Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : TeLSCombe (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : University of Sussex — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 305488.74 €

Precise timing has led to many advances, such as GPS and the Internet, which depend critically on frequency and time standards. The currently limited accuracy, however, is hindering the progress towards societal-changing technologies such as telecommunications beyond 5G or precise earth mapping. Optical atomic clocks based on optical frequency combs – Nobel prize in Physics, 2005 to Hall and Hänsch – are the only technology capable of providing timing accurate up to 10^(-18) seconds, answering such a demand of time precision. The realisation of such clocks in portable scale is expected to change the technology landscape. Micro-combs – based on miniature optical resonators – have galvanized the attention of the world over the past ten years with the promise to realise the full potential of frequency combs in a compact form. However, these devices still do not meet the demand of practical atomic clocks which require reliable optical sources and currently depend on bulky pulsed lasers, which are well-known for their robustness but unfit for portable applications. Developing energy-efficient micro-combs with the reliability and versatility of control of modern pulsed lasers will require to surpass the intrinsic limitations of the nonlinear physics exploited so far for their generation. Here we propose a high-gain/ high-risk research plan which steers from the state-of-the-art and builds on a different physics for developing micro-combs with control capabilities beyond the current miniature solutions. Specifically, we will exploit the generation of localised waves called temporal laser cavity-solitons in complex resonators exhibiting lasing and parametric nonlinear interactions. Such a setting is mostly unexplored and this proposal will demonstrate the unique features of these waves and their general impact in broader physics. Eventually, this study will pave the way to a class of robust micro-combs which can be controlled with user-friendly machine learning approaches.
Projet ERC : TeLSCombe (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : Loughborough University — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1189194.26 €

Precise timing has led to many advances, such as GPS and the Internet, which depend critically on frequency and time standards. The currently limited accuracy, however, is hindering the progress towards societal-changing technologies such as telecommunications beyond 5G or precise earth mapping. Optical atomic clocks based on optical frequency combs – Nobel prize in Physics, 2005 to Hall and Hänsch – are the only technology capable of providing timing accurate up to 10^(-18) seconds, answering such a demand of time precision. The realisation of such clocks in portable scale is expected to change the technology landscape. Micro-combs – based on miniature optical resonators – have galvanized the attention of the world over the past ten years with the promise to realise the full potential of frequency combs in a compact form. However, these devices still do not meet the demand of practical atomic clocks which require reliable optical sources and currently depend on bulky pulsed lasers, which are well-known for their robustness but unfit for portable applications. Developing energy-efficient micro-combs with the reliability and versatility of control of modern pulsed lasers will require to surpass the intrinsic limitations of the nonlinear physics exploited so far for their generation. Here we propose a high-gain/ high-risk research plan which steers from the state-of-the-art and builds on a different physics for developing micro-combs with control capabilities beyond the current miniature solutions. Specifically, we will exploit the generation of localised waves called temporal laser cavity-solitons in complex resonators exhibiting lasing and parametric nonlinear interactions. Such a setting is mostly unexplored and this proposal will demonstrate the unique features of these waves and their general impact in broader physics. Eventually, this study will pave the way to a class of robust micro-combs which can be controlled with user-friendly machine learning approaches.
Projet ERC : BugTheDrug (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : National University of Ireland — Pays : Irlande — Rôle : PI
Acronyme de l'entité : NUI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1687458.0 €

Precision medicine is an emerging paradigm that aims at maximizing the benefits and minimizing the harm of drugs. Realistic mechanistic models are needed to understand and limit heterogeneity in drug responses. Consequently, novel approaches are required that explicitly account for individual variations in response to environmental influences, in addition to genetic variation. The human gut microbiota metabolizes drugs and is modulated by diet, and it exhibits significant variation among individuals. However, the influence of the gut microbiota on drug failure or drug side effects is under-researched. In this study, I will combine whole-body, genome-scale molecular resolution modeling of human metabolism and human gut microbial metabolism, which represents a network of genes, proteins, and biochemical reactions, with physiological, clinically relevant modeling of drug responses. I will perform two pilot studies on human subjects to illustrate that this innovative, versatile computational modeling framework can be used to stratify patients prior to drug prescription and to optimize drug bioavailability through personalized dietary intervention. With these studies, BugTheDrug will advance mechanistic understanding of drug-microbiota-diet interactions and their contribution to individual drug responses. I will perform the first integration of cutting-edge approaches and novel insights from four distinct research areas: systems biology, quantitative systems pharmacology, microbiology, and nutrition. BugTheDrug conceptually and technologically addresses the demand for novel approaches to the study of individual variability, thereby providing breakthrough support for progress in precision medicine.

