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Heat shock protein 70 is an evolutionary conserved molecular chaperone responsible for the protein quality control functions. It is involved in many critical cellular processes, including folding protein ‘clients’, modulation of protein-protein interactions, and transport of proteins across membranes. Hsp70s are critical for maintaining cell viability in response to a large variety of cellular stresses. Perturbation of the proteostasis network is implicated in many diseases ranging from cancer and neurodegeneration to genetic disorders. Hsp70s are highly modified at the post-translational level. All these modifications together are referred to as the “chaperone code. These modifications fine-tune chaperone function, altering chaperone activity, localization, and selectivity. Understanding the regulation of these modifications will provide new insights into the protein folding process and characterize the direct interplay between chaperones and major signal transduction pathways. This thesis investigates a critical post-translational modification (PTM) site on yeast Heat Shock Protein 70 (Hsp70) that undergoes phosphorylation during heat shock response. Here, we focus on threonine 492 (T492), a highly conserved residue on Hsp70, which is conserved across all domains of life. Elucidating its upstream regulation and downstream effects. Yeast cells respond rapidly to heat stress by activating multiple protective mechanisms to maintain proteostasis. These include Hsf1 and Msn2/4-mediated transcriptional activation, cell integrity signaling, stress-induced bimolecular condensate formation and resolution, and protein translation inhibition. However, these pathways' rapid activation and coordination have remained poorly understood. Our findings reveal that heat-induced membrane stretch is detected by the Mechanosensor Mid2, triggering rapid phosphorylation of the cytosolic Yeast Hsp70 at T492. This phosphorylation event has several crucial downstream effects, which include altered interactome, altered dynamics of P-body resolution, maintenance of translational fidelity, amplification of the cell-wall integrity pathway, proper activation of heat-shock response, and regulation of clients Bck1 and Edc3. These results provide a comprehensive, unified theory of the global yeast shock response mediated by the Hsp70 chaperone code.
ABSTRACT
GRANT W. BIDNEY: Fabrication, Numerical Modeling, and Testing of Silicon Micropyramid Arrays and Retroreflectors
(Under the direction of DR. VASILY N. ASTRATOV)
This dissertation is devoted to the optical properties of mesoscale and nanoscale photonic arrays, specifically regarding two different areas: i) silicon (Si) micropyramidal photonics aimed at enhancing photodetectors and emitters, and ii) plasmonic Littrow retroreflectors in the optical regime.
In the first area, we show that Si anisotropic wet etching is attractive for the fabrication of large-scale arrays of micropyramids, or microvoids, with an extraordinary level of uniformity over centimeter-scale wafers. This is related to the self-terminating nature of the etching process when two (111)-type planes meet under the conditions when a surfactant is used to slow down the undercutting rate of the SiO2 layer. Although this technology is generally well studied by the microelectromechanical (MEMS) community, it seems that this particular property did not receive sufficient attention in previous studies. However, it is this property which enables the fabrication of uniform micropyramid arrays suitable for integration with detector and emitter arrays in optoelectronics applications. The optical properties of such arrays are studied by 3-D finite-difference time-domain (FDTD) numerical modeling in two realms represented by different boundary conditions (BCs). Periodic BCs result in Talbot self-images experimentally observed in this work. Perfectly matched layer BCs describe mesoscale interference effects resulting in the subwavelength focusing properties of individual micropyramids. It is proposed that integration with micropyramid arrays can enhance the collection of photons, signal-to-noise ratio, and operational temperatures of mid-wave infrared photodetector focal plane arrays (FPAs). It is also proposed that Si micropyramid arrays can be used to enhance light extraction and directionality of quantum sources and infrared scene projectors. Additionally, micropyramids were monolithically integrated with silicon-platinum silicide (PtSi/p-Si) Schottky barrier photodetectors to experimentally demonstrate an improved signal obtained by these micropyramid arrays. These results were compared with 3-D FDTD numerical modeling, as well as the modeling of a novel resonator cavity micropyramid structure as a way to further increase the enhancement capabilities of these micropyramids based on using a silicon-on-insulator (SOI) wafer. This structure demonstrated increased absorption of up to 11× compared to a planar reference device of the same size.
