Matlab r2015b

Manufactured by MathWorks

Sourced in United States, United Kingdom

MATLAB R2015b is a high-performance numerical computing environment and programming language. It provides a platform for algorithm development, data analysis, and visualization. MATLAB R2015b includes a comprehensive set of tools for a wide range of applications, including signal processing, communications, control systems, and image processing.

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Lab products found in correlation

Spm12, by MathWorks (2 mentions) Spss version 17, by IBM (2 mentions) Cogent 2000 v125, by MathWorks (2 mentions) Magnetom prisma, by Siemens (2 mentions) Hp 0921, by Agilent Technologies (2 mentions) Agilent 6550 ifunnel qtof lc ms system, by Agilent Technologies (2 mentions) Spss statistics 23, by IBM (2 mentions) Scikit learn 0, by Anaconda (1 mentions) Ss25la, by Biopac (1 mentions) Psychophysics toolbox, by MathWorks (1 mentions) Achieva 3.0 tx, by Philips (1 mentions) Multiscan fc photometer, by Thermo Fisher Scientific (1 mentions) F96 microwell plate, by Thermo Fisher Scientific (1 mentions) Glass beads, by Carl Roth (1 mentions) Spss version 21, by IBM (1 mentions) Psychtoolbox, by MathWorks (1 mentions) Stata 16, by StataCorp (1 mentions) Spss version 28, by IBM (1 mentions) Inverted fluorescence microscope, by Nikon (1 mentions) 3 tesla mri scanner, by Philips (1 mentions)

Spelling variants of this product

MATLAB (20315 citations) MATLAB version R2015b (15 citations) MATLAB R2015b software (4 citations)

164 protocols using matlab r2015b

Multivariate Analytical Modeling Techniques

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Outlier detection was conducted in MATLAB R2015b (The MathWorks, Natick, MA, USA). SNV, MSC, and detrending were performed in the Unscrambler X 10.1 (Camo AS, Oslo, Norway). FD was undertaken in MATLAB R2015b (The MathWorks, Natick, MA, USA). For the model establishment, the construction of the PLS model was performed in the Unscrambler X 10.1 (Camo AS, Oslo, Norway). SVR was carried out in the scikit-learn 0.23.1 (Anaconda, Austin, TX, USA) using python 3.1. The CNN model and fine-tuning were conducted in MXNet1.4.0 (MXNetAmazon, Seattle, WA, USA).
The coefficients of determination (R²) and root mean square error (RMSE) of calibration, validation and prediction set were calculated to evaluate model performance. The R² of a robust model should approach 1, while the RMSE is close to 0.

Xiao Q., Tang W., Zhang C., Zhou L., Feng L., Shen J., Yan T., Gao P., He Y, & Wu N. (2022). Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves. Plant Phenomics, 2022, 9813841.

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Metabolic Profiling and Statistical Analysis

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Absolute metabolic values were normalized by the protein content of the cell pellet for each replicated sample (S2 Table). Metabolites with more than 62.5% of missing values or with coefficients of variation greater than 15% were excluded.
For unsupervised analyses, normalized and averaged metabolic values per time-point were z-scored by subtracting to each value the mean for each metabolite-cell and then dividing by the respective standard-deviation. Principal component analysis and hierarchical clustering was performed in Matlab R2015b (MathWorks, Natick MA) and in Perseus software [44 (link)], respectively.
Steady-state fold changes were statistically tested by performing a two-sample t-test, two-sided, assuming the two samples comes from independent random samples from normal distributions with equal means and equal but unknown variances. The Benjamini-Hochberg method was used to correct for multiple testing errors using a false discovery rate of 5% [45 ]. These fold-changes were log2-transformed for depiction in volcano plots and in the Pearson Correlation matrices. These statistical tests and correlations were performed in Matlab R2015b (MathWorks, Natick MA).

Vasconcelos e Sá J., Simão D., Terrasso A.P., Silva M.M., Brito C., Isidro I.A., Alves P.M, & Carrondo M.J. (2020). Unveiling dynamic metabolic signatures in human induced pluripotent and neural stem cells. PLoS Computational Biology, 16(4), e1007780.

