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If its evaluation can be easy for a single system, the modelling of its components for the global efficiency assessment is much more difficult (taking into account redundancies, temporal scale factors …).
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The global efficiency for the MMS subnetwork before and after prediction is 0.21 and 0.34, respectively.
Post hoc ROI-to-ROI analysis and graph theoretical analysis showed that there were higher correlations between the left insula and the right insula, left inferior parietal cortex and right OP1 for all networks and that the global efficiency was more sensitive than the local efficiency to detect differences between notes in a network.
Furthermore, combinations of different enzymatic cocktails were evaluated in order to improve the global efficiency of cocktails for cellulose hydrolysis.
For two of the four networks (L-P and L-D), the global efficiency indices between nodes within the network were different (Table 4).
The global efficiency was 83.0%.
The global efficiency is calculated by using two different models because the data regarding the costs was only available for the second year of study.
Here, two basic measures for each node (ROI) within the network, global efficiency and local efficiency, were computed as measures of the connectivity using graph theory.
Significant group differences were also observed for strength and global efficiency indices in the sub-network detected by NBS.
In contrast, the connectivity (global efficiency) was improved for every slim GO term examined, with a median increase of more than 200% connectivity (data not shown).
A roundabout search can be avoided using the Tabu rule and aspiration criterion, for improving the global search efficiency.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com