Simple randomization was performed for both the tooth and the biomaterial group depending on a set of randomized numbers generated with a computer using Matlab version 8.0 (https://nl.mathworks.com/products/matlab.html , accessed on 10 July 2021, The Mathworks Inc., Natick, MA, USA) software. The randomization list was concealed in a sealed envelope. The chosen tooth was randomly allocated by an independent individual (S.R) to one of the two groups: the Biodentine (Septodont, Saint Maur des Faussés, France) group or the ProRoot WMTA (Dentsply, Tulsa Dental, OK, USA) group. The other tooth was automatically allocated to the other group. All teeth were coded with an eight-digit code, where the first four digits were coded for the sheep number and the last four digits were coded for the tooth number and the biomaterial used. These unique codes ensured that the assessors (oral pathologists) were blinded to the type of treatment performed. A single precalibrated well-experienced operator (L.M) performed all the intervention and was blinded to the treatment group until the placement of the biomaterial.
Matlab version 8
Manufactured by MathWorks
Sourced in United States, United Kingdom
MATLAB version 8.3 is a high-level programming language and numerical computing environment designed for technical computing. It provides a platform for algorithm development, data visualization, and numerical analysis.
Automatically generated - may contain errors
Lab products found in correlation
Minolta ls 110, by Konica Minolta (2 mentions)
Proroot wmta, by Dentsply (1 mentions)
Biodentine, by Dentsply (1 mentions)
Psychophysics toolbox, by MathWorks (1 mentions)
Spss statistical package version 16, by IBM (1 mentions)
Spss software, by IBM (1 mentions)
Mps2 autosampler, by Gerstel (1 mentions)
Pegasus 3 mass spectrometer, by Leco (1 mentions)
Imaging biomarker explorer, by MathWorks (1 mentions)
Sigmaplot version 10, by Merck Group (1 mentions)
21 protocols using matlab version 8
1
Randomized Comparative Dental Biomaterial Study
2
Functional MRI Preprocessing Pipeline
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Imaging data were transferred and processed on a Linux platform running MATLAB version 8.0 (The MathWorks, Natick, Massachusetts, USA). Preprocessing was performed with Statistical Parametric Mapping software (SPM8; Wellcome Trust Centre for Neuroimaging, UK) using a preprocessing pipeline as reported in Kerestes et al.24 (link) In brief, preprocessing involved the following steps: slice timing correction, motion correction, co-registration of each participant's echo planar images to their respective co-registered T1 image, spatial normalisation of both the structural and functional images (re-sliced to 2 mm) and spatial smoothing with a 6 mm full width half maximum Gaussian filter (see supplementary material for additional information).
3
Evaluating Balance during Walking Tasks
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We performed a paired t-test with Bonferroni correction to determine significant differences in balance measures between the three different walking tasks. The significant level was set at 0.05/3. The effect size for t-tests was also reported using Cohen's d, with values of 0.2–0.4, 0.4–0.8 and greater than 0.8 indicating small, moderate and large effects, respectively [39 ]. Receiver operating characteristic (ROC) analysis was also performed, after which the area under the curve (AUC) was calculated and used to investigate the discriminatory power of each balance measure between the normal walking task and the unstable walking tasks (i.e. dual-task and arm-restricted walking). An AUC between 0.5 and 0.7, between 0.7 and 0.9 and above 0.9 indicates low, medium and high discriminatory power, respectively [40 (link)].
Paired t-test and effect size analyses were performed using Microsoft Excel (Microsoft, Redmond, WA, USA). Effect size and ROC analyses were performed using Matlab version 8.3 (Mathworks, Natick, MA, USA).
Paired t-test and effect size analyses were performed using Microsoft Excel (Microsoft, Redmond, WA, USA). Effect size and ROC analyses were performed using Matlab version 8.3 (Mathworks, Natick, MA, USA).
4
Elderberry Juice Quality Analysis
5
Solvent Effects on Cocrystal Formation
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Principal component analysis (PCA) [34 ] was used to analyze the spectra in order to compare the spectrum of the PM with the spectrum of the products obtained with each solvent. In this way, it can be infer the effect of the solvents in the formation and purity of the cocrystals.
