Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : UbRegulate (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Life Science
Panel : Integrative Biology: from Genes and Genomes to Systems
Entité participante : University of Edinburgh — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

Dynamic regulation of gene expression is vital for all organisms and enables them to respond to the ever-changing environment. Plant cells display dramatic reprogramming of the transcriptome in response to pathogen attack, ensuring prioritisation of immune responses over normal cellular functions. The immunizing hormone salicylic acid (SA) plays indispensable roles in this process, establishing long-lasting disease resistance against future pathogen attack. Chemical and genetic approaches that impinge on SA signalling have been developed to combat economically costly plant diseases that threaten future food security. However, these protection strategies are often environmentally unsustainable or have penalties on plant growth and yield. Understanding how SA reprograms the transcriptome to establish durable immunity will enable the urgent design of improved sustainable technologies for crop protection. My team recently pioneered discoveries that demonstrate SA employs the small post-translational modifier, ubiquitin, to establish durable immunity. SA induces dynamic modification of regulatory proteins with ubiquitin chains of diverse linkage types, generating the potential to control their functions and activities. Understanding the relevance of distinct ubiquitin chain types and their protein targets is a major challenge and is only in its infancy in plants. The ‘UbRegulate’ project will reveal how SA-induced ubiquitin signalling: (i) engages the transcriptional machinery and reveal its potential for exploitation by agrichemical biology; (ii) avoids transcription-induced genome instability; (iii) orchestrates chromatin permissiveness to allow gene expression; and (iv) utilises diverse chain linkage types to establish durable, broad-spectrum immunity. In addition to advancing fundamental insights into dynamic ubiquitin signalling, this ambitious project will reveal how ubiquitin signalling can be harnessed to revolutionise plant protection strategies.

Mots-clés : salicylic acid,plant immunity,ubiquitin,proteasome,post translational modification
Projet ERC : EMBED (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : University of Cambridge — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : U. Cambridge
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1211863.63 €

EMBED takes a microeconomic approach to investigating the macroeconomic implications of market transactions being embedded in social relationships. Sociologists and economists have documented the importance of relationships for mediating trade in a wide range of market settings. EMBED seeks to investigate the implications of this for the economy as a whole. Ethnographic work suggests that relationships foster common understandings which limit opportunistic behaviour. Subproject 1 will develop a first relational contacting theory of networked markets to capture this, and test these predictions using data from the Bundesbank. Formally modelling dynamic business-relationships, these relationships can be viewed as social capital. We will investigate whether this social capital is destroyed by economic shocks, and if so how long it takes to rebuild. Subproject 2 will run a field experiment. We will intervene in a networked market to create new relationships in a variety of ways. The varying success of these approaches will help us better understand the role of relationships in markets. Moreover, as a result we’ll get exogenous variation in the market structure that will help identity the affects relationships have on market outcomes. Subproject 3 will explore frictions in the clearing of networked markets. As the data requirements to empirically test between different theories are extremely demanding, laboratory experiments will be run. Breaking convention, these experiments will be protocol-free, although interactions will be closely monitored. This will create a more level playing field for testing different theories while also creating scope for the market to develop efficiency enhancing norms. Subproject 4 will examine firm level multi-sourcing and production technology decisions, and how these feed into the creation of supply chains. The fragility of these supply chains will be investigated and equilibrium supply chains compared across countries.

Mots-clés : networks,embeddedness,networked markets,field experiments,finance,relational contracts,random graphs
Projet ERC : ENUBET (Horizon 2020, appel 2015, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : University of Padua — Pays : Italie — Rôle : PI
Acronyme de l'entité : UNIPD
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 957605.0 €

