Environmental influences on family similarity in afternoon cortisol levels: twin and parent-offspring designs.
ABSTRACT Modest genetic effects on morning, but not late-day, cortisol levels have been established. Environmental demands may influence basal cortisol levels later in the day. Thus, we anticipated that individuals in the same family would have similar afternoon cortisol levels to the extent that they share aspects of their environment. We examined afternoon basal cortisol levels measured across 3 consecutive days in mothers and fathers and in multiple offspring in two separate large and longitudinal studies. Study I involved 321 families with singletons while study II involved 233 families with twins. Modest family similarity was apparent for afternoon basal cortisol levels in both studies. Spouses' cortisol levels were also correlated. Data from study II demonstrated that family resemblance in afternoon cortisol was accounted for by underlying shared environmental factors, but not underlying genetic factors. Shared environment accounted for 62% of the variation in twin afternoon basal cortisol levels and 14% of the variation in parent afternoon basal cortisol levels. We used pooled data from the two studies to examine whether parental depression, socioeconomic status (SES), and offspring sex and age impacted cortisol levels. Female offspring had higher cortisol levels than males, and cortisol decreased with age until about 9 years of age, after which cortisol increased with age. Family similarity persisted after accounting for parental depression, SES, time of day, and offspring sex and age, which suggests that the shared family environment influences parent and offspring stress hormone levels throughout the childhood years.
Article: Detection and identification of bacteria in an isolated system with near-infrared spectroscopy and multivariate analysis.[show abstract] [hide abstract]
ABSTRACT: Near-infrared (NIR) transflectance spectra of Listeria innocua FH, Lactococcus lactis, Pseudomonas fluorescens, Pseudomonas mendocina, and Pseudomonas putida suspensions were collected and investigated for their potential use in the identification and classification of bacteria. Unmodified spectral data were transformed (first and second derivative) using the Savitzsky-Golay algorithm. Principal component analysis (PCA), partial least-squares discriminant analysis (PLS2-DA), and soft independent modeling of class analogy (SIMCA) were used in the analysis. Using either full cross-validation or separate calibration and prediction data sets, PLS2 regression classified the five bacterial suspensions with 100% accuracy at species level. At Pseudomonas genus level, PLS2 regression classified the three Pseudomonas species with 100% accuracy. In the case of SIMCA, prediction of an unknown sample set produced correct classification rates of 100% except for L. innocua FH (77%). At genus level, SIMCA produced correct classification rates of 96.7, 100, and 100% for P. fluorescens, P. mendocina, and P. putida, respectively. This successful investigation suggests that NIR spectroscopy can become a useful, rapid, and noninvasive tool for bacterial identification.Journal of Agricultural and Food Chemistry 06/2008; 56(10):3431-7. · 2.82 Impact Factor