Doctor of Philosophy, The Ohio State University, 2024, Environmental Science

Despite significant investments in preparedness, decision making systems in the United States were largely unable to effectively navigate the scale, intensity, and extended duration of the COVID-19 pandemic. This dissertation offers a reference framework for organizational and technological decision support that can augment institutional capabilities in a crisis. It examines three unique stages of a decision support cycle: (1) course of action determination, including problem definition, identification of alternative interventions, and assessment of the tradeoffs between interventions; (2) agile and adaptive deployment of interventions, often under severe resource constraints and time pressure; and (3) active monitoring that seeks to understand the factors influencing the success or failure of interventions. The first decision support stage is examined through the Comprehensive Monitoring Team (CMT) model, which we developed to serve as a repeatable process for producing intelligence analysis in the crisis setting. The model is presented and explored through case studies of its implementation within a state government and a large public university. The second decision support stage is examined through our Flexible Adaptive Algorithmic Surveillance Testing (FAAST) model, which provides an analytical approach to balancing scarce resource allocation problems using bandit search algorithms. Building on a real-world pilot study of FAAST, a playbook is presented that can be used as a template for modifying and re-deploying the approach under a range of distinct circumstances in the future. Finally, the third decision support stage is examined through a concept for misinformation monitoring that we call the “Sentinel Node” approach, which provides a structured means of analyzing the complex online information ecosystem that influences behavior in a public health emergency. The tools themselves, as well as the principles underlying their design and application, collectively (open full item for complete abstract)

Committee: Tanya Berger-Wolf (Advisor); Ayaz Hyder (Committee Member); Jeffrey Bielicki (Committee Member); Mike Rayo (Committee Member); Amy Fairchild (Committee Member) Subjects: Environmental Health; Information Science; Public Health; Systems Design

MS, University of Cincinnati, 2024, Medicine: Cancer and Cell Biology

Glioblastoma multiforme poses significant treatment challenges, attributed to its distinctive characteristics and immune evasion mechanisms, and carries a grim prognosis despite advancements in therapy. Therefore, rigorous efforts are necessary to uncover additional therapies or other unconventional strategies. Recently, immunotherapies have gained significant traction due to their success in certain malignancies. Immunotherapies use the host's immune system to enhance the ability to combat cancer. These therapies have been in development for over 100 years, and many of them are currently under investigation for treating Glioblastoma multiforme in preclinical and clinical studies. Immunotherapies include antibody therapies, including immune checkpoint blockade, adoptive T cell therapies, cytokine therapies, cancer vaccines, and oncolytic viruses. A deeper understanding of the origins of immunotherapy, immune system dynamics in response to cancer, immune evasion mechanisms of glioblastoma, and the various immunotherapeutic strategies may facilitate the development of novel and effective treatment strategies for glioblastoma. Here, we review the different immunotherapeutic approaches and propose possible combinatory strategies.

Committee: Qing Lu Ph.D. (Committee Chair); David Plas Ph.D. (Committee Member); Biplab DasGupta Ph.D. (Committee Member); David Haslam M.D. (Committee Member) Subjects: Oncology

Doctor of Business Administration (D.B.A.), Franklin University, 2022, Business Administration

The COVID-19 pandemic has challenged student success and the ability of colleges to deliver the education students need for the workplace. The purpose of this qualitative dissertation case study was to investigate how the organizational resiliency of a Midwest community college impacted student success during the COVID-19 pandemic by examining the contributions of static and dynamic resiliency characteristics and dimensions. Exploration and analysis of the resilient characteristics and dimensions of student success focus on the key factors of instructional modality, instructor-student engagement, and socioeconomic influences. Conclusions are based on thematic analysis of semi-structured interviews with the college's senior leaders triangulated with information from public documents and a student survey. The researcher concludes that the college's static resiliency components of solid planning and infrastructural preparation for probable events, a longstanding collaborative commitment to achieving key elements of student success, and effective internal communication processes produced the robust capacity for flexibility and innovation that distinguishes dynamic resiliency. Innovations that accelerated decision processes, faculty and staff encouraged to experiment based on regular feedback, and energetic action on non-academic stresses had positive effects on student success during the COVID-19 pandemic. One result was that for the fiscal year 2021-2022, the college awarded the highest number of degrees in its history to all students and to African American males, with 99 percent of students rating the quality of their education as good to excellent.

