Matlab r2020b

Manufactured by MathWorks

Sourced in United States

MATLAB R2020b is a software package used for technical computing and programming. It provides a high-level language and interactive environment for numerical computation, visualization, and programming. MATLAB R2020b is a product of MathWorks, a leading developer of mathematical computing software.

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MATLAB (20315 citations) MATLAB version R2020b (12 citations) MATLAB 9.9 R2020b (4 citations) Matlab v. R2020b (2 citations) MATLAB R2020b software (2 citations) Matlab (ver. R2020b) (2 citations)

178 protocols using matlab r2020b

Porcine Duodenum Tensile Properties

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The linear regression analyses between the measured values of T o and E -0.5 in porcine duodenum specimens were performed using Microsoft Excel (version 16.66.1, Microsoft co. ltd). Analysis of covariance (ANCOVA) was performed to compare the slopes of the trendlines resulting from the linear regression using MATLAB r2020b (The MathWorks, 2020). One-way analysis of variance (ANOVA) and posthoc analyses (Tukey-Kramer test) were also performed using MATLAB r2020b (The MathWorks, 2020) to assess the specimens' tearing angles after the second tensile test.

Yamamoto K., Hara K., Kobayashi E., Yuki A, & Sakuma I. (2024). Tissue histology on the correlation between fracture energy and elasticity. International journal of computer assisted radiology and surgery, 19(3).

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Kinematic analysis of baseball pitches

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The Kolmogorov–Smirnov test was used to confirm that the EMG data were normally distributed (p > 0.05). Depending on whether the physical characteristics and EMG data were normally distributed, paired t-tests and Wilcoxon signed-rank tests were used to identify differences in the variables between fastball and curveball pitching in each participant. A probability p value of <0.05 was considered statistically significant. Cohen’s d effect sizes (ESs) were calculated for all analyses and showed the magnitude of the differences between fastball and curveball pitching. Data analyses were conducted using IBM SPSS Statistics for Windows version 24.0. (IBM Corporation, Armonk, NY, USA). One-dimensional statistical parametric mapping (SPM[t]) paired t-tests were performed to compare each individual point of the averaged EMG curves from the late cocking phase to the follow-through phase between fastball and curveball pitching. The threshold of significance was set at α = 0.05 for all analyses. SPM(t) paired t-tests were conducted using the open-source SPM1d code (www.spm1d.org, accessed on 6 January 2023) in MATLAB_R2020b (MathWorks Inc., Boston, MA, USA).

Tamura A, & Saito M. (2023). Muscle Activity Characteristics of the Pronator Teres during Throwing in Baseball Pitchers: A Pilot Study. Healthcare, 11(4), 618.

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Batch Correction for Flow Cytometry

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Flow cytometric gating was based on fluorescence minus one controls in cases where bimodal distribution was not apparent. To generate histogram comparisons of fluorescence intensity across samples run with the same cytometer settings on different days, we first performed batch correction with the function SwiftReg(70 (link)) on MATLAB_R2020b (The MathWorks, Inc., Natick, MA, USA). Comparisons were then performed with concatenations of all samples containing equal proportions of the cell type of interest. For comparison of two groups without matching, a Mann-Whitney test was performed. Comparisons of two groups with matching samples were performed with Wilcoxon matched-pairs signed rank tests. For comparisons of three or more groups, a Friedman test was performed. For paired comparisons across three or more groups, a mixed-effects analysis was performed. For all plots derived from flow cytometric data, no correction was made for multiple comparisons and all comparisons made in the statistical analysis are displayed. All statistical analyses were done using Prism 9.0.2 (GraphPad Software, San Diego, CA, USA). Statistical significance is indicated by one or more asterisks, and the number of asterisks shown correspond to p-values less than 0.05, 0.01, 0.001, or 0.0001, respectively.

Jackson Z., Hong C., Schauner R., Dropulic B., Caimi P.F., de Lima M., Giraudo M.F., Gupta K., Reese J.S., Hwang T.H, & Wald D.N. (2022). Sequential single cell transcriptional and protein marker profiling reveals TIGIT as a marker of CD19 CAR-T cell dysfunction in patients with non-Hodgkin’s lymphoma. Cancer discovery, 12(8), 1886-1903.

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Wearable Data for Epilepsy Monitoring

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Wearable data will be made available on EpilepsyEcosystem.org. Analyses are described in detail above, and MATLAB scripts are available from the author by reasonable request. Analyses used MATLAB_R2020b (MathWorks^®, mathworks.com, Natick MA, USA).

, & Lyon M.F. (2000). LINE-1 elements and X chromosome inactivation: A function for “junk” DNA?. Proceedings of the National Academy of Sciences of the United States of America, 97(12), 6248-6249.

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Image Processing of IP-SEM Data

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The IP-SEM signal data of amplitude and phase stored in the data recorder were transferred to a personal computer (Intel Core i7, 3.8 GHz, Windows 10) and the IP-SEM images were processed by the image processing toolbox in Matlab R2020b (Math Works Inc., USA). The original IP-SEM images were filtered through a two-dimensional Gaussian filter (GF) with a kernel size of 7×7 pixels and a radius of 1.2σ. The background was removed by subtracting the IP-SEM images from the filtered images using a broad GF (kernel size 201×201 pixels, radius 100σ). Finally, amplitude and phase images were converted to 8-bit grey scale format.

, & Ogura T. (2022). Development of multi-frequency impedance scanning electron microscopy. PLoS ONE, 17(1), e0263098.

