The proposed Coordination and Support Action (CSA) has the overall objective to establish an international network of experts and stakeholders in the field of microbiome food system research, elaborating microbiomes from various environments such as terrestrial, plant, aquatic, food and human/animal and assess their applicability and impact on the food system. MICROBIOMESUPPORT will follow the approach of food system and integrate actors and experts from all stages in this circular economy of food. The food system approach is part of the FOOD 2030 concept to promote a systems approach to research and innovation (R&I). MICROBIOMESUPPORT will be one of the key drivers to implement FOOD 2030 strategies, will facilitate multi-actor engagement to align, structure and boost R&I in microbiome and will support the European Commission by coordinating the activities, meetings, workshops and results from the International Bioeconomy Forum (IBF) working group ‘Food Systems Microbiome’. The main concept behind MICROBIOMESUPPORT IS to boost the bioeconomy and the FOOD 2030 strategy, by focusing on the new avenues generated by microbiome R&I efforts. MICROBIOMESUPPORT WILL have a main impact on the coordination of commonly defined R&I agendas which will be incorporated into regional, national, European but also global funding programmes related to microbiomes in food systems. MICROBIOMESUPPORT will create a collaborative international network and integrate know-how in plant, terrestrial, animal, human and aquatic microbiome R&I as well as expertise in bioeconomy applications. MICROBIOMESUPPORT has integrated international partners form Brazil, Canada, South Africa, China, Argentina, Australia, New Zealand, India and USA in order to improve the international cooperation and coordination of common bioeconomy research programmes and set a basis for common microbiome R&I agendas.

Quantum sensors use the properties of quantum physics, a theory that describes phenomena at the atomic scale. We now know how to perfectly manipulate photons, atoms and electrons and place them on demand in a given quantum state. These states can be extremely sensitive to the slightest disturbance. It is on this principle that quantum sensors are based to detect, with great precision, quantities such as acceleration, rotation, magnetic field ... Their unparalleled sensitivity allows them to detect tiny variations but also to make very precise recordings over very long periods, thus opening the way to applications ranging from the measurement of the gravitational attraction of a buried object to the mapping of the magnetic fields emitted by our brain. The VeQSeNse project focuses on laser-cooled atomic inertial quantum sensors. At a temperature below microKelvin, it is thus possible to create waves of matter. By then using a laser pulse, we can form copies of these waves which will move away from each other in the direction of the laser beams. We thus create a quantum superposition of matter waves whose trajectories we will control using a series of light pulses, to form an interferometer with the possibility of observing interference fringes in the probability of detecting atoms at the exit of the interferometer. These fringes will be sensitive to accelerations along the laser, and it is possible to detect tiny changes, on the order of a billionth of the acceleration, and record these variations over very long periods of time. All these properties open the field of new applications, such as positioning without GPS, resource management without drilling or monitoring with a view to preventing disasters such as earthquakes. These sensors have been studied extensively and are commercially available today, but they are also very limited. Only one direction is measurable, whereas three-dimensional vector-type measurements would be required. In addition, there is still a lot of “dead time” in the measurement which degrades the accuracy, especially over long term use. The VeQSeNse project will study and develop a new generation of vector quantum sensors which will be used via a network of correlated sensors to meet the two challenges of vector measurement and reduced “dead time”. Vector sensors already exist by sequentially interrogating atoms on three axes, but this only partially solves the problem as it increases “dead times”. A series of correlated measurements can also be created by interrogating many clouds of atoms with the same laser, but this can only be done at short scales and therefore cannot be deployed over large areas as surveillance would require. and disaster forecasting. Through close collaboration between European and Canadian research institutes, we propose to develop a quantum link between “classic” vector sensors in order to improve sensitivity and precision and to develop a new 3D quantum manipulation tool that will lead to to a new generation of multi-axis quantum sensors. The combination of these two methods will increase the ability of these networks to record an acceleration vector map for geosphere monitoring applications. The aim is to set up a consortium of experts on aspects of sensors, synchronization networks and geophysical application. The work established in this context will also make it possible to foreshadow a consortium led by industrial partners aiming to accelerate the commercial development of gravimetric sensors.

