Common pool resources (CPRs) are noted for their excludability and subtractability issues and early academic commentary stressed that due to the resources' complexity and uncertainty, management efforts were futile and a "tragedy of the commons" was the end result. Recent academic commentary has challenged this end result and has elaborated institutional design principles to sustainably manage CPRs which include the need for nested institutional arrangements (NIAs). However, little is known about how to move between the two extremes, that is, how we change public policy in a move towards and the sorts of institutional innovations that lead us to greater sustainability. This research begins to unravel nested institutional arrangements. It develops a framework for what constitutes a nested institutional arrangement and measures their effect on groundwater policy changes (frequency, type, magnitude) under different conditions of uncertainty as applied to a comparative case study between the Great Lakes Basin (high uncertainty; Ontario, New York) and the Ogallala Aquifer in the U.S. Midwest (low uncertainty; Nebraska). This dimensional mapping reveals the centrality of the nature of the linkages between governance units (especially linkage functionality), linkage complementarity and the effects of diffuse authority structures. In short, it is possible to unravel what an NIA is from the various strands in the literature and to develop linkages between NIAs and outcomes for particular situations (e.g. high vs. low uncertainty areas) in relation to common pool resources (e.g. groundwater). The results provide theoretical guidance for the study of groundwater policy changes by staking out the broad parameters of a strategy for groundwater policy change. / Thesis / Doctor of Philosophy (PhD)

An endmember mixing analysis (EMMA) is a hydrograph separation technique used to identify and quantify stream source contributions, but the error within the results of the analysis itself can be difficult to quantify. Employing EMMA to accurately quantify these contributions is particularly important for critical watersheds that supply water to large populations, such as montane watersheds. We applied EMMA to the Provo River, a nested catchment with three monitoring locations in northern Utah, to understand the limitations and potential improvements that could be made to EMMA. Four main endmembers (quartzite groundwater, soil water, snow and carbonate groundwater) were identified for the watershed and differentiated using the conservative tracers δ18O, δ2H, Si, HCO3-, Mg2+, K+, and Ca2+. In a traditional EMMA approach, a principal components analysis (PCA) is used to identify endmembers for a single location in a watershed, and the principal component (PC) scores are used to calculate the fractional contributions of each endmember. However, we found that calculating the fractional contributions of the endmembers in tracer space resulted in less error in the calculations compared to performing the calculation in PC defined space (U-space). Performing the mixing in tracer space with four endmembers showed that during spring runoff, snow was the main endmember with inputs ranging from 23 – 66% for the highest part of the watershed and 14 – 60% for the lowest part of the watershed. During baseflow, the stream was dominated by groundwater with contributions ranging from 23 – 60% quartzite groundwater for the upper part of the watershed and 30 – 57% carbonate groundwater for the lower part of the watershed. The amount of error present in the results depended on the scale of the catchment and the number of endmembers included, with more error in downstream locations relative to upstream locations. The nested catchment approach is a further improvement on traditional EMMA because it allows for identification of missing endmembers and error analysis for characterizing stream chemistry in several locations in a complex watershed.

Quinolones are a group of antibiotics that have gained significant popularity on a global scale since the end of the last century. This popularity was predominantly based on their proven potency, broad coverage against a wide range of bacteria, in addition to possessing a favorable pharmacologic profile. Whereas quinolone-associated adverse reactions are generally tolerable and self-limiting, some reactions have generated heightened concerns due to their serious nature, which have resulted in label changes or even market withdrawal in some instances. This thesis investigates the association between quinolone antibiotics and two adverse reactions of an acute and serious nature: acute liver failure and retinal detachment. Each adverse reaction is investigated through integrating evidence from three studies utilizing different designs based on data from different sources, with each source offering a unique perspective on this issue. The first study type (chapter 2 for acute liver failure ‘ALF’ and Chapter 5 for retinal detachment ‘RD’) analyzes spontaneous reports submitted to the US Food & Drug Administration (FDA) adverse event reporting system database. Chapters 3 and 6 systematically identified all relevant (published and unpublished) clinical trials for occurrences of ALF and RD, respectively, among trial participants. Finally, chapters 4 (ALF) and 7 (RD) involved case-control analysis of a major US database of electronic health records for nearly 70 million inpatients admitted to more than 500 hospitals between 2000 and 2016. The FAERS analysis revealed a positive ALF signal with ciprofloxacin and a marginal signal for RD with moxifloxacin. Examination of the evidence from clinical trials revealed only two cases of ALF, one associated with gemifloxacin and one with moxifloxacin. No cases of RD were reported in any of the identified clinical trials. Primary analyses of the Health Facts® data revealed no overall association between quinolones and the risk of ALF or RD. However, elevated risk was identified in some subgroups, including African Americans (ALF, RD), Caucasians (ALF), women (ALF, RD), men (ALF), those ≤60 years of age (ALF) or 56-70 years of age (RD), and those with no or few comorbidities (ALF). Evidence from analyses of data from spontaneous reports and clinical trials provided some evidence for an elevated risk of ALF or RD following the systemic administration of quinolone antibiotics. Some evidence of elevated risk was also identified in the case-control analyses of inpatient EHR records. Findings from our six epidemiologic studies are in line with current advisories by FDA and Health Canada.

