Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
MEDICALLY RECOGNIZED, EVIDENCE-BASED PATIENT MENTAL HEALTH JOURNEY MANAGEMENT PLATFORM POWERED BY DATA SCIENCE (Predictix)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Other destinations
Action type: EIC actions
Countries involved: Israel
Call: EIC Accelerator 2024 - Short application (HORIZON-EIC-2024-ACCELERATOR-01, 2024)
Duration (months): 24.0
Total project cost (EUR): 2500000.0
EU funding (EUR): 2500000.0
Participants: 1
Project status: SIGNED

The World Health Organization highlights mental disorders, especially depression, as a major global public health issue due to their high prevalence, burden of disease, and impact on disability. The European Commission (EC) and WHO emphasize the need for equitable access to mental health services without financial barriers, advocating for treatments that balance efficiency with financial fairness. Personalized medicine has emerged as the most rapid, cost-effective, and optimized approach to treating depression. Taliaz, established in 2012 by Dr. Dekel Taliaz and Oren Taliaz, is at the forefront of this movement, blending data science and expertise in central nervous system disorders with genetic sequencing and machine learning technologies. Our mission is to redefine personalized medicine, enhancing the quality of life for individuals suffering from depression. The Taliaz Predictix™ platform, supported by the SME Instrument in 2020, exemplifies this vision. Initially focusing on medical practitioners, it provides them with up-to-date, precise knowledge on each case, enhancing patient care and generating economic benefits. This platform is noted for improving the delivery of existing treatments, a key factor in increasing remission rates. Our current project expands this focus to directly empower patients, creating a comprehensive, end-to-end software solution. This patient-centric approach combines with our existing practitioner-focused system to form a unified, data-rich platform. This integration not only enhances patient care but also feeds back valuable insights into the system, optimizing both ends of the treatment spectrum. The new solution offers faster, more cost-effective, evidence-based treatment, significantly expanding access to crucial care for millions more patients. This holistic approach embodies Taliaz's commitment to setting a new standard in personalized medicine for depression treatment.

Keywords: depression predictive analysis antidepressants artificial intelligence machine learning
TECHNOLOGY FOR EUROPEAN INDEPENDENCE IN SPACE IMAGE SENSOR (TENSIS)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Digital, Industry and Space
Theme: Space
Destination: Open Strategic Autonomy in Developing, Deploying and Using Global Space-Based Infrastructures, Services, Applications and Data
Action type: Research and Innovation actions
Countries involved: France,Belgium,Spain
Call: Space technologies for European non-dependence and competitiveness (HORIZON-CL4-2024-SPACE-01, 2024)
Start date: 2025-01-01
End date: 2027-12-31
Duration (months): 36.0
Total project cost (EUR): 4049287.82
EU funding (EUR): 2999530.48
Participants: 5
Project status: SIGNED

The objective of the Technology for European iNdependence in Space Image Sensors (TENSIS) project is to develop, manufacture, and validate a scalable, large-area, high-resolution, radiation tolerant, customizable, and affordable, 180nm CMOS detector, with the key technical characteristics of a 24K x 16K format matrix of 400MPixels. The targeted pixel pitch is 3.5µm to keep the overall matrix manufacturable with the proposed X-Fab 0.18µm CMOS Image Sensor process, and with high Full Well Capacity. Overall pixel electro-optical performances including MTF and spectral features will be enhanced by the design and implementation of specific optical stack in Back Side Illumination configuration, combining RGB&NIR filters with flat microlenses. As a by product of the design of the 24K x 16K detector, a 4K x 4K detector will also be designed and manufactured by Te2v in FSI then BSI configuration. Te2v will package produced dies in PCB package and deliver them to Airbus Defence and Space in Toulouse for detailed functional electro-optical characterization. Before this characterization test campaign, Airbus will develop dedicated test means including proximity boards both matching with the 24K x 16K and the 4K x 4K detectors, in close cooperation with Tev. A group of 4K x 4K BSI detectors initially electro-optically characterized by Airbus will then be delivered to ALTER, at Séville to be submitted to dedicated environmental tests, including TID, TNID and heavy ions radiation campaigns as well as burn-in and power on/off cycling tests. Thanks to this test campaign, the 4K x 4K detector die will reach TRL7 by the end of the project . The project will further aim at creating a strategic competitive advantage for the European partners, prime and equipment manufacturers and reduce the dependence on critical technologies and capabilities from outside EU. It will rely on the X-Fab foundry, which is key for the TENSIS project.
Unraveling the regulatory code of gene expression in plants (multiCODE)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Integrative B iology : from Genes and Genomes to S ystems
Panel description: For all organis ms: Genetics, epigenetics, genomics  and other ‘omics studies , bioinformatics, systems biology , genetic diseases , gene editing, innovative methods and modelling,  ‘omics for personalised  medicine
Countries involved: Belgium
Call: ERC ADVANCED GRANTS (ERC-2024-ADG, 2024)
Start date: 2025-11-01
End date: 2030-10-31
Duration (months): 60.0
Total project cost (EUR): 2498937.0
EU funding (EUR): 2498937.0
Participants: 1
Project status: SIGNED

