Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Taming Irregular Computations On Heterogeneous processors (TICOH)

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: Norway
Call: Individual Fellowships (H2020-MSCA-IF-2016, 2016)
Start date: 2017-05-01
End date: 2019-06-30
Duration (months): 24.0
Total project cost (EUR): 208400.4
EU funding (EUR): 208400.4
Participants: 1
Project status: CLOSED

In the past decade, more and more heterogeneous processors are equipping modern supercomputers. Unfortunately, despite the progress of hardware infrastructure, the utilization of heterogeneous computing is still relatively low in practice. The utilization mainly suffers from two bottlenecks: distributed memories that bring high cost for software engineering and degraded performance, and lack of heterogeneity-oriented scalable approaches for irregular problems. The objective of the project TICOH is to address the issue of currently unsatisfactory utilization of heterogeneous computing for irregular problems such as graph and sparse matrix processing. Following a multi-level approach which bridges the domains of performance measurement, benchmark data analysis, modeling, data structure construction, algorithm design and application integration, TICOH will explore best practices that toward the best performance for irregular computations on the best hardware selection. Specifically, the main focus of the project will be to (a) identify and understand bottlenecks of current heterogeneous computing, (b) benchmark and model heterogenous processors composed of CPU, GPU and high-bandwidth memories, (c) design and evaluate new data structures and algorithms for irregular problems aiming for fully use computing and memory resources, and (d) integrate and apply the newly designed approaches for high-level applications. By empirically investigating these issues, the ultimate goal of the project is to allow a broad range of real-world applications to further benefit from heterogeneous hardware in the new era. Prof. Anne C. Elster will be hosting this action at the HPC-Lab of NTNU. Her research team is known for its experience with heterogeneous computing, and is one of the main partners in the H2020 CloudLightning project. This environment will guarantee successful bidirectional transfer of knowledge.

Keywords: heterogeneous processor,graph processing,sparse matrix computations,irregular problems
Brain network based stratification of mental illness (STRATIFY)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Prevention, Diagnosis and Treatment of Human Diseases
Panel description: Medical technologies and tools for prevention, diagnosis and treatment of human diseases, therapeutic approaches and interventions, pharmacology, preventative medicine, epidemiology and public health, digital medicine
Countries involved: Germany,Ireland,United Kingdom,United Kingdom,United Kingdom
Call: ERC Advanced Grant (ERC-2015-AdG, 2015)
Start date: 2016-10-01
End date: 2023-09-30
Duration (months): 84.0
Total project cost (EUR): 3394214.88
EU funding (EUR): 988895.34
Participants: 5
Project status: SIGNED

To reduce the burden of mental disorders it is a formidable aim to identify widely applicable disease markers based on neural processes, which predict psychopathology and allow for targeted interventions. We will generate a neurobehavioural framework for stratification of psychopathology by characterising links between network properties of brain function and structure and reinforcement–related behaviours, which are fundamental components of some of the most prevalent mental disorders, major depression, alcohol use disorder and ADHD. We will assess if network configurations define subtypes within and if they correspond to comorbidity across these diagnoses. We will identify discriminative data modalities and characterize predictors of future psychopathology. To identify specific neurobehavioural clusters we will carry out precision phenotyping of 900 patients with major depression, ADHD and alcohol use disorders and 300 controls, which we will investigate with innovative deep machine learning methods derived from artifical intelligence research. Development of these methods will optimize exploitation of a wide range of assessment modalities, including functional and structural neuroimaging, cognitive, emotional as well as environmental measures. The neurobehavioural clusters resulting from this analysis will be validated in a longitudinal population-based imaging genomics cohort, the IMAGEN sample of over 2000 participants spanning the period from adolescence to adulthood and integrated with information generated from genomic and imaging-genomic meta-analyses of >300.000 individuals. By targeting specific neural processes the resulting stratification markers will serve as paradigmatic examples for a diagnostic classification, which is based upon quantifiable neurobiological measures, thus enabling targetted early intervention, identification of novel pharmaceutical targets and the establishment of neurobehaviourally informed endpoints for clinical trials.
Ensuring the future of EUPATI beyond 2020 (EFOEUPATI)

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,Ireland,Germany,Belgium,Switzerland,Belgium,United States,Denmark,Denmark,United Kingdom,United Kingdom
Call: Exploitation of IMI project results (H2020-JTI-IMI2-2017-11-single-stage, 2017)
Start date: 2018-09-01
End date: 2020-09-30
Duration (months): 25.0
Total project cost (EUR): 604042.5
EU funding (EUR): 365242.5
Participants: 13
Project status: CLOSED

