Medical imaging has transformed clinical medicine in the last 40 years. Diagnostic imaging provides the means to probe the structure and function of the human body without having to cut open the body to see disease or injury. Imaging is sensitive to changes associated with the early stages of cancer allowing detection of disease at a sufficient early stage to have a major impact on long-term survival. Combining imaging with therapy delivery and surgery enables 3D imaging to be used for guidance, i.e. minimising harm to surrounding tissue and increasing the likelihood of a successful outcome. The UK has consistently been at the forefront of many of these developments. Despite these advances we still do not know the most basic mechanisms and aetiology of many of the most disabling and dangerous diseases. Cancer survival remains stubbornly low for many of the most common cancers such as lung, head and neck, liver, pancreas. Some of the most distressing neurological disorders such as the dementias, multiple sclerosis, epilepsy and some of the more common brain cancers, still have woefully poor long term cure rates. Imaging is the primary means of diagnosis and for studying disease progression and response to treatment. To fully achieve its potential imaging needs to be coupled with computational modelling of biological function and its relationship to tissue structure at multiple scales. The advent of powerful computing has opened up exciting opportunities to better understand disease initiation and progression and to guide and assess the effectiveness of therapies. Meanwhile novel imaging methods, such as photoacoustics, and combinations of technologies such as simultaneous PET and MRI, have created entirely new ways of looking at healthy function and disturbances to normal function associated with early and late disease progression. It is becoming increasingly clear that a multi-parameter, multi-scale and multi-sensor approach combining advanced sensor design with advanced computational methods in image formation and biological systems modelling is the way forward. The EPSRC Centre for Doctoral Training in Medical Imaging will provide comprehensive and integrative doctoral training in imaging sciences and methods. The programme has a strong focus on new image acquisition technologies, novel data analysis methods and integration with computational modelling. This will be a 4-year PhD programme designed to prepare students for successful careers in academia, industry and the healthcare sector. It comprises an MRes year in which the student will gain core competencies in this rapidly developing field, plus the skills to innovate both with imaging devices and with computational methods. During the PhD (years 2 to 4) the student will undertake an in-depth study of an aspect of medical imaging and its application to healthcare and will seek innovative solutions to challenging problems. Most projects will be strongly multi-disciplinary with a principle supervisor being a computer scientist, physicist, mathematician or engineer, a second supervisor from a clinical or life science background, and an industrial supervisor when required. Each project will lie in the EPSRC's remit. The Centre will comprise 72 students at its peak after 4 years and will be obtaining dedicated space and facilities. The participating departments are strongly supportive of this initiative and will encourage new academic appointees to actively participate in its delivery. The Centre will fill a significant skills gap that has been identified and our graduates will have a major impact in academic research in his area, industrial developments including attracting inward investment and driving forward start-ups, and in advocacy of this important and expanding area of medical engineering.

This proposal focuses on the electricity network of 2050. In the move to a decarbonised energy network the heat and transport sectors will be fully integrated into the electricity system. Therefore, the grand challenge in energy networks is to deliver the fundamental changes in the electrical power system that will support this transition, without being constrained by the current infrastructure, operational rules, market structure, regulations, and design guidelines. The drivers that will shape the 2050 electricity network 2050 are numerous: increasing energy prices; increased variability in the availability of generation; reduced system inertia; increased utilisation due to growth of loads such as electric vehicles and heat pumps; electric vehicles as randomly roving loads and energy storage; increased levels of distributed generation; more diverse range of energy sources contributing to electricity generation; and increased customer participation. These changes mean that the energy networks of the future will be far more difficult to manage and design than those of today, for technical, social and commercial reasons. In order to cater for this complexity, future energy networks must be organised to provide increased flexibility and controllability through the provision of appropriate real time decision-making techniques. These techniques must coordinate the simultaneous operation of a large number of diverse components and functions, including storage devices, demand side actions, network topology, data management, electricity markets, electric vehicle charging regimes, dynamic ratings systems, distributed generation, network power flow management, fault level management, supply restoration and fuel choice. Additionally, future flexible grids will present many more options for energy trading philosophies and investment decisions. The risks and implications associated with these decisions and the real-time control of the networks will be harder to identify and quantify due to the increased uncertainty and complexity.We propose the design of an autonomic power system for 2050 as the grand challenge to be investigated. This draws upon the computer science community's vision of autonomic computing and extends it into the electricity network. The concept is based on biological autonomic systems that set high-level goals but delegate the decision making on how to achieve them to the lower level intelligence. No centralised control is evident, and behaviour often emerges from low-level interactions. This allows highly complex systems to achieve real-time and just-in-time optimisation of operations. We believe that this approach will be required to manage the complex trans-national power system of 2050 with many millions of active devices. The autonomic power system will be self-configuring, self-healing, self-optimising and self-protecting. This proposal is not focused on the application of established autonomic computing techniques to power systems (as they don't exist) but the design of an autonomic power system, which relies on distributed intelligence and localised goal setting. This is a significant step forward from the current Smart Grid vision and roadmaps. The autonomic power system is a completely integrated and distributed control system which self-manages and optimises all network operational decisions in real time. To deliver this, fundamental research is required to determine the level of distributed control achievable (or the balance between distributed, centralised, and hierarchical controls) and its impact on investment decisions, resilience, risk and control of a transnational interconnected electricity network. The research within the programme is ambitious and challenges many current philosophies and design approaches. It is also multi-disciplinary, and will foster cross-fertilisation between power systems, complexity science, computer science, mathematics, economics and social sciences.

