Pancreatic cancer (PDAC) is usually detected at late stages and most patients die within one year after diagnosis. In PANCAID we will therefore develop a blood test for early detection of PDAC. Despite tremendous technological advances in Liquid Biopsy Diagnostics (LBx), this goal is very ambitious because small tumors release only minute amounts of cells or cellular products (e.g., DNA, RNA, protein, metabolites) into the circulation. Thus, tests with a high sensitivity are required but increases in sensitivity are usually achieved on the expenses of reduced specificity which can lead to significant overdiagnosis leading to unnecessary stress for the individuals with a false-positive blood test and high costs for the health system. In PANCAID, we will therefore establish a blood test with high accuracy by analyzing large cohorts of patients with PDAC and its precursor lesions, individuals at risk to develop PDAC and appropriate age matched control groups(healthy and non-cancer diseasesfrequent in the targeted population). Ambitious objectives of PANCAID include (1) establishment of a unique resource of blood samples of early PDAC and risk groups (WP1); (2) Establishment of a breakthrough blood test for early diagnosis of PDAC (WP2); (3) Identification of the best composite biomarker panel by integrating multimodal features in an AI-assisted computational analysis; (4) Analysis of the socio-economic impact of early PDAC diagnosis (WP4); and (5) Definition of the ethics parameters relevant to early PDAC detection (WP5). A robust multi-biomarker panel will be determined during the training period (year 1-3) and subsequently validated on bio-banked blood samples (year 4-5). Depending on the outcome of this comprehensive analysis, PANCAID will provide the design of a future prospective study for validation of the developed composite blood test in an international multi-center setting required to introduce LBx into screening programs for high-risk individuals. This action is part of the Cancer Mission cluster of projects on ‘Prevention, including Screening’.

Heart failure (HF) is a pandemic currently affecting up to 15 million people in Europe. It is a complex clinical syndrome presenting with impaired heart function and is associated with poor quality of life for patients and high healthcare costs. There is a high clinical demand for novel artificial intelligence (AI) tools which will facilitate risk stratification, early diagnosis, and disease progression assessment in HF. Such tools are essential to allow prompt initiation of evidence-based prevention and treatment strategies which will improve patient quality of life, reduce morbidity and mortality and the HF burden on healthcare. STRATIFYHF aims to develop, validate and implement the first AI-based, decision support system (DSS) for risk stratification, early diagnosis, and disease progression assessment in HF to accommodate both primary and secondary care clinical needs. The DSS will integrate patient-specific demographic and clinical data using existing and novel technologies and establish AI-based tools for risk stratification and HF prediction using machine learning. Additionally, a mobile app will be developed to empower patients to better manage their condition, and health care professionals to make informed decision in selection of evidence-based HF prevention and treatment strategies. Our multidisciplinary consortium, including three small-to-medium enterprises (SMEs) and two stakeholder organisations, will be guided by medical advice and regulatory and health technology experts to deliver the DSS as a medical class 2b device, reaching TRL 8 by the end of the project. STARTIFYHF will change the way in which HF is diagnosed today and thereby improve the quality and length of patients’ lives and lead to efficient and sustainable healthcare systems by reducing the number of HF-related hospital admissions and unnecessary referrals from primary to secondary care in Europe and beyond.

