Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Inspection Magnetique de Surface (Contacless magnetic inspection) (IM2S)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: France
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2018)
Start date: 2018-05-01
End date: 2018-10-31
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

90% of Oil and Gas products are transported and distributed through a network of pipelines. These networks are, for the most part, buried metallic structures which makes them prone to corrosion. To ensure their operational safety most countries require compulsory inspection every 6/10 years, minimum. The inspections are usually conducted through instruments that are inserted into the pipeline to assess its structural integrity. However, due to operational constraints, 30% of the worldwide network (1.5 million KM or 38 times the circumference of planet earth) cannot be inspected with such tool. This constitutes a real security threat for operators with potentially significant human, environmental and material consequences. Skipper NDT has developed an innovative technology that allows the contactless inspection (from the surface of the ground) of pipeline that cannot be tested with the technology currently available. This technology will help reduce incidents which result in toxic products being spilled on the ground (Oil) or evaporated in the atmosphere and contributing to the greenhouse effect (Gas) in addition to protecting the people in proximity.
Assessing compounds targeting DNA replication licensing complexes as anti-tumor agents (DynaCOMP)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Countries involved: Greece
Call: ERC-Proof of Concept-2016 (ERC-2016-PoC, 2016)
Start date: 2017-07-01
End date: 2018-12-31
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: CLOSED

Cancer is a major clinical, societal and economic burden worldwide and development of novel anti-cancer therapies constitutes a major investment of public and private funds. Despite intense research over decades however, which has led to a much improved understanding of cancer biology, cancer treatment remains a challenge. This is to a large extent due to the genetic heterogeneity of cancer and the ability of cancer cells to escape treatment by constantly undergoing further genetic alterations. During our ERC funded work, we have shown that aberrations in the DNA replication licensing pathway may contribute to the genome plasticity of cancer cells, and appear a common feature of cancer cells. They may however also constitute an Achilles foot, as cancer cells appear more dependent on negative regulators of the licensing system for survival. Cancer cells may therefore be more sensitive than normal cells to compounds targeting this control (inhibition of untimely licensing). We have identified compounds which target the DNA replication licensing inhibitor Geminin. The proposed PoC study will enable us to: - assess the efficacy and specificity of the identified compounds in cells and preclinical models and study their mechanism of action. - investigate the potential use of these compounds for studying cell cycle processes. - assess whether the functional imaging approaches developed under the mother ERC project, which quantify protein-protein interactions within living cells, may constitute a powerful tool for in-cell analysis of novel lead compounds. The project thus aims to characterize, protect and commercialize novel putative anti-tumor agents as well as the in-cell methods developed for their characterization.

Keywords: anti tumour drug,bioactive compounds,dna replication licensing,synthetic lethality,functional imaging,cell based assays,cancer
Liver steatosis drives muscle atrophy in type 2 diabetes patients. (LiSDMA)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Reintegration panel
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: Netherlands
Call: Individual Fellowships (H2020-MSCA-IF-2016, 2016)
Start date: 2018-02-21
End date: 2021-04-12
Duration (months): 24.0
Total project cost (EUR): 165598.8
EU funding (EUR): 165598.8
Participants: 1
Project status: CLOSED

Problem: Muscle atrophy is defined as a decrease in muscle mass, and is a major problem in many patients with type 2 diabetes (T2D). Previous studies have linked a fatty liver to the development of muscle atrophy in T2D patients, but the exact mechanisms are unknown. Background: Many T2D patients are characterised by hepatic steatosis. We have preliminary data to suggest that secretion products from the steatotic liver play an important role in the development of muscle atrophy. Aim: we will identify how hepatic steatosis affects the liver secretory profile, and we will determine the impact of these secretion products on muscle protein metabolism. We will also identify novel liver-secreted proteins that are causally related to muscle atrophy, and validate these proteins in patients with hepatic steatosis and T2D. Hypothesis: We hypothesize that hepatic steatosis changes the liver protein secretion profile which negatively impacts muscle mass. We also expect to identify proteins that are causally related to muscle atrophy, and to validate these proteins in patients with liver steatosis and T2D. Methods: We will use a translational approach in which leads from human studies will be investigated in cell- and animal experiments and subsequently tested again in human subjects. We will combine functional metabolic assays, high-throughput molecular biology approaches, cell biology and in vivo studies to investigate whether secretion products from the liver play a role in the development of muscle atrophy. Outcome: This project is the first to investigate whether secretion products from the liver directly contribute to the loss of muscle mass in T2D patients. Our research is innovative and will provide crucial proof-of-concept information to further develop therapeutic approaches to treat or prevent muscle atrophy in T2D patients.

