Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : subLETHAL (Horizon Europe, appel 2021, Advanced grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Radboud University Medical Centre — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : Radboud UMC
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2120588.0 €

Immunotherapy combined with cytotoxic and molecular therapies have entered centre stage as novel treatments for cancer. Despite this advance, toxicity and insufficient efficacy in many patients remain major obstacles and illustrate the need for therapy improvement based on a deeper understanding of the cellular damage and repair responses to drugs and immunotherapy. To overcome this roadblock, I propose a previously unappreciated approach by combining immunotherapy with cancer therapies at ultra-low, sublethal dose. We demonstrated that cytotoxic T cells induce sublethal membrane and DNA damage and oxidative stress which, when repaired, allows for tumor cell survival. However, when damage adds up, the tumor cell dies. I hypothesize that sublethal effects reveal novel vulnerabilities in cancer cells which can be exploited by complementary multi-targeted therapies at ultra-low dose which are nontoxic individually but, when combined, achieve lethality by an additive mechanism. Taking advantage of innovative microscopy, I aim to produce a catalogue of damages and repair types, and their combined effects in tumor cells. Single-cell analyses combined with advanced statistics and mathematical modeling will be used to derive the damage profile and duration of damage for each modality in tumor cells, yet without side effects towards immune effector cells. I will design algorithms for multi-targeted regimens to achieve additive damage, reduced repair, and death induction and validate them in immunotherapy models in vitro and in local and disseminated preclinical cancer in vivo. If resistance occurs, survival programs will be detected by single-cell transcriptomics and countered by orthogonal targeting. subLETHAL will 1) identify sublethal damage as the basis of cytotoxic therapy, 2) enable rational design of multi-targeted ultra-low dose additive regimens and advance 3) the understanding, efficacy and tolerability of chemoimmunotherapy for 4) rapid clinical translation.

Mots-clés : tumor immunotherapy,microscopy,mathematical modeling
Projet ERC : Tree2Globe (Horizon Europe, appel 2023, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Université de Leeds — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 291963.0 €

Three trillion trees lie behind a carbon sink that has taken up about 30% of humanity’s carbon emissions over recent decades. Yet whether we can continue to rely on this huge natural subsidy is highly uncertain. The sink emerges from systematic perturbations in the dynamics of growth, death and establishment of individual trees. Taken together across the forest, these lead to the net uptake or release of carbon. Both theory and recent observations point towards profound changes in these dynamics of the world’s forests being underway, potentially causing the sink to saturate and decline. But our ability to accurately assess and quantify such on-going changes worldwide is severely limited. The reasons for this are twofold: a lack of standardised measurements of the sink and the dynamics that cause it across the world, and the difficulty of modelling the dynamics of trillions of trees. Tree2Globe solves both of these limitations, using a novel model-data assimilation approach to consistently integrate the dynamics of millions of observations of individual trees collected under disparate protocols worldwide and link together this information in space and time based on physically-consistent principles. The resulting global forest reanalysis system will (a) provide a step-change in capability to make accurate and timely assessment of the rates of tree growth, death and establishment globally and (b) quantify how these combine with the legacies of past land-use and disturbance to explain the size, location and trajectory of the global carbon sink in trees over the period 1990-2027. This will enable us to make a definitive assessment of whether hypothesised changes in forest dynamics and the resulting carbon sink are coming to pass. This unique insight into how the world’s forests and their contribution to climate change mitigation are changing will provide key information to help guide the world to meet its net zero emissions policy commitments.

Mots-clés : forest carbon sink forest dynamics reanalysis vegetation modelling
Projet ERC : BLUE-AFRICA (Horizon Europe, appel 2023, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Université de Montpellier — Pays : France — Rôle : PI
Acronyme de l'entité : UM
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2497250.0 €

