Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
New therapies for uveal melanoma (UM Cure 2020)

Framework: Horizon 2020 — Societal Challenges
Programme: Health, demographic change and wellbeing
Theme: Treating and managing disease
Action type: Research and Innovation actions
Countries involved: Germany,Italy,Sweden,Poland,France,France,Netherlands,Netherlands,United Kingdom,United Kingdom,Portugal
Call: New therapies for rare diseases (H2020-PHC-2015-two-stage, 2015)
Start date: 2016-01-01
End date: 2021-06-30
Duration (months): 66.0
Total project cost (EUR): 7971101.25
EU funding (EUR): 6183456.25
Participants: 13
Project status: CLOSED

Uveal melanoma (UM) is a rare intraocular tumour with an incidence of 5 cases per million individuals per year. Up to 50% of UM patients develop metastases, most often in the liver, and these are invariably fatal. Despite new discoveries in the genetic and molecular background of the primary tumour, little is known about the metastatic disease; furthermore, there is no therapy to either prevent or treat UM metastases. In UM Cure 2020, we aim to identify and validate at the preclinical level novel therapeutic approaches for the treatment of UM metastases. For this purpose, the consortium brings together the major experts of UM in both patient care and basic/translational/clinical research, as well as patient representatives. An ambitious multidisciplinary approach is proposed to move from patient tissue characterisation to preclinical evaluation of single or combinations of drugs. This approach includes the characterisation of the genetic landscape of metastatic UM and its microenvironment, proteomic studies to address signal pathway deregulation and establishment of novel relevant in vitro and in vivo UM models. We also aim to validate accurate surrogate endpoint biomarkers to evaluate therapies and detect metastases as early as possible. Underpinning this will be the UM Cure 2020 virtual biobank registry, linking existing biobanks into a harmonised network, which will prospectively collect primary and metastatic UM samples. Together, our approach will lead to the identification of new therapies, allowing the initiation of UM-dedicated clinical trials sponsored by academia or pharma. Dissemination of results will include the building of a patient network across the countries as part of the consortium as well as a dedicated UM patient and caregiver’s data portal as part of the UM Cure 2020 website, in order to increase patient information and disease awareness.

Keywords: eye cancer,uveal melanoma,new treatment,targeted therapy,personalised medicine,preclinical research,animal models
Making Terrestrial Planets (METAL)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Earth system science
Panel description: Physical geography, geology, geophysics, atmospheric sciences, oceanography, climatology, cryology, ecology, global environmental change, biogeochemical cycles, natural resources management
Countries involved: France
Call: ERC CONSOLIDATOR GRANTS (ERC-2020-COG, 2020)
Start date: 2021-10-01
End date: 2026-09-30
Duration (months): 60.0
Total project cost (EUR): 1998750.0
EU funding (EUR): 1998750.0
Participants: 2
Project status: SIGNED

Understanding the formation and early evolution of terrestrial planets is one of the most important goals in sciences. The objectives of this proposal, METAL (Making tErresTriA pLanets), are to study the accretion and differentiation processes that have shaped the present composition of the Earth, Moon, Mars and differentiated asteroids including understanding the origin, and timing of delivery of their volatile and siderophile elements. To reach this goal we have identified the best-suited isotopic tools, which are sensitive to the different physico-chemical processes acting at different stages of planetary formation. This work will involve: 1) Development and use of new cutting-edge stable isotope systems for moderately volatile elements (e.g. In, Sb, Sn) in terrestrial, lunar and meteoritic materials, in order to constrain the origin of solar system’s volatile element depletion. 2) Quantifying experimentally the isotopic effects during metal/silicate partitioning and evaporation in all conditions relevant to planetary accretion and differentiation. 3) Building a physical model of volatile loss. 4) Studying the timing, proportions, fate and nature of the material that accreted to Earth and Mars after core formation (i.e. the late-veneer) by using a new method based on the stable isotopes of a highly siderophile element, Pt. This high-risk high-rewards approach seeks to link innovative novel isotopic systems, experiments under extreme conditions, and dynamical modelling, to solve long-standing major scientific questions related to the formation and evolution of the terrestrial planets.

