Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Segments in animal vocalizations: A novel approach to understanding non- human vocal behavior (SEGVOC)

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: Austria
Call: Individual Fellowships (H2020-MSCA-IF-2020, 2020)
Start date: 2022-02-01
End date: 2026-01-30
Duration (months): 24.0
Total project cost (EUR): 186167.04
EU funding (EUR): 186167.04
Participants: 1
Project status: TERMINATED

Research comparing human and non-human animal communication has made great strides over the past half-century, yet, aspects of vocal behavior remain unexplored. In non-human animal vocal behavior, the smallest unit of analysis is the syllable or breath group, where intakes of breath divide the acoustic stream. Humans, however, can utter full words, phrases, and even sentences between intervals of silence. If alien researchers used the breath group as the smallest domain of analysis in human vocal behavior, they would be confused as to how “five spiders” and “more than four spiders” elicits similar responses but “five spiders” and “five ciders” are treated completely differently. In humans, segments – units that build words and phrases and are divided by acoustic transitions rather than silent intervals – are central to our understanding of language and have clarified the extent and limits to cross-population variability, how humans categorize acoustic stimuli, how languages change over time, and how learning and biology interact to create culture. Because of the central role in understanding language, segment analyses can greatly enhance our understanding of animal vocal behavior, as well. Building on my PhD research, I will further develop tools, methods, and experimental protocols to study the production and perception of non-human vocal segments. While the experimental work will focus on the non-stereotyped vocalizations of budgerigars (a small parrot species), I will apply a segmental approach to the vocalizations of a range of species. Because these segments are more acoustically analogous, they provide better units of comparison across species.

Keywords: bio acoustics
iPerm: Guided wave monitoring tool (iPerm)

Framework: Horizon 2020 — Industrial Leadership
Action type: Innovation actions
Countries involved: Türkiye,Greece,Netherlands,United Kingdom,United Kingdom
Call: Fast Track to Innovation Pilot (H2020-FTIPilot-2016-1, 2016)
Start date: 2016-10-01
End date: 2019-03-31
Duration (months): 30.0
Total project cost (EUR): 3153506.63
EU funding (EUR): 2553360.13
Participants: 7
Project status: CLOSED

Today, there are more than 40 major transnational oil and gas pipelines traversing Europe, accounting for over 34,565 km of strategically critical pipeline infrastructure, 40% of which are over 40 years old and subject to corrosion induced catastrophic failures impacting negatively upon European society and its economy. With such an extensive and growing network of pipelines, and a corollary increasing incidence of failures, it is no longer feasible to rely upon current intermittent manual inspection techniques to maintain safe operation, from the twin perspectives of probability of detection over such large network, and consequence of failure given the hazardous materials being distributed. Clearly, a wide-area coverage Structural Health Monitoring solution is overdue. The iPerm project seeks to address this challenge by developing a permanent, intelligent and energy-efficient wireless monitoring network which will deliver a step-change improvement over existing inspection systems, and thus a step change in the safety and reliability of Europe’s pipeline network. The FTI Pilot is the ideal instrument us, a trans-disciplinary and cross-sectoral consortium, to utilize in order to commercialize a technological solution that has already gained traction in the market. It will enable us leverage our own investment and reduce the time required to refine and scale up our concept in order to deliver it to international markets. Our vision is to capture 3,3% of the Oil & Gas segment, the fastest growing segment of the global NDT market, within 5 years following product launch aiming to add (cumulatively) €26.79 million to our top-line and generate more than €6.93 million in operating profit resulting to an investment multiple of 2 times the European funding. In addition, we will create 215 new direct jobs as a result of the generated revenue, and more indirectly. iPerm is the definite answer to a more secure and safe environment for large-scale pipeline operations.

Keywords: oil and gas,non destructive testing,structural health monitoring,guided waves ultrasonics
ROBOTT-NET - A shared infrastructure to sustainably optimise technology transfer throughout Europe (ROBOTT-NET)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Innovation actions
Partnership: Robotics SPARC (cPPP)
Countries involved: Spain,Germany,United Kingdom,Denmark
Call: Robotics (H2020-ICT-2015, 2015)
Start date: 2016-01-01
End date: 2019-12-31
Duration (months): 48.0
Total project cost (EUR): 7692516.26
EU funding (EUR): 7564337.5
Participants: 5
Project status: CLOSED

