Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : hASTROCURE (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Gouvernement basque — Pays : Espagne — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 1999875.0 €

The pathogenesis of Alzheimer´s disease (AD) is not fully understood and no cure is available so far. Current therapeutic targets are insufficient and there is an urgent need for finding new pathways and mechanisms and novel targets to treat this terrible disease. Due to critical species-specific differences and the limitations of rodent models of AD, humanized models are necessary to capture the complexity of the disease process. My lab has pioneered the use of humanized chimeric mice to study human astrocytes (h-astrocytes), integral components of the brain and understudied in AD. I propose here to investigate with a multidisciplinary approach their contribution to AD development and progression. We have already showed the potential of chimeric mice to identify novel and unexpected contributions of h-astrocytes to AD pathology. hASTROCURE will take advantage of our unique model to drive breakthrough discoveries in our understanding of h-astrocytes and their role in AD. We will investigate in humanized chimeras what is the impact of the genetic make-up (risk vs protective variants in AD risk genes) on h-astrocytes molecular states and functions as disease evolves and how they contribute to pathological features and AD progression. The overarching aim is to dissect central pathways leading to h-astrocyte activation towards detrimental states and target those pathways, which can provide the necessary knowledge to enable the development of novel astrocyte-specific therapeutic strategies. This pioneering project involves a set of innovative approaches as an essential platform for studying human genetics in h-astrocytes, in an AD relevant context. My experience and the work that my lab has developed these last years support the conceptual basis of the proposal and demonstrate its feasibility. hASTROCURE will go beyond the state-of-the-art providing groundbreaking information about h-astrocytes and their contribution to AD, and will help to define new strategies for AD.

Mots-clés : alzheimer s disease,human astrocytes,humanized chimeric models,human pluripotent stem cells hipscs
Projet ERC : JANUS (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : Université de Berne — Pays : Suisse — Rôle : Partner
Acronyme de l'entité : Unibe
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 275000.0 €

For the decades to come, the pioneering investigation of the fundamental laws of nature at the Large Hadron Collider (LHC) and at future collider experiments will rely on the interpretation of complex scattering events by means of accurate theoretical calculations, which are instrumental to find small signals of elusive new phenomena. These calculations must describe the evolution of the system from the few particles produced in the high-energy scattering to the tens or hundreds of low-energy particles observed in the detectors. This proposal aims at transforming the core of our theoretical understanding of such a multi-scale evolution, which nowadays is often too inaccurate to cope with the striking experimental precision. The goal of JANUS is to develop innovative ideas and theoretical methods to advance significantly both approaches to the problem: Resummations and Parton-Shower-Monte-Carlo (PSMC) generators. JANUS will establish a deep connection between the two fields, resulting in novel technology for the accurate modelling of multi-scale effects. Its main objectives are:   - To formulate new theoretical methods to tackle the resummation of complex collider observables in reactions with more than two emitters and scales with state-of-the-art accuracy. - To lay the theoretical foundations for a new generation of PSMC algorithms, capable of describing accurately the hard scattering and subsequent parton-shower evolution for reactions with different jet multiplicities with a new standard of precision.  This technology will be applied to obtain state-of-the-art theoretical predictions for a broad range of critical observables in the important fields of Higgs and jet physics, enabling their optimal exploration at the LHC and future collider experiments.

Mots-clés : perturbative qcd,resummation,parton showers,event generators,collider phenomenology
Projet ERC : HIGHRES (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Université libre de Bruxelles — Pays : Belgique — Rôle : PI
Acronyme de l'entité : ULB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1987368.75 €

