Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Dissecting the cellular and molecular dynamics of bone marrow fibrosis for improved diagnostics and treatment (deFIBER)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting 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: Netherlands
Call: ERC Starting Grant (ERC-2017-STG, 2017)
Start date: 2018-01-01
End date: 2023-12-31
Duration (months): 72.0
Total project cost (EUR): 1498544.0
EU funding (EUR): 1498544.0
Participants: 1
Project status: SIGNED

Bone marrow (BM) fibrosis is the continuous replacement of blood forming cells in the bone marrow by scar tissue, ultimately leading to failure of the body to produce blood cells. Primary myelofibrosis (PMF), an incurable blood cancer, is the prototypic example of the step-wise development of BM fibrosis. The specific mechanisms that cause BM fibrosis are not understood, in particular as the cells driving fibrosis have remained obscure. My recent findings demonstrate that Gli1+ cells are fibrosis-driving cells in PMF, that their frequency correlates with fibrosis severity in patients, and that their ablation ameliorates BM fibrosis. These results indicate that Gli1+ cells are the primary effector cells in BM fibrosis and that they represent a highly attractive therapeutic target. This puts me in a unique position to vastly expand our knowledge of the BM fibrosis pathogenesis, improve diagnostics, and discover new therapeutic strategies for this fatal disease. I will do this by: 1) dissecting the molecular and cellular mechanisms of the fibrotic transformation, 2) defining the stepwise disease evolution by genetic fate tracing and analysis of the previously unknown critical effector cells of BM fibrosis , 3) understanding early forms of BM fibrosis for improved diagnostics in patients, all with the ultimate aim to identify novel therapeutic targets to directly block the cellular and molecular changes occuring in BM fibrosis. I will apply state-of-the-art techniques, including genetic fate tracing experiments, conditional genetic knockout mouse models, tissue engineering of the bone marrow niche and in vivo and in vitro CRISPR/Cas9 gene editing, to unravel the complex molecular and cellular interaction between fibrosis-causing cells and the malignant hematopoietic cells. I will translate these findings into patient samples with the aim to improve the early diagnosis of the disease and to ultimately develop novel targeted therapies with curative intentions.

Keywords: fibrosis,hematology,myeloproliferative neoplasms,hematopoeitic stem cells
Evolutionary ATM. A modelling framework to assess the impact of ATM evolutions (EVOAtm)

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: Spain,Italy,Spain,Italy,Italy,Portugal
Call: ATM Operations, Architecture, Performance and Validation (H2020-SESAR-2016-2, 2016)
Start date: 2018-01-01
End date: 2019-12-31
Duration (months): 24.0
Total project cost (EUR): 968880.0
EU funding (EUR): 968880.0
Participants: 7
Project status: CLOSED

The overall goal of EvoATM is to build a framework to better understand and model how architectural and design choices influence the ATM system and its behaviors, and vice versa how the expected ATM overall performances drive the design choices. The EvoATM project will model a specific part of ATM system combining the agent based paradigms with evolutionary computing. Specifically it will define a solver which finds an optimal tuning of the design of new/modified ATM components to accomplish the expected performances. It will adopt sensitivity analysis strategies in order to understand the influence of ATM components parameters on the behaviours at component performances level (behaviours of other components) and at whole system performances level. It will test the framework by using known scenarios and quantitative indicators to validate its effectiveness in terms of: change impact assessment, support to design and support to strategic thinking. The project will provide methodological guidelines to extend the proposed frameowrk to any part of ATM to be modelled. The EvoATM consortium involves a multidisciplinary group of experts representing the different expertise classes: complex systems modelling, evolutionary computing, ATM modelling, human aspects, aerospace engineering, verification and validation. The chosen Advisory Board will guarantee the coordination/dialogue with the impacted stakeholders domains: ASDA (Association for the Scientific Development of ATM in Europe),ENAV (Italian air navigation service provider), EUROCONTROL, LEONARDO (Italian industry).

