Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
MItigating Negative Impacts of Monitoring high levels of Automation (MINIMA)

Framework: Horizon 2020 — Societal Challenges
Programme: Smart, green and integrated transport
Theme: Public-private partnerships
Destination: Single European Sky Air Traffic Management Research
Action type: Research and Innovation actions
Partnership: SESAR (Art-187)
Countries involved: Italy,Germany,France
Call: Automation in ATM (H2020-SESAR-2015-1, 2015)
Start date: 2016-05-01
End date: 2018-04-30
Duration (months): 24.0
Total project cost (EUR): 582780.0
EU funding (EUR): 582779.5
Participants: 3
Project status: CLOSED

Assigning tasks, formerly executed by human operators, to automation can increase the performance in many aspects of ATM. However, the absence of automation errors can often not be guaranteed. Consequently a human operator is required to monitor the automation and to intervene in the rare cases of automation errors. It has been shown that this monitoring role of human operators results in negative effects like lack of attention, loss of situation awareness and – in the long term – skill degradation. This project will develop solutions to mitigate these effects. As an example a highly automated arrival management task in which the aircraft follow their predefined 4D-trajectories will be investigated. As neither the automatic detection and resolution of all conflicts nor the ability of all aircraft to follow their trajectories with the required precision can be guaranteed all the time, a human operator is needed to monitor and handle situations in which automation fails. New human-automation interaction design concepts for this task will be developed from scratch in three steps in this project: First, the task environment will be analyzed to identify all the necessary tasks that can be assigned either to the human operator or the automation. Secondly, concept for different solutions will be developed considering the human performance envelope. This includes adaptive or adaptable automation with dynamic task distributions, tools to direct the operator attention, and suitable human-machine interfaces. In a third step, different human-automation design concepts will be evaluated. Identifying how to apply higher automation to complex systems while mitigating the negative effects of monitoring tasks will allow benefiting from performance increases of higher levels of automation while keeping the human operator performance on a high level to ensure safe operations.
Knowledge graph completion using Artificial Neural Networks for Herb-Drug Interaction discovery (kANNa)

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: France
Call: Individual Fellowships (H2020-MSCA-IF-2017, 2017)
Start date: 2019-04-01
End date: 2021-03-31
Duration (months): 24.0
Total project cost (EUR): 185076.0
EU funding (EUR): 185076.0
Participants: 1
Project status: CLOSED

With the growing popularity of herbal drugs an increasing number of scientific studies report information about herb-drug interactions that can significantly alter the effects of a drug. Keeping up with the current publication rate is not feasible, therefore there is a clear need for computational methods for early detection of herb-drug interactions that will enable better public and physician understanding of herbal products. But the costs of manually representing knowledge about herb-drug interactions in a machine processable way are prohibitive, therefore domain expertise has to be leveraged indirectly from domain-specific corpora using Information Extraction. This Marie Curie European Fellowship proposes a Deep Learning approach based on Artificial Neural Networks (ANN) and Information Extraction to monitor medical literature and construct a knowledge base of herb-drug interactions together with supporting evidence in the form of interaction mechanisms. To cope with the problem of incorrect or missing information we will consolidate the resulting knowledge graph using knowledge graph completion that predicts the probability of existence or correctness of typed edges in the graph. Advanced graph visualization techniques will be employed to develop intuitive interfaces for analyzing and comparing herb-drug interactions and underlying mechanisms. The Fellowship is expected to increase knowledge on clinically significant herb-drug interactions which will contribute to improved public safety. The Host will provide training on Deep Learning approaches for knowledge extraction which will open opportunities for a senior researcher position, in turn the Fellow will transfer Natural Language Processing skills and European collaborations to the host.

Keywords: artificial neural networks,deep learning,biomedical informatics,herb drug interactions,semantic web,graph vizualization
Multivariate Analysis of Big Data in Software Defined Networks (MAD-SDN)

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: Spain
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2021-03-01
End date: 2023-02-28
Duration (months): 24.0
Total project cost (EUR): 172932.48
EU funding (EUR): 172932.48
Participants: 1
Project status: CLOSED