Spectroscopy and metrology are amongst the most important applications of electromagnetic fields The very fast oscillations of light (up to 200 THz in the infrared) permit very precise distance measurements as well as broadband analysis of radiations emitted and absorbed by matter. The technologies have evolved tremendously over the years and, at the turn of the century, a ground-breaking technology was proposed. By using two sources of optical pulse trains (corresponding to optical frequency combs in the spectral domain), no moving parts would be required, and much faster measurements could be performed. So-called dual comb spectroscopy and ranging have had a very significant impact on their respective fields and have established capabilities in terms of speed and accuracy that are still relevant today. Yet some important limitations remain. Most importantly, there is a trade-off between resolution an acquisition time which is currently treated as a fundamental limit of dual comb systems. It prevents these systems from being used for high resolution spectroscopy or long-distance ranging, which are critical for some applications such as gas sensing or large-scale metrology. In HIGHRES, I propose a novel technique which permits overcoming this fundamental limit. I expect it will improve the current state of the art by as much as three orders of magnitude in terms of resolution (or distance). We will start by a theoretical analysis of the general principle we propose. We will then build a dual comb source with unprecedented characteristics based on soliton formation in driven fiber resonators. Finally, using this novel source, we will experimentally demonstrate two proof of principle applications which would greatly benefit from this novel technique, namely THz spectroscopy and very long-distance ranging.

Mots-clés : frequency combs thz spectroscopy optical ranging temporal solitons
Projet ERC : iBack-epic (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Université technique de Munich — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999993.0 €

Chronic back pain is a major burden and source of disability worldwide. It is primarily attributed to different biomechanical factors, but can also have inflammatory, neurological or psychological causes. Clinical findings and conventional imaging cannot reliably distinguish different causes of back pain. In contrast, individual biomechanical models can quantify diverse (pathologic) loading patterns and thus could be used to distinguish different aetiologies of back pain, to better understand individual pathophysiology and guide preventive strategies. During my recent ERC-StG “iBack”, I developed quantitative imaging methods and deep-learning based image processing to automatically generate a fully individualized biomechanical model of the thoracolumbar spine. Simultaneously, two large-scale epidemiologic studies collected clinical and high-resolution imaging data of the spine of more than 15,000 participants so far, aiming at more than 35,000 participants by mid 2022 The high-level objective of iBack-epic is to use such novel image analysis techniques to identify different biomechanical and inflammatory causes of back pain in study participants. I will adopt and extend my recently developed deep-learning based spine labelling and segmentation algorithms to fully automatically calculate individual biomechanical, functional and morphometric parameters of the spine. In this large-scale population data, I will identify different biomechanical loading patterns, use quantitative image-based parameters to discriminate normal ageing from pathologic degeneration and identify pathological conditions that are linked to back pain or subsequent development of chronic back pain. Such a differentiation – for the first time based on quantitative image data – will allow for a better understanding of the underlying pathophysiology of back pain, an improved risk stratification, a tailored investigation of genetic causes and thus will help to better guide preventive strategies.

Mots-clés : back pain,computational imaging,machine learning,magnetic resonance imaging,
Projet ERC : LongTx (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Université technique de Munich — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1626995.0 €

The perception that RNAs are passive carriers of genetic information has been overturned. Due to the diverse targeting abilities and extensive research in RNA modification and delivery systems, nucleic acid-based therapies have emerged as suitable treatment options for many diseases. Targeting RNAs offers opportunities to modulate numerous cellular processes, including those linked to the large portion of ‘undruggable’ proteins. Currently approved protein-targeted therapies interact with <700 gene products, meaning that only 0.05% of the human genome is presently utilized for treatment. On the contrary, a large fraction of the human genome (>70%) is transcribed into non-coding RNAs. Humans produce more than 15.000 long non-coding RNAs (lncRNA), with a substantial subset of these likely being ‘druggable’ via RNA interference strategies, such as antisense oligonucleotides and siRNAs. In my current LongTx proposal, we aim at identifying suitable lncRNAs with relevance to vascular disease development and progression. To be successful, we made sure that we have identified suitable lncRNAs by profiling diseased human tissue from patients with carotid artery disease and stroke, as well as abdominal aortic aneurysms. Both diseases are currently being treated with suboptimal surgical interventions, and the growing affected patient population would tremendously benefit from novel treatment strategies that enable stabilization of advanced vulnerable atherosclerotic lesions, and also limit the risk for acute aortic ruptures and dissections. The two lncRNAs we have identified in preliminary studies using single-cell, bulk, and spatial transcriptomics are called CRNDE and NKILA. We propose to study these lncRNAs in disease-relevant preclinical in vitro (arteries-on-chips) and in vivo (genetically mutated mice and mini-pigs) models, with a strong focus on novel local delivery concepts for antisense oligonucleotide inhibitors that site-specifically target both transcripts.

