Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : SIDSCA (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: Biological Mechanisms, Structures and Functions
Entité participante : University of Sussex — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1783034.0 €

DNA single-strand breaks (SSBs) are the most frequent DNA lesions arising in cells and are a major threat to cell survival and genome integrity, as indicated by the elevated genetic deletion, embryonic lethality, or neurological disease observed if single-strand break repair (SSBR) is attenuated. In particular, SSBR defects are associated with hereditary neurodegeneration in humans, as illustrated by the genetic diseases ataxia oculomotor apraxia-1 (AOA1), spinocerebellar ataxia with axonal neuropathy-1 (SCAN1), and microcephaly with early onset seizures (MCSZ). However, two major questions remain: what are the mechanisms by which SSBs trigger neurodegeneration, and to what extent do SSBs contribute to other genetic and/or sporadic neurodegenerative disease? Based on exciting new data we now propose that the impact of SSBs on neurodegeneration extends beyond rare SSBR-defective diseases to include more common motor neurone diseases (amyotrophic lateral sclerosis) and the genetically dominant spinocerebellar ataxias (SCAs). Ultimately, we suggest that SSBs might also be an etiological factor in normal human ageing. Finally, again based on new data, we propose that SSBs induce neurodegeneration by triggering over-activation of the SSB sensor protein, PARP1; thereby identifying inhibitors of this protein (currently licensed for cancer treatment) as a possible therapy for neurodegeneration. We will now address these hypotheses using a range of cutting edge molecular/cellular techniques. In particular we will (a), systematically examine all relevant amyotrophic lateral sclerosis/motor neurone disease (ALS/MND) and spinocerebellar ataxia (SCA) proteins for involvement in the DNA damage response, (b) Identify the mechanism/s by which ALS and SCA proteins engage in the DNA damage response, (c) Identify the role of ALS and SCA proteins in the DNA damage response, and (d) Explore PARP1 as a possible therapeutic target for treatment of neurodegenerative disease.

Mots-clés : the relationship between dna strand breakage,transcription,and neurodegenerative disease
Projet ERC : TOPOmics (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Life Science
Panel : Integrative Biology: from Genes and Genomes to Systems
Entité participante : Spanish National Research Council — Pays : Espagne — Rôle : Partner
Acronyme de l'entité : CSIC
Type d'entité : Recherche
Financement ERC de l'entité : 1740909.06 €

DNA topoisomerases are conserved nuclear enzymes that regulate DNA topology by transiently cleaving and resealing the DNA molecule, fulfilling a fundamental role in virtually every aspect of chromosome metabolism. Nevertheless, erroneous or abortive topoisomerase activity can result in persistent DNA strand breaks with the enzyme covalently attached to 3’ or 5’ DNA ends by a phosphotyrosyl bond, an anomalous structure that can compromise cell survival and/or genome integrity with the consequent implications in tumourigenesis. This peculiarity of topoisomerase catalysis also underlies the anticancer efficacy of topoisomerase poisons, which inhibit the re-ligation step of the reaction inducing the formation of DNA breaks that preferentially target highly proliferating and/or repair defective tumour cells. In addition to this link with cancer therapy, defects in the repair of topoisomerase-induced DNA damage have been linked to neurological disease. Understanding the cellular response to topoisomerase-induced breaks is therefore key for important aspects of human health, with possible implications in the development of novel diagnostic, prognostic and therapeutic tools. This project aims at acquiring a comprehensive picture of the dynamics of topoisomerase-induced DNA breaks: from their occurrence and repair to the consequences for genome expression and integrity. We rely on the development of completely novel assays to detect and isolate the different intermediates of topoisomerase-induced break repair, and which overcome major traditional limitations in the field. These tools are subsequently used to integrate the time-dependent and genome-wide distribution of the different steps and final outcomes of the process of topoisomerase-induced DNA break repair. Furthermore, we outline original proteomic and genetic screenings to identify novel factors and pathways specifically involved the cellular response to this important type of DNA lesion.

