UC Riverside

The traditional prescribing approaches in clinical therapies, such as “one drug fits all”, have limits due to consideration of drug effectiveness and safety. It is now well recognized that the Single Nucleotide Polymorphism (SNP) plays an important role in pharmacogenomics and personalized medicine by serving as the predictive biomarker for patient stratification and dose selection. Considering the economic efficiency of drug development, searching for the single best predictive SNP has just begun and has great potential. Current statistical methods for the best predictive biomarker selection rely on a variety of ranking procedures. However, these ordering approaches face three main issues: (1) they can potentially fail to distinguish predictive biomarkers from prognostic biomarkers; (2) the ordering is not necessarily correlated with the true significance, especially when the signal-to-noise is small; (3) such ranking approaches provide no control on the incorrect assertion in terms of the best predictive SNP detection.

In this paper, we propose to overcome the first issue by quantifying the predictive ability of each candidate SNP in two parallel approaches: (1) adjusting the model with Least Square mean (LSmean); (2) adjusting the data with Virtual Matching (VM) before doing estimation. For the selection of the best predictive SNP, we propose to apply the Multiple Comparisons with the Best (MCB) to the ranking and selection procedures. Specifically, we will do the indifference zone selection with MCB lower bounds; and the subset selection with MCB upper bounds (suppose a larger response is better). The probability that both inferences are correct is controlled. Simulation studies show that our proposed method is more reliable in distinguishing between predictive and prognostic effects, and has a larger chance to detect the true best predictive SNP than varieties of methods in both multiple testing and machine learning approaches. Furthermore, the adjusting-model approach is generally better than the adjusting-data approach when we assume there is one single predictive SNP; however, the latter one is easier to be extended to the scenario with multiple predictive SNPs.

The pallid bat (Antrozous pallidus), a gleaning bat found in the western United States and Mexico, hunts a wide variety of ground-dwelling prey, including scorpions. Anecdotal evidence suggests that the pallid bat is resistant to scorpion venom, but no systematic study has been performed. Here we show with behavioral measures and direct injection of venom that the pallid bat is resistant to venom of the Arizona bark scorpion, Centruroides sculpturatus. Our results show that the pallid bat is stung multiple times during a hunt without any noticeable effect on behavior. In addition, direct injection of venom at mouse LD50 concentrations (1.5 mg/kg) has no effect on bat behavior. At the highest concentration tested (10 mg/kg), three out of four bats showed no effects. One of the four bats showed a transient effect suggesting that additional studies are required to identify potential regional variation in venom tolerance. Scorpion venom is a cocktail of toxins, some of which activate voltage-gated sodium ion channels, causing intense pain. Dorsal root ganglia (DRG) contain nociceptive neurons and are principal targets of scorpion venom toxins. To understand if mutations in specific ion channels contribute to venom resistance, a pallid bat DRG transcriptome was generated. As sodium channels are a major target of scorpion venom, we identified amino acid substitutions present in the pallid bat that may lead to venom resistance. Some of these substitutions are similar to corresponding amino acids in sodium channel isoforms responsible for reduced venom binding activity. The substitution found previously in the grasshopper mouse providing venom resistance to the bark scorpion is not present in the pallid bat, indicating a potentially novel mechanism for venom resistance in the bat that remains to be identified. Taken together, these results indicate that the pallid bat is resistant to venom of the bark scorpion and altered sodium ion channel function may partly underlie such resistance.The pallid bat (Antrozous pallidus) is a gleaning bat found in western North America and is a well known hunter of scorpions. Previous work has shown the pallid bat is highly resistant to the venom of the Arizona barks scorpion, Centruroides sculpturatus. Here we build upon that work and show how the pallid bat may overcome the venom via cellular and genetic adaptations. Pallid bats were injected with doses of C. sculpturatus venom up to 20 mg/kg and showed only minor symptoms and made full recoveries. The serum neutralizing effects of pallid bat blood serum was tested via incubation of pallid bat blood serum with the venom before injection into mice. The serum incubated venom retained equivalent potency as the non-serum-incubated venom suggesting the pallid bat does not inactivate the venom via serum-based mechanisms. The sensory neurons in pallid bat dorsal root and trigeminal ganglia were tested via the Constellation Pharmacology method. Pallid bat sensory neurons experienced more calcium signal when challenged with C. sculpturatus venom than mouse sensory neurons, suggesting pallid bat venom resistance is not dependent on preventing increases in somatic calcium concentrations. The sequences of two voltage gated sodium channels were compared to other species and possible sites influencing venom action showed signs of positive selection in scorpion hunting bats.

This thesis presents progress in the adoption of NMR crystallography – the synergistic combination of X-ray crystallography, NMR spectroscopy, and Ab initio computational approaches – to study structures and dynamics of enzymes. The objective is to develop exceptionally definite and chemically-detailed structures of the chemical active sites by focusing on the intermediates along the pyridoxal-5'-phosphate catalyzed reaction pathway of tryptophan synthase.

