Greenland Ice Sheet (GrIS) mass loss acceleration is driven by increasing rates of surface melt and calving of marine-terminating outlet glaciers. The links between increasing surface melt and ice-flow dynamics are poorly understood, in part because we do not mechanistically understand where and under what conditions meltwater accesses the ice-sheet bed at a continental scale. Surface meltwater must reach the ice-bed interface via a surface-to-bed meltwater pathway for meltwater to affect GrIS flow dynamics and, in most cases, for meltwater to contribute to sea level. Surface-to-bed pathways have been manually mapped in local regions (<500 km2), but these methodologies are not practical at the continental scale (~10 to the power of 6 km2). Automated characterization and mapping of ice-sheet surface features is required to fill this gap in knowledge and advance our understanding of the features and processes driving meltwater's influence on ice-sheet dynamics. To understand the formation of surface-to-bed meltwater pathways across the GrIS and their impact on ice-flow dynamics, this three-year project will use a combination of remote-sensing observations, deep learning, and physics-based models to: (1) detect continent-wide surface fractures, moulins and supraglacial lake drainage events with satellite imagery; (2) determine the ice-sheet conditions required to trigger supraglacial lake drainage via hydrofracture and create surface-to-bed pathways; and (3) model the impact of supraglacial lake drainage events on ice-flow dynamics at a regional scale. These objectives will produce the first comprehensive, continental-scale database of GrIS surface-to-bed meltwater pathways and supraglacial lake drainage dates and mechanisms.

We conducted two studies that demonstrated the need to improve the way trial participants are told about potential trial benefits and harms. In the first, we found that half of the participants in trials who take a placebo treatment (like a sugar pill) report having a negative 'side effect.' 1 in 5 of the participants who took a placebo dropped out due to an intervention 'side effect.' There are many reasons for this, including negative expectations. A trial participant might be warned about a possible side effect in a way that caused them to expect, and then actually experience this side effect. Negative side effects among patients taking placebos were more common in pain-related, cancer, and mental health trials. For example, in a trial of aspirin or sulfinpyrazone for treating chest pain, some patients were told the drug might cause stomach pain, and others were not. The patients who were told about experiencing stomach pain were six times more likely to withdraw from the trial because of stomach pain. In our second study, we found that patient information leaflets (PILs) do not always tell trial participants what they understand or want. We looked at 33 PILs and found that the way information about harms was shared did not seem to follow any logical pattern. Most of them had more information about harms than benefits, and some did not mention benefits at all. This seems to be because researchers feel they have to disclose every problem as part of ethical informed consent. Our background research also highlighted an ethical issue. The way in which information about harms of participating in trials is shared can actually cause harm. Therefore, presenting information about trial harms in a scary way (especially if benefits are not mentioned) could be unethical if it causes unnecessary harm. Patient representatives and other stakeholders assist in developing PILs. However, there is currently no guidance specifically on how they should reflect about the way potential benefits and harms are presented to avoid worrying trial participants unnecessarily. We propose to develop a method for presenting benefit and harm information within PILs that rigorously considers patient, doctor and research ethics committee member views. We will then test to see whether they reduce side effects and improve trial recruitment rates. We will achieve our aim in five steps. 1. We will survey stakeholders to understand their views about how the information about trial participation harms and benefits should be communicated in PILs. The information should provide facts and at the same time not cause 'information-induced harm.' The stakeholders will include patients, research ethics committee members, clinicians, medico-legal experts, regulators, and clinical trial managers. They will be chosen based on their experience with one of the conditions that our background research identified as being sensitive to information-induced harm (see 'Objectives'). 2. We will then work with our advisory group to identify principles from the stakeholder interviews. The group will include applicants, patient representatives, and REC members. 3. Using the principles, we will design PILs for five trials. We will call these 'PrinciPILs'. 4. We will then compare PrinciPILs with standard PILs in the trials. We will test whether patients exposed to PrinciPILs had fewer negative side effects. 5. Finally, we will develop and disseminate guidance to relevant stakeholders so that those providing information about potential trial or treatment benefits and harms can generate PrinciPILs. The main outputs from our study will be a report on effects of using PrinciPILs and guidelines designing them. We expect our findings will be useful outside trials and in clinical practice, where clinicians also need to present information about harms and benefits of interventions to patients.

