Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : MODulATE (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : Institut national de recherche pour l'agriculture, l'alimentation et l'environnement — Pays : France — Rôle : Partner
Acronyme de l'entité : Inrae
Type d'entité : Recherche
Financement ERC de l'entité : 961708.75 €

Tryptophan (Trp) is an essential amino acid required for protein biosynthesis and is also a biochemical precursor of metabolites which have major effects on mammalian physiology. In the gastrointestinal tract, Trp metabolism can follow three major pathways, all of which are under the control of the gut microbiota: (i) the kynurenin pathway in immune and epithelial cells via indoleamine 2,3-Dioxygenase 1, (ii) the serotonin production pathway in enterochromaffin cells via Trp hydroxylase 1 and (iii) the direct use of Trp by the microorganisms of the gut microbiota into several molecules including ligands of the Aryl Hydrocarbon Receptor. The end products of these pathways play key roles in modulating the immune response, intestinal and metabolic functions and behaviour. Several diseases which involve the gut microbiota in their pathogenesis are also impacted by Trp metabolite. This suggests that the effect of the microbiota in these diseases could be at least partially mediated by impaired Trp metabolism. We recently observed that impaired Trp metabolism by the gut microbiota is involved in inflammatory bowel disease pathogenesis and preliminary data suggest a potential role in other major human diseases. The aims of the current proposal are (i) to identify the components of the gut microbiota, including both bacteria and fungi, involved in the control of the 3 Trp metabolism pathways in the gut, (ii) to decipher the reciprocal equilibrium between the pathways and to evaluate the potential of its modulation as a therapeutic target, and finally (iii) to assess the relevance of these phenomena in human patients. This challenging project will involve multi-disciplinary aspects, from microbiology to metabolism, inflammation and medicine, the use of multiple cutting edge technologies and translational analysis from mice to human. Beside scientific importance, it will have societal impact by identifying new therapeutic strategies in several human diseases with unmet needs.

Mots-clés : gastroenterology,intestinal homeostasis,microbiota,inflammatory bowel disease
Projet ERC : TRANSREAD (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Karolinska Institutet — Pays : Suède — Rôle : PI
Acronyme de l'entité : KI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2480889.0 €

Tumor suppressor genes such as TP53, RB1, PTEN and APC are frequently inactivated in sporadic and inherited human tumors. A significant fraction of the mutations in these genes are nonsense mutations that lead to premature termination of translation and expression of truncated unstable and non-functional proteins. We will identify and characterize novel molecules that can induce translational read-through of premature termination codons in nonsense mutant TP53 and expression of full length p53 protein. Hits will be tested for their effect on nonsense mutant RB1, PTEN and APC. We will also elucidate the molecular mechanism of action of the identified compounds by CETSA and other techniques. Moreover, we will test hit compounds on primary tumor cells from patients ex vivo, and generate mouse knock-in tumor models with nonsense mutant tumor suppressor genes for further characterization of hits. We will take the most promising hits through preclinical development towards clinical trials. Our aim is to develop novel and efficient targeted therapy for tumors with nonsense mutations in the TP53, RB1, PTEN and/or APC tumor suppressor genes.

Mots-clés : tumor suppressor genes,tp53,nonsense mutations,translational read through,cancer therapy
Projet ERC : INITIATOR (Horizon 2020, appel 2019, Proof of concept grants)
Domaine scientifique : Life Science
Panel : Integrative Biology: from Genes and Genomes to Systems
Entité participante : Vlaams Instituut voor Biotechnologie — Pays : Belgique — Rôle : PI
Acronyme de l'entité : VIB
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

Tumors are characterized by a disorganized vasculature, leading to profound levels of hypoxia. Normalizing the blood vessel formation (angiogenesis) in tumors has therefore become an attractive therapeutic strategy, leading to the development of approved drugs that inhibit major angiogenic pathways. Resistance however restricts the success of these anti-angiogenic drugs in some patients. There is thus a great clinical need for readily accessible markers that serve as reliably predictive biomarkers of anti-angiogenic therapies. However, all markers discovered to date have a limited predictive power. In the ERC Consolidator Grant CHAMELEON, we discovered how tumor hypoxia causes DNA hypermethylation and how changes in DNA methylation underlie resistance to anti-angiogenesis. Here, we propose to validate how methylation patterns detectable in plasma circulating free DNA from a prospective collection of patients with metastatic colorectal cancer can be used to predict an early response to anti-angiogenic therapies by using our in-house developed cfDNA co-methylation test, referred as the ‘INITIATOR’ test. If successful, we anticipate that this project will overcome many of the issues that are currently associated with anti-angiogenic drugs: cfDNA is readily accessible and can be serially sampled during treatment or follow-up, cfDNA also mirrors the entire tumor and not just the biopsied site, while the epigenetic changes detectable in cfDNA may be a dynamic representation of the tumor in response to treatment.

