Owing to advances in single cell and omics level profiling methodologies, nuances of cell identity and characterization of novel sub populations has become an active research area for many tissues and organs. However, utilization of these techniques for similar discoveries in the neural crest field have been limited due to technical challenges associated with collecting a transient and highly migratory cell type. Recent advances in human pluripotent stem cell models of neural crest differentiation, allowing for spatial and temporal patterning of neural crest identity, offer an attractive alternative due to their reproducibility, scalability, and availability. Utilization of these systems for omics level experiments with subsequent validation in in vivo models offers a novel approach for advancing the understanding of neural crest identity and fate competency.

This dissertation begins with a history and overview of the works describing neural crest development, identity, and potency, eventually focusing on the melanocyte lineage and how neural crest heterogeneity presents in melanoma. Applications for stem cell-based models to advance these areas of research are also discussed. Stemming from these points, I utilize a stem cell-based model of temporal neural crest patterning to characterize the heterogeneity among temporally distinct neural crest populations and focus in on how temporal patterning affects the melanocyte lineage and melanoma.

To characterize heterogeneity, I employ single cell RNA sequencing with comprehensive downstream expression and lineage analysis to reveal temporally and transcriptionally distinct trajectories of melanocyte specification. I develop a new protocol to differentiate melanocytes from human pluripotent stem cell-derived Schwann cell precursors to perform the first functional and transcriptional comparison of melanocytes derived from temporally distinct progenitor populations. Finally, I leverage data from the cancer genome atlas and an in vivo CRISPR interference screen to reveal that melanoma cases with transcriptional signatures similar to Schwann cell precursor-derived melanocytes display higher rates of metastasis.

Altogether, this work identifies a temporal switch in melanocyte sub population competency in human neural crest cells and systematically characterizes this developmental origin of heterogeneity in melanocyte populations and melanoma. Moreover, it highlights the utility of stem cell-based models of neural crest patterning for novel discovery and lays a framework for future complementary analysis focusing on additional aspects of neural crest spatiotemporal identity or other neural crest lineages.

Proper decision-making is crucial to the day-to-day lives of all organisms, whether the situation requiring a choice be mundane and seemingly trivial, or critical and life-or-death. Simply defined, a decision occurs when an organism is presented with input stimuli that induce multiple alternative responses, but given the situation a single behavioral response can only be selected and executed as motor output. Given its importance, decision-making studies have been performed in numerous organisms, across numerous academic disciplines.

One advantage that Drosophila melanogaster possesses as a model organism is the vast array of neurogenetic tools available to answer biological questions. Given the experimental utility of the fruit fly, we set out to use Drosophila to study the behavioral mechanisms and neurophysiology underlying decision-making. We developed a two-choice assay that simultaneously measures the positional aversion and egg-laying attraction responses flies exhibit to a single environmental compound. Using our straightforward yet robust assay, we use acetic acid preferences to substantiate the Drosophila oviposition program as a model for choice-like behavior. Subsequently, we identified a number of genes potentially important to the Drosophila oviposition program, and initially characterized the sensory systems and brain regions mediating these responses.

Next, to expand our acetic acid-based model for choice-like behavior into a more general paradigm of decision-making, and to perform more in-depth analysis of the neural circuitry governing this process, we modified our assay to employ bitter-tasting compounds like lobeline that induce contradictory positional and egg-laying responses. Subsequently, we identified the specific sensory neurons that mediate these preferences, and surprisingly find that the same gustatory receptor is required for both responses. We show Drosophila can employ tissue-specific activation of taste-neurons expressing this same receptor complex to elicit these very different and competing behavioral responses. Finally, we identified a higher-order brain structure, the mushroom body, which is an intersection point between the neural circuits governing the competing positional aversion and egg-laying attraction pathways, thereby offering suggestive evidence that the mushroom body is a candidate integration center in a true decision-making process that occurs within the Drosophila oviposition program.

