Matching Items (7)
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- All Subjects: MRI
- All Subjects: Knowledge
- Creators: Kostelich, Eric
- Creators: Brewer, Gene A
- Resource Type: Text
Description
The increase in obesity since the 1980's has been associated with fast-food consumption. In hopes that calorie labeling will be an effective tool to combat obesity, congress included a provision in the Patient Protection and Affordable Care Act of 2010 (ACA) that will require all restaurants with twenty or more locations to post calorie information for each menu item. Current research has provided mixed results regarding the effectiveness of calorie labeling, but overall seems to suggest that calorie labeling may only be effective among certain populations. In September, 2012 McDonald's began to post calorie labels on their menu boards before it was federally mandated under the ACA. This policy provided the opportunity to study the impact of calorie labeling on the purchasing behavior of McDonald's patrons. This cross-sectional study was designed to determine if self-perception of diet, self-perception of health, smoking, physical activity, fruit and vegetable intake, or knowledge of daily calorie requirements is associated with the likelihood of noticing or using calorie labels, or total calories purchased. In addition, relationships between noticing or using calorie labels with total calories purchased were also examined. Receipts and survey responses were collected from 330 participants who purchased food and beverage items from 27 different McDonald's locations within a 20 mile radius of downtown Phoenix, Arizona. Results indicated that only 16.1% of the sample reported using calorie labels, and those who reported using calorie labels purchased an average of 136 fewer calories. Multivariate analysis indicated there were no statistically significant relationships between self-perception of diet, self-perception of health, smoking, physical activity, fruit and vegetable intake, or knowledge of daily calorie requirements with the likelihood of noticing or using calorie labels, or total calories purchased. However, it is possible that the small sample size of participants using calorie labeling precluded any statistically significant relationships among these variables from emerging. Further research with larger sample sizes should be conducted, to investigate individual level factors that may be associated with use of calorie labeling.
ContributorsBrown, Alan (Author) / Ohri-Vachaspati, Punam (Thesis advisor) / Bruening, Meredith (Committee member) / Wharton, Christopher (Christopher Mack), 1977- (Committee member) / Arizona State University (Publisher)
Created2013
Description
Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and includes chemotherapy, radiation therapy, and surgical removal if the tumor is surgically accessible. Treatment seldom results in a significant increase in longevity, partly due to the lack of precise information regarding tumor size and location. This lack of information arises from the physical limitations of MR and CT imaging coupled with the diffusive nature of glioblastoma tumors. GBM tumor cells can migrate far beyond the visible boundaries of the tumor and will result in a recurring tumor if not killed or removed. Since medical images are the only readily available information about the tumor, we aim to improve mathematical models of tumor growth to better estimate the missing information. Particularly, we investigate the effect of random variation in tumor cell behavior (anisotropy) using stochastic parameterizations of an established proliferation-diffusion model of tumor growth. To evaluate the performance of our mathematical model, we use MR images from an animal model consisting of Murine GL261 tumors implanted in immunocompetent mice, which provides consistency in tumor initiation and location, immune response, genetic variation, and treatment. Compared to non-stochastic simulations, stochastic simulations showed improved volume accuracy when proliferation variability was high, but diffusion variability was found to only marginally affect tumor volume estimates. Neither proliferation nor diffusion variability significantly affected the spatial distribution accuracy of the simulations. While certain cases of stochastic parameterizations improved volume accuracy, they failed to significantly improve simulation accuracy overall. Both the non-stochastic and stochastic simulations failed to achieve over 75% spatial distribution accuracy, suggesting that the underlying structure of the model fails to capture one or more biological processes that affect tumor growth. Two biological features that are candidates for further investigation are angiogenesis and anisotropy resulting from differences between white and gray matter. Time-dependent proliferation and diffusion terms could be introduced to model angiogenesis, and diffusion weighed imaging (DTI) could be used to differentiate between white and gray matter, which might allow for improved estimates brain anisotropy.
ContributorsAnderies, Barrett James (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Stepien, Tracy (Committee member) / Harrington Bioengineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Collaborative learning is a potential technique for teachers to use to meet the diverse learning needs of the students in their classrooms. Previous studies have investigated the contexts in which the benefits of collaborative learning show greater presence. The most important factor found was the quality of the interactions. Studies have suggested that high achieving students are capable of improving the quality of interactions. This bears the question if prior knowledge plays an influence in the learning outcome of students in collaborative learning. Results show that high prior knowledge students do not face a detriment in having low prior knowledge students as a partner comparing to having another high prior knowledge student and that low prior knowledge students show significantly higher learning outcome when partnered with a high prior knowledge partner than with another low prior knowledge student. It is therefore likely that having a high prior knowledge student within a dyad improves the quality of interaction, resulting in greater learning outcome through collaborative learning.