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Measuring Photobleaching Dynamics in Yeast

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S. cerevisiae cells were loaded and cultured in the microfluidic chip as described (see Measurement of fluorophore brightness and signal-to-noise ratio for details). To correct for bleaching of background fluorescence, a strain carrying no fluorescent protein tag was imaged under the same conditions. The light intensity of the fluorescence excitation light source was adjusted for all channels to be imaged with 2.91 mW cm⁻² h⁻¹.
To estimate the in vivo photobleaching, cells were imaged every 10 sec with an exposure time of 2 sec for 200 iterations. The resulting images were segmented (see Image analysis for detail) and the mean fluorescence intensity values (

μ_{s i g}

and

μ_{b g}

) were extracted (see Measurement of fluorophore brightness and signal-to-noise ratio for detail). The mean fluorescence intensity was corrected for background fluorescence (

μ_{s i g}

μ_{b g}

) and plotted against the accumulated light dose. A two-term exponential decay model (a*e^(b*x) + c*e^(d*x)) was fitted using the ”fit” function of Matlab R2015b (Mathworks, Natick, USA). The light dose at which the fluorescence intensity dropped to 50% of the initial value was determined (

L D_{B l e a c h 50}

, by solving the fitted exponential model using the ”solve” function of Matlab R2015b (Mathworks, Natick, USA).

Schmidt G.W., Cuny A.P, & Rudolf F. (2020). Preventing Photomorbidity in Long-Term Multi-color Fluorescence Imaging of Saccharomyces cerevisiae and S. pombe. G3: Genes|Genomes|Genetics, 10(12), 4373-4385.

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Effort-Discounting and Information-Seeking Tasks

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Participants performed two separate tasks: a physical effort-discounting task (performed outside the scanner), and a non-instrumental information-seeking task with physical effort costs (while being scanned). Participants completed both tasks in a single session, with the order of tasks counter-balanced across participants. Stimuli were presented using the Psychophysics Toolbox implemented in MATLAB R2015b (Mathworks Inc., US). Participants held an fMRI-compatible dynamometer (SS25LA, BIOPAC Systems, USA) in their dominant (right) hand, and provided button responses with their non-dominant (left) hand.

Goh A.X., Bennett D., Bode S, & Chong T.T. (2021). Neurocomputational mechanisms underlying the subjective value of information. Communications Biology, 4, 1346.

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Modeling Biomedical Dynamics in MATLAB

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All analysis was written in Matlab R2015b (The Mathworks, Inc, Natick, Mass). The model equations were solved using the appropriate ODE solver in Matlab. The parameters values were verified by fitting the solution of the differential equations to experimental data found in literature.

Miller S.E., Bell C.S., McClain M.S., Cover T.L, & Giorgio T.D. (2016). Dynamic Computational Model of Symptomatic Bacteremia to Inform Bacterial Separation Treatment Requirements. PLoS ONE, 11(9), e0163167.

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Solar Cell Power Output Analysis

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The main result of interest is the output power of the solar cells

P_{S}

[W], which is calculated by multiplying the corresponding values of current

I_{S}

and voltage

U_{S}

. This gives a power-profile for the whole day.

\bar{P_{D}}

[W] is the arithmetic mean of one day (24 h) and

\bar{P_{M}}

[W] is the arithmetic mean of a month (i.e. the mean of all

\bar{P_{D}}

during one month). The recordings usually cover 24 h of a day. In rare cases, study participants forgot to wear the logger and reported this in the questionnaire as a lack of data. In this case, data was included in the analysis if at least 12 h were valid (00:00–12:00 or 12:00–24:00), otherwise data was discarded. Calculation and statistical analysis was performed with MATLAB R2015b (Mathworks, USA). The output power for statistical considerations and

\bar{P_{M}}

are reported as mean values with standard deviation. A two-sided Wilcoxon signed-rank test was performed for statistical analysis. A p value ≤0.05 was considered significant.

Bereuter L., Williner S., Pianezzi F., Bissig B., Buecheler S., Burger J., Vogel R., Zurbuchen A, & Haeberlin A. (2017). Energy Harvesting by Subcutaneous Solar Cells: A Long-Term Study on Achievable Energy Output. Annals of Biomedical Engineering, 45(5), 1172-1180.

