Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : AutoLiqHand (Horizon 2020, appel 2016, Proof of concept grants)
Entité participante : Technical University of Munich — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : TUM
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 149750.0 €

Liquid handling is an integral part of biological and medical assays. For many applications the method of choice is manual pipetting, which has significant limitations. Chiefly, it is user-time intensive and prone to sample handling issues (poor time accuracy and pipetting errors). While liquid handling robots do exist, they are expensive instruments, shared by many users and targeted for high-throughput applications. This leaves, in practice, most research and diagnostics without an automated solution. Here, we present an innovative solution to this problem: a compact and mobile automated liquid handling (AutoLiqHand) device for the individual user. This platform will enable to automate biomedical experiments and diagnostic routines which are currently done by hand. Moreover, its unique design enables it to run in a much larger range of biomedical settings than currently offered by existing solutions, and at the fraction of the cost. This platform was developed as part of the ERC-funded project SelfOrg and is routinely used in our lab for a variety of specialized and standard routines (e.g. drug treatment and immunostaining). The AutoLiqHand system mimics the main advantages of manual pipetting, namely simplicity and versatility, through a unique design of a fully integrated and microfluidic-based platform. In addition, when interfaced with well-established biomedical equipment such as ELISA readers or PCR machines, our platform can form a fully automated lab at significantly lower costs than commercially available devices. Thus, it has the potential to become a standard tool for researchers both in basic and early pharmaceutical/clinical research as well as for clinicians in point-of-care diagnostics. The aim of this Proof-of-Concept proposal is to adapt the AutoLiqHand platform to market needs and optimize it for production in order to make it available to the market.

Mots-clés : liquid handling,automation,life science,biomedical diagnostics,point of care,biotechnology,cell biology,laboratory instrumentation,microfluidics
Projet ERC : LiAnMAT (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Humboldt-Universität zu Berlin — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Lithium (Li) ion batteries – present in all consumer electronics and battery-powered vehicles – are produced in a capital and know-how intensive way, in particular during the initial steps of materials synthesis and cell manufacturing. The anodes of Liion batteries are currently prepared by mixing an ink of binders, solvents, silicon (Si) nanoparticles and graphite under strictly controlled conditions (order, timing, temperature), and they undergo several steps like application of the slurry to metal contacts, drying and pressing before completion of the actual anode. In the course of the ERC Starting Grant BEGMAT we have developed a new Li-ion anode material (LiAnMAT), that (a) works without any classical additives (binder, graphite) and hence cuts down on material weight, cost, and problems in materials handling, (b) does not require mixing and application of a mixed ink to metal contacts during the preparation of the active materials – the anode material comes ready as-received on copper (Cu) metal, and (c) it does not require post-production like pressing – the films are uniformly flat as-received and can be tuned in thickness from several nm up to ~20 µm. Further, the material properties are fantastic and close to the theoretical limit for LiSi-ion batteries. Hence, the objective of this ERC PoC Grant LiAnMAT is to develop this technology further and to achieve together with our tech transfer liaison Humboldt-Innovation GmbH, and our industrial partner VARTA Micro Innovation GmbH the following goals: • Elimination of (most or) all conventional additives (binder, graphite, Si NPs) in anode materials. • Minimising the number of manufacturing steps in anode and battery assembly. • Elimination of the solid-electrolyte interface (SEI) and associated capacity loss in the 1st lithiation cycle. • Transfer of the LiAnMAT synthesis to large-scale industrial processes. • Finding a market for LiAnMAT anodes/batteries and value creation by licensing and spin-offs.

Mots-clés : lithium ion battery,anode,porous polymer,organic semiconductor,organic conductor
Projet ERC : LifeLikeMat (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Weizmann Institute of Science — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999572.0 €

