Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : ImmuneCheckpointsAD (Horizon 2020, appel 2016, Advanced grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Weizmann Institute of Science — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2287500.0 €

Understanding, and ultimately treating Alzheimer’s disease (AD) is a major need in Western countries. Currently, there is no available treatment to modify the disease. Several pioneering discoveries made by my team, attributing a key role to systemic immunity in brain maintenance and repair, and identifying unique interface between the brain’s borders through which the immune system assists the brain, led us to our recent discovery that transient reduction of systemic immune suppression could modify disease pathology, and reverse cognitive loss in mouse models of AD (Nature Communications, 2015; Nature Medicine, 2016; Science, 2014). This discovery emphasizes that AD is not restricted to the brain, but is associated with systemic immune dysfunction. Thus, the goal of addressing numerous risk factors that go awry in the AD brain, many of which are -as yet- unknown, could be accomplished by immunotherapy, using immune checkpoint blockade directed at the Programmed-death (PD)-1 pathway, to empower the immune system. In this proposal, we will adopt our new experimental paradigm to discover mechanisms through which the immune system supports the brain, and to identify key/novel molecular and cellular processes at various stages of the disease that are responsible for cognitive decline long before neurons are lost, and whose reversal or modification is needed to mitigate AD pathology, and prevent cognitive loss. Achieving our goals requires the multidisciplinary approaches and expertise at our disposal, including state-of-the art immunological, cellular, molecular, and genomic tools. The results will pave the way for developing a novel next-generation immunotherapy, by targeting additional selective immune checkpoint pathways, or identifying a specific immune-based therapeutic target, for prevention and treatment of AD. We expect that our results will help attain the ultimate goal of converting an escalating untreatable disease into a chronic treatable one.

Mots-clés : immune checkpoint blockade,alzheimer s disease
Projet ERC : MiMo (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : University of Cambridge — Pays : Royaume-Uni — Rôle : Partner
Acronyme de l'entité : U. Cambridge
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 777726.05 €

Unobserved differences between economic agents are an important driver behind the differences in their economic outcomes such as schooling decisions, wages, and employment durations. Allowing for such unobserved heterogeneity in economic modeling equips the specification with an additional dimension of realism but presents major challenges for econometric practice. Hence, reconciling heterogeneity in the data with econometric models is an issue of utmost importance. The aim of this project is to develop inference methods for models with unobserved heterogeneity by exploiting the identifying power of longitudinal (panel) data. The project consists of three blocks. Together, they span the largest part of modern applications of panel data. The first block deals with inference on nonlinear models and enhances the performance of statistical hypothesis tests. So far, the literature has focused on point estimation. However, it is statistical inference that accounts for uncertainty in the data and forms the basis for testing economic restrictions. The second block makes progress on the estimation of models for network data. The importance of social and economic connections is well established but few formal results are available. We exploit the fact that network data can be seen as a type of panel data to derive such results. The third block uses panel data to non-parametrically estimate dynamic discrete-choice models with unobserved type heterogeneity and/or latent state variables. Such results are inexistent even though dynamic discrete-choice models are a workhorse tool in labor economics and industrial organization. The performance of the tools will be assessed theoretically and via simulation, and they will be applied to various empirical problems. Two examples of applications that we will study are the extensive margin of labor force participation and the determinants of the import and export behavior of firms and countries.

Mots-clés : discrete choice models,microeconometrics,networks,incidental parameters,statistical inference,panel data,unobserved heterogeneity
Projet ERC : COMPASS (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of Bern — Pays : Suisse — Rôle : PI
Acronyme de l'entité : UB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499250.0 €

