Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
The Invisible Women: Nameless and Forgotten Stories of the Rubber Boom (Bolivian Amazonia, 19-20th centuries) (NAMELESS-STORIES)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Social Sciences and Humanities
Countries involved: Italy
Call: Individual Fellowships (H2020-MSCA-IF-2020, 2020)
Start date: 2021-12-01
End date: 2023-11-30
Duration (months): 24.0
Total project cost (EUR): 183473.28
EU funding (EUR): 183473.28
Participants: 1
Project status: SIGNED

Mainly limited to a male gaze, the history of the Rubber Boom in Bolivian Amazonia (1870-1920) seems to be somewhat biased. The sources of the period present us with a hyper-masculinized landscape in the Amazonian jungle, and neglect the numerous women – Creoles, Europeans or Indigenous – involved in the extractive machinery by forgetting, anonymizing or relegating rubber women as minor, transparent, and almost invisible social actors. By developing an innovative and multidisciplinary approach that combines ethno-history, ethnography, gender studies, digital humanities and visual anthropology, the project aims to trace, reconstruct and revalue female agency during the Bolivian rubber boom. The aim is to rescue testimonies of and about native women (siringueras, cooks, maids, washers, lovers, “bush wives”) neglected by the sources due to their ethnic condition, but also related to the working Creole or European women who arrived in the Amazon to be silenced by the sexist bias of regional historiography, or either those women who were rendered invisible as immigrant settlers that joined their relatives in the great rubber adventure. Therefore, the first objective of the project is to create a participatory and open access web archive about multiethnic women during the rubber era, making available the information gathered to a broader public: the digital archive will consist of historical documentation and above all ethnographic data, unpublished texts and little-known pictures about the extractive experience of rubber women. The second objective is a critical re-reading of the oral, documentary and visual information in order to analyze the multiple modalities of female participation in the extractive endeavor, and reconstruct an heterogeneous continuum of relations linking women and rubber that ranged from barter to wage labor, from marriage alliance to compadrazgo (fictive kinship), and from commerce to sexual violence, kidnapping and even enslavement.

Keywords: bolivian amazonia,rubber boom women,extractivism forgotten hsitories,memory,digital humanities,anthropology,ethnography,history
Understanding soil fertility impacts on terrestrial biomass production in a changing environment (FIBER)

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: Environment and Geosciences
Countries involved: Spain
Call: Marie Skłodowska-Curie Individual Fellowships (IF-EF) (H2020-MSCA-IF-2015, 2015)
Start date: 2017-02-28
End date: 2019-02-27
Duration (months): 24.0
Total project cost (EUR): 158121.6
EU funding (EUR): 158121.6
Participants: 1
Project status: CLOSED

The feedback between climate and the land carbon (C) cycle poses one of the largest uncertainties in climate change projections. FIBER targets the unresolved challenge for Dynamic Global Vegetation Models (DGVM) to simulate effects of soil fertility and nutrient deposition on biomass productivity (BP) and the land C balance. Accumulating evidence documents how plants adjust their growth strategies and C allocation under multiple limiting resources. Current DGVMs lag behind these new insights, produce widely diverging results for C cycling and nutrient limitation under future scenarios and fail to explain the observed land C sink. This work will provide a new global modelling approach to simulating flexible plant C allocation following optimality principles. A better understanding of the controls on BP is crucial for assessing climate change impacts on ecosystem services and to reduce uncertainty in C cycle and climate change projections. I will develop a new type of plant growth model to predict increased root growth and export of labile C to soil biota on infertile soils and under low N inputs, consistent with powerful data from forest inventories and ecosystem manipulation experiments. By accounting for trade-offs between different growth strategies and a C cost of nutrient uptake, I will simulate the plant C economy under optimality constraints – a powerful approach, supported by observations but not exploited for DGVMs. The project is conceived to combine the relevant expertise and exploit the pioneering science of leading European researchers with my integrating role and demonstrated model development skills. Collaboration with two secondment hosts will facilitate the mining of their large data resources and fusing data into model predictions using Bayesian statistical tools. This project will integrate new model components developed at my current host institute and will be a crucial step on the way to building the next generation of vegetation models.

