Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : DEVOCEAN (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : Lund University — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2500000.0 €

Motivated by a series of recent discoveries, DEVOCEAN will provide the first comprehensive evaluation of the emergence of diatoms and their impact on the global biogeochemical cycle of silica, carbon and other nutrients that regulate ocean productivity and ultimately climate. I propose that the proliferation of phytoplankton that occurred after the Permian-Triassic extinction, in particular the diatoms, fundamentally influenced oceanic environments through the enhancement of carbon export to depth as part of the biological pump. Although molecular clocks suggest that diatoms evolved over 200 Ma ago, this result has been largely ignored because of the lack of diatoms in the geologic fossil record with most studies therefore focused on diversification during the Cenozoic where abundant diatom fossils are found. Much of the older fossil evidence has likely been destroyed by dissolution during diagenesis, subducted or is concealed deep within the Earth under many layers of rock. DEVOCEAN will provide evidence on diatom evolution and speciation in the geological record by examining formations representing locations in which diatoms are likely to have accumulated in ocean sediments. We will generate robust estimates of the timing and magnitude of dissolved Si drawdown following the origin of diatoms using the isotopic silicon composition of fossil sponge spicules and radiolarians. The project will also provide fundamental new insights into the timing of dissolved Si drawdown and other key events, which reorganized the distribution of carbon and nutrients in seawater, changing energy flows and productivity in the biological communities of the ancient oceans.

Mots-clés : diatom,past oceans,silica,mesozoic
Projet ERC : eQG (Horizon 2020, appel 2016, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : Max Planck Society — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : MPG
Type d'entité : Recherche
Financement ERC de l'entité : 1918750.0 €

Motivated by the overwhelming success of symmetry concepts in formulating basic laws of physics, the present proposal (eQG) seeks to develop a new symmetry based approach to the problem of reconciling Quantum Mechanics and Einstein’s General Relativity into a consistent theory of Quantum Gravity, regarded by many as the greatest challenge of contemporary theoretical physics. The need for such a theory is most pressing for the resolution of black hole singularities and the Big Bang, but it is equally crucial to the search for a consistent UV completion of the Standard Model of Particle Physics and the unification of the fundamental interactions. eQG aims to tackle this problem from a new perspective, bringing together very different strands of development: on the one hand, by paying particular attention to recent advances in our understanding of cosmological singularities and the evidence for novel infinite-dimensional duality symmetries near the singularity that has emerged in supergravity and string theory, and to recent progress in formulating ‘exceptional geometries’ transcending Riemannian geometry; on the other hand, by exploiting insights from modern canonical quantisation towards a better understanding of the basic degrees of freedom and the dynamics of quantum space-time. The main focus of eQG will be the ‘maximally extended’ exceptional hyperbolic Kac–Moody symmetry E10, whose uniquely distinguished status makes it a prime candidate symmetry for unifying the known dualities of string and M theory, for a conceptually precise scenario of emergent (quantum) space and time near the singularity, and finally, for replacing supersymmetry as a guiding principle for unification. Consequently, the principal goal of eQG will be to explore how this symmetry can define a theory of quantum gravity, how it acts on its fundamental degrees of freedom, what the special features are of the quantised theory, and what physical predictions can be derived from it.
Projet ERC : MotMotLearn (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : University of Birmingham — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1497885.0 €

Motor learning (the ability of the brain to learn and update how an action is executed) is a fundamental process which influences many aspects of our lives such as learning to walk during childhood; the day-to-day behavioural adjustments required as an adult or in healthy ageing; and the rehabilitation process following an illness or injury. Despite the impact to society, it has proved extremely difficult to develop interventions that significantly enhance human motor learning. Therefore, devising protocols which optimise motor learning is a state-of-the-art research question that promises to deliver scientific, clinical and societal impact.Seeking reward and avoiding punishment are powerful factors in motivating humans to alter behaviour during cognition-based learning (selecting which action to perform), with sensitivity to reward and punishment being biased by the availability of dopamine in the brain. Intriguingly, reward and punishment are also known to affect generic motor learning (deciding how an action is executed) tasks which involve multiple underlying mechanisms. However to establish their potential for optimizing motor learning, we must understand how explicit reward- and punishment-based motivational feedback impact motor learning systems with unique computational and anatomical features (use-dependent/model-free/model-based). Using an unprecedented combination of behavioural analysis, computational modelling, genetics and pharmacology, MotMotLearn will provide the first systems-based account of how reward, punishment and dopamine influence motor learning. This novel approach will enable MotMotLearn to develop theoretically-grounded protocols that utilise reward/punishment in conjunction with dopaminergic medication to optimise motor learning in healthy individuals and stroke patients suffering motor impairments. MotMotLearn will have a profound scientific impact in motor learning with applications to development, ageing, rehabilitation and sports.