The main objective of the TT-Net initiative is to create a sustainable European infrastructure to support novel robotic technologies on their path to market. TT-Net will develop and test mechanisms for sharing expertise and physical research infrastructures, and a unique multidisciplinary approach that enhances cross-fertilisation of ideas between academia and industry. We transform successful national structures and mechanisms used by Research and Technology Organisations (RTOs) in Denmark, Germany, Spain and the UK; merging them into a Europe-wide, composite, technology transfer system. Together we will test and improve a four-stage process to accelerate the path to market for new robotic technologies. The first stage involves promoting state-of-the-art industrial and professional service robot technologies to over 1,200 end-users and robot developers (from industry and academia) in a bidirectional exchange of views and opinions about robotic research trends, needs and technology developments, and soliciting participation in TT-Net. The second stage involves technical and business feasibility studies for both end-users and robot developers at about TRL 5. The third stage implements first field trials at TRL 6 for a novel technology and then produces complete TRL 7+ robotic automation solutions at end-user sites. The fourth stage is the completion to TRL 9, facilitated within TT-Net but happening outside project control. Accelerating the path to market of new robot technologies will increase Europe’s market share in both industrial and professional service robotics. Installing European-standard robotic automation at end-user sites instead of the present national-standard automation will contribute substantially to increasing the competitiveness of Europe's manufacturing sector and in particular SMEs.

Keywords: network,technology transfer,sustainable infrastructure,pilots,multidisciplinary,business feasibility studies
Bayesian Peer Influence: Group Beliefs, Polarisation and Segregation (BPI)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Individuals, Markets and Organisations
Panel description: Economics, finance and management
Countries involved: United Kingdom
Call: ERC Consolidator Grant (ERC-2015-CoG, 2015)
Start date: 2016-08-01
End date: 2022-01-31
Duration (months): 66.0
Total project cost (EUR): 1662942.0
EU funding (EUR): 1662942.0
Participants: 1
Project status: CLOSED

The objective of this research agenda is to provide a new framework to model and analyze dynamics of group beliefs, in order to study phenomena such as group polarization, segregation and inter-group discrimination. We introduce a simple new heuristic, the Bayesian Peer Influence heuristic (BPI), which is based on rational foundations and captures how individuals are influenced by others' beliefs. We will explore the theoretical properties of this heuristic, and apply the model to analyze the implications of belief dynamics on social interactions. Understanding the formation and evolution of beliefs in groups is an important aspect of many economic applications, such as labour market discrimination. The beliefs that different groups of people have about members of other groups should be central to any theory or empirical investigation of this topic. At the same time, economic models of segregation and discrimination typically do not focus on the evolution and dynamics of group beliefs that allow for such phenomena. There is therefore a need for new tools of analysis for incorporating the dynamics of group beliefs; this is particularly important in order to understand the full implications of policy interventions which often intend to "educate the public''. The BPI fills this gap in the literature by offering a tractable and natural heuristic for group communication. Our aim is to study the theoretical properties of the BPI, as well as its applications to the dynamics of group behavior. Our plan is to: (i) Analyze rational learning from others’ beliefs and characterise the BPI. (ii) Use the BPI to account for cognitive biases in information processing. (iii) Use the BPI to analyze the diffusion of beliefs in social networks. (iv) Apply the BPI to understand the relation between belief polarization, segregation in education and labour market discrimination. (v) Apply the BPI to understand the relation between belief polarization and political outcomes.

Keywords: group beliefs,peer influence,belief polarization,segregation,discrimination,group dynamics,bayesian learning,political polarization,information diffusion
Tackling secondhand tobacco smoke and e-cigarette emissions: exposure assessment, novel interventions, impact on lung diseases and economic burden in diverse European populations. The TackSHS Project. (TackSHS)

Framework: Horizon 2020 — Societal Challenges
Programme: Health, demographic change and wellbeing
Theme: Treating and managing disease
Action type: Research and Innovation actions
Countries involved: Spain,Spain,Ireland,Italy,Italy,Spain,Belgium,Greece,United Kingdom,United Kingdom
Call: Global Alliance for Chronic Diseases. Prevention and treatment of lung diseases (H2020-HCO-2015, 2015)
Start date: 2015-11-01
End date: 2019-10-31
Duration (months): 48.0
Total project cost (EUR): 2987795.0
EU funding (EUR): 2987794.25
Participants: 12
Project status: CLOSED