One of the main problems of the Internet is the rapidly growing volume of diverse data. The Future Internet needs new efficient methods to support data management, processing and analysis. The Software Defined Networking (SDN) is a novel network architecture that overcomes the limitations of traditional networks, separating the control and data planes, and providing programmability capabilities of network functionalities. Yet, modern SDN deployments are difficult to manage and optimize. Big Data analysis techniques can be useful in SDN to identify problems, troubleshoot them and optimize network performance. One promising approach for this is the Multivariate Big Data Analysis (MBDA), which extends multivariate analysis to Big Data sets. However, MBDA has not been applied to SDN yet. During this project, MBDA will be used to detect anomalies and classify network traffic in complex SDN environment. In addition, in order to ensure privacy, MBDA will be extended with Federated Learning, a cutting-edge approach recently developed by Google with application to distributed data analysis problems. This project will be carry out by the experienced researcher (ER) who worked during her PhD thesis on network traffic analysis using advanced statistical methods on time series. The ER will cooperate with the Supervisor who is an expert in the field of multivariate analysis for anomaly detection and optimization of networks, and the principal developer of the MBDA approach.

Keywords: network traffic analysis,network management,big data analytics,multivariate analysis,anomaly detection,traffic classification
Gas sensing in plants: Oxygen- and nitric oxide-regulated chromatin modification via a targeted protein degradation mechanism (GasPlaNt)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Cellular, Developmental and Regenerative Biology
Panel description: Structure and function of the cell, cell-cell communication, embryogenesis, tissue differentiation, organogenesis, growth, development, evolution of development, organoids, stem cells, regeneration, therapeutic approaches
Countries involved: United Kingdom
Call: ERC Starting Grant (ERC-2016-STG, 2016)
Start date: 2017-03-01
End date: 2023-06-30
Duration (months): 76.0
Total project cost (EUR): 1495340.75
EU funding (EUR): 1495340.75
Participants: 1
Project status: SIGNED

Oxygen (O2) and nitric oxide (NO) are gases that function as key developmental and stress-associated signals in plants. Investigating the molecular basis of their perception has the potential to identify new targets for crop improvement. In previous ground breaking work I showed that the direct transcriptional response to O2/NO is mediated by controlled degradation of specialised ‘gas-sensing’ transcription factors. We have now linked this degradation mechanism to a new functional class of ‘sensor’, a chromatin modifying protein that regulates the epigenetic silencing of genes. Here we will investigate the hypothesis that this protein acts as a previously undiscovered link between O2/NO and chromatin dynamics, and that plants have evolved a unique system for transducing gaseous signals into rapid transcriptional responses, and longer term epigenetic changes, through targeting different types of protein to the same degradation pathway. Using multidisciplinary genetic, biochemical and omics approaches we will investigate the molecular basis of this novel gas perception system, which appears to be a plant-specific innovation. We will identify its global gene targets (the ‘gas-responsive epigenome’), and uncover its growth and stress-associated functions in Arabidopsis and barley. We will also investigate how manipulating this pathway using genome editing and synthetic biology techniques alters plant performance, focusing on traits of agronomic significance. This ambitious and timely research will take our knowledge of O2/NO-signaling and the control of chromatin dynamics beyond the current state of the art by offering insight into a completely novel signaling mechanism operating at the interface of gas-perception, protein degradation, and epigenetics. GasPlaNt will therefore provide a step-change in our understanding of how plants synchronise their gene expression in response to signals to optimise growth and development within a dynamic environment.

Keywords: hypoxia,stress responses,crop species
The asymmetry of life: towards a unified view of the emergence of biological homochirality (A-LIFE)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Physical and analytical chemical sciences
Panel description: Analytical chemistry, chemical theory, physical chemistry/chemical physics
Countries involved: France
Call: ERC Starting Grant (ERC-2018-STG, 2018)
Start date: 2019-04-01
End date: 2024-09-30
Duration (months): 66.0
Total project cost (EUR): 1500000.0
EU funding (EUR): 1500000.0
Participants: 1
Project status: SIGNED

What is responsible for the emergence of homochirality, the almost exclusive use of one enantiomer over its mirror image? And what led to the evolution of life’s homochiral biopolymers, DNA/RNA, proteins and lipids, where all the constituent monomers exhibit the same handedness? Based on in-situ observations and laboratory studies, we propose that this handedness occurs when chiral biomolecules are synthesized asymmetrically through interaction with circularly polarized photons in interstellar space. The ultimate goal of this project will be to demonstrate how the diverse set of heterogeneous enantioenriched molecules, available from meteoritic impact, assembles into homochiral pre-biopolymers, by simulating the evolutionary stages on early Earth. My recent research has shown that the central chiral unit of RNA, ribose, forms readily under simulated comet conditions and this has provided valuable new insights into the accessibility of precursors of genetic material in interstellar environments. The significance of this project arises due to the current lack of experimental demonstration that amino acids, sugars and lipids can simultaneously and asymmetrically be synthesized by a universal physical selection process. A synergistic methodology will be developed to build a unified theory for the origin of all chiral biological building blocks and their assembly into homochiral supramolecular entities. For the first time, advanced analyses of astrophysical-relevant samples, asymmetric photochemistry triggered by circularly polarized synchrotron and laser sources, and chiral amplification due to polymerization processes will be combined. Intermediates and autocatalytic reaction kinetics will be monitored and supported by quantum calculations to understand the underlying processes. A unified theory on the asymmetric formation and self-assembly of life’s biopolymers is groundbreaking and will impact the whole conceptual foundation of the origin of life.

