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For the development of the prognostic model, we involved potential users from three settings: pre-hospital, battlefield, and emergency departments.
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The model we considered involved coupling only through the mean of a function, s, of the variable V i which, in the limit N → ∞, can be thought of as an integral or, more generally, as a functional of V.
The model we employ involves explicit all-atom accounts of the energetics of structural adaptation of the protein and the DNA upon complex formation; the van der Waals and electrostatic interactions between the protein and the DNA; and the electrostatic polarization and screening effects, van der Waals components, and cavitation effects of solvation.
The model we propose involves a relevant organizational change in the surgical pre-admission process but it is applicable and appropriate to include different clinical urgency criteria.
Since the combined end-point is not a validated clinical model, we have developed a multivariate model, in which, among others, we involved also the most commonly used biomarkers of acute coronary syndrome, that is, troponin I and BNP.
As a model we chose butyrylcholinesterase (BChE), involved in termination of cholinergic signaling, and widely considered as a poor immunogen.
In support of this model, we detected several transcripts involved in the production of A. apis hydrolytic enzymes, listed in Additional file 1: Table S1.
Some diseases such as Neuromyelitis optica may be worsened by IFN-β [ 30], but the EAE model we have studied neither involves aquaporin IV antibodies nor is dependent on an antibody response [ 34].
When the dispersal models are involved, we also refer to [20, 21, 28 31].
Because a Monte Carlo-based uncertainty analysis is computationally very demanding, especially when complex models are involved, we developed a fast indicative uncertainty assessment method that allows for generating proxies of the Monte Carlo-based result in terms of the mean prediction and its associated uncertainty based on a single model evaluation.
In this study we investigate the model uncertainties involved in predicting long-term permeable reactive barrier (PRB) remediation efficiency based on a lab-scale column experiment under accelerated flow conditions.
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