UC Davis

This dissertation aims to investigate three distinct problems. Firstly, it aims to enhance lung cancer diagnosis and survival predictions through the implementation of deep learning techniques and CT imaging. Secondly, it delves into understanding the differences in distortion patterns present in adversarial images generated by various attack methods. Lastly, it explores the application of the Minimum Description Length (MDL) principle for optimal threshold determination in microbiome community detection.

Supervised by Professor Thomas Lee and Professor James Sharpnack, Chapter \ref{ch:chap2} proposes the utilization of convolutional neural networks to model the intricate relationship between the risk of lung cancer and the morphology of the lungs depicted in CT images. Introducing a mini-batched loss extending the Cox proportional hazards model, this approach accommodates the non-convexity induced by neural networks, enabling training on large datasets. The combination of mini-batched loss and binary cross-entropy facilitates the prediction of both lung cancer occurrence and the risk of mortality. Results from simulations and real data experiments highlight the potential of this method to advance lung cancer diagnosis and treatment.

Supervised by Professor Thomas Lee, Chapter \ref{ch:chap4} discusses the application of the MDL principle in microbiome data analysis, particularly focusing on community detection methods. Addressing the challenge of subjective threshold selection in correlation-based techniques, MDL is employed to identify the optimal community structure by minimizing the subjectivity in choosing a cut-off for correlation strength. The chapter provides a detailed derivation of the MDL principle, discusses its consistency in threshold selection, and validates its effectiveness through simulations. A real data experiment involving microbiome data from the Great Lakes offers practical insights into the application of MDL in a real-world context.

Supervised by Professor Thomas Lee, Professor Yao Li, and Professor Cho-Jui Hsieh, Chapter \ref{ch:chap3} explores the vulnerability of deep neural networks to adversarial examples. Focusing on three common attack families – gradient-based, score-based, and decision-based – the research aims to recognize distinct types of adversarial examples. By identifying the information possessed by attackers, effective defense strategies can be developed. The study demonstrates that adversarial images from different attack families can be successfully identified with a simple model. Experiments on CIFAR10 and Tiny ImageNet reveal differences in distortion patterns between various attack types for both $L_2$ and $L_\infty$ norms.

In this study, we report the generation of CD209 gene knockout sheep, utilizing electroporation-mediated CRISPR-Cas9 genome editing, as a model to test whether this might make cattle resistance against Bovine Leukemia Virus (BLV). This approach exploits the CD209 gene's role as a receptor for BLV, hypothesizing that its knockout would confer resistance to infection. Our methodology involves specific guide RNAs targeting the sheep CD209 gene, followed by electroporation into ovine zygotes to induce targeted gene disruptions. It was hypothesized that a gene knockout of CD209 would result in the inability of the virus to bind and enter the cell; therefore, creating disease resistance. The resultant lambs exhibited varied mosaicism and phenotypic outcomes associated with the gene edit, indicative of the CRISPR-Cas9 system's effectiveness and efficiency. This study not only demonstrates a novel application of gene editing in livestock but also underlines the potential of sheep as surrogate models in BLV research, due to their analogous immunological responses and shorter gestational periods compared to cattle. The successful application of this technology paves the way for future research in genetic engineering for livestock disease resistance, with significant implications for animal health management and agricultural productivity.

Predicting bread baking performance with knowledge of flour composition or dough behavior under varying stresses would be of great significance to the baking industry. Various studies have attempted to relate characteristics in flour, such as protein content, protein quality, stability, water absorption, and degree of damaged starch as well as dough rheological properties, such as elasticity, strength, and extensibility to baking performance. The complex mechanisms occurring within the bread processing stages (mixing, sheeting, proofing, and baking) are not fully understood and thus make it difficult to determine a true relationship. The study consisted of using 15 different flour types, making a specific protocol for mixing and taking measurements at each processing stage (e.g. mixing sheeting, and proofing) and assessing any significant relationships between the quality parameters of the baked bread and input parameters from each process stage. A programable mixer was successfully used to produce uniform and consistent doughs across replicates, an instrumented dough sheeter was used to measure dough behavior under varying stress during sheeting and height profiles during proofing. Multiple factors at each stage of development and processing influenced the developing dough. Multiple linear regression models for baked bread quality parameters did show sheeting parameters as significant but measuring across all processes can better predict baked bread quality than simply looking at one process. For example in the current study there was lack of evidence examining linear correlations between loaf firmness and sheeting parameters, however, incorporating sheeting parameters into a comprehensive predictive model showed sheeting as a significant factor in predicting loaf firmness.