Mots-clés : dna damage and repair,dna breaks,dna topoisomerases,genome instability
Projet ERC : ArsNova (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : University of Pavia — Pays : Italie — Rôle : Partner
Acronyme de l'entité : UNIPV
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 937500.0 €

Dante Alighieri at the dawn of the 1300s, as well as Eustache Deschamps almost a century later, conceived poetry as music in itself. But what happens with poetry when it is involved in the complex architecture of polyphony? The aim of this project is to study for the first time the corpus of 14th- and early 15th-century poetry set to music by Ars Nova polyphonists (more than 1200 texts). This repertoire gathers different poetic and musical traditions, as shown by the multilingual anthologies copied during the last years of the Schism. The choice of this corpus is motivated by two primary goals: a) to offer a new interpretation of its meaning and function in the cultural and historical context, one that may be then applied to the rest of coeval European lyric poetry; b) to overcome current disciplinary divisions in order to generate a new methodological balance between the project’s two main fields of interest (Comparative Literature / Musicology). Most Ars Nova polyphonists were directly associated with religious institutions. In many texts, the language of courtly love expresses the values of caritas, the theological virtue that guides wise rulers and leads them to desire the common good. Thus, the poetic figure of the lover becomes a metaphor for the political man, and love poetry can be used as a device for diplomacy, as well as for personal and institutional propaganda. From this unprecedented point of view, the project will develop three research lines in response to the following questions: 1) How is the relationship between poetry and music, and how is the dialogue between the different poetic and musical traditions viewed in relation to each context of production? 2) To what extent does Ars Nova poetry take part in the ‘soft power’ strategies exercised by the entire European political class of the time? 3) Is there a connection between the multilingualism of the manuscript tradition and the perception of the Ars Nova as a European, intercultural repertoire?

Mots-clés : medieval latin and romance philologies and literatures,history of collective identities,history of philosophy,religious studies,symbolic representation,lexicography,digital humanities
Projet ERC : TRADE LAW 4.0 (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of Lucerne — Pays : Suisse — Rôle : PI
Acronyme de l'entité : UNILU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1600975.0 €

Data has been conceptualized as the ‘new oil’ and although this is a flawed statement, it catches well the value attached to data as a driver of economic growth and innovation, and as a force of change in all facets of societal life. Accordingly, data has emerged as an important topic in contemporary law and policy—on the one hand, because it is critical to understand whether and how different societal areas have been affected by digital transformations, including disruptive phenomena like Big Data and Artificial Intelligence (AI), and on the other hand, because governance toolkits, including legal rules, need to adapt to reflect these implications. Despite the urgency attached to both tasks and the intensified mobilization of policy and research efforts to address them, the topic of data-driven transformation has been explored in a fragmented manner. The domain of trade law has been particularly slow to react—neither do we have a full understanding of the impact of digitization on the entire body of global trade rules, nor have we seen legal adaptation. To put it plainly, despite living in times of industries 4.0, we have trade rules grounded at 1.0. The project addresses these flaws by conducting research in three interrelated fields that seek to: (1) enhance our understanding of the implications of digital disruption for trade law and vice versa, including through a thorough analysis of all existing trade rules that matter for data, as it flows across borders and is regulated domestically; (2) boost our toolkits for tackling the tensions inherent to the data-driven economy by mapping and analyzing all available mechanisms for reconciling economic and non-economic objectives, with a strong focus on data protection but including also other values, such as freedom of speech, that may be important for national constituencies; and ultimately, (3) suggest design for trade law that can be sustainable in an environment of fluid technological change.
Projet ERC : FACTORY (Horizon 2020, appel 2015, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
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 : 1931776.25 €

Data is often available in matrix form, in which columns are samples, and processing of such data often entails finding an approximate factorisation of the matrix in two factors. The first factor yields recurring patterns characteristic of the data. The second factor describes in which proportions each data sample is made of these patterns. Latent factor estimation (LFE) is the problem of finding such a factorisation, usually under given constraints. LFE appears under other domain-specific names such as dictionary learning, low-rank approximation, factor analysis or latent semantic analysis. It is used for tasks such as dimensionality reduction, unmixing, soft clustering, coding or matrix completion in very diverse fields. In this project, I propose to explore three new paradigms that push the frontiers of traditional LFE. First, I want to break beyond the ubiquitous Gaussian assumption, a practical choice that too rarely complies with the nature and geometry of the data. Estimation in non-Gaussian models is more difficult, but recent work in audio and text processing has shown that it pays off in practice. Second, in traditional settings the data matrix is often a collection of features computed from raw data. These features are computed with generic off-the-shelf transforms that loosely preprocess the data, setting a limit to performance. I propose a new paradigm in which an optimal low-rank inducing transform is learnt together with the factors in a single step. Thirdly, I show that the dominant deterministic approach to LFE should be reconsidered and I propose a novel statistical estimation paradigm, based on the marginal likelihood, with enhanced capabilities. The new methodology is applied to real-world problems with societal impact in audio signal processing (speech enhancement, music remastering), remote sensing (Earth observation, cosmic object discovery) and data mining (multimodal information retrieval, user recommendation).

