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.

MaPPLexiC aims to fill the gap in modern language theories concerning the interpretation and description of one type of idiosyncratic word constructions that stands out in terms of its frequency of use and as a measure of language proficiency, namely lexical collocations, and refute the wide-spread assumption that their production is ad hoc and therefore cannot be described in systematic terms. Inspired by the dramatic advances in Deep Neural Networks (DNNs) MaPPLexiC considers a lexical collocation identification and classification DNN as a cognitive model, whose internal neuron activation vectors during the assessment of the collocation status of a given word combination can be translated into interpretable semantic, contextual, and socio-cultural features of the collocation elements and generalized into “collocation production principles” that dictate which features lexical items must possess to form a lexical collocation. In other words, what MaPPLexiC targets is a lexical collocation grammar. For this purpose, the Project will adapt and advance state-of-the-art techniques for the derivation of interpretable features from DNN’s activation vectors of individual lexical collocation samples and neural clustering techniques that will facilitate the generalization of the obtained “profiles” for the individual samples to more generic collocation production principles. Considering that collocation construction differs from language to language, the Project has a strong multilingual orientation. The investigation will be carried out on pairs of Germanic (English, German), Romance (French, Spanish), Finno-Ugric (Finnish, Hungarian), and Slavic (Czech, Russian) languages. The collocation profiles of translation-equivalent collocation samples and the language-specific collocation production principles will be contrasted with the goal to develop cross-language collocation production transfer techniques, which will highly benefit collocationally under-resourced languages.

The HPC Digital Autonomy with RISC-V in Europe (DARE) will invigorate the continent’s High Performance Computing ecosystem by bringing together the technology producers and consumers, developing a RISC-V ecosystem that supports the current and future computing needs, while at the same time enabling European Digital Autonomy. DARE takes a customer-first approach (HPC Centres & Industry) to guide the full stack research and development. DARE leverages a co-design software/hardware approach based on critical HPC applications identified by partners from research, academia, and industry to forge the resulting computing solutions. These computing solutions range from general purpose processors to several accelerators, all utilizing the RISC-V ecosystem and emerging chiplet ecosystem to reduce costs and enable scale. The DARE program defines the full lifecycle from requirements to deployment, with the computing solutions validated by hosting entities, providing the path for European technology from prototype to production systems. The six year time horizon is split into two phases, enabling a DARE plan of action and set of roadmaps to provide the essential ingredients to develop and procure EU Supercomputers in the third phase. DARE defines SMART KPIs for the hardware and software developments in each phase, which act as gateways to unlock the next phase of development. The DARE HPC roadmaps (a living document) are used by the DARE Collaboration Council to maximize exploitation and spillover across all European RISC-V projects. DARE addresses the European HPC market failure by including partners with different levels of HPC maturity with the goal of growing a vibrant European HPC supply chain. DARE Consortium partners have been selected based on the ability to contribute to the DARE value chain, from HPC Users, helping to define all the requirements, to all parts of the hardware development, software development, system integration and subsequent commercialization.

Seasonal Forecasts are critical tools for early-warning decision support systems, that can help reduce the related risk associated with hot or cold weather and other events that can strongly affect a multitude of socio-economic sectors. Recent advances in both statistical approaches and numerical modeling have improved the skill of Seasonal Forecasts. However, especially in mid-latitudes, they are still affected by large uncertainties that make their application often complicated. The ARTIST project aims at improving our knowledge of climate predictability at the seasonal time-scale, focusing on the role of unexplored drivers, to finally enhance the performance of current prediction systems. This effort is meant to reduce uncertainties and make forecasts efficiently usable by regional met-services and private bodies. A statistical/dynamical hybrid model will be designed through the synthesis of (a) a cutting-edge dynamical Seasonal Prediction System and (b) a statistical model based on advanced Machine Learning (ML) techniques. Such a hybrid approach may become critical to improve climate forecasts, because it combines the theoretical foundation and interpretability of physical modeling with the power of Artificial Intelligence (AI), that can reveal unknown or disregarded spatio-temporal features. ARTIST will focus on seasonal prediction of temperature hot/cold extremes in Europe, but its scalable nature can make it applicable across a wide range of variables and geographical areas. Besides the employment of AI, a strength of the action stands in the use of local land surface predictors to instruct the empirical model. The fellowship, which includes a variety of training activities, will be mainly conducted at the Barcelona Supercomputing Centre (Spain), a world-renowned institute for climate predictions and applications. A secondment period is projected at the Max Planck Institute for BGC (Germany), prominent in land studies and ML employment in earth science.

Type 2 diabetes (T2D) is a major public health problem, affecting 55 million European citizens. T2D ensues in individuals who develop a progressive pancreatic beta cell failure. T2D probably comprises a heterogeneous group of diseases. A new molecular taxonomy of T2D is essential for the development of medical care that is predictive, preventive and personalized. Currently available T2D therapies are not disease modifying: they treat hyperglycaemia without addressing its underlying cause, i.e. beta cell failure. In this proposal we seek to identify pathogenic molecular events that operate in the diseased tissue, i.e. the failing human beta cell, at their true level of complexity. T2DSystems will accomplish this ambitious goal by integrating human islet genetic and epigenetic data with disease-relevant environmental perturbations, metabolomics and functional studies, and use this knowledge to identify distinct human islet phenotypes in subgroups of patients. In closely interacting work packages, we will achieve the following goals: • Compile and expand existing European bio-banks and datasets to create the Translational human pancreatic Islet Genotype tissue-Expression Resource (TIGER), a T2D systems biomedicine resource of unprecedented scale; • Develop large-scale data analysis tools and both data driven and mechanistic probabilistic modelling frameworks to exploit TIGER towards system level biological insight; • Translate these findings to identify stratified beta cell phenotypes in human cohorts. This will provide understanding of beta cell pathophysiology in vivo and enable stratified prevention and therapeutics. T2DSystems will enable the development of personalized diagnostic tests, taking into account individual environmental and genetic risk factors. The newly identified molecular disease mechanisms will provide the basis for development of novel therapies and for patient stratification to test individualized therapies.