Keywords: agent based modelling,optimization,evolutionary computing,change impact assessment,strategic thinking
Flexible medium voltage DC electric railway systems (MVDC-ERS)

Framework: Horizon 2020 — Societal Challenges
Programme: Smart, green and integrated transport
Theme: Public-private partnerships
Destination: European rail initiative
Action type: Coordination and support actions
Partnership: Shift2Rail (Art-187)
Countries involved: Romania,United Kingdom
Call: Innovative/breakthrough mobility concepts (with rail as backbone) (H2020-S2RJU-OC-2018, 2018)
Start date: 2018-12-01
End date: 2022-04-30
Duration (months): 41.0
Total project cost (EUR): 125000.0
EU funding (EUR): 125000.0
Participants: 2
Project status: CLOSED

The overall aim of MVDC-ERS is to investigate the next generation railway electrification systems, DC power electronic traction transformers and technologies for integrating renewable sources, to challenge the traditional rail approach to develop innovative and breakthrough technological concepts. The key objectives of the project are to: RO1: To introduce new high-efficiency topologies of power conversion systems to convert medium-voltage AC power into medium-voltage DC power with the capability of limiting the short-circuit current. RO2: To introduce high-power density topologies of power converters for on-board DC transformers. RO3: To investigate the impact of the forthcoming wide band-gap semiconductor devices in terms of efficiency and voltage level for the converters of the feeder stations and in terms of weight and volume for the traction converters. RO4: To understand how the new railway electrification system should be controlled and protected when renewable power sources are integrated. RO5: To understand how on-board energy storage can be exploited and how it can optimise the operations of the network. RO6: To work with industrial stakeholders to investigate the marketability of the new electrification system and trains.

Keywords: static converters
Monitoring multidimensional aspects of QUAlity of Life after cancer ImmunoTherapy - an Open smart digital Platform for personalized prevention and patient management (QUALITOP)

Framework: Horizon 2020 — Societal Challenges
Programme: Health, demographic change and wellbeing
Theme: Treating and managing disease
Action type: Research and Innovation actions
Countries involved: Italy,Spain,Germany,Belgium,France,France,United States,Netherlands,Netherlands,United Kingdom,Portugal
Call: Big data and Artificial Intelligence for monitoring health status and quality of life after the cancer treatment (H2020-SC1-DTH-2019, 2019)
Start date: 2020-01-01
End date: 2024-06-30
Duration (months): 54.0
Total project cost (EUR): 5196772.5
EU funding (EUR): 5196772.5
Participants: 17
Project status: SIGNED

Cancer immunotherapy brought about significant progress in cancer treatment. It resulted in high efficacy in some cancers; e.g., up to 60% objective response rate in melanoma and 80% complete response rate in acute lymphoblastic leukaemia. Nevertheless, two main challenges still impede improving cancer patients’ health status and quality of life (QoL) after immunotherapy initiation: 1) a crucial need for “predictive markers” of occurrence of immunotherapy-related adverse events (IR-AEs) to predict and improve patients’ health status and promote their QoL; and, 2) the lack of knowledge on patients after start of immunotherapy outside randomised controlled trials. To reach these goals, significantly more diversified sources of data are required. Project QUALITOP aims at developing a European immunotherapy-specific open Smart Digital Platform and using big data analysis, artificial intelligence, and simulation modelling approaches. This will enable collecting and aggregating efficiently real-world data to monitor health status and QoL of cancer patients given immunotherapy. Through causal inference analyses, QUALITOP will identify the determinants of health status regarding IR-AEs and define patient profiles in a real-world context. For this, heterogeneous data sources (big data), both retrospective and prospective --collected for QUALITOP from clinical centres in four EU countries—will integrate lifestyle, genetic, and psychosocial determinants of QoL. Using machine learning approaches, QUALITOP will provide "real-time" recommendations stemming from patient profiles and feedbacks via the Smart Digital Platform. Furthermore, an increased visibility on patients’ behaviour, a better IR-AEs prediction, and an improvement of care coordination will help analysing through simulation modelling approaches the gain in cost-effectiveness. Guidelines will be issued over the short and long-term.

Keywords: immunotherapy,real world data,patient reported outcomes,immune related adverse events,psychosocial environment,cost effectiveness,confidentiality,open smart digital platform,employability
Artificial Intelligence System for Multi-Cancer Detection Support (AI-MICADIS)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Romania
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2018)
Start date: 2019-01-01
End date: 2019-06-30
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

