Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : HealthAEye (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Université de médecine de Vienne — Pays : Autriche — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999809.0 €

The gift of longevity carries with it a host of complexities in ensuring that lifespans are not only prolonged but also healthy and functional. Effective health strategies must be deeply embedded in a framework that provides continuous access to healthcare services. This involves persistent monitoring of both health and illness. The human retina stands out as an invaluable window into one’s health, shown to reflect not only ocular diseases but also aging, neurodegeneration, and heart function. High-end optical imaging devices such as optical coherence tomography (OCT) provide clinicians with remarkably clear pictures of the living retina. However, they are not widely accessible and portable for personal use. With the drastic improvements in smartphone optical and sensory capabilities, taking diagnostic-quality snapshots of the eye fundus is becoming a viable option. This poses a paramount opportunity to turn the smartphone imaging with the support of artificial intelligence (AI) into a powerful health monitoring tool.  The proposed research will advance the AI methodology and provide a proof-of-concept in retinal disease monitoring. It is based on the hypothesis that the disease-specific biomarkers visible on gold-standard 3D OCT imaging are recoverable with AI from 2D portable fundus imaging provided by smartphones. In a unique interdisciplinary setting, it aims to (i) build multimodal foundation AI models based on a large amount of available retrospective imaging data, (ii) advance the AI methodology to distill across the imaging modalities the knowledge accrued by the models, and (iii) in a clinical study demonstrate that AI models operating on smartphone-based fundus images are comparable to their high-end optical imaging counterparts across diagnostic tasks. The work will lead to a potentially disruptive, accessible and scalable technology for monitoring of retinal changes and disease activity having a lasting impact on patients and public health.

Mots-clés : artificial intelligence,medical imaging,image computing,retina,optical coherence tomography,fundus
Projet ERC : DiabetManager (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Université de médecine de Vienne — Pays : Autriche — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999201.0 €

Diabetes is a major world health problem, approaching epidemic proportions and causing long term health damages due to inappropriate blood glucose regulation. Pharmacological therapies are limited by numerous side effects (as renal insufficiency or heart failure) and dose management issues. Their efficacy is also hindered by often problematic, patients’ compliance. This results in an unmet need for automatized, closed-loop glucose metabolism regulation, adaptable to variable patients’ conditions. Vagus nerve (VN) is a major component of the autonomous nervous system innervating internal organs, and its electrical stimulation showed preliminary results in modulating glucose metabolism. However, underlying mechanisms remain unknown, posing a high-risk to its feasibility, and could lead to indiscriminate stimulation causing multiple side effects (compromising breathing or heart rate). Fine glucose regulation cannot be achieved without better understanding of nerve-implant interaction and circuits stimulated by VNS. I aim to fill present scientific gaps by developing the first VNS neuroprosthesis for personalized closed-loop-regulation of glucose levels in diabetics. This results in a significant challenge: defining stimulation policies able to selectively block or activate specific neural pathways, while counteracting metabolic shifts and implant-nerve alterations. To that aim I will exploit models-driven interface design and AI-based stimulation policies. Animal experiments will investigate the physiological responses to optimal policies, maximising glucose regulation while minimizing adverse events, inspecting both cervical and abdominal VNS. Optimal VNS will be validated in human trials achieving device assessment in patients. DiabetManager will shed light on the underlying mechanisms of VNS-mediated glucose metabolism control, while providing versatile VN in-silico model, unprecedent AI-architecture for its adaptive stimulation, and disruptive health treatment.

Mots-clés : neurostimulation,computational models,vagus nerve,ai
Projet ERC : MATRICs (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Université de Milan-Bicocca — Pays : Italie — Rôle : PI
Acronyme de l'entité : UniMiB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999968.75 €

