Doctor of Philosophy (PhD), Ohio University, 2024, Civil Engineering (Engineering and Technology)

Colloidal aggregation is a critical phenomenon influencing various environmental processes. However, limited research has been conducted on the aggregation of particles with heterogeneous physical and chemical properties, which are more representative of practical environmental systems than homogeneous particles. The central hypothesis of this dissertation is that primary particle size polydispersity along with chemical and material heterogeneity of primary particles exert non-trivial effects on the aggregate growth rate and the fractal dimensions of aggregates. In this dissertation, the aggregation and stability of heterogeneous nano- and sub-micrometer particles in aqueous systems were investigated using in situ microscopy and image analysis. Initially, the study examined the growth kinetics and structures of aggregates formed by polystyrene microplastics in mono- and bidisperse systems. Findings indicated that while the primary particle size distribution did not affect the scaling behavior of aggregate growth, it delayed the onset of rapid aggregation. Structural analysis revealed a power law dependence of the aggregate fractal dimension in both mono- and bidisperse systems, with mean fractal dimensions consistent with aggregates from diffusion-limited cluster aggregation. The results also suggested that aggregate fractal dimension was insensitive to shape anisotropy. The dissertation further explored the structure of DLCA aggregates in heterogeneous systems composed of particles with varying sizes, surface charges, and material compositions. The fractal dimensions of DLCA aggregates in these heterogeneous particle systems were similar, ranging from 1.6 to 1.7, and consistent with theoretical predictions and experimental evidence for homogeneous DLCA aggregates. This confirmed the universality of aggregate structures in the DLCA regime, regardless of particle composition. Additionally, a scaling relationship was demonstrated between aggregat (open full item for complete abstract)

Committee: Lei Wu (Advisor); Guy Riefler (Committee Member); Daniel Che (Committee Member); Sumit Sharma (Committee Member); Natalie Kruse Daniels (Committee Member) Subjects: Chemical Engineering; Civil Engineering; Environmental Engineering; Physical Chemistry

Master of Science (MS), Ohio University, 2020, Civil Engineering (Engineering and Technology)

Asphaltene aggregation is critical to many aspects of petroleum utilization, from oil recovery, transportation to refining. As such, this area has attracted significant amounts of research attention in the past few decades. The majority of studies have focused on the characterization of the colloidal properties of asphaltene with limited effort to linking asphaltene aggregation to its chemical composition. However, new evidence suggests that sulfur, the most important heteroatom in asphaltene molecule, has non-trivial effects on asphaltene aggregation. Specifically, sulfur-rich asphaltenes form dense consolidated nanoaggregates which will ultimately affect the kinetics and morphology of micro-sized asphaltene aggregates. However, the mechanisms that govern aggregates formation of sulfur-rich asphaltenes remain poorly understood. The central hypothesis of this study is that the aggregation behavior of sulfur-rich asphaltene has different characteristics compared to those predicted by colloid aggregation theories. To test this hypothesis, the growth kinetics of sulfur-rich asphaltene aggregates and their morphology evolution were investigated by customized in-situ microscopy. The aggregates formation kinetics, colloidal structural evolutions, and aggregate morphologies were examined by the analysis of growth curve, particle size distribution, and fractal structure, respectively. This analysis was based on advanced image processing algorithms, allowing for the examination of sulfur-rich asphaltene aggregation in greater detail. To isolate the effects of sulfur on the formation and geometry of aggregates, model oil composed of toluene and asphaltene was used to reduce the effects of complex components typically found in crude oil. The measurements indicate that aggregates assembled from sulfur-rich asphaltene obtain time-dependent self-similarity with morphologies and growth rates that are aligned with a crossover behavior between classic reaction-limited aggregation an (open full item for complete abstract)

Committee: Lei Wu (Advisor); Daniel Che (Committee Member); Sumit Sharma (Committee Member); Gang Chen (Committee Member) Subjects: Chemical Engineering; Civil Engineering; Energy; Petroleum Engineering; Physics

Doctor of Philosophy (PhD), Ohio University, 2019, Chemistry and Biochemistry (Arts and Sciences)