Mots-clés : motor system,motor learning,stroke motor rehabilitation,motivation,reward,punishment,dopamine,action execution,motor control
Projet ERC : UncertainENV (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Tel Aviv University — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Much of the research on the foundations of graph algorithms is carried out under the assumption that the algorithm has full knowledge of the input data. In spite of the theoretical appeal and simplicity of this setting, the assumption that the algorithm has full knowledge does not always hold. Indeed uncertainty and partial knowledge arise in many settings. One example is where the data is very large, in which case even reading the entire data once is infeasible, and sampling is required. Another example is where data changes occur over time (e.g., social networks where information is fluid). A third example is where processing of the data is distributed over computation nodes, and each node has only local information. Randomization is a powerful tool in the classic setting of graph algorithms with full knowledge and is often used to simplify the algorithm and to speed-up its running time. However, physical computers are deterministic machines, and obtaining true randomness can be a hard task to achieve. Therefore, a central line of research is focused on the derandomization of algorithms that relies on randomness. The challenge of derandomization also arise in settings where the algorithm has some degree of uncertainty. In fact, in many cases of uncertainty the challenge and motivation of derandomization is even stronger. Randomization by itself adds another layer of uncertainty, because different results may be attained in different runs of the algorithm. In addition, in many cases of uncertainty randomization often comes with additional assumptions on the model itself, and therefore weaken the guarantees of the algorithm. In this proposal I will investigate the power of randomization in uncertain environments. I will focus on two fundamental areas of graph algorithms with uncertainty. The first area relates to dynamic algorithms and the second area concerns distributed graph algorithms.

Mots-clés : graph algorithms,distributed computing,dynamic algorithms,randomized algorithms
Projet ERC : INVADERS (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : Institut national de la santé et de la recherche médicale — Pays : France — Rôle : PI
Acronyme de l'entité : Inserm
Type d'entité : Recherche
Financement ERC de l'entité : 1850000.0 €

Mucus-Penetrating Microbiota: Characterization, Mechanism and Therapeutic in Metabolic Disease Humanity is facing an epidemic of inter-related metabolic disorders, including obesity, insulin resistance, hyperglycemia, hyperlipidemia, and hepatic steatosis, that altogether have major impact on the promotion of cardiovascular diseases. The increasing incidence of these complex metabolic disorders and their highly morbid, chronic and costly downstream diseases threatens to overwhelm the world’s health care systems and economies, making it a top public health priority in dire need of investigation. The intestinal tract is inhabited by a large and diverse community of bacteria, collectively referred to as the intestinal microbiota. When stably maintained at an appropriately safe distance from the epithelial cell monolayer, the microbiota provides important benefits to its host. However, disturbance of the microbiota-host relationship, promoted by genetic or non-genetic factors, can alter intestinal homeostasis and drive chronic low-grade intestinal inflammation, ultimately leading to metabolic abnormalities. We previously reported that a ubiquitous class of food additives, emulsifiers, detrimentally impact the microbiota resulting in its encroachment into the mucus layer that associated with low-grade inflammation and development of metabolic disorders. The central goal of this proposal is to investigate the hypothesis that bacteria that penetrate the inner part of the mucus layer, referred as invaders, promote development of metabolic alterations. We herein propose to identify mucus-invaders, in preclinical models and clinical conditions, and investigate mechanisms by which they promote inflammatory and metabolic abnormalities. Furthermore, we propose to define original approaches to modulate the intestinal microbiota in order to counteract microbiota encroachment and protect against associated metabolic abnormalities.

