Respiratory syncytial virus (RSV) is an extremely common viral respiratory infection that currently has no vaccine or treatment. One of the issues in developing a treatment has been that immune system responses in both humans and rats vary in their susceptibility to RSV across different age groups. In this study, we use a mathematical model to quantify the viral kinetics of RSV and analyze its relationship to age. After fitting the model to experimental data, six parameter values were determined and used to calculate the eclipse phase length, infection phase length, basic reproductive number, and infecting time. These values were compared by age and collection site. After running several statistical tests, there was no major trend with the parameter values in relation to either age or collection site. This result provides the foundations for further studies to explore how viral models can better represent RSV and understand the immune response in general.

Sagittarius (Sgr), a dwarf galaxy and satellite to the Milky Way, is currently being tidally torn apart. To study the chemistry of
Sgr, we have taken thousands of stellar spectra across the galaxy. We have analyzed the stellar component of Sgr member
stars by using The Cannon, a machine learning algorithm for determining stellar parameters (temperature, surface gravity, chemical
abundances) from stellar spectra. A subset of our stars have previously been observed as part of SDSS/APOGEE survey, at higher
quality, which allows us to use these spectra to train The Cannon so that we can obtain accurate abundances for the ~1,100 Sgr
member stars. This will allow us to confidently study the formation history and stellar evolution of Sgr, and place it within the
context of other dwarf galaxies.

In order to determine correct dosage of chemotherapy drugs, the effect of the drug must be properly quantified. There are two important values that characterize the effect of the drug: Emax is the maximum possible effect from a drug, and IC50 is the drug concentration where the effect diminishes by half. Currently, the technique used to measure these quantities gives estimates of the values that depend on the time at which the measurement is made. We use mathematical modeling to test a new method for measuring Emax and IC50 that gives estimates independent of measurement time. We fit treatment data from the literature to determine values for Emax and IC50 using mathematical models under two assumptions: that the drug reduces growth rate, or maximum number of cells. Our method produced IC50 estimates similar to estimates derived using current techniques. This work is intended to characterize the efficacy of anticancer drug treatments and determine the correct doses before trying those in patients to get the most effective therapeutic treatment.

Since the invention of on optical microscope various biological structures have been observed. Today we have a need to study subcellular structures and their dynamics. Here we encounter diffraction limit – two objects located closer than the half of the wavelength cannot be resolved as two distinct objects. Superresolution techniques have been developed to overcome this limit. They can be divided into two types: stochastic and deterministic. Stochastic ones (STORM, PALM) utilize natural ability of fluorescent molecules to blink. These methods require sparse labeling and significant amount of some time to acquire image. Deterministic ones (STED) utilize an additional pulsed light source to de-excite populated state. These methods require advanced technology. Our method is similar to deterministic superresolution techniques. We utilize long-living fluorescent dyes whose excited state population can be significantly enhanced by bursts of pulses. Enhancement occurs only when time delay between pulses within burst is shorter than the lifetime of the dye. By varying bursts and single pulses one may observe varying intensity of a dye, hence, achieve superresolution. Regular labeling methods become an advantage in this case, and such an experimental setup is not very different from conventional microscopy methods.

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.