Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Clausal Selection: Integrating Theoretical with Experimental Linguistics (ClauSeInTEL)

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: United Kingdom
Call: Marie Skłodowska-Curie Individual Fellowships (IF-EF) (H2020-MSCA-IF-2014, 2014)
Start date: 2015-09-01
End date: 2017-08-31
Duration (months): 24.0
Total project cost (EUR): 183454.8
EU funding (EUR): 183454.8
Participants: 1
Project status: CLOSED

The nature of the relationship between a verb and its grammatical dependents is an ancient question in linguistics, tracing back to Aristotelian notions of the subject-predicate relation. A major debate in current theoretical linguistics is how to model this, and the standard view (that the relevant information is stored as part of the grammatical specification of the verb) has been challenged by the development of a new idea: that both the verb and its noun-dependents are embedded inside syntactic hierarchies of fundamentally grammatical elements (socalled “functional categories” encoding notions such as tense, aspect, definiteness and numerosity) and that it is the interaction between the functional category structures that embed the verb and the noun that is responsible for establishing the dependency. This viewpoint has allowed a new and empirically successful understanding of the relationship between a verb and its dependents, capturing how definiteness and grammatical number interact with aspectual categories across different languages. However, this new approach has never been applied to the other major grammatical dependency that verbs set up: clausal complementation. That is, the parallel relation between a verb and its clausal, as opposed to nominal, dependents. This project investigates, using interdisciplinary methods, whether these new theoretical ideas can be applied to this empirical domain, comparing two languages (Greek and English) whose clausal syntax is quite different. The objective is to both test the theoretical model in a new empirical area, leading to new findings about how clausal embedding works syntactically, and to determine how best to develop and extend the model to domains that it was not designed for. The action requires extended training in experimental methods and design (both behavioural and neurolinguistic), statistical analysis, development of systematic theoretical models, as well as interaction with a non-academic partner

Keywords: syntactic theory,experimental linguistics,greek language,english language
Multi-scale Composite Material Selection Platform with a Seamless Integration of Material Models and Multidisciplinary Design Framework. (COMPOSELECTOR)

Framework: Horizon 2020 — Industrial Leadership
Programme: Nanotechnologies, Advanced Materials, Advanced Manufacturing and Processing, and Biotechnology
Theme: Advanced materials
Action type: Research and Innovation actions
Countries involved: Italy,Italy,Germany,Switzerland,France,France,France,Czechia,Netherlands,Netherlands,United Kingdom,Luxembourg
Call: Advancing the integration of Materials Modelling in Business Processes to enhance effective industrial decision making and increase competitiveness (H2020-NMBP-2016-two-stage, 2016)
Start date: 2017-01-01
End date: 2020-12-31
Duration (months): 48.0
Total project cost (EUR): 3904186.03
EU funding (EUR): 3904186.03
Participants: 15
Project status: CLOSED

The mission of COMPOSELECTOR is to develop a Business Decision Support System (BDSS), which integrates materials modelling, business tools and databases into a single workflow to support the complex decision process involved in the selection and design of polymer-matrix composites (PMCs). This will be achieved by means of an open integration platform which enables interoperability and information management of materials models and data and connects a rich materials modelling layer with industry standard business process models. In order to satisfy the need for effectively designing and producing increasingly sophisticated materials, components and systems with advanced performance on a competitive time scale there is a particular need in industry for chemistry/physics-based materials models and modelling workflows which capture the performance of materials, accounting for material internal microstructure and effects of processing, provide accuracy/validation of predicted data, and relevant management of uncertainty and assemble knowledge ready for decision makers to act upon. COMPOSELECTOR will address these needs by integration of (discrete and continuum) materials models and process models as well as structured and unstructured data into a standards-based, open integration framework, implementing uncertainty management and multi-criteria optimisation in order to provide actionable choices, and building tailored knowledge apps to support decision makers. The human interface of COMPOSELECTOR will be supported by Visual Analytics capable of integrating qualitative, quantitative and cognitive aspects for a user-friendly management of the vast quantity of available data. The COMPOSELECTOR BDSS will be applied to and validated by end users targeting accurate, reliable, efficient and cost effective decision-making and management of polymer matrix composite (PMC) materials in the transport and aerospace value chains.
Defining the Role of Antioxidants on Cancer progression and metastasis (RAC)

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: Sweden
Call: Marie Skłodowska-Curie Individual Fellowships (IF-EF) (H2020-MSCA-IF-2015, 2015)
Start date: 2016-03-01
End date: 2018-02-28
Duration (months): 24.0
Total project cost (EUR): 173857.2
EU funding (EUR): 173857.2
Participants: 1
Project status: CLOSED

