Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Advanced skills for data analytics in neurological diseases (NeuroInsight)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Co-funding of regional, national and international programmes (COFUND)-Fellowship programmes
Action type: Marie Skłodowska-Curie actions
Countries involved: Ireland
Call: Co-funding of regional, national and international programmes (H2020-MSCA-COFUND-2020, 2020)
Start date: 2021-06-01
End date: 2026-05-31
Duration (months): 60.0
Total project cost (EUR): 4854960.0
EU funding (EUR): 2427480.0
Participants: 1
Project status: SIGNED

The NeuroInsight Fellowship programme, led by RCSI, will offer a combination of Incoming and Global, 24-month fellowships to 33 Experienced Researchers (ERs) at the interface of the two, world-leading Science Foundation Ireland (SFI) Research Centres; FutureNeuro and Insight. The vision of NeuroInsight is to deliver the future generation of research leaders equipped with the skills to assemble, integrate and mine, using machine and deep learning, a wide variety of data types to yield novel insights into high-definition precision medicine for neurological disorders. We will offer individually-tailored, as well as programme-wide training in aspects of relevance to all recruited researchers, including advanced research skills, transferable skills and experience in industry and or/clinical settings. NeuroInsight helps meet the diverse challenges presented by COVID-19 and is adaptable to associated restrictions. Our intersectoral partners (industry, clinical and patient groups) will take an active part in the Fellows’ progression, through compulsory intersectoral secondments, clinical events, interdisciplinary short visits and specialized training. NeuroInsight is patient-focused, prioritising their needs in training activities and research. We will enable fellows to develop their research-specific and transferable skill sets to actively interact, collaborate and engage with industry, clinical partners and patient groups, and create a culture of patient-centric entrepreneurship. The net result of NeuroInsight will be research leaders equipped to transform the patient journey for people with chronic and rare neurological conditions. Adhering to the COFUND principles, applicants will freely choose, according to their individual preferences, from a wide range of research fields, academic host organisations, supervisors and secondment organisations. The total cost of the programme is €4,741,704, with the consortium providing a contribution of €2,314,224 (49%).

Keywords: neurology,analytics,genomics,ethnographic,machine learning,artificial intelligence,patient impact,ehealth,wearable sensors,public patient involvement,precision medicine,neuroanalytics
Connected Health Early Stage Researcher Support System (CHESS)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Innovative Training Networks (ITN)-European Training Networks
Action type: Marie Skłodowska-Curie actions
Panel: Social Sciences and Humanities
Countries involved: Ireland,Ireland,Spain,Finland,Greece,Switzerland,Finland,Greece,France,France,Netherlands,United Kingdom,United Kingdom
Call: Marie Skłodowska-Curie Innovative Training Networks (ITN-ETN) (H2020-MSCA-ITN-2015, 2015)
Start date: 2015-09-01
End date: 2019-08-31
Duration (months): 48.0
Total project cost (EUR): 3950971.2
EU funding (EUR): 3950971.2
Participants: 18
Project status: CLOSED

Changing demographics and increasing pressure on health related resources mean that new models of care must be found. Fortunately, technology developments and increasing digital literacy mean that we can now put the correct information in the correct hands at the correct time. This is connected health and it allows patients, clinicians, and planners to make better decisions. Ultimately, these decisions can save lives, save money and ensure a better quality of life for the patient during and after treatment. Connected Health is an emerging market and an emerging science. It requires “t-shaped” individuals with deep disciplinary expertise allied to a broad understanding of a variety of domains including business, economics, life science, health science, computer science, social science and engineering. Current research and training programmes in Europe do not address this need and adoption of Connected Health solutions and practices is limited as a result. CHESS will develop connected health scientists and champions who have a broad understanding of multiple domains, who can communicate in an interdisciplinary world and who can operate across the education, industry, health and policy sectors. CHESS will include intersectoral secondments, interdisciplinary communication skills, public engagement and outreach with particular focus on patient, clinician and policy-maker audiences. This will build on the programme of interrelated core research projects which will address gaps in the knowledge and evidence base for Connected Health in terms of health economics, use of big data, personal sensing, business modelling, navigating markets and ecosystems, change management and user-led design.

