Matlab 2016b

Manufactured by MathWorks

Sourced in United States, United Kingdom

MATLAB 2016b is a high-level programming language and numerical computing environment developed by MathWorks. It provides a platform for matrix manipulation, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages. MATLAB 2016b includes a range of toolboxes for various scientific and engineering applications.

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Lab products found in correlation

Prism 6, by GraphPad (4 mentions) Prism 8, by GraphPad (3 mentions) Spss version 22, by IBM (3 mentions) Prism 7, by GraphPad (2 mentions) Spss 21, by IBM (2 mentions) Activetwo, by BioSemi (2 mentions) Spss for mac v22, by IBM (2 mentions) Spss statistics 24, by IBM (2 mentions) Propixx dlp projector, by VPixx Technologies (2 mentions) Prism 9, by GraphPad (2 mentions) Sigmaplot, by Grafiti LLC (2 mentions) Du401 dd, by Oxford Instruments (1 mentions) W plan apochromat, by Zeiss (1 mentions) Uhts 300, by WITec (1 mentions) Xtra 2, by Toptica (1 mentions) Spss statistics version 24, by IBM (1 mentions) Cdp star, by Roche (1 mentions) Qubit 2.0 fluorometer, by Thermo Fisher Scientific (1 mentions) Dneasy blood and tissue kit, by Qiagen (1 mentions) Imagequant las 4000, by Cytiva (1 mentions)

183 protocols using matlab 2016b

EEG Signal Preprocessing Workflow

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Band-pass filter 0.3–30 Hz is implemented in MATLAB 2016b (MathWorks, Inc., Natick, MA, USA). The filter characteristics are Butterworth IIR 10th order. The filter is constructed with zero-pole-gain form converted into a Second Order Section (SOS) and the non-linear phase is corrected by the “filtfilt” function. The EEG down sampling to 100 Hz is also implemented in MATLAB 2016b (MathWorks, Inc., Natick, MA, USA) with the function “resample” which has been called to use a polyphase antialiasing filter.

Lacourse K., Yetton B., Mednick S, & Warby S.C. (2020). Massive online data annotation, crowdsourcing to generate high quality sleep spindle annotations from EEG data. Scientific Data, 7, 190.

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Confocal Raman Spectral Acquisition Methodology

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Confocal Raman spectral acquisition was performed on a Raman micro-spectroscope (alpha300R + , WITec, Ulm, Germany). The light source used was a 785 nm laser (Toptica XTRA II) with a 63 × /1.0 NA water immersion microscope objective lens (W Plan-Apochromat, Zeiss, Oberkochen, Germany). The scattered light was collected via a 100 μm fibre with a 600 groove mm⁻¹ grating spectrograph (UHTS 300, WITec, Ulm, Germany) and spectra were acquired using a thermoelectrically cooled back-illuminated CCD camera (iDus DU401-DD, Andor, Belfast, UK) with a spectral resolution of 3 cm⁻¹ and 85 mW laser power at the sample. Laser control was performed remotely via a serial connection and custom MATLAB (2016b, The Mathworks, MA, USA) scripts.

Penders J., Pence I.J., Horgan C.C., Bergholt M.S., Wood C.S., Najer A., Kauscher U., Nagelkerke A, & Stevens M.M. (2018). Single Particle Automated Raman Trapping Analysis. Nature Communications, 9, 4256.

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Radiotherapy Plan Optimization and Analysis

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Plotting and data fitting were performed using MATLAB 2016b (MathWorks; Natik, MA). The data were organized into eight categories characterized by plan type (DCAT or CAT), and PD (15, 18, 21, or 24 Gy). Each category consisted of 50 sets of dosimetric data, each set corresponding to a treatment plan. For each category, logTV12/1cc vs logPTV/1cc was plotted together with the corresponding fit to a first‐order polynomial. In this case, “log[]” implied “log₁₀[],” and each volume value was divided by “1 cc” so that for each point the argument was dimensionless. In this way, the TV12 vs PTV relationship within each data category was characterized by two dimensionless fit parameters: slope (m) and intercept (b). For each plan type (CAT or DCAT), we plotted and fit both (m vs PD) and (b vs PD) data sets. Combining the data fit functions and parameter values determined during each round of data fitting, we generated a relationship for TV12 as a function of both PTV and PD.