A vital element of any human-rated mission is the Environmental Control and Life Support System (ECLSS), composed of multiple subsystems, including an Air Revitalization subsystem that maintains a breathable atmosphere. Tracking performance, identifying performance degradation, predicting remaining useful life of components, and performing maintenance on such a critical system are paramount to creating a safe, habitable environment and are thus key research areas at the UC Davis Center for Spaceflight Research. This thesis outlines the design, build, and test of the ZeoDe (Zeolite Capacity Degradation) testbed at the UC Davis Center for Spaceflight research, as well as the background research that went into its conception. This testbed is a chemically functional CO2 removal system that generates degradation data for prognostics through the introduction of humidity into the system. The introduction of humidity can occur in a space habitat due to leaks or other faults. Humidity build-up within the system leads to CO2 removal capacity degradation of the sorbent. Thus, the study of sorbent degradation is of paramount importance to any zeolite-based CO2 removal system deployed on future spacecraft. The maintenance of such a system is equally important. The base requirements of the ZeoDe system take both human and robotic maintainability into account, along with the development of a twin robotically manipulable mockup that was also built up at the UCD Center for Spaceflight Research. The ZeoDe testbed will allow UC Davis, NASA, and any visiting researcher to investigate sensor criticality, degradation physics, detection sequences, and maintenance plans for a degraded ECLSS CO2 removal unit in both autonomous robotic tasks and integrated robot/human teaming scenarios. The modular build will also allow for future research and visiting research to take place at the center to further ECLSS research for future space habitation.

Groundwater is a crucial component of global water resources, especially in semi-arid and arid regions where it may be the only or predominant source of water. The intensive use of this resource deeply impacts hydrological systems at a basin-scale, causing widespread aquifer depletion. As consequence, recent decades have seen an increasing interest in complex hydrogeologic modeling and analysis has been observed worldwide to support the decision-making process for sustainable water resource management. These numerical groundwater models offer several capabilities to represent the physical systems and to assess different water management policies, but they are often constrained by limited data availability and several sources of model uncertainty (e.g., parameters, variables, inputs, and assumptions). This dissertation uses a physically-based groundwater model to explore to explore several strategies to overcome data gap conditions during the water resource modeling process and to evaluate the performance of spatially distributed aquifer management alternatives for the case study of the Ukiah Valley Groundwater Basin in northern California.First, the development of a three-dimensional finite-difference groundwater model is described in Chapter 1. This Chapter indicates that the bias in the model related to limited data availability could be reduced when taking specific steps to improve model representation of the system. These steps include; 1) taking advantage of the knowledge and practical insights of technical experts and locals to methodically define model inputs (e.g., the stream network consistency with the actual flowing stream segments), 2) conducting a large review and cross validation of all available geological and hydrogeologic representations is best practice for designing a robust aquifer 1 layering system; and 3) by integrating the appropriate software components (packages) to accurately represent the predominant hydrologic mechanisms within the basin. Chapter 2 aims to use the developed physical model to compare the impact on groundwater heads and stream-aquifer interaction of the coupled effects of a set of six managed aquifer recharge (MAR) alternatives combined with different surface water storage scenarios. In four MAR alternatives, the water availability was investigated from the hypothetical construction of four new small dams, and in two MAR alternatives, water availability was explored from the reoperation of the existing reservoir. Numerical modeling results confirm that four out of six of the proposed recharge alternatives in the alluvial aquifer have an important impact on hydraulic heads, with substantial (greater than 7 m) increases that last over an average of 4 months and smaller increases (greater than 0.01 m) that are visible for most of the year. These results highlight how the combination of a smaller infiltration basin with a larger reservoir capacity improves the groundwater levels basin-wide more than the opposite scheme, with a larger infiltration basin and smaller reservoir capacity. Additionally, combining expansive infiltration basins and high flows from reservoirs can considerably increase the net aquifer-to-stream flux along the main stem and tributaries, depending on the location of the reservoirs. In Chapter 3, we develop a method to support decision making regarding efficient data collection that could address the model uncertainties for this case study (e.g., parameters). We used a time variant global sensitivity (TVSA) analysis to assign the variation in model outputs (i.e., RMSE metrics for simulated groundwater head, NSE metrics for simulated streamflows) to the variations in model inputs (i.e., a set of 11 parameters). we then perform these TVSA methods across four well observation subsets (i.e., W1, W2, W3, and W4) and three stream gage observation subsets (i.e., SG1, SG2, and SG3) to evaluate the independent effects of record length and number of 2