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Neuroimaging Data Analysis Protocol

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Data were analyzed using MATLAB R2020b (MATLAB version 9.9, 2020; The MathWorks, Inc.) and the R System for Statistical Computing version 4.0.3 (R Development Core Team, 2020). SPM12 software package (http://www.fil.ion.ucl.ac.uk/spm/; Wellcome Centre for Human Neuroimaging) was used for fMRI data analyses.

Chen K., Garbusow M., Sebold M., Kuitunen-Paul S., Smolka M.N., Huys Q.J., Zimmermann U.S., Schlagenhauf F, & Heinz A. (2022). Alcohol Approach Bias Is Associated With Both Behavioral and Neural Pavlovian-to-Instrumental Transfer Effects in Alcohol-Dependent Patients. Biological Psychiatry Global Open Science, 3(3), 443-450.

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Markerless Animal Pose Tracking with DeepLabCut

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Markerless tracking of animal body parts was conducted using the DeepLabCut (DLC) Toolbox (Mathis et al. 2018 (link)) and analysis of movement features based on these tracked coordinates was conducted in Matlab R2020b (Mathworks). All DLC analysis was conducted on a Dell G7–7590 laptop running Windows 10 with an Intel Core i7–9750H CPU, 2.60Ghz, 16 GB RAM, and an NVIDIA GeForce RTX 2080 Max-Q 8GB GPU. DeepLabCut 2.1.10 was installed in an Anaconda environment with Python 3.7.7 and Tensorflow 1.13.1. Videos (944 × 480 resolution) were recorded with a sampling frequency of 30 frames per second using a TIGERSECU Super HD 1080P 16-Channel DVR system.

Cheung P.Y, & Barrington K.J. (2001). The effects of dopamine and epinephrine on hemodynamics and oxygen metabolism in hypoxic anesthetized piglets. Critical Care, 5(3), 158-166.

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Molecular Weight Analysis by AcquireMP

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Measurements were recorded using AcquireMP 2.4.2 (Refeyn Ltd., Oxford, UK) and analysed with DiscoverMP (v2023 R1.2) (Refeyn Ltd., Oxford, UK). For data analysis, the bin width was set to 40 for all measurements. A Gaussian function was used to fit the ratiometric contrast distribution yielding the molecular weight of the respective subpopulation. All bar plots were visualised with Matlab R2020b (MathWorks, Natick, MA, USA).

Wagner C., Fuchsberger F.F., Innthaler B., Pachlinger R., Schrenk I., Lemmerer M, & Birner-Gruenberger R. (2024). Automated Mass Photometry of Adeno-Associated Virus Vectors from Crude Cell Extracts. International Journal of Molecular Sciences, 25(2), 838.

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Preprocessing of rs-fMRI Data

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The rs‐fMRI data underwent preprocessing using SPM12 (Welcome Department of Imaging Neuroscience, UK, http://www.fil.ion.ucl.ac.uk/spm/) within MATLAB R2020b (MathWorks Inc., Sherborn, MA, USA). We employed a standard preprocessing pipeline (including realignment, reslicing, co‐registration, segmentation, normalization, and smoothing) similar to that described in our previous work (see Roger et al., 2020 (link)) with specific details mentioned in Appendix S1.

Guichet C., Banjac S., Achard S., Mermillod M, & Baciu M. (2024). Modeling the neurocognitive dynamics of language across the lifespan. Human Brain Mapping, 45(5), e26650.

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Optimizing Saturation Pulse Designs for Spatially Varying B1+ Fields

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To explore the pulse design performance, saturation pulses with different number of sub‐pulses (1, 2, and 3) were designed offline for a spatially invariant β ranging from 0.1μT to 2μT in steps of 0.1μT. The optimizations were performed for both 2D axial slices and 3D volume of brain transmit maps from an eight‐channel pTx system (details below), with each 2D single slice/3D optimization taking ≈7/22 s in Matlab R2020b (Mathworks Inc., Natick, MA) on a desktop computer (Intel i9‐10900X @ 3.70GHz, 64GB of RAM, not parallelized). The solutions were analyzed in terms of their B1rms maps and normalized RMS error (NRMSE).
To predict the impact of spatial variation in mean squared B1+ on the MT contrast, magnetization transfer ratio (MTR) maps were simulated using the definition:

MTR (%) = 100 \times \frac{M_{ref} - M_{sat}}{M_{ref}}

where Msat and Mref are the steady‐state signals acquired with and without the saturation pulse, respectively. For the MTR simulations, the steady state of an SPGR sequence was calculated by solving Equation (2) assuming the whole brain to have uniform tissue parameters similar to white matter³⁹, ⁴⁰: R1f=0.4s−1,T2f=60ms,f=M0s/M0s+M0f=0.1357,k=ksf/(1−f)=kfs/f=32.79s−1,R1s=1.85s−1,T2s=9.6μs, and a Super‐Lorentzian
absorption lineshape (centered at ‐773 Hz).²⁷ Different saturation pulses optimized offline forβ=1μT were applied combined with a small excitation flip angle of αexc=5∘.

Leitão D., Tomi‐Tricot R., Bridgen P., Wilkinson T., Liebig P., Gumbrecht R., Ritter D., Giles S.L., Baburamani A., Sedlacik J., Hajnal J.V, & Malik S.J. (2022). Parallel transmit pulse design for saturation homogeneity (PUSH) for magnetization transfer imaging at 7T. Magnetic Resonance in Medicine, 88(1), 180-194.

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