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When instead spread is based on node-specific thresholds that are distributed uniformly in [0, 1] (the Linear Threshold Model), we observe that even very noisy network observations provide substantial value.
In the context of this model, we observe that anisotropy creates new states and alters the connectivity between existing states.
While we cannot find any significant CNEs (in the final model), we observe significant negative SNEs on both user sides.
In our model, we observe activation of the trigeminal ganglion using the inflammatory substances CFA and IS.
For the ML model, we observe chaotic dynamics around a strange attractor, where small perturbations can grow, leading to a positive largest Lyapunov exponent of 0.6738.
When comparing RMS error of predictions (training + test set results) for Tang's, MLR and ANN model, we observe the values 1.76, 2.14, and 2.25 respectively.
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In a reverse cancer-then-sepsis model we observed that sepsis may conversely inhibit tumor growth.
By using equivalent electrical model, we observed the dynamic processes of the discharge.
To judge the discrimination ability of the D-CNN model, we observed the distributions of the different neurons' excitations.
For the validation of the final model, we observed the estimate for the coefficients which represented the research hypotheses paths.
In the Snord116 deletion mouse model, we observed normal locomotor activity and excellent wire-hanging ability.
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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