Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
PROtein SEQuencing using Optical single molecule real-time detection (PROSEQO)

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: Spain,Germany,Italy,Italy,France,France
Call: FET-Open research projects (H2020-FETOPEN-2014-2015-RIA, 2015)
Start date: 2016-03-01
End date: 2019-02-28
Duration (months): 36.0
Total project cost (EUR): 2906801.25
EU funding (EUR): 2906801.25
Participants: 6
Project status: CLOSED

The advent of analytical techniques with extremely low limits of detection has led to dramatic progresses mostly in the field of nucleic acids sequencing. Despite the advent of the next generation sequencing platforms, the current genome sequencing task remains formidable, and revolutionary advances in DNA sequencing technology are still demanded. Nevertheless, the primary actors in virtually all life processes are the proteins coded by DNA sequences known as genes. Proteins can yield far more compelling revelations than may be gleaned from DNA alone. Protein sequencing may radically transform patient treatment, enabling precise monitoring of disease response to therapeutics at the molecular level. Single-molecule sequencing of proteins is of enormous value, offering the potential to detect diminishingly small quantities of proteins that may have been altered by alternative splicing or post-translational modification. In this project, we build upon current state-of-the-art sequencing technologies to develop novel proof-of-principle technologies for high-throughput protein sequencing and single molecule DNA/RNA sequencing. The work proposed herein will provide: (i) a new sequencing technology development that utilizes plasmonic nanostructures in order to enhance the optical detection and to control the molecules movement by means of optical trapping; (ii) a novel approach of plasmonic based optical spectroscopy for sequencing of protein; (iii), a rigorous analytical model to reconstruct the exact sequence from the signals recorded; and (iv) a plasmonic device that can perform both nucleic acids and amino-acids sequencing in one functional unit. These research efforts provide a foundation for the novel use of systems for a wide range of applications, such as the framework to investigate next generation protein sequencing, as well as high-throughput DNA sequencing and genetic diagnostics.

Keywords: third generation sequencing,protein sequencing,plasmonics,optical spectroscopy,fret,nanotrapping
Liposome-based Biomimetic Treatment for auto-immune diabetes (T1D) (LIPOBITS)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Spain
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2018)
Start date: 2018-12-01
End date: 2019-05-31
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

Autoimmune diseases (AD) affect 5-10% of the global population and require chronic palliative treatments, resulting in lower quality of life and great costs for society. Moreover, a steady rise in the incidence of some AD such as Type 1 diabetes (T1D) has been detected worldwide. Main treatments for T1D are currently based on the continuous administration of exogenous insulin, but none of the 20 existing marketed insulins can achieve a sustained glycaemic level control. Thus, they cannot fully prevent T1D’s long term and acute side effects. Ahead Therapeutics is a start-up company exploiting a proprietary encapsulation technology to develop treatments for different AD. It has developed an innovative solution for T1D based on the use of liposomes containing auto-autoantigens. Through a biomimetic process, these liposomes can induce antigen specific immune tolerance, stopping the autoimmune reaction and allowing insulin-producing β-cells to recover. Despite its potential for preventive use, Ahead’s treatment is mainly foreseen as a therapeutic reverse vaccine on the onset of T1D, leading to a permanent cure of the disease. Ahead solution has several competitive advantages derived from its biomimetic/physiologic mechanism of action: long-lasting/permanent effect, non-systemic immunosuppressive, no secondary effects/toxicity, low-cost production, complementary with other therapies. The liposome-based platform has already been validated in animal models and preclinical non-regulatory assays are being completed. Current feasibility study will be now focus on defining a Regulatory Roadmap & IPR strategy, enlarging key partners network, discover new potential licensees, scaling up liposomes production and analyse deeper market & competitors. LIPOBITS is a solid high-potential innovation project expected to support Ahead’s global dimension and to generate 7 high qualified jobs total incomes of 10,5 M€ by 2022, and < 300 M € considering the whole life cycle of the asset.

