Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : PaVeS (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Technical University of Munich — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : TUM
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2354000.0 €

Parameterized systems consist of an arbitrary number of replicated agents with limited computational power, interacting to achieve common goals. They pervade computer science. Classical examples include families of digital circuits, distributed algorithms for leader election or byzantine agreement, routing algorithms, and multithreaded programs. Modern examples exhibit stochastic interaction between mobile agents, and include robot swarms, molecular computers, and cooperating ant colonies. A parameterized system is in fact an infinite collection of systems, one for each number of agents. Current verification technology of industrial strength can only check correctness of a few instances of this collection. For example, model checkers can automatically prove a distributed algorithm correct for a small number of processes, but not for any number. While substantial progress has been made on the theory and applications of parameterized verification, in order to achieve large impact the field has to face three ``grand challenges'': - Develop novel algorithms and tools for p-verification of classical p-systems that bypass the high complexity of current techniques. -Develop the first algorithms and tools for p-verification of modern stochastic p-systems. -Develop the first algorithms and tools for synthesis of correct-by-construction p-systems. Addressing these challenges requires fundamentally new lines of attack. The starting point of PaVeS are two recent breakthroughs in the theory of Petri nets and Vector Addition Systems, one of them achieved by the PI and his co-authors. PaVeS will develop these lines into theory, algorithms, and tools for p-verification and p-synthesis, leading to a new generation of verifiers and synthesizers.

Mots-clés : computer aided verification,model checking,automatic synthesis,parameterized verification,probabilistic verification,automata theory,constraint solving,petri nets,vector addition systems
Projet ERC : DynamicAssemblies (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : Commissariat à l’énergie atomique et aux énergies alternatives — Pays : France — Rôle : PI
Acronyme de l'entité : CEA
Type d'entité : Recherche
Financement ERC de l'entité : 2499150.0 €

Paramyxoviruses, including, measles and a number of dangerous human pathogens, are negative strand RNA viruses that express their own machinery for transcription and replication. Different interactions between the nucleoprotein (N) and the phosphoprotein (P) are essential for chaperoning and assembly of N on newly synthesized RNA genomes to form nucleocapsids (NCs), as well as for initiating replication and transcription. Both N and tetrameric P exhibit extensive conformational disorder, with very long, unfolded regions that host important post-translational modification sites as well as regulatory interactions with host and viral partners. The presence of this level of disorder, in viruses whose genetic information is normally so parsimoniously exploited, remains unexplained. The elaboration of time-resolved, atomic resolution descriptions of the interaction trajectories of these highly disordered N:P complexes is extremely challenging, requiring the development of adapted methodologies that can account for their intrinsic flexibility. The role of N and P has been rendered yet more enigmatic following our recent observation that when mixed in solution they form liquid-like droplets. Such membraneless organelles are revolutionizing our understanding of cellular chemical biology, although their physical basis is poorly understood. Our aim is to describe these important complexes at atomic resolution, in particular to understand the role of the extensive conformational dynamics of N and P in the replication cycle. Our recent success in engineering soluble N:P complexes from measles that assemble into NCs, combined with ongoing development of NMR-based methods to investigate the structure, dynamics and interaction kinetics of large, intrinsically disordered proteins, fluorescence spectroscopy, cryoEM, SAXS, crystallography and molecular simulation, will provide the essential tools to investigate the functional mechanisms of these previously inaccessible complexes.

Mots-clés : intrinsically disordered proteins,liquid liquid phase separation,paramyxoviruses
Projet ERC : WARMCOASTS (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : University of Bremen — Pays : Allemagne — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 616611.25 €

