Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : IndiGene (Horizon 2020, appel 2018, Synergy grants)
Entité participante : Karlsruhe Institute of Technology — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : KIT
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 600000.0 €

We propose to thoroughly investigate and characterise the sources of variation that results in varying phenotypes in a complex vertebrate. As well as characterising the genetic and environmental sources of variation, we will also investigate individual stochastic variation present even in fixed settings (both genetically and environmentally). To achieve this we will exploit the unique properties of Medaka fish, which can be fully inbred from the wild. We have already inbred and performed whole genome sequencing of a panel of 111 diverse Medaka fish from a single location; we propose to phenotype these fish in depth with high replication structure, ranging from organismal to molecular phenotypes. We will also phenotype entirely wild fish from the same source population as the panel with a subset of the phenotypes. We will analyse the data using state of the art methods to partition variation between genetic, environmental and stochastic components, and their interactions. We will integrate across both the different levels of phenotypic information across the cardiovascular system, and also across vertebrate phenotypes, in particular the extensive human phenotypes. By using genetic crosses and CRISPR-Cas9 techniques we will definitively prove specific interactions. We will host a “Research Hotel” for other phenotyping schemes to be applied to this panel, in particular from the Zebrafish community. This comprehensive and carefully replicated study will allow us to understand the opportunities and limitations of genetic stratification and personalised medicine in humans.
Projet ERC : BASIC (Horizon 2020, appel 2017, Proof of concept grants)
Entité participante : Zellmechanik dresden gmbh — Pays : Allemagne — Rôle : PI
Type d'entité : Organismes privés (hors enseignement)
Financement ERC de l'entité : 150000.0 €

We want to prepare the commercialisation of a novel clinical diagnostic test for the status of the immune system – the mechanical haemogram – with the purpose of improving the therapy management of severe infections, in particular in case of sepsis. Sepsis is ranked the third most common cause of death worldwide, and represents one of the most challenging problems even in modern health care systems. Our test is based on the analysis of single cell mechanical properties of white blood cells found in peripheral blood. Those properties serve as functional biomarker for the activation status of the immune system. With this test, we aim to provide an early detection of sepsis and direct monitoring of treatment effects for an efficient therapy management. The goal of the proposed project is to create and evaluate a business case for the successful implementation of this in vitro diagnostic test for clinical application in sepsis management.

Mots-clés : biomarker,sepsis,haemogram,rt dc,zellmechanik,cell mechanics
Projet ERC : SGCR (Horizon 2020, appel 2014, Advanced grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems Engineering
Entité participante : UK Research and Innovation — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : UKRI
Type d'entité : Recherche
Financement ERC de l'entité : 2475344.0 €

We will build an orthogonal translation system for the encoded synthesis of unnatural polymers in cells. The steps towards this goal are each significant and substantial and, importantly, our solutions for each step are both scalable and modular with those for other steps in the process. In the first step we create a scalable and modular route to multiple new aminoacyl-tRNA synthetase/tRNA pairs that are orthogonal to each other and to endogenous synthetases and tRNAs. In a second step we engineer a cell for orthogonal translation to enable each orthogonal tRNA to quantitatively and efficiently decode a distinct quadruplet codon. In a third step we invent a modular and scalable route to program the active sites of mutually orthogonal synthetases to uniquely recognize distinct unnatural building blocks. The modular combination of the advances in each step enables the conversion of genetic code expansion with one or two unnatural amino acids into a scalable technology for genetic code reprogramming, facilitating the encoded synthesis of unnatural polymers using a synthetic genetic code with more encoded monomers than the natural genetic code.

