Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : THOR (Horizon 2020, appel 2015, Proof of concept grants)
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 : 148333.75 €

The aim of the PoC project is to develop a portable, low-cost device for highly sensitive and fast identification of molecules. For demonstration of the capability of the device we will focus on (i) toxic compounds in food and home environment, and (ii) compounds that are important for the cosmetics research industry. These applications all have the need of sample pretreatment, scalability, sensitivity, speed and ease of use that the device will offer. In addition, detection of both compound classes is commercially important. The aim is to, within the project period, to identify the most viable commercialization process. The device consist of two major parts: (i) microfluidics disc and (ii) Raman scattering detection system. (i) Microfluidics disc. Fluids that contain target molecules can be guided through microfluidics channels using controlled centrifugation. Analyte can be manipulated and introduced into reservoirs that contain so-called surface-enhanced Raman scattering (SERS) substrates. SERS substrate is a nanostructured surface that is used to amplify the Raman scattering signal. Amplification of the Raman scattering signal enables identification of compounds at extremely low concentrations. We employ world-leading SERS substrates (“amplifiers”) that are successfully sold worldwide. (ii) Molecular identification is recorded using a portable Raman scattering system. The detection system is designed to collect large amounts of data to perform a more reliable statistical analysis. Every component in the readout system is based on existing technologies which dramatically reduces device manufacturing costs. To the best of our knowledge, there are no similar SERS-based commercial products. Compared to existing Raman scattering systems, our technology will offer a much cheaper, automated solution for molecular identification.

Mots-clés : microfluidics,portable raman device,surface enhanced raman scattering,sers
Projet ERC : RELEVEN (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The study of the human past
Entité participante : Austrian Academy of Sciences — Pays : Autriche — Rôle : Partner
Acronyme de l'entité : OAW
Type d'entité : Recherche
Financement ERC de l'entité : 383443.75 €

The aim of the RELEVEN project is to develop and test new ways for digital data about historical phenomena to be created and curated so that it is most useful to historians, and to apply these methods to a methodologically challenging yet very significant aspect of medieval history. The approach is to re-frame both existing and new historical data as assertions, often sourced but always linked to an authority; this allows data to be manipulated according to source and authority, and also allows assertions themselves to be linked depending on whether they corroborate, depend on, or conflict with each other. The novel aspect of this methodology is that it takes to its logical conclusion something that historians all readily acknowledge and that is especially apparent for pre-modern history: that there are very few, if any, simple and undisputed facts. A related challenge is the contextualisation and reuse of existing online data for the period, to avoid its going to waste. The approach is tested by taking a broad trans-regional approach to the history of the late 11th century (c. 1030–1095), centred broadly in the eastern half of Christendom but incorporating developments elsewhere, especially in the newly Christianised kingdoms of central Europe. The looming weight of the First Crusade at the century's end means that while certain regional or proto-national narratives—particularly for western Europe—are well-developed, they tend to obscure the larger trans-regional trends of communication and contact, particularly in eastern Christendom. By drawing upon the depth of scholarship and the plethora of digital resources that have emerged for this period in sub-disciplines such as prosopography, textual scholarship, corpus-based research, and archaeology, and by framing this scholarship in terms of assertions whose authority is traceable, it will become possible to look at the history not just from "the eastern perspective", but from several.

Mots-clés : data modeling,linked open data,trans regional studies,christian near east,medieval studies
Projet ERC : StrepCryptPath (Horizon 2020, appel 2018, Proof of concept grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems Engineering
Entité participante : University of Oviedo — Pays : Espagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

