Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : MDFT (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 1534159.0 €

Density Functional Theory (DFT) is one of the most famous methods used in quantum physics and chemistry to describe matter at the microscopic scale. The purpose of the proposal is to investigate the mathematical foundations of this theory. The questions which have to be solved involve advanced tools from nonlinear analysis, partial differential equations, spectral theory, optimal transport, and numerical analysis. Several have stayed unsolved for many years. The project is prone to have an impact in many areas of mathematics, as well as in physics and chemistry. The proposal is divided into three main tasks. The first is focused on some important questions on the foundations of DFT, including excited states, its time-dependent formulation, and the local density approximation based on the uniform electron gas model. DFT can be formulated as an inverse problem, the main question being to find the external potential knowing only the density of particles in the system. We will investigate the invertibility of the potential-to-density map, both in the stationary and time-dependent cases. The second task deals with the derivation of simple DFT models from the true Schrödinger equation, a problem which has been largely discussed in the literature. We will work on the most challenging open questions, including for instance the relativistic Scott correction, or the proof of Bose-Einstein condensation for an infinite Bose gas in the mean-field limit. Finally, in the last task we study some particular DFT models, which are particularly challenging from the mathematical point of view. This includes for example a highly nonlinear model for neutrons and protons, infinite crystals with deterministic and random perturbations, and a time-dependent electromagnetic field solving Maxwell's equations coupled to the quantized Dirac quantum vacuum.

Mots-clés : density functional theory,variational methods,quantum theory,numerical analysis
Projet ERC : ApoptoMDS (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : University of Freiburg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1372525.0 €

Deregulated apoptotic signaling in hematopoietic stem and progenitor cells (HSPCs) strongly contributes to the pathogenesis and phenotypes of congenital bone marrow failure and myelodysplastic syndromes (MDS) and their progression to acute myeloid leukemia (AML). HSPCs are highly susceptible to apoptosis during bone marrow failure and early MDS, but AML evolution selects for apoptosis resistance. Little is known about the main apoptotic players and their regulators. ApoptoMDS will investigate the impact of apoptotic deregulation for pathogenesis, correlate apoptotic susceptibility with the kinetics of disease progression and characterize the mechanism by which apoptotic susceptibility turns into resistance. ApoptoMDS will draw on a large collection of patient-derived samples and genetically engineered mouse models to investigate disease progression in serially transplanted and xenotransplanted mice. How activated DNA damage checkpoint signaling contributes to syndrome phenotypes and HSPC hypersusceptibility to apoptosis will be assessed. Checkpoint activation confers a competitive disadvantage, and HSPCs undergoing malignant transformation are under high selective pressure to inactivate it. Checkpoint abrogation mitigates the hematological phenotype, but increases the risk of AML evolution. ApoptoMDS aims to analyze if inhibiting apoptosis in HSPCs from bone marrow failure and early-stage MDS can overcome the dilemma of checkpoint abrogation. Whether inhibiting apoptosis is sufficient to improve HSPC function will be tested on several levels and validated in patient-derived samples. How inhibiting apoptosis in the presence of functional checkpoint signaling influences malignant transformation kinetics will be assessed. If, as hypothesized, inhibiting apoptosis both mitigates hematological symptoms and delays AML evolution, ApoptoMDS will pave the way for novel therapeutic approaches to expand the less severe symptomatic period for patients with these syndromes.

Mots-clés : bcl2,bone marrow failure,myelodysplastic syndrome,malignant transformation,dna damage checkpoint
Projet ERC : EPI-X4Health (Horizon 2020, appel 2018, Proof of concept grants)
Domaine scientifique : Life Science
Panel : Immunity, Infection and Immunotherapy
Entité participante : Fraunhofer Society — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : FHG
Type d'entité : Recherche
Financement ERC de l'entité : 26950.0 €

