Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : RINGE3 (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: Biological Mechanisms, Structures and Functions
Entité participante : Cancer Research UK — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : CRUK
Type d'entité : Recherche
Financement ERC de l'entité : 2000000.0 €

Ubiquitin (Ub) conjugation regulates a myriad of cellular processes in the eukaryotic cell. Ub-ligases (E3) play a pivotal role in deciding the substrate’s fate and function by catalyzing the transfer of Ub from Ub-conjugating enzyme (E2) to a substrate protein lysine sidechain. Successive rounds of E3-catalyzed substrate ubiquitination lead to the formation of poly-Ub chains or multi-monoubiquitination, which direct the substrate to different biological fates such as degradation by the 26S proteasome. RING E3s comprise the largest family of E3s with approximately 600 members in humans. Over the last fifteen years, structural biology and biochemical studies have paved the way for understanding how RING E3s interact with E2s and substrates, and how they are regulated. Recently my group has trapped the crystal structure of a RING E3 bound to an E2 covalently-linked to Ub (E2~Ub), thus providing a molecular snapshot of how RING E3 optimizes E2~Ub for catalysis. Despite these advances, the mechanisms of RING E3-catalyzed ubiquitination are not completely understood. Here, we propose to investigate three key aspects of RING E3 functions. First, we will determine structures of several RING E3s bound to E2~Ub to dissect the molecular basis for RING E3-E2~Ub selectivity. Second, our recent structure of a RING E3, Cbl-b, bound to E2~Ub and a substrate peptide provides a starting point for structural determination of a more challenging RING E3-E2~Ub-intact substrate complex to elucidate the mechanisms of substrate ubiquitination. Third, we have developed an ubiquitinated Cbl-substrate mimetic to study the mechanisms of RING E3-catalyzed poly-ubiquitination using structural and biochemical approaches. Expected results will greatly expand our knowledge of RING E3-mediated ubiquitination and will foster strategies in exploiting E3s for therapeutic development, since deregulation of E3s underlies many diseases including cancers.

Mots-clés : ubiquitin,e3 ligase,ring e3,protein degradation
Projet ERC : Ub-BAC (Horizon 2020, appel 2016, Advanced grants)
Domaine scientifique : Life Science
Panel : Immunity, Infection and Immunotherapy
Entité participante : Goethe University Frankfurt — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2500000.0 €

Ubiquitination represents one of the most versatile post-translational modifications in eukaryotes and is involved in regulation of numerous cellular processes. Despite prokaryotes lacking this system, facultative intracellular bacteria like Salmonella, Shigella and Legionella have evolved multiple strategies to manipulate the host ubiquitin (Ub) system to their own benefit. This proposal is focussed on gaining a fundamental understanding of the Ub system in the course of bacterial infections. We will employ advanced quantitative proteomics to perform a global analysis of the dynamic changes in the ubiquitinome and phosphoproteome of epithelial cells and macrophages upon infection with Salmonella, Shigella and Legionella. The comprehensive datasets will constitute an invaluable resource freely available to the scientific community. We expect to identify novel pathways triggered by selected bacterial ligases and will characterise their contribution to pathogenicity and virulence, as well as their suitability for being targeted in a therapeutic setting. Both medicinal chemistry and structural biology approaches will be exploited to identify inhibitors for NEL-type bacterial ligases. Hypothesis-driven projects within the work program focus on (i) a novel chemical modification of Ub controlling Legionella infections and (ii) the observation that Ub chains on Salmonella can form nanoscale clusters that recruit and activate multiple signalling pathways within the host cell. Super-resolution microscopy and single-molecule imaging will be used to visualise and dissect these Ub-triggered complexes. Taken together, the combination of unbiased global proteome analysis and hypothesis-driven projects creates a unique scientific program within the biomedical field of understanding and combatting bacterial infections. Along the same lines, this proposal holds a great potential for the future development of novel strategies for antibacterial therapies.

