The receding Greenland Ice Sheet (GrIS) is now the largest contributor to global sea-level rise. A major driving force behind this recession is the encroachment of warm ocean water through fjords to the faces of marine-terminating outlet glaciers (MTOGs) that drain the ice sheet. Satellite data confirm that these glaciers have thinned, accelerated and retreated over the past few decades, but with significant temporal and spatial variability. Despite this information, our ability to predict how, and at what rate, the ice sheet will respond to future warming is made difficult by a lack of direct observations from these remote and often ice-infested areas and by the limited time-series of existing datasets. Constraining Greenland's likely decay trajectory is necessary to evaluate policy options with regard to its contribution to sea level rise. However, the wider effects of this decay also encompass the marine environments bordering the landmass. Increasing the supply of freshwater to these areas (as meltwater and icebergs) alters circulation patterns and impacts North Atlantic weather systems, including those affecting the UK. It also brings nutrients to offshore areas that promote marine productivity, which in turn has the potential to draw down more atmospheric CO2 and bury organic carbon in fjord and shelf sediments. To date, these processes have not been quantified and we need to improve our understanding of this negative feedback to climate change before it can be incorporated into predictive models. One way to determine which ice-ocean-marine ecosystem scenarios are analogues for future warming scenarios is to extend the record of modern observations back over the last 11,700 years of the Holocene using proxies from marine sediment cores. A few records of 20th Century iceberg calving and warm water encroachment exist around Greenland but there are no comprehensive, coupled records of past glacier change, ocean warming and marine productivity for earlier periods. Here, we propose to generate these long-term records for the Holocene era for a key location in SE Greenland (Kangerlussuaq Fjord) calibrated by observations of the present-day system over three annual cycles. We will then use numerical modelling constrained by our new data to test how the Greenland Ice Sheet responded to climatic warming during the Holocene, particularly during the Holocene Thermal Maximum when summer temperatures were analogous to those predicted for 2100. We will acquire a full suite of oceanographic, biological and geological observations during a 6-week multidisciplinary cruise to SE Greenland on the UK's new polar research vessel, the RRS Sir David Attenborough, making full use of its state-of-the-art capabilities as a logistical platform. We will use cruise datasets to determine modern interactions between warm water inflows and glacial meltwater outflows, and to quantify marine productivity, sedimentation and nutrient cycling. At the same time, we will collect long and short marine-sediment cores and terrestrial rock samples to constrain past changes in glacier dynamics and derive coupled proxy records of ocean temperatures and carbon burial/storage. To do this, we will calibrate the sediment-core signals with our modern observations using an anchored mooring and repeat observations.

The world's rapid population growth and climate change pose challenges to sustainable food production. Agricultural crop production has long relied on Process-based models (PBMs) to forecast yields and understand how plant physiological processes interact with the environment, influencing crop growth and development. However, the PBMs suffer limitations in making accurate predictions due to complex weather/plant interactions. This is especially true for extreme events (drought, heat waves), pests, diseases, and stresses not accounted for. Process-based models' predictive abilities are hindered by uncertainties in structure, inputs, and parameters, exceeding observed yield variations over time/space. Machine Learning (ML) offers quick crop yield prediction by learning from data, but it's often a black box needing explanations. Integrating PBMs and ML has shown promise in improving predictions. Challenges remain in effective integration: choosing the right ML for accurate simulation, balancing interpretability and uncertainty. Environmental impact assessment is often overlooked. Building on our existing foundations, this partnership brings together leading researchers in agri-environment sciences, crop modelling from Germany and computer science (big data/machine learning/AI) from UK, and aims to develop an innovative AI framework by synergising process-based and machine learning models for accurate and explainable crop yield prediction coupling with environmental impact assessment. The overarching aim is to build and foster a long-term partnership between UK and Germany's top research groups to address the call theme- AI in sustainable agriculture and food and provides the added value to our ongoing research in climate-smart agriculture solutions. To achieve this, we will conduct a series of research activities including feasibility study, staff exchanges/early career researchers (ECRs) visits, facility and data access, workshops, and joint publications/funding applications. The integration of AI with agricultural modeling represents an emerging paradigm that pushes the boundaries of agricultural research. It not only offers improved crop yield predictions and climate change impact mitigation but also opens up new avenues for understanding crop dynamics, resource optimization, and sustainable farming practices. The proposed approach has the potential to be applied at different scales, ranging from individual farm fields to regional and global levels. This scalability and generalization make the AI-driven synergy suitable for addressing complex agricultural challenges and adapting to diverse environmental conditions. It has the capacity to revolutionize agriculture, leading to more efficient, sustainable, and resilient food production systems. This research offers potential benefits to farmers, consumers, policymakers, and the environment. Improved predictions will enhance agricultural decision-making, increase food security, promote climate change adaptation and mitigation, and optimize resource utilization. Additionally, the research will advance scientific knowledge and benefit industry and academic institutions.