Unprecedented concentration in agri-food value chains is reinforcing global inequality. Waves of land grabbing threaten the livelihoods of millions. Reshaping the effects of agricultural investment, land use, and trade on human well-being is thus an urgent challenge. Certification schemes (CS) such as “Fairtrade” have become a common strategy to meet this challenge. However, accumulating evidence shows that many CS have limited effects on well-being. Inclusive business (IB) and solidarity economy (SE) strategies are emerging alternatives. Inclusiveness and solidarity are widely believed to enhance well-being, but evidence and theories disprove this common belief. Environmental justice may be a necessary condition to understand and reshape the effects of CS, IB, and SE on well-being. However, lack of reliable data and comparative analyses limits understanding of these links. COMPASS will tackle these challenges. This project aims to demonstrate how environmental justice influences the effects of CS, IB, and SE strategies on human well-being. COMPASS is organized in four work packages (WPs) and focuses on the cocoa and coffee sectors of Peru and Switzerland. WP1 surveys organizations (n=120) to compare their instruments used in CS, IB, and SE strategies. WP2 surveys households (n=840) and uses set-theoretic and process-tracing methodology to explain the effects of CS, IB, and SE on well-being. WP3 identifies the rules that organizations (n=18) create to regulate land use, investment and trade, assesses their environmental justice, and explains how they influence well-being. WP4 generates context-sensitive generalizations of these effects, and it tests and advances pertinent theories. COMPASS breaks new ground by systematically comparing CS, IB, and SE strategies and their effects on human well-being. It develops a new strand of environmental justice research on private-sector strategies and it tests the transformative potential of environmental justice.
Projet ERC : MAGIC (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
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 : 1108723.08 €

Unraveling the cause for Cenozoic global climate cooling is one of the most important unresolved questions challenging the Earth and Environmental sciences community today. Increased erosion and weathering of the uplifted Tibetan Plateau and Himalayas, is advocated as the primary cause for the enigmatic pCO2 drawdown, that led to global cooling 50 to 34 Myrs ago from the warm ice-free Greenhouse world to the bi-polar Icehouse conditions still prevailing today. Asian Monsoons are genetically linked to high orography associated with the India-Asia collision starting ca. 50 Myrs ago, however, the relation between Greenhouse to Icehouse cooling and Asian Monsoons remains to be explore as they were previously thought to intensify only much later ca. 25 Myrs ago. Our recent findings of monsoonal activity in Asia since at least 45 Myrs ago raises the fascinating possibility that Asian Monsoons may have triggered global cooling from Greenhouse to Icehouse conditions. Testing this novel hypothesis and exploring its implications on feedback mechanisms between regional environments, Asian Monsoons and global climate, will constitute the stimulating objectives of MAGIC. 3 PhDs will provide end-member monsoonal archives well-dated during greenhouse to icehouse cooling from 3 key locations (NE Tibet, SE Asia and Paratethys Sea). These will be analyzed by three postdocs expert in novel proxy methods tailored for MAGIC to infer temperatures, precipitation, salinity, seasonality, paleoaltimetry, wind-patterns, paleoecology and paleogeography at infra-annual, orbital and tectonic time scales. Ultimately, these records and boundary conditions will be integrated into climate models by a dedicated postdoc to unravel the role and behavior of Asian Monsoons with respect to long-term Greenhouse to Icehouse cooling, pCO2 levels as well as global hyperthermal and cooling event such as the PETM, MECO and EOT.
Projet ERC : UbiProPox (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Life Science
Panel : Immunity, Infection and Immunotherapy
Entité participante : University of London — Pays : Royaume-Uni — Rôle : Partner
Acronyme de l'entité : U. London
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1753821.25 €

Vaccinia virus (VACV), the prototypic poxvirus, is a large, enveloped, DNA virus characterized by its cytoplasmic site of replication and large subset of genes. Due to the complexity of VACV, the majority of studies focus on the virus rather than the host cell. Thus the repertoire of cell factors and functions required for its replication remain largely unknown. Our previous work to define a subset of these, revealed the cellular degradation machinery as a key requirement of VACV replication. Our findings indicated that ubiquitin (Ub), Ub ligase activity, and proteasome-mediated degradation are required for multiple stages of the virus lifecycle. The aim of this proposal is to reveal how VACV differentially modulates or takes advantage of the Ub proteasome system during genome uncoating, the initiation of DNA replication, and the assembly of progeny virions. For genome uncoating we will characterize the spatial and temporal interactions between ubiquitinated viral proteins, proteasomes, the viral uncoating factor, and the viral genome that occur on cytoplasmic cores. To ascertain how Cullin-3 based ubiquitination and proteasome degradation facilitate the switch from uncoating to replication of the viral genome, we will identify the relevant Cullin-3 substrates in the context of a detailed characterization of viral replication initiation sites. Coming full circle, we will explore the mechanisms used by VACV to modulate cellular degradation such that ubiquitinated viral core proteins are packaged into newly forming virions without being degraded. Using systems biology, virology, cell biology, biochemistry, molecular biology and a wide range of microscopy approaches we will unravel the complex interactions between poxviruses and the host cell degradation machinery. In turn, as viruses often serve as valuable tools to study cell function, this work is likely to uncover new insights into how cells spatially and temporally regulate their own degradative capacities.

