Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : TerrACE (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The study of the human past
Entité participante : University of Barcelona — Pays : Espagne — Rôle : Partner
Acronyme de l'entité : UB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 157910.0 €

Large areas of agricultural terracing have been abandoned and are being destroyed in Europe. TerrACE will develop a transferrable methodology of archaeological protocols for understanding the creation, maintenance and use of terraces over the longue durée, thereby informing strategies for the sustainable management of these environmental-fragility hotspots. Terraces and lynchets are hallmarks of complex Prehistoric to Post-Medieval societies and were critical for the sustainability of many European societies (including the Greeks & Romans). However, we typically know less about them than any other archaeological site type, although they were one of the first transformations of natural into cultural landscapes, which in many cases persisted to the 20th century. Key archaeological questions are: how old are terrace systems, how were they used, did they reduce soil erosion and are they carbon sinks? The answers to these questions have high relevance in Europe today as terraces can combine high yields with decreased erosion and fire risk, whilst increasing social resilience and food security in the face of environmental change. TerrACE will be a step-change in our archaeological approach by integrating a raft of new and innovative scientific techniques through an exemplary study of the creation, operation and abandonment of terraced landscapes on a N-S climatic transect of well-known/excavated terraces across Europe. TerrACE has 5 objectives; 1) improving terrace-landscape mapping, 2) multiple dating typical terrace systems, 3) determining history of use and management, 4) understanding the role of terraces in carbon storage, and 5) outreach to promote terraces as part of Globally Important Agricultural Heritage Landscapes (UNESCO GIAHS). TerrACE is only now feasible due to the development of techniques including LiDAR, direct sediment dating, palaeoenvironmental proxies, soil carbon modelling and the innovatory use of ancient DNA on terrace palaeosols.

Mots-clés : environmental history,complex societies,social change,palaeoecology,archaelogical survey,biomolecular methods,sediment dating,agricultural history,rural sustainability
Projet ERC : ELECTRO-POM (Horizon 2020, appel 2017, Proof of concept grants)
Entité participante : University of Glasgow — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Large scale energy storage demands are set to increase dramatically during the next years due to the expansion of renewables. One of the most promising large-scale electrical storage technologies are Redox Flow Battery (RFB) systems, which can convert electrical energy to chemical energy and back again. Here the electrolyte is an electro-active species where the chemical energy is converted to electricity in a flow cell. RFBs can act as both batteries and a fuel generation device depending on the needs of the user, which is advantageous because they can be recharged without replacing the electro-active material. The vanadium RFB is a promising technology, but is critically limited by only being able to store one electron per species giving a low energy density (~20 W h kg-1) and poor stability restricting many applications. Using the artificial intelligence driven discovery system of the ERC Advanced Grant SMART-POM, we aimed at the discovery of new metal oxide molecular polyoxometalate (POM) clusters showing unexpected properties. For instance, we found a molecule that can store > 10 times more electrons reversibly than the vanadium RFB making these the molecules the most reduced molecules ever discovered. Here we want to make a major step in translating this ground-breaking outcome of SMART-POM from discovery of new clusters, to scale up so the molecule can be tested in a flow battery device set up. The heart of any flow battery is the electron storage redox electrolyte. The more electrons the electrolyte can store reversibly the higher the energy density and we aim here to beat the state of the art by at least an order of magnitude aiming >1000 Wh L-1 (at this point applications in electric cars are possible). We will licence the technology with the University of Glasgow spin out company, Astrea Power, as a partner to co-develop the innovation with several potential multinational companies as customers who are eager to utilize the technology.

Mots-clés : polyoxometalates,artificial intelligence,redox flow batteries,metal oxides,electrochemistry
Projet ERC : E4I (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : University Hospital Heidelberg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1492375.0 €