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Spectral and Coherence Analysis in MATLAB

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Data analysis was performed in MATLAB R2015b (MathWorks). Spectral and coherence calculations were implemented with functions from the Chronux toolbox ³ (Mitra and Bokil, 2008 ).

Guidera J.A., Taylor N.E., Lee J.T., Vlasov K.Y., Pei J., Stephen E.P., Mayo J.P., Brown E.N, & Solt K. (2017). Sevoflurane Induces Coherent Slow-Delta Oscillations in Rats. Frontiers in Neural Circuits, 11, 36.

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Surface-Enhanced Raman Spectroscopy of Peptide-Bound Nanoparticles

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SERS and TERS spectra and maps
were plotted using Matlab R2015b (Mathworks). Raw SERS spectra of
the peptide-GNPs (with or without αvβ3 binding) were preprocessed
through a weighted least-squares (WLS, Whittaker filter, fifth order
polynomial) automatic baseline subtraction to remove differences due
only to the background in each spectrum. These spectra were used to
decompose the pure components of the SERS data using multivariate
curve resolution (MCR), and further used to classify the TERS data
in order to determine the class of each spectrum in the TERS map.
TERS maps and MCR maps were reconstructed in MATLAB according to single-peak
intensities and MCR scores, respectively. Individual SERS and TERS
spectra of three different peptides bound with αvβ3 were
analyzed by principal component analysis (PCA) and hierarchical cluster
analysis (HCA). MCR, PCA, and HCA were performed using PLS toolbox
(eigenvector).

Xiao L., Bailey K.A., Wang H, & Schultz Z.D. (2017). Probing Membrane Receptor–Ligand Specificity with Surface- and Tip- Enhanced Raman Scattering. Analytical Chemistry, 89(17), 9091-9099.

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MRI Image Preprocessing Using SPM12

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All of the MR images were processed using spm12 (Wellcome Department of Imaging Neuroscience, University College London, UK, http://www.fil.ion.ucl.ac.uk/spm) running on matlab R2015b (MathWorks, Natick, MA, USA) on Ubuntu 16.04 based Lin4Neuro.24 Prior to preprocessing, all data were co‐registered to ‘icbm152’ standard image implemented in spm12 so that the origin of images would be close to the anterior commissure–posterior commissure (AC‐PC) and so that images would be aligned with the AC‐PC line. Each image was segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using the segment function of spm12. Subsequently, the segmented GM images were spatially normalized using diffeomorphic anatomical registration through an exponentiated lie algebra (DARTEL) algorithm.25 Custom DARTEL templates were generated from all the GM and WM images of the participants. After spatial normalization, the GM images were modulated to preserve the volume, followed by smoothing with an 8‐mm full width at half maximum Gaussian kernel. For this preprocessing, default parameters were used. In addition to that, total intracranial volume (TIV) was calculated by summing GM image, WM image, and CSF image using the ‘Tissue Volumes’ function of spm12.

Nemoto K., Shimokawa T., Fukunaga M., Yamashita F., Tamura M., Yamamori H., Yasuda Y., Azechi H., Kudo N., Watanabe Y., Kido M., Takahashi T., Koike S., Okada N., Hirano Y., Onitsuka T., Yamasue H., Suzuki M., Kasai K., Hashimoto R, & Arai T. (2019). Differentiation of schizophrenia using structural MRI with consideration of scanner differences: A real‐world multisite study. Psychiatry and Clinical Neurosciences, 74(1), 56-63.

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Validating WSS-Plaque Thickness Correlation

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WSS values projected on radial bins, after different relocation distances of the WSS stack, were compared to the original WSS-histology registration using a Wilcoxon signed rank test. For each relocation case, the correlation between WSS and plaque thickness per radial bin was calculated (MATLAB R2015b, Mathworks Inc., Natick (MA), USA) at a 0.05 significance level.

Moerman A.M., Dilba K., Korteland S., Poot D.H., Klein S., van der Lugt A., Rouwet E.V., van Gaalen K., Wentzel J.J., van der Steen A.F., Gijsen F.J, & Van der Heiden K. (2019). An MRI-based method to register patient-specific wall shear stress data to histology. PLoS ONE, 14(6), e0217271.

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