Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
A Flexible Platform for the Application of SAM-dependent enzymes (AppSAM)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Biotechnology and Biosystems Engineering
Panel description: Biotechnology using all organisms, biotechnology for environment and food applications, applied plant and animal sciences, bioengineering and synthetic biology, biomass and biofuels, biohazards
Countries involved: Germany
Call: ERC Starting Grant (ERC-2016-STG, 2016)
Start date: 2017-10-01
End date: 2023-09-30
Duration (months): 72.0
Total project cost (EUR): 1499219.0
EU funding (EUR): 1499219.0
Participants: 1
Project status: SIGNED

AppSAM will unlock the synthetic capability of S-adenosyl¬methionine (SAM)-dependent methyltransferases and radical SAM enzymes for application in environmentally friendly and fully sustainable reactions. The biotechnological application of these enzymes will provide access to chemo-, regio- and stereoselective methylations and alkylations, as well as to a wide range of complex rearrangement reactions that are currently not possible through traditional approaches. Methylation reactions are of particular interest due to their importance in epigenetics, cancer metabolism and the development of novel pharmaceuticals. As chemical methylation methods often involve toxic compounds and rarely exhibit the desired selectivity and specificity, there is an urgent need for new, environmentally friendly methodologies. The proposed project will meet these demands by the provision of modular in vitro and in vivo systems that can be tailored to specific applications. In the first phase of AppSAM, efficient in vitro SAM-regeneration systems will be developed for use with methyltransferases as well as radical SAM enzymes. To achieve this aim, enzymes from different biosynthetic pathways will be combined in multi-enzyme cascades; methods from enzyme and reaction engineering will be used for optimisation. The second phase of AppSAM will address the application on a preparative scale. This will include the isolation of pure product from the in vitro systems, reactions using immobilised enzymes and extracts from in vivo productions. In addition to E. coli, the methylotrophic bacterium Methylobacter extorquens AM1 will be used as a host for the in vivo systems. M. extorquens can use C1 building blocks such as methanol as the sole carbon source, thereby initiating the biotechnological methylation process from a green source material and making the process fully sustainable, as well as being compatible with an envisaged “methanol economy”.

Keywords: methylation,s adenosylmethionine,enzyme application
Mechanobiology of Bovine Reproduction (MechBiolRep)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Biotechnology and Biosystems Engineering
Panel description: Biotechnology using all organisms, biotechnology for environment and food applications, applied plant and animal sciences, bioengineering and synthetic biology, biomass and biofuels, biohazards
Countries involved: Israel
Call: ERC Starting Grant (ERC-2015-STG, 2015)
Start date: 2016-06-01
End date: 2021-11-30
Duration (months): 66.0
Total project cost (EUR): 1500000.0
EU funding (EUR): 1500000.0
Participants: 1
Project status: CLOSED

The global demand for dairy products is expected to surge by 36% over the next decade in a manner that is progressively insatiable by existing technologies. The dairy industry relies on bovine reproduction, yet cow fertility is declining and the exact causes are not fully understood. It is clear, however, that the quality of bovine oocytes is decreasing. In mammals, the ovarian reserve of oocytes stored within quiescent primordial follicles is non-renewable. Oocyte develop and mature within distinctive follicular microenvironments under tightly regulated molecular and physical conditions. Similarly, preimplantation embryo development is supported within a specialized microenvironment that is surrounded by the zona pellucida and insulated from external soluble and mechanical inputs. Characterizing and understanding these environments and how they affect reproductive processes is a key toward improving assisted reproductive technologies in bovine species. Our premise is that molecular characterization of endocrine and paracrine signalling pathways must be complemented with understanding the mechanical regulation of reproductive biology. This premise is supported by recent finding showing that physical stresses and the mechanical compliance of the extracellular surroundings serve as potent regulators of cell fates in regeneration processes, development, and disease. I propose to employ a biophysical and computational toolbox to study the mechanobiology of reproduction with application to bovine embryo-based technologies. By mimicking the mechanical properties of the ovarian cortical niche, which I will characterize using freshly derived ovaries, I will design an in vitro system for supporting follicle growth. Mechanical profiling of the entire developmental course from oocyte maturation to preimplantation embryogenesis will generate mechanistic insights into the physical regulation of reproductive processes.