Exposure to secondhand tobacco smoke (SHS) has been classified as a "Group 1” carcinogen (known human carcinogen) by the International Agency for Research on Cancer and has been shown to have adverse health effects on adults and children, including heart disease and respiratory disorders. Electronic cigarettes (e-cigarettes), the most common “electronic nicotine delivery system”, have irrupted in the past 5 years with sales volumes increasing considerably across the European Union. The TackSHS Project will try to elucidate the comprehensive impact that SHS and e-cigarettes emissions have on the respiratory health of the European population and how health impacts vary according to socio-economic parameters with particular emphasis on specific vulnerable groups (patients suffering from pre-existing chronic lung diseases, heavy smokers, and other disadvantaged groups). By means of an integrated series of work packages, we will investigate the determinants of SHS exposure, assessed at the individual level and in the environment (survey and air quality assessment in 12 countries), the overall burden of disease caused (lung diseases and cardiovascular diseases), including the specific respiratory health changes in patients and healthy people, the economic impact of both mortality and morbidity caused by these exposures, the methods to better characterize these exposures and novel interventions to reduce them. This comprehensive, integrated approach will enable significant step-change beyond the current state-of-the-art in understanding SHS and e- cigarette emission exposure. The participating partners have been at the forefront of cutting edge research in this discipline, with prior collaboration between them in specific projects. The TackSHS Project will put together for the first time all these first-line research teams, and the conjunction of the work packages will result in a step forward to tackle exposure to SHS and e-cigarettes emissions.

Keywords: secondhand tobacco smoke,electronic cigarettes,vulnerable populations,health inequalities,copd,asthma,chronic respiratory diseases,mortality,morbidity,economic burden
Machine learning based analytics for bacteria cell cycle characterization using super resolution microscopy (BALTIC)

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: Switzerland
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2020-04-01
End date: 2022-10-07
Duration (months): 24.0
Total project cost (EUR): 191149.44
EU funding (EUR): 191149.44
Participants: 1
Project status: CLOSED

The cellular life cycle, or cell cycle (CC), is the fundamental backbone of the cellular machinery: it orchestrates processes over multiple scales, in space and time. In bacteria, it consists of an internal clock associated with, for instance, cell-size homeostasis at a population level. Although some analytical tools dedicated to characterizing CC are available for eukaryotic cells, such approaches are still lacking when it comes to bacteria cells study. Moreover, existing eukaryotic cell cyclers are highly limited in terms of both resolution (spatial or temporal) and applications. However, with the rise of antibiotic resistance, there is a real need for reliable quantitative platforms dedicated to bacteria CC and allowing for high throughput comparison studies. This research proposal aims at producing a novel approach for bacteria CC investigation, whilst over-passing the drawbacks associated with existing tools. The developed methodology will rely on cutting edge high throughput super resolution microscopy. We will firstly explore proteins contribution to characterizing CC at the nano-scale, taking a step back from the unreliable and limited size or time dependent estimation. Relying on state of the art machine learning strategies and the identified CC reporting features, I will develop tactics to circumvent the trade-off between temporal and spatial resolution constraining fluorescence nanoscopy when it comes to the study of dynamic processes such as CC. I will implement a methodology to extract, for the first time, dynamic models of bacteria CC from fixed cells super resolved images. It is a considerable step forward: enabling to benefit from a spatial resolution around 10 nm, whilst inferring live-cell akin quantitative information. The highly innovative approaches to bacteria CC quantification developed here will be made generalizable across cell types and applications, providing a unique platform for complex studies, and therapeutics development.

Keywords: super resolution microscopy,machine learning,analysis tools,cell cycle
Epitranscriptomics at the crossroads of metabolism and cellular ageing (EpiMetAgeing)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Reintegration panel
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: Spain
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2020-09-15
End date: 2022-09-14
Duration (months): 24.0
Total project cost (EUR): 160932.48
EU funding (EUR): 160932.48
Participants: 1
Project status: CLOSED

