Browsing by Subject "Quantitative"
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Item Open Access Childhood Obesity, Development, and Self-Regulation in Girls: Three Essays(2013) Gearing, Maeve EThis dissertation encompasses three essays which examine the development of obesity in black and white girls and its responses to interventions.
The first chapter asks the question, how does obesity develop in girls? Using the National Growth and Health Study (NGHS), a longitudinal study of 2400 girls from age 9 to age 18, this chapter aims to address gaps in knowledge about the development and persistence of obesity in girls. Analyses using multivariate regression and growth-mixture modeling describe trajectories of body mass change in children and their correlates. Results suggest that obesity in children begins early and persists in most cases--BMI at age 17 is, on average, 1.3 times BMI at age 9. However, change does occur; 0.8 percent of the sample move from being obese at age 9 to healthy weight at age 17, and 2.2 percent of the sample make the reverse journey from healthy weight to obese. Where change occurs, it is most commonly seen among those who socio-demographically were anomalies among their body mass cohort at age 9. These results emphasize the importance of early interventions as well as the need for more study into body mass mutability in population subsamples.
The second chapter investigates 1) what motivates children to pursue weight loss; and 2) what aspects of interventions may most effectively support healthy child motivations and program success. These questions are qualitatively studied among a sample of 45 obese girls aged 9 to 13 girls participating in a behavior modification intervention. In total, 106 interviews were conducted. All of the girls in the study were interested in losing weight, most commonly in order to fit in (n=11), reduce teasing (n=10), or express particular social identities (n=6). However, not all of the girls were able to translate this desire to lose weight into a healthy and effective lifestyle change motivation. Several factors were associated with adopting healthy motivation and behavior, including familial involvement, self-regulation skills, non-social weight desires, realistic weight loss goals, and clear messages about body ideals. Other program protocols also supported motivation during difficult periods for those who adopted healthy motivation, including nutrition information, incentives, lack of physician judgment, and patient autonomy. Finally, two other potential program protocols were mentioned by girls in the study as useful aids. More support services, particularly during the summer, and more information on the expected course of weight loss could, these girls argued, help sustain motivation. Together, these findings suggest a role for self-regulation theory in the design of lifestyle change motivation and for more directly addressing expectations in weight loss treatment.
The third chapter investigates the relationship between self-worth and obesity among girls, again using the National Growth and Health Study. Results indicate a negative relationship between self-worth and obesity across all participants. However, this relationship only has predictive power from early body mass to later self-worth and self-worth trajectories. That is, higher body mass at age 9 predicts lower self-worth at age 17 and decreases in self-worth from age 9 to age 17. The effect is larger for Caucasians and for those in young adolescence but persists across the sample. Mechanisms for this relationship are also investigated, and some support found for stigma. Analyses using self-worth components suggest most of the self-worth effects are driven by social concerns, while mediational analyses suggest social body image pressures explain the relationship between global self-worth and body mass. Overall, the findings suggest a complex interrelation between self-worth and body mass in girls, meriting further investigation as well as a more nuanced discussion in the public realm.
