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Imiqualines are analogues of the immunomodulatory drug imiquimod. EAPB02303, the lead of the second-generation imiqualines, is characterized by significant anti-tumor effects with IC50s in the nanomolar range. We used Caenorhabditis elegans transgenic and mutant strains of two key signaling pathways (PI3K-Akt and Ras-MAPK) disrupted in human cancers to investigate the mode of action of EAPB02303. The ability of this imiqualine to inhibit the insulin/IGF1 signaling (IIS) pathway via the PI3K-Akt kinase cascade was explored through assessing the lifespan of wild-type worms. Micromolar doses of EAPB02303 significantly enhanced longevity of N2 strain and led to the nuclear translocation and subsequent activation of transcription factor DAF-16, the only forkhead box transcription factor class O (Fox O) homolog in C. elegans . Moreover, EAPB02303 significantly reduced the multivulva phenotype in let-60 /Ras mutant strains MT2124 and MT4698, indicative of its mode of action through the Ras pathway. In summary, we showed that EAPB02303 potently reduced the activity of IIS and Ras-MAPK signaling in C. elegans . Our results revealed the mechanism of action of EAPB02303 against human cancers associated with hyperactivated IIS pathway and oncogenic Ras mutations., Competing Interests: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

As a part of modern technological environments, virtual microscopy enriches histological learning, with support from large institutional investments. However, existing literature does not supply empirical evidence of its role in improving pedagogy. Virtual microscopy provides fresh opportunities for investigating user behavior during the histology learning process, through digitized histological slides. This study establishes how students' perceptions and user behavior data can be processed and analyzed using machine learning algorithms. These also provide predictive data called learning analytics that enable predicting students' performance and behavior favorable for academic success. This information can be interpreted and used for validating instructional designs. Data on the perceptions, performances, and user behavior of 552 students enrolled in a histology course were collected from the virtual microscope, Cytomine®. These data were analyzed using an ensemble of machine learning algorithms, the extra-tree regression method, and predictive statistics. The predictive algorithms identified the most pertinent histological slides and descriptive tags, alongside 10 types of student behavior conducive to academic success. We used these data to validate our instructional design, and align the educational purpose, learning outcomes, and evaluation methods of digitized histological slides on Cytomine®. This model also predicts students' examination scores, with an error margin of <0.5 out of 20 points. The results empirically demonstrate the value of a digital learning environment for both students and teachers of histology., (© 2023 American Association for Anatomy.)

Toxoplasma gondii is the etiologic agent of toxoplasmosis, a highly prevalent parasitosis. Toxoplasma gondii ( T. gondii ) transits in the brain from acute (AT) to chronic toxoplasmosis (CT), under host immune control. In immunocompromised patients, reactivation of CT is potentially life-threatening. Behavioral and neurological complications have been associated with CT. Furthermore, an effective treatment targeting CT is still lacking. We previously reported the efficacy of imiquimod against CT. Here, we demonstrate the molecular effects of imiquimod or imiquimod followed by the clinically used combination of sulfadiazine and pyrimethamine (SDZ + PYR) on CT-associated behavior in a rat model. Imiquimod decreased the number of cysts in the brains of chronically infected rats due to an induced reactivation of bradyzoites into tachyzoites. Importantly, this decrease was more pronounced in rats treated with imiquimod followed by SDZ + PYR. Rats chronically infected with T. gondii exhibited an anxiety-like behavior. Notably, treatment with imiquimod reversed this behavior aberrancy, with even a more pronounced effect with imiquimod followed by SDZ/PYR. Similarly, rats chronically infected with T. gondii exhibited learning deficits, and imiquimod alone or followed by SDZ/PYR reversed this behavior. Our results enhance our knowledge of the implications of CT on behavioral aberrancies and highlight the potency of imiquimod followed by SDZ + PYR on these CT-associated complications., Competing Interests: The authors declare no conflicts of interest.