Committee: Charles Fenner (Committee Chair); John Nadalin (Committee Member); Gary White (Committee Member) Subjects: Business Administration

Doctor of Philosophy, The Ohio State University, 2020, Industrial and Systems Engineering

Responding to anomalies in the critical digital services domain involves coordination across a distributed system of automated subsystems and multiple human roles (Allspaw, 2015; Grayson, 2019). Exploring the costs of this joint activity is an underexamined area of study (Woods, 2017) but has important implications for managing complex systems across distributed teams and for tool design and use. It is understood that anomaly recognition is a shared activity between the users of the service, the automated monitoring systems, and the practitioners responsible for developing and operating the service (Allspaw, 2015). In addition, multiple, diverse perspectives are needed for their different views of the system and its behavior and their ability to recognize unexpected and abnormal conditions. While the collaborative interplay and synchronization of roles is critical in anomaly response (Patterson et al, 1999; Patterson & Woods, 2001), the cognitive costs for practitioners (Klein et al, 2005; Klinger & Klein, 1999; Klein, 2006) can be substantial. The choreography of this joint activity is shown to be a subtle and highly integrated into the technical efforts of dynamic fault management. This work uses process tracing to take a detailed look at a corpus of five cases involving software engineers coping with unexpected service outages of varying difficulty. In doing so, it is noted that the practices of incident management work very differently than domain models suggest and the tooling designed to aid coordination incurs a cognitive cost for practitioners. Adding to the literature on coordination in ambiguous, time pressured and non co-located groups, this study shows that adaptive choreography enables practitioners to cope with dynamic events – and dynamic coordination demands. These demands can also be a function of the coordination strategies of others – in particular when they shift costs of coordination across time and organizational boundaries.

Committee: David Woods (Advisor); Michael Rayo (Committee Member); Philip Smith (Committee Member) Subjects: Computer Science; Engineering

Doctor of Philosophy, University of Akron, 2019, Biomedical Engineering

Cancer treatments have shifted toward using therapies that target specific oncogenic molecules in cells. However, cancer cells develop adaptive responses to such targeted therapies and render them ineffective. Understanding mechanisms of resistance to targeted therapies is critical to improve the treatments outcome. We addressed this unresolved problem using three-dimensional (3D) tumor spheroid cultures under long-term cyclic treatment and recovery cycles. This regimen mimicked how cancer patients receive chemotherapy and allowed cancer cells to develop adaptive resistance to inhibitors of oncogenic protein kinases. Our phenotypic compound testing paralleled with comprehensive molecular analysis of tumor spheroids identified activation of specific signaling pathways that promoted adaptive drug resistance of colorectal cancer cells. Using a design-driven approach, we developed drug combinations that effectively suppressed both initially active and treatment-induced oncogenic signaling in cancer cells. To address potential toxicity of drug combinations to normal tissues, we identified low-dose synergistic drug pairs and demonstrated their use in a sequential combination treatment regimen to effectively inhibit growth of tumor spheroids and minimize toxicity to normal cells. Incorporating patient-derived cells in our 3D tumor model will help identify drivers of drug resistance toward realizing personalized cancer medicine with effective and safe combinations of targeted drugs.