Keywords: chirality,origin of life,circularly polarized light,multidimensional chromatography,circular dichroism
Aligning pigmentation and repair: a holistic approach for UV protection dynamics (UVdynamicsProtection)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Physiology in Health, Disease and Ageing
Panel description: Organ and tissue physiology, comparative physiology, physiology of ageing, pathophysiology, interorgan and tissue communication, endocrinology, nutrition, metabolism, interaction with the microbiome, non-communicable diseases including cancer (and except disorders of the nervous system and immunity-related diseases)
Countries involved: Israel
Call: ERC Consolidator Grant (ERC-2016-COG, 2016)
Start date: 2017-04-01
End date: 2022-03-31
Duration (months): 60.0
Total project cost (EUR): 1971875.0
EU funding (EUR): 1971875.0
Participants: 1
Project status: CLOSED

The human body takes different measures in order to protect itself against the results of UV exposure and its accompanied hazards, such as skin cancer. Despite extensive studies regarding the molecular regulation of the two main UV protection mechanisms, namely, the DNA repair system and the pigmentation system, a comprehensive theory that simultaneously accounts for the two systems is still missing. Hence, the ground-breaking goal of this proposal is to elucidate, for the first time, the dynamic control used to schedule and synchronize the UV protection subsystems. Since these two systems serve the same physiological purpose, but on different time scales (DNA repair takes minutes, while pigmentation lasts hours to days), I propose to take the novel approach of focusing on their timing as an opportunity to uncover their regulation. As a first step, we exposed human and mouse skin to UV and found that UV exposure at 48hr intervals resulted in higher skin pigmentation than did exposure at 24hr intervals, even after controlling for total UV dosage. Furthermore, we found that the expression level of the melanocyte central regulator, MITF, exhibits damped oscillatory behaviour during this 48hr interval. I therefore hypothesize that the dynamic behaviour of the central regulator dictates the UV–response timing of the two protection systems. In the proposed research, I will take a holistic approach and address this issue from three complementary perspectives: (1) transcriptional dynamics, (2) temporal effects on cellular output, and (3) DNA repair after UV. This will be achieved by utilizing and developing new experimental and analytical tools that will allow us to correlate the temporal behaviours of a wide set of molecular markers. Reaching our goals will provide a breakthrough in our understanding of skin protection from UV and the underlying mechanisms that control it. These findings may offer exciting new avenues for future skin cancer prevention.
Plant conditioner enhancing plants' drought resistance based on proline aminoacid (DRYFIT)

Framework: Horizon 2020 — Societal Challenges
Programme: Food security, sustainable agriculture and forestry, marine and maritime and inland water research and the bioeconomy
Theme: Sustainable agriculture and forestry
Action type: SME instrument
Countries involved: Germany
Call: Stimulating the innovation potential of SMEs for sustainable and competitive agriculture, forestry, agri-food and bio-based sectors (H2020-SMEINST-1-2016-2017, 2016)
Start date: 2017-01-01
End date: 2017-03-31
Duration (months): 3.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

Drought and extreme heat events cause 25% of crop losses in Europe, and reduce availability of irrigation water for agriculture, leading to severe economic impact on farmer’s income. The low proportion of irrigated agricultural land (only 6.7% of total European cultivated area), and the strict limitations for growing genetically modified crops (e.g. resistant to drought), rest much competitiveness to a sector that is extremely vulnerable to weather conditions. Thus, a clear business opportunity exists in Europe for methods to enhance crop drought resistance, which is indeed favoured by the current shift of plant protection product’s market towards preventive tools for optimizing yield performance (12% annual growth, €2,3 billion by 2021). DRYFIT is a new plant conditioner composed by a natural blend of substances already present in plants, that enhances their resistance to water or combined water-heat stress caused by droughts and extreme high-temperature events. DRYFIT is based on proline aminoacid, which increases the cells' (plant) water retention by 20-30%, so they are not harmed by water deficit. The Salicylic acid, its second ingredient, provides additional protection against other abiotic stresses (salinity, metals), and maximizes the performance of proline. Our product value is not only given by the preventive protection to external stresses, but also by its innocuity, and the easy-to-use foliar application mode that does not require exposing roots to air (as is the case of pesticides), thus minimizing the probability of infection by external disease agents. DRYFIT is the result of the intense R&D activity of StarAgro Deutschland GmbH, a German SME currently commercializing other plant conditioners. By this new product, StarAgro forecasts to achieve between 2019 and 2021 a cumulative revenue of €4,6 million with €0,8 million profit (profitability ratio of 25%), increasing workforce in 10 people.

