The delivery of technology-enhanced learning is of increasing relevance to the training and development of researchers in the social sciences. Online resources not only provide a valuable personal development resource for researchers unable to participate in face-to-face training, but also provide an important repository of social science knowledge. There has been a considerable ESRC investment in online resources through initiatives such as the Research Methods Programme (RMP), the Researcher Development Initiative (RDI), Quantitative Methods Initiative (QMI) and the National Centre for Research Methods (NCRM).
The development of an online resource is time-consuming and expensive and the full value of the resource only comes into play close to the point at which funding ends. Following the completion of the initial award, the value of the resource will often deteriorate, seriously limiting the returns on the initial investment by ESRC.
The purpose of the ReStore project is to come up with a solution to this problem. The project is the result of extended discussion between RMP, NCRM and ESRC and is hosted by the NCRM Hub.
Readiness for REF (R4R) will investigate and implement how to streamline the REF data gathering exercise by building an interoperable institutional infrastructure, including repositories, that would capture and manage research outputs and other factors. The project will work with the existing REF Stakeholders Group (research administrators and information and computing professionals from over 100 UK institutions with particular interests in the REF) via the REF Steering Group to determine a suitable candidate for an interoperable data model to deliver repository and CRIS interoperability for the REF. The project intends to investigate and implement the CERIF (Common European Research Information Format) data model as the ââ¬Ëglueââ¬â¢ that will hold disparate information systems together, allowing for interoperability and exchange of data within and across institutions, including external systems.R4R is a partnership between between King's and the University of Southampton.
This is a collaborative interdisciplinary project that is seeking to develop intelligent agents (and other machine learning techniques) within the smart grid in order to reduce energy use within domestic settings. The project brings together an interdisciplinary team comprising experts in the fields of intelligent agents and multi-agent systems (School of Electronics and Computer Science), renewable energy and energy efficiency in the built environment, and human factors in the design of automated control and feedback systems (Sustainable Energy Research Group and Transportation Research Group in the School of Civil Engineering and the Environment) at the University of Southampton.
This proposal aims to exploit a novel platform for parallel on-chip electrophysiology, developed at the University of Southampton, for the functional characterization of a family of voltage-gated sodium channels, including human/bacterial chimeras, for which the expression, purification and reconstitution into liposomes is being developed at Birkbeck College. Specifically, the project will use this high-throughput platform to identify novel ligands/drugs that modulate the conductance properties of the sodium channels.
This ongoing project aims to develop a series of complimentary methods to systematically study the interaction of nanoparticles with synthetic cell membranes, in order to gain an understanding of the role of the physical and chemical properties of nanoparticles, with and without a protein corona, in cellular interactions and to develop an assay that screens for a hallmark of cellular toxicity.
The aim of this project is to develop silicon nanowire arrays, the only technology that has been shown to enable highly specific and ultrasensitive analysis of protein biomarkers with electronic rather than costly optical detection, into a robust user platform for the simultaneous analysis of a large number of biomarkers in the same clinical sample. We will optimize a unique method to fabricate extensive arrays of silicon nanowires with a cost-effective mass-production technology that is similar to that used by the microelectronics industry. The silicon nanowires will be incorporated in an advanced microfluidic matrix that will not only allow the sample volume to be very small (a blood droplet obtained with a simple finger prick could be sufficient), but will also provide the means to divide the nanowire array, which can consist of up to a thousand parallel nanowires, into many individually addressable sets of nanowires. Through appropriate functionalization chemistry, each nanowire set can be made to recognize and quantify a different biomarker, enabling a maximum amount of information to be extracted from a minimal amount of sample.
The NEUNEU research programme is concerned with the development of mass-producible chemical information processing components and their interconnection into functional architectures. The individual supramolecular components will crudely resemble biological neurons and will be capable of excitation and self-repair. Self-organisation of organic compounds and proteins will be complemented with dielectrophoretic manipulation to fabricate small devices from interconnected supramolecular components. State-of-the-art micro- and nanoscale technologies will be exploited to take well established physico-chemical phenomena into the new context of forming a ï¬âexible and efï¬?cient substrate for a chemistry-based information technology. Through integrated modeling from component to architecture level a broad understanding of the capabilities and limitations of the implemented as well as related technologies will be established. This ambitious collaboration among computer scientists, biophysicists, chemical physicists, biochemists, chemical biologists and electrical engineers will develop the core science needed to build a future massively parallel computing infrastructure, will deliver prototype devices, and will pave the ground to harnessing bio- and nano-materials for a novel approach to cognitive computing.
Ion channels are membrane proteins of interest to medical research, drug discovery, and biosensing applications. Expressing ion channels and inserting them into lipid bilayers for characterisation using electrophysiology is conventionally a multi-step process involving the growth and transformation of cell lines followed by cell lysis, protein purification and reconstitution. This is a labour intensive, time consuming and cumbersome process that is often limited by low yields. In vitro transcription/translation is a fast, cell-free and commercially available approach to expressing proteins. A cell-free expression mixture contains all the necessary components for expressing proteins from a supplied DNA template. One drawback of this approach is that commercial cell-free systems are expensive, which has restricted their use to a small number of specific applications. However this is not an issue for lab-on-chip technology, where sample volumes are reduced to the microliter scale.
The aim of this project is to simultaneously express and characterise ion channels on-chip inside microdroplets using in vitro transcription/translation and electrophysiology. This is achieved using a droplet dielectrophoresis device capable of forming lipid bilayers by manipulating two microdroplets into contact inside a well that contains a lipid-oil solution. This transforms the conventional multi-day, multi-step single ion-channel electrophysiology method into a quick and economical process.
Our modelling of thermal damage in carbon fibre composites after lightning strike aims to simulate coupled electric current flow and thermal fields in composites structures during lightning strike, and associated degradation of the material. (The lightning strike protection is an essential part of any modern development, such as aircraft wings or wind turbines' rotating blades.) Our main objective is to develop a qualitative mathematical model and an effective computational method to predict the composites behaviour during lightning strike through a robust understanding of the physics of the associated high-voltage and high-current processes.
The project concerns flexible materials in the form of high added value smart fabrics/textiles which are able to sense stimuli and react or adapt to them in a predetermined way. The project will exploit screen and inkjet printing together with microfabrication based strategies to achieve functionality. This will result in a low-cost, easy to design, flexible, rapid way to manufacture multi function smart textiles for a large set of multi-sectorial applications.