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- All Subjects: Cancer
- Creators: School of Mathematical and Statistical Sciences
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Status: Published
Gardner and Dr. Jones to model the surface brightness of astrophysical jets. We attempt to accomplish this goal by modeling the astrophysical jet HH30 in the spectral emission lines [SII] 6716Å, [OI] 6300Å, and [NII] 6583Å. In order to do so, we used the jet model to simulate the temperature and density of the jet to match observational data by Hartigan and Morse (2007). From these results, we derived the emissivities in these emission lines using Cloudy by Ferland et al. (2013). Then we used the emissivities to determine the surface brightness of the jet in these lines. We found that the simulated surface brightness agreed with the observational surface brightness and we conclude that the model could successfully be extended to model the surface brightness of a jet.
Over time, tumor treatment resistance inadvertently develops when androgen de-privation therapy (ADT) is applied to metastasized prostate cancer (PCa). To combat tumor resistance, while reducing the harsh side effects of hormone therapy, the clinician may opt to cyclically alternates the patient’s treatment on and off. This method,known as intermittent ADT, is an alternative to continuous ADT that improves the patient’s quality of life while testosterone levels recover between cycles. In this paper,we explore the response of intermittent ADT to metastasized prostate cancer by employing a previously clinical data validated mathematical model to new clinical data from patients undergoing Abiraterone therapy. This cell quota model, a system of ordinary differential equations constructed using Droop’s nutrient limiting theory, assumes the tumor comprises of castration-sensitive (CS) and castration-resistant (CR)cancer sub-populations. The two sub-populations rely on varying levels of intracellular androgen for growth, death and transformation. Due to the complexity of the model,we carry out sensitivity analyses to study the effect of certain parameters on their outputs, and to increase the identifiability of each patient’s unique parameter set. The model’s forecasting results show consistent accuracy for patients with sufficient data,which means the model could give useful information in practice, especially to decide whether an additional round of treatment would be effective.
Stellar mass loss has a high impact on the overall evolution of a star. The amount<br/>of mass lost during a star’s lifetime dictates which remnant will be left behind and how<br/>the circumstellar environment will be affected. Several rates of mass loss have been<br/>proposed for use in stellar evolution codes, yielding discrepant results from codes using<br/>different rates. In this paper, I compare the effect of varying the mass loss rate in the<br/>stellar evolution code TYCHO on the initial-final mass relation. I computed four sets of<br/>models with varying mass loss rates and metallicities. Due to a large number of models<br/>reaching the luminous blue variable stage, only the two lower metallicity groups were<br/>considered. Their mass loss was analyzed using Python. Luminosity, temperature, and<br/>radius were also compared. The initial-final mass relation plots showed that in the 1/10<br/>solar metallicity case, reducing the mass loss rate tended to increase the dependence of final mass on initial mass. The limited nature of these results implies a need for further study into the effects of using different mass loss rates in the code TYCHO.
Cancer rates vary between people, between cultures, and between tissue types, driven by clinically relevant distinctions in the risk factors that lead to different cancer types. Despite the importance of cancer location in human health, little is known about tissue-specific cancers in non-human animals. We can gain significant insight into how evolutionary history has shaped mechanisms of cancer suppression by examining how life history traits impact cancer susceptibility across species. Here, we perform multi-level analysis to test how species-level life history strategies are associated with differences in neoplasia prevalence, and apply this to mammary neoplasia within mammals. We propose that the same patterns of cancer prevalence that have been reported across species will be maintained at the tissue-specific level. We used a combination of factor analysis and phylogenetic regression on 13 life history traits across 90 mammalian species to determine the correlation between a life history trait and how it relates to mammary neoplasia prevalence. The factor analysis presented ways to calculate quantifiable underlying factors that contribute to covariance of entangled life history variables. A greater risk of mammary neoplasia was found to be correlated most significantly with shorter gestation length. With this analysis, a framework is provided for how different life history modalities can influence cancer vulnerability. Additionally, statistical methods developed for this project present a framework for future comparative oncology studies and have the potential for many diverse applications.