Escherichia coli multidrug resistant to widely available antibacterials poses a threat to humans, their poultry and their environment when the prevalence is high, and containment is low.

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Extended-spectrum, β-lactamase-producing Escherichia coli (ESBL-E) harboring the bla -encoding plasmid (ESBL-E55) has been reported to be associated with urinary tract infection (UTI). The aims of this study were to clarify the prevalence of ESBL-E55 in pork meats and workers from the same wholesale market, as well as patients with UTI from a nearby hospital in Vietnam; we also investigated the plasmids encoding bla. Sequencing analysis showed that 66.6% of the ESBL-E isolated from pork meats contained bla , whereas the gene was present in 25.0% of workers and 12.5% of patients with UTI. Plasmid analysis showed that several sizes of plasmid encoded bla in ESBL-E55 isolated from pork meats, whereas ESBL-E55 isolated from workers and patients with UTI contained only 104-139 kbp of bla -encoding plasmids. This indicates that the 104-139 kbp sizes of bla-encoding plasmids were commonly disseminated in pork meats, wholesale market workers, and patients with UTI.

Stratified random sampling (SRS) is a widely used sampling technique for approximate query processing. We consider SRS on continuously arriving data streams, and make the following contributions. We present a lower bound that shows that any streaming algorithm for SRS must have (in the worst case) a variance that is Ω(r ) factor away from the optimal, where r is the number of strata. We present S-VOILA, a streaming algorithm for SRS that is locally variance-optimal. Results from experiments on real and synthetic data show that S-VOILA results in a variance that is typically close to an optimal offline algorithm, which was given the entire input beforehand. We also present a variance-optimal offline algorithm VOILA for stratified random sampling. VOILA is a strict generalization of the well-known Neyman allocation, which is optimal only under the assumption that each stratum is abundant, i.e. has a large number of data points to choose from. Experiments show that VOILA can have significantly smaller variance (1.4x to 50x) than Neyman allocation on real-world data.