EuroPOND will develop a data-driven statistical and computational modeling framework for neurological disease progression. This will enable major advances in differential and personalized diagnosis, prognosis, monitoring, and treatment and care decisions, positioning Europe as world leaders in one of the biggest societal challenges of 21st century healthcare. The inherent complexity of neurological disease, the overlap of symptoms and pathologies, and the high comorbidity rate suggests a systems medicine approach, which matches the specific challenge of this call. We take a uniquely holistic approach that, in the spirit of systems medicine, integrates a variety of clinical and biomedical research data including risk factors, biomarkers, and interactions. Our consortium has a multidisciplinary balance of essential expertise in mathematical/statistical/computational modelling; clinical, biomedical and epidemiological expertise; and access to a diverse range of datasets for sporadic and well-phenotyped disease types. The project will devise and implement, as open-source software tools, advanced statistical and computational techniques for reconstructing long-term temporal evolution of disease markers from cross-sectional or short-term longitudinal data. We will apply the techniques to generate new and uniquely detailed pictures of a range of important diseases. This will support the development of new evidence-based treatments in Europe through deeper disease understanding, better patient stratification for clinical trials, and improved accuracy of diagnosis and prognosis. For example, Alzheimer’s disease alone costs European citizens around €200B every year in care and loss of productivity. No disease modifying treatments are yet available. Clinical trials repeatedly fail because disease heterogeneity prevents bulk response. Our models enable fine stratification into phenotypes enabling more focussed analysis to identify subgroups that respond to putative treatments.

This project addresses the scientific, technological and clinical problem of recovery of hand function after amputation. Despite decades of research and development on artificial limbs and neural interfaces, amputees continue to use technology for powered prostheses developed over 40 years ago, namely myoelectric prostheses controlled via superficial electrodes. These devices do not purposely provide sensory feedback and are known for their poor functionality, controllability and sensory feedback, mainly due to the use of surface electrodes. The consortium has pioneered the use of osseointegration as a long-term stable solution for the direct skeletal attachment of limb prostheses. This technology aside from providing an efficient mechanical coupling, which on its own has shown to improve prosthesis functionality and the patient’s quality of life, can also be used as a bidirectional communication interface between implanted electrodes and the prosthetic arm. This is today the most advanced and unique technique for bidirectional neuromuscular interfacing, suited for the upper limb amputees, which was proven functional in the long term. The goal of the DeTOP project is to push the boundaries of this technology –made in Europe– to the next TRL and to make it clinically available to the largest population of upper limb amputees, namely transradial amputees. This objective will be targeted by developing a novel prosthetic hand with improved functionality, smart mechatronic devices for safe implantable technology, and by studying and assessing paradigms for natural control (action) and sensory feedback (perception) of the prosthesis through the implant. The novel technologies and findings will be assessed by three selected patients, implanted in a clinical centre. DeTOP bridges several currently disjointed scientific fields and is therefore critically dependent on the collaboration of engineers, neuroscientists and clinicians.

HARIA re-defines the nature of physical human-robot interaction (HRI), laying the foundations of a new research field, i.e., human sensorimotor augmentation, whose constitutive elements are: i) AI-powered wearable and grounded supernumerary robotic limbs and wearable sensorimotor interfaces; ii) methods for augmentation enabling users to directly control and feel the extra limbs exploiting the redundancy of the human sensorimotor system through wearable interfaces; iii) clear target populations, i.e., chronic stroke and spinal cord injured individuals, and real-world application scenarios to demonstrate the extraordinary value of the paradigm shift that HARIA represents in HRI and the great impact on the motivation to re-use the paretic arm(s), with consequent improvement of the quality of life. Supernumerary limbs will be partially controlled by artificial intelligence, and partially under the direct control of the human who gains the agency of some motion parameters of the supernumerary limbs. From the control point of view, it is fundamental to find the right trade-off between motion task parameters that are controlled by the user, and the level of robot autonomy. This interplay is enabled by the wearable sensorimotor interface that establishes a connection between the human sensorimotor system and the system of actuators and sensors of the robot, allowing reciprocal awareness, trustworthiness and mutual understanding. HARIA finds its natural application in assisting people with uni- or bi-lateral upper limbs chronic motor disabilities. Technology and methodology developments will follow a user-centered design approach, as only patients with disabilities are fully aware of their real (still unmet) needs in real life activities. This project will also go beyond the application to health, starting a new era of intuitive and seamless human-robot augmentation by wearable sensorimotor interfaces and supernumerary limbs.

The SYSCID consortium aims to develop a systems medicine approach for disease prediction in CID. We will focus on three major CID indications with distinct characteristics, yet a large overlap of their molecular risk map: inflammatory bowel disease, systemic lupus erythematodes and rheumatoid arthritis. We have joined 15 partners from major cohorts and initiatives in Europe (e.g.IHEC, ICGC, TwinsUK and Meta-HIT) to investigate human data sets on three major levels of resolution: whole blood signatures, signatures from purified immune cell types (with a focus on CD14 and CD4/CD8) and selected single cell level analyses. Principle data layers will comprise SNP variome, methylome, transcriptome and gut microbiome. SYSCID employs a dedicated data management infrastructure, strong algorithmic development groups (including an SME for exploitation of innovative software tools for data deconvolution) and will validate results in independent retrospective and prospective clinical cohorts. Using this setup we will focus on three fundamental aims : (i) the identification of shared and unique "core disease signatures” which are associated with the disease state and independent of temporal variation, (ii) the generation of "predictive models of disease outcome"- builds on previous work that pathways/biomarkers for disease outcome are distinct from initial disease risk and may be shared across diseases to guide therapy decisions on an individual patient basis, (iii) "reprogramming disease"- will identify and target temporally stable epigenetic alterations in macrophages and lymphocytes in epigenome editing approaches as biological validation and potential novel therapeutic tool . Thus, SYSCID will foster the development of solid biomarkers and models as stratification in future long-term systems medicine clinical trials but also investigate new causative therapies by editing the epigenome code in specific immune cells, e.g. to alleviate macrophage polarization defects.