In the second area devoted to Littrow grating retroreflectors, we tackle the problem of simultaneous and efficient TE and TM polarization retroreflection. We developed the guidelines for designing such retroreflectors. Optimized performance at wavelengths in the vicinity of λ = 633 nm is expected for top metal slot arrays with thickness in the 20-40 nm range. However, this can vary for different metals such as Au, Ag, Al, and Cu. The most interesting development is our proposal to use the experimentally measured index values for thin films with different thicknesses to study and optimize the performance of real physical retroreflector devices. To the best of our knowledge this approach was proposed for the first time in our work. Using this approach, we showed that there is potentially plasmonic enhancement mechanisms involved, caused by their confinement in the metal stripes of the arrays. We demonstrated that, despite presence of absorption, such Au Littrow retroreflectors reach simultaneous ~0.2 and ~0.6 efficiency levels at TE and TM polarizations simultaneously in the same structure.
For centuries trusted advisors have helped leaders address knowledge gaps and provided an opportunity to evaluate logic processes and ideas before executing them. In industry, management consultants have turned the trusted advisor role into a profession that has increasingly garnered academic focus over time. While the benefits of management consulting may be difficult to quantify, the study of those benefits has been primarily case based and focused on publicly traded companies. Family businesses constitute 59% of the private sector workforce and 54% of private sector GDP in the US, representing a significant impact on the economy. But we know little about what influences a family business to seek external help or when a family business hires management consultants. The present study extended bounded systems theory to explore how family influence and succession intentions affect the intention to hire management consultants, and how performance aspirations moderate this relationship. The research identified a positive relationship between succession intentions and the intention to hire management consultants. It also demonstrated that family influence is not a statistically significant determinant of intention to seek external help. The results from this study help advance academic knowledge and provide useful insights to practitioners.
As cyber threats continue to grow in both volume and sophistication, automated and effective threat hunting has become essential for proactively detecting and responding to cyber threats. Unlike traditional defenses, an automated end-to-end threat-hunting approach involves analyzing vast amounts of unstructured data to identify actionable intelligence for timely detection and mitigation. Generative AI-driven threat hunting provides a more efficient and effective alternative due to the capability of understanding complex natural language patterns, enabling faster response times and greatly reducing human effort in identifying and analyzing threats. This dissertation aims to develop an automated end-to-end threat hunting model, harnessing the power of Large Language Models (LLMs) to enhance threat detection and response. This dissertation has three main objectives: 1) developing an approach to identify threat-related information from a large amount of unstructured text, 2) developing a model to extract actionable intelligence and explain them to gain the trust of security analysts, and 3) developing a model to generate search queries for log analysis, allowing security teams to investigate potential threats in a network.
The results of every step of the automated end-to-end threat hunting process have demonstrated the effectiveness of the approach. This dissertation achieved 94.93% precision and 88.22% recall in distinguishing between threat-related and non-threat-related real-time messages. The extraction step extracted critical threat information like IOCs, observable technical manifestations of attacks, and TTPs from threat-related messages. Additionally, by integrating a knowledge-graph based validation approach, the system ensured the accuracy of the extracted information and successfully reduced the hallucination rate from 34.6% to 1.58% and the error rate from 36.9% to 7.21%. Finally, this dissertation utilized the relational context in Kibana query generation and increased accuracy from 41.03% (without relational context) to 58.97%.
This dissertation presents several major contributions to automate the end-to-end threat-hunting process, transforming cyber threat intelligence messages into actionable Kibana queries to search logs for evidence of the attack described by the intelligence. This prototype implementation, leveraging OpenAI APIs, utilizes the robust language capabilities of Large Language Models (LLMs) to identify threat-related messages and extract actionable threat intelligence from even the most cryptic real-time threat-sharing messages. The core idea is to apply an explainable AI approach that explains the logical reasoning behind extracting threat intelligence, addressing a fundamental problem of LLM: hallucinations. This research explains the extracted intelligence in terms of specific MITRE ATT&CK TTP using a knowledge graph that includes “is-a” and “part-of” relationships, which are also extracted using a Large Language Model by OpenAI. The benefits of this explanation-based approach include significantly overcoming LLM hallucinations and gaining the trust of security analysts by providing explained results. Finally, this system leverages the explained “is a” and “part of” relationships to automate Kibana query generation for log analysis. This dissertation has demonstrated that explanation can improve LLM’s accuracy in generating Kibana queries and can be further extended by enriching the knowledge graph with additional relationships.
Medically unexplained symptoms (MUS), defined as symptoms lacking objective test findings or known biological causes, are highly prevalent and pose significant challenges for healthcare providers. Often associated with complex biopsychosocial origins, MUS can lead to diagnostic uncertainty. Consequently, providers may rely on patient characteristics, such as gender and mental health history, when making a diagnosis or determining appropriate treatments, which may introduce bias into their decision-making. This study investigated how these factors influence provider decision-making in diagnosing and treating MUS, focusing on two key research questions: (1) How does knowledge of a patient’s gender and mental health history affect diagnostic assessment? And (2) How does it impact treatment likelihood?
A sample of 152 primary care providers participated in the study, through an online survey, which implemented a 2x2 factorial between-subjects design. Participants were randomized into one of four conditions and reviewed clinical case vignettes, responding to questions regarding diagnostic and treatment considerations. The findings revealed a significant effect of patient gender and mental health history on treatment decisions. Providers were less likely to recommend medical follow-up for female patients with a history of depression and anxiety compared to male patients without a history of mental health concerns. For symptoms specifically involving generalized pain and fatigue, providers were more likely to attribute them to behavioral health factors than medical causes in female patients with histories of depression and anxiety compared to other groups. Conversely, for patients without a mental health history, providers favored medical follow-up over behavioral health interventions, regardless of patient gender. No significant differences emerged for diagnostic assessment or behavioral health treatment recommendations across groups.
These results suggest that patient gender and mental health history influence provider decision-making regarding the management of MUS, highlighting the need for strategies to reduce bias and improve equity in clinical decision-making. Additional research is warranted to explore these relationships further and better understand how various factors impact the assessment and treatment of ambiguous symptoms.
Transportation electrification is a critical global policy, and the effective integration of electric vehicles (EVs) is key to ensuring power grid reliability and resilience. This dissertation proposes solutions to address the integration of fleet EVs (FEVs) into distribution grids, using cloud-based and edge-based approaches.
First, a centralized, cloud-based optimization model is developed for the strategic placement of FEV charging stations, designed to enhance grid resilience during high-impact, low-probability events like hurricanes.
Second, a bilevel optimization framework is introduced to aggregate FEV charging with other distributed energy resources (DERs), enabling their participation in the ISO day-ahead energy and reserve markets. This approach models FEVs as part of a Distributed Energy Resource Aggregator (DERA), providing energy and ancillary services.
Third, a novel cloud-edge collaboration framework is proposed to enable decentralized control of FEV charging stations at the grid edge. This framework uses federated reinforcement learning, allowing individual FEVs to coordinate their actions locally while contributing to voltage regulation and grid stability.
These solutions offer a comprehensive approach for optimizing the deployment and control of FEV charging stations, addressing both operational efficiency and market integration in modern power grids.
Black women are underrepresented in secondary math education, but their presence is critical for young black girls who dream of STEM careers. Some researchers believe that the number of Black women pursuing secondary math licensure can be increased through improved recruitment strategies, while others focus on causes of leakage in the education pipeline. This study sought to discover types of capital that Black women, who are preservice Mathematics teachers (PSMTs), relied on to persist towards the completion of their teacher preparation programs.
Framed using Critical Race Feminism and Black Feminism, this study employed Counternarrative Inquiry to discover the capital that the five PSMTs credited for their ability to stay the course. The findings indicate that, while PSMTs might use the same capital, they use it in a variety of ways and for different purposes.
A key implication of the findings from this study is that, if undergraduate math educators can mitigate or eliminate the conditions within the math classroom that triggers the use of many of the capitals in this study, Black women who are PSMTs may be able to divert their energies to developing robust mathematical identities.
