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- All Subjects: Chemistry
- Genre: Academic theses
- Creators: Arizona State University
Description
Chimeric antigen receptor (CAR) T-cell therapies present transformative potentials for progressive and refractory cancer treatment. However, therapy-associated neuronal toxicities, cytokine release syndromes, relapse rates, and the complex responses of patients and medical management have increased the cost of patient care. Prompt point-of-care (POC) quantification of circulating CAR T-cells and associated cytokines could enhance safety, simplify patients' management, and decrease patient care costs. While effective, existing standard detection methods, such as Enzyme-Linked Immunosorbent Assay (ELISA), quantitative Polymerase Chain Reaction(qPCR), and Flow cytometry, are not conducive to quick POC testing due to their complexity and expense. This research introduces a centrifuge-free Rapid Optical Imaging (ROI)-based platform to quantify CAR T-cells and therapy-related cytokine (Interleukin-6) from a single drop of whole blood. Through machine learning, label-free ROI-based CAR T-cell detection has been improved for accuracy compared with fluorescent staining results, and the morphological characteristics of CAR-T cells have been applied to attribute for differentiation and reduce false positives. This multi-layered microfluidic chip integrates cell and cytokines separation, collection, and detection steps, reducing the need for centrifugation or staining procedures. The microfluidic channel system separates white blood cells from whole blood after red blood cell agglutination and membrane filtration. The non-agglutinated samples are then extracted into a subchannel with a functionalized sensor surface for CAR-T-specific detection. Calibration curves were established using blood samples spiked with varying CAR-T cell concentrations. Another subchannel, featuring dual-layer membrane filtration, has been designed for cytokine detection using gold nanoparticle-labeled detection antibodies. Cytokine concentrations are digitally measured by tracking the number of gold nanoparticles in designated zones. This platform aims to offer a rapid and cost-efficient prognostic tool for timely assessment of key molecular and cellular biomarkers of CAR-T therapy patients, facilitating timely and evidence-based treatment adjustments.
ContributorsYu, Nanxi (Author) / Wang, Shaopeng SW (Thesis advisor) / Forzani, Erica EF (Thesis advisor) / Borges, Chad CB (Committee member) / Liu, Yan YL (Committee member) / Arizona State University (Publisher)
Created2023
Description
In this thesis, applications of sparsity, specifically sparse-tensors are motivated in physics.An algorithm is introduced to natively compute sparse-tensor's partial-traces, along with direct implementations in popular python libraries for immediate use. These applications include the infamous exponentially-scaling (with system size) Quantum-Many-Body problems (both Heisenberg/spin-chain-like and Chemical Hamiltonian models). This sparsity aspect is stressed as an important and essential feature in solving many real-world physical problems approximately-and-numerically. These include the original motivation of solving radiation-damage questions for ultrafast light and electron sources.
ContributorsCandanedo, Julio (Author) / Beckstein, Oliver (Thesis advisor) / Arenz, Christian (Thesis advisor) / Keeler, Cynthia (Committee member) / Erten, Onur (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation centers on treatment effect estimation in the field of causal inference, and aims to expand the toolkit for effect estimation when the treatment variable is binary. Two new stochastic tree-ensemble methods for treatment effect estimation in the continuous outcome setting are presented. The Accelerated Bayesian Causal Forrest (XBCF) model handles variance via a group-specific parameter, and the Heteroskedastic version of XBCF (H-XBCF) uses a separate tree ensemble to learn covariate-dependent variance. This work also contributes to the field of survival analysis by proposing a new framework for estimating survival probabilities via density regression. Within this framework, the Heteroskedastic Accelerated Bayesian Additive Regression Trees (H-XBART) model, which is also developed as part of this work, is utilized in treatment effect estimation for right-censored survival outcomes. All models have been implemented as part of the XBART R package, and their performance is evaluated via extensive simulation studies with appropriate sets of comparators. The contributed methods achieve similar levels of performance, while being orders of magnitude (sometimes as much as 100x) faster than comparator state-of-the-art methods, thus offering an exciting opportunity for treatment effect estimation in the large data setting.