Need – Each year, over 8M people die of cancer and now the World Health Organization is urging governments to accelerate action to reduce premature mortality. The stage of a cancer progression at the time of diagnosis is strongly associated with patient survival. Unfortunately, the current diagnostic tests and screening tools have very disappointing accuracy rates and many patients remain undiagnosed. Solution – Artificial Intelligence Expert (AIE) is a Romanian start-up company with a ground-breaking solution that will enable early diagnoses and screening for a wide variety of cancers. AIE aims to leverage the fast transition to liquid biopsies, microarray and next generation sequencing (NGS) for molecular diagnostics. AIE can detect cancer at the earliest stages by using circulating microRNAs instead of the commonly used circulating tumour DNA or circulating cancer cells. AIE is developing an extremely accurate diagnostic tool for detection of the earliest stages of cancer progression using powerful Deep Learning Artificial Intelligence to analyse microRNA profiles from microarray and next generation sequencing (NGS) data. Market opportunity – The fast increase of big data collection and the associated need to extract knowledge from it drives the AI in healthcare market, which is growing at a CAGR of 53% to $8B by 2022. This market growth is fuelled by a strong growth of the NGS diagnostic market, which is growing at a CAGR of 73%. Competition – The competition is focusing on optimizing currently used circulating tumour DNA detection tools and simpler machine learning models, that extract a small number of meaningful biomarkers. This keeps the competitor’s data processing needs low, but infringes on the tests’ accuracy and sensitivity. Feasibility assessment – AIE will use the SMEi phase 1 funding to identify partnerships, perform user involvement study, set out a regulatory path, and optimize the best business strategy to bring the AI-MICADIS to the market.

Keywords: deep learning,microrna,in vitro diagnostics,cancer staging,early cancer detection,breast cancer,gastro intestinal cancers,reproductive cancers,ensemble methods,hyperparameter tuning
EMERGING VIRAL DISEASES IN TOMATOES AND CUCURBITS: IMPLEMENTATION OF MITIGATION STRATEGIES FOR DURABLE DISEASE MANAGEMENT (VIRTIGATION)

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: Research and Innovation actions
Countries involved: Italy,India,Spain,Italy,Germany,Spain,Israel,Germany,Spain,Spain,Spain,Austria,Belgium,Belgium,Belgium,France,France,France,Netherlands,United Kingdom,Morocco,Luxembourg
Call: New and emerging risks to plant health (H2020-SFS-2020-2, 2020)
Start date: 2021-06-01
End date: 2025-05-31
Duration (months): 48.0
Total project cost (EUR): 7358170.15
EU funding (EUR): 6998668.34
Participants: 25
Project status: SIGNED

Viral diseases can cause severe losses to vegetable crop production which has an estimated annual value of 34,5 billion EUR in Europe. Therefore several billions are lost every year due to the prominence of viral diseases and the emergence of new viruses in European fields and greenhouses. Importantly viral diseases also lead to the extensive use of pesticides, thereby exposing European growers and consumers to pesticide residues. In relation to the work programme, the project aims at developing rapid and lasting solutions to emerging viral diseases caused by begomoviruses (whitefly-transmitted) and tobamoviruses (mechanically transmitted) on cucurbits and tomato in Northern Europe and the Mediterranean Basin as well as at increasing knowledge to better control and manage the viral diseases. The project is structured in 6 objectives : 1. Knowledge sharing and engagement of stakeholders in research activities (Short term impact) 2. Develop robust diagnostic tests, quarantine measures and identify ecological factors driving disease outbreaks (Short term impact) 3. Understand plant-virus(es)-vector interactions (Medium term impact) 4. Develop IPM solutions (Medium term impact) 5. Pyramidize natural resistance (Long term impact) 6. Train the value chain (Medium term impact) The objectives will build on 1) detailed study of virus biology and transmission under climate change conditions; 2) development of classical solutions (IPM and natural resistance) to control viral diseases with two distinct modes of transmission; 3) testing novel approaches (biopesticides, biological control, cross-protection) to mitigate viral diseases and to reduce pesticide usage. In order to take into account the diversity of vegetable cropping systems and viral diseases, focus groups involving extension services, commercial companies and growers will help co-designing research activities and mitigation strategies from the onset of the project.

Keywords: tomatoes,cucurbits,tobamovirus,begomovirus,natural virus resistance,virus diagnostics,biopesticides,high throughput sequencing,cross protection vaccines,natural extracts,parasitoids
dEcentralized repositories for traNsparent and efficienT vIrtual maChine opErations (ENTICE)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Theme: Future Internet: Software, hardware, Infrastructures, technologies and services
Action type: Research and Innovation actions
Countries involved: Spain,Spain,Austria,Slovenia,Hungary,United Kingdom
Call: Advanced Cloud Infrastructures and Services (H2020-ICT-2014-1, 2014)
Start date: 2015-02-01
End date: 2018-01-31
Duration (months): 36.0
Total project cost (EUR): 2767565.0
EU funding (EUR): 2767563.0
Participants: 7
Project status: CLOSED

