Data-Gouv-ML/pcri-actions-marie-curie-participations-et-projets-laureats huggingface dataset:
Projet 3F-FutureFreshFruit (Horizon 2020, appel à projets 2014)
Action : Postdoctoral Fellowships - Global Fellowships
Panel : Environment and Geosciences
Statut du projet : CLOSED

Water stress is a key environmental factor that causes important quality and economical losses before and after fruit harvest. As water depletion is becoming increasingly problematic, improving water efficiency and tolerance of plants to dehydration is a priority of the European Union, as reflected in the societal challenge of Climate action, environment and resource efficiency. Likewise, food waste reduction is another key issue in the framework of Horizon 2020, as described in Food security and sustainable agriculture and forestry challenge. Indeed, a large proportion of fresh produce is wasted during postharvest (25-50%) of horticultural crops, which is largely caused by dehydration. Therefore, this project deals with the urgent need of understanding factors influencing water loss to develop innovative solutions geared to saving water and reducing food waste. Based on the idea that cuticular waxes are critical for limiting water loss from fruit, and thus for resisting desiccation and spoilage, we propose to uncover the structural and regulatory pathways that mediate its biosynthesis, transport and assembly in response to drought stress through a multidisciplinary approach. Thus, SEM and TEM microscopy, GC/MS, as well as up-to-date technologies, such as Laser Capture Microdissection (LCM) and RNA sequencing, will be used. Tomato and citrus fruit will be selected as model systems to address this poorly understood area of plant biochemistry, physiology and molecular biology. The availability of mutants of both fruits, as well as the application of stress-related hormones during postharvest, will allow comparative surveys to better understand the regulation of these processes. The outcomes of this proposal will represent a major advance in the understanding of the crosslink between water stress, wax metabolism and fruit biology, and will suggest strategies for enhancing shelf life, drought hardiness and fruit quality in both climacteric and non-climacteric fruit.

Mots clés : water stress, cuticle propierties, epicuticular wax, cutin, fruit quality, postharvest, tomato, citrus, ethylene, transcriptomic, rna sequencing, laser capture microdissection, desiccation

Participants :
- Cornell University (Etats-Unis) — Partner, organization partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
- Spanish National Research Council (Espagne) — Coordinator, beneficiary, Recherche, subvention : 239191.2 €
Projet SuperFlotillin (Horizon 2020, appel à projets 2017)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Life Sciences
Statut du projet : CLOSED

We aim to convert an industrial waste product (glycerol) to an environment-friendly biofuel (biobutanol), by using a competitive and efficient system, based on bacterial lipid rafts with gain of function mutants of flotillin, their main scaffold component. Bacterial cells can be used as hosts for biosynthesis of a variety of value-added products as antibiotics or vitamins. The use of recently discovered bacterial lipid rafts to confine the reactions is expected to greatly improve the production levels, when comparing with traditional systems. We will now further improve the yields of synthesis by generating gain of function mutants of B. subtilis flotillin (FloA), a key-role player in lipid rafts scaffolding. These Superflotillins or Flo* will organize more stable, robust and functional rafts. Therefore, they will confine more complex biosynthesis pathways with more enzymes and improve the production yields. We will then test the efficiency of these improved mutants in biosynthesis of biofuels. Five Clostridium spp. enzymes confined in B. subtilis Flo*-rafts will produce biobutanol, an alternative for traditional petroleum, using glycerol waste as a unique carbon source. After the metabolic optimisation of the process, together with our industrial partner (Enantis, CZ) we will scale up the production and assess the project viability for biobutanol production at industrially relevant conditions.

Mots clés : bacterial lipid rafts, membrane microdomains, flotillin, biofuels, industrial application of bioprocesses, applied microbiology, synthetic biology, systems biology

Participants :
- Spanish National Research Council (Espagne) — Coordinator, beneficiary, Recherche, subvention : 158121.6 €
Projet ORIENTATE (Horizon 2020, appel à projets 2020)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Information Science and Engineering
Statut du projet : SIGNED

