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EU policies, such as the EU biodiversity strategy 2030 and the Birds and Habitats Directives, demand unbiased, integrated and regularly updated biodiversity and ecosystem service data. However, efforts to monitor wildlife and other species groups are spatially and temporally fragmented, taxonomically biased, and lack integration in Europe. To bridge this gap, the MAMBO project will develop, test and implement enabling tools for monitoring conservation status and ecological requirements of species and habitats for which knowledge gaps still exist. MAMBO brings together the technical expertise of computer science, remote sensing, social science expertise on human-technology interactions, environmental economy, and citizen science, with the biological expertise on species, ecology, and conservation biology. MAMBO is built around stakeholder engagement and knowledge exchange (WP1) and the integration of new technology with existing research infrastructures (WP2). MAMBO will develop, test, and demonstrate new tools for monitoring species (WP3) and habitats (WP4) in a co-design process to create novel standards for species and habitat monitoring across the EU and beyond. MAMBO will work with stakeholders to identify user and policy needs for biodiversity monitoring and investigate the requirements for setting up a virtual lab to automate workflow deployment and efficient computing of the vast data streams (from on the ground sensors, and remote sensing) required to improve monitoring activities across Europe (WP4). Together with stakeholders, MAMBO will assess these new tools at demonstration sites distributed across Europe (WP5) to identify bottlenecks, analyze the cost-effectiveness of different tools, integrate data streams and upscale results (WP6). This will feed into the co-design of future, improved and more cost-effective monitoring schemes for species and habitats using novel technologies (WP7), and thus lead to a better management of protected sites and species.

Tens of millions of images from biological collections have become available online over the last two decades. In parallel, there has been a dramatic increase in the capabilities of image analysis technologies, especially those involving machine learning and computer vision. While image analysis has become mainstream in consumer applications, it is still used only on an artisanal basis in the biological collections community, largely because the image corpora are dispersed. Yet, there is massive untapped potential for novel applications and research if images of collection objects could be made accessible in a single corpus. In this paper, we make the case for infrastructure that could support image analysis of collection objects. We show that such infrastructure is entirely feasible and well worth investing in.

In this study, TiO2 nanospheres (TiO2-NS) were synthesized by the solvothermal method. Firstly, the synthesized nanomaterial was characterized by X-ray diffraction (XRD), Fourier Transformed Infrared (FTIR), scanning electron microscopy (SEM) and UV-Vis Diffuse Reflectance Spectroscopy (DRS). To study the photocatalytic degradation of Tartrazine (TTZ) and Naphthol Blue Black (NBB) in a binary mixture, the influence of some key parameters such as pH, pollutant concentration and catalyst dose was taken into account under visible and UV light. The results show a 100% degradation efficiency for TTZ after 150 min of UV irradiation and 57% under visible irradiation at 180 min. The kinetic study showed a good pseudo-first-order fit to the Langmuir–Hinshelwood model. Furthermore, in order to get closer to the real conditions of textile wastewater, the influence of the presence of salt on TiO2-NS’s photocatalytic performance was explored by employing NaCl as an inorganic ion. The optimum conditions provided by the Response Surface Methodology (RSM) were low concentrations of TTZ (2 ppm) and NBB (2.33 ppm) and negligible salt (NaCl) interference. The percentage of photodegradation was high at low pollutant and NaCl concentrations. However, this yield became very low as NaCl concentrations increased. The photocatalytic treatment leads to 31% and 53% of mineralization yield after 1 and 3 h of visible light irradiation. The synthesis of TiO2-NS provides new insights that will help to develop an efficient photocatalysts for the remediation of contaminated water.

Temporal predictions can be formed and impact perception when sensory timing is fully predictable: for instance, the discrimination of a target sound is enhanced if it is presented on the beat of an isochronous rhythm. However, natural sensory stimuli, like speech or music, are not entirely predictable, but still possess statistical temporal regularities. We investigated whether temporal expectations can be formed in non-fully predictable contexts, and how the temporal variability of sensory contexts affects auditory perception. Specifically, we asked how “rhythmic” an auditory stimulation needs to be in order to observe temporal predictions effects on auditory discrimination performances. In this behavioral auditory oddball experiment, participants listened to auditory sound sequences where the temporal interval between each sound was drawn from gaussian distributions with distinct standard deviations. Participants were asked to discriminate sounds with a deviant pitch in the sequences. Auditory discrimination performances, as measured with deviant sound discrimination accuracy and response times, progressively declined as the temporal variability of the sound sequence increased. Moreover, both global and local temporal statistics impacted auditory perception, suggesting that temporal statistics are promptly integrated to optimize perception. Altogether, these results suggests that temporal predictions can be set up quickly based on the temporal statistics of past sensory events and are robust to a certain amount of temporal variability. Therefore, temporal predictions can be built on sensory stimulations that are not purely periodic nor temporally deterministic.

