Climate change is one of several drivers of recurrent outbreaks and geographical range expansion of zoonotic infectious diseases in Europe. Policy and decision-makers need tailored monitoring of climate-induced disease risk, and decision-support tools for timely early warning and impact assessment for proactive preparedness and timely responses. The abundance of open data in Europe allows the establishment of more effective, accessible, and cost-beneficial prevention and control responses. IDAlert will co-create novel policy-relevant pan-European indicators that track past, present, and future climate-induced disease risk across hazard, exposure, and vulnerability domains at the animal, human and environment interface. Indicators will be sub-national, and disaggregated through an inequality lens. We will generate tools to assess cost-benefit of climate change adaptation and mitigation measures across sectors and scales, to reveal novel policy entry points and opportunities. Surveillance, early warning and response systems will be co-created and prototyped to increase health system resilience at regional and local levels, and explicitly reduce socio-economic inequality. Indicators and tools will be co-produced through multilevel engagement, innovative methodologies, existing and new data streams and citizen science, taking advantage of intelligence generated from selected hotspots in Spain, Greece, The Netherlands, Sweden, and Bangladesh that are experiencing rapid urban transformation and heterogeneous climate-induced disease threats. For implementation, IDAlert has assembled European authorities in climate modelling, infectious disease epidemiology, social sciences, environmental economics, One Health and EcoHealth. Further, by engaging critical stakeholders from the start, IDAlert will ensure long-lasting impacts on EU climate policy, and provide new evidence and tools for the European Green Deal to strengthen population health resilience to climate change.

Deriving mammalian retina from stem cells has had a large impact on the study of the biology of vision and is called organoid. Compared to in vivo retina, retinal organoids are far less functionally sophisticated in terms of their synapses, connectivity, discrimination between different light stimuli and their electrical action potentials. This project will overcome this functional constraint of retinal organoids by studying electrophysiological events-derived functional maturation of mouse retina during retinal development and then stimulating those events with the help of mathematical models in order to induce the same functionality in mouse and human retinal organoids. NeuFRO will achieve a resonance in the field by generating retinal organoids with the neuronal connectivity and the natural diversity of functions using interdisciplinary fields including electrophysiology, developmental biology, and computationally-derived electrical stimulation. Initially, I will create a holistic roadmap of the electrical features of immature mouse retina during development that shows self-organization through electrophysiology. With milli- to nanometer imaging precision, electrical activities derived the circuit formation will be spatiotemporally documented. Then I will decode this space-time code of intrinsic electrical patterns and neuronal connectivity using an ambitious strategy incorporating Hodgkin-Huxley and linear-nonlinear models. Next, such electrical response models will be applied to immature retinal organoids (mouse and human) by an innovative ‘sandwich’ electrophysiology technique during the development in vitro. With this approach, I will induce naturalistic electrical features in the retinal organoid, allowing the functional neurons to wire and fire appropriately into retinal organoids, particularly visual circuits. This ground-breaking approach will advance techniques for generating functional human retina.

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.