ENUBET has been designed to open a new window of opportunities in accelerator neutrino physics. The proposed project enables for the first time the measurement of the positrons produced in the decay tunnel of conventional neutrino beams: these particles signal uniquely the generation of an electron neutrino at source. Neutrino facilities enhanced by the ENUBET technique will have an unprecedented control of the neutrino flux. This will allow to reduce by one order of magnitude the uncertainties on neutrino cross sections: a leap that has been sought after since decades and that is needed to address the challenges of discovering matter-antimatter asymmetries in the leptonic sector. The apparatus is a highly specialized electromagnetic calorimeter with fast response, sustaining particle rates as high as 0.5 MHz/cm^2, having excellent electron/pion separation capabilities with a reduced number of read-out channels. ENUBET will boost technologies that have been envisaged for high energy colliders to address this new challenge. On the other hand it will operate in a substantially different configuration. The experiment will be performed at the CERN Neutrino Platform, a recently approved facility where innovative neutrino detectors will be developed exploiting dedicated hadron beam-lines from the SPS accelerator. In the first phase of the project, ENUBET will address the challenges of particle identification from extended sources, developing innovative optical readout systems and cost-effective solutions for radiation imaging. This approach is based on cutting-edge technologies for single photon sensitive devices. During the second phase, the detector will be assembled and characterized at CERN with particle beams. Finally, it will be operated in time coincidence with Liquid Argon neutrino detectors, achieving a major step towards the realization of the concept of tagging individual neutrinos both at production and interaction level, on an event-by-event basis.

Mots-clés : neutrino beam tagging positron kaon calorimeter cross section leptonic cp violation silicon photomultiplier
Projet ERC : BayesianGDPR (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Basque Center for Applied Mathematics — Pays : Espagne — Rôle : Partner
Acronyme de l'entité : BCAM
Type d'entité : Recherche
Financement ERC de l'entité : 113750.0 €

EU's GDPR prescribes that "Personal Data shall be processed lawfully, fairly, and in a transparent manner." The vision of this BayesianGDPR project is to integrate into automated machine learning systems using a novel Bayesian approach, in a transparent manner, the legal non-discriminatory principles of GDPR, taking into account feedback from humans and future consequences of their outputs. We aim to achieve this ambitious vision by 1) developing a machine learning framework for addressing fairness in classification problems and beyond, and under uncertainty about data, models, and predictions about future data (algorithmic fairness under uncertainty), 2) extending the framework to a setting where data points arrive over time, and models have to be dynamically updated when taking general feedback (feedback-driven setting), and 3) ensuring a human could understand how non-discrimination is defined and achieved by using, among others, uncertainty estimates for building interpretable models and/or explicitly explaining about changes being made to the models to enforce non-discriminatory principles (transparency in fairness). The BayesianGDPR project is "doubly timely"; not just in terms of the criticality of the fairness and transparency in machine learning at this point in time, but also because recent breakthroughs in scalability have finally made it feasible to explore Bayesian approaches that are uniquely capable of addressing one of the most central aspects of the problem, i.e. uncertainty. BayesianGDPR will, in the short term, ensure that organisations relying on machine learning technologies are provided with concrete tools to comply with the non-discriminatory principles of GDPR and similar laws. In the medium term, it will impact research in computational law, and its integration into mainstream legal practice. In the long term, it will also ensure continued confidence of the general public in the deployment of machine learning systems.

Mots-clés : bayesian machine learning models,fairness models,and transparency in fairness models
Projet ERC : MOBETA (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
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 : 1988189.0 €

Each of us performs thousands of actions during the course of daily tasks, ranging from relatively simple (e.g. clicking on a mouse button), to highly complex (e.g. holding a sheet of paper in one hand while using scissors to cut it in a straight line with the other). All of these actions involve an intricate interplay between sensory integration, muscle coordination, and movement evaluation processes. Motor cortical activity in the beta frequency range (13-30Hz) is a hallmark signature of healthy and pathological movement, but its behavioral relevance remains unclear. Recently, it has become apparent that oscillatory beta activity actually occurs in discrete, transient bursts, and that short-lasting, high-powered bursts of activity only appear to be sustained oscillations when averaged over multiple trials. This renders previous theories of beta activity’s functional relevance, which involve slow changes of oscillatory power, untenable. However, this insight provides an exciting opportunity to examine beta activity on a trial-by-trial basis and directly relate it to forthcoming and ongoing motor behavior. Here, I propose a new theory for understanding motor-related beta activity that has the potential to overturn current thinking of the motor role of beta. The proposed project will build upon my recent research and use a multi-modal (electrophysiological recordings, non-invasive neuroimaging, and computational modeling), multi-scale (from cortical columns to multi-region networks), comparative (monkey and human), and developmental (longitudinal measures of human infants) approach to develop and test this theory. Not only will results from this project substantially extend our knowledge on the role of motor-related beta activity, they will have important implications for personalized, online treatments for pathophysiological disorders characterized by aberrant beta signaling.

