Search results

Title: Stigma, depression, and help-seeking: Experiences of parents/caregivers of children with mental health challenges
Creator: Serchuk, Marisa D.
Date: 2023
Description: The impacts of stigma on people with lived-experience are widely recognized, however, stigma has been noted to extend to family members. The...
Show moreThe impacts of stigma on people with lived-experience are widely recognized, however, stigma has been noted to extend to family members. The current investigation examines how specific types of stigma experienced by parents/caregivers (N=250) of children with mental health challenges are related to symptoms of depression and attitudes towards help-seeking. Results found that higher levels of public stigma, self-stigma, and vicarious stigma were associated with higher levels of depression and were differentially associated with attitudes towards help-seeking. Findings from this investigation add to the small body of literature examining stigma experienced by parents/caregivers of children with mental health challenges.
Show less

Title: Certified Rehabilitation Counselors' Knowledge, Stigma, and Self-Efficacy in Working with Non-Suicidal Self-Injury
Creator: Tseng, Yen Chun
Date: 2023
Description: Certified Rehabilitation Counselors (CRCs) are professionals who are responsible for supporting the rights and independence of people with...
Show moreCertified Rehabilitation Counselors (CRCs) are professionals who are responsible for supporting the rights and independence of people with disabilities. They provide services such as mental health counseling, vocational counseling, advocacy, and psychoeducation to people with disabilities. Suicide prevention and safety education are within the scope of services provided by CRCs as well. Non-suicidal self-injury (NSSI), one of the strongest risk factors for suicide attempts (Franklin et al., 2017; Kiekens et al., 2018), has received more attention as people with disabilities have elevated risk to engage in such behaviors (Coduti et al., 2016). NSSI refers to the socially unacceptable behavior causing intentional and direct injury to one’s own body tissue without conscious suicidal intent (Nock & Favazza, 2009). As the prevalence of NSSI increases, it is likely that in their professional tenure, CRCs will interact with clients who have engaged in NSSI. It is within CRCs scope of practice to advocate at individual, group, institutional, and societal levels to promote opportunity and access, improve quality of life for individuals with disabilities (Commission on Rehabilitation Counselor Certification, [CRCC], 2023). However, few studies have explored CRCs’ training, stigma, and self- efficacy when working with NSSI. The purpose of this study was to explore the nature and extent of NSSI training received by CRCs, CRCs’ stigma towards individuals engaging in NSSI, and factors associated with CRCs’ self-efficacy for working with clients with NSSI. CRCs practicing in the United States participated in the study (N = 91). Less than half of the participants reported that they received NSSI training in the past. In addition, they demonstrated some knowledge of NSSI while holding some misconceptions of NSSI at the same time. In addition, CRCs reported generally positive attitudes toward NSSI. Predictors examined in the study included training, knowledge, familiarity, and attitudes toward individuals engaged in NSSI while controlling for participants’ age and gender. Hierarchical regression analysis was used to analyze whether these factors were associated with self-efficacy to work with clients with NSSI. Results indicated that training and stigma (helping attitude) were significant predictors of CRCs’ self-efficacy for working with clients experiencing NSSI. Additionally, the variance in self-efficacy was accounted for by NSSI training and stigma. CRCs who received NSSI training in the past reported more positive attitudes and perceived themselves as more capable to work with clients who engaged in NSSI. This study is among the few to examine specific factors impacting CRCs’ self-efficacy in working NSSI. Implications for practice and research are discussed.
Show less

Title: Large Language Model Based Machine Learning Techniques for Fake News Detection
Creator: Chen, Pin-Chien
Date: 2024
Description: With advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into...
Show moreWith advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into content creators on social media or the streaming platforms sharing their personal ideas regardless of their education or expertise level. Distinguishing fake news is becoming increasingly crucial. However, the recent research only presents comparisons of detecting fake news between one or more models across different datasets. In this work, we applied Natural Language Processing (NLP) techniques with Naïve Bayes and DistilBERT machine learning method combing and augmenting four datasets. The results show that the balanced accuracy is higher than the average in the recent studies. This suggests that our approach holds for improving fake news detection in the era of widespread content creation.
Show less

