Automorphisms and opposition in twin buildings

We show that every automorphism of a thick twin building interchanging the halves of the building maps some residue to an opposite one. Furthermore we show that no automorphism of a locally finite 2-spherical twin building of rank at least 3 maps every residue of one fixed type to an opposite. The main ingredient of the proof is a lemma that states that every duality of a thick finite projective plane admits an absolute point, i.e., a point mapped onto an incident line. Our results also hold for all finite irreducible spherical buildings of rank at least 3, and as a consequence we deduce that every involution of a thick irreducible finite spherical building of rank at least 3 has a fixed residue

Sublethal effects of fipronil on the ability of honeybees (Apis mellifera L.) to orientate in a complex maze

contribution to session IVTest methodology Background: The recent fipronil-based pesticide is accused by bee-keepers of causing depopulations in hives of honeybees (Apis mellifera L.). Behavioural effects during the flight of foraging honeybees would have been evoked. To test whether the insecticide fipronil may disorientate foragers, its impact on orientation in a maze was examined. Bees had to fly through a sequence of boxes to reach the target, which was a feeder containing a reward of sugar solution. After being trained to associate a green mark with the reward, foragers received 1 μg kg-1 fipronil orally and their capacity to orientate through the maze following the colour mark was tested and compared to control. Results: The rate of foragers entering the maze, and so responding to the mark placed at the entrance, was reduced with fipronil-fed animals. Before and after treatment, 86-89% of bees equally flew through the whole path and arrived to the goal without mistakes. The rate of fipronil-treated bees finding path without mistakes decreased to 60%. Conversely, the rate of bees with unsuccessful searches for the goal notably increased with treatment (34% in treated bees versus 4% in control bees). Conclusion: Our results show that orientation capacities of foragers in a complex maze were affected by fipronil. Keywords: Apis mellifera L., pesticide, maze, conditioning, visual learning, flight

An individual-based model of Zebrafish population dynamics accounting for energy dynamics

International audienceDeveloping population dynamics models for zebrafish is crucial in order to extrapolate from toxicity data measured at the organism level to biological levels relevant to support and enhance ecological risk assessment. To achieve this, a dynamic energy budget for individual zebrafish (DEB model) was coupled to an individual based model of zebrafish population dynamics (IBM model). Next, we fitted the DEB model to new experimental data on zebra-fish growth and reproduction thus improving existing models. We further analysed the DEB-model and DEB-IBM using a sensitivity analysis. Finally, the predictions of the DEB-IBM were compared to existing observations on natural zebrafish populations and the predicted population dynamics are realistic. While our zebrafish DEB-IBM model can still be improved by acquiring new experimental data on the most uncertain processes (e.g. survival or feeding), it can already serve to predict the impact of compounds at the population level

AVIS de l'ANSES relatif à " l'évaluation du rapport bénéfice risque des pratiques de lutte anti-vectorielle habituellement mises en oeuvre pour lutter contre la dengue, dans le contexte actuel de confinement global "

Dans le contexte de la gestion de crise liée à l'épidémie de Covid-19 en France, l'Anses a été saisie en urgence le 14 avril 2020 par la Direction Générale de la Santé pour réaliser l'expertise suivante : " Évaluation du rapport bénéfice-risque des pratiques de lutte anti-vectorielle habituellement mises en oeuvre pour lutter contre la dengue, dans le contexte actuel de confinement global "

Seascape ecology : identifying research priorities for an emerging ocean sustainability science

Seascape ecology, the marine-centric counterpart to landscape ecology, is rapidly emerging as an interdisciplinary and spatially explicit ecological science with relevance to marine management, biodiversity conservation, and restoration. While important progress in this field has been made in the past decade, there has been no coherent prioritisation of key research questions to help set the future research agenda for seascape ecology. We used a 2-stage modified Delphi method to solicit applied research questions from academic experts in seascape ecology and then asked respondents to identify priority questions across 9 interrelated research themes using 2 rounds of selection. We also invited senior management/conservation practitioners to prioritise the same research questions. Analyses highlighted congruence and discrepancies in perceived priorities for applied research. Themes related to both ecological concepts and management practice, and those identified as priorities include seascape change, seascape connectivity, spatial and temporal scale, ecosystem-based management, and emerging technologies and metrics. Highest-priority questions (upper tercile) received 50% agreement between respondent groups, and lowest priorities (lower tercile) received 58% agreement. Across all 3 priority tiers, 36 of the 55 questions were within a ±10% band of agreement. We present the most important applied research questions as determined by the proportion of votes received. For each theme, we provide a synthesis of the research challenges and the potential role of seascape ecology. These priority questions and themes serve as a roadmap for advancing applied seascape ecology during, and beyond, the UN Decade of Ocean Science for Sustainable Development (2021-2030)

Prediction of bioconcentration factors in fish and invertebrates using machine learning

© 2018 The Authors The application of machine learning has recently gained interest from ecotoxicological fields for its ability to model and predict chemical and/or biological processes, such as the prediction of bioconcentration. However, comparison of different models and the prediction of bioconcentration in invertebrates has not been previously evaluated. A comparison of 24 linear and machine learning models is presented herein for the prediction of bioconcentration in fish and important factors that influenced accumulation identified. R2 and root mean square error (RMSE) for the test data (n = 110 cases) ranged from 0.23–0.73 and 0.34–1.20, respectively. Model performance was critically assessed with neural networks and tree-based learners showing the best performance. An optimised 4-layer multi-layer perceptron (14 descriptors) was selected for further testing. The model was applied for cross-species prediction of bioconcentration in a freshwater invertebrate, Gammarus pulex. The model for G. pulex showed good performance with R2 of 0.99 and 0.93 for the verification and test data, respectively. Important molecular descriptors determined to influence bioconcentration were molecular mass (MW), octanol-water distribution coefficient (logD), topological polar surface area (TPSA) and number of nitrogen atoms (nN) among others. Modelling of hazard criteria such as PBT, showed potential to replace the need for animal testing. However, the use of machine learning models in the regulatory context has been minimal to date and is critically discussed herein. The movement away from experimental estimations of accumulation to in silico modelling would enable rapid prioritisation of contaminants that may pose a risk to environmental health and the food chain.Biotechnology and Biological Sciences Research Council (BBSRC) CASE industrial scholarship scheme (Reference BB/K501177/1), iNVERTOX project (Reference BB/P005187/1) and AstraZeneca Global SHE research programme. This work was additionally supported by the Francis Crick Institute which receives its core funding from Cancer Research UK (FC001999), the UK Medical Research Council (FC001999), and the Wellcome Trust (FC001999)

Lethal and Sublethal Effects of Pyriproxyfen on Apis and Non-Apis Bees

Pyriproxyfen is a juvenile hormone mimic used extensively worldwide to fight pests in agriculture and horticulture. It also has numerous applications as larvicide in vector control. The molecule disrupts metamorphosis and adult emergence in the target insects. The same types of adverse effects are expected on non-target insects. In this context, the objective of this study was to evaluate the existing information on the toxicity of pyriproxyfen on the honey bee (Apis mellifera) and non-Apis bees (bumble bees, solitary bees, and stingless bees). The goal was also to identify the gaps necessary to fill. Thus, whereas the acute and sublethal toxicity of pyriproxyfen against A. mellifera is well-documented, the information is almost lacking for the non-Apis bees. The direct and indirect routes of exposure of the non-Apis bees to pyriproxyfen also need to be identified and quantified. More generally, the impacts of pyriproxyfen on the reproductive success of the different bee species have to be evaluated as well as the potential adverse effects of its metabolites