Deregulation of CXCL12 signaling via the CXCR4 chemokine receptor plays a key role in many diseases, including cancer, rheumatoid arthritis, chronic inflammation and cardiovascular disorders. Thus, agents modulating CXCR4 signaling offer many prospects for therapeutic development. CXCR4 is also a major coreceptor for HIV entry. Thus, we used HIV as tool to screen peptide libraries encompassing the entire blood peptidome for novel CXCR4 ligands. This approach allowed the identification of a small fragment of human serum albumin as potent HIV inhibitor. Remarkably, this naturally occurring peptide, named EPI-X4 (Endogenous Peptide Inhibitor of CXCR4), blocks CXCL12/CXCR4 signaling and suppresses cancer cell migration and invasion. Preclinical studies in mice show that EPI-X4 mobilizes hematopoietic stem/progenitor cells and inhibits lung inflammation in an asthma model. Preliminary analyses suggest that EPI-X4 is more specific and better tolerated than Mozobiol® (AMD3100), the only CXCR4 antagonist approved for clinical application. Recently, we developed synthetic derivatives of EPI-X4 showing about three orders of magnitude improved potency against HIV as well as increased plasma stability. Key goals of this project are to determine the therapeutic potential of these improved derivatives against cancers (Aim 1) and inflammatory diseases (Aim 2) using established mouse models of colon carcinoma, osteosarcoma, glioblastoma, airway inflammation and dermatitis. These disorders were selected from the large number of CXCR4-linked diseases because no safe and effective treatment exists and since preclinical mouse models are available to determine whether EPI-X4 derived agents are more efficient and better tolerated than other compounds. In parallel to the experimental assessment of the therapeutic potential of these novel CXCR4 antagonists, we will perform market analyses and develop an IPR strategy to pave the way towards commercialization and future clinical application.

Mots-clés : g protein coupled receptors,chemokines,cxcr4 signaling,antagonist,colon carcinoma,osteosarcoma,glioblastoma,inflammation,allergy,endogenous peptides,peptide optimization,mouse models
Projet ERC : EPI-X4Health (Horizon 2020, appel 2018, Proof of concept grants)
Domaine scientifique : Life Science
Panel : Immunity, Infection and Immunotherapy
Entité participante : Université de Strasbourg — Pays : France — Rôle : Partner
Acronyme de l'entité : UNISTRA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 26950.0 €

Deregulation of CXCL12 signaling via the CXCR4 chemokine receptor plays a key role in many diseases, including cancer, rheumatoid arthritis, chronic inflammation and cardiovascular disorders. Thus, agents modulating CXCR4 signaling offer many prospects for therapeutic development. CXCR4 is also a major coreceptor for HIV entry. Thus, we used HIV as tool to screen peptide libraries encompassing the entire blood peptidome for novel CXCR4 ligands. This approach allowed the identification of a small fragment of human serum albumin as potent HIV inhibitor. Remarkably, this naturally occurring peptide, named EPI-X4 (Endogenous Peptide Inhibitor of CXCR4), blocks CXCL12/CXCR4 signaling and suppresses cancer cell migration and invasion. Preclinical studies in mice show that EPI-X4 mobilizes hematopoietic stem/progenitor cells and inhibits lung inflammation in an asthma model. Preliminary analyses suggest that EPI-X4 is more specific and better tolerated than Mozobiol® (AMD3100), the only CXCR4 antagonist approved for clinical application. Recently, we developed synthetic derivatives of EPI-X4 showing about three orders of magnitude improved potency against HIV as well as increased plasma stability. Key goals of this project are to determine the therapeutic potential of these improved derivatives against cancers (Aim 1) and inflammatory diseases (Aim 2) using established mouse models of colon carcinoma, osteosarcoma, glioblastoma, airway inflammation and dermatitis. These disorders were selected from the large number of CXCR4-linked diseases because no safe and effective treatment exists and since preclinical mouse models are available to determine whether EPI-X4 derived agents are more efficient and better tolerated than other compounds. In parallel to the experimental assessment of the therapeutic potential of these novel CXCR4 antagonists, we will perform market analyses and develop an IPR strategy to pave the way towards commercialization and future clinical application.

Mots-clés : g protein coupled receptors,chemokines,cxcr4 signaling,antagonist,colon carcinoma,osteosarcoma,glioblastoma,inflammation,allergy,endogenous peptides,peptide optimization,mouse models
Projet ERC : FIDELIO (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of Cambridge — Pays : Royaume-Uni — Rôle : Partner
Acronyme de l'entité : U. Cambridge
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 518563.69 €

Designating Protected Areas (PAs) is the most important policy tool for biodiversity conservation. Over 100 new PAs have been established in the past 36 months in European Union territory. However, effective management of PAs is often obstructed by conflicts mainly associated with the social impacts (SIs) imposed on local communities by their establishment. Despite the importance of these SIs there are certain aspects in this field that remain significantly under-researched. There is now an increasing need to incorporate SI assessments in decision making processes by providing a clear framework explaining how perceptions of these impacts are created and predicting their change in the future. This will support the achievement of international goals for biodiversity conservation and adaptation to climate change as well as better accounting for social justice issues for communities dependent on protected natural resources. The aim of FIDELIO is to develop for the first time a new paradigm in order to understand how perceptions of SIs are formulated taking into consideration the dimensions of space and time. FIDELIO will assist in increasing public engagement and the incorporation of local opinions in decision-making. It will also facilitate the process of policy development and a reduction in conflicts between different stakeholders in PAs. FIDELIO will last 5 years and its research objectives will be explored through the application of a mixed-methods approach including the implementation of two rounds of social surveys in 4 PAs across Europe and the testing of the framework in 15 additional PAs. This is an extremely timely project considering the steady increase of new PAs and the re-designation of current ones. FIDELIO will contribute to the better understanding of SIs and facilitate predictions for their change in the future, while assisting in maximizing social benefits for local communities arising from the designation of a PA.

