Frontotemporal dementia (FTD) has a debilitating effect on patients and their caregivers and leads to substantial economic costs. 15-30% of patients have familial FTD caused by known pathogenetic mutations. For the other 70-85% of patients, termed sporadic FTD, diagnosis is slow (~3.6 years) with frequent misdiagnosis due to clinical, genetic and molecular heterogeneity. Thus, there is great need for biomarkers for early diagnosis of sporadic FTD and its pathological subtypes. In PREDICTFTD, we will validate a set of biomarkers and create a diagnostic tool for early diagnosis of familial and sporadic FTD, which will facilitate tailored support and symptomatic treatments and care. We will apply several new approaches to achieve this: 1) we combine 11 geographically diverse cohorts of sporadic and familial FTD with retrospective and prospective longitudinal liquid biopsy samples and extensive clinical and behavioural data; 2) we are the first to use multimodal clinical and liquid biomarker data to train an AI-algorithm as a diagnostic tool for quick and early clinical FTD diagnosis; and 3) we implement a novel robust two-stage strategy for biomarker and AI algorithm validation, where phase I validates biomarkers and algorithms on a cohort of genetic and autopsied cases and phase II assesses biomarker value for diagnosis of sporadic FTD and at-risk pre-symptomatic mutation carriers. We will apply this two-stage validation strategy to address three critical clinical challenges: i) To distinguish sporadic FTD from (non-) neurodegenerative disorders that show significant clinical/symptomatic overlap, ii) To robustly detect FTD pathological subtypes in sporadic FTD and iii) pre-symptomatic identification of FTD onset. Thus, PREDICTFTD will transform FTD diagnosis, offering potential for early disease confirmation, guiding treatment decisions, facilitating patient recruitment for clinical trials, guidance of patients and caregivers, and enabling preventive measures.

The HAL4SDV proposal aligns with the EU Strategic Research and Innovation Agenda 2022 on Electronic Components and Systems. It aims to pioneer methods, technologies, and processes for series vehicle development beyond 2030, driven by anticipated advancements in microelectronics, communication technology, software engineering, and AI. HAL4SDV envisions a future where vehicles are fully integrated into smart cities, intelligent highways, and cyberspace, blurring the lines between inside and outside the vehicle. Assumptions include data-centricity, code portability, efficient data fusion, unlimited scalability, real-time capabilities, and robust cybersecurity. The objectives encompass unifying software interfaces, creating a hardware abstraction framework, enabling Over-The-Air (OTA) updates, designing platform architectures, ensuring hardware abstraction and virtualization, offering hardware support, automating integration, supporting safety features, harnessing edge computing, implementing security measures, and providing essential development tools. By focusing on these objectives, HAL4SDV aims to establish a unified ecosystem for software-defined vehicles, positioning Europe's automotive industry for continued leadership post-2030 while leveraging existing results and technologies to accelerate progress.

In the face of a rapidly advancing digital healthcare terrain, the DTRIP4H project emerges as a momentous effort to revolutionize predictive, preventive, personalized, and participatory health paradigms within the EU. Amid significant incidence of chronic conditions and cancer, there is a pressing need for a proactive shift in health strategies. Yet, the full potential of European research infrastructures (RIs) is curtailed by investment deficits, fragmentation, and the intricacies of data management. Digital Twin (DT) technology introduces a new age of precision by enabling sophisticated simulations and analyses of intricate biological processes. In DTRIP4H, we start a new initiative in Europe “decentralized health digital twin ecosystem consisting of RIs”. Using DTs, we aim to resolve critical challenges around data harmonization, equitable access, and stringent privacy safeguards. Incorporating technologies such as federated learning, Generative AI, and Virtual Reality (VR), the project aspires to create a decentralized digital twin environment (DDTE). This will empower both internal and external RI users, such as researchers, innovators, and SMEs, to craft DT applications that address specific scientific challenges, utilizing a blend of real-world and synthetic data in compliance with regulatory frameworks, i.e. GDPR. We will develop 7 innovative proof of concept thematic health-related Use cases fulfilling the needs of scientists, SMEs, and industrial end users, particularly in health topics related to cancer treatment, drug development, human environmental exposome, precision treatment for schizophrenia and personalized medicine through Artificial Intelligence (AI), AR/VR empowered DTs utilizing DDTE, while adhering to FAIR data principles. DTRIP4H adopts a human-centric methodology to elevate research efficacy, narrow the skills gap, and align with the objectives of the European Research Area (ERA) and the Sustainable Development Goals (SDGs) by 2030.

Cynergy4MIE is a visionary project poised to revolutionize Europe's industrial landscape by bridging the gap between foundational technology layers, cross-sectional technologies, and key application areas. The project addresses the pressing need for efficient resource utilization and synergy creation across ecosystems. By actively managing requirements from various key application areas, Cynergy4MIE aims to steer developments in foundational technology layers and cross-sectional technologies, enabling unparalleled collaboration and resource optimization. This approach promises faster time-to-market, efficient resource utilization, and enhanced technological exchange between key application areas. Cynergy4MIE's strategy aligns with the EU's agenda and emphasizes urgency, resilience, technological partnerships, and cross-domain integration to champion European competitiveness. The project's long-term impact rests on embracing urgency, fostering competitive resilience, strengthening technological partnerships, harnessing ecosystem synergies, promoting cross-domain integration, advancing AI competence, prioritizing sustainability, enhancing productivity, and ensuring user-centric digitalization while forming strategic alliances. Cynergy4MIE envisions a future where emergent cyber-physical systems serve human-centric needs, drive domain convergence, and secure Europe's position as a global technology leader.

Nature-based solutions (NBS) are considered essential to solving the climate crisis. They have the potential to simultaneously address climate change, biodiversity loss and human wellbeing and development, by providing different ecosystem services. However, the typically small size of NBS projects, the difficulties to measure and monetize their co-benefits as well as the long time frame for investment returns and the risk profile, undermine their attractiveness for funding. In comparison to NBS alone, the combination of NBS and ecosystem-based approaches, physical measures and technological measures is able to provide measurable outcomes quicker, reducing the risk profile and facilitating faster implementation and long-time maintenance, broadening opportunities for ecosystem innovation and increasing investment attractiveness. Aiming for bankability-by-design, RISE-IN will assess a set of climate resilient solutions (CRS), including NBS and ecosystem- based approaches and physical measures, by mapping and quantifying their co-benefits and identifying innovative financing and investment opportunities accordingly. Co-creation and participatory processes with local stakeholders, including investors and businesses, will enable the screening of the most adequate solutions for each site, in terms of functionality and funding and within a framework of just resilience. The assessment will be complemented by 3 climate risk assessment models that will reduce uncertainty regarding risks associated with extreme events, water run-off and capital loss. RISE-IN will demonstrate this concept for flood management (most costly type of disaster in Europe) in Cesena (IT), Christchurch (NZ) and Póvoa de Varzim (PT). RISE-IN demonstrations will be further tailored for replication in Ghent (BE), Zhytomyr (UA), Kadikoy (TR) and in 3 additional sites of Demonstrator Cities (Demo Cities) to prove the impact of the concept to facilitate bankability and upscale implementation across EU and Internationally.