Title: REDUCED-ORDER MODELING OF UNSTEADY FLOW OVER TWO COLLINEAR PLATES AT LOW REYNOLDS NUMBERS
Creator: Almashjary, Abdulrahman N
Date: 2021
Description: Wakes of bluff bodies that exhibit unsteady behavior are a topic of great interest in the study of fluid dynamics. Vortex formation in these...
Show moreWakes of bluff bodies that exhibit unsteady behavior are a topic of great interest in the study of fluid dynamics. Vortex formation in these wakes depends significantly on the Reynolds number and the arrangement of the bluff bodies in the computation domain. To attain a comprehensive understanding of the unsteady wakes of adjacent bodies, we examine the emerged flow patterns in the wake of two bodies when subjected to different flow regimes and geometric configurations. This work aims to develop a reduced-order model that can capture the dynamics and predict the time evolution of specific parameters in the flowfield. Investigations including direct numerical simulations of two collinear plates normal to the flow were performed. Flowfield data and forces exerted on the plates were collected using a numerical code of an immersed boundary projection method (IBPM). The conducted numerical simulations pursued classifying the flow patterns by systematically varying the Reynolds number and the gap between the two plates. It was found that at small gap spacings, a typical von Karman vortex street is observed. Whereas at larger gap spacings, both a biased and a flip-flopping gap flow are detected. Prevalent coherent structures present in various flow regimes can be extracted via data-driven modeling techniques. The proper orthogonal decomposition (POD) method is used in this framework, from which projection-based reduced-order models are developed utilizing the governing equations of fluid flows. Single and broadband spectra are observed in the unsteady wake of the two-plate configuration. The amplitude and frequency of the time-evolution of the true POD modes and the predicted models are assessed using the spectral proper orthogonal decomposition (SPOD), an empirical method to extract coherent structures one frequency at a time from fluid flows. It was found that these reduced-order models are able to recover the frequency content from non-time resolved data.
Show less

Title: Dynamic Risk and Dynamic Performance Measures Generated by Distortion Functions and Diversification Benefits Optimization
Creator: Liu, Hao
Date: 2023
Description: This thesis consists of two major parts, and it contributes to the fields of risk management and optimization.One contribution to risk...
Show moreThis thesis consists of two major parts, and it contributes to the fields of risk management and optimization.One contribution to risk management is made via developing dynamic risk measures and dynamic acceptability indices that can be characterized by distortion functions. In particular, we proved a representation theorem illustrating that the class of dynamic coherent risk measures generated by distortion functions coincides with a specific type of dynamic risk measures, the dynamic WV@R. We also investigate thoroughly various types of time consistencies for dynamic risk measures and dynamic acceptability indices in terms of distortion functions. Another contribution to risk management is proving strong consistency and asymptotic normality of two estimators of dynamic WV@R. In contrast to the exist- ing literature, our results do not rely on the assumptions of distribution of random variables. Instead, we investigate the asymptotic normality of estimators in terms of the generating distortion functions. Last but not least, we give counterexample to show that a sufficient condition of asymptotic normality is not necessary. The contribution to optimization is twofold. On the one hand, we formulate the (scalar) diversification optimization problem as a vector optimization problem (VOP), and show that a set-valued Bellman principle is satisfied by this VOP. On the other hand, we derive explicit policy gradient formula and implement the deep neural network to solve diversification optimization problem numerically. This deep learning technique allows to overcome computation difficulty caused by the non-convexity of VOP.
Show less

