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Mitigating the impacts of global warming on wildlife entails four practical steps. First, we need to study how processes of interest vary with temperature. Second, we need to build good temperature scenarios. Third, processes can be forecast accordingly. Only then can we perform the fourth step, testing mitigating measures. While having good temperature data is essential, this is not straightforward for stream ecologists and managers. Water temperature (WT) data are often short and incomplete and future projections are currently not routinely available. There is a need for generic models which address this data gap with good resolution and current models are partly lacking. Here, we expand a previously published hierarchical Bayesian model that was driven by air temperature (AT) and flow (Q) as a second covariate. The new model can hindcast and forecast WT time series at a daily time step. It also allows a better appraisal of real uncertainties in the warming of water temperatures in rivers compared to the previous version, stemming from its hybrid structure between time series decomposition and regression. This model decomposes all-time series using seasonal sinusoidal periodic signals and time varying means and amplitudes. It then links the contrasted frequency signals of WT (daily and six month) through regressions to that of AT and optionally Q for better resolution. We apply this model to two contrasting case study rivers. For one case study, AT only is available as a covariate. This expanded model further improves the already good fitting and predictive capabilities of its earlier version while additionally highlighting warming uncertainties. The code is available online and can easily be run for other temperate rivers.

Résumé: En 1997, Joseph PARÉ publiait son ouvrage Écritures et Discours dans le roman africain francophone. Dans son livre, l’auteur se proposait d’observer spécifiquement le discours romanesque, dans la littérature africaine francophone. L’objet observé selon l’auteur est spécifique en ce sens qu’il est ontologiquement hybride, c’est-à-dire qu’il est un dérivé de deux imaginaires différents, clairement identifiables. Il s’agit d’une part de la culture occidentale acquise grâce ou à cause de la colonisation et qui se manifeste par l’utilisation de la langue (le français) ; et d’autre part la culture africaine, à travers la littérature orale et certaines pratiques langagières. La spécificité de l’objet à examiner nécessitait la construction d’une démarche méthodologique en vue de le prendre en charge. Cette démarche s’appelle, l’herméneutique de l’hybridité. La présente réflexion est une proposition qui vise un double objectif : repenser la démarche critique (l’herméneutique de l’hybridité) et envisager son prolongement. Le corpus de base dans cette étude est constitué entre autres de « Les Soleils des indépendances » (A. Kourouma) ; « Ça tire sous le Sahel » ( T. Pacéré) ; « Les Dieux délinquants » ( A. S. Coulibaly), etc. Mots-clés : sémiotique ; hybridité ; Hybridation ; littérature africaine francophone

Abstract The use of machine learning to build subgrid parametrizations for climate models is receiving growing attention. State‐of‐the‐art strategies address the problem as a supervised learning task and optimize algorithms that predict subgrid fluxes based on information from coarse resolution models. In practice, training data are generated from higher resolution numerical simulations transformed in order to mimic coarse resolution simulations. By essence, these strategies optimize subgrid parametrizations to meet so‐called a priori criteria. But the actual purpose of a subgrid parametrization is to obtain good performance in terms of a posteriori metrics which imply computing entire model trajectories. In this paper, we focus on the representation of energy backscatter in two‐dimensional quasi‐geostrophic turbulence and compare parametrizations obtained with different learning strategies at fixed computational complexity. We show that strategies based on a priori criteria yield parametrizations that tend to be unstable in direct simulations and describe how subgrid parametrizations can alternatively be trained end‐to‐end in order to meet a posteriori criteria. We illustrate that end‐to‐end learning strategies yield parametrizations that outperform known empirical and data‐driven schemes in terms of performance, stability, and ability to apply to different flow configurations. These results support the relevance of differentiable programming paradigms for climate models in the future.

Microencapsulation technologies have experienced much growth over the past decades and are commonly used for food, cosmetic, pharmaceutical and biomedical applications. Certain application fields impose stricter requirements on the polymer capsules. In many biomedical applications including bioencapsulation, cell therapy and drug delivery applications, capsules are required to have a controlled shape and size, as well as a defined mechanical stability and porosity.This data article reports the alginate capsule production using common centrifugal technology, which enables the production of microcapsules with highly viscous biopolymers. We describe the experimental data generated in a parametric study, where the main control parameters of the centrifugal encapsulation system (alginate viscosity, rotating speed, nozzle diameter, collecting distance) were varied. The geometric properties of the produced hydrogel capsules were analysed by microscope photography and image processing. The dataset presented here contains the experimental data, the raw capsule images, the analysis scripts, the analysed images, and tables with extracted geometric information. All extracted data was compiled into a table containing geometric properties of more than 50000 analysed capsules.These data allow (i) to reproduce quickly the encapsulation experiments and be able to choose in a straight-forward manner the combination of parameters needed in order to generate capsules with desired properties; (ii) to create more general phase diagrams of the centrifugal encapsulation technology which can be widely used for prediction and/or parameter selection; (iii) to analyse more thoroughly the sensitivity of capsule properties to given stages of the encapsulation process.The research article on these data [1] was published in the journal Colloids and Surfaces A: Physicochemical and Engineering Aspect, with the title: Three-dimensional phase diagram for the centrifugal Calcium-alginate microcapsules production technology.

ABSTRACT Some Bacillus species, such as B. velezensis, are important members of the plant-associated microbiome, conferring protection against phytopathogens. However, our knowledge about multitrophic interactions determining the ecological fitness of these biocontrol bacteria in the competitive rhizosphere niche is still limited. Here, we investigated molecular mechanisms underlying interactions between B. velezensis and Pseudomonas as a soil-dwelling competitor. Upon their contact-independent in vitro confrontation, a multifaceted macroscopic outcome was observed and characterized by Bacillus growth inhibition, white line formation in the interaction zone, and enhanced motility. We correlated these phenotypes with the production of bioactive secondary metabolites and identified specific lipopeptides as key compounds involved in the interference interaction and motile response. Bacillus mobilizes its lipopeptide surfactin not only to enhance motility but also to act as a chemical trap to reduce the toxicity of lipopeptides formed by Pseudomonas. We demonstrated the relevance of these unsuspected roles of lipopeptides in the context of competitive tomato root colonization by the two bacterial genera. IMPORTANCE Plant-associated Bacillus velezensis and Pseudomonas spp. represent excellent model species as strong producers of bioactive metabolites involved in phytopathogen inhibition and the elicitation of plant immunity. However, the ecological role of these metabolites during microbial interspecies interactions and the way their expression may be modulated under naturally competitive soil conditions has been poorly investigated. Through this work, we report various phenotypic outcomes from the interactions between B. velezensis and 10 Pseudomonas strains used as competitors and correlate them with the production of specific metabolites called lipopeptides from both species. More precisely, Bacillus overproduces surfactin to enhance motility, which also, by acting as a chemical trap, reduces the toxicity of other lipopeptides formed by Pseudomonas. Based on data from interspecies competition on plant roots, we assume this would allow Bacillus to gain fitness and persistence in its natural rhizosphere niche. The discovery of new ecological functions for Bacillus and Pseudomonas secondary metabolites is crucial to rationally design compatible consortia, more efficient than single-species inoculants, to promote plant health and growth by fighting economically important pathogens in sustainable agriculture.