Keywords: isotope geochemistry,the moon,meteorites,plate tectonics,earth s differentiation,volatiles
Development of an accurate, early diagnostic tool for ovarian cancer (EARLYDETECT)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Panel: Products and processes engineering
Panel description: Product and process design, chemical, civil, environmental, mechanical, vehicle engineering, energy processes and relevant computational methods
Countries involved: United Kingdom
Call: ERC Proof of Concept Grant (ERC-2019-PoC, 2019)
Start date: 2021-01-01
End date: 2022-06-30
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: CLOSED

Every year, ovarian cancer (OC) kills more than 40 000 women in Europe and 150 000 worldwide. In large part this is due to the fact that most women are not diagnosed until advanced stages. There is an urgent unmet clinical need for screening tools for the early, accurate identification of OC. OC is associated with alterations in glycosylation of secreted glycoproteins, and as such, they can serve as early OC biomarkers. However, this knowledge has still not been translated into clinic due to the lack of assays that can differentiate subtle differences between glycan chains. For the first time, under the ERC consolidator grant-GLYCOSURF, we developed a synthetic glycan recognition platform that can sharply discriminate between different overall glycan structures (Patent No: WO2015/118294). Capitalizing on the knowledge of glycosylation variations in OC and our unique glycan recognition technology, we will be developing and clinically validating a new glycan-based test for detecting OC earlier and more effectively through a simple serum test. OC-associated glycans will be molecularly casted on fluorescent nanoparticles to create surface nanopockets with glycan receptors in a specific spatial arrangement to fit only OC-associated glycans. A microplate immunoassay with high sensitivity, selectivity and reproducibility will be developed, wherein the glycan-casted nanoparticles will provide a means for selectivity, signal amplification and fluorescent readout. Serum samples from OC patients and controls from women with benign tumours will be tested. The assay will be minimally-invasive, cost-effective and will be suitable for rapid, easily automatable, high-throughput screening, promoting swift translation into the clinical setting. This project offers therefore the potential to radically change the diagnostic standard of care for OC, ultimately saving lives and reducing the health, social and economic burden imposed by OC.

Keywords: highly specific molecular diagnostic,nanoparticle glycan recognition,glycan based assay,early ovarian cancer detection
Advancing Modelling for Process-Product Innovation, Optimization, Monitoring and Control in Life Science Industries (ModLife)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Innovative Training Networks (ITN)-European Training Networks
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Germany,Germany,Germany,Belgium,France,United Kingdom,United Kingdom,Denmark,United Kingdom
Call: Marie Skłodowska-Curie Innovative Training Networks (ITN-ETN) (H2020-MSCA-ITN-2015, 2015)
Start date: 2015-11-01
End date: 2019-10-31
Duration (months): 48.0
Total project cost (EUR): 4090249.44
EU funding (EUR): 4090249.44
Participants: 12
Project status: CLOSED

The ModLife project is a European Training Network initiative that brings together 5 leading European universities, 4 global industrial players and 2 SMEs to undertake research and training in the area of product-process innovation, optimization monitoring and control for life sciences and biotechnology industries. Modlife aims to develop Advanced Model-Based Optimization, Monitoring and Control as Enabling Technologies for bioprocess-product development and innovation tailored for the needs in life science industries. ModLife ETN will address excellence in research and training of next generation biochemical and process engineers in life sciences industries through: (1) Offering comprehensive training and knowledge transfer opportunities in multidisciplinary and multi-sectoral fields for training early stage researchers with multidisciplinary knowledge and competences bridging engineering and life sciences (2) Integrating and creating synergies among individually excellent but otherwise fragmented bioprocess engineering research centers across the European union in life sciences and biotechnology industries, (3) Building on advances in modeling and simulation, to develop cutting edge model-based enabling technologies and applications for optimization, monitoring and control for bioprocess and product development and innovation. The ModLife ETN aims at next generation of high performance computing tools and in-situ measurements for increasing efficiency, innovation and competitiveness of Europe’s life sciences and processing industries. Ultimately Modlife aims to help Europe realize the promising potential of life sciences and biotechnology- considered the next frontier technologies with profound impacts to knowledge based economy, by building the capacity to translate lab-scale life science discoveries to large scale new products and processes to match the human needs for health, nutrition and wellbeing.