Living organisms are sophisticated self-assembled structures that exist and operate far from thermodynamic equilibrium and, as such, represent the ultimate example of dissipative self-assembly. They remain stable at highly organized (low-entropy) states owing to the continuous consumption of energy stored in "chemical fuels", which they convert into low-energy waste. Dissipative self-assembly is ubiquitous in nature, where it gives rise to complex structures and properties such as self-healing, homeostasis, and camouflage. In sharp contrast, nearly all man-made materials are static: they are designed to serve a given purpose rather than to exhibit different properties dependent on external conditions. Developing the means to rationally design dissipative self-assembly constructs will greatly impact a range of industries, including the pharmaceutical and energy sectors. The goal of the proposed research program is to develop novel principles for designing dissipative self-assembly systems and to fabricate a range of dissipative materials based on these principles. To achieve this goal, we will employ novel, unconventional approaches based predominantly on integrating organic and colloidal-inorganic building blocks. Specifically, we will (WP1) drive dissipative self-assembly using chemical reactions such as polymerization, oxidation of sugars, and CO2-to-methanol conversion, (WP2) develop new modes of intrinsically dissipative self-assembly, whereby the activated building blocks are inherently unstable, and (WP3&4) conceive systems whereby self-assembly is spontaneously followed by disassembly. The proposed studies will lead to new classes of "driven" materials with features such as tunable lifetimes, time-dependent electrical conductivity, and dynamic exchange of building blocks. Overall, this project will lay the foundations for developing new synthetic dissipative materials, bringing us closer to the rich and varied functionality of materials found in nature.

Mots-clés : self assembly,nanoparticles,nanoscience
Projet ERC : datarev (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The study of the human past
Entité participante : University of Vienna — Pays : Autriche — Rôle : PI
Acronyme de l'entité : Univie
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Long before Big Data invaded the countryside with datafication and High Precision Farming in the 2010s, at least another data-revolution had taken place in Europe. The spread of bookkeeping to ordinary farmers that started in the second half of the 19th century represents a revolutionary phenomenon whose importance and magnitude have been underestimated by historians. After overseas competition hit European farmers in the 1870s, the diffusion of bookkeeping techniques among ordinary European farmers was an important component of the recovery. The farm accountancy offices that mushroomed in northern and central Europe thanks to the work of economists and agricultural unions were crucial to reach medium and small farmers. Such offices acted as consulting agencies to farmers, helping them to be more efficient. But the offices also aggregated farm accountancy data from individual farms to produce benchmarks and for statistical purposes. Farmers unions could thus use micro-data to enlighten macro-economic issues and guide policy decisions at different levels in a striking example of “stakeholders’ statistics.” DATAREV investigates the diffusion of farm accountancy offices of continental Europe since the 1870s and the use of farm accountancy data by state and non-state actors in the first half of the 20th century. With its four subprojects, DATAREV thoroughly explores different aspect of the first data revolution, its actors and its consequences. It examines how economic and socially change was conceptualized and direct through accountancy data and statistics and how the peculiar structure of the agricultural business in Europe imposed constraints on bookkeeping unlike those at work in industry. It questions the agency in the datafication process and explores the conflicting claims to the data. Finally, it clarifies how farm accountancy data played a crucial role in the reorganization of the governance of European agriculture after the Long Depression.

Mots-clés : history and sociology of quantification,agricultural history,science and technology studies
Projet ERC : datarev (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The study of the human past
Entité participante : University of Geneva — Pays : Suisse — Rôle : Partner
Acronyme de l'entité : UNIGE
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

Long before Big Data invaded the countryside with datafication and High Precision Farming in the 2010s, at least another data-revolution had taken place in Europe. The spread of bookkeeping to ordinary farmers that started in the second half of the 19th century represents a revolutionary phenomenon whose importance and magnitude have been underestimated by historians. After overseas competition hit European farmers in the 1870s, the diffusion of bookkeeping techniques among ordinary European farmers was an important component of the recovery. The farm accountancy offices that mushroomed in northern and central Europe thanks to the work of economists and agricultural unions were crucial to reach medium and small farmers. Such offices acted as consulting agencies to farmers, helping them to be more efficient. But the offices also aggregated farm accountancy data from individual farms to produce benchmarks and for statistical purposes. Farmers unions could thus use micro-data to enlighten macro-economic issues and guide policy decisions at different levels in a striking example of “stakeholders’ statistics.” DATAREV investigates the diffusion of farm accountancy offices of continental Europe since the 1870s and the use of farm accountancy data by state and non-state actors in the first half of the 20th century. With its four subprojects, DATAREV thoroughly explores different aspect of the first data revolution, its actors and its consequences. It examines how economic and socially change was conceptualized and direct through accountancy data and statistics and how the peculiar structure of the agricultural business in Europe imposed constraints on bookkeeping unlike those at work in industry. It questions the agency in the datafication process and explores the conflicting claims to the data. Finally, it clarifies how farm accountancy data played a crucial role in the reorganization of the governance of European agriculture after the Long Depression.