ContributorsKrantsevich, Nikolay (Author) / Hahn, P Richard (Thesis advisor) / McCulloch, Robert (Committee member) / Zhou, Shuang (Committee member) / Lan, Shiwei (Committee member) / He, Jingyu (Committee member) / Arizona State University (Publisher)
Created2023
Description
In image classification tasks, images are often corrupted by spatial transformationslike translations and rotations. In this work, I utilize an existing method that uses
the Fourier series expansion to generate a rotation and translation invariant representation of closed contours found in sketches, aiming to attenuate the effects of distribution shift caused by the aforementioned transformations. I use this technique to
transform input images into one of two different invariant representations, a Fourier
series representation and a corrected raster image representation, prior to passing
them to a neural network for classification. The architectures used include convolutional neutral networks (CNNs), multi-layer perceptrons (MLPs), and graph neural
networks (GNNs). I compare the performance of this method to using data augmentation during training, the standard approach for addressing distribution shift, to see
which strategy yields the best performance when evaluated against a test set with
rotations and translations applied. I include experiments where the augmentations
applied during training both do and do not accurately reflect the transformations encountered at test time. Additionally, I investigate the robustness of both approaches
to high-frequency noise. In each experiment, I also compare training efficiency across
models. I conduct experiments on three data sets, the MNIST handwritten digit
dataset, a custom dataset (QD-3) consisting of three classes of geometric figures from
the Quick, Draw! hand-drawn sketch dataset, and another custom dataset (QD-345)
featuring sketches from all 345 classes found in Quick, Draw!. On the smaller problem space of MNIST and QD-3, the networks utilizing the Fourier-based technique to
attenuate distribution shift perform competitively with the standard data augmentation strategy. On the more complex problem space of QD-345, the networks using the
Fourier technique do not achieve the same test performance as correctly-applied data
augmentation. However, they still outperform instances where train-time augmentations mis-predict test-time transformations, and outperform a naive baseline model
where no strategy is used to attenuate distribution shift. Overall, this work provides
evidence that strategies which attempt to directly mitigate distribution shift, rather
than simply increasing the diversity of the training data, can be successful when
certain conditions hold.
ContributorsWatson, Matthew (Author) / Yang, Yezhou YY (Thesis advisor) / Kerner, Hannah HK (Committee member) / Yang, Yingzhen YY (Committee member) / Arizona State University (Publisher)
Created2023
Description
Scientific research encompasses a variety of objectives, including measurement, making predictions, identifying laws, and more. The advent of advanced measurement technologies and computational methods has largely automated the processes of big data collection and prediction. However, the discovery of laws, particularly universal ones, still heavily relies on human intellect. Even with human intelligence, complex systems present a unique challenge in discerning the laws that govern them. Even the preliminary step, system description, poses a substantial challenge. Numerous metrics have been developed, but universally applicable laws remain elusive. Due to the cognitive limitations of human comprehension, a direct understanding of big data derived from complex systems is impractical. Therefore, simplification becomes essential for identifying hidden regularities, enabling scientists to abstract observations or draw connections with existing knowledge. As a result, the concept of macrostates -- simplified, lower-dimensional representations of high-dimensional systems -- proves to be indispensable. Macrostates serve a role beyond simplification. They are integral in deciphering reusable laws for complex systems. In physics, macrostates form the foundation for constructing laws and provide building blocks for studying relationships between quantities, rather than pursuing case-by-case analysis. Therefore, the concept of macrostates facilitates the discovery of regularities across various systems. Recognizing the importance of macrostates, I propose the relational macrostate theory and a machine learning framework, MacroNet, to identify macrostates and design microstates. The relational macrostate theory defines a macrostate based on the relationships between observations, enabling the abstraction from microscopic details. In MacroNet, I propose an architecture to encode microstates into macrostates, allowing for the sampling of microstates associated with a specific macrostate. My experiments on simulated systems demonstrate the effectiveness of this theory and method in identifying macrostates such as energy. Furthermore, I apply this theory and method to a complex chemical system, analyzing oil droplets with intricate movement patterns in a Petri dish, to answer the question, ``which combinations of parameters control which behavior?'' The macrostate theory allows me to identify a two-dimensional macrostate, establish a mapping between the chemical compound and the macrostate, and decipher the relationship between oil droplet patterns and the macrostate.
ContributorsZhang, Yanbo (Author) / Walker, Sara I (Thesis advisor) / Anbar, Ariel (Committee member) / Daniels, Bryan (Committee member) / Das, Jnaneshwar (Committee member) / Davies, Paul (Committee member) / Arizona State University (Publisher)
Created2023
Description
Banded iron formations (BIFs) are among the earliest possible indicators for oxidation of the Archean biosphere. However, the origin of BIFs remains debated. Proposed formation mechanisms include oxidation of Fe(II) by O2 (Cloud, 1973), photoferrotrophy (Konhauser et al., 2002), and abiotic UV photooxidation (Braterman et al., 1983; Konhauser et al., 2007). Resolving this debate could help determine whether BIFs are really indicators of O2, biological activity, or neither.