Mots-clés : host,microbiota interactions
Projet ERC : MultiRev (Horizon 2020, appel 2014, Proof of concept grants)
Entité participante : Johannes Gutenberg University Mainz — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Multi-revolution sensors that detect multiple rotations are being used in many areas of the automotive and automation industry. The current generation of discrete multi-revolution sensors has major drawbacks, such as complex architecture, limited applicability and high cost. MultiRev will demonstrate the proof-of-concept of a novel monobloc multi-revolution sensor based on non-volatile low power domain wall motion induced by the rotating fields that are being sensed to count revolutions. A large number of rotations can be sensed by combining multiple curved domain wall conduit devices with coprime numbers of loops developed with results from the PI’s ERC starting grant. This novel non-volatile sensor will operate without power during sensing and only for eventual read-out power is needed. This new multi-revolution sensor will be disruptive by replacing current expensive and complex incumbent non-magnetic multi-revolution sensors by a simple energy self-sufficient magnetic device that will open up a large market by extending the range of magnetic sensors from the present sensing of only one or a few revolutions to the sensing of thousands of revolutions.The goal of this PoC is to: (i) demonstrate that the building blocks for the sensor developed in the lab can be combined and fabricated for reliable operation using industrially compatible processes to achieve volume production at competitive costs and (ii) explore exploitation routes by analyzing market, cost, and pricing and developing an IP strategy and business model. To achieve this, a postdoc and an engineer will be hired and external support will be contracted to support the team. Our novel sensor solution for growing markets will generate high added value for European companies, which are under constant price pressure for their currently produced simple sensors. The high innovation level will secure the adoption of this high value technology with mass-market potential in Europe strengthening the economic area.

Mots-clés : magnetic sensor,rotation,revolution sensor,magnetic domain wall device,spintronics device
Projet ERC : LostInZoom (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Institut national de l'information géographique et forestière — Pays : France — Rôle : PI
Acronyme de l'entité : IGN
Type d'entité : Recherche
Financement ERC de l'entité : 2000000.0 €

Multi-scale interactive maps such as Google Maps or OpenStreetMap have replaced paper topographic maps for most professional and daily uses. Past research told us how to design paper topographic maps at a given scale to make them readable and understandable by human users. But these rules/guidelines do not apply anymore, and map designers lack guidelines to make maps that are smooth to explore through scales, and it is common for a multi-scale map user to feel lost for a few seconds after a zooming interaction. The LostInZoom project (Not Getting Lost in Multi-Scale Maps: Exploring the Anchor Theory in Cartographic Zoom Interactions in the Context of Crisis Management) seeks to establish a new zooming paradigms for multi-scale maps, based on multi-scale visual landmarks that act as anchors during the zoom. To achieve this novel zooming paradigm, we need new grounding knowledge on how people perceive and understand interactive multi-scale maps, in order to design maps and interaction that make multi-scale explorations smoother. The LostInZoom project will be based on three main pillars. In the first one, we will explore the cognition of multi-scale interactive maps with an experimentation approach, in order to identify the anchors or landmarks that help a map reader locate himself when zooming. In the second pillar, we will design new cartographic generalisation techniques to derive multi-scale maps that magnify the landmarks that are important for multi-scale exploration. Finally, the third pillar will be dedicated to the design of new zooming interactions that focus on these landmarks to smooth even more the zooming exploration.

Mots-clés : cartography,multi scale visualisation,zoom,scale,risk management,map generalisation,spatial cognition
Projet ERC : Macro-EpiK (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
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 : 1499945.0 €