The European Patients' Academy on Therapeutic Innovation (EUPATI) IMI project resulted in a number of key outputs for the field of patient engagement. The core activities continue running until 2019 under a PPP consortium under an EPF programme focusing on the Patient Expert Training Course, the multilingual public Toolbox, and the EUPATI National Platforms Network. It is imperative that these pillars that support the patient engagement landscape exist in the medium- and long-term to address the growing field of patient engagement. Further investment is needed to develop sustainable models of collaboration to ensure this, and put into place the infrastructure required for these to work, building on those already established under IMI-EUPATI.

Keywords: patient engagement,patient involvement,patient education
SPOT - Synthesis of Pretargeted Oncology Theranostics (SPOT)

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: Life Sciences
Countries involved: Spain
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2020-07-15
End date: 2022-07-14
Duration (months): 24.0
Total project cost (EUR): 160932.48
EU funding (EUR): 160932.48
Participants: 1
Project status: CLOSED

To overcome size-related drawbacks of intact antibodies (Ab) for active tumor targeting, small but multivalent Ab-fragments, which fall in the “ideal tumor targeting zone”, are gaining increasing interest. However, the achieved benefits of Ab-fragments are lost when conjugated to imaging probes or drug carriers. Therefore, the SPOT action is going to focus on the generation of small Ab-fragments with highly specific targeting capacities and deep tissue penetration that are able to subsequently undergo a bioorthogonal click reaction at the tumor site. As cancer model, pancreatic ductal adenocarcinoma (PDAC), which currently still has an extremely poor prognosis, is going to be targeted. Two different click reaction will be evaluated: The cycloaddition between tetrazine and trans-cyclooctene (TCO) as well as azide and dibenzocyclooctyne (DBCO), whereby the Ab-fragments are functionalized with the TCO and DBCO groups. The opponent moieties are attached to alginate and hyaluronic acid-based ligands, which are subsequently introduced to the nanoparticle’s (NP) surface, additionally providing shielding from the immunological system. After the accumulation of the clickable Ab-fragments at the tumor site and their clearance from healthy tissue, the imaging or drug-delivering NPs with the opponent clickable moieties are injected, allowing for their exclusive accumulation at the pretargeted tumor site. For effective visualization of PDAC tissue, state-of-the-art silver sulfide NPs, with a high contrast-to-noise ratio and deep tissue imaging, are applied. The use of these NPs can be a significant step towards early PDCA diagnosis and therefore providing better chances for a positive treatment outcome. For the pretargeted drug-delivery approach, poly(lactic acid)-based NPs with encapsulated gemcitabine or docetaxel generated via electrospraying are going to be tested as a potential chemotherapeutic approach for PDAC, preforming small animal in vivo studies with PDAC xenografts.

Keywords: ab fragments,in vivo click reactions,pretargeted imaging,pretargeted drug delivery,pancreatic ductal adenocarcinoma,electrospraying,nanoparticles,nanoparticle shielding,carbohydrate ligands
Deciphering Regulatory DNA and Transcription Factor Binding Sites in C3 and C4 Species with Varying Water Use Efficiencies (Trans C4)

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: Life Sciences
Countries involved: United Kingdom
Call: Individual Fellowships (H2020-MSCA-IF-2016, 2016)
Start date: 2018-06-26
End date: 2020-06-25
Duration (months): 24.0
Total project cost (EUR): 195454.8
EU funding (EUR): 195454.8
Participants: 1
Project status: CLOSED

Water-related stress is the number one limiting factor for plant productivity and human well-being. One-third of the current world population faces water shortages and by 2025, two-thirds are expected to experience water stress conditions, i.e 1.8 billion people will be subjected to absolute water scarcity. The C4 photosynthetic pathway boosts plant productivity by ~50% but also increases water use efficiency. C4 photosynthesis is a remarkable trait that is thought to have evolved in response to environmental factors including increased aridity and seasonality. All C4 plants concentrate CO2 in leaves, increasing productivity by ~50%, but also maintaining lower stomatal conductance than C3 species. For example, under heat stress induced by a temperature rise from 20°C to 30°C, C3 plants double water loss via transpiration whilst C4 plants are able to decrease the diffusive efflux of water vapour by 50%, and are therefore considered as water-efficient users. A fuller understanding of C4 photosynthesis would facilitate water efficient and productive crops to be engineered in the future. In this programme the researcher will become familiar with state-of-the-art, genome-wide approaches that are used and operational in the host laboratory to better understand the genetic basis of C4 photosynthesis. Specifically regions of the rice and sorghum genomes that are bound by transcription factors as leaves develop will be determined. These data will be interrogated to test the hypothesis that genes of the C4 pathway evolved to become induced by light in C4 leaves. Secondly, transcription factor footprints associated with genes expressed in M or BS cells of sorghum will be identified. These footprints (DNA sequences) will test the hypothesis that multiple genes preferentially expressed in M or BS cells are regulated by the same cis-elements.

