RESUMEN
A collection of 74 Enterobacteriaceae isolates found in Bo, Sierra Leone, were tested for quinolone antibiotic susceptibility and resistance mechanisms. The majority of isolates (62%) were resistant to quinolones, and 61% harbored chromosomal gyrA and/or parC mutations. Plasmid-mediated quinolone resistance genes were ubiquitous, with qnrB and aac(6')-Ib-cr being the most prevalent. Mutated LexA binding sites were found in all qnrB1 genes, and truncated qnrB pseudogenes were found in the majority of Citrobacter isolates.
Asunto(s)
Antibacterianos/farmacología , Proteínas Bacterianas/metabolismo , Farmacorresistencia Bacteriana/genética , Enterobacteriaceae/efectos de los fármacos , Quinolonas/farmacología , Serina Endopeptidasas/metabolismo , Proteínas Bacterianas/genética , Sitios de Unión , Girasa de ADN/genética , Topoisomerasa de ADN IV/genética , Farmacorresistencia Bacteriana/efectos de los fármacos , Enterobacteriaceae/genética , Enterobacteriaceae/aislamiento & purificación , Infecciones por Enterobacteriaceae/epidemiología , Infecciones por Enterobacteriaceae/microbiología , Humanos , Pruebas de Sensibilidad Microbiana , Mutación , Seudogenes , Sierra Leona/epidemiologíaRESUMEN
Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal's body parts directly from images or videos. However, currently available animal pose estimation methods have limitations in speed and robustness. Here, we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2x with no loss in accuracy compared to currently available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings-including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.