Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Exploiting the Untapped potential of Fruit tree Wild DIVersity for Sustainable Agriculture (FRUITDIV)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Food, Bioeconomy Natural Resources, Agriculture and Environment
Theme: Bio-Environment
Destination: Biodiversity and Ecosystem Services
Action type: Research and Innovation actions
Countries involved: France,Greece,Italy,Slovenia,Serbia,Armenia,Romania,Belgium,Germany,Spain,Bosnia and Herzegovina,Sweden,Austria,North Macedonia
Call: Crop wild relatives for sustainable agriculture (HORIZON-CL6-2023-BIODIV-01, 2023)
Start date: 2024-01-01
End date: 2028-12-31
Duration (months): 60.0
Total project cost (EUR): 5998693.75
EU funding (EUR): 5998693.75
Participants: 28
Project status: SIGNED

Crop wild relatives (CWR) are wild plant taxa closely related to a crop. They represent an important source of genetic diversity for the improvement of agronomic traits. In the context of the One Health Initiative, temperate fruit trees are essential for human nutrition and health, yet CWR resources have hitherto been underused. Moreover, fruit tree long lifespan and a current production dominated by a few cultivars make them particularly vulnerable to the effects of global changes. To address this challenge, the FRUITDIV project will monitor, characterise, use, and conserve the diversity of emblematic fruit tree CWR, with a particular emphasis on Malus, Pyrus and Prunus. To better characterise the genetic and phenotypic diversity of CWR fruit trees and identify favourable traits for future introgression into cultivars, FRUITDIV will use a combination of floristic, ethnogeography and population genomics on genebanks and historical European hotspots of diversity. We will then develop new multiomics-based breeding strategies that combine marker-assisted introgression for traits of interest (e.g. resilience, resistance to pests and diseases, fruit quality) with pangenomic prediction and a reduction of CWR-associated genetic load. In addition to breeding programs, FRUITDIV will also work with networks of farmers and associations to help characterise CWR progeny in various pedo-climatic conditions in Europe. An European-wide online platform that provides genotyping and phenotyping data for free will be implemented to promote the use of CWR genitors by breeders and farmers and help disseminate plant material of interest for various usages and cultivation systems. Overall, the FRUITDIV multi-actor approach involving geneticists, forestry officers, germplasm curators, farmers and citizens, will foster the in- and ex-situ conservation of CWR and promote sustainable agricultural practices across Europe.

Keywords: fruit tree biodiversity conservation forestry phenotypic variability resilient crops breeding pangenome genetic load,genetic diversity,sustainable agriculture,cwr
Mutations in healthy tissues: a double-edged sword for tissues homeostasis (MUTAHOME)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Synergy grants
Action type: ERC actions
Countries involved: United Kingdom
Call: ERC SYNERGY GRANTS (ERC-2024-SyG, 2024)
Start date: 2025-09-01
End date: 2031-08-31
Duration (months): 72.0
Total project cost (EUR): 9808142.0
EU funding (EUR): 9808142.0
Participants: 2
Project status: SIGNED

Healthy tissues become progressively populated of clonally expanded cells that have acquired somatic mutations. Often these mutations modify cancer genes promoting cell plasticity, self-renewal, and growth, all features known to decline with ageing. Despite being pervasive across tissues and persistent throughout life, only rarely mutant cells become pathogenic. Here we hypothesise that somatic mutations enhance tissue integrity by increasing the fitness of stem/progenitor cells (SPCs) to counterbalance their age-induced decline. Increased SPC fitness at the cellular level increases the fitness at the tissue level, thus preserving the overall tissue regenerative potential. To test this hypothesis, we propose to investigate the effect of somatic mutations on SPC fitness in solid tissues and blood using complementary and synergistic approaches specific to our teams, bridging the historically distinct fields of evolutionary genetics and stem cell biology. Through the analysis of human tissues and the use of complex mouse models, we will dissect the interactions between mutant SPCs and their niche, at homeostasis and upon insults. We will determine whether the expansion of mutant SPCs over time leads to a progressive decline in SPC diversity, which eventually increases disease risk. We will adopt a progressive approach. We will first characterise the mutant clone (WP1) and its crosstalk with the niche (WP2) at homeostasis. We will then test the response of mutant clone and niche to physiological (WP3) and pathological (WP4) challenges. We will finally assess the occurrence of a multiorgan crosstalk conveyed by mutant blood on mutant solid tissues (WP5). This study will radically transform our understanding of tissue homeostasis and clarify the relationship between somatic mutations, ageing and disease. A deeper knowledge of the mechanisms that maintain the homeostatic equilibrium over time is the first step to guide intervention and disease prevention.

