Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Computational methods and modeling to decipher organelle nanophysiology (OrganellenanoComp)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced 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: France
Call: ERC Advanced Grant (ERC-2019-ADG, 2019)
Start date: 2021-03-01
End date: 2026-02-28
Duration (months): 60.0
Total project cost (EUR): 2374698.0
EU funding (EUR): 2374698.0
Participants: 1
Project status: SIGNED

Neuronal and glial physiology in nanodomains remains poorly understood due to the spatio-temporal limitation of direct unperturbed in vivo measurements. Yet, it is the scale of voltage regulation, ionic, proteins and molecular trafficking, metabolism control and local signal transduction. The goal of this proposal is to determine how the flow of ions and molecules is regulated in the cytoplasm in relation with organelles, such as the endoplasmic reticulum and mitochondria in various physiological conditions such as steady-state, induction of plastic changes or ionic depletion. The approach is based on mathematical modeling, large data analysis, simulation methods, and developing the associated fast and efficient algorithms. We developed in the past 15 years computational tools such as molecular modeling, stochastic simulations and data analysis at a molecular level to study various signals such as voltage recordings or super-resolution microscopy single particle trajectories (SPTs). However, these theoretical approaches are not sufficient today to face the novel data revolution coming from SPTs, but also voltage dyes, voltage recorded by nanopipettes, or photoconversion in nanocompartments. The aim of the project is to develop physical models of molecular diffusion and electro-diffusion. These models will be applied to reconstruct and interpret the local transport, which is not homogeneous because molecules or receptors could aggregate in some specific nano-regions. We will explore the mechanisms underlying this heterogeneity. Second, we will apply these modeling and numerical simulations to analyze data about flux regulation inside the cytoplasm but also in organelles, such as the ER and mitochondria.Third, we analyze calcium fluorescent data (photoactivation, local uncaging) to study how calcium is exchanged in nanodomains during synaptic transmission in dendrites and dendritic spines.
Unknown functions of Osteocyte DEath (ODE)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Physiology in Health, Disease and Ageing
Panel description: Organ and tissue physiology, comparative physiology, physiology of ageing, pathophysiology, interorgan and tissue communication, endocrinology, nutrition, metabolism, interaction with the microbiome, non-communicable diseases including cancer (and except disorders of the nervous system and immunity-related diseases)
Countries involved: Germany
Call: ERC CONSOLIDATOR GRANTS (ERC-2020-COG, 2020)
Start date: 2021-06-01
End date: 2026-05-31
Duration (months): 60.0
Total project cost (EUR): 2008062.0
EU funding (EUR): 2008062.0
Participants: 1
Project status: SIGNED

Osteocytes are long-lived cells within the bone matrix that have a variety of functions in the control of bone remodeling. They are the most frequent cells of the bone by far and mediate the regulation of the mechanical loading-induced bone renewal at the systemic level. Little is known how osteocytes die and how this process affects local bone homeostasis. Nonetheless several local bone diseases such as fracture, osteonecrosis and arthritis are characterized by enhanced osteocyte death and local bone resorption. My preliminary data show that osteocytes, when dying, undergo secondary necrosis, due to their secluded localization within the bone and the absence of phagocyting cells. Hence substantial amounts of damage-associated molecular patterns (DAMPs) are released into the bone micro-environment. I can show that DAMPs effectively osteoclast differentiation via binding to the C-type lectin receptor Mincle. My proposal ODE aims to characterize osteocyte death, the nature of the released DAMPs and the molecular link between osteocyte death and stimulation of osteoclasts. I specifically aim to delineate, in which way osteocytes die within the bone matrix (apoptosis, necrosis, necroptosis, ferroptosis or pyroptosis) and which specific DAMPs are released into the bone marrow via the canalicular network. In this context, local bone diseases such as fracture, osteonecrosis and arthritis will be investigated. In aim 1, I will molecularly characterize osteocyte cell death and block the corresponding death pathways. In aim 2, I will determine putative molecular mechanisms driving osteoclast maturation through the pathways triggered by myeloid specific C-type lectin receptors. In aim 3, I will test the molecular mechanisms of osteocyte death-induced osteoclastogenesis. Overall, my proposal will gain new insights into local bone homeostasis, i.e. the molecular regulation of osteocyte death and the molecular links to an altered local bone microenvironment.

