USGS ScienceSearch

Geology topics

V. Shankar

Publications and source records attributed to V. Shankar.

2 recordsLinked to original sources

Lack of association between ectoparasite intensities and rabies virus neutralizing antibody seroprevalence in wild big brown bats (Eptesicus fuscus), Fort Collins, Colorado

Recently, bat ectoparasites have been demonstrated to harbor pathogens of potential importance to humans. We evaluated antirabies antibody seroprevalence and the presence of ectoparasites in big brown bats (Eptesicus fuscus) sampled in 2002 and 2003 in Colorado to investigate if an association existed between ectoparasite intensity and exposure to rabies virus (RV). We used logistic regression and Akaike's Information Criteria adjusted for sample size (AICc) in a post-hoc analysis to investigate the relative importance of three ectoparasite species, as well as bat colony size, year sampled, age class, colony size, and year interaction on the presence of rabies virus neutralizing antibodies (VNA) in serum of wild E. fuscus. We obtained serum samples and ectoparasite counts from big brown bats simultaneously in 2002 and 2003. Although the presence of ectoparasites (Steatonyssus occidentalis and Spinturnix bakeri) were important in elucidating VNA seroprevalence, their intensities were higher in seronegative bats than in seropositive bats, and the presence of a third ectoparasite (Cimex pilosellus) was inconsequential. Colony size and year sampled were the most important variables in these AICc models. These findings suggest that these ectoparasites do not enhance exposure of big brown bats to RV. ?? 2007 Mary Ann Liebert, Inc.

Vector-Borne and Zoonotic Diseases

Texture analysis for automated classification of geologic structures

Texture present in aeromagnetic anomaly images offers an abundance of useful geological information for discriminating between rock types, but current analysis of such images still relies on tedious, human interpretation. This study is believed to be the first effort to quantitatively assess the performance of texture-based digital image analysis for this geophysical exploration application. We computed several texture measures and determined the best subset using automated feature selection techniques. Pattern classification experiments measured the ability of various texture measures to automatically predict rock types. The classification accuracy was significantly better than a priori probability and prior weights-of-evidence results. The accuracy rates and choice of texture measures that minimize the error rate are reported. ?? 2006 IEEE.

Conference Paper