The cultivation of seed mixtures for local pastures is a traditional mixed cropping technique of cereals and legumes for producing, at a low production cost, a balanced animal feed in energy and protein in livestock systems. By considerably improving the autonomy and safety of agricultural systems, as well as reducing their impact on the environment, it is a type of crop that responds favorably to both the evolution of the European regulations on the use of phytosanitary products and the expectations of consumers who wish to increase their consumption of organic products. However, farmers find it difficult to adopt it because cereals and legumes do not ripen synchronously and the harvested seeds are heterogeneous, making it more difficult to assess their nutritional value. Many efforts therefore remain to be made to acquire and aggregate technical and economical references to evaluate to what extent the cultivation of seed mixtures could positively contribute to securing and reducing the costs of herd feeding. The work presented in this paper proposes new Artificial Intelligence techniques that could be transferred to an online or smartphone application to automatically estimate the nutritional value of harvested seed mixes to help farmers better manage the yield and thus engage them to promote and contribute to a better knowledge of this type of cultivation. For this purpose, an original open image dataset has been built containing 4,749 images of seed mixes, covering 11 seed varieties, with which 2 types of recent deep learning models have been trained. The results highlight the potential of this method and show that the best-performing model is a recent state-of-the-art vision transformer pre-trained with self-supervision (Bidirectional Encoder representation from Image Transformer). It allows an estimation of the nutritional value of seed mixtures with a coefficient of determination R2 score of 0.91, which demonstrates the interest of this type of approach, for its possible use on a large scale.

Analytical methods are significantly useful tools for engineers in computational modeling. These methods provide fast simulations, while preserving physical meaning. In this paper, a full analytical formulation is developed based on the circuit approach, also known as Intermediate Level Circuit Model (ILCM), to evaluate the shielding effectiveness (SE) of metallic structures. The proposal focuses on rectangular enclosures containing apertures of complex geometrical shape, illuminated by an external plane wave. The enclosure is taken as a short-circuited waveguide. The aperture is treated as a transmission line with an intrinsic impedance that depends on the aperture polarizability, according to Bethe’s theory of small holes. The formulation easily takes into account high-order resonant modes and different apertures, such as circular, elliptical, round-ended and cross-shaped apertures. The ILCM technique reveals good agreement with a numerical full-wave method for various configurations of enclosures, covering a large range of frequencies from 0.5 GHz to 2.5 GHz, thus offering perspectives for parametric and/or optimization SE studies.

Species Distribution Models (SDMs) are fundamental tools in ecology for predicting the geographic distribution of species based on environmental data. They are also very useful from an application point of view, whether for the implementation of conservation plans for threatened species or for monitoring invasive species. The generalizability and spatial accuracy of an SDM depend very strongly on the type of model used and the environmental data used as explanatory variables. In this article, we study a country-wide species distribution model based on very high resolution (VHR) (1 m) remote sensing images processed by a convolutional neural network. We demonstrate that this model can capture landscape and habitat information at very fine spatial scales while providing overall better predictive performance than conventional models. Moreover, to demonstrate the ecological significance of the model, we propose an original analysis based on the t-distributed Stochastic Neighbor Embedding (t-SNE) dimension reduction technique. It allows visualizing the relation between input data and species traits or environment learned by the model as well as conducting some statistical tests verifying them. We also analyze the spatial mapping of the t-SNE dimensions at both national and local levels, showing the model benefit of automatically learning environmental variation at multiple scales.

