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The first analysis was descriptive, and categorized sample respondents by demographics, socioeconomics and clinical characteristics and compared respondents in underweight, normal, overweight and obese BMI categories.
Based on recommendations from regulatory agencies for safety assessment [ 11, 12, 16, 17], we categorized sample sizes into six levels: 1) < 100 patients; 2) 100 to 299 patients; 3) 300 to 599 patients; 4) 600 to 1499 patients; 5) 1500 to 5000 and 6) > 5000 patients.
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We categorized samples in multiple breast cancer data sets by PAM50 gene signature (Parker et al., 2009).
PAH DNA adducts were categorized as detectable or nondetectable; for analytical purposes, we categorized samples with < 15% inhibition as nondetectable (Gammon et al. 2002b).
We categorized samples into three groups: 1) <33% methylation (hypomethylated), 2) 33-66% methylation (median methylation), and 3) >66% methylation (hypermethylation) as previously described [ 11, 26].
Additionally, electronic tracking of the methodically categorized samples is essential for swift retrieval of those that are suitable for a specific request among the ones kept in freezers.
In order to do that, we categorized samples into two groups: high miR-21-5p expression (expression above the established cutoff, 70,000 cpm) and low miR-21-5p expression.
We categorized samples with mean membership probability of >0.6 to one of the clusters as 'pure' samples (for use in the outlier tests and LD analyses) and the others as 'mixed' samples.
We further categorized samples according to whether they were sold as lean red tuna (akami in Japanese) or fatty tuna (toro) because mercury and lipid concentrations are inversely proportional in tuna (Balshaw et al. 2008 a ).
A participatory wealth-ranking approach was used to categorize sample households into various wealth categories.
Standard clinical lab cut-off points were used to categorize samples as positive or negative.
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