A distributed architecture for the monitoring and analysis of time series data

It is estimated that the quantity of digital data being transferred, processed or stored at any one time currently stands at 4.4 zettabytes (4.4 × 2 70 bytes) and this figure is expected to have grown by a factor of 10 to 44 zettabytes by 2020. Exploiting this data is, and will remain, a significant challenge. At present there is the capacity to store 33% of digital data in existence at any one time; by 2020 this capacity is expected to fall to 15%. These statistics suggest that, in the era of Big Data, the identification of important, exploitable data will need to be done in a timely manner. Systems for the monitoring and analysis of data, e.g. stock markets, smart grids and sensor networks, can be made up of massive numbers of individual components. These components can be geographically distributed yet may interact with one another via continuous data streams, which in turn may affect the state of the sender or receiver. This introduces a dynamic causality, which further complicates the overall system by introducing a temporal constraint that is difficult to accommodate. Practical approaches to realising the system described above have led to a multiplicity of analysis techniques, each of which concentrates on specific characteristics of the system being analysed and treats these characteristics as the dominant component affecting the results being sought. The multiplicity of analysis techniques introduces another layer of heterogeneity, that is heterogeneity of approach, partitioning the field to the extent that results from one domain are difficult to exploit in another. The question is asked can a generic solution for the monitoring and analysis of data that: accommodates temporal constraints; bridges the gap between expert knowledge and raw data; and enables data to be effectively interpreted and exploited in a transparent manner, be identified? The approach proposed in this dissertation acquires, analyses and processes data in a manner that is free of the constraints of any particular analysis technique, while at the same time facilitating these techniques where appropriate. Constraints are applied by defining a workflow based on the production, interpretation and consumption of data. This supports the application of different analysis techniques on the same raw data without the danger of incorporating hidden bias that may exist. To illustrate and to realise this approach a software platform has been created that allows for the transparent analysis of data, combining analysis techniques with a maintainable record of provenance so that independent third party analysis can be applied to verify any derived conclusions. In order to demonstrate these concepts, a complex real world example involving the near real-time capturing and analysis of neurophysiological data from a neonatal intensive care unit (NICU) was chosen. A system was engineered to gather raw data, analyse that data using different analysis techniques, uncover information, incorporate that information into the system and curate the evolution of the discovered knowledge. The application domain was chosen for three reasons: firstly because it is complex and no comprehensive solution exists; secondly, it requires tight interaction with domain experts, thus requiring the handling of subjective knowledge and inference; and thirdly, given the dearth of neurophysiologists, there is a real world need to provide a solution for this domain

Stress-induced visceral pain in rodents: neurochemical, hormonal, immune and epigenetic mechanisms

Visceral pain is a debilitating disorder which affects up to 25% of the population at any one time. It is a global term used to describe pain originating from the internal organs, which is distinct from somatic pain. Currently the treatment strategies are unsatisfactory, with development of novel therapeutics hindered by a lack of detailed knowledge of the underlying mechanisms. The work presented in this thesis aimed to redress this issue and look in more detail at the molecular mechanisms of visceral pain in preclinical models. Stress has long been implicated in the pathophysiology of visceral pain in both preclinical and clinical studies. Here a mouse model of early-life stress-induced visceral hypersensitivity was validated. Moreover, mouse strain differences were also apparent in visceral sensitivity suggesting a possible genetic component to the underlying pathophysiology. Furthermore, gender and sex hormones were also implicated in stress sensitivity and visceral pain. Using the rat model of maternal separation, some of the epigenetic mechanisms underpinning visceral hypersensitivity, specifically the contribution of histone acetylation were unravelled. Glutamate has been well established in somatic pain processing, however, its contribution to visceral pain has not been extensively characterised. It was found that glutamate uptake is impaired in viscerally hypersensitive animals, an effect which could be reversed by treatment with riluzole, a glutamate uptake activator. Moreover, negative modulation of the metabotropic glutamate (mGlu) receptor 7 was sufficient to reverse visceral hypersensitivity in a stress sensitive rat strain, the Wistar Kyoto rat. Furthermore, toll-like receptor 4 (TLR4) was implicated in chronic stress-induced visceral hypersensitivity. Taken together, these findings have furthered our knowledge of the pathophysiology of visceral pain. In addition, we have identified glutamate transporters, mGlu7 receptor, histone acetylation and TLR4 as novel targets, amenable to pharmacological manipulation for the specific treatment of visceral pain.

Challengers to provisional republicanism: the official republican movement, people’s democracy and the Irish republican socialist party, 1968–98

The Provisional IRA and its political wing Sinn Féin have attracted by far the greatest scholarly interest of all the players in the Northern Irish conflict. This emphasis is perfectly legitimate, given the centrality of the Provos to so many turning-points in the conflict, from the collapse of Stormont in the early 1970s to the hunger strikes of the following decade and the ceasefires which were followed by the Belfast Agreement. My project, however, looks at political groups that at one time or another challenged the Provos for leadership of the militant, anti-state constituency in Northern Ireland (chiefly based in the Catholic working class). Although never as large or influential as the Provisional republicans, groups such as the Official IRA and the Irish Republican Socialist Party sometimes had a discernible impact on the course of events which is overlooked by most studies, and often pioneered ideas and tactics that were later adopted by the Provos themselves. The idea that republicans should embrace political action and work in broad campaigning alliances was promoted by the IRSP and socialist groups such as People’s Democracy before it was taken up by Gerry Adams and his allies, while the Official IRA supported the principle of a settlement based on democratization of the Northern Irish state, which was later accepted by Sinn Féin in the form of the Belfast Agreement. The goal of my research is to provide a novel perspective on the conflict in Northern Ireland, while engaging with theoretical debates about its character.