Item Open Access Examining Wastewater Treatment Struggles in Lowndes County, AL(2018-04-27) Meza, EmilyMany poor, rural Americans currently live without access to basic wastewater treatment, raising sobering environmental justice and public health issues. Recent research found a 34.5% incident rate of N. americanus (hookworm) within vulnerable populations of Lowndes County, Alabama, a disease spread through contact with soils exposed to human fecal matter. Working with the Alabama Center for Rural Enterprise (ACRE), I seek to assess likely predictors of seeing raw sewage on the ground, as well as broadly define the scale and scope of the struggles with wastewater treatment faced by Lowndes County. My analysis relies on an EPA funded community survey conducted by ACRE and community volunteers in 2011-2012. Approximately 2,450 households (~ 56% of households countywide) were interviewed in person about sanitation conditions in their home and on their property. Four main types of wastewater disposal methods were identified—full sewer connection, settling tank connected to sewer, septic systems, and straight pipes (lack of any treatment). While 92% of the county reported being served by a municipal drinking water utility, only 21.8% were served by a sewer system. As expected, residents that used straight pipes to dispose of their wastewater were ~36 times more likely than residents connected to a full sewer to report raw sewage on the ground. Additionally, those whose septic or settling tanks were not operating properly were ~35 times more likely to see raw sewage. This includes residents served by Hayneville’s wastewater utility, as they use a hybrid lagoon system, with settling tanks on each property. Improving sanitation and reducing exposure to raw sewage in Lowndes County requires addressing both private household needs as well as the municipal utilities with failing infrastructure.Item Open Access The Effects of PET Reconstruction Parameters on Radiotherapy Response Assessment and an Investigation of SUV-peak Sampling Parameters(2013) Rankine, Leith JohnPurpose: Our primary goal was to examine the effect of PET image reconstruction parameters on baseline and early-treatment FDG-PET/CT quantitative imaging. Early-treatment changes in tumor metabolism in primary tumor and nodes can potentially determine if the patient is responding to therapy, but this assessment can change based on the reconstruction parameters. We investigated the effect of the following reconstruction parameters: number of Ordered-Subset-Expectation-Maximization (OSEM) iterations, post-reconstruction smoothing, and quantitative metrics (SUV-max, SUV-mean, SUV-peak).
A concurrent investigation explored in detail the sampling parameters of SUV-peak by way of a Monte Carlo digital phantom study. SUV-peak was proposed as a compromise between SUV-max and SUV-mean, in hope to retain key attractive features of these two metrics (inter-physician independence of SUV-max, noise-averaging of SUV-mean) but reduce unwanted errors (noise dependence of SUV-max, contour-dependence of SUV-mean). Sampling parameters have vaguely been defined, in particular, the scanning resolution (i.e. 1 voxel, 1/2 voxel, 1/4 voxel, etc.) of the SUV-peak spherical ROI . We examined the role that partial-voxel scanning plays in tumor SUV recovery in both noise-free and realistic OS-EM noise environments.
Materials and Methods: The response assessment investigation involved 19 patients on an IRB-approved study who underwent 2 baseline PET scans (mean-separation = 11 days) prior to chemoradiotherapy (70 Gy, 2 Gy/fraction). An intra-treatment PET scan was performed early in the course of therapy (10-20 Gy, mean = 14 Gy). The images were reconstructed with varying OS-EM iterations (1-12) and Gaussian post-smoothing (0-7 mm). Patients were analyzed in two separate groups, distinguished by the PET/CT scanner used to acquire data: (1) GE Discovery STE; and (2) Siemens Biograph mCT. For each combination of iterations and smoothing, Bland-Altman analysis was applied to quantitative metrics (SUV-max, SUV-mean, SUV-peak) from the baseline scans to evaluate metabolic variability (repeatability, R = 1.96&sigma). The number and extent of early treatment changes that were significant, i.e., exceeding repeatability, was assessed.
An original SUV-peak algorithm was developed, which measures SUV-max and SUV-peak for as small as 1/32 voxel scanning. Two rounds of digital phantoms were generated for the SUV-peak investigation. First, 10,000 spherical tumors were generated at a random matrix location for each diameter 1-4 cm and smoothed with an isotropic Gaussian, FWHM = 0.8 cm, then evaluated using the SUV-peak algorithm. Next, realistic body-sized phantoms were generated with background activity, and 1,000 spherical tumors of activity 4 time the background for each diameter (1-4cm) were placed inside (8 tumors per phantom, location randomized within certain constraints). These images received realistic corrections in projection space for attenuation, spatial resolution, and noise, were reconstructed with an in-house OS-EM algorithm, and then assessed using the SUV-peak algorithm. The mean recovered activity above background and its coefficient of variation were calculated for all metrics for each tumor size, for both simulations. For the realistic noise simulation, various levels of Gaussian smoothing was applied post-reconstruction, the effects summarized in plots showing coefficient of variation vs. mean recovered activity above background - a comparison of the effectiveness of SUV-max and SUV-peak.