By preventing all the Earth’s carbon to be released into the oceans and atmosphere or to be stored within rocks, the geological carbon cycle acts as a global long term thermostat, linking the evolution of climate and life to plate tectonics. The uncertainty regarding CO2 emissions from continental magmatic arcs, a primary natural input of carbon into the ocean and atmosphere, is currently the greatest limitation to our quantitative understanding of the geological carbon cycle. The high-gain target of MATRICs is to reconstruct the time history of magmatic CO2 emissions from the Neo-Tethyan convergent plate margin and its critical contribution to early Cenozoic climate changes. This ambitious goal will be achieved by iterative geologic data acquisition and state-of-the-art numerical modeling.  I propose to couple three established techniques to assess temporal changes in the source and amount of CO2 emissions from the Neo-Tethyan magmatic arc and assess their effects on early Cenozoic climate: (1) studies of melt inclusions, pockets of melts preserved within magmatic rocks, (2) analyses of trace elements concentrations (e.g., Hg, Te) within the sedimentary record, and (3) numerical petro-thermo-mechanical geodynamic and climate carbon cycle modeling. Undertaking this multi-disciplinary and ground-breaking project is now possible, owing to my success in using numerical modeling and diverse geological data to unravel the interactions between tectonics and climate accounting for magmatism.  Engaging in a high-gain win-win challenge, MATRICs will either overturn or finally validate untested paradigms about the tectonic forcing of Cenozoic climate. In either case, the knowledge produced about the geological carbon cycle will allow us to better assess the drivers of natural climate variability and, by comparison, the climatic consequences of current anthropic emissions.

Mots-clés : geological carbon cycle,neo tethyan plate margin,continental collision subduction,data modeling approach
Projet ERC : BraveNewWord (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Compl exity
Entité participante : Université de Milan-Bicocca — Pays : Italie — Rôle : PI
Acronyme de l'entité : UniMiB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1387005.0 €

We learn new words almost on a daily basis: as adults, a new element is introduced in our vocabulary every other day. With new words, we also learn about new objects and ideas - in most cases new words are not simply additional labels to be applied to familiar objects: they connote meanings that are unknown to the speaker of a language. However, when we experience, as adults, an unfamiliar word, typically its referent is not immediately available in the same context. How then can language, by itself, constitute such a reliable instrument for the acquisition of novel meanings? What do we exploit to induce new meanings on the basis of an unfamiliar sequence of sounds or graphical elements? BraveNewWord addresses these questions in an innovative multidisciplinary perspective, combining cutting-edge proposals from computational linguistics and empirical investigation techniques from experimental psychology and cognitive neuroscience. BraveNewWord posits three main sources for lexically-driven meaning acquisition: linguistic context, word structure, form-meaning mapping. The project advances a computational framework that models these mechanisms through data-driven, psychologically plausible distributional systems trained on examples of natural language usage. The quantitative characterizations and algorithmic definitions offered by these models constitute, in turn, the basis for BraveNewWord large-scale empirical investigation, involving both behavioral (reaction times, mouse-tracking trajectories, diachronic language changes) and neuroscience data (event-related potentials, neuroimaging). With its innovative perspective and advanced computational and empirical approach, BraveNewWord will constitute a non-incremental contribution to understanding how human speakers use new lexical information as a mean for enriching their semantic system, and provide a ground-breaking perspective on the cognitive processes relating language and thought.

Mots-clés : distributional semantic models semantic representations word processing lexical morphology form meaning mapping adult novel word learning cognitive modelling
Projet ERC : OSPAPIK (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Université de Bretagne Occidentale — Pays : France — Rôle : PI
Acronyme de l'entité : UBO
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1996646.0 €

What is the role of contemporary Indigenous artists, and non-Indigenous artists engaging with Indigenous people knowledges, in making ocean and space pollution visible? How are Indigenous knowledges, know-how, histories, and memories mobilised to address current environmental crises? Strongly grounded in anthropology and the arts, OSPAPIK is both pluri- and interdisciplinary. It offers innovative approaches to pollution, Indigenous knowledges, and the arts through its systematic focus on materiality and on the relationship that people have with waste. It intends to develop novel, critical, and ethnographically-informed analyses of the socio-environmental life of waste by investigating how creative and artistic expressions allow the artists themselves, scientists, expedition project organisers, and audiences to better understand how marine ecosystems and (outer) space are impacted by pollution. It will also interrogate whether the study of arts provides means to better understand the different professional sectors and actors involved in depolluting. The whole project is designed to rigorously analyse conjointly 1/ the motifs and patterns used by Indigenous artists and non-Indigenous artists collaborating with Indigenous people and 2/ the ways these artists use ocean and outer space waste and debris as artistic material. It focuses on the ocean and space, where pollution can be invisible to the eyes, and which are spaces that are often deemed sacred according to Indigenous cosmogonies, but have been perceived, according to dominant Western modern conceptions, as uninhabited. The project aims to study comparatively affective, professional, sensorial, and historical relationships to marine, nuclear, and space debris and waste, through an analysis of Indigenous artistic practices and non-Indigenous practices engaging with Indigenous knowledges.

Mots-clés : indigenous arts and literatures,contemporary art,indigenous knowledges,ocean pollution,space debris,light pollution,nuclear waste,plastics,persistant organic pollutants,pacific islands,oceania,australia,french guiana,european spaceport,collaboration,museum,environmental protection,transmission,stories,creativity
Projet ERC : MULTIMODAL (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Université de Tampere — Pays : Finlande — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1998760.0 €

WHAT: MULTIMODAL will develop sensory-motorized material systems that perceive several coupled environmental stimuli and respond to a combination of these via controlled motor functions, shape-change or locomotion. The sensory-motorized materials will be “trained” to strengthen upon repetitive action, they can “heal” upon injury, and mechanically adapt to different environments. They will be utilized in the design of soft robots with autonomous and interactive functions.  HOW: We will utilize shape-changing liquid crystal networks (LCNs) that undergo controlled untethered motions in response to photochemical, (photo)thermal, and humidity-triggered activation. Coupling between these stimuli will allow for gated control strategies over the shape changes. I expect that the gated control strategies, in combination with stimuli-induced diffusion from surface to bulk of the LCN, will enable advanced robotic functionalities. The diffusion process will be used for supramolecular crosslinking and formation of interpenetrated dynamic polymer networks with the LCN, to allow for trainable gaiting for versatile locomotion control. We will also make mechanically adaptable amphibious grippers for autonomous object recognition.  WHY: Technological disruptions are often due to new materials and fabrication technologies. Paradigm changes on how materials are perceived have profound effects on our society, well-being, and the ways we see the world. Here, we strive for a paradigm change in robotic materials. By taking inspiration from biological sensory-motor interactions, we will develop MULTIMODAL materials with autonomous and interactive features. These features go far beyond the capabilities of conventional stimuli-responsive materials, allowing us to take inanimate, shape-changing materials one ambitious step closer to motor functions of living species.

Mots-clés : life like materials liquid crystal networks photoactuation azobenzene soft robotics
Projet ERC : CR-INTERFEROMETRY (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe Sciences
Entité participante : Université Radboud de Nimègue — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 3000000.0 €

The universe hosts a population of subatomic particles with astonishingly high kinetic energies, so-called cosmic rays. Where and how they obtain this energy is unknown. These particles initiate cascades of particles in the atmosphere called air showers. The particle interactions within air showers are not fully understood and probe a regime not easily accessible by particle accelerator experiments. I developed a new technique that applies interferometry on the radio emission from air showers. It improves the reconstruction accuracy of air shower properties and hence allows for better determination of the features of cosmic particles. The largest facility for ultra-high-energy cosmic ray detection, the Pierre Auger Observatory, is upgrading its infrastructure to include 1660 radio antennas. This CoG facilitates the interferometric technique for the upgrade by deploying an accurate synchronisation system. The Southern Wide-view Gamma-ray Observatory is a next-generation facility, starting construction in 2026. This CoG augments it with 800 antennas to perform interferometry on air showers. By improving the air shower reconstruction of these facilities, we aim to extend their capabilities to address major open questions in astroparticle physics. Specifically, the combination of particle detection and interferometry is used to reach these objectives: - Determine which particles contribute to the cosmic-ray flux at the highest energies, by measuring air shower depth. - Accurately measure the hadronic interactions in air showers, by simultaneously detailed observations of the muon and electromagnetic components of air showers. The same technique is applied over a wide cosmic-ray energy range. - Observe astrophysical photons with energies above 1015 eV, to identify the most extreme particle accelerators in our galaxy. This CoG pioneers the interferometric air shower reconstruction on a large scale and will pave the way for its use in future projects.

Mots-clés : cosmic rays,air showers,hadronic interactions,gamma ray astronomy
Projet ERC : COCONUT (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université Radboud de Nimègue — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

Modern society runs on concurrent software: different processes (threads) jointly process massive data sets and serve many clients and users simultaneously. Good methods to ensure the correctness of concurrent software are lacking due to the enormous space of concurrent executions. But it is vital to have some correctness guarantees, e.g., “every thread will eventually perform an action” (liveness) or “private data cannot leak to an attacker” (non-interference). Recent years saw an active development and industry adoption of new programming languages that automatically enforce correctness guarantees through a type system that disables programmers from writing “bad programs”. Yet, existing concurrent programming languages cannot enforce “deep” concurrency properties such as liveness and non-interference. Moreover, there is no guarantee that the good properties of high-level programs are preserved after compilation to executable machine code, because compilers perform increasingly complicated unverified optimizations.  The COCONUT project will design and verify new type systems, compilers and logical abstractions for fully-fledged concurrent programming languages that automatically enforce deep program properties. COCONUT will address this objective through rigorous machine-checked foundations, leveraging recent innovations in the development of powerful program logics for concurrency (in which I played a key role). The foundations are essential to prove the validity of our results, but they will also play a key role for experiments and to bring together recent results from the fields of programming language theory, concurrency theory, compilers, security and proof assistants.  COCONUT will impact the development of future concurrent programming languages but also the practice of concurrent programming itself, and thereby contribute towards the grand challenge of enabling software engineers to write correct and secure concurrent software.

Mots-clés : programming language theory concurrent separation logic type systems compiler verification
Projet ERC : ULTIMASero (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Institut Pasteur de Dakar — Pays : Sénégal — Rôle : Partner
Type d'entité : Recherche
Financement ERC de l'entité : 250000.0 €

Enormous progress is being made in the fight against parasitic diseases such as malaria in sub-Saharan Africa, which is driving a long-term epidemiological shift in the burden of infectious diseases. Until recently, most febrile patients presenting to rural health facilities were presumptively treated for malaria. However, the decline in malaria cases coupled with the roll out of rapid diagnostic tests has revealed a substantial burden of non-malaria fevers. I hypothesise that the predominant disease burden in Africa is changing from parasites to viruses, and that the diversity of infecting pathogens is increasing over time.  ULTIMASero aims to investigate long-term trends in the epidemiology of infectious diseases by taking a multi-pathogen approach using multiplex serological and molecular assays, epidemiological studies, and statistical modelling. This will be achieved through four aims: (i) Investigate febrile patients in longitudinal studies in Senegal, Cameroon, and Madagascar; (ii) Develop a multiplex serological assay for viral, parasitic, and bacterial pathogens in Africa; (iii) Create statistical and machine learning tools for multiplex data; and (iv) Implement sero-epidemiological analyses of long-term trends in infectious disease in general populations.  The key technological output will be a multiplex serological assay using Luminex bead-based technology to measure antibodies to infectious diseases transmitted in Africa. The assay will test small volume blood samples for antibodies to >300 antigens from >100 pathogens. We will test samples from prospective cohorts of febrile patients to provide an in-depth assessment of the burden of febrile illness, and from 50,000 biobanked samples dating back over 30 years to assess long-term trends in multi-pathogen epidemiology in the general population.  The knowledge generated from this interdisciplinary project will have major implications for how we study, track, prevent and treat febrile illness in Africa.

Mots-clés : infectious disease epidemiology serological diagnostics malaria statistical models machine learning surveillance fever
Projet ERC : CreateKnow (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Compl exity
Entité participante : Université Radboud de Nimègue — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

In this project I want to unravel how we build-up and update our knowledge networks depending on the amount of previous experience we have by testing my new theory on experience dependent learning. Current theory postulates memories to be stored initially as sets of neural representations spanning the hippocampus and weakly interacting neocortical modules. The spontaneous reactivation of new memories during a consolidation phase, would lead to strengthening of the neocortical memory trace. Consequently, pre-existing knowledge is proposed to be coded in the brain as a cortical network of neurons that allows for more efficient consolidation of new information. I recently developed a new theory, proposing that the existence of previous knowledge and therefore the extent of the cortical memory network, is a gradient of experience instead of being either present or not. The size and complexity of the knowledge network would influence where in the brain memories are encoded and how fast they are consolidated. I propose combining my innovative behavioural paradigm – the HexMaze - that enables investigatory access to various levels of experience, with different techniques in three species to test this theory. In mice, with immediate early gene expression techniques to visualize and manipulate the brain-wide memory network with the resolution of individual neurons. In rats, with electrophysiology to measure and manipulate memory reactivations during sleep as the mechanisms to enable consolidation. In humans, with targeted memory reactivation and magnetic resonance imaging to follow the evolution of learning over one year. The combination of species with their respective methods enables to observe effects as well as test for causality. The unique combination of meaningful behaviour with appropriate, precise techniques would provide ground-breaking insight into how we create and update our knowledge networks and change the way we view and test memory.

Mots-clés : sleep,memory reactivations,schemas,previous knowledge,memory consolidation