Largely due to the ageing of their populations and changing lifestyles, middle-income countries (MICs) are facing a rapidly increasing burden of heart attacks and strokes. Most of these cardio- and cerebrovascular disease (CCVD) events are preventable through successful treatment of three major risk factors: diabetes, dyslipidaemia, and hypertension. Yet despite the existence of inexpensive and effective medications, only a small minority of adults with these risk factors in MICs successfully transition through the care continuum from screening to effective treatment. There is currently little to no evidence from these settings on what health services interventions are most effective in reducing the loss of patients along the CCVD risk factor care continuum. Focussing on the four most populous MICs – which jointly account for 43% of the world’s population – E4I thus aims to i) determine at which of the main steps in the care continuum – screening, linkage to care, and retention in care – the greatest loss of patients occurs; ii) establish which health services interventions have been most effective in reducing the loss of patients at each of these three care steps; and iii) ascertain the causal effect of reducing the loss of patients along the care continuum on individuals’ health and economic outcomes. To do so, E4I will use novel causal inference techniques from different academic disciplines on large population-based cross-sectional and cohort datasets with jointly over seven million participants, challenging the frequently-held beliefs in public health that only randomised trials can provide causal effect estimates and that cohort data’s principal value is the study of disease aetiology. By generating urgently needed knowledge on how to more effectively deliver proven treatments for a major public health problem in MICs, E4I will decisively advance public health research and has the potential to have an important impact on population health globally.

Mots-clés : global health,health services research,population health,clinical epidemiology,applied econometrics,health policy evaluation,health economics
Projet ERC : MALMECC (Horizon 2020, appel 2014, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2112081.65 €

Late medieval European court cultures have traditionally been studied from a mono-disciplinary and national(ist) perspective. This focus has obscured much of the interplay of cultural performances that informed “courtly life”. Recent research has begun to reverse this, focusing on issues such as the tensions between orality, writing, and performance; the sociocultural dimensions of making and owning manuscripts (musical and otherwise); the interstices between musical, literary and visual texts and political, social and religious rituals; and the impact of gender, kinship, and social status on the genesis and transmission of culture and music. These “new medievalist” studies have significantly enhanced our understanding of the cultural meanings of singing, listening, and sound in late medieval times. Taking a decisive step further, MALMECC will, for the first time, systematically explore late medieval (c. 1280-1450) court cultures and their music synoptically across Europe. England, the Low Countries, Avignon, Bohemia, south-eastern Germany/Salzburg, Savoy, and Cyprus have been selected for study as each was a vibrant site of cultural production but has been relatively neglected due to prevailing discursive formations favouring “centres” like Paris and Florence. Linking these courts in a large-scale comparative study focused on the role of music in courtly life but embedded within a multidisciplinary framework encompassing all the arts as well as politics and religion will reveal the complex ecology of late medieval performances of noblesse in unheard-of depth while at the same time throwing the unique qualities of each court into distinct relief. The project will apply an innovative research paradigm that develops a trans-disciplinary and post-national(ist), “relational” approach to the study of music in late-medieval court cultures. In doing so it will integrate all late medieval arts and re-constitute the fullness of their potential meanings.

Mots-clés : cultural studies,court cultures,entangled histories,art history,literary history,political history,history of religion,history of gender,manuscript studies,archival studies,anthropology
Projet ERC : SPADE (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
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 : 1499375.0 €

Lately, deep learning (DL) has become one of the most powerful machine learning tools with ground-breaking results in computer vision, signal & image processing, language processing, and many other domains. However, one of its main deficiencies is the lack of theoretical foundation. While some theory has been developed, it is widely agreed that DL is not well-understood yet. A proper understanding of the learning mechanism and architecture is very likely to broaden the great success to new fields and applications. In particular, it has the promise of improving DL performance in the unsupervised regime and on regression tasks, where it is currently lagging behind its otherwise spectacular success demonstrated in massively-supervised classification problems. A somewhat related and popular data model is based on sparse-representations. It led to cutting-edge methods in various fields such as medical imaging, computer vision and signal & image processing. Its success can be largely attributed to its well-established theoretical foundation, which boosted the development of its various ramifications. Recent work suggests a close relationship between this model and DL, although this bridge is not fully clear nor developed. This project revolves around the use of sparsity with DL. It aims at bridging the fundamental gap in the theory of DL using tools applied in sparsity, highlighting the role of structure in data as the foundation for elucidating the success of DL. It also aims at using efficient DL methods to improve the solution of problems using sparse models. Moreover, this project pursues a unified theoretical framework merging sparsity with DL, in particular migrating powerful unsupervised learning concepts from the realm of sparsity to that of DL. A successful marriage between the two fields has a great potential impact of giving rise to a new generation of learning methods and architectures and bringing DL to unprecedented new summits in novel domains and tasks.