Mots-clés : history and sociology of quantification,agricultural history,science and technology studies
Projet ERC : NovelNMDA (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Universitätsmedizin Göttingen — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : UMG
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1340544.0 €

Long-term synaptic plasticity is an experience dependent increase or decrease in synaptic signaling between nerve cells that can last from hours to days or even longer. It is thought to represent the cellular substrate of learning and memory by providing an information storage mechanism in the brain. At many excitatory synapses, plasticity-induction processes involve NMDA receptors, glutamate-gated ion channels. I recently discovered a new form of NMDA receptor signaling during spike-timing dependent long-term depression at layer-4-to-layer-2/3 synapses in rodent somatosensory cortex, which requires glutamate binding to the NMDA receptor but not ion flux through its channel. This non-ionic signaling mechanism via the NMDA receptor represents a strikingly overlooked signaling pathway at glutamatergic synapses, and its discovery may overturn much of the long-term depression field, which has focused on calcium influx through the NMDA receptors as the relevant signal in plasticity. Indeed, the discovery of non-ionic NMDA receptor signaling in the postsynaptic membrane even calls into question our current understanding of the basic properties of synaptic signaling. By combining electrophysiology and two-photon imaging and uncaging, we will address the fundamental properties of non-ionic NMDAR dependent plasticity at cortical layer-4-to-layer-2/3 synapses, determine in which synaptic subcompartment plasticity occurs, assess whether signaling depends on a particular NMDA receptor subtype, and examine whether non-ionic NMDA receptor signaling represents a general mechanism at other glutamatergic synapses. This work will lead to a detailed functional understanding of the novel non-ionic NMDA receptor signaling process and its role in synapse function and plasticity.

Mots-clés : nmda receptor,spike timing dependent plasticity,long term depression,synaptic plasticity
Projet ERC : DynaOmics (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : University of Turku — Pays : Finlande — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499869.0 €

Longitudinal omics data hold great promise to improve biomarker detection and enable dynamic individualized predictions. Recent technological advances have made proteomics an increasingly attractive option but clinical longitudinal proteomic datasets are still rare and computational tools for their analysis underdeveloped. The objective of this proposal is to create a roadmap to detect clinically feasible protein markers using longitudinal data and effective computational tools. A biomedical focus is on early detection of Type 1 diabetes (T1D). Specific objectives are: 1) Novel biomarker detector using longitudinal data. DynaOmics introduces novel types of multi-level dynamic markers that are undetectable in conventional single-time cross-sectional studies (e.g. within-individual changes in abundance or associations), develops optimization methods for their robust and reproducible detection within and across individuals, and validates their utility in well-defined samples. 2) Individualized disease risk prediction dynamically. DynaOmics develops dynamic individualized predictive models using the multi-level longitudinal proteome features and novel statistical and machine learning methods that have previously not been used in this context, including joint models of longitudinal and time-to-event data, and one-class classification type techniques. 3) Dynamic prediction of T1D. DynaOmics builds a predictive model of dynamic T1D risk to assist early detection of the disease, which is crucial for developing future therapeutic and preventive strategies. T1D typically involves a relatively long symptom-free period before clinical diagnosis but current tools to predict early T1D risk have restricted power. The objectives involve innovative and unconventional approaches and address major unmet challenges in the field, having high potential to open new avenues for diagnosis and treatment of complex diseases and fundamentally novel insights towards precision medicine.

Mots-clés : computational biomedicine,systems biology,longitudinal data analysis,molecular networks,biomarker,statistical data mining,predictive modeling,clinical sample material,type 1 diabetes
Projet ERC : S4F (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : University of Copenhagen — Pays : Danemark — Rôle : PI
Acronyme de l'entité : UCPH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999659.0 €