Mots-clés : virus genome uncoating,viral dna replication,virus assembly,ubiquitin proteasome system,proteomics,rnai screening
Projet ERC : AVeriTeC (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
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 : 1982824.0 €

Verification of textual claims is the task of assessing the truthfulness of a statement in natural language. It is commonly conducted manually by journalists on claims made by public figures such as politicians, with the aim of reducing misinformation. However, the proliferation of social media has created the need to apply verification to a larger volume of claims coming from a greater variety of sources, thus calling for automation. Research in automated verification of textual claims is at an early stage. The methods developed either assess the truthfulness of the claim without considering evidence, or handle very simple claims such as “UK has 3.2 million EU immigrants” that requires the retrieval of a single factoid from a knowledge base. While useful, claims are often more complex, and taking evidence into account is necessary for the verdicts to be credible. AVeriTeC will transform automated verification by enabling the verification of more complex claims than previously attempted, such as “the United Kingdom has ten times Italy’s number of immigrants”, which require multiple pieces of evidence. We will achieve this by developing methods able to generate multiple questions per claim, retrieve answers from both knowledge bases and textual sources, and combine them into verdicts. As these tasks are interdependent, we will develop novel machine learning approaches able to handle them jointly so that the verdicts are accompanied by suitable justifications in the form of questions and answers. The latter will be formulated in natural language, thus the process followed by the models developed will be explainable to the users, while the evidence itself can be useful even if the overall verdict is incorrect. Beyond developing novel methods and creating publicly available evaluation resources, AVeriTeC will establish verification of textual claims as a real-world challenge to stimulate progress in natural language processing, machine learning and related fields.

Mots-clés : automated verification,fact checking,computational journalism
Projet ERC : trainABL (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : Freie Universität Berlin — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : FU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1364414.66 €

Vertical exchange in the atmospheric boundary is primarily due to turbulence, but turbulence may cease locally as a consequence of stable density stratification. This state of turbulence intermittency challenges traditional geophysical approaches to represent turbulent mixing. Field observations of the phenomenon are hard to obtain because of broad-scale interacting processes. Existing numerical approaches based on bulk turbulence closures reach their limits because they neglect the relevance of large-scale intermittency for turbulent mixing. Hence, process-level insight to turbulence intermittency in the atmospheric boundary layer is lacking which has dramatic consequences for the forecast of minimum temperature, of frost and fog situations, and of the potential for wind-power extraction. trainABL recognizes the geophysical phenomenon of turbulence intermittency in the atmospheric boundary layer as a fluid mechanics problem. A virtual-lab approach based on direct numerical simulation yields an appropriate turbulence-resolving representation of the intermittently turbulent atmospheric boundary layer. The quantitative insight into large-scale intermittency offered by direct numerical simulation in combination with large-eddy simulation and observational data allows to transfer the emerging physical understanding to the geophysical range of parameters. This paves the avenue towards a novel turbulence mixing representation based on factorization of the turbulent flux into a reference flux and pre-factor accounting for large-scale intermittency. trainABL will thus provide a first physically consistent turbulent mixing parametrization that acknowledges the importance of turbulence intermittency, covers the entire vertical range of the atmospheric boundary layer, and is valid for all regimes of stratification.

Mots-clés : planetary boundary layer,numerical simulation,turbulent mixing,turbulence parameterization,stratified turbulence,scale adaptive,similarity theory
Projet ERC : VHERAPY (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : Weizmann Institute of Science — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Very High Energy Electrons (VHEE) as those produced by compact laser plasma accelerators are ideal candidate in radiotherapy (RT). The corresponding dose distribution of the already produced low divergence and quasi-monoenergetic electron beam portends significant potential to treat deep tissue tumors, due to VHEE’s narrow radial dose deposition profile and long penetration distance. Our technological breakthrough is creating VHEE beam suitable for radiation therapy with a single laser and in relatively small space. Therefore, we expect our discovery to enable smaller, simpler and cheaper RT machinery with superior therapy performance. This will bring added value to RT device manufacturers and operators. Our approach substantially reduces the size of the acceleration complex leading to significantly smaller footprint, and investments in such facilities. We also enable increasing patient throughput while facilitating lower radiation protection requirements. The approach is safer for patients – for instance, our numerical studies of dose deposition in cases of prostate cancer indicate that VHEE reduces 20% of the ionizing radiation in healthy tissues. Moreover, obesity makes cancer treatment more difficult and adverse side effects more common, for which VHEE-RT represents an efficient and economically pertinent solution. In addition to increasing the technology maturity in this PoC, we will study and prepare commercialization plans of the approach and variety of VHEE-RT components. Moreover, we will carry out IP protection and networking tasks with collaborators, potential customers and investors for improving our chances of commercialization success.

