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Transdiagnostic Emotional Factors as Mediators of the Relation between Obsessive-Compulsive Symptoms and Tobacco Use and Dependence in a Large Community Sample of Adolescents
No full textThis study investigated the associations between obsessive compulsive disorder symptoms (OCS) and tobacco use and tobacco dependence in a large community sample of adolescents. Specifically, transdiagnostic emotional vulnerability factors (i.e., anhedonia, distress tolerance and anxiety sensitivity) were explored as potential mediators of the relation between OCS and tobacco use/dependence. Weighted logistic regression models found that OCS was significantly positively associated with likelihood of tobacco use in the past six months (when suppressor variables were excluded). Similarly, zero-inflated negative binomial regression models found a significant positive relation between OCS and tobacco dependence (when suppressor variables were excluded). There was no significant weighted indirect effect via anhedonia for the OCS-Tobacco Use association, which suggests that anhedonia was not a significant mediator in this case. However, the indirect effect via anhedonia for the OCS-Tobacco Dependence association was significant at the p < .05 level, but not the adjusted p < .001 threshold (when suppressor variables were removed). The weighted indirect effect via distress tolerance for the OCS-Tobacco Use association was significant at the p < .05 level, but not the p < .001 threshold (when suppressor variables were removed). There was no significant indirect effect via distress tolerance for the OCS-Tobacco Dependence association. There was a significant weighted indirect effect via anxiety sensitivity for the OCS-Tobacco Use association (when suppressor variables were removed). There were no significant indirect effects via anxiety sensitivity for the OCS-Tobacco Dependence association. When overlapping variance was accounted for by including all three mediators simultaneously, anhedonia was still not a significant mediator of the OCS-Tobacco Use association; there were still significant indirect effects via distress tolerance and anxiety sensitivity, under specific model parameters. Our results also revealed that while Asian adolescents demonstrated lower likelihood of tobacco use compared to those who were Hispanic/Latinae, Asian youth demonstrated more severe tobacco dependence symptoms
Characterization and Migration of Silver Nanoparticles from Electron-Beam Irradiated Low-Density Polyethylene
No full textPolymer nanocomposites (PNCs) and engineered nanomaterials (ENMs) may find use in a wide range of commercial applications, including food and medical product packaging. Migration of nanofillers from polymer nanocomposites into food matrices could be a source of human dietary exposure to ENMs. Electron beam (e-beam) irradiation is a processing method used for microbial inactivation as well as for modifying properties of polymer films, such as stretch resistance and shrink tension. Process treatment of nanotechnology-based packaging materials either for sterilization or for strengthening of the polymer films may have a significant effect on the migration of ENMs into food matrices. The primary objective of this study is to investigate the effect of e-beam irradiation treatments of LDPE containing silver nanoparticles (AgNPs) and the subsequent migration of AgNPs into a food simulant under intended use conditions. The study observes a correlation between e-beam irradiation dose quantity and the release of AgNPs into a food simulant
Towards Utility-Driven Data Analytics with Differential Privacy
No full textThe widespread use of personal devices and dedicated recording facilities has led to the generation of massive amounts of personal information or data. Some of them are high-dimensional and unstructured data, such as video and location data. Analyzing these data can provide significant benefits in real-world scenarios, such as videos for monitoring and location data for traffic analysis. However, while providing benefits, these complicated data always raise serious privacy concerns since all of them involve personal information. To address privacy issues, existing privacy protection methods often fail to provide adequate utility in practical applications due to the complexity of high-dimensional and unstructured data. For example, most video sanitization techniques merely obscure the video by detecting and blurring sensitive regions, such as faces, vehicle plates, locations, and timestamps. Unfortunately, privacy breaches in blurred videos cannot be effectively contained, especially against unknown background knowledge. In this thesis, we propose three different differentially private frameworks to preserve the utility of video and location data (both are high-dimensional and unstructured data) while meeting the privacy requirements, under different well-known privacy settings. Specifically, to our best knowledge, wepropose the first differentially private video analytics platform (VideoDP) which flexibly supports different video queries or query-based analyze with a rigorous privacy guarantee. Given the input video, VideoDP randomly generates a utility-driven private video in which adding or removing any sensitive visual element (e.g., human, and object) does not significantly affect the output video. Then, different video analyses requested by untrusted video analysts can be flexibly performed over the sanitized video with differential privacy. Secondly, we define a novel privacy notion ϵ-Object Indistinguishability for all the predefined sensitive objects (e.g., humans, vehicles) in the video, and then propose a video sanitization technique VERRO that randomly generates utility-driven synthetic videos with indistinguishable objects. Therefore, all the objects can be well protected in the generated utility-driven synthetic videos which can be disclosed to any untrusted video recipient. Third, we propose the first strict local differential privacy (LDP) framework for location-based service (LBS) (“L-SRR”) to privately collect and analyze user locations or trajectories with ε-LDP guarantees. Specifically, we design a novel LDP mechanism “staircase randomized response” (SRR) and extend the empirical estimation to further boost the utility for
a diverse set of LBS Apps (e.g., traffic density estimation, k nearest neighbors search, origin-destination analysis, and traffic-aware GPS navigation). Finally, we conduct experiments on real videos and location dataset, and the experimental results demonstrate all frameworks can have good performance
Latent Price Model for Market Microstructure: Estimation and Simulation
No full textThis thesis focuses on exploring and solving several problems based on partiallyobserved diffusion models. The thesis has two parts.
