John M. Easton

Metabolic changes in flatfish hepatic tumours revealed by NMR-based metabolomics and metabolic correlation networks

Histopathologically well-characterized fish liver was analyzed by 800 MHz 1H NMR metabolomics to identify metabolic changes between healthy and tumor tissue. Data were analyzed by multivariate statistics and metabolic correlation networks, and results revealed elevated anaerobic metabolism and reduced choline metabolism in tumor tissue. Significant negative correlations were observed between alanine-acetate (p = 3.0 x 10(-5)) and between proline-acetate (p = 0.003) in tumors only, suggesting alanine and proline are utilized as alternative energy sources in flatfish liver tumors.

Proposed reporting requirements for the description of NMR-based metabolomics experiments

The amount of data generated by NMR-based metabolomic experiments is increasing rapidly. Furthermore, diverse techniques increase the need for informative and comprehensive meta-data. These factors present a challenge in the dissemination, interpretation, reviewing and comparison of experimental results using this technology. Thus, there is a strong case for unification and standardisation of the data representation for both academia and industry. Here, a systems analysis of an NMR-based metabolomics experiment is presented in order to reveal the reporting requirements. An in-depth analysis of the NMR component of a metabolomics experiment has been produced, and a first round of data standard development completed. This has focussed on both one- and two-dimensional 1H NMR experiments, but is also applicable to higher dimensions and other nuclei. We also report the modelling of this schema using Unified Modelling Language (UML), and have extended this to a proof-of-concept implementation of the standard as an XML schema.

nmrML: A Community Supported Open Data Standard for the Description, Storage, and Exchange of NMR Data

NMR is a widely used analytical technique with a growing number of repositories available. As a result, demands for a vendor-agnostic, open data format for long-term archiving of NMR data have emerged with the aim to ease and encourage sharing, comparison, and reuse of NMR data. Here we present nmrML, an open XML-based exchange and storage format for NMR spectral data. The nmrML format is intended to be fully compatible with existing NMR data for chemical, biochemical, and metabolomics experiments. nmrML can capture raw NMR data, spectral data acquisition parameters, and where available spectral metadata, such as chemical structures associated with spectral assignments. The nmrML format is compatible with pure-compound NMR data for reference spectral libraries as well as NMR data from complex biomixtures, i.e., metabolomics experiments. To facilitate format conversions, we provide nmrML converters for Bruker, JEOL and Agilent/Varian vendor formats. In addition, easy-to-use Web-based spectral viewing, processing, and spectral assignment tools that read and write nmrML have been developed. Software libraries and Web services for data validation are available for tool developers and end-users. The nmrML format has already been adopted for capturing and disseminating NMR data for small molecules by several open source data processing tools and metabolomics reference spectral libraries, e.g., serving as storage format for the MetaboLights data repository. The nmrML open access data standard has been endorsed by the Metabolomics Standards Initiative (MSI), and we here encourage user participation and feedback to increase usability and make it a successful standard.

Mining Open and Crowdsourced Data to Improve Situational Awareness for Railway

This paper describes on-going research developing a system to harvest and utilise open and crowdsourced data related to the UK railway systems. This system will allow the controllers and decision makers to listen to the messages posted on social networks by passengers or other members of the public and relate these messages to specific (physical) trains that are referred to in those messages, by fusing information from other open sources. This will enable the railway controllers to take prompt actions in case of any emergency or simply to improve the quality of customer service.

Applications of linked data in the rail domain

This paper presents early findings from a larger study, into the use of linked data in the rail domain. The study and other literature has shown there to be benefits from improved integration of data in this domain and proposes that linked data in general and ontology in particular will address this. The paper will set out the current state of data integration in the British rail domain, highlighting issues found there. The manner in which linked data is employed in the broader transport domain will then be examined along with previous work pertaining to the rail domain.