Health informatics is the multidisciplinary field of study that uses computing and information technology (IT) to collect, store, interpret, and deliver health information to doctors, patients, and consumers. Health informatics applications have historically been limited by the computing and IT capabilities of the time, whether it be equipment cost, size, processing abilities or general availability. However, in the past two decades, personal computers and peripheral equipment overcame these hurdles enabling an abundant amount of Electronic Healthcare (eHealth) applications, such as patient/doctor communication via telecommunication, electronic health record management, emergency information delivery, and medical data transmission. Furthermore, mobile devices such as smartphones and tablets are becoming a dominant computing source in the consumer market with over 1 billion smartphone and tablet sales in 2013 alone. Because of their prevalence, inexpensive costs, processing power, and small form factor, mobile devices are also becoming primary computing devices in Health Informatics, satisfying Mobile Healthcare (mHealth) applications. This thesis discusses 4 mHealth applications and the corresponding tablet-apps developed to handle the health information. Specifically, each tablet-app has a particular focus in either the collection, interpretation, or delivery of health information. The applications of the 4 mHealth apps are: surveying youth in Swaziland, Africa to research the connections between relationship-oriented, psychological development and impoverished environments, pain classification for pain treatment of veterans with spinal cord injuries and disorders (SCI/D), pressure ulcer prevention training for occupational therapists working with veterans with SCI/D, and self-health reporting and information delivery for senior citizens prescribed to anticoagulant medications. These apps aim to yield more extensive surveys and research studies, more standard diagnosis suggestions, less prevalence of pressure ulcers, and healthier senior citizens.

Quantum computers have the potential to revolutionize the computational power accessible to humanity. But because the scale and quality of current devices is limited, maximizing their usefulness requires that researchers make efficient use of the resources that they provide.

Motivated by this, we introduce in this work several techniques for efficient verification and operation of near-term quantum devices. We first develop and demonstrate a stochastic quantum compilation technique (STOQ) which produces approximate compilations of unitaries in terms of any arbitrary native instruction set. We then construct a novel verification protocol for quantum devices called randomized analog verification (RAV), which uses STOQ to generate verification sequences that ideally leave the system near a measurement basis state, which allows for efficient estimation of the success rate.

We first apply RAV to the context of quantum computers with continuously-parameterized native gate sets. We demonstrate both numerically and experimentally that RAV provides an efficiency advantage over cross-entropy benchmarking (XEB) when estimating an error rate experimentally. We then adapt the RAV protocol for verification of analog quantum simulators, and we demonstrate its sensitivity to various types of error both numerically and experimentally.

In addition, we discuss practical techniques for efficient operation of quantum computers. We develop and implement a surrogate-based optimization (SBO) technique for variational quantum algorithms, and we demonstrate that the technique has significant performance advantages over the most commonly-used optimization methods. Then, applying a more experimental lens, we describe work toward realistic simulation of various aspects of a trapped-ion experiment, such as experimental control sequences and calibration runs.

Taken together, the techniques introduced in this work are a collection of incremental steps toward the broader goal of increasing efficiency in near-term quantum computers and quantum simulators.

Despite the global burden of prostate cancer, we lack an understanding of the genetic events that drive tumorigenesis and drug resistance, impeding the development of precision cancer medicine. Only a few genetic models of the disease exist, and of the limited number of prostate cancer cell lines, many lack androgen receptor expression and dependence, significantly curtailing their utility. Moreover, the lack of representation in genetic cohorts has led to systematic under-studying of genes more commonly mutated in populations of color, exacerbating racial and geographic healthcare disparities. To address these shortcomings, we developed a novel functional genomic system in which we induced mismatch repair deficiency in mouse prostate organoids with defined genetic backgrounds. In doing so, we generated heterogeneous pools of organoids that rapidly accumulated mutations, which we then injected into mice. Through this work, we determined how tumor genotype and treatment impacted the selective pressure on genomic alterations and the evolutionary trajectories of tumors. We also uncovered novel genetic drivers of tumorigenesis and drug resistance, all three of which have been profoundly understudied despite their prevalence in patients of African and Asian descent. Finally, we showed that we can generate a virtually unlimited number of ex vivo tumoroid lines and demonstrated their utility for further mechanistic study.