Mots-clés : latent factor estimation,matrix factorisation,dictionary learning,low rank approximation,optimisation,statistical estimation,audio signal processing,remote sensing,data mining
Projet ERC : FlexAnalytics (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : Universidad de Málaga — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UMA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1203125.0 €

David against Goliath: Could small consumers of electricity compete in the wholesale markets on equal footing with the other market agents? Yes, they can and FlexAnalytics will show how. Activating the demand response, although a major challenge, may also bring tremendous benefits to society, with potential cost savings in the billions of euros. This project will exploit methods of inverse problems, multi-level programming and machine learning to develop a pioneering system that enables the active participation of a group of price-responsive consumers of electricity in the wholesale electricity markets. Through this, they will be able to make the most out of their flexible consumption. FlexAnalytics proposes a generalized scheme for so-called inverse optimization that materializes into a novel data-driven approach to the market bidding problem that, unlike existing approaches, combines the tasks of forecasting, model formulation and estimation, and decision-making in an original unified theoretical framework. The project will also address big-data challenges, as the proposed system will leverage weather, market, and demand information to capture the many factors that may affect the price-response of a pool of flexible consumers. On a fundamental level, FlexAnalytics will produce a novel mathematical framework for data-driven decision making. On a practical level, FlexAnalytics will show that this framework can facilitate the best use of a large amount and a wide variety of data to efficiently operate the sustainable energy systems of the future.

Mots-clés : power systems operations,electricity demand response,electricity markets,energy trading,decision making in energy,renewable energy sources,smart grids,microgrids,unit commitment
Projet ERC : MobileDeaf (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : Heriot-Watt University — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499985.0 €

Deaf signers’ international mobilities are rapidly increasing. These mobilities are unique in a number of respects: while being biologically deaf leads to certain limitations and to discrimination and inequalities, being skilled in visual language also creates possibilities and opportunities for communication across national and linguistic borders. There are two main research questions in the project. First, within contexts of international deaf spaces, how does the status of being deaf intersect with other statuses, particularly ethnicity, nationality, education, religion and gender, and which meaningful connections or accumulated inequalities occur? Second, how do deaf signers in these contexts practice and experience translanguaging, by making strategical use of multiple languages and language modalities, and International Sign? Four subprojects will focus on structurally different kinds of international deaf mobilities: (1) forced migration, (2) labour migration, (3) professional mobility, and (4) tourist mobility. The research team will be all-deaf as to maximise access to various sign languages, access to distinct deaf networks, and insights into deaf ways of living. This is a unique endeavour as most deaf-related research is hearing-led. The methodology will be ethnographic but neither logocentric nor audiocentric as visual methods (photography, video, mapping, and the production of four ethnographic documentaries) will be heavily employed, doing justice to the visual nature of sign language communication. By scrutinizing and bridging the concepts of intersectionality and translanguaging, this study will contribute to the study of growing complexity in diversity and mobility; the production/delimitation of social spaces particularly through language practices, strategies and ideologies; while engaging with issues of researchers’ embodiment, positionality and engagement, concerns which are central to the so-called third wave in deaf studies.

Mots-clés : spatial patterns of social behaviour,multilingual and multimodal linguistic strategies
Projet ERC : CENGIN (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Life Science
Panel : Cellular, Developmental and Regenerative Biology
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 : 2500000.0 €