This study investigates the presence of biocides and other semi-volatile organic compounds (SVOCs) in cleaning products used in daycare centers and health impact through ingestion of settled dust by young children. In Paris metropolitan area, 106 daycares area were investigated between 2019-2022. Fifteen substances were analyzed in settled indoor dust by gas chromatography-tandem mass spectrometry. Detection rates and concentrations ranged from 5 to 100%, and

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., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2024 The Author(s).)

The biological effects of exposure to electromagnetic fields due to wireless technologies and connected devices are a subject of particular research interest. Ultrashort high-amplitude electromagnetic field pulses delivered to biological samples using immersed electrodes in a dedicated cuvette have widely demonstrated their effectiveness in triggering several cell responses including increased cytosolic calcium concentration and reactive oxygen species (ROS) production. In contrast, the effects of these pulses are poorly documented when electromagnetic pulses are delivered through an antenna. Here we exposed Arabidopsis thaliana plants to 30,000 pulses (237  kV m -1 , 280 ps rise-time, duration of 500 ps) emitted through a Koshelev antenna and monitored the consequences of electromagnetic fields exposure on the expression levels of several key genes involved in calcium metabolism, signal transduction, ROS, and energy status. We found that this treatment was mostly unable to trigger significant changes in the messenger RNA accumulation of calmodulin, Zinc-Finger protein ZAT12, NADPH oxidase/respiratory burst oxidase homolog (RBOH) isoforms D and F, Catalase (CAT2), glutamate-cystein ligase (GSH1), glutathione synthetase (GSH2), Sucrose non-fermenting-related Kinase 1 (SnRK1) and Target of rapamycin (TOR). In contrast, Ascorbate peroxidases APX-1 and APX-6 were significantly induced 3 h after the exposure. These results suggest that this treatment, although quite strong in amplitude, is mostly ineffective in inducing biological effects at the transcriptional level when delivered by an antenna. © 2023 The Authors. Bioelectromagnetics published by Wiley Periodicals LLC on behalf of Bioelectromagnetics Society., (© 2023 The Authors. Bioelectromagnetics published by Wiley Periodicals LLC on behalf of Bioelectromagnetics Society.)

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., Competing Interests: No conflict of interest to declare Disclaimer: This article is (co-)authored by any of the Editors-in-Chief, Managing Editors or their deputies in this journal., (Quentin Groom, Mathias Dillen, Wouter Addink, Arturo H. H. Ariño, Christian Bölling, Pierre Bonnet, Lorenzo Cecchi, Elizabeth R. Ellwood, Rui Figueira, Pierre-Yves Gagnier, Olwen M Grace, Anton Güntsch, Helen Hardy, Pieter Huybrechts, Roger Hyam, Alexis A. J. Joly, Vamsi Krishna Kommineni, Isabel Larridon, Laurence Livermore, Ricardo Jorge Lopes, Sofie Meeus, Jeremy A. Miller, Kenzo Milleville, Renato Panda, Marc Pignal, Jorrit Poelen, Blagoj Ristevski, Tim Robertson, Ana C Rufino, Joaquim Santos, Maarten Schermer, Ben Scott, Katja Chantre Seltmann, Heliana Teixeira, Maarten Trekels, Jitendra Gaikwad.)

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 R 2 score of 0.91, which demonstrates the interest of this type of approach, for its possible use on a large scale., Competing Interests: Competing interests: The authors declare that they have no competing interests., (Copyright © 2023 Shamprikta Mehreen et al.)