Species distribution models (SDMs) are widely used numerical tools that rely on correlations between geolocated presences (and possibly absences) and environmental predictors to model the ecological preferences of species. Recently, SDMs exploiting deep learning and remote sensing images have emerged and have demonstrated high predictive performance. In particular, it has been shown that one of the key advantages of these models (called deep-SDMs) is their ability to capture the spatial structure of the landscape, unlike prior models. In this paper, we examine whether the temporal dimension of remote sensing images can also be exploited by deep-SDMs. Indeed, satellites such as Sentinel-2 are now providing data with a high temporal revisit, and it is likely that the resulting time-series of images contain relevant information about the seasonal variations of the environment and vegetation. To confirm this hypothesis, we built a substantial and original dataset (called DeepOrchidSeries) aimed at modeling the distribution of orchids on a global scale based on Sentinel-2 image time series. It includes around 1 million occurrences of orchids worldwide, each being paired with a 12-month-long time series of high-resolution images (640 x 640 m RGB+IR patches centered on the geolocated observations). This ambitious dataset enabled us to train several deep-SDMs based on convolutional neural networks (CNNs) whose input was extended to include the temporal dimension. To quantify the contribution of the temporal dimension, we designed a novel interpretability methodology based on temporal permutation tests, temporal sampling, and temporal averaging. We show that the predictive performance of the model is greatly increased by the seasonality information contained in the temporal series. In particular, occurrence-poor species and diversity-rich regions are the ones that benefit the most from this improvement, revealing the importance of habitat's temporal dynamics to characterize species distribution.

The demonstration of enhanced spatial control of nanosecond microwave plasmas generated by the time reversal plasma source is presented in this paper. This new microwave plasma source relies on the spatio-temporal control of the electric field inside an all-metal plasma reactor by modifying the waveform of a high power microwave signal. More specifically, it originally used the spatio-temporal focusing capabilities of the time reversal method to focus a high electric field in a small location. However, a parasitic microwave breakdown can still occur at sharp corners or wedges inside the cavity due to the local enhancement of the residual electric field during time reversal focusing. Thus, it is proposed to use the linear combination of configuration field method to improve field control inside the reactor. Its transient electric field shaping capabilities turn out to be a good candidate for the development of a low pressure microwave “plasma brush”.

We introduce a Domain Decomposition Spectral Method (DDSM) as a solution for Maxwell’s equations in the frequency domain. It will be illustrated in the framework of the Aperiodic Fourier Modal Method (AFMM). This method may be applied to compute the electromagnetic field diffracted by a large-scale surface under any kind of incident excitation. In the proposed approach, a large-size surface is decomposed into square sub-cells, and a projector, linking the set of eigenvectors of the large-scale problem to those of the small-size sub-cells, is defined. This projector allows one to associate univocally the spectrum of any electromagnetic field of a problem stated on the large-size domain with its footprint on the small-scale problem eigenfunctions. This approach is suitable for parallel computing, since the spectrum of the electromagnetic field is computed on each sub-cell independently from the others. In order to demonstrate the method’s ability, to simulate both near and far fields of a full three-dimensional (3D) structure, we apply it to design large area diffractive metalenses with a conventional personal computer.

Plant invasions generate massive ecological and economic costs worldwide. Predicting their spatial dynamics is crucial to the design of effective management strategies and the prevention of invasions. Earlier studies highlighted the crucial role of long-distance dispersal in explaining the speed of many invasions. In addition, invasion speed depends highly on the duration of its lag phase, which may depend on the scaling of fecundity with age, especially for woody plants, even though empirical proof is still rare. Bayesian dynamic species distribution models enable the fitting of process-based models to partial and heterogeneous observations using a state-space modeling approach, thus offering a tool to test such hypotheses on past invasions over large spatial scales. We use such a model to explore the roles of long-distance dispersal and age-structured fecundity in the transient invasion dynamics of Plectranthus barbatus, a woody plant invader in South Africa. Our lattice-based model accounts for both short and human-mediated long-distance dispersal, as well as age-structured fecundity. We fitted our model on opportunistic occurrences, accounting for the spatio-temporal variations of the sampling effort and the variable detection rates across datasets. The Bayesian framework enables us to integrate a priori knowledge on demographic parameters and control identifiability issues. The model revealed a massive wave of spatial spread driven by human-mediated long-distance dispersal during the first decade and a subsequent drastic population growth, leading to a global equilibrium in the mid-1990s. Without long-distance dispersal, the maximum population would have been equivalent to 30% of the current equilibrium population. We further identified the reproductive maturity at three years old, which contributed to the lag phase before the final wave of population growth. Our results highlighted the importance of the early eradication of weedy horticultural alien plants around urban areas to hamper and delay the invasive spread.

Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted "punctual models"). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure ("ablation experiments"). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.

Plant diseases have a significant impact on global food security and the world's agricultural economy. Their early detection and classification increase the chances of setting up effective control measures, which is why the search for automatic systems that allow this is of major interest to our society. Several recent studies have reported promising results in the classification of plant diseases from RGB images on the basis of Convolutional Neural Networks (CNN). These studies have been successfully experimented on a large number of crops and symptoms, and they have shown significant advantages in the support of human expertise. However, the CNN models still have limitations. In particular, CNN models do not necessarily focus on the visible parts affected by a plant disease to allow their classification, and they can sometimes take into account irrelevant backgrounds or healthy plant parts. In this paper, we therefore develop a new technique based on a Recurrent Neural Network (RNN) to automatically locate infected regions and extract relevant features for disease classification. We show experimentally that our RNN-based approach is more robust and has a greater ability to generalize to unseen infected crop species as well as to different plant disease domain images compared to classical CNN approaches. We also analyze the focus of attention as learned by our RNN and show that our approach is capable of accurately locating infectious diseases in plants. Our approach, which has been tested on a large number of plant species, should thus contribute to the development of more effective means of detecting and classifying crop pathogens in the near future.

Abstract 1. Successful monitoring and management of plant resources worldwide needs the involvement of civil society to support natural reserve managers. Because it is difficult to correctly and quickly identify plant species for non‐specialists, the development of recent techniques based on automatic visual identification should facilitate and increase public engagement in citizen science initiatives. 2. Automatic identification platforms are new to most citizen scientists and land managers. Pl@ntNet is such a platform, available since 2013 on web and mobile environments, and now included in several workflows such as invasive alien species management, endemic species monitoring, educational activities and eco‐tourism practices. The successful development of such platforms needs to identify their strengths and weaknesses in order to improve and facilitate their use in all aspects of ecosystem management. 3. Here we present two Pl@ntNet citizen science initiatives used by conservation practitioners in Europe (France) and Africa (Kenya). We discuss various perspectives, including benefits and limitations. Based on the experiences of field managers, we formulate several recommendations for future initiatives. The recommendations are aimed at a diverse group of conservation managers and citizen science practitioners.

Summary: The increasing availability of digital images, coupled with sophisticated artificial intelligence (AI) techniques for image classification, presents an exciting opportunity for biodiversity researchers to create new datasets of species observations. We investigated whether an AI plant species classifier could extract previously unexploited biodiversity data from social media photos (Flickr). We found over 60,000 geolocated images tagged with the keyword “flower” across an urban and rural location in the UK and classified these using AI, reviewing these identifications and assessing the representativeness of images. Images were predominantly biodiversity focused, showing single species. Non-native garden plants dominated, particularly in the urban setting. The AI classifier performed best when photos were focused on single native species in wild situations but also performed well at higher taxonomic levels (genus and family), even when images substantially deviated from this. We present a checklist of questions that should be considered when undertaking a similar analysis. The Bigger Picture: Recent reports of global biodiversity decline make it more important than ever to monitor biodiversity so that we can detect changes and infer their drivers. Online digital media, such as social media images, may be a new source of biodiversity observations, but they are far too numerous for a human to practically review. In this paper we apply an AI image classifier, designed to identify plants from images, to social media imagery to assess this method as a way to generate new biodiversity observations. We find that this approach is able to generate new data on species occurrence but that there are biases in both the social media data and the AI image classifier that need to be considered in analyses. This approach could be applied outside the biodiversity domain, to any phenomena of interest that may be captured in social media imagery. The checklist we provide at the end of this paper should therefore be of interest to anyone considering this approach to generating new data.