Coastal and rural villages in Eastern Africa are more likely to have mitigated or even avoided poverty and outmigration during the last three decades of land desertification and natural disasters when developing a sustainable blue economy providing alternative resources and livelihoods. To test this core hypothesis, we will rely on a long-term causal inference framework combining satellite imagery, artificial intelligence algorithms, and spatial matching statistical methods, but also multidisciplinary field surveys. More precisely, BLUE-AFRICA has the ambition to quantitatively and causally assess on the long term whether (i) maintaining productive coral reefs through the establishment of fisheries management, (ii) investing in mariculture, or (iii) developing nature-based tourism can be potential transformation pathways on the eastern African coast to improve wealth assets and prevent outmigration when arable land and terrestrial resources are vanishing under climate change. Four main objectives will be reached: 1. Mapping and characterizing all rural socio-ecological systems (RSES) on the coasts of Madagascar, Mozambique, and Tanzania, but also blue economy activities. 2. Building, testing and validating artificial intelligence algorithms accurately predicting the level of human poverty and outmigration across space and time in Africa from satellite imagery and auxiliary covariates. 3. Testing the causal link between the establishment of a blue economy activity and the level of poverty and outmigration in the drying rural eastern Africa over a long time period (up to 30 years) using a spatial matching method between control and treated RSES and then fitting several response curve models. 4. Identifying, visiting and understanding the ‘bright’ vs. the ‘dark’ spots, i.e., the RSES with the highest vs. lowest level of economic development and net migration using a deviance approach from model expectations, in-depth field surveys and statistical comparisons.

Mots-clés : food insecurity,outmigration,satellite imagery,artificial intelligence,spatial matching,bright spots,asset wealth,marine protected areas,mariculture,tourism,natural disasters
Projet ERC : HI-Audio (Horizon Europe, appel 2021, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Institut Mines-Télécom — Pays : France — Rôle : PI
Acronyme de l'entité : IMT
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2482317.5 €

Machine Listening, or AI for Sound, is defined as the general field of Artificial Intelligence applied to audio analysis, understanding and synthesis by a machine. The access to ever increasing super-computing facilities, combined with the availability of huge data repositories (although largely unannotated), has led to the emergence of a significant trend with pure data-driven machine learning approaches. The field has rapidly moved towards end-to-end neural approaches which aim to directly solve the machine learning problem for raw acoustic signals but often only loosely taking into account the nature and structure of the processed data. The main consequences are that the models are 1) overly complex, require massive amounts of data to be trained and extreme computing power to be efficient (in terms of task performance), and 2) remain largely unexplainable and non-interpretable. To overcome these major shortcomings, we believe that our prior knowledge about the nature of the processed data, their generation process and their perception by humans should be explicitly exploited in neural-based machine learning frameworks. The aim of HI-Audio is to build such hybrid deep approaches combining parameter-efficient and interpretable signal models, musicological and physics-based models, with highly tailored, deep neural architectures. The research directions pursued in HI-Audio will exploit novel deterministic and statistical audio and sound environment models with dedicated neural auto-encoders and generative networks and target specific applications including speech and audio scene analysis, music information retrieval and sound transformation and synthesis.

Mots-clés : audio models,speech and audio scene analysis,music information retrieval,deep learning,curriculum learning,attention models,source separation,physics based and statistical model of reverberation,sparse representations
Projet ERC : DansMol (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Université de Groningue — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : RUG
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2500000.0 €

Inspired by natural oscillatory systems that govern countless processes, from circadian rhythms to cellular metabolism, and the essential role of catalysis in both bio- and synthetic chemistry, this project aims to systematically design and construct modular, tunable organic chemical oscillators with catalytic functions. These oscillators will not only serve as time-keeping systems but also autonomously control and catalyze additional chemical reactions. The Belousov-Zhabotinsky reaction demonstrated that sustained chemical oscillations are possible, sparking decades of research. However, most existing chemical oscillators are based on redox chemistry and their harsh conditions and lack of versatility make them unsuitable for broader applications. In preliminary studies we developed the first catalytic oscillator based on small organic molecules. Building on this proof-of-concept, we propose to develop methods that allow the construction of biocompatible chemical oscillators tunable to a wide range of potential applications. The project is structured into three closely interconnected Work Packages (WP1-WP3). WP1 focuses on streamlining the design and optimization of oscillators using predictive models and parameter estimation techniques. WP2 will explore a variety of organic catalysts and solvents to create a versatile suite of oscillators with a diverse range of catalytic functions adaptable to various conditions. WP3 will explore a number of promising use cases of biocompatible catalytic oscillators to demonstrate their potential in real-world scenarios, including time-controlled polymerization processes and periodic drug synthesis. Integrating innovative computational modeling with automated experimental workflows will overcome the current limitations in oscillator design and pave the way for the systematic development of catalytic oscillators with specific, tailored properties, opening new frontiers in chemical synthesis, materials science, and beyond.
Projet ERC : Commander (Horizon Europe, appel 2023, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe Sciences
Entité participante : Société Max-Planck pour le développement des sciences — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : Société Max Planck
Type d'entité : Recherche
Financement ERC de l'entité : 218750.0 €