ContributorsKeyvani, Kewmars (Author) / Chi, Michelene (Thesis director) / Wylie, Ruth (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / School of Life Sciences (Contributor)
Created2014-05
Description
Glioblastoma Multiforme (GBM) is an aggressive and deadly form of brain cancer with a median survival time of about a year with treatment. Due to the aggressive nature of these tumors and the tendency of gliomas to follow white matter tracks in the brain, each tumor mass has a unique growth pattern. Consequently it is difficult for neurosurgeons to anticipate where the tumor will spread in the brain, making treatment planning difficult. Archival patient data including MRI scans depicting the progress of tumors have been helpful in developing a model to predict Glioblastoma proliferation, but limited scans per patient make the tumor growth rate difficult to determine. Furthermore, patient treatment between scan points can significantly compound the challenge of accurately predicting the tumor growth. A partnership with Barrow Neurological Institute has allowed murine studies to be conducted in order to closely observe tumor growth and potentially improve the current model to more closely resemble intermittent stages of GBM growth without treatment effects.
ContributorsSnyder, Lena Haley (Author) / Kostelich, Eric (Thesis director) / Frakes, David (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
Description
This dissertation develops a second order accurate approximation to the magnetic resonance (MR) signal model used in the PARSE (Parameter Assessment by Retrieval from Single Encoding) method to recover information about the reciprocal of the spin-spin relaxation time function (R2*) and frequency offset function (w) in addition to the typical steady-state transverse magnetization (M) from single-shot magnetic resonance imaging (MRI) scans. Sparse regularization on an approximation to the edge map is used to solve the associated inverse problem. Several studies are carried out for both one- and two-dimensional test problems, including comparisons to the first order approximation method, as well as the first order approximation method with joint sparsity across multiple time windows enforced. The second order accurate model provides increased accuracy while reducing the amount of data required to reconstruct an image when compared to piecewise constant in time models. A key component of the proposed technique is the use of fast transforms for the forward evaluation. It is determined that the second order model is capable of providing accurate single-shot MRI reconstructions, but requires an adequate coverage of k-space to do so. Alternative data sampling schemes are investigated in an attempt to improve reconstruction with single-shot data, as current trajectories do not provide ideal k-space coverage for the proposed method.
ContributorsJesse, Aaron Mitchel (Author) / Platte, Rodrigo (Thesis advisor) / Gelb, Anne (Committee member) / Kostelich, Eric (Committee member) / Mittelmann, Hans (Committee member) / Moustaoui, Mohamed (Committee member) / Arizona State University (Publisher)
Created2019
Description
Capacity limits of the human nervous system require important or rewarding information to be prioritized and encoded over less important or rewarding information. The present dissertation aims to identify structural and functional neural correlates of reward-motivated memory encoding. Chapter 1 reviews studies of reward-motivated memory encoding and their neural correlates, as well as the structure and function of dopaminergic midbrain circuits. Chapter 2 presents a study that utilizes electroencephalography (EEG) to determine which of two hypothesized processes underly the influence of reward value on episodic memory. One hypothesis is that value engages prefrontal executive control processes, so that valuable stimuli engage an elaborative rehearsal strategy that benefits memory. A second hypothesis is that value acts through the reward-related midbrain dopamine system to modulate synaptic plasticity in hippocampal and cortical efferents, thereby benefiting memory encoding. The results revealed that EEG signals thought to index dopamine-driven attention allocation were modulated by reward value and were positively correlated with individual differences in behavioral measures of memory prioritization. Chapter 3 employs diffusion-weighted magnetic resonance imaging (MRI) to dissociate heterogenous functional circuits of the midbrain reward system. The results comport with primate histology and show that midbrain circuits are differentially predictive of impulsivity and of attention-deficit hyperactivity disorder (ADHD). Chapter 4 presents a study that also employs diffusion-weighted MRI. The findings replicate Chapter 3 in dissociating heterogenous functional circuits of the midbrain reward system. Additionally, the structural integrity of midbrain-hippocampus circuits was quantified. Structural integrity of these circuits was positively correlated to behavioral measures of memory prioritization. These findings suggest that structural and functional measures of the dopaminergic reward system may underlie reward-motivated memory encoding in humans.
ContributorsElliott, Blake Louis (Author) / Brewer, Gene A (Thesis advisor) / McClure, Samuel M (Committee member) / Sanabria, Federico (Committee member) / Bae, Gi-Yeul (Committee member) / Arizona State University (Publisher)
Created2021
Description
Individuals encounter problems daily wherein varying numbers of constraints require delimitation of memory to target goal-satisfying information. Multiply-constrained problems, such as compound remote associates, are commonly used to study this type of problem solving. Since their development, multiply-constrained problems have been theoretically and empirically related to creative thinking, analytical problem solving, insight problem solving, intelligence, and a multitude of other cognitive abilities. Critically, in order to correctly solve a multiply-constrained problem the solver must have the solution available in memory and be able to target and access to that information. Experiment 1 determined that the cue – target relationship affects the likelihood that a problem is solved. Moreover, Experiment 2 identified that the association between cues and targets predicted inter- & intra-individual differences in multiply-constrained problem solving. Lastly, Experiment 3 found monetary incentives failed to improve problem solving performance likely due to knowledge serving as a limiting factor on performance. Additionally, problem solvers were shown to be able to reliably assess the likelihood they would solve a problem. Taken together all three studies demonstrated the importance of knowledge & knowledge structures on problem solving performance.
ContributorsEllis, Derek (Author) / Brewer, Gene A (Thesis advisor) / Homa, Donald (Committee member) / Blais, Chris (Committee member) / Goldinger, Stephen (Committee member) / Arizona State University (Publisher)
Created2021