Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : LimitMDR (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Life Science
Panel : Integrative Biology: from Genes and Genomes to Systems
Entité participante : Technical University of Denmark — Pays : Danemark — Rôle : PI
Acronyme de l'entité : DTU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1492453.0 €

Drug resistance is limiting our ability to treat most infectious diseases and forms of cancer. Indeed this relentless evolution is the major driver of treatment failure for diseases that are responsible for over half of the global disease related mortality. Yet, the underlying principles that guide this evolutionary response are poorly understood, in particular with regards to understanding the impact of multidrug treatment. LimitMDR will characterize evolutionary trajectories leading to multidrug resistance in response to individual and combination drug treatment through the execution of large-scale adaptive evolution experiment with two bacterial pathogens followed by genome sequencing and phenotyping. This effort will enable testing of contrasting hypotheses regarding the evolution of multidrug resistance in response to combination treatment. We will characterize the cause-and-effect of resistance and sensitivity mutations identified in our global data set and map comprehensive fitness landscapes of mutations accumulated during drug resistance evolution to understand the evolutionary dynamics underlying resistance evolution. To accomplish these bold goals we shall develop novel multiplexed methodologies enabling unprecedented scale of construction and phenotypic testing of identified mutations. While genetic epistasis is considered of key importance to resistance evolution most studies focus on mutations within an individual gene. Through the development of a novel experimental approach we shall elucidate complex epistatic interaction networks between mutations accumulated during resistance evolution. Finally, we will conduct mechanistic studies to uncover the mechanisms of collateral sensitivity. These studies will shed light on this underappreciated phenomenon, which is of critical relevance to drug discovery and the evolution of drug resistance. In conclusion LimitMDR will develop groundbreaking novel methodologies and scientific insights that will c

Mots-clés : antibiotic resistance,collateral sensitivity,adaptive evolution,epistasis,fitness,drug interactions,combination treatment,sequential drug treatment
Projet ERC : TOFDRY (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : University of Copenhagen — Pays : Danemark — Rôle : PI
Acronyme de l'entité : UCPH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1711467.0 €

Drylands cover approximately 65 million km² of the Earth’s land surface but their tree and shrub cover is a major unknown in terrestrial research. This is because a large proportion of dryland trees grow isolated without canopy closure and most scientific and non-scientific interest is devoted to forests, while the density and size of trees outside of forests is not well documented. However, these non-forest trees play a crucial role for biodiversity and provide ecosystem services such as carbon storage, food resources, livelihoods and shelter for humans and animals. The limited attention devoted to the quantification of dryland trees leads to an underrepresentation of non-forest trees in development strategies and climate\vegetation models, and the economic and ecological importance of non-forest trees is largely unknown at large scale. Through this project I will work towards a wall-to-wall identification of trees in global drylands, and study their ecological services and socio-environmental determinants. The breakthrough is that trees are not assessed as canopy fraction of an area, but as individuals, allowing to identify not only their coverage but also their density, crown size, and key ecological services. I will apply a new generation of satellite imagery at sub-meter resolution and extensive field data in conjunction with fully convolutional neural networks, a deep learning technique being able to identify objects within imagery at an unprecedented accuracy. In doing so, I will lay the groundwork for new insights into the contribution of human agency and climate change to the distribution of dryland trees and their role in mitigating degradation, climate change and poverty.

Mots-clés : drylands,ecosystem services,tree cover,deep learning,carbon stocks,savannas
Projet ERC : FeelAgain (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
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 : 1385032.0 €

Due to sensory loss diabetic patients are prone to falls and to foot ulcers, which consequently increase the risk of amputations. Because of the lack of sensory feedback amputees experience falls, perceive the prosthesis as a foreign body and therefore do not rely on it during walking. This causes counterbalancing movements that increase fatigue. Both types of patients suffer neuropathic pain, associable to aberrant sensory inputs. Neural pathways between the periphery and the brain are still functional above the damage or the amputation. Targeting these structures with peripheral neural interfaces could allow the restoration of natural sensory functionalities. The aim of project is to develop the first neuroprosthesis restoring natural foot sensations, through sciatic nerve stimulation, to patients with diabetic neuropathy or leg amputation. To that aim we will develop a detailed computational model of the sensory loop of the sciatic nerve. It will merge the electrical stimulation effects on sensory fibers and transduction of mechanical deformations of the skin into action potentials. Modelling results will be validated. Applying this modeling framework we will optimize the geometry of a peripheral neural interface, its surgical placement and define stimulation protocols that mimic natural sensory feedback responses. Effective device for feedback restoration will be constructed, able to translate the signals recorded by sensorized sole placed under the prosthetic or diabetic foot into the natural foot sensations perceived by subject. The interventional tools for embodiment boosting and pain relief will be developed. Clinical tests on amputee and diabetic subjects will assess the efficacy of the FeelAgain conceptual and technological framework by examination of pain, embodiment, ulcer prevention, falls avoidance and walking ability.