Mots-clés : protein translation
Projet ERC : DIM-CrIC (Horizon 2020, appel 2019, Proof of concept grants)
Domaine scientifique : Life Science
Panel : Immunity, Infection and Immunotherapy
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 : 140200.0 €

We will develop and test a treatment for pancreatic ductal adenocarcinoma (PDAC). To this aim, we will synthesize and evaluate the efficacy of novel inhibitors of nucleic acid-induced inflammation that we have identified. Indeed, nucleic acid-induced chronic inflammation is a major driver of tumorigenesis. Because the canonical pathway that triggers nucleic acid-induced chronic inflammation relies on the STING protein, identifying ways to activate or inhibit STING is a major goal of current research. We have identified a novel endogenous pathway that antagonizes STING though the production of a second messenger molecule. This pathway prevents spurious STING activation and regulates nucleic acid-associated inflammation. We have developed a set of analogues of this small molecules for wich we demonstrate the ability to prevent STING activation in vitro and now wish to ameliorate the intracellular delivery of these molecules to assess their in vivo efficacy on PDAC-associated inflammation. PDAC is a suitable model for establishing the proof of concept of efficacy of the treatment we propose, because we show that knocking-out STING impairs PDAC growth. Therefore, DIM-CriC will provide novel means to tackle STING-dependent inflammation in PDAC. Ultimately, this work should pave the way to assessment in additional tumorigenesis models that present a STING-dependent inflammatory profile, unraveling a novel route to block cancer-related inflammation to the benefit of patients.

Mots-clés : sting,inhibitors,pancreatic adenocarcinoma,inflammation,cancer
Projet ERC : ULIGHT (Horizon 2020, appel 2019, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : Heriot-Watt University — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

We will use a new class of deep ultraviolet light source to address the biggest challenge facing modern medicine: the emergence of antimicrobial resistance (AMR): the resistance of bacteria (so-called “super bugs”) to anti-biotics. AMR is anticipated to cause 10 million deaths annually by 2050. The golden era of antibiotics is over, and the antibiotic pipeline is failing. We need to develop new technologies to target pathogens without antibiotics and to which pathogens cannot develop resistance. The germicidal properties of ultraviolet light (wavelength around 254 nm) from lamps are well established. Unfortunately, this wavelength is mutagenic to human cells and unsuitable for therapy. However, it has recently been shown that shorter-wavelength deep ultraviolet light (DUVL), such as 207 nm light, maintains germicidal efficacy while limiting mutagenic effects. While there are existing ways to generate DUVL, they all fail to meet the constraints that must be met for ubiquitous clinical use: compact size, low cost, long lifetime, high robustness and no need for serviceability. During my ERC Starting Grant project I developed a new route to create high brightness DUVL sources. In this project we will demonstrate the Proof of Concept of a business providing compact, robust, bright and low-cost DUVL sources—based on a down-scaling of my Starting Grant technology—for delivery to confined regions of the body for safe (non-mutagenic to human cells) killing of pathogens.

Mots-clés : ultraviolet,laser,healthcare,healthcare technology,antimicrobial,germicidal,medical,medicine,antibiotic resistance,antimicrobial resistance
Projet ERC : UEMHP (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : Board of the Swiss Federal Institutes of Technology — Pays : Suisse — Rôle : PI
Acronyme de l'entité : ETH Zurich
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2494725.0 €

We wish to understand how magnetic fields are generated in the cores of planets. This process is called the dynamo mechanism, whose understanding remains one of the geophysical grand challenges. In cores of rocky planets and in gas giants, thermal forcing results in convection and the resulting flow of liquid metal transports heat and generates magnetic fields as a result of the electrical currents that are induced by their motion. Magnetic field generation affects heat transport and cooling, controlling planetary history and evolution (on Earth it is connected with the growth of the inner core); and ultimately the presence or not of magnetic fields can control the existence of life. To understand the process, the momentum, induction and heat equations must be solved in three dimensions as a function of time in a spherical geometry and linked to the numerous extant data sets that are available. Present-day computer simulations do this by implementing fluid viscosities that are too large by many orders of magnitude. Our aim is to correct this by creating a working computer code that correctly implements a novel theory for the dynamo process in which viscosity and inertia have negligible effects, as is correct for the planets. These dynamos are likely to be different from any previous computational dynamo presented over the last 25 years. The potential rewards of such a correct theory and implementation will be great. We will understand how self-sustained dynamos equilibrate in this never-explored regime, and discover strengths and geometries of generated magnetic fields as a function of forcing. We will discover how to find reversing dynamos, just like on Earth, and understand the mechanisms at play. We will understand the energy requirements, ultimately understand core cooling and also be able to make more accurate predictions of future field evolution. We also aim to understand the new data returning from missions to the giant planets.