Title: Development of data assimilation for analysis of ion drifts during geomagnetic storms
Creator: Hu, Jiahui
Date: 2024
Description: The primary objective of this dissertation is to gain insight into geomagnetic storm effects at mid-latitudes induced by solar activity....
Show moreThe primary objective of this dissertation is to gain insight into geomagnetic storm effects at mid-latitudes induced by solar activity. Geomagnetic storms affect our everyday lives because they give rise to transient signal loss, data transmission errors, negatively impacting users of satellite navigation systems. The Nighttime Localized Ionospheric Enhancement (NILE) is a localized plasma enhancement that because it is not well understood, drives the design of satellite-based augmentationsystems. To better secure operation of technological infrastructure, it is essential to build a comprehensive understanding of the atmospheric drivers, especially during solar active periods. Instrument measurements and climate models serve as valuable tools in obtaining information regarding the occurrence of space weather events; nonetheless, both sources exhibit quantitative and qualitative limitations. Data assimilation, an evolving technique, integrates measurements and model information to optimize the state estimations. This dissertation presents developments in a data assimilation algorithm known as Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE), and its applications in investigating the atmospheric behaviors under varying solar conditions. EMPIRE is a data assimilation algorithm specifically designed for upper atmospheric driver estimation of neutral wind and ion drifts at user-defined spatial and temporal scales. The EMPIRE application in this work aims to contribute to a more comprehensive understanding of the effects of the NILE. EMPIRE utilizes the Kalman filter to optimize state calculations primarily based on electron density rates, provided by other data assimilation algorithms. Earlier runs of the algorithm used pre-defined values for the background state covariance cross time. To address model limitations under changing geomagnetic conditions, the algorithm is enhanced by concurrently updating the background state covariance during assimilation processes. Additionally, representation error is incor- porated as a component of the observation error, and error analysis is performed through a synthetic-data study. Previously, EMPIRE fused Fabry-Perot Interferometer (FPI) neutral wind measurements, demonstrating increased agreement with validation neutral wind data. In this work, this approach is extended to augment Coherent Scatter Radar (CSR) ion drift measurements from Super Dual Auroral Radar Network (SuperDARN), providing additional insights into EMPIRE’s estimated field-perpendicular ion motion. For an in-depth exploration of storm-related NILE, both EMPIRE and another data assimilation method, the Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension coupled with Data Assimilation Research Testbed (WACCM-X + DART), is implemented for a storm event to test the proposed NILE driving mechanism. Furthermore, this dissertation introduces a Kalman smoother technique into the EMPIRE to enhance its ability to assess past storm events, and to explore the potential for algorithm improvements.
Show less

Title: Self-Stigma, Disclosure, and Care-Seeking in People with Self-Reported Mental Illness
Creator: Shah, Binoy Biren
Date: 2023
Description: Objective: The longstanding mental illness treatment gap has only been exacerbated by the COVID-19 pandemic. One reason for this is the self...
Show moreObjective: The longstanding mental illness treatment gap has only been exacerbated by the COVID-19 pandemic. One reason for this is the self-stigma of mental illness, which has been shown to decrease care-seeking. This study aims to better understand the relationships between self-stigma and care-seeking by identifying novel mediators of this relationship. Method: A sample of 125 individuals with mental health difficulties, obtained from MTurk, completed measures of self-stigma, disclosure, care-seeking. Self-stigma was conceptualized as a distal antecedent to disclosure, and novel proximal antecedents of disclosure (i.e., approach goals, avoidance goals, and the “Why Try?” effect) were unpacked. Hypotheses were tested in steps via path analysis. Results: We found partial evidence to support our model of self-stigma. Disclosure did not mediate the relationship between self-stigma and care-seeking. Findings regarding proximal antecedents of disclosure were mixed. Conclusion: Results should be interpreted with caution due to data quality concerns. Additional research is needed to better understand how self-stigma impacts disclosure. This line of inquiry has noteworthy implications for research, policy, and clinical practice.
Show less

Title: Learning Stochastic Governing Laws from Noisy Data Using Normalizing Flows
Creator: McClure, William Jacob
Date: 2021
Description: With the increasing availability of massive collections of data, researchers in all sciences need tools to synthesize useful and pertinent...
Show moreWith the increasing availability of massive collections of data, researchers in all sciences need tools to synthesize useful and pertinent descriptors of the systems they study. Perhaps the most fundamental knowledge of a dynamical system is its governing laws, which describe its evolution through time and can be lever-aged for a number of analyses about its behavior. We present a novel technique for learning the infinitesimal generator of a Markovian stochastic process from large, noisy datasets generated by a stochastic system. Knowledge of the generator in turn allows us to find the governing laws for the process. This technique relies on normalizing flows, neural networks that estimate probability densities, to learn the density of time-dependent stochastic processes. We establish the efficacy of this technique on multiple systems with Brownian noise, and use our learned governing laws to perform analysis on one system by solving for its mean exit time. Our approach also allows us to learn other dynamical behaviors such as escape probability and most probable pathways in a system. The potential impact of this technique is far-reaching, since most stochastic processes in various fields are assumed to be Markovian, and the only restriction for applying our method is available data from a time near the beginning of an experiment or recording.
Show less