Keywords: somatic mutations,tissue stem cells,homeostasis
Lomustine with or without reirradiation for first progression of glioblastoma: a pragmatic randomized phase III study (LEGATO)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Mission
Theme: Cancer
Destination: Cancer
Action type: Research and Innovation actions
Countries involved: Belgium,Austria,Hungary,United Kingdom,Italy,Switzerland
Call: Pragmatic clinical trials to optimise treatments for patients with refractory cancers (HORIZON-MISS-2022-CANCER-01, 2022)
Start date: 2023-06-01
End date: 2028-05-31
Duration (months): 60.0
Total project cost (EUR): 5214733.75
EU funding (EUR): 5214733.75
Participants: 6
Project status: SIGNED

Recurrent glioblastoma is an invariably fatal refractory cancer with dismal prognosis. Patients suffer from low quality of life and very burdensome symptoms. This project is proposing an investigator-initiated phase III randomised pragmatic clinical trial. We will assess whether the addition of radiation treatment to lomustine chemotherapy has superior efficacy as compared to lomustine chemotherapy alone for treatment of patients with recurrent glioblastoma. The study will apply minimal patient eligibility criteria, and thus be as close as possible to the routine clinical setting. Cost-effectiveness analysis will be performed to investigate the economic value of adding radiation treatment to lomustine chemotherapy in different countries to support evidence-informed policy decisions. If successful, the outcomes of the project will define a new evidence-based standard of care for recurrent glioblastoma. The proposed research could lead to improved patient survival and quality of life but also improving health system sustainability. This is a multidisciplinary and multistakeholder consortium involving clinical oncologists, radiation oncologists, health economists and patient representatives. The study design was successfully discussed with patients. This action is part of the Cancer Mission cluster of projects on ‘Diagnosis and treatment.”

Keywords: glioblastoma
SAFE: Tsunami early warning System using Availableseafloor Fiber cablEs (SAFE)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Transition
Destination: Open to supporting projects in all areas of science, technology,...
Action type: EIC actions
Countries involved: Spain,France,Portugal
Call: EIC Transition Open 2022 (HORIZON-EIC-2022-TRANSITION-01, 2022)
Start date: 2022-12-01
End date: 2026-02-28
Duration (months): 39.0
Total project cost (EUR): 1162210.25
EU funding (EUR): 1162209.25
Participants: 7
Project status: SIGNED

According to the World Health Organization, more than 700 million people live in low-lying coastal areas and Small Island Developing States exposed to extreme sea-level events including tsunamis. The cost of the most reliable currently-available tsunami warning systems (based on offshore sensors) is extremely high and essentially only affordable to rich countries like the US or Japan. As such, the vast majority of all this endangered population is not yet protected by this type of tsunami warning system. Luckily, the vast majority of these coastal areas have already-available fibre-optic cables installed (for communication purposes) which could additionally serve as a key component for tsunami early warning, at a marginal extra cost. The objective of this project is to fulfill the need of protecting these communities, which often have limited resources, by providing a tsunami early warning solution based on the already-available fibre optic infrastructure. The solution provided in this project will be simple to deploy and maintain (just connecting an interrogator in the dry end of the fiber-optic cable), low cost (it will be several orders of magnitude cheaper than current systems), reliable (tsunami alerts will be confirmed by the same device using indirect measurements of sea level) and timely (the response time is expected to be faster than current systems). The main market of this solution will be official meteorological and environmental institutions of different countries responsible for issuing early warnings.

Keywords: tsunamis,submarine optical fiber cables,early warning systems,product development
Verifying Emissions of Climate Forcers (EYE-CLIMA)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Climate
Destination: Climate sciences and responses for the transformation towards climate neutrality
Action type: Research and Innovation actions
Countries involved: Norway,Austria,Greece,Germany,United Kingdom,France,Finland,Switzerland
Call: Verification and reconciliation of estimates of climate forcers (HORIZON-CL5-2022-D1-02, 2022)
Start date: 2023-01-01
End date: 2026-12-31
Duration (months): 48.0
Total project cost (EUR): 4994262.5
EU funding (EUR): 4994262.25
Participants: 17
Project status: SIGNED