Keywords: auto immune diseases,type 1 diabetes,biomimicry,antigen specific immune tolerance,liposome based nanoplatform
How Work Organizations Shape Ethnic Stratification across Immigrant Generations: Assimilation, Segregation, and Workplace Contexts (OrgMIGRANT)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: The Social World and Its Diversity
Panel description: Sociology, social psychology, social anthropology, education sciences, communication studies
Countries involved: Norway
Call: ERC Starting Grant (ERC-2019-STG, 2019)
Start date: 2020-09-01
End date: 2025-08-31
Duration (months): 60.0
Total project cost (EUR): 1498079.0
EU funding (EUR): 1498079.0
Participants: 1
Project status: SIGNED

Large-scale immigration has introduced salient new dimensions of ethnic stratification in Europe’s rich, liberal democracies—successful incorporation of disadvantaged, newcomer immigrant minorities now poses a critical challenge for the 21st century. Despite a vast literature on labor market inequalities between immigrants and natives, the great majority of these studies is based on surveys of individual workers and yield limited knowledge about the role of firms and workplace contexts. Still, there has been no systematical attempt to exploit linked employer-employee (LEE) data to assess how work organizations are linked to economic assimilation across immigrant generations. Here I bring a new organizational focus on workplaces as key sites where contemporary dynamics of ethnic stratification unfold at the micro level. The objective of OrgMIGRANT is to demonstrate how work organizations both contribute to and reflect changing patterns of ethnic stratification across immigrant generations. We will study workplace segregation and probe whether, how, and why ethnic boundary salience and immigrant-native inequalities vary by organizational context, net of worker traits. To this end, we will use economy-wide LEE data from Norway and comparisons with selected high-income countries (i.e., Sweden, Denmark, Germany, France, Canada, and the United States). Our world-class data allow us to situate workers inside workplaces, enabling the study of the organizational context of immigrant-native labor market inequalities in high detail using state-of-the-art panel data techniques. OrgMIGRANT will be organized into three work packages: (1) organizational sources behind native-immigrant workplace segregation; (2) organizational determinants of immigrant-native inequalities within workplaces; (3) a cross-national comparison of organizational variation in workplace-specific immigrant-native pay inequalities and the size of within-job pay gaps (i.e., same occupation and workplace).

Keywords: organizations,immigration,assimilation,ethnic stratification,segregation,workplaces,firms,labor markets,administrative data,linked employer employee data
Window to the future: Understanding and assessing the vulnerability of marine biodiversity to ocean acidification (Future4Oceans)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Global Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Environment and Geosciences
Countries involved: Italy,United States
Call: Marie Skłodowska-Curie Individual Fellowships (IF-GF) (H2020-MSCA-IF-2015, 2015)
Start date: 2016-12-01
End date: 2019-11-30
Duration (months): 36.0
Total project cost (EUR): 244269.0
EU funding (EUR): 244269.0
Participants: 2
Project status: CLOSED

Ocean acidification (OA) is expected to profoundly alter the diversity and function of marine ecosystems, as well as the services they provide to society. Thus, understanding how future oceans will function in the face of OA represents one of the major challenges and needs for marine science and management. However, most of the studies to date on OA have consisted of short- to medium- timescale laboratory experiments of single species exposed in isolation to OA. Consequently, our understanding and ability to predict the impacts of OA at the community and ecosystem levels are extremely limited. There is, therefore, a great need for empirical investigations of the long-term effects of OA on marine ecosystems exposed to high pCO2, as found around naturally acidified systems. To fill the remaining knowledge gap of the emergent effects of OA on coastal marine ecosystems, I will adopt a multidisciplinary perspective by integrating three novel approaches: 1) functional-community level studies in natural CO2 vent systems; 2) use of newly developed multi-stressors experimental FOCE systems; and 3) genomic studies of the adaptive capacity of marine species to OA. Moreover, we will use virtual reality to increase public understanding of OA. This research, based on innovative and high-level training, is expected to generate a better understanding and ability to assess marine functional biodiversity vulnerability. Future4Oceans has a strong link to management and policy advice at EU and international levels. It directly addresses the H2020 Work Programme priorities of climate action and sustainable environment. This project will not only reinforce, extend, and diversify the scientific expertise of the fellow by facilitating the development of methodological skills but propel her career towards scientific independence and professional maturity.