Title: Large Language Model Based Machine Learning Techniques for Fake News Detection
Creator: Chen, Pin-Chien
Date: 2024
Description: With advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into...
Show moreWith advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into content creators on social media or the streaming platforms sharing their personal ideas regardless of their education or expertise level. Distinguishing fake news is becoming increasingly crucial. However, the recent research only presents comparisons of detecting fake news between one or more models across different datasets. In this work, we applied Natural Language Processing (NLP) techniques with Naïve Bayes and DistilBERT machine learning method combing and augmenting four datasets. The results show that the balanced accuracy is higher than the average in the recent studies. This suggests that our approach holds for improving fake news detection in the era of widespread content creation.
Show less

Title: UNDERSTANDING MARIJUANA USE AS A TREATMENT OPTION FOR PEOPLE WITH EPILEPSY: USE, ATTITUDES, AND QUALITY OF LIFE
Creator: Johnson, Kristina
Date: 2021
Description: Epilepsy is the most common neurological disorder worldwide with a heterogeneous range of negative symptoms. Current treatments for epilepsy...
Show moreEpilepsy is the most common neurological disorder worldwide with a heterogeneous range of negative symptoms. Current treatments for epilepsy have side effects that can negatively impact the quality of a person’s life. Alternative treatments are being explored, including marijuana. This study aimed to understand marijuana use in adults with epilepsy across U.S. states. Rates of use, preferred method of use, and reasons to use and not use marijuana were examined. Additionally, levels of comfort discussing marijuana compared to other treatment options and with different types of providers were explored. Lastly, this was one of the first studies to examine the relationship between quality of life (QOL) and marijuana use for people with epilepsy. Participants included 128 individuals with epilepsy from 26 states, with non-legal states having significantly fewer people who reported using marijuana. Smoking was reported as the primary method of use, knowing someone else that uses as their primary reason for using, and health concerns as the primary reason not to use. There was no difference in level of comfort discussing marijuana compared to other treatments, and participants reported feeling most comfortable discussing marijuana with neurologists compared to other providers. Finally, total QOLIE-31 and the social functioning subscale were significantly lower among marijuana users; however, this difference did not remain when anxiety was entered as a covariate. In fact, the relationship between anxiety and QOL was significant, with anxiety accounting for η2 = .12 to η2 = .57 of the variance in QOLIE-31 subscale scores, controlling for marijuana use. Findings from this study further the understanding of marijuana use by people with epilepsy in the United States.
Show less

Title: Large Language Model Based Machine Learning Techniques for Fake News Detection
Creator: Chen, Pin-Chien
Date: 2024
Description: With advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into...
Show moreWith advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into content creators on social media or the streaming platforms sharing their personal ideas regardless of their education or expertise level. Distinguishing fake news is becoming increasingly crucial. However, the recent research only presents comparisons of detecting fake news between one or more models across different datasets. In this work, we applied Natural Language Processing (NLP) techniques with Naïve Bayes and DistilBERT machine learning method combing and augmenting four datasets. The results show that the balanced accuracy is higher than the average in the recent studies. This suggests that our approach holds for improving fake news detection in the era of widespread content creation.
Show less

Title: Two essays on corporate finance and risk management
Creator: LI, YANFENG
Date: 2021
Description: This dissertation consists of two essays. The first essay examines the interaction effect of human capital investment in firms with dual-class...
Show moreThis dissertation consists of two essays. The first essay examines the interaction effect of human capital investment in firms with dual-class shares (DCS) structure. In this study, I find that although more input in human capital, measured by employee welfare index (EWI), can enhance the valuation of single-class (SCS) firms, human capital investment in DCS firms is not valued by the market, but even hurts firm value. This result is consistent with the prediction of agency theory. The management entrenchment effect in DCS firms causes valuation discount when managers can transfer private benefit through investing in humans. To get a robust result, I use propensity score matched data of SCS and DCS firms and get the same conclusion. Overall, my paper provides the evidence that human capital investment plays a different role in firm value under different circumstances, especially under different ownership structures.The second essay examines the relationship between director network centrality and firm credit risk. By using a comprehensive data including both rated firms and unrated firms, I discover that director network is positively associated with firm’s probability of default. This positive effect is more robust in firms without agency credit ratings. I further examine that when firm’s cash flow increases, firm’s default risk increases with director network. But when investment increases and firm’s debt finance increases, the default risk decreases with director network. These combined results imply that director network leads to more agency problems when firms have plenty of cash flow but benefit firms when the cash flow goes into investment or when directors utilize their network to get more debt finance for firms. Also, I find that director network helps loss firms other than profitable firms and decreases firm default risk during the financial crisis.
Show less

