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Matlab vr2012b

Manufactured by MathWorks
Sourced in United States

MATLAB vR2012b is a technical computing software that provides an interactive environment for algorithm development, data visualization, and numerical computation. It includes a wide range of built-in functions and tools for various engineering and scientific applications.

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7 protocols using matlab vr2012b

1

Transcriptome Profiling of E. coli Strain MG1655

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Background adjustment, normalization, and summarization calculations were performed in MATLAB v. R2012b (MathWorks, Natick, MA, USA) using GCRMA [26] (link). Using pooled data, permutation t-tests (10,000 permutations) were performed to identify transcripts with statistically significant changes in abundance (q-value <0.05). Additionally, a fold change (log2(treatment/control)) cutoff of ±1 was applied to identify genes that demonstrated a sufficient magnitude of variation between experimental conditions. Probes not annotated as MG1655, but orthologous to E. coli strain MG1655 were included. Probes relating to other species were excluded from analysis. Additionally, overrepresentation of Gene Ontology terms associated with significant expression perturbations was examined and visualized using the Cytoscape tool [27] (link) and BiNGO [28] (link).
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2

Comparing Functional Brain Networks in AD

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Paired t-tests were used to determine whether significant within-group differences were present between WM-seed and GM-seed networks for both controls and AD subject groups. Two-sample t-tests were utilized to assess differences between controls and AD in either WM-seed networks or GM-seed networks. Equality of variance was tested using F-tests prior to performing two-sample t-tests, and all effect sizes are presented as Cohen’s d values. Statistics were performed in MATLAB vR2012b (MathWorks, Natick, MA, USA) and JMP Pro v12.1.0 (SAS, Cary, NC, USA).
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3

Pharmacokinetic Modeling of Albumin Distribution

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A six compartment model was used to estimate the distribution of human albumin produced by the liver model. The albumin production rate was estimated for each interaction period by assuming a constant production rate calculated to yield the measured concentration at the 17 hr time point. The production rate was assumed constant for the remaining 31 hr interaction period and then re-estimated during the new interaction period. The initial concentration was assumed zero everywhere. The equations are shown below and were simulated in Matlab v. R2012b (Mathworks, Natick, MA).
dCMidt=1VMi[(QL1+Qhpv)CLi+QL2CL2+QL3CL3-QMiCMi]dCL1dt=QL1VL1[CMi-CL1]dCLidt=1VLi[QhpvCMi+QL1CL1-(QL1+Qhpv)CLi+Kp]dCL2dt=QL2VL2[CMi-CL2]dCL3dt=QL3VL3[CMi-CL3]dCL4dt=0 where CMi = Drug concentration in mixer [mol/L], CLn= Drug concentration in Airway #n compartment [mol/L](n=1 to 4), CLi= Drug concentration in liver compartment [mol/L], VMi = Volume in Mixer [L], VLn = Volume in Airway#n compartment [L] (n=1 to 4), VLi= Volume in Liver [L], QMi = Flow Thru Mixer [L/min], QLn= Flow Thru Airway#n compartment [L/min] (n=1 to 3), QLi= Flow Thru HPV (Mixer to Liver) [L/min].
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4

Statistical Methods for Biomedical Data Analysis

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Depending on the nature of data distribution, analysis of variance (ANOVA), Fisher’s, or chi-square test was applied to test differences among groups. Significance in linear relations was gauged via Pearson’s correlation coefficient, engaging multinomial logistic regression for multivariate relations. By default, confidence intervals of binary variables involved binomial distributions. In neural network performance analysis, the following metrics were generated: accuracy, sensitivity, specificity, positive predictive value, negative predictive value, Fleiss κ, Cohens κ, and F-score. All computations were driven by standard software (MATLAB vR2012b; The MathWorks Inc., Natick, MA, USA), setting significance p <  0.05.
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5

Structural Brain Analysis using VBM

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Voxel-based morphometry (VBM) was performed using SPM12 (Wellcome Trust Centre for Neuroimaging, SPM12">https://www.fil.ion.ucl.ac.uk/spm/software/SPM12) implemented in MATLAB v. R2012b (The MathWorks). First each participant’s structural image was segmented into grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) using the ‘unified segmentation’ set of algorithms in SPM12. The image segments of interest (the GM segments) were then normalised to MNI space using the diffeomorphic anatomical registration through exponentiated lie-algebra (DARTEL) registration method in SPM1252 (link). The GM images were smoothed using a Gaussian kernel of 8 mm full width at half maximum. An 8 mm smoothing kernel is optimal for detecting morpho-metric differences in both large and small neural structures53 (link).
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6

Statistical Analysis of Experimental Data

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Depending on the nature of data distribution, analysis of variance (ANOVA), Fisher’s exact test, or chi-square test was applied to assess differences among groups. Significance in linear relations was gauged via Pearson’s correlation coefficient, using multinomial logistic regression for multivariate relations. By default, confidence intervals of binary variables involved binomial distributions. In neural network performance analysis, the following metrics were generated: accuracy, sensitivity, specificity, positive predictive value, negative predictive value, Fleiss’ κ, Cohen’s κ, and F-score. All computations were driven by standard software (MATLAB vR2012b; The MathWorks Inc, Natick, MA, USA), setting significance at p < 0.05.
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7

Behavioral State Dynamics Analysis

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Data were analyzed using Spike2 v 7.0 (Cambridge Electronic Design) and MATLAB v R2012b (Mathworks). MATLAB and SigmaPlot v 11.0 (Jandel Scientific) were used for statistical analyses. After visual inspection of long stretches of data from many conditions, we described the states of activity as 1 of 8 categories. Each data set was then manually scored, across time, temperature and modulatory condition, with the behavior of one of these categories. Transitions from one category to another shorter than one second, were not noted. Subsequently, transition data sets were counted for number of transitions, pair-wise transitions present and their frequency and how long preparations exhibited the denoted behavior.
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