Murine Model for Measuring Effects of Humanized-Dosing of Antibiotics on the Gut Microbiome.

There is a current need for enhancing our insight in the effects of antimicrobial treatment on the composition of human microbiota. Also, the spontaneous restoration of the microbiota after antimicrobial treatment requires better understanding. This is best addressed in well-defined animal models. We here present a model in which immune-competent or neutropenic mice were administered piperacillin-tazobactam (TZP) according to human treatment schedules. Before, during and after the TZP treatment, fecal specimens were longitudinally collected at established intervals over several weeks. Gut microbial taxonomic distribution and abundance were assessed through culture and molecular means during all periods. Non-targeted metabolomics analyses of stool samples using Quadrupole Time of Flight mass spectrometry (QTOF MS) were also applied to determine if a metabolic fingerprint correlated with antibiotic use, immune status, and microbial abundance. TZP treatment led to a 5-10-fold decrease in bacterial fecal viability counts which were not fully restored during post-antibiotic follow up. Two distinct, relatively uniform and reproducible restoration scenarios of microbiota changes were seen in post TZP-treatment mice. Post-antibiotic flora could consist of predominantly Firmicutes or, alternatively, a more diverse mix of taxa. In general, the pre-treatment microbial communities were not fully restored within the screening periods applied. A new species, closely related t

The Tell-Tale Hat: Surfacing the Uncertainty in Folklore Classification

Classification is a vexing problem in folkloristics. Although broad genre classifications such as "ballad", "folktale", "legend", "proverb", and "riddle" are well established and widely accepted, these formal classifications are coarse and do little more than provide a first level sort on materials for collections that can easily include tens, if not hundreds, of thousands of records. Many large collections of folklore have been classified using systems designed for very specific tasks, usually related to early theories about the spread of folk narrative. Beyond the straightforward parsing of folk expressions into easily recognized formal genres (e.g. ballad, riddle, joke, legend, fairytale, etc.), the overarching emphasis of these schemas is on topic indexing. Perhaps best known of these indices is the ATU index of fairy tales, designed to assist scholars who are interested in the comparison of fairy tales from one or more cultures. Another well-known index, the motif index of folk literature compiled by Stith Thompson (1955-58), is designed to assist scholars in discovering the relationships between complete narratives and their component parts, as well as the movement of motifs across time and space, where the motif is conceptualized as "the smallest element in a tale having a power to persist in tradition." Other genre specific classification schemes include the Migratory Legend [ML] catalog and Danmarks gamle Folkeviser [DgF]. More collection specific indices include the Child Ballads and, of particular interest to this study, the typological indices to the Danish folklore collector Evald Tang Kristensen's legend collections

Practical Issues in Dependability Benchmarking

Much of the work to date on dependability benchmarks has focused on costly, comprehensive measurements of whole-system dependability. But benchmarks should also be useful for developers and researchers to quickly evaluate incremental improvements to their systems. To address both audiences, we propose dividing the space of dependability benchmarks into two categories: competitive benchmarks that take the holistic approach, and less expensive developer benchmarks aimed at day-to-day development tasks. In this paper we differentiate the goals of these two types of benchmarks, discuss how each type might be appropriately realized, and propose simplifyin

Proteomics and metabolomics for analysis of the dynamics of microbiota.

Expert Rev Proteomics 2018 Feb; 15(2):101-104