Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : INTERSECT (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Tmf - technologie und methodenplattform fur die vernetzte medizinische forschung ev — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : TMF
Type d'entité : Recherche
Financement ERC de l'entité : 387500.0 €

The health effects of the migration trajectory, from pre- to post-migration in new environments, have yet been poorly studied. Methodological challenges and selection effects limit the possibilities for causal inferences on how differences in social, economic, or political factors at small-area level affect variation in health among migrants (e.g. differential mortality or self-reported health). Knowledge about such relations would not only inform better and health and integration policies for migrants. Considering migrants as humans in new environments (not as singularity), may allow to conclude on the relationship between context and health more general, and inform public health beyond immigration. However, randomisation of individuals into new contexts is not possible, and establishment of prospective cohorts (from pre- to post-migration settings) is challenging, resource-intensive and prone to selection bias. The aim of INTERSECT is to study human mobility and health consequences of different social, economic and policy environments at small-area level. To this end, INTERSECT builds on three innovations: i) it re-constructs the migration trajectory by using existing data among German-bound re-settlement refugees through data-linkage or distributed computation techniques, ii) it capitalises on mandatory dispersal as natural experiment to minimise selective migration into contexts, iii) it prospectively collects primary data on health of resettlement refugees and combines these with administrative and contextual data. Using these methodological innovations, INTERSECT creates a unique research infrastructure that allows answering important research questions on social, economic and political factors at small-area level which causally explain A) mortality differences among refugees while considering baseline health status; B) changes in self-reported health status depending on changes in contexts; and C) individual and/or contextual factors moderating such effects.

Mots-clés : social epidemiology migration trajectory natural experiments social policy small area contexts
Projet ERC : BrokenSymmetries (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : DESY — Pays : Allemagne — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 2000000.0 €

Symmetries serve as our main guide in studying physical phenomena. The more symmetric a system is, the more constrained are its degrees of freedom and the better prospects we have to understand and solve it. Significant progress has been achieved in the study of theories with high amounts of symmetry in the last decades. Supersymmetry and conformal invariance are the main reasons why the beautiful idea of holography materialised into the AdS/CFT correspondence and the discovery of hidden symmetries (integrability) in gauge theories was possible. Thanks to supersymmetry, modern mathematical techniques allowed the evaluation of the otherwise unfathomable path integral and the comprehensive study of a long list of physical observables. Finally, the successful revival of the conformal Bootstrap program is based on conformal invariance. These breakthroughs are, unfortunately, applicable only to theories with unrealistic amounts of symmetry. BrokenSymmetries will break this impasse by applying the aforementioned ideas to more realistic theories, where some of the supersymmetry and/or conformal invariance are broken. The key innovation of BrokenSymmetries is to still make use of a broken symmetry. Symmetries are captured by Ward identities which we can still derive in various cases of symmetry breaking. Broken symmetries also imply powerful non-perturbative relations that observables obey. We will combine these with the developments mentioned above, producing novel exact results. Our approach will give a handle on otherwise insoluble problems. The objectives of BrokenSymmetries are to obtain exact results while breaking: 1) conformal invariance spontaneously, keeping supersymmetry intact; 2) supersymmetry explicitly, keeping conformal invariance intact; 3) both supersymmetry and conformal invariance turning on the temperature; 4) (super)symmetry generators in the context of integrability, which get upgraded to quantum groups' generators.

Mots-clés : exact results,ads cft,cft,integrability,localization,agt,bootstrap,conformal manifold
Projet ERC : ESCAPE (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe Sciences
Entité participante : Aix-Marseille Université — Pays : France — Rôle : PI
Acronyme de l'entité : amU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999996.0 €

