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For the experiments on both strains of L. pneumophila that we present, we used the following values for the algorithm parameters.
In other analyses, when there was only one level of the B comparison present, we used a one way randomized ANOVA and SNK.
Our rail is track beam instead of the ordinary steel rail, which is very different at present, we used to adopt pre-stressed concrete track beam (referred to PC track beam).
So literally in the shooting of the present, we used time to break down and erode the performance, and then we used time in the past to give the freshness and spontaneity to performances.
Where multiple potential confounding variables were present, we used multivariate logistic regression models to determine independent predictors of important outcomes (e.g., delayed presentation).
To provide a test of the ability of our models to predict the distribution of human or canine blastomycosis in areas from which no input data are present, we used a spatially stratified subsetting procedure (see references [17] and [20] for other examples).
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In the examples we present, we use a simplified one-covariate model, encompassing all possible differences between the two datasets.
In the work presented, we used mathematical tools to construct a predictive model of cellular outcomes.
When medians and means were not presented, we used the category midpoint.
In a sensitivity analysis that yielded similar results (not presented), we used African American-specific RRs (Zhu et al, 2005).
Both approaches led to very similar conclusions, therefore for all other data presented we used the unfiltered data.
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CEO of Professional Science Editing for Scientists @ prosciediting.com