Keywords: muscle atrophy,liver steatosis,type 2 diabetes,proteomics
Research of Aviation PM Technologies, mOdelling and Regulation (RAPTOR)

Framework: Horizon 2020 — Societal Challenges
Programme: Smart, green and integrated transport
Theme: Public-private partnerships
Destination: Clean Sky 2
Action type: Research and Innovation actions
Partnership: CS2 (Art-187)
Countries involved: Germany,Switzerland,France,France,Netherlands,Netherlands,United Kingdom,United Kingdom
Call: Research for the development of Particulate Matter (PM) regulations and guidelines (H2020-CS2-CFP09-2018-02, 2018)
Start date: 2019-11-01
End date: 2022-04-30
Duration (months): 30.0
Total project cost (EUR): 995210.0
EU funding (EUR): 995210.0
Participants: 9
Project status: CLOSED

Particulate Matter (PM) emissions from aircraft engines adversely affect air quality in and around airports, contributing to public health concerns for airport workers and within neighbouring communities. RAPTOR will bring together relevant stakeholders to bridge a wide and multidimensional portfolio of existing European research to: i) provide enhanced insights into PM including the ultrafine PM (UFP) component within aircraft exhaust emissions; ii) explore interdependencies between noise and emissions particularly NOx and UFP; iii) work in association with other national and EU funded projects to determine a more nuanced understanding of the potential impact of UFP on health outcomes; iv) provide new insights on emission/exhaust measurement and emission modelling uncertainties; v) create a more comprehensive understanding on the composition and potential importance of emissions from unregulated engines; and vi) support the European Commission and associated Agencies to develop a coherent European roadmap for action on aircraft engine particulate matter regulation. To achieve these ambitious goals RAPTOR will undertake an in-depth review of available literature to assess knowledge gaps. This will include non-volatile nvPM measurement techniques & corrections and their associated uncertainties as these directly impact modelling studies of local air quality and inform aircraft-induced PM related toxicity and health effects. Findings will be communicated in an open access database, highlighting interdependencies between the measurements, modelling and health disciplines. RAPTOR will forge synergistic links with existing national, EU and international projects to ensure a high degree of additionality and provide access to desensitised proprietary data. RAPTOR will also generate new data using a representative combustor rig to assess uncertainty of the current CAEP/11 nvPM standards, utilising two reference ICAO appendix 7-compliant nvPM systems.

Keywords: aviation,particulates,nvpm,air quality,health impact,volatiles,regulation,pm modelling,airport,small engines,rig test
Genomic Data-Fusion Platform for Omics-Driven Precision Medicine (GENETTA)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Panel: Computer science and informatics
Panel description: Informatics and information systems, computer science, scientific computing, intelligent systems
Countries involved: Spain
Call: Call for proposals for ERC Proof of Concept Grant (ERC-2020-PoC, 2020)
Start date: 2020-09-01
End date: 2023-08-31
Duration (months): 36.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: SIGNED

This PoC project aims to pave the road to commercialize a comprehensive data analytics platform enabling data-driven biomedical innovation and precision medicine. The platform is specifically designed to efficiently fuse and mine heterogeneous omic data, including genomes, epigenomes, proteomes, metabolomes, patient clinical profiles, drugs and their chemical similarities, disease and other ontologies, and other relevant omic data. The goal of this development is to provide the most advanced software platform for fusion and analytics of numerous heterogeneous multi-scale omic data that takes advantage of novel non-negative matrix trifactorization (NMTF)-based and network mining algorithms, providing dramatic improvements in the number and type of fused data, quantity of data, computational efficiency and biomedical accuracy compared to the most advanced omic data analytics platforms currently existing. The main goal of the PoC is to close the gap between the research results of ERC Consolidator Grant, ICON-BIO, and the production of a commercial data analytics platform for the bio-pharmaceutical sector. In particular, the solution will target biopharma companies to embed the platform into their existing Data Science resources and enable effective and efficient application of the platform’s explainable AI methods (resulting from ICON-BIO) to optimize and guide their discovery processes. The result of this effort will be the achievement of a market-ready Data Analytics Platform.