Mots-clés : laser plasma accelerators,plasmas,laser,radiotherapy
Projet ERC : IMPACT (Horizon 2020, appel 2015, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : École normale supérieure de Lyon — Pays : France — Rôle : Partner
Acronyme de l'entité : ENS de Lyon
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 60000.0 €

Very little is understood of the physics governing the Giant Impact and the subsequent formation of the Moon. According to this model an impactor hit the proto-Earth; the resulting energy was enough to melt and partially vaporize the two bodies generating a large protolunar disk, from which the Earth-Moon couple formed. Hydrodynamic simulations of the impact and the subsequent evolution of the protolunar disk are currently based on models of equations of state and phase diagrams that are unconstrained by experiments or calculations. Estimates of the positions of critical points, when available at all, vary by one order of magnitude in both temperature and density. Here we propose to compute the thermodynamics of the major rock-forming minerals and rock aggregates, and use it to study the formation and evolution of the protolunar disk. For this we employ a unique combination of atomistic state-of-the-art ab initio simulations. We use large-scale density-functional theory (DFT) molecular dynamics to study bulk fluids, coupled with Green functions (GW) and time-dependent DFT techniques to analyze atomic clusters and molecular species. We compute the vaporization curves, position the supercritical points, and characterize the sub-critical and supercritical regimes. We construct equations of state of the rocks at the conditions of the giant impact that are beyond current experimental capabilities. We employ a multiscale approach to bridge the gap between atomic, geological sample, and planetary scales via thermodynamics; we simulate the thermal profile through the disk, the ratio between liquid and vapor, and the speciation. From speciation we predict elemental and isotopic partitioning during condensation. Plausible impact scenarios, features of the impactor and of the proto-Earth will be constrained with a feedback loop, until convergence between predictions of final Earth-Moon compositions and observations is reached.
Projet ERC : VideoPlus (Horizon 2020, appel 2016, Proof of concept grants)
Entité participante : Università della Svizzera italiana — Pays : Suisse — Rôle : PI
Acronyme de l'entité : USI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Video content uploaded on social video sharing systems such as YouTube, Vimeo, or Dailymotion has experienced an exponential growth in the past years, making them the favorite medium for advertisement. The ease of sharing and accessing video content has also provoked significant efforts on the side of content creators and owners aimed at protecting their video assets from illegal reproduction and distribution, including the distribution through different channels, the use of digital right management (DRM) encrypted systems, and video fingerprinting systems for the detection of illegally uploaded video content. The ambition of VideoPlus is to develop a close-to-market system that would serve as a “uniform resource identifier” for video content, employing technologies which are based on video fingerprinting to precisely synchronize spatio-temporal metadata with any video content, without being limited to any specific video source or distribution channel. VideoPlus builds on three main components: (i) the algorithmic core is the result of the ERC-funded project COMET, where we developed an efficient fingerprinting algorithm capable of extracting a short binary signature (hash) of image and video content and instantaneously locate it in a very large dataset; (ii) the back-end would employ specifically tailored data structures to store and retrieve spatio-temporal information (e.g. video subtitles and annotations) together with frame hashes and align it with the video; (iii) the front-end would capture the video content, extract its hash, communicate with the back-end to retrieve the spatio-temporal metadata, display them synchronously with the video, and allow user interaction. In particular, we will develop front-ends for two platforms: an Android mobile device (smartphone or tablet), allowing to playback local or remote videos or capture video content through the mobile device camera, and Microsoft HoloLens augmented reality glasses.

Mots-clés : video content identification,video metadata synchronisation,similarity preserving hashing