In the first part we present a tractable sufficient condition for the consistency
of maximum likelihood estimators (MLEs) in partially observed diffusion models,
stated in terms of stationary distributions of the associated test processes, under the
assumption that the set of unknown parameter values is finite. We illustrate the
tractability of this sufficient condition by verifying it in the context of a latent price
model of market microstructure. Finally, we describe an algorithm for computing
MLEs in partially observed diffusion models and test it on historical data to estimate
the parameters of the latent price model.
In the second part we provide a thorough analysis of the particle filtering
algorithm for estimating the conditional distribution in partially observed diffusion
models. Specifically, we focus on estimating the distribution of unobserved processes
using observed data. The algorithm involves several steps and assumptions, which are
described in detail. We also examine the convergence of the algorithm and identify
the sufficient conditions under which it converges. Finally, we derive an explicit
upper bound of the convergence rate of the algorithm, which depends on the set of
parameters and the choice of time frequency. This bound provides a measure of the
algorithm’s performance and can be used to optimize its parameters to achieve faster
convergence
Developing Advanced Materials for Carbon Dioxide Electroreduction to Value-Added Chemicals and Fuels
No full textDeveloping highly efficient electrocatalysts for the carbon dioxide reductionreaction (CO2RR) to value-added fuels and chemicals offers a feasible pathway for
renewable energy storage and could help mitigate the ever-increasing carbon dioxide
(CO2) emissions from human activities. Different catalysts are known to catalyze
CO2RR in aqueous solutions. Most known catalysts are only capable of transferring
2 electrons with needed protons to CO2 producing either carbon monoxide (CO)
or formic acid (HCOOH). Copper (Cu) is the only electrocatalytic material that
converts CO2 into different types of hydrocarbon products. Additionally, owing to
Cu’s natural abundance and low cost, it has been intensively studied for CO2RR
for decades. However, the required high input energy (overpotential), low product
selectivity towards valuable fuel products, and the lack of long-term stability remain
major challenges for Cu-based catalysts. This work aims to develop new materials
that produce hydrocarbons at lower overpotentials with higher rates and greater
selectivity than current copper catalysts. By implementing a process referred to as
the electrocatalyst discovery cycle iterations between predications, catalyst testing,
and active site characterization allow for the rational design and discovery of new
and improved electrocatalysts for CO2RR. This methodology led to the discovery of
different heteroatomic catalysts as low overpotential catalysts for electroreduction of
CO2 high energy density hydrocarbon products
Computational Genomics of Human-Infecting Microsporidia Species from the Genus Encephalitozoon
No full textMicrosporidia are obligate intracellular pathogens classified as category B priority pathogens by the National Institute of Allergy and Infectious Diseases (NIAID), a division of the National Institutes of Health (NIH). Microsporidian species from the genus Encephalitozoon infect humans and can cause encephalitis, keratoconjunctivitis or enteric diseases in both immunocompromised and immunocompetent individuals. The main treatment for disseminated microsporidiosis available in the United States is albendazole, an anthelmintic benzimidazole that is also used to treat fungal infections, but species from the Encephalitozoonidae have already shown signs of resistance against this drug. The Encephalitozoonidae harbors highly specialized pathogens with the smallest known eukaryote genomes, with Encephalitozoon cuniculi featuring a genome of only 2.9 Mbp and coding for a proteome of roughly 2,000 proteins. Pathogens are in an everlasting race to quicken their adaptation pace against host defenses. This adaptation is often driven by gene duplication, recombination and/or mutation, and due to the potentially disruptive nature of duplication and recombination processes, many of these evolutions in pathogens are taking place outside conserved genomic loci. As such, genes involved in virulence and drug resistance in pathogens are often localized in the (sub)telomeres rather than in chromosome cores.
