Summary• Ectomycorrhizal interactions established between the root systems of terrestrial plants and hyphae from soil-borne fungi are the most ecologically widespread plant symbioses. The efficient uptake of a broad range of nitrogen (N) compounds by the fungal symbiont and their further transfer to the host plant is a major feature of this symbiosis. Nevertheless, we far from understand which N form is preferentially transferred and what are the key molecular determinants required for this transfer.• Exhaustive in silico analysis of N-compound transporter families were performed within the genome of the ectomycorrhizal model fungus Laccaria bicolor. A broad phylogenetic approach was undertaken for all families and gene regulation was investigated using whole-genome expression arrays.• A repertoire of proteins involved in the transport of N compounds in L. bicolor was established that revealed the presence of at least 128 gene models in the genome of L. bicolor. Phylogenetic comparisons with other basidiomycete genomes highlighted the remarkable expansion of some families. Whole-genome expression arrays indicate that 92% of these gene models showed detectable transcript levels.• This work represents a major advance in the establishment of a transportome blueprint at a symbiotic interface, which will guide future experiments.

Lyme disease is one of the most common infectious vector-borne diseases in the world. In the early stage, the disease manifests itself in most cases with erythema migrans (EM) skin lesions. Better diagnosis of these early forms would allow improving the prognosis by preventing the transition to a severe late form thanks to appropriate antibiotic therapy. Recent studies show that convolutional neural networks (CNNs) perform very well to identify skin lesions from the image 2 but, there is not much work for Lyme disease prediction from EM lesion images. The main objective of this study is to extensively analyze the effectiveness of CNNs for diagnosing Lyme disease from images and to find out the best CNN architecture for the purpose. There is no publicly available EM image dataset for Lyme disease prediction mainly because of privacy concerns. In this study, we utilized an EM dataset consisting of images collected from Clermont-Ferrand University Hospital Center (CF-CHU) of France and the internet. CF-CHU collected the images from several hospitals in France. This dataset was labeled by expert dermatologists and infectiologists from CF-CHU. First, we benchmarked this dataset for twenty-three well-known CNN architectures in terms of predictive performance metrics, computational complexity metrics, and statistical significance tests. Second, to improve the performance of the CNNs, we used transfer learning from ImageNet pre-trained models as well as pre-trained the CNNs with the skin lesion dataset "Human Against Machine with 10000 training images (HAM1000)". In that process, we searched for the best performing number of layers to unfreeze during transfer learning finetuning for each of the CNNs. Third, for model explainability, we utilized Gradient-weighted Class Activation Mapping to visualize the regions of input that are significant to the CNNs for making predictions. Fourth, we provided guidelines for model selection based on predictive performance and computational complexity. Our study confirmed the effectiveness and potential of even some lightweight CNNs for building Lyme disease pre-scanner mobile applications to assist people with an initial diagnosis in the absence of an expert dermatologist. We also made all the trained models publicly available at https://dappem.limos.fr/download.html, which can be used by others for transfer learning and building pre-scanners for Lyme disease.

Mass-participation events in temperate forests are now well-established features of outdoor activities and represent high-risk activities regarding human exposition to tick bites. In this study we used a citizen science approach to quantify the space–time frequency of tick bites and undetected tick bites among orienteers that participated in a 6-day orienteering competition that took place in July 2018 in the forests of Eastern France, and we looked at the use and efficacy of different preventive behaviors. Our study confirms that orienteers are a high-risk population for tick bites, with 62.4% of orienteers bitten at least once during the competition, and 2.4 to 12.1 orienteers per 100 orienteers were bitten by ticks when walking 1 km. In addition, 16.7% of orienteers bitten by ticks had engorged ticks, meaning that they did not detect and remove their ticks immediately after the run. Further, only 8.5% of orienteers systematically used a repellent, and the use of repellent only partially reduced the probability of being bitten by ticks. These results represent the first attempt to quantify the risk of not immediately detecting a tick bite and provide rare quantitative data on the frequency of tick bites for orienteers according to walking distance and time spent in the forest. The results also provide information on the use of repellent, which will be very helpful for modeling risk assessment. The study also shows that prevention should be increased for orienteers in France.

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L’enjeu d’une culture scientifique pour tous est d’importance face à la difficulté des citoyens à critiquer les données de la science avec des arguments rationnels, notamment en ce qui concerne la biologie et la santé (vaccination, procréation médicalement assistée, etc.), car le grand public ne veut plus croire sur parole ce que disent les experts. Dans ce contexte, rapprocher les citoyens de la recherche scientifique représente un réel défi pour l’avenir, que les laboratoires ouverts Tous Chercheurs ont voulu relever.

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