Despite living inside the Milky Way, we do not know well basic quantities such as its detailed chemical makeup at the level needed to fundamentally tie the Milky Way to studies of evolution in other galaxies. One key observable is the radial chemical abundance gradient. Open star clusters provide an age datable sample by which to measure this gradient. This measurement has previously been made using a diverse and regularly conflicting compilation of clusters from various literature studies. We present the first measurement using a large (462 stars in 28 open clusters), uniform sample of open clusters abundances. Our measurements show a general agreement with recent studies of the overall metallicity gradient, with a measured ∆ [Fe/H]/∆ RGC of -0.050 ± 0.004 dex/kpc. We also explore trends with distance from the galactic plane and cluster age, and finally investigate the existence of a "knee" in the overall abundance gradient, between 12-14 kpc, within the range suggested by previous work. We show strong evidence for this phenomenon.

Since a number of medical conditions require simultaneous treatment and diagnostics, the field of molecular therapeutics has recently turned to multifunctional approaches allowing for both therapy and biomedical imaging. A number of such molecular and nanoformulations are combined with fluorophores that allow for imaging of the delivery pathways of the drug in the visible. This is optimal for in-vitro or ex-vivo work, however, cannot be utilized well in-vivo. Thus, there is a need in nanoformulations optimized for both in-vitro and in-vivo studies. Graphene quantum dots, possessing intrinsic stable fluorescence in the visible and near-IR stand out as candidates for such complex application.

In this work, we for the first time produce biocompatible graphene quantum dots (GQDs) that exhibit multi-color emission both in visible and NIR possess a capability for biological pH sensing. These GQDs show the crystalline graphitic structure in TEM and average sizes of c.a. 5 nm beneficial for cellular internalization. They show no cytotoxicity even at high doses of 1 mg/mL that are used for imaging. As opposed to related structures such as graphene oxide and other graphene derivatives GQDs show high quantum yield in green (~500 nm) of ~50%. Near-IR emission at ~860 nm is located in the water window with reduced absorption and lower autofluorescence backgrounds providing a promising potential route for in-vivo studies. Emission of GQDs also depends on pH of the surrounding medium. The change in pH of as-prepared GQDs from 2.70 to 8.0 yields an increase of fluorescence intensity up to ~60%. Additionally, pH-dependent shifts of the spectral features allow differentiating between acidic cancerous and neutral healthy exocellular environments allowing to use GQDs for cancer detection. Therefore, our results indicate that GQDs have a significant potential in bio-applications because of their capacity for multi-color green/near-IR imaging for in-vitro/in-vivo studies, pH sensitivity, water solubility, low cytotoxicity and high capacity for cellular internalization.

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.

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.