Keywords: ocean acidification,climate change,multiple stressors,marine benthic communities and ecosystems,functional traits,biodiversity,evolutionary adaptation,naturally acidified systems
SensPD: Early Diagnosis of Autism (SensPD)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Israel
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

About 1 in 45 babies born today will be diagnosed later in life with Autism Spectrum Disorder (ASD). But final diagnosis will come only at age four, too late for early interventions that would allow them to markedly improve their level of functionality, enabling them to acquire needed social skills to be integrated in society. When interventions are applied before the age of two the outcome is an over 90% success rate of social integration, as compared to after the age of five, where the success rate drops below 11%. People with ASD in all its forms have Sensory Processing (SP) issues, SensPD monitors SP performance by measuring the Oto-Acoustic Emissions (OAE). This procedure is objective, short, non-invasive and can be performed hours after birth. SensPD aims to include this procedure in the standard battery of tests performed on every new-born, enabling early interventions, and realizing the cost savings associated with this improved functionality. During Phase I, SensPD will complete the technological and economic feasibility of the device and the service. SensPD will conduct three small scale clinical studies, each with 30 diagnosed subjects between the ages of two to five and 30 controls. The readings will be compared with results of the current diagnoses, thus establishing the technological feasibility. SensPD’s economic viability will be tested from the perspective of using the device as an add-on to an already existing OAE measuring device (and not as a stand-alone device). We will negotiate with an OAE device company as a strategic partner and complete the approval of the patent in Europe and USA. In Phase II, SensPD will conduct widened multi-site clinical studies (~250 subjects per site), will start the regulatory process and the establishment of a cloud global data base. By the completion of the clinical trials, the process for obtaining the needed regulatory approvals, as well for obtaining the insurance reimbursement code, will be underway.

Keywords: autism,oto acoustic emissions oae,sensory perception,newborn hearing screening
EPP manufacturing evolution: Using SLM techniques, advance EPP manufacturing shape complexity possibilities and create efficiencies that reduce industry production costs by 16% (SLM-Tools)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Germany
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2018)
Start date: 2019-01-01
End date: 2019-03-31
Duration (months): 3.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

Expanded Polypropylene (EPP), is commonly used in many industries but particularly in automotive for varying applications for its light weight, high-energy absorption, structural strength and recyclability properties. We have developed an innovative technique that exploits Selective Laser Melting (SLM) technology to produce EPP mold tools at a significantly lower cost, with an excellent lifetime as well as offer increased shape complexity capability. Compared to traditional techniques, our worldwide unique innovation is a highly efficient manufacturing process that has the industry disrupting effect of reducing the cost of EPP products by a massive 16% as well as advancing shape complexity possibilities, offering customers absolute design freedom. At the same time, it is environmentally beneficial, offering high energy and materials savings: reducing production energy consumption by 70%, reducing cooling water consumption by <90%, and increasing plant output by 100%. As the first of its kind and with IP protection in progress, we expect for SLM Tools to become the industry standard and launch us into becoming Germany’s EPP tooling market leader with a significant presence across the globe, as such, it is key for us to plan this market entry strategy carefully to ensure success is maximised. This feasibility study will allow us to: refine our market knowledge; map our Phase 2 work; scope the IP landscape; finalise Phase 2 project collaborators and produce a fully elaborated business plan. Building on our legacy as an innovative company, at Overath, we possess the know-how necessary to develop, manufacture and market such world novelties and innovations. Having already proven TRL6, at Phase 2 we shall: validate an efficient production environment; optimise the technology productivity capability; prove to the market our quality and efficiency capability and prepare for market entry.

Keywords: expanded polypropylene,expanded polystyrene,selective laser melting,automotive
WEB-RADR 2 (WEB-RADR 2)

Framework: Horizon 2020 — Societal Challenges
Programme: Health, demographic change and wellbeing
Theme: Public-private partnerships
Destination: Innovative Medicines Initiative 2
Action type: Research and Innovation actions
Partnership: IMI2 (Art-187)
Countries involved: Germany,Spain,Switzerland,Switzerland,Croatia,Sweden,United States,Netherlands,United Kingdom,United Kingdom,Denmark,United Kingdom
Call: Exploitation of IMI project results (H2020-JTI-IMI2-2017-11-single-stage, 2017)
Start date: 2018-09-01
End date: 2020-06-30
Duration (months): 22.0
Total project cost (EUR): 2140980.0
EU funding (EUR): 1168750.0
Participants: 12
Project status: CLOSED

