UC Riverside

Over the last two decades, the universe of insect taste has significantly expanded, from the initial identification of receptors expressed on peripheral neurons to the elucidation of complete neuronal circuits governing memory, locomotor output, homeostasis, and numerous behaviors associated with feeding. In parallel, neurobiologists have leveraged the genetic workhorse Drosophila melanogaster in both its larval and adult form to pursue these directions, with each developmental stage affording unique advantages as a model system for dissecting taste. Our work includes efforts to optimize the process of behavioral data analysis in adults, where we develop an adaptable pipeline for high-resolution analyses of multiple features associated with feeding on liquid food sources. Using this approach and established choice behavior assays, we identify the regulation of appetitive tastant feeding via pharyngeal gustatory receptor neuron (GRN) populations, and specifically a subset of pharyngeal GRNs that express sugar receptor Gr43a. However, how taste sensing and feeding behavior is shaped across metamorphosis is less understood. To better understand the taste system’s influence on behavior across development, we first developed a model for larval tastant exposure that permits us to assay adult behavior using non-toxic amounts of bitter tastants. We identified that exposure to certain tastants as larvae imbued attenuated avoidance to innately aversive tastants as adults across behavior assays such as food choice and proboscis extension responses. This shift in behavior was specifically linked to the identity of the tastant encountered during larval development. Additionally, we observed that behavioral modification required both functional bitter taste and intact mushroom body and dopaminergic neuron activity, where gustatory memory is formed and stored. Our results suggest that attenuation of avoidance to innately bitter compounds may require multiple levels of putative taste circuits, from the periphery to central processing components. Interestingly, silencing of dopaminergic neurons implicated in learned avoidance seemed to potentiate avoidance behavior, revealing the required regulation of learned aversion pathways in habituating avoidance. Overall, this work represents the first genetic and circuit-wide dissection of how a tastant response may be modulated across development following exposure during early life.

Fluid dynamics is the field of study that examines the motion of fluids such as liquids and gases. It can be used to investigate large-scale phenomena, such as ocean currents, as well as small-scale systems, like blood circulation. Fluid flows can be classified into two broad categories: laminar and turbulent flows. Laminar flows are smooth and streamlined, while turbulent flows are irregular and unpredictable. One of the fundamental tasks in analyzing fluid flows is to determine the flow rates and pressure values in a flow network, given its topology, channel dimensions, fluid properties, and boundary conditions.

In the first project, we study fluid mixing in microfluidic chips (MFCs), which are micro-scale fluid systems. In MFCs, flows are laminar, and for laminar flows, computing flow rates and pressure values are straightforward, but simulating the mixing process is computationally challenging. We present an approach for modeling concentration profiles in grid-based MFCs. Our algorithm outperforms COMSOL Multiphysics® software --- commercial software that uses finite element analysis method to model physics processes --- in terms of runtime while producing results that approximate those of COMSOL.

In the second project, we study turbulent flows in large-scale pipe systems such as water distribution systems and sewage networks. Unlike laminar flow systems, solving flows in turbulent models involves a system of nonlinear equations, and iterative algorithms have been widely applied in practice. We focus on the Hardy Cross loop-based algorithm (HC-loop) and the Newton-Raphson loop-based algorithm (NR-loop). We provide a mathematical analysis of the local convergence of these two algorithms, showing that, under certain conditions, NR-loop algorithm achieves quadratic convergence while HC-loop algorithm only converges linearly. This confirms earlier experimental observations reported in the literature.

In the third project, we investigate the minimum spanning tree congestion problem (STC), motivated by its application to improve the efficiency of the NR-loop algorithm for pipe flows analysis. We study the complexity of K-STC (STC for a fixed integer K) and prove that K-STC is NP-complete for K >= 5, improving the earlier hardness result and leaving only the case = 4 open. We also investigate K-STC restricted to graphs of radius 2, establishing that this variant is NP-complete for K >= 6. Additionally, we explore a variant of STC, denoted K-STC-D, where the objective is to determine if a graph has a depth-D spanning three of congestion K. We provide a tight bound for bipartite graphs by proving that 6-STC-2 is NP-complete, while 5-STC-2 is solvable in polynomial time. Finally, we present polynomial-time algorithms for two special cases involving bipartite graphs with restrictions on vertex degrees.