Mots-clés : protected areas,social impacts,europe
Projet ERC : D3 (Horizon 2020, appel 2016, Starting 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 : 1482761.0 €

Designers draw extensively to externalize their ideas and communicate with others. However, drawings are currently not directly interpretable by computers. To test their ideas against physical reality, designers have to create 3D models suitable for simulation and 3D printing. However, the visceral and approximate nature of drawing clashes with the tediousness and rigidity of 3D modeling. As a result, designers only model finalized concepts, and have no feedback on feasibility during creative exploration. Our ambition is to bring the power of 3D engineering tools to the creative phase of design by automatically estimating 3D models from drawings. However, this problem is ill-posed: a point in the drawing can lie anywhere in depth. Existing solutions are limited to simple shapes, or require user input to “explain” to the computer how to interpret the drawing. Our originality is to exploit professional drawing techniques that designers developed to communicate shape most efficiently. Each technique provides geometric constraints that help viewers understand drawings, and that we shall leverage for 3D reconstruction. Our first challenge is to formalize common drawing techniques and derive how they constrain 3D shape. Our second challenge is to identify which techniques are used in a drawing. We cast this problem as the joint optimization of discrete variables indicating which constraints apply, and continuous variables representing the 3D model that best satisfies these constraints. But evaluating all constraint configurations is impractical. To solve this inverse problem, we will first develop forward algorithms that synthesize drawings from 3D models. Our idea is to use this synthetic data to train machine learning algorithms that predict the likelihood that constraints apply in a given drawing. In addition to tackling the long-standing problem of single-image 3D reconstruction, our research will significantly tighten design and engineering for rapid prototyping.

Mots-clés : 3d modeling,sketch based modeling,computer assisted drawing and design,non photorealistic rendering
Projet ERC : SUBLINEAR (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Board of the Swiss Federal Institutes of Technology — Pays : Suisse — Rôle : PI
Acronyme de l'entité : ETH Zurich
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1473175.0 €

Designing efficient algorithms for fundamental computational tasks as well as understanding the limits of tractability has been the goal of computer science since its inception. Polynomial runtime has been the de facto notion of efficiency since the introduction of the notion of NP-completeness. As the sizes of modern datasets grow, however, many classical polynomial time (and sometimes even linear time) solutions become prohibitively expensive. This calls for sublinear algorithms, i.e. algorithms whose resource requirements are substantially smaller than the size of the input that they operate on. We propose to design a toolbox of powerful algorithmic techniques with sublinear resource requirements that will form the theoretical foundation of large data analysis, as well as develop a nuanced runtime, space and communication complexity theory to show optimality of our algorithms. Specifically, we propose to: 1. design an algorithmic toolkit for sublinear graph processing that will form the basis of large scale graph analytics; 2. design a new generation of sublinear algorithms for signal processing that will become the method of choice for a wide range of applications; 3. develop a far-reaching set of techniques for lower bounding runtime, space and communication complexity of sublinear algorithms. The problems that we propose to solve are among the most fundamental algorithmic questions on the forefront of the rapidly developing algorithmic theory of large data analysis, which has been the focus of an extensive body of work in the research community. The algorithms and complexity theoretic results that we propose to design will cut at the core of fundamental computational problems and form the theoretical foundation of computing with constrained resources.
Projet ERC : ScienceGraph (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Institut fur angewandte informatik (infai) ev — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : Infai
Type d'entité : Recherche
Financement ERC de l'entité : 167500.0 €