Determining the frequency of life in the Universe is one of the main challenges of the next decades. It requires a large high-contrast imager in space such as LUVOIR or HabEx, able to characterize dozens of nearby earth-like planets. The detection of such planets, 10^-10 fainter than their star and lost in the bright and varying glare of the star in the images, is a formidable challenge. In the race to remove the contaminating starlight, both the coronagraphic instrument and the image processing have a key role to play. Yet, the science and technological definition of these mission concepts relies entirely on the coronagraphic instrument to reject the starlight, assuming a simple gain of 10 in sensitivity with image processing based on 15-year-old techniques developed for HST. The cost of this approach is a daunting wavefront stability requirement of 20pm rms and conservative exoplanet yield estimates.  With ESCAPE I propose to develop innovative image processing methods that make use of the specific hardware in these high-contrast imagers (their wavefront sensors and deformable mirrors) and of the data accumulated in their archives to bring a significant gain in starlight subtraction. I will use the unique opportunity of the timely launch of the Roman Space Telescope during the ERC timeframe, which precisely includes the CGI technology demonstrator of high-contrast imaging in space, to demonstrate my methods and achieve 20x improved detection limits. In parallel I will deploy the ESCAPE methods on the public archives of the VLT-SPHERE and JWST NIRCam and MIRI coronagraphic instruments to deliver higher-grade data to the community, obtain new planetary systems discoveries, and constrain the giant planet population on scales yet-unexplored and comparable to the Solar System. These ambitious goals and timeline will pave the way for the implementation of the ESCAPE methods in the future space imaging missions and facilitate the determination of the frequency of life.

Mots-clés : direct imaging high contrast imaging image processing data analysis
Projet ERC : LACRIMA (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Vrije Universiteit Brussel — Pays : Belgique — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999590.0 €

Under a continued increase in global warming, extreme events such as heatwaves will further rise in frequency, intensity, and duration over the next decades. Climate change impact studies routinely assess hazard and exposure change across discrete time windows, but thereby fail to grasp how vulnerability and climate risk evolve across a person’s lifetime. Conversely, demographic research has a long tradition of assessing population processes and vulnerability from a cohort perspective, but generally ignores climate impacts. This project will develop novel concepts and methodologies to express climate change impacts and risk from a cohort perspective. More specifically, the project pursues (i) to reconstruct two iconic climate change impacts on people around the world using machine learning (heat-related mortality and burned area), (ii) to uncover age-specific vulnerability to climate extremes including heatwaves, wildfires, river floods, droughts, tropical cyclones, and crop failures, (iii) to detect and attribute changes in lifetime extreme event exposure and climate impacts on mortality across generations and regions, (iv) to quantify how these attributable cohort impacts change country-level life expectancy around the world under a range of warming scenarios, and finally (v) to project how lifetime exposure to extreme events including compound events may trigger irreversible impacts under scenarios of temporary overshooting of long-term warming targets.  By bridging physical climate science, demography, and planetary health, LACRIMA will comprehensively identify whether and where people will live an unprecedented life in terms of climate impacts, and how collective mitigation choices can alter the climate change burden on current young generations around the world.

Mots-clés : detection and attribution climate extremes inter sectoral impact modelling isimip community land model
Projet ERC : APHELEIA (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Institut national de recherche en sciences et technologies du numérique — Pays : France — Rôle : PI
Acronyme de l'entité : Inria
Type d'entité : Recherche
Financement ERC de l'entité : 1999375.0 €

Despite the undeniable success of machine learning in addressing a wide variety of technological and scientific challenges, the current trend of training predictive models with an evergrowing number of parameters from an evergrowing amount of data is not sustainable. These huge models, often engineered by large corporations benefiting from huge computational resources, typically require learning a billion or more of parameters. They have proven to be very effective in solving prediction tasks in computer vision, natural language processing, and computational biology, for example, but they mostly remain black boxes that are hard to interpret, computationally demanding, and not robust to small data perturbations. With a strong emphasis on visual modeling, the grand challenge of APHELEIA is to develop a new generation of machine learning models that are more robust, interpretable, and efficient, and do not require massive amounts of data to produce accurate predictions. To achieve this objective, we will foster new interactions between classical signal processing, statistics, optimization, and modern deep learning. Our goal is to reduce the need for massive data by enabling scientists and engineers to design trainable machine learning models that directly encode a priori knowledge of the task semantics and data formation process, while automatically prefering simple and stable solutions over complex ones. These models will be built on solid theoretical foundations with convergence and robustness guarantees, which are important to make real-life trustworthy predictions in the wild. We will implement these ideas in an open-source software toolbox readily applicable to visual recognition and inverse imaging problems, which will also handle other modalities. This will stimulate interdisciplinary collaborations, with the potential to be a game changer in the way scientists and engineers design machine learning problems.