We are clearly on the verge of Industry 4.0. The European Commission is investing on European Industry digitalisation not only to improve competitiveness but also to reach climate-neutrality. Indoor Positioning (IP) will play a key role in Industry 4.0 by, for instance, supporting unmanned vehicles navigation or tracking goods within the value chain. Despite there are already technologies coping with the technical requirements for IP in industrial environments, they present drawbacks that might prevent its usage. Moreover, a direct comparison of the current IP technologies is not possible due to the diversity of scenarios used in empirical evaluation. This project aims to provide a significant contribution to Industrial IP by: i) proposing a new scalable high-accurate solution; ii) dynamically integrating multiple IP technologies; iii) creating an open evaluation framework. From the methodological point of view, a novel solution -based on Visible-Light Communications principles- will be proposed. Moreover, data provided by positioning modules will be processed with neural networks and local outlier factor to detect anomalous behaviour. Trusted positioning information will be coded as hyperspectral images to allow dynamic sensor fusion through Deep Neural Networks. Finally, the Experienced Researcher, with the support of 3 industrial partners and 12 international researchers, will agree an evaluation framework for Industrial IP. The project outcomes will contribute to the IP community with significant advances to the state-of-the-art solutions and, specially, with a new open evaluation framework and generated datasets. The Experienced Researcher will emerge from the project with acquired multidisciplinary competences necessary to conduct high-impact research projects and deliver high-level consultancy services. Thus, it will grant the ER the capacity of achieving maturity and independence on Industrial IP, a research area with great growth potential.

Mots clés : indoor positioning, autonomous robotic navigation, sensor fusion, accurate and dependable positioning, reproducible research, standardisation of evaluation procedures, technology transfer, patents

Participants :
- University of Minho (Portugal) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 159815.04 €
Projet NU 4 nu (Horizon 2020, appel à projets 2020)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Physics
Statut du projet : SIGNED

We are entering an era of high-precision neutrino oscillation experiments (T2HK, DUNE), which potentially hold answers to some of the most exciting questions in particle physics. These future scientific discoveries require a precise knowledge of neutrino-nucleus interactions for a wide range of energies and nuclear targets, mainly medium-mass nuclei like oxygen and argon. Presently, this goal is far from being reached due to the simplistic nuclear models used in experimental analyses performed with the Monte Carlo (MC) event generators. In view of these needs, “NU 4 ν: nuclear ab initio methods for neutrino physics” gives an insight from more fundamental nuclear studies and delivers consistent theoretical predictions. “NU 4 ν” is an interdisciplinary endeavour which pushes nuclear structure calculations towards new applications and higher energy regions. We propose to employ the coupled cluster (CC) framework to deliver and benchmark a set of tools that will be implemented in the MC event generators. For the first time we want to employ a fundamental many-body theory to give results on various steps of simulation done within the MC generators. Not only we encapsulate the physics of the nuclear ground state through an established formalism of spectral functions, but we also account for the effects of nuclear correlations in the intra-nuclear cascade, an integral part of the MC generators, leading to an unprecedented theoretical consistency. The CC theory is perfectly suited to describe systems as large as oxygen and argon, pivotal for the neutrino experiments. Recently it has been combined with Lorentz integral transform (LIT-CC) method opening the door to calculate neutrino-nucleus cross-sections from first principles. The concurrent planned research of the host group using the LIT-CC method will give a unique chance to compare both approaches and perform an analysis of theoretical uncertainties.

Mots clés : neutrino nucleus interactions, electroweak interactions, monte carlo event generator

Participants :
- Johannes Gutenberg University Mainz (Allemagne) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 162806.4 €
Projet PHOTO ORGANO-GOLD (Horizon 2020, appel à projets 2015)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Chemistry
Statut du projet : CLOSED

We are in a changing era for drug discovery: the growing perception is that basic chemical research will play a greater role in pharmaceutical development. One current challenge is to develop a new kind of chemistry that yields a screening collection comprising optimal chiral molecules that increase the probability of success in identifying drug-candidate structures. Also, the chemistry community has recently recognized how visible light-mediated photoreactions can open new avenues in modern organic synthesis and the past seven years have witnessed its remarkable prosperity. Therefore, developing photo-facilitated asymmetric reactions focusing on rapidly generating privileged chiral natural-like compounds for drug discovery are highly desirable. The proposed research aims to combine the fellow’s experience in gold catalysis and the host’s experience in photo-triggered organocatalysis (two powerful fields of molecule activation, which have to date remained unrelated) to develop otherwise unachievable catalytic asymmetric photoreactions. Specifically, we will focus on using readily available, unactivated aryl/alkyl chloride or bromide, which cannot be easily activated by the commonly used Rh- or Ir-based photoredox catalysts. The resulting strategies will be used as an ideal platform for assembling libraries comprising enantiopure chiral small molecules bearing α,α-disubstituted α-amino acid & 3,3-disubstituted oxindole frameworks, which, along with biological screening carried out in collaboration with a world-wide recognized pharma-company (Lundbeck A/S, Copenhagen, DK), will increase the probability of success in identifying drug-candidate structures. The multi-cultural nature of this project will greatly contribute to broaden the fellow competencies and will place him in an excellent position for the next career move