Past interglacials are periods of the earth’s history when climate was warmer than the pre-industrial, and are often considered as process-analogs for a future warmer climate. During the Last Interglacial (LIG, ~128-116 ka), polar temperatures were few degrees higher than pre-industrial, ice sheets were smaller and sea level was higher than today. Studies also suggest that waves in the North Atlantic might have been more intense in the LIG than today. Understanding sea level changes and extreme wave intensity during the LIG is key to assess the future of the world’s ice sheets and coastlines under warmer climatic conditions. For this reason, the LIG is the most studied among past interglacials, but recent research highlighted that the LIG is far from a ‘solved problem’, especially for which concerns sea level and coastal dynamics. There are in fact three relevant research gaps. First, widely accepted estimates suggest that LIG global mean sea level was 5-10 m higher than today, but recent studies proved that previously unrecognized processes concur to make current LIG sea level estimates very uncertain. Second, it is unclear if LIG sea level was characterized by rapid oscillations that caused sea level to rise abruptly at rates higher than at present (up to 10 mm per year in the LIG, compare with 3 mm per year today). A third research gap is related to the highly controversial notion that the LIG was characterized by ‘superstorms’, producing waves more intense than those observed today. In this project, we want to employ a multidisciplinary combination of methods to study Last Interglacial peak sea level, sea level variations and extreme waves. WARMCOASTS will develop both new datasets and merge methods from geology, earth modeling, surface processes modeling and hydrodynamic modeling to advance the current state-of-the-art. The results of this project will be functional to better understand coastal processes under slightly warmer climate conditions.

Mots-clés : paleo sea level changes
Projet ERC : WARMCOASTS (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : Ca' Foscari University of Venice — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 883353.75 €

Past interglacials are periods of the earth’s history when climate was warmer than the pre-industrial, and are often considered as process-analogs for a future warmer climate. During the Last Interglacial (LIG, ~128-116 ka), polar temperatures were few degrees higher than pre-industrial, ice sheets were smaller and sea level was higher than today. Studies also suggest that waves in the North Atlantic might have been more intense in the LIG than today. Understanding sea level changes and extreme wave intensity during the LIG is key to assess the future of the world’s ice sheets and coastlines under warmer climatic conditions. For this reason, the LIG is the most studied among past interglacials, but recent research highlighted that the LIG is far from a ‘solved problem’, especially for which concerns sea level and coastal dynamics. There are in fact three relevant research gaps. First, widely accepted estimates suggest that LIG global mean sea level was 5-10 m higher than today, but recent studies proved that previously unrecognized processes concur to make current LIG sea level estimates very uncertain. Second, it is unclear if LIG sea level was characterized by rapid oscillations that caused sea level to rise abruptly at rates higher than at present (up to 10 mm per year in the LIG, compare with 3 mm per year today). A third research gap is related to the highly controversial notion that the LIG was characterized by ‘superstorms’, producing waves more intense than those observed today. In this project, we want to employ a multidisciplinary combination of methods to study Last Interglacial peak sea level, sea level variations and extreme waves. WARMCOASTS will develop both new datasets and merge methods from geology, earth modeling, surface processes modeling and hydrodynamic modeling to advance the current state-of-the-art. The results of this project will be functional to better understand coastal processes under slightly warmer climate conditions.

Mots-clés : paleo sea level changes
Projet ERC : RESISTANCE (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : University of Zurich — Pays : Suisse — Rôle : PI
Acronyme de l'entité : UZH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 988041.25 €

Pathogens are prevalent across all ecosystems. Given the threats imposed by pathogens on their hosts, the ability to resist infection is one key determinant of an individual’s reproductive success and survival. According to theory, resistance evolution is driven by pathogen-imposed selection and constrained by host life-history trade-offs. However, resistance evolution is traditionally studied within the “one host-one pathogen” framework, although it is becoming increasingly clear that a single host individual is exploited by diverse pathogen communities. Unravelling this diversity is the key to understanding selection for resistance, and the key aim of this proposal is to bridge this gap between theory and data. The specific objectives of this proposal are to: i) Assess spatio-temporal variation in pathogen communities and their determinants through community modeling; ii) Quantify the role of host resistance in shaping its pathogen community; iii) Unravel resistance mechanisms that determine pathogen communities by combining experimental and molecular approaches; iv) Quantify immediate and cross-generational fitness consequences that different pathogen communities inflict on their host, and v) Validate the experimental results by assessing how past disease communities have shaped host resistance in natural populations. This ambitious goal is now attainable for the first time because over the past decade my research group has amassed long-term data on hundreds of Plantago lanceolata populations in the Åland Islands, and an extensive genetic sample and seed collection that allow estimating past disease communities and resistance evolution through time. Jointly the objectives of this proposal will provide an unprecedented synthesis of how resistance functions and evolves under realistic pathogen loads, with far reaching implications for both redefining the conceptual framework for resistance evolution and for tackling real-world health and food security problems.