Keywords: large scale distributed software,omics and molecular data analytics,precision medicine,data fusion,large scale data mining
Perception Ultrasound by Learning Sonographic Experience (PULSE)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Prevention, Diagnosis and Treatment of Human Diseases
Panel description: Medical technologies and tools for prevention, diagnosis and treatment of human diseases, therapeutic approaches and interventions, pharmacology, preventative medicine, epidemiology and public health, digital medicine
Countries involved: United Kingdom
Call: ERC Advanced Grant (ERC-2015-AdG, 2015)
Start date: 2016-11-01
End date: 2023-04-30
Duration (months): 78.0
Total project cost (EUR): 2462015.0
EU funding (EUR): 2462015.0
Participants: 1
Project status: SIGNED

PULSE will develop a new generation of ultrasound imaging capabilities to revolutionize the use of this low-cost and portable imaging technology across clinical medicine worldwide. The greatest barrier to the universal implementation of ultrasound (US) in clinical medicine today is the need to train sonographers to the highest level to ensure diagnostic images are of consistently high quality and fit for purpose. Unfortunately, the non-expert finds US images very difficult to interpret by eye alone. Perception Ultrasound by Learning Sonographic Experience (PULSE) is an innovative inter-disciplinary project designed to eliminate the need for highly skilled operators of the technology. It is motivated by the observation that sonographers find it easier to interpret their own scans than review those taken by others. The innovation in PULSE is to apply the latest ideas from machine learning and computer vision to build, from real world training video data, computational models that describe how an expert sonographer performs a diagnostic study of a subject from multiple perceptual cues. Novel machine-learning based computational models will be derived based on probe and eye motion tracking, image processing, and knowledge of how to interpret real-world clinical images and videos acquired to a standardised protocol. By building models that more closely mimic how a human makes decisions from US images we believe we will build considerably more powerful assistive interpretation methods than have previously been possible from still US images and videos alone. Software demonstrators will be developed and evaluated on real world obstetric US data in collaboration with clinical experts and novices to demonstrate the new approach and its potential to move routine US scanning services from hospitals into the community which would have clear economic, healthcare and social benefits across Europe and beyond.

Keywords: ultrasound imaging,biomedical image analysis
Dark energy, non-linearites and effective theories (DEFT)

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: Physics
Countries involved: France
Call: Marie Skłodowska-Curie Individual Fellowships (IF-EF) (H2020-MSCA-IF-2014, 2014)
Start date: 2015-10-01
End date: 2017-09-30
Duration (months): 24.0
Total project cost (EUR): 173076.0
EU funding (EUR): 173076.0
Participants: 1
Project status: CLOSED

The objective of this project is improving substantially our understanding of the acceleration of the Universe by developing novel effective techniques and semi-analytical methods for non-linear cosmological perturbation theory that will be applied to the data of upcoming high precision surveys. The accelerated expansion of the Universe was first detected in 1998 with Type Ia supernova data, and today there is ample evidence for it from various other observations such as the cosmic microwave background radiation (CMB) and the distribution of galaxies at large scales. We call dark energy the mysterious force that drives the acceleration of the Universe and understanding its origin is one of the major problems in cosmology of our time. The high precision of the near-future probes designed for elucidating the nature of dark energy needs to be matched with very accurate theoretical predictions, in particular concerning non-linear effects in cosmological perturbation theory. Developing efficient methods to compute these effects is a crucial requirement for the interpretation of the future dark energy data. I will apply the methods of effective field theories, that have been widely successful in many areas of physics, to develop new tools for describing dark matter and dark energy perturbations in the linear and non-linear regimes. I will combine these tools with state-of-the-art forecasting techniques to determine the prospects for the detection of dark energy parameters from future large scale structure data (specifically from Euclid) in combination with CMB data from Planck.

Keywords: cosmology,dark energy,effective theories,non linearitites
Research Centre in Interactive Media, Smart Systems and Emerging Technologies (RISE)

Framework: Horizon 2020 — Spreading excellence and widening participation
Programme: Widening participation and spreading excellence
Theme: Improved access to Excellence
Action type: Framework Partnership Agreement
Countries involved: Germany,Cyprus,United Kingdom
Call: Teaming (H2020-WIDESPREAD-2014-1, 2014)
Start date: 2015-05-06
End date: 2020-05-05
Duration (months): 60.0
Total project cost (EUR): 0.0
EU funding (EUR): 0.0
Participants: 7
Project status: CLOSED