Mots clés : enantioselective catalysis, organocatalysis, photoredox catalysis, gold catalysis, synergistic catalysis, drug discovery

Participants :
- Institut Català d'Investigació Química (Espagne) — Coordinator, beneficiary, Recherche, subvention : 158121.6 €
Projet FLOW (Horizon 2020, appel à projets 2019)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Life Sciences
Statut du projet : CLOSED

We are incorporating increasingly complex systems and information streams into our daily lives that involve flow (e.g. autonomous cars, social feeds, disaster information systems). This type of flow is created by a body of individual entities that show both collective and individual behaviours following a coordinated set of rules (e.g. flocks of birds, schools of fish, cars on highways). Designs implicitly assume that the human visual system processes collective flow in an effortless and veridical manner. But which properties of collective flow can we perceive and how sensitive are we to its parameters? Can we perceive 3D (e.g. smoke) as well as 2D flow (e.g. traffic) structures? By studying the human visual system and the mechanisms it relies on while performing complex tasks on flow we will learn how to efficiently deal with large streams of information under highly varying conditions. We will study this by gathering perceptual data of human observers and developing models that align with these human observations. Combining empirical studies involving psychophysical testing and theoretical inferences using neural networks we will arrive at an understanding of collective flow perception. Understanding human perception and the information basis of complex dynamics is of major importance for the design of human-centered complex dynamic systems. This fellowship will be carried out in Delft University of Technology (TU Delft), in the Netherlands, under the supervision of Prof. Sylvia Pont, in the Perceptual Intelligence lab. This multidisciplinary task force is renowned for its multidisciplinary work on real-world perception problems and offers unique training in application-oriented quantitative psychophysical work and cognitive ergonomics. It will offer me the opportunity to return to the Netherlands as a postdoc while broadening my expertise towards the applied realm and information communication design.

Mots clés : psychophysics

Participants :
- Delft University of Technology (Pays-Bas) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 175572.48 €
Projet LANTERN (Horizon 2020, appel à projets 2020)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Information Science and Engineering
Statut du projet : SIGNED

We are living in a world where connected devices outnumber human population, and this trend keeps growing: around 24.6 billion connections are forecasted in 2025—more than three times the estimated population. This gives rise to the Internet of Things (IoT) in which virtually all devices are interconnected and continuously share data. The IoT is a key enabler for a host of applications, such as intelligent transportation systems, smart cities, and smart grids. Thus it promises to transform the way we live. To realize the IoT, it is crucial and timely to develop a communication and computation infrastructure that is able to support the processing of a vast amount of time-sensitive data, for which a centralized computation is inadequate. Edge computing has emerged as a novel paradigm to guarantee very low-latency and high-bandwidth computing services. It involves moving the computation power from the cloud to where data is generated, by pooling the available resources at the network edge. In this project, we investigate how low-latency and private edge computing protocols can be developed in wireless random-access networks. Relying on tools from information theory and coding theory, we will tackle the two following challenging objectives: i) to establish a foundation for privacy and reliability in latency-critical, multi-client and multi-server edge computing in random-access networks; and ii) to devise resilient coding schemes together with energy-efficient and scalable wireless random-access protocols to achieve low latency and preserve privacy in distributed edge computing. The results of this project will help paving the way to the full realization of the IoT in the near future.

Mots clés : internet of things, edge computing, computation, latency, privacy, random access networks, wireless communications, information theory, coding theory

Participants :
- Chalmers University of Technology (Suède) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 191852.16 €
Projet GenGeoRisk (Horizon 2020, appel à projets 2019)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Life Sciences
Statut du projet : CLOSED

