were analysed with respect to sugar and fat content (control condition). As a marker of sugar (fat) wanting, within-subject correlations were computed between the trial-wise 8-point rating and the sugar (fat) content of each item (see Results for details). Hereby, high correlations can be considered as a stronger willingness to consume food with high sugar (fat) content.
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4

Quantifying Notch Ligand Activation

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All data processing was performed off-line using a commercial software package (MATLAB R2016, the MathWorks Inc.). A semi-automatic analysis code was used for cell segmentation and data extraction. N1G4-citrine and Dll1-mCherry cells were segmented separately using the yellow (515nm laser) and red (561nm laser) channels, respectively. For the two-cell assay, TEC was defined as total fluorescence in the yellow channel (green in images shown) found in the area of the Dll1-mCherry cell. Background fluorescence, calculated from pixels outside the segmented area, was subtracted from the TEC signal. Fluorescence signal from the boundary between cells as defined by the overlap of the segmentation of the two cells, was not included in the calculation of TEC. This is because we consider as TEC only N1G4-Citrine within the sender cell and not on the boundary. nTEC was calculated by dividing the TEC by total Dll1-mCherry signal in each time frame. The correlation coefficient between nTEC and contact width and the corresponding p-value was estimated using Pearson correlation analysis. The reporter analysis (Fig. S1N-P) was performed by a semi-automatic tracking protocol in which the total fluorescence in the nucleus was calculated in each frame.
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5

Perception Task in Dark Room

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Participants performed the task in a dark room, while seated 57 cm from the monitor. All stimuli were presented on a black background on a 21-inch CRT monitor operating at 60 Hz refresh rate (1280×960 pixels). Stimuli were generated using custom-made software in MatlabR2016 (Mathworks, Inc, Natick, MA, USA) utilising Psychtoolbox (Brainard, 1997 (link); Kleiner et al., 2007 ; Pelli, 1997 (link)).
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6

Graph Theoretical Analysis of c-Fos Activity

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All data values are presented as the mean ± SEM. Mann–Whitney U test was used to analyze statistical differences using GraphPad Prism 7 software. Statistical significance was set at p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001. Functional network analyses of the c-Fos dataset were computed with the Rubinov and Sporns (2010) Brain Connectivity Toolbox1 in MATLAB R2016 (The Mathworks Inc.) Graph theoretical analysis of c-Fos data between gentle handling and non-handling conditions was used to calculate the relationship among the 16 ROIs. Correlations among the 16 ROIs arranged by rank-order were conducted using Kendall’s Tau correlation coefficient. Community correlation network and participation coefficients were analyzed according to previous studies, and a threshold value of 0.35 was set (Newman, 2006 (link); Power et al., 2013 (link); Vetere et al., 2017 (link); Rogers-Carter et al., 2018 (link)).
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7

PET Imaging of Ketamine Effects on Glucose Metabolism in Rats

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This procedure was based on previous studies [84 (link), 85 (link)]. Rats were habituated to experimenter handling and the open field arena. Rats were fasted 16 h before the experiment. On the day of the experiment, rats received a continuous IV infusion of vehicle (buffered saline), (S)-ketamine (10 mg/kg), or (R)-ketamine (10 mg/kg) over 40 min in an open field arena. Ten minutes after start of infusion, rats were injected (IP) with 13 MBq of 2-deoxy-2-[18F]fluoro-D-glucose (FDG; Cardinal Health). After 30 min, rats were anesthetized with 1.5% isoflurane, placed on a custom-made bed of a nanoScan small animal PET/CT scanner (Mediso Medical Imaging Systems) and scanned for 20 min on a static acquisition protocol, followed by a CT scan. The PET data were reconstructed and coregistered to an MRI template as described above. All statistical parametric mapping analyses were performed using Matlab R2016 (Mathworks) and SPM12 (University College London).
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8

Biomechanical Evaluation of Center of Rotation

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During testing, the compressive force and the shear force were recorded using an integrated 6DOF force transducer. The ROM in FE was obtained using the integrated rotation transducer of the corresponding rotatory channels. NZ was defined as half the total laxity at zero load in the complementary direction, as described by Wilke et al. [16 (link)].
The effects of CORs on the compressive force, shear force, NZ, and ROM in FE were assessed using repeated-measures analysis of variance (ANOVA) with a statistical significance level of 0.05. Prior to the analysis, ROMs of flexion, extension, and NZ of FE were deemed a baseline when the COR was located at the middle point of the horizontal middle plane. Subsequently, ROMs of flexion, extension, and NZ of FE for the 9 CORs were normalized to this baseline, respectively. All statistical analyses were performed with MATLAB R2016 version (MathWorks, Natick, MA, USA).
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9

Longitudinal Analysis of MS Progression

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All results are presented as median with interquartile range (IQR). Statistical differences in demographic parameters were tested by Chi-square test or Fisher exact test for sex and Mann-Whitney test for age. The level of statistical significance between the results of MS patients at different time-points was tested by Wilcoxon signed-rank test and the difference between the study population at the different time points and HC was tested using the Kruskal Wallis rank test. In order to compensate for multiple comparisons, the level of significance was adjusted according to Holm-Bonferroni. All data handling and statistical analyses were made using SAS 9.4 (SAS Institute Inc, Cary, NC, USA) and Matlab R2016 (MathWorks Inc, USA).
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10

PET Imaging of Ketamine Effects on Glucose Metabolism in Rats

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This procedure was based on previous studies [84 (link), 85 (link)]. Rats were habituated to experimenter handling and the open field arena. Rats were fasted 16 h before the experiment. On the day of the experiment, rats received a continuous IV infusion of vehicle (buffered saline), (S)-ketamine (10 mg/kg), or (R)-ketamine (10 mg/kg) over 40 min in an open field arena. Ten minutes after start of infusion, rats were injected (IP) with 13 MBq of 2-deoxy-2-[18F]fluoro-D-glucose (FDG; Cardinal Health). After 30 min, rats were anesthetized with 1.5% isoflurane, placed on a custom-made bed of a nanoScan small animal PET/CT scanner (Mediso Medical Imaging Systems) and scanned for 20 min on a static acquisition protocol, followed by a CT scan. The PET data were reconstructed and coregistered to an MRI template as described above. All statistical parametric mapping analyses were performed using Matlab R2016 (Mathworks) and SPM12 (University College London).
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