Deciphering and engineering the assembly of cellular organelles is a key pursuit in biology. The centriole is an evolutionarily conserved organelle well suited for this goal, and which is crucial for cell signaling, motility and division. The centriole exhibits a striking 9-fold radial symmetry of microtubules around a likewise symmetrical cartwheel containing stacked ring-bearing structures. Components essential for generating this remarkable architecture from alga to man have been identified. A next critical step is to engineer assays to probe the dynamics of centriole assembly with molecular precision to fully understand how these components together build a functional organelle. Our ambitious research proposal aims at taking groundbreaking steps in this direction through four specific aims: 1) Reconstituting cartwheel ring assembly dynamics. We will use high-speed AFM (HS-AFM) to dissect the biophysics of SAS-6 ring polymer dynamics at the root of cartwheel assembly. We will also use HS-AFM to analyze monobodies against SAS-6, as well as engineer surfaces and DNA origamis to further dissect ring assembly. 2) Deciphering ring stacking mechanisms. We will use cryo-ET to identify SAS-6 features that direct stacking of ring structures and set cartwheel height. Moreover, we will develop an HS-AFM stacking assay and a reconstituted stacking assay from human cells. 3) Understanding peripheral element contributions to centriole biogenesis. We will dissect the function of the peripheral centriole pinhead protein Cep135/Bld10p, as well as identify and likewise dissect peripheral A-C linker proteins. Furthermore, we will further engineer the HS-AFM assay to include such peripheral components. 4) Dissecting de novo centriole assembly mechanisms. We will dissect de novo centriole formation in human cells and water fern. We will also explore whether de novo formation involves a phase separation mechanism and repurpose the HS-AFM assay to probe de novo organelle biogenes

Mots-clés : centriole biology,self assembly,cell free assay
Projet ERC : DBSModel (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : National University of Ireland — Pays : Irlande — Rôle : PI
Acronyme de l'entité : NUI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999474.0 €

Deep brain stimulation (DBS) is an effective therapy for treating the symptoms of Parkinson’s disease (PD). Despite its success, the mechanisms of DBS are not understood and there is a need to improve DBS to improve long-term stimulation in a wider patient population, limit side-effects, and extend battery life. Currently DBS operates in ‘open-loop’, with stimulus parameters empirically set. Closed-loop DBS, which adjusts parameters based on the state of the system, has the potential to overcome current limitations to increase therapeutic efficacy while reducing side-effects, costs and energy. Several key questions need to be addressed before closed loop DBS can be implemented clinically. This research will develop a new multiscale model of the neuromuscular system for closed-loop DBS. The model will simulate neural sensing and stimulation on a scale not previously considered, encompassing the electric field around the electrode, the effect on individual neurons and neural networks, and generation of muscle force. This will involve integration across multiple temporal and spatial scales, in a complex system with incomplete knowledge of system variables. Experiments will be conducted to validate the model, and identify new biomarkers of neural activity that can used with signals from the brain to enable continuous symptom monitoring. The model will be used to design a new control strategy for closed-loop DBS that can accommodate the nonlinear nature of the system, and short- and long-term changes in system behavior. Though challenging, this research will provide new insights into the changes that take place in PD and the mechanisms by which DBS exerts its therapeutic influence. This knowledge will be used to design a new strategy for closed-loop DBS, ready for testing in patients, with the potential to significantly improve patient outcomes in PD and fundamentally change the way in which implanted devices utilise electrical stimulation to modulate neural activity.

Mots-clés : neural engineering,electrical stimulation,deep brain stimulation
Projet ERC : MEMFLUX (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : IBM Research - Zurich — Pays : Suisse — Rôle : PI
Type d'entité : Organismes privés (hors enseignement)
Financement ERC de l'entité : 150000.0 €

Deep neural networks (DNNs), loosely inspired by biological neural networks, consist of parallel processing units called neurons interconnected by plastic synapses. By tuning the weights of these interconnections, these networks are able to perform certain cognitive tasks remarkably well. DNNs are being deployed all the way from cloud data centers to edge servers and even end devices and is projected to be a tens of billion Euro-market just for semiconductor companies in the next few years. There is a significant effort towards the design of custom ASICs based on reduced precision arithmetic and highly optimized dataflow. However, one of the primary reasons for the inefficiency, namely the need to shuttle millions of synaptic weight values between the memory and processing units, remains unaddressed. In-memory computing is an emerging computing paradigm that addresses this challenge of processor-memory dichotomy. For example, a computational memory unit with resistive memory (memristive) devices organized in a crossbar configuration is capable of performing matrix-vector multiply operations in place by exploiting the Kirchhoff’s circuits laws. Moreover, the computational time complexity reduces to O(1). The goal of this project is to prototype such an in-memory computing accelerator for ultra-low latency, ultra-low power DNN inference.

Mots-clés : deep learning,in memory computing,phase change memory