Tau protein plays a crucial role in stabilizing microtubules inside neuronal axons and maintaining the structural integrity of neurons. Binding of tau to microtubules at tau repeat domains (R) is regulated by phosphorylation. This phosphorylation is regulated by a family of enzymes called kinases. Under pathological conditions, tau is hyperphosphorylated by elevated activity of kinases such as the microtubule affinity-regulating kinase (MARK) proteins, leading to complete detachment of tau, microtubule collapse and ultimately, neuronal cell death. The free, hyper-phosphorylated tau proteins aggregate into insoluble prion-like oligomers which have been implicated in neurodegenerative diseases, including Alzheimer's disease (AD) and frontotemporal dementia. There is currently no treatment to prevent the progression of AD; all medications available today only reduce the symptoms of the disease. Moreover, using small molecule kinase inhibitors as treatment can cause serious negative side effects because of their lack of specificity. The research outlined in this work aims to develop a metabolically stable, selective peptide-based MARK kinase inhibitor that targets MARK proteins. This peptide-based inhibitor, designated tR1, was designed as a direct sequence memetic of the microtubule-binding R1 repeat domain of tau. Here, we show that tR1 peptides can inhibit MARK2 activity and reduce the level of tau phosphorylation in vitro and in cultured rat primary cortical neurons. In the second segment of this project, we attempted to inhibit tau aggregation in vitro using peptide-based aggregation inhibitors. Here, we synthesized peptides designated (an-R3, PHF6, and PHF6*) which mimic nucleating sites in the microtubule binding repeat domain of full-length tau. We hypothesized that these peptides would associate with tau protein and block further tau aggregation. We assessed the ability of these three peptides to inhibit tau aggregation using in vitro heparin-induced tau (open full item for complete abstract)

Committee: Justin Holub M. (Advisor); Marcia Kieliszewski (Committee Member); Robert Colvin (Committee Member); Jana Houser (Committee Member); Jixin Chen (Committee Member) Subjects: Biochemistry

MS, University of Cincinnati, 2003, Engineering : Computer Science

We work with temporal data stored in distributed databases that are spread over a region. We have considered a sensor network where a lot of sensor nodes are spread in a grid like manner. These sensor nodes are capable of storing data and thus act as a separate dataset. The entire network of these sensors act as a set of distributed datasets. An algorithm is introduced that mines global temporal patterns from these datasets and results in the discovery of linear trajectories of moving objects under supervision. Each of these datasets has its local temporal dataset along with spatial data and the geographical coordinates of a given object or target. The main objective here is to perform in-network aggregation between the data contained in the various datasets to discover global spatio-temporal patterns; the main constraint is that there should be minimal communication among the participating nodes. We present the algorithm and analyze it in terms of the communication costs. The cost of our algorithm is much smaller than that of the alternative in which the data must be transferred to a single site and then mined. In addition to this, we vary the requirements of our algorithm slightly and present a variant of it that enhances its performance in terms of the overall complexity of computations. We go on to show that the while the efficiency of the algorithm increases in terms of the number of messages exchanged between nodes, the amount of information available to all the nodes in the system decrease. The advantages and drawbacks of this variant of our algorithm is also presented.

Committee: Dr. Raj Bhatnagar (Advisor) Subjects: Computer Science

Master of Science, The Ohio State University, 2005, Graduate School

Committee: Not Provided (Other) Subjects:

Doctor of Philosophy, Case Western Reserve University, 2024, Physiology and Biophysics

TDP-43 is a nucleic acid-binding, nucleocytoplasmic protein involved in several cellular processes, including transcription, RNA processing, translation, and stress granule formation. Proteinaceous inclusions made up of misfolded TDP-43 are the hallmark of several neurodegenerative diseases, such as amyotrophic lateral sclerosis, frontotemporal lobar degeneration, and limbic-predominant age-related TDP-43 encephalopathy. Much of the TDP-43 within these inclusions is post-translationally modified, with hyperphosphorylation drawing particular interest as phosphorylated TDP-43 segregates specifically to said inclusions. Like several other RNA-binding proteins associated with neurodegenerative diseases with intrinsically-disordered domains, TDP-43 can undergo liquid-liquid phase separation (LLPS) under physiologic conditions. This process involves the protein self-associating into a reversible, liquid-like droplet phase. While LLPS plays a vital physiologic role in the spatial organization of cellular contents, increasing evidence has linked dysregulated, aberrant phase separation to pathogenesis. In the work described in this thesis, we demonstrated that phosphorylation (modeled via Ser-to-Asp phosphomimetic substitutions) can profoundly affect the LLPS of TDP-43. In the first study, which explored phosphorylation at pathologically-relevant C-terminal sites (residues S403, S404, S409, and S410), we ascertained that phosphorylation changes the forces driving TDP-43 LLPS and imparts a biphasic dependence on NaCl concentration to the protein's LLPS. This biphasic dependence was due to attractive electrostatic forces tuning hydrophobic forces to drive LLPS at low ionic strengths, and pure hydrophobic forces driving LLPS at high ionic strengths where all electrostatic interactions had been screened out. In the second study, we explored the impact of phosphorylation within the transiently α-helical region (at residues S332 and S333) of TDP-43, a subdomain shown to be cruci (open full item for complete abstract)

