For wave speed analysis, A and Q curves were not interpolated or filtered. For WIA the A and Q curves were interpolated to 1-ms temporal resolution using a cubic spine and filtered using a zero-phase, low-pass, 2nd order Butterworth filter with cut-off frequency of 20 Hz. All signal processing was performed in Matlab 2012a (Mathworks).
Matlab 2012a
Manufactured by MathWorks
Sourced in United States, United Kingdom
MATLAB 2012a is a high-level programming language and numerical computing environment. It is designed for technical computing and visualization. MATLAB 2012a provides tools for matrix manipulation, function plotting, algorithm implementation, and data visualization.
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Lab products found in correlation
Tim trio, by Siemens (5 mentions)
Systems biology toolbox, by MathWorks (3 mentions)
Tx300, by Tobii (2 mentions)
Spss 19, by IBM (1 mentions)
306 channel meg system, by Elekta (1 mentions)
Igor pro, by Wavemetrics (1 mentions)
Spss 20.0 for windows package, by IBM (1 mentions)
Mpro110, by 3M (1 mentions)
Matlab 2015a, by MathWorks (1 mentions)
Rdf223g, by Mitsubishi (1 mentions)
Spss statistics for window, by IBM (1 mentions)
Spss statistics 19, by IBM (1 mentions)
Ephus, by MathWorks (1 mentions)
Prism 8, by GraphPad (1 mentions)
Opus 7, by Bruker (1 mentions)
Spss statistics version 22, by IBM (1 mentions)
Spss 20, by IBM (1 mentions)
Brainamp amplifier system, by Brain Products (1 mentions)
Brain vision analyzer software, by Brain Products (1 mentions)
Neural network toolbox, by MathWorks (1 mentions)
80 protocols using matlab 2012a
1
Wave Speed and WIA Analysis Protocol
2
EEG Data Preprocessing and ICA Analysis
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Data were preprocessed using custom routines in MATLAB 2012a (MathWorks). ICA was performed using functions from the EEGLAB toolbox57 (link). Preprocessing was identical to our previous studies, see ref. 10 (link) for details.
3
Eye-Tracking Study on Self-Recognition
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Participants were tested individually in a soundproof, dimly lit room at the Department of Experimental Psychology. Prior to the experimental session, a single photograph of each participant was taken from the back and superimposed onto the set of experimental stimuli using MATLAB 2012a (The MathWorks Inc., MA) and a graphics software suite (Gimp, Version 2.6). Participants first completed the self-report questionnaires. Participants were then seated in front of the eye-tracker and a 9-point calibration (20%, 50% 80% of both horizontal and vertical display span) was run. Calibration was considered satisfactory if at least 12 gaze samples within one degree of visual angle were collected per calibration point in the screen area corresponding to stimuli presentation. The task started with instructions displayed on the screen and followed by a practice trial that exposed the participant to her own photograph for the first time. The experimenter assisted the participant during the practice to make sure that she understood the instructions. The task was approximately 25 min long and was split into three parts separated by short breaks. At the end of the experimental session, participants were debriefed about the nature of the research and received the gift voucher.
4
Competitive Ligand Binding Assay
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The data were fit using a standard saturation isotherm (see the Supporting Information for further information).
For DNGSH fluorescence, λL was measured directly
and found to be 572.5 nm. Inverse titrations with high SdKefQCTD concentrations
allowed a good estimation of the emission maximum for the bound form
(λML) as 530 nm with a quantum yield (Q) of 4. These three parameters were kept fixed during the fitting
while Kd and n were optimized.
A mutated protein bearing a Q419K mutation that abrogates activation
of Kef by GSH adducts7 (link) was used to measure
background binding. The level of nonspecific binding was observed
to be very low, and thus, no routine corrections were performed. For
competition experiments, depletion of DNGSH (L) as well as of the
competing nonfluorescent ligand B was taken into consideration during
the analysis (see theSupporting Information ). Data were analyzed according to Thrall et al.,13 (link) using a Kd of 6 μM for
DNGSH (derived in this study). The dissociation constant for the competing
ligand B, KB, and n were
optimized using Matlab2012a (Mathworks) taking dilution of ligands
and protein during the titration into account. Equations are shown
in theSupporting Information .
For DNGSH fluorescence, λL was measured directly
and found to be 572.5 nm. Inverse titrations with high SdKefQCTD concentrations
allowed a good estimation of the emission maximum for the bound form
(λML) as 530 nm with a quantum yield (Q) of 4. These three parameters were kept fixed during the fitting
while Kd and n were optimized.
