Digital technologies and services are shaping our lives. Work, education, finance, health, politics and society are all affected. They also raise concomitant and complex challenges relating to the security of and trust in systems and data. TIPS (Trust, Identity, Privacy and Security) issues thus lie at the heart of our adoption of new technologies and are critical to our economic prosperity and the well-being of our citizens. Identifying and addressing such issues requires a coherent, coordinated, multi-disciplinary approach, with strong stakeholder relationships at the centre. SPRITE+ is a vehicle for communication, engagement, and collaboration for people involved in research, practice, and policy relevant to TIPS in digital contexts. Since launching in 2019, we have established ourselves as the go-to point of contact to engage with the broadest UK network of interdisciplinary, cross-sector digital TIPS experts. The second phase of SPRITE+ ('SPRITE+2') will continue to build our membership, whilst expanding the breadth and depth of our innovation, and deepen our impact through proactive engagement. SPRITE+2 will have the following objectives: 1. Expand our TIPS community, harnessing the expertise and collaborative potential of the national and international TIPS communities 2. Identify and prioritise future TIPS research challenges 3. Explore and develop priority research areas to enhance our collective understanding of future global TIPS challenges 4. Stimulate innovative research through sandpits, industry led calls, and horizon scanning 5. Deepen engagement with TIPS research end users across sectors to accelerate knowledge Exchange 6. Understand, inform, and influence policy making and practice at regional, national and international level These will be delivered through four work packages and two cross cutting activities. All work packages will be led by the PI (Elliot) to ensure that connections are made and synergies exploited. Each sub-work package will be led by a member of the Management Team and supported by our Expert Fellows and Project Partners. WP1 Develop the Network We will deliver a set of activities designed to expand, broaden, and engage the network, from expert meetings and workshops to student bootcamps and international conferences. WP2 Engage stakeholders to enhance knowledge exchange and deliver impact. We will be greatly enhancing our purposive engagement activity in SPRITE+2. This activity will include a new business intelligence function and PP engagement grants, designed to enhance mutual understanding between researchers and stakeholders. WP3 Identify, prioritise, and explore future TIPS challenges We will select and then investigate priority areas of future TIPS. Two areas are pre-scoped based on the work we have done so far in SPRITE+ (TIPS in digital cities; trustworthy digital identities) with a further two be identified during the lead up to SPRITE+2. WP4 Drive innovation in research This WP concerns the initiation and production of high-quality impactful research. Through horizon scanning, sandpits and industry-led calls, we will steer ideas through an innovation pipeline ensuring SPRITE+2 is future focused. Cross cutting activities The first cross-cutting activity will accelerate the translation of TIPS research into policy and practice for public and private sector end uses. The second focuses on mechanisms to facilitate communication within our community. The experiences of SPRITE+ and the other DE Network+s demonstrate that it takes years of consistent and considerable effort for a new network to grow membership and develop productive relationships with stakeholders. In SPRITE+2 grant we would hit the ground running and maximise the impact of four additional years of funding. A successful track record, a well-established team, and a raft of ambitious new plans provide a solid foundation for strong delivery in 2023-27.

In this fellowship, I will use a radical new approach to improve the radiotherapy treatment of patients suffering from inoperable non-small cell lung cancer (NSCLC). NSCLC is a cancer of unmet need for which the actual chemo-radiotherapy treatment has remained mostly unchanged for more than 30 years, with a poor 16.4% 5-year survival. This poor survival is caused by the limitation of the 'one-dose-fits-all' paradigm which neglects the diverse spectrum of clinical presentation in NSCLC. To improve the treatment, my group and I will harness the capacities of novel cutting-edge artificial intelligence techniques combined with a massive retrospective database of patients data to answer a fundamental question about lung cancer which is "How will the disease progress?". More precisely, the deep learning approach will be used to extract general trends relating patient's data features (histopathology, anatomy, tumour stage, tumour activity, treatment plan) to an outcome (death, recurrence, secondary fibrosis, heart failure and success). The methodology output will then be used for two endpoints of the study. It will first be directly used to inform and personalise the radiotherapy treatment planning strategy to improve patient survival. It will also serve as a basis to define a new stratification procedure for lung cancer patients to refine the clinical trials selection system. This framework will enact a paradigm change in treatment planning for radiotherapy and has the potential to enable a jump in performance of the treatment by tailoring the dose to the patient; thereby lowering the secondary effects and improving overall survival.