Mots-clés : gut microbiota,nutrition,therapeutic stratification,precision medicine,drug metabolism,human metabolism,colorectal cancer,parkinson s disease,physiology based pharmacokinetic modeling
Projet ERC : BEMOTHER (Horizon 2020, appel 2019, Advanced grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Autonomous University of Barcelona — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UAB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1608531.0 €

Pregnancy involves biological adaptations that are necessary for the onset, maintenance and regulation of maternal behavior. We were the first group to find (1, 2) that pregnancy is associated with consistent, pronounced and long-lasting reductions in cerebral gray matter (GM) volume in areas of the social-cognition network. The aim of BEMOTHER is to develop an integrative model of the adaptations for motherhood that occur during pregnancy and the postpartum period by: i) establishing when the brain of pregnant women begins to change and how it evolves; ii) characterizing the dynamics of cognitive performance, theory-of-mind, maternal-infant bonding and psychiatric measures; iii) assessing the effect of environmental and/or psychological factors in the maternal adaptations, iv) identifying the metabolomics biomarkers associated with maternal adaptations, and v) integrating the previous findings within the Research Domain Criteria framework (RDoC) (3). We will use a prospective longitudinal design at 5 time points (1 pre-pregnancy session, 2 intra-pregnancy sessions and 2 postpartum sessions) during which neuroimaging, psychological, behavioral and metabolomics data will be acquired in 3 groups of women: a group of nulliparous women who will be undergoing a full-term pregnancy, another group of nulliparous women whose same-sex partners will undergo a full-term pregnancy, and a group of control nulliparous women. We will provide the longitudinal RDoC-based model at the end of the study, but we will also deliver intermediate longitudinal evaluations after the postpartum session, as well as cross-sectional analyses after the first intra-pregnancy session and the postpartum session. BEMOTHER is timely and innovative. It adopts the translational RDoC framework in order to provide a pioneering, comprehensive and dynamic characterization of the adaptations for motherhood, addressing the interaction among different functional domains at different levels of analysis.

Mots-clés : mohterhood,neuroplasticity,pregnancy,maternal infant bonding,theory of mind
Projet ERC : BEMOTHER (Horizon 2020, appel 2019, Advanced grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : CERCA Institution — Pays : Espagne — Rôle : Partner
Acronyme de l'entité : CERCA;I-CERCA
Type d'entité : Recherche
Financement ERC de l'entité : 334068.75 €

Pregnancy involves biological adaptations that are necessary for the onset, maintenance and regulation of maternal behavior. We were the first group to find (1, 2) that pregnancy is associated with consistent, pronounced and long-lasting reductions in cerebral gray matter (GM) volume in areas of the social-cognition network. The aim of BEMOTHER is to develop an integrative model of the adaptations for motherhood that occur during pregnancy and the postpartum period by: i) establishing when the brain of pregnant women begins to change and how it evolves; ii) characterizing the dynamics of cognitive performance, theory-of-mind, maternal-infant bonding and psychiatric measures; iii) assessing the effect of environmental and/or psychological factors in the maternal adaptations, iv) identifying the metabolomics biomarkers associated with maternal adaptations, and v) integrating the previous findings within the Research Domain Criteria framework (RDoC) (3). We will use a prospective longitudinal design at 5 time points (1 pre-pregnancy session, 2 intra-pregnancy sessions and 2 postpartum sessions) during which neuroimaging, psychological, behavioral and metabolomics data will be acquired in 3 groups of women: a group of nulliparous women who will be undergoing a full-term pregnancy, another group of nulliparous women whose same-sex partners will undergo a full-term pregnancy, and a group of control nulliparous women. We will provide the longitudinal RDoC-based model at the end of the study, but we will also deliver intermediate longitudinal evaluations after the postpartum session, as well as cross-sectional analyses after the first intra-pregnancy session and the postpartum session. BEMOTHER is timely and innovative. It adopts the translational RDoC framework in order to provide a pioneering, comprehensive and dynamic characterization of the adaptations for motherhood, addressing the interaction among different functional domains at different levels of analysis.