observations (i.e., groundwater head and streamflows) on the temporal (i.e., annual and seasonal) parameters sensitivities. We find that, though the error in the heads and flows exhibited some differences in temporal trends, drought cycles largely governed the variation of parameter sensitivities in both metrics. Findings suggest that the length of record of monitoring data are more important than the number of wells in screening the parameter temporal sensitivities, and more data could be collected for the regulated segments of the main stem of the watershed, particularly during dry years. These highlight how sensitivity analysis methods can be expanded to inform decision-making in term of data prioritization.The methods developed during this dissertation could be valuable tools to apply in other Mediterranean or semi-arid alluvial basins and to respond to different groundwater modeling challenges. Specifically, the frameworks developed can be used to overcome limited groundwater elevation data availability, to evaluate managed aquifer recharge alternatives impacts on the hydrologic system, and to apply time variant sensitivity analysis for supporting the design of new data acquisition. Such analyses could assist communities as they invest in surface- and groundwater modeling to adapt to unpredictable water supplies and a changing future climate.

Groundwater overdraft in the state of California has resulted in many undesirable results, including land subsidence, water quality degradation, loss of interconnected surface water/ groundwater locations, seawater intrusion, and overall reduction in groundwater storage. These consequences were exacerbated by the 2012-2016 drought period, resulting in the passage of the California Sustainable Groundwater Management Act, the first legislation that explicitly required sustainable use of groundwater resources in the state. This legislation also acknowledged the importance of conjunctive use of surface water and groundwater resources, in which excesses of one resource can supplement deficiencies of the other. This conjunctive use of resources is the main motivation for relevant parties incorporating managed aquifer recharge projects into their groundwater sustainability portfolios.Managed aquifer recharge projects have the potential to allow for increased surface water resources in the wet season to be transferred to aquifers for future use. Many of these projects are in use in the state and range from injection wells to large scale flooding of agricultural fields. These projects can be costly to implement and are limited in locations due to the need to create infrastructure for diversions, find willing landowners to allow the project to occur, and receive the proper permits. Because of these costs, there is an importance in understanding what specific parameters are most important to understand and quantify when determining whether these recharge projects will be successful. This dissertation focuses on the creation of models for managed aquifer recharge sites utilizing large amounts of publicly available data and understanding how the uncertainty of that data impacts recharge results. The first body chapter focuses on the creation and utilization of a data-dense fine resolution geologic model of the recharge site and surrounding area using previously proprietary geologic data. Many realizations were developed to quantify the uncertainty of this geology. Results of this chapter acknowledge the importance of geologic characterization of recharge study sites because there can be great uncertainty between realizations of geologic results, specifically in the location of high conductivity connected geologic units. In the second body chapter, these geologic realizations are incorporated into groundwater models, with publicly available information for pumping and recharge. This geology was then simplified using a vertical upscaling process, to conclude if computationally intensive geologic models could be simplified and still produce similar groundwater flux and head results. Geologic upscaling resulted in similar groundwater head results at low levels of upscaling, but as upscaling increased, the impact of the pumping and recharge boundary conditions increased, resulting in increasing unrealistic model results. Finally, artificial recharge scenarios were applied at varying magnitudes and times to the recharge sites in a transient groundwater model in the third body chapter to quantify any changes in large scale or local scale model results. Large amounts of recharge applied at once resulted in increased gradients at the recharge locations, which drove more flow out of the model domain than in a no recharge case, but overall, recharge provided a benefit to river dynamics and cumulative storage rates. This work emphasizes the importance of subsurface characterization and understanding the impact of the boundary conditions that are applied to groundwater model results and provides scenario results that can be presented to relevant parties to discuss how the timing and magnitude of recharge at the study sites can affect the underlying groundwater table. Future work could include the incorporation of more data, such as isotopes for groundwater dating and recharge pathways and modeling of the unsaturated zone to visualize how recharge flows from the surface to the water table. 

