Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : OPOSSUM (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : Istituto nazionale di fisica nucleare — Pays : Italie — Rôle : Partner
Acronyme de l'entité : INFN
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 200000.0 €

The observation of neutrinoless double-beta decay (0νββ) would revolutionise our understanding of neutrinos by affirming their Majorana nature and shedding light on their absolute mass scale and hierarchy. Detecting its monochromatic peak at MeV levels, with half-lives over 1026 years, is a significant challenge. Scientists are striving to push the scale of experimental exposures to tons of material per years. It is only in a background-free experiment that sensitivity scales linearly with exposure, maximising the benefits of increased detector mass. The forefront of this search lies with ton-scale arrays of bolometers at mK temperatures, such as CUORE, which investigates 0νββ in 130Te using TeO2 crystals, known for their exceptional energy resolution but plagued by background event identification challenges. With its 34% isotopic abundance 130Te emerges as the leading 0νββ candidate, bypassing the need for the now-challenged enrichment process. OPOSSUM will revolutionise event detection by outfitting 12 CUORE-crystals with six quantum limited sensors (Microwave Kinetic Inductance Detectors) and a thermistor. This innovative approach will enable discrimination of events by classifying them as Single Site or Multi Site Events. Through pulse-shape analysis, OPOSSUM is set to tag 0νββ signals with 3σ distinction from any background. This method has the potential to lower CUORE's background from 10-2 to below 10-4 counts/keV/kg/year. The success of OPOSSUM will facilitate rapid deployment within the existing setup without extensive R&D or major financial outlay. OPOSSUM-1ton is poised to become the first bolometric ton-scale, background-free experiment that can aim at surpassing the inverted hierarchy scenario, i.e. <10 meV Majorana mass. My vast experimental knowledge in low-temperature detectors is crucial for OPOSSUM's mission to push the boundaries of particle physics as per the EU Astroparticle committee recommendation for the neutrino physics programme.

Mots-clés : rare events searches neutrinoless double beta decay millikelvin detector low temperature calorimeters bolometers underground physics mkids low background experiments
Projet ERC : TargMacropin (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
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 : 1627988.0 €

Tumors must survive microenvironmental stresses, such as nutriment deprivation, low levels of oxygen and anticancer treatments. To do so, tumor cells have developed numerous adaptation mechanisms, and we have recently shown that induction of macropinocytosis is associated with tumor cell survival. In our previous work, we determined that the Galectin-3 (Gal-3)/KRAS/alphavbeta3 integrin complex acts as a driver of macropinocytosis, an endocytic process by which tumor cells are able to engulf large amounts of nutrients from the local microenvironment. In glioblastoma (GBM) cells, which do not harbor mutations in KRAS, we interestingly uncovered the same phenomenon of macropinocytosis induction, which was associated with the mesenchymal subset of GBM. My recent work challenged this paradigm of oncogenes, such as oncogenic KRAS, essentiality for macropinocytosis activity in cancer cells. Although the mesenchymal subset is associated with variable expression of several genes, a molecular signature rather than a mutation in a single oncogene defines macropinocytosis. In this context, we also found Gal-3 to be involved in macropinocytosis induction in mesenchymal GBM cells, confirming the central role of Gal-3 in this process. Altogether, my results demonstrate that induction of macropinocytosis is not restricted to tumors bearing mutations in specific oncogenes but can potentially be a universal scavenger process used by tumor cells to survive environmental stress. In this context, the overarching goal of this proposal is to define the molecular mechanisms underlying cancer cell addiction to macropinocytosis, with the ultimate aim to identify targets for the therapeutic exploitation of this mechanism. To achieve this goal, we will combine various functional assays, metabolomic, transcriptomic, and proteomic analyses with a cutting-edge technique of laser capture microdissection together with mass-spectrometry-guided protein identification of the macropinosomes.
Projet ERC : ITSaMATCH (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Life Science
Panel : Immunity, Infection and I mmunotherapy
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 : 1165000.0 €