Multicellular plants control the fine-grained expression of thousands of genes during growth, development, and in response to external stimuli. While gene expression varies strongly between different organs and tissues, how this specificity is encoded in the genome is largely unknown. Although various regulatory DNA sequences and sequence-specific transcription factors controlling diverse transcriptional responses have been described, the combinatorial nature of the regulatory code makes the application of rational approaches to learn and predict context-specific gene expression challenging. This is a major obstacle towards engineering plant promoters with predictable and tuneable expression. multiCODE’s aim is uniting single-cell genomics, explainable artificial intelligence (xAI), and synthetic promoter engineering, to efficiently learn and validate regulatory sequences controlling gene expression in plants. Based on high-resolution single-cell gene expression profiling in the model Arabidopsis and the crop Brassica rapa, xAI models will be built to predict gene expression in leaves under control and stress conditions while at the same time identifying the underlying regulatory DNA sequences and syntax. The power of these predictive models will be validated by exploiting inter- and intra-species sequence variation, revealing the evolutionary conservation of the regulatory code. Furthermore, a novel multi-tier synthetic promoter engineering approach will be used to experimentally validate, directly in plants, the learned regulatory code in a high-throughput manner. By combining the power of advanced computational and experimental methods, I will identify novel regulatory sequences and design novel synthetic promoters controlling gene expression in complex cellular contexts. This represents a crucial step towards a comprehensive understanding of gene regulation and will significantly contribute to developing stress-resilient crops.

Keywords: gene regulation,synthetic promoters,plant biotechnology,deep learning,single cell genomics
COMPUTER-AIDED EFFECTIVE FRACTURE RISK STRATIFICATION OF PATIENTS WITH VERTEBRAL METASTASES FOR PERSONALISED TREATMENT THROUGH ROBUST COMPUTATIONAL MODELS VALIDATED IN CLINICAL SETTINGS (METASTRA)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Health
Theme: Health
Destination: Unlocking the full potential of new tools, technologies and digital solutions for a healthy society
Action type: Research and Innovation actions
Countries involved: Italy,Germany,Switzerland,United Kingdom,Hungary,Spain,Netherlands,France,Croatia
Call: Computational models for new patient stratification strategies (HORIZON-HLTH-2022-TOOL-12-two-stage, 2022)
Start date: 2023-07-01
End date: 2028-06-30
Duration (months): 60.0
Total project cost (EUR): 5087742.5
EU funding (EUR): 5087742.5
Participants: 16
Project status: SIGNED

