Land cover mapping is the process of spatially labelling the Earth's surface according to its biophysical land cover type, and has uses including land-use planning and natural resource management that can be used to inform public policy. Remote sensing offers the potential for efficient provision of continuous sensor data, at a range of spatial and temporal scales, enabling detection of change. This allows land cover mapping to be applied to address a variety of environmental risks, including monitoring deforestation, forest degradation, sea ice extent, urbanisation and cropland expansion. The insights gained from land cover analysis can be used to help monitor 14 of the 17 UN Sustainable Development Goals. To date, the dominant paradigm for land cover mapping has been to use supervised learning in combination with models such as random forests and convolutional neural networks. While these approaches can accurately segment land cover, they suffer from three key drawbacks: (1) Annotation cost. To achieve good performance, large quantities of high-quality manually annotated semantic segmentation data are required; however, this can be extremely expensive to collect (e.g., 90 minutes per image ). (2) Robustness. Existing supervised approaches struggle to generalise beyond their narrow training distribution, restricting their use to small geographic regions and limited land cover types. (3) Fixed category labels. Supervised learning models can only predict labels from the fixed set of categories they are trained on, and retraining is needed to predict additional categories Due to the interconnectivity and scale of the environmental challenges we currently face, a 'big-picture' view encompassing multiple domains is needed to effectively address them. This PhD intends to provide a flexible and searchable interactive land cover investigation tool that can be used by researchers working to overcome these issues. The tool could be used to look at land cover in a (1) generalised setting (e.g., deforestation/urbanisation); (2) more specific setting (e.g., food security/forest degradation); and (3) object detection and analytics setting (e.g., locating renewable assets). Current approaches are insufficient to feasibly create this functionality; however, we believe leveraging vision-language models offer promising potential. Aims The two key aims of this project are: Aim 1. Develop computer vision models for land cover segmentation with an accuracy approaching that of a human expert. Aim 2. Develop a tool (similar in style to Google Earth1, Maps2 and electricityMap3) that provides an interactive land cover visualisation, language-driven object search, and analytics dashboard

We aim to use a simple model organism, the harmless nematode worm C. elegans to investigate how growth is controlled. Growth is a defining property of living organisms including humans. How fast we grow and to what size we grow are both influenced by both external factors such as the quality of our diet and by internal factors. Growth problems can cause serious disease in themselves and may underlie increased susceptibility to other problems such as heart disease. We have found that changes in a particular gene alter both the growth rate and size of the animal. This gene codes for part of the network of proteins that allow animals to transmit messages within cells using the simple molecule calcium. This signalling network is one of the most common and important signalling systems in animals and humans. Our aim is to discover how this system regulates growth by identifying which other genes are involved and where in the animal (in which cells or tissues) it acts. Using C. elegans will allow us to use state-of-the art genetic approaches on a large scale so that we can simultaneously test a large number of ideas about how the system might work and identify new components of the system. By doing this we will increase our understanding of growth regulation which in turn should help us to ensure that diseases caused by alterations in growth can be treated more effectively and that people can grow healthily.

The project is aimed principally at addressing the issues encountered when analysing helioseismic and asteroseismic data from ground-based and space-borne observations. Unlike most astronomical data, seismic information is not immediately readily interpretable, even after the data have been 'reduced'. It is first necessary to understand how to assess the different physical contributions to the mode frequencies, and then to design procedures for isolating different properties of the underlying structure of the star under study. This demands first a thorough understanding of the manner in which diferent physical processes influence oscillation mode structure, even if the degree of that influence is not known - the so-called forward problem - and second, the design of analysis procedures from which the structure and internal motion of the star can be inferred - the so-called inverse problem.

Recent research in leukaemia and other cancers suggests that a critical subpopulation of cells within the cancer control the growth and spread of these tumours, the so called cancer stem cells A likely explanation for the lack of effect of many cancer therapies or the recurrence of tumours following therapy is that current drugs do not affect this important population of cells. Using leukaemia as a model for all cancers, this proposal aims to identify the mechanisms which control the renewal of these cancer stem cells and therefore the ability of the cancers to continue growing. This research should improve our understanding of the mechanisms of cancer growth and help to identify new therapies to target these critical cells and improve the treatment of cancer patients

Heart disease is the most common cause of death in patients with diabetes mellitus, and the combination of type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD) is a major cause of premature cardiovascular morbidity and mortality. Therapy to improve glucose metabolism by the heart have not been widely adopted as they are cumbersome, offer limited benefit and have therefore not been widely adopted in clinical practice. This proposed research builds on existing funding from the British Heart Foundation and the MRC to assess the effect of metabolic factors to improve the ability of the heart to tolerate the effects of a reduced blood supply due to coronary artery disease (ischaemia, clinically recognised as angina). A peptide, glucagon-like peptide 1 (GLP-1), that is secreted mainly by upper intestine in response to food has recently been found to improve the action of insulin in patients with T2DM and improve glucose metabolism. A number of drugs that limit the breakdown of this peptide have now been licensed to treat T2DM, and the proposed research will investigate whether increasing the level of GLP-1 in the blood improves the ability of the heart to tolerate ischaemia. Our preliminary results suggest that GLP-1 does indeed protect the heart against contractile dysfunction that occurs both during and after ischaemia. I addition, pilot studies in a small number of patients undergoing coronary angioplasty and stenting suggest that an infusion of GLP-1 protects the heart during the therapeutic procedure, which is particularly important in those with T2DM. The research will be informative by allowing the efficacy of a safer metabolic therapy to be tested and confirmation of efficacy in patients with T2DM will provide novel information about the mediation of a therapeutic effect and provide the basis for subsequent interventional studies in patients with CAD and T2DM.