Mots-clés : neural stimulation,computational modeling,sensory feedback,neuroprosthetics
Projet ERC : LEXICAL (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Cambridge — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : U. Cambridge
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1989203.0 €

Due to the growing volume of textual information available in multiple languages, there is a great demand for Natural Language Processing (NLP) techniques that can automatically process and manage multi-lingual texts, supporting information access and communication in core areas of society (e.g. healthcare, business, science). Many NLP tasks and applications rely on task-specific lexicons (e.g. dictionaries, word classifications) for optimal performance. Recently, automatic acquisition of lexicons from relevant texts has proved a promising, cost-effective alternative to manual lexicography. It has the potential to considerably enhance the viability and portability of NLP technology both within and across languages. However, this approach has been explored for a very small number of resource-rich languages only, leaving the vast majority of worlds’ languages without useful technology. The ambitious goal of this project is to take research in lexical acquisition to the level where it can support multi-lingual NLP, involving also languages for which no parallel language resources (e.g. corpora, knowledge resources) are available. Building on an emerging line of research which uses mainly naturally occurring supervision (connections between languages) to guide cross-lingual NLP, we will develop a radically novel approach to lexical acquisition. This approach will transfer lexical knowledge from one language to another as well as will learn it simultaneously for a diverse set of languages using new methodology based on guiding joint learning and inference with rich knowledge about cross-lingual connections. We not only aim to create next generation lexical acquisition technology but also aim to take cross-lingual NLP a big step toward to the direction where it is no longer dependent on parallel resources. We will use our approach to support fundamental tasks and applications aimed at broadening the global reach of NLP to areas where it is now critically needed.

Mots-clés : natural language processing,automatic lexical acquisition,lexical resources,lexical semantics,cross lingual nlp,multilingual nlp,language transfer,joint learning and inference,machine learning
Projet ERC : ImageToSim (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Leibniz University Hannover — Pays : Allemagne — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 514581.91 €

Due to the intricate process of transferring diagnostic imaging data into patient-specific models, simulation workflows involving complex physiological geometries largely rely on the manual intervention of specially trained analysts. This constitutes a significant roadblock for a wider adoption of predictive simulation in clinical practice, as the associated cost and response times are incompatible with tight budgets and urgent decision-making. Therefore, a new generation of imaging-through-analysis tools is needed that can be run autonomously in hospitals and medical clinics. The overarching goal of ImageToSim is to make substantial progress towards automation by casting image processing, geometry segmentation and physiology-based simulation into a unifying finite element framework that will overcome the dependence of state-of-the-art procedures on manual intervention. In this context, ImageToSim will fill fundamental technology gaps by developing a series of novel comprehensive variational multiscale methodologies that address robust active contour segmentation, upscaling of voxel-scale parameters, transition of micro- to macro-scale failure and flow through vascular networks of largely varying length scales. Focusing on osteoporotic bone fracture and liver perfusion, ImageToSim will integrate the newly developed techniques into an imaging-through-analysis prototype that will come significantly closer to automated operation than any existing framework. Tested and validated in collaboration with clinicians, it will showcase pathways to new simulation-based clinical protocols in osteoporosis prevention and liver surgery planning. Beyond its technical scope, ImageToSim will help establish a new paradigm for patient-specific simulation research that emphasizes full automation as a key objective, accelerating the much-needed transformation of healthcare from reactive and hospital-centered to preventive, proactive, evidence-based, and person-centered.

