Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : Z-EURECA (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : KU Leuven — Pays : Belgique — Rôle : PI
Acronyme de l'entité : KUL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1498500.0 €

Approximately 9 out of 10 chemical processes use a solid catalyst, as they increase the efficiency of chemical reactions while being recyclable. Zeolites are one of the most iconic types. These porous crystalline oxides are built from networks of aluminum and silicon nodes and are characterized by regular pores with dimensions similar to most of the molecules that sustain modern society. Although zeolite synthesis is essentially an assembly of silica and alumina around positively charged template molecules, the outcome heavily depends on kinetics. Yet, classic reactors and strategies barely allow any fine-control over the kinetic interplay, and thus the zeolite made, due the complex and not well understood role of Coulomb and other interactions among the assembling species, and their concentration profiles. Even for established catalysts, synthetic control over their key properties is often lacking. However, such fine-control is essential to develop better catalytic processes in the light of global challenges. Z-EURECA wants to revolutionize zeolite synthesis, not by the usual search for ingredients, but by introducing new reactor-based handles, specifically external electric fields and fed-batch modes, to exert control over kinetic pathways. Based on new reactor designs, active manipulation of the various Coulomb and interspecies interactions and local concentration gradients during synthesis will be possible. This bottom-up revolution will i) provide missing insights into the fundamentals of relevant zeolite synthesis, ii) develop active tools that engineer access to synthetic fine-control over zeolites and iii) validate this fine-control in relevant catalytic reactions for superior acid and redox zeolites in prominent sustainable catalysis challenges of our resource-hungry society.

Mots-clés : zeolite synthesis,reactors for porous material synthesis,bottom up catalyst design,material chemistry,sustainable heterogeneous catalysis
Projet ERC : OXYGEN (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : University of Geneva — Pays : Suisse — Rôle : PI
Acronyme de l'entité : UNIGE
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2406972.0 €

Arc magmatism at subduction zones is responsible for much of the mass transfer of chemical elements between the Earth’s lower and upper spheres. Arc magmas are significantly more oxidized and richer in volatile elements than other voluminous magma types on Earth. These characteristics promote the genesis of large magmatic-hydrothermal ore deposits and potentially also the build-up of the oxygen budget of the Earth’s atmosphere. Despite its great significance, the origin of the higher oxidation state of arc magmas is still one of the most debated questions in petrology. I will combine high-pressure-temperature experiments, field-based studies and computational simulations to obtain quantitative understanding of redox reactions taking place during magma genesis, differentiation and degassing. Subsequently, I will apply this new knowledge to assess if arc magmatism may have been a key to the oxygenation of the Earth’s atmosphere, and to pinpoint the most prospective regions for the generation of giant ore deposits. Most experiments will rely on revolutionary new instrumentation and methodologies, which I have recently developed or will develop as a part of the project. For example, we will determine for the first time the speciation of sulfur in aqueous fluids in situ at magmatic temperatures and upper crustal pressures by using a prototype spectroscopic cell, so that its critical role in redox transfer and ore genesis can be quantified. Similarly, the field-based studies will employ a new method to constrain the redox evolution of magmas with unparalleled precision, which will be developed experimentally by using a prototype high-pressure apparatus with a unique capability to control redox conditions. In addition, these will also apply a powerful combination of novel and challenging analytical methods including the analysis of Au, Pt, Pd and Re concentrations and S isotope ratios in silicate melt inclusions in minerals to identify the key agents of magma oxidation.

Mots-clés : experimental petrology,magma genesis,plate tectonics,mineral resource,volcanic degassing,atmospheric science,planetary evolution
Projet ERC : Q-ARCTIC (Horizon 2020, appel 2020, Synergy grants)
Entité participante : Max Planck Society — Pays : Allemagne — Rôle : CO-PI
Acronyme de l'entité : MPG
Type d'entité : Recherche
Financement ERC de l'entité : 7427695.96 €