Mots-clés : mohterhood,neuroplasticity,pregnancy,maternal infant bonding,theory of mind
Projet ERC : ObesityDevelop (Horizon 2020, appel 2014, Advanced grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : University of Bristol — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1863321.0 €

Pregnant women who are overweight/obese have increased risks of adverse perinatal outcomes, and of having offspring who are subsequently overweight/obese. A potentially important consequence of this is that it will drive the obesity epidemic across generations. Evidence from animal models supports this, but it has not been explored in humans. Furthermore, we need to know the mechanisms linking maternal adiposity to adverse offspring and next generation outcomes in order to develop preventive interventions. I will use data from up to 100,000 participants from nine cohorts and two consortia to determine the effects of maternal pregnancy levels of adiposity and associated circulating nutrients on levels of adiposity and cardiometabolic health at three periods of the lifecourse: (i) fetal development, (ii) infancy to adulthood and (iii) in the next generation. My team is world leading in this area, and we will use state-of-the-art methods to advance the field by: (i) assessing a larger number of maternal nutrients than previously; (ii) accurately assessing maternal gestational fat deposition; (iii) determining the effects of maternal exposures on fetal fat and lean mass, and metabolic response; and (iv) measuring outcomes into the next generation. Effects will be replicated in several independent cohorts and triangulated across different state-of-the-art statistical methods: (i) cross-cohort comparisons between European and low and middle income country cohorts, in which confounding structures differ; (ii) comparisons of associations of maternal exposures to equivalent associations of paternal exposures, under the assumption that intrauterine effects are maternal specific; and (iii) Mendelian randomization using genetic variants as unconfounded proxies for maternal exposures. My proposed research is important because of how many women start pregnancy overweight/obese. It will provide a step-change in knowledge of how to prevent adverse outcomes across generations.

Mots-clés : developmental origins,obesity,cross generation effects
Projet ERC : NACS (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Edinburgh — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1468303.0 €

Probabilistic models have been adopted in almost all scientific disciplines. Many related computational problems arise, involving estimating the probability of certain events, or drawing samples from a desired distribution. These problems, technically known as counting and sampling problems, are the main focus of the NACS project, with an emphasis on rigorous mathematical analysis. Markov chain Monte Carlo algorithms have been designed and applied for counting and sampling long before the computational complexity theory, and they are among the oldest randomised algorithms. There had been dramatic progress in the rigorous analysis of Markov chains in the golden era of early 90s, which has lead to many landmark results. Nevertheless, after about 30 years' development, the complexity of many fundamental problems remains open. In the last few years, a number of exciting approaches have emerged, resulting from new perspectives and surprising connections. Consequently, a lot of old challenges start to crumble. It is now the right time to revisit some of the oldest problems in counting complexity. The emerging techniques include brand new sampling frameworks, such as partial rejection sampling, as well as new ways to analyse traditional algorithms, especially Markov chain algorithms. They can be classified under two themes: those connected with the Lovasz local lemma and those with the geometry of polynomials. The goal of this project is to unleash the full power of the new approaches, to establish novel algorithmic paradigms, and to attack major open problems, such as sampling perfect matchings in general graphs. Due to the fundamental nature of counting and sampling problems, success of the project will also benefit a number of related areas, ranging from combinatorial optimisation, machine learning, and randomised algorithms, to combinatorics, probability theory, statistical physics, and quantum computation.