Goldbaum D.S., Hurley J.D, & Hamilton R.J. (2019). A simple knowledge‐based tool for stereotactic radiosurgery pre‐planning. Journal of Applied Clinical Medical Physics, 20(12), 97-108.

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Statistical Methods for Diagnostic Accuracy

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All statistical calculations were performed in the matlab2016b platform (MathWorks, Inc, see text footnote 5). The exact Clopper–Pearson method was used to calculate the 95% confidence intervals (CIs) of sensitivity, specificity, and accuracy (Agresti and Coull, 1998 (link)). The CIs of AUC was calculated by the DeLong methods (DeLong et al., 1988 (link); Mercaldo et al., 2007 (link); Mei et al., 2020 (link)). McNemar’s test (Bates and McNemar, 1964 (link)) was used to calculate the two-sided P-value for AUC between MCInc vs. MCIc, AD vs. MCIc.

Zhang T., Liao Q., Zhang D., Zhang C., Yan J., Ngetich R., Zhang J., Jin Z, & Li L. (2021). Predicting MCI to AD Conversation Using Integrated sMRI and rs-fMRI: Machine Learning and Graph Theory Approach. Frontiers in Aging Neuroscience, 13, 688926.

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Envelope Detection Sensitivity Analysis

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Behavioral data were analyzed in MATLAB 2016b (The MathWorks; RRID:SCR_001622) using the Palamedes toolbox 1.5.1 (RRID:SCR_006521; Prins and Kingdom, 2009 ). For each AM envelope type, there were 60 stimuli, half of which had a tone embedded. A two-by-two confusion matrix was created for each AM envelope type by treating the trials with the tone embedded as “target” and the other trials as “noise.” Correct detection of the tone in the target trials was counted as “hit,” while reports of hearing a tone in the noise trials were counted as “false alarm”; D-prime values were computed based on hit rates and false alarm rates of each table. A half artificial incorrect trial was added to the table with all correct trials (Macmillan and Creelman, 2004 ).

Teng X., Meng Q, & Poeppel D. (2021). Modulation Spectra Capture EEG Responses to Speech Signals and Drive Distinct Temporal Response Functions. eNeuro, 8(1), ENEURO.0399-20.2020.

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PET and MRI Image Preprocessing Workflow

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Data preprocessing for both PET and MRI images was done using Statistical Parametric Mapping 12 (the Wellcome Department of Neurology, London, UK) package implemented in Matlab2016b (Mathworks Inc.). First, ¹⁸F-FDG PET scan for each subject was aligned with corresponding T1-weighted MRI scan. Second, MRI images were segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) tissue probability maps. Then, the GM map was registered to the Montreal Neurological Institute (MNI) stereotaxic template using nonlinear transformation parameters. The aligned PET image was also normalized to the MNI template using the same transformation parameters. Finally, the normalized MRI and PET images were smoothed equivalent to a convolution with an isotropic Gaussian kernel of 8 mm to increase signal-to-noise ratios.

Liu C., Jiang J., Zhou H., Zhang H., Wang M., Jiang J., Wu P., Ge J., Wang J., Ma Y, & Zuo C. (2020). Brain Functional and Structural Signatures in Parkinson’s Disease. Frontiers in Aging Neuroscience, 12, 125.

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Humeral Head Position Analysis Workflow

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All imaging data of the MRI and video camera were analyzed after the initial six seconds in order to exclude the transitional period from resting position. The humeral head position and rotation angles on MR imaging and video camera were measured using Matlab 2016b (Mathworks Inc., Massachusetts, USA) before computing the translation of the humeral head. All the data analyses were undertaken by one examiner.

Matsui K., Tachibana T., Nobuhara K, & Uchiyama Y. (2018). Translational movement within the glenohumeral joint at different rotation velocities as seen by cine MRI. Journal of Experimental Orthopaedics, 5, 7.

