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This model therefore predicts the expected in-hospital GIB at presentation, and as such, the predictions could be helpful for identifying vulnerable patients, allocating relevant medical resources, implementing contrapuntal prophylactic strategies, such as the use of histamine H2 receptor antagonists (H2RAs) or proton pump inhibitors (PPIs) during subsequent hospitalization.
This type of Lagrangian model therefore predicts lower concentrations, in conditions of low wind speeds and stable stratification, in comparison with a standard Lagrangian model.
The model therefore predicts that careful project management will be necessary to match discovery output over time with downstream development resource capacity. Figure 18 Time chart showing simulated preclinical candidate output as a function of year for a working group comprised of the following: (A) 10 chemists and 10 biologists with an average output of 0.6.
The model therefore predicts a key role for IκBα-actin interactions possibly related to sustaining optimal pathway regulation by adjusting NF-κB-IκB complex formation at the steady-state, and controlling negative feedback following activation.
This model, therefore, predicts the expected outcome at one year after ICH at presentation.
The FG hydrogel model, therefore, predicts the correct order of magnitude for the passive channel width.
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Our logistic regression model, therefore, predicted seedling survival as a function of population, acclimation and heat challenge.
The simplest evolutionary model, therefore, predicted that one of the four hominoid SULT1A loci was orthologous to the rodent SULT1A1 gene.
The behavior of GAL4, which is expected to switch off, shows no expression at all times in our predicted model, therefore predicting the time series except for the initial time point.
The model therefore predicted that every 9.5 years (95% confidence interval: 5.2 – 13.9 years)the difference between a quitter's risk of age-related macular degeneration and that of a never-smoker will be halved, because when t = ln(2)*τ, (RR t -1) = (RR t -1)/2k, where k is an arbitrary integer.
The proposed model allows the user to incorporate the effects of fire into the evacuation simulation (importing fire data/results from another model) and, therefore, predicts the number of people directly affected by the smoke (victims).
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Justyna Jupowicz-Kozak
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