Mots-clés : liquid biopsy,dna methylation,cancer therapy,anti angiogenesis
Projet ERC : UniTED (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : University of Bayreuth — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1724375.0 €

Turbulence governs essentially all large-scale flows on our planet, including our atmosphere and oceans. With a vast number of engineering applications in transportation technology, renewable energies, and more, turbulence has a direct impact on our lives. However, developing predictive theories of turbulence, which ultimately all modeling applications rely on, remains one of the outstanding scientific challenges. Moreover, while massive simulations on the largest supercomputers are nowadays an established tool, reaching realistically high Reynolds numbers remains prohibitive. Already today, analyzing the sheer amount of peta-scale simulation data requires new paradigms for making meaningful progress. Fundamentally new approaches are needed to achieve a breakthrough. UniTED will deliver such an approach by a unique, synergistic combination of data-driven theory and large-scale computations. How? Recently, I showed that the complex statistics of turbulence can be disentangled into much simpler sub-ensembles. This significant reduction of complexity points toward exciting new theoretical pathways and novel computational methodologies, which I will explore in this project. In UniTED, we will (A) dissect the multi-scale structure of turbulence through massively parallel computations. This will (B) provide the foundation for a statistical theory of turbulence which is based on a novel ensemble decomposition approach. Combining (A) and (B), we will (C) develop a novel ensemble-based simulation approach, enabling unprecedented insights into turbulence at high Reynolds numbers. We will then use this approach to (D) provide big data for modeling small-scale turbulence using physics-informed machine learning. UniTED will boost our fundamental understanding of turbulence at very high Reynolds numbers and provide new modeling approaches in a breadth of fields such as computational engineering, the Earth sciences, renewable energy, and plasma physics.

Mots-clés : turbulence,computational fluid dynamics,statistical field theory,reduced order models
Projet ERC : COTURB (Horizon 2020, appel 2014, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Carlos III University of Madrid — Pays : Espagne — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 278750.0 €

Turbulence is a multiscale phenomenon for which control efforts have often failed because the dimension of the attractor is large. However, kinetic energy and drag are controlled by relatively few slowly evolving large structures that sit on top of a multiscale cascade of smaller eddies. They are essentially single-scale phenomena whose evolution can be described using less information than for the full flow. In evolutionary terms they are punctuated ‘equilibria’ for which chaotic evolution is only intermittent. The rest of the time they can be considered coherent and predictable for relatively long periods. Coherent structures studied in the 1970s in free-shear flows (e.g. jets) eventually led to increased understanding and to industrial applications. In wall-bounded cases (e.g. boundary layers), proposed structures range from exact permanent waves and orbits to qualitative observations such as hairpins or ejections. Although most of them have been described at low Reynolds numbers, there are reasons to believe that they persist at higher ones in the ‘LES’ sense in which small scales are treated statistically. Recent computational and experimental advances provide enough temporally and spatially resolved data to quantify the relevance of such models to fully developed flows. We propose to use mostly existing numerical data bases to test the various models of wall-bounded coherent structures, to quantify how often and how closely the flow approaches them, and to develop moderate-time predictions. Existing solutions will be extended to the LES equations, methods will be sought to identify them in fully turbulent flows, and reduced-order models will be developed and tested. In practical situations, the idea is to be able to detect large eddies and to predict them ‘most of the time’. If simple enough models are found, the process will be implemented in the laboratory and used to suggest control strategies.