Mots-clés : dynamo theory,earth s core
Projet ERC : CONT-ACT (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : Max Planck Society — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : MPG
Type d'entité : Recherche
Financement ERC de l'entité : 1495500.0 €

What are the algorithmic principles that would allow a robot to run through a rocky terrain, lift a couch while reaching for an object that rolled under it or manipulate a screwdriver while balancing on top of a ladder? Answering this seemingly naïve question resorts to understanding the fundamental principles for robot locomotion and manipulation, which is very challenging. However, it is a necessary step towards ubiquitous robots capable of helping humans in an uncountable number of tasks. The fundamental aspect of both locomotion and manipulation is that the dynamic interaction of the robot with its environment through the creation of physical contacts is at the heart of the tasks. The planning of such interactions in a general manner is an unsolved problem. Moreover, it is not clear how sensory information (e.g. tactile and force sensors) can be included to improve the robustness of robot behaviors. Most of the time, it is simply discarded. CONT-ACT has the ambition to develop a consistent theoretical framework for motion generation and control where contact interaction is at the core of the approach and an efficient use of sensory information drives the development of high performance, adaptive and robust planning and control methods. CONT-ACT develops an architecture based on real-time predictive controllers that fully exploit contact interactions. In addition, the structure of sensory information during contact interactions is experimentally analyzed to create sensor representations adapted for control. It is then possible to learn predictive models in sensor space that are used to create very reactive controllers. The robot constantly improves its performance as it learns better sensory models. It is a step towards a general theory for robot movement that can be used to control any robot with legs and arms for both manipulation and locomotion tasks and that allows robots to constantly improve their performances as they experience the world.

Mots-clés : legged locomotion,manipulation,force control,motion planning
Projet ERC : GeoMeG (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : University of Fribourg — Pays : Suisse — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 550326.19 €

What are the best trajectories to park a truck with several trailers? How fast can a lattice grow? These are some of the questions studied in this project because both the infinitesimal control structure of movement of a truck and the asymptotic geometry of a (nilpotent) lattice are examples of metric groups: Lie groups with homogeneous distances. The PI plans to study geometric properties of metric groups and their implications to control systems and nilpotent groups. In particular, the plan is to exploit the relation between the regularity of distinguished curves, sets, and maps in subRiemannian groups, volume asymptotics in nilpotent groups, and embedding results. The general goal is to develop an adapted geometric measure theory. SubRiemannian spaces, and in particular Carnot groups, appear in various areas of mathematics, such as control theory, harmonic and complex analysis, asymptotic geometry, subelliptic PDE's and geometric group theory. The results in this project will provide more links between such areas. The PI has developed a net of high-level international collaborations and obtained several results via a combination of analysis on metric spaces (differentiation of Lipschitz maps, tangents of measures, and Gromov-Hausdorff limits) and the theory of locally compact groups (Lie group techniques and the solutions of the Hilbert 5th problem). This allowed the PI to solve a number of open problems in the field, such as the analogue of Myers-Steenrod theorem on the smoothness of isometries, the analogue of Nash isometric embedding and the non-minimality of curves with corners. Some of the next aims are to establish an analogue of the De Giorgi's rectifiability result for finite-perimeter sets and prove the smoothness of geodesics, a 30-year-old open problem. The goal of this project is to tackle them, together with many more related questions. The PI received his first degree at SNS Pisa (advisor: M.Abate) and his PhD from Yale University (advisor: B.Kleiner). Before obtaining a permanent position only three years after graduation, he was at ETH, Orsay, and MSRI. He received the prestigious position of research fellow of the Academy of Finland.