Mots-clés : salmonella,shigella,legionella,bacterial effectors,ubiquitination,ubiquitinome,phosphoproteome,e3 ligase,drug development,host pathogen interaction,innate immunity,autophagy
Projet ERC : DISCONNECT (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : Ghent University — Pays : Belgique — Rôle : PI
Acronyme de l'entité : UGent
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499860.0 €

Ubiquitous connectivity increases our autonomy: We can connect to content, contacts and services without time or place constraints. Paradoxically, however, ubiquitous connectivity also threatens that very autonomy: The addictive design of digital technologies diverts attention away from our primary activities, and in our contemporary culture of connectivity we face increasing pressures to be permanently online and permanently connected. This mobile connectivity paradox presents an urgent challenge: How can we balance connectivity and disconnectivity so that we experience digital wellbeing? Current scholarship lacks answers to this question. It fails to account for the dynamic nature of digital wellbeing and to sufficiently address the interplay between psychological, technological and social factors. This research project overcomes these limitations by building a dynamic pathway model of digital wellbeing. The model is tested via a multi-method and multi-paradigmatic empirical approach that integrates traditional data sources with behavioral data gathered via device logging and dynamic data on users’ momentary states and contexts gathered via mobile experience sampling. Data are analyzed using innovative digital ethnographic and machine learning methods. This empirical test of the dynamic pathway model of digital wellbeing gives insight into (1) how individuals understand and practice digital wellbeing, (2) which constellations of person-, device- and context-specific factors form pathways that lead to short-term changes in digital wellbeing and long-term changes in indicators of overall wellbeing such as burnout or depression, and (3) what impact digital wellbeing interventions have on digital wellbeing. Armed with these insights, stakeholders such as users, technology developers and policy makers can take steps to increase digital wellbeing.

Mots-clés : digital wellbeing,digital media use,wellbeing,computational social sciences,digital ethnography
Projet ERC : SPRINT (Horizon 2020, appel 2015, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : National Research Council — Pays : Italie — Rôle : PI
Acronyme de l'entité : CNR
Type d'entité : Recherche
Financement ERC de l'entité : 1990011.0 €

Ultra-short light pulses with large instantaneous intensities can probe light-matter interaction phenomena, capture snapshots of molecular dynamics and drive high-speed communications. In a semiconductor laser, mode-locking is the primary way to generate ultrafast signals. Despite the intriguing perspectives, operation at Terahertz (THz) frequencies is facing fundamental limitations: engineering "ultrafast" THz semiconductor lasers from scratch or finding an integrated technology to shorten THz light pulses are currently two demanding routes. SPRINT aims to innovatively combine the groundbreaking quantum cascade laser (QCL) technology with graphene, to develop a new generation of passive mode-locked THz photonic laser resonators, combined with unexplored electronic nanodetectors for ultrafast THz sensing and imaging. To achieve these ambitious objectives, the versatile quantum design of QCLs will be exploited to engineer the laser gain spectrum on purpose. Resonators of unusual symmetry and shape, like photonic quasi-crystals or random patterns, will be implemented, offering the flexibility to control and guide photons and the lithographic capability to embed miniaturized intra-cavity passive components to probe and modulate light. Graphene, owing to its gapless nature and ultrafast, gating-tunable carrier dynamic, will lead to a major breakthrough: integration in the THz QCL cavity will allow superbly manipulating its functionalities. Antenna-coupled quantum-dot nanowires will be also devised to sense and probe ultra-short THz pulses. The project will target radically new concepts and interdisciplinary approaches encompassing unconventional THz QCL micro-resonators, graphene and polaritonic THz saturable absorbers, non-linear ultra-low dimensional detection architectures. Pushing forward the understanding of ultrafast dynamics in complex THz electronic and photonic systems, SPRINT prospects new directions and long-term impacts on fundamental and applied science.
Projet ERC : TerAqua (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : Ruhr University Bochum — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : RUB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1415417.0 €

