No abstract

Emerging next-generation sequencing technologies have revolutionized the collection of genomic data for applications in bioforensics, biosurveillance, and for use in clinical settings. However, to make the most of these new data, new methodology needs to be developed that can accommodate large volumes of genetic data in a computationally efficient manner. We present a statistical framework to analyze raw next-generation sequence reads from purified or mixed environmental or targeted infected tissue samples for rapid species identification and strain attribution against a robust database of known biological agents. Our method, Pathoscope, capitalizes on a Bayesian statistical framework that accommodates information on sequence quality, mapping quality, and provides posterior probabilities of matches to a known database of target genomes. Importantly, our approach also incorporates the possibility that multiple species can be present in the sample and considers cases when the sample species/strain is not in the reference database. Furthermore, our approach can accurately discriminate between very closely related strains of the same species with very little coverage of the genome and without the need for multiple alignment steps, extensive homology searches, or genome assembly-which are time-consuming and labor-intensive steps. We demonstrate the utility of our approach on genomic data from purified and in silico ''environmental'' samples from known bacterial agents impacting human health for accuracy assessment and comparison with other approaches.

SummaryMedical implants are sometimes colonized by biofilm-forming bacteria that are very difficult to treat effectively. The combination of gentamicin and ultrasonic exposure for 24 h was previously shown to reduce the viability of E. coli biofilms in vivo. This article shows that such treatment for 48 h reduced viable E. coli bacteria to nearly undetectable levels. However, when P. aeruginosa biofilms were implanted and treated for 24 and 48 h, no significant ultrasonic-enhanced reduction of viable bacteria was observed. The difference in response of these two organisms is attributed to greater impermeability and stability of the outer membrane of P. aeruginosa.

Escherichia coli biofilms on two polyethylene disks were implanted subcutaneously into rabbits receiving systemic gentamicin. Ultrasound was applied for 24 h to one disk. Both disks were removed, and viable bacteria were counted. Pulsed ultrasound significantly reduced bacterial viability below that of nontreated biofilms without damage to the skin.

This study investigated Type I error rates for tests of fixed effects in mixed linear models using Wald F-statistics with the Kenward-Roger adjustment. Data were generated using 15 covariance structures. Correct covariance structures as well as those selected using the Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC) criteria were examined. Performance of the AIC and BIC criteria in selecting the true covariance structure was also studied. Type I error rates for the correct models were often adequate depending on the sample size and complexity of covariance structure. Type I error rates for the best AIC and BIC models were always higher than target values, but those obtained using BIC were closer to the target value than those obtained using AIC. For unbalanced data, Type I error rates for the between-subjects effect were closer to target values for positive pairing while those for the within-subject effect were closer for negative pairing. Success of AIC and BIC in selecting the correct covariance structure was low.