The aim of the StrepCrypPath is to test the industrial potential of a new approach to enhance Streptomyces secondary metabolism, discovered in our ERC-StG Strp-differentiation (280304) project. We discovered a pleiotropic regulator of differentiation and secondary metabolism in the S. coelicolor model strain. The S. coelicolor strain lacking this regulator is the first Streptomyces reported to produce secondary metabolites during its whole developmental cycle, including germination, the exponential growth phase and the stationary stage. S. coelicolor encodes 30 secondary metabolites, but only two are produced in high amounts under the culture conditions used in our lab. The expression of 15 secondary metabolite clusters (50% of the total), including 6 predicted to participate in secondary metabolite biosynthesis never observed in the lab (cryptic pathways), is activated/enhanced, in our mutant. The gene encoding this new regulator is highly conserved in Streptomyces. We will explore if the inactivation of the orthologues of this regulatory gene in industrial streptomycetes causes the same phenotype observed in S. coelicolor: secondary metabolism and cryptic pathway activation. We will create integrative conjugative plasmids harbouring antisense mRNAs to inactivate this gene. We do not expect that this method will enhance and/or activate all secondary metabolite clusters. However, if as it happens in S. coelicolor, we can activate/enhance 50% of the secondary metabolite pathways, including several cryptic pathways, in any streptomycete, we can revolutionise the screening for new secondary metabolites from streptomycetes.

Mots-clés : streptomycetes,antimicrobials,antibiotics,secondary metabolites,microbial resistance,screening,pharma
Projet ERC : SICLE (Horizon 2020, appel 2014, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : Collège de France — Pays : France — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1959283.0 €

The aim of the interdisciplinary investigation project SICLE is to investigate in a completely new way the intellectual and cultural history of the Saadian period in Morocco (1554-1660), using primarily the Saadian sultans’ library, exceptionally preserved as a ‘time capsule’ in the El Escorial monastery following king Felipe III’s decision to include it in his own library. The period is a time of direct involvement of Morocco in international politics and exchanges, both in their East-West (with the Ottoman empire) and North-South (with Europe) dimensions, without forgetting Timbuktu and the Songhay empire conquered by Ahmad al-Mansur, the most brilliant ruler of the dynasty. The manuscripts kept in El Escorial mirror the interests and the tastes of the Saadian rulers and of the elite at large. The paratextual information they contain (but ignored by the catalogues) like the various notes, the colophons, the illuminations or the bindings will be systematically collected and analyzed in a trans-disciplinary approach which can be defined as an ‘archaeology of the book’, combining codicology, philology, archaeometry, anthropology and art history. The same approach will be applied to the books of the ‘ordinary people’ from Saadian times preserved in Moroccan collections. SICLE goals are to offer: a) a history of the Saadian sultans’ library and an analysis of their interests; b) a cultural and intellectual history of Morocco during the 16th century, with groundbreaking studies on the history of the book, on its economy and on education; c) an evaluation of the relationship with the ‘outside’ (Christian Europe, the Ottoman empire and sub-Saharan Africa) and the possible influence it had on Saadian intellectual production. Special attention will be paid to the Jewish and Morisco communities in their role of culture brokers.

Mots-clés : maghreb,intellectual history,king s kibrary 17th century
Projet ERC : ICON-BIO (Horizon 2020, appel 2017, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Barcelona Supercomputing Center — Pays : Espagne — Rôle : PI
Acronyme de l'entité : BSC
Type d'entité : Recherche
Financement ERC de l'entité : 1893460.0 €

The aim of the project is to develop a comprehensive framework for generalizing network analytics and fusion paradigms of non-negative matrix factorization to medical data. Heterogeneous, interconnected, systems-level omics data are becoming increasingly available and important in precision medicine. We are seeking to better stratify and subtype patients into risk groups, discover new biomarkers for complex and rare diseases, personalize medical treatment based on genomics and exposures of an individual, and repurpose known drugs to different patient groups. Existing methodologies for dealing with these big data are limited and a paradigm shift is needed to achieve quantitatively and qualitatively better results. The project is motivated by the recent success of non-negative matrix tri-factorization (NMTF) based methods for fusion of heterogeneous data in biomedicine. Though these methods have been known for some time, the availability of large datasets, coupled with modern computational power and efficient optimization methods, allowed for creation and efficient training of complex models that can make a qualitative breakthrough. For example, NMTF has recently achieved unprecedented performance on exceptionally hard problems of simultaneously utilizing the wealth of diverse molecular and clinical data in precision medicine. However, research thus far has been limited to special variants of this problem and used only fixed point methods to address these exciting examples of hard non-convex high-dimensional non-linear optimization problems. The ambition of the project is to develop general data fusion methods, from mathematical models to efficient and scalable software implementation, and apply them to the domain of biomedical informatics. The project will lead to a paradigm shift in biomedical and computational understanding of data and diseases that will open up ways to solving some of the major bottlenecks in precision medicine and other domains.

