Magnetic resonance images were acquired on a 9.4T scanner (Bruker BioSpec, Billerica, MA) using proton quadrature transmit/receive volume coils (see SI ). T1-weighted data for T1 mapping were acquired using saturation recovery and rapid acquisition with refocused echoes (RARE) of variable repetition times (TRs). For the diffusion-in study, samples were imaged continuously every 22 minutes for 14 hours in solution. For the equilibrium study, samples were imaged in solution after a 24-hour immersion. The diffusion studies RARE parameters were: effective echo time=9.8ms; field-of-view (FOV)=25.6 × 25.6mm2; matrix=256 × 256; RARE factor=2; slice thickness=1mm (single slice). For the equilibrium study, a T1 map was acquired with the following parameters: effective echo time=9.8ms; FOV=25.6 × 20.0mm2; matrix=256×192; RARE factor=2; slice thickness=1mm (see SI ). Cartilage and solution object maps were used to obtain serial T1 relaxation times (MRI Mapper, Beth Israel Deaconess Medical Center, Brookline, MA and Matlab2012b, MathWorks, Natick, MA).
Matlab 2012b
Manufactured by MathWorks
Sourced in United States, United Kingdom, Germany
MATLAB 2012b is a high-level programming language and numerical computing environment designed for technical and scientific computing. It provides a wide range of built-in functions and tools for data analysis, visualization, and algorithm development. MATLAB 2012b is primarily used for matrix manipulation, function plotting, implementation of algorithms, and creation of user interfaces.
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Lab products found in correlation
Spss 22, by IBM (2 mentions)
Eyelink 1000 eye tracker, by SR Research (2 mentions)
9.4t scanner, by Bruker (1 mentions)
Visage mkii stimulus generator, by Cambridge Research Systems (1 mentions)
Spss statistics v22, by IBM (1 mentions)
Eyelink 1000 plus, by SR Research (1 mentions)
Acqknowledge 3, by Biopac (1 mentions)
Mp100 system, by Biopac (1 mentions)
Originpro 8, by OriginLab (1 mentions)
Osirix software, by Pixmeo (1 mentions)
Quadro k600, by NVIDIA (1 mentions)
Image processing toolbox, by MathWorks (1 mentions)
Gannet 2.0 toolkit, by MathWorks (1 mentions)
Active two eeg amplifier system, by BioSemi (1 mentions)
Spss statistic, by IBM (1 mentions)
Stata se 14, by StataCorp (1 mentions)
Origin 8, by OriginLab (1 mentions)
Winwcp, by Molecular Devices (1 mentions)
Clampfit, by Molecular Devices (1 mentions)
Eight channel head coil, by Philips (1 mentions)
73 protocols using matlab 2012b
1
T1 Relaxation Mapping of Cartilage
2
Histological Features and Gene Expression
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Feature-expression heatmaps were generated according to a previously described approach29 . Only BPD samples were included for the visualization. Histologically assessed features were grouped into degenerative changes, hepatic activity, endogenous hepatic activity, steatosis, fibrosis, and inflammation. Depending on the data type of the histological parameter, ordinal logistic regression or binary logistic regression was performed for the association analyses between histological parameters and log2 intensity values of the top 10 differentially expressed genes obtained from the over-represented functional pathways analysis. The effect size, standard error, p-value and the FDR adjusted p-value from the Benjamini and Hochberg correction were calculated and plotted as feature-expression heatmaps using R. An FDR adjusted P < 0.2 was considered statistically significant. Heatmap, significance, and FDR plots were merged using MATLAB 2012b (The MathWorks, Inc., Natick, Massachusetts, USA).
3
Phylogenetic Analysis of Viral Envelope Sequences
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Founder and chronic DNA sequences, as well as the HXB2 reference envelope sequence [55 (link)], were converted to AA sequences (nt2aa, MATLAB 2012b, The MathWorks Inc., Natick MA, USA), and then aligned using a progressive multiple alignment method (multialign). Pairwise distances were calculated with the Jukes-Cantor method, with the phylogenetic tree generated using the Unweighted Pair Group Method Average (seqlinkage). AA positions in the aligned sequences were numbered relative to HXB2 according to the convention of Korber et al. [31 ].
4
lncRNA-Transcription Factor Network Analysis
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Differentially expressed lncRNAs were defined as co-expressed with potentially trans-regulated protein-coding genes, which is beyond 100kb in genomic distance from, or on the other allele of the lncRNAs. MATLAB 2012b (The MathWorks) was used with the hypergeometric cumulative distribution function to construct the lncRNAs-Transcription factors (TFs) network. Cytoscape 3.01 (Agilent and IBS) was used to draw the graph of the lncRNAs-TFs network. We can predict that those lncRNAs may take part in pathways regulated by lncRNAs-Transcription factors when the intersection of these two groups is large enough (FDR < 0.01, under the control of the Benjamini and Hochberg procedure and P < 0.01, calculated by hypergeometric cumulative distribution function).