To examine the viability of abiotic UV photooxidation of Fe, laboratory experiments were conducted in which Fe-bearing solutions were irradiated with different regions of the ultraviolet (UV) spectrum and Fe oxidation and precipitation were measured. The goal was to revisit previous experiments that obtained conflicting results, and extend these experiments by using a realistic bicarbonate buffered solution and a xenon (Xe) lamp to better match the solar spectrum and light intensity.
In experiments reexamining previous work, Fe photooxidation and precipitation was observed. Using a series of wavelength cut-off filters, the reaction was determined not to be caused by light > 345 nm. Experiments using a bicarbonate buffered solution, simulating natural waters, and using unbuffered solutions, as in prior work showed the same wavelength sensitivity. In an experiment with a Xe lamp and realistic concentrations of Archean [Fe(II)], Fe precipitation was observed in hours, demonstrating the ability for photooxidation to occur significantly in a simulated natural setting.
These results lead to modeled Fe photooxidation rates of 25 mg Fe cm-2 yr-1—near the low end of published BIF deposition rates, which range from 9 mg Fe cm-2 yr-1 to as high as 254 mg Fe cm-2 yr-1 (Konhauser et al., 2002; Trendall and Blockley, 1970). Because the rates are on the edge and the model has unquantified, favorable assumptions, these results suggest that photooxidation could contribute to, but might not be completely responsible for, large rapidly deposited BIFs such those in the Hamersley Basin. Further work is needed to improve the model and test photooxidation with other solution components. Though possibly unable to fully explain BIFs, UV light has significant oxidizing power, so the importance of photooxidation in the Archean as an environmental process and its impact on paleoredox proxies need to be determined.
To examine the viability of abiotic UV photooxidation of Fe, laboratory experiments were conducted in which Fe-bearing solutions were irradiated with different regions of the ultraviolet (UV) spectrum and Fe oxidation and precipitation were measured. The goal was to revisit previous experiments that obtained conflicting results, and extend these experiments by using a realistic bicarbonate buffered solution and a xenon (Xe) lamp to better match the solar spectrum and light intensity.
In experiments reexamining previous work, Fe photooxidation and precipitation was observed. Using a series of wavelength cut-off filters, the reaction was determined not to be caused by light > 345 nm. Experiments using a bicarbonate buffered solution, simulating natural waters, and using unbuffered solutions, as in prior work showed the same wavelength sensitivity. In an experiment with a Xe lamp and realistic concentrations of Archean [Fe(II)], Fe precipitation was observed in hours, demonstrating the ability for photooxidation to occur significantly in a simulated natural setting.
These results lead to modeled Fe photooxidation rates of 25 mg Fe cm-2 yr-1—near the low end of published BIF deposition rates, which range from 9 mg Fe cm-2 yr-1 to as high as 254 mg Fe cm-2 yr-1 (Konhauser et al., 2002; Trendall and Blockley, 1970). Because the rates are on the edge and the model has unquantified, favorable assumptions, these results suggest that photooxidation could contribute to, but might not be completely responsible for, large rapidly deposited BIFs such those in the Hamersley Basin. Further work is needed to improve the model and test photooxidation with other solution components. Though possibly unable to fully explain BIFs, UV light has significant oxidizing power, so the importance of photooxidation in the Archean as an environmental process and its impact on paleoredox proxies need to be determined.
ContributorsCastleberry, Parker (Author) / Anbar, Ariel D (Thesis advisor) / Herckes, Pierre (Committee member) / Lyons, James (Committee member) / Arizona State University (Publisher)
Created2017
Description
Late first row transitional metals have attracted attention for the development of sustainable catalysts due to their low cost and natural abundance. This dissertation discusses the utilization of redox-active ligands to overcome one electron redox processes exhibited by these base metals. Previous advances in carbonyl and carboxylate hydrosilylation using redox active ligand-supported complexes such as (Ph2PPrPDI)Mn and (Ph2PPrDI)Ni have been reviewed in this thesis to set the stage for the experimental work described herein.The synthesis and electronic structure of late first row transition metal complexes featuring the Ph2PPrPDI chelate was pursued. Utilizing these complexes as catalysts for a variety of reactions gave a recurring trend in catalytic activity. DFT calculations suggest that the trend in activity observed for these complexes is associated with the ease of phosphine arm dissociation.