Keywords: plant conditioner,plant protection product,proline aminoacid,salicylic acid,drought,temperature resistance,crops,agriculture,extreme temperature
Entreprise Europe Ouest H2020 (EEN OUEST H2020)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: Specific Grant agreement
Countries involved: France,France
Call: H2020 ENTREPRISE EUROPE NETWORK SGA4 2020-2021 (H2020-EEN-SGA4-2020-2021, 2019)
Start date: 2020-01-01
End date: 2021-12-31
Duration (months): 24.0
Total project cost (EUR): 234745.0
EU funding (EUR): 234745.0
Participants: 3
Project status: CLOSED

EEN OUEST aims at providing high added value services to SMEs willing to access new markets and innovate. Part of the services foreseen in the implementation strategy (WP4) concerns the capacity of businesses to innovate and succeed in their SME Instrument projects. Two kind of actions are proposed : - Innovation Management : enhancing innovation capacity is at stake for many small companies which need to remain competitive. EEN Ouest delivers tailored made services through a diagnosis and an action plan, thanks to a methodology called "Check Innov" - Key Account Management for SME Instrument beneficiaries : SME which succeeded in the SME instrument calls can benefit from coaching services from EEN advisors. The objective is to maximise their chances to succeed in their project. The KAM has been extended to other EIC tools.

Keywords: innovation management
Global Omic Data Integration on Animal, Vegetal and Environment Sectors (GLOMICAVE)

Framework: Horizon 2020 — Industrial Leadership
Programme: Nanotechnologies, Advanced Materials, Advanced Manufacturing and Processing, and Biotechnology
Theme: Biotechnology
Action type: Research and Innovation actions
Countries involved: Spain,Germany,Germany,Spain,Spain,Belgium,France,France,France,Denmark,Portugal,Portugal
Call: Multi-omics for genotype-phenotype associations (RIA) (H2020-NMBP-TR-IND-2020-twostage, 2020)
Start date: 2020-11-01
End date: 2024-04-30
Duration (months): 42.0
Total project cost (EUR): 6372583.75
EU funding (EUR): 6372583.75
Participants: 16
Project status: SIGNED

Massive increases in analytical throughput together with reductions in costs have enabled multi-omics studies to be routinely performed at a scale not previously imagined. Two main barriers hamper our ability to reveal the mechanism behind a specific genotype-phenotype link: the cross-talk between multiple molecular layers cannot be properly assessed by a reductionist approach that analyses each omics layer in isolation; and the ever-growing amount of buried information in scientific literature and public omics datasets cannot be extracted without intelligent computational approaches. GLOMICAVE project addresses the need for building systems that allow streamlining both the experimental design and the analysis and integration at a systems level of large-scale omics experiments by maximizing the utility of pre-existing massive omics datasets and scientific literature to increase the understanding of biological systems as a whole. The main outcome will be a multi-omics data analysis cloud-based platform, relying on Big Data Analytics and Artificial Intelligence techniques – and using large-scale publicly available and experimental omics datasets, enhanced with an automatic processing of scientific literature, to assist experts and non-experts in identifying and understanding new links between genotype and phenotype which apply to different domains. GLOMICAVE integrative approach will be validated in 3 different industrial sectors (livestock, agro-biotechnology and environment) addressing specific challenges in 6 business cases, which will pave the way for further uptake in other business areas. The success of GLOMICAVE builds upon the expertise of a multidisciplinary team of 14 partners with expertise in omics sciences; standardization; bioinformatics; environmental assessment; mathematical modelling; AI and BDA; plant and animal physiology; food quality, microbiology; bioengineering as well as ethical, legal and social aspects.

Keywords: natural language processing,interoperability,data integration,multi omics,animal reproduction,meat quality,fruit quality,plant growth,phosphorus removal,recovery,bioenergy production