Ecosystems are intricately connected by the exchange of organisms, inorganic materials, energy, and information that traverse ecotones, forming a complex network of interactions. These inputs from donor systems, known as spatial subsidies, can profoundly shape habitats by influencing primary productivity, altering community interactions, impacting resilience, and changing species composition in recipient habitats. However, human activities such as global shipping and urbanization can disrupt these subsidies through increased nutrient flow, species introductions, and climate-mediated range shifts. In this dissertation, I investigate how invasive species in donor systems and human-mediated movement of organic material across habitats impact subsidy-dependent communities.Chapter 1 explores the impacts of an invasion-mediated shift in seaweed wrack from native kelp, Macrocystis pyrifera, to invasive Devilweed, Sargassum horneri, on subsidy- dependent communities of rocky shores. The study assesses the species-specific impacts on common detritivores (Pagurus samuelis [Blue banded hermit crab], Pachygrapsus crassipes [Striped shore crab], and Tegula funebralis [Black turban snail]), the historically important but now endangered wrack-consuming giant snail (Haliotis cracherodii, [Black abalone]), an assemblage of these grazers, and common native benthic seaweeds (Centroceras clavulatum, Silvetia compressa, and Ulva spp.). Performance impacts are evaluated through long-term feeding assays using common Black turban snails and Haliotis rufescens (Red abalone), as a proxy for the rare Black abalone. Food preference was determined through feeding choice assays using individual species of these wrack detritivores, and a “community assay” in which an assemblage of these grazers fed on three native benthic seaweeds along with either native kelp or invasive Devilweed wrack are conducted. Performance varied among consumers, with abalone growing better on a kelp diet compared to Devilweed and showing intermediate growth on a mixed diet. Contrary to predictions, Black turban snails grow more on the Devilweed diet over kelp, and those that fed a mixed diet grow similarly to those that ate Devilweed alone. Preference assays reveal that although Black turban snails grow more on Devilweed diets, they prefer kelp, while abalone also display a high preference for kelp. Blue banded hermit crabs prefer Devilweed, and Striped shore crabs show no preference. The assemblage of wrack detritivores shifts consumption away from wrack when kelp is replaced by Devilweed, and increases consumption on S. compressa, an already sensitive, canopy-forming rockweed, suggesting Devilweed has the potential to indirectly impact native benthic seaweeds in subsidy-dependent communities. Chapter 2 aims to understand the spatiotemporal variation in native and invasive wrack deposition on beaches that receive large inputs from adjacent kelp forests, recently invaded by Sargassum horneri. We conducted surveys at seven sites on one of the California Channel Islands at four time points across 2022. Our findings reveal spatiotemporal variation in native and invasive wrack inputs to beaches, with Giant kelp, Macrocystis pyrifera, dominating wrack inputs throughout the year, and S. horneri being relatively rare. Kelp was most abundant on west-northwest facing shores, while S. horneri was even more rarely found on west-facing shores. The peak deposition periods for kelp and S. horneri differed, with kelp deposition peaking in September and S. horneri deposition peaking in March. This chapter highlights the complex spatiotemporal variation in native and invasive wrack inputs, and their potential to shape recipient communities.

In Chapter 3, we delve into the intricate interconnections between ecosystems, focusing on the unique phenomenon of Sargassum wrack deposition along coastlines in the Mexican Caribbean. These massive deposits, resulting from seaweed blooms in the Sargasso Sea and the Great Sargassum Belt, present significant ecological and economic challenges. Our study investigates the ecological implications of Sargassum deposition, employing manipulative field experiments simulating realistic biomass inputs of several cubic meters in both beach and forest ecosystems. Contrary to expectations based on ecological theory, our findings reveal comparable decomposition rates between beach and forest ecosystems, challenging the notion that naïve ecosystems are incapable of processing novel subsidies. We assess the relative contributions of arthropods and microbes to Sargassum decomposition, with microbial communities dominating decomposition in the forest and a combination of microbes and talitrid amphipods driving decomposition on the beach. Furthermore, our study provides insights into the long-term effects of Sargassum deposition on nutrient cycling within these two ecosystems. After 12 months, we found that Sargassum may serve as a nutrient subsidy to native plants in the forest, albeit with slower utilization rates compared to non-native plants such as Bermuda grass found on beaches in the area. Overall, our results highlight the capacity of forest ecosystems to assimilate and utilize foreign organic matter, challenging traditional ecological paradigms and offering new perspectives on ecosystem functioning.

Community Based Organizations (CBOs) play a crucial role in supporting clients to navigate healthcare and social services, often funded through grants and donations. Nevertheless, the burden of extensive documentation for both service providers and clients can erect barriers that inhibit CBOs from addressing community needs or deter clients from availing services that could enhance their standard of living. This research focuses on how neoliberal policies engender bureaucratic entanglements, thereby escalating the obstacles to access for older individuals in rural settings. The burden of excessive paperwork, combined with other obstacles such as poor quality of care, discourages service seekers from applying for programs beyond their most immediate needs due to fear of mistreatment. By centering the voices of these older individuals, the study aims to understand the impact of these policies on the healthcare safety net. In addition, service providers of Community Based Organizations share how the management of grant deliverables, funding and interorganizational collaboration impact service delivery. Finally, the research explores the concept of Community Cultural Wealth (CCW) as a potential tool for rural unincorporated communities to overcome the challenges that arise from healthcare access barriers.

