[Extract from the AEF dispatch of January 5, 2022] As part of its drive to promote “science with and for society”, on March 18, 2024 the ANR launched a new call specifically aimed at research projects it had already funded in 2021. The aim is for these winning projects to propose and implement “scientific mediation, communication or promotion actions” on their issues and results, aimed at a non-specialist audience. Submission of proposals to this call, entitled SAPS-CSTI-Générique21, is open until April 25, 2024. This call “aims to implement scientific communication, mediation and promotion actions around the issues, methods and results of research projects supported by the ANR as part of the 2021 generic project calls, under the JCJC (young researchers) and PRC (collaborative research projects) funding schemes”, states the call text. “All forms of scientific mediation, communication and promotion can be envisaged”, emphasizes the ANR. However, “they must be of a structuring nature at local, regional or national level, by jointly mobilizing the project coordinators [...] within the establishments, but also the scientific culture structures [...], and in particular those referenced by the Ocim (Office de coopération et d'information muséales)”. The aim is to create or reinforce a real synergy between scientists and professionals in the fields of scientific communication, mediation and promotion, “backed up by a steering mechanism to ensure the coherence and visibility of the actions carried out”, explains the ANR.

Most of our cognitive activities require processing and memorizing sequences of information. Whether sequences of motor gestures, sounds, letters, or words, we memorize them by associating the elements that make up these sequences. The field of implicit statistical learning aims at understanding the associative mechanisms that allow us to memorize these sequences of information. The HEBBIAN project aims to better understand these fundamental associative mechanisms by relying on recent theoretical models whose general framework is Hebbian learning. This project aims to experimentally study three main questions concerning 1) the role of the spacing between two repetitions of the same sequence in the memorization of this sequence ; 2) the dynamic of sequence encoding as a function of sequence size, number, and learning context ; 3) the problem of parts/whole relations between sequences of different sizes. This experimental work will be carried out in a comparative perspective with humans and non-human primates (Guinea baboons, Papio papio) using serial pointing tasks and classical psycholinguistic tasks, such as the naming or lexical decision tasks, in which a sequence is systematically repeated without the subjects being informed. This experimental work will be done in conjunction with the development and evaluation of two types of models based on the principles of Hebbian learning (psychological models and others that are more plausible on the neurobiological level). All of this work should allow for significant progress in our understanding and conception of these fundamental associative mechanisms.

In the context of movement assistance, where physical contact between robotic systems and the human body is expected to increase, the long-term physical health of patients must be investigated. This is precisely one of the commercial arguments promoting assistive devices, which are claimed to limit the impact of traumatic tasks on health or to bridge the gap between disabled and able-bodied patients. Paradoxically, there is little to no consideration of the human body biomechanics in the control of these devices. The reasons for such a shortcoming are twofold: i) the complexity and lack of fidelity of neuromusculoskeletal (MSK) models and ii) the slowness of algorithms relying on these models which disqualify them for real-time applications, such as typically needed for controlling assistive devices. B-IRD stands at a crossroad between robotics and biomechanics to develop faithful and personalized MSK models and fast algorithms that will enable to take into account the complex dynamics of the human body in the control of assistive devices. The ambition of the first axis is to develop biomechanical estimation methods, fast enough to be used in feedback for the control of assistive devices, without sacrificing their accuracy. The objective of the second axis is to develop MSK personalization methods which exploit the high kinematic accuracy of robots. The last axis presents the integration of these algorithms and models in the control of assistive devices to generate a customized motion assistance that truly respects the biomechanical limits of the human body while exploiting its assets. To improve the benefits of assistive technologies, B-IRD will enable to objectively quantify the quality of their assistance and to change their control paradigm. The foreseen approaches are based on robotics, automatic control and machine learning tools, not commonly used in biomechanics.