Keywords: modeling,optimization,monitoring,control,thermodynamics,process analytics,chemometrics,process design,formulated products,scale up,food,healthcare,pharmaceuticals,biotechnology
Einstein Relation for the Variable Range Hopping model (EinsteinVRH)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Mathematics
Countries involved: France
Call: Marie Skłodowska-Curie Individual Fellowships (IF-EF) (H2020-MSCA-IF-2014, 2014)
Start date: 2016-02-01
End date: 2018-01-31
Duration (months): 24.0
Total project cost (EUR): 173076.0
EU funding (EUR): 173076.0
Participants: 1
Project status: CLOSED

The Variable Range Hopping is considered in the Physics literature as an effective model for the analysis of conductivity in semiconductors. Understanding how the macroscopic parameters depend on the small-scale randomness of the environment and proving the Einstein Relation for this model is the ambitious aim of this project. Main objectives: 1) Extend recent results (law of large numbers, existence of a stationary state) for long-range reversible random walks on point processes including the possibility of traps. 2) Analyze how an external field influences the limiting velocity of the Variable Range Hop- ping, in comparison to similar models from Mathematical Physics. 3) Establish the first rigorous Einstein Relation for a physically relevant model, the Variable Range Hopping. The mathematical techniques we have at our disposal nowadays (such as the weak Einstein Relation and the control of long range models) are a solid basis for the investigation of the problem: This would be the first time an Einstein Relation is rigorously proven for a relevant physical model. Furthermore, the richness of the subject guarantees also many intermediate results of great relevance in the field of Probability Theory. Besides the big scientific relevance of the expected results, the project will have a strong impact also on the career of the experienced researcher, completing his international profile of independent scientist, and will also strengthen the interplay between the Probability Theory communities of France, Germany and Italy. Finally, a positive outcome of the action will bring a significant insight on the physical study of semiconductors.

Keywords: random walks in random environment,einstein relation
Machine learning based Software Toolkit for Automated identification in atomic-REsolution operando nanoscopy (STARE)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Panel: Synthetic Chemistry and Materials
Panel description: New materials and new synthetic approaches, structure-properties relations, solid state chemistry, molecular architecture, organic chemistry
Countries involved: Germany
Call: Call for proposals for ERC Proof of Concept Grant (ERC-2020-PoC, 2020)
Start date: 2021-01-01
End date: 2022-06-30
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: CLOSED

In recent years, the analysis of large data sets is becoming increasingly important in the fields of material science and engineering. There is a strong demand for real-time automated identification algorithms in electron microscopy (EM) for the analysis of atomic-structure, phases, and defects. Unfortunately, it is non-trivial to obtain or extract meaningful scientific information from raw EM output digital data. It requires a tedious process of filtering/fitting and the expertise of a seasoned microscopist. With the rapid development of information technology and computer science, automated computer-assisted analysis of electron microscopy images/data is becoming a reality. In the past decade, different techniques have been developed and applied to digital data analysis. Meanwhile, the rapid development of novel microscopy techniques and instrumentation, e.g. in situ/operando and pixelated detector-based techniques, require high-speed data execution and analysis. Currently, several groups worldwide are concentrating their efforts into implementing machine learning and deep learning algorithms for image/data analysis. However, this is still a very undeveloped direction in the field of electron microscopy for materials science, especially in Europe. According to the Digital Transformation Monitor, artificial intelligence-based technologies will play a major role in future economy. The ability to analyse levels of data that are beyond human comprehension will allow business to personalize experiences, customize products and services and identify growth opportunities with a speed and accuracy that has never been possible before. The objective of this PoC is to generate an innovative software package that enables the analysis of large sets of EM data (i) at high throughput with (ii) low costs, in (iii) a standardized approach and (iv) under operando conditions, based on advanced machine learning algorithms.

Keywords: machine learning,transmission electron microscopy,big data,peak finding algorithm
Russian Nationalism and the Ukraine Crisis: The Impact of Nationalist Actors on Russian Foreign Policy (RUSNAT)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Social Sciences and Humanities
Countries involved: United Kingdom
Call: Individual Fellowships (H2020-MSCA-IF-2016, 2016)
Start date: 2017-09-01
End date: 2022-01-01
Duration (months): 24.0
Total project cost (EUR): 195454.8
EU funding (EUR): 195454.8
Participants: 1
Project status: CLOSED