Artificial intelligence technology along with deep neural networks (DNNs) have evolved rapidly in recent years, demonstrating remarkable performance in complex tasks. Despite their huge impact and general effectiveness, the theoretical foundation of DNNs is not well established. In this dissertation, we investigate the statistical properties of two sparse DNNs, convolutional neural networks (CNNs), and a novel sparse DNN named sparse deep ReLU neural network (SDRN). We consider the neural network estimators obtained from empirical risk minimization with a Lipschitz loss function and develop non-asymptotic excess risk bounds for these estimators. Our established bounds demonstrate both CNNs and SDRNs can achieve robust approximation accuracy. Additionally, these bounds provide theoretical insights into optimally aligning network complexity with sample size, ensuring the best possible learning and performance. Furthermore, we establish the theories showing that under certain assumptions, CNNs and SDRNs estimators can alleviate the \textquotedblleft curse of dimensionality \textquotedblright. These theoretical results provide important insights into the understanding of the DNNs.

To bridge the gap between theory and application, this dissertation also focuses on the practical implementation of DNNs in sleep research. Raw electroencephalogram (EEG) signals in sleep data typically exhibit class imbalance and individual heterogeneity problems, which significantly impact the classification performance of machine learning algorithms. We propose a new GAN (called EGAN) architecture adapted to the features of EEG signals for data augmentation to solve the class imbalanced problem and design a cost-free ensemble learning strategy with CNNs to alleviate the heterogeneity problem. We show that the proposed method improves classificationaccuracy compared to several existing state-of-the-art methods. This application not only showcases the practical utility of CNNs but also serves as an example of how theoretical foundations can be translated into real-world solutions.

This study investigates the potential impact of passive learning for retaining spatial memory using an open-field Minecraft environment. The goal was to understand the extent to which memory of object locations can be retained when learned from watching a video. We wanted to understand the role of hippocampus-dependent development of episodic and spatial memory as it pertains to the consolidation of memories from this passive learning experience. We examined how 59 participants retained object-location associations from viewing a video featuring an avatar navigating to 12 specific objects. After watching the video, participants were given a paper-based object recognition and sequence recall test to gauge immediate object memory. Following this, they navigated the Minecraft environment to locate the objects, with their accuracy measured through Euclidean distances calculated with recorded coordinates. Our findings reveal strong positive correlations within and between the Paper-Based Object Recognition and Sequence Task and the In-Vivo Environment Task. This indicates that better memory for identifying objects and recalling their sequence of appearance corresponds, respectively, with the ability to find the correct objects and navigate accurately between objects. This study suggests there is a viable approach to enhancing spatial memory through passive learning in enriched virtual environments. It aligns with prior research suggesting that complex tasks requiring spatial skills conducted in virtual settings, like Minecraft, can positively impact hippocampal-dependent memory processes.