Pneumocystis pneumonia (PCP) remains the most frequent AIDS-defining illness in developed countries. This infection also occurs in non-AIDS immunosuppressed patients, e.g., those who have undergone an organ transplantation. Moreover, mild Pneumocystis jirovecii infections related to low pulmonary fungal burden, frequently designated as pulmonary colonization, occurs in patients with chronic pulmonary diseases, e.g., cystic fibrosis (CF). Indeed, this autosomal recessive disorder alters mucociliary clearance leading to bacterial and fungal colonization of the airways. This mini-review compiles and discusses available information on P. jirovecii and CF. It highlights significant differences in the prevalence of P. jirovecii pulmonary colonization in European and Brazilian CF patients. It also describes the microbiota associated with P. jirovecii in CF patients colonized by P. jirovecii. Furthermore, we have described P. jirovecii genomic diversity in colonized CF patients. In addition of pulmonary colonization, it appears that PCP can occur in CF patients specifically after lung transplantation, thus requiring preventive strategies. In other respects, Pneumocystis primary infection is a worldwide phenomenon occurring in non-immunosuppressed infants within their first months. The primary infection is mostly asymptomatic but it can also present as a benign self-limiting infection. It probably occurs in the same manner in CF infants. Nonetheless, two cases of severe Pneumocystis primary infection mimicking PCP in CF infants have been reported, the genetic disease appearing in these circumstances as a risk factor of PCP while the host-pathogen interaction in older children and adults with pulmonary colonization remains to be clarified.

Premise Weed removal in agriculture is typically achieved using herbicides. The use of autonomous robots to reduce weeds is a promising alternative solution, although their implementation requires the precise detection and identification of crops and weeds to allow an efficient action. Methods We trained and evaluated an instance segmentation convolutional neural network aimed at segmenting and identifying each plant specimen visible in images produced by agricultural robots. The resulting data set comprised field images on which the outlines of 2489 specimens from two crop species and four weed species were manually drawn. We adjusted the hyperparameters of a mask region‐based convolutional neural network (R‐CNN) to this specific task and evaluated the resulting trained model. Results The probability of detection using the model was quite good but varied significantly depending on the species and size of the plants. In practice, between 10% and 60% of weeds could be removed without too high of a risk of confusion with crop plants. Furthermore, we show that the segmentation of each plant enabled the determination of precise action points such as the barycenter of the plant surface. Discussion Instance segmentation opens many possibilities for optimized weed removal actions. Weed electrification, for instance, could benefit from the targeted adjustment of the voltage, frequency, and location of the electrode to the plant. The results of this work will enable the evaluation of this type of weeding approach in the coming months.

Phenology—the timing of life-history events—is a key trait for understanding responses of organisms to climate. The digitization and online mobilization of herbarium specimens is rapidly advancing our understanding of plant phenological response to climate and climatic change. The current practice of manually harvesting data from individual specimens, however, greatly restricts our ability to scale-up data collection. Recent investigations have demonstrated that machine-learning approaches can facilitate this effort. However, present attempts have focused largely on simplistic binary coding of reproductive phenology (e.g., presence/absence of flowers). Here, we use crowd-sourced phenological data of buds, flowers, and fruits from >3,000 specimens of six common wildflower species of the eastern United States (Anemone canadensis L., A. hepatica L., A. quinquefolia L., Trillium erectum L., T. grandiflorum (Michx.) Salisb., and T. undulatum Wild.) to train models using Mask R-CNN to segment and count phenological features. A single global model was able to automate the binary coding of each of the three reproductive stages with >87% accuracy. We also successfully estimated the relative abundance of each reproductive structure on a specimen with ≥90% accuracy. Precise counting of features was also successful, but accuracy varied with phenological stage and taxon. Specifically, counting flowers was significantly less accurate than buds or fruits likely due to their morphological variability on pressed specimens. Moreover, our Mask R-CNN model provided more reliable data than non-expert crowd-sourcers but not botanical experts, highlighting the importance of high-quality human training data. Finally, we also demonstrated the transferability of our model to automated phenophase detection and counting of the three Trillium species, which have large and conspicuously-shaped reproductive organs. These results highlight the promise of our two-phase crowd-sourcing and machine-learning pipeline to segment and count reproductive features of herbarium specimens, thus providing high-quality data with which to investigate plant responses to ongoing climatic change.

