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By more accurately modeling many aspects of human in vivo tissues, the 3-D intestinal cell model generated in this study offers a novel approach for studying microbial infectivity from the perspective of the host-pathogen interaction.
The PASE model generated in this study lacks primitive endoderm and trophoblast, and thus cannot form yolk sac and placenta.
To this end, we employed a viable SGK1 knockout mouse model generated in a 129/SvJ background.
An experimentally validated model generated in finite element code LS-DYNA was utilized to study crushing behavior of these structures under axial impact.
The predictive model generated in this study can be used as a valid tool for the identification of hydrogel compositions with desired, selected characteristics.
The CART model generated in Experiment 1 was used to predict participants' classification accuracy in Experiment 2. Indeed, a key purpose of classification and regression trees is to allow, based on predictor variables (i.e. kinematic features), accurate predictions of outcome for future experimental samples.
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The stochastic model generates in particular both the mean and the standard deviation of the auxin concentrations, which enables us to characterise the expected variability.
Test results were compared to crack growth models generated in AFGROW.
Different from the mathematical interpolation function based human body synthesis methods, the models generated in our method have the approximation errors minimized.
the models generated in the training stage are employed in the two proposed use-cases.
In the GUI, models generated in confidential models are clearly highlighted in the model list, to facilitate their identification.
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