Keywords: mechanobiology,follicle in vitro growth,bovine embryo based technologies,preimplantation embryo development,embryo morphokinetics,machine learning
Armored Liftable anti-Intrusion Control Equipment (ALICE)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Italy,Italy
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2019)
Start date: 2019-09-01
End date: 2020-02-29
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 2
Project status: CLOSED

The increasing number of vehicle attacks in Europe and North America has led to an unprecedented demand for new security products for public and private sector. A.L.I.C.E. is the first-ever product of this kind: it's a passive barrier specifically designed to restrict entry of unauthorized vehicles into specific facility areas preserving their open and public nature. This disruptive innovation is a tool able to protect “soft targets”, that can be integrated in urban architecture as a design object with multiple functions (plant pot/bicycle stands) reducing psychological and sociological impact related to artificial barrier.

Keywords: anti ram vehicle barrier,anti terrorism barrier,urban design,urban furniture,soft target protection
From discovery to scale up of cluster based electrolytes for Ultra-high energy storage flow batteries (ELECTRO-POM)

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: United Kingdom
Call: ERC-Proof of Concept (ERC-2017-PoC, 2017)
Start date: 2018-03-01
End date: 2019-08-31
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: CLOSED

Large scale energy storage demands are set to increase dramatically during the next years due to the expansion of renewables. One of the most promising large-scale electrical storage technologies are Redox Flow Battery (RFB) systems, which can convert electrical energy to chemical energy and back again. Here the electrolyte is an electro-active species where the chemical energy is converted to electricity in a flow cell. RFBs can act as both batteries and a fuel generation device depending on the needs of the user, which is advantageous because they can be recharged without replacing the electro-active material. The vanadium RFB is a promising technology, but is critically limited by only being able to store one electron per species giving a low energy density (~20 W h kg-1) and poor stability restricting many applications. Using the artificial intelligence driven discovery system of the ERC Advanced Grant SMART-POM, we aimed at the discovery of new metal oxide molecular polyoxometalate (POM) clusters showing unexpected properties. For instance, we found a molecule that can store > 10 times more electrons reversibly than the vanadium RFB making these the molecules the most reduced molecules ever discovered. Here we want to make a major step in translating this ground-breaking outcome of SMART-POM from discovery of new clusters, to scale up so the molecule can be tested in a flow battery device set up. The heart of any flow battery is the electron storage redox electrolyte. The more electrons the electrolyte can store reversibly the higher the energy density and we aim here to beat the state of the art by at least an order of magnitude aiming >1000 Wh L-1 (at this point applications in electric cars are possible). We will licence the technology with the University of Glasgow spin out company, Astrea Power, as a partner to co-develop the innovation with several potential multinational companies as customers who are eager to utilize the technology.

Keywords: polyoxometalates,artificial intelligence,redox flow batteries,metal oxides,electrochemistry
Understanding and exploiting competitive solvent interactions in supramolecular polymerization: from solutions to bulk materials (SolSupPol)

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: Chemistry
Countries involved: Netherlands
Call: Individual Fellowships (H2020-MSCA-IF-2020, 2020)
Start date: 2021-08-01
End date: 2023-07-31
Duration (months): 24.0
Total project cost (EUR): 175572.48
EU funding (EUR): 175572.48
Participants: 1
Project status: SIGNED

During the last three decades, tremendous progress has been made in the field of Supramolecular Polymers (SPs), and several fundamental issues have been disclosed. As a result, these materials are reaching technological maturity. The intrinsic dynamicity and responsive properties of SPs render them intriguing candidates for a plethora of applications, ranging from life-like systems to optoelectronics. Thus, investigation on SPs is expected to have a profound impact on the knowledge-based society. Remarkably, the role of solvent in supramolecular polymerization is still poorly understood. The current picture of solvent effects on SPs is fragmented and mainly descriptive. With SolSupPol, I propose a detailed study of the role of competitive solvent interactions in the helical bias and thermal behaviour of SPs in solution, based on the use of chiral solvents. Combining a systematic investigation on solvent effects at a macroscopic level with a microscopic study of their molecular origin, an unprecedent control over competitive solvent interactions in supramolecular polymerization will be achieved. Hence, I aim to exploit solvent effects to tune the properties of SPs in the bulk. These findings will constitute a proof-of-concept, revealing emerging solvent-induced structure-property relationships in the solid state. The project includes the synthesis of a library of monomers, the empirical and theoretical study of their assembly in solution, the preparation and characterization of thin films, and the probing of the spin-filtering and long-range exciton transfer properties of solvent-induced chiral supramolecular structures in the bulk. The outcomes of this project will enable the design of tailored interactions between SPs and solvents and will foster the development of functional, adaptive materials based on solvent effects. Dissemination and communication activities will guarantee the great impact of SolSupPol.