Keywords: health care,data,change management,business models,connected health,e health,health technology,health systems,health promotion
Transparent Aluminium Networks (TALNET)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Panel: Synthetic Chemistry and Materials
Panel description: New materials and new synthetic approaches, structure-properties relations, solid state chemistry, molecular architecture, organic chemistry
Countries involved: Ireland
Call: ERC Proof of Concept Grant (ERC-2019-PoC, 2019)
Start date: 2020-03-01
End date: 2022-02-28
Duration (months): 24.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: CLOSED

The aim of this PoC proposal is to determine the economic and technical feasibility of using electrospun polymer templates to fabricate mechanically robust, corrosion resistant transparent conductors (TC) on flexible substrates for display, lighting, solar cells and other applications where light transmission is critical to device operation. This innovation exploits the use of inexpensive aluminium networks that surpass the performance of indium tin oxide (ITO) - the industry standard. The brittle nature of ITO and the scarcity of indium worldwide means that alterative solutions are required to meet the enormous growth in display production and the increasing demand for light-weight, mechanically robust and flexible form factors.

Keywords: transparent,conductor,electrospinning,polymer template,metal wires,metal mesh,flexible,displays,lighting,photovoltaics
Deconstructing the Translational Control of Myelination by Specialized Ribosomes (MyeRIBO)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator 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,Spain
Call: ERC Consolidator Grant (ERC-2019-COG, 2019)
Start date: 2020-10-01
End date: 2025-09-30
Duration (months): 60.0
Total project cost (EUR): 1874996.0
EU funding (EUR): 1860519.27
Participants: 2
Project status: SIGNED

The myelin sheath is essential for neuronal function and health: myelinating glial cells speed up propagation of axonal potentials, fuel the energetic demands and regulate the ionic environment of neurons. Lesions to the myelin sheath thus result in devastating neurological disorders that include multiple sclerosis, diabetic neuropathy and Charcot-Marie-Tooth disease. Myelination involves a striking expansion of the glial cell membrane that relies on an exceptional increase in protein and lipid synthesis rates. Decades of dedicated research has uncovered a complex transcriptional program that drives this process, whereas translational control mechanisms, on the other hand, have received little attention. There is emerging evidence, enabled by modern techniques, that ribosomes, typically viewed as invariant, passive molecular machines, may instead be heterogeneous in composition, with particular ribosomal components having a ‘specialized’ regulatory capacity for preferential translation of specific mRNAs. In MyeRIBO, I propose that translation control by specialized ribosomes is a novel layer of regulation that shapes the proteome of the myelinating glial cell. I will exploit advances in cryo-EM and quantitative proteomics analyses to discover the nature and diversity of ribosomes in myelinating cells, employ genome-wide ribosome profiling to obtain mechanistic insights into selective mRNA translation by heterogeneous ribosomes, and generate genetic mouse models to determine the functional consequences of this specialization for myelination in vivo. Notably, I will study the implication of this mechanism in pathogenesis of injury-induced demyelination and diabetic neuropathy, and evaluate the targeting of specialized ribosomal components as a preclinical strategy. MyeRIBO will push further the boundaries of our current understanding of the molecular control of myelination, which could have a profound impact for understanding neural development and myelin disorders.

Keywords: myelin,schwann cells,translation control,specialised ribosomes
Accurate, Reliable and Optimized functional MAgnetic resonance imaging at unprecedented field strength for unique exploration of the human brain (AROMA)

Framework: Horizon 2020 — Excellent Science
Programme: Future and Emerging Technologies (FET)
Theme: EIC Pathfinder
Destination: Novel ideas for radically new technologies
Action type: Research and Innovation actions
Countries involved: Germany,Switzerland,France,Netherlands,United Kingdom
Call: FET-Open Challenging Current Thinking (H2020-FETOPEN-2018-2019-2020-01, 2019)
Start date: 2021-01-01
End date: 2025-12-31
Duration (months): 60.0
Total project cost (EUR): 3601526.25
EU funding (EUR): 3601526.25
Participants: 6
Project status: SIGNED