Arctic permafrost has been identified as a critical element in the global climate system, since it stores a vast amount of carbon that is at high risk of being released under climate change. The feedbacks between permafrost carbon and climate change are moderated by many factors, including hydrology, topography, and biology. Shifts in these factors lead to highly complex feedbacks between biogeochemical and biogeophysical processes. These are only rudimentarily represented in current Earth System Models (ESMs), in particular due to a scaling gap between processes and model grid. Q-ARCTIC will establish a next generation coupled land-surface model that explicitly resolves highest resolution landscape features and disturbance processes in the Arctic. Model development will be informed by novel remote sensing methodologies linking landscape characteristics and change potential at an exceptional level of detail. Interdisciplinary observations at multiple spatiotemporal scales will deliver novel insight into permafrost carbon cycle processes. All components are essential for our objective to generate an unprecedented process-based hindcast of glacial permafrost carbon state and projection of permafrost sustainability under future scenarios with a focus on abrupt changes. Our ground-breaking research is based on the newly developed ICON-ESM that enables highest-resolution simulations based on high-performance computing infrastructure. The required remote sensing information can for the first time be produced from new pan-Arctic data streams, such as the European Sentinel satellites. Finally, recent breakthroughs in ultraportable instrumentation and mobile air- and water-borne platforms facilitate bridging the gap between in-situ process understanding and landscape-scale surface-atmosphere exchange. The Q-ARCTIC PI-consortium will combine their world-leading expertise in these fields to close the scaling gap between high-resolution processes and the coarser ESM resolution.

Mots-clés : permafrost,carbon cycle,climate feedback,arctic climate change,hydrological disturbance,scaling,glacial co2
Projet ERC : AGENSI (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : NORCE Norwegian Research Centre — Pays : Norvège — Rôle : PI
Acronyme de l'entité : NORCE
Type d'entité : Recherche
Financement ERC de l'entité : 2615858.0 €

Arctic sea ice decline is the exponent of the rapidly transforming Arctic climate. The ensuing local and global implications can be understood by studying past climate transitions, yet few methods are available to examine past Arctic sea ice cover, severely restricting our understanding of sea ice in the climate system. The decline in Arctic sea ice cover is a ‘canary in the coalmine’ for the state of our climate, and if greenhouse gas emissions remain unchecked, summer sea ice loss may pass a critical threshold that could drastically transform the Arctic. Because historical observations are limited, it is crucial to have reliable proxies for assessing natural sea ice variability, its stability and sensitivity to climate forcing on different time scales. Current proxies address aspects of sea ice variability, but are limited due to a selective fossil record, preservation effects, regional applicability, or being semi-quantitative. With such restraints on our knowledge about natural variations and drivers, major uncertainties about the future remain. I propose to develop and apply a novel sea ice proxy that exploits genetic information stored in marine sediments, sedimentary ancient DNA (sedaDNA). This innovation uses the genetic signature of phytoplankton communities from surface waters and sea ice as it gets stored in sediments. This wealth of information has not been explored before for reconstructing sea ice conditions. Preliminary results from my cross-disciplinary team indicate that our unconventional approach can provide a detailed, qualitative account of past sea ice ecosystems and quantitative estimates of sea ice parameters. I will address fundamental questions about past Arctic sea ice variability on different timescales, information essential to provide a framework upon which to assess the ecological and socio-economic consequences of a changing Arctic. This new proxy is not limited to sea ice research and can transform the field of paleoceanography.

Mots-clés : palaeoceanography,molecular ecology,metabarcoding,organic geochemistry,palynology
Projet ERC : PaCE (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Trinity College Dublin — Pays : Irlande — Rôle : PI
Acronyme de l'entité : TCD
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1952848.0 €