Pneumocystis jirovecii is a transmissible fungus responsible for severe pneumonia (Pneumocystis pneumonia [PCP]) in immunocompromised patients. Missense mutations due to atovaquone selective pressure have been identified on cytochrome b (CYB) gene of P. jirovecii. It was recently shown that atovaquone prophylaxis can lead to the selection of specific P. jirovecii CYB mutants potentially resistant to atovaquone among organ transplant recipients. In this context, our objectives were to provide data on P. jirovecii CYB mutants and the putative selective pressure exerted by atovaquone on P. jirovecii organisms in France. A total of 123 patients (124 P. jirovecii specimens) from four metropolitan hospitals and two overseas hospitals were retrospectively enrolled. Fourteen patients had prior exposure to atovaquone, whereas 109 patients did not at the time of P. jirovecii detection. A 638 base-pair fragment of the CYB gene of P. jirovecii was amplified and sequenced. A total of 10 single nucleotide polymorphisms (SNPs) were identified. Both missense mutations C431T (Ala144Val) and C823T (Leu275Phe), located at the Qo active site of the enzyme, were significantly associated with prior atovaquone exposure, these mutations being conversely incidental in the absence of prior atovaquone exposure (P < 0.001). Considering that the aforementioned hospitals may be representative of the national territory, these findings suggest that the overall presence of P. jirovecii CYB mutants remains low in France., (© The Author(s) 2023. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology.)

Cutaneous melanoma is one of the most aggressive human cancers and is the deadliest form of skin cancer, essentially due to metastases. Novel therapies are always required, since cutaneous melanoma develop resistance to oncogenic pathway inhibition treatment. The Imiqualine family is composed of heterocycles diversely substituted around imidazo[1,2- a ]quinoxaline, imidazo[1,2- a ]pyrazine, imidazo[1,5- a ]quinoxaline, and pyrazolo[1,5- a ]quinoxaline scaffolds, which display interesting activities on a panel of cancer cell lines, especially melanoma cell lines. We have designed and prepared novel compounds based on the [1,2,4]triazolo[4,3- a ]quinoxaline scaffold through a common synthetic route, using 1-chloro-2-hydrazinoquinoxaline and an appropriate aldehyde. Cyclization is ensured by an oxidation-reduction mechanism using chloranil. The substituents on positions 1 and 8 were chosen based on previous structure-activity relationship (SAR) studies conducted within our heterocyclic Imiqualine family. Physicochemical parameters of all compounds have also been predicted. A375 melanoma cell line viability has been evaluated for 16 compounds. Among them, three novel [1,2,4]triazolo[4,3- a ]quinoxalines display cytotoxic activities. Compounds 16a and 16b demonstrate relative activities in the micromolar range (respectively, 3158 nM and 3527 nM). Compound 17a shows the best EC 50 of the novel series (365 nM), even if EAPB02303 remains the lead of the entire Imiqualine family (3 nM).

Numerous studies report different types of responses following exposure of plants to high frequency electromagnetic fields (HF-EMF). While this phenomenon is related to tissue heating in animals, the situation is much less straightforward in plants where metabolic changes seem to occur without tissue temperature increase. We have set up an exposure system allowing reliable measurements of tissue heating (using a reflectometric probe and thermal imaging) after a long exposure (30 min) to an electromagnetic field of 2.45 GHz transmitted through a horn antenna (about 100 V m -1 at the plant level). We did not observe any heating of the tissues, but we detected rapid increases (60 min) in the accumulation of transcripts of stress-related genes (TCH1 and ZAT12 transcription factor) or involved in ROS metabolism (RBOHF and APX1). At the same time, the amounts of hydrogen peroxide and dehydroascorbic acid increased while glutathione (reduced and oxidized forms), ascorbic acid, and lipid peroxidation remained stable. Therefore, our results unambiguously show that molecular and biochemical responses occur rapidly (within 60min) in plants after exposure to an electromagnetic field, in absence of tissue heating., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Francoise Paladian reports was provided by SNCF Réseau., (Copyright © 2023 Elsevier GmbH. All rights reserved.)