Mots-clés : computer vision image processing optimization
Projet ERC : WICE (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Université de Lettonie — Pays : Lettonie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1996386.25 €

The negative attitudes towards immigration in Central and Eastern European (CEE) countries were on clear display during the 2015/2016 immigration crisis, particularly in the Visegrád Group countries, but also in the Baltics. Against this backdrop, the overwhelming support for Ukrainians fleeing the war in CEE has come as a surprise to many. It has highlighted the huge inequalities in the treatment of Ukrainians vs. other third country asylum seekers and clearly revealed conditionality of migration and integration systems as well as their potential for change. While there have been countless examples of populist leaders being able to successfully stoke hostility towards immigrants, there is little evidence to indicate what can systematically decrease anti-immigrant sentiments in CEE on such a broad scale.  Making use of the unique historical conditions, the project draws from scientific disciplines often overlooked in research on attitudes towards migrants (memory studies, history, security studies, political science, behavioural economics, communication etc.) aiming to generate an original understanding of societal attitudes that go beyond ‘xenophobia’ and ‘prejudice’, and raises broader questions about the relationship between structural, political, cultural, economic, ideological, and agential influences on attitudes towards immigration.  The project is based on case studies of three CEE countries (Poland, Latvia, Hungary) and Italy as a benchmark case. While most previous research focuses on negative narratives surrounding immigration, this project seeks to learn from the experience with Ukrainian refugees in CEE and looks for practical solutions on how to effectively counter the negative narratives. In addition to qualitative research methods, it relies on a novel experimental survey design based on full factorial analysis to test how attitudes are influenced by different characteristics of migrants and different framing (information treatment)

Mots-clés : migration,migrant reception,xenophobia
Projet ERC : ELIMINATE-CKD (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : University Medical Center Groningen — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : UMCG
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

The objective of ELIMINATE-CKD is to demonstrate that combining measures of kidney structure and function will strongly enhance detection of chronic kidney disease (CKD). To reach this objective, I will develop a new method to quantify the number of nephrons, the smallest functional units in the kidney.  Rationale: - CKD affects ~10% of the global population and causes 2.6 million deaths/year. CKD-associated costs (140 billion euros/year in Europe) exceed diabetes and cancer. Limitations in CKD detection impede the success of emerging therapies. - Current CKD definition is based on creatinine-based estimated glomerular filtration rate, which is often inaccurate due to interindividual differences in body composition and compensation for early kidney damage as remaining nephrons increase their function. - Using a unique combination of multi-omics and machine learning approaches in human cohorts and innovative animal models, I will dissect pathways that drive nephron number and enable its quantification.  Concrete objectives are to: 1) Develop a high-throughput method to quantify nephron number in large human cohorts and a novel rat model of gradual nephron reduction 2) Construct and validate unbiased and hypothesis-driven nephron number algorithms based on kidney-derived proteins, lipids and metabolites in blood and urine 3) Demonstrate that nephron number algorithms enable earlier and more accurate CKD detection 4) Provide proof of principle that nephron number-triggered therapy prevents or halts CKD  Impact: This project will lead to a new CKD classification, based on both structure and function. Identification of mechanisms driving nephron number will boost drug development and regenerative medicine towards nephron-preserving therapies. Finally, the concept of structure-function relationship is relevant in many other organs (brain, heart). My passion to beat kidney disease by innovative diagnostics drives me towards my ultimate goal: to eliminate CKD.

Mots-clés : chronic kidney disease,kidney function,biomarker epidemiology,machine learning,resilience
Projet ERC : CONVERGENCE (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Life Science
Panel : Environmental B iology, Ecology  and E volution
Entité participante : Centre de recherche de Biologie de Szeged — Pays : Hongrie — Rôle : PI
Acronyme de l'entité : BRC
Type d'entité : Recherche
Financement ERC de l'entité : 1997688.0 €

In this project, we aim to study the genetic mechanisms of convergent evolution by uncovering the mechanistic details of two highly replicated transitions in organismal complexity. Convergent evolution is widespread in nature, even on macroevolutionary timescales. To explain its pervasiveness, recent studies have proposed the idea of predisposing precursor traits that, if easily co- or exapted for new functions, can increase the likelihood of convergence. However, most of these hypotheses remain untested because of the lack of tractable model systems. We identified two fungal case studies that offer optimal model systems to mechanistically test the hypothesis that precursor traits increase the likelihood of convergence: (i) 8-11 repeated origins of complex multicellularity in mushrooms and (ii) >14 losses of multicellularity in yeast-like fungi. We hypothesize that both of these occurred by the repeated exaptation of ancient morphogenetic programs and, in the case of yeasts, additionally, by the emergence of mechanisms for bypassing multicellular growth. Our hypotheses imply that both complex multicellular and yeast-like lifestyles are only a few mutations away for any filamentous fungus because precursor traits shorten the mutational path for evolution. Although these are bold hypotheses, we obtained promising preliminary results that support them. We designed an experimental plan involving phylogeny-aware comparative -omics, reverse genetics, and evo-devo, which, when combined with our preliminary results, will provide a robust entry point for testing the role of predisposition in convergent evolution and will ultimately allow us to “replay the tape of major fungal transitions” in the laboratory. We expect this project to contribute to uncovering the general principles of convergent evolution and to be one of the first to mechanistically test if certain precursor traits can promote convergence.