Mots-clés : computational complexity,approximate counting,sampling,markov chain monte carlo
Projet ERC : EXPC (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : University of East Anglia — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : UEA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1464713.0 €

Probabilistic models of brain function, which propose that the brain can be understood as implementing the principles of optimal statistical inference, have become extremely influential in recent years. Predictive coding is perhaps the most widely held, and best supported, of these models, particularly within cognitive neuroscience. However, current models of predictive coding and its neuronal substrates are still relatively simple, and do not explain how humans solve a number of fundamental problems. This limits their power to explain brain function. I propose a series of experiments designed to test how human subjects address a number of these core problems. I will use behavioural and neuroimaging data to develop and test extensions to current models (or, if necessary, provide an alternative framework for understanding brain function). The purpose of this is two-fold, to give insight into the computations that underlie cognitive function, and to provide understanding of the neurobiological processes that support those computations. The project will thus constitute a stepping stone towards developing a mechanistic model of how the brain implements cognition.

Mots-clés : predictive coding,bayesian brain,neuroimaging
Projet ERC : TANDEng (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Spanish National Research Council — Pays : Espagne — Rôle : PI
Acronyme de l'entité : CSIC
Type d'entité : Recherche
Financement ERC de l'entité : 1982188.0 €

Process intensification and catalysis are considered key stepping-stones towards a future sustainable chemical industry. Potentially, a major intensification can be achieved by the integration of two catalysts in a single reactor, to steer sequential chemical reactions in a tandem fashion. Yet, tandem catalytic processes face a notorious barrier towards industrial realization: the challenge of harmonizing the rates at which the integrated catalysts individually process and collectively exchange molecules, to avoid e.g. derivation of intermediate products through undesired reaction pathways, something elegantly achieved in nature's enzymatic tandem reactions via complex substrate channelling phenomena. TANDEng is set to break through this frontier limitation via an effort which encompasses aspects of chemistry, material science, physics and reaction engineering. The challenge is to tackle the current constraining dichotomy that close proximity of tandem catalysts, which is mandatory for an efficient transport of intermediate products between their active sites, prohibits individual adjustment of other key performance parameters such as temperature. To break through this paradigm, the project seeks to realize a disengaged engineering of the chemical and thermal "intimacies" in tandem solid catalysts. Fundamental studies will guide the development of innovative catalysts featuring i) bespoke porosities for fast molecular transport, able to mimic a nanoscale proximity even for active sites residing in different macroscopic particles, and ii) implanted ancillary functions designed to achieve a catalyst-specific heating and thermometry in compact reactors with unconventional electromagnetic power supply. The novel concept will be validated to unlock currently unfeasible tandem processes to address the timely challenge of valorising delocalized feedstocks, alternative to petroleum (unconventional gas and biomass), where process intensification is an enabling prerequi
Projet ERC : DEEP-MAPS (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2261100.0 €

Processes in Earth's lower mantle govern our planet's inner dynamics and control surface plate tectonics. As such, a quantitative understanding of the physical and chemical properties of the lower mantle is pivotal to model Earth’s dynamic evolution, including the long-term chemical interactions between mantle and atmosphere that are vital to the development of habitability on Earth, and possibly other planets. While seismic tomography is providing increasingly detailed three-dimensional maps of the lower mantle, the interpretation of tomographic models to elucidate key factors such as mantle geochemical heterogeneity or dynamic mantle flow processes has proven to be highly ambiguous. All evidence points to phase transitions being the missing link needed to converge to a consistent interpretation of seismic observations. The same phase transitions also play a key role in governing mantle dynamics. But even fundamental properties, such as the location of major phase transition boundaries in Earth’s mantle, are poorly constrained. This is because the parameter space (pressure-temperature-composition) is huge and experimental measurements at planetary interior conditions are extremely slow. DEEP-MAPS will employ a novel class of time-resolved high-pressure/-temperature experiments that reduce by several orders of magnitude the time for key experiments. This will allow DEEP-MAPS to map lower mantle phase transitions, their impact on physical properties and their seismic signature with practically continuous coverage in relevant pressure-temperature-composition-space. DEEP-MAPS will further probe the time-dependence of phase transitions, transforming our understanding of how to scale from laboratory measurements to geophysical processes. DEEP-MAPS will provide a step-change in our ability to interpret mantle seismic observables and to quantify the geodynamic impact of mantle phase transitions, ultimately leading to a holistic picture of Earth’s deep mantle.

Mots-clés : phase transition in earth,structure and dynamics of earth s mantle,seismic signature of the deep earth