Matlab r2018a

The fMRI data will be analyzed with SPM12 software (Wellcome Department of Imaging Neuroscience, London, UK), MATLAB_R2018a (Mathworks, Inc., Natick, MA, USA), DPARSF_V2.2 (http://www.rfmri.org/DPABI), and Freesurfer (http://surfer.nmr.mgh.harvard.edu/) software. Original data will be corrected; slice timing, affine head motion, and nonbrain extraction, spatial smoothing and temporal filtering will be applied.
Baseline ReHo and amplitude of low-frequency fluctuations (ALFF) values and functional connectivity maps from the real and sham acupuncture groups and the healthy control group will be compared using independent t tests. Independent t tests will be used for comparison of ReHo or ALFF values from the two groups after acupuncture. Paired t tests will be used to compare the pre-acupuncture and post-acupuncture data within each group. Average time series data from significant areas will be extracted to test the regional relationship with the rest of the brain using voxel-based general linear modeling. Fisher’s R-to-Z transformation will be applied to compare resting state functional connectivity using independent-sample t tests between groups [34 (link)].
Pearson’s correlation analyses will be performed to evaluate the relationship of clinical symptoms with ReHo or ALFF values in regions demonstrating significant differences between groups.

In the present study, we aim to compare treatment outcomes between two different surgical strategies for mTLE. Kaplan-Meier estimator will be conducted to compare the seizure outcome between two groups at 3, 6, and 12 months’ interval. Cognitive function, visual field defects, and Engel class I outcomes will be compared between the groups using χ² tests or t tests. Subgroup analysis will be made if the results are positive. Average complication rate and hospitalisation expenses will be qualitatively compared between the two groups. For each separate group, a logistic analysis will be made to further explore the factors which may affect the outcomes (p < 0.05). Statistical analysis will be performed using SPSS version 21 (SPSS Inc., Chicago, IL, USA) and/or MATLAB_R2018a (MathWorks Inc., Natick, MA, USA).

Biofilm enumeration data were analyzed by performing t-tests comparing specific time points of interest using Graphpad Prism 7 (GraphPad Software Inc., California, USA). SIFT-MS data for each independent P. aeruginosa biofilm were extracted using MATLAB R2018a (The MathWorks, Inc., Natick, Massachusetts, United States). In order to remove signal noise from the data the mean concentration of each compound was calculated for each hour of analysis.

Before Z-disc ablation, turboRFP-α-actinin expressing CMs were incubated with 2 µM of the calcium indicator Fluo 4-AM at 37 °C and 5% CO₂ atmosphere. After 40 min, the culture medium was replaced and cells were incubated for 20 min at 37 °C and 5% CO₂ atmosphere. Afterwards, CMs were randomly selected and positions were saved. The Fluo 4 fluorescence intensity was recorded (30 images with a frame rate of 2 Hz) before and 10 s, 15 min, 30 min, 45 min and 60 min after single Z-disc ablation. The fluorescence intensity of a selected region of interest near the ablation site was determined for all time points and standardized to the mean fluorescence intensity of the first image. After plotting of the standardized fluorescence intensity against the time, the number of calcium oscillation peaks was determined using a self-written Matlab (Matlab R2018a, The MathWorks) program.

The datasets were converted to ISMRMRD format^{41 (link)} and a vendor-neutral processing was implemented in Matlab R2018a (Mathworks, Natick, MA) on a server with 40 CPU cores. The overall data processing is presented by a flowchart in Supporting Information Fig. 1 and detailed in the following section.

Due to the 1D time-varying properties of the ankle and MTP joint kinematics, a factorial SnPM was applied in MATLAB R2018a (The MathWorks, Natick, MA, USA) using open-access one-dimensional SPM scripts [16 (link),24 (link)]. Two-way repeated-measures ANOVA was used to assess the effect of AS on foot–ankle kinematics during expected and unexpected gait terminations. Bonferroni correction was applied to adjust the alpha risk of post hoc tests in the case of significance. The level of statistical significance was set at p < 0.05.