Keywords: plant biology,photosynthesis,c4 pathway,regulatory dna
Interrogating native CRISPR arrays to achieve scalable combinatorial screens and dissect genetic redundancy (CRISPRcombo)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Biotechnology and Biosystems Engineering
Panel description: Biotechnology using all organisms, biotechnology for environment and food applications, applied plant and animal sciences, bioengineering and synthetic biology, biomass and biofuels, biohazards
Countries involved: Germany
Call: ERC Consolidator Grant (ERC-2019-COG, 2019)
Start date: 2020-06-01
End date: 2025-05-31
Duration (months): 60.0
Total project cost (EUR): 2000000.0
EU funding (EUR): 2000000.0
Participants: 1
Project status: SIGNED

A ubiquitous yet poorly understood theme pervading biology is redundancy, wherein seemingly equivalent components drive shared processes. In cases from development to pathogenesis, untangling the ensuing web of potential genetic interactions can be virtually impossible with conventional techniques. CRISPR technologies, with their propensity for multiplexing, are well poised to address this challenge. However, current CRISPR-based screens have not exceeded more than two targets at a time. Here, I will achieve a major leap forward for CRISPR-based screens and dissecting redundancy by harnessing a core yet underexplored part of CRISPR: CRISPR arrays. CRISPR arrays naturally form the immunological memory of CRISPR-Cas systems and produce multiple targeting gRNAs processed from a single transcript. The arrays are highly compact, genetically stable, and can encode hundreds of gRNAs. However, the repetitive “repeats” within each array have hampered their construction and widespread adoption. My group recently made a breakthrough with the modular one-pot assembly of long arrays and array libraries. This capability grants us the unique opportunity to develop the first high-throughput, CRISPR-based screens that readily scale to many gene targets at a time. In parallel, our first assembled arrays highlighted technical constraints to designing robust and highly active arrays. I posit that native CRISPR arrays have faced similar limitations and thus can inform the design of array libraries. I thus propose to 1) Develop design rules for CRISPR arrays yielding only intended and uniformly abundant guide RNAs. 2) Elucidate and exploit why CRISPR arrays are genetically stable. 3) Perform scalable combinatorial screens using redundancy by small RNAs in E. coli as a compelling case study. If successful, this project will reveal unexplored properties of CRISPR arrays and, for the first time, achieve scalable combinatorial screens for interrogating redundancy throughout biology.

Keywords: combinatorial screens,crispr,guide rnas,rna folding,rna processing,bacteria
A multidimensional analysis on the joint-effect of retention forestry and landscape structure on large mammals communities (ConFoBiNM)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Reintegration panel
Action type: Marie Skłodowska-Curie actions
Panel: Environment and Geosciences
Countries involved: Germany
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2021-01-01
End date: 2022-12-31
Duration (months): 24.0
Total project cost (EUR): 162806.4
EU funding (EUR): 162806.4
Participants: 1
Project status: CLOSED

The integration of conservation into production forest management is crucial to achieve desired biodiversity goals at the EU level. Retention forestry, which was introduced as a tool to mitigate the negative impact of transformation and homogenization of forests, is now common practice in Central European forests. The design of efficient retention strategies hinges on ecological knowledge, yet research-based evidence for its effectiveness is lacking. In particular, responses of large terrestrial mammals to variable retention harvesting are unknown. I will address this knowledge gap by using a combination of advanced techniques and statistical methods to investigate how the large mammal community changes in response to varying levels of retention forestry in multiple-use forests, and assess the interplay between retention forestry and the surrounding landscape. I will (a) quantify mammal species richness, species-specific abundance and β-diversity across a gradient of retention forestry and landscape forest cover, and (b) determine a threshold of retention at which significant responses occur. I expect that retention will enhance mammal diversity, but not below a certain threshold or below a certain amount of forest cover. I will use the Black Forest in Southwestern Germany as a model system for multiple-use forests in Central Europe, and conduct camera-trapping in 135 plots embedded in patches of mixed-montane forests which differ along 2 gradients of retention and landscape fragmentation. I will use multispecies hierarchical modeling in a Bayesian framework to quantify the effects of retention forestry on mammal species at different spatial grains. This multidisciplinary research combines spatial ecology, with landscape and forest ecology and will integrate for the first time ecological study of large mammals and retention forestry in Central European landscapes.

