All Raman spectra were pre-processed in bespoke scripts or Toolboxes in Matlab 2017 (Mathworks, USA). A cosmic ray removal was performed using a median filter, followed by a baseline correction with an asymmetric least square smoothing and a vector normalisation. K-Means cluster analysis was performed using a Toolbox in Matlab 2017 (Mathworks, USA).
Matlab 2017
Manufactured by MathWorks
Sourced in United States, United Kingdom
MATLAB 2017 is a high-level programming language and numerical computing environment used for scientific and engineering calculations. It provides a wide range of built-in functions and tools for data analysis, algorithm development, and visualization.
Automatically generated - may contain errors
Lab products found in correlation
Dimension icon, by Bruker (2 mentions)
Spss 24, by IBM (2 mentions)
4 cm millipede rf imaging probe, by Agilent Technologies (1 mentions)
Labview, by National Instruments (1 mentions)
Letswave, by MathWorks (1 mentions)
Letswave 6, by MathWorks (1 mentions)
Spss 23, by IBM (1 mentions)
Labchart software, by ADInstruments (1 mentions)
Spss statistics 20, by IBM (1 mentions)
Eyelink 1000 plus, by SR Research (1 mentions)
Tetrodotoxin ttx, by Abcam (1 mentions)
Eeglab 14.1.2b, by MathWorks (1 mentions)
Prism 6, by GraphPad (1 mentions)
Nirscout, by NIRx Medical Technologies (1 mentions)
Nirslab software, by NIRx Medical Technologies (1 mentions)
Polaris vicra, by Northern Digital (1 mentions)
46 protocols using matlab 2017
1
Raman Spectra Pre-processing and Clustering
2
Automated Image Stitching for Matrix Tablets
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A novel image stitching algorithm was used to stitch the collected images to develop a single overall matrix tablet surface using MATLAB® 2017 software (The MathWorks, Inc. USA). Briefly, this process consists of two stages; (a) image stitching and (b) image blending. Firstly the, stitching was performed by generating the relative position of collected images followed by the identification of the correlation point which was performed by sliding the adjacent edges of the image in both directions until a best match of edge features are established. This method entails the choice of an optimal search space as illustrated in Fig. 2a , in which a detailed assessment was performed to identify the best correlation (which was a 20% overlap in the present study). The normalised cross-correlation can be expressed by the following equation (eqn (1) ); where, w(x, y) represents a pixel value of the image. w̄ is the mean value of the overall pixels included in the box area. f(x + i, y + j) represents a pixel value of the composite image inside the box area. f̄(i, j) is the mean value of the of the composite image inside the box area. K, L represents the box dimensions.
Secondly, an advanced image blending algorithm was applied to improve the visual quality of the composite image. The process is illustrated inFig. 2b where an overlap between a new image and the composite image is shown.
Secondly, an advanced image blending algorithm was applied to improve the visual quality of the composite image. The process is illustrated in
3
High-Field MRI Imaging Protocols
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All images were acquired using a 9.4 T (400 MHz for protons) 89 mm vertical bore magnet (Varian, Walnut Creek, CA) with a 4 cm Millipede RF imaging probe with triple axis gradients (100 G/cm max). Specific imaging sequences and parameters that were used are provided in the relevant Methods sections. All acquired images were then processed in MATLAB 2017 (MathWorks, Inc., Natick, MA) as described in the relevant methods sections.
4
Statistical Analysis of Aging Hallmarks
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Statistical analysis for physiological hallmarks of aging was done as described previously in Miller et al.2 (link). Briefly, 100 cells were randomly selected from each experimental group (data depicted in Supplementary Figs. 2 –5 ), and they were then pooled in a unique population of 800 cells for aged fibroblasts (100 cells × 8 individuals for both aged and aged treated); 300 cells for young fibroblasts (100 cells × 3 individuals); 700 cells for aged endothelial cells (100 cells × 7 individuals for both aged and aged treated); 300 cells for young endothelial cells (100 cells × 3 individuals). Box distribution plots display the fluorescence intensity quantification of 100 cells from each patient. Distributions were compared by statistical analysis by using multiple-comparison ANOVA. Arbitrary units for frequency distributions of different cell types should not be compared because staining was performed at different times. Matlab 2017 (MathWorks) was used for data presentation and analysis.