Although cleaning tasks are frequently performed in daycare, no study has focused on exposures in daycares in relation to respiratory health. The CRESPI cohort is an epidemiological study among workers (n~320) and children (n~540) attending daycares. The purpose is to examine the impact of daycare exposures to disinfectants and cleaning products (DCP) on the respiratory health of workers and children. A sample of 108 randomly selected daycares in the region of Paris has been visited to collect settled dust to analyze semi-volatile organic compounds and microbiota, as well as sample indoor air to analyze aldehydes and volatile organic compounds. Innovative tools (smartphone applications) are used to scan DCP barcodes in daycare and inform their use; a database then matches the barcodes with the products' compositions. At baseline, workers/parents completed a standardized questionnaire, collecting information on DCP used at home, respiratory health, and potential confounders. Follow-up regarding children's respiratory health (monthly report through a smartphone application and biannual questionnaires) is ongoing until the end of 2023. Associations between DCP exposures and the respiratory health of workers/children will be evaluated. By identifying specific environments or DCP substances associated with the adverse respiratory health of workers and children, this longitudinal study will contribute to the improvement of preventive measures.

Metallic bismuth and Bi-oxyfluoride nanoparticles (NPs) are successfully synthesized by non-reactive and reactive sputtering of a Bi target onto 1-butyl-3-methylimidazolium bis(trifluoro-methylsulfonyl)imide ([BMIM][TFSI]) ionic liquid (IL). Non-reactive sputtering is realized in pure Ar plasma, where isotropic, well crystallized and dispersed Bi NPs of 3-7 nm are obtained. The diameter and the size distribution of these NPs do not significantly vary with the power, gas pressure, and sputtering time; but these sputtering parameters seem to influence the NP concentration. Then, the introduction of O 2 and CF 4 gases in addition to Ar enables the reaction of radicals from plasma with Bi clusters at the liquid's top surface to form Bi-oxyfluoride NPs of 3-12 nm in diameter with photocatalytic activity. Hence, the reactive sputtering onto an IL is an efficient, original and promising method for synthesizing Bi-based compound NPs. Finally, we propose a mechanism based on reactions of species from plasma at the IL surface to explain the formation of Bi-compounds by reactive sputtering.

Toll-like receptor 7 (TLR7) is a class of pattern recognition receptors (PRRs) recognizing the pathogen-associated elements and damage and as such is a major player in the innate immune system. TLR7 triggers the release of pro-inflammatory cytokines or type-I interferons (IFN), which is essential for immunoregulation. Increasing reports also highlight that the abnormal activation of endosomal TLR7 is implicated in various immune-related diseases, carcinogenesis as well as the proliferation of human immunodeficiency virus (HIV). Hence, the design and development of potent and selective TLR7 antagonists based on small molecules or oligonucleotides may offer new tools for the prevention and management of such diseases. In this review, we offer an updated overview of the main structural features and therapeutic potential of small-molecule antagonists of TLR7. Various heterocyclic scaffolds targeting TLR7 binding sites are presented: pyrazoloquinoxaline, quinazoline, purine, imidazopyridine, pyridone, benzanilide, pyrazolopyrimidine/pyridine, benzoxazole, indazole, indole, and quinoline. Additionally, their structure-activity relationships (SAR) studies associated with biological activities and protein binding modes are introduced.

Pneumocystis jirovecii infections occur in patients treated with methotrexate (MTX) because of immunosuppressive effects of this highly potent dihydrofolate reductase (DHFR) inhibitor. Conversely, MTX may act as an anti -P. jirovecii drug and consequently may exert a selective pressure on this fungus. In this context, we compared the sequences of the dhfr gene of P. jirovecii isolates obtained from two groups of patients with P. jirovecii infections. The first group, with systemic diseases or malignancies, had prior exposure to MTX (21 patients), whereas the second group (22 patients), the control group, did not. Three single nucleotide polymorphisms (SNPs) were observed at positions 278, 312, and 381. The first one was located in the intronic region and the two others were synonymous. Based on these SNPs, three P. jirovecii dhfr alleles, named A, B, and C, were specified. Allele A was the most frequent, as it was observed in 18 patients (85.7%) and in 16 patients (72.7%) of the first and second groups, respectively. No significant difference in P. jirovecii dhfr gene diversity in the two patient groups was observed. In conclusion, these original results suggest that MTX does not exert an overt selective pressure on P. jirovecii organisms.