Mots-clés : coinfection,plant pathogen interactions,resistance evolution
Projet ERC : TrueBrainConnect (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Charité - Universitätsmedizin Berlin — Pays : Allemagne — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 995540.0 €

Pathological communication between different brain regions has been implicated in various neurological disorders. However, the computational tools for assessing such communication from neuroimaging data are not sufficiently developed. The goal of TrueBrainConnect is to establish brain connectivity analysis using non-invasive electrophysiology as a practical and reliable neuroscience tool. To achieve this, we will develop novel signal processing and machine learning techniques that address shortcomings in state-of-the-art reconstruction and localization of neural activity from sensor data, the estimation of genuine neural interactions, the prediction of external (e.g., clinical) variables from estimated neural interactions, and the interpretation of the resulting models. These techniques will be thoroughly validated and then made publicly available. We will use the TrueBrainConnect methodology to characterize the neural bases underlying dementia and Parkinson's disease (PD), two of the most pressing neurological health challenges of our time. In collaboration with clinical experts, we will address practically relevant issues such as how to determine the onset of 'freezing' episodes in PD patients, and how to detect different variants and precursors of dementia. The outcome of TrueBrainConnect will be a versatile methodology allowing researchers, for the first time, to reliably estimate and anatomically localize important types of interactions between different brain structures in humans within known confidence bounds. The proposed clinical applications will improve our understanding of the studied diseases and will lay the foundation for the development of novel diagnostic markers for these diseases.

Mots-clés : brain connectivity,electroencephalography,magnetoencephalography,inverse problem,classification,regression,interpretation,validation,open source software
Projet ERC : MAP-IN-HEART (Horizon 2020, appel 2019, Proof of concept grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Centre hospitalier universitaire de Bordeaux — Pays : France — Rôle : Partner
Acronyme de l'entité : CHU Bordeaux
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 80500.0 €

Patients at risk of ventricular tachycardia (VT), the primary cause of sudden cardiac death, commonly undergo catheter ablation to cauterize the areas within myocardial scars responsible for arrythmias. However, the initial phase of the intervention devoted to identifying the ablation targets currently involves the intra-cardiac insertion of a mapping catheter characterized for being invasive, often inaccurate, and time-consuming. Pre-operative implementation of 3D cardiac imaging approaches to provide detailed structural information on ablation targets could overcome the shortcomings of standard catheter-based mapping. Still, state-of-the-art techniques have not lived up to their potential so far. During his ERC Starting Grant ECSTATIC (2017-2022), Prof. Hubert Cochet at University of Bordeaux developed a novel 3D image-processing technology (MAP-IN-HEART) which would allow cardiologists to locate VT ablation targets and guide ablation procedures in a non-invasive, highly precise and rapid manner using widely available cardiac CT images, without the need for a mapping catheter. In this ERC PoC project, we will investigate the technical feasibility of the innovative MAP-IN-HEART approach by assessing its efficiency and safety in a limited clinical study, and performing a cost-efficiency analysis. Moreover, we will ensure Freedom-To-Operate and explore all the potential paths for commercialization to finally develop a viable business strategy based on the technological aspects, the market needs and trends. Lastly, during this project we will gain technical and commercial proof-of-concept, providing the necessary information for potential commercialisation routes.

Mots-clés : cardiovacular diseases,ventricular tachycardia,computed tomography,catheter ablation,cardiac 3d image
Projet ERC : PROFECI (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : Hebrew University of Jerusalem — Pays : Israël — Rôle : PI
Acronyme de l'entité : HUJI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499732.0 €