Mots-clés : turbulence,structure identification,reduced order models
Projet ERC : POWVER (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Saarland University — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2425000.0 €

Twenty years ago we were able to repair cars at home. Nowadays customer services repair coffee machines. By installing software updates. Soon you will no longer be able to repair your bike. Embedded software innovations boost our society; they help us tremendously in our daily life. But we do not understand what the software does, regardless of how well educated or smart we are. Proprietary embedded software has become an opaque layer between functionality and user. That layer is thick enough to possibly induce malicious or unintended behaviour. Proprietary embedded software locks us out of the products we own. We need a turn to open and hence customisable embedded software. However, a minor customisation might well have strong unexpected impact, for instance on the longevity of an embedded battery, or the safety of the battery charging process. We thus need means to detect, quantify and prevent such implications. The POWVER project lays the foundations. It provides quantitative verification technology for system-level correctness, safety, dependability, and performability. In this endeavour, POWVER takes up a hard scientific challenge, a challenge where discrete and continuous, real-time, stochastic as well as data- and user-dependent aspects are all deeply intertwined: embedded software for electric power management. Electric power is intricate to handle by software, is safety-critical, but vital for mobile devices and their longevity. Since ever more tools, gadgets, and vehicles run on batteries and use power harvesting, power management is a pivot of the future. POWVER will demonstrate that quantitative verification of open embedded software is feasible, and can ensure safe and dependable operation of safety-critical devices. A proof of concept will target the field of electric mobility, set up as a blueprint for other battery-powered appliances. As such, POWVER is the nucleus for a radical change in the way embedded software quality is assured in general.

Mots-clés : markov chain stochastic probabilistic systems model checking embedded automated verification energy concurrency theory abstraction refinement automata dependable performance temporal logic game time
Projet ERC : FORESTDIET (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of Copenhagen — Pays : Danemark — Rôle : PI
Acronyme de l'entité : UCPH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1447389.0 €

Two billion people across the planet suffer from nutrient deficiencies. Dietary diversification is key to solving this problem, yet food security policy, especially in low-income countries, still focuses on increasing agricultural production and access to sufficient calories as the main solution. But calories are not all equal. Indeed, this approach has created a blind spot with respect to the role of forests, which are often overlooked but may be important for dietary diversification for the rural poor by providing wild foods, high-value products which can be sold and thereby enable food purchases, and fodder for livestock which then provide meat, milk and eggs as well as manure to improve agricultural production. This project will identify how forest loss and fragmentation affect people’s dietary quality. I will apply a cutting-edge multi-scale, multi-country, and data-rich approach. First, I will take a broad-scale perspective to explore where forest loss, forest fragmentation, and dietary changes are taking place and identify hotspots of forest change and deteriorating diets. To do so, I will spatially link publicly available household data on food consumption, with metrics on the proportion and spatial arrangement of forest from longitudinal datasets. Second, I will unravel the mechanisms linking forests and diets to develop a comprehensive framework on forest-diet linkages. Third, I will zoom in on selected sites in two countries and use in-depth fieldwork to elucidate causal relations between forests and dietary outcomes locally. The project will break new ground by providing the first generalizable empirical evidence of how and why forests influence dietary quality in low-income countries, thereby laying the ground for a shift in how we think about pathways to food security – that is, to move from conceptualizing food security as driven by agriculture alone to seeing it as dependent on socio-ecological interactions at the forest-agriculture nexus.

Mots-clés : forest loss,dietary diversity,food security,low income countries
Projet ERC : FOCUS (Horizon 2020, appel 2017, 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 : 2067522.5 €

Two-thirds of the Earth’s surface is covered by water and thus largely inaccessible to modern networks of seismological instruments. The FOCUS project is poised to revolutionize seismic monitoring of the seafloor through a novel use of fiber optic cables to improve hazard assessment and increase early warning capability. Laser reflectometry using BOTDR, commonly used for structural health monitoring of large-scale engineering structures (e.g. - bridges, dams, pipelines, etc.), can measure very small strains (< 1 mm) at very large distances (10 - 200 km). It has never been used to monitor deformation caused by active faults on the seafloor. The objective of the FOCUS project is to demonstrate that this technique can measure small (1 - 2 cm) displacements on a primary test site offshore Sicily where the 28 km long EMSO Catania cable crosses the recently mapped North Alfeo Fault. BOTDR observations must be calibrated by other independent measurements. Therefore, targeted marine geophysical surveys of the seafloor along the trace of the cable and faults are planned, with micro-bathymetry, high-resolution seismics, seafloor seismic stations and use of seafloor geodetic instruments to quantify fault displacement. Once the BOTDR fault-monitoring technique has been tested and calibrated offshore Sicily, the goal is to expand it to other fiber optic cable networks, either existing research networks in earthquake hazard zones (Japan, Cascadia) or to the Mediterranean region through access to retired telecommunication cables, or through the development of dual-use cables with industry partners, (two of the anticipated outcomes of the FOCUS project). The novel secondary use of fiber optic cables as described by FOCUS represents a potentially tremendous breakthrough in seismology, tectonics and natural hazard early warning capability, one that could turn Earth’s future undersea communication infrastructure into a seismological monitoring network of unprecedented scale.

