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Longitudinal recursive partitioning (LRP) is a tree-based method for longitudinal data. It takes a sample of individuals that were each measured repeatedly across time, and it splits them based on a set of covariates such that individuals with similar trajectories become grouped together into nodes. LRP does this by fitting a mixed-effects model to each node every time that it becomes partitioned and extracting the deviance, which is the measure of node purity. LRP is implemented using the classification and regression tree algorithm, which suffers from a variable selection bias and does not guarantee reaching a global optimum. Additionally, fitting mixed-effects models to each potential split only to extract the deviance and discard the rest of the information is a computationally intensive procedure. Therefore, in this dissertation, I address the high computational demand, variable selection bias, and local optimum solution. I propose three approximation methods that reduce the computational demand of LRP, and at the same time, allow for a straightforward extension to recursive partitioning algorithms that do not have a variable selection bias and can reach the global optimum solution. In the three proposed approximations, a mixed-effects model is fit to the full data, and the growth curve coefficients for each individual are extracted. Then, (1) a principal component analysis is fit to the set of coefficients and the principal component score is extracted for each individual, (2) a one-factor model is fit to the coefficients and the factor score is extracted, or (3) the coefficients are summed. The three methods result in each individual having a single score that represents the growth curve trajectory. Therefore, now that the outcome is a single score for each individual, any tree-based method may be used for partitioning the data and group the individuals together. Once the individuals are assigned to their final nodes, a mixed-effects model is fit to each terminal node with the individuals belonging to it.
I conduct a simulation study, where I show that the approximation methods achieve the goals proposed while maintaining a similar level of out-of-sample prediction accuracy as LRP. I then illustrate and compare the methods using an applied data.
I conduct a simulation study, where I show that the approximation methods achieve the goals proposed while maintaining a similar level of out-of-sample prediction accuracy as LRP. I then illustrate and compare the methods using an applied data.
ContributorsStegmann, Gabriela (Author) / Grimm, Kevin (Thesis advisor) / Edwards, Michael (Committee member) / MacKinnon, David (Committee member) / McNeish, Daniel (Committee member) / Arizona State University (Publisher)
Created2019
Description
There is a need to reinvent evidence-based interventions (EBIs) for pediatric anxiety problems to better address the demands of real-word service delivery settings and achieve public health impact. The time- and resource-intensive nature of most EBIs for youth anxiety has frequently been noted as a barrier to the utilization of EBIs in community settings, leading to increased attention towards exploring the viability of briefer, more accessible protocols. Principally, this research reports between-group effect sizes from brief-interventions targeting pediatric anxiety and classifies each as well-established, probably efficacious, possibly efficacious, experimental, or questionable. brief interventions yielded an overall mean effect size of 0.19 on pediatric anxiety outcomes from pre to post. Effect sizes varied significantly by level of intervention: Pre to post-intervention effects were strongest for brief-treatments (0.35), followed by brief-targeted prevention (0.22), and weakest for brief-universal prevention (0.09). No participant or other intervention characteristic emerged as significant moderators of effect sizes. In terms of standard of evidence, one brief intervention is well-established, and five are probably efficacious, with most drawing on cognitive and behavioral change procedures and/or family systems models. At this juncture, the minimal intervention needed for clinical change in pediatric anxiety points to in-vivo exposures for specific phobias (~3 hours), cognitive-behavioral therapy (CBT) with social skills training (~3 hours), and CBT based parent training (~6 hours, eight digital modules with clinician support). This research concludes with a discussion on limitations to available brief EBIs, practice guidelines, and future research needed to capitalize on the viability of briefer protocols in enhancing access to, and impact of, evidence-based care in the real-world.
ContributorsStoll, Ryan (Author) / Pina, Armando A. (Thesis advisor) / Gonzales, Nancy (Committee member) / MacKinnon, David (Committee member) / Perez, Marisol (Committee member) / Arizona State University (Publisher)
Created2019
Description
The research on the topology and dynamics of complex networks is one of the most focused area in complex system science. The goals are to structure our understanding of the real-world social, economical, technological, and biological systems in the aspect of networks consisting a large number of interacting units and to develop corresponding detection, prediction, and control strategies. In this highly interdisciplinary field, my research mainly concentrates on universal estimation schemes, physical controllability, as well as mechanisms behind extreme events and cascading failure for complex networked systems.