Title: WIM BASED LIVE LOAD FACTORS FOR CONSISTENT ILLINOIS BRIDGE RELIABILITY
Creator: Chi, Jingya
Date: 2019
Description: The Load and Resistance Factor Rating (LRFR) approach was developed in the early 2000s. The live-load factors were calibrated at that time so...
Show moreThe Load and Resistance Factor Rating (LRFR) approach was developed in the early 2000s. The live-load factors were calibrated at that time so that bridges rated by the LRFR approach could achieve a uniform structural reliability. However, the first calibration of the live-load factors was intent on the applications to the entire nation, without considering state-specific traffic conditions and truck restrictions. In addition, the calibration was carried out using limited data collected from the weigh stations of Ontario, Canada, in the 1970s. Therefore, to develop a practice that is consistent with the current LRFR approach as well as considering the state-specific live-load effects has motivated us to conduct this study.We study the weigh-in-motion (WIM) data that have been collected by the Department of Transportation of several states (i.e., Michigan, New York, Minnesota, California, Illinois, Oregon, Kentucky and Pennsylvania). These data contain approximate four years (i.e., from 2013 to 2017) of continuously-recorded trucks. They provide the information about truck weight and configuration, as well as the truck traffic pattern. In this research, we focus on the data collected by the Illinois DOT to calibrate live-load factors for the Illinois LRFR highway bridge evaluation.We first propose and verify a simulation method to statistically restore the missing trucks in the second lanes in the Illinois data. Based on the concept of relative calibration, we propose 3 sets of live-load factors for the Illinois legal, routine permit and special permit load ratings. Then we conduct a sensitivity analysis on the overweight trucks. Finally, we study the effect of law enforcement on the calibration.
Show less

Title: THE SPATIAL BLOCK: NATURAL VENTILATION IN HOT AND DRY CLIMATES OF TURKEY
Creator: BAY, EZGI
Date: 2020
Description: The housing deficit is a global problem. In Turkey, solutions to remedy scarce, unaffordable, and low-grade housing are being proposed by TOKI...
Show moreThe housing deficit is a global problem. In Turkey, solutions to remedy scarce, unaffordable, and low-grade housing are being proposed by TOKI, the governmental mass housing administration. Its residential projects based on ‘standard regulations’ and ‘high-rise typologies’ have been widely criticized. The ‘one size fits all’ approach is known for its limited exploration of contemporary needs of this society. Low quality urban and architectural conditions in TOKI projects are believed to marginalize the living standards of the residents. Sprawling rapidly throughout different regions around the country, a permanent complaint of TOKI residents is related to outdoor and indoor thermal conditions. As consequence of this ‘homogenization effect’, overheated and underheated conditions are experienced in these ‘naturally ventilated buildings’ designed with few considerations regarding the surrounding environment. Minimal research has been done on how TOKI towers perform under extreme seasonal conditions and what other building forms could be used in consonance with localized Turkish climates. Most TOKI projects have been developed for ‘hot and dry climates’ that also correspond to areas with larger urban growth from recent migrations. Through post-occupancy evaluations, this dissertation investigates a TOKI built in this climatic context. At the same time, this study brings new ‘typological’ alternatives analyzed through energy simulations and computer fluid dynamics (CFD). These methods are intended to bring clarity about the dynamic of thermal stress inside this project, and how renewable sources, such as prevailing winds, could be used to alleviate thermal related problems in consonance with ‘building forms’ derived from ‘vernacular architecture’ in this region.Inputs from residents illustrate the dynamics of thermal stress and reliance on natural ventilation in summer conditions. It is confirmed through results of the Predicted Percentage Dissatisfied (PPD) and the Air Changes per Hour (ACH) obtained from Simulations in the IES-VE software. The relationship between human thermal comfort and indoor microclimate in TOKI housing can be improved through the reformulation of its residential typologies. The ‘Spatial Block’ approach presented in this dissertation brings the idea of how urban and architectural decisions in addition to improving indoor climatic conditions and thermal satisfaction or residents, brings them improved social integration.
Show less

