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In binocular rivalry experiments, the network has two different learned patterns.
In this simulation experiments, the network area is 1000 × 1000 m2 approximately.
Last, because the edges in the network model described here are supported by causal relationships directly observed in published experiments, the network model contains a unique level of biological transparency.
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Alike to laboratory invasion experiments, the network-based model was a better predictor of tomato plant wilting compared with the diversity-based model (Table 1).
While they are based on different types of data from many experiments, the networks could still be enhanced by future studies from various "omic" approaches.
These relationships persisted throughout the rest of the invasion experiment, the network model being the best predictor of disease spread during the intermediate (R2=0.75, Table 1; Fig. 3) and late infection stages (R2=0.73, Table 1; Fig. 3).
In each experiment, the network is subjected to increasingly severe attacks.
In the first experiment, the network was scaled in terms of number of nodes, but with constant transmission data rate.
However, in our experiment the network variables are constant: for any given respondent the network is the same in all trials (apart from randomness of actual travel times).
It is worth noting again that in our experiment the network performance is stationary and the differences of simulated actual travel times in successive trials only depend on random dispersion.
To build a network of interaction among the differentially regulated genes in our experiment, the network building tool in MetaCore with the "Direct interaction" algorithm was used, which resulted in a statistically significant (FDR < 0.05) network.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com