Antioxidants, by neutralizing ROS and ROS-induced DNA damages, are thought to protect against cancer. Recently this common view was challenged by the observation that antioxidants may accelerate tumor progression in mouse models of lung cancer, by turning down the DNA-damage-gH2AX-p53 signaling pathway. This study raised new questions: Are antioxidants able to induce metastasis from benign tumors, by silencing p53 expression? Are cytosolic ROS equivalent to mitochondrial ROS for lung cancer progression? In a first part, I will test the hypothesis that antioxidants favor the metastatic process in lung cancer in state-of the art mouse models. I will administer NAC or vitamin E in the drinking water or the diet of the mice (Kras2LSL) after tumor initiation, and define their impact on metastasis and survival. Preliminary data showed that antioxidants are able to induce metastasis in this non-metastatic model. RNA from tumors and metastases of antioxidant-treated and control mice will be used for RNAseq to identify molecular events that accompany the antioxidant effect. These experiments should allow me to answer my hypothesis and provide new leads on the metastatic process of lung cancer. Lung cancer is indeed a metastatic disease that is still challenging to treat and in great need of new treatments. In a second part, I will investigate the impact of mitochondria-targeted antioxidant (mitoQ, a quinone targeting the inner membrane of the mitochondria) in order to specifically assess the role of mitochondrial ROS on tumor progression. I will administer mitoQ in the drinking water of mice after tumor initiation, and assess its impact on tumor progression. These experiments will allow me to determine whether mitochondrial ROS are pro-tumorigenic, as we hypothesize. Altogether this project will undoubtedly deepen our knowledge of the importance of ROS and ROS scavenging in tumors and will generate a treasure trove of hypotheses for future studies. .

Keywords: lung cancer,antioxidants,reactive oxygen species,mouse models
Acetogenic bacteria: from basic physiology via gene regulation to application in industrial biotechnology (ACETOGENS)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced 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 Advanced Grant (ERC-2016-ADG, 2016)
Start date: 2017-10-01
End date: 2023-09-30
Duration (months): 72.0
Total project cost (EUR): 2497140.0
EU funding (EUR): 2497140.0
Participants: 1
Project status: SIGNED

Demand for biofuels and other biologically derived commodities is growing worldwide as efforts increase to reduce reliance on fossil fuels and to limit climate change. Most commercial approaches rely on fermentations of organic matter with its inherent problems in producing CO2 and being in conflict with the food supply of humans. These problems are avoided if CO2 can be used as feedstock. Autotrophic organisms can fix CO2 by producing chemicals that are used as building blocks for the synthesis of cellular components (Biomass). Acetate-forming bacteria (acetogens) do neither require light nor oxygen for this and they can be used in bioreactors to reduce CO2 with hydrogen gas, carbon monoxide or an organic substrate. Gas fermentation using these bacteria has already been realized on an industrial level in two pre-commercial 100,000 gal/yr demonstration facilities to produce fuel ethanol from abundant waste gas resources (by LanzaTech). Acetogens can metabolise a wide variety of substrates that could be used for the production of biocommodities. However, their broad use to produce biofuels and platform chemicals from substrates other than gases or together with gases is hampered by our very limited knowledge about their metabolism and ability to use different substrates simultaneously. Nearly nothing is known about regulatory processes involved in substrate utilization or product formation but this is an absolute requirement for metabolic engineering approaches. The aim of this project is to provide this basic knowledge about metabolic routes in the acetogenic model strain Acetobacterium woodii and their regulation. We will unravel the function of “organelles” found in this bacterium and explore their potential as bio-nanoreactors for the production of biocommodities and pave the road for the industrial use of A. woodii in energy (hydrogen) storage. Thus, this project creates cutting-edge opportunities for the development of biosustainable technologies in Europe.
Gaining an in-depth understanding of the technical feasibility and commercialization potential for the cold-active amylase enzyme (CLEANprocesses)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Greenland
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2018)
Start date: 2018-06-01
End date: 2018-11-30
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