Cancer patients (2.7M in Europe) with a positive prognosis are exposed to a high incidence of secondary tumours (≈1M). Bone metastases spread to the spine in 30-70% cases, reducing the load bearing capacity of the vertebrae and triggering fracture in 30% cases. Clinicians have only two options: either operate to stabilise the spine, or leave the patient exposed to a high fracture risk. The decision is highly subjective and can either lead to unnecessary surgery, or a fracture significantly affecting the quality of life and cancer treatment. The standard-of-care to classify patients with vertebral metastasis are scoring systems based on radiographic images, with little consideration of the local biomechanics. Current scoring systems are unable to establish an indication for surgery in around 60% of cases. Thus, there is an unmet need to accurately and timely quantify the risk of fracture to improve patient stratification and identify the best personalised treatment. This interdisciplinary project will develop Artificial Intelligence (AI)- and Physiology-based (VPH) biomechanical computational models to stratify patients with spine metastasis who are at high risk of fracture and to identify the best personalised surgical treatment. After rigorous model training with clinical (2000 retrospective cases) and biomechanical (120 ex vivo specimens) data, the new approach will be tested in a multicentric prospective observational study (200 patients). The models will be combined in a decision support system (DSS) enabling clinicians to successfully stratify metastatic patients. The models and the DSS will be designed so as to be suitable for regulatory requirements and future exploitation. METASTRA will propose new guidelines for the stratification and management of metastatic patients. METASTRA approach is expected to cut the uncertain diagnoses from the current 60% down to 20% of cases. This will reduce patient suffering, and allow cutting expenditure by 2.4B€/year.

Keywords: patient stratification explainable artificial intelligence fracture risk vertebral metastasis data driven model mechanistic model image analysis validation decision support system guidelines
INnovative oFfshore wInd techNologies In deep waTErs (INFINITE)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Energy
Destination: Sustainable, secure and competitive energy supply
Action type: Innovation actions
Countries involved: Spain,United Kingdom,Greece,France,Portugal
Call: Innovation on floating wind energy deployment optimized for deep waters and different sea basins (Mediterranean Sea, Black Sea, Baltic Sea, North-east Atlantic Ocean) (HORIZON-CL5-2021-D3-03, 2021)
Start date: 2022-11-01
End date: 2025-05-31
Duration (months): 31.0
Total project cost (EUR): 22398250.0
EU funding (EUR): 15455944.89
Participants: 10
Project status: TERMINATED

The INFINITE project demonstrates a 4.8 MW floating offshore wind system at 100m water depth with two key technology innovations. The first is a disruptive and environment-friendly concrete tension leg platform anchored with an innovative tendon-based mooring system. The platform is designed to work with commercially available WTGs and is scalable, modular and self-installing, showing a vast potential for industrialisation. The second is an innovative aluminium dynamic cable design that is safer, lighter, cheaper and allows for more standardisation in O&M. The demonstrator makes use of a cost optimised O&M strategy that increases accessibility and turbine availability. Moreover, best practices for value co-creation with local stakeholders are applied leading to increased public acceptance of offshore wind developments and an improved Maritime Spatial Planning. The innovations result in an LCOE of 85.3 EUR/MWh at project end and set the path to achieve 43.3 EUR/MWh by 2030. An LCA of the technology innovations developed and an industrial roadmap bringing together innovation needs, supply chain readiness and policy frameworks to allow mass production and deployment complement the project activities.

Keywords: floating offshore wind,tension leg platform,dynamic cable,demonstration,oandm,local stakeholders
BIOMAT4CAST - PETRU PONI INSTITUTE OF MACROMOLECULAR CHEMISTRY MULTI-SCALE IN SILICO LABORATORY FOR COMPLEX AND SMART BIOMATERIALS (BioMat4CAST)

Framework: Horizon Europe — Widening Participation and Strengthening the European Research Area
Programme: Widening participation and spreading excellence
Theme: Attracting and mobilising the best talents
Destination: Attracting and mobilising the best talents
Action type: Coordination and support actions
Countries involved: Romania
Call: ERA Chairs (HORIZON-WIDERA-2022-TALENTS-01, 2022)
Start date: 2022-12-01
End date: 2027-11-30
Duration (months): 60.0
Total project cost (EUR): 2500000.0
EU funding (EUR): 2500000.0
Participants: 1
Project status: SIGNED