Committee: Hossein Tavana PhD (Advisor); Marnie Saunders PhD (Committee Member); Ge Zhang PhD (Committee Member); Sailaja Paruchuri PhD (Committee Member); Nic Leipzig PhD (Committee Member) Subjects: Biology; Biomedical Engineering; Engineering

Master of Science in Mechanical Engineering (MSME), Wright State University, 2019, Mechanical Engineering

Integrally Bladed Rotors (IBR) of aircraft turbine engines suffer from fluctuations in the dynamic response that occurs due to blade to blade geometric deviations. The Stochastic Approach for Blade and Rotor Emulation (SABRE) framework has been used to enable a probabilistic study of mistuned blades in which a reduced order modeling technique is applied in conjunction with sets of surrogate models, called emulators, to make predictions of mistuned mode shapes. SABRE has proven useful for non-switching mode shapes. However, switching mode shapes have non-stationary or discontinuous response surfaces which reduce the accuracy of the surrogate models used in SABRE. To improve emulator accuracy, the methodology proposed in this thesis was developed. This methodology improves prediction quality by identifying and eliminating non-stationary and discontinuous portions of the response with the classification decision boundary methodology, efficiently identifying areas of inaccuracy while improving the surrogate as efficiently as possible with adaptive sampling, and alleviating the computational burden associated with large numbers of finite element samples required to build accurate emulators.

Committee: Harok Bae Ph.D. (Advisor); Ahsan Mian Ph.D. (Committee Member); Jeffrey Brown Ph.D. (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2018, Comparative Veterinary Medicine

Porcine reproductive and respiratory syndrome (PRRS) continues to be a huge threat to the swine industry globally. Development of effective vaccines is still a best measure for controlling PRRS and reducing economic losses. Due to advancements in global trade, co-infection of both PRRSV species occurs in the same herd. In this study, we employed new vaccine development strategies to induce an increased breadth of immunity against both PRRSV species. In PRRSV-1 and PRRSV-2 co-infected MARC-145 cells we observed a strong interference of PRRSV-2 on PRRSV-1 replication, especially in concurrent co-infected cells. We used PRRSV-1 and PRRSV-2 modified live virus vaccines (MLV) to analyze the efficacy of both consecutive and concurrent vaccination methods administered intramuscularly, either 3 days apart (PRRSV-1 MLV followed by PRRSV-2 MLV, (consecutive) or together on the same day (concurrent) against homologous challenge in pigs. We showed that the consecutive vaccination method appears to provide better clearance of both PRRSV species through induction of increased frequency of PRRSV-1 and PRRSV-2 specific IFN-¿ positive T-helper/memory and cytotoxic T cells responses, with increased virus neutralization antibody titers against both PRRSV-1 and PRRSV-2. Further, PRRSV MLVs have a safety concern, however killed vaccines are safe but have an efficacy concern due to their less immunogenicity. In that study, we employed killed vaccines against PRRSV-1 and PRRSV-2 species along with a potent mucosal adjuvant, like non-toxic heat labile (LT) enterotoxin for priming, followed by oral boosting with immunogenic conserved T-cell epitopes of PRRSV expressed by non-pathogenic E. coli against respective homologous challenge virus infections in pigs. We showed that our vaccination strategy induced a robust cell-mediated immune response, especially T cell responses against both PRRSV-1 and PRRSV-2, and improved the overall adaptive immune response in pigs. Further studies are nee (open full item for complete abstract)

Committee: Renukaradhya Gourapura PhD (Advisor); Daral Jackwood PhD (Committee Member); Jianrong Li PhD (Committee Member); Qiuhong Wang PhD (Committee Member) Subjects: Immunology; Microbiology; Molecular Biology; Veterinary Services; Virology

Doctor of Philosophy, The Ohio State University, 2011, Veterinary Preventive Medicine