Keywords: osteocytes,damps,c type lectin,osteoclasts,bone diseases
Big Time Series Analytics for Complex Economic Decisions (BigTime)

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: Economic Sciences
Countries involved: Netherlands
Call: Individual Fellowships (H2020-MSCA-IF-2018, 2018)
Start date: 2019-05-01
End date: 2021-04-30
Duration (months): 24.0
Total project cost (EUR): 175572.48
EU funding (EUR): 175572.48
Participants: 1
Project status: CLOSED

Big time series data are commonplace in economics. Their variety and sheer size provide nearly endless opportunities to improve economic decision making at European governments, companies and universities: amongst others, internet search data could shed light on consumer sentiment, social media provide opportunities for improving economic policy analysis, and high-frequency volatility data could be informative for financial risk analysis. While the expansion of these Big Data sources bring possibilities, it also raises ever-increasing statistical challenges since novel methods (for instance, 'penalized' methods) are needed to estimate high-dimensional models containing many parameters. The development of such methods has flourished in the statistical learning community, but they are not geared towards the specificities of economic time series. Econometric time series models typically differ from traditional statistical models in that they require (i) an accurate assessment of the certainty of the economic findings and predictions, (ii) a description of how the economy responds, over time, to exogenous shocks, and (iii) an identification strategy that maps the observed data to the relevant economic parameters of interest. The proposal builds a partnership between econometrics, statistics and machine learning with the aim of addressing these three econometric objectives. It develops statistical learning methods for (i) honest uncertainty quantification (inference), (ii) interpretable economic impulse response functions analysis and (iii) identification of high-dimensional time series models. The suitability of the developed Big Time Series methods is demonstrated for economic applications including financial risk analysis and macro-economic policy analysis. As such, the proposal provides a Big Time Series Analytics toolbox to modern empirical economists that aims to support and improve economic decision making in big, dynamic and complex time series problems.

Keywords: econometric time series,identification,high dimensional statistics,penalized estimation,post selection inference
Multi-aspect and diffErenTiable Evaluation of Rankings (METER)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Denmark
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2020-10-01
End date: 2022-09-30
Duration (months): 24.0
Total project cost (EUR): 207312.0
EU funding (EUR): 207312.0
Participants: 1
Project status: CLOSED

Information Retrieval (IR) deals with the automatic retrieval and ranking of information conveying items, which are relevant to a specific information need, from a large collection of items. Search engines are the most popular and well known examples of IR systems. State-of-the-art IR systems use sophisticated Machine Learning (ML) and Deep Learning (DL) models. Those models usually minimize a loss function which is built upon an IR evaluation measure, i.e. a measure that evaluates the quality of a ranked list of items. This project, Multi-aspect and diffErenTiable Evaluation of Rankings (METER), will tackle two open challenges for state-of-the-art IR systems. First, traditionally IR systems ranks items only by relevance, estimated as the semantic similarity between the user query and the information conveying items. However, beside relevance, understandability and trustworthines are fundamental for health search, or credibility and correctness should be considered for news search. Therefore, the first goal of METER will be to extend IR evaluation measures to deal with mutiple aspects. Then, these new evaluation measures will be integrated in ML algorithms, to develop multi-aspect IR systems. Second, IR measures are non-continuous and non-differentiable. This represents an issue for ML algorithms, which usually exploit gradient based approaches to minize the loss function. Therefore, the second goal of METER will be to thoroughly analyze IR evaluation measures and propose differentiability like properties which will help for the search of minima of the loss function. Therefore, METER has the potential for making both a scientific and a societal impact: 1) multi-aspect measures will be used to account for several aspect and improve the effectiveness of IR systems in different domains; 2) differentiability like properties will be exploited to improve the search of local minima for IR loss functions and better understand how this search is performed.

Keywords: information retrieval,evaluation,search engines,web search
Deep multi-scale modelling of electrified metal oxide nanostructures (DeepProton)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Physical and analytical chemical sciences
Panel description: Analytical chemistry, chemical theory, physical chemistry/chemical physics
Countries involved: Sweden
Call: ERC STARTING GRANTS (ERC-2020-STG, 2020)
Start date: 2021-01-01
End date: 2025-12-31
Duration (months): 60.0
Total project cost (EUR): 1400600.0
EU funding (EUR): 1400600.0
Participants: 1
Project status: SIGNED

