RESUMEN

BACKGROUND: Bacteriophages (phages) are widespread in the environment and play a crucial role in the evolution of their bacterial hosts and the emergence of new pathogens. RESULTS: LSB-1, a reference coliphage strain, was classified as a member of the Podoviridae family with a cystic form (50 ± 5 nm diameter) and short tail (60 ± 5 nm long). The double stranded DNA was about 30 kilobase pairs in length. We identified its host range and determined the gp17 sequences and protein structure using shotgun analysis and bioinformatics technology. CONCLUSIONS: Coliphage LSB-1 possesses a tailspike protein with endosialidase activity which is probably responsible for its specific enteroinvasive E.coli host range within the laboratory.

Asunto(s)

Colifagos/genética , Colifagos/fisiología , Especificidad del Huésped , Podoviridae/genética , Podoviridae/fisiología , Secuencia de Aminoácidos , Colifagos/clasificación , Colifagos/ultraestructura , Biología Computacional , ADN Viral/química , ADN Viral/genética , Escherichia coli/virología , Modelos Moleculares , Datos de Secuencia Molecular , Filogenia , Podoviridae/clasificación , Podoviridae/ultraestructura , Estructura Secundaria de Proteína , Estructura Terciaria de Proteína , Análisis de Secuencia de ADN , Proteínas Virales/genética , Virión/ultraestructura

RESUMEN

Objective To establish an autoregression moving average (ARMA) model for predicting general traffic accidents and analyzing distributional difference on time series and frequency of common traffic accident so as to provide certain basis for a prediction model with better stability and accuracy. Methods The data of road traffic accidents in one newly developed zone of Chongqing in 2000-2005 were collected. The monthly distribution regularity of road traffic accidents was analyzed with descriptive epidemiologic method. ARMA model was set up for retrospective and prospective prediction. The predicted data were compared. Results Based on the characteristics of monthly distribution, the frequency of general traffic accidents in this area showed a cyclic fashion. The frequency of general traffic accidents predicted by ARMA model had over 80% of coincidence with the actual value. Conclusion The ARMA model can be used to predict the frequency of general traffic accidents, with better accuracy of short-term prediction than the long-term prediction.