RESUMEN

Proliferation of Phormidium biofilms in rivers is becoming a worldwide sanitation problem for humans and animals, due to the ability of these bacteria to produce anatoxins. To better understand the environmental conditions that favor the development of Phormidium biofilms and the production of anatoxins, we monitored the formation of these biofilms and their toxins for two years in the Tarn River, biofilms from which are known to have caused the deaths of multiple dogs. As previously observed in New Zealand, Phormidium biofilm development occurred in riffle areas. The coverage of these biofilms at the bottom of the river exhibited strong spatial and temporal variations, but was positively correlated with water temperature and depth. Anatoxin-a was detected in less than 50% of the biofilms. The concentrations of these toxins in the biofilms exhibited high spatiotemporal variability, with the highest concentrations being recorded at the end of the summer period at the upstream sampling sites. These findings suggest that the maturity of the biofilms, combined with the local environmental conditions, have an impact on the production of anatoxin, making risk assessment for these benthic proliferations challenging.

Asunto(s)

Biopelículas , Cianobacterias/fisiología , Contaminantes del Agua , Toxinas de Cianobacterias , Monitoreo del Ambiente , Francia , Ríos/química , Ríos/microbiología , Tropanos/análisis , Tropanos/toxicidad , Contaminantes del Agua/análisis , Contaminantes del Agua/toxicidad

RESUMEN

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This ninth installment of Explorations in Statistics explores the analysis of ratios and normalized-or standardized-data. As researchers, we compute a ratio-a numerator divided by a denominator-to compute a proportion for some biological response or to derive some standardized variable. In each situation, we want to control for differences in the denominator when the thing we really care about is the numerator. But there is peril lurking in a ratio: only if the relationship between numerator and denominator is a straight line through the origin will the ratio be meaningful. If not, the ratio will misrepresent the true relationship between numerator and denominator. In contrast, regression techniques-these include analysis of covariance-are versatile: they can accommodate an analysis of the relationship between numerator and denominator when a ratio is useless.

Asunto(s)

Modelos Estadísticos , Humanos , Modelos Biológicos , Análisis de Regresión

RESUMEN

Massively univariate regression and inference in the form of statistical parametric mapping have transformed the way in which multi-dimensional imaging data are studied. In functional and structural neuroimaging, the de facto standard "design matrix"-based general linear regression model and its multi-level cousins have enabled investigation of the biological basis of the human brain. With modern study designs, it is possible to acquire multiple three-dimensional assessments of the same individuals - e.g., structural, functional and quantitative magnetic resonance imaging alongside functional and ligand binding maps with positron emission tomography. Current statistical methods assume that the regressors are non-random. For more realistic multi-parametric assessment (e.g., voxel-wise modeling), distributional consideration of all observations is appropriate (e.g., Model II regression). Herein, we describe a unified regression and inference approach using the design matrix paradigm which accounts for both random and non-random imaging regressors.

RESUMEN

Regressions of aluminum against potentially toxic elements in the sediments of freshwater aquatic systems in Louisiana were used to distinguish natural variability from anthropogenic pollution when elemental concentrations exceeded screening effects levels. The data were analyzed using geometric mean model II regression methods to minimize, insofar as possible, bias that would have resulted from the use of model I regression. Most cadmium concentrations exceeded the threshold effects level, but there was no evidence of an anthropogenic impact. In Bayou Trepagnier, high concentrations of Cr, Cu, Pb, Ni, and Zn appeared to reflect anthropogenic pollution from a petrochemical facility. In Capitol Lake, high Pb concentrations were clearly associated with anthropogenic impacts, presumably from street runoff. Concentrations of potentially toxic elements varied naturally by as much as two orders of magnitude; hence it was important to filter out natural variability in order to identify anthropogenic effects. The aluminum content of the sediment accounted for more than 50% of natural variability in most cases. Because model I regression systematically under-estimates the magnitude of the slope of the regression line when the independent variable is not under the control of the investigator, use of model II regression methods in this application is necessary to facilitate hypothesis testing and to avoid incorrectly associating naturally high elemental concentrations with human impacts.

RESUMEN

Convergent growth regulation, where individuals adjust their growth trajectories to reach a targeted final body size, has been reported for many arthropod taxa. Divergent growth, where larger individuals grow proportionately more than smaller individuals, is seldom observed. Most studies based their conclusions on growth increment analysis: correlation or regression between body size at a particular molt and the increment grown during the next molt. These studies interpreted a negative relationship as evidence for convergent growth regulation, since smaller individuals appeared to grow more during the subsequent molt than larger individuals. Using random data simulations and an analysis of the statistics, I demonstrate that autocorrelation in these statistics generates false evidence for convergent growth, even when divergent growth actually occurred. I suggest model II geometric mean (GM) regression as an alternative method because it does not suffer from these statistical problems. A GM regression reanalysis of two published studies revealed evidence for divergent growth or no growth regulation in cases where the original studies reported convergent growth regulation, suggesting that the reported prevalence of convergent growth may be a statistical artifact.