In the last decade, the proliferation of online collaboration has vastly broadened the landscape of knowledge work. Individuals form online communities and accomplish complicated intelligent work together, such as developing software or planning healthy diets. These collaborations vary in levels of joint interaction and collaboration strategies, partially dependent on the technologies as well. While technological advancements have enabled large-scale flexible participation, they have also resulted in a proliferation of unorganized and overlapping knowledge contributions. Also, it may hinder misleading information from separating from credible knowledge. This over- whelming abundance and disorganization pose considerable challenges both for individuals and the community to leverage the knowledge to solve problems and make high-stakes decisions. These tasks demand not only cognitive resources but also advanced meta-cognitive skills. These obstacles highlight the need to concentrate on structures and distilling knowledge, from creation and integration to dissemination.

In my thesis, I explore the designs and understand the impacts of computational and visualizaion scaffolds that structure and highlight relevant and semantic knowledge, taking into account the socio-cognitive dynamics among collaborators. I aim to use technological support and social-cognitive mechanisms to mitigate cognitive and attentional limitations in online community-based cooperative work.

To help individuals actively explore and understand knowledge shared in online communities, such as videos, we first dive into the sharing from both video producers and audiences, and explore the idea of structuring semantic representations of video contents and audience comments, which aids in discerning high-quality videos and supports diverse video exploration compared to conventional video watching experiences. Empirical study results also illuminate the adoption and priority of structured overview for interpretation. Moving the understanding forward, accessing relevant knowledge doesn’t guarantee analysis and knowledge integration. We investigate social nudging approaches to scaffold engagement in higher-order thinking for high-stakes topic analysis, and compare the influences between common documentation tools and a concept- mapping-based space which also plays as thinking scaffold, DeepThinkingMap. Two lab studies reveal the effectiveness of social nudging in fostering both reflective and critical thinking, and confirmed the synergetic effect of nudging with other thinking scaffolds. Finally, we shift focus to synchronous video-based interactions and non-verbal cues. Our secondary analysis of group brainstorming sessions demonstrates the significant impact of metaphoric hand gestures on both individual and partner creativity and found that the positive effect of metaphoric gestures is independent of media richness of communication medium.

In conclusion, this dissertation underscores the potential of computational and social support in reshaping how knowledge is explored, integrated, and co-created within online communities. Based on the empirical findings about the socio-technical-cognitive mechanisms and the design space, this dissertation paves the way for future research that promotes organized, reflective, and efficient knowledge collaboration in online communities.

This work will present experimental results on two projects measuring the electronic properties of unconventional superconductors. The first project focuses on the iron-based superconductor system of FeSe1-xSx. We used scanning tunneling microscopy/spectroscopy (STM/S) to make detailed measurements of the electronic states of tetragonal FeSe0.89S 0.19. We also performed theoretical band structure calculations that were calibrated by angle-resolved photoemission spectroscopy measurements for comparison. We utilize the high spatial resolution of the STM/S measurements to separately analyze modulations in the local density of states (LDOS) in regions near and away from iron-vacancies. This analysis revealed two types of features: (i) energy dispersive quasiparticle interference patterns which can explained by our band structure calculations and (ii) a much stronger modulation just above the Fermi level centered at q=0.12 Å-1 which does not disperse with energy and cannot be explained by our band structure model. Local rotational symmetry analysis shows that while the modulations are four-fold symmetric on average, they are actually comprised of small domains with two-fold symmetry. Statistical analysis demonstrates that the boundaries of these domains are spatially correlated with the locations of iron-vacancies.

The second project studies charge order in the La-based cuprate La1.475Nd0.4Sr0.125CuO4. We performed temperature and uniaxial strain dependent resonant x-ray scattering studies on the charge order and the low-temperature orthorhombic to low-temperature tetragonal (LTO-LTT) structural transition. Before applying any strain, we found a precursor charge order peak existing up to 200 K, well above the static charge order onset temperature. Upon applying uniaxial tensile strain of about 0.1%, we observed a reduction in the onset of charge order by 50 K and a 20 K reduction in the LTO-LTT transition temperature. We also saw a preference for the charge order to form in the direction of applied strain due to a 6 K difference in onset temperatures for the two directions.