Are there recurring patterns in the escalation and emergence of wars? The idea that history may repeat itself is old. But recent advances overcoming methodological and data barriers present an opportunity to identify these recurrences empirically and to examine whether these patterns can be classified to improve forecasts and inform theories of conflict. I propose to combine new methods—using the shape of the sequence of events rather than its raw values—and novel data on conflict from finance, diplomatic cables, and newspapers, to extract typical pre-war motifs. Just as DNA sequencing has been critical to medical diagnoses, PaCE aims to diagnose international politics by uncovering the relevant patterns in the area of conflict. Our goals are to: (i) Identify patterns in the pre-conflict actions using data on conflict events—from the onset of WWI to Hamas’s rocket launches—and in their perceptions using data from financial markets (the “crowd’s” perception), news articles (the “experts”), and diplomatic documents (the policy-makers). This will allow us to evaluate the patterns of escalation over different timescales—from the decade to the minute. The similarity between temporal sequences will be measured using algorithms which allow for flexible matching, such as Dynamic Time Warping. (ii) Evaluate the utility of these patterns to improve forecasts of conflict with both historical and live out-of-sample predictions. Our results, using shape-based classification methods, will be made public and evaluated in real time. Moreover, using new measures of complexity to distinguish regular, chaotic, and random behavior, I will measure possible fundamental limits to the predictability of conflict events. (iii) Summarize the core features of dangerous patterns into motifs—recurring patterns—that can help build new theories of conflict emergence and escalation. PaCE will build a repository of shapes—a grammar of patterns—to be used as the building blocks of new theories.

Mots-clés : forecasting,civil conflict,interstate wars,international politics,armed violence,conflict onset,machine,learning
Projet ERC : ARGO (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: Biological Mechanisms, Structures and Functions
Entité participante : Wageningen University & Research — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : WUR
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2177157.5 €

Argonaute nucleases are key players of the eukaryotic RNA interference (RNAi) system. Using small RNA guides, these Argonaute (Ago) proteins specifically target complementary RNA molecules, resulting in regulation of a wide range of crucial processes, including chromosome organization, gene expression and anti-virus defence. Since 2010, my research team has studied closely-related prokaryotic Argonaute (pAgo) variants. This has revealed spectacular mechanistic variations: several thermophilic pAgos catalyse DNA-guided cleavage of double stranded DNA, but only at elevated temperatures. Interestingly, a recently discovered mesophilic Argonaute (CbAgo) can generate double strand DNA breaks at moderate temperatures, providing an excellent basis for this ARGO project. In addition, genome analysis has revealed many distantly-related Argonaute variants, often with unique domain architectures. Hence, the currently known Argonaute homologs are just the tip of the iceberg, and the stage is set for making a big leap in the exploration of the Argonaute family. Initially we will dissect the molecular basis of functional and mechanistic features of uncharacterized natural Argonaute variants, both in eukaryotes (the presence of an Ago-like subunit in the Mediator complex, strongly suggests a regulatory role of an elusive non-coding RNA ligand) and in prokaryotes (selected Ago variants possess distinct domains indicating novel functionalities). After their thorough biochemical characterization, I aim at engineering the functionality of the aforementioned CbAgo through an integrated rational & random approach, i.e. by tinkering of domains, and by an unprecedented in vitro laboratory evolution approach. Eventually, natural & synthetic Argonautes will be selected for their exploitation, and used for developing original genome editing applications (from silencing to base editing). Embarking on this ambitious ARGO expedition will lead us to many exciting discoveries.

Mots-clés : argonaute,rna interference,rnai,crispr,mediator,protein design,laboratory evolution,genome editing
Projet ERC : CANCER-RADIOMICS (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Maastricht University — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : UM
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

Artificial Intelligence (AI), deep-learning in particular, is propelling the field of radiology forward at a rapid pace. In oncology, AI can characterize the radiomic phenotype of the entire tumor and provide a non-invasive window into the internal growth patterns of a cancer lesion. This is especially important for patients treated with immunotherapy as, despite the remarkable success of these novel therapies, the clinical benefit remains limited to a subset. As immunotherapy is expensive and could bring unnecessary toxicity there is a direct need to identify beneficial patients, but this remains difficult in clinical practice today. Radiomic biomarkers could address this, as, unlike biopsies that only represent a sample within the tumor, radiomics can depict a full picture of each cancer lesion with a single non-invasive examination. Previous work found significant connections between radiomic data, molecular pathways, and clinical outcomes. However, a direct link between radiomics and immunotherapy response has not yet been established. This project will address this problem by analyzing unique multicentre clinical data, including non-invasive imaging, clinical outcomes, and extensive biologic characterization of patients with lung or melanoma cancer. Specifically, I will develop deep-learning radiomic biomarkers to predict immunotherapy response using baseline (WP1) and follow-up imaging (WP2). I will also investigate if radiomics can characterize underlying biological factors, and, in turn, can be used to improve response predictions (WP3). Successful completion of this proposal will demonstrate the potential of radiomics to help physicians in selecting patients who will likely benefit from immunotherapy, while sparing this expensive and potentially toxic treatment for patients who don't. This work has implications for the use of imaging-based biomarkers in the clinic, as they can be applied noninvasively, repeatedly, and at low additional cost.
Projet ERC : CELLULO-EPI (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
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 : 1499734.0 €