5
Correlation Analysis of Pain Perception
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Statistical testing for correlation between thermode temperature, pain ratings and the CSQ measures was carried out in MATLAB 2012b (The MathWorks, Inc., Natick, USA) using Spearman’s Correlation (two-sided). Due to the strong inter-dependencies of the six active sub-scales of the CSQ (diverting attention, reinterpreting pain sensations, coping self-statements, ignoring pain sensations, increasing activity level, increasing pain behaviors), Bonferroni correction would be too conservative to apply (Abdi 2007 ). Therefore, we undertook a principal component analysis for all subjects and all 6 active score sub-scales using single value decomposition to identify the first principal component that best represents the participant data of the six active CSQ sub-scales. This measure has the advantage of using the structure of the questionnaire (division into six sub-scales) as well as all sub-scales to a varying degree.
We then checked for correlation between this first component and pain ratings as well as thermode temperature.
We then checked for correlation between this first component and pain ratings as well as thermode temperature.
6
Quantitative PET Image Analysis Workflow
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PET image analysis was performed as previously described [35 (link)]. Interframe correction for head movement during the scan was conducted by denoising the nonattenuation-corrected dynamic images using a level 2, order 64 Battle-Lemarie wavelet filter. Frames were hence realigned to a single ‘reference’ frame, acquired 8 min after injection, employing a mutual information algorithm [34 (link)]. The transformation parameters were then applied to the corresponding attenuated-corrected dynamic images. The realigned frames were then summated, creating a movement-corrected dynamic image, which was used in the analysis. Subsequently, the realigned images were spatially normalized by registering their summed image to the [18F] DOPA template created in a previous study [36 (link)]. Region of interest (ROI) time–activity curves (TACs) were hence extracted using atlas maps for the whole striatum, and its associative and limbic [37 (link)]. The cerebellum was used as the reference region as it is a region with minimal dopaminergic projections [38 (link)]. Finally, using the cerebellar TAC as a reference region input, the Gjedde–Patlak plot39 was applied at ROI to derive the Kicer relative to the cerebellum. The analysis was performed using a combination of SPM5 package (http://www.fil.ion.ucl.ac.uk/spm) and in-house code based on Matlab2012b (The Mathworks, Natick, MA, USA).
7
Visual Stimuli Presentation and Response Recording
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Stimuli were presented on a Sony GDM-F520 CRT monitor driven by a ViSaGe MKII Stimulus Generator (Cambridge Research Systems) 1 . The experimental software was written using MATLAB 2012b (MathWorks) 2 and the CRS toolbox. Responses were recorded using a wireless CT6 Response Box and infrared receiver (Cambridge Research Systems). Participants used a chin rest, positioned at a 57 cm distance from the screen.
8
Structural Brain Differences Analysis using VBM8
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For detection of structural brain differences, the VBM8 toolbox embedded in SPM8 (Statistical Parametric Mapping; Wellcome Department of Cognitive Neurology, London, UK) and running on Matlab 2012b (The MathWorks Inc., Natick, MA, USA) was used. Scans were manually checked for movement and ringing-like artifacts. Images were segmented into gray and white matter (WM), spatially normalized to MNI space using the high-dimensional DARTEL template (Ashburner, 2007 (link)) and resampled into 1.5 mm isotropic voxels corrected for global brain volume using non-linear modulation. Hidden Markov Random Fields (HMRF) of 0.15 and a multi-threaded Spatial Adaptive Non-Local Means (SANLM) de-noising filter were applied to remove spatial noise. Smoothing was performed with an 8-mm Full Width at Half Maximum (FWHM) Gaussian kernel and the smoothed GM images were used for statistical analysis.
9
Nanotubular Topography-Induced Coexpression Network Analysis
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Coexpression networks were created to identify the interactions among lncRNAs and the protein-coding genes involved in nanotubular topography-induced biological and molecular pathways. On the basis of Guttman et al18 (link)–20 (link), lncRNAs that participate in special biological pathways are regulated by critical transcription factors (TFs) of those pathways. In order to determine whether the lncRNAs could possess trans-regulating functions, we compared the mRNAs that are coexpressed with lncRNAs with particular TFs regulated mRNAs. To investigate the mutual regulatory relationships among miRNA, lncRNA, and mRNA, we performed miRNA–lncRNA–mRNA coexpression network analysis based on Pearson’s correlation coefficient. Coexpression relationships with more than three shared miRNAs at p<0.05 were selected to build the miRNA–lncRNA–mRNA interaction network. The networks were constructed using the hypergeometric cumulative distribution function in MATLAB 2012b (The MathWorks, Natick, USA). The graphs were generated in Cytoscape 3.01 (Agilent Technologies).
10
Cardiovascular Responses to Head-Up Tilt
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Offline Data analysis was performed using MATLAB 2012b (Mathworks, Natick, USA). ECG data were pre-processed in Kubios HRV software (ver. 2.2, University of Eastern Finland, Finland) and inspected for artefacts. MAP, CPPe, MCA Vmean, HR and rScO2 was reported as baseline (300 s), the first 10 s during 30°, 60° and 80° HUT, the last 10 s of HUT (HUT0) and the post-tilt period (300 s after HUT0).
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