The small and streamlined nature of microsporidian genomes makes them excellent candidates to investigate the adaptation of pathogens to host defenses, the evolution of their virulence, and the development of their resistance to drugs from a genomic perspective. However, microsporidian genomes are highly divergent at the DNA sequence level and the ones that have been sequenced so far are incomplete and are lacking the telomeres. This high level of sequence divergence hinders standard sequence homology-based functional annotations, blurring our understanding of what these organisms are capable of from a metabolic perspective. The gap in our knowledge of what is encoded in the microsporidia telomeres could lead to an underestimation of their pathogenic capabilities. Therefore, deciphering the functions of unknown proteins in microsporidia genomes and unraveling the content of their telomeres is important to fully assess their potential for adaptability to host defenses and predisposition to drug resistance. Likewise, a better understanding of the genetic diversity in microsporidia will help assess the extent by which host-pathogen interactions are shaping the adaptation of these parasites to humans. As observed in the COVID-19 pandemic, genetic diversity can influence the speed at which pathogens adapt to host defenses and thus can pose a big challenge to disease control. The development of strategies for controlling microsporidiosis outbreaks will likely benefit from the work performed in this thesis.
As part of my PhD work, I investigated the virulence and host-adaptation capabilities of human-infecting microsporidia species from the genus Encephalitozoon with computational genomic approaches. This work included: 1) using structural homology to infer the functions of unknown proteins from the microsporidia proteome; 2) sequencing the complete genomes from telomere-to-telomere of three distinct Encephalitozoon spp. (E. cuniculi, E. hellem and E. intestinalis) to determine the genetic makeup of their telomeres and better understand the extent of their diversity; and 3) assessing the intraspecies genetic diversity that exists between Encephalitozoon species
Efficacy and Mechanisms of Power Ultrasound-Based Hurdle Technology for Reduction of Pathogens in Fresh Produce
No full textMinimally processed produce is frequently contaminated with foodborne bacterial pathogens. Power ultrasound is a non-thermal and cost-effective technology that can be combined with other chemical sanitization methods. This study investigated the reduction of Listeria monocytogenes and Salmonella Newport on grape tomato, romaine lettuce, and spinach washed with water, chlorine, or peroxyacetic acid alone or in combination with 25 or 40 kHz power ultrasound for 1, 2, or 5 min. Produce items were inoculated with selected pathogens at approximately 10 log CFU/g, air dried for 2 h, and then treated. Combined treatment of ultrasound and sanitizers resulted in 1.44-3.99 log CFU/g reduction of L. monocytogenes and 1.35-3.62 log CFU/g reduction of S. Newport on washed produce items, with significantly higher reductions observed on grape tomato. Synergistic effects were achieved with the combined treatment of power ultrasound coupled with the chemical sanitizers when compared to the single treatments. An additional 0.48-1.40 log CFU/g reduction of S. Newport was obtained with the combined treatment on grape tomato. In general, no significant differences (p<0.05) were observed in pathogen reductions between the selected ultrasound frequencies, sanitizers, or treatment lengths. Results from this study suggest that incorporation of power ultrasound to current treatment can enhance bacterial pathogen reduction on fresh produce surface, but cannot completely eliminate bacterial pathogens. Transcriptomic study revealed significant (|Log2 fold change|<1 and false discovery rate < 0.05) transcriptional changes in L. monocytogenes LS810 in response to the 2 min power ultrasound treatment. The up-regulation of genes encoding TPI, LLO, and PTS indicates increased energy requirements, enhanced virulence, and demand for sugar sources in bacteria. On the other hand, the down-regulation of genes involved in cyclic dimeric GMP hydrolysis and transcriptional regulation suggests a modulation of intracellular signaling, cellular processes, and metabolisms to enhance survival and recovery. The GO and KEGG analysis demonstrated defense mechanisms against ultrasound stress more comprehensively. L. monocytogenes adjusts its metabolism, repairs cell membranes, and conserves energy for survival. These findings enhance our understanding of its adaptation to environmental stress. Results of this study can be used as a start point for optimizing the efficacy of ultrasound-based hurdle treatments for fresh produce disinfection
Investigation in the Uncertainty of Chassis Dynamometer Testing for the Energy Characterization of Conventional, Electric and Automated Vehicles
