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We used sex, observed parenting quality at 18 months, and three variants of the catechol-O-methyltransferase gene (Val[superscript 158]Met [rs4680], intron1 [rs737865], and 3′-untranslated region [rs165599]) to predict mothers' reports of inhibitory and attentional control (assessed at 42, 54, 72, and 84 months) and internalizing symptoms (assessed at 24, 30, 42,

We used sex, observed parenting quality at 18 months, and three variants of the catechol-O-methyltransferase gene (Val[superscript 158]Met [rs4680], intron1 [rs737865], and 3′-untranslated region [rs165599]) to predict mothers' reports of inhibitory and attentional control (assessed at 42, 54, 72, and 84 months) and internalizing symptoms (assessed at 24, 30, 42, 48, and 54 months) in a sample of 146 children (79 male). Although the pattern for all three variants was very similar, Val[superscript 158]Met explained more variance in both outcomes than did intron1, the 3′-untranslated region, or a haplotype that combined all three catechol-O-methyltransferase variants. In separate models, there were significant three-way interactions among each of the variants, parenting, and sex, predicting the intercepts of inhibitory control and internalizing symptoms. Results suggested that Val[superscript 158]Met indexes plasticity, although this effect was moderated by sex. Parenting was positively associated with inhibitory control for methionine–methionine boys and for valine–valine/valine–methionine girls, and was negatively associated with internalizing symptoms for methionine–methionine boys. Using the “regions of significance” technique, genetic differences in inhibitory control were found for children exposed to high-quality parenting, whereas genetic differences in internalizing were found for children exposed to low-quality parenting. These findings provide evidence in support of testing for differential susceptibility across multiple outcomes.
Created2015-08-01
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Description
This thesis details a Python-based software designed to calculate the Jones polynomial, a vital mathematical tool from Knot Theory used for characterizing the topological and geometrical complexity of curves in 3-space, which is essential in understanding physical systems of filaments, including the behavior of polymers and biopolymers. The Jones polynomial serves as a topological

This thesis details a Python-based software designed to calculate the Jones polynomial, a vital mathematical tool from Knot Theory used for characterizing the topological and geometrical complexity of curves in 3-space, which is essential in understanding physical systems of filaments, including the behavior of polymers and biopolymers. The Jones polynomial serves as a topological invariant capable of distinguishing between different knot structures. This capability is fundamental to characterizing the architecture of molecular chains, such as proteins and DNA. Traditional computational methods for deriving the Jones polynomial have been limited by closure-schemes and high execu- tion costs, which can be impractical for complex structures like those that appear in real life. This software implements methods that significantly reduce calculation times, allowing for more efficient and practical applications in the study of biological poly- mers. It utilizes a divide-and-conquer approach combined with parallel computing and applies recursive Reidemeister moves to optimize the computation, transitioning from an exponential to a near-linear runtime for specific configurations. This thesis provides an overview of the software’s functions, detailed performance evaluations using protein structures as test cases, and a discussion of the implications for future research and potential algorithmic improvements.
ContributorsMusfeldt, Caleb (Author) / Panagiotou, Eleni (Thesis director) / Richa, Andrea (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor)
Created2024-05
Description
In this paper, a novel model of Hotelling duopoly is introduced that explains horizontal product variety as the result of consumer preferences, expanding on and meshing the works of Hotelling (1929) and Neven (1985). From this model, two opposing forces from consumer preferences are found that impact the variety and

In this paper, a novel model of Hotelling duopoly is introduced that explains horizontal product variety as the result of consumer preferences, expanding on and meshing the works of Hotelling (1929) and Neven (1985). From this model, two opposing forces from consumer preferences are found that impact the variety and price decisions of firms: market share revenues and price revenues. As firms face consumers with highly linear (weak) preferences over variety, the profit incentive is to simply capture the market by offering products that appeal to the middle consumer. However, as firms face consumers with highly quadratic (strong) preferences over variety, the profit incentive is to carve out and exploit a market segment by offering a distinct variety. Thus, observed product variety between minimal and maximal differentiation is emergent from consumer preferences, as firms face a balance of price and market share incentives.
ContributorsMalaki, Adam (Author) / Leiva Bertran, Fernando (Thesis director) / Hanemann, Michael (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / School for the Future of Innovation in Society (Contributor)
Created2024-05
ContributorsGe, Kara (Author) / Collins, Gregory (Thesis director) / Printezis, Antonios (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Supply Chain Management (Contributor)
Created2024-05
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Description
The purpose of this study was to examine whether dispositional sadness predicted children's prosocial behavior and if sympathy mediated this relation. Constructs were measured when children (n = 256 at time 1) were 18, 30, and 42 months old. Mothers and non-parental caregivers rated children's sadness; mothers, caregivers, and fathers rated

The purpose of this study was to examine whether dispositional sadness predicted children's prosocial behavior and if sympathy mediated this relation. Constructs were measured when children (n = 256 at time 1) were 18, 30, and 42 months old. Mothers and non-parental caregivers rated children's sadness; mothers, caregivers, and fathers rated children's prosocial behavior; sympathy (concern and hypothesis testing) and prosocial behavior (indirect and direct, as well as verbal at older ages) were assessed with a task in which the experimenter feigned injury. In a panel path analysis, 30-month dispositional sadness predicted marginally higher 42-month sympathy; in addition, 30-month sympathy predicted 42-month sadness. Moreover, when controlling for prior levels of prosocial behavior, 30-month sympathy significantly predicted reported and observed prosocial behavior at 42 months. Sympathy did not mediate the relation between sadness and prosocial behavior (either reported or observed).
Created2015-01-01
Description
Deforestation in the Amazon rainforest has the potential to have devastating effects on ecosystems on both a local and global scale, making it one of the most environmentally threatening phenomena occurring today. In order to minimize deforestation in the Amazon and its consequences, it is helpful to analyze its occurrence

Deforestation in the Amazon rainforest has the potential to have devastating effects on ecosystems on both a local and global scale, making it one of the most environmentally threatening phenomena occurring today. In order to minimize deforestation in the Amazon and its consequences, it is helpful to analyze its occurrence using machine learning architectures such as the U-Net. The U-Net is a type of Fully Convolutional Network that has shown significant capability in performing semantic segmentation. It is built upon a symmetric series of downsampling and upsampling layers that propagate feature information into higher spatial resolutions, allowing for the precise identification of features on the pixel scale. Such an architecture is well-suited for identifying features in satellite imagery. In this thesis, we construct and train a U-Net to identify deforested areas in satellite imagery of the Amazon through semantic segmentation.
ContributorsGiel, Joshua (Author) / Douglas, Liam (Co-author) / Espanol, Malena (Thesis director) / Cochran, Douglas (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Sustainability (Contributor)
Created2024-05