Data Quality Assessment and Anomaly Detection Via Map / Reduce and Linked Data: A Case Study in the Medical Domain

Recent technological advances in modern healthcare have lead to the ability to collect a vast wealth of patient monitoring data. This data can be utilised for patient diagnosis but it also holds the potential for use within medical research. However, these datasets often contain errors which limit their value to medical research, with one study finding error rates ranging from 2.3%???26.9% in a selection of medical databases. Previous methods for automatically assessing data quality normally rely on threshold rules, which are often unable to correctly identify errors, as further complex domain knowledge is required. To combat this, a semantic web based framework has previously been developed to assess the quality of medical data. However, early work, based solely on traditional semantic web technologies, revealed they are either unable or inefficient at scaling to the vast volumes of medical data. In this paper we present a new method for storing and querying medical RDF datasets using Hadoop Map / Reduce. This approach exploits the inherent parallelism found within RDF datasets and queries, allowing us to scale with both dataset and system size. Unlike previous solutions, this framework uses highly optimised (SPARQL) joining strategies, intelligent data caching and the use of a super-query to enable the completion of eight distinct SPARQL lookups, comprising over eighty distinct joins, in only two Map / Reduce iterations. Results are presented comparing both the Jena and a previous Hadoop implementation demonstrating the superior performance of the new methodology. The new method is shown to be five times faster than Jena and twice as fast as the previous approach

On the development of an experimental rig for hydrogen micromix combustion testing

This work describes the development of a combustion rig, aimed at testing hydrogen-fuelled micromix burners for aero gas-turbines at pressures up to 15barg, inlet-air temperatures up to 600K and equivalence ratios (Φ) from leanblow- out to 0.5. It discusses the test facility used, and the design procedure of the experimental apparatus: the requirements of it, the design choices and implementation of instrumentation. Emphasis is placed on the design and manufacture of the burner. Comparison between Additive Manufacturing (AM) and micro-machining techniques for the sub-millimetre injection points shows that further research is needed in this area, to achieve adequate geometric accuracy of the injection holes economically. This rig forms a unique facility for hydrogen micromix testing, offering simultaneous measurements of NOx emissions, Flame-Transfer–Function (FTF) and flame imaging

Learning to Self-Manage by Intelligent Monitoring, Prediction and Intervention

Despite the growing prevalence of multimorbidities, current digital self-management approaches still prioritise single conditions. The future of out-of-hospital care requires researchers to expand their horizons; integrated assistive technologies should enable people to live their life well regardless of their chronic conditions. Yet, many of the current digital self-management technologies are not equipped to handle this problem. In this position paper, we suggest the solution for these issues is a model-aware and data-agnostic platform formed on the basis of a tailored self-management plan and three integral concepts - Monitoring (M) multiple information sources to empower Predictions (P) and trigger intelligent Interventions (I). Here we present our ideas for the formation of such a platform, and its potential impact on quality of life for sufferers of chronic conditions

Learning to self-manage by intelligent monitoring, prediction and intervention

Despite the growing prevalence of multimorbidities, current digital self-management approaches still prioritise single conditions. The future of outof- hospital care requires researchers to expand their horizons; integrated assistive technologies should enable people to live their life well regardless of their chronic conditions. Yet, many of the current digital self-management technologies are not equipped to handle this problem. In this position paper, we suggest the solution for these issues is a model-aware and data-agnostic platform formed on the basis of a tailored self-management plan and three integral concepts - Monitoring (M) multiple information sources to empower Predictions (P) and trigger intelligent Interventions (I). Here we present our ideas for the formation of such a platform, and its potential impact on quality of life for sufferers of chronic conditions

In muro fragmentation of the rhamnogalacturonan I backbone in potato (Solanum tuberosum L.) results in a reduction and altered location of the galactan and arabinan side-chains and abnormal periderm development

Rhamnogalacturonan (RG) I is a branched pectic polysaccharide in plant cell walls. Rhamnogalacturonan lyase (eRGL) from Aspergillus aculeatus is able to cleave the RG I backbone at specific sites. Transgenic potato (Solanum tuberosum L.) plants were made by the introduction of the gene encoding eRGL, under the control of the granule-bound starch synthase promoter. The eRGL protein was successfully expressed and translated into an active form, demonstrated by eRGL activity in the tuber extracts. The transgenic plants produced tubers with clear morphological alterations, including radial swelling of the periderm cells and development of intercellular spaces in the cortex. Sugar compositional analysis of the isolated cell walls showed a large reduction in galactosyl and arabinosyl residues in transgenic tubers. Immunocytochemical studies using the LM5 (galactan) and LM6 (arabinan) antibodies also showed a large reduction in galactan and arabinan side-chains of RG I. Most of the remaining LM5 epitopes were located in the expanded middle lamella at cell corners of eRGL tubers, which is in contrast to their normal location in the primary wall of wild type tubers. These data suggest that RG I has an important role in anchoring galactans and arabinans at particular regions in the wall and in normal development of the periderm