Revealing the underlying structure and dynamics of complex networked systems from observed data without of any specific prior information is of fundamental importance to science, engineering, and society. We articulate a Markov network based model, the sparse dynamical Boltzmann machine (SDBM), as a universal network structural estimator and dynamics approximator based on techniques including compressive sensing and K-means algorithm. It recovers the network structure of the original system and predicts its short-term or even long-term dynamical behavior for a large variety of representative dynamical processes on model and real-world complex networks.
One of the most challenging problems in complex dynamical systems is to control complex networks.
Upon finding that the energy required to approach a target state with reasonable precision
is often unbearably large, and the energy of controlling a set of networks with similar structural properties follows a fat-tail distribution, we identify fundamental structural ``short boards'' that play a dominant role in the enormous energy and offer a theoretical interpretation for the fat-tail distribution and simple strategies to significantly reduce the energy.
Extreme events and cascading failure, a type of collective behavior in complex networked systems, often have catastrophic consequences. Utilizing transportation and evolutionary game dynamics as prototypical
settings, we investigate the emergence of extreme events in simplex complex networks, mobile ad-hoc networks and multi-layer interdependent networks. A striking resonance-like phenomenon and the emergence of global-scale cascading breakdown are discovered. We derive analytic theories to understand the mechanism of
control at a quantitative level and articulate cost-effective control schemes to significantly suppress extreme events and the cascading process.
Revealing the underlying structure and dynamics of complex networked systems from observed data without of any specific prior information is of fundamental importance to science, engineering, and society. We articulate a Markov network based model, the sparse dynamical Boltzmann machine (SDBM), as a universal network structural estimator and dynamics approximator based on techniques including compressive sensing and K-means algorithm. It recovers the network structure of the original system and predicts its short-term or even long-term dynamical behavior for a large variety of representative dynamical processes on model and real-world complex networks.
One of the most challenging problems in complex dynamical systems is to control complex networks.
Upon finding that the energy required to approach a target state with reasonable precision
is often unbearably large, and the energy of controlling a set of networks with similar structural properties follows a fat-tail distribution, we identify fundamental structural ``short boards'' that play a dominant role in the enormous energy and offer a theoretical interpretation for the fat-tail distribution and simple strategies to significantly reduce the energy.
Extreme events and cascading failure, a type of collective behavior in complex networked systems, often have catastrophic consequences. Utilizing transportation and evolutionary game dynamics as prototypical
settings, we investigate the emergence of extreme events in simplex complex networks, mobile ad-hoc networks and multi-layer interdependent networks. A striking resonance-like phenomenon and the emergence of global-scale cascading breakdown are discovered. We derive analytic theories to understand the mechanism of
control at a quantitative level and articulate cost-effective control schemes to significantly suppress extreme events and the cascading process.
ContributorsChen, Yuzhong (Author) / Lai, Ying-Cheng (Thesis advisor) / Spanias, Andreas (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Ying, Lei (Committee member) / Arizona State University (Publisher)
Created2016
Description
Positive alcohol outcome expectancies (AOEs) are consistent longitudinal predictors of later alcohol use; however, exclusion of solitary drinking contexts in the measurement of AOEs may have resulted in an underestimation of the importance of low arousal positive (LAP) effects. The current study aimed to clarify the literature on the association between AOEs and drinking outcomes by examining the role of drinking context in AOE measurement. Further, exclusion of contextual influences has also limited understanding of the unique effects of AOEs relative to subjective responses (SR) to alcohol. The present study addressed this important question by exploring relations between AOEs and SR when drinking context was held constant across parallel measures of these constructs. Understanding which of these factors drives relations between alcohol effects and drinking behavior has important implications for intervention. After conducting confirmatory factor analysis (CFA) and tests of measurement invariance for the AOE and SR measures, 4 aims collectively examined the role of context in reporting of AOEs (Aims 1 and 2), the extent to which context specific AOEs uniquely relate to drinking outcomes (Aim 3), and the importance of context effects on correspondence between AOEs and SR (Aim 4). Results of Aims 1 and 2 demonstrated that participants are imagining contexts when reporting on measures of AOEs that do not specify the context, and found significant mean differences in high and low arousal positive AOEs across contexts. Contrary to the hypotheses of Aim 3, context-specific AOEs were not significantly associated with drinking behavior. Results of Aim 4 indicated that while LAP AOEs for both unspecified and solitary contexts were associated with LAP SR in a solitary setting, unspecified context AOEs had a stronger relation than the solitary context AOEs. No significant relations between high arousal positive (HAP) AOEs and HAP SR emerged. The findings suggest that further investigation of the relation between context-specific AOEs and drinking outcomes/SR is warranted. Future studies of these hypotheses in samples with a wider range of drinking behavior, or at different stages of alcohol involvement, will elucidate whether mean level differences in context specific AOEs are important in understanding alcohol related outcomes.