Title: LOCAL VISCOELASTIC PROPERTIES OF SOFT ANISOTROPIC FIBROUS TISSUE
Creator: Gallo, Nicolas Remy
Date: 2020
Description: The current aging population, with more than 80 million "baby boomers", will present a steep medical challenge for our society in a...
Show moreThe current aging population, with more than 80 million "baby boomers", will present a steep medical challenge for our society in a foreseeable future. Half of the adults over 85 years old are predicted to be diagnosed with Alzheimer's disease by 2050. With healthcare cost reaching over 700 billion dollars in the United States, early detection of Alzheimer's disease (AD) and other co-existing neurodegenerative diseases is crucial to improve the recovery odds in patients and to decrease individual care cost. This work seeks to tackle this problem by proposing a novel computational framework toward improving the measurement of shear visco-elastic properties of brain white matter (WM), which vary with age. These measurements practically represent the effective (average) response of many cells and are typically obtained by using rheology or elastography. Although the former is direct, the latter requires the solution of an inverse problem based on a priori mechanical tissue model. The mechanical anisotropy of WM has previously not been fully explored although many inconsistencies have been reported in brain MRE experiments. To account for these inconsistencies a transversely isotropic constitutive model for the brain WM is proposed to interpret prior experiments involving 7 young and 4 older healthy men. By employing a novel inversion scheme, we report the local variation of the effective transverse and axial shear moduli in two well aligned WM structures (corpus callosum: CC; and cortical spinal tract: CST) for both the young and old cohort of healthy subjects part of the study. This work reports statistically significant changes in local regional variation of the transverse modulus across the CC for the young cohort. In the older cohort, the trend was similar yet not statistically significant. A novel candidate biomarker, the shear anisotropy metric, defined as the ratio of the transverse and axial shear moduli, found statistically significant local regional variation across the CC but not in the CST. Healthy aging was observed to decrease both transverse and axial in both CC and CST, although the variation was significant only for the CC. Finally, in an effort to understand the cause of effective transverse mechanical properties variation in WM with aging, the connection between effective and intrinsic contribution of WM cellular constituents is established. The intrinsic mechanical contributions of axons and glial matrix are separated by fitting the estimates of the effective shear moduli to a microscopic composite fiber model of myelinated axons embedded in the glial matrix. This work provides a method to establish a baseline for healthy brain mechanical properties thus promising to increase the specificity of MRE toward early diagnosis of neurodegenerative diseases. Additional oscillating disc rheology experiments with decellularized porcine myocardium, and the fabrication of a stable heterogeneous phantom matching the mechanical, diffusional and electrical properties of the WM provide foundational knowledge for due development and validation of MRE methodologies employed in other tissues.
Show less

Title: WHY AND WHY-NOT PROVENANCE FOR QUERIES WITH NEGATION
Creator: Lee, Seokki
Date: 2020
Description: Explaining why an answer is in the result of a query or why it is missing from the result is important for many applications including...
Show moreExplaining why an answer is in the result of a query or why it is missing from the result is important for many applications including auditing, debugging data and queries, hypothetical reasoning about data, and data exploration. Both types of questions, i.e., why and why-not provenance, have been studied extensively, but mostly in isolation. A recent study shows that unification of why and why-not provenance can be achieved by developing a provenance model for queries with negation. In many complex queries, negation is natural and yields more expressive power. Thus, supporting both types of provenance and negation together can be useful for, e.g., debugging (missing) data over complex queries with negation. However, why-not provenance and — to a lesser degree — why provenance, can be very large resulting in severe scalability and usability challenges.In this thesis, we introduce a framework that unifies why and why-not provenance. We develop a graph-based provenance model that is powerful enough to encode the evaluation of queries with negation (First-Order queries). We demonstrate that our model generalizes a wide range of provenance models from the literature. Using our model, we present the first practical approach that efficiently generates explanations, i.e., parts of the provenance that are relevant to the query outputs of interest. Furthermore, we present a novel approximate summarization technique to address the scalability and usability challenges. Our technique efficiently computes pattern-based provenance summaries that balance informativeness, conciseness, and completeness. To achieve scalability, we integrate sampling techniques into provenance capture and summarization. We implement these techniques in our PUG (Provenance Unification through Graphs) system which runs on top of a relational database. We demonstrate through extensive experiments that our approach scales to large datasets and produces comprehensive and meaningful (summaries of) provenance.
Show less