In line with WIDESPREAD-2014-1 TEAMING, this proposal puts forward the vision for establishing a “Research centre on Interactive media, Smart systems and Emerging technologies” (RISE). RISE aims to become a Centre of Excellence and a hub across the three continents bordering Cyprus, facilitating thus the local scientific, technological, and economic growth of the region. The proposal identifies the potential of Interactive Media to bring together several scientific areas, yielding applications in the priority areas of the Smart Specialisation Strategy of Cyprus. Therefore, research in RISE integrates the Visual Sciences, Human Factors and Design, and Communications and Artificial Intelligence, in a tight synergy that provides a unique interdisciplinary research perspective that emphasizes an “Inspired by Humans, Designed for Humans” philosophy. To support the implementation of the research and innovation agenda of RISE, the proposal puts forward the a consortium coordinated by the Nicosia Municipality, the largest regional authority of Cyprus with extensive experience in managing large projects and commitment in establishing and supporting an innovation ecosystem in Nicosia, along with all three public universities of Cyprus. The four local partners will contribute their research, infrastructure, and financial potential for the establishment of RISE and with guidance by two Experienced Partners (MPI and UCL) with unequivocal scientific excellence and demonstrated success in running Centres of Excellence, aim to establish a sustainable Centre of Excellence in Cyprus. With the long-term sustainable growth of RISE in mind, the proposal designates an administrative structure for the Centre to facilitate its interdisciplinary view of research, its outreach perspective towards human users and businesses, and its multi-institutional composition.

Keywords: interactive media,visual sciences,human factors,cyprus smart specialisations
Continuous Resolution and Deracemization of Chiral Compounds by Crystallization (CORE)

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,Switzerland,Romania,France,France,Netherlands,Netherlands,Netherlands,United Kingdom,United Kingdom,United Kingdom
Call: Innovative Training Networks (H2020-MSCA-ITN-2016, 2016)
Start date: 2016-10-01
End date: 2020-12-31
Duration (months): 51.0
Total project cost (EUR): 3926004.12
EU funding (EUR): 3926004.12
Participants: 15
Project status: CLOSED

Industries are in need of highly skilled academically trained experts and powerful sets of tools enabling the design, control & prediction of optimized & efficient production process of future high-value products such as chiral pharmaceuticals. The CORE Network will in parallel train 15 ESRs and develop tools, approaches and methods within the area of Continuous Resolution (CORE), the process to obtain enantiopure molecules of chiral compounds. The training objective of the CORE network is to deliver a CORE skills toolbox of knowledge, personal, organizational and impact skills to a core of multi-disciplinary scientists and engineers in the interdisciplinary and cross-sectional field of Continuous Resolution. Each ESR obtains dedicated training through their research project, network events, a webinar course, management involvement and an academic & industrial secondment. The research objective of the CORE Network is to jointly construct a CORE Industrial Toolbox on Continuous Resolution that provides next generation tools, approaches and methods to industry for the development continuous resolution processes. The strongly involved industrial partners will ensure that the CORE Industrial Toolbox fulfils their requirements in the skills gap areas Towards Continuous, Hybrid Resolution and Enabling Resolution. CORE brings together 8 academic and 7 industrial partners resulting in an unparalleled combination of chirality, synthesis and crystallization training and research covering the areas of Chemical Engineering, Chemistry and Applied Physics.

Keywords: continuous manufacturing,resolution,chirality,crystallization,chiral separation,chiral,synthesis
INtelligeNt ApplicatiOns oVer Large ScAle DaTa StrEams (INNOVATE)

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: Information Science and Engineering
Countries involved: United Kingdom
Call: Individual Fellowships (H2020-MSCA-IF-2016, 2016)
Start date: 2018-04-01
End date: 2020-03-31
Duration (months): 24.0
Total project cost (EUR): 195454.8
EU funding (EUR): 195454.8
Participants: 1
Project status: CLOSED

Large scale data analytics is the key research domain for future data driven applications as numerous of devices produce huge volumes of data in the form of streams. Analytics services can offer the necessary basis for building intelligent decision making mechanisms to support novel applications. Due to the huge volumes of data, analytics should be based on efficient schemes for querying large scale data partitions. Partitions contain only a piece of data and a dedicated processor manages the incoming queries. The management of continuous queries over data streams is a challenging research issue requiring intelligent methods to derive the final outcome (i.e., query response) in limited time with maximum performance. The management process of continuous queries involves their assignment to specific processors and the processing of the derived responses. We focus on a group of query controllers serving the incoming queries and, thus, becoming the connection of big data systems with the real world. INNOVATE proposes solutions for the management of the controllers behavior. We propose an intelligent decision making process for each controller in three axes: (i) top-down, by realizing a mechanism that assigns queries to the underlying processors; (ii) bottom-up, by proposing decision making mechanisms for returning responses to users/applications on top of early results; (iii) horizontal, by proposing optimization schemes for queries management. We adopt a pool of learning schemes and an ensemble learning model dealing with how and on which processors each query should be assigned. We also propose specific schemes for combining processors responses. Intelligent and optimization techniques are adopted for the controllers group management. Machine learning, Computational Intelligence and optimization are the key adopted technologies that, when combined, provide efficient solutions to a challenging problem like the support of intelligent analytics over big data streams.

Keywords: large scale data,data streams,query streams,computational intelligence