ContributorsScott, Caitlin (Author) / Corbin, William (Thesis advisor) / MacKinnon, David (Committee member) / Barrera, Manuel (Committee member) / Chassin, Laurie (Committee member) / Arizona State University (Publisher)
Created2016
Description
Recent advances in hierarchical or multilevel statistical models and causal inference using the potential outcomes framework hold tremendous promise for mock and real jury research. These advances enable researchers to explore how individual jurors can exert a bottom-up effect on the jury’s verdict and how case-level features can exert a top-down effect on a juror’s perception of the parties at trial. This dissertation explains and then applies these technical advances to a pre-existing mock jury dataset to provide worked examples in an effort to spur the adoption of these techniques. In particular, the paper introduces two new cross-level mediated effects and then describes how to conduct ecological validity tests with these mediated effects. The first cross-level mediated effect, the a1b1 mediated effect, is the juror level mediated effect for a jury level manipulation. The second cross-level mediated effect, the a2bc mediated effect, is the unique contextual effect that being in a jury has on the individual the juror. When a mock jury study includes a deliberation versus non-deliberation manipulation, the a1b1 can be compared for the two conditions, enabling a general test of ecological validity. If deliberating in a group generally influences the individual, then the two indirect effects should be significantly different. The a2bc can also be interpreted as a specific test of how much changes in jury level means of this specific mediator effect juror level decision-making.
ContributorsLovis-McMahon, David (Author) / Schweitzer, Nicholas (Thesis advisor) / Saks, Michael (Thesis advisor) / Salerno, Jessica (Committee member) / MacKinnon, David (Committee member) / Arizona State University (Publisher)
Created2015
Description
Humanity’s demand for energy is increasing exponentially and the dependence on fossil fuels is both unsustainable and detrimental to the environment. To provide a solution to the impending energy crisis, it is reasonable to look toward utilizing solar energy, which is abundant and renewable. One approach to harvesting solar irradiation for fuel purposes is through mimicking the processes of natural photosynthesis in an artificial design to use sunlight and water to store energy in chemical bonds for later use. Thus, in order to design an efficient energy conversion device, the underlying processes of the natural system must be understood. An artificial photosynthetic device has many components and each can be optimized separately. This work deals with the design, construction and study of some of those components. The first chapter provides an introduction to this work. The second chapter shows a proof of concept for a water splitting dye sensitized photoelectrochemical cell followed by the presentation of a new p-type semiconductor, the design of a modular cluster binding protein that can be used for incorporating catalysts, and a new anchoring group for semiconducting oxides with high electron injection efficiency. The third chapter investigates the role of electronic coupling and thermodynamics for photoprotection in artificial systems by triplet-triplet energy transfer from tetrapyrroles to carotenoids. The fourth chapter describes a mimic of the proton-coupled electron transfer in photosystem II and confirms that in the artificial system a concerted mechanism operates. In the fifth chapter, a microbial system is designed to work in tandem with a photovoltaic device to produce high energy fuels. A variety of quinone redox mediators have been synthesized to shuttle electrons from an electron donor to the microbial system. Lastly, the synthesis of a variety of photosensitizers is detailed for possible future use in artificial systems. The results of this work helps with the understanding of the processes of natural photosynthesis and suggests ways to design artificial photosynthetic devices that can contribute to solving the renewable energy challenge.
