Classification tree method for bacterial source tracking with antibiotic resistance analysis data.

Various statistical classification methods, including discriminant analysis, logistic regression, and cluster analysis, have been used with antibiotic resistance analysis (ARA) data to construct models for bacterial source tracking (BST). We applied the statistical method known as classification trees to build a model for BST for the Anacostia Watershed in Maryland. Classification trees have more flexibility than other statistical classification approaches based on standard statistical methods to accommodate complex interactions among ARA variables. This article describes the use of classification trees for BST and includes discussion of its principal parameters and features. Anacostia Watershed ARA data are used to illustrate the application of classification trees, and we report the BST results for the watershed.

Spiroplasma atrichopogonis sp. nov., from a ceratopogonid biting midge.

Spiroplasma sp. strain GNAT3597T was isolated from the biting midge genus Atrichopogon (Diptera: Ceratopogonidae). It was serologically distinct from other Spiroplasma species, groups or subgroups. Dark-field microscopy of the cells revealed the classical helical shape and subsequent transmission electron microscopy revealed cells surrounded by only a cell membrane (i.e. lacking a cell wall). Growth of strain GNAT3597T occurred in M1D medium at 30 degrees C. Strain GNAT3597T catabolized both glucose and arginine, but did not hydrolyse urea. The DNA G+C content of strain GNAT3597T was 29+/-1 mol%. Only one strain, SMCAT (Spiroplasma mirum), is serologically related to strain GNAT3597T, although the relationship is weak (positive reaction to only a 1 : 80 dilution). It is therefore proposed that strain GNAT3597T (=ATCC BAA-520T=NBRC 100390T) represents a novel species, Spiroplasma atrichopogonis sp. nov. (class Mollicutes: order Entomoplasmatales: family Spiroplasmataceae).