Keywords: camera trapping,large terrestrial mammals,spatial ecology,temperate forests
Human-Centric Machine Learning (HumanML)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Computer science and informatics
Panel description: Informatics and information systems, computer science, scientific computing, intelligent systems
Countries involved: Germany
Call: ERC STARTING GRANTS (ERC-2020-STG, 2020)
Start date: 2020-11-01
End date: 2025-10-31
Duration (months): 60.0
Total project cost (EUR): 1495000.0
EU funding (EUR): 1495000.0
Participants: 1
Project status: SIGNED

With the advent of mass-scale digitization of information and virtually limitless computational power, an increasing number of social, information and cyber-physical systems evaluate, support or even replace human decisions using machine learning models and algorithms. Machine learning models and algorithms have been traditionally designed to take decisions autonomously, without human intervention, on the basis of passively collected data. However, in most social, information and cyber-physical systems, algorithmic and human decisions feed on and influence each other. As these decisions become more consequential to individuals and society, machine learning models and algorithms have been blamed to play a major role in an increasing number of missteps, from discriminating minorities, causing car accidents and increasing polarization to misleading people in social media. In this project, we will develop human-centric machine learning models and algorithms for evaluating, supporting and enhancing decision making processes where algorithmic and human decisions feed on and influence each other. These models and algorithms will account for the feedback loop between algorithmic and human decisions, which currently perpetuates or even amplifies biases and inequalities, and they will learn to operate under different automation levels. Moreover, they will anticipate how individuals will react to their algorithmic decisions, often strategically, to receive beneficial decisions and they will provide actionable insights about their algorithmic decisions. Finally, we will perform observational and interventional experiments as well as realistic simulations to evaluate their effectiveness in a wide range of applications, from content moderation, recidivism prediction, and credit scoring to medical diagnosis and autonomous driving.

Keywords: decision making,fairness in machine learning,strategic machine learning,human ai collaboration,human centric machine learning,social aspects of machine learning
Non-semisimple differential graded modular functors (Modular Functors)

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: Mathematics
Countries involved: Denmark
Call: Individual Fellowships (H2020-MSCA-IF-2020, 2020)
Start date: 2022-01-01
End date: 2023-12-31
Duration (months): 24.0
Total project cost (EUR): 207312.0
EU funding (EUR): 207312.0
Participants: 1
Project status: CLOSED

Non-semisimple differential graded modular functors: While semisimple modular categories can be entirely understood in terms of three-dimensional topological field theory, an equally satisfactory topological understanding of non-semisimple modular categories is not available. The proposed project will solve concrete problems related to the topological understanding of non-semisimple modular categories by unraveling within a homotopy coherent framework the relation between the homological algebra of a modular category (in particular, its Hochschild complex) and low-dimensional topology. The backbone of this approach is the differential graded modular functor associated to any modular category (a consistent system of projective mapping class group representations on chain complexes satisfying excision) that I have recently established in joint work with Schweigert. Among the concrete objectives is a generalization of the Verlinde formula to a statement about two compatible E_2-structures on the differential graded conformal block for the torus. This will naturally link the Verlinde formula to the Deligne conjecture. Moreover, rigidity requirements for categories that can be extracted from a modular functor will be studied systematically using cyclic and modular operads and results of Costello and Giansiracusa. This will lead to a vast generalization of existing string-net techniques, namely string-net complexes for any pivotal Grothendieck-Verdier category in the sense of Boyarchenko-Drinfeld. These string-net complexes can be used to compute differential graded conformal blocks for modular categories which are the Drinfeld center of a spherical pivotal finite tensor category and to create a link to Morrison-Walker blob homology. The key techniques that I will learn during the fellowship involve graph models for mapping class group actions and multiplicative structures on Hochschild complexes. My host Nathalie Wahl is an expert in these areas.