Gopherus polyphemus, commonly known as the Gopher Tortoise, is a dryland reptile native to the southeastern United States. It is commonly a resident of longleaf pine and dry oak sand hill habitats. It is considered a keystone species because they dig deep burrows that provide shelter to them as well as many other animals. Habitat loss, fragmentation, and disease are major threats and have caused this species to be federally listed as a threatened species under the Endangered Species Act (ESA). Disease is a major threat to the gopher tortoise’s survival, and with declining populations, the need to investigate pathogens is crucial. Herpesvirus, is known to contribute to upper respiratory tract diseases (URTD) in G. polyphemus and is the primary focus of this project. Due to high mutation rates in the virus, a modified version of PCR, nested PCR, was conducted on eye and nose swabs and blood samples obtained from G. polyphemus to detect the presence of the alpha herpesvirus pathogen. The positive samples were then sent for genetic sequencing to confirm the occurrence of the pathogen. The detectability of Herpesvirus in eye and nose swabs was compared to blood and lymph samples and statistical tests concluded that both sample types had the same detectability.

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Distributed transactional memory (DTM) is an emerging, alternative concurrency control model that promises to alleviate the difficulties of lock-based distributed synchronization. In DTM, transactional conflicts are traditionally resolved by a contention manager. A complementary approach for handling conflicts is through a transactional scheduler, which orders transactional requests to avoid or minimize conflicts. We present a suite of transactional schedulers: Bi-interval, Commutative Requests First (CRF), Reactive Transactional Scheduler (RTS), Dependency-Aware Transactional Scheduler} (DATS), Scheduling-based Parallel Nesting} (SPN), Cluster-based Transactional Scheduler} (CTS), and Locality-aware Transactional Scheduler} (LTS). The schedulers consider Herlihy and Sun's dataflow execution model, where transactions are immobile and objects are migrated to invoking transactions, relying on directory-based cache-coherence protocols to locate and move objects. Within this execution model, the proposed schedulers target different DTM models. Bi-interval considers the single object copy DTM model, and categorizes concurrent requests into read and write intervals to maximize the concurrency of read transactions. This allows an object to be simultaneously sent to read transactions, improving transactional makespan. We show that Bi-interval improves the makespan competitive ratio of DTM without such a scheduler to O(log(N)) for the worst-case and (log(N - k) for the average-case, for N nodes and k read transactions. Our implementation reveals that Bi-interval enhances transactional throughput over the no-scheduler case by as much as 1.71x, on average. CRF considers multi-versioned DTM. Traditional multi-versioned TM models use multiple object versions to guarantee commits of read transactions, but limit concurrency of write transactions. CRF relies on the notion of commutative transactions, i.e., those that ensure consistency of the shared data-set even when they are validated and committed concurrently. CRF detects conflicts between commutative and non-commutative write transactions and then schedules them according to the execution state, enhancing the concurrency of write transactions. Our implementation shows that transactional throughput is improved by up to 5x over a state-of-the-art competitor (DecentSTM). RTS and DATS consider transactional nesting in DTM, and focus on the closed and open nesting models, respectively. RTS determines whether a conflicting outer transaction must be aborted or enqueued according to the level of contention. If a transaction is enqueued, its closed-nested transactions do not have to retrieve objects again, resulting in reduced communication delays. DATS's goal is to boost the throughput of open-nested transactions by reducing the overhead of running expensive compensating actions and acquiring/releasing abstract locks when the outer transaction aborts. The contribution of DATS is twofold. First, it allows commutable outer transactions to be validated concurrently and allows non-commutable outer transactions -- depending on their inner transactions -- to be committed before others without dependencies. Implementations reveal effectiveness: RTS and DATS improve throughput (over the no-scheduler case), by as much as 1.88x and 2.2x, respectively. SPN considers parallel nested transactions in DTM. The idea of parallel nesting is to execute the inner transactions that access different objects concurrently, and execute the inner transactions that access the same objects serially, increasing performance. However, the parallel nesting model may be ineffective if all inner transactions access the same object due to the additional overheads needed to identify both types of inner transactions. SPN avoids this overhead and allows inner transactions to request objects and to execute them in parallel. Implementations reveal that SPN outperforms non-parallel nesting (i.e., closed nesting) by up to 3.5x and 4.5x on a micro-benchmark (bank) and the TPC-C transactional benchmark, respectively. CTS considers the replicated DTM model: object replicas are distributed across clusters of nodes, where clusters are determined based on inter-node distance, to maximize locality and fault-tolerance, and to minimize memory usage and communication overhead. CTS enqueues transactions that are aborted due to early validation over clusters and assigns their backoff times, reducing communication overhead. Implementation reveals that CTS improves throughput over competitor replicated DTM solutions including GenRSTM and DecentSTM by as much as 1.64x, on average. LTS considers the genuine partial replicated DTM model. In this model, LTS exploits locality by: 1) employing a transaction scheduler, which enables/disables object ownership changes depending on workload fluctuations, and 2) splitting hot-spot objects into multiple replicas for reducing contention. Our implementation reveals that LTS outperforms state-of-the-art competitors (Score and CTS) by up to 2.6x on micro-benchmarks (Linked List and Skip List) and by up to 2.2x on TPC-C. / Ph. D.