Raw EGG data were collected at a sampling frequency of 2000 Hz and then preprocessed using MATLAB (Mathworks®, Natick, Massachusetts) as follows: (1) data were down sampled to 500 Hz using MATLAB function “downsample”; (2) a 3rd order polynomial fit was performed with MATLAB function “polyfit” for data detrending to obtain a temporal trend for the down-sampled time series; (3) the fitted trend was subtracted from the down-sampled data52 (link),53 (link); (4) the detrended time series was filtered using a low-pass filter at 1 Hz with the “filtfilt” filter in MATLAB to avoid any filtering-induced phase shift.
Matlab
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MATLAB is a high-performance programming language and numerical computing environment used for scientific and engineering calculations, data analysis, and visualization. It provides a comprehensive set of tools for solving complex mathematical and computational problems.
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Lab products found in correlation
Labview, by National Instruments (106 mentions)
Eeglab, by MathWorks (102 mentions)
Eyelink 1000, by SR Research (87 mentions)
Psychtoolbox, by MathWorks (76 mentions)
Prism 6, by GraphPad (74 mentions)
Igor pro, by Wavemetrics (73 mentions)
Pls toolbox, by Eigenvector Research (68 mentions)
Psychophysics toolbox, by MathWorks (62 mentions)
Prism 8, by GraphPad (61 mentions)
Prism 7, by GraphPad (49 mentions)
Offline sorter, by Plexon (48 mentions)
Prism 9, by GraphPad (46 mentions)
Spss statistic, by IBM (45 mentions)
Imagej, by MathWorks (43 mentions)
Spss software, by IBM (41 mentions)
Spike2, by Cambridge Electronic Design (40 mentions)
Prism 5, by GraphPad (39 mentions)
Visual3d, by C-Motion (37 mentions)
Spm12, by MathWorks (37 mentions)
Sigmaplot, by Grafiti LLC (32 mentions)
20 292 protocols using matlab
1
EEG Signal Preprocessing Workflow
2
Volleyball Blocking Technique Biomechanics
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Each jump trial was processed in the QTM motion capture system (Version 2.15), exported, and calculated by using MATLAB (MathWorks®, Version R2017a). Dependent measure was jumping height. Jumping height was analyzed using the marker at the back of the participants. The vertical distance between the back marker in standing (static measurement) and in the highest point of each jump was calculated with MATLAB (MathWorks®, Version R2017a).
As a supplementary measure of motor behavior, we analyzed the length of the first step after ready-block position. The length of the first step was calculated by using the big-toe marker of the foot that made the first step to the right or left side. The distance between the starting position directly before initiating the jump and the first touch on ground was calculated with MATLAB (MathWorks®, Version R2017a). All participants used the same volleyball-specific blocking technique (i.e., swing block, which is the preferred technique in elite volleyball), consisting of a three-step approach.
Further parameters were volleyball-specific errors (e.g., net touching) and decision accuracy in all trials and conditions. They were recorded by the experimenter via protocol. An invalid trial in decision accuracy was defined when participants performed a step in the wrong direction.
As a supplementary measure of motor behavior, we analyzed the length of the first step after ready-block position. The length of the first step was calculated by using the big-toe marker of the foot that made the first step to the right or left side. The distance between the starting position directly before initiating the jump and the first touch on ground was calculated with MATLAB (MathWorks®, Version R2017a). All participants used the same volleyball-specific blocking technique (i.e., swing block, which is the preferred technique in elite volleyball), consisting of a three-step approach.
Further parameters were volleyball-specific errors (e.g., net touching) and decision accuracy in all trials and conditions. They were recorded by the experimenter via protocol. An invalid trial in decision accuracy was defined when participants performed a step in the wrong direction.
3
Digital Image Analysis Protocols
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4
Digital Image Analysis Workflow
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5
Red Blood Cell Parameterization via MATLAB
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RBCs were manually identified and tracked by the user utilizing a user interface developed in MATLAB (Mathworks, Natick, MA). Following cell selection and tracking, a region of interest containing the cell was generated, and image processing was performed to obtain the parametrization. Briefly, image processing to obtain binary images involved the following: (1) contrast thresholding ([μi − σi, μi] → [0, 255]), (2) Gaussian Smoothing (kernel: 5, σ = 7), (3) Thresholding (T = 68), and (4) morphological opening (kernel: 3x3). All image processing was performed in MATLAB (Mathworks, Natick, MA). Contour extraction involved edge detection of the processed binary images, obtained utilizing a MATLAB implementation of the Moore-Neighbor algorithm for binary image edge detection (Reddy et al., 2012 (link)). Following edge detection, parameterizations of edge pixel positions were obtained utilizing MATLAB (Mathworks, Natick, MA). Image transformations are expressed in detail in Figure 2 .