Premise Herbarium specimens represent an outstanding source of material with which to study plant phenological changes in response to climate change. The fine‐scale phenological annotation of such specimens is nevertheless highly time consuming and requires substantial human investment and expertise, which are difficult to rapidly mobilize. Methods We trained and evaluated new deep learning models to automate the detection, segmentation, and classification of four reproductive structures of Streptanthus tortuosus (flower buds, flowers, immature fruits, and mature fruits). We used a training data set of 21 digitized herbarium sheets for which the position and outlines of 1036 reproductive structures were annotated manually. We adjusted the hyperparameters of a mask R‐CNN (regional convolutional neural network) to this specific task and evaluated the resulting trained models for their ability to count reproductive structures and estimate their size. Results The main outcome of our study is that the performance of detection and segmentation can vary significantly with: (i) the type of annotations used for training, (ii) the type of reproductive structures, and (iii) the size of the reproductive structures. In the case of Streptanthus tortuosus, the method can provide quite accurate estimates (77.9% of cases) of the number of reproductive structures, which is better estimated for flowers than for immature fruits and buds. The size estimation results are also encouraging, showing a difference of only a few millimeters between the predicted and actual sizes of buds and flowers. Discussion This method has great potential for automating the analysis of reproductive structures in high‐resolution images of herbarium sheets. Deeper investigations regarding the taxonomic scalability of this approach and its potential improvement will be conducted in future work.

Con base en las recientes aplicaciones exitosas de técnicas de Aprendizaje Profundo en la clasificación, detección y segmentación de plantas, proponemos un enfoque de segmentación de instancias utilizando un modelo Mask R-CNN para la detección de malezas y cultivos en tierras de cultivo. Evaluamos el rendimiento de nuestro modelo con la métrica de precisión promedio de MSCOCO, contrastando el uso de técnicas de aumento de datos. Los resultados obtenidos muestran cómo el modelo se adapta muy bien en este contexto, abriendo nuevas oportunidades para soluciones automatizadas de control de malezas, a gran escala.

The use of naturalist mobile applications have dramatically increased during last years, and provide huge amounts of accurately geolocated species presences records. Integrating this novel type of data in species distribution models (SDMs) raises specific methodological questions. Presence-only SDM methods require background points, which should be consistent with sampling effort across the environmental space to avoid bias. A standard approach is to use uniformly distributed background points (UB). When multiple species are sampled, another approach is to use a set of occurrences from a Target-Group of species as background points (TGOB). We here investigate estimation biases when applying TGOB and UB to opportunistic naturalist occurrences. We modelled species occurrences and observation process as a thinned Poisson point process, and express asymptotic likelihoods of UB and TGOB as a divergence between environmental densities, in order to characterize biases in species niche estimation. To illustrate our results, we simulated species occurrences with different types of niche (specialist/generalist, typical/marginal), sampling effort and TG species density. We conclude that none of the methods are immune to estimation bias, although the pitfalls are different: For UB, the niche estimate fits tends towards the product of niche and sampling densities. TGOB is unaffected by heterogeneous sampling effort, and even unbiased if the cumulated density of the TG species is constant. If it is concentrated, the estimate deviates from the range of TG density. The user must select the group of species to ensure that they are jointly abundant over the broadest environmental sub-area.

A better knowledge of tree vegetative growth phenology and its relationship to environmental variables is crucial to understanding forest growth dynamics and how climate change may affect it. Less studied than reproductive structures, vegetative growth phenology focuses primarily on the analysis of growing shoots, from buds to leaf fall. In temperate regions, low winter temperatures impose a cessation of vegetative growth shoots and lead to a well-known annual growth cycle pattern for most species. The humid tropics, on the other hand, have less seasonality and contain many more tree species, leading to a diversity of patterns that is still poorly known and understood. The work in this study aims to advance knowledge in this area, focusing specifically on herbarium scans, as herbariums offer the promise of tracking phenology over long periods of time. However, such a study requires a large number of shoots to be able to draw statistically relevant conclusions. We propose to investigate the extent to which the use of deep learning can help detect and type-classify these relatively rare vegetative structures in herbarium collections. Our results demonstrate the relevance of using herbarium data in vegetative phenology research as well as the potential of deep learning approaches for growing shoot detection.