Before PCA, MIRS, and NIRS spectra were pre-processed with standard normal variate (SNV) to correct for disturbances caused by light scattering (e.g., resulting from variable particle size). Data were then mean centred immediately before the estimation of the PCA models. Matlab version 8.3 (MathWorks, Natick, MA, USA) and the PLS Toolbox version 7.5 (Eigenvector Research Inc., Wenatchee, WA, USA) were used for this analysis.
Before PCA, MIRS, and NIRS spectra were pre-processed with standard normal variate (SNV) to correct for disturbances caused by light scattering (e.g., resulting from variable particle size). Data were then mean centred immediately before the estimation of the PCA models. Matlab version 8.3 (MathWorks, Natick, MA, USA) and the PLS Toolbox version 7.5 (Eigenvector Research Inc., Wenatchee, WA, USA) were used for this analysis.
6
Visual Span and Reading Speed Measurements
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The 26 lowercase Courier font letters of the English Alphabet—a serif font with fixed width and normal spacing—were used for both visual span and reading speed tasks. Trigrams, random strings of three letters, were used to measure visual span profiles. All the letters were black on a uniform gray background with a contrast of 99% (Fig. 1 A) and a letter size of 0.8° (in x-height) at the 57-cm viewing distance.
All stimuli were generated and controlled using a computing environment (MATLAB version 8.3 and Psychophysics Toolbox extensions47 (link),48 (link); MathWorks, Inc., Natick, MA, USA) for a commercial operating system (Windows 7; Microsoft Corp., Redmond, WA, USA) running on a PC desktop computer (Dell Precision Tower 5810; Dell, Inc., Round Rock, TX, USA). Stimuli were presented on a liquid crystal display monitor (model: Asus VG278HE; refresh rate: 144 Hz; resolution: 1920 × 1080, subtending 60° × 34° visual angle at a viewing distance of 57 cm) with the mean luminance of the monitor at 159 cd/m2. Luminance of the display monitor was made linear using an 8-bit lookup table in conjunction with photometric readings from a luminance meter (MINOLTA LS-110; Konica Minolta, Inc., Tokyo, Japan).
All stimuli were generated and controlled using a computing environment (MATLAB version 8.3 and Psychophysics Toolbox extensions47 (link),48 (link); MathWorks, Inc., Natick, MA, USA) for a commercial operating system (Windows 7; Microsoft Corp., Redmond, WA, USA) running on a PC desktop computer (Dell Precision Tower 5810; Dell, Inc., Round Rock, TX, USA). Stimuli were presented on a liquid crystal display monitor (model: Asus VG278HE; refresh rate: 144 Hz; resolution: 1920 × 1080, subtending 60° × 34° visual angle at a viewing distance of 57 cm) with the mean luminance of the monitor at 159 cd/m2. Luminance of the display monitor was made linear using an 8-bit lookup table in conjunction with photometric readings from a luminance meter (MINOLTA LS-110; Konica Minolta, Inc., Tokyo, Japan).
7
Crowded Letter Perception Experiment
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The 26 lowercase Courier font letters of the English alphabet—a serif font with fixed width and normal spacing—were used. For the crowded condition, trigrams—random strings of three letters—were used. All letters were black and had a letter size of 0.8° (in x-height) at the 57-cm viewing distance and were presented on a uniform gray background with a contrast of 99%.
All stimuli were generated and controlled using a computing environment (MATLAB version 8.3 and Psychophysics Toolbox extensions39 ,40 ; MathWorks, Inc., Natick, MA) for a commercial operating system (Windows 7; Microsoft Corp., Redmond, WA) running on a PC desktop computer (Dell Precision Tower 5810; Dell, Inc., Round Rock, TX). Stimuli were presented on a liquid crystal display monitor (model: Asus VG278HE; refresh rate: 144 Hz; resolution: 1920 × 1080, subtending 60° × 34° visual angle at a viewing distance of 57 cm) with the mean luminance of the monitor at 159 cd/m2. The luminance of the display monitor was made linear using an 8-bit lookup table in conjunction with photometric readings from a luminance meter (MINOLTA LS-110; Konica Minolta, Inc., Tokyo, Japan).