We propose to use recently developed technologies and sequence information to create and trial a novel transcriptomics-based resource for the wheat community. This resource, which will be fully upgradable without further cost to the community, will consist of a wheat array capable of monitoring the expression of up to 90,000 different homoeolog and paralog transcripts. This approach will remove many of the seen and unseen problems associated with the existing GeneChip and EST-based platforms and will be a valuable source of new and novel information on the formation of Triteace polyploids and the differential expression of the three different genomes that make up allohexaploid wheat. We propose to open up this resource to the entire wheat community as an unencumbered facility free of MTAs and follow on IP agreements.

Applications such as hydrogen storage, separation, catalysis, delivery of poorly soluble drugs all demand internally micro- or meso-porous inorganic materials, with specific requirements for pore size and available surface area, which can be produced reliably, easily and cheaply. Therefore there is a great need to improve existing methods for production of porous materials. The proposal aims to investigate, by experiment, entirely novel micro- and meso-porous silica particles using nano/microbubbles as templating material. Recently published developments on the stability and long life of nano/microbubbles in aqueous and organic solvents have paved the way for their application in various fields and the proposed research intends to use stable nano/microbubbles to tune the internal porosity/architecture of an inorganic material. The work aims to identify the main parameters influencing the nano/microbubble size and relate it to the resulting internal structure as well as those influencing the silica particle size and uniformity. An efficient method (ultrasound sonicator and cavitation venturi tube) will be used to generate the nano/microbubbles and their size and stability will be validated allowing their use as templating material within the silica droplets to tailor the internal structure of spherical silica particles. Improved production of silica droplets containing nano/microbubbles using membrane emulsification will be a significant leap toward reducing surfactant templating methods and slow batch operation to grow silica particles. The aim is to facilitate the development of an eco-friendly process (that does not rely on templating surfactants) for the production of highly uniform porous spherical silica particles. Although silica will be used as a case study material, the process has the potential to be applied to tailor the internal architecture of both inorganic and polymeric nanostructures. Such nanostructures have great potential for applications in drug delivery, energy (e.g. hydrogen) storage as well as catalyst supports.

In optimizing the properties of functional materials it is essential to understand in detail how structure influences properties. Identification of the most important structural parameters is time-consuming and usually investigated by preparing many different chemical modifications of a material, determining their crystal structures, measuring their physical properties and then looking for structure-property correlations. It is also necessary to assume that the chemical modifications have no influence other than to distort the structure, which is often not the case. High pressure offers a way around these difficulties. Pressure can be used to distort a material without the need for chemical modification. Both crystal structures and physical property measurements can be conducted at high pressure, so that the properties of the same material can be studied in different states of distortion, providing the most direct way to study correlations between structure and properties. In this proposal we focus on structure-property relationships in molecule-based magnets connected into extended chains, networks or frameworks using a combination of high pressure crystallography, magnetic measurements, spectroscopy and simulation which will exploit the UK's unique capabilities in extreme conditions research. Extended materials are of great interest because a small distortion at one site is propagated throughout the material by the strong chemical links between the magnetic centres, making the magnetic properties very sensitive to structural changes. We will design and build new instruments for magnetic susceptibility and diffraction measurements at high pressure and low temperature and we will exploit these new instruments and methodology to study two important classes of magnetic material. 1-D magnetic materials represent a fertile playground for discovering and understanding exotic physical phenomena. The magnetic behaviour of Single-Chain Magnets (SCMs) is fundamentally governed by the magnitude of nearest neighbour exchange interactions (intra-chain exchange), the extent of inter-chain interactions, and Ising-like anisotropy - all of which are sensitive to pressure. We have already shown that these parameters can be pressure-tuned in Single-Molecule Magnets (SMMs) and the same should be true for SCMs In 3-D frameworks magnetism can be combined with porosity, so that inclusion of different guest molecules provides another means for controlling magnetic properties. Prussian Blue Analogues consist of different metal cations linked by cyanide anions, while metal carboxylates build diamond-like frameworks. In both cases guest molecules influence magnetic ordering temperatures. Some metal-organic frameworks show spin-crossover behaviour, where different electronic configurations of the metal ions are stable under different conditions. The transition from one form to another is influenced by guest molecules which occupy the pores of the framework. High pressure will enable us to control the structure of the framework itself, the interactions between the host and the guest, and the number of guest molecules incorporated into the pores, providing a quantitative link between host-guest interactions and magnetism.