A mutated protein bearing a Q419K mutation that abrogates activation
of Kef by GSH adducts7 (link) was used to measure
background binding. The level of nonspecific binding was observed
to be very low, and thus, no routine corrections were performed. For
competition experiments, depletion of DNGSH (L) as well as of the
competing nonfluorescent ligand B was taken into consideration during
the analysis (see the
DNGSH (derived in this study). The dissociation constant for the competing
ligand B, KB, and n were
optimized using Matlab2012a (Mathworks) taking dilution of ligands
and protein during the titration into account. Equations are shown
in the
5
Extracting Multimodal Imaging Features for Accurate Diagnosis
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For an accurate diagnosis, more texture features and digital information needed to be extracted from PET/CT images. All data were standardized and normalized to facilitate the statistical analysis of index evaluation values. In total, 2662 features were extracted, including 2436 CT-features of primary tumor and lymph node extracted using the PyRadiomics platform. The features were developed to standardize the calculation of the radiomic feature algorithms and ease the feature extraction process to improve reproducibility of the findings (Griethuysen et al. 2017 (link)). Additionally, 216 PET-features of primary tumors and lymph nodes were automatically extracted using the Chang Gung Image Texture Analysis package in MATLAB 2012a (MathWorks Inc., Natick, MA, USA) (Fang et al. 2014 (link)). The CT-features were extracted based on the original image and by applying Laplacian of Gaussian and wavelet filters. To extract the PET-features, the SUV values contained within the ROIs were relatively resampled to 64 different values to yield a limited range of values; this was done to reduce the noise and to normalize the images (Yang et al. 2017 (link)).
6
Preprocessing fMRI Data Using BrainVoyager
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We performed fMRI data analysis using BrainVoyager software 2.8 (Brain Innovation, Maastricht, The Netherlands) and MATLAB 2012a (The MathWorks Inc., Natick, MA, USA). We discarded the first two volumes of each functional run to avoid T1 saturation effects. We performed a standard pre-processing protocol: slice scan time correction using sync interpolation, standard three-dimensional motion correction to adjust for head movements as well as linear-trend removal and temporal high-pass filtering at 0.006 Hz. After alignment with the anatomical scan, we transformed individual datasets into Talairach space [17 ] and overlaid them on inflated cortical hemispheres.
7
Analyzing Orientation Effects on Sensory Perception
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All analyses were performed offline using Matlab 2012a (The MathWorks, Inc., Natick, MA) and SPSS 19 (IBM Corp, Armonk, NY). We compared the effect of group (patient vs. control) and orientation (upright vs. 90° RED) on SBT and SVV performance using a two-way univariate analysis of variance with subject as a random factor. Interaction effects were post hoc analyzed using a Bonferroni-corrected paired sample t-test. All statistical tests were performed at the 0.05 level (P < 0.05).
8
Voxel-Based Morphometry Analysis of MRI Data
9
Cardiac Segmentation and Visualization Protocol
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Segmentation of the left ventricle on the short axis CINE and LGE MRI data is done in the end diastolic phase in approximately 20 slices located from apex to base. The segmentations are done automatically and checked on the long axis images using the freely available software Segment version 1.9 R2507 (http://segment.heiberg.se ) [16 (link)] available for Matlab (MATLAB 2012a, The MathWorks Inc., Natick, MA, 2012). Segmentations are done to create a 3D surface mesh (cine mesh) of the LV endocardium for surface registration and projection of the acquired data. Furthermore the local wall thickening (WT) of the myocardium was assessed using the CINE images. The myocardial infarct was segmented on the LGE images using the area based semi-automatic segmentation [17 (link)]. If necessary both the LV and the infarct segmentations were manually adjusted by an experienced radiologist. Area based IT values were calculated in 80 circumferential segments of all slices using the bullseye function of Segment [17 (link)]. The IT data was projected on the CINE derived endocardial surface mesh using the TriScatteredInterp function of Matlab. The endocardial LV surface mesh is used for registration of the EMM points, image guided injection procedures, and post processing of the EMM and MRI data.
10
Evaluating VH and VL Structural Similarity
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To investigate the effect of the length of the linker peptide on the structure of VH and VL, the structural similarity of VH and VL in (Gly4Ser)n was calculated at different values of n. In addition, a space spherical shell hierarchical matching algorithm, based on spherical coordinates and described by Zhang and Chen (9 ), was used to calculate the level of similarity. In this algorithm, the protein was treated as the spherome, and the euclidean coordinates of each atom in the protein were transformed into spherical coordinates. According to the radius, the protein was then divided into several layers of spherical shells, and the same number of same atoms in each layer were collected. On the basis of predetermined weights, the number of atoms were determined, thus, obtaining the final value of atoms in each layer. Each layer of atomic values of VH-(Gly4Ser)n or VL-(Gly4Ser)n and VH (or VL) were stacked into vector a and vector b, respectively. The included angle cosine function was treated as a similarity function. The formula used (1 (link)), the algorithm of which was achieved using MATLAB 2012a software (The MathWorks, Inc., Natick, MA, USA) was as follows:
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