NMR can supply direct chemical shifts to certain selected atoms in enzyme-substrate complexes; however, it is extremely sensitive to the local environment, making large complexes, such as enzyme complexes, unable to be interpreted. When the NMR shifts combine with X-ray crystallography, they can provide the framework to build computational models of active sites. The results of Ab initio calculations can reveal the unprecedented level of structural details. This is addressed in particular by the ketoenamine and enolimine tautomerization, in which a proton transfer points to the importance of protonation/deprotonation at ionizable sites on the coenzyme, substrates, and side residues to activate key steps in the catalytic process.

Solid-state NMR suffers from low sensitivity due to low polarization and slow polarization recovery times. With aid of dynamic nuclear polarization at low temperatures, solid-state NMR can not only enhance sensitivity but also capture some kinetic intermediates. These intermediates can be tautomers if solid-state NMR with dynamic nuclear polarization can selectively stabilize a tautomer. The use of NMR crystallography, which builds models for tautomers, provides a holistic view of the protonation states and dynamics of tautomers.

Intermittently-powered embedded devices are getting widespread attention these days. However, running real-time tasks on these devices remains a challenging problem due to the lack of support for data freshness guarantee, time keeping, and schedulability analysis. Furthermore, while many sensing applications require low-level sensor readings to be done in an atomic way, meaning that the operations cannot be suspended and resumed later, existing solutions for intermittently-powered devices assume compute-only workloads and disregard such sensor operations. In this research, we provide an energy harvesting model for intermittently-powered devices, and based on that, propose techniques to utilize intermittent power sources efficiently and to schedule real-time periodic tasks that need atomic operations. We present a hardware-software co-design scheme to keep track of time and to ensure the periodic execution of sensing tasks. We provide schedulability analysis to determine if a single task is schedulable in a given charging setup, and extend this idea to scheduling multiple tasks. As a proof-of-concept, we design a custom programmable RFID tag device, called R'tag, and demonstrate the effect of our proposed techniques in a realistic sensing application. We also show the device parameters' effect on energy harvesting performance in simulation. We compare the baseline approach and the proposed method both in simulation and experimental evaluations. Evaluation results, both on simulation and experiment, verify that the proposed method outperforms the baseline approach in terms of task scheduling, time keeping, and periodic sensing.

Urban vegetation represents a novel ecosystem where classical theories of vegetation ecology interact with systems of management and control not found in wildland areas. These interactions provide unique circumstances to test classical ecological constructs of how vegetation responds to climate while under the influence of urban actors. Cities can be hotter than their rural counterparts, include a diverse array of vegetation, and are directly and inadvertently treated with additional water and nitrogen resources. All these factors make urban regions novel common gardens to examine vegetation ecology. Furthermore, understanding the nexus of urbanization, vegetation, and climate will aid in quantifying ecosystem services as well as provide insight into how a diverse array of vegetation responds to multiple stressors. The research contained within this dissertation aims to explore how the dynamics of cities influence vegetation responses to extreme climates. To capture the many possible interactions, I explore urban vegetation ecology at multiple levels of organization, including the organismal, community, and ecosystem scale. Moreover, these studies examine both within the city and across city dynamics, comparing cities from different regional climates. I use a combination of ecophysiological traits, community diversity sampling, and remote sensor networks to understand the interactions of cities, climate, and plant-based ecosystem services. Overall, I find that the abundance of water resources in arid cities causes urban trees to decouple their carbon and water-use strategies and that decoupling is increased in desert climates. When comparing plant communities in parks in mesic and arid cities, taxonomic diversity was strongly driven by climate, but aspects of functional diversity were more determined by management practice. Across cities, the ecosystem service of vegetation-derived nocturnal cooling was tightly correlated to atmospheric aridity, highlighting the relationship between transpiration and ecosystem services. Taken together this dissertation connects how increases in water availability can result in shifts in plant function, community diversity, and resulting plant-derived ecosystem services

The Early Postclassic (900-1150 A.D.) metropolis of Chichen Itza, Yucatan, Mexico, arose from the ashes of the Classic Maya collapse and ushered in a new era that synthesized the old and new with the local and foreign. One of the many unique things about Chichen Itza was an ideology that centered around a single event—the ascent of the dawning sun on the road of the plumed serpent out of the eastern sea. This daily event was at the center of an ideology based upon solar worship. Despite the perplexing little amount of solar imagery from Classic and epi-Classic Central Mexico, it is likely that the origins of the solar cult can be found among the earlier Classic Maya with it reaching a level of state ideology at Chichen Itza. Solar ideology was taken to new heights at Chichen Itza where there are more concentrated representations of the sun god than anywhere in all Mesoamerica. In this dissertation, I will look at the cultural and material dynamics of solar ideology at Chichen Itza in order to gain a more nuanced understanding of cultural exchange, art, and religion during the Early Postclassic Period. The sun god, plumed serpent, and souls of heroic warriors were closely associated with heart sacrifice and blood that sustained them on their daily journey to the eastern solar paradise, a place of shimmering brilliance and beauty characterized by a rain of flowers, jewels, and flames. Chichen Itza likely represented this symbolic paradise on earth and may have even been recognized as such by their contemporaries in western Mesoamerica. The solar ideology reflected at Chichen Itza made the site a virtual “City of the Sun” where conceptions of the dawning sun, plumed serpent, heart sacrifice, and paradise blended effortlessly together. This solar ideology spread into Central Mexico by way of the ideological relationship shared between Chichen Itza and contemporaneous Tula, a legacy that can be seen centuries later in the Late Postclassic International Style, including the Aztec and Mixteca-Puebla substyles.