Ultrafast laser driven sources of few-cycle THz pulses have progressed from being specialized systems to widespread laboratory tools. This has enabled THz time-domain spectroscopy to emerge as a powerful technique for time-resolved studies of the dynamics of many fundamental constituents of matter. Recent progress in the generation of few-cycle THz pulses with electric field strengths exceeding several MV/cm, has allowed nonlinear THz spectroscopy to open new exciting fields of research. However, many fields suffer from a persistent lack of table-top THz sources combining high field strength and high repetition rate (i.e. high average power). So far, these parameters can only be achieved by costly and restrictive accelerator facilities. In most cases, scientists will either abandon research lines where high pulse energy is required or operate at low repetition rate (typically <1 kHz). This imposes severe limits on the explored parameter space in many measurements, for which minuscule average powers result in very long integration time and low signal-to-noise ratio. My objective is to fill this gap by achieving table-top strong-field THz sources with watt-level average power. The key feature is to leverage existing THz generation techniques to higher efficiency by driving them inside the resonator of a high-power ultrafast thin-disk oscillator, operated in the mid-infrared (2 µm) spectral region. This source will have impact in various fields of physics, physical chemistry, engineering, biology and medicine. I envision to apply this source in physical chemistry, to gain understanding of the role of water as “the solvent of life”. THz spectroscopy is perfectly suited to study aqueous samples, however, so far, most THz studies in liquid phase have been performed in the linear domain, and in a restricted parameter space due to the inadequacy of the sources. Our source will enable us to expand these studies into the nonlinear domain, which was previously impossible.
Projet ERC : SuperComp (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : Institute of Photonic Sciences — Pays : Espagne — Rôle : PI
Acronyme de l'entité : ICFO;ICFO-CERCA
Type d'entité : Recherche
Financement ERC de l'entité : 1997381.0 €

Unconventional superfluids, where frictionless flow appears combined with features such as topological excitations or crystalline order, are some of the most surprising collective quantum phases of matter. Although the corresponding topological superfluids and supersolids were originally predicted in condensed matter, ultracold quantum gases provide a more controlled experimental approach to these phases, and promise microscopic access to their exotic properties. A key mechanism towards unconventional superfluidity is the competition in the same system of interactions of different origins, a situation naturally addressed by multicomponent gases. When these interactions have opposite signs, new and surprising phases emerge. A prime example is the ultradilute quantum liquid phase recently observed by my group in a mixture of Bose-Einstein condensates, which is most likely only the first item in a long list of new unconventional phases. The goal of SuperComp is to exploit the full potential of quantum gases with competing interactions to unlock the observation of unconventional superfluid phases that have until now defied experimental realization. To this end, I will explore three distinct mechanisms resulting in unconventional superfluid behavior: quantum fluctuations, engineered dispersion relations, and interactions with non-zero orbital angular momentum. Exploiting combinations of bosonic and fermionic potassium atoms, I will realize novel types of ultradilute quantum liquids, supersolid-like gases and liquids, density-dependent artificial gauge fields, elastic multi-body interactions, and investigate a new approach towards the long-sought px+ipy topological superfluid phase of 2D Fermi gases. These experiments will deepen our understanding of the mechanisms responsible for unconventional superfluidity, and impact not only the field of quantum gases, but also the much broader range of disciplines where unconventional superfluids or superconductors play a key role.

Mots-clés : ultracold quantum mixtures,dilute quantum liquids,spin orbit coupling,p wave interactions,topological superfluids
Projet ERC : UnDark (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : University of Amsterdam — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : UvA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

Uncovering the particle nature of dark matter (DM) is one of the primary goals of fundamental physics and cosmology today. DM accounts for 85% of the matter in the Universe, but only its gravitational effects on the motion of celestial objects have been observed. DM research is arguably in a critical state: the long-prevailing WIMP (weakly interacting massive particle) paradigm has not been confirmed experimentally up to now, putting the focus on the electroweak scale under serious pressure. I played a leading role in both identifying and bringing to fall some of the most promising WIMP signal candidates, thanks to new data analysis and modeling techniques that I continuously develop and employ with my group. Furthermore, I am an initiator and lead developer of a powerful new analysis framework for combining a vast range of astrophysical, cosmological, collider and laboratory data, which now forms the core of the most accurate global studies of DM theories. My goal is to discover particle DM in the post-WIMP era, both by introducing new approaches and by adapting methods that I successfully developed for WIMP searches. I will conclusively probe astrophysical hints for sterile neutrino and axion DM, which are the most promising DM candidates next to the WIMP. However, given the hundreds of valid DM models on the market, one should be wary of repeating the fixation on specific energy scales yet again. I will develop an urgently needed 'principled lampposts' approach to identify gaps in searches for a large number of DM models across the scales. I will cover substantial unexplored territory in the axion and sterile neutrino DM parameter space with upcoming radio and X-ray data, study indirect detection signals over a large range of frequencies, and constrain the substructure of DM with upcoming optical, infrared and radio observations. The results will lead to a detection of the most promising DM candidates, or provide the strongest constraints.

