Matlab 2017b

Novel objects were created using Matlab 2017b (MathWorks, Natick, MA) with Psychtoolbox. The gratings were generated in Python using the Psychopy version 1.9 toolbox (Peirce, 2009). For the testing procedure, a Dell PC running Matlab 2016b and Psychtoolbox was used to conduct the experiment. Subjects were placed in front of a DELL monitor with a 1600x900 resolution at 70 Hz. The training paradigm was made with Unity^® (version 2017.2.03f). For the training procedure, subjects used their personal computer running on Windows. For the course of the experiment, subjects were asked to uphold an upright position and to limit forward and backward movements from the screen during testing and training.

Surface-electromyographic (EMG) recordings were acquired from RF, BF, TA, and soleus of both legs using pairs of silver-silver chloride electrodes (Intec Medizintechnik GmbH, Klagenfurt, Austria), placed with an inter-electrode distance of 3 cm in accordance with the Surface Electromyography for the Non-Invasive Assessment of Muscles (SENIAM) recommendations (www.seniam.org). A common ground electrode was placed over the fibular head of the right leg. Abrasive paste (Nuprep, Weaver and Company, Aurora, CO) was used for skin preparation to reduce EMG electrode resistance below 5 kΩ. EMG signals were acquired using the Phoenix multichannel EMG system (EMS-Handels GmbH, Korneuburg, Austria) set to a gain of 502 (or 229, in case of amplifier saturation by large evoked potentials) over a bandwidth of 10–1000 Hz and digitized at 2048 samples per second and channel. EMG data were additionally bandpass-filtered offline between 10 and 1000 Hz using a 2^nd order Butterworth filter (Matlab 2017b, The MathWorks, Inc., Natick, MA). All recordings were conducted with the participants lying comfortably in the supine position.

The analyses were conducted using MATLAB 2017b (MathWorks Inc., MA, USA). The hierarchical consensus framework was adapted from Jeub et al.⁴⁶ (https://github.com/LJeub/HierarchicalConsensus). We adopted the Louvain implementation from Jeub et al.⁶¹ (https://github.com/GenLouvain/GenLouvain). The Random Matrix Theory method was adopted from MacMahon et al.⁴¹ (https://mathworks.com/matlabcentral/fileexchange/49011). Miscellaneous network tools were used from the Network Community Toolbox (http://commdetect.weebly.com) and the Brain Connectivity Toolbox^{90 (link)} (https://sites.google.com/site/bctnet/Home). The brain plots were vizualized with the Connectome Workbench software⁹¹ (https://github.com/Washington-University/workbench). Our code and hierarchical brain maps are available on GitHub (https://github.com/emergelab/hierarchical-brain-networks).

The fractured tibiae were harvested at 28 dpf, and the ends were secured with methacrylate (MMA) in 1.2-cm-long cylinders to place the fracture site in the middle. The fracture tibiae were tested in terms of torsion using a custom LabVIEW (National Instruments) program until failure. The maximum torque and displacement at maximum torque were recorded and processed by a custom MATLAB 2017b program (Mathworks).

Peak detection, integration, deconvolution and alignment were carried out using XCMS software^{18 (link)} in R 3.4.1. Principal component analysis (PCA), guided PCA, hierarchical cluster analysis (HCA) and univariate (t-test, False Discovery Rate (FDR) adjusted p-values using the Benjamini-Hochberg procedure²³ ) analysis were carried out in MATLAB 2017b (Mathworks Inc., Natick, MA, USA) using in-house written scripts and the PLS Toolbox 8.3 (Eigenvector Research Inc., Wenatchee, USA). Support Vector Regression models were carried out in MATLAB using the LIBSVM library²⁴ . The datasets and scripts generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Raw UPLC-MS data in mzXML format are also accessible via the MetaboLights repository (www.ebi.ac.uk/metabolights) under accession number MTBLS906.