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The most straightforward interpretation of our data is, assuming a regular assembly, that the G88R mutant acts in a dominant-active manner (Fig 6B).
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For this paper, the data is assumed to be a realization of Equation 1, where the parameters are estimated using a method similar to the Expectation Maximization (EM) algorithm [29].
The overall distribution of the data is assumed to be a mixture of several such distributions.
The mean depth is 95 feet with a standard deviation of 109 ft. This data will hereafter be referred to as "private well data" and this data is assumed to represent a deeper aquifer model of groundwater NO3– (n = 22 067).
The data is assumed to come from a Gaussian mixture model.
This volume of data is assumed to increase at a yearly rate of 5%.
The data is assumed to lie on a known social network containing K processes, where each of the K processes is a pairwise rivalry between two gangs.
The data is assumed to consist of a periodic baseline level and irregularly occurring epidemics.
In kernel-based detection algorithms the data is assumed to be implicitly mapped into a high-dimensional kernel feature space by a nonlinear mapping, which is associated with a kernel function.
The distribution of the data is assumed to be normally distributed and a violation of normality can impact coverage.
The longitudinal data are assumed to follow a mixed effects model, and a proportional hazards model depending on the longitudinal random effects and other covariates is assumed for the survival endpoint.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com