Half of the world’s population is at risk for mosquito-borne diseases. Yet, less than 3% of the mosquito species on earth can transmit pathogens to humans. Even within a species that specializes in biting humans and is the major vector for dengue virus (Aedes aegypti), mosquito populations on the globe transmit DENV with a wide range of efficiencies. Thus, some virus-mosquito pairs “match” with each other, and enable viral transmission, while others don’t. Understanding the biological processes that determine virus-mosquito compatibility is a longstanding question that has not yet been addressed, mostly owing to a lack of appropriate methods. Here, I propose to leverage advances in single-cell technology, gene editing and computational tools to understand the basis of virus-mosquito matchmaking. I will address three related challenges:  1 – Obtain single-cell transcriptional and epigenetic atlases for key organs of “matched” or “unmatched” virus-mosquito pairs. To be retransmitted, a virus needs to infect and transit through key organs in a mosquito’s body. Unknown factors that interfere with viral infection and impact further transmission exist in mosquito cells. They will be detected with single-cell technologies. 2 – Identify the key drivers of virus-mosquito matchmaking. Using cutting-edge single-cell data analysis methods, I will determine which genetic or epigenetic processes are associated with “matched” and “unmatched” virus-mosquito pairs. 3- Reprogram virus-mosquito matchmaking using genome editing. With key factors of matchmaking identified, I will genetically interfere with their function and determine whether virus-mosquito pairs can artificially be “matched” or “unmatched”. ITSaMATCH will combine new technologies to unravel the basis for virus-mosquito matchmaking. The project has the potential to substantially advance our understanding of virus-mosquito interactions and inform novel disease control strategies.

Mots-clés : insect immunity,arboviruses,single cell sequencing,mosquito transgenesis
Projet ERC : AIS-CaP (Horizon Europe, appel 2021, Starting 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 : 1494810.0 €

Computation pathology has the potential to revolutionize cancer care and research, specifically through improving assessment of patient prognosis and treatment selection by applying advanced machine learning methods to digitized tissue sections, i.e. whole-slide images (WSIs). This will allow us to replace the current state-of-the-art of human-developed cancer grading systems. However, the field is currently hindered by significant knowledge gaps: we do not know how to effectively leverage both global and local information in WSIs, how to identify pan-cancer prognostic features, and how to make machine learning models explainable and interpretable. In this project, I will address these key knowledge gaps by building on the novel stochastic streaming gradient descent developed in my group. Specifically, I will integrate innovative multi-task and cross-task learning algorithms with SSGD. Furthermore, I will leverage the latest advances in self-supervision, self-attention and natural language processing to endow deep neural networks with unprecedented transparency and explainability. Last, the project will validate our developed methodology in the largest dataset of oncological WSIs in the world, and, for the first time, identify links between morphological prognostic features and genetic features. By publicly releasing all developed tools and data, the proposed project will have a scientific multiplier effect for the fields of oncology, computational pathology and machine learning. Specifically, the derived cancer-specific and pan-cancer biomarkers can be leveraged in clinical care and cancer research, the enhanced SSGD method for other tasks in computational pathology and our novel multi-task and explainability algorithms can impact other research areas in machine learning, such as remote sensing and self-driving cars.

Mots-clés : computation pathology,digital pathology,machine learning
Projet ERC : IN-THE-KIDNEY (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
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 : 1500000.0 €

BACKGROUND | Renal tubulopathies are a large group of genetic disorders that disturb the function of the kidney tubule. A major challenge for diagnostics and treatment of magnesium (Mg2+) wasting tubulopathies, is absence of functional cell models and tools to study the pathophysiology. Diagnostics of hereditary Mg2+ wasting tubulopathies based on genetic screening, rather than biological measurements of kidney processes. Therapy largely depends on oral supplements because therapeutics that target the cause of disease are not available. VISION | In my vision, better insights in the cellular functional processes in the kidney are the next essential step forward towards better diagnostics, pathophysiological understanding and development of therapy. Development of functional Mg2+ transport measurements and innovative use of patient-specific models is required to increase diagnostic yield and to develop therapy. AIMS AND APPROACH | In this project, I aim to develop a urine-based diagnostic approach using scRNA sequencing and extracellular vesicle proteomics to provide biological diagnosis of renal tubulopathies. Using urine-derived adult stem cells, I will perform the first ever functional studies of ion reabsorption in kidney organoids. This approach allows personalized analysis of the pathophysiology as well as patient-specific drug screenings. Lastly, I will be the first to apply intravital multiphoton imaging in mice to measure Mg2+ reabsorption in the kidney tubule. The combination of patient-derived organoids and mice models will be used to test novel therapeutics. IMPACT AND INNOVATION | IN-THE-KIDNEY establishes the novel concept of biological diagnostics for renal tubulopathies, develops for the first-time patient-derived tissue models for functional analysis and introduces intravital ion transport measurements in mice. The methodology and approaches in this project will result in the identification of novel therapeutics for Mg2+-wasting tubulopathies.