A variety of medical conditions (e.g., traumatic brain injury, cerebral edema, stroke, brain tumors) necessitate surgical craniectomy to access the brain, followed by the placement of a cranial implant to replace the excised cranial bone. Cranial implants provide mechanical protection to the brain, and are made from a variety of materials ranging from polymers to metals and ceramics. Despite this variety of implant options, all current cranial implants for patient use lack optical transparency which could allow for brain imaging or therapy without additional open-skull procedures. The “Window to the Brain” is a novel transparent cranial implant made from a tough, biocompatible ceramic called nanocrystalline-Yttria Stabilized Zirconia. Preliminary studies have shown that this material allows for acute brain imaging in vivo, but its suitability for chronic use has not been established. In this dissertation, I investigated (1) the stability of the optical access provided by the window for chronic brain imaging in vivo; (2) functional and structural imaging techniques across the window; (3) laser bacterial antifouling strategies for use with the window; (4) characterization of the window’s optical properties which impact imaging. This work provides answers to some of the key remaining questions regarding the feasibility of a transparent cranial implant for chronic structural and functional brain imaging based on nc-YSZ.

The spread of human settlement has imperiled fresh waterbodies; however, it has also led to the generation of novel water conservation strategies, including the reuse of treated wastewater, or effluent. Effluent reuse is an increasingly common aspect of watershed management, and thus far, research has been concentrated on its effects to water quality and efforts to describe effects to wildlife species have been relatively piecemealed. In this study, we evaluate the overlap between wastewater treatment plants and federal and state-listed endangered and threatened wildlife species in order to present a holistic view of the intersection of effluent and species management and the potential need for effluent considerations in species conservation. We show that there is substantial overlap between the presence of species and the presence of treatment plants in California watersheds, and with this overlap, a large potential for unintended consequences. As such, species conservation goals should be considered when making decisions related to effluent reuse.

This work explores the role played by reproductions in American museums from the end of the nineteenth century onwards. It does so by examining the history of a single collection of objects - the Wanamaker Bronzes produced by the Chiurazzi foundry. It traces their history, considering the cultural context in which they were produced and in which the foundry’s artisans operated, as well as their history at the museum that houses them, with its ever changing cast of collectors, keepers, audiences and interests. In doing so, this work hopes to illustrate the manner in which perspectives on and uses of the collection and, by extension, reproductions as a whole, have evolved over time and, through the consideration of broader trends in contemporary museum studies and archaeological research, how they may continue to evolve in the future through the application of modern reproduction technologies.

The rapid development of computing devices and artificial intelligence (AI) in recent decades have dramatically reshaped the ecosystem of critical infrastructure systems. Intelligent control and data-driven algorithms have received widespread interests due to their great potential in reducing the operating costs and improving the system efficiency and reliability. The increasing data collected from different sectors of infrastructure provide abundant resources for scientific studies propelled by machine learning and statistics. Nevertheless, successful design and applications of novel intelligent algorithms on infrastructure can be challenging due to the complex domain-specific contexts and constraints therein. The goal of this dissertation is to investigate and design innovative solutions to emerging problems in power systems, transportation, energy efficient buildings, and wildfire camera networks. For power transmission systems, we developed automatic event detection and identification algorithms based on real-world synchrophasor data. For power distribution systems, we investigated and characterized the spatio-temporal correlation between distribution feeders with statistical models. We analyzed and quantified the impacts of different socioeconomic and weather factors on residents' electricity consumption. For transportation systems, we initially formulated the electric vehicle (EV) routing for ride-hailing services as a mixed integer programming problem. This framework does not scale well to large amount of EVs. To address this issue, we developed a reinforcement learning based algorithm to operate an EV fleet, which is characterized by decentralized learning and centralized decision making. For energy efficient buildings, we designed an innovative change-point logistic regression model to provide an accurate forecast of building occupancy. A novel building HVAC control algorithm, which aims at reducing the energy consumption, was developed by embedding the occupancy prediction model into a model predictive control framework. \par For wildfire camera networks, we developed an efficient video smoke detection framework designed for embedded applications on local cameras. We also proposed an optimal wildfire camera placement strategy by maximizing the overall camera network benefits with limited budget.