As a paediatrician who specialized in immunology and as a physicist who specialized in mathematical modeling of infectious diseases, I want to introduce with this project a paradigm shift in infectious disease epidemiology through the concept of celluloepidemiology. Celluloepidemiology is a term I invented to describe my proposed interdisciplinary approach combining unique cellular immune responses against pathogens on a population level with mathematical modeling, thereby generating unique and otherwise not obtainable multidimensional T-cell profiles. CELLULO-EPI will develop and use such a highly innovative model to simulate how T-cells against pathogens evolve in a synthetic population as a function of age, gender, time since infection and other relevant variables. This model will be parameterized and fitted by cross sectional T-cell data against a wide set of pathogens from 500 individuals (sampled again after 1 year), unique data from individuals with known first infections with dengue and measles and longitudinal data from individuals re-exposed to chickenpox and parvovirus B19. The insights of CELLULO-EPI will be pivotal for public health. One important example: Varicella-zoster virus (VZV) causes chickenpox but also shingles after VZV reactivation. Vaccination can prevent chickenpox, but the predicted increase in shingles incidence has blocked chickenpox vaccination in many EU-countries. Indeed, re-exposure to chickenpox is hypothesized to protect against shingles through boosting of T-cells. Unfortunately, none of the available epidemiological or immunological tools allow for adequate validation of the boosting hypothesis. However, CELLULO-EPI will be able to solve this persisting VZV vaccination dilemma. Furthermore, CELLULO-EPI will also redefine infectious disease epidemiology, for example by allowing us to back-calculate the time since last exposure. I am convinced CELLULO-EPI can revolutionize infectious disease epidemiology and public health.

Mots-clés : varicella,zoster,mathematical modeling,t cell,individual based modeling,seroepidemiology
Projet ERC : ProCovar (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Life Science
Panel : Integrative Biology: from Genes and Genomes to Systems
Entité participante : University of London — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : U. London
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2433679.0 €

As a result of the rapid development of next generation sequencing, we now have access to hundreds and often many thousands of sequences which belong to the same family. Such a large amount of sequence data for a particular protein family, along with recent developments in computational statistics, enables an entirely new kind of evolutionary analysis to be performed on sequences, where for the first time we can compute statistically significant networks of correlated mutations. The proposal describes an integrated programme of work to fully explore the potential applications of the new amino acid covariation techniques in predicting aspects of protein structure and function. A particular emphasis in this proposal are proteins which are difficult to study by experimental techniques i.e. disordered proteins, transmembrane proteins and large complexes. The first objective will be to explore key developments in the underpinning algorithms, tackling both the issue of needing very large numbers of homologous sequences and also the downstream 3-D embedding to produce viable models. The second objective will involve experimental work with a collaborator where the idea that de novo protein design techniques might be exploited to artificially expand the set of available sequences for a given proto-family will be explored. The third objective will focus specifically on transmembrane protein modelling, where covariation-based approaches have proven to be highly effective. Here the goal will be to extend our existing FILM3 method to encompass both beta-barrel type TM proteins, but also to try to handle the issue of homomultimers, which is a critical aspect of TM protein modelling as so many families are known to adopt higher orders of structure than the fold level alone. Finally, applications of covariation analysis to probing multiple conformations of disordered proteins will be developed, with a specific focus on interactions of disordered proteins with DNA and RNA.

Mots-clés : protein co evolution,protein structure prediction,transmembrane proteins,natively disordered proteins,protein design,correlated mutations,protein protein interactions