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PET Image Preprocessing Procedure

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The PET acquisition process is detailed in the online information of the ADNI project. In 207 cases, dynamic 3D scans with six 5-min frames were acquired 30 min after injection of 185 ± 18.5 MBq FDG. In the remaining cases (n = 56), patients were scanned for a static 30-min acquisition period. In the case of dynamic scans, all frames were motion-corrected to the first frame and then summed to create a single image file.
Individual PET scan preprocessing was performed by statistical parametric mapping (SPM12) software (Wellcome Department of Imaging Neuroscience, Institute of Neurology, London, United Kingdom) using Matlab2016b (Mathworks Inc, Sherborn, MA, United States). Scans from each subject were spatially normalized into Montreal Neurological Institute (MNI) space with linear and non-linear 3D transformations. The normalized PET images were then smoothed by a Gaussian filter of 8 mm full-width at half-maximum (FWHM) over a 3D space to blur the individual anatomical variations and to increase the signal-to-noise ratio for subsequent analysis. Finally, given that the difference in the FDG uptake value of each individual, the smoothed image was normalized to the range of 0 to 255.

Zhou H., Jiang J., Lu J., Wang M., Zhang H, & Zuo C. (2019). Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer’s Disease. Frontiers in Neuroscience, 12, 1045.

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Torque and EMG Data Acquisition for Movement Analysis

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Torque data was amplified (FORZA, OTBioelettronica, Turin, Italy; gain : 1,000 V/V). Torque and electromygraphic data were recorded by means of an EMG amplifier (QUATTROCENTO, OTBioelettronica, Turin, Italy; gain: 150 V/V, A/D depth: 16 bits per sample, sampling frequency: 2,048 samples per second, analog inputs: DC coupled, EMG: band-pass filter: 10–900 Hz, 8^th-order Bessel filter) (Hermens et al., 2000 (link)). Data were recorded via a proprietary recording software (OTBiolab V.2.06, OTBioelettronica, Turin, Italy) and post-processed via custom written software in Matlab (2016b, the Mathworks, Natick, Massachusetts). Normalized torque values were displayed to the subject (visual feedback) on a computer screen (13 inches, 60 Hz refresh rate) which was placed roughly 1.2 m away from the subjects, in their line of sight.

Gizzi L., Yavuz U.Ş., Hillerkuss D., Geri T., Gneiting E., Domeier F., Schmitt S, & Röhrle O. (2021). Variations in Muscle Activity and Exerted Torque During Temporary Blood Flow Restriction in Healthy Individuals. Frontiers in Bioengineering and Biotechnology, 9, 557761.

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Knee Joint Biomechanics Analysis

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The two peaks in the resultant tibiofemoral contact force were identified and the coinciding knee adduction moment (KAM), contact force magnitude and mean pressure were analyzed. All variables, except KAM were analyzed for the medial and lateral condyle separately. KAM was scaled to body mass, contact forces were scaled to bodyweight (BW) and contact pressures were scaled to bodyweight and knee dimensions (BW*A²). Furthermore, peak ligament strains were determined for the anterior and posterior cruciate ligament (ACL and PCL), as well as the medial and lateral collateral ligament (MCL and LCL). Ligament strain was calculated by:

ϵ = \frac{l - l_{0}}{l_{0}}

, with l₀ being the ligament length in the reference position and l being the ligament length.
A repeated-measures ANOVA was used to check for significant differences in contact force, contact pressure and ligament strain between the different geometries or alignments separately. When a significant main effect for geometry or alignment was found, all imposed geometries or alignments were individually compared to the reference simulation using a paired t-test. Significance level was set at α = 0.05 and a Bonferroni correction was applied to correct for the multiple testing (α_BC= 0.003125). All statistical tests were conducted in MATLAB (MATLAB 2016b, The Math Works, Inc., Natick, Massachusetts, USA).

Van Rossom S., Wesseling M., Smith C.R., Thelen D.G., Vanwanseele B., Van Assche D, & Jonkers I. (2019). The influence of knee joint geometry and alignment on the tibiofemoral load distribution: a computational study. The Knee, 26(4), 813-823.

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