Keywords: supramolecular polymers,supramolecular assemblies,self organization,non covalent interactions,helical structures,helicity control,supramolecular chirality,circular dichroism,solvents,monomers
Leveraging Big Data to Manage Transport Operations (LeMO)

Framework: Horizon 2020 — Societal Challenges
Programme: Smart, green and integrated transport
Action type: Coordination and support actions
Countries involved: Germany,Belgium,Norway,Netherlands,United Kingdom
Call: Big data in Transport: Research opportunities, challenges and limitations (H2020-MG-2017-SingleStage-INEA, 2017)
Start date: 2017-11-01
End date: 2020-10-31
Duration (months): 36.0
Total project cost (EUR): 1492627.5
EU funding (EUR): 1492627.0
Participants: 6
Project status: CLOSED

Big data has opened a wide spectrum of opportunities in the field of transport research. Observing the recent emergent interest in the application of big data within transport, as well as the extended scope of its applications, it is evident that most of the challenges have yet to be addressed. Leveraging Big Data to Manage Transport Operations (LeMO) project will explore the implications of the utilisation of big data to enhance the economic sustainability and competitiveness of European transport sector. The project will study and analyse big data in the European transport domain in particular with respect to five transport dimensions: mode, sector, technology, policy and evaluation. LeMO will accomplish this by conducting a series of case studies, in order to provide recommendations on the prerequisites of effective big data implementation in the transport field. Through case studies, LeMO will investigate methodological, technological, governmental and institutional issues, which in turn contribute to evidence-based decision making. LeMO will supplement these case studies with a horizontal analysis that identifies the barriers and limitations of the transportation system to exploit big data opportunities. In collaboration with strong advisory and reference group, and expert stakeholders, LeMO will devise and develop research and policy roadmap that will provide incremental steps necessary towards data openness and sharing to make transport safer, more efficient and more sustainable. Notably, LeMO will bring crucial issues linked to privacy, data security and legal aspects to the forefront, paving the way for future legal framework for the collection and exploitation of big data in transport. Furthermore, LeMO will disseminate project findings to a large population of stakeholders, including transport authorities and industries leading to better understanding of travellers' and consumers' behaviour, targeted information and identify policy interventions.

Keywords: big data,data exploitation,transport,smart transport,open data,data analysis,socio economic science,data privacy,data security,logistics
A unique noninvasive diagnostic test based on endometrium tissue analysis, which assesses the presence or absence of endometriosis without surgery, helping the physician to make his diagnosis rapidly (EndoSearch)

Framework: Horizon 2020 — Societal Challenges
Programme: Health, demographic change and wellbeing
Theme: Treating and managing disease
Action type: SME instrument
Countries involved: France
Call: Supporting innovative SMEs in the healthcare biotechnology sector (H2020-SMEINST-2-2016-2017, 2017)
Start date: 2017-09-01
End date: 2021-08-31
Duration (months): 48.0
Total project cost (EUR): 4446991.25
EU funding (EUR): 4231613.0
Participants: 1
Project status: CLOSED

Endometriosis is a chronic and disabling gynecological disease affecting 10% of women of reproductive age, representing 176 million patients worldwide. It is defined as the presence of endometrial-like tissue outside the uterus, capable of invading several organs. It is associated with acute symptoms, mainly consisting of severe pelvic pain and infertility, imposing an annual economic burden estimated at $77,46 billion in 9 European countries and $78.05 billion in the USA. The current gold standard for diagnosis of endometriosis is an invasive surgical procedure performed under general anesthesia (laparoscopy) enabling the visualization of the lesions by the surgeon and histological confirmation. Once diagnosed, endometriosis management frequently combines surgery and non-specific hormonal therapies with limited efficacy. Since these treatments only address the symptoms and not the root causes, disease recurrence is frequent and occurs in up to 50% of the patients. In close collaboration with international endometriosis experts, Endodiag has developed EndoSearch®, a unique noninvasive diagnostic solution based on endometrium tissue analysis, which assesses the presence or absence of endometriosis without surgery, helping the physician to make his diagnosis rapidly, cost-effectively, with a non invasive procedure and enabling him to take decisions on the most suitable patient management. With this research programme, Endodiag is addressing a major healthcare need, clearly identified by the European Community in its statement of June 2005 at the European parliament. EndoSearch® successful validation and deployment will improve the quality of life of millions of patients while significantly reducing endometriosis-associated healthcare costs.