Mots-clés : beta bursts,naturalistic actions,movement planning,sensorimotor processing,computational model,development
Projet ERC : MOLEGAZER (Horizon 2020, appel 2019, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : University of Southampton — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Early detection of melanoma improves survival. Individuals with multiple naevi (moles) are at an increased risk of developing melanoma, but sequential monitoring by dermatologists is time-consuming and inefficient. Artificial intelligence (AI) methods can potentially diagnose melanoma from single time-point lesion images, but a more clinically-relevant question is whether melanoma can be detected early, based on automated detection of changes in naevi using total body photography (TBP; high-resolution standardised images of body-parts). Cutting edge astronomical surveys use sequential images to monitor the night sky. With state-of-the-art AI techniques, these surveys identify and track subtle changes, robustly classifying the nature and prognosis of each event from just three images. Both astronomy and dermatology therefore face similar challenges: robustly predicting the evolution of sources from sparsely-sampled images. With this motivation we propose an innovative solution: adapting AI algorithms, developed in astronomy and the ERC SPCND project, for use in the automated detection, characterisation and monitoring of skin lesions. With a wealth of experience tackling this problem in astronomy, this proof-of-concept project will characterise the properties and evolutionary path of naevi in preparation for the next stage: the early detection of melanoma.

Mots-clés : melanoma,skin cancer,medicine,astronomy,imaging,time series,machine learning,dermatology
Projet ERC : Baby DCs (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Life Science
Panel : Immunity, Infection and Immunotherapy
Entité participante : Ludwig-Maximilians-Universität München — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : LMU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Early life immune balance is essential for survival and establishment of healthy immunity in later life. We aim to define how age-dependent regulation of dendritic cell (DC) development contributes to this crucial immune balance. DCs are versatile controllers of immunity that in neonates are qualitatively distinct from adults. Why such age-dependent differences exist is unclear but newborn DCs are considered underdeveloped and functionally immature. Using ontogenetic tracing of conventional DC precursors, I have found a previously unappreciated developmental heterogeneity of DCs that is particularly prominent in young mice. Preliminary data indicate that distinct waves of DC poiesis contribute to the functional differences between neonatal and adult DCs. I hypothesize that the neonatal DC compartment is not immature but rather that DC poiesis is developmentally regulated to create essential age-dependent immune balance. Further, I have identified a unique situation in early life to address a fundamental biological question, namely to what extent cellular function is pre-programmed by developmental origin (nature) versus environmental factors (nurture). In this proposal, we will first use novel models to fate map the origin of the DC compartment with age. We will then define to what extent cellular origin determines age-dependent functions of DCs in immunity. Using innovative comparative gene expression profiling and integrative epigenomic analysis the cell intrinsic mechanisms regulating the age-dependent functions of DCs will be characterized. Because environmental factors in utero and after birth critically influence immune balance, we will finally define the impact of maternal infection and metabolic disease, as well as early microbial encounter on DC poiesis. Characterizing how developmentally regulated DC poiesis shapes the unique features of early life immunity will provide novel insights into immune development that are vital to advance vaccine strategies.

Mots-clés : dendritic cell,neonatal immunity,immune development,fate mapping,obesity,schistosoma mansoni,epigenomic profiling,next generation sequencing,microbiome
Projet ERC : RUTTER (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The study of the human past
Entité participante : University of Lisbon — Pays : Portugal — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 496314.36 €