Mots-clés : sparse representations,compressed sensing,low dimensioanl modeling,theory of deep learning
Projet ERC : LEAF-FALL (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : University of Antwerp — Pays : Belgique — Rôle : PI
Acronyme de l'entité : UAntwerp
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499250.0 €

Leaf phenology is a key component in the functioning of temperate and boreal deciduous forests. The environmental cues for bud-burst in spring are well known, but little is known about the cues controlling the timing of leaf fall in autumn. Leaf fall is the last stage of leaf senescence, a process which allows trees to recover leaf nutrients. We urgently need to understand the controls timing leaf senescence to improve our projections of forest growth and climate change. I propose a new general paradigm of the onset of leaf senescence, hypothesizing that leaf senescence is triggered by the cessation of tree growth in autumn. I expect that: (i) in the absence of growth-limiting environmental conditions, tree growth cessation directly controls leaf-senescence onset; and (ii) in the presence of growth-limiting conditions, photoperiod controls leaf-senescence onset – this prevents trees from starting to senesce too early. I will test these hypotheses with a combination of: (i) manipulative experiments on young trees - these will disentangle the impact of photoperiod from that of other factors affecting tree growth cessation, namely: temperature, drought and soil nutrient availability; (ii) monitoring leaf senescence and growth in mature forest stands; (iii) comparing the leaf senescence dynamics of four major tree species (Fagus sylvatica, Quercus robur, Betula pendula and Populus tremula) in four European locations spanning from 40º to 70º N; and (iv) integrating the new paradigm into a model of forest ecosystem dynamics and testing it for the major forested areas of Europe. The aim is to solve the conundrum of the timing of leaf senescence in temperate and boreal deciduous trees, provide a new interpretation of the relationship between leaf senescence, tree growth and environment, and deliver a modelling tool able to predict leaf senescence and tree growth, for projections of forest biomass production and climate change.

Mots-clés : plant functioning,relationships plant environment,eco physiology,phenology,leaf senescence,seasonal tree growth,temperate and boreal deciduous trees
Projet ERC : BeeDanceGap (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : University of Leeds — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 21961.86 €

Learning from others is fundamental to ecological success across the animal kingdom, but a key theme to emerge from recent research is that individuals respond differently to social information. Understanding this diversity is an imposing challenge, because it is hard to replicate the overwhelming complexity of free-living groups within controlled laboratory conditions. Yet here I propose that one of the most complex social models that we know of— the sophisticated eusocial societies of honeybees— offer unrivaled and yet unrecognized potential to study social information flow through a natural group. The honeybee “dance language” is one of the most celebrated communication systems in the animal world, and central to a powerful information network that drives our most high-profile pollinator to food, but bee colonies are uniquely tractable for two reasons. Firstly, next-generation transcriptomics could allow us to delve deep into this complexity at the molecular level, on a scale that is simply not available in vertebrate social systems. I propose to track information flow through a natural group using brain gene expression profiles, to understand how dances elicit learning in the bee brain. Secondly, although bee foraging ranges are vast and diverse, social learning takes place in one centralized location (the hive). The social sciences now offer powerful new tools to analyze social networks, and I will use a cutting-edge network-based modelling approach to understand how the importance of social learning mechanisms shifts with ecology. In the face of global pollinator decline, understanding the contribution of foraging drivers to colony success has never been more pressing, but the importance of the dance language reaches far beyond food security concerns. This research integrates proximate and ultimate perspectives to produce a comprehensive, multi-disciplinary program; a high-risk, high-gain journey into new territory for understanding animal communication.