Low-mass stars like our Sun are formed in the centers of dark clouds of dust and gas that obscure their visible light. Deep observations at infrared and submillimeter wavelengths are uniquely suited to probe the inner regions of these young stellar objects and unravel their structures, as well as the physical and chemical processes involved. These earliest stages are particularly interesting because the properties of the deeply embedded objects reflect the star formation process itself and how it relates to its environment. It is for example during this stage that the final mass of the star and the properties of its disk – and thus ability to form planets – are determined. It is also during these stages that the first seeds for the chemical evolution of the protoplanetary disk are planted and where some complex organic, possibly prebiotic, molecules may be formed. I here apply for an ERC Consolidator Grant that will support an ambitious program to map the physics and chemistry of the early Solar System. The proposed research program intends to use new high resolution, high sensitivity observations from the Atacama Large Millimeter Array (ALMA) - including a number of recently approved large programs – coupled to state-of-the-art radiative transfer tools and theoretical simulations to address some of the key questions concerning the physics and chemistry of the earliest stages of the Solar System: How is the chemistry of the earliest protostellar stages related to the physical structure and evolution of the young stellar object and its surrounding environment? Which complex organic molecules are present in the inner regions of low-mass protostars? What are the chances the rich chemistry of the earliest stages is incorporated into planetary systems such as our own?
Projet ERC : COLORAMAP (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Mons — Pays : Belgique — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1291750.0 €

Low-rank matrix approximation (LRA) techniques such as principal component analysis (PCA) are powerful tools for the representation and analysis of high dimensional data, and are used in a wide variety of areas such as machine learning, signal and image processing, data mining, and optimization. Without any constraints and using the least squares error, LRA can be solved via the singular value decomposition. However, in practice, this model is often not suitable mainly because (i) the data might be contaminated with outliers, missing data and non-Gaussian noise, and (ii) the low-rank factors of the decomposition might have to satisfy some specific constraints. Hence, in recent years, many variants of LRA have been introduced, using different constraints on the factors and using different objective functions to assess the quality of the approximation; e.g., sparse PCA, PCA with missing data, independent component analysis and nonnegative matrix factorization. Although these new constrained LRA models have become very popular and standard in some fields, there is still a significant gap between theory and practice. In this project, our goal is to reduce this gap by attacking the problem in an integrated way making connections between LRA variants, and by using four very different but complementary perspectives: (1) computational complexity issues, (2) provably correct algorithms, (3) heuristics for difficult instances, and (4) application-oriented aspects. This unified and multi-disciplinary approach will enable us to understand these problems better, to develop and analyze new and existing algorithms and to then use them for applications. Our ultimate goal is to provide practitioners with new tools and to allow them to decide which method to use in which situation and to know what to expect from it.

Mots-clés : linear dimensionality reduction,hyperspectral imaging,document classification,nonnegative matrix factorization,robust principal component analysis,missing data
Projet ERC : INTEGRATE (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Trinity College Dublin — Pays : Irlande — Rôle : PI
Acronyme de l'entité : TCD
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1754543.0 €

Lower back pain is a global epidemiological and socioeconomic problem. Biomaterial and cell-based therapies have been pursued for the treatment of degenerated intervertebral disc (IVD), with a number of clinical trials underway. However, the degenerated intervertebral disc has a distinct environment (e.g. altered oxygen, glucose, acidity, inflammatory cytokine levels) that is unique to an individual (i.e. patient-specific) and will ultimately determine the likelihood and rate at which regeneration can occur. A “one size fits all” approach will lead to the failure to demonstrate efficacy of advanced therapies, as they are not being designed or personalised for individual patients. This proposal envisions a future whereby advanced gene activated cell therapies are personalised (targeting regeneration or modulating inflammation) to treat back pain based on knowing the individuals unique disc microenvironment. This will be achieved through profiling of individual patient disc microenvironmental factors, with in vitro screening and in silico modelling to design cell therapies and predict regeneration outcomes (Aim 1) combined with the development of tailored functionalised gene activated biomaterials (Aim 2), to enhance matrix formation and modulate the inflammatory processes (Aim 3). Gene-based therapy offers several advantages over direct delivery of proteins or small molecules, among them the possibility of sustained efficacy and endogenous synthesis of growth factors or suppression of inflammatory factors and pathways. The platform technology (personalised gene activated biomaterials to regulate regeneration and inflammation) and knowledge (tailoring cell therapies to suit patient-specific microenvironments) generated through this research are beyond the current state-of-the-art and will provide a significant transformative scientific and clinical step change opening new horizons in minimally-invasive therapeutic strategies.

Mots-clés : injectable hydrogels,tissue regeneration,spine,stem cells