Analysis of electrophysiological recordings was performed in Matlab R2016a (Mathworks). All statistical testing was performed in Matlab R2016a (Mathworks) and Prism (GraphPad). Values are reported as mean ± standard error of the mean (SEM, where n = number of trials) unless otherwise stated. A one- or two-way ANOVA was performed, followed by Bonferroni’s or Dunnett’s post-hoc tests. In testing the significance of changed incidence of SLEs, a Fisher’s Exact test was used with a confidence interval (CI) of 95%.
Matlab r2016a
Manufactured by MathWorks
Sourced in United States, United Kingdom
MATLAB R2016a is a high-level programming language and numerical computing environment developed by MathWorks. It provides a wide range of tools for data analysis, algorithm development, and visualization. MATLAB R2016a supports a variety of matrix manipulations, plotting of functions and data, implementation of algorithms, and the creation of user interfaces.
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Lab products found in correlation
Spss statistics version 19, by IBM (3 mentions)
Prism 5, by GraphPad (2 mentions)
Eyelink 1000, by SR Research (2 mentions)
Canoco 5, by Microcomputer Power (2 mentions)
Spss software version 11.0 for windows, by IBM (2 mentions)
Originpro 8, by OriginLab (2 mentions)
Simca version 14, by Sartorius (2 mentions)
Spss version 24, by IBM (2 mentions)
Spss version 22, by IBM (2 mentions)
Unity inova 500, by Agilent Technologies (1 mentions)
Mestrenova version 8, by Mestrelab Research (1 mentions)
Psychtoolbox, by MathWorks (1 mentions)
Eeglab, by MathWorks (1 mentions)
Spss v20, by IBM (1 mentions)
Spss version 18, by IBM (1 mentions)
Eyelink 1000 eye tracker, by SR Research (1 mentions)
Hd 280 pro, by Sennheiser (1 mentions)
Matlab r2016a, by GraphPad (1 mentions)
Lsqnonlin, by MathWorks (1 mentions)
Ingenia cx scanner, by Philips (1 mentions)
241 protocols using matlab r2016a
1
Electrophysiological Analysis of SLEs
2
Comprehensive EEG Preprocessing Workflow
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EEG data was preprocessed in the open-source software Letswave5 (https://github.com/NOCIONS/Letswave5 ), running in MATLAB R2016a (MathWorks, USA) according to well-validated procedures as reported previously24 (link),25 (link). After importation, continuous blocks of recording were aligned to the first block to correct for vertical jumps due to voltage drift during pauses. EEG data were first band-pass filtered between 0.05 to 100 Hz with a 4th order zero-phase Butterworth filter and then notch-filtered to remove the 50 Hz frequency and 2 following harmonics (100 and 150 Hz, width 0.5 Hz). Data were downsampled to 256 Hz to reduce computational load. The whole EEG trace was then segmented relative to the starting trigger of each sequence, with an additional 2 s before and after each sequence (−2 to 76 s). Eyeblink artifacts more than 0.2 times/s on average were corrected by applying independent component analysis (ICA) on one participant (P10)19 (link). No interpolation of bad channels was required in our participants. Finally, the EEG sequences were re-referenced to the average of all 128 electrodes.
3
Color Analysis of Fruits and Vegetables
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The colors of the fruits and vegetables were measured by using computer vision-based image analysis in MATLAB® R2016a (The MathWorks, Inc., Natick, Massachusetts, United States). Digital images were taken from an image acquisition system consisting of a digital camera placed vertically at a distance of 25 cm from the sample as described previously (Mogol and Gokmen, 2014 (link)). The angle between the axes of the lens and the sources of illumination (day light fluorescent lamps with a color temperature 6500 K) is adjusted to approximately 45°. Captured images were stored in a personal computer in jpeg format without compression. The HSV values were calculated from RGB values of the images by using a built-in function of MATLAB on the region of interest of the digital image, where H, S, and V indicate hue, saturation and value, respectively (Appendix ). The hue angle values were used for further investigations. Each measurement was performed in triplicate.
4
Proton NMR Spectroscopy for Sample Analysis
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Proton NMR spectra were acquired with a Varian Unity Inova 500 spectrometer (Agilent Technologies, Santa Clara, CA, USA) operating at 499.84 MHz at 300 K. For each spectrum, 256 scans were collected into 32 k data points over a spectral width of 6000 Hz, with a 45° pulse, an acquisition time of 1.5 s, and a relaxation delay of 4 s. The solvent (water) residual signal was suppressed by applying a presaturation technique with low power radiofrequency irradiation for 1.5 s. An exponential function corresponding to 0.3 Hz was applied to each Free Induction Decay (FID) before Fourier transformation as well as a zero-filling to 65 k data points. NMR spectra were manually phased and the baseline corrected using MestReNova (Version 8.1, Mestrelab Research SL). Spectral chemical shift referencing was also performed in all spectra on the TSP CH3 signal at 0.00 ppm. The NMR spectral data were then converted into ASCII, imported into MATLAB R2016a (Mathworks, MA, USA) and a data matrix, sized 60 × 65,536 (samples × variables), was built.
