The novel macrolide-Lincosamide-Streptogramin B resistance gene erm(44) is associated with a prophage in Staphylococcus xylosus.

A novel erythromycin ribosome methylase gene, erm(44), that confers resistance to macrolide, lincosamide, and streptogramin B (MLSB) antibiotics was identified by whole-genome sequencing of the chromosome of Staphylococcus xylosus isolated from bovine mastitis milk. The erm(44) gene is preceded by a regulatory sequence that encodes two leader peptides responsible for the inducible expression of the methylase gene, as demonstrated by cloning in Staphylococcus aureus. The erm(44) gene is located on a 53-kb putative prophage designated ΦJW4341-pro. The 56 predicted open reading frames of ΦJW4341-pro are structurally organized into the five functional modules found in members of the family Siphoviridae. ΦJW4341-pro is site-specifically integrated into the S. xylosus chromosome, where it is flanked by two perfect 19-bp direct repeats, and exhibits the ability to circularize. The presence of erm(44) in three additional S. xylosus strains suggests that this putative prophage has the potential to disseminate MLSB resistance.

Simulating Energy Transfer and Upconversion in β-NaYF 4 : Yb 3+ , Tm 3+

The design of upconversion phosphors with higher quantum yield requires a deeper understanding of the detailed energy transfer and upconversion processes between active ions inside the material. Rate equations can model those processes by describing the populations of the energy levels of the ions as a function of time. However, this model presents some drawbacks: energy migration is assumed to be infinitely fast, it does not determine the detailed interaction mechanism (multipolar or exchange), and it only provides the macroscopic averaged parameters of interaction. Hence, a rate equation model with the same parameters cannot correctly predict the time evolution of upconverted emission and power dependence under a wide range of concentrations of active ions. We present a model that combines information about the host material lattice, the concentration of active ions, and a microscopic rate equation system. The extent of energy migration is correctly taken into account because the energy transfer processes are described on the level of the individual ions. This model predicts the decay curves, concentration, and excitation power dependences of the emission. This detailed information can be used to predict the optimal concentration that results in the maximum upconverted emission.