We are modeling the effect of the Hill coefficient on the volume of a tumor. This is to test drugs that may bind to multiple receptors and compare them to each other. We are using Python and used 4 main parameters and one equation. We modeled the Volume and the Dose Response Curves as well as the Emax and Ic50. We used the different positive Hill Coefficients and studied the effect on dose and carrying capacity.

Non-invasive temperature sensing is necessary for the analysis of biological processes occurring in the human body including cellular enzyme activity, protein expression, and ion regulation. Considering that a variety of such biological processes occur at the microscopic scale, a novel mechanism allowing for the detection of the temperature changes in microscopic environments is desired. One-dimensional graphene quantum dots can serve as agents for such detection: they are promising non-invasive probes that because of their 2-5 nm size and optical sensitivity to temperature change enable sub-cellular resolution imaging. Both biocompatible bottom-up synthesized nitrogen-doped graphene quantum dots and quantum dots produced from reduced graphene oxide via top-down approach exhibit temperature-induced fluorescence variations. This response observed for the first time is utilized for deterministic temperature sensing in bulk suspension as well as inside mammalian cells. Distinctive quenching of quantum dot fluorescence by up to 19.8 % is observed, in a temperature range from 25℃ to 49℃, in aqueous solution, while the intensity is restored to the original values as the temperature decreases back to 25℃. A similar trend is observed in vitro in HeLa cells as the cellular temperature is increased from 25℃ to 41℃. Our findings suggest that the temperature-dependent fluorescence quenching of bottom-up and top-down-synthesized graphene quantum dots can serve as non-invasive reversible deterministic mechanism for temperature sensing in microscopic sub-cellular biological environments.

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: ε_max is the maximum possible effect from a drug, and IC_50 is the drug concentration where the effect diminishes by half. We use mathematical models to estimate how the values depend on measurement time and model choice. Improper choice of growth model is problematic and can lead to differences in predictions of treatment outcomes for patients. This work intends to understand how choice of model and measurement time affects the relative drug effect and causes the differences in predictions for the most effective dose of anticancer drug for a patient. This work determines the correct doses before trying those in patients to get the most effective therapeutic treatment.

New anti-cancer drugs are constantly being developed and tested. Effectiveness of these drugs is currently assessed by measuring the reduction in number of cancer cells cultured in experiments as a function of the applied drug dose. These measurements determine the drug dose needed to achieve half of the maximum reduction in cells (IC50) and the maximum effect of the drug (εmax). However, the technique that measures values of IC50 and εmax depends on the time chosen to make the measurements. We have developed a method to analyze the growth of cancer cells in different concentrations of drugs that will provide estimates of both parameters that are independent of measurement time. Here, we computationally simulated the growth of cancer cells according to a logarithmic model, adding different levels of noise. And, we found the error in IC50 and εmax as a function of the level of noise. Development of this new technique will lead to more consistent measurement of the efficacy of known and novel anti-cancer therapies.

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.

Antimicrobial action of micro- and nanoscale ZnO particles has been documented, but the fundamental physical mechanisms driving this action are still not identified . We hypothesize that one of the key mechanisms behind the antibacterial action of ZnO is rooted in interactions between ZnO surfaces and extracellular material. Crystalline structure of ZnO results in two distinct types of crystallographic surfaces: polar (charged) and non-polar (neutral). The excess charge and electronic states at the polar surfaces of micro- and nano-scale ZnO particles may affect interfacial phenomena with surrounding media. Therefore, it is feasible that the relative abundance of such polar surfaces could significantly influence their antibacterial action. In this study we use a hydrothermal growth method established in our lab to synthesize ZnO crystals with different controllable surface morphologies. We study the effects of relative abundance of polar surfaces on antibacterial action. These experiments performed in conjunction with optoelectronic studies of ZnO crystals yield information regarding the fundamental nature of their antibacterial action.

Polysulfone is a stable and strong semitransparent thermoplastic material that is applicable in many industries due to its resistance to low and high temperatures, as well as unique hydrophobic properties. Hydrophobic films are frequently used in waterproofing devices and to improve the efficiency of water vessels. It was recently discovered that polysulfone has a unique behavior as it changes from being hydrophobic to hydrophilic after exposure to a UV radiation. In order to elucidate the mechanisms behind this phenomenon we are performing surface photovoltage (SPV) studies on polysulfone thin films, which is done for the first time, to the best of our knowledge. Whereas SPV is sensitive to buried interfaces, SPV spectral features contain contributions not only from the polysulfone films, but from the silicon wafer and the silicon oxide layer beneath the polymer films. Thereby, to identify the signal germane to the polysulfone properly, we employ in our studies polysulfone films of varying and controllable thicknesses. To establish controllable methods for producing such films by spin coating, we use different concentrations of polysulfone in solutions with different spin rates. Film thickness is determined employing a thin film analyzer. From these thicknesses, trends are established relating film thickness to solution concentration and spin rate. SPV studies provide initial investigations into surface electronic transitions and mechanisms behind the hydrophobic ‘flipping’ of polysulfone.

High-power laser excitation systems are critical in observing and studying nanomaterials and their optoelectronic properties on a single specie level. These systems enable inducing fluorescence and observing emission microscopically from individual flakes and or molecules. As the fluorescence of nanomaterials is often excitation dependent, multiple laser with different frequencies are needed to probe their optical properties. In this work we construct such multi-laser setup to use for a microscopy system to enable imaging nanocarbons: flakes of functional derivatives of graphene, carbon nanotubes, and graphene quantum dots.
The system is composed of four lasers of varying wavelength: blue at 450 nm, green at 532 nm, red at 637 nm, and near-infrared (NIR) at 808 nm. An additional near-infrared laser at 980 nm is included for special applications with deep NIR imaging. These lasers were set up to be turned on and off remotely and traverse through a system of dichroic and regular mirrors and a periscope coupled to a fluorescence microscope. A neutral density filter wheel designed and set up in the light path enables altering the intensity of the lasers leading to optimized fluorescence and imaging. The resulting laser set up allowed effective imaging of graphene oxide flakes, graphene quantum dots, and carbon nanotubes both on a microscope slide and in biological cells and tissues.

