REELER - Responsible Ethical Learning with Robotics Robots are the next ICT-related technology on the horizon ready to radically alter human societies. It is a major societal concern that up to 40% jobs may be replaced by robots over the next 20 years. Few empirical studies have been made in how roboticists’ visions may differ from users/affected stakeholders’ needs and concerns with these pervasive and radical changes. The REELER project aims at aligning the roboticists’ visions of a future with robots with empirically-based knowledge of human needs and societal concerns. Based on extensive robotics/SSH-RRI collaboration, REELER will offer proactive steps towards ethical and responsible robots by suggesting radical changes in current robot design procedures. Moreover, REELER will formulate guidelines in the REELER Roadmap for distributed responsibility among roboticists, users/affected stakeholders and policy-makers by closing the current gap between these. At the core of these guidelines is the concept of collaborative learning which permeates all aspects of REELER and will guide future SSH-ICT research. The main outcome of REELER is the research-based roadmap presenting a) ethical guidelines for Human Proximity Levels, b) prescriptions for how to include the voice of new types of users and affected stakeholders through Mini-Publics and call forth roboticists’ assumptions via sociodrama and c) an agent-based simulation tool for policy-making. The high level of multidisciplinarity (8 robot designers from the LEIT-ICT batch 23, anthropologists, psychologists, economists and philosophers) of the REELER research, will assure cooperation, comprehension and acceptance of SSH-research by the robotics research community. Integrating the recommendations of the REELER Roadmap for Responsible and Ethical Learning in Robotics in future robot design processes will ensure a European robotics community that take humans needs and societal concerns into account.

Environmental factors, including air and noise pollution, and the built environment, are typically associated with cardiovascular and metabolic non-communicable diseases (NCDs), e.g. obesity, type 2 diabetes, heart diseases and atherosclerosis. The extent to which these exposures may cause their attributed health effects (via molecular mediation) directly or indirectly as a result of associations to an individual’s psychosocial context is largely unknown. NCDs arise from a lifelong process influencing anthropometric, glycaemic, cardiac and lipid-related health trajectories. Risks may start as early as during the fetal period and are modified during sensitive periods in childhood, adolescence and adulthood. Despite this, research has not focused enough on the life-course characterisation of the exposome and the application of this to health and disease. In 5 years, LONGITOOLS, a partnership of 15 academic groups and 3 small companies will harness a catalogue of birth cohorts, longitudinal data, registers and biobanks. We will characterise coincident longitudinal trajectories of exposure and cardiometabolic health combining the study of longitudinal effects and internal responses. The latter will include measures of DNA methylation, RNA expression and read outs of metabolic pathways. LONGITOOLS will implement this longitudinal approach in 11 work packages designed to generate a catalogue of FAIR data and a novel analytical toolbox. Evidence-based life-course causal models will estimate how clinical and policy interventions may sustainably affect the health and economic burden of NCDs. A key objective will be to generate evidence-based predictions which can ultimately translate into innovative healthcare applications (apps) and policy options. LONGITOOLS will also allow researchers and policy makers to generate new knowledge - identifying the likely causal (direct and indirect) mechanisms through which exposures to man-made environmental factors affect the risk of NCDs. LONGITOOLS is one of the nine projects composing the European Human Exposome Network.

HappyMums is designed to improve our understanding on the biological mechanisms underlying the development of depressive symptoms in pregnancy, and the efficacy of interventions. It will interrogate a large collection of cohorts with multiple biological, medical, clinical, socio-demographic and environmental and lifestyle data to identify the most robust risk factors triggering depressive symptoms, but also moderators of the risk. By putting together unique human samples of placenta, chorionic villi and amniotic fluid, and animal models, HappyMums will improve the understanding of the neurobiological mechanisms affected by depressive symptoms in pregnancy that lead to alterations in the foetal environment, shaping offspring risk for developing negative mental outcomes. The use of three complimentary rodent models will allow to achieve a proof of causality, and the presence of an innovative fish model will elucidate the mechanisms specific to placenta by which adverse maternal conditions are transmitted to the offspring without the potentially confounding mitigating effects of compensatory postnatal maternal care. This knowledge will allow the identification of new targets for the development of novel medications, for the repurposing of existing medications or for the development of non-pharmacological interventions. HappyMums will also develop a digital platform where AI tools-based data can be collected, together with biological, clinical, medical, environmental and lifestyle data, through a mobile phone App that will be at the interface with clinicians via a dedicated dashboard. This will allow early screening of depressive symptoms, prompt diagnoses, personalized treatments, and the promotion of protective lifestyle attitudes. Overall, HappyMums will not only increase the knowledge in the field of mental disorders in pregnancy, but also improve the wellbeing of these women, providing unprecedented benefits also to the offspring and thus to society at large.