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Investigation of Sniffing as a Viable Measure of Odor Habituation in Mice

Description

Mammalian olfaction relies on active sniffing, which both shapes and is shaped by olfactory stimuli. Habituation to repeated exposure of an olfactory stimuli is believed to be mediated by decreased sniffing; however, this decrease may be reserved by exposure to novel odorants. Because of this, it may be possible to use sniffing itself as a measure of novelty, and thus as a measure of odorant similarity. Thus, I investigated the use of sniffing to measure habituation, cross-habituation, and odorant similarity. During habituation experiments, increases in sniff rate seen in response to odorant presentation decreased in magnitude between the first and second presentations, suggesting of habituation. Some of this reduction in sniff rate increases was revered by the presentation of a novel odorant in cross-habituations. However the effect sizes in cross-habituation experiments were low, and the variability high, forestalling the conclusion that sniffing accurately measured cross-habituation. I discuss improvements to the experimental protocol that may allow for cross-habituation to be more accurately measured using sniffing alone in future experiments.

ContributorsVigayavel, Nirmal (Author) / Smith, Brian (Thesis director) / Sanabria, Federico (Committee member) / Gerkin, Rick (Committee member) / Barrett, The Honors College (Contributor)

Created2015-12

Accurate State Estimation From Uncertain Data and Models: An Application of Data Assimilation to Mathematical Models of Human Brain Tumors

Description

Background:
Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor.

Results:
We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise.

Conclusions:
The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling.

ContributorsKostelich, Eric (Author) / Kuang, Yang (Author) / McDaniel, Joshua (Author) / Moore, Nina Z. (Author) / Martirosyan, Nikolay L. (Author) / Preul, Mark C. (Author) / College of Liberal Arts and Sciences (Contributor)

Created2011-12-21

Modeling Brain Tumors: Simulating individual Patient Cases of Glioblastoma Multiforme

Description

Glioblastoma multiforme (GBMs) is the most prevalent brain tumor type and causes approximately 40% of all non-metastic primary tumors in adult patients [1]. GBMs are malignant, grade-4 brain tumors, the most aggressive classication as established by the World Health Organization and are marked by their low survival rate; the median survival time is only twelve months from initial diagnosis: Patients who live more than three years are considered long-term survivors [2]. GBMs are highly invasive and their diffusive growth pattern makes it impossible to remove the tumors by surgery alone [3]. The purpose of this paper is to use individual patient data to parameterize a model of GBMs that allows for data on tumor growth and development to be captured on a clinically relevant time scale. Such an endeavor is the rst step to a clinically applicable predictions of GBMs. Previous research has yielded models that adequately represent the development of GBMs, but they have not attempted to follow specic patient cases through the entire tumor process. Using the model utilized by Kostelich et al. [4], I will attempt to redress this deciency. In doing so, I will improve upon a family of models that can be used to approximate the time of development and/or structure evolution in GBMs. The eventual goal is to incorporate Magnetic Resonance Imaging (MRI) data into a parameterized model of GBMs in such a way that it can be used clinically to predict tumor growth and behavior. Furthermore, I hope to come to a denitive conclusion as to the accuracy of the Koteslich et al. model throughout the development of GBMs tumors.

ContributorsManning, Miles (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Preul, Mark (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)

Created2012-12