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Le genre Chlorophytum Ker Gawl. (Asparagales, Asparagaceae) à Madagascar, avec la description de 12 nouvelles espèces, 4 nouvelles sous-espèces et 1 nouvelle variété
No full textPignal M., Phillipson P.B. & Bardot-Vaucoulon M. 2025. The genus Chlorophytum Ker Gawl. (Asparagales, Asparagaceae) in Madagascar, with the description of 12 new species, 4 new subspecies and 1 new variety. European Journal of TaxonomyThe genus Chlorophytum Ker Gawl. (Asparagaceae) is revised taxonomically for Madagascar. It is a palaeotropical genus of more than 200 species, all of which are geophytes that are distributed throughout Africa, the Arabian Peninsula (Oman), Southeast Asia, the Indian subcontinent, China and Australia. We recognise 39 endemic taxa for Madagascar (30 species, 4 subspecies, 3 varieties and 2 potential species not formally named due to incomplete material). Descriptions and geographical data are provided for all taxa, of which 17 are newly described in this revision: 12 species (C.Le genre Chlorophytum Ker Gawl (Asparagaceae) fait l'objet d'une révision taxonomique pour Madagascar. Il s'agit d'un genre paléotropical comptant plus de 200 espèces, toutes géophytes, réparties en Afrique, dans la péninsule arabique (Oman), en Asie du Sud.-Est., dans le sous-continent indien en Chine et en Australie. Nous reconnaissons 39 taxons endémiques à Madagascar (30 espèces, 4 sous-espèces 3 variétés et 2 espèces potentielles non nommées officiellement en raison d’un matériel incomplet). Les descriptions et les données géographiques sont fournies pour tous les taxons, dont 17 sont nouvellement décrits dans cette révision : 12 espèces, (C. albociliatum sp. nov., C. aspidistrifolium sp. nov., C. basivaginatum sp. nov., C. candelabrum sp. nov., C. darainense sp. nov., C. helvillae sp. nov., C. meridionale sp. nov., C. nigrogranulosum sp. nov., C. nusbaumeri sp. nov., C. ranirisonii sp. nov., C. ratovosonii sp. nov., C. tolyanum sp. nov.) ; 4 sous-espèces (C. sofiense subsp. gautieri subsp. nov., C. graniticum subsp. ambrense subsp. nov., C. meridionale subsp. ihoyense subsp. nov., C. meridionale subsp. tulearense subsp. nov.) et 1 variété. (C. madagascariense var. boinense var. nov.). Chlorophytum gramineum (Baker) H.Perrier, est basé sur un nom illégitime (Anthericum gramineum Baker), et est remplacé par le nom légitime postérieur, C. madagascariense Baker, que nous considérons comme un synonyme taxonomique. Nous conservons les variétés décrites par Perrier de la Bâthie au sein de cette espèce, en les transférant à C. madagascariense, et nous décrivons une variété supplémentaire : C. madagascariense var. pervillei. De plus, nous désignons un lectotype pour huit espèces C. decaryanum, C. decipiens, C. dianellifolium, C. geayanum, C. graniticum, C. namorokense H.Perrier, C. sofiense, C. subligulatum, et deux variétés : C. dianellifolium var. transiens et C. madagascariense var. sciaphilum comb. nov.) . Une clé d'identification et des évaluations préliminaires du statut de conservation selon les critères de la Liste rouge de l'UICN sont fournies pour toutes les espèces.Mots-clefs : Anthericum, Anthericaceae, Asparagacea
Une tête de pont de l'Université de Strasbourg en Rhénanie occupée: le Centre d'Etudes Germaniques à Mayence (1921-1930)
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DemIstifyCPS: A Domain-Specific Language for Influence Modeling in Cyber-Physical Systems
No full textInternational audienceCyber-Physical Systems (CPS) are integrated systems, composed of various system parts, comprising physical and computational processes. CPS development generates multiple artifacts, including design models and implementation code developed by several stakeholders, that may interact with each other and the environment in which the CPS operates. Teams rely on Model-Based Systems Engineering (MBSE) to manage this complexity. Standard MBSE relations capture functional exchanges between artifacts and between the system and its environment. However, many couplings across artifacts and the environment remain implicit, only partially understood, or unknown at design time. The absence of explicit modeling of these couplings makes it hard to understand the effects of design or environmental changes. In this paper, we model these hidden couplings as Influences. We propose DEMISTIFYCPS, a domain-specific language for design influences as first-class relations. A mobile robot use case demonstrates the language, showing how it surfaces influences and offers actionable feedback. Overall, our work indicates that making Influences explicit helps demystify CPS behavior and supports better decision-making