Aim: Pudendal and inferior cluneal nerve entrapment can cause a neuropathic pain syndrome in the sensitive areas innervated by these nerves. Diagnosis is challenging and patients often suffer several years before diagnosis is made. The purpose of the review was to inform healthcare workers about this disease and to provide a basis of anatomy and physiopathology, to inform about diagnostic tools and invasive or non-invasive treatment modalities and outcome., Methods: A description of pudendal and inferior cluneal nerve anatomy is given. Physiopathology for entrapment is explained. Diagnostic criteria are described, and all non-invasive and invasive treatment options are discussed., Results: The Nantes criteria offer a solid basis for diagnosing this rare condition. Treatment should be offered in a pluri-disciplinary setting and consists of avoidance of painful stimuli, physiotherapy, psychotherapy, pharmacological treatment led by tricyclic antidepressants and anticonvulsants. Nerve blocks are efficient at short term and serve mainly as a diagnostic tool. Pulsed radiofrequency (PRF) is described as a successful treatment option for pudendal neuralgia in patients non-responding to non-invasive treatment. If all other treatments fail, surgery can be offered. Different surgical procedures exist but only the open transgluteal approach has proven its efficacy compared to medical treatment. The minimal-invasive ENTRAMI technique offers the possibility to combine nerve release with pudendal neuromodulation., Conclusions: Pudendal and inferior cluneal nerve entrapment syndrome are a challenge not only for diagnosis but also for treatment. Different non-invasive and invasive treatment options exist and should be offered in a pluri-disciplinary setting.

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.

This study deals with the toxicity of the treated solutions of two types of dyes, namely, the anthraquinonic Reactive Bleu 19 dye (RB19) and the bi-azoic Direct Red 227 dye (DR227), which are treated in single and binary mixture systems. The target molecules were removed by the photocatalysis process using ZnO as a catalyst, which was calcined at two temperatures 250 and 420 °C (ZnO 250 and ZnO 420 ) prepared in the lab by the one-step calcination method. XRD, TEM, EDX, XPS, FT-IR, BET, RAMAN, and EPR analyses were carried out to characterize the catalyst material. While the phytotoxicity was being conducted using watercress seeds, the cytotoxicity took place using a cell line (raw) and an intestinal cell (caco-2). The XRD analysis showed the partial calcination of ZnO 250 and the presence of anhydrous zinc acetate along with the ZnO nanoparticles (NPs). This result was not observed for ZnO 420 . Despite the complete discoloration (100%) of all the final solutions, ZnO 250 exhibited a high cytotoxicity and phytotoxicity against the RB19 dye after the photocatalytic treatment; however, it was not the case of ZnO 420 which was selected as an eco-friendly photocatalyst for the degradation of organic dyes based on the results of removal efficiency, cytotoxicity, and phytotoxicity., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Deneu, Joly, Bonnet, Servajean and Munoz.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Estopinan, Servajean, Bonnet, Munoz and Joly.)