Committee: Witold Surewicz (Advisor); Sudha Chakrapani (Committee Chair); Matthias Buck (Committee Member); Ashleigh Schaffer (Committee Member); Brian Appleby (Committee Member); James Leverenz (Committee Member) Subjects: Biophysics

Doctor of Philosophy, The Ohio State University, 2024, Chemistry

Investigating dielectric behavior in inhomogeneous systems remains an open question, despite significant progress with homogeneous systems like the continuum. This work shifts the paradigm, interpreting dielectric behavior through a molecular picture instead of a continuum one. Our investigation focuses on water's dielectric behavior near a spherical ion—a model widely applicable to biological processes and electrokinetics. We demonstrate that the total bound charge near an ion is identical for both continuum and molecular systems. While polarization charge density is confined to an infinitely thin layer in continuum systems, it distributes within a finite range near the ion in molecular systems. We derive the formulations of dielectric properties in terms of polarization charge density compatible with both continuum and molecular solvents. Furthermore, we critically discuss a theoretical approach to interfacial dielectric constant that was recently proposed and underscore the necessity of appropriate treatment of dielectric properties under periodic boundary conditions. Building on dielectric behavior, we explore ion solvation through molecular dynamics simulations. We propose a theoretical methodology for correcting the finite size effect inherent in the periodic boundary condition, without employing Ewald summation. This correction leads to a free energy value and the corresponding Born radius for an infinite system aligning with existing literatures. Through simulations and our methodology, we provide a physical interpretation of the Born radius in molecular systems. We unveil unique overcompensations from alternating charge layers near the ion, which converge to the Born radius further from the ion. It has been observed that the Born radius in molecular systems is smaller than the radius where solvent molecules begin to appear. To interpret this smaller Born radius, we utilize a simple model for cumulative bound charge, where (open full item for complete abstract)

Committee: Sherwin Singer (Advisor); Alexander Sokolov (Committee Member); Steffen Lindert (Committee Member) Subjects: Chemistry; Physical Chemistry

Master of Science, Miami University, 2024, Computer Science

Fuzzy Cognitive Maps (FCMs) are powerful semi-quantitative simulation models capable of investigating the long-term behavior of complex systems. They are quick and easy to build, aggregate knowledge from stakeholders, and can evaluate interventions in the system (e.g., if we increase the availability of public transportation, how are emissions affected?). Although FCMs are powerful, they have three critical limitations. First, they cannot represent every aspect of complexity; they do not represent time (e.g., there are no delays or ramp-up of effects) or nonlinear relationships and have a limited representation of uncertainty, hindering their ability to model complex systems. Thus, researchers have developed numerous extensions of FCMs to incorporate additional information. Second, aggregating knowledge from several stakeholders can result in a model whose perspective corresponds to none of the individual viewpoints. Third, FCMs commonly represent mental models: an individual's representation of knowledge that permits reasoning in a particular domain. However, when two FCMs interact, cognitive dissonance may arise, potentially distorting an individual's view of the domain. This thesis addresses these limitations and empowers modelers to effectively use FCMs by reviewing and providing interoperability among numerous extensions, proposing properties and developing new algorithms for aggregation, and automatically resolving dissonance within FCMs.

Committee: Philippe Giabbanelli (Advisor); Honglu Jiang (Committee Member); Garrett Goodman (Committee Member) Subjects: Artificial Intelligence; Computer Science

Doctor of Philosophy, University of Akron, 0, Chemical Engineering

Accumulating evidence demonstrates that misfolded proteins associated with various amyloid diseases, such as Alzheimer's disease, Parkinson's disease, type 2 diabetes, and cardiovascular disease, can interact across disease boundaries to promote amyloid aggregation through a process known as amyloid cross-seeding. This cross-seeding mechanism is crucial for the spreading of common pathologies across different cells and tissues, thus exacerbating the progression of these diseases. To deepen our comprehension of the amyloid cross-seeding process, we identified and studied a series of amyloid cross-seeding systems of hIAPP (associated with T2D)/TKEQVTNV (associated with PD), Aβ (associated with AD)/ANP (associated with CVD), and Aβ/SEVI (associated with HIV/AIDs). The “Amyloid Cascade Hypothesis” has long been regarded as a primary pathological factor initiating amyloid diseases. However, the complex, multifactorial nature of amyloid diseases has rendered single-target strategies largely ineffective, even in preclinical trials. This ineffectiveness hints at the significant role of an additional factor, the “Microbial Infection Hypothesis”, in the pathology of amyloid diseases. Against this backdrop, we proposed and validated a novel “Anti-Amyloid and Antimicrobial Hypothesis” to identify several antimicrobial peptides containing β-rich structures including intestinal defensins, PG-1, and aurein, with multifunctional and multi-targeting properties. This innovative hypothesis not only demonstrates the cross-seeding process between amyloid and antimicrobial peptides, but also integrate a crucial antimicrobial perspective, which presents a more effective prevention strategy against amyloid diseases by targeting various pathological pathways. The early diagnosis of amyloid diseases represents another significant challenge that has garnered considerable efforts in recent years. Traditional approaches have often been constrained to molecules that offer either a single (open full item for complete abstract)

Committee: Jie Zheng (Advisor); Qixin Zhou (Committee Member); Xiong Gong (Committee Member); Ge Zhang (Committee Member); Linxiao Chen (Committee Member) Subjects: Biochemistry; Chemical Engineering

Doctor of Philosophy, Miami University, 2023, Chemistry and Biochemistry

Tannins are plant secondary metabolites belonging to the larger group of plant polyphenols. Polyphenols have many desired nutritional and health benefits such as antimicrobial, anticarcinogen, anti-inflammatory activities as well as popular antioxidant activity to offer. Tannins are unique polyphenols because of their high affinity for protein and ability to precipitate protein under favorable conditions. Throughout this work, the association of tannin with tannin and tannin with protein is studied. Part of this dissertation focuses on tannic acid, which is commercially produced as a mixture of different chain length gallate esters. Many published studies have assumed that tannic acid is a pure single compound, ignoring the true structural and chemical diversity of this material. In this work, the overlooked compositional diversity of different supplies of tannic acids is highlighted. This work addresses that knowledge gap by coating zein protein nanoparticles with compositionally different tannic acids and then characterizing their structure and function using analytical tools such as RP-HPLC to characterize tannin acids, DLS to determine particles size and polydispersity, zeta analyzer for zeta potential, electrophoresis for digestibility experiments, FT-IR to investigate bond formation, SEM to confirm sizes and aggregation patterns and circular dichroism to investigate secondary structure changes of zein protein. This work highlights the need to acknowledge and explore the compositional diversity of tannic acid to achieve its full potential as coating material in nanotechnology work. An additional set of studies that explore the self-association of condensed tannin are described here. As a result of this work, it is proposed that aggregation is a phenomenon that stands in the way of effective use of chromatographic techniques to characterize condensed tannins. Dynamic light scattering (DLS) was extensively used in this work. The data lead to the idea that underst (open full item for complete abstract)

Committee: Ann Hagerman (Advisor) Subjects: Analytical Chemistry; Biochemistry; Chemistry

Doctor of Philosophy, University of Akron, 2023, Polymer Science

With sizes ranging from 1 to 10 nm, macroions demonstrate attractive and unique solution behaviors which cannot be described either by Derjaguin-Landau-Verwey-Overbeek (DLVO) theory or Debye-Huckel theory. With charges carried by macroions, most of them show great solubility in polar solvent, and electrostatic interaction possesses a significant role in their solution behaviors. As single molecules, the association of counterions to macroions, based on electrostatic interaction, could show spatial predilection due to variable constructive units within macroions and the large size disparity between macroions and counterions. With moderate charges, many of macroions are capable to spontaneously self-assemble into single-layer hollow spherical blackberry-like structures via counterion-mediated attraction, which can be accurately controlled by pH, solvent polarity, and electrolyte concentration. Based on the complex structures of macroions, various interactions, such as hydrophobic interaction, hydrogen bonding, and sigma -π interactions, can be involved to compete or cooperate with electrostatic interaction to regulate their solution behaviors and achieve multiple self-assembled structures. Furthermore, with the larger supramolecular structures formed by macroions, phase transitions, such as sol-gel transition, take place. Therefore, exploring the solution behaviors of macroions at their single-molecular, self-assembled states, and consequent phase transition would be an efficient method to understand weak non-covalent interactions in the system, which are difficult to evaluate via traditional tools. In this work, the structural well-defined metal-organic cages (MOCs) and uranyl peroxide molecular clusters serve as great models for understanding the solution behaviors of macroions either at single-molecular state or during their self-assembly processes. A series of MOCs with aggregation-induced emission (AIE) luminophores are designed and prepared. The change in fluor (open full item for complete abstract)

Committee: Tianbo Liu (Advisor); Toshikazu Miyoshi (Committee Chair); Junpeng Wang (Committee Member); Mesfin Tsige (Committee Member); Chunming Liu (Committee Member) Subjects: Chemistry; Physical Chemistry

Doctor of Philosophy, The Ohio State University, 2022, Microbiology

Orthopedic procedures significantly enhance the quality of life for hundreds of thousands of people each year. Despite these profound medical advances, a primary risk of any invasive surgery is still the development of an infection. The implantation of medical hardware, which is associated with joint replacement procedures, dramatically increases the chances of biofilm formation at the postsurgical site. For several reasons, bacterial biofilm formation on an implanted device, or adjacent tissue, severely complicates the resolution of an infection. Because antibiotic treatment is highly ineffective against biofilm-implicated infections, patients are often subjected to a secondary surgery to debride and remove the hardware. While current treatment methods are surgeon-specific, most revision protocols include surgical explantation, rigorous antibiotic therapy, and eventual re-implantation surgery. There is an urgent need for novel methods of prevention and treatment for periprosthetic joint infections. To develop effective, targeted strategies, it is imperative that we first understand the underlying mechanisms by which bacterial pathogens enter, proliferate, and establish in the postsurgical joint space. While several bacterial pathogens have been found to invade the postoperative joint environment, including gram-positive and gram-negative species, Staphylococcus aureus is one of the most common and virulent. In establishing a chronic infection, S. aureus engages a wide range of virulence factors- including biofilm establishment, host polymer binding, and a range of secreted toxins. To date, investigations probing the bacterial pathogenesis of periprosthetic joint infections have primarily centered around biofilms. Although the generalized stages of biofilm formation have been well defined for decades, the mechanisms through which the invading bacteria survive initial entry into a specific host environment are highly variable. The post-operative joint does not provi (open full item for complete abstract)

Committee: Paul Stoodley (Advisor); John Gunn (Committee Member); Daniel Wozniak (Committee Member); Chad Rappleye (Committee Member) Subjects: Microbiology

Master of Science (MS), Ohio University, 2021, Biological Sciences (Arts and Sciences)

Parkinson's disease (PD) is a progressive neurodegenerative movement disorder characterized by the loss of dopaminergic (DA) neurons in substantia nigra pars compacta and the formation of Lewy Bodies (LBs), cytoplasmic protein deposits of α-Synuclein (αSyn). In recent years, an intriguing concept of prion-like spreading of pathogenic proteins such as αSyn has emerged. Released αSyn spreads between neurons causing neurodegeneration, but the actual propagation mechanism is still under investigation. In order to test cell-to-cell propagation of αSyn, I investigate αSyn release. In my project, I develop a larval neuromuscular junction (NMJ) model in order to study αSyn release mechanisms. I hypothesize that neuronal activity regulates pathological αSyn release. Thus, using optogenetics to stimulate neurons that co-express αSyn and Channel Rhodopsin (ChR2) in Drosophila melanogaster larvae, I examine αSyn release induced by neuronal depolarization. I use ELISA technique to detect and compare released αSyn levels in the hemolymph of different fly lines. Results show activity-dependent αSyn release. This activity-dependent αSyn release is also influenced by synaptic transmission, mutations, and phosphorylation of αSyn. Hence, αSyn release might be induced in some regions of PD brain in response to excitability, and this αSyn release might underlie the disease progression. Therefore, targeting αSyn release could be further studied in hope of establishing new therapeutic interventions to stop or slow PD pathology.

Committee: Daewoo Lee (Advisor); Corinne Nielsen (Committee Member); Robert Colvin (Committee Member) Subjects: Biology; Neurobiology; Neurosciences

Doctor of Philosophy, The Ohio State University, 2021, Earth Sciences

Anthropogenic particulate matter is an emerging form of environmental contaminate encompassing nanomaterials and microplastics. These human-made materials have renewed interest in colloid science and toxicology with the goal of answering two questions. (1) What processes affect the distribution of anthropogenic particulate matter in the environment? (2) Could anthropogenic particulate matter harm humans and/or the environment? It is difficult to answer these questions because the study of anthropogenic particulate matter exists between classical physics and chemistry, and concepts from both must be invoked to understand the processes that govern the fate and effects of these particles. As an example, I examined what unit is best used to express nanoparticle toxicity. Researchers typically express the dose of nanoparticles delivered to an organism as grams per liter, while the biochemically appropriate unit is moles. The use of the ‘gram per liter' unit implies that 1 mole of a nanoparticles is equivalent to 1 mole of the dissolved particle material, rather than the number of particles or the active surface area of the particles, which may be appropriate measures of a mole of nanoparticles depending on mode of action. To determine which dosing unit is most appropriate, I performed the meta- analysis described in Chapter 2, which has been peer-reviewed and published in the journal NanoImpact. Through this work I discovered that units of surface area better reduced the heterogeneity of data for dissolvable particles, namely silver and zinc oxide tested on crustaceans, indicating that toxicity in these cases is dependent on surface reactions. However, no conclusion could be reached for non-dissolvable particles due to limited available data. The bigger takeaway from this work is that researchers have not been reporting sufficient meta-data to build a proper dataset, and more careful primary research is required. Research into the processes (open full item for complete abstract)

Committee: Steven Lower (Advisor); Motomu Ibaraki (Committee Member); Nicholas Basta (Committee Member); John Lenhart (Committee Member) Subjects: Chemistry; Environmental Science; Geochemistry; Toxicology

MS, University of Cincinnati, 2021, Medicine: Biomedical Research Technology

Severe congenital neutropenia (SCN) is a heterozygous dominant autosomal hematopoietic disorder associated with differentiation arrest and cell death at the promyelocytic/myelocytic stages of granulopoietic differentiation, resulting in very low levels or absence of mature neutrophils in bone marrow and peripheral blood. Granulocyte-colony stimulating factor (G-CSF) is the cornerstone of therapy for this disease, however some mutations are associated with G-CSF resistance. Mutation in the ATG start codon of the ELANE gene interestingly gives rise to alternatively translated truncated peptides of neutrophil elastase (NE). The alternatively translated peptides are frequently misfolded which generates insoluble protein aggregates which induces apoptosis and cell death of granulocyte precursors. The cell death of ATG mutant promyelocytes is not due to unfolded protein response pathway mediated endoplasmic reticulum stress (UPR/ER-stress). The insoluble protein aggregates induce aggrephagy processes, as evidenced by the NE co-localization, with adaptor protein p62 in promyelocytes derived from ATG mutant patient's iPSC and GTG knock-in iPSC lines. The ELANE ATG mutant granulopoiesis is insensitive to high concentration G-CSF therapy. We found that SERF1, a protein known to induce insoluble protein amyloid formation in neurons, is upregulated in ATG mutant promyelocytes. Downregulation of SERF1 rescues the survival of ATG mutant promyelocytes and myelocytes. Taken together, our data illustrates the mechanism of SCN associated with ATG mutations of ELANE and demonstrates the consequence of aggrephagy, which opens a new avenue for the therapy of G-CSF resistant SCN patients.