As nationalist sentiments are on the rise among broader Russian public, nationalist actors may exercise a more significant role in Russian politics. The main objectives of RUSNAT are: 1) to analyse the impact of the events in Ukraine on representative organizations of grassroots nationalism in Russia; 2)to analyse the ideologies of Russian nationalists and the Russian government with respect to the events in Ukraine;3)to develop a theoretical and conceptual framework appropriate for the analysis of the impact of Russian nationalists’ activism on the government’s foreign policy towards neighbouring countries with Russian populations, e.g. the Baltic states. They will be achieved through Discourse Analysis, Critical Discourse Analysis and close reading of primary sources generated by nationalists and representatives of Russian foreign policy from 2014 to 2016. I will construct a database covering nationalist frames, elite discourse (civic, ethnic, imperialist), and nationalist disruptive events. The influence of nationalists will be considered as positive if a nationalist frame is adopted by the elite discourse at a later date and if elite discourse changes after sudden disruption events organized by the nationalists. This research offers insight into the influence of nationalist actors on policy-making in post-communist societies; it helps assess the extent to which in non-democratic contexts foreign policy is driven by nationalist ideals in its initial phases; it contributes to our understanding of Russia’s relations with neighbouring countries with Russian populations and with the European Union as a geostrategic partner with whom Russia shares this neighbourhood; and, it will help us understand whether the growing influence of nationalist actors directly encourages the radicalization of Russian policies in near abroad. This is relevant for the design and implementation of EU policies regarding its Eastern neighbourhood and strategic partnership with Russia.

Keywords: nationalism,russia,ukraine,social movements
TRADExpress Client Clearing (TRADECC)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: SME instrument
Countries involved: Sweden,Sweden
Call: Open Disruptive Innovation Scheme (implemented through the SME instrument) (H2020-SMEINST-2-2014, 2014)
Start date: 2015-04-01
End date: 2016-09-30
Duration (months): 18.0
Total project cost (EUR): 3148200.0
EU funding (EUR): 2203740.0
Participants: 2
Project status: CLOSED

The objective of this project is to create a real-time risk margining and multi-asset client clearing product for banks. Banks and their clients will receive an instant and complete view of their risk exposure, which will enable them to use posted collateral efficiently across all markets they trade — a disruptive shift in the client clearing business, moving from batch to real-time clearing.

Keywords: client clearing real time bank
Study of the role of BANK1 in antibody production and antibody-independent functions of B cells in SLE (RoBE)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Career Restart panel
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: Spain
Call: Individual Fellowships (H2020-MSCA-IF-2018, 2018)
Start date: 2019-09-16
End date: 2022-10-21
Duration (months): 36.0
Total project cost (EUR): 259398.72
EU funding (EUR): 259398.72
Participants: 1
Project status: CLOSED

Systemic Lupus Erythematosus (SLE, Lupus) is a life-threatening and incurable autoimmune disease that affects primarily women, its pathogenesis is related with an exaggerated B cells activation that results both in the generation of antibodies against self-nuclear acid antigens and in the generation of subsets of B cells that can exert antibody-independent roles in Lupus. However, the mechanisms that regulate aberrant B cell functions are poorly understood. In this project, we propose to investigate the role of the B cell scaffold protein with ankyrin repeats 1 (BANK1) in the B cell activation signaling pathways in a mouse model of Lupus. We have previously reported that the absence of BANK1 signaling in mice susceptible to Lupus resulted in the reduction of the activation of B cells, circulating total IgG2c and IgG autoantibodies, pro-inflammatory cytokines, and transcription factors related with TLR7 activation. Our data suggest that BANK1 is an important mediator of B cell hyperactivation. We hypothesize that BANK1 plays an important role in B cell signaling affecting their activation and effector functions in Lupus, however we do not fully understand yet the mechanism behind the function of BANK1. Our specific aims are: 1) to define how BANK1 signaling drives class switch recombination, 2) to determine the mechanism by which BANK1 regulates the cytokine production in B cells, and 3) to examine the participation of BANK1 signaling in the differentiation of pathogenic B cell subsets. To accomplish our objectives, we will use flow and mass cytometry, RNA-based next-generation sequencing (RNA-seq) analysis, the assay for transposase-accessible chromatin followed by deep sequencing (ATAC-seq), and a chromatin immunoprecipitation (ChIP) assay. We expect that the results of these studies will help to understand the molecular mechanism underlying the pathogenic B cell response in Lupus and will lead to identification of new avenues for therapeutic development.

Keywords: systemic lupus erythematosus,b cell scaffold protein with ankyrin repeats 1 bank1,b cells,signalling,toll like receptors tlr