Nucleophosmin-1 (NPM1) is a pleiotropic protein involved in numerous cellular processes. NPM1 shuttles between the nucleus and the cytoplasm, but exhibits a predominant nucleolar localization, where its fate and functions are exquisitely controlled by dynamic post-translational modifications (PTM). Sentrin/SUMO Specific Peptidase 3 (SENP3) and ARF are two nucleolar proteins involved in NPM1 PTMs. SENP3 antagonizes ARF-mediated NPM1 SUMOylation, to promote ribosomal biogenesis. In Acute Myeloid Leukemia (AML), NPM1 is frequently mutated, and exhibits an aberrant cytoplasmic localization (NPM1c). NPM1c mutations define a separate AML entity with good prognosis in some AML patients, rendering NPM1c as a potential therapeutic target. SENP3-mediated NPM1 de-SUMOylation induces resistance to therapy in NPM1c AML. Here, we demonstrate that the imidazoquinoxaline EAPB0503 prolongs the survival and results in selective reduction in the leukemia burden of NPM1c AML xenograft mice. Indeed, EAPB0503 selectively downregulates HDM2 expression and activates the p 53 pathway in NPM1c expressing cells, resulting in apoptosis. Importantly, we unraveled that NPM1c expressing cells exhibit low basal levels of SUMOylation paralleled with high SENP3 and low ARF basal levels. EAPB0503 reverted these molecular players by inducing NPM1c SUMOylation and ubiquitylation, leading to its proteasomal degradation. EAPB0503-induced NPM1c SUMOylation is concurrent with SENP3 downregulation and ARF upregulation in NPM1c expressing cells. Collectively, these results provide a strong rationale for testing therapies modulating NPM1c post-translational modifications in the management of NPM1c AML.

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.

Objectives: The aim of this study was to assess the predictive value of the Vandenberg and Kuse Mental Rotation Test (MRT) on performance of novice medical students for manipulation of a nasal endoscope on a cadaveric model., Material and Method: We randomly selected 39 medical students who had never handled a nasal endoscope and subjected them to the MRT. General information including experience in manual, technical, or surgical activities and testing of anatomical knowledge were collected to exclude possible confounding factors. They were then asked to perform series of cadaveric model exercises using a nasal endoscope. Their cadaver performance was evaluated by 2 blinded observers, using a standardized scale., Results: We found that medical students with higher mental rotation skills had significantly increased endoscopic sinus performance ( P = .0002 using multivariate regression adjusted for specialty choice, previous surgical exposure, and anatomy knowledge). Higher anatomy knowledge was also associated with better endoscopic sinus performance ( P = .0141). Other parameters had no impact on endoscopic sinus performance measured by the endoscopic scale ( P > .05)., Conclusion: The score obtained on the MRT was correlated with the practical performance of manipulating the nasal endoscope in cadaver. It could therefore be a useful spatial ability tool for directing targeted training in rhinology.

Echinocandins are noncompetitive inhibitors of the GSC1 subunit of the enzymatic complex involved in synthesis of 1,3-beta-d-glucan, a cell wall component of most fungi, including Pneumocystis spp. Echinocandins are widely used for treating systemic candidiasis and rarely used for treating Pneumocystis pneumonia. Consequently, data on P. jirovecii gsc1 gene diversity are still scarce compared to that for the homologous fks1 gene of Candida spp. In this study, we analyzed P. jirovecii gsc1 gene diversity and the putative selection pressure of echinocandins on P. jirovecii. gsc1 gene sequences of P. jirovecii specimens from two patient groups were compared. One group of 27 patients had prior exposure to echinocandins, whereas the second group of 24 patients did not, at the time of P. jirovecii infection diagnoses. Two portions of the P. jirovecii gsc1 gene, HS1 and HS2, homologous to hot spots described in Candida spp., were sequenced. Three single-nucleotide polymorphisms (SNPs) at positions 2204, 2243, and 2303 close to the HS1 region and another SNP at position 4540 more distant from the HS2 region were identified. These SNPs represent synonymous mutations. Three gsc1 HS1 alleles, A, B, and C, and two gsc1 HS2 alleles, a and b, and four haplotypes, Ca, Cb, Aa, and Ba, were defined, without significant difference in haplotype distribution in both patient groups ( P =  0.57). Considering the identical diversity of P. jirovecii gsc1 gene and the detection of synonymous mutations in both patient groups, no selection pressure of echinocandins among P. jirovecii microorganisms can be pointed out so far.