5
Micro-Optical Characterization of Mucociliary Epithelium
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To assess the functional microanatomy parameters of ASL depth and ciliary beat frequency (CBF), micro–optical coherence tomography (μOCT) imaging of MNSE cultures was performed with incident illumination of the apical cell surface. To mitigate errors in geometric measurements, the imaging optics axis was placed within 10 degrees of normal to the cell plane as previously described [33] , [34] (link). All imaging was performed at 4 regions of interest per each well (2 points at 1 mm from the center and another at 1 mm from the edge for 2 separate locations). ASL was quantitatively evaluated by directly gauging the visible thickness of the respective layers in the image. To account for refractory properties of the liquid, layer thickness dimensions were corrected for the index of refraction of the liquid (n = 1.33). CBF was calculated using a time series of images and quantitatively measured by identifying peak amplitude frequency in the temporal Fourier transform of areas demonstrating oscillatory behavior [33] . All parameters were judged at 5 uniformly distributed areas of the image. All images were analyzed utilizing ImageJ version 1.50i (National Institutes of Health, Bethesda, MD, USA) and MATLAB® R2016a (The MathWorks, Natick, MA, USA).
6
Analyzing Valve Biogenesis via Live-Cell Imaging
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The raw movie files were analyzed using Fiji (ImageJ, National Institute of Health) and Matlab R2016a (Mathworks). To examine valve biogenesis, live-cell imaging data of four cells were analyzed as follows. The raw movies were rotated and cropped to align the cells vertically. The intensities of all nine z-planes recorded were summarized for each frame yielding Fig. 5 . The background was corrected by setting the global minimum fluorescence intensity outside the cell to zero. The movie was further processed in Matlab correcting for drift via a cross-correlation image registration [43 (link)]. The movie was then split into rectangular subregions as indicated in the corresponding figures. The region size was linearly interpolated between key frames to account for cell growth during the recording time. The fluorescence intensity was summarized for each region and frame, and plotted over time. The total fluorescence intensity prior to cytokinesis was normalized to 1.0 to allow for comparison of different cells. To build an average intensity plot of all cells, the peak intensity for PDMPO fluorescence was used for post-synchronization. Time-lapse data from four cells were used to generate an average fluorescence intensity plot.
7
Extracting Imaging Biomarkers from Contrast-Enhanced CT
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Pre-treatment contrast-enhanced CT scan images of patients were exported for analysis. The primary tumour was delineated by experienced radiation oncologists on the mediastinal window of the planning CT scan. IBMs were extracted by internal programming software using MATLAB R2016a (Mathworks, Natick, USA) and its toolbox. From the contrast-enhanced CT images of each patient, 96 IBMs were extracted, including the following types: (1) 24 CT intensity IBMs, describing the distribution of voxel parameter values in the volume of interest, such as the min, max and skewness of the primary tumour intensity; (2) 20 geometric IBMs that calculated the size and shape of the volume of interest, such as sphericity, volume, surface and long axis length; and (3) 52 texture IBMs, that described the difference in voxel density distribution of the three-dimensional contoured structure and consisted of four different matrices: grey level co-occurrence (GLCM) [23 (link)], grey level run-length (GLRLM) [24 (link)], neighbourhood grey-tone difference (NGTDM) [25 (link)], and grey level size-zone (GLSZM) matrices [26 ]. More details on the algorithms for IBM extraction and application have been discussed in previous studies [14 (link), 27 (link)].
8
Whole-Night Sleep EEG Spectral Power Analysis
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The whole-night sleep EEG data were processed in MATLAB R2016a (The MathWorks Inc., Natick, MA). Continuous EEG signals were referenced offline to the left or right mastoid (M1 or M2). All signals were filtered using a 0.5 Hz high-pass filter, a 40 Hz low-pass filter, and a 50 Hz notch filter. Then, sleep recordings were divided into 6 s epochs, and artifacts in the 6 s epochs were visually removed. Portions in which the absolute amplitude of the EEG signal exceeded 150μV were marked as artifacts. If more than 25% of artifacts were removed for one participant, this participant would be excluded from the analysis. Finally, only one participant was excluded from the spectral power analysis. Whole-night spectral power averages were obtained across all artifact-free epochs of stage N2.
Fast Fourier transformation (FFT) was used in MATLAB R2016a to calculate the spectral power density, and the truncation error was reduced by applying a Hanning window (50% overlap). The obtained power spectral data were divided into two bands (delta (0.5–4 Hz) and beta (13–30 Hz)). In this study, we only evaluated the beta/delta power ratio as an integrated EEG index of activation.
Fast Fourier transformation (FFT) was used in MATLAB R2016a to calculate the spectral power density, and the truncation error was reduced by applying a Hanning window (50% overlap). The obtained power spectral data were divided into two bands (delta (0.5–4 Hz) and beta (13–30 Hz)). In this study, we only evaluated the beta/delta power ratio as an integrated EEG index of activation.
9
Soft Sensor for Biomass Monitoring
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The real-time process data was used for implementation of a soft sensor, meaning that information about the actual composition of biomass subpopulations could be extracted from multiple real-time measurements for monitoring purposes. The data evaluation and set up of the soft sensor was performed with MatLab (MATLAB R2016a, MathWorks, U.S.A.). All model parameters and shown residuals were estimated using the experimental data of the fed-batches described above.
10
Normalizing and Analyzing Glioblastoma Transcriptomes
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The Affymetrix U133A CEL files (n = 548) were normalized with IRON33 (link) using sample 5500024037497121008340.A12.CEL as median sample. The CEL file with the smallest RMSD was selected if there were multiple CEL files for a single sample, resulting in 529 files. The molecular subtypes for these GBM samples were as described10 (link), resulting in 53 mesenchymal, 25 neural, 52 proneural and 34 classical GBM samples. Group comparisons were performed using Mann-Whitney test and the two-tailed p-value is reported. Fold change is reported as fold change (fc). Box plots and scatter plots were generated using MATLAB R2016a (TheMathWorks, Natick, MA, USA) and the “Alternative box plot” toolbox and “gramm (complete data visualization toolbox, ggplot2/R-like)” toolbox from MATLAB File Exchange.
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