Ageing is an unavoidable consequence of living and constitutes the highest risk factor for most human diseases in modern “western” societies. Despise the frequently observed correlation between metabolic alterations and cellular ageing, the molecular mechanisms linking these two complex processes are far from being understood. Here the EpiMetAgeing project proposes to take a novel approach to investigate cell decline linked to obesity and ageing through the lenses of RNA modifications, specifically TET2-mediated 5-hydroxymethylation of cytosines (5hmC). For that purpose, we will characterize the impact of obesity throughout ageing in: (1) the distribution and dynamics of 5hmC epitranscriptomic modification and (2) the key RNA-binding proteins that mediate TET2 function. Given that RNA modifications are reversible, the ultimate goal of this project is to develop novel strategies for cellular rejuvenation. To this end the ER will use cutting-edge technologies, including next-generation sequencing techniques, proteomics, CRISPR activation and repression systems, metabolic phenotyping and iPSC technology. EpiMetAgeing will provide a deeper understanding of the implication of a specific RNA chemical modification in orchestrating metabolism and ageing. Our research has the potential to facilitate the identification of potential druggable targets for therapeutic intervention in age-associated metabolic diseases. EpiMetAgeing has been carefully designed by combining the ER and host expertise in a highly interdisciplinary research environment to boost her career opportunities, and enhance her possibilities to achieve a position of greater professional maturity and independence that will allow her transition to become a principal investigator in the European R&D system.

Keywords: epitranscriptomics,metabolism,somatic cell reprogramming,ageing,cellular rejuvenation
Dissecting and Engineering Allosteric Activation in a Biosynthetic Enzyme (DEAllAct)

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: Sweden
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2020-11-16
End date: 2022-11-15
Duration (months): 24.0
Total project cost (EUR): 203852.16
EU funding (EUR): 203852.16
Participants: 1
Project status: CLOSED

Allosteric regulation of enzyme catalysis is widespread in nature and presents challenges and opportunities in synthetic biology. The enzyme ATP-phosphoribosyltransferase (ATPPRT) catalyses the first step in histidine biosynthesis, and is subject to complex allosteric inhibition by histidine. The short form of the enzyme, HisGS, is found in complex with a regulatory protein, HisZ. Such regulatory protein has a dual function: it allosterically enhances catalysis by HisGS, and it binds histidine and therefore mediates allosteric inhibition. The scientific aim of "DEAllAct" is to explore the design of a computational framework by combining state-of-the-art EVB/MM computational simulations and biophysical experimental studies to discover specific mutations at the protein-protein interface between HisGS and HisZ, that directly impact the transmission mechanism of the allosteric regulation. The fellow, Marina Corbella will carry out the project in Uppsala University under the supervision of Prof. Lynn Kamerlin who has extensive experience in computational chemistry and enzyme evolution. The first goal of "DEAllAct" is to elucidate the molecular details of the catalytic process of HisGS in the absence/presence of the regulatory protein HisZ via molecular dynamics simulations. Based on the information extracted from these simulations, a novel simulation tool will be developed to predict residues of key importance for binding interactions between the enzyme and the regulatory protein. Finally, the fellow will undergo a secondment at the University of St Andrews to test the hypothesis experimentally by introducing gain-of-function mutations on HisGS at the protein-protein interface to mimic the allosteric activation. Altogether, "DEAllAct" will provide the fellow with a highly competitive multidisciplinary profile by complementing her previous acquired expertise, putting her in a strong position to initiate her career as an independent and innovative research leader.

Keywords: enzymes,allostery
CLIMB THE DATA MOUNTAIN (CTDM)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: Coordination and support actions
Countries involved: Germany
Call: European SME innovation Associate - pilot (H2020-INNOSUP-2020-02, 2020)
Start date: 2020-11-01
End date: 2021-10-31
Duration (months): 12.0
Total project cost (EUR): 176785.71
EU funding (EUR): 123750.0
Participants: 1
Project status: CLOSED

There is one leading question dominating the manufacturing industry across all sectors: "I need to produce cheap and fast without quality defects – how can I optimize that?". After several huge improvements like lean manufacturing, the next big hope for enhancements is the so-called Industry 4.0. While there exist different definitions of Industry 4.0, there is one common denominator: data-driven improvements. However, new technologies also place new demands on manufacturing companies in terms of manpower for data analysis and know-how. Particularly the backbone of the European economy – small and medium-sized companies –, often lack in resources like data scientists and internal knowledge for advanced analytics. According to a study of the Laboratory for Machine Tools and Production Engineering only 37% of the today processed data is explored across various industries, which shows there is still a long way to go for many companies. However, the topic of data analysis in production had already been widely researched, but the research did not reach the broad masses yet. DatenBerg aims to address that topic with its automated data-analytics software and service offer. During the proposed projects new data-analytics approaches in the area of deep learning shall be developed.

Keywords: industry 4 0,smart factory,data driven decisions