Keywords: diagnostic,noninvasive,endometriosis,women health
GoJelly - A gelatinous solution to plastic pollution (GoJelly)

Framework: Horizon 2020 — Societal Challenges
Programme: Food security, sustainable agriculture and forestry, marine and maritime and inland water research and the bioeconomy
Theme: Cross-cutting marine and maritime research
Action type: Innovation actions
Countries involved: Germany,Germany,Israel,Italy,Germany,Italy,Norway,China,Slovenia,France,Portugal,Denmark
Call: Blue green innovation for clean coasts and seas (H2020-BG-2017-1, 2017)
Start date: 2018-01-01
End date: 2021-12-31
Duration (months): 48.0
Total project cost (EUR): 6222816.5
EU funding (EUR): 5998114.75
Participants: 16
Project status: CLOSED

The objective of the GoJelly project is to develop, test and promote a gelatinous solution to microplastic pollution by developing a TRL 5-6 prototype microplastics filter (GoJelly) for commercial and public use, where the main raw material is jellyfish mucus. In doing so, the consortium addresses two environmental issues with one approach by removing the commercially and ecologically destructive sea and coastal pollution of both jellyfish and microplastics. This innovative approach will ultimately lead to less plastic in the ocean, municipal demand (and thereby competitive prices) for jellyfish raw material to fill the "mucus-need" by filter developers, and in turn more jobs for commercial fishers in off-seasons. The by-products of the GoJelly biomass have other uses as well, ensuring that GoJelly also delivers a green innovation, resulting in novel, valuable resource for the food and feed industry as well as agro-biological fertilizer for organic farming. The GoJelly prototype products will be tested and demonstrated in three different European seas (Norwegian, Baltic and Mediterranean), by a range of stakeholders, including commercial fishers and industry partners. Tying it together, the project will also ensure the possibilities for broader European promotion and utilization of GoJelly at the local, regional and global level by delivering a socio-ecological methodological toolbox for forming and implementing policies. GoJelly will broadly communicate its results in several formats such as traditional social media, open lab ship cruise, and in the form of an experimental online game depicting different management scenarios under different jellyfish- and microplastics combinations. An interdisciplinary and international consortium consisting of technology developers, business analysts, fishing companies, research institutes, and both natural and social scientists will realize GoJelly, and will ensure the uptake of GoJelly products by industry and policy makers.

Keywords: microplastics,jellyfish,forecasting,waste water treatment,organic farming,cosmetics,nutriceuticals,food science,aquafeed,socioecological systems,trade offs,blue jobs,circular economy
Trafisense is a real-time monitoring and early-warning service for high-risk situations in dry-type distribution and power transformers based on proprietary machine-learning technology. (Trafisense)

Framework: Horizon 2020 — Societal Challenges
Programme: Secure, clean and efficient energy
Action type: SME instrument
Countries involved: Greece
Call: Stimulating the innovation potential of SMEs for a low carbon and efficient energy system (H2020-SMEINST-1-2016-2017, 2016)
Start date: 2017-02-01
End date: 2017-07-31
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

Trafisense is a monitoring service for dry-type distribution and power transformers based on proprietary machine-learning technology. Currently only 5% of dry-type power transformers are monitored beyond a very basic set of properties. As a result, owners cannot predict the state of the transformers accurately and face hardware failures and downtime. Even worse, the lack of monitoring prevents identifying the original cause of failure. The last 4 years several hundred datacenters where shut down due to dry transformer failures. 50% of failures and 60% of downtime per year in wind farms correspond to electrical component failures and transformer failures are amongst the most common sources. Additional issues arise in offshore sites due to advanced monitoring requirements. Trafisense offers a combined hardware/software solution for continuous real-time monitoring of dry-type power transformers. We process data feeds from more than 10 electrical and environmental properties to identify the risk status of the hardware and notify the customer of exceptional conditions several days or weeks before they manifest. Trafisense does not just display statistics leaving the deciphering job to the customer. Our service generates detailed actionable insights leading maintenance engineers to the root problem and suggesting specific maintenance actions. In addition to the daily and weekly automated reports, our customers receive semiannual summary reports for each monitored transformer by a team of in-house experts. Initial comparison with standard maintenance practices indicates considerable savings in maintenance cost mainly in offshore installations and onshore remote or critical installations. The reduction of false negatives results to less downtime that translates to fewer disruptions in production. Our proprietary technology generates early warnings regarding high-risk hardware states and it can reduce accidents by 80%.

Keywords: dry type power transformer,real time monitoring,predictive analytics,prescriptive analytics,analytics,actionable insights,critical energy infrastructure