Committee: Rashmi Hegde Ph.D. (Committee Chair); Jose Cancelas-Perez M.D. Ph.D. (Committee Member); Carolyn Lutzko Ph.D. (Committee Member) Subjects: Biology

MS, University of Cincinnati, 2021, Engineering and Applied Science: Computer Science

Rankings represent the process of evaluating a subject's preference over different objects. It is used from user reporting to user recommendations. Dynamically updating rankings by adding new objects results in storage and computational constraints. Machine learning including deep learning algorithms have been widely implemented and used in a scale previously thought impossible. This is due to the increase of computing power and storage space available to users. This study explores the idea of allowing rankings to be updated using less computational and storage resources than generally required using machine learning. Using machine learning techniques such as Linear Regression and Support Vector Machine, this study explores models that can be used to overcome the computational and storage intensive problem of incrementally updating rankings.

Committee: Anca Ralescu Ph.D. (Committee Chair); Kenneth Berman Ph.D. (Committee Member); Dan Ralescu Ph.D. (Committee Member) Subjects: Computer Science

Master of Science (M.S.), University of Dayton, 2020, Chemistry

In the area of photochemistry, many molecules can undergo photoisomerization. Within these photoswitches is a family of molecules derived from azobenzene, which can form what are called supramolecular aggregates. When these molecules are in the presence of one another, they can form large structures. In addition to this, such molecules may also be able to maintain their ability to photoswitch while in a supramolecular structure. These supramolecular photoswitching aggregates have been used in many different applications including hydrogels and sol-gels, and the effort of cataloging and characterization of these molecules is a novel endeavor. Previous testing has concluded that ADA (Azobenzene-4,4'-dicarboxylic acid) and M0423 (4-Dimethylaminoazobenzene4'-carboxylic acid) show aggregation properties as well as photoswitching properties when paired together. When by themselves they show remarkable self-aggregation, however, they both do not show photoswitching properties. ADA shows photoswitching properties while M0423 does not. This data was confirmed through additional tests. The addition of the molecule A1598 (Azobenzene-3,3'-dicarboxylic acid) shows shocking similarities in aggregation to ADA in addition to possessing its own unique attributes. A1598 was confirmed to have formed aggregates with itself and M0423. These tests also confirmed a preferred pH and mixing ratio for the ADA and A1598-M0423 aggregate as well as assessing their photoswitching capabilities and aggregation under the stresses of photoswitching. Showing that both molecules when aggregated, regardless of the high degree of order displayed in the spectra, locked up. This prevented further photoswitching from occurring. This research shows that there is still more that can be learned from these molecules and similar azobenzene derivatives with respect to photoswitching aggregates.

Committee: Angela Mammana Ph.D. (Advisor); Vladimir Benin Ph.D. (Committee Member); Mark Masthay Ph.D. (Committee Member) Subjects: Chemistry; Organic Chemistry

Doctor of Philosophy, Case Western Reserve University, 2020, Physiology and Biophysics

Protein aggregation is a pathological hallmark of many neurodegenerative diseases. In these diseases, proteins misfold and assemble into ordered filaments, known as amyloid fibrils. These aggregates have a unique structure that is believed to underlie their toxicity to the brain. Therefore, immense effort has been put toward understanding their architectures and mechanisms of assembly. More recently, liquid-liquid phase separation (LLPS) of proteins was revealed to underlie the formation of membrane-less organelles within the cell. However, the intrinsically disordered proteins that undergo phase separation also have a great propensity to form amyloid aggregates. Accordingly, LLPS has been proposed to mediate the formation of amyloid fibrils and, consequently, the prevention of aberrant phase transitions deemed of therapeutic value. This work describes three interrelated areas of research. In the first, the relationship between LLPS and amyloid aggregation for a specific protein, TDP-43, is described: LLPS per se can promote amyloid aggregation. In the second, the structures of these amyloid aggregates formed by TDP-43 are investigated in addition to the role that LLPS has in structural permutations. Finally, the capacity for a small molecule to greatly modulate the LLPS of TDP-43, among many other important proteins, is demonstrated. The mechanism by which this regulation occurs and the chemical features that impart this capacity are also described. In all, each of these studies emphasizes the intersection between LLPS and protein aggregation, providing insights into the molecular basis of protein aggregation in disease and potential therapeutic avenues.

Committee: Sudha Chakrapani (Committee Chair); Eckhard Jankowsky (Committee Member); Alberto Costa (Committee Member); Matthias Buck (Committee Member); Blanton Tolbert (Committee Member); Witold Surewicz (Advisor) Subjects: Biochemistry; Biophysics; Condensation

PhD, University of Cincinnati, 2019, Engineering and Applied Science: Environmental Engineering

Nanotechnology is one of the most prospective technologies of this century and promises groundbreaking innovation in many fields due to the unique physicochemical characteristics of engineered nanomaterials (ENMs). The potential impacts of ENMs on aquatic environments and humans currently receive significant attention by both regulators and academia. Currently, approximately a quarter of all nano-enabled consumer products (CPs) contain silver nanoparticles (AgNPs). AgNPs are incorporated into a wide range of CPs (e. g., textiles, disinfectants, household appliances, industrial, medical, and scientific applications). The increased application of AgNPs will inevitably lead to their release into environmental systems. Therefore, the investigation and quantification of AgNPs in environmental matrices becomes critical to answer questions regarding their fate/transport and potential risks to the environment and human health. This dissertation aims to systematically explore the release of AgNPs in various environmental media. Also, this research work will aid in developing a rapid and sensitive approach for quantification of AgNPs-CPs in various matrices. First, the silver-containing nanoparticles were characterized in 22 consumer products that advertised the use of silver or colloidal silver as the active ingredient. A high degree of variability between measured and claimed values for total silver was detected. Primary silver particle size distributions by transmission electron microscopy showed two categories of particles - smaller particles (<5 nm) and larger particles (20-40 nm). This characterization study helps us to understand the potential human exposure risks posed by these CPs. Second, the dissolution trends of colloidal AgNPs in five products were investigated in deionized and tap water. These five CPs were selected from the characterization study. To expand our understanding on the fundamental mechanisms of dissolution of AgNPs in CPs, the dissolution beh (open full item for complete abstract)

Committee: Dionysios Dionysiou Ph.D. (Committee Chair); Souhail Al-Abed Ph.D. (Committee Member); Margaret Kupferle Ph.D. (Committee Member); Phillip M. Potter Ph.D. (Committee Member); George Sorial Ph.D. (Committee Member) Subjects: Environmental Engineering

Doctor of Philosophy (PhD), Wright State University, 2019, Biomedical Sciences PhD

Amyotrophic Lateral Scleroses (ALS) is a neurodegenerative disease characterized by the degeneration of upper and lower motor neurons in the brain and spinal cord leading to progressive paralysis and ultimately death within 5 years of symptom onset. Only two drugs are approved by the Food and Drug Administration (FDA) for treating ALS that slow the disease progression by about 3 months. Mutations in the gene Fused in Sarcoma (FUS) cause inherited forms of ALS. FUS is a nuclear, multifunctional RNA-binding protein (RBP) involved in multiple RNA metabolic pathways. Mutations in FUS cause mislocalization of the protein from the nucleus to cytoplasm, where it forms inclusion that colocalize with stress granules. Over-expression of human FUS in the budding yeast, Saccharomyces cerevisiae, results in cellular toxicity, cytoplasmic aggregation and localization to stress granules, recapitulating phenotypes of mutant FUS in mammalian models and patients. In recent years, perturbations in RNA metabolism and RNA binding proteins has emerged as an underlying defect in ALS pathogenesis. FUS is one of the first RBPs linked to ALS, and modeling FUS toxicity in yeast will enhance our understanding of how RBPs contribute to neuronal toxicity in ALS. Using a yeast model of FUS toxicity, we designed and completed a genetic screen to identify human genes that suppress FUS induced toxicity. Enrichment analysis of the suppressor genes showed an over representation of genes with RNA binding function and ribonucleoprotein complex localization. A subset of the suppressors physically interact with FUS and colocalize with FUS aggregates. We focused on the FUS suppressor TATA-Box Binding Protein Associated Factor (TAF) for further study since TAF has been found in a complex with FUS and has a similar function and structure to FUS. We tested TAF against three other toxic neurodegenerative disease proteins; TAF suppressed toxicity of FUS and not the others. TAF did not reduce FUS pr (open full item for complete abstract)

Committee: Shulin Ju Ph.D. (Advisor); Paula Bubulya Ph.D. (Committee Member); Weiwen Long Ph.D. (Committee Member); Mark Rich M.D., Ph.D. (Committee Member); Quan Zhong Ph.D. (Committee Member) Subjects: Molecular Biology