Applications, value and barriers of common data frameworks in the rail industry of Great Britain

In the rail industry, the exchange of data across system and organisational boundaries is an essential step in the delivery of advances such as intelligent infrastructure, end-to-end ticketing, improved passenger information, real-time capacity management and greater interoperability between stakeholders. The industry, however, faces a serious challenge in the form of siloed, legacy ICT systems based around different technologies and data formats. One solution to this problem can be found in the implementation of open data interfaces that use a conceptual representation of the railway network to facilitate the exchange of information while preserving its underlying meaning. The work reported in this paper used two engagement exercises involving industry stakeholders to, first, generate a set of applications for wider information sharing in the rail industry of Great Britain; second, compile a list of the benefits of such an approach to the rail industry; and, third, identify the most significant barriers to implementation. The paper should therefore be valuable for anyone in industry or in academia working on projects that depend on the exchange of data between systems and stakeholders to support rail operations and strategy.

The development of a graphical user interface, functional elements and classifiers for the non-invasive characterization of childhood brain tumours using magnetic resonance spectroscopy

Magnetic resonance spectroscopy (MRS) is a non-invasive method, which can provide diagnostic information on children with brain tumours. The technique has not been widely used in clinical practice, partly because of the difficulty of developing robust classifiers from small patient numbers and the challenge of providing decision support systems (DSSs) acceptable to clinicians. This paper describes a participatory design approach in the development of an interactive clinical user interface, as part of a distributed DSS for the diagnosis and prognosis of brain tumours. In particular, we consider the clinical need and context of developing interactive elements for an interface that facilitates the classification of childhood brain tumours, for diagnostic purposes, as part of the HealthAgents European Union project. Previous MRS-based DSS tools have required little input from the clinician user and a raw spectrum is essentially processed to provide a diagnosis sometimes with an estimate of error. In childhood brain tumour diagnosis where there are small numbers of cases and a large number of potential diagnoses, this approach becomes intractable. The involvement of clinicians directly in the designing of the DSS for brain tumour diagnosis from MRS led to an alternative approach with the creation of a flexible DSS that, allows the clinician to input prior information to create the most relevant differential diagnosis for the DSS. This approach mirrors that which is currently taken by clinicians and removes many sources of potential error. The validity of this strategy was confirmed for a small cohort of children with cerebellar tumours by combining two diagnostic types, pilocytic astrocytomas (11 cases) and ependymomas (four cases) into a class of glial tumours which then had similar numbers to the other diagnostic type, medulloblastomas (18 cases). Principal component analysis followed by linear discriminant analysis on magnetic resonance spectral data gave a classification accuracy of 91% for a three-class classifier and 94% for a two-class classifier using a leave-one-out analysis. This DSS provides a flexible method for the clinician to use MRS for brain tumour diagnosis in children.

Linked Metabolites: A tool for the construction of directed metabolic graphs

Metabolic pathway diagrams provide a wealth of information on how reactions combine to perform biological functions. While pathway diagrams are arranged in a way that allows a specific area of metabolism to be visualised, the inherent complexity of each pathway makes it difficult to identify the sets of reactions linking groups of compounds; a common task for researchers attempting to explain observed correlations or looking for further compounds of potential interest to use in validation work. Here we introduce Linked Metabolites, a tool that identifies sets of reactions linking groups of compounds in the context of the KEGG pathway diagrams.

Enabling Data Integration in the Rail Industry Using RDF and OWL: The RaCoOn Ontology

AbstractAs traditionally infrastructure-centric industries such as the railways deploy ever more complex information systems, data interoperability becomes a challenge that must be overcome in order to facilitate effective decision making and management. In this paper, the authors propose a system based on semantic data modeling techniques to allow integration of information from diverse and heterogeneous sources. The results of work, which aimed to demonstrate how semantic data models can be used in the rail industry, are presented. These include a novel domain ontology for the railways, and a proof-of-concept real time passenger information system based on semantic web technologies. Methods and patterns for creating such ontologies and real world systems are discussed, and ontology-based techniques for integrating data with legacy information systems are shown.

Innovative Applications of Big Data in the Railway Industry

Innovative Applications of Big Data in the Railway Industry is a pivotal reference source for the latest research ndings on the utilization of data sets in the railway industry. Featuring extensive coverage on relevant areas such as driver support systems, railway safety management, and obstacle detection, this publication is an ideal resource for transportation planners, engineers, policymakers, and graduate-level engineering students seeking current research on a speci c application of big data and its effects on transportation.