Furthermore, the synthesis and characterization of a phosphine-substituted aryl diimine ligand, Ph2PPrADI-H was explored. Addition of Ph2PPrADI-H to CoCl2 resulted in C-H activation of the ligand backbone and formation of [(Ph2PPrADI)CoCl][Co2Cl6]0.5. Reduction of [(Ph2PPrADI)CoCl][Co2Cl6]0.5 afforded the precatalyst, (Ph2PPrADI)Co, that was found to effectively catalyze carbonyl hydrosilylation. At low catalyst loading, TOFs of up to 330 s-1 could be achieved, the highest ever reported for metal-catalyzed carbonyl hydrosilylation.
This dissertation also reports the first cobalt catalyzed pathway for dehydrocoupling diamines or polyamines with polymethylhydrosiloxanes to form crosslinked copolymers. At low catalyst loading, (Ph2PPrADI)Co was found to catalyze the dehydrocoupling of 1,3-diaminopropane and TMS-terminated PMHS with TOFs of up to 157 s-1, the highest TOF ever reported for a Si-N dehydrocoupling reaction. Dehydrocoupling of diamines with hydride-terminated polydimethylsiloxane yielded linear diamine siloxane copolymers as oils.
Finally, dehydrocoupling between diamines and organosilanes catalyzed by a manganese dimer complex, [(2,6-iPr2PhBDI)Mn(μ-H)]2, has allowed for the preparation of silane diamine copolymers. Exceptional solvent absorption capacity was demonstrated by the solid networks, which were found to absorb up to 7 times their own weight. Furthermore, degradation of these networks revealed that their Si-N backbones are easily hydrolysable when exposed to air. The use of lightly crosslinked copolymers as coatings was also studied using SEM analysis.
ContributorsSharma, Anuja (Author) / Trovitch, Ryan J. (Thesis advisor) / Seo, Dong-Kyun (Committee member) / Moore, Gary F. (Committee member) / Arizona State University (Publisher)
Created2024
Description
Microplastics, plastics smaller than 5 mm, are an emerging concern worldwide due to their potential adverse effects on the environment and human health. Microplastics have the potential to biomagnify through the food chain, and are prone to adsorbing organic pollutants and heavy metals. Therefore, there is an urgent need to assess the extent of microplastic contamination in different environments. The occurrence of microplastics in the atmosphere of Tempe, AZ was investigated and results show concentrations as high as 1.1 microplastics/m3. The most abundant identified polymer was polyvinyl chloride. However, chemical characterization is fraught with challenges, with a majority of microplastics remaining chemically unidentified. Laboratory experiments simulating weathering of microplastics revealed that Raman spectra of microplastics change over time due to weathering processes. This work also studied the spatial variation of microplastics in soil in Phoenix and the surrounding areas of the Sonoran Desert, and microplastic abundances ranged from 122 to 1299 microplastics/kg with no clear trends between different locations, and substantial total deposition of microplastics occurring in the same location with resuspension and redistribution of deposited microplastics likely contributing to unclear spatial trends. Temporal variation of soil microplastics from 2005 to 2015 show a systematic increase in the abundance of microplastics. Polyethylene was prominent in all soil samples.
Further, recreational surface waters were investigated as a potential source of microplastics in aquatic environments. The temporal variation of microplastics in the Salt River, AZ over the course of one day depicted an increase of 8 times in microplastic concentration at peak activity time of 16:00 hr compared to 8:00 hr. Concurrently, microplastic concentrations in surface water samples from apartment community swimming pools in Tempe, AZ depicted substantial variability with concentrations as high as 254,574 MPs/m3. Polyester and Polyamide fibers were prevalent in surface water samples, indicating a release from synthetic fabrics. Finally, a method for distinguishing tire wear microplastics from soot in ambient aerosol samples was developed using Programmed Thermal Analysis, that allows for the quantification of Elemental Carbon. The method was successfully applied on urban aerosol samples with results depicting substantial fractions of tire wear in urban atmospheric environments.
ContributorsChandrakanthan, Kanchana (Author) / Herckes, Pierre (Thesis advisor) / Fraser, Matthew (Committee member) / Shock, Everett (Committee member) / Arizona State University (Publisher)
Created2024