This proposal seeks to build on the mobile application (app) functionality delivered through the original WEB-RADR project to expand access to the platform and the reach of the information contained within it. It will achieve this by making the functionality available through application programming interfaces (APIs), meaning that third party organisations will be able to embed WEB-RADR platform functionality into their own systems, applications and websites. An important component to this will be a terminology/classification mapping activity involving the three core healthcare terminology owners; MedDRA MSSO, SNOMED International and WHO. The mapping will facilitate communication between regulatory and healthcare databases by establishing mapping protocols and delivering an initial mapping between a subset of key pharmacovigilance terms. The project has established use cases and adopters for the proposed functionality through the networks established through the original WEB-RADR delivery, and has a diverse range of partners keen to utilise different aspects of the proposed solutions including regulatory authorities, health charities and system providers.
Understanding human action from unstructured 3D point clouds using deep learning methods (3DInAction)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Global Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Australia,Israel
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2021-01-01
End date: 2023-12-31
Duration (months): 36.0
Total project cost (EUR): 276205.44
EU funding (EUR): 276205.44
Participants: 2
Project status: SIGNED

Human action recognition and forecasting is an integral part of autonomous robotic systems that require human-robot interaction as well as other engineering problems. Action recognition is typically achieved using video data and deep learning methods. However, other tasks, e.g. classification, showed that it is often beneficial to additionally use 3D data. Namely, 3D point clouds that are sampled on the surfaces of objects and agents in the scene. Unfortunately, existing human action recognition methods are somewhat limited, motivating the following research. In this action, we describe a new class of algorithms for 3D human action recognition and forecasting using a deep learning-based approach. Our approach is novel in that it extends a recent body of work on action recognition from 2D to the 3D domain which is particularly challenging due to the unstructured, unordered and permutation invariant nature of 3D point clouds. Our algorithms use the global and local statistical properties of 3D point clouds along with a 3D convolutional neural network to devise novel multi-modal representation of human action. It is inherently robust to spatial changes in the 3D domain, unlike previous works which rely on the 2D projections. In practice, deep learning methods allow us to learn an inference model from real-world examples. A common methodology for action recognition includes creating an annotated dataset, training an inference model and testing its generalization. Our research objectives cover all of these tasks and suggest novel methods to tackle them. Overall, the proposed research offers a new point of view for these long-standing problems, and with the vast related work in other domains, it may bridge the gap to arrive at a generalizable, effective and efficient 3D human action recognition and forecasting machinery. The resulting algorithms may be used in several scientific and engineering domains such as human-robot interaction among other applications.

Keywords: 3d point clouds,action recognition
Measuring and Modelling Air Pollution Within Vehicles – Implications for daily EXposure and Human Health. (MMAP-VEX)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Global Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Environment and Geosciences
Countries involved: United States,United Kingdom
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2020-07-01
End date: 2023-06-30
Duration (months): 36.0
Total project cost (EUR): 271732.8
EU funding (EUR): 271732.8
Participants: 2
Project status: SIGNED

Outdoor air pollution is accountable for at least 4.2 million deaths per year worldwide, whilst more than 80% of urban residents are exposed to outdoor air pollution levels greater than the legislative limits. As this outdoor polluted air undergoes exchange with the air inside vehicle cabins, vehicle occupants may suffer elevated exposure to ambient/outdoor air pollutants of varying extents during this time. Additionaly, personal air pollution exposure within vehicles varies not only under different ventilation settings, but also during time of the day and road type. MMAP-VEX project aims to investigate how the outdoor air quality is a determinable factor of health and personal air pollution exposure in passenger cars. MMAP-VEX will perform concurrent measurements of air pollutants within vehicles and directly outside/ambient of multiple air pollutants such as concentrations of PM10, PM2.5, PM1 (particulate matter with aerodynamic diameter ≤ 10, 2.5 and 1 µm, respectively), ultrafine particles (UFP, with aerodynamic diameter < 300 nm), lung surface deposited area (LSDA), nitric oxide (NO), nitrogen dioxide (NO2), ozone (O3), carbon dioxide (CO2) and volatile organic compounds (VOC), which are critical species in terms of air pollution exposure comparisons. The concurent measurements will take place under different ventilation settings on urban, sub-urban and rural roads. The insights from these experiments, while significant themselves, will then be used to develop modelling techniques, which, with the use of the state-of-the-art machine learning algorithms, will be trained in order to estimate personal exposure inside vehicle cabins under different conditions such as cold/hot weather, air conditioning on/off or driving in urban/rural roads. MMAP-VEX research is critical for effective indoor air quality management policies, alternative, greener commuting strategies and control of daily exposure to air pollution.

Keywords: air pollution exposure,within vehicle exposure,indoor,personal exposure,inhalation dose,modelling,machine learning,nitrogen dioxide,particulate matter