We can all name a family member, close friend, colleague or acquaintance who has suffered from depression. It is a common, pervasive and devastating illness characterised by persistent episodes of low mood. Depression affects individuals across all geographical locations and by the year 2030 it will be the largest contributor to disease burden worldwide. Depression that occurs in women during pregnancy and post-partum is referred to as maternal depression (MD). MD is the leading cause of perinatal mortality and it accounts for ∼20% of all postpartum deaths. Despite this profound individual and societal burden, the aetiology of depression remains poorly understood in part, due to three research barriers: 1) lack of success in identifying genetic variants specific to MD 2) inadequate account of environmental risk factors in genetic research and 3) heterogeneity. GenGeoRisk will address each of these research barriers in the following ways: 1) I will use aggregates of genetic variants (polygenic scores) derived from other successful studies of psychiatric traits to calculate genetic p—a general dimension, which sits at the top of a hierarchical structure of psychopathological dimensions and captures one’s general liability to psychopathology. Genetic p will be used to predict MD symptom risk and resilience in the world’s largest (N= 240 000) pregnancy cohort (MoBa; the Norwegian, mother, father and offspring cohort study) 2) I will use geographical location data from the entire Norwegian population (> 7 000 000) that has been recently linked to MoBa data to illuminate how MD symptoms and genetic p vary across neighbourhoods and diverse Norwegian municipalities 3) I will focus on MD, a less heterogeneous form of major depression, and use genetic p to discover MD subtypes. GenGeoRisk will be the first intergenerational, multi-environmental and DNA based investigation of MD to date.

Mots clés : geocode, neighbourhood factors, depression, polygenic scores

Participants :
- University of Oslo (Norvège) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 214158.72 €
Projet HYBSPN (Horizon 2020, appel à projets 2017)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Information Science and Engineering
Statut du projet : CLOSED

We have recently witnessed a considerable interest in probabilistic models within deep learning, leading to e.g. generative adversarial networks, deep generative networks, neural auto-regressive density estimators and Pixel-RNNs/CNNs. Furthermore, sum-product networks (SPNs) are a recent deep architecture with a unique advantage over the aforementioned models: they allow both exact and efficient inference, implemented in terms of simple network passes. However, SPNs are a constrained type of neural network and do not reach the full flexibility of the deep learning tool kit available to date. This calls for hybrid learning systems which exploit the superior inference properties of SPNs within other deep learning approaches. In this project, I will investigate two such approaches. First, I will structurally combine a deep learning architecture (front-end), which extracts a representation from a set of inputs, controlling the parameters of an SPN (back-end) over a set of outputs. This yields a hybrid conditional SPN which facilitates full inference over the output space, and which is naturally applied in structural prediction tasks. Such hybrid SPNs can be expected to be highly expressive and to set new state-of-the-art results in e.g. semantic image segmentation. The second approach is to use SPNs as variational distributions, i.e. for approximating a given target distribution by minimizing Kullback-Leibler divergence. On the one hand, this allows to capture intractable models with SPNs, with the goal to enable fast amortized approximate inference. On the other hand, this approach allows to use hybrid conditional SPNs as so-called inference networks for intractable generative models with latent variables, for the purpose of variational posterior inference and learning. This approach would represent a substantial improvement over state-of-the-art approaches, which are usually limited to expensive inference via Monte Carlo estimation.

Mots clés : probabilistic models, sum product networks, tractable inference, deep learning

Participants :
- University of Cambridge (Royaume-Uni) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 179166.9 €
Projet TEMD (Horizon 2020, appel à projets 2020)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Information Science and Engineering
Statut du projet : SIGNED

With explosive development and demand of implantable bio-applications, battery replacement becomes a key issue to achieve permanent implantation in vivo. Recent advances in energy nanogenerators have allowed for self-power function by conversing mechanical energy to electric energy, promising the battery-free implantation of bio-applications. Among the emerging nano harvesting technologies, triboelectrification initially proposed in 2012 is the front one due to universal availability, from enormous to tiny movements and even low-frequency motion in vivo. Another advantage of triboelectrification is the abundant choices of materials to meet the requirement of biocompatibility. Hence, the triboelectrification is the enabling technology for the next generation self-powered implant. Recently, researchers have commenced implanting triboelectric nanogenerators (TENG) in animals to evaluate the potential of energy harvesting from heart beating and respiration. However, the understanding of interactions between triboelectrification and muscle dynamics for energy harvesting is unclear. The experiments are limited in measuring, explaining and quantifying the performance of TENG by ignoring the complex dynamics of muscles, significantly hindering the application of TENG as implantable device. The proposal aims to develop a triboelectrification-muscle dynamics (TEMD) framework based on experiment and modelling to support the design, characterization and optimization of TENG for implantable bio-applications under different muscle dynamics on macro/nano scales. The framework will be able to (1) predict the output performance of TENG at any position of specific muscle, and (2) to design and optimize TENG in certain circumstances for the improvement of performance and durability. Such framework will also provide solid foundations and physical-mechanical guidance for other implantable energy harvesters, such as piezoelectric and flexoelectric nanogenerators.

Mots clés : triboelectrification, muscle dynamics, triboelectric nanogenerators

Participants :
- Leibniz University Hannover (Allemagne) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 162806.4 €