Mots-clés : sterile neutrino dark matter,axion dark matter,indirect dark matter searches,small scale probes of dark matter,deep learning,statistical methods,global scans
Projet ERC : TORYD (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : Sorbonne Université — Pays : France — Rôle : PI
Acronyme de l'entité : Sorbonne U
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1900000.0 €

Understanding and controlling quantum matter is a key challenge for basic research and for the development of applications. The richness of quantum physics is notoriously difficult to handle for strong interactions, usually leading to massive entanglement between particles, especially when it is associated with a nontrivial topology of the Hamiltonian. The realization of well-controlled experiments probing strongly correlated quantum matter is thus a major objective to explore these perspectives. In this project, I will investigate many-body states of quantum matter in- and out-of-equilibrium. I will study the interplay between topology and interactions in tailored model systems and explore new quantum phases of matter. I will focus on two main objectives: (i) The study of dynamical properties of strongly interacting 1D Bose gases using quantum transport experiments. (ii) The realization of fractional Chern insulator states in topological lattices. These objectives will be achieved thanks to ultracold gases of Rydberg atoms, where the excellent control of quantum gases is extended thanks to the strong interactions between Rydberg particles.

Mots-clés : topology,rydberg atoms,bose einstein condensates,edge states,optical lattices
Projet ERC : EDUCAGE (Horizon 2020, appel 2014, Proof of concept grants)
Entité participante : Hebrew University of Jerusalem — Pays : Israël — Rôle : PI
Acronyme de l'entité : HUJI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Understanding behavior is still one of the holy grails in the natural and social sciences. Behavior is often utterly complex because it is a result of an extremely rich set of past experiences, the present state of the animal and the animal’s predictions about the future; all of which affect learning, decision making and consequently behavior. Given the complexity of behavior, most researchers work in the realm of highly simplified learning tasks but these only test very basic attributes of learning and are constraining discovery of more sophisticated behavior. To date, there are only few available platforms for rigorous study of complex behavioral paradigms in experimental animals; not in basic science nor in biomedical research. Our goal is to bring to completion (and potential commercialization) a novel platform for analyzing complex animal behavior named “The Educage” to allow fully automatic, hands free assessment of higher cognitive functions in freely behaving animals. Potential customers are research labs, and the biomedical industry. The Educage will allow researchers to study behavior at unprecedented resolution, 24/7, for any duration of time. The learning paradigms can be tailored to the specific task of interest. The Educage has many advantages that outperform existing technologies by allowing rigorous statistical assessment of complex behaviors in laboratory animals. The Educage allows researchers the flexibility to monitor, analyze and manipulate the experiment during the behavior. Our system can be reliably used to analyze perceptual learning in mice and is well suited for being a new and rigorous behavioral platform. It has great potential to become a central tool to fuel discovery in animal research both in biology and biomedical research.

Mots-clés : behavior,learning,automated,natural,perception,auditory,olfactory
Projet ERC : CausalStats (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
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 : 2184375.0 €

Understanding cause-effect relationships between variables is of great interest in many fields of science. However, causal inference from data is much more ambitious and difficult than inferring (undirected) measures of association such as correlations, partial correlations or multivariate regression coefficients, mainly because of fundamental identifiability problems. A main objective of the proposal is to exploit advantages from large-scale heterogeneous data for causal inference where heterogeneity arises from different experimental conditions or different unknown sub-populations. A key idea is to consider invariance or stability across different experimental conditions of certain conditional probability distributions: the invariants correspond on the one hand to (properly defined) causal variables which are of main interest in causality; andon the other hand, they correspond to the features for constructing powerful predictions for new scenarios which are unobserved in the data (new probability distributions). This opens novel perspectives: causal inference can be phrased as a prediction problem of a certain kind, and vice versa, new prediction methods which work well across different scenarios (unobserved in the data) should be based on or regularized towards causal variables. Fundamental identifiability limits will become weaker with increased degree of heterogeneity, as we expect in large-scale data. The topic is essentially unexplored, yet it opens new avenues for causal inference, structural equation and graphical modeling, and robust prediction based on large-scale complex data. We will develop mathematical theory, statistical methodology and efficient algorithms; and we will also work and collaborate on major application problems such as inferring causal effects (i.e., total intervention effects) from gene knock-out or RNA interference perturbation experiments, genome-wide association studies and novel prediction tasks in economics.

Mots-clés : application in biology and economics,causal inference,genetic pertubations,genomics,high dimensional inference,invariant prediction