Workplace drug testing, different matrices different objectives

Drug testing is used by employers to detect drug use by employees or job candidates. It can identify recent use of alcohol, prescription drugs, and illicit drugs as a screening tool for potential health and safety and performance issues. Urine is the most commonly used sample for illicit drugs. It detects the use of a drug within the last few days and as such is evidence of recent use; but a positive test does not necessarily mean that the individual was impaired at the time of the test. Abstention from use for three days will often produce a negative test result. Analysis of hair provides a much longer window of detection, typically 1 to 3 months. Hence the likelihood of a falsely negative test using hair is very much less than with a urine test. Conversely, a negative hair test is a substantially stronger indicator of a non-drug user than a negative urine test. Oral fluid (saliva) is also easy to collect. Drugs remain in oral fluid for a similar time as in blood. The method is a good way of detecting current use and is more likely to reflect current impairment. It offers promise as a test in post-accident, for cause, and on-duty situations. Studies have shown that within the same industrial settings, hair testing can detect twice as many drug users as urine testing. Copyright (C) 2012 John Wiley & Sons, Ltd.

Short communication: Principal components and factor analytic models for test-day milk yield in Brazilian Holstein cattle

A total of 46,089 individual monthly test-day (TD) milk yields (10 test-days), from 7,331 complete first lactations of Holstein cattle were analyzed. A standard multivariate analysis (MV), reduced rank analyses fitting the first 2, 3, and 4 genetic principal components (PC2, PC3, PC4), and analyses that fitted a factor analytic structure considering 2, 3, and 4 factors (FAS2, FAS3, FAS4), were carried out. The models included the random animal genetic effect and fixed effects of the contemporary groups (herd-year-month of test-day), age of cow (linear and quadratic effects), and days in milk (linear effect). The residual covariance matrix was assumed to have full rank. Moreover, 2 random regression models were applied. Variance components were estimated by restricted maximum likelihood method. The heritability estimates ranged from 0.11 to 0.24. The genetic correlation estimates between TD obtained with the PC2 model were higher than those obtained with the MV model, especially on adjacent test-days at the end of lactation close to unity. The results indicate that for the data considered in this study, only 2 principal components are required to summarize the bulk of genetic variation among the 10 traits.