Functionalized nitrogen- and chalcogen-containing heterocyclic compounds as aromatase inhibitors: Design, synthesis and biological evaluation
No full textInternational audienceAromatase inhibition remains a key therapeutic strategy for hormone-dependent breast cancer (HDBC). Among non-steroidal aromatase inhibitors (NSAIs), letrozole and anastrozole are well-established treatments. To further probe structure-function relationships within the human aromatase active site, we synthesized a library of 42 novel azole derivatives. Several compounds displayed nanomolar inhibitory activity, with potencies approaching that of letrozole in vivo, while maintaining favorable selectivity against other steroidogenic enzymes. Notably, benzoselenazolinone 75 emerged as the most promising candidate, exhibiting potency comparable to letrozole with improved in vitro selectivity, thereby justifying further evaluation in vivo.</div
Mg2NiH4 under stress: Coupling transition metal doping with structural deformation for improved hydrogen storage
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An improved optimization algorithm for solving arrival aircraft scheduling problem in the Terminal Maneuvering Area
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S4 modal sequent calculus as intermediate logic and intermediate language
No full textInternational audienceIn this short paper, we advocate for the idea that continuation-based intermediate languages correspond to intermediate logics. The goal of intermediate languages is to serve as a basis for compiler intermediate representations, allowing to represent expressive program transformations for optimisation and compilation while preserving the properties that make programs compilable efficiently in the first place, such as the “stackability” of continuations. Intermediate logics are logics between intuitionistic and classical logic in terms of provability. The S4 modal logic can be seen as an intermediate logic owing to the Gödel-McKinsey-Tarski theorem, which states the intuitionistic nature of its modal fragment. In this paper, we propose a polarised sequent calculus for (classical) S4 modal logic together with an operational machine model with which we study the stackability properties of its modal fragment
A reliable framework for brain tumor segmentation via multi-modal fusion and uncertainty modeling
No full textInternational audienceAccurate brain tumor segmentation from MRI scans is critical for effective diagnosis and treatment planning. Recent advances in deep learning have significantly improved brain tumor segmentation performance. However, these models still face challenges in clinical adoption due to their inherent uncertainties and potential for errors. In this paper, we propose a novel MR brain tumor segmentation approach that integrates multi-modal data fusion and uncertainty quantification to improve the accuracy and reliability of brain tumor segmentation. Recognizing that each MR modality contributes unique insights into the tumor’s characteristics, we propose a novel modality-aware guidance by explicitly categorizing the modalities into ”teacher” (FLAIR and T1c) and ”student” (T2 and T1) groups. Since the teacher modalities are the most informative modalities for identifying brain tumors, we propose a multi-modal teacher-student fusion strategy. This strategy leverages the teacher modalities to guide the student modalities in both spatial and channel feature representation aspects. To address prediction reliability, we employ Monte Carlo dropout during training to generate multiple uncertainty estimates. Additionally, we develop a novel uncertainty-aware loss function that optimizes segmentation accuracy while quantifying the uncertainty in predictions. Experimental results conducted on three BraTS datasets demonstrate the effectiveness of the proposed components and the superior performance compared to the state-of-the-art methods, highlighting their potential for clinical applicatio