Results: For the GE Discovery STE 2D cases averaged over all metrics (SUV-max, SUV-mean, SUV-peak) and structures (GTV, LN), repeatability, R, improved with increasing smoothing and decreasing iterations. Individually, SUV-mean repeatability was less affected by the number of iterations, but demonstrated the same relationship with smoothing. SUV-mean outperformed SUV-max and SUV-peak with regards to the number of cases exceeding repeatability, N. Considering R, N, and the sum of relative metric change outside repeatability, &Omega, averaged over all metrics and all structures, and normalized, several combinations of reconstruction parameters produced five optimal combinations above set thresholds: 1 iteration with 0.1-3.0 mm smoothing; and 2 iterations with 2.0-3.0 mm smoothing. Current GE 2D reconstruction protocol for HN cases uses 2 iterations and 3.0 mm post-smoothing, which lies on the edge, but within these recommendations.
The relationship between repeatability and number of iterations for the 3D cases was more complex; SUV-max demonstrated the best repeatability with 2 iterations, with both SUV-mean and SUV-peak reaching the best repeatability with 4 iterations. The same dependence on smoothing was noted, i.e. increased smoothing gives lover (desirable) repeatability. SUV-mean once again outperformed SUV-max and SUV-peak with regards to the number of cases exceeding repeatability, N. The calculations of N and &Omega averaged over all metrics were limited severely by the low number of cases, damaging the statistical significance of the following recommendation. Three optimal combinations with averaged and normalized R, N, &Omega, above a set threshold are recommended as most effective reconstruction parameter combinations: 4 iterations with 2.0-4.0 mm smoothing. Current Siemens 3D reconstruction protocol for HN cases uses 4 iterations and 3.0 mm post-smoothing, which lies within these recommended parameters. However, no statistically significant conclusions could be drawn from this analysis for this scanner, and performing similar data analysis on a larger patient pool is proposed.
The minimum spherical tumor diameter required for full recovery was 3.0-3.5 cm for SUV-peak, and 2.5-3.0 cm for SUV-max. SUV-max was found to overestimate the recovered value of tumors by up to 46% (vs. 10% for SUV-peak); above the minimum diameter for full recovery, SUV-peak values were significantly closer to actual tumor activity. Considering only the realistic noise tumors, the coefficient of variation for SUV-max ranged from 5.5-17.7%, whereas for SUV-peak these values were lower, 2.7-13.2%. Partial-voxel scanning did not substantially affect the coefficient of variation (<0.2%). Comparison of coefficient of variation vs. mean recovered value demonstrated that SUV-max with additional Gaussian smoothing outperforms SUV-peak by up to 0.8% for 1 cm tumors and 0.2% for 4 cm tumors. Other tumor sizes showed little difference between the two metrics.
Conclusion: For patients scanned on the GE Discovery STE using the HN protocol (2D acquisition mode), images reconstructed for quantitative analysis may benefit from a low number of OS-EM iterations (≤ 2). Some post-reconstruction smoothing proved to be beneficial (1.0 mm ≤ FWHM ≤ 3.0 mm), however, over-smoothing for the sake of more qualitatively appealing images or improved image quality metric (e.g. SNR, CNR) may prove detrimental to quantitative response assessment analysis. Our results for the Siemens Biograph mCT using the HN protocol (3D acquisition mode) demonstrated favor towards 4 iterations and limited range of smoothing (2.0 mm ≤ FWHM ≤ 4.0 mm). These results are statistically limited, further cases are necessary for any conclusive recommendations on reconstruction parameters.
SUV-peak was shown to reduce uncertainties associated with quantitative PET image analysis when compared directly to SUV-max. Above the minimum tumor diameter required for full recovery, SUV-peak also provides a better estimate of the actual tumor activity. However, initial comparisons of SUV-peak and SUV-max over various levels of additional Gaussian smoothing found SUV-max more favorable. Partial-voxel scanning of SUV-peak did not reduce the metric's coefficient of variation in images with realistic noise. Therefore, a phantom investigation is proposed to compare SUV-peak and SUV-max of real scanned images with various levels of post-smoothing, which may conclusively eliminate the need for SUV-peak.