ContributorsBrown, Chelsea L (Author) / Moore, Ana L (Thesis advisor) / Gust, Devens (Committee member) / Woodbury, Neal (Committee member) / Arizona State University (Publisher)
Created2015
Description
The purpose of information source detection problem (or called rumor source detection) is to identify the source of information diffusion in networks based on available observations like the states of the nodes and the timestamps at which nodes adopted the information (or called infected). The solution of the problem can be used to answer a wide range of important questions in epidemiology, computer network security, etc. This dissertation studies the fundamental theory and the design of efficient and robust algorithms for the information source detection problem.
For tree networks, the maximum a posterior (MAP) estimator of the information source is derived under the independent cascades (IC) model with a complete snapshot and a Short-Fat Tree (SFT) algorithm is proposed for general networks based on the MAP estimator. Furthermore, the following possibility and impossibility results are established on the Erdos-Renyi (ER) random graph: $(i)$ when the infection duration $<\frac{2}{3}t_u,$ SFT identifies the source with probability one asymptotically, where $t_u=\left\lceil\frac{\log n}{\log \mu}\right\rceil+2$ and $\mu$ is the average node degree, $(ii)$ when the infection duration $>t_u,$ the probability of identifying the source approaches zero asymptotically under any algorithm; and $(iii)$ when infection duration $
In practice, other than the nodes' states, side information like partial timestamps may also be available. Such information provides important insights of the diffusion process. To utilize the partial timestamps, the information source detection problem is formulated as a ranking problem on graphs and two ranking algorithms, cost-based ranking (CR) and tree-based ranking (TR), are proposed. Extensive experimental evaluations of synthetic data of different diffusion models and real world data demonstrate the effectiveness and robustness of CR and TR compared with existing algorithms.
For tree networks, the maximum a posterior (MAP) estimator of the information source is derived under the independent cascades (IC) model with a complete snapshot and a Short-Fat Tree (SFT) algorithm is proposed for general networks based on the MAP estimator. Furthermore, the following possibility and impossibility results are established on the Erdos-Renyi (ER) random graph: $(i)$ when the infection duration $<\frac{2}{3}t_u,$ SFT identifies the source with probability one asymptotically, where $t_u=\left\lceil\frac{\log n}{\log \mu}\right\rceil+2$ and $\mu$ is the average node degree, $(ii)$ when the infection duration $>t_u,$ the probability of identifying the source approaches zero asymptotically under any algorithm; and $(iii)$ when infection duration $
In practice, other than the nodes' states, side information like partial timestamps may also be available. Such information provides important insights of the diffusion process. To utilize the partial timestamps, the information source detection problem is formulated as a ranking problem on graphs and two ranking algorithms, cost-based ranking (CR) and tree-based ranking (TR), are proposed. Extensive experimental evaluations of synthetic data of different diffusion models and real world data demonstrate the effectiveness and robustness of CR and TR compared with existing algorithms.
ContributorsZhu, Kai (Author) / Ying, Lei (Thesis advisor) / Lai, Ying-Cheng (Committee member) / Liu, Huan (Committee member) / Shakarian, Paulo (Committee member) / Arizona State University (Publisher)
Created2015
Description
The manipulation of biological targets using synthetic compounds has been the focal point of medicinal chemistry. The work described herein centers on the synthesis of organic small molecules that act either as probes for studying protein conformational changes or DNA–protein interaction, or as multifunctional radical quenchers.
Fluorescent labeling is of paramount importance to biological studies of proteins. For the development of new extrinsic small fluorophores, a series of tryptophan analogues has been designed and synthesized. Their pdCpA derivatives have been synthesized for tRNA activation and in vitro protein synthesis. The photophysical properties of the tryptophan (Trp) analogues have been examined, some of which can be selectively monitored even in the presence of multiple native tryptophan residues. Further, some of the Trp analogues form efficient FRET pairs with acceptors such as acridon-2-ylalanine (Acd) or L-(7-hydroxycoumarin-4-yl)ethylglycine (HCO) for the selective study of conformational changes in proteins.