Prior to the nationwide survey on indoor air quality in 100 age care and social facilities across France, which is planned for 2019, a questionnaire was sent by email to the directors of all 18,432 institutions of these types to determine the potential emission sources of indoor pollutants. Among these, cleaning practices and cleaning products were targeted. The questionnaire included items regarding cleaning frequency and periods, cleaning techniques, storage areas, the use of air fresheners and the commercial names of the products used. A total of 2140 questionnaires were collected and processed, and 1109 cleaning products, along with their commercial names, were listed. From them, 341 different products were identified, for which 299 safety data sheets (SDSs) were available and analyzed. A total of 216 different chemical substances were identified in the SDSs. The boiling points were retrieved to classify the substances according to their volatility. Finally, information regarding their hazard classification was collected, and six categories of human health effects were considered. A total of 41 chemical substances were classified, among which 10 are carcinogenic, 6 are mutagenic, 1 has reproductive toxicity, 3 have specific target organ toxicity - repeat exposure, 19 are possible endocrine disruptors, 14 are skin sensitizers, and 2 are respiratory sensitizers. Of these 41 chemicals, 51% (n = 21) are volatile organic compounds (VOCs). Despite the limitations of using SDSs, which do not report the exact compositions of the products, this study shows that a large spectrum of volatile substances may be emitted from cleaning products used in age care and social facilities, which may have a potential impact on the indoor air quality.

Machine learning (ML) can accelerate the extraction of phenological data from herbarium specimens; however, no studies have assessed whether ML-derived phenological data can be used reliably to evaluate ecological patterns. In this study, 709 herbarium specimens representing a widespread annual herb, Streptanthus tortuosus, were scored both manually by human observers and by a mask R-CNN object detection model to (1) evaluate the concordance between ML and manually-derived phenological data and (2) determine whether ML-derived data can be used to reliably assess phenological patterns. The ML model generally underestimated the number of reproductive structures present on each specimen; however, when these counts were used to provide a quantitative estimate of the phenological stage of plants on a given sheet (i.e., the phenological index or PI), the ML and manually-derived PI’s were highly concordant. Moreover, herbarium specimen age had no effect on the estimated PI of a given sheet. Finally, including ML-derived PIs as predictor variables in phenological models produced estimates of the phenological sensitivity of this species to climate, temporal shifts in flowering time, and the rate of phenological progression that are indistinguishable from those produced by models based on data provided by human observers. This study demonstrates that phenological data extracted using machine learning can be used reliably to estimate the phenological stage of herbarium specimens and to detect phenological patterns.

In this work, we present a dataset, which provides information on the structural diversity of some endemic tropical species in Madagascar. The data were from CIRAD xylotheque (since 1937), and were also collected during various fieldworks (since 1964). The field notes and photographs were provided by French botanists; particularly by Francis Hallé. The dataset covers 250 plant species with anatomical, morphological, and architectural traits indexed from digitized wood slides and fieldwork documents. The digitized wood slides were constituted by the transverse, tangential, and radial sections with three optical magnifications. The main specific anatomical traits can be found within the digitized area. Information on morphological and architectural traits were indexed from digitized field drawings including notes and photographs. The data are hosted in the website ArchiWood (http://archiwood.cirad.fr). Keywords: Morpho-architectural traits, Plant architecture, Wood anatomy, Madagascar

Abstract Background Hundreds of herbarium collections have accumulated a valuable heritage and knowledge of plants over several centuries. Recent initiatives started ambitious preservation plans to digitize this information and make it available to botanists and the general public through web portals. However, thousands of sheets are still unidentified at the species level while numerous sheets should be reviewed and updated following more recent taxonomic knowledge. These annotations and revisions require an unrealistic amount of work for botanists to carry out in a reasonable time. Computer vision and machine learning approaches applied to herbarium sheets are promising but are still not well studied compared to automated species identification from leaf scans or pictures of plants in the field. Results In this work, we propose to study and evaluate the accuracy with which herbarium images can be potentially exploited for species identification with deep learning technology. In addition, we propose to study if the combination of herbarium sheets with photos of plants in the field is relevant in terms of accuracy, and finally, we explore if herbarium images from one region that has one specific flora can be used to do transfer learning to another region with other species; for example, on a region under-represented in terms of collected data. Conclusions This is, to our knowledge, the first study that uses deep learning to analyze a big dataset with thousands of species from herbaria. Results show the potential of Deep Learning on herbarium species identification, particularly by training and testing across different datasets from different herbaria. This could potentially lead to the creation of a semi, or even fully automated system to help taxonomists and experts with their annotation, classification, and revision works.

