The Literature of Disaffection: Political Dysphoria and British Modernism after 1930

This dissertation argues that “disaffection” is an overlooked but foundational posture of mid-twentieth-century British and Anglophone literature. Previously misdiagnosed as quietism or apathy, disaffection instead describes how many late modernist writers mediated between their ideological misgivings and the pressure to respond to dire political crises, from the Second World War to the creation of new postcolonial nations. Stylists of disaffection—such as Henry Green, Virginia Woolf, Elizabeth Bowen, and V. S. Naipaul—grappled with how limiting cultural assumptions, for instance, about class and nation, seemed to inhere in particular aesthetic techniques like stream of consciousness or realism. Disaffected literature appeals to but then disrupts a given technique’s projection of these assumptions and the social totality that they imagine. This literary “bait-and-switch” creates a feeling of dysphoria whereby readers experience a text unnervingly different from what they had been led to expect. Recognizing the formative work of literary disaffection in late modernism offers an original way to conceptualize the transition between modernist and postmodernist literature in the twentieth century.

Genetic manipulation of Anopheles stephensi immunity to increase plasmodium falciparum salivary gland sporozoite infection levels

Human malaria is responsible for over 700,000 deaths a year. To stay abreast of the threat posed by the parasite, a constant stream of new drugs and vector control methods are required. This study focuses on a vaccine that has the potential to protect against parasite infection, but has been hindered by developmental challenges. In malaria prevention, live, attenuated, aseptic, Plasmodium falciparum sporozoites (PfSPZ) can be administered as a highly protective vaccine. PfSPZ are produced using adult female Anopheles stephensi mosquitoes as bioreactors. Production volume and cost of a PfSPZ vaccine for malaria are expected to be directly correlated with Plasmodium falciparum infection intensity in the salivary glands. The sporogonic development of Plasmodium falciparum in A. stephensi to fully infected salivary gland stage sporozoites is dictated by the activities of several known components of the mosquito’s innate immune system. Here I report on the use of genetic technologies that have been rarely, if ever, used in Anopheles stephensi Sda500 to increase the yield of sporozoites per mosquito and enhance vaccine production. By combining the Gal4/UAS bipartite system with in vivo expression of shRNA gene silencing, activity of the IMD signaling pathway downstream effector LRIM1, an antagonist to Plasmodium development, was reduced in the midgut, fat body, and salivary glands of A. stephensi. In infection studies using P. berghei and P. falciparum these transgenic mosquitoes consistently produced significantly more salivary gland stage sporozoites than wildtype controls, with increases in P. falciparum ranging from 2.5 to 10 fold. Using Plasmodium infection assays and qRT-PCR, two novel findings were identified. First, it was shown that 14 days post Plasmodium infection, transcript abundance of the IMD immune effector genes LRIM1, TEP1 and APL1c are elevated, in the salivary glands of A. stephensi, suggesting the salivary glands may play a role in post midgut defense against the parasite. Second, a non-pathogenic IMD signaling pathway response was observed which could suggest an alternative pathway for IMD activation. The information gained from these studies has significantly increased our knowledge of Plasmodium defense in A. stephensi and moreover could significantly improve vaccine production.

SEEKING CULTURAL FAIRNESS IN A MEASURE OF RELATIONAL REASONING

Relational reasoning, or the ability to identify meaningful patterns within any stream of information, is a fundamental cognitive ability associated with academic success across a variety of domains of learning and levels of schooling. However, the measurement of this construct has been historically problematic. For example, while the construct is typically described as multidimensional—including the identification of multiple types of higher-order patterns—it is most often measured in terms of a single type of pattern: analogy. For that reason, the Test of Relational Reasoning (TORR) was conceived and developed to include three other types of patterns that appear to be meaningful in the educational context: anomaly, antinomy, and antithesis. Moreover, as a way to focus on fluid relational reasoning ability, the TORR was developed to include, except for the directions, entirely visuo-spatial stimuli, which were designed to be as novel as possible for the participant. By focusing on fluid intellectual processing, the TORR was also developed to be fairly administered to undergraduate students—regardless of the particular gender, language, and ethnic groups they belong to. However, although some psychometric investigations of the TORR have been conducted, its actual fairness across those demographic groups has yet to be empirically demonstrated. Therefore, a systematic investigation of differential-item-functioning (DIF) across demographic groups on TORR items was conducted. A large (N = 1,379) sample, representative of the University of Maryland on key demographic variables, was collected, and the resulting data was analyzed using a multi-group, multidimensional item-response theory model comparison procedure. Using this procedure, no significant DIF was found on any of the TORR items across any of the demographic groups of interest. This null finding is interpreted as evidence of the cultural-fairness of the TORR, and potential test-development choices that may have contributed to that cultural-fairness are discussed. For example, the choice to make the TORR an untimed measure, to use novel stimuli, and to avoid stereotype threat in test administration, may have contributed to its cultural-fairness. Future steps for psychometric research on the TORR, and substantive research utilizing the TORR, are also presented and discussed.

Real-time analytics on large dynamic graphs

In today's fast-paced and interconnected digital world, the data generated by an increasing number of applications is being modeled as dynamic graphs. The graph structure encodes relationships among data items, while the structural changes to the graphs as well as the continuous stream of information produced by the entities in these graphs make them dynamic in nature. Examples include social networks where users post status updates, images, videos, etc.; phone call networks where nodes may send text messages or place phone calls; road traffic networks where the traffic behavior of the road segments changes constantly, and so on. There is a tremendous value in storing, managing, and analyzing such dynamic graphs and deriving meaningful insights in real-time. However, a majority of the work in graph analytics assumes a static setting, and there is a lack of systematic study of the various dynamic scenarios, the complexity they impose on the analysis tasks, and the challenges in building efficient systems that can support such tasks at a large scale. In this dissertation, I design a unified streaming graph data management framework, and develop prototype systems to support increasingly complex tasks on dynamic graphs. In the first part, I focus on the management and querying of distributed graph data. I develop a hybrid replication policy that monitors the read-write frequencies of the nodes to decide dynamically what data to replicate, and whether to do eager or lazy replication in order to minimize network communication and support low-latency querying. In the second part, I study parallel execution of continuous neighborhood-driven aggregates, where each node aggregates the information generated in its neighborhoods. I build my system around the notion of an aggregation overlay graph, a pre-compiled data structure that enables sharing of partial aggregates across different queries, and also allows partial pre-computation of the aggregates to minimize the query latencies and increase throughput. Finally, I extend the framework to support continuous detection and analysis of activity-based subgraphs, where subgraphs could be specified using both graph structure as well as activity conditions on the nodes. The query specification tasks in my system are expressed using a set of active structural primitives, which allows the query evaluator to use a set of novel optimization techniques, thereby achieving high throughput. Overall, in this dissertation, I define and investigate a set of novel tasks on dynamic graphs, design scalable optimization techniques, build prototype systems, and show the effectiveness of the proposed techniques through extensive evaluation using large-scale real and synthetic datasets.