The AROMA project will provide the means enabling in vivo non-invasive exploration of cognitive functions of the human brain at the mesoscopic scale (500 μm). This will be achieved with non-incremental Magnetic Resonance Imaging (MRI) technologies at unprecedented field strength (11.7 Tesla). The following ambitious objectives are considered: -Maximize signal by means of advanced sequence developments, innovative coil designs, motion correction and paradigm shift radiofrequency pulse designs. -Exploit the emerging field sensors technology to minimize the noise induced by subject’s motion and physiology to achieve the functional targeted detection sensitivity. -Test and validate the disruptive scientific progress with tens of healthy adult volunteers for validation of the anatomical scan quality as well as reliable detection of cortical-depth dependent neural activations. AROMA is a multidisciplinary project engaging 6 EU partners (with young researchers and first-time participants to FET) from 5 countries (France, Germany, Switzerland, Netherlands and United Kingdom) covering the academic and industrial worlds. An implementation plan is presented in the form of 5 work packages, 4 of which are technical in nature. Synergy in communication and dissemination by the several partners and stakeholders (external advisory board, letters of intent are provided) will permit to maximize AROMA project impact. The proposal presents solutions to overcome the fundamental barriers and considers appropriate deliverables, tasks, milestones and risks in order to complete the project objectives in due time. Medical sector will be impacted by the AROMA project outcomes, with implications for computational psychiatry and deciphering the encoding of words, mathematical objects and conscious thoughts. The arsenal of techniques deployed in the project will constitute paradigm shifts with a unified solution, and with commercial opportunities and benefits for the MRI industry in general.

Keywords: ultra high field,methods,rf pulse design,rf coils,field sensors,motion correction,sequence programming,brain exploration,neuroscience
Investors and Climate Change (FinanceCC)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Individuals, Markets and Organisations
Panel description: Economics, finance and management
Countries involved: United Kingdom
Call: ERC Advanced Grant (ERC-2019-ADG, 2019)
Start date: 2020-07-01
End date: 2025-06-30
Duration (months): 60.0
Total project cost (EUR): 1738670.0
EU funding (EUR): 1738670.0
Participants: 1
Project status: SIGNED

Should firms maximize financial value without regard to any environmental and social damages? If not, how should shareholders convey their social and environmental preferences? And, how should investors form their portfolio strategies and engage with the companies they invest in to mitigate climate change and other externalities? These are new fundamental questions firms and investors are confronted with in the face of accelerating climate change, and the new role financial markets are expected to play in the fight against climate change. Another transformational development is the rise of institutional investors –asset managers, insurance companies, pension funds, and sovereign wealth funds—who together own more than 70% of the shares of publicly traded companies. These institutional investors are de facto the critical actors to aggregate investor preferences and engage with companies on their behalf. This research project is divided into two major themes: (A) to determine how financial markets provide the asset price signals and information to help investors shape their portfolio strategies in the face of climate change risk; (B) to identify how institutional investors approach climate change and engage with companies to lead them to accelerate the transition towards renewable energy. These two themes cannot be separated from public policy towards climate change, and the broader context of the global fight against climate change by governments, municipalities, regulators and civil society. Accordingly, a major aspect of the research is to make explicit how public policy shapes corporate behaviour through the actions of institutional investors.
Intelligence for precision therapies against antimicrobial resistance: The first diagnostic panel that probes all the clinically relevant antibiotics at once (BacterOMIC)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Poland
Call: SME instrument (H2020-SMEInst-2018-2020-2, 2019)
Start date: 2019-08-01
End date: 2021-12-31
Duration (months): 29.0
Total project cost (EUR): 2752387.5
EU funding (EUR): 1926671.25
Participants: 1
Project status: CLOSED

In the recent years, we have seen a drastic increase in the antibiotic resistance among bacterial pathogens. This is considered as one of the biggest threats to global health in the current era. The reason for that is inappropriate use of antibiotics - patients are often treated with not optimal antibiotic that need to be changed after few days. Number of resistant microorganisms constantly elevates due to inappropriate use of antibiotics - patients are often treated with inappropriate drugs that need to be changed after few days if bacteria turns to be resistant. The reason for that is that current manual antibiotic resistance tests are time-consuming and laborious, while automatic antimicrobial tests offer analysis of a short lists of antibiotics. BacterOMIC is a response to the growing global threat of antibiotic resistance. We provide the first fully comprehensive rapid Antimicrobial Susceptibility Testing (AST) system to determine already in 4h which from all common antibiotics will be most successful in treating certain bacterial infection. Unlike current methods, our system also allows to investigate antimicrobial effect of not only single antibiotics but also combinations of multiple antibiotics – the future of infection disease treatment. Constantly growing antimicrobial susceptibility testing market was valued at EUR 2.4 billion in 2018 and is expected to reach EUR 3.1 billion by 2022 at the CAGR of 5.1%. With our clear business model we target pharmaceutical and biotechnology companies, contract research organizations, hospitals/private healthcare centers, and diagnostic laboratories. We have a network of partnerships across the EU (PL, FR, ES, CH, DE, and PT) and we are ready for rapid scale-up, with more than 30 million cumulative profit by 2025. We need SME Instrument phase 2 to optimize the design, prepare for mass production, validate the system in clinical trials, and certify it for successful commercialization.