Mots-clés : earthquake hazard,active faults,seafloor deformation,montitoring,fiber optic cables,laser reflectometry,botdr,strain measurements,mediterranean,sicily,ionian sea
Projet ERC : SUPERSTARS (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : Queen's University Belfast — Pays : Royaume-Uni — Rôle : Partner
Acronyme de l'entité : QUB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 107989.7 €

Type Ia supernovae (SNe Ia) are the incredibly luminous deaths of white dwarfs in binaries. They play a vital role in chemical enrichment, galaxy feedback, stellar evolution, and were instrumental in the discovery of dark energy. However, what are the progenitor systems of SNe Ia, and how they explode remains a mystery. My recent work has concluded the controversial result that there may be more than one way to produce SNe Ia. As SN Ia cosmology samples reach higher precision, understanding subtle differences in their properties becomes increasingly important. A surprising diversity in white-dwarf explosions has also been uncovered, with a much wider-than-expected range in luminosities, light-curve timescales and spectral properties. A key open question is ‘What explosion mechanisms result in normal SNe Ia compared to more exotic transients?’ My team will use novel early-time observations (within hours of explosion) of 100 SNe Ia in a volume-limited search (<75 Mpc). The targets will come from the ATLAS and Pan-STARRS surveys that will provide unprecedented sky coverage and cadence (>20000 square degrees, up to four times a night). These data will be combined with key progenitor diagnostics of each SN (companion interaction, circumstellar material, central density studies). The observed zoo of transients predicted to result from white-dwarf explosions (He-shell explosions, tidal-disruption events, violent mergers) will also be investigated, with the goal of constraining the mechanisms by which white dwarfs can explode. My access to ATLAS/Pan-STARRS and my previous experience puts me in a unique position to obtain ‘day-zero’ light curves, rapid spectroscopic follow-up, and late-time observations. The data will be analysed with detailed spectral modelling to unveil the progenitors and diversity of SNe Ia. This project is timely with the potential for significant breakthroughs to be made before the start of the next-generation ‘transient machine’, LSST in ~2021.
Projet ERC : SNeX (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : Technion – Israel Institute of Technology — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

Type-Ia supernovae (SNe) are thought to originate from thermonuclear explosions of carbon-oxygen (CO) white-dwarfs (WDs). They play a key role in the evolution of the universe (producing most of the Iron-peak elements); and serve as critical cosmological distance-indicators. The main proposed SNe progenitors are CO-WDs accreting material from stellar companions; and mergers of two CO-WDs. However, all suggested models fail to reproduce the diverse physical characteristics of Ia-SNe; their inferred rates/ages/luminosity distrbution; and their puzzling wide sub-types diversity. Finding the origins and the evolutionary pathways of thermonuclear SNe remains one of the most important “holy grail” open questions in modern astronomy. Here we propose novel directions and potential solutions to this question, and suggest new scenarios for the origin of all sub-types of thermonucelar SNe. Supported by preliminary results, we propose that (1) the little-explored mergers of CO-WDs with hybrid He-CO WDs play a key-role in producing most types of SNe, and may provide a viable model for the origin of the majority of thermonuclear SNe, their diversity and their distrbutions; (2) neutron star-WD mergers may explain the origin of peculiar rapidly evolving SNe; (3) the channel of exploding accretion-grown massive CO-WDs never/rarely gives rise to standard Ia-SNe. We propose an end-to-end open-source-based modelling of SNe (providing easy access and reproducibiliy of our results) including stellar evolution of their progenitors; 3D hydro simulations of WD mergers; 2D (+3D) thermonuclear-hydrodynamical+radiative-transfer models (predicting detailed light-curve/spectra/compostion observables); and population synthesis studies. Our proposed science can potentially transform the field; solve the century-long puzzle of Ia-SNe and explain their origins; and provide critical input for understanding the evolution of the universe and the measurements of its fundamental cosmological parameters.

Mots-clés : supernova,thermonuclear,nucleosynthesis,stellar evolution,white dwarfs,neutros stars,binary stars,standard candles,type ia supernova,iax supernova,type ib supernova,light curve,spectra,chemical evolution of galaxies