The strategic objective of the BioMat4CAST project is to implement a structural change within in the scientific management paradigm of “Petru Poni” Institute of Macromolecular Chemistry (ICMPP), Iasi, Romania, by setting up a competitive research group in the field of computational chemistry under the supervision of an outstanding researcher manager in the field of computational chemistry, Prof Aatto Laaksonen (Arrhenius Laboratory, Stockholm University, Sweden). The objectives of the BioMat4CAST project addresses directly to the targets of the Horizon Europe program: "to support research organisations to attract outstanding researcher and/or innovator in the chosen scientific domain thus to achieve excellence on a sustainable basis". The BioMat4CAST project results matches the Destination 2: Attracting and Mobilising the Best Talents of the Horizon Europe program. The BioMat4CAST project will raise the RTD standards of ICMPP – as the first NE Romanian Region research center having a critical mass enabling it to spread excellence in the specific field of computational chemistry able to link the Romanian computational school to the World Association of Theoretical and Computational Chemists through on-going cooperation, partnership and shearing of know-how/technical expertise. The ambition of BioMat4CAST project is to become, after the project ends, an example of good practice and a model of knowledge sharing.

Keywords: complex biological material,high performance computing,machine learning,computational biology,in silico design,drug gene transport and delivery
Design, synthesis and optimization of Fe(III) based porphyrins for use as next-generation MRI probes (PorphIRON)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Postdoctoral Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Chemistry
Countries involved: France
Call: MSCA Postdoctoral Fellowships 2023 (HORIZON-MSCA-2023-PF-01, 2023)
Start date: 2025-09-01
End date: 2027-08-31
Duration (months): 24.0
Total project cost (EUR): 195914.88
EU funding (EUR): 195914.88
Participants: 1
Project status: SIGNED

Magnetic resonance imaging (MRI) plays a pivotal role in early diagnosis and therapy follow-up of many diseases. The substitution of current Gd-based MRI contrast agents (CA), which raise both safety and environmental concerns, is one of today’s most important challenges in imaging-related chemistry. High complex stabilities and inertness attainable with Fe(III) make it a high potential alternative to Gd. Porphyrins have met great clinical success, exhibit biocompatibility, stable metal complexes and easy structural modulation., but formation of μ-oxo-dimers in Fe(III) porphyrins (FePo) reduces their relaxation efficacy. PorphIRON will tackle these limitations and develop new families of biocompatible FePo fine-tuned for MRI use. PorphIRON will focus on structural optimization to i) maintain the oxidation and the high spin state of Fe(III), yielding relaxation effect comparable to clinical CA, and ii) adjust physicochemical properties to preserve biocompatibility. To achieve this, PorphIRON will rely on a synergic combination of the ER’s experience in porphyrin chemistry and computational studies with that of the supervisor in MRI CA development, and an iterative approach with: i) initial synthesis of newly designed FePo, followed by their physicochemical and biological evaluation; ii) computational rationalization of the experimental results and prediction of new optimized structures; iii) second-round optimized FePo synthesis and characterization. PorphIRON will deliver fully characterized, stable, biocompatible and high efficacy FePos with preliminary preclinical in vivo MRI validation, thereby actively contributing to the implementation of next-generation non-toxic CA in clinic. PorphIRON will strengthen ER’s experience in organic synthesis, computational chemistry, while significantly expanding his knowledge to CA development and preclinical MRI. It will expand his career perspectives and open new opportunities in the emerging field of theragnostics.

Keywords: mri,fe iii porphyrins,contrast agents
Operational Heat-Health-Social Early Warning System (HHS-EWS)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Countries involved: Spain
Call: ERC PROOF OF CONCEPT GRANTS1 (ERC-2022-POC1, 2022)
Start date: 2022-10-01
End date: 2024-03-31
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: CLOSED

