Physiological Effects of Alcohol on Crayfish Escape Circuitry

Alcohol is one of the oldest and most widely used drugs on the planet, but the cellular mechanisms by which it affects neural function are still poorly understood. Unlike other drugs of abuse, alcohol has no specific receptor in the nervous system, but is believed to operate through GABAergic and serotonergic neurotransmitter systems. Invertebrate models offer circuits of reduced numerical complexity and involve the same cell types and neurotransmitter systems as vertebrate circuits. The well-understood neural circuits controlling crayfish escape behavior offer neurons that are modulated by GABAergic inhibition, thus making tail-flip circuitry an effective circuit model to study the cellular mechanisms of acute alcohol exposure. Crayfish are capable of two stereotyped, reflexive escape behaviors known as tail-flips that are controlled by two different pairs of giant interneurons, the lateral giants (LG) and the medial giants (MG). The LG circuit has been an established model in the neuroscience field for more than 60 years and is almost completely mapped out. In contrast, the MG is still poorly understood, but has important behavioral implications in social behavior and value-based decision making. In this dissertation, I show that both crayfish tail-flip circuitry are physiologically sensitive to relevant alcohol concentrations and that this sensitivity is observable on the single cell level. I also show that this ethyl alcohol (EtOH) sensitivity in the LG can be changed by altering the crayfish’s recent social experience and by removing descending inputs to the LG. While the MG exhibits similar physiological sensitivity, its inhibitory properties have never been studied before this research. Through the use of electrophysiological and pharmacological techniques, I show that the MG exhibits many similar inhibitory properties as the LG that appear to be the result of GABA-mediated chloride currents. Finally, I present evidence that the EtOH-induced changes in the MG are blocked through pre-treatment of the potent GABAA receptor agonist, muscimol, which underlines the role of GABA in EtOH’s effects on crayfish tail-flip circuitry. The work presented here opens the way for crayfish tail-flip circuitry to be used as an effective model for EtOH’s acute effects on aggression and value-based decision making.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.