Mots-clés : molecular networks
Projet ERC : LEMAN (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Imperial College London — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1335250.0 €

The aim of the project is to develop a geometrically meaningful framework that allows generalizing deep learning paradigms to data on non-Euclidean domains. Such geometric data are becoming increasingly important in a variety of fields including computer graphics and vision, sensor networks, biomedicine, genomics, and computational social sciences. Existing methodologies for dealing with geometric data are limited, and a paradigm shift is needed to achieve quantitatively and qualitatively better results. Our project is motivated by the recent dramatic success of deep learning methods in a wide range of applications, which has literally shaken the academic and industrial world. Though these methods have been known for decades, the computational power of modern computers, availability of large datasets, and efficient optimization methods allowed creating and effectively training complex models that made a qualitative breakthrough. In particular, in computer vision, deep neural networks have achieved unprecedented performance on notoriously hard problems such as object recognition. However, so far research has mainly focused on developing deep learning methods for Euclidean data such as acoustic signals, images, and videos. In fields dealing with geometric data, the adoption of deep learning has been lagging behind, primarily since the non-Euclidean nature of objects dealt with makes the very definition of basic operations used in deep networks rather elusive. The ambition of the project is to develop geometric deep learning methods all the way from a mathematical model to an efficient and scalable software implementation, and apply them to some of today’s most important and challenging problems from the domains of computer graphics and vision, genomics, and social network analysis. We expect the proposed framework to lead to a leap in performance on several known tough problems, as well as to allow addressing new and previously unthinkable problems.

Mots-clés : geometric deep learning,convolutional neural networks,intrinsic geometry,shape analysis,shape correspondence,signal processing on graphs,spectral methods,face analysis,social network analysis
Projet ERC : LEMAN (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Twitter (United States) — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Organismes privés (hors enseignement)
Financement ERC de l'entité : 375000.0 €

The aim of the project is to develop a geometrically meaningful framework that allows generalizing deep learning paradigms to data on non-Euclidean domains. Such geometric data are becoming increasingly important in a variety of fields including computer graphics and vision, sensor networks, biomedicine, genomics, and computational social sciences. Existing methodologies for dealing with geometric data are limited, and a paradigm shift is needed to achieve quantitatively and qualitatively better results. Our project is motivated by the recent dramatic success of deep learning methods in a wide range of applications, which has literally shaken the academic and industrial world. Though these methods have been known for decades, the computational power of modern computers, availability of large datasets, and efficient optimization methods allowed creating and effectively training complex models that made a qualitative breakthrough. In particular, in computer vision, deep neural networks have achieved unprecedented performance on notoriously hard problems such as object recognition. However, so far research has mainly focused on developing deep learning methods for Euclidean data such as acoustic signals, images, and videos. In fields dealing with geometric data, the adoption of deep learning has been lagging behind, primarily since the non-Euclidean nature of objects dealt with makes the very definition of basic operations used in deep networks rather elusive. The ambition of the project is to develop geometric deep learning methods all the way from a mathematical model to an efficient and scalable software implementation, and apply them to some of today’s most important and challenging problems from the domains of computer graphics and vision, genomics, and social network analysis. We expect the proposed framework to lead to a leap in performance on several known tough problems, as well as to allow addressing new and previously unthinkable problems.

Mots-clés : geometric deep learning,convolutional neural networks,intrinsic geometry,shape analysis,shape correspondence,signal processing on graphs,spectral methods,face analysis,social network analysis
Projet ERC : ImpAncCit (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The study of the human past
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 : 2483075.0 €