Biological taxonomy involves the classification and naming of plants, animals and all the other living organisms on earth, however taxonomists often disagree over how the organisms should be classified, so we end up with many different taxonomies. Generally, individual taxonomists can only manage to classify a small group of organisms, and so many taxonomists' work is joined together to form larger taxonomies. When we have many alternative taxonomies, combining them into one large hierarchy to picture the relationships between organisms is difficult and problems also arise when the taxonomies get too large to comprehend. To compound these problems, different classifications often result in different names for what some people may consider the same organism. This results in one name in one classification meaning something else in another classification, so taxonomists also have to handle and process this information if they are to understand the overall relationship between taxonomies.This project aims to help taxonomy users through 'Information Visualization', a technique which allows data to be displayed and manipulated graphically, rather than as pages of raw text. This approach is effective because people perceive information and relationships more easily when they are displayed diagrammatically, in terms of shapes, sizes, colours and positions. In this project we will create and develop techniques that graphically show taxonomic hierarchies and their associated relationships, to allow taxonomists to intuitively interact with and query the data.This should allow taxonomists to focus on the questions they need to ask of the data and the answers that are returned, rather than the process of how to ask the questions or how to cope with untangling the relationships of the information that they are faced with.Not only will the project result in effective tools for the taxonomists but we will advance data visualisation techniques applicable to any area where it is important to compare large alternative hierarchies of information.

The reliance of military systems and armed forces on the EM spectrum creates vulnerabilities and opportunities for electronic warfare (EW) in support of military operations. EW is concerned with detecting, recognising then exploiting and countering the enemy's electronic order of battle, and calls for the development of innovative algorithmic solutions for information extraction and delivery of signals in contested electromagnetic environment. Traditionally, the subject of signal sensing/information extraction has been developed separately from the area of signal delivery. In contrast, this visionary project conducted at Imperial College London and University College London aims at leveraging the consortium complementary expertise in various areas of signal processing (sparsity, super-resolution and subspace methods, communications, radar, and machine learning) for civilian and defence applications to design and develop novel and innovative solutions for a cohesive treatment of information extraction and delivery of signals in contested electromagnetic environment. To put together this novel approach in a credible fashion, this project is organized in two major work packages. The first work package will analyze, separate and characterize signals across time, frequency, and space and extract useful information from those signals by developing and leveraging novel super-resolution, subspace and deep learning methods. The second work package will leverage progress made in the first work package and design signals and system responses for sensing and signaling in congested RF environments. Novel waveform design approaches will be derived for sensing using an extended ambiguity function-based framework, for precise spatiotemporal energy delivery using network-wide time-reversal and for joint sensing and signaling. Attention will also be drawn to the design of signals resilient to hardware and nonlinear channel responses. The project will be performed in partnership with academia/research institutes (University of Kansas, Fraunhofer) and industrial leaders in civilian and military equipment design and manufacturing (IBM, US Army Research Lab, Thales). The project demands a strong track record in a wide range of signal processing techniques and it is to be conducted by a unique research consortium with a right mix of theoretical and practical skills. With the above and given the novelty and originality of the topic, the research outcomes will be of considerable value to transform the future of electronic warfare and give the industry and defence a fresh and timely insight into the development of signal processing for contested electromagnetic environment, advancing UK's research profile in the world. Its success would radically change the design of electronic support measures, electronic coutermeasures and electronic counter-coutermeasures and have a tremendous impact on the defence sector and industry.

Recent observations suggest that the Greenland Ice Sheet, is losing ice at an accelerating rate. Because the ice sheet contains 2.85 million cubic km, equivalent to a global sea level rise of 7.2 m, this melting could contribute substantially to future sea level rise. Despite this, current sea level projections specifically exclude changes in the ice flow of the Greenland ice sheet. This is because of difficulties in knowing whether projected changes in the ice sheet are being correctly modelled. Improving the ability to be able to model ice flow dynamics in Greenland is an urgent problem. To quantify the potential threat that Greenland poses to longer-term global sea-level rise we need a more profound understanding of the internal ice sheet structure. This will help inform how the ice sheet may responses to changing climate. Airborne geophysics provides a means to observe the internal structure of the Greenland ice sheet. Previously, technical difficulties in extracting information, have prevented the use of existing aerogeophysical data. Here, it is proposed to apply new means to overcome these difficulties. The new methods are based on using techniques which have been developed in other research fields. They will be applied here to this new ice sheet aerogeophysical problem, thereby helping to make the best use of pre-existing expensive aerogeophyscial observations. The new methods will describe quantitatively the internal ages of the ice sheet. The new age information extracted will allow glaciologists to test, at the continental-scale, whether models of ice flow are correct. This will help to provide a better understanding of ongoing Greenland ice sheet changes, helping to understand the past and predict the future. This is a necessary step in reducing uncertainties on Greenland mass loss predictions.