Climate change is one of the most urgent problems facing mankind. Implementation of the Paris climate agreement relies on robust scientific evidence. Yet, the uncertainty of non-greenhouse gas forcing associated with aerosol-cloud interactions limits our constraints on climate sensitivity. Radically new ideas are required. While the majority of forcing estimates are model based, model uncertainties remain too large to achieve the required uncertainty reductions. The quantification of aerosol cloud climate interactions in Earth Observations is thus one of the major challenges of climate science. Progress has been hampered by the difficulty to disentangle aerosol effects on clouds and climate from their covariability with confounding factors, limitations in remote sensing, very low signal-to-noise ratios as well as computationally, due to the scale of the big (>100Tb) datasets and their heterogeneity. Such big data challenges are not unique to climate science but occur across a wide range of data science applications. Innovative techniques developed by the AI and machine learning community show huge potential but have not yet found their way into climate sciences – and climate scientists are currently not trained to capitalise on these advances. The central hypothesis of IMIRACLI is that merging machine learning and climate science will provide a breakthrough in the exploration of existing datasets, and hence advance our understanding of aerosol-cloud forcing and climate sensitivity. Its innovative training plan will match each ESR with supervisors from climate and data sciences as well as a non-academic advisor and secondment and provide them with state-of-the-art data and climate science training. Partners from the non-academic sector will be closely involved in each of the projects and provide training in a commercial context. This ETN will produce a new generation of climate data scientists, ideally trained for employment in the academic and commercial sectors.

Lebers congenital amaurosis (LCA) is a devastating disease leading to the failure of vision because the light-sensing photoreceptor cells in the eye do not work properly even within the first few years of life. There is currently no cure or treatment for LCA. Inherited genetic mistakes in the AIPL1 gene are one of the causes of LCA. In this study, we want to create a new laboratory system that accurately models the human disease, so that we can test novel therapies to treat LCA caused by faults in the AIPL1 gene. Using state-of-the-art technologies optimised in our group, we will take cells isolated from the urine of LCA patients and reprogramme them to so called induced pluripotent stem cells (iPSC) from which we will grow three dimensional 'optic cups' in a dish in the laboratory, the 'retina in a dish'. This is important, as it is not possible to isolate and culture the light sensitive photoreceptor cells of the retina that are faulty in LCA directly from patients. Therefore, the derivation of these photoreceptor cells from LCA patients via formation of optic cups is critical to model the disease in humans and will be invaluable to validate new AIPL1-targeted therapies that may treat or cure this devastating disease. We will first test a pharmacological approach that targets a class of mutations encountered in up to 60% of all LCA patients with AIPL1 faults. These drugs, which can be safely applied as an eye drop, or as a tablet, target the cell machinery that converts a gene to a protein. We will test whether these drugs can rescue the normal AIPL1 protein from the faulty AIPL1 gene. The rescued AIPL1 protein will be able to perform its normal function in the cell. We will also develop treatments for a different kind of mutation in the AIPL1 gene that interferes with the way different parts of the AIPL1 gene are stitched or 'spliced' together to form the full-length message for the AIPL1 protein. We will test whether our custom-design therapeutic agents can 'override' these splice mutations to form a normal full-length AIPL1 message coding for a normal full-length and functional AIPL1 protein. These novel therapies will be tested on optic cups made from patients harbouring these classes of different AIPL1 mutations. The findings of this research are a critical step towards the application of these novel therapies to treat AIPL1 LCA patients in the clinical setting. The drug that will be used in this study has already been approved by NICE for the treatment of another disease caused by stop mutations, and a similar therapeutic agent targeting splice mutations in another gene causing LCA will shortly enter phase I clinical trials. Hence, the potential for our findings to rapidly enter the clinical phase and make a difference to patients' lives is very high.

Child marriage is a major determinant of physical and mental ill health and affects educational and economic attainment. Despite being illegal, it is common in Nepal, having been ingrained over generations, with strong social and financial incentives. Attempts to reduce this have been unsuccessful. Rather than mere education, we plan to work with adults who have married young. We will engage with couples (both men and women) to think deeply about how child marriage has affected their lives and how to tell their story through film. We will create a film together, take it to communities where child marriage is common and show it in large public screenings. A trained facilitator will engage the audience in discussions and help them to reflect on the issues presented. The community members will then have the opportunity to discuss the issues at length in smaller meetings and think about how they may address the issue locally. We hope that by engaging the public in this process, we can help communities take action to prevent child marriage and help both them and us understand the barriers to change.