6
Cardiac Interbeat Interval Analysis
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Cardiac interbeat time intervals (RR) were collected during each SCWT in both the NFb and Ctrl groups (Figure 1 ) using a bipolar electrode transmitter belt Polar H10© (Polar®, Finland). The accuracy of this device has been demonstrated elsewhere [39 (link)]. The RR time series were imported to Matlab (Matlab Release 2021a, Mathworks, Natick, MA, USA) for subsequent analyses using existing Matlab functions and custom-designed algorithms. First, occasional ectopic beats (the irregularity of the heart rhythm involving extra or skipped heartbeats, e.g., extrasystoles and consecutive compensatory pause), were visually identified and manually replaced with interpolated adjacent values.
7
Confocal Microscopy Protocol for GFP/mCherry
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Colony images were gathered using a Confocal Multispectral Leica SP5 system with a HCX PL APO CS 10 × 0.40 DRY UV objective using 488 and 561 nm laser lines to detect GFP and mCherry fluorescent signals, respectively. Images were captured at 8-bit resolution (1024 × 1024) with no amplification factor and a frequency rate of 400 Hz. Distance between XY pixels and gathered Z planes included in stack images were 1.5137 and 6 μm, respectively. The numerical method was implemented in a script written in MATLAB (The Mathworks) containing the imaging toolbox and the MATLAB compatible bioformats package (MATLAB/index.html">https://docs.openmicroscopy.org/bio-formats/5.9.2/users/MATLAB/index.html# ) on a regular PC. Confocal images shown in the manuscript were treated to enhance brightness and contrast using ImageJ software.
8
Image Processing and Analysis Techniques
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For digital image processing, MATLAB (MathWorks) and ImageJ (National Institute of Health) were used. For graphics, MATLAB (MathWorks), Imaris (Bitplane) and ImageJ (National Institute of Health) were used. MATLAB (MathWorks) was used for statistical analysis and Mathematica (Wolfram Research) for Mathematical analysis.
9
Cine-MRI and TPM-MRI Analysis of Cardiac Function
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Segmentation of the cine-MRI was conducted using Matlab (The MathWorks Inc., USA), where endocardium and epicardium were detected in a semiautomatic manner. Briefly, the semiautomatic method took the centrum of the left ventricle as user input and converted the images into polar coordinates. The contrast between blood and myocardium were automatically detected and were used to generate endo- and epicardial masks. These masks were then manually validated and adjusted, if necessary, by a single examiner. From the cine-MRI data, left ventricular (LV) mass, LV end-diastolic volume (LVEDV), LV end-systolic volume (LVESV), stroke volume (SV), and LV ejection fraction (LVEF) were calculated.
Analysis of the TPM-MRI data was conducted using Matlab (The MathWorks Inc., USA) as previously described in detail [27 (link)]. TPM-MRI was used to measure global longitudinal strain (GLS, %), peak early diastolic strain rate (SRe’, 1/s) and peak systolic strain rate (SRs’, 1/s) in the septum and the LV free wall, and global. SRs’ and SRe’ were measured from the peaks in the strain rate curve gained from TPM using Matlab (The MathWorks Inc., USA). All MRI-analysis was conducted by staff blind to the treatment of the animals.
Analysis of the TPM-MRI data was conducted using Matlab (The MathWorks Inc., USA) as previously described in detail [27 (link)]. TPM-MRI was used to measure global longitudinal strain (GLS, %), peak early diastolic strain rate (SRe’, 1/s) and peak systolic strain rate (SRs’, 1/s) in the septum and the LV free wall, and global. SRs’ and SRe’ were measured from the peaks in the strain rate curve gained from TPM using Matlab (The MathWorks Inc., USA). All MRI-analysis was conducted by staff blind to the treatment of the animals.
10
Image Processing and Visualization Tools
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