All stimuli were generated and controlled using a computing environment (MATLAB version 8.3 and Psychophysics Toolbox extensions39 ,40 ; MathWorks, Inc., Natick, MA) for a commercial operating system (Windows 7; Microsoft Corp., Redmond, WA) running on a PC desktop computer (Dell Precision Tower 5810; Dell, Inc., Round Rock, TX). Stimuli were presented on a liquid crystal display monitor (model: Asus VG278HE; refresh rate: 144 Hz; resolution: 1920 × 1080, subtending 60° × 34° visual angle at a viewing distance of 57 cm) with the mean luminance of the monitor at 159 cd/m2. The luminance of the display monitor was made linear using an 8-bit lookup table in conjunction with photometric readings from a luminance meter (MINOLTA LS-110; Konica Minolta, Inc., Tokyo, Japan).
8
Tri-axial Accelerometry for Running Biomechanics
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Tri-axial accelerometry (X50-2 wireless accelerometer, range ± 50g, sampling at 1024 Hz, 0.016g/count resolution, 33g weight, Gulf Coast Data Concepts, MS, USA) was acquired during each running trial. The accelerometer was securely positioned over L3 spinous process of the trunk [25] (link), and directly mounted to the skin using double sided tape and adhesive spray.
Accelerometer position was unaltered between all running trials and was routinely checked between running trials for security. Trials were discarded in the case the investigators deemed the accelerometer to be not securely fastened upon its removal (after data collection).
All signal processing of acceleration curves was performed using customized software in MATLAB version 8.3 (The Mathworks Inc., Natick, MA, USA). Accelerometry-derived parameters were computed from the middle ten consecutive strides of the 10-70m measurement zone, that were first trigonometrically tilt-corrected and filtered using a zero-lag 4 th order low-pass Butterworth filter (cut-off frequency 50 Hz) [20, (link)25] (link). Accelerometry-derived parameters were averaged over two running trials per surface per participant.
Accelerometer position was unaltered between all running trials and was routinely checked between running trials for security. Trials were discarded in the case the investigators deemed the accelerometer to be not securely fastened upon its removal (after data collection).
All signal processing of acceleration curves was performed using customized software in MATLAB version 8.3 (The Mathworks Inc., Natick, MA, USA). Accelerometry-derived parameters were computed from the middle ten consecutive strides of the 10-70m measurement zone, that were first trigonometrically tilt-corrected and filtered using a zero-lag 4 th order low-pass Butterworth filter (cut-off frequency 50 Hz) [20, (link)25] (link). Accelerometry-derived parameters were averaged over two running trials per surface per participant.
9
Machine Learning Protocol for Disease Classification
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The hold-out validation was used to assess the performance of the developed methods. The dataset was randomly split into two independent 70% training and 30% test sets73 . Also, four-fold cross-validation was employed for further performance assessment to guard against testing hypotheses suggested by the data (Type III errors)74 . The classifiers were assessed in terms of the performance indices such as sensitivity, specificity, precision, accuracy, and diagnostic odds ratio (DOR) whose definitions are given in Table S1 . The Q-Cochran’s test and McNemar’s post-hoc test were used to identify whether the proposed system significantly outperformed the state-of-the-art. The Bonferroni correction was also applied for multiple comparisons and the adjusted P-values were then used for interpretation. The random permutation test75 was used to compare the performance of the mGMDH-AFS machine with real DT/non-DT classes and ten random sets whose class labels were randomly permuted. MATLAB version 8.6 (The MathWorks Inc., Natick, MA, USA) was used for offline processing.
10
Genetic Variation in Dyslipidemia
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Results are reported as mean ± standard deviation (for interval variables) and frequencies (for categorical variables).The pairwise χ2 analysis was used to test for allele frequency differences (and nominal features) between dyslipidemia and normal groups and when the Cochran conditions were not met, the Fisher exact test was used. The χ2 analysis was used to test genotype frequency deviations from what predicted by the Hardy Weinberg equation. P-values less than 0.05 were considered significant. The entire data processing was performed off-line using Matlab version 8.6 (The MathWorks Inc., Natick, MA, USA). The statistical analysis and calculations were performed using the SPSS statistical package, version 16.0 (SPSS Inc., Chicago, IL, USA).
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