Mots-clés : evo devo,multicellularity,convergent evolution
Projet ERC : PredictCOPD (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : University Medical Center Groningen — Pays : Pays-Bas — Rôle : Partner
Acronyme de l'entité : UMCG
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 253750.0 €

Chronic Obstructive Pulmonary Disease (COPD) has been traditionally understood as a self-inflicted disease caused by tobacco smoking occurring in old individuals. Over the past few years, however, our group and others have proposed that the pathogenesis of COPD goes beyond smoking, and that there is a range of lung function trajectories through life (trajectome); some of them have roots in early life and can lead to COPD, cardiovascular and metabolic morbidity as well as premature death, while others are associated with healthy ageing. Here I propose that, detrimental gene (G) and environment (E) interactions occurring early in life (T) constitute a ‘first injury hit’ that alters the normal lung developmental program and modify the pace of normal lung aging by reducing the resilience of the lungs to future GxExT interactions. Accordingly, lifelong GxExT interactions determine the individual trajectome and, eventually, the occurrence of COPD and associated multimorbidity. PredictCOPD aims to identify the interactions and mechanisms that determine which individuals will develop COPD and multimorbidty at some point of their life. The specific aims are: 1) to identify the lifelong environmental and host risk factors associated with the trajectome, COPD and multimorbidity, 2) to use a novel liquid biopsy method to identify biological factors driving the trajectome, COPD and multimorbidity; 3) to identify clinically relevant preventive and/or early therapeutic targets integrating the results from aims 1 and 2 with novel analytical approaches and 4) to validate findings both in vitro and in other available cohorts. The project will leverage from several available population and COPD patient studies with available clinical data and biological samples. The results of PredictCOPD have the potential to: 1) promote healthy ageing by preventing and eventually eradicate COPD and associated multimorbidity; and, 2) change COPD treatment from palliative to causal.

Mots-clés : lung trajectories,copd,multiborbidity,aging
Projet ERC : AIDose (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Université de Copenhague — Pays : Danemark — Rôle : PI
Acronyme de l'entité : KU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999972.75 €

Radiotherapy (RT) is a critical component of cancer treatment, potentially benefiting approximately 50% of patients. However, less than 80% of patients who could benefit from RT actually receive it in Europe. The risk of toxicities, i.e., the radiation-induced damage of healthy tissues, is one of the main reasons why oncologists may exclude a potentially beneficial RT from the treatment regimen. For example, a recent analysis of the RTOG 0617 trial highlighted that the tumor control benefits of high-dose lung RT are accompanied by higher mortality rates due to extensive heart irradiation. The AIDose project will leverage my work on hepatobiliary toxicity prediction, artificial intelligence (AI) advancements, and medical image analysis to develop the first toxicity risk atlas for thoracic and abdominal organs-at-risk (OARs), i.e., organs located in close proximity to tumors. The atlas is envisioned as a three-dimensional map of OARs with pinpointed anatomical subregions associated with high toxicity risks. The AIDose project has four aims: a) developing AI-driven solutions for morphological and radiomic profiling of OARs; b) extracting and analyzing non-dosimetric clinical features associated with toxicities; c) utilizing AI to identify consistent patterns in radiation doses delivered to OAR subregions, and correlating them with toxicity risks; d) validating the resulting toxicity atlas against multi-hospital RT data. The feasibility of AIDose is supported by my previous research on liver and head-and-neck RT planning, which resulted in publications in key journals in the field and an award from the American Association of Medical Physicists. The use of AI for radiotherapy planning and outcome prediction is believed to be the main area of impact by the American and European Societies for Radiation Oncology. This proposal represents a unique opportunity to advance this highly innovative and clinically important research direction.

Mots-clés : medical imaging,artificial intelligence,radiotherapy,cancer,toxicity,image segmentation,image registration