The goal of study was to understand the persistence distribution and immunopathogensis of infectious bursal disease virus in chickens. A series of experiments were designed to determine the persistence, distribution and quantification of IBDV strains in lymphoid and non-lymphoid tissues of specific pathogen free (SPF) chickens and commercial broiler chickens. Five separate experiments were conducted using 2 and 4 weeks old SPF chickens and 2 weeks old commercial broilers having maternally derived antibodies. The virus strains were detected the longest in bursal tissues followed by spleen, thymus and bone marrow. In non-lymphoid tissues, both of the strains were detected for the longest period in the caecum followed by liver, kidney, pancreas, lungs, thigh muscles and breast muscles. Although the virus can persist in the bursa of SPF chickens for 4 weeks, it is very unlikely that the infectious virus will be present in the processed meat. In addition, the RT-PCR results are not sufficient to indicate the presence of the infectious virus. A study was conducted to better understand the innate and adaptive immune responses in chickens against IBDV. Three-week-old specific pathogen free chickens were inoculated intraocularly with standard challenge strain (STC) (cIBDV) and a variant strain Indiana (IN) (vIBDV). The cIBDV produced more pronounced bursal damage and inflammatory response as compared to vIBDV. There were significant differences in the expression of innate (IFN-α and IFN-β), proinflammatory cytokine and mediator (IL-6 and iNOS) in cIBDV- and vIBDV-infected bursas. The expression of chemokines genes, IL-8 and MIP-α was also higher in cIBDV-infected chickens during the early phase of infection. The expression of Toll like receptor 3 (TLR3) was downregulated at PIDs 3, 5, and 7 in the bursas of vIBDV-infected chickens whereas TLR3 was upregulated at PIDs 3 and 5 in cIBDV-infected bursas. In vIBDV-infected bursa, TLR7 expression was downregulated at PIDs 3 and 5 (open full item for complete abstract)

Committee: Yehia Saif Dr. (Advisor); Daral Jackwood Dr. (Committee Member); Chang Won Lee Dr. (Committee Member); Gireesh Rajashekara Dr. (Committee Member); Renukaradhya Gourapura Dr. (Committee Member) Subjects: Virology

Doctor of Philosophy, The Ohio State University, 2008, Ohio State Biochemistry Program

During cancer progression, cells encounter many stress signals and all along they have built-in mechanisms to eliminate themselves. The successful cancer cells managed to foil this hardwired stress response. Emerging evidence indicates that some of the genes that normally function to eliminate the cells are co-opted to become oncogenes. How the cellular (normal vs. cancerous) context determines that some genes undergo this “Jekyll-and-Hyde” conversion is an intriguing but largely unresolved issue in cancer biology. In my work over the past 5 years, I found ATF3, an ATF/CREB family transcription factor encoded by an adaptive-response gene, is a new regulatory molecule with a dichotomous role. It enhances apoptosis in untransformed cells, but protects the cells from stress-induced death and promotes cell motility in malignant cancer cells. In an in vivo xenograft mouse model, in addition to promoting primary tumor growth, ATF3 also increases lung metastasis. To explore the potential mechanisms by which ATF3 promotes metastasis, I set out to address three major questions. First, as a transcription factor, will ATF3 regulate the expression of some target genes involved in cell motility? Second, as an adaptive response gene, will ATF3 be induced in cancer cells in response to stromal signals and whether the induction of ATF3 mediates cancer cells' response to stromal signals? Third, will the induction of ATF3 in cancer cells feed back on stromal cells to affect stroma-cancer interaction? By examining potential ATF3 target genes, I found ATF3 regulates a set of genes involved in cell motility, some of which were proved to be direct targets of ATF3. As an adaptive-response gene, ATF3 was induced by multiple stromal signals, such as TGFβ, TNFα and IL-1β. When focusing on one multifunctional cytokine, TGFβ, I found that ATF3 can mediate TGFβ effects on target gene expression and cell motility regulation. The interaction between ATF3 and Smad2/3 offers a partial mechan (open full item for complete abstract)

Committee: Tsonwin Hai (Advisor); James DeWille (Committee Member); Anthony Young (Committee Member); Mike Zhu (Committee Member) Subjects: Biochemistry; Cellular Biology; Molecular Biology; Oncology