Molecules which can bind with high sequence selectivity to a chosen target in a gene sequence are of interest for the development of gene therapy, diagnostic devices for genetic analysis, and as molecular tools for nucleic acid manipulations. Stereoselective synthesis of different alanyl nucleobase amino acids is described. Their pdCpA derivatives have been synthesized for tRNA activation and site-specific incorporation into the DNA-binding protein RRM1 of hnRNP LL. It is proposed that the nucleobase moieties in the protein may specifically recognize base sequence in the i-motif DNA through H-bonding and base-stacking interactions.
The mitochondrial respiratory chain accumulates more oxidative damage than any other organelle within the cell. Dysfunction of this organelle is believed to drive the progression of many diseases, thus mitochondria are an important potential drug target. Reactive oxygen species (ROS) are generated when electrons from the respiratory chain escape and interact with oxygen. ROS can react with proteins, lipids or DNA causing cell death. For the development of effective neuroprotective drugs, a series of N-hydroxy-4-pyridones have been designed and synthesized as CoQ10 analogues. All the analogues synthesized were evaluated for their ability to quench lipid peroxidation and reactive oxygen species (ROS).
Fluorescent labeling is of paramount importance to biological studies of proteins. For the development of new extrinsic small fluorophores, a series of tryptophan analogues has been designed and synthesized. Their pdCpA derivatives have been synthesized for tRNA activation and in vitro protein synthesis. The photophysical properties of the tryptophan (Trp) analogues have been examined, some of which can be selectively monitored even in the presence of multiple native tryptophan residues. Further, some of the Trp analogues form efficient FRET pairs with acceptors such as acridon-2-ylalanine (Acd) or L-(7-hydroxycoumarin-4-yl)ethylglycine (HCO) for the selective study of conformational changes in proteins.
Molecules which can bind with high sequence selectivity to a chosen target in a gene sequence are of interest for the development of gene therapy, diagnostic devices for genetic analysis, and as molecular tools for nucleic acid manipulations. Stereoselective synthesis of different alanyl nucleobase amino acids is described. Their pdCpA derivatives have been synthesized for tRNA activation and site-specific incorporation into the DNA-binding protein RRM1 of hnRNP LL. It is proposed that the nucleobase moieties in the protein may specifically recognize base sequence in the i-motif DNA through H-bonding and base-stacking interactions.
The mitochondrial respiratory chain accumulates more oxidative damage than any other organelle within the cell. Dysfunction of this organelle is believed to drive the progression of many diseases, thus mitochondria are an important potential drug target. Reactive oxygen species (ROS) are generated when electrons from the respiratory chain escape and interact with oxygen. ROS can react with proteins, lipids or DNA causing cell death. For the development of effective neuroprotective drugs, a series of N-hydroxy-4-pyridones have been designed and synthesized as CoQ10 analogues. All the analogues synthesized were evaluated for their ability to quench lipid peroxidation and reactive oxygen species (ROS).
ContributorsTalukder, Poulami (Author) / Hecht, Sidney M. (Thesis advisor) / Woodbury, Neal (Committee member) / Gould, Ian (Committee member) / Arizona State University (Publisher)
Created2016
Description
Variability in subjective response to alcohol has been shown to predict drinking behavior as well as the development of alcohol use disorders. The current study examined the extent to which individual differences in alcohol pharmacokinetics impact subjective response and drinking behavior during a single session alcohol administration paradigm. Participants (N = 98) completed measures of subjective response at two time points following alcohol consumption. Pharmacokinetic properties (rate of absorption and metabolism) were inferred using multiple BAC readings to calculate the area under the curve during the ascending limb for absorption and descending limb for metabolism. Following the completion of the subjective response measures, an ad-libitum taste rating task was implemented in which participants were permitted to consume additional alcoholic beverages. The amount consumed during the taste rating task served as the primary outcome variable. Results of the study indicated that participants who metabolized alcohol more quickly maintained a greater level of subjective stimulation as blood alcohol levels declined and reported greater reductions in subjective sedation. Although metabolism did not have a direct influence on within session alcohol consumption, a faster metabolism did relate to increased ad-libitum consumption indirectly through greater acute tolerance to sedative effects and greater maintenance of stimulant effects. Rate of absorption did not significantly predict subjective response or within session drinking. The results of the study add clarity to theories of subjective response to alcohol, and suggest that those at highest risk for alcohol problems experience a more rapid reduction in sedation following alcohol consumption while simultaneously experiencing heightened levels of stimulation. Variability in pharmacokinetics, namely how quickly one metabolizes alcohol, may be an identifiable biomarker of subjective response and may be used to infer risk for alcohol problems.