Keywords: bacteria,antibiotic,diagnostics,resistance,antibiotic resistance,minimum inhibitory concentration,resistance mechanism
How should automated profiling be regulated? (PROFILE)

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: Belgium
Call: Individual Fellowships (H2020-MSCA-IF-2016, 2016)
Start date: 2018-01-01
End date: 2019-12-31
Duration (months): 24.0
Total project cost (EUR): 172800.0
EU funding (EUR): 172800.0
Participants: 1
Project status: CLOSED

Profiling may threaten values that the law aims to protect, and undermine goals that the law aims to achieve. Profiling involves automated processing of personal or other data to develop profiles that can be used to make decisions about people. Profiling can be used in different contexts. For instance, (i) with retail price discrimination, online shops charge different consumers different prices for the same or similar products. (ii) Lenders use profiling to estimate a consumer’s creditworthiness. Lenders can adapt interest rates to certain consumers, or refuse to lend to them. (iii) Predictive policing refers to the use of profiling technology to predict criminal behaviour.However, profiling has drawbacks. For instance, profiling can discriminate unintentionally, when an algorithm learns from data reflecting biased human decisions. Additionally, profiling is opaque: people may not know why they are treated differently. Making profiling transparent is difficult, among other reasons because of the complexity and the possibly ever-changing nature of algorithms. The project’s overarching research question is: considering the rationales for the rules in different sectors, is additional regulation needed, and if so: how should profiling be regulated? The project aims to develop guidelines for regulating profiling. I examine profiling in three sectors: retail price discrimination, consumer credit, and predictive policing. For each case study, I analyse current rules that apply to profiling. Next, I analyse these rules’ rationales, which are partly different for each sector. A rule may, for example, aim to protect a human right, or express a legal principle, such as equality, contractual freedom, or the right to a fair trial. Rules may also have economic rationales, which are different for each sector. Drawing from the three case studies, I develop guidelines to regulate profiling. Policymakers, NGOs, and other stakeholders expressed great interest in the results.

Keywords: profiling,discrimination,human rights,fundamental rights,privacy,data protection,fairness,price discrimination,algorithmic decision making,machine learning,predictive policing,big data
Skill Acquisition in Humans and Robots (SAHR)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Systems and communication engineering
Panel description: Electrical, electronic, communication, optical and systems engineering
Countries involved: Switzerland
Call: ERC Advanced Grant (ERC-2016-ADG, 2016)
Start date: 2017-10-01
End date: 2024-03-31
Duration (months): 78.0
Total project cost (EUR): 2492036.0
EU funding (EUR): 2492036.0
Participants: 1
Project status: SIGNED

Society is rapidly opening its doors to robots in our daily life with autonomous vehicles, rehabilitation devices and autonomous appliances. These robots will face unexpected changes in their environment, to which they will have to react immediately and appropriately. Even though robots exceed largely humans’ precision and speed of computation, they are far from matching humans’ capacity to adapt rapidly to unexpected changes. In the past decades, robotics has made leaps forward in the design of increasingly complex robotic platforms to meet these challenges. In this endeavour, it has benefited from advances in optimization for solving high-dimensional constrained problems and in machine learning (ML) to analyse vast amounts of data. These methods are powerful for planning in slow-paced tasks and when the environment is known. This project addresses a growing need for methods that show fast and on-line reactivity. We design controllers that can plan at run time and adapt to new environmental constraints. We offer a novel approach to robot learning that follows stages of skill acquisition in humans. To inform modelling, we conduct a longitudinal study of the acquisition of dexterous bimanual skills in craftsmanship. We study how humans exploit task uncertainty to overcome their sensory-motor noise, and how humans learn bimanual synergies to reduce the control variables. This study informs the design of novel learning strategies for robots that exploit failures as much as successes. We combine planning and ML to learn feasible control laws, retrievable at run time with no need for further optimization. We exploit properties of dynamical systems (DS), which have received little attention in robot control, and use ML to identify characteristics of DS, in ways that were not explored to date. The approach is assessed in live demonstrations of coordinated adaptation of a multi-arm/hand robotic system engaged in a fast-paced industrial task, in the presence of humans.

Keywords: robot learning,bimanual coordination