No full textFor conventional and electric vehicles tested in a standard chassis dynamometer environment precise regulations on the evaluation of their energy performance exist. However, the regulations do not include requirements on the confidence value to associate with the results. As vehicles become more and more efficient to meet the stricter regulations mandates on emissions, fuel and energy consumption, traditional testing methods may become insufficient to validate these improvements, and may need revision. Without information about the accuracy associated with the results of those procedures however, adjustments and improvements are not possible, since no frame of reference exists. For connected and automated vehicles, there are no standard testing procedures, and researchers are still in the process of determining if current evaluation methods can be extended to test intelligent technologies and which metrics best represent their performance. For these vehicles is even more important to determine the uncertainty associated with these experimental methods and how they propagate to the final results. The work presented in this dissertation focuses on the development of a systematic framework for the evaluation of the uncertainty associated with the energy performance of conventional, electric and automated vehicles. The framework is based on a known statistical method, to determine the uncertainty associated with the different stages and processes involved in the experimental testing, and to evaluate how the accuracy of each parameter involved impacts the final results. The results demonstrate that the framework can be successfully applied to existing testing methods and provides a trustworthy value of accuracy to associate with the energy performance results, and can be easily extended to connected-automated vehicle testing to evaluate how novel experimental methods impact the accuracy and the confidence of the outputs. The framework can be easily be implemented into an existing laboratory environment to incorporate the uncertainty evaluation among the current results analyzed at the end of each test, and provide a reference for researchers to evaluate the actual benefits of new algorithms and optimization methods and understand margins for improvements, and by regulators to assess which parameters to enforce to ensure compliance and ensure projected benefits
Development of a Model To Investigate Inflammation Using Peripheral Blood Mononucleated Cells
No full textOur modern culture in our society is facing one of the biggest risks in health which is high-calorie diet-related postprandial inflammation. Chronic diseases may be
caused if the energy-dense food is the choice meaning if it is uncontrolled, clinical
studies have demonstrated this with the body's post-meal inflammatory response. We
aimed to find the causes of postprandial inflammation in response to various dietary
treatments and provide a model to demonstrate. We aimed to make use of in vivo and in
vitro techniques and statistics to create a model. The created model would help us to
design specific treatments to minimize inflammation with response to dietary. In addition
to figuring out vital dietary additives, the model additionally facilitates the layout of
individualized interventions to reduce inflammation, thereby improving long-time period
health outcomes. We aim to understand the clinical observations of diet-induced
postprandial inflammation on the molecular level. We desire to make contributions to
reduce the impact of chronic inflammatory disorders that is associated with postprandial
inflammation
Development of a Model To Investigate Inflammation Using Peripheral Blood Mononucleated Cells
No full textOur modern culture in our society is facing one of the biggest risks in health which is high-calorie diet-related postprandial inflammation. Chronic diseases may be
caused if the energy-dense food is the choice meaning if it is uncontrolled, clinical
studies have demonstrated this with the body's post-meal inflammatory response. We
aimed to find the causes of postprandial inflammation in response to various dietary
treatments and provide a model to demonstrate. We aimed to make use of in vivo and in
vitro techniques and statistics to create a model. The created model would help us to
design specific treatments to minimize inflammation with response to dietary. In addition
to figuring out vital dietary additives, the model additionally facilitates the layout of
individualized interventions to reduce inflammation, thereby improving long-time period
health outcomes. We aim to understand the clinical observations of diet-induced
postprandial inflammation on the molecular level. We desire to make contributions to
reduce the impact of chronic inflammatory disorders that is associated with postprandial
inflammation