Keywords: climate change,carbon cycle,soil fertility,nitrogen deposition,allocation,biomass productivity,plant soil interactions
Learning to Control - Smart and Data-Driven Formal Methods for Cyber-Physical Systems control (L2C)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Systems and communication engineering
Panel description: Electrical, electronic, communication, optical and systems engineering
Countries involved: Belgium
Call: ERC Consolidator Grant (ERC-2019-COG, 2019)
Start date: 2020-09-01
End date: 2025-08-31
Duration (months): 60.0
Total project cost (EUR): 1911250.0
EU funding (EUR): 1911250.0
Participants: 1
Project status: SIGNED

The engineered systems surrounding us are increasingly hard to control. Not only the complicated interaction of the physical processes with the machines that control them, but also specifications (cyber-security, safety, privacy, resilience, resource-efficiency, decentralization) are more and more complex, and critical. Last but not least, in an increasing number of situations, no model of the system is available (or the model is too complex), and one needs to ‘learn’ the optimal way of controlling the system by the mere observation of data. Our technological world is living a paradigm shift, which is often coined as the Cyber-Physical Revolution, or the Industry 4.0. In view of these specificities, the only sensible way of controlling these complex systems is often by discretizing the different variables, thus transforming the model into a simple combinatorial problem on a finite-state automaton, called an abstraction of this system. Until now, this approach has not been proved useful beyond academic, small examples, as it scales very poorly. The goal of L2C is to transform this approach into an effective, scalable, cutting-edge technology that will address the CPS challenges and unlock their potential. This ambitious goal will be achieved by leveraging powerful tools from Mathematical Engineering. Out of this research, a state-of-the-art software platform will promote our results and translate them into directly usable solutions for the scientific and industrial communities. L2C is a pluridisciplinary project at the frontier between Control Engineering, Computer Science and Applied Mathematics. It bridges the gap between rich innovative techniques and emerging challenges in Control. It impacts both fundamental Science and Engineering, as the theoretical research is driven and fostered by cutting edge technological challenges.

Keywords: hybrid systems,switching systems,formal methods,optimization and control
Dementia modelling (DEMO)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Innovative Training Networks (ITN)-European Industrial Doctorates
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: United Kingdom,United Kingdom,Denmark
Call: Innovative Training Networks (H2020-MSCA-ITN-2016, 2016)
Start date: 2016-09-01
End date: 2020-08-31
Duration (months): 48.0
Total project cost (EUR): 853451.64
EU funding (EUR): 853451.64
Participants: 3
Project status: CLOSED

Dementia constitutes a major burden on society, both in monetary costs and the suffering of patients and their relatives. It comprises a number of diseases including Alzheimer’s disease (AD) and vascular dementia (VaD). In recent years, imaging biomarkers have been developed including measures of brain morphology (MRI T1), vascular pathologies (MRI T2*/FLAIR), white matter abnormalities (MRI DWI), perfusion (MRI ASL), glucose turnover (PET FDG), and accumulation of pathological proteins (PET PIB/AV45). Quantitative measures using these biomarkers in large cohort studies have the potential to model the pathological process of the disease. This proposal would create an innovative training network, in which early stage researchers will develop new computational imaging biomarkers, under the supervision of experienced researchers, for the purpose of modeling dementia etiology. One researcher will investigate quantification of vascular pathologies, another will develop quantitative measures of white matter abnormalities from structural MRI, and the final researcher will construct a quantitative model of disease etiology using a maximum-likelihood framework. The early stage researchers will be enrolled as PhD students at University College London (UCL) under the EPSRC Centre for Doctoral Training in Medical Imaging (CDT), which is based in Centre for Medical Image Computing (CMIC), with the Dementia Research Centre (DRC) being one of the main clinical collaborators for CDT studentships. However, they will spend the majority of time at the research facilities of Biomediq A/S, Copenhagen Denmark, where they will be exposed to industry and work under professional guidance.

Keywords: alzheimer s disease,imaging biomarkers,machine learning,quantitative etiollogical modelling,vascular dementia,vascular pathology,white matter pathology
Imaging the Motion of Magneto-Excitons in New Emerging Materials (IMME-NEM)

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: Netherlands
Call: Individual Fellowships (H2020-MSCA-IF-2016, 2016)
Start date: 2017-10-01
End date: 2019-09-30
Duration (months): 24.0
Total project cost (EUR): 165598.8
EU funding (EUR): 165598.8
Participants: 1
Project status: CLOSED