Item Open Access Utilizing Natural Variation and De Novo Mutation to Understand Cryptococcus Evolution(2022) Sauters, Thomas John CThe evolution of pathogenesis, in many cases, is a story of competition between host and microbe; however, many opportunistic pathogens are primarily found in niches other than the host environment. Such pathogens frequently lack host-to-host transmission, and there may be limited opportunities for an infectious population to be re-dispersed back into the environment. Observations such as these motivate the hypothesis that the evolution of virulence traits in opportunistic pathogens may be primarily driven by environmental selective pressures, rather than the host-environment per se.
For Cryptococcus the ability to survive interactions with macrophages and the ability to grow at host body temperatures are indispensable to its pathogenic capabilities. The work presented here aims to dissect the genetic underpinnings of these virulence traits using the abundant natural variation of Cryptococcus and using the accumulation de novo mutations associated with growth under relevant stressors.
An important aspect of the hypotheses surrounding Cryptococcus evolution is the predator-prey interactions it has with free-living amoeba. Amoebae are able to consume Cryptococcus cells in a manner similar to how macrophages phagocytose and digest infectious cells. This similarity is the basis of the “Amoeboid Predator-Fungal Animal Virulence Hypothesis” which posits that amoeba act as training grounds for environmental fungal pathogens and thus inadvertently select for resistance to immune phagocytes. I tested this hypothesis by using QTL mapping to identify genes and alleles that are involved in amoebae resistance in both C. neoformans and C. deneoformans. I identified QTL that contribute to amoeba resistance, and discovered that the largest effect QTL in both species localize to homologous regions of the genome, suggesting a shared mechanism of amoeba resistance. In C. neoformans, this QTL also contributes to variation in melanization. I identified a causal variant for this QTL, a non-coding deletion upstream of a transcription factor, BZP4. Contrary to the predictions of the Amoeboid Predator-Fungal Animal Virulence Hypothesis, I did not find an association between the ability to survive amoeba predation and virulence in either in vitro or in vivo models of infection. These findings suggest a re-evaluation of the amoeba predation model for the evolution of pathogenesis, suggesting that factors other than amoeba may provide the significant selective pressures that underlie virulence ability.
I extended my quantitative analyses of Cryptococcus to two important factors involved in both environmental and disease contexts: thermal and low pH tolerance. In doing so, I discovered multiple pleiotropic QTL involved in general growth that also dictate stress tolerance in both high temperature and low pH environments. By fitting growth data to a Gompertz growth model and QTL mapping based on the parameters of this model, I discovered a novel QTL that effects lag, the time it takes for a population of cells to begin growing at an exponential rate. This lag QTL is pleiotropic across growth conditions. I identified a candidate allele for the lag QTL, a 9-bp deletion in CNAG_01111, a gene that has been found to impact growth initiation in other species of fungi.
Finally, taking a complimentary approach to understanding the role of genes in environmental survival, I experimentally evolved a C. neoformans strain in conditions of thermal stress and fludioxonil stress. I discovered that strains evolved at high temperatures lose tolerance to fludioxonil and strains evolved in fludioxonil lose temperature tolerance. Furthermore, the loss of fludioxonil tolerance in the high temperature evolved strains can be partially rescued by growing them on media containing fludioxonil. This rescue results in a proportional loss of thermal tolerance. Studying the genomic changes behind the evolved phenotypes I discovered multiple large scale deletions and one multi-gene duplication associated with fludioxonil resistance and a single multi-gene deletion associated with thermal tolerance. There are also a variety of small scale mutations associated with each evolved condition, including mutations of genes in the HOG and ergosterol pathway that are responsible for fludioxonil resistance. Mutations in uncharacterized multidrug transporters are frequently associated with fludioxonil resistance, suggesting that the evolved strains might also have altered resistance to other antifungals. These findings highlight the polygenic and pleiotropic genetic architecture of adaptation in C. neoformans on an ever warming planet with increased use of agricultural antifungals. The trade-offs found may represent a good sign for the use of phenylpyrroles as an agricultural antifungal.
Collectively, my work sheds light on genes and alleles involved in environmental survival while also making important connections back to human disease. It also exhibits the importance of utilizing the natural variation of fungal pathogens to study the evolutionary hypothesis surrounding virulence traits. The studies reported here also provide significant groundwork for many new insights into virulence genes and the origins of Cryptococcus pathogenicity.