Title: FUNCTIONALIZED NANOSCALE MATERIALS FOR PROTEIN BIOMARKER DETECTION
Creator: Zhang, Youwen
Date: 2020
Description: Proteins are vital biomolecules in living organisms which function as the working element for many aspects of life. An abnormal expression of...
Show moreProteins are vital biomolecules in living organisms which function as the working element for many aspects of life. An abnormal expression of proteins or expression of unique proteins is often associated with certain disease. Accordingly, proteins have become valuable biomarkers for disease diagnosis and prognosis. So far, numerous methods have been developed for detections of protein biomarkers. However, most of them suffer from the lack of accuracy, sensitivity, and specificity for clinical diagnostic applications. With the rapid advancement in nanotechnology, functional nanoscale materials, which could overcome the biocompatibility and biological recognition ability, have been widely used to develop sensitive and selective biosensors.In this dissertation, two kinds of functionalized nanoscale materials-based sensing strategies are investigated for protein biomarker detection. One strategy takes advantages of graphene oxide (GO) and utilizes fluorescence resonance energy transfer (FRET) for fast and sensitive protease detection by covalent attaching fluorescently labeled protease substrate peptide to the GO surface. This type of GO-based fluorescence sensor is highly sensitive (with a detection limit of picomolar concentration) and selective (other structure similar proteases does not interfere with the target analyte detection). In addition, it could accurately analyze serum samples. With this strategy, we have successfully achieved the detection of the HIV-1 PR (HIV-1 protease, a significant biomarker for AIDS) and ADAMs (a disintegrin and metalloproteinases, a biomarker for human cancers). It could be visualized that this GO platform could be utilized to detect various proteases by only changing the peptide substrate and solution pH. In addition, by coupling multiple substrate peptides on the GO surface, we developed a multiplex GO sensing system for simultaneously profiling of the activities of a panel of MMPs/ADAMs. Under the assistance of joint entropy and programming, our sensor could identify up to 5 types of human cancers, and offers the potential to detect other cancer types by changing biomarkers.The other strategy is to utilize nanopore stochastic sensing to detect proteins, which involves measuring the ionic current modulation generated by analytes’ electro-osmotic flow through a chemical functionalized nanoscale sized pore. As a sensitive and label-free technique, nanopores have been highly recognized as one of the emerging techniques to detect analytes at the single-molecule level. Unlike DNA molecules which are uniformly charged, proteins are an isotropically charged molecules, which have low translocation probability through a nanopore. Since the protein pore-based sensing system is not suitable as deployable tools for detection of proteins due to the size limitation and fragile nature of the biological membranes. In this project, we fabricated solid-state nanopores using PET membranes followed by chemical functionalization of their inner surfaces. The modified- PET nanopore was sensitive and could detect HIV-1 protease at picomolar concentration. More importantly, the modified-nanopore sensor was selective, and could differentiate the target protein from others such as Trypsin, BSA and HSA. Furthermore, the modified PET nanopore strategy developed in this work provide a general platform for exploring fundamental protein dynamics and rapid detection of proteins at the single-molecule level
Show less

Title: Far Eastern Spatial Techique Utilized in Architectural Design
Creator: Qian, Zhao
Date: 2010-05-01, 2010-05-01
Description: This project spans techniques used in path, enclosure, play of light, perspective illusion, and time measurement. Concepts are arrived at by...
Show moreThis project spans techniques used in path, enclosure, play of light, perspective illusion, and time measurement. Concepts are arrived at by rational thought, expressing individual experience accessible by persons practicing meditation.
Show less

