Matlab r2010

In total, 20 individual fruit were sampled for the sequencing experiment. Four fruit were sampled at harvest, and after four of months storage another 16 fruit were sampled. In each one of them, 1 cm thick slices were cut through the equator of the fruit. From each fruit, five tissue samples of 1 cm diameter were taken from the inner and the outer cortex (Additional file 1: Figure S1), immediately frozen, crushed in liquid nitrogen and stored at −80°C. To assess browning, pictures were taken from the complementary tissue slice immediately after cutting using a digital camera and controlled light conditions. For each tissue sample a visual BI was calculated using an in-house developed MATLAB program (Matlab R2010, The MathWorks, Inc., Natick, MA, USA) as described previously [2 ]. The colour scale was based on ten classes ranging from yellow (one) to brown (ten). Tissues were classified as healthy (low BI) or affected (high BI) (Additional file 1: Figure S1; Additional file 2: Table S7). Three groups of apples were identified: 1) those with both healthy inner and outer cortex, 2) those with only inner cortex affected, and 3) those with both inner and outer cortex affected. Tissues from all three groups of apples were taken to cover the initiation and development of the disorder in a tissue-specific manner.

Preprocessing was performed using the Data Processing Assistant for Resting-State fMRI (http://www.restfmri.net) and the Resting-State fMRI Data Analysis Toolkit (Rest, V1.8, http://www.restfmri.net), based on statistical parametric mapping-8 (SPM8, www.fil.ion.ucl.ac.uk/spm/) and Matlab R2010 (www.mathworks.com). The first 10 images were excluded from the analysis. The remaining images were corrected for slice timing with the middle slice used as a reference, realigned to remove head motion, normalized into the standard space, and resampled to a 3 × 3 × 3 mm³ voxel size. The resulting images were then smoothed using a 4-mm Gaussian kernel before proceeding to the next step.

To remove external magnetic noise from the MEG recordings, data were preprocessed offline using the signal‐space‐separation method implemented in Maxfilter 2.1 (Elekta Neuromag).32 MEG data were also corrected for head movements, and substitutions were made for bad channels using interpolation algorithms implemented in the software. Subsequent analyses were performed using Matlab R2010 (Mathworks®, Natick, MA). Heartbeat and EOG artifacts were detected using independent component analysis (ICA) and linearly subtracted from recordings. The ICA decomposition was performed using the Infomax algorithm implemented in the Fieldtrip toolbox.33