Many human pathologies such as cancer are due to complex biochemical alterations that start at a sub-cellular level and lead to progressive changes that result in a heterogeneous tumor composition. The polyclonality of tumor cells hampers the diagnosis and the therapy giving rise to tumor clones that lead to therapy resistance and promote metastases. An accurate diagnosis of tumor biopsies to identify these particular cell clones is crucial to provide targeted therapy tailored to the tumor characteristics, to improve the patient outcomes and increase survival rates. For this vision to come true, we introduce ulTRafast hOlograPHic FT-IR microscopY (TROPHY) as a paradigm shift in vibrational microscopy, blending elements of photo-thermal infrared (PT-IR), Fourier transform (FT)-IR, and Digital Holography Microscopy (DHM). TROPHY brings these techniques to the unprecedented ultrafast timescale, where the refractive index change induced by coherent IR vibrations is probed at its peak value before thermal relaxation. TROPHY borrows from PT-IR the combination of IR vibrational excitation with visible probing for high spatial resolution, from FT-IR the use of time-domain interferometry to obtain a high spectral resolution from broadband excitation, from DHM highly sensitive and quantitative detection of the refractive index (phase) change. Combined with artificial intelligence algorithms, this technology will enable quantitative concentration imaging of molecular biomarkers with high spatial resolution, high chemical selectivity and high speed, with a transformative impact on medical research and clinics. In oncology, it will be applied to intraoperative diagnosis of tumor biopsies, providing tumor grading, staging and subtyping, and supporting complete tumor resection. It will also allow to determine the best therapeutic approach tailored to the patient and identify resistant tumor clones under targeted therapy, paving the way for precision medicine in cancer.

The European energy system is undergoing a significant transformation: decarbonization, security of supply, deployment of renewables and their integration into the market, generating significant opportunities and challenges for energy stakeholders. Despite all energy efficiency efforts, overall demand for decarbonized electricity is set to be significantly higher in 2050 than today due to the decarbonization of the heating, cooling, transport and many industrial sectors, which can only be achieved via efficient and smart electrification. Hydropower is a key technology in supporting the European pathway to a decarbonized energy system and to achieve global leadership in renewable energy generation. It consists a renewable and highly sustainable electricity resource and can supply the European power system with stability and valuable flexibility. In addition, hydropower reduces EU’s dependency on fossil imports and renders multiple extra benefits for society in the river basins such as support to irrigation, water supply and flood control. The D HYDROFLEX project will advance excellence in research on digital technology for hydropower paving the way towards more efficient, more sustainable, and more competitive hydropower plants in modern power markets. D-HYDROFLEX will develop a toolkit for digitally ‘renovating’ the existing hydroelectric power plants based on sensors, digital twins, AI algorithms, hybridization modelling (power-to-hydrogen), cloud-edge computing and image processing. The core pillars of the project will be: (i) digitalization, (i.e., digital twins for hydro dams and machinery, weather and flow forecasts, cyber-resilience), (ii) flexibility, (i.e., coordination with hydrogen, storage and VPP operation) and (iii) sustainability, (i.e., biodiversity environmental issues). Validation will take place in real hydro plants of EDF (France), TEE (Poland), PPC (Greece), TASGA (Spain) and INTEX (Romania), covering different geographical areas of Europe.

Graph-X aims at the development of a novel hardware platform based on graphene photonic integrated circuits for ultra-high speed and scalable sub-THz D- (110-170 GHz) and H-band (170-240 GHz) wireless links. The proposed technology will be the basic building block for high-speed radio back haul links, multi beam forming antennas for massive MIMO, short distance high resolution RADAR sensing. GraPh-X targets the distribution and detection of multi Gbit/s radio signals over sub-THz tunable carrier frequencies. The main outcome of GraPh-X will be a monolithic electronic and photonic chip (EPIC) that will constitute the basic building block of a completely new class of photonic/electronic antenna arrays for the next generation sub-THz communication and RADAR systems. The proposed approach will allow to overcome the technical bottlenecks of current sub-THz technology, such as increasing detrimental effect of phase noise at higher carrier frequencies, and carrier frequency stability. To reach the goal, the wafer scale graphene photonic technology is needed and must go beyond the state of the art. A new technique (HMG-Stack technology) will be developed to allow multi-stacking of graphene layers maintaining the same properties of single layer graphene. The key component of GraPh-X is a novel optoelectronic efficient frequency mixer able to mix two optical wavelengths and a high data rate electrical signal. The photonic chip will be realized using a SiN photonic platform that will integrate HMG-Stack as active material. The monolithic integration of the graphene photonic mixer with SiGe BiCMOS electronics for mmWave amplification will enable high output power and reduced footprint (<500x500µm2), matching the requirements of a single element of mmWave antenna array system.