This paper presents an equilibrium model in which the process of firm formation and technology adoption is endogenous. Individuals decide whether to work in an existing firm for a posted wage, or to establish a new firm. Entrepreneurs hire a single worker and choose a production technology from a fixed set. The stochastic properties of different technologies are known with different, and exogenously specified, degrees of precision. We use Dempster's (967) lower probabilities to characterize these differences in objective precision of risk information. Individuals in the model are heterogeneous with respect to their tolerance of imprecise risk. This heterogeneity determines which technologies are adopted in equilibrium, the number of firms adopting each active technology, firm structure (risk attitudes of owner and worker), and the wage differentials across firms adopting different technologies. We can also parametrically alter the risk precision associated with a given technology to examine the effect on equilibrium. This comparative static exercise suggests an explanation for the commonly observed S-shaped diffusion profile for successful innovations.

Climate change and human-driven landscape alterations pose critical threats to riverine organisms. In California, aquatic taxa have evolved to cope with stream intermittency, but novel flow regimes including extreme disturbances can cause profound shifts in these ecosystems. My dissertation presents the findings of three studies on aquatic insect communities, based on large biomonitoring datasets, and a field study on community changes over time in a Southern California intermittent headwater stream. This research aimed to understand how dispersal connectivity, variable flow regimes, and environmental factors influence taxonomic and functional biodiversity. In the first chapter, I utilized over 8,000 benthic community samples to build machine learning models associating taxa, traits, and trait clusters with flow, climate, and landscape variables. These models were then tested for their effectiveness in predicting similar relationships across ecoregions. The findings revealed that certain trait-based groups, linked to flow conditions, surpassed traditional bioindicator groups in predictive performance. Additionally, a subset of specialist trait groups and taxonomic orders showed consistent environmental factor associations, regardless of ecoregion. My second chapter involved analyzing aquatic insect communities through repeated temporal observations. I found that downstream communities in river networks were more influenced by dispersal mechanisms, while upstream communities were primarily shaped by environmental processes over time. My third chapter extended these analyses, evaluating whether network theory-based connectivity measures could surpass traditional stream order metrics as proxies for dispersal. These indices proved effective, demonstrating that more connected communities were more resilient to variable flow regimes compared to isolated ones. Finally, my fourth chapter details a field study in an intermittent montane headwater stream, focusing on fine-scale temporal and spatial biodiversity shifts. Here I found that remnant pools served as biodiverse refuges during drying periods. Notably, functional diversity showed resistance to reductions in water volume until streams shrank to very low pools. Overall, my dissertation highlights the interplay of dispersal, environmental conditions, and flow regimes on aquatic biodiversity, providing insights into protecting these ecosystems in the face of climate change.

A novel technique for beta-delayed neutron spectroscopy has been developed using trapped radioactive ions. The neutron energy spectrum was reconstructed by measuring the time of flight (TOF) of the nuclear recoil following neutron emission, thereby avoiding all the challenges associated with neutron detection such as backgrounds from scattered neutrons and gamma rays and complicated detector-response functions. A proof-of-principle measurement was conducted on 137I+ by delivering ions from a 252Cf source, confining them in a linear Paul trap surrounded by radiation detectors, and measuring the neutron energy spectrum and branching ratio by detecting the beta and recoil ions in coincidence. Systematic effects were explored by determining the branching ratio three ways. Improvements to achieve higher detection efficiency, better energy resolution, and a lower neutron energy threshold were implemented by upgrading the detectors and optimizing the trapping apparatus. These improvements were demonstrated in a campaign of measurements including 138I, which is a standard beta-delayed neutron precursor outlined by a recent IAEA report on delayed neutron measurements.