Mots-clés : stroke,aortic aneurysm,long non coding rna
Projet ERC : PlantSoilAdapt (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Life Science
Panel : Environmental B iology, Ecology  and E volution
Entité participante : Université de Tartu — Pays : Estonie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999386.0 €

European grasslands have been shaped by millennia of low-intensity management and are unique cultural and biodiversity hotspots providing critical ecosystem services. However, the area of traditionally-managed grassland has declined dramatically during the last century, with land-use intensification on productive soil and abandonment of unfertile land both causing species loss and deterioration of ecosystem services. Recent evidence suggests that land-use change also leads to genetic and phenotypic changes in plant populations. How population-level processes mediate the impact of land use on ecosystem functions and affect adaptive potential to future perturbations is entirely unknown. Filling this knowledge gap is urgent as grasslands face additional pressure from climate change, particularly an increasing frequency of droughts. I hypothesise that land use intensification and abandonment lead to evolutionary shifts in plant function away from resource conservation towards fast resource acquisition and low stress tolerance. Combined with the disruption of co-evolved mutualistic plant-microbial interactions, this has cascading effects on essential ecosystem services provided by soils and their resilience to drought-induced perturbation. The hypothesis will be tested using laboratory tests of the mechanisms underlying eco-evolutionary dynamics in plant-soil interactions during land-use change and community-level experiments to uncover the consequences of adaptation for soil functioning and resilience to drought. I will use the world's longest-running fertilization experiment and grassland networks in three European regions, representing different histories and contrasting management regimes. The project will provide a step-change in our understanding of the selective pressures imposed on plant-soil systems by human land use and will inform future policies for sustainable land management and maintenance of adaptive potential in the face of climate change.

Mots-clés : plant microbial interactions,local adaptation,land use,drought resistance,functional traits,ecosystem functioning,grasslands,adaptive potential
Projet ERC : RandomHypGra (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1621875.0 €

The central theme of this proposal is the enumeration of large structures obeying a family of local constraints. There are numerous well-studied instances of this general problem in combinatorics, probability theory, and statistical physics. We propose to study a variety of interrelated open problems, spanning extremal and probabilistic combinatorics, Ramsey theory, and large deviation theory, whose unifying theme is the distribution of copies of a given graph in large random graphs.  The first part of the proposal revolves around the 'infamous' upper tail problem for subgraph counts in random graphs, which has seen spectacular progress in the last few years. We propose to advance the combinatorial approach introduced in our recent work on a special case of the problem to solve this problem completely and make progress on several related questions.  The second part of the proposal discusses the global structure of 'sparse' subsets in hypergraphs. We propose to prove a far-reaching generalisation of the hypergraph container theorem that provides a useful description of the family of all 'sparser-than-average' sets. We plan to use it to resolve the lower tail problem for subgraph counts in the uniform random graph and to prove a sparse analogue of the counting lemma for regular graphs.  The third part of the proposal concerns the enumeration of independent sets in hypergraphs with cardinalities below the threshold for appearance of global structure. In order to obtain precise asymptotics for the number of such sets, we propose to extend the cluster expansion method from statistical physics to models with non-pairwise interactions.  The fourth part of this proposal deals with thresholds for fundamental extremal and Ramsey properties of random graphs and hypergraphs. We propose to resolve several outstanding open problems in this area that have resisted the recent dramatic advances in the field.