Mots-clés : social learning,communication,pollinator behaviour
Projet ERC : Lensless (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : Vrije Universiteit Amsterdam — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : VU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Lensless imaging is an elegant approach to microscopy, in which a sharp image of an object is retrieved by numerical means rather than by actual optical components such as lenses. Such an approach allows high-resolution imaging using only a light source and an image sensor. Multi-wavelength phase retrieval (Witte et al., patent application PCT/NL2013/050618) provides a general framework for lensless imaging, which allows the development of surprisingly simple yet high-resolution imaging devices. By detecting wavelength-dependent differences in wave propagation, I have recently shown that amplitude and phase of an object field can be reconstructed with high contrast and resolution. This approach works for complex objects in both reflection and transmission. As such, lensless microscopes have advantages in size, cost, robustness and simplicity, and provide a powerful alternative to conventional imaging systems in many applications. My aim with this proposal is to advance this new technology towards high-impact applications, and to forge a coordinated research program on lensless imaging technology and its applications. I will develop lensless microscopes to image many cell cultures in parallel, as well as smartphone-based lensless microscopes for low-cost point-of-care diagnostics in third-world countries. Lensless microscopy through optical fibers will be implemented as a novel tool for minimally invasive endoscopic imaging. Since the technology does not require precision optical components, I will be able to extend it to shorter wavelengths. Applications in EUV metrology and soft-X-ray imaging become feasible, such as ultra-high-resolution surface profiling of lithographic wafers. This program provides exciting new prospects for fundamental science, industrial metrology and medical diagnostics alike. My expertise in lensless imaging and laser development provide me with an excellent starting point for the realization of such a new generation of imaging technology.

Mots-clés : microscopy,lensless imaging,biomedical optics,light scattering,cell biology,optical coherence,extreme ultraviolet and soft x ray imaging
Projet ERC : CAMBAT (Horizon 2020, appel 2016, Starting 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 : 1688705.0 €

Li-ion battery is ubiquitous and has emerged as the major contender to power electric vehicles, yet Li-ion is slowly but surely reaching its limits and controversial debates on lithium supply cannot be ignored. New sustainable battery chemistries must be developed and the most appealing alternatives are to use Ca or Mg metal anodes which would bring a breakthrough in terms of energy density relying on much more abundant elements. Since Mg and Ca do not appear to be plagued by dendrite formation like Li, metal anodes could thus safely be used. While standard electrolytes forming stable passivation layers at the electrode/electrolyte interfaces enabled the success of the Li-ion technology, the migration of divalent cations through a passivation layer was thought to be impossible. Thus, all research efforts to date have been devoted to the formulation of electrolytes that do not form such layer. This approach comes with complex electrolyte, highly corrosive and with narrow electrochemical stability window leading to incompatibility with high voltage cathodes thus penalizing energy density. The applicant demonstrated that calcium can be reversibly plated and stripped through a stable passivation layer when transport properties within the electrolyte are tuned (decreasing ion pair formation). CAMBAT aims at developing new electrolytes forming stable passivation layers and allowing the migration of Ca2+ and Mg2+. Such a dramatic shift in the methodology would allow considering a completely new family of electrolytes enabling the evaluation of high voltage cathode materials that cannot be tested in the electrolytes available nowadays. 1Ah prototype cells will be assembled as proof of concept, targets for energy density and cost being ca. 300 Wh/kg and 250 $/kWh, respectively, thus doubling the energy density while dividing by at least a factor of 2 the price when compared to state of the art Li-ion batteries and having the potential for being SAFER (absence of dendrite).

Mots-clés : battery,divalent cations,electrolyte
Projet ERC : NOVA (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Goethe University Frankfurt — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499693.71 €

Life expectancy in the European Union is rising and the prevalence of age-induced cardiovascular disease increases concomitantly. Endothelial dysfunction is a hallmark of aging in the vasculature and is the underlying cause of many cardiovascular disorders, like hypertension, acute myocardial infarction and stroke. This proposal aims to better understand the molecular mechanisms behind aging that lead to endothelial dysfunction. Non-coding RNAs are emerging as novel key regulators of cellular functions and we hypothesize that non-coding RNAs contribute to vascular aging. Preliminary experiments show that several long non-coding RNAs (lncRNAs) are regulated during aging of the cardiovascular system, including the lncRNA H19. We propose to extensively characterize the role of H19 in endothelial aging and to identify other lncRNAs that are involved in vascular aging. We will use state-of-the-art in vitro and in vivo models to assess endothelial aging and function upon gain-of-function and loss-of-function of lncRNAs. Understanding the role that H19 and other lncRNAs play in aging endothelium will highlight novel potential therapeutic targets to attenuate age-induced endothelial dysfunction. The focus on aging will also yield insight in molecular mechanisms that may have been overlooked when studying endothelial function in young cells or organisms.

Mots-clés : non coding rna