Early modern nautical rutters (sailing directions) are the earliest Western documents that testify to the stable and regular lived experience of traversing the earth’s oceans on a global, planetary scale. Nautical rutters (and ship’s loogbooks) are technical documents that collect and analyse critical information for the successful accomplishment of oceanic navigation. This includes elements of strict nautical nature (courses, distances, and latitudes), as well as information on oceanography (currents and tides), meteorology (winds and storms), geography, geophysics (magnetic declination) and the natural world. Their unique value lies not only in the fact that they are exceptional historical repositories of information about the world on a planetary scale but, more importantly, that they document the emergence of global concepts about the earth. In fact, no earlier documents contain information about the earth on a comparable worldwide scale. Thus, their historical value is peerless. Using these exceptional, yet poorly known sources, the main objective of this project is to write a narrative of the scaling up of a scientific description of the earth in the sixteenth and seventeenth centuries, from the lived experience of travelling and observing the earth in long-distance sea voyages. As a preliminary task, a systematic search, identification and classification of the information contained in early modern Iberian rutters and ship’s logbooks will be performed. This will be followed by an extensive multidisciplinary study of their content aiming at radically improving our present knowledge of the historical process that led to the formation of global concepts about the earth.

Mots-clés : global environment,history of navigation,climate history,history of oceanography
Projet ERC : USMILE (Horizon 2020, appel 2019, Synergy grants)
Entité participante : Columbia University — Pays : Etats-Unis — Rôle : PI
Acronyme de l'entité : CU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2129361.0 €

Earth system models are fundamental to understand climate change. Although they have improved significantly, considerable biases and uncertainties in their projections remain. Process parameterisations limit the models’ ability to simulate both global and regional Earth system responses, which are key for assessing climate change and its impacts on ecosystems and society. In recent years, the volume of data from high-resolution models and observations has substantially increased to petabyte scales. Concomitantly, the field of machine learning (ML) has quickly developed, promising breakthroughs in detecting and analysing non-linear relationships and patterns in large multivariate datasets. Yet, traditionally, physical modelling and ML have been often treated as two different worlds with opposite scientific paradigms (theory-driven versus data-driven). Thus, despite its great potential, ML has not yet been widely adopted for addressing the urgent need of improved understanding and modelling of the Earth system. USMILE will combine multi-disciplinary expertise in ML and process-based atmosphere and land modelling to completely rethink model development and evaluation. ML will further allow us to define novel observational constraints on Earth system feedbacks and climate projections. We will (1) develop ML algorithms to enhance Earth observation datasets accounting for spatio-temporal covariations, (2) deploy ML-based parameterisations and sub-models for clouds and land-surface processes that have hindered progress in climate modelling for decades, and (3) detect and understand modes of climate variability, multivariate extremes and uncover dynamical aspects of the Earth system with novel deep learning and causal inference techniques. USMILE will drive a paradigm shift in the current modelling of the Earth system towards a new data-driven physics-aware science and to an unprecedented reduction of uncertainties in projections.

Mots-clés : earth system modelling,climate projections,climate sensitivity,earth system feedbacks,earth system model evaluation,causal inference,anomaly detection,deep learning,recurrent neural networks,gaussian processes
Projet ERC : TECTONIC (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : Sapienza University of Rome — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2603500.0 €

Earthquakes represent one of our greatest natural hazards. Even a modest improvement in the ability to forecast devastating events like the 2016 sequence that destroyed the villages of Amatrice and Norcia, Italy would save thousands of lives and billions of euros. Current efforts to forecast earthquakes are hampered by a lack of reliable lab or field observations. Moreover, even when changes in rock properties prior to failure (precursors) have been found, we have not known enough about the physics to rationally extrapolate lab results to tectonic faults and account for tectonic history, local plate motion, hydrogeology, or the local P/T/chemical environment. However, recent advances show: 1) clear and consistent precursors prior to earthquake-like failure in the lab and 2) that lab earthquakes can be predicted using machine learning (ML). These works show that stick-slip failure events –the lab equivalent of earthquakes– are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. Remarkably, ML predicts the failure time and in some cases the magnitude of lab earthquakes. Here, I propose to connect these results with field observations and use ML to search for earthquake precursors and build predictive models for tectonic faulting. This proposal will support acquisition and analysis of seismic and geodetic data and construction of new lab equipment to unravel earthquake physics, precursors and forecasts. I will use my background in earthquake source theory, ML, fault rheology, and geodesy to address the physics of earthquake precursors, the conditions under which they can be observed for tectonic faults and the extent to which ML can forecast the spectrum of fault slip modes. My multidisciplinary team will train the next generation of researchers in earthquake science and foster a new level of broad community collaboration.

Mots-clés : earthquakes,machine learning,prediction