Mots-clés : random graph,random structures,random hypergraph,large deviations,thresholds,extremal graph theory,concentration inequalities
Projet ERC : EngineerBcells (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1996250.0 €

B cells have an important role in the immune response against cancer. Tumor specific B cells in tertiary lymphoid structures and anti-tumor antibodies in the plasma are associated with a favorable prognosis and with an improved response to checkpoint inhibition in different sarcomas and carcinomas. Antigen specific B cells home to tumors and prolong survival in mice, while B cell based vaccines allow durable anti-tumor activity in cervical cancer patients. We have recently demonstrated both ex vivo and in vivo B cell engineering for the expression of anti-HIV antibodies. Here, we propose a novel cancer immunotherapy approach based on engineered B cells. In particular, we use CRISPR/Cas9 and AAV to target the integration of anti-tumor antibody genes into the IgH locus. In diverse tumor models, we plan to demonstrate localized B cell activation upon antigen engagement. The B cells will exert multiple anti-tumor effects. Secreted antibodies will induce ADCC, CDC and ADCP. In addition, a polyclonal T cell response with epitope spreading will be facilitated by engineered B cells acting as APCs as well as by antibodies forming immune complexes to be taken up by dendritic cells and macrophages for cross-presentation. The B cell will be co-engineered to locally secrete additional immune effectors upon activation. These include: stimulatory cytokines, BiTEs, checkpoint inhibitors, CD40/27 agonists and cell penetrating nanobodies. Localized secretion is predicted to increase efficacy while reducing systemic toxicities. When targeting self-antigens, B cells will be engineered to co-express a CAR, relaying CD40 or TLR signals for T cell independent B cell activation and allowing allogeneic therapy. We will further demonstrate in vivo B cell engineering for increased scalability, and ensure safety using a suicide cassette for inducible B cell elimination. B cell engineering is thus a flexible and robust platform technology that may revolutionize cancer immunotherapy.

Mots-clés : gene therapy,cancer immunotherapy,b cell,antibody,aav,car,hpv
Projet ERC : GPCR Volt Dep Role (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1992500.0 €

G protein coupled receptors (GPCRs) are broadly expressed in the brain, mediate responses to many molecules, and are crucial for normal brain function and therapeutic intervention. 20 years ago, it was shown that the activity of many GPCRs is regulated by membrane potential. e.g. the activity of cholinergic M2R muscarinic and metabotropic glutamate mGluR3 receptors is reduced by depolarization, and that of M1R and mGluR1a is increased. However, due to high technical challenges, a crucial question remains unanswered: what are the physiological roles of this voltage dependency, its effect on neural activity, or its relevance to behaviour? Recently, we showed that M1R voltage dependence is crucial for its recruitment. Not only that under physiological conditions, in vivo, M1R could not be activated without depolarization, depolarization alone was sufficient to activate M1R. Furthermore, flies with a voltage independent M1R had increased odor habituation indicating a paramount effect on behavior. These findings are pivotal in our thinking on GPCR recruitment and activity. However, to create a real paradigm shift, we need to unravel whether GPCR voltage dependence has a role in other types of GPCRs and neuronal processes. The fly is an ideal model system to explore GPCR voltage dependence roles because it has a low variety of receptors with no functional overlap. In particular, the Drosophila dopaminergic and muscarinic receptors that are highly expressed in the olfactory system seem ideal. I will use a multidisciplinary approach of electrophysiology, two-photon imaging, genetics, and behaviour to examine GPCR voltage dependency and means to manipulate it, unravel these GPCR physiological roles, and examine whether abolishing GPCR voltage dependence affects neuronal activity and behavioural output. The understanding that there is a “voltage rheostat” that controls GPCR activity will open an entirely new field of research and can serve for new therapeutic intervention.

Mots-clés : gpcr voltage dependence drosophila olfactory learning and memory
Projet ERC : LLMinPeerProduction (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1853125.0 €

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.

Mots-clés : generative ai,llm,ai,engagement,online content,online communities,open source