Global water scarcity poses a major challenge to agricultural productivity. This dissertation investigates the use of recycled water for irrigation, focusing on the occurrence of pharmaceutical and personal care products (PPCPs), their accumulation in edible crops, and the impact of irrigation water quantity on this accumulation. Analysis reveals that PPCPs, including sulfamethoxazole, are present in recycled water at concentrations ranging from 130-1400 ng/L in secondary effluent and 25-400 ng/L in tertiary effluent. The study shows that PPCP uptake and accumulation vary between leafy and fruity vegetables, with diclofenac and fluoxetine being most prevalent in each, respectively. Key factors affecting PPCP accumulation include transpiration rate and osmotic adjustments under limited water availability. The research explores two strategies: recycled wastewater effluent irrigation and limited irrigation rates, aimed at mitigating PPCPs accumulation and conserving irrigation water. A 14-week field trial on St. Augustine turfgrass assesses the effects of UV persulfate (UV/PS) treatment and limited irrigation rates on PPCPs accumulation and plant health. Results indicate that UV/PS treatment effectively removes 60% of carbamazepine and over 99% of other PPCPs from recycled water, significantly reducing PPCP levels in turfgrass leaves and roots. Limited irrigation at 60% ETo increases carbamazepine accumulation and canopy temperature, suggesting higher water stress compared to 80% ETo. Additionally, greenhouse experiments with lettuce, carrot, and tomato, using PPCP-spiked recycled water, UV/PS treated recycled water, and tap water at 60%, 80%, and 100% crop evapotranspiration rates (ETc), show that UV/PS treatment reduces PPCP accumulation by over 99%. Lettuce benefits from reduced irrigation, while carrot and tomato show increased accumulation due to osmotic adjustment. Combining UV/PS treatment with deficit irrigation conserves water, maintains crop yield, and minimizes PPCP accumulation. The findings offer valuable insights for developing strategies to safely and effectively reuse recycled water in agriculture, supporting sustainable practices and improving food safety.

CRISPR-Cas12a has revolutionized genome editing, offering precise control over genetic modifications while also serving as extraordinarily rapid and reliable diagnostic tools. This thesis provides comprehensive insights into Cas12a through advanced molecular dynamics simulations and experimental approaches. The second chapter involves multi-microsecond molecular dynamics simulations to reveal the allosteric switches governing conformational activation in Cas12a. It demonstrates how target DNA binding activates the complex, marked by a significant increase in the coupled dynamics between the REC2 and Nuc domains. Taking the investigation forward, the third chapter addresses the broader question of how Cas12a generates double-strand DNA breaks using its single RuvC nuclease domain through sequential cleavage of the non-target strand (NTS) followed by target strand (TS). Here, continuous tens of microsecond- long molecular dynamics and free-energy simulations uncovers the pivotal role of an α-helical lid within the RuvC domain. This lid anchors the crRNA:target strand duplex and guides the target strand toward the RuvC core, a mechanism corroborated by DNA cleavage experiments. The fourth chapter further investigates the role of the α-helical lid by examining R-loop formation using cryo-electron microscopy and advanced free-energy simulations. Structural and dynamic insights reveal that the lid assumes an unstructured loop at the 5-bp seed state, accompanied by distinct REC domain rearrangements. As the R-loop progresses to the 16-bp and 20-bp states, the lid resets into an α-helical structure, aiding in the accommodation of the non-target strand (NTS) followed by the target strand (TS). These structural insights rationalize Cas12a’s specificity and highlight mechanistic comparisons to other class 2 effectors. The fifth chapter focuses on the trans-cleavage property of Cas12a, which is the basis for nucleic acid detection. Kinetic studies show that the trans-cleavage activity rate of Cas12a is significantly enhanced due to its improved affinity (Km) for hairpin DNA structures, also providing mechanistic insights through molecular dynamics simulations. This enhanced signal transduction enables faster detection of clinically relevant double-stranded DNA targets with improved sensitivity and specificity. Finally, the sixth chapter investigates CRISPR-Cas9-based Adenine Base Editors (ABEs), which hold significant promise for addressing human genetic diseases caused by point mutations. We identify critical residues and demonstrate that the dimerization of TadA8e (the deaminase domain) and its unique juxtaposition to Cas9 are pivotal for efficient DNA deamination by ABE8e, the most efficient ABE to date. Overall, this thesis advances our understanding of CRISPR-based (Cas12a and Cas9) genome-editing tools, providing mechanistic insights into critical processes that will enrich fundamental knowledge and facilitate further engineering strategies for genome editing and diagnostic applications.