Doctor of Philosophy, The Ohio State University, 2008, Statistics

Computer simulations have become increasingly popular as a method for studying physical processes that are difficult to study directly. These simulations are based on complex mathematical models that are believed to accurately describe the physical process. We consider the situation where these simulations take a long time to run (several hours or days) and hence can only be conducted a limited number of times. As a result, the inputs (design) at which to run the simulations must be chosen carefully. For the purpose of fitting a response surface to the output from these simulations, a variety of designs based on a fixed number of runs have been proposed.In this thesis, we consider sequential adaptive designs as an “efficient” alternative to fixed-point designs. We propose new adaptive design criteria based on a cross validation approach and on an expected improvement criterion, the latter inspired by a criterion originally proposed for global optimization. We compare these new designs with others in the literature in an empirical study and they shown to perform well. The issue of robustness for the proposed sequential adaptive designs is also addressed in this thesis. While we find that sequential adaptive designs are potentially more effective and efficient than fixed-point designs, issues such as numerical instability do arise. We address these concerns and also propose a diagnostic tool based on cross validation prediction error to improve the performance of sequential designs. We are also interested in the design of computer experiments where there are control variables and environmental (noise) variables. We extend the implementation of the proposed sequential designs to achieve a good fit of the unknown integrated response surface (i.e., the averaged response surface taken over the distributions of the environmental variables) using output from the simulations. The goal is to find an optimal choice of the control variables while taking into account the distr (open full item for complete abstract)

Committee: WILLIAM NOTZ PhD (Advisor); THOMAS SANTNER PhD (Committee Member); ANGELA DEAN PhD (Committee Member) Subjects: Statistics

Doctor of Philosophy, The Ohio State University, 2005, Computer and Information Science

At a cocktail party, we can selectively attend to a single voice and filter out other interferences. This perceptual ability has motivated a new field of study known as computational auditory scene analysis (CASA) which aims to build speech separation systems that incorporate auditory principles. The psychological process of figure-ground segregation suggests that the target signal should be segregated as foreground while the remaining stimuli are treated as background. Accordingly, the computational goal of CASA should be to estimate an ideal time-frequency (T-F) binary mask, which selects the target if it is stronger than the interference in a local T-F unit. This dissertation investigates four aspects of CASA processing: location-based speech segregation, binaural tracking of multiple moving sources, binaural sound segregation in reverberation, and monaural segregation of reverberant speech. For localization, the auditory system utilizes the interaural time difference (ITD) and interaural intensity difference (IID) between the ears. We observe that within a narrow frequency band, modifications to the relative strength of the target source with respect to the interference trigger systematic changes for ITD and IID resulting in a characteristic clustering. Consequently, we propose a supervised learning approach to estimate the ideal binary mask. A systematic evaluation shows that the resulting system produces masks very close to the ideal binary ones and large speech intelligibility improvements. In realistic environments, source motion requires consideration. Binaural cues are strongly correlated with locations in T-F units dominated by one source resulting in channel-dependent conditional probabilities. Consequently, we propose a multi-channel integration method of these probabilities in order to compute the likelihood function in a target space. Finally, a hidden Markov model is employed for forming continuous tracks and automatically detecting the number of act (open full item for complete abstract)

Committee: DeLiang Wang (Advisor) Subjects:

Master of Sciences, Case Western Reserve University, 2008, Biology

Helicobacter pylori (H. pylori) infects the stomach of over half of the worlds population and often results in gastritis and ulcers. In addition, the World Health Organization has categorized H. pylori as a class I carcinogen. Still, little is known about how H. pylori induced gastritis arises and resolves. The adaptive immune response to H. pylori shapes proclivity to gastritis and clearance of infection. Recently, interaction of Leptin with its receptor has been recognized to modulate immune responses, and particularly reduces the T cells ability to incite inflammation. We hypothesized that the LepR modulates the adaptive immune response and thereby plays a significant role in the clearance of H. pylori from gastric mucosa. The data indicate that the LepR on CD4+ T cells is required for clearance of H. pylori from infected SCID mice adoptively transferred with T cells.

Committee: James Zull PhD (Committee Chair); Nancy DiIulio PhD (Advisor); John Nedrud PhD (Committee Member); Radhika Atit PhD (Committee Member) Subjects: Biology; Biomedical Research; Immunology