Detecting inflationary gravitational waves from the Big Bang and mapping cosmic structure formation rank among the most important goals in modern cosmology, and detailed cosmic microwave background (CMB) measurements is a uniquely powerful probe of these effects. However, the predicted signatures are tiny, and their detections require unprecedented instrumental sensitivity and systematics control. In this project I propose to develop one single massively parallel end-to-end framework for the joint analysis of past, present and future CMB experiments, and use this to combine current data from WMAP, Planck LFI+HFI and others with forthcoming measurements from Simons Observatory (SO), all processed at the level of time ordered data (TOD). I will also prepare for the analysis of future CMB experiments, including LiteBIRD, CMB-S4, and a Voyage 2050 CMB spectral distortion probe. This framework will build on an Open Source Bayesian CMB Gibbs sampler called Commander that has already played a transformational role in the field for more than two decades, and that has recently been used successfully to derive new state-of-the-art frequency maps for both Planck LFI and WMAP. However, the existing code only scales well up to O(100) computing cores, and I propose in this project a new organization that will scale it up to O(100,000) cores, as required for next-generation experiments. I will also implement a wide range of ground-breaking TOD-level corrections for key systematic effects (non-linear ADCs, cosmic ray glitches, atmospheric fluctuations, detector cross-correlations etc.) that optimally exploit synergies between experiments. Once operational, I will use this global framework to establish a new state-of-the-art model of the microwave sky; shed new light on several hotly debated LCDM tensions; and, perhaps, make the world's first detection of primordial gravitational waves. This work represents a paradigm shift in the field of computational cosmology.

Mots-clés : cosmic microwave background,bayesian methods
Projet ERC : LabFree (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed Matter Physics
Entité participante : Université de Groningue — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : RUG
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2500000.0 €

Self-assembly of viral particles remains a poorly understood process. What typically is known is the starting state, i.e. what the individual proteins and the genome look like, as well as the end state, i.e. the structure of a complete virus. However, what happens in between is largely unclear. The reason for that is that it is difficult to capture the intermediate assembly states with standard techniques. In this project, this problem will be tackled by using state-of-the-art single molecule techniques that are able to follow the early steps in assembly of individual particles. This means that viral assembly initiation can be observed particle by particle. While traditional ensemble averaging techniques are very useful to decipher the overall thermodynamics of viral assembly, they do not account for individual variations. Single particle techniques have an exactly opposite approach as these variations are captured, because each individual particle is followed. This will allow for understanding the variability in assembly and lay bare possible pathways of this process. In order to interfere as minimally as possible with assembly, label-free techniques are used that do not influence or alter the particle-particle interactions. In this manner, assembly initiation of RNA containing viruses will be studied. The new insights into assembly that will be generated are essential for constructing fundamental knowledge on this poorly understood process. The results will additionally be available for using viral nanoparticles in nanotechnology and medicine, as well as for fighting viruses more efficiently.

Mots-clés : physical virology,self assembly,high speed afm,optical tweezers
Projet ERC : UBICODEWRITERS (Horizon Europe, appel 2023, Advanced grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: B iological  Mechanisms , Structures  and Functions
Entité participante : Institute of Molecular Biology — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : IMB
Type d'entité : Recherche
Financement ERC de l'entité : 2499799.0 €

Ubiquitylation is an essential posttranslational modification that governs the activities and interactions of cellular proteins. The structural diversity of polyubiquitin chains, collectively called the ‘ubiquitin code’, is thought to determine the fate of the modified proteins. Although many analytical and inhibitory ubiquitin-specific reagents exist, we lack the tools to create polyubiquitin chains of defined linkage on a protein of interest in cells to investigate their signalling functions. We recently established a method to induce substrate-specific polyubiquitylation via three major linkages, M1, K48, and K63. Based on this technology, we now propose to develop a new generation of research tools for the scientific community. This will include the identification and design of custom enzymes for assembling the rarer, still poorly characterised non-canonical linkages and a functionalisation with modules for chain editing, branching, and the identification of effectors. In a combination of biochemical, cell biological, and proteomic approaches, we will then apply these tools to major genome maintenance pathways with prominent roles in the defence against disorders such as cancer and premature ageing. Specifically, we will analyse the significance of non-canonical polyubiquitylation in the response to DNA double-strand breaks and the interplay between degradative and non-degradative ubiquitylation in the regulation of essential DNA replication and repair factors. We envision that our research will not only provide new insight into ubiquitin signalling in genome maintenance, but the new technology developed in this project will facilitate future investigations of polyubiquitin chains, their readers, and their writers in other signalling pathways. Situated at the interface between proteostasis and genome maintenance, this project will thus advance our understanding of two essential cellular surveillance systems with critical functions in human health and well-being.