Ambient temperatures are associated with more than 5 million annual deaths globally, 300,000 of which in Western Europe alone. Many European countries have implemented heat early warning systems, but they are generally based on temperature thresholds from weather forecasts that do not account for the inequalities in vulnerability of the exposed populations. This ERC-funded project will create the first operational Heat-Health-Social Early Warning System (HHS-EWS) by integrating weather forecasting, environmental epidemiology and the social drivers of vulnerability. Towards this aim, I will calibrate epidemiological models to transform bias-corrected weather forecasts into predictions of health outcomes. To validate the path from ground-breaking research to innovation, I will analyse and compare the spatiotemporal scales of predictability, and determine if the epidemiological models reduce or suppress the window of predictability of the weather forecasts. HHS-EWS will develop an operational, fit-for-purpose early warning system representing the health impacts of environmental temperatures, which will better inform potential end-users such as public health agencies to activate emergency plans directly targeting vulnerable groups.
dAta driveN cosT Effective 5G iNtegrated cNs As A sErvice (ANTENNAE)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Public-private partnerships
Destination: Single European Sky Air Traffic Management Research
Action type: Research and Innovation actions
Partnership: SESAR (JU-JTI)
Countries involved: Ireland,Finland,Germany,Spain
Call: ATM application-oriented research for connected and automated ATM (HORIZON-SESAR-2023-DES-ER-02, 2023)
Start date: 2024-09-01
End date: 2027-02-28
Duration (months): 30.0
Total project cost (EUR): 1999706.5
EU funding (EUR): 1999706.0
Participants: 4
Project status: SIGNED

Emerging U-Space and UAM concepts envisage a new generation of small, highly manoeuvrable, and highly automated aircraft operating at low altitude, alongside existing helicopter and general aviation users. Coordination & deconfliction of large numbers of such aircraft operating in primarily urban environments requires new Communications, Navigation, and Surveillance (CNS) infrastructure to ensure safety of passengers, the public, and other stakeholders while supporting complex low-altitude operations. Leveraging the scalable waveforms of 5G New Radio (NR), modern IP-based software-defined networking, and distributed computing capabilities, ANTENNAE (dAta driveN cosT-Effective 5G iNtegrated CommuNication, Navigation, and Surveillance (CNS)-as-A-ServicE) proposes a flexible and resilient integrated CNS-as-a-Service model supporting both low-altitude piloted and U-Space operations, and builds upon the mature and growing family of 3GPP 5G standards including system architecture, deployment models, and commercial implementations. ANTENNAE will apply advanced modelling to validate the applicability of 3GPP standards to deliver low-altitude CNS functions, including the full range of aeronautical data services (through 5G eMBB & URLLC), navigation (through 5G-based A-PNT), and surveillance (through emerging A-SUR and joint communication & sensing (JCS) concepts). ANTENNAE will examine the architectural benefits of established 5G deployment models for providing distributed data services, network resilience, and scalability. ANTENNAE will also look to the future of the 3GPP standards by examining technologies under development in the 3GPP working groups for beyond 5G ("6G”) services. Finally, ANTENNAE will conduct a rigorous quantitative techno-economic analysis informed by these engineering models to assess the financial feasibility of deploying a scalable integrated CNS-as-a-Service through a 5G access network, with comparison to alternative technological approaches.
Ultrafast 2.1µm Holmium Lasers for GHz ablation (Giga2u)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Countries involved: Germany
Call: ERC PROOF OF CONCEPT GRANTS (ERC-2023-POC, 2023)
Start date: 2023-10-01
End date: 2025-03-31
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: CLOSED

Femtosecond lasers are ubiquitous tools in science and industry, their widespread applications aided by their increasingly wide commercial availability. However, most femtosecond lasers operate at a wavelength of around 1µm, which hinders many potential new uses. In the last years, there is an increased interest in longer wavelengths, in particular the wavelength region between 2-3 µm, however only very few laser systems exist in the laser market at these wavelengths. This wavelength region has large potential for many fields in material processing, laser surgery, gas sensing; as well as in a variety of scientific applications, such as spectroscopy. In this project, we propose to explore the market potential of a femtosecond laser system based on a new laser gain material demonstrated in the context of our ERC St.G Project, that operates in this wavelength region with record high average power and short pulse durations. The unique parameter set this laser offers at this wavelength has the potential to disrupt material processing applications; and this project aims at validating the potential of the laser for this market.