Recently discovered two-dimensional (2D)-honeycomb semiconductor materials have two inequivalent, degenerate valleys in their electronic band structure. This leads to a new “valley” degree of freedom known as pseudospin that, similar to real spin, has been proposed as an extra information carrier for new classes of electronic and optoelectronic devices. Monolayer transition-metal dichalcogenides (TMDs) are an important type of 2D material, in which, due to the strong 2D confinement and a reduced dielectric screening of the Coulomb interactions, electron-hole (e-h) correlations are extremely strong. This results in creation of e-h pairs (excitons) that are so strongly bound that excitonic effects completely dominate the optical properties of TMDs even up to room temperature. The pronounced excitonic effects in single-layer TMDs, therefore, provide a unique opportunity to investigate strong light-matter interactions associated with valley effects exhibiting exotic behaviour. However, the key fundamental question regarding the exact excitonic band structure, the valley-exciton energy-momentum (dispersion) relationship, and the corresponding excitonic transport properties, remains open. This proposal is devoted to a fundamental understanding of the valley-exciton band structure. It consists of two main research objectives: (i) to determine the exciton dispersion and the corresponding transport using momentum- and real-space optical imaging, and (ii) to achieve an external control of the exciton dispersion via applied magnetic and electric fields, charge density and strain. The results will deepen the understanding of valley-exciton transport in single-layer TMDs and help the development of novel valley-based technologies.

Keywords: single layers,excitons,optical imaging,magnetic fields,photonics,optoelectronics
A patent pending solution/microchip for the IoT cybersecurity market requirements: no access to online software updates, very small size, inexpensive hardware, low energy consumption. (TFence)

Framework: Horizon 2020 — Societal Challenges
Programme: Secure societies - protecting freedom and security of Europe and its citizens
Action type: SME instrument
Countries involved: Israel
Call: Engaging SMEs in security research and development (H2020-SMEINST-1-2016-2017, 2017)
Start date: 2017-08-01
End date: 2017-11-30
Duration (months): 4.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

Terafence is developing a state-of-the-art Firmware/microchip, “TFence™”, patents pending, for cyber-secured connectivity, and mechanical waves to control medical implants. This advanced Firmware/chip ensures total protection from tampering by enabling data outflow while completely blocking data entry and with it malicious attacks. · The “TFence™” is designed to meet the IoT cyber security market requirements. Data outflow while completely blocking data entry and malicious attacks · One-way communication by means of small & cheap hardware Converting multi protocols from the secured network to multi protocols for the unsecured network · Supporting numerous sensors at the secured network and analyzing initial real time profiling/statistics · IoT/sensors profiling to protect from botnet activities

Keywords: security,cybersecurity
GENOMIC AND METABOLIC REGULATION OF METASTATIC CANCER STEM CELLS (GENMETASTEM)

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: Life Sciences
Countries involved: United Kingdom
Call: Marie Skłodowska-Curie Individual Fellowships (IF-EF) (H2020-MSCA-IF-2014, 2014)
Start date: 2016-09-01
End date: 2018-08-31
Duration (months): 24.0
Total project cost (EUR): 183454.8
EU funding (EUR): 183454.8
Participants: 1
Project status: CLOSED

The major causes of cancer deaths are relapse and resistance to current therapies associated with the presence of cancer stem cells (CSCs) and metastatic growth in distant organs. CSCs have the ability to self-renew and differentiate in non-CSCs. In breast cancer, acquisition of stemness properties has been closely related to epithelial-mesenchymal transition (EMT), a key process in cancer invasion and metastasis triggered via Rho-ROCK mediated actomyosin contractility. Interestingly, in melanoma, transition from elongated-mesenchymal to amoeboid mode of movement (MAT) driven by Rho-ROCK signalling has been associated with increased stemness. Furthermore, preliminary data from host lab shows that actomyosin cytoskeletal regulates glutamine metabolism in both melanoma and breast cancer cells. Metabolic cues participate in stem cell self-renewal regulation, suggesting that, in very contractile cells, the regulation of EMT, metastatic spread and tumour initiation might be functionally linked to stemness via metabolic clues. Nevertheless, how very contractile cells regulate genes involved in all these processes remains unexplored. As increasing contractility via EMT in carcinoma cells or via MAT in melanoma cells correlates with increasing stemness, we hypothesize a molecular link between the pathways regulating both migration and stemness abilities, which will be maintained across tumour types (from carcinoma to melanoma). The main goal of this proposal is to understand how tumour cells can acquire stem cell traits to successfully metastasize and how this can be regulated by the actomyosin cytoskeletal by using an interdisciplinary approach that combines state-of-the-art techniques in molecular and cellular biology, biochemistry, in vivo imaging and animal models. This will allow to identify key important genes regulating both stemness traits and metastatic spread with the ultimate goal of unravelling novel drug targets and prognostic markers of distant relapse.
Towards more accessIble and iNCLUSIve mObility solutions for EuropeaN prioritised areas (INCLUSION)

