Integrating both of these dose-response assessment ideologies into the Bayesian framework enables us to create a risk assessment model that accounts for both inter-batch and experimental inconsistencies in the relevant data, and also captures the uncertainty surrounding the toxicity potential of many NMs [51].
It is widely accepted that culture conditions alone may exert effects on gene expression, resulting in experimental inconsistencies [38], [39].
However, if the cell number and media volume are not considered, this will likely result in applying different doses of ligand per cell across the different experiments, which may lead to experimental inconsistencies.
It is also found that there exist uncertainty and inaccuracy in the available models when compared with the experimental data and inconsistency among the models.
However, this is marked as experimental, so various inconsistencies should be expected.
It is clear that many of the past experimental failures and inconsistencies in culturing hNSCs were likely because of insufficient understanding of the insulin sensitivity specific to human neuronal cells.
The inconsistency in experimental results can be linked to several variables relative to experimental design, such as BP measurement monitoring at differing times postexercise and use of different exercise parameters such as volume, intensity, rest between sets, and training status.
The effect of dietary zinc (Zn) exposure to Daphnia magna fed living algae remains unsure as existing experimental data exhibit considerable inconsistency.
However, calculating dynamic mechanical properties, i.e. tan δ, from this master curve leads to a pronounced inconsistency with experimental results.
We demonstrate the existence and recovery of state inconsistency through experimental analysis using a drive-by-wire (DVW) application and a position control system (PCS) application running state-based schedules.