“Let Me LOOK at You:” The Post-9/11 Representational Imperative & Muslim* Refusal argues that the War on Terror produces hegemonic notions of Muslim American identity in order to contain anti-imperialist critique. My dissertation makes an intervention in Critical Muslim Studies by articulating how gendered racialization works to depoliticize “the Muslim” within US imperial ideology. In addition to demonstrating how the post-9/11 representational imperative functions, I consider how Muslim women and queer Muslim poets and performance artists mobilize form to reject recognition by the settler-imperialist state. By looking at the work of poets and performance artists Naomi Shihab-Nye, Solmaz Sharif, Safia Elhillo, Fatimah Asghar, Andrea Abi-Karam, Arshia Fatima Haq, and more, my project asks: what kind of ethics of care emerges when Muslim poets and performance artists betray the impulse for representation? Post-9/11 Representational Imperative & Muslim* Refusal works alongside the poetry and performance of women and queer people caught under the post-9/11 spotlight of “the Muslim” to understand how they language a world beyond coloniality and the US imperial project it engenders.

Face recognition errors occur frequently, with consequences that range from personal embarrassment to eyewitness misidentification. Established interventions have taken a variety of approaches in attempts to improve face recognition, yet they have lacked in their capacity for practical use. With this in mind, I created an application-oriented training program in an effort to improve face recognition in real-world contexts. Specifically, I designed the training to teach individuals how to process faces holistically, or in terms of how facial features spatially relate to one another. After developing the training, I conducted three experiments to assess the general efficacy of the training, examine its capacity to improve recognition over time, and compare its impact against two established other-race face recognition interventions. Experiment samples consisted of 196 to 320 participants, whom I recruited through the UC Riverside Psychology subject pool and CloudResearch Connect. In all experiments, participants completed a baseline recognition memory task, followed by the training or an alternate condition (matched control in Experiments 1 and 2, individuation or cross-race effect awareness in Experiment 3), then completed another recognition memory task. In Experiment 2, participants completed two additional recognition memory tasks 24 hours and 1 week after the manipulation. Memory strength, operationalized as accuracy on the recognition memory task, was compared before versus after the manipulation to determine whether the training produced an improvement in recognition memory ability. Across the experiments, multilevel modeling revealed that the training did not lead to improved face recognition ability. Instead, training participants generally displayed poorer recognition memory ability after the manipulation compared to their average recognition ability at baseline. Slight fluctuations in recognition ability had returned to baseline levels after one week (Experiment 2), and only the previously established interventions – not the training – led to improved other-race face recognition (Experiment 3). The training may have incited depletion and fatigue among participants, which I seek to address in future research. In future work I will also measure the extent to which the training promotes holistic processing.

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The origin and evolution of pectoral fins remains one of the most important components of vertebrate history. Sharks represent one of the oldest representatives to exhibit paired fins. Yet, there is a paucity of data on the functional and evolutionary aspects of shark pectoral fins. This dissertation aims to bridge those gaps. In Chapter 1, we measured the external pectoral fin morphology of a shark species, the scalloped hammerhead (Sphyrna lewini), that undergoes ecological shifts through ontogeny. We found the pectoral fins changed in shape that are most likely in response the difference in ecological demands that this shark experiences. In Chapter 2, we measured the pectoral fin aspect ratio (AR) of nearly every known (89%) extant shark species and various extinct species. In addition, we coded each species based on their ecology and performed sophisticated evolutionary model fitting analyses. We determined sharks were most likely benthic (i.e., bottom-dwelling) or benthopelagic (i.e., near bottom) in origin and that when sharks shifted to the pelagic (open water) zone of the marine ecosystem, their pectoral fin morphology also changed, clearly demonstrating adaptive evolution. We also realized temperature was a critical driver in shark evolution. In Chapter 3, we used the computational fluid dynamics (CFD) approach to understand the functional aspects of pelagic shark pectoral fins. Our results showed that pelagic shark pectoral fins have different functions compared to benthic sharks. In Chapter 4, we followed up our work in Chapter 1, and compared the scaling trends of shark pectoral fins with various ecologies to determine if any noticeable patterns were present. We realized the scaling of shark pectoral fins is highly complex and that further broader research is warranted.