Framework: Horizon 2020 — Societal Challenges
Programme: Smart, green and integrated transport
Action type: Research and Innovation actions
Countries involved: Italy,Italy,Spain,Germany,Spain,Italy,Italy,Belgium,Belgium,Hungary,France,United Kingdom,United Kingdom
Call: Improving accessibility, inclusive mobility and equity: new tools and business models for public transport in prioritised areas (H2020-MG-2017-SingleStage-INEA, 2017)
Start date: 2017-10-01
End date: 2020-09-30
Duration (months): 36.0
Total project cost (EUR): 2969007.5
EU funding (EUR): 2969007.5
Participants: 16
Project status: CLOSED

The main objective of the INCLUSION project is to understand, assess and evaluate the accessibility and inclusiveness of transport solutions in European prioritised areas, identify gaps and unmet needs, propose and experiment with a range of innovative and transferable solutions, including ICT-enabled elements, ensuring accessible, inclusive and equitable conditions for all and especially vulnerable user categories. INCLUSION will address a large set of case studies (at least 50) involving different forms of geographical areas and transport contexts, demographic categories, population groups and mobility solutions, providing concrete experiences from various European sites and pilot initiatives involving a variety of regulatory and business frameworks, supporting technologies, organisational and operational conditions. Complementary to this, a number of innovative solutions will be concretely tried out and validated through real-life experiments (Innovation Pilot Labs) in a mix of urban, peri-/sub-urban and rural target areas in Belgium, Germany, Hungary, Italy, Spain and the UK, offering a variety of different transport environments, socio-economic contexts, cultural and geographical conditions. Expected results include: an understanding of the main challenges affecting transport provision and accessibility in the different type of prioritised areas; a structured view highlighting the dependencies among spatial and environmental characteristics, populations segments and their mobility needs; in-depth examination of 10 innovative PT approaches and a wider catalogue of at least 40 promising cases contributing to more accessible, inclusive and equitable transport solutions, complemented by innovative solution components validated in real-life experiments; a set of recommendations and an “option generator” for the development and deployment of mobility solutions addressing the needs of vulnerable user communities in European prioritised areas

Keywords: accessible mobility,transport equity,vulnerable users,social innovation,flexible transport,combined transport,business model
Autonomous Computing Artificial Cells (AMIGA)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Physical and analytical chemical sciences
Panel description: Analytical chemistry, chemical theory, physical chemistry/chemical physics
Countries involved: Netherlands
Call: ERC CONSOLIDATOR GRANTS (ERC-2020-COG, 2020)
Start date: 2022-02-01
End date: 2027-01-31
Duration (months): 60.0
Total project cost (EUR): 1999497.0
EU funding (EUR): 1999497.0
Participants: 1
Project status: SIGNED

We propose an ambitious 5-year multidisciplinary program that seeks to pioneer and establish a fundamentally new paradigm in molecular information systems that is based on novel conceptual and experimental advances on the integration of DNA-based chemical reaction networks (CRNs) and semipermeable microcapsules, i.e. protocells. In AutonoMous computInG Artificial cells (AMIGA), we will establish a platform technology, based on molecular communication between interacting protocells, capable of revolutionary new modes of molecular sensing, computation and data storage/retrieval. Progress in this emerging field requires i) the development of computer-aided design (CAD) strategies to implement large-scale CRNs consisting of hundreds of components, ii) formulating suitable micro-substrates, such as droplets or vesicles, to spatially localize CRNs and ways to manipulate their interconnection and iii) strategies that allow direct recording of molecular operations onto a chemical storage medium such as DNA. We address these challenges via a comprehensive research program in which we implement large-scale, DNA-based CRNs by localization of components in interacting protocells resulting in distributed molecular circuits programmed to display advanced computational functions such as (i) asynchronous logic, (ii) integral feedback control and (iii) molecular pattern recognition. Combining protocell localization with recent advances in CRISPR base editors, we will construct an integrated system where molecular operations can write instructions on permanent memory storage elements. The developed methodology finds applications in emerging technologies aimed at using molecular circuits for in-vitro diagnostics and the use of synthetic DNA as a storage medium for digital data.

Keywords: dna computing,protocell communities,bottom up synthetic biology,dna nanotechnology,systems chemistry,artificial cells