Increasing tensions in American society surrounding social equity issues and minority statuses like race and sexual orientation have prompted competing social narratives. Historically marginalized groups face disparate socioeconomic, housing, and educational opportunities. The existing body of research and governmental data contend that there are strong relationships between minority status(es) and housing instability. However, most of the presently available research does not examine these relationships within the school district’s economic context and local homelessness response efforts. This dissertation investigated the association of housing instability with minority status(es), school district, and homelessness response efforts. A descriptive quantitative case study was conducted of Black and White adolescents, between the ages of twelve and eighteen, identifying as heteronormative or LGBTQ+. Data used came from the Oakland Unified School District and the Los Angeles Unified School District. This study employed Critical Race Structuralism and Quantitative Critical Theory to guide the study’s analysis. Cross-sectional data from the 2017 Youth Risk Behavior Surveillance System (YRBSS) survey was used for secondary data analysis. The present study analyzed intersections between race, sexual orientation, school district, and housing instability.Opportunities for further data collection and exploration were identified and implications for policy and programming were discussed.
Keywords: race, sexual orientation, housing instability
As the demand for stabilized and fast-tuning laser sources continues to grow in metrology applications, there is a corresponding need for innovative laser sources that satisfies this need. This dissertation aims to create a novel stabilized and frequency-modulated (FM) laser source utilizing Volume Bragg Grating (VBG) as a wavelength selection method. In order to produce this source, six subsystem components need to be integrated. These include: 1) selecting the proper laser source, 2) implementing a PID control to maintain temperature and source current, 3) laser source fast and slow axis collimation, 4) optimizing the bonding between components of the mechanical frequency-based modulator, 5) opto-mechanical design of a fixture for the modulator to enhance adjustment, and 6) interferometric measurement of the modulation depth using a Michelson configuration.
The periodic refractive index written within the bulk VBG provides a feedback mechanism to act as a wavelength selective mirror as an external cavity to the laser diode. Two piezoelectric (PZT) actuators bonded at each end of the VBG generate the oscillatory force to create a mechanical modulation of the diffraction wavelength in traditional external cavity frequency modulation techniques. This novel approach requires bonding of the actuators. To minimize damping by adhesive layers, it is necessary to choose an appropriate adhesive. Among the adhesives studied, E60HP has proven to give the best result in terms of amplitude. A mechanism that provides 6 degrees-of-freedom adjustment of the oscillator to align VBG diffraction grating to the wavefront of the laser beam is developed and tested. The VBG alignment results gave a spectrometer limited linewidth of 0.27 nm. Modulation depth study still needs to be investigated. The final system is comprised of a 775 nm external cavity laser diode, a 3033-layer VBG at replay wavelength at 778.17 nm, and with frequency modulation at 530.7 kHz.
The number of low-performing schools has drastically increased since COVID-19. During the 2018-2019 school year, there were 488 low-performing schools (North Carolina Department of Public Instruction, 2024). The number increased to 736 schools during the 2023-2024 school year, a 50.8% increase (North Carolina Department of Public Instruction, 2024). Understanding factors related to climate in these schools is pertinent to making teachers’ jobs more rewarding while improving student outcomes (Rosenburg & Anderson, 2021). Though there is research on school climate and student achievement, more research is needed to examine school climate in low-performing elementary schools in North Carolina.
This quantitative study explored whether two school-level characteristics, namely, schools’ Education Value-Added Assessment System (EVAAS) growth status and school performance grade, impact teachers' confidence levels regarding principal leadership, teacher leadership, student discipline, and teacher retention. Thus, this quantitative study sought to answer the overarching research question: whether there are statistically significant differences in perceptions of principal leadership, teacher leadership, student discipline, and teacher retention based on their school’s EVAAS growth measure and performance grade.
This study’s participants were certified staff members from 293 low-performing public elementary schools ranging from pre-kindergarten to fifth grade in North Carolina during the 2021-2022 school year. The statistical analysis used was a 2 x 2 factorial MANOVA, which measured the dependent variables (principal leadership, teacher leadership, student discipline, and teacher retention). Additionally, the 2 x 2 factorial MANOVA examined the EVAAS growth measure (met or not met) and the school performance grade from each school (D or F) based on the certified staff perspectives of the dependent variables. Findings suggest that teachers’ perceptions of teacher leadership and teacher retention differ significantly based on the school’s EVAAS growth measure and performance grade in low-performing elementary schools in North Carolina. However, the results did not align with previous research on teachers’ perceptions of principal leadership and student discipline, as there was no statistically significant difference.