EnTimeMent aims at a radical change in scientific research and enabling technologies for human movement qualitative analysis, entrainment and prediction, based on a novel neuro-cognitive approach of the multiple, mutually interactive time scales characterizing human behaviour. Our approach will afford the development of computational models for the automated detection, measurement, and prediction of movement qualities from behavioural signals, based on multi-layer parallel processes at non-linearly stratified temporal dimensions, and will radically transform technology for human movement analysis. EnTimeMent new innovative scientifically-grounded and time-adaptive technologies operate at multiple time scales in a multi-layered approach: motion capture and movement analysis systems will be endowed with a completely novel functionality, achieving a novel generation of time-aware multisensory motion perception and prediction systems. The proposed model and technologies will be iteratively tested and refined, by designing and performing controlled and ecological experiments, ranging from action prediction in a controlled laboratory setting, to prediction in dyadic and small group interaction. EnTimeMent scenarios include health (healing and support of everyday life of persons with chronic pain and disability), performing arts (e.g. dance), sports, and entertainment group activities, with and without living architectures. EnTimeMent will create and support community-building and exploitation with concrete initiatives, including a community of users and stakeholders, innovation hubs and SME incubators, as premises for the consolidation beyond the end of the project in a broader range of market areas.

Exploratory behaviour lies at the core of human essence. Being confronted with novel terrain, materials, tools, or artistic creations, humans instinctively explore the unknown in order to acquire information about it, to make sense of it, to act on it, and to appreciate what is in front of them. This quest for understanding involves dynamic engagement like walking, observing, handling objects, and manipulating them to witness changes in sight, touch, or sound. Active exploration is pivotal in shaping our sensory experiences of the world. Yet, the mutual relationships between perceptual experiences and the dynamic structures of the multisensorial information generated by active exploration are still not well understood. This presents a significant scientific challenge in itself and for comprehending the human mind's workings in natural settings. It also negatively impacts the development of multisensory interactive technologies and the innovative design of immersive systems, as well as the modelling of intelligent adaptive behaviour, thereby affecting nearly every facet of our life, including healthcare, education, mobility, AI, robotics and culture. EXPLORA aims to uncover the mutual interactions between perceptual impacts and active exploration systematically, quantitatively, and empirically. We propose a paradigm shift, approaching perception of materials, objects, and space as dynamic interactions with complex ecological systems. Through our innovative, cross-sectoral, multi-disciplinary, and sustainable research and training programme, we aim to catalyse this shift, creating impact through transfer between science, engineering, design, arts and industry and by connecting multiple levels of STEAM (Science, Technology, Engineering, Arts, and Mathematics) education from middle schools to doctoral education to enhance talent and knowledge circulation across the European R&I (Research and Innovation) landscape.

BACKGROUND Optimal care for older patients with complex chronic conditions (CCC) is challenging. Not only do older patients with CCC present with multiple conditions and functional impairments, these often interact with each other, as well as with their treatments. Patients with CCC are concentrated in home care and nursing home settings. Professionals working in these settings often lack appropriate decision support that mirrors the medical and functional complexity of these persons. AIM To improve prognoses and estimation of treatment impact for older care recipients with CCC in home care and nursing homes settings, and develop, validate, and test next generation individualised decision support. IMPACT Better informed decision making for clinical management of older care recipients with CCC in home care and nursing homes, through (1) high quality internationally validated predictive algorithms on disease trajectories and treatment outcomes; (2) a multi-nationally tested e-platform for health professionals to receive predictive scenarios on course and treatment outcomes of newly assessed care recipients at point of care; and (3) dissemination among health professionals working in nursing homes and home care. APPROACH We collated longitudinal data from 52 million older recipients of home care and nursing home care from eight countries including (1) highly reliable, valid and harmonised comprehensive assessments of functional capacities, diseases, and treatments, linked with (2) administrative repositories on mortality and care use. We develop and validate decision support algorithms using a variety of techniques including machine learning to better predict (i) outcomes (eg death, acute admissions, quality of life) and the modifying impact of (ii) pharmacological and (iii) non-pharmacological treatments. We co-create decision support output with health professionals and patients and pilot it's applicability at point of care with an e-platform.