Onchocerciasis or river blindness is a parasitic disease affecting 17 million in sub-Saharan Africa. It causes visual impairment, sometimes leading to irreversible blindness, and intense itching of the skin. The symptoms of the disease can be controlled by annual treatment with the drug ivermectin, which kills the larval stages in the skin, but the adult worms are not eliminated and can survive for more than 10 years. Diagnosis of the disease has been dependent for many decades on microscopic examination of skin snips for the larval stage. This is a painful procedure that, not surprisingly, is being met with reduced compliance in communities that have been sampled repeatedly over many years. In this proposal, we aim to radically improve the diagnosis of onchocerciasis by testing for the presence of adult worms using a patient's urine sample. We have discovered that the worms release proteins and small RNA molecules into urine that can be detected by mass spectrometers and sequencing technology. The molecules are associated with small "packets" called extracellular vesicles. We will isolate these vesicles from urine samples to identify the best markers for the disease. To facilitate the process, we will also test urine samples from African cattle, which are infected with a parasite very closely related to that which causes human onchocerciasis. By treating the cattle with drugs that kill the adult parasites, we will test whether the molecules released in urine are a good indicator of whether live worms remain in the host's body. This is a particularly important at the present time, as several drugs with potential activity against adult worms are being tested in clinical trials in humans, and currently the only way to know whether the worms have been killed is to perform surgery on the volunteers. In the final stages of the proposal, we will investigate methods to capture parasite molecules from urine in an efficient manner. This will be necessary to move away from a diagnostic test that is dependent on expensive laboratory equipment to a kit that ultimately could be used in rural Africa.

This multi-country project aims to establish the health benefits of large-scale land restoration in Africa's Sahel region. We will leverage the Great Green Wall (GGW) of Africa initiative, the largest land restoration effort in the world, as a natural experimental system. Drylands host nearly 40% of the global population. The GGW and other similar land-restoration efforts currently underway around the world are set to reshape landscapes and the lived experiences of billions of people globally. Such restoration efforts are increasingly being regarded as potential 'Nature-based solutions' as the world seeks to confront and adapt to the triple challenges of climate change, biodiversity loss and food security. At present however, human health considerations play a very minor role in the design and implementation of restoration projects, including the GGW. This project aims to fill this critical gap, to ensure restoration projects can maximally serve human health alongside other objectives. We will use a novel combination of activities spanning 4 integrated work packages to do this. Briefly, WP1 will comprise a literature review and community consultations to develop an iteratively refined, gender-sensitive logic model describing the causal linkages between dryland restoration and human health. This will guide the project by helping to refine key hypotheses and identify a suitable subset of secondary health outcomes to be evaluated in subsequent WPs. In WP2 we will collate as much existing data as possible for GGW countries to conduct a Sahel-wide village-matched health impact evaluation. The primary outcome to be investigated will be weight-for-age z score (WAZ) of children (0-59 months) as a measure of acute nutritional status. A subset of secondary outcomes in children and women emerging from WP1 as of particular relevance will also be considered. We will compare health outcomes between communities with and without GGW activities to evaluate the health impacts of restoration. WP3 will be a follow-up of WPs1-2 in which we will conduct a more targeted, community-prioritised, village-matched health impact evaluation with primary data collection in three focal countries (The Gambia, Senegal, Burkina Faso). Based on our current understanding of the linkages between health and environmental restoration, these are likely to include other anthropometric measures (e.g., height-for-age z score, HAZ), and outcomes reflecting risk factors on the nutrition, infection and mental health / well-being pathways. We will again focus on children and non-pregnant women. Some secondary outcomes require collection of biological samples from children for laboratory analysis. Follow-up sampling will give information on seasonal effects and an opportunity to compare child growth over a 12-14 month period between groups with and without GGW interventions. WP4 comprises a set of integrating tasks aimed at marrying the results of the health impact evaluations with current activities guiding the design and implementation of the GGW and understanding the role of and benefits to health of completing the GGW. With an anticipated cost of around $50 billion to reach its 100 million hectare target of restored drylands by 2030, it is essential for health impacts (benefits and costs) to be brought into existing decision-support tools for applied purposes. We will do this via a combination of steps from health economic evaluation, cost-benefit and trade-off analysis, and systems and scenario modelling in the context of a changing climate. In all WPs, our Project Partners and Scientific Steering Committee will further ensure local relevance and streamline the research-to-practice pipeline, enhancing impact.

Germany