ContributorsBoyd, Stephen (Author) / Corbin, William R. (Thesis advisor) / Chassin, Laurie (Committee member) / MacKinnon, David (Committee member) / Olive, Michael Foster (Committee member) / Arizona State University (Publisher)
Created2014
Description
The present study utilized longitudinal data from a high-risk community sample (n=254, 52.8% female, 47.2% children of alcoholics, 74% non-Hispanic Caucasian) to test questions concerning the effects of genetic risk, parental knowledge, and peer substance use on emerging adult substance use disorders (SUDs). Specifically, this study examined whether parental knowledge and peer substance use mediated the effects of parent alcohol use disorder (AUD) and genetic risk for behavioral undercontrol on SUD. The current study also examined whether genetic risk moderated effects of parental knowledge and peer substance use on risk for SUD. Finally, this study examined these questions over and above a genetic "control" which explained a large proportion of variance in the outcome, thereby providing a stricter test of environmental influences.
Analyses were performed in a path analysis framework. To test these research questions, the current study employed two polygenic risk scores. The first, a theory-based score, was formed using single-nucleotide polymorphisms (SNPs) from receptor systems implicated in the amplification of positive effects in the presence of new/exciting stimuli and/or pleasure derived from using substances. The second, an empirically-based score, was formed using a data-driven approach that explained a large amount of variance in SUDs. Together, these scores allowed the present study to test explanations for the relations among parent AUD, parental knowledge, peer substance use, and SUDs.
Results of the current study found that having parents with less knowledge or an AUD conferred greater risk for SUDs, but only for those at higher genetic risk for behavioral undercontrol. The current study replicated research findings suggesting that peer substance use mediated the effect of parental AUD on SUD. However, it adds to this literature by suggesting that some mechanism other than increased behavioral undercontrol explains relations among parental AUD, peer substance use, and emerging adult SUD. Taken together, these findings indicate that children of parents with AUDs comprise a particularly risky group, although likelihood of SUD within this group is not uniform. These findings also suggest that some of the most important environmental risk factors for SUDs exert effects that vary across level of genetic propensity.
Analyses were performed in a path analysis framework. To test these research questions, the current study employed two polygenic risk scores. The first, a theory-based score, was formed using single-nucleotide polymorphisms (SNPs) from receptor systems implicated in the amplification of positive effects in the presence of new/exciting stimuli and/or pleasure derived from using substances. The second, an empirically-based score, was formed using a data-driven approach that explained a large amount of variance in SUDs. Together, these scores allowed the present study to test explanations for the relations among parent AUD, parental knowledge, peer substance use, and SUDs.
Results of the current study found that having parents with less knowledge or an AUD conferred greater risk for SUDs, but only for those at higher genetic risk for behavioral undercontrol. The current study replicated research findings suggesting that peer substance use mediated the effect of parental AUD on SUD. However, it adds to this literature by suggesting that some mechanism other than increased behavioral undercontrol explains relations among parental AUD, peer substance use, and emerging adult SUD. Taken together, these findings indicate that children of parents with AUDs comprise a particularly risky group, although likelihood of SUD within this group is not uniform. These findings also suggest that some of the most important environmental risk factors for SUDs exert effects that vary across level of genetic propensity.
ContributorsBountress, Kaitlin (Author) / Chassin, Laurie (Thesis advisor) / Crnic, Keith (Committee member) / Lemery-Chalfant, Kathryn (Committee member) / MacKinnon, David (Committee member) / Arizona State University (Publisher)
Created2015