Predicting future climate change is one of the biggest scientific and societal challenges facing humankind. Whist carbon emissions from human activities are the main determinant of future climate change, the response of the earth system is also extremely important. Earth system processes provide 'feedbacks' to climate change, either reinforcing upward trends in greenhouse gas concentrations and temperature (positive feedbacks) or sometimes dampening them (negative feedbacks). A crucial feedback loop is formed by the terrestrial global carbon cycle and the climate. As carbon dioxide concentrations in the atmosphere and temperature rise, carbon fixation by plants increases due to the CO2 fertilisation effect and the lengthening of the growing season at high latitudes (this is a negative feedback). But at the same time, increasing temperatures lead to increased decomposition of the carbon stored in soils and this results in more carbon dioxide being released back to the atmosphere (this is a positive feedback). The balance of these competing processes is especially important for peatlands because they are very large carbon stores. Northern Hemisphere peatlands hold about the same amount of carbon that is stored in all the world's living vegetation including forests, so determining the response of this large carbon store to future climate change is especially critical. One hypothesis is that warming will increase decomposition rates in peatland soils to such an extent that large amounts of carbon will be released in the future. However, the vast majority of peatlands are in relatively cold and wet areas and evidence from past changes in accumulation rates suggest that for these regions, warming may lead to increased productivity that more than compensates for any increase in decay rates, leading to increased carbon sequestration overall. Furthermore, in the northernmost areas of the Arctic, there is potential for further lateral expansion of peatlands, increasing the total area over which peat accumulates. We intend to answer the question of whether changes in accumulation in Arctic peatlands plus the increased spread of peatlands in cold regions will lead to an overall increase in their carbon storage capacity. Our approach will be to use a novel combination of data from the fossil record stored in peatlands together with satellite data to test a global model that simulates changes in both carbon accumulation rates and the extent of peatland vegetation over Arctic regions. If we can demonstrate that the model performs well in simulations of past changes, we can then confidently use it to make projections of future changes in response to warming for several hundred years into the future. We know that fluctuations in Arctic climate over the past 1000 years should have been sufficient to drive changes in peat accumulation rates and lateral spread, so we are focusing our analyses on this period. In particular, we know there were increases in temperature over the last 150-200 years and especially over the last 30-40 years. If our hypothesis that increased temperature leads to increasing accumulation and spread of Arctic peatlands is correct, we expect to see the evidence for this in the fossil record of peat accumulation and spread, and also in satellite data of vegetation change. Our previous work and our new pilot studies show that we can reconstruct accumulation rate changes and also that our proposed remote sensing techniques can detect peatland vegetation increases since the mid-1980s, so we are confident in our methodology. The model will provide estimates of northern peatland carbon storage change for different climate change scenarios over the next century and longer term to the year 2300. If we can show that there is a potential increase or even no change in carbon storage in Arctic peatlands, it will radically change our perception of the role of the Arctic terrestrial carbon store in mediating climate change.

Light is energy. Sunlight can be harnessed by solar cells, for instance, turning light into electricity, which can, in turn, be used to power big and small devices. This is, however, a rather inefficient process and light can be used differently for various applications. One way to efficiently use light is through a photothermal material, which converts light into heat. Heat is an important user of fossil fuels: industrial processes for instance consume vast quantities of fossil fuels. It has been reported that 4.2% of worldwide delivered energy is consumed manufacturing basic inorganic, organic, and agricultural chemicals. Of this 17 quadrillion Btu, 78% comes from liquid fuels, natural gas, and coal, leading to greenhouse gas emissions. [1] A substantial fraction of these fuels are used to heat up chemical reactions, while free, green, and abundant sunshine could instead provide the required energy via a photothermal material. Heat also heals: photothermal materials injected near cancer cells can be excited by an otherwise non-interacting infrared light, leading to local temperature rise (of the order of 10s of degrees) sufficient to kill cancer cells without any surgery or chemotherapy. This proposal targets the development of a new class of biocompatible photothermal material based on the 8th most abundant element in earth's crust, magnesium. We have shown previously that small particles of magnesium are stable in air and interact strongly with light. Magnesium, like gold and silver, is extraordinarily good at absorbing light because its interaction is different than that of simple "black" materials. Indeed, these nanoparticles act like antenna for light and consequently absorb more light than their physical footprint. This phenomenon is truly nanoscale; it involves the light-driven oscillation of electrons in small metallic particles and is called localized surface plasmon resonance. In the two years of this project, we first aim to develop ways to make large quantities of magnesium nanostructures, suitable for industrial-scale production. We will then demonstrate their ability to efficiently produce heat from light, and will study how to best match the particle size to the specific application, for both sunlight-matched and medical applications. At the end of the project, we will be in a position to approach industrial partners to discuss further development and commercialization of these new green technologies. [1] Energy Information Administration, Government Publications Office, International Energy Outlook: 2016 with Projections to 2040. U.S. Government Printing Office: 2016.

EPSRC : Max Hird : EP/T517793/1 Algorithms that learn and sample from probability distributions form an important part of machine learning, AI, and the natural sciences. One needn't look far to find such algorithms at the bleeding edge of methodology, and in everyday scientific pursuit. The Wang-Landau algorithm is an example. It combines a sampling step with a learning step, to learn a probability distribution about which our knowledge is limited. The probability distribution may be over physical states, so an efficiently running algorithm would allow the simulation of the dynamics of protein folding, for instance. The learning step incorporates information gained from the sampling step, forming a more complete picture of the distribution. The particular form of the learning step is foundational in many neural networks and is called stochastic approximation. Due to our incomplete knowledge of the distribution, we cannot apply standard sampling methods. We therefore need to employ a more exotic sampler. Coupling exotic samplers alongside stochastic approximation is underexplored, and potentially fruitful. We will try to assess the behaviour of such a coupling, an assessment not yet existing in the literature.