The goal of this project was to engineer complexes of antibiotics and nanomaterials that address gram negative bacteria more efficiently than antibiotics alone. The gram-negative class of bacteria has two cell membranes, as opposed to the gram-positive class which has only one; this second membrane poses an additional challenge for antibiotic cell entry. Theoretically, the amphiphilic nanomaterials may aid the antibiotics by assisting them through both membranes and masking their entry. A number of nanomaterials were tested including graphene quantum dots, single-walled carbon nanotubes, and graphene oxide, and antibiotics including Penicillin, Methicillin, Amoxicillin, Norfloxacin and Linezolid were tested as well. Carbon nanotubes were supplemented with polyethylene-glycol coating agent, while water-soluble GQDs and graphene oxide were used as synthesized in our laboratory. The complex of the antibiotic Norfloxacin and Graphene Quantum Dots (GQDs) was selected as the most efficacious. It allowed killing of the gram-negative bacteria E. Coli at moderate concentrations significantly more efficiently than unaccompanied Norfloxacin. Its colocalization with bacteria was verified via high quantum yield (over 62%) intrinsic fluorescence of GQDs in the visible. This may lead to substantial improvement of antibacterial techniques against gram negative bacteria, increase in antibiotic efficacy, and potentially the recycling of antibiotics to which bacteria exhibit resistance.

The interaction between low-mass galaxies are of critical importance for the growth and evolution of galaxies. The star formation can be enhanced during interactions between massive galaxies, but very few studies focus on the interaction between low-mass galaxies. In this work, we explored the current star-formation surface density in both isolated and interacting galaxies and look for enhanced star formation during the interactions. A galaxy will be considered as a galaxy pair candidate if the physical separation between it and its closest low-mass galaxies is smaller than 5000 light years, otherwise it will be put into the isolate galaxy sample. This sample intentionally excludes galaxies with a massive galaxy neighbor nearby as massive neighbors can harass low-mass companion galaxies and can cause them to become quenched. This project is the first attempt to systematically study how the internal star-formation activities of low-mass galaxies are influenced by outer environment.

Large galaxies are made up of smaller satellite galaxies. This makes these satellite galaxies crucial to understanding how stars form. Shallow gravity wells make them extremely sensitive to internal and external disturbances. Therefore, they are excellent laboratories to explore stellar physics. We use multi-body simulations of a Milky Way-like galaxy to explore the stellar properties of satellite galaxies surrounding a possible Large Magellanic Cloud (LMC). LMC is the largest satellite galaxy of the Milky Way. We compare the resulting properties such as chemical composition, light, radial distribution to observations from McConnachie et al. 2012.

In this experiment we take the differential equation model from Heldt 2012 for the viral life cycle and apply a stochastic algorithm in order to simulate random events on a molecular level. We then introduce a known mechanism by which to mutate the produced virus particles and attempt to understand the relationship between surface proteins and these random mutations. This work will shed light on the efficacy of particular antiviral drugs that act on the binding of surface proteins to the cell membrane.

Nearby, the Large Magellanic Cloud galaxy (LMC), has ejected massive amounts of gaseous material, some of which is headed toward the Milky Way. The material consists of ionized hydrogen gas which is a consequence of significantly energetic events that have occurred in the LMC. Such events are not only the cause of the ionized material, but also the immense amount of material being thrown out. This ejected wind holds a substantial amount of information regarding both galaxies in general and the LMC’s physical processes. Studying this ionized outflow will reveal new details concerning the internal processes that produce such massive ejections, the potential for galactic outflows to replenish gas reservoirs for future star formation, and the environments surrounding galaxies. The latter will influence our view of a galaxy’s environment and how it may interact with nearby neighbors such as our Milky Way galaxy.

Fluorescence anisotropy is a common measurement that helps provides important information on molecular mobility, solvent (environment) viscosity, or/and molecular size. Fluorescence anisotropy involves measurement of two orthogonally polarized light emission intensities. One of the common issues of fluorescence anisotropy measurements is that most optical detection systems respond differently to the parallel and perpendicular polarization of light. The challenging task is to estimate the calibration curve, often called as the instrumental G-factor (grating factor), a parameter indicates the contributions and/or distortion of the optical detection system to the parallel and/or perpendicular light polarization, so that one can correct their polarized emission intensity and obtain a proper fluorescence anisotropy result. Here we present novel techniques that we have been developed in our laboratory that help achieve the G-Factor curves for different instruments.

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.

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.

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.

Galaxies are not alone in space; often, they have neighboring galaxies with which they gravitationally interact. These interactions foster diverse characteristics, such as size, morphology, and color. This project studies the properties of galaxies in the context of their neighbors and environment. More specifically, I examine how the proximity between galaxies affects their evolution. I do this by exploring two samples: 1) galaxy pairs within a few galactic diameters of each other and 2) isolated galaxies separated from the next nearest galaxy by more than ~450,000 light years. Using existing Mapping Nearby Galaxies at Apache Point Observatory observations, part of the Sloan Digital Sky Survey IV, I determine the various types of ionization conditions present at different radii throughout each galaxy. Through these efforts, I explore which processes promote and hinder star formation within galaxies.

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.