Mots-clés : ubiquitin,polyubiquitin chain linkage,proteostasis,genome stability,dna repair,dna double strand breaks,dna replication stress,dna damage signalling
Projet ERC : Nab-Heart (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Istituti Clinici Scientifici Maugeri — Pays : Italie — Rôle : Partner
Type d'entité : Organismes privés (hors enseignement)
Financement ERC de l'entité : 0.0 €

In cardiac cells, kinase enzymes are key modulators of calcium homeostasis, mitochondrial function, excitation-contraction coupling, and metabolism. Of these, the delta isoform of Ca 2+ /Calmodulin-dependent kinase (Camk2d) is particularly relevant to the induction of life-threatening cardiac arrhythmias.Of the several promising small molecule and peptide Camk2d inhibitors proposed in pre-clinical investigations, none has entered clinical applications. The severity of side effects, a well-known issue for kinase inhibitors, in this case is further amplified by the high homology and the unique Ca 2+ -dependent regulatory physiology among four Camk2 isoforms, plus the delicate role of the ubiquitously expressed Camk2d in non-cardiac tissues. Since the major obstacle to developing a cardio-specific, Camk2d-specific inhibitor mainly relies on reaching high isoform selectivity, the Nab-Heart project will conjugate selected Nanobodies (Nbs) to a new Camk2 inhibitory peptide to develop the first selective kinase inhibitor. Such a molecule will prevent the development of life-threatening arrhythmias in a variety of cardiac conditions, spanning from inherited arrhythmias such as Long QT Syndrome to acquired and highly prevalent diseases such as atrial fibrillation and heart failure. The team will identify Nb candidates with the desired Camk2d isoform-selectivity and affinity. Optimized substrate-mimicking Camk2d-inhibitory peptides will be covalently linked to the selected Nbs (Nb-inibs) to ensure delivery of the inhibitory module to the pathologically active Camk2d target with outstanding selectivity and affinity. The strategy will be validated in vivo through cardio-specific AAV9 delivery of the Nb-inib candidates in a mouse model of Catecholaminergic Polymorphic Ventricular Tachycardia and in a Swine model of acquired heart failure, where Camk2d is known to be the trigger of life-threatening arrhythmias.
Projet ERC : PAIN ENSEMBLES (Horizon Europe, appel 2023, Advanced grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : University Hospital Heidelberg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2468700.0 €

Chronic pain remains a fundamental unsolved puzzle in biology and an unmet global challenge in clinical practice. Progress is limited by major gaps in knowledge on neural substrates, owing to overwhelming redundancy of implicated regions and pathways with several pain-unrelated sensory, cognitive and emotional functions as well as lack of understanding on the origins of plasticity and cellular substrates for diverse psychosocial and environmental influences on pain. Here we propose that neocortical neuronal ensembles, i.e., discrete cohorts of co-active neurons intermingled within seemingly redundant pathways, represent a mechanistic correlate for imparting specificity to pain perception, while also providing adaptive flexibility to context and introspective bodily states, and serve as convergent substrates for divergent influences, such as past experience of pain, fear memory and expectation. Using cutting-edge in vivo methods, such as multiphoton imaging of activity and structure, electrophysiological parallel recordings of hundreds of neurons, fine-grained behavioral analyses, activity-based cell tagging approaches, opto-chemogenetic manipulations and single cell transcriptomics in mouse models of acute and chronic pain, we aim to (i) identify neocortical pain ensembles that delineate pain from other sensory percepts, valence states and cognitive functions, and determine their composition, connectivity, coordinated activity in local and across distant networks and functional relevance to pain, (ii) study their dynamic plasticity over the transition from acute to chronic pain and delineate bottom-up (peripheral-spinal) and top-down (brain intrinsic) influences, with a focus on studying the role of the salience network in channeling context, expectation and fear memory and its dysfunction in chronic pain and (iii) study the potential of reversing plasticity of neuronal ensembles by non-invasive neurostimulation and behavioral approaches for achieving pain relief.

Mots-clés : chronic pain,neuronal ensembles,salience detection and plasticity