Respiratory coinfections are commonly found in patients hospitalized with influenza-like illness, but it is not clear whether these infections are more severe than single infections. Mathematical models can be used to help understand the dynamics of respiratory viral coinfections and their impact on the severity of the illness. Most models of viral infections use ordinary differential equations (ODEs) which reproduce the average behavior of the infection, however, they might not be accurate in predicting certain events because of the stochastic nature of the viral replication cycle. Stochastic simulations of single virus infections have shown that there is an extinction probability that depends on the size of the initial viral inoculum and parameters that describe virus-cell interactions. Thus the coexistence of viruses predicted by the ODEs might be difficult to observe in reality. In this work we develop a stochastic numerical implementation of the deterministic coinfection model using the Gillespie algorithm. Stochastic extinction probabilities for each viruses are calculated analytically and will be verified by stochastic simulations. Preliminary analyses of the model have showed that even if the two viruses are given the same initial growth rates, one virus can have higher probability of extinction than the other, namely competitive exclusion, opposing the coexistence cases predicted by the deterministic model.

Star clusters are key chemical and age tracers of Milky Way evolution. The use of star clusters to provide significant constraints on galaxy evolution, however, has been limited due to discrepancies between different studies. This work seeks to add additional open clusters into an existing large, uniform chemical abundance system. We analyze spectra of giant stars in 31 open clusters and, using a machine learning method called The Cannon, determine iron abundances. This uniform analysis is compared with previous results, and we present new chemical abundances of 12 star clusters.

Nanoscale zinc oxide (ZnO) is an inexpensive, widely accessible material used in numerous well-established and emerging applications due to the unique optoelectronic, structural and chemical properties as well as the variety of synthesis methods. One of these emerging applications of ZnO nanostructures is in the field of antibacterial tools. The antibacterial nature of this material is being actively investigated, yet the mechanisms behind remain largely unknown. Some studies indicate that there is an influence of the polarity of exposed ZnO surfaces on their antibacterial action. Crystalline ZnO forms hexagonal prisms due to an anisotropic hexagonal lattice, which in turn produces three primary surface types: Zn-polar, O-polar and nonpolar. The hexagonal faces of these prism-shaped crystals are polar while the rectangular surfaces are nonpolar. In this study we employ a hydrothermal chemical method for growing ZnO nanocrystals having tunable morphology with the aim of obtaining a reliable control of the predominant polarity of the exposed nanocrystalline surfaces. This in turn can serve as a platform to investigate mechanisms of antibacterial action. Using Scanning Electron Microscopy as a probe of the microcystal morphology we demonstrate that the predominant ZnO surface polarity can be affected through the variations in the chemical precursors of the hydrothermal process. The ability to control the morphology and prominent surface polarity of ZnO nanocrystals would allow us to investigate fundamental phenomena governing antibacterial characteristics of nanoscale ZnO.

In vitro experiments are necessary to understand the processes driving viral infections and to develop antivirals and vaccines. However, experiments do not completely replicate the in vivo environment, and not all cell lines used in these experiments have the components necessary to support viral replication. In these cases, the missing elements are added to the medium to facilitate viral infections. Trypsin is an enzyme usually added to facilitate influenza infections in cell cultures. We use data from infections of influenza in different cell lines in the presence and absence of trypsin to parameterize a within-host mathematical model of influenza infection, and in this way understand the impact of trypsin in the dynamics of the infection.

Interaction between galaxies is of critical importance to the formation and evolution of galaxies. We are conducting a study on both isolated and interacting low-mass galaxies to determine how their environment impacts their star-formation ability. We compare the features of gas and stars in isolated and interacting galaxies to examine the differences and similarities. The interaction-triggered star-formation activity will be further discussed to analyse how the internal properties of galaxies are influenced by the outer environment. This investigation is based on data from the fourth-generation Sloan Digital Sky Survey (SDSS-IV) / Mapping Nearby Galaxies at Apache Point Observatory (MaNGA), and is part of the project No.0285 in SDSS-IV.