Despite an improved digital access to scientific publications in the last decades, the fundamental principles of scholarly communication remain unchanged and continue to be largely document-based. The document-oriented workflows in science have reached the limits of adequacy as highlighted by recent discussions on the increasing proliferation of scientific literature, the deficiency of peer-review and the reproducibility crisis. In ScienceGRAPH we aim to develop a novel model for representing, analysing, augmenting and exploiting scholarly communication in a knowledge-based way by expressing and linking scientific contributions and related artefacts through semantically rich, interlinked knowledge graphs. The model is based on deep semantic representation of scientific contributions, their manual, crowd-sourced and automatic augmentation and finally the intuitive exploration and interaction employing question answering on the resulting ScienceGRAPH base. Currently, knowledge graphs are still confined to representing encyclopaedic, factual information. ScienceGRAPH advances the state-of-the-art by enabling to represent complex interdisciplinary scientific information including fine-grained provenance preservation, discourse capture, evolution tracing and concept drift. Also, we will demonstrate that we can synergistically combine automated extraction and augmentation techniques, with large-scale collaboration to reach an unprecedented level of knowledge graph breadth and depth. As a result, we expect a paradigm shift in the methods of academic discourse towards knowledge-based information flows, which facilitate completely new ways of search and exploration. The efficiency and effectiveness of scholarly communication will significant increase, since ambiguities are reduced, reproducibility is facilitated, redundancy is avoided, provenance and contributions can be better traced and the interconnections of research contributions are made more explicit and transparent.

Mots-clés : scholarly communication,knowledge graphs,semantic technologies,question answering
Projet ERC : HOWPER (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Weizmann Institute of Science — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2493441.25 €

Despite decades of intensive research, there is no agreement about the general scheme of perception: Is the external object a trigger for a brain-internal process (open-loop perception, OLP) or is the object included in brain dynamics during the entire perceptual process (closed-loop perception, CLP)? HOWPER is designed to provide a definite answer to this question in the cases of human touch and vision. What enables this critical test is our development of an explicit CLP hypothesis, which will be contrasted, via specific testable predictions, with the OLP scheme. In the event that CLP is validated, HOWPER will introduce a radical paradigm shift in the study of perception, since almost all current experiments are guided, implicitly or explicitly, by the OLP scheme. If OLP is confirmed, HOWPER will provide the first formal affirmation for its superiority over CLP. Our approach in this novel paradigm is based on a triangle of interactive efforts comprising theory, analytical experiments, and synthetic experiments. The theoretical effort (WP1) will be based on the core theoretical framework already developed in our lab. The analytical experiments (WP2) will involve human perceivers. The synthetic experiments (WP3) will be performed on synthesized artificial perceivers. The fourth WP will exploit our novel rat-machine hybrid model for testing the neural applicability of the insights gained in the other WPs, whereas the fifth WP will translate our insights into novel visual-to-tactile sensory substitution algorithms. HOWPER is expected to either revolutionize or significantly advance the field of human perception, to greatly improve visual to tactile sensory substitution approaches and to contribute novel biomimetic algorithms for autonomous robotic agents.

Mots-clés : active sensing,embodied cognition,morphological coding,event based processing,sensory motor loops
Projet ERC : HypoFlam (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : Helmholtz Zentrum München — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HMGU
Type d'entité : Recherche
Financement ERC de l'entité : 2402280.0 €

Despite educational, political and biomedical research efforts, obesity, type 2 diabetes and related metabolic diseases are increasing worldwide at an alarming rate. Failure to deliver efficient and safe medical therapies is a result of our incomplete understanding of the pathogenesis of the metabolic syndrome. Current knowledge implicates impaired CNS control over appetite, body weight and systemic metabolism as a key pathogenic process leading to obesity and type 2 diabetes. Yet, years of intense study focused on neuronal signalling have produced no transformative breakthroughs. Control centers located in the hypothalamic arcuate nucleus (ARC) serve as primary targets of afferent hormone signals regulating systemic control of body weight and metabolism and appear to be most affected by high fat high sugar (HFHS) diets. Recently, we discovered that in the early stages of the metabolic syndrome induced by consumption of a HFHS diet, significant changes beyond neuronal pathologies occur in hypothalamic nuclei responsible for metabolic control, such as the ARC. Specifically, we observe in hypothalami of mice, rats and humans increased reactive microgliosis and astrocytosis as well as a decline in regulatory T-cell presence. We hypothesize that hypothalamic inflammatory processes triggered by hypercaloric environments impair hormone sensing, disrupt glial homeostasis and incapacitate these hypothalamic control centers, ultimately contributing to development of obesity and diabetes. Building on a considerable body of preliminary data, we will apply an array of advanced technologies to A) develop a functional understanding of the pathophysiology of diet-induced hypothalamic inflammation, B) test if hypothalamic inflammation plays a critical role in the development of the metabolic syndrome, and C) attempt to target these novel pathogenic processes for the first time using novel targeted therapeutic approaches.