1) Wider research context / theoretical framework This project aims at the development of a novel framework for high-order discretization of partial differential equations on general domains. The latter pose challenges related to their topology and in particular at the vicinity of, so called, extraordinary vertices where smoothness requirements and superior approximation power are paramount for efficient simulations. 2) Hypotheses/research questions /objectives We focus on the paradigm of isogeometric analysis that uses spline functions for design and analysis on non-linear geometries. We propose a framework of geometrically continuous splines called RFF-Splines (Refinable FreeForm Splines) that shall enable numerical schemes for topologically unrestricted design and analysis. 3) Approach/methods The project goes all the way from the theoretical construction to its algorithmic derivation and the efficient implementation in C++, as well as experimental evaluation in demanding applications involving high order partial differential equations. 4) Level of originality / innovation The novelty of the construction stems from the efficient construction of the basis functions (notably for evaluation and numerical integration), adaptivity by local refinement (via a truncation mechanism) as well as the good approximation power, supported by theoretical results. The idea of RFF-Splines is inspired from the work of Hartmut Prautzsch and is based on composing polynomial mappings with spline parameterizations. 5) Primary researchers involved The project involves Bert Juettler (JKU Linz), Angelos Mantzaflaris (Researcher at INRIA), Bernard Mourrain and Regis Duvigneau (Research directors at INRIA), and two PhD students (one at JKU and one at INRIA).

Durable use of genetically resistant plant varieties is a major step toward sustainable crop protection. Yet, defining the optimal deployment strategy for plant resistances is still a matter of active debate. We advocate that enhancing resistance durability requires the integration of knowledge on molecular events leading to virulence into relevant evolutionary-epidemiological models. The ENDURANCE project addresses this issue: thanks to population genetics concepts and tools, we will embed genetic determinism(s) of resistance breakdown into pathogen population dynamics. ENDURANCE involves five partners and gathers 30 persons (researchers, associate professors, engineers and technicians) who are at the forefront of studies on resistance durability, either through evolutionary epidemiology modelling, or through the molecular study of host-pathogen interactions. Our consortium includes mathematicians, modelers, plant pathologists, functional genomicists and population geneticists. Fabien Halkett, who has a strong expertise in population genetics and adaptation of plant pathogens, will lead ENDURANCE. As a building block for ENDURANCE, all partners joined forces to summarize the current knowledge about plant resistance durability in a recently published opinion note. Molecular studies document a large variability of mechanisms, but little is known about the evolutionary outcomes of such variability. Conversely, most models deal so far with “generic” pathogens (haploid asexual) and need to be adapted to the specificities of life cycles of pathogens in agro-ecosystems. To overcome these limitations, ENDURANCE focuses on three pathogen species (Melampsora larici-populina, Leptosphaeria maculans, Plasmopara viticola) with different ploidies and life cycles to build contextualized evolutionary epidemiology models. Our main working hypothesis is that ecological and biological differences between pathogen species, as well as molecular constraints, translate into various dynamics of emergence and spread of virulence alleles. Each species is mastered by a biological partner. We will broaden the scope of our findings through available data on resistance breakdown dynamics in other pathosystems. Our objectives are: i) to extend our knowledge on the genetic determinisms of virulence through genome association studies, genomic analyses and functional validations; ii) to assess the temporal changes in virulence allele frequencies as well as in genetic structure in pathogen populations thanks to time series samples; iii) to develop new evolutionary epidemiological models that consider the diversity in genetic determinisms of virulence, as well as the characteristics of fungal pathogen life cycles. These objectives delineate three operational tasks, each task fostering the two others. This will strengthen collaborations among partners. Besides scientific collaboration, ENDURANCE also aims at sharing technical knowledge and ensuring methodological transfers among partners in order to leverage our knowledge. In ENDURANCE, special interest will be devoted to highlighting complex interactions among loci and to measuring the cost of virulence. We will benefit from large historical collections to highlight transient dynamics in pathogen evolutionary trajectories. We will develop both mathematical models (based on the semi-discrete formalism that matches the seasonality of pathogen life cycles) and simulation models that take all the specificities of the three pathogens into account. Theoretical predictions will be challenged with empirical data and when appropriate, model parameters will be inferred from the time series data acquired from historical surveys and population genetics analyses. Ultimately, the outcome of our project is to determine the best deployment strategies of genetically resistant cultivars by integrating, from gene to population, all relevant and contextual biological knowledge into sound theoretical models.