The aim of the project is to re-examine the impact of urbanism in the Greco-Roman Mediterranean on subsequent urban history, both locally, in the impact of individual cities that survive into modern times on their development, and globally in the impact of ideas of ancient urbanism derived both from writings and the visible remains of ancient cities on urban formation and development, both within and beyond the Mediterranean area. What leaves this field wide open for investigation is the collapse of an older thesis that saw orthogonal planning as the most distinctive and significant contribution of antiquity to subsequent urban development. The explicitly colonialist underpinnings of that thesis, which saw the Roman empire as a model for modern imperialism, and which rejected the urbanism of the Islamic world as the antithesis to its form of 'civilization', have rendered it unsustainable. This project aims to rethink the issue fundamentally. There is evident discontinuity between the cities of antiquity and their medieval and modern successors, variously represented as a catastrophic collapse or a creative transformation: resilience theory provides a framework within which to see both catastrophe and continuity as parts of a cycle of adaptive response to ecological change. The project aims to investigate patterns of response and adaption across time in areas of the Mediterranean with contrasting subsequent histories, most obviously across the divide between Christian Europe and the Islamic world, by taking a series of detailed case histories in Italy, the western and eastern Mediterranean, and asking a set of questions about how changing urban form responds to changing social needs. It will also re-examine the role of Greek and Roman writing in the history of thought about and representation of urbanism in the European and Islamic traditions. Finally it will use Rome and Constantinople/Istanbul as major case studies of the role of social memory in urban continuity.

Mots-clés : urbanism,islamic cities,reception of classical antiquity,resilience theory,use and abuse of past
Projet ERC : Nanodevice (Horizon 2020, appel 2017, Proof of concept grants)
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 : 149873.0 €

The aim of this ERC Proof of Concept Grant proposal is the construction, validation, and patenting of a nanoscale device that can accurately measure the binding and unbinding kinetics of target molecules. Accurate measurement of binding and unbinding kinetics of molecules is of great interest to both pharmaceutical companies and academic research groups, and is essential for the development of novel therapeutic compounds. Our proposed nanodevice is based on our knowledge about dynamic nanostructures gained during the course of the ERC starting grant “DNA ORIGAMI DEVICES” and the current ERC consolidator grant “DNA ORIGAMI MOTORS”, and offers a range of advantages compared to existing competing technologies, such as significantly lower initial and running costs, a label-free assay with fluorescent readout, no necessity of time-resolved sample preparation, a solution-based measurement, greater experimental throughput, and control over target molecule arrangements on a molecular scale. In the course of this project, we will build a demonstrator nanodevice, secure relevant intellectual property rights, validate and improve the nanodevice in close collaboration with prospective end-users, and determine promising commercialization approaches. The expected outcomes are a licensable patent, a provisional business model, a technology with TRL 4-6, and foundations for subsequent collaboration projects with customers, suppliers, and competitors.

Mots-clés : nanotechnology,bioanalytics,label free sensing,biomedical diagnostics,biotechnology,life science
Projet ERC : SMIRP (Horizon 2020, appel 2014, Proof of concept grants)
Entité participante : University of Surrey — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 149967.0 €

The aim of this PoC proposal (SMIRP) is the fabrication, characterisation and validation of commercially competitive silicon mid-infrared (IR) photodetector prototypes, operating in the 2-15 microns range, for demonstration to commercialisation partners and designed to replace current detectors made from mercury cadmium telluride (MCT), lead sulphide, lead selenide and arsenic containing alloys. Silicon detectors currently completely dominate the UV, visible and very near-IR regions - however they do not work above 1.2 microns. Following the discovery of band edge modified rare earth optical transitions, specific to europium, ytterbium and cerium in silicon, under the current SILAMPS (Silicon Integrated Lasers and Optical Amplifiers) Advanced Investigator Grant, we have demonstrated that we can extend silicon responsivity from 1.2 microns, at the silicon band gap, out to the important mid-IR region and beyond. Experimentally the responsivities and detectivities of our latest devices now offer a real challenge to existing detector materials and devices in the 2 to 15 microns range currently dominated by more exotic and expensive materials such as MCT. Replacing these materials with silicon based detectors would offer enormous benefits in cost, reliability, performance and integration with the silicon microelectronics for detection and imaging, as well as using much less toxic materials and production processes. Low leakage currents achievable in silicon based photodiodes mean that further development of this new silicon based technology may lead to room temperature or thermoelectrically cooled detectors, replacing current detectors that have to be cooled to liquid nitrogen temperatures (77 K) to achieve sufficiently high detectivity values to be useful.

Mots-clés : silicon mid infrared photodetectors rare earths sensors