People’s lives, both as individuals and at the collective level, are guided by projections about the future. This project aims to understand the complex processes through which projections about political issues, such as the outcomes and implications of elections, referenda, crises or wars, are formulated and negotiated within and through the media. It develops a new interdisciplinary approach and research tools for studying how projections evolve over time and through the contribution of various social actors (such as politicians, experts and journalists), and how they are received and acted upon by the public. The focus is on at least three retrospective case studies (projections about the Brexit referendum; the civil war in Syria; and Donald Trump's presidential bid) as well as three real-time case studies (to be selected based on geopolitical developments), characterised by high-visibility and involving projections with far-reaching implications. PROFECI integrates cutting-edge qualitative, quantitative and automated approaches to capture the interactive process underlying the construction and evolution of public projections, and their reciprocal relationship with people’s expectations and behaviour. It begins with an in-depth investigation of the construction, transformation and reception of projections made by key actors, using textual analysis of the source projections and their journalistic coverage, reconstruction interviews with experts and journalists, and focus groups with public members. The results of this stage inform the development of tools for a large-scale diachronic automated text analysis and panel surveys, which will be applied to the real-time and the retrospective case studies. By elucidating the life cycle of projections in a mediated environment, PROFECI opens up new avenues for understanding and researching adaptive social processes, such as self-fulfilling and self-defeating prophecies, and the role of the media in shaping the future.

Mots-clés : predictions,journalism,news,political communication,political judgment,expectations,public perceptions,social construction,social dynamics,automated text analysis,mixed methods
Projet ERC : BISON (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems 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 : 3003125.0 €

Peptide building blocks serve as very attractive bio-inspired elements in nanotechnology owing to their controlled self-assembly, inherent biocompatibility, chemical versatility, biological recognition abilities and facile synthesis. We have demonstrated the ability of remarkably simple aromatic peptides to form well-ordered nanostructures of exceptional physical properties. By taking inspiration from the minimal recognition modules used by nature to mediate coordinated processes of self-assembly, we have developed building blocks that form well-ordered nanostructures. The compact design of the building blocks, and therefore, the unique structural organization, resulted in metallic-like Young's modulus, blue luminescence due to quantum confinement, and notable piezoelectric properties. The goal of this proposal is to develop two new fronts for bio-inspired building block repertoire along with co-assembly to provide new avenues for organic nanotechnology. This will combine our vast experience in the assembly of aromatic peptides together with additional structural modules from nature. The new entities will be developed by exploiting the design principles of small aromatic building blocks to arrive at the smallest possible module that form super helical assembly based on the coiled coil motifs and establishing peptide nucleic acids based systems to combine the worlds of peptide and DNA nanotechnologies. The proposed research will combine extensive design and synthesis effort to provide a very diverse collection of novel buildings blocks and determination of their self-assembly process, followed by broad chemical, physical, and biological characterization of the nanostructures. Furthermore, effort will be made to establish supramolecular co-polymer systems to extend the morphological control of the assembly process. The result of the project will be a large and defined collection of novel chemical entities that will help reshape the field of bioorganic nanotechnology.

Mots-clés : peptide self assembly,short aromatic peptides,bionanotechnological building blocks,peptide nanotubes,peptide semiconductors,supramolecular biochemistry
Projet ERC : PURFECT (Horizon 2020, appel 2019, Proof of concept grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Perception Ultrasound for Reassuring Fetal Echo Clinical Trainees (PURFECT) builds on the results of ERC Advanced Grant PULSE to develop an AI-assistive system to guide a user to find a standard mid-trimester fetal ultrasound imaging plane. Such a system would provide reassurance to newly qualified sonographers as they begin to perform real-world diagnostic imaging, may support experts perform scanning more efficiently, and would contribute to the desired trend to use ultrasound more widely in healthcare delivery. The novelty of the proposed system is that the guidance provided by the PURFECT solution is provided by artificial intelligence (AI) multi-modal deep learning models which predict where an expert would look. These models have been built from ultrasound video and gaze tracking of full ultrasound scans as part of the PULSE project. The Proof of Concept award aims to build on the algorithm research conducted in PULSE to systems engineer a working PURFECT prototype of an AI-assisted ultrasound image guidance system. This will require an inter-disciplinary approach to design, build and test a system suitable for use by the intended user. In addition to addressing the engineering challenges, the project will also conduct a series of pilot usability studies to understand how AI-assisted image plane guidance can assist a trainee in diagnostic imaging and give them reassurance just as a human tutor or more experienced colleague might do. The project aims to generate licensable intellectual property and therefore will consider as well commercialization options for the developed technology for post-project follow up.

Mots-clés : ultrasound imaging,biomedical image analysis,machine learning,diagnostic ultrasound,obstetrics ultrasound,clinical skills training and assessment