Multicellular organisms (e.g., animals, fungi and plants) are the best-studied eukaryotes but their ancestors and the vast majority of eukaryotic diversity correspond to microbial species (“protists”). The evolutionary history of protists is closely connected to the evolution of the eukaryotic cell itself. However, most protist diversity is still genomically unexplored, limiting our investigation of eukaryotic evolution. For example, while the importance of lateral gene transfer (LGT) in prokaryotic evolution is well recognized, its role in eukaryotic evolution is still debated. In addition, although epigenetic mechanisms represent a hallmark of eukaryotic genome regulation, we know surprisingly little about the evolution of these mechanisms across eukaryotic diversity. The overarching goal of my project is to understand how epigenetic mechanisms and LGT have shaped the macroevolution of eukaryotic genomes. This project has several inter-related intermediate objectives, which each in themselves will bring crucial insights into eukaryotic evolution: 1) reconstructing a robust phylogeny of eukaryotes; 2) inferring the gene content of the Last Eukaryotic Common Ancestor; 3) tracing the evolution of genes involved in epigenetic mechanisms and obtaining epigenomic maps from under-studied protists; 4) investigating the intriguing hypothesis of a possible interplay between epigenetic regulation and horizontal gene transfer and its influence on eukaryotic genome evolution: Have genes involved in epigenomic mechanisms been transferred between eukaryotes? Do epigenomic modifications affect the frequency of LGT in different lineages? To achieve this, I will characterize the transcriptomes, genomes, methylomes and small RNAs of understudied eukaryotic microbes selected for their key phylogenetic position, and to analyse them using state-of-the-art bioinformatic methods. I will target uncultivated protists, using single-cell techniques and novel genome-scaffolding approaches.

Mots-clés : eukaryotic microbes,evolution,phylogenetics,epigenetics,lateral gene transfer,singe cell,genomics,comparative genomics
Projet ERC : ECHO (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Murcia — Pays : Espagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999955.0 €

Multicore processors are present nowadays in most digital devices, from smartphones to high-performance servers. The increasing computational power of these processors is essential for enabling many important emerging application domains such as big-data, media, medical, or scientific modeling. A fundamental technique to improve performance is speculation, a technique that consists in executing work before it is known if it is actually needed. In hardware, speculation significantly increases energy consumption by performing unnecessary operations, while speculation in software (e.g., compilers) is not the default thus preventing performance optimizations. Since performance in current multicores is limited by their power budget, it is imperative to make multicores as energy-efficient as possible to increase performance even further. In a multicore architecture, the cache coherence protocol is an essential component since its unique but challenging role is to offer a simple and unified view of the memory hierarchy. This project envisions that extending the role of the coherence protocol to simplify other system components will be the key to overcome the performance and energy limitations of current multicores. In particular, ECHO proposes to add simple but effective extensions to the cache coherence protocol in order to (i) reduce and even eliminate misspeculations at the processing cores and synchronization mechanisms and to (ii) enable speculative optimizations at compile time. The goal of this innovative approach is to improve the performance and energy efficiency of future multicore architectures. To accomplish the objectives proposed in this project, I will build on my 14 years expertise in cache coherence, documented in over 40 publications of high impact.

Mots-clés : cache coherence,processor design,synchronization mechanisms,compiler infrastructure,heterogeneous architectures
Projet ERC : complexNMR (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : University of Warwick — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999044.0 €

Multidrug resistant bacteria that render worthless the current arsenal of antibiotics are a growing global problem. This grave challenge could be tackled by polyketide synthases (PKSs), which are gigantic modular enzymatic assembly lines for natural products. PKSs could be developed for industry to produce chemically difficult to synthesize drugs, but cannot be harnessed until we understand how they work on the molecular level. However, such understanding is missing because we cannot easily investigate large complexes with current structural biology and modeling methods. A key puzzle is how the function of these multicomponent systems emerges from atomic-scale interactions of their parts. Solving this puzzle requires a holistic approach involving measuring and modeling the relevant interacting parts together. Our goal is to develop a multidisciplinary approach rooted in solid and solution state NMR that will make possible studies of complexes from PKSs. The two main challenges for the NMR of PKSs are increasing sensitivity and resolution. Recent innovations from our lab allow application of solid-state to study large complexes in 2–10 nanomole quantities. Building on this approach, with a protein-antibody complex as a test case, we will develop new NMR methods that will enable a study of structure and motions of domains in complexes. We will probe, for the first time, the structural dynamics of PKSs of enacyloxin and gladiolin, which are antibiotics against life-threatening multidrug resistant hospital-acquired Acinetobacter baumannii infections and tuberculosis. These studies will guide rational engineering of the PKSs to enable synthetic biology approaches to produce new antibiotics. If successful, this project will go beyond the state of the art by: enabling studies of unknown proteins in large complexes and providing unique insights into novel mechanisms for controlling biosynthesis in PKSs, turning them into truly programmable synthetic biology devices.

Mots-clés : solid state nmr,protein complexes,magic angle spinning,protein structure and dynamics