Mots-clés : finite element method,computational solid mechanics,computational fluid mechanics,multiscale analysis,bone fracture,liver perfusion,variational and pde based image processing
Projet ERC : DoReMI (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
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 : 1398620.0 €

Due to the ubiquity of the C-C bond in diverse areas such as pharmaceuticals, agrochemicals or materials, development of atom-economical and efficient methods ensuring its formation is an active research field. This field witnessed incredible breakthroughs during the last decades, especially thanks to organometallic catalysis applied to cross-coupling or C-H activation methods. However, the most efficient C-C bond formation reactions rely on noble-metal catalysts, which lead to economic and toxicity issues. Iron catalysis appears as a promising alternative, since this metal is cheap and environmentally-friendly. Yet, current methods of Fe-mediated C-C bond formation suffer from strong scope limitations and lead to processes with a poor atom economy, as they often require stoichiometric organometallics or sacrificial reactants (metalation reagents, or oxidants). Dominating the reactivity of the C-Fe bond is pivotal to address those issues, since organoiron derivatives are key intermediates in Fe-mediated C-C bond formations. This reactivity is rich and complex, and is governed both by redox and spin effects. The DoReMI project aims to use this complexity as an advantage to develop new synthetic strategies, which cannot be envisioned with noble metal catalysis, aiming at overtaking the aforementioned pitfalls. In a first work package (WP1), the mechanistic features of the reaction of organoirons with electrophiles will be unveiled, shedding light on the reactivity of the C-Fe bond in a C-C bond formation context. WP2 will show for the first time that a stoichiometric C-H functionalization reaction between non-activated hydrocarbons and electrophiles can be achieved via the formation of an organoiron intermediate. Using the results gathered in WP1 and WP2, WP3 will demonstrate that this C-H functionalization methodology can be turned into an efficient catalytic method applicable to both unfunctionalized C-H substrates and to targets of synthetic interest.

Mots-clés : iron catalysis,reaction mechanisms,elementary steps
Projet ERC : CNSentinels (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Life Science
Panel : Immunity, Infection and Immunotherapy
Entité participante : Institut national de la santé et de la recherche médicale — Pays : France — Rôle : PI
Acronyme de l'entité : Inserm
Type d'entité : Recherche
Financement ERC de l'entité : 1912500.0 €

Due to the vital importance of the Central Nervous System (CNS), its infection and inflammation have to be tightly controlled. The surface of the CNS is connected to the periphery by a rich and complex tissue, the meninges. They contain a vast network of macrophages subdivided in two populations endowed with elusive functions. Using innovative depletion strategies in experimental mouse models, I discovered that meningeal macrophage populations represent the first line of protection against neuroinvasive pathogens. In their absence, specific areas in the meninges become highly infected, leading to fatal brain disease. The goal of ‘CNSentinels’ is to understand 1/the mechanisms controlling the spatiotemporal distribution of macrophage populations at the brain surface and 2/the relative contribution of the two macrophage populations in protecting the CNS against neuroinvasive pathogens. To this aim, I developed innovative strategies to visualize and manipulate meningeal macrophages in vivo that I will combine with cutting-edge gene editing techniques, in mice infected with lymphocytic choriomeningitis virus (LCMV). This pioneer work will help understand the spatial organization of the brain defence system and the molecular mechanisms involved in CNS protection, and will provide new avenues to design therapeutic strategies.

Mots-clés : macrophages,meninges,neuroinvasion,meningitis
Projet ERC : MAREP (Horizon 2020, appel 2019, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : Italian Institute of Technology — Pays : Italie — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

Due to the vital role of the heart, cardiac safety assessment has become one of the major gatekeepers in all stages of the drug development process, increasing costs and resources required for new drugs. Despite the recent advancements of the Comprehensive In vitro ProArrhythmia (CiPA) initiative, there are still important obstacles related to chronic cardiotoxicities and to unreliable cell models. Thus, there is a strong need for new solutions that can improve the efficacy of drug development processes. The MAREP project aims at tackling this problem by demonstrating a novel automated system for the comprehensive pharmacologic investigation of drug effects on in-vitro cardiac cell cultures. The novel approach combines laser excitation of novel metamaterials with high density multielectrode arrays (HD-MEA), providing high quality and high throughput recording of cardiac cells activity and offering an unprecedented and affordable method for the global study of drug effects on the heart. The outcome of the project is the prototype of a system that combines electrical recording of action potentials of thousands of cardiac cells with laser-based cell poration and intracellular recording. In essence, cardiac cells can be investigated in physiological conditions while drugs are administered and their effects studied in details on a large and reliable biological sample. The MAREP technology will boost the development of new drugs through the establishment of a novel, affordable, cost-effective and automated experimental procedure. This project intends to exploit a research innovation born, though not exploited, in the parent ERC project Neuro-plasmonics. This innovation may bring the laser-based bio-nanotechnologies developed in Neuro-plasmonics closer to commercialization by introducing a novel interface that is fully compatible with standard CMOS technology and with large scale production.