Virtualization is a key enabler of cloud computing that allows to run multiple virtual machines (VM) with their own software environment and applications on top of physical hardware with the promise to increase efficient usage of hardware resources at lower cost and increased elasticity. Typically VMs are created using provider-specific templates (so called VM images) that are stored in proprietary repositories which leads to provider lock-in and hinder portability or simultaneous use of multiple federated Clouds. Optimization at the level of the VM images is needed both by the Cloud applications as well as by the underlying Cloud providers for improved resource usage, speed, elasticity, redundancy, fault tolerance and other much desired Quality of Service (QoS)-related features. Critical barriers exist that prevent many users from industry, business and academia to effectively use cloud resources and virtualization environments for their computing and data processing needs. In this project we will create a novel (ENTICE) environment targeting federated Cloud infrastructures for: (i) simplifying the creation of lightweight and highly optimized VM images; (ii) automatic decomposition and distribution of VM images based on multi-objective optimization (performance, economic costs, storage size, and QoS requirements) and a knowledge collection and reasoning infrastructure, and (iii) auto-scaling of Cloud resources that supports interoperability of VMs across Cloud infrastructures without provider lock-in. We gathered an interesting selection of complementary use cases from energy management to earth observation and cloud orchestration which will be used to validate the ENTICE environment and that are provided by two SME and one industrial partners of the project.

Keywords: virtual machine images,creating and distributing virtual machine images,cloud resource provisioning,multi objective optimization,cloud interoperability
Rescuing seeds’ heritage: engaging in a new framework of agriculture and innovation since the 18th century (ReSEED)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: The study of the human past
Panel description: Archaeology, history and memory
Countries involved: Portugal,Portugal
Call: ERC Starting Grant (ERC-2017-STG, 2017)
Start date: 2018-11-01
End date: 2024-10-31
Duration (months): 72.0
Total project cost (EUR): 1467727.0
EU funding (EUR): 1326335.0
Participants: 2
Project status: SIGNED

Humanity is facing a huge challenge: how to feed a growing population in a sustainable way? Scientists from different fields are looking for answers and ReSEED aims to assist such endeavours. Solutions depend on one key issue: seeds. We can have appropriate soil, climate or technologies, however without proper seeds it is impossible to guarantee food production. As historiography has given little attention to the role of seed varieties, there are many gaps in scientific knowledge. I argue that long-term historical analysis is critical to provide the best answers to current questions. ReSEED examines the changing connections between seeds, environment and human action, the triangle that has always underpinned agriculture, since the 18th century. The main objectives are as follows. 1) To map geographical changes in local crop distribution, paying attention to the new seeds made available by Columbian Exchange. 2) To outline which were the social networks supporting the circulation and cultivation of edible seed varieties, and at later date, checking how they articulated with state services. 3) To identify human factors that contribute to reducing, increasing, maintaining or restoring regional agro-biodiversity. 4) To assess the impacts of national and international decisions on local management of the triangle, mainly on farmers’ innovation. 5) To re-examine the long-term dynamics behind various European agricultural modernization itineraries. Based on innovative interdisciplinary and transdisciplinary methodologies, I build robust empirical research on the case of Iberian Peninsula in connection to empires, which allows thorough comparisons with other regions in Europe and beyond. ReSEED promotes strategies for win-win environmental/society outcomes, linking edible seeds to places and to innovations needed for food production. This is crucial to better understand how historical experiences can contribute to create solutions that ensure sustainable futures.

Keywords: food production,new seeds,historiography,regional agro biodiversity,sustainable futures
TRPV1-targeted ligand-drug conjugates to treat prostate cancer (TRPLigDrugConj)

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: Portugal
Call: Individual Fellowships (H2020-MSCA-IF-2016, 2016)
Start date: 2017-03-01
End date: 2019-05-04
Duration (months): 24.0
Total project cost (EUR): 160635.6
EU funding (EUR): 160635.6
Participants: 1
Project status: CLOSED

Cancer remains a leading cause of death and morbidity worldwide. Prostate cancer (PC) is among the most frequently diagnosed non-skin cancers. Despite major advances in the understanding of cancer biology and development of candidate therapeutic agents, there is still an urgent need of exploiting innovative chemotypes and disrupting novel signalling pathways. Evidence suggests that targeted therapies may provide an original means of selectively modulating diseased prostate cells. Herein we propose the validation and devlopment of an unprecedented approach to tackle PC, addressing the overexpressed transient receptor potential vanilloid channel V1 (TRPV1) with ligand-drug conjugates. This strategy provides a pioneering technological advance by aiming at the disruption of calcium signalling, while selectively targeting cancer cells for the delivery of cytotoxic payloads. The approach will additionally yield chemical probes for studying TRPV1 biology and for whole-animal optical imaging of TRPV1-overexpressed cancer cells.

Keywords: targeted therapeutics,chemical biology,cancer