One promising solution toward a sustainable society and a green economy is to use metal oxide-based materials. Metal oxides are a class of inorganic materials that have various energy and environmental applications such as heterogeneous catalyst, fuel cell, lithium-ion battery, supercapacitor, water treatment and antimicrobial application. Most metal oxides are synthesized as nanostructures which leads to unique properties and reduced economical costs. The very properties that make the metal oxide nanostructures attractive and indispensable in modern science and technology also cause an issue for the environment and human safety. In both the functioning and the degradation of metal oxide nanostructures, aqueous interface plays a vital role. The metal oxide-aqueous solution aqueous interface is electrified in working conditions due to acid-base chemistry and composed of protonic electric double layer. Given the importance of metal oxide surfaces in practical applications, surprisingly little is known about the relation between atomic structure of protonic double layer and the interfacial reactivity. This is largely due to the fact that our knowledge is mostly based on macroscopic observations such as current and concentration in electrochemistry and microscopic information of protonic double layer is difficult to be obtained in experiments. Therefore, developing a novel deep-learning empowered multi-scale modelling framework and providing a revolutionizing understanding at microscopic level of the functioning and degradation of electrified metal oxide nanostructures are the aims of this proposal. The outcome of this project will not only lead to the knowledge discovery about the impact of protonic electric double layer on porous metal oxide-based supercapacitors and on the degradation of metal oxide nanoparticles, but it will also propose useful design principles for synthesis and fabrication.

Keywords: electric double layer,surface chemistry,supercapacitor,dissolution,electric field,molecular dynamics,machine learning,density functional theory
Data-driven Inference of Models from Embodied Neural Systems In Vertebrate Experiments (DIMENSIVE)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: United Kingdom
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2020-05-12
End date: 2022-05-25
Duration (months): 24.0
Total project cost (EUR): 212933.76
EU funding (EUR): 212933.76
Participants: 1
Project status: CLOSED

A major challenge in cognitive neuroscience is to understand how behaviour arises from the dynamical interaction of an organism’s nervous system, its body, and its environment. Understanding embodied neural activity involves the resolution of various conceptual, technical and methodological issues in explaining how living organisms self-organize at many levels (from neural bio-chemistry to behaviour and learning). Currently, two important obstacles hinder this endeavour: the difficulties in recording neural activity in behaving animals, and the lack of mathematical tools to characterize the complex brain-body-environment interactions in living organisms. In this project we will address current limits by implementing an interdisciplinary combination of novel animal behaviour neuroimaging setups and large-scale statistical methods, with the goal of recording and modelling whole-brain activity of locomoting vertebrates. We will study fictively swimming larval zebrafish during active behaviour in a pioneering experimental setup, recording neural activity utilizing light-sheet microscopy for calcium imaging in different virtual reality scenarios involving sensorimotor manipulations. In this setup, we will collect data from the distributed neural circuits that integrate sensory signals from the environment (exafferent input) and their own movements (reafferent input), as well as plastic processes of habituation to new sensorimotor contingencies. From this data, we will infer large-scale generative models (i.e. models capable of yielding synthetic data resembling the studied phenomena) of embodied neural circuits by complementing dynamical models and techniques from statistical mechanics with innovative information theoretic and Bayesian inference methods and approximations for very large systems in non-equilibrium and non-stationary conditions.

Keywords: computational neuroscience,animal behaviour,zebrafish,calcium imaging,virtual reality,statistical models,neural networks,information theory,bayesian inference
Harvesting energy via aligned porosity pyroelectrics (HEAPPs)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: United Kingdom
Call: Marie Skłodowska-Curie Individual Fellowships (IF-EF) (H2020-MSCA-IF-2015, 2015)
Start date: 2016-06-10
End date: 2018-06-09
Duration (months): 24.0
Total project cost (EUR): 195454.8
EU funding (EUR): 195454.8
Participants: 1
Project status: CLOSED

Energy harvesting continues to receive increasing amounts of industrial and academic interest since it provides a route for the realisation of autonomous and self-powered low-power electronic devices. Pyroelectric materials, the focus of this proposal, are of interest since under the correct conditions they have the ability to operate with a high thermodynamic efficiency for converting temperature fluctuations into useable electrical power. To improve the power generation capability of these materials, the improvement and optimisation of the pyroelectric properties and relevant figure of merits are imperative for expanding the field of applications. In this proposal we will examine pyroelectric ceramics with tailored porosity since they have the potential to exhibit high mechanical strength and performance figures of merit by maintaining a high pyroelectric coefficient with a reduced permittivity and volume specific heat. To date, reports on the porous pyroelectric ceramics are limited due to poor control of pore size and mechanical properties. This research will focus on the development of aligned porous pyroelectric ceramics with readily adjustable pyroelectric properties. The Fellowship will allow me develop professional maturity in terms of leading knowledge transfer to a new institution, gaining experience of a boarder range of skill sets (modelling, energy, harvesting systems) for thermal energy harvesting applications. In addition, the fellowship will contribute to the strengthening of the European energy harvesting research community to the establishment of a long-term collaboration between the host and the applicant's country. By expanding my research and academic training, supported by Prof. Bowen and University of Bath, this fellowship will be a key step in the applicant’s career development, as the applicant aims to take a leadership role and build a better platform for energy harvesting applications upon returning to her home country.