The paper deals with the stochastic collocation analysis of a time domain response of a straight thin wire scatterer buried in a lossy half-space. The wire is excited either by a plane wave transmitted through the air-ground interface or by an equivalent current source representing direct lightning strike pulse. Transient current induced at the center of the wire, governed by corresponding Pocklington integrodifferential equation, is determined analytically. This antenna configuration suffers from uncertainties in various parameters, such as ground properties, wire dimensions, and position. The statistical processing of the results yields additional information, thus enabling more accurate and efficient analysis of buried wire configurations.

L’anatomie de 1re année de bachelier (BAC 1) en médecine est dispensée grâce à un dispositif hy- bride de formation. L’évaluation se compose de trois types de QCM, dont le QCM 2 qui recourt à des exercices d’analyse de coupes anatomiques re- présentées dans différents plans de l’espace. Nous avons évalué si la mise en ligne d’exercices en- traînant à l’analyse de coupes est valorisée par les étudiants et favorise la réussite du QCM 2. Afin de contextualiser nos recherches, nous nous sommes interrogés sur la perception de la cohérence entre les objectifs, les méthodes et l’évaluation de l’en- semble de notre dispositif. Ensuite, cette perception a été mise en lien avec le score global de l’examen. Le QCM 2 est significativement meilleur pour les étudiants ayant pratiqué les nouveaux exercices en ligne et pour les étudiants ayant perçu la valeur de cette activité. La perception de la cohérence entre les objectifs, les méthodes et l’évaluation n’est, quant à elle, pas associée au score global. Le nou- veau scénario d’apprentissage proposé enrichit fa- vorablement l’environnement d’apprentissage des

Le cours d’introduction à l’anatomie en Faculté de médecine, ULg, Belgique intègre une formation à distance et un cours présentiel. Un module introductif a été développé en EAD pour fournir une méthode progressive d’entraînement à la vision dans l’espace et à la description anatomique, sur la base des concepts prérequis indispensables à la compréhension des autres modules. Ces concepts identifiés ont été disséqués puis organisés pour permettre la généralisation et ensuite le transfert, et pour que tous les étudiants puissent réaliser à loisir les exercices, passant par toutes les étapes des raisonnements suivant leur rythme personnel. Ce module a été régulé après deux années d’utilisation en situation réelle. Cet article met l’accent sur les principes méthodologiques qui fondent la structure micrograduée du cours à distance.

Boris Johnson’s term in office has been marked by many twists and turns: an epic confrontation with Parliament; the achievement of Brexit; a pandemic; a series of scandals and the beginning of the war in Ukraine. Throughout these three eventful years, Johnson often had to take the floor to justify his choices, celebrate his successes, explain his mistakes and so on. This article looks at one particular aspect of his rhetoric: storytelling.Storytelling is the art of telling stories in order to capture the attention or win the support of an audience. It is not limited to language and can draw on non-verbal semiotic resources such as images and visual representations. In politics, storytelling plays a key role in shaping the discursive ethos, i.e., the self-image that speakers construct in their discourse. It combines collective narratives with personal stories to create a vision of the world that appeals to voters. This narrativized representation of the world plays with the audience’s emotions, which facilitates the transmission of a political ideology, since the willing suspension of disbelief induced by the narrative format reduces our capacity to counter-argue and invites us to share the hero’s point of view.A former journalist and well-known orator, Boris Johnson is well versed in the art of telling stories. Johnson’s storytelling, which created the image of a charismatic, irreverent leader, close to the people and in opposition to patriarchal authority, was particularly well suited to the Brexit crisis, when the United Kingdom supposedly wanted to extricate itself from European authoritarianism and elitism. The narrative of Vote Leave, of which Johnson was an influential member, was both simple and highly structured: by undermining the country’s sovereignty, the EU posed an existential threat to a freedom-loving Britain lulled by the heroic tales of its exceptionalism and defence of democracy in the Second World War. The situation changed radically, however, when the country was faced with a global health crisis.This article proposes to analyse Johnson’s storytelling in his speeches at the 2019, 2020 and 2021 Conservative Party annual conferences. The aim is to see whether Johnson’s storytelling, which contributed to his success in the 2019 election, has evolved, to convince Britons that he was still the right man for the job during the pandemic.