Mots-clés : analysis on metric spaces,subriemannian geometry,geometric group theory
Projet ERC : DiversiPHI (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : Utrecht University — Pays : Pays-Bas — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 464375.0 €

What determines if a phage can infect a host? This question arises as we work to understand the ecological roles of the hundreds of thousands of unknown viruses that I and others have discovered around the world. Phages are the most abundant life forms on Earth with important applications in medicine and biotechnology and far-ranging effects on microbial community functioning in all environments. Phage-host interactions (PHI) are an emergent trait that depends on the complex integration of factors like their taxonomic identity, the environment, and phage- and host-encoded proteins. With DiversiPHI, I propose a research program to unravel PHI by 1) measuring, 2) modelling, and 3) experimentally testing these diverse factors to develop a predictive understanding of host-range evolution. I will first measure a range of evolutionary, ecological, and molecular factors contributing to PHI at high resolution using newly developed computational tools that exploit high-throughput datasets from thousands of natural environments around the world. Next, I will apply deep learning to integrate these measurements to simultaneously (i) quantify the relative importance and complex inter-dependencies of the different factors, and (ii) create a unique predictive model of host-range evolution. To complement these in silico predictions, I will develop an experimental evolution setup that tests the effect of the different PHI factors on host-range evolution in vitro. Little is known about the abundant phages and their role in shaping our microbial world. DiversiPHI will vastly elevate this understanding and contribute new fundamental knowledge on how species-species interactions evolve in complex environments. Moreover, I will provide valuable new analysis tools to the community and consolidate my strong international reputation as a pioneering researcher in the cross-disciplinary field encompassing microbial ecology, virology, metagenomics, bioinformatics, and computer learning.

Mots-clés : bacteriophages,virus host interactions,host range,microbial ecology,eco evolutionary processes,complexity,metagenomics,bioinformatics,machine learning,experimental evolution,host switching
Projet ERC : HealthcareLabour (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Imperial College London — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1412767.5 €

What determines the quality of public services? How do shocks to the economy affect the delivery of public services? Why is there such variation in the efficiency of public service providers and how does this affect those who use their services? My aim is to make a fundamental contribution to our understanding of the labour supply behaviour of public service providers and the impact of their behaviour on the quality and distribution of critical outcomes. To achieve this I will primarily focus on the healthcare sector. The importance of the healthcare sector to social wellbeing, the existence of shocks that create ‘natural’ experiments, and the availability of large administrative datasets makes the healthcare market the ideal test-bed. Further, understanding how labour markets in healthcare operate is crucial for public expenditure and central because society cares about the output produced. I will adopt two broad approaches. The first is to examine the micro-foundations of behaviour for critical agents. The second is to examine the effect of policy and macro shocks to the economy on the reallocation of labour within, and between, healthcare and other sectors. In all cases my focus is on understanding labour supply responses and how these impact on the level and distribution of critical outcomes in society. The ideas are applicable to all labour markets characterised by high levels of investment in human capital and where market failures mean society cares about the outcomes. My research will contribute to the fields of labour and health economics. My research will also inform the development of policies to increase the uptake and spread of medical innovation, increase the quality of the medical labour force and improve the design of healthcare systems.
Projet ERC : HealthcareLabour (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Institute for Fiscal Studies — Pays : Royaume-Uni — Rôle : Partner
Acronyme de l'entité : IFS
Type d'entité : Recherche
Financement ERC de l'entité : 74980.0 €

What determines the quality of public services? How do shocks to the economy affect the delivery of public services? Why is there such variation in the efficiency of public service providers and how does this affect those who use their services? My aim is to make a fundamental contribution to our understanding of the labour supply behaviour of public service providers and the impact of their behaviour on the quality and distribution of critical outcomes. To achieve this I will primarily focus on the healthcare sector. The importance of the healthcare sector to social wellbeing, the existence of shocks that create ‘natural’ experiments, and the availability of large administrative datasets makes the healthcare market the ideal test-bed. Further, understanding how labour markets in healthcare operate is crucial for public expenditure and central because society cares about the output produced. I will adopt two broad approaches. The first is to examine the micro-foundations of behaviour for critical agents. The second is to examine the effect of policy and macro shocks to the economy on the reallocation of labour within, and between, healthcare and other sectors. In all cases my focus is on understanding labour supply responses and how these impact on the level and distribution of critical outcomes in society. The ideas are applicable to all labour markets characterised by high levels of investment in human capital and where market failures mean society cares about the outcomes. My research will contribute to the fields of labour and health economics. My research will also inform the development of policies to increase the uptake and spread of medical innovation, increase the quality of the medical labour force and improve the design of healthcare systems.