5
Coherent Raman Microscopy for Virtual H&E Staining
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SRS images were processed in bespoke scripts60 or Toolboxes in Matlab 2017 (Mathworks, USA). All images were acquired using the same parameters with a field of view of 200 μm × 200 μm (512 × 512 pixels). The SRS images were acquired at 2845 and 2930 cm−1 for CH2 and CH3 bonds, which correspond to the lipids and proteins signal, respectively.52,53 (link) The subtraction of the SRS2930cm−1 to SRS2845cm−1 images evidences the cell nuclei. By overlaying in false colour the SRS2845cm−1 and SRS2930cm−1 − SRS2845cm−1 images, it is possible to produce a virtual H&E stained image.
6
3D Surface Texture Analysis of Printed Tablets
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3D parametric surface texture analysis of printed formulations was examined using Talysurf CCI 3000 optical 3D surface profiler and the method was adopted as previously described [28 (link)]. Briefly, printed tablets placed on a clean stainless steel stage using double-sided transparent tape and 1.2 × 1.2 mm2 area was scanned and then 3D parametric surface texture parameters were determined using MATLAB 2017 (The Math Works, Inc. Natick, Massachusetts, USA) [28 (link),32 (link),33 (link),34 (link)].
7
EEG Processing of Frequency-Tagging Responses
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All EEG processing was performed using Letswave 6 (https://www.letswave.org/ ) and MATLAB 2017 (the MathWorks). EEG data was segmented in 67-s segments (2s before and 5s after each sequence), bandpass filtered (0.1 to 100 Hz) using a fourth-order Butterworth filter, and downsampled to 256 Hz. Next, noisy electrodes were linearly interpolated from the three spatially nearest electrodes (not more than 5% of the electrodes, -i.e., three electrodes, were interpolated). All data segments were re-referenced to a common average reference. While in frequency-tagging studies we typically apply blink correction (using ICA) for any participant blinking more than 2 SD above the mean [e.g., (68 (link)–70 (link))], in the present study we did not perform any blink correction as none of the participants blinked excessively, i.e., more than two standard deviations above the mean across all participants (0.36 times per second). Note that frequency-tagging yields responses with a high SNR at specific frequency bins, while blink artefacts are broadband and thus do not generally interfere with the responses at the predefined frequency (67 ). Hence, blink correction (or removal of trials with many blinks) is not systematically performed in such studies [e.g., (71 (link)–73 (link))].
8
fNIRS Signal Processing and Analysis
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The fNIRS data were exported without prior preprocessing, and all analyses were performed with MATLAB 2017 (The MathWorks). All frequencies < 0.01 Hz and > 0.5 Hz were excluded using a bandpass filter. Additionally, the correlation‐based signal improvement procedure (CBSI; Cui et al., 2010 ) was used to correct motion artifacts. All further analyses were run with the resultant cbsi‐hb. Independent component analysis (ICA; Delorme & Makeig, 2004 ) was used to exclude residual artifacts. After preprocessing the data, a model‐based analysis for event‐related fNIRS data (Plichta et al., 2007 ) was applied. The resulting ß values were used for all further tests, which were run using SPSS 22 (SPSS Inc.).
9
Cortical Evoked Potential Analysis
10
Nocturnal HRV Analysis using ECG Recordings
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HRV analysis was performed on ECG recordings in accordance with standard guidelines [10 (link)] using HRV algorithms implemented in signal processing software (MATLAB 2017, version 9.2.0.538062 (R2017a), Mathworks, Natick, Massachusetts). HRV parameters were calculated using 2-min epochs, shifted by 30 s, across the entire ECG signal, and then averaged across segments of N2 sleep (stage 2 non-rapid eye movement). N2 sleep is the preferred stage for nocturnal HRV analysis due to its relative stability, as it is primarily controlled by the parasympathetic nervous system and has fewer arousals that may impact the sensitivity of HRV measures [15 (link)]. Epochs with mixed sleep stages and ECG sections associated with respiratory events or cardiac arrhythmia (including atrial and ventricular ectopic beats) were excluded from HRV analysis. If the total exclusion period exceeded 12 s (10% of epoch length), the entire epoch was excluded [16 ].
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