CLEANprocesses (CoLd-active Enzymes and New sustainable Processes) will analyse the technical feasibility of a cold-active amylase enzyme as well as its commercial potential in various markets. Enzymes are a key ingredient in many consumer products, such as food & drinks, textile, pharmaceuticals and detergents, and the enzyme market has become a multibillion dollar industry worldwide. For decades, it has been a goal for enzyme producers to discover truly cold-active enzymes, due to their performance at low temperature industrial processes resulting in significant reduction in energy and costs. However until now, results have been insufficient, as only partially cold-active enzymes have been achieved. The cold-active enzymes on the market are mainly active at temperatures down to 30° and the best in class is active down to 16°. But if the goal is to be able to use these enzymes in tap water with a temperature around 7°, there is still a long way to go as even the best in class will be almost inactivated at this level. Coldzymes (CZ) is the first company in the world to discover and develop truly cold-active enzymes with industrial relevance and potential. These stem from ikaite pillars in Greenland, which are made up of unusual minerals that precipitate when ion-rich spring water from Greenland mixes with seawater. In particular, CZ has discovered the α-amylase AmyI3C6, which has already been proven in cold wash detergents. It acts optimally at 5-20°C and still maintains over 70% of its activity at 0-1℃. The market for enzymes is global, and the top three enzyme producers hold app. 75 % of the market share. CZ will develop and test the cold-active AmyI3C6 before selling the DNA sequence to the large enzyme producers. In turn CZ will receive royalties for the sales of the AmyI3C6, as the enzyme producers will include it in their portfolios on equal terms with their other enzymes. This business model entails that the enzyme is commercialised broadly.

Keywords: cold active,enzyme,detergent,soap,washing,water,greenland,energy efficiency,sustainable,environment,amylase,biotech
Feasibility Study for the introduction into pre-clinical study domain of the first-of-a-kind Multi Organ On Device (MOOD) technology (MOOD)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Italy
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2018)
Start date: 2018-08-01
End date: 2019-01-31
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

Advances in molecular biology have significantly increased the understanding of the biology of different diseases. Nevertheless, these advances are not fully translated yet into improved treatments for patients, specifically for what pertains to the oncological disorders. One of main reasons for this shortcoming is the lack of reliable in vivo models that accurately predict drug efficacy and toxicity, so to translate these results from preclinical studies to the clinic. In present project we address the major challenge of the preclinical stage in drug development, by offering a proprietary Multi-Organ-On-Device (MOOD), being an advanced fluidic multi-chamber bioreactor, a 3D model of breast cancer tissue cultured onto a membrane mimicking the blood vessel barrier, which jointly reproduce the physio mimetic environment of the cancer disease. In this way, MOOD technology enables the execution of standard pre-clinical assays, such as tumor cell migration and intravasation, drug efficacy and toxicity assays, thus bypassing unreliable, costly and ethically relevant mice studies. The validation of the MOOD device paves the way for the replacement, reduction and refinement (3Rs) of animal studies on global scale, allowing for dramatic cost reduction in the pre-clinical stage and for accelerated time to market for pharmaceutical companies and CROs. The objectives of this feasibility study will be the deep understanding of the market and its technological and economic requirements, so to properly stream line the industrialization of MOOD platform. We expect our MOOD technology to exert a disruptively beneficial impact on drug development process and thus, human health and national economies.

Keywords: in vitro device,alternative to mice models,pre clinical device,fluidic 3d model,tissue engineering,cancer in vitro models,breast cancer,neuroblastoma
Beyond classical conditioning: Hippocampal circuits in higher-order memory processes (HighMemory)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Neuroscience and Disorders of the Nervous System
Panel description: Nervous system development, homeostasis and ageing, nervous system function and dysfunction, systems neuroscience and modelling, biological basis of cognitive processes and of behaviour, neurological and mental disorders
Countries involved: Spain
Call: ERC STARTING GRANTS (ERC-2020-STG, 2020)
Start date: 2021-05-01
End date: 2026-04-30
Duration (months): 60.0
Total project cost (EUR): 1499826.0
EU funding (EUR): 1499826.0
Participants: 1
Project status: SIGNED

Animals and humans adapt to changes in the environment through the encoding and storage of previous experiences. Although associative learning involving a reinforcer has been the major focus in the field of cognition, other forms of learning are gaining popularity as they are likely more relevant and frequent in human daily choices. Indeed, associations between non-reinforcing stimuli represent the most evolutionarily advanced way to increase the chances of predicting future events and adapting individuals’ behavior. Animals are also able to form these higher-order conditioning processes, but more research is needed to understand how the brain encode and store these complex cognitive processes. In this project, I propose to study the role of hippocampo-cortical circuits in higher-order conditioning processes. These processes explain why subjects are often repulsed or attracted by stimuli, which do not have intrinsic repellent or appealing value and they were never explicitly paired with negative or positive outcomes. A proposed explanation of these “ungrounded” aversion or attraction is that these stimuli were incidentally associated with other cues directly reinforced, through a process called mediated learning (ML). However, with increased incidental associations, the subjects acquire more information, allowing them to separate the real saliences of the different stimuli. Therefore, ML evolves into “reality testing”(RT), a behavioral process that has been even less studied. These processes involve multiple brain regions and are characterized by accessible phases, making them perfect models to study the circuit-level regulation of complex behavior. By using genetic, pharmacological, imaging and mouse behavioral approaches (sensory preconditioning), HighMemory proposes to characterize at macro- (brain regions), meso- (cell-types) and micro-scale (activity changes), the causal involvement of hippocampo-cortical projections in higher-order conditioning processes.