Mots-clés : intracellular recording,drug screening,cardiac electrophysiology,multielectrode arrays,laser cell,poration,pharmacology,toxicology
Projet ERC : LSDiASD (Horizon 2020, appel 2015, Proof of concept grants)
Entité participante : University of Milan — Pays : Italie — Rôle : PI
Acronyme de l'entité : UNIMI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 107750.0 €

Duplication of chromosome region 7q11.23 (7Dup), comprising 26-28 genes, is one of best characterized copy number variations (CNV) causing Autism Spectrum Disorder (ASD), a very prevalent neurodevelopmental condition affecting more than 1% individuals worldwide, and for which no effective treatment is available. Despite the remarkable consistency of core ASD symptoms (impairment in language and sociability along with stereotyped behaviours), over 400 causative genetic lesions have been thus far identified for ASD, splitting it de facto into an aggregate of rare and highly penetrant genetic diseases. In turn, the convergence of symptoms grounds the hope that few paradigmatic syndromes may yield generalizable therapeutic inroads across the autistic spectrum. 7Dup is a case in point since it is symmetrically opposite to Williams Syndrome, a condition caused by 7q11.23 hemideletion and featuring symmetrically opposite phenotypes in language and sociability, thus offering unique opportunities to dissect the dosage-vulnerable circuits that affect language competence and sociability. Specifically, convergent evidence points to transcription factor GTF2I as the key gene underlying the cognitive/behavioural traits of 7Dup. Within my ERC-Consolidator project DISEASEAVATARS we discovered that increased GTF2I dosage represses key genes involved in intellectual disability, autism and neuronal function by associating with lysine demethylase 1 (LSD1), a key chromatin modifier, and found that LSD1 inhibition rescues GTF2I-dependent alterations. Therefore, we now wish to establish in vivo proof of concept for the efficacy of novel LSD1 inhibitors in rescuing cognitive-behavioural deficits in relevant ASD mouse models recapitulating GTF2I CNV, developing an adequate business plan and industrial partnerships as prerequisites for advancing the most effective LSD1 inhibitor towards pre-clinical development for 7Dup and ASD more broadly.

Mots-clés : autism,demethylase inhibitors,sociability,7q11 23 duplication,gtf2i
Projet ERC : ComplEvol (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : University of Sheffield — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1498275.0 €

During evolution, organisms adapt to diverse environmental conditions by evolving new morphological and/or biochemical traits, some of which are of impressive complexity. This is for example the case of eyes, wings or complex biochemical pathways, which all involve multiple components. The evolution of such complex traits has always intrigued evolutionary biologists, including Charles Darwin, and is still only partially understood. How can natural selection on random mutations lead over time to novel complex ecological adaptations that allow organisms to thrive in diverse environments? This question will be addressed here by studying a species complex that presents exceptional variation in a key ecological adaptation, namely C4 photosynthesis. This trait results from multiple anatomical and biochemical components that function together to increase plant productivity in warm and dry environments. Capitalizing on a species complex of grasses that includes C4 as well as the ancestral C3 photosynthetic types and multiple intermediate states, the ComplEvol project will combine methods from different fields to infer (i) the history of mutations that generated components for C4 photosynthesis during the dispersal into different ecological conditions, (ii) the factors controlling the spread of these mutations among populations, (iii) the effects of these mutations on the properties of the encoded C4 enzymes, (iv) the effects of different anatomical and biochemical C4 components on the performance of the plants (fundamental niche), and (v) the relationships between these components and the distribution of individuals in contrasted environments (realised niche). The incorporation of these different dimensions of evolution and ecology will shed new lights on the processes that allow over time the emergence of major ecological novelties through the repeated action of natural selection on minor changes within populations.

Mots-clés : complex traits,evolution,phylogenetics,genomics,phylogeography,molecular ecology,biochemistry,plant physiology