Title: RECIPROCAL INTERACTIONS BETWEEN RED RASPBERRY POLYPHENOLS AND GUT MICROBIOME COMPOSITION AND METABOLIC HEALTH
Creator: Zhang, Xuhuiqun
Date: 2020
Description: Red raspberries (RRB) and fructo-oligosaccharides (FOS) have been associated with reduced risk of developing cardio-metabolic diseases. RRB...
Show moreRed raspberries (RRB) and fructo-oligosaccharides (FOS) have been associated with reduced risk of developing cardio-metabolic diseases. RRB are uniquely high in anthocyanin- and ellagitannin- type (poly)phenols, however, these (poly)phenols have low bioavailability. Gut microbiota can improve (poly)phenol bioavailability through fermentation processes generating absorbable metabolites and altering gut microbiota structure. However, clinical evidence on the effects of RRB intake on the gut microbiome, (poly)phenolic metabolites and metabolic health is lacking. Further, fermentable carbohydrates (FOS) that selectively stimulate gut microbiota growth may enhance metabolite generation. Therefore, the aim of this research is to investigate the interactions between the gut microbiome and RRB, and explore added effects of FOS as a possible nutritional strategy for improving metabolic health of at risk individuals with prediabetes and insulin resistance (PreDM-IR). Through a series of investigations drawn from a randomized clinical trial (RCT), the following hypotheses were tested: 1) Individuals with PreDM-IR will have a distinctive gut microbiome, and lower capacity to metabolize (poly)phenols compared to healthy individuals; 2) RRB intake for 4-week will increase microbial-derived (poly)phenolic metabolites and adding FOS will augment the effect; 3) RRB intake will improve metabolic risk factors in PreDM-IR and adding FOS will augment the RRB effect; 4) RRB and RRB+FOS supplementations will alter the structure of the gut microbiome explaining variances observed in metabolites and metabolic outcomes. In this single-blinded, crossover RCT, adults with PreDM-IR (n=26) and a healthy group (n=10) consumed 1 cup RRB (fresh weight equivalence) per day or RRB with 8g FOS per day for 4 weeks in random order separated by 4-week washout. Metabolic risk factors, (poly)phenolic metabolites and metagenomic profile were assessed before and after supplementation. Baseline characterization before supplementation revealed distinctive metabolites and metagenomics profiles related to metabolic status. After 4-week RRB, microbial (poly)phenolic metabolites, metabolic health indices and gut microbiome structure beneficially shifted in PreDM-IR group. Adding FOS increased specific microbial species and phenolic metabolites that correlated with β-cell function in PreDM-IR. Overall, nutritional strategies incorporating RRB and FOS may improve metabolic health of individuals with PreDM-IR through modulating gut microbiome composition and the capacity to metabolize RRB (poly)phenols.
Show less

Title: WIENER-HOPF FACTORIZATION FOR TIME-INHOMOGENEOUS MARKOV CHAINS AND BAYESIAN ESTIMATIONS FOR DIAGONALIZABLE BILINEAR STOCHASTIC PARTIAL DIFFERENTIAL EQUATIONS
Creator: Cheng, Ziteng
Date: 2021
Description: This thesis consists of two major parts, and contributes to two areas of research in stochastic analysis: (i) Wiener-Hopf factorization (WHf)...
Show moreThis thesis consists of two major parts, and contributes to two areas of research in stochastic analysis: (i) Wiener-Hopf factorization (WHf) for Markov Chains, (ii) statistical inference for Stochastic Partial Differential Equations (SPDEs).WHf for Markov chains is a methodology concerned with computation of expectation of some types of functionals of the underlying Markov chain. Most results in WHf for Markov chains are done in the framework of time-homogeneous Markov chains. The major contribution of this thesis in the area of WHf for Markov chains are: • We extend the classical theory to the framework of time-inhomogeneous Markov chains. • In particular, we establish the existence and uniqueness of solutions for a new class of operator Riccati equations. • We connect the solution of the Riccati equation to some expectations of interest related to a time-inhomogeneous Markov chain. Statistical inference for SPDEs regards estimating parameters of a SPDE based on available and relevant observations of the underlying phenomenon that is modeled by the given SPDE. We summarize the contribution of this thesis in the area statistical inference for SPDEs as follows: • We conduct the statistical inference for a diagonalizable SPDE driven by a multiplicative noise of special structure, using spectral approach. We show that the corresponding statistical model fits the classical uniform asymptotic normality (UAN) paradigm. • We prove a Bernstein-Von Mises type result that strengthens the existing results in the literature. • We prove the asymptotic consistency, asymptotic normality and asymptotic efficiency of two Bayesian type estimators.
Show less

repository.iit

Search the repository

Pages

Pages