Keywords: higher order conditioning,sensory preconditioning,mediated learning,reality testing,psychotic states,hippocampus,cortical regions
Analysis of Structural Economic Models: Misspecification and Flexible Estimation (NPSkills)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Individuals, Markets and Organisations
Panel description: Economics, finance and management
Countries involved: Germany
Call: ERC STARTING GRANTS (ERC-2020-STG, 2020)
Start date: 2020-12-01
End date: 2025-11-30
Duration (months): 60.0
Total project cost (EUR): 1050161.0
EU funding (EUR): 1050161.0
Participants: 1
Project status: SIGNED

Structural models are key tools of economists to evaluate and design policies. These models specify economic environments, estimate mechanisms that determine outcomes, and can be used for counterfactual predictions. One important class of models deals with skill and human capital formation, which is an important driver of economic growth and inequality. These models study the determinants of skill formation and the timing of optimal investments in children. Since structural models require simplifying assumptions, they are also prone to misspecification. The proposed research shows that existing skill formation models rely on seemingly innocuous normalizations, which can severely impact counterfactual predictions. For example, simply changing the units of measurements of observed variables can yield ineffective investment strategies and misleading policy recommendations. I plan to tackle these problems by providing a new comprehensive identification analysis and by focusing on a novel set of important policy-relevant parameters that yield robust conclusions. These issues and solutions might extend to many other structural models with latent variables. In addition, I will provide a new flexible estimator for the policy-relevant features and analyze various data sets to reevaluate policy recommendations with potentially large impacts on costs and benefits of large public investments in children, economic growth, and inequality. Estimation will rely on other objectives of this proposal, which aim to develop new econometric tools. These tools are important contributions on their own rights and are applicable in a wide range of settings. They allow researchers to obtain more precise nonparametric estimators and more reliable conclusions by using shape restrictions implied by economic theory and data-driven dimension reduction techniques. By also providing guidance on which estimation method to use in practice, these results can have a large impact on empirical research.

Keywords: structural models,skill formation,misspecification,normalizations,counterfactual implications,nonparametric methods,shape restrictions,dimension reduction
Simulation-based design and development of a novel 3D printed ankle-foot orthosis (SPRINTAFO)

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-2020, 2020)
Start date: 2021-06-01
End date: 2023-05-31
Duration (months): 24.0
Total project cost (EUR): 224933.76
EU funding (EUR): 224933.76
Participants: 1
Project status: CLOSED

The research action involves an experienced researcher, Dr Xijin Hua, previously working at ETH Zurich in Switzerland and now working in the Institute for Manufacturing (IfM), Department of Engineering at University of Cambridge in the UK, to work on the project “Simulation-based design and development of a novel 3D printed ankle-foot orthosis (SPRINTAFO)” for 24 months under the supervision of Dr Sebastian Pattinson. The project aims to develop a simulation-based platform for the personalised design and development of 3D printed orthoses, and apply the platform to the design and optimization of a novel 3D printed ankle-foot orthosis (AFO). The action also aims to provide a platform for the fellow to acquire new knowledge and skills, and for the fellow and the host organization to share and transfer knowledge and skills in the fields of Musculoskeletal computational simulation, Biomechanics, Optimization, 3D printing, Material Sciences and Medical Devices. The outcome of the project will provide a leap forward in the development and manufacturing of cost-effective, highly personalised and functional tailored orthoses, through the integration of personalised biomechanical simulation model and 3D printing technologies, which will further strengthen the host organization’s leading position and competence as a multidisciplinary research unit in 3D printed medical devices. Moreover, the project will aim to develop long-term research collaborations between University of Cambridge and ETH Zurich, as well as between Europe and China by taking advantages of existing academic networks and industrial partnerships.

Keywords: musculoskeletal simulation,biomechanics,3d printing,medical devices,orthoses