The Global Stocktake, and the success of the Paris Agreement, hinges on the information nations provide about their emissions through National Greenhouse Gas Inventories (NGHGIs). Current methodologies laid-out by the IPCC for reporting emissions are generally built around the use of statistical data and emission factors. Although they are designed to be transparent, they can have significant uncertainties owing to incomplete or inaccurate information. The 2019 refinement of the IPCC Guidelines highlights the need for independent verification of NGHGIs especially using atmospheric observations. However, the technical complexity and the hitherto limited resolving power of atmospheric constraints makes it challenging for NGHGI compilers to adopt this type of verification. EYE-CLIMA will address this need for independent verification by developing observation-based methods (using both satellite remote sensing and ground-based observations) to a level of readiness where they can be used to determine emissions at national and sub-national scales and for verification of NGHGIs. The methodology involves using process-based and data-driven models to simulate GHG fluxes, first without atmospheric observations, then these fluxes are then combined with models of atmospheric transport and chemistry to assimilate atmospheric observations, which are used to correct the first flux estimates. Through engagement with stakeholders, i.e., NGHGI compilers, EYE-CLIMA, will develop flux data products for CO2 (LULUCF sector), CH4, N2O, and emissions data of F-gases (SF6, HFC-23, HFC-143a, HFC-125, HFC-134a, HFC-32) and black carbon (BC), which will be tailored to their needs. The fluxes will be attributed to natural versus anthropogenic sources, and for the latter, to source sectors that can be compared with groups of IPCC sectors in NGHGIs. The methodology for the atmospheric inversions and how to use these for verification of NGHGIs will be described in best practice guidelines.

Keywords: greenhouse gases,inverse modelling,satellite remote sensing,methane,nitrous oxide,carbon dioxide,f gases,black carbon,emissions,inventories,global stocktake,verification,climate forcers
Digital Twin for Predictive Lung Health (WeResp)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Other destinations
Action type: EIC actions
Countries involved: Italy
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

Respiratory diseases, including chronic obstructive pulmonary disease (COPD), lung cancer, tuberculosis, and lower respiratory tract infections (like pneumonia), are major global health issues. In Europe, these conditions account for one in ten deaths, while globally, one in six deaths is attributed to these lung diseases. Within the EU, respiratory diseases cause 15% of deaths: 5.8% from lung cancer, 3.5% from pneumonia, and 3.4% from COPD. Globally, about 700M people suffer from respiratory diseases, highlighting the critical need for better diagnostics and treatment. MedLea, an innovative healthcare technology firm based in Rome, Italy, addresses this need by developing advanced digital solutions for lung health. Using state-of-the-art computer science and AI, MedLea analyzes radiological and clinical data to enhance respiratory disease diagnosis and treatment. Its flagship platform, WeResp, offers a comprehensive digital twin of the lung, enabling real-time predictive diagnostics that surpass conventional methods. WeResp predicts lung surgery outcomes by providing detailed pre-operative evaluations of a patient’s respiratory function, optimizing interventional strategies. MEDLEA has raised €600k in seed funding and received €200k in grants from Rome Technopole and LazioInnova. The company is seeking €5.5M to advance its Artificial Intelligence-based digital twin from TRL5-6 to TRL 8 and prepare for commercialiSation. Key milestones include clinical trials and regulatory approvals in Europe, with North American expansion planned subsequently. By 2029, MEDLEA projects €50M in revenues, positioning itself within a global respiratory diagnostics market expected to reach $8.2B by 2029, and a respiratory disease testing market estimated to grow from $14B in 2022 at a CAGR of 6.5%. Together, these markets constitute a total addressable market of $25B by 2029, demonstrating WeResp’s potential to transform respiratory care globally.

Keywords: predictive diagnostics,respiratory diseases,lung health,ai healthcare,digital twin,respiratory diagnostics market,pulmonary treatment,copd,medtech
The Effect of Large Language Models (LLM) on Engagement in Online Peer Production Communities (LLMinPeerProduction)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Individuals, Markets and Organisations
Panel description: Economics, finance , management
Countries involved: Israel
Call: ERC CONSOLIDATOR GRANTS (ERC-2024-COG, 2024)
Start date: 2025-06-01
End date: 2030-05-31
Duration (months): 60.0
Total project cost (EUR): 1853125.0
EU funding (EUR): 1853125.0
Participants: 1
Project status: SIGNED

This project explores the impact of generative AI technologies, specifically large language models (LLMs) on online peer production content communities, such as Wikipedia and GitHub. These communities depend heavily on the existence of a vibrant community and its members’ wiliness to contribute voluntarily and the introduction of LLM applications is expected to have transformative effect on the engagement dynamics within those production communities. The proposal is structured around three primary aims: (1) the effects of LLM applications on individuals' willingness to engage and contribute to online communities, focusing on the quantity, type and quality of contributions; (2) the impact of LLM applications on communities’ openness, inclusiveness and trans-community mobility; and (3) the effects of LLM applications on users' demand for community-produced content, analyzing overall demand shifts and changes in content consumption patterns. The project is based on an empirical analysis of publicly available data collected via APIs, as well as field experiments conducted in collaboration with existing online platforms. We are currently witnessing an explosion of new LLM applications over time, which are already disrupting various content communities and will contribute to do so during the lifetime of this project. We will collect longitudinal data on content communities in different domains and use the introduction of different LLM applications as quasi-experiments. We also present preliminary results, based on a quasi-experiment that leverages the selective rollout of GitHub Copilot, a ground-breaking coding-focused LLM, in October 2021. This project is pivotal in gaining in-depth understanding of the evolving landscape of digital collaboration and content creation. It aims to provide actionable insights into the role of advanced LLM in shaping online communities, guiding future technological developments, policy-making, and community management strategies.

Keywords: generative ai,llm,ai,engagement,online content,online communities,open source
New Frontiers for Computational Solid Mechanics based on eXtended Level Set representation. Applications to damage mechanics, contact mechanics and stress analysis. (XLS)

Framework: FP7 — Ideas
Programme: European Research Council
Theme: European research council
Destination: Advanced grants
Action type: Support for frontier research (ERC)
Panel: Products and process engineering
Countries involved: France
Call:  (ERC-2011-ADG_20110209, 2011)
Start date: 2012-04-01
End date: 2017-03-31
Duration (months): 60.0
Total project cost (EUR): 1957909.0
EU funding (EUR): 1957909.0
Participants: 1
Project status: CLOSED

The present project intends to introduce new uses of the level set representation of surfaces in order to develop simulation methods able to solve problem currently unsatisfactorily addressed. Three main applications are targeted: localisation phenomena and very complex cracking patterns, precise determination of stresses around contact zones and, finally, introduction in reasonably coarse meshes of the mechanical influence of geometrically small but vitally important reinforcement for the structure safety (weld points, cables, fillets, …). We plan to address them with a unified technology, parts of which are already being incorporated in codes worldwide because of the contribution of the PI and colleagues in the past ten years. The PI is a specialist in the field of computational mechanics and has been the founder and among the major promoters of the eXtended Finite Element Method (X-FEM), now widely used for crack growth and material interfaces modelling. With the X-FEM, internal or external mechanical boundaries do not need to be explicitly meshed and may conveniently be stored as level sets. The project proposes new algorithm based an original and extended use of the level set concept in conjunction with the X-FEM : *Thick Level Set (TLS): We consider that the mechanical variable responsible for the localization of deformation in a softening behaviour (say damage for instance) is not local but tied to the movement of a degradation front located by a level set. *Inequality Level Set (ILS): We rephrase variational inequality formulation (such as contact) as shape optimization. The shape of the active contact zone is sought and represented by a level set. For any given level set location, a variational equality is solved and a sensitivity analysis is performed to update the level set location. *Subgrid Level Set (SLS): The structural features (cables, fillets, …) are represented on a subgrid different from the mesh used to perform the computation.

Keywords: extended finite element method level set damage mechanics fracture mechanics contact mechanics stress analysis
Flow Machines: Interacting with Style (FLOWMACHINES)

Framework: FP7 — Ideas
Programme: European Research Council
Theme: European research council
Destination: Advanced grants
Action type: Support for frontier research (ERC)
Panel: ERC Advanced Grant Interdisciplinary Panel
Countries involved: France,United Kingdom
Call:  (ERC-2011-ADG_20110209, 2011)
Start date: 2012-08-01
End date: 2017-07-31
Duration (months): 60.0
Total project cost (EUR): 2240120.0
EU funding (EUR): 2240120.0
Participants: 2
Project status: CLOSED

Content creation is a fundamental activity for developing identities in modern individuals. Yet creativity is hardly addressed by computer science. This project addresses the issue of content creation from the perspective of Flow machines. Flow machines are interactive systems that learn how to generate content, text or music, in the user’s style. Thanks to controlled generation mechanisms, the user can then steer the machine to generate content that fits with their intentions. Flow interactions induce a multiplicative effect that boosts creativity and prompts the user to reflect on their own style. This vision stems from the success stories of several computer-assisted musical systems that showed how interactive dialogs with self-learning interactions provoke flow states. To enables full control of stylistic generation, the scientific challenge is the reification of style as a flexible texture. This challenge will be addressed by pursuing three original directions in the fields of statistical learning and combinatorial optimization: 1) the formulation of Markov-based generation as a constraint problem, 2) the development of feature generation techniques for feeding machine learning algorithms and 3) the development of techniques to transform descriptors into controllers. Two large-scale studies will be conducted with well-known creators using these Flow machines, during which the whole creation process will be recorded, stored, and analyzed, providing the first complete chronicles of professional-level artifacts. The artifacts, a music album and a novel, will be published in their respective ecosystems, and the reaction of the audience will be measured and analyzed to further assess the impact of Flow machines on creation. The technologies developed and the pilot studies will serve as pioneering experiments to turn Flow machines into a field of study and explore other domains of creation.

Keywords: style learning,content generation,music,text generation,creativity studies