Mots-clés : magnesium,tubulopathies,distal convoluted tubule,hypomagnesemia,gitelman syndrome
Projet ERC : CANEUTREAT (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
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 : 1836141.0 €

Cancer neuroscience has emerged as a major axis of cancer biology, at the crossroad of two biological areas. Multi-layered dialogs between cancer and nervous contingents have started to be unravelled, with pivotal roles of the nervous system in tumor initiation, growth and invasion, in turn amplified by cancer-induced hijacking of nervous functions. Specifically, brain tumors were shown to be integrated in bona fide neuron-to-cancer synaptic networks but also to capture neural-activity dependent released factors that boost their tumor properties. In the last years, this completely rewired the view of high-grade gliomas, especially those arising in the pediatric population, suggesting specific vulnerabilities when neural networks are being shaped. The knowledge regarding neuro-cancer crosstalk in the peripheral nervous system (PNS) is far more fragmented, and is even obscure in malignancies arising within peripheral nervous tissues. Neuroblastoma (NB) is an emblematic, deadly, pediatric cancer of the PNS, with primary tumors that develop in sympathetic ganglia or in the adrenal gland. Despite its occurrence in sites of active central-to-PNS synaptic contacts, whether neural activity impacts on NB tumor features and metastatic progression is fully unknown. We have conceived a model of NB that reproduces the embryonic and nervous microenvironment in which it arises but also the sequence of its metastatic progression. Guided by preliminary data, the objective of the proposal is to decrypt the panel of neuro-cancer functional interplays in NB, at the anatomical, electrophysiological and molecular level. We will deploy multidisciplinary and cutting-edge approaches to provide a comprehensive and dynamic picture of neuro-NB dialogs, in relevant models for this pediatric cancer. Our underlying aims are to identify novel entry points for therapeutic strategies, and to define subtypes of NB that would benefit from neuro-related therapies.

Mots-clés : cancer neuroscience neuroblastoma peripheral nervous system embryonal cancer neuronal activity
Projet ERC : LEAPHY (Horizon Europe, appel 2021, Starting 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 : 1684046.0 €

Population growth and the expected-to-increase (a)biotic stresses due to climate change are putting the agro-ecosystems under pressure. The dependence on inorganic agrochemicals (IAs) for fertilization and plant protection will lead to an increase in their use. Yet, current IAs do not efficiently reach their target. They lead to waste of resources, pollutions, and environmental degradations. Foliar application of nanostructures is one of the proposed solutions to optimize IAs in order to better protect crops, but also their agro-ecosystem. Nano-IAs can exhibit reduced leaf leaching and increased bioavailability, allowing to strictly apply the right dose of IA. However, the lack of knowledge on IA behaviour at the leaf interface hinders our ability to predict optimized nanostructures. The LEAPHY project aims to establish a rationale for the design of such nano-IAs. Model nano-IAs with controlled morphologies and surface properties will be designed and exposed to isolated plant cells or model leaves characterized for their surface characteristics and interfacial functional groups. The pathways and associated rates of uptake, transformations, and in planta behaviour will be quantified. These results will be used to establish a predictive modelling framework for the biological and chemical interactions that govern IA adhesion, uptake, and translocation from leaves to other plant tissues and organs. This knowledge will be leveraged to design and test bio- and geo-inspired copper-based fertilizers and pesticides with improved delivery efficacy. The team's expertise in tuning (in)organic reactivity at plant interfaces and studying the resulting interactions and speciation changes is the backbone of LEAPHY's state-of-the-art experimental strategy. This project will be a cornerstone in implementing solutions to contribute to building a rationale for safe foliar phytoprotection and fertilization strategies.

Mots-clés : nano bio interface speciation copper agrochemicals plant biological barriers inorganic reactivity cellular intake nanoparticles fertilizer pesticide impacts
Projet ERC : POLLOT (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Fondation nationale des sciences politiques — Pays : France — Rôle : PI
Acronyme de l'entité : FNSP
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499999.0 €

Political lotteries randomly select individuals to take a political decision. Recently, lotteries have put citizens together to find solutions to today’s contentious, polarizing issues, such as disaffection, abortion reform, and climate change. What explains their varied success? Lotteries equalise opportunity of access to all. Random chance can strengthen those otherwise in the minority. A wider variety of opinions can also improve debate and reduce polarization. The ideal, but unfeasible experiment would randomly vary group characteristics, assign real political power, and observe effects over time. Instead, this project draws lessons from past experiments during democratisation in Europe - whereby legislators were randomly assigned to groups within legislatures - and experiments with lotteries in online citizens’ assemblies today. Lotteries ensured that minorities participated in deliberation in small groups, and were assigned influential legislative tasks. These natural experiments with lotteries have never before been investigated using modern statistical methods, and their role in opening access to politics is almost entirely absent from empirical studies of democratisation. This project compiles a novel, comparative dataset of these political lotteries, legislators, parliamentary activity and offices from different European countries. Next, the decisions of randomly assigned groups will be analysed within each natural experiment. A theory of debate in randomly drawn groups reducing polarization will be verified by cross-national comparison across experimental contexts, and in a present-day experiment that allows controlled conditions. By opening access to minorities and contributing to strong, deliberative party and committee systems, lotteries stabilised political conflict. They have the potential to do so again today. The research will help inform evidence-based innovation of our representative democracies, as experiments with lotteries have already begun.

Mots-clés : lotteries,legislation,legislative procedure,selection,deliberation,parliaments
Projet ERC : QuanThermal (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Institut de recherches mathématiques Alfréd-Rényi — Pays : Hongrie — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 1470693.0 €

As low-noise medium-scale quantum devices are on the horizon, it is becoming increasingly important to find useful quantum algorithms that can be run on such devices. However, existing quantum algorithms typically only offer an advantage compared to advanced classical computers when run on large-scale quantum devices for rather long running times, urging us to find more effective quantum algorithms.  This project aims to design new efficient Nature-inspired quantum algorithms and find new applications and use cases, through quantum generalisations of the Metropolis algorithm and Glauber dynamics, whose classical counterparts were also distilled from deeper understanding of physical models and thermodynamical processes, but turned out to be foundational in computer science through applications to a wide range of problems outside their original scope. The primary focus is on quantum algorithms related to open quantum systems and thermalisation. Such algorithms can be described by Quantum Markov Chains (QMC), which unify the powerful paradigms of classical randomized algorithms and traditional coherent quantum algorithms. Due to their rich, complex structure such processes have proven to be hard to construct and analyse. The PI's recent breakthroughs in designing detailed-balanced QMCs opened new ground in quantum algorithms design, and the PI intends to explore and exploit the resulting QMC algorithms and adapt them to the requirements of early quantum devices.  The PI's interdisciplinary approach already led to the successful translation of the physics inspired "Quantum Signal Processing" protocol to a major unification of various quantum algorithms via "Quantum Singular Value Transformation" (QSVT) (which has been presented at STOC'19 and cited 750 times since). Further building on physics intuition and CS insights, this project aims to pave the way of QMC-based algorithms for advancements in quantum simulation, optimization and information processing.

Mots-clés : quantum algorithms,quantum markov chains,metropolis algorithm,quantum channels
Projet ERC : CONTEXT (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Imperial College London — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : ICL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1397886.46 €

Climate change and the race to decarbonise our society is making extreme events in fluids more prevalent. These are rare events where the flow suddenly takes extreme states far from its normal state. These can be found in any flow systems, such as in the atmosphere with atmospheric blocking causing extreme heatwaves, or in our oceans with rogue waves (waves of extreme heights) capable of capsizing boats, or in engineering flows in hydrogen-based clean combustors with flashback events where the flame suddenly moves back into the injection system. Currently, we cannot accurately predict such extreme events due to several roadblocks. First, the chaotic nature of these turbulent flows makes them hard to predict: any infinitesimal perturbation leads to drastically different evolutions (the butterfly effect). Second, extreme events originate from complex nonlinear interactions which are very different for systems with different physical mechanisms. This makes any past development difficult to generalize across different flow systems. Third, we have very limited observations of such events. To revolutionize how we tackle extreme events, the CONTEXT project will create a cutting-edge scientific machine learning framework that blends deep learning with physics-based techniques. CONTEXT’s framework will provide the means to (i) identify precursors and mechanisms of extreme events, (ii) forecast the flow evolution before and during extreme events and (iii) control the flows to prevent extreme events. CONTEXT’s framework will be able to handle diverse and disparate physics, with this being demonstrated across different flows of increasing complexity and with different physics, culminating in a demonstration of the practical impact of the framework on the engineering-relevant multiphysics test case of a flashbacking hydrogen combustor. CONTEXT will provide a comprehensive framework to achieve the understanding, prediction, and prevention of extreme events in turbulent flows.

Mots-clés : extreme events,scientific machine learning,chaos