Keywords: aligned pore structure,energy harvesting applications,pyroelectric ceramics,improvement of mechanical and pyroelectric properties
Copper Acquisition (CORA)

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: Environment and Geosciences
Countries involved: Austria
Call: Marie Skłodowska-Curie Individual Fellowships (IF-EF) (H2020-MSCA-IF-2015, 2015)
Start date: 2016-06-01
End date: 2018-05-31
Duration (months): 24.0
Total project cost (EUR): 178156.8
EU funding (EUR): 178156.8
Participants: 1
Project status: CLOSED

It has recently been discovered that archaea are promoting ammonia oxidation, an important process in nitrogen cycling. Ammonia oxidizing archaea (AOA) use Cu containing enzymes to oxidize ammonia to nitrate. Therefore, they have an elevated Cu requirement and thus the bioavailability of Cu to archaea may impact the nitrogen cycle. However, the molecular mechanism of Cu uptake by Archaea from terrestrial and aquatic environments remains unknown. Understanding the molecular mechanism of Cu uptake by these archaea will be an important step in understanding Cu acquisition by the Archaea. The first objective of this study will be to determine what types of proteins are potentially involved in Cu uptake under Cu limiting conditions. The hypothesis that low concentrations of Cu will result in expression of Cu acquisition genes will be tested using different strains of AOA originating from soil and hotspring environments and varying the availability of Cu to the AOA. The gene expression of AOA cultures as a function of Cu availability will be monitored using transcriptome illumina sequencing. The hypothesis that soil AOA will secrete extracellular reactive compounds to mobilize Cu as part of a Cu acquisition strategy will be tested. The presence of a strong copper binding ligands using a colorimetric assay and by an electrochemical method. The second objective of this study will be to use different strains of AOA in batch incubation experiments with different previously characterized Cu containing soils and minerals. The hypothesis that AOA isolated from soil environments will have high efficiency Cu uptake systems compared to AOA from the aquatic environment will be tested in batch incubations with AOA cell cultures and different Cu containing soils and minerals. This study will be innovative as this problem has not previously been addressed.

Keywords: archaea,copper binding ligand,copper,ammonia oxidation,nutrient acquisition,dissolution rates
Multiplex phOtonic sensor for pLasmonic-based Online detection of contaminants in milK (MOLOKO)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Innovation actions
Partnership: Photonics (cPPP)
Countries involved: Italy,Italy,Germany,Italy,Italy,Italy,Switzerland,Finland,Hungary,Belgium,Netherlands,United Kingdom
Call: Photonics KET 2017 (H2020-ICT-2017-1, 2017)
Start date: 2018-01-01
End date: 2022-03-31
Duration (months): 51.0
Total project cost (EUR): 6050891.45
EU funding (EUR): 5479159.0
Participants: 14
Project status: CLOSED

The main objective of MOLOKO project is the manufacturing, implementation and validation of a self-managing and automatic miniaturized integrated photonic sensor to be used as process analytical instrumentation for fast-response on-site monitoring of interest analytes for security and quality within milk supply chain. These challenging objectives are achieved by integrating within the same device platform forefront technologies as organic photonics, nanoplasmonics, immunoassay diagnostics and microfluidics. Specifically, we aim at realizing multiplexing quantitative detection of up to 10 analytes among which food safety parameters, e.g. antibiotics (i.e. penicillin, ampicillin, cephalonium) and toxins (i.e. mycotoxins and bacterial toxins) and food quality parameters e.g. lactoferrin and caseins by implementing a highly-integrated optoplasmonic-microfluidic sensor in the strategic checkpoints along the entire supply and value chain of milk. The MOLOKO miniaturized integrated photonic sensor is specifically designed according to milk primary production, processing and distribution end-users in order to enable and guarantee fast, low-cost, robust, quantitative and high-sensitive multiplexing detection for the rapid acceptance screening of milk from primary producers (farm tank milk) or single bovine. The inherent versatility of the sensor guarantees disruptive effectiveness in multiple real settings applications, i.e. as automated sensor integrated in milking machine as handheld reusable analytical instrument for specialized and non-specialised milk operators. The effectiveness and market-placement of the engineered functional prototype is quantitatively evaluated by direct comparison with respect to standard analytical methods and commercially available optical biosensors.

Keywords: food quality,analytical instruments,dairy industry,early stage monitoring