Radical cystectomy (RC) with ileal conduit urinary diversion has become a common surgical procedure. Stomal complications and particularly parastomal hernia (PH) are the most frequent complications and causes of reintervention after RC with ileal conduit urinary diversion. PH is usually asymptomatic. When PH increases in size and becomes symptomatic, it clearly impairs patients' quality of life in terms of physical limitation, mental distress and social interaction. The aim of this article was to review the current knowledge on PH after RC with ileal conduit urinary diversion, regarding its natural history, risk factors, prevention and surgical management. There is no strong recommendation on the ideal surgical technique for repair of PH after RC, but laparoscopic Sugarbaker or Sandwich techniques with non-absorbable meshes are emerging as the preferred modern means of PH repair. Techniques for prevention and repair of PH after RC need to be specifically evaluated in future prospective studies.

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.

Introduction and Hypothesis: Chronic perineal pain syndrome due to pudendal nerve impingement is difficult to diagnose and to treat. All the known treatment options leave room for improvement considering the outcome. Early neuromodulation of the pudendal nerve after its surgical release could improve outcomes., Objectives: The aim of the study was to evaluate the potential beneficial effect of pudendal neuromodulation combined with release surgery using the ENTRAMI technique (endoscopic transgluteal minimally invasive technique)., Study Design: This is a single-center prospective descriptive study. Between March 2019 and March 2020, 16 patients (2 males, 14 females) were included. Data were collected at baseline and 1 month after surgery., Methods: Patients eligible for inclusion had chronic perineal pain for at least 3 months in the area served by the pudendal nerve. We combined pudendal nerve release with neuromodulation., Results: At 1 month, the numeric pain rating scale (NPRS) dropped from 9.5 at baseline to 3.5 (p = 0.003). Seventy-six percent of patients showed a global impression of change (PGIC) of > 50% at 1 month, and optimal treatment response (PGIC ≥ 90%) was found in 41% of patients., Limitations: The drawback of our study was that it was not randomized or blinded. The peripheral nerve evaluation lead (PNE) used could only be implanted for 1 month because of infection risk and is also prone to dislocations and technical failures., Conclusion: Pudendal nerve liberation by the ENTRAMI technique combined with short-term pudendal neuromodulation seems feasible and promising in treating patients with chronic perineal pain. Clinical trial number: NCT03880786., (© 2020. The International Urogynecological Association.)

Field photographs of plant species are crucial for research and conservation, but the lack of a centralized database makes them difficult to locate. We surveyed 25 online databases of field photographs and found that they harboured only about 53% of the approximately 125,000 vascular plant species of the Americas. These results reflect the urgent need for a centralized database that can both integrate and complete the photographic record of the world's flora., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Pulmonary specimen pairs from five patients who presented with pulmonary colonization and later developed Pneumocystis Pneumonia (PcP) were retrospectively examined for P. jirovecii genotyping. A match of genotypes in pulmonary specimen pairs of three patients was observed, whereas a partial match and a mismatch were observed in the fourth and fifth patients, respectively. The genotyping results suggest that the colonization state can differ from PcP but can also represent the incubation period of PcP. Clinicians should not systematically rule out the treatment of putative colonized patients and should at least discuss the initiation of prophylaxis on a case-by-case basis., (© The Author(s) 2020. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology.)

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., Competing Interests: The authors have declared that no competing interests exist.

Toxoplasma gondii is a prevalent parasite of medical and veterinary importance. Tachyzoïtes and bradyzoïtes are responsible for acute and chronic toxoplasmosis (AT and CT), respectively. In immunocompetent hosts, AT evolves into a persistent CT, which can reactivate in immunocompromised patients with dire consequences. Imiquimod is an efficient immunomodulatory drug against certain viral and parasitic infections. In vivo , treatment with Imiquimod, throughout AT, reduces the number of brain cysts while rendering the remaining cysts un-infectious. Post-establishment of CT, Imiquimod significantly reduces the number of brain cysts, leading to a delay or abortion of reactivation. At the molecular level, Imