Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : DEVENDRA (Horizon Europe, appel 2021, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : GFZ Helmholtz Centre for Geosciences — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : GFZ
Type d'entité : Recherche
Financement ERC de l'entité : 500285.0 €

The chemical weathering of rocks on the Earth’s surface draws down atmospheric CO2, balancing emissions from volcanoes and maintaining habitable temperatures. Basalt and carbonate rocks are particularly crucial in this balance, because they are efficiently weathered: as estimated from studies of the dissolved elements in rivers, basalt accounts for 20-35% of modern global silicate weathering and CO2 consumption flux, despite only covering 5% of Earth’s surface. To formulate sensitivities and feedbacks between weathering of these rocks and climate, we need an accurate description of the exact processes that drive the conversion of rock to soil by weathering. Besides water flow, erosion rate and vegetation are thought to exercise significant control. Currently, however, we lack the tools needed to decipher these controls, because the preferred approach used to quantify erosion and weathering rates – cosmogenic nuclides, produced in situ in quartz – does not work in basalt and carbonate lithologies. The aim of DEVENDRA, dedicated to the pioneer of cosmogenic nuclide geochemistry Devendra Lal (1920 – 2012), is to eliminate this blind spot using a novel method never applied to basalt and carbonate lithologies: the ratio of cosmogenic beryllium-10 rained out from the atmosphere to stable beryllium-9 released by weathering. DEVENDRA will develop this system as a novel erosion and weathering rate meter for these rocks, and will use this new method to calibrate – using globally-distributed soil profiles and catchments of differing climate and erosion rate – the laws that govern weathering and CO2 drawdown in these rocks. The outcomes from DEVENDRA will refine the global weathering models that are used to understand Earth’s carbon cycle on geological time scales, to predict the trajectory of anthropogenic CO2 in coming centuries, and to estimate the potential for negative CO2 emissions by artificially-enhanced weathering of basalts.

Mots-clés : weathering,erosion,cosmogenic nuclides,co2,climate feedback,vegetation
Projet ERC : SOFT-PLANET (Horizon Europe, appel 2022, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 3262944.0 €

How and when plate tectonics (PT) developed on Earth, and why the Earth is currently the only rocky planet of the solar system with PT, are two of the main enigmas of Earth and Planetary Sciences. The evolution of a planet is conditioned by heat transfer in its most viscous envelope, the mantle. This heat transfer is mostly due to convection, and strongly depends on the convective patterns, which in turn depends critically on mantle rheology. However, the link between rock rheology at small time- and length-scales, and mantle « effective » rheology at the long/large scales of mantle convection is still missing. I propose that it resides in the meso-scale structure of the mantle and lithosphere that are acquired through convective motions and partial melting. Hence, deciphering the evolution of the Earth or any other planet requires describing and understanding the interplay between « texture » (i.e. material multi-scale organization), rheology, two-phase flow and convection. Our team recently showed that aqueous nanoparticle colloids, fluids commonly used by soft matter physicists, can reproduce planetary phenomena such as one-sided subduction, and the two-phase flow dynamics of mid-ocean ridges. Building on this, SOFT-PLANET will combine convection in these soft materials with state-of-the-art visualization techniques to relate surface morphology (including faults, wrinkle ridges and volcanism), lithospheric structure and internal flow patterns, and characterize the development of texture and rheology from the nano-scale to the macro-scale of convection. SOFT-PLANET will map the different regimes of convection that can develop, and will derive a physical understanding of each. This new physical framework, together with geomorphology observations, will be used to decipher the current state of Venus, and the divergent evolution of Venus, Mars and the Earth.

Mots-clés : convection plate tectonics mantle plumes subduction accretion volcanism planetary evolution soft matter
Projet ERC : WOLF (Horizon Europe, appel 2023, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 2500000.0 €

Single cell molecular profiling allows to map cellular development at an unprecedented level of detail. Optimal transport (OT) enables the analysis of this dynamical process as a trajectory inference problem, using OT flows. These flows treat cells as particles evolving on an energy landscape over an "omics'' space (such as transcriptomic, epigenomic, proteomic and location). Learning this model from large scale omics datasets poses however formidable mathematical and computational challenges, which will be tackled by WOLF. The first one is the joint learning of both the gene embedding space and the energy landscape. Existing approaches use ad-hoc Euclidean embeddings, ignoring biological relationships between genes. WOLF will develop a new type of non-Euclidean OT flows, which takes into account complex genetic relations. The second challenge is the fusion of multiple omics dataset (for instance transcriptomics, proteomic and space) without having access to an explicit pairing between the cells across the omics. Multi-omics is the next frontier in developmental analysis, and the corresponding trajectories cannot be captured with existing OT flows. WOLF will develop a new class of multi-linear OT flows where interaction terms couple particles together across different omics. These advances will be integrated in an efficient computational package where the parameters of the models are learned using parallelizable OT flow solvers. Leveraging the connexion between OT flows and attention mechanisms in deep learning, these methods will be approximated using transformers architectures and optimized using implicit differentiation. These theoretical and numerical contributions will work hand in hand to offer the first comprehensive framework for multi-omics trajectory inference. This will unlock biological findings for the characterization of developmental molecular pathways and the understanding of disease mechanisms.

Mots-clés : optimal transport,deep learning,transformers,genomics,trajectory inference
Projet ERC : 3TMAAP (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Study of the Human Past
Entité participante : Institut national de l'information géographique et forestière — Pays : France — Rôle : Partner
Acronyme de l'entité : IGN
Type d'entité : Recherche
Financement ERC de l'entité : 391250.0 €

This project begins with a “book:” eight historical case studies detailing environmental change will serve to produce 8 machine learning training data sets to develop a methodology for the automatic analysis of quantitative and visual environmental data from historical aerial photographs. This will allow for a new approach to study the 1940s-1990s Great Acceleration, which caused unprecedented environmental change. Becoming ubiquitous in the 1940s, aerial photography has a sub-1-meter resolution which is key to identifying non-linear, non-monumental, and pre-industrial rural environmental infrastructure (e.g. trees, waterholes, fields) and how it has changed over time. Since satellite imagery only gained a comparable high resolution in the early 2000s, the ability to machine-read aerial photographs will quadruple the availability of high-resolution geospatial data, thereby allowing a more robust and nuanced analysis of environmental and climate change. The project goals are: (1) to develop a historical ground truthing method to enhance the interpretation of historical aerial photographs to imagine historical landscapes and how they changed; (2) to produce machine deep learning training data sets by deploying historical ground truthing to identify and extract a set of features from sample areas in the study regions; (3) to use a more systematic and more historically informed object-based methodology to machine read archival aerial photography that is deployable at different spatial (local, regional, global) and temporal scales and make it available as an Open Access online software to read historical aerial photography world-wide. By unlocking the mass data contained in archival aerial photography 3TMAAP opens a new path of research by systematically integrating historical ground truthing into the automatization of geospatial analysis, dramatically deepening the time depth of high-resolution mass data for historical analysis and geospatial and climate modelling.

Mots-clés : environmental history climate history archival aerial photographs historical aerial photographs historical ground truth historical data sets for machine learning automatic analysis historical aerial photography historical data and climate modelling new high resolution geo spatial data computing history
Projet ERC : MECHPAIN (Horizon Europe, appel 2023, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Compl exity
Entité participante : Central Institute of Mental Health — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : CIMH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2424481.0 €

Chronic pain is a major health problem typically associated with mental disorders such as depression or anxiety and is still difficult to treat despite enormous efforts in the biomedical community. This project proposes a radical shift from the symptomatic focus on chronic pain and predefined diagnostic categories to a mechanistic analysis and treatment of psychobiological factors that shape the transition to chronic pain and its associated mental diorders in a transdiagnostic approach. The proposal aims to identify transdiagnostic mechanisms of the development of chronic pain across three major diagnostic subtypes of pain (primary musculoskeletal pain, neuropathic pain, primary headache) that are usually analyzed and treated in isolation and conducts a proof of principle study of mechanism-based modular digital and virtual reality-based interventions. Work package (WP) 1 will use meta-analyses to determine common and specific transdiagnostic mechanisms of chronicity across these three diverse pain disorders, WP 2 will analyze longitudinal data from existing databases on pain development, using machine learning and knowledge graph approaches. WP 3 will develop an assessment battery for core psychobiological mechanisms and identify predictors of chronicity as well as comorbidity with mental disorder across diagnostic groups using advanced unsupervised learning algorithms. WP 4 will devise easy to apply real life digital and virtual reality interventions that target core psychobiological processes. WP 5 will contain a proof of principle study that contrasts standard cognitive-behavioral pain management with the mechanism-based intervention modules. They will be applied in a digital format and contain virtual reality-based interventions. Statistical analyses will include linear mixed models, an n=1 design as well as a microrandomized trial. We believe that this transdiagnostic mechanistic approach will make a major contribution to the prevention of chronic pain.

Mots-clés : prevention,subacute pain,machine learning,psychobiological mechanisms,psychological interventions,digital assessment,virtual reality
Projet ERC : CellGeom (Horizon Europe, appel 2022, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed Matter Physics
Entité participante : Université Louis-et-Maximilien de Munich — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : LMU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2498813.0 €

Can systems with life-like properties be built from scratch with only a minimal set of components? While progress has been made experimentally in the development of such minimal systems, there is a lack of theoretical underpinnings that could provide mechanistic principles. My goal is to discover such principles by combining theoretical modeling and in-silico evolution to explore the potential for life-like functions of minimal systems consisting of only two core elements of cells: reaction compartments enclosed by lipid membranes (liposomes) and equipped with a protein reaction network. Towards this goal, we will develop new multi-scale approaches to investigate the mechanistic interplay between the ability of protein networks to form spatiotemporal patterns by decoding information about the membrane geometry and to reshape the membrane through mechano-chemical feedback. Using methods from differential geometry we will develop projection techniques that reduce the model to the two-dimensional manifold of the membrane. Building on my expertise with protein pattern formation I will design coarse-graining methods using machine learning concepts to link scales. These theories will give unprecedented insights into the relative role of reaction networks, membrane elasticity, and mechanochemical feedback in forming different types of protein patterns and membrane morphologies. Moreover, they will provide an efficient computational platform, which I will use to in-silico explore the potential of supported lipid bilayers with adhering liposomes as a platform to generate functions such as cell migration, cell division, and collective cell-cell communication. This will lead to theoretical insights into the mechanistic principles of the emergent behavior of these systems, make specific predictions for established bottom-up experimental model systems, and provide innovative suggestions for the rational design of systems with targeted functionalities.

Mots-clés : theoretical physics,nonequilibrium dynamics,self organization,cell membranes,protein pattern formation,continuum field theory,model reduction and coarse graining,evolutionary algorithms,numerical simulations
Projet ERC : AI-Twin (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université Louis-et-Maximilien de Munich — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : LMU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2495475.0 €

We aim to establish a scientific foundation for AI-twins of human experience - personalized multi-modal generative AI systems that capture and encode an individual’s real-world experiences and tacit knowledge. Through continuous data capture from wearable devices, we will explore and develop AI-twins as advanced personal systems for storing and processing human experiences. We believe AI-twins can augment human cognitive and social abilities and enhance human executive functions such as memory creation and retrieval, metacognition, planning and prioritizing, decision making, and creativity. AI-twins will enable personal simulations of future scenarios grounded in one's own experience. By effective cognitive augmentation, AI-twins will allow a symbiotic human-AI relationship built on real and individual experiences. Using ubiquitous sensing, we hope to continuously add new and authentic real-world information (e.g. texts, conversations, images, gaze, location) to ensure the sustainability of the generative AI model, reducing the risk of model degradation or collapse. We will adopt an experimental approach, prototyping functional systems that facilitate in-situ capture, processing, storage, and access. We will empirically explore applications in skill acquisition, human memory aids, self-reflection, and social networking to assess the potential impact qualitatively and quantitatively. In addressing technical and ethical challenges, we will look at the implications of design decisions on user privacy and the system's utility. This project has the potential to transform personal data utilization, enabling cognitive augmentation and social connectivity through AI. We will also explore the broader impact of AI-twins beyond personal use, envisioning societal knowledge sharing through the fusion of these models. Ultimately, this research aims to pioneer human-AI symbiosis, leading to a form of personalized symbiotic intelligence grounded in authentic human experiences.

Mots-clés : human computer interaction,hci,ubiquitous computing,human augmentation,wearable computing,genai,generative ai,generative ai models,generative ai systems,large language modals,llms,multimodal large language models,mllms
Projet ERC : EXPEDITE (Horizon Europe, appel 2022, Advanced grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Next generation diagnostic srl — Pays : Italie — Rôle : Partner
Type d'entité : Organismes privés (hors enseignement)
Financement ERC de l'entité : 100000.0 €

In vivo gene therapy based on a single administration of adeno-associated viral (AAV) vectors is emerging as an effective therapeutic option for both monogenic and complex diseases. Given the episomal nature of the AAV genome, its applications are limited to non-replicating tissues like retina or adult liver. Several AAV-based products that target these tissues are either approved or in advanced clinical development. Despite this, some limitations still remain, including: the potential for insertional mutagenesis associated with genome-wide AAV integration; the loss of transgene expression from replicating tissues like newborn liver; and the challenge to counteract toxic gain-of-function mutations, which cause dominant diseases for which canonical gene replacement is ineffective. EXPEDITE aims to integrate therapeutic DNA at desired genomic loci safely and effectively, thus overcoming the above limitations. EXPEDITE will go beyond the current state-of-the-art by implementing and comparing two parallel strategies: (i) novel Cas fusion proteins to recruit DNA repair machineries at induced double stand breaks (DSBs) to maximize on-target donor DNA integration; (ii) novel cleavage-free platforms for therapeutic DNA integration, based on transposases or bacterial single strand-DNA annealing proteins. The therapeutic relevance of these platforms will be tested in the retina and liver, two highly relevant tissues for gene therapy, using animal models of inherited retinal degenerations and lysosomal storage diseases, respectively, and, ultimately, non human primates. To reduce the risk of potential off-targets, EXPEDITE will also test non-viral vectors for transient delivery of the genome editing tools while delivering the donor DNA via AAV. The results from EXPEDITE will allow significant expansion of the patient population that can benefit from in vivo gene therapy and may represent a change of paradigm for gene therapy by replacing canonical gene addition approaches.

Mots-clés : gene therapy,adeno associated viral vectors,aav,genome editing,liver,retina
Projet ERC : ARCHIATER (Horizon Europe, appel 2022, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Texts and Concepts
Entité participante : Université Louis-et-Maximilien de Munich — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : LMU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 716886.2 €

After the Black Death of 1348, hospitals were newly designed to face social disease, further plague’s waves, and new illnesses, e.g. syphilis. Social distancing (like recently with an unknown virus) was the main method to avoid all kinds of contagion. New spatial and safety concepts were developed to care and confine the sick and the poor in structures enhancing the visibility of political or religious agendas. While wars and early globalization contributed to spreading pandemics, charitable bodies exploited their roles to gather capital, which they reinvested in art. Despite an extensive scholarship on hospitals as social institutions, the question of why art and architecture were so vital for hospitals remains open. ARCHIATER tackles this gap, focusing on three objectives: 1) To map hospital visual cultures, agencies and imaginations in European cities before 1750 along terrestrial and maritime routes and according to hospital typology and networks. 2) To analyse the spaces, forms, themes and designs of hospital art and architecture as liminal mediators to manage disease and contagion as well as life passages, healing, death, and salvation. 3) To reconstruct transitions and changing values of hospital materiality and develop ways of ‘curating’ the impressive heritage of premodern hospitals. Bringing together art, architecture, history, and curatorial studies, the project looks at these relations in comparative and interconnected ways. It combines geographical and micro-historical investigations, object-based analysis, and a new conceptual questioning on the ‘liminality’ of hospital art and architecture as mediators between spaces, actors, and intentions in the ‘monumentalization’ of disease, which characterizes historical cityscapes. Addressing these issues will reveal, not only why so many artworks in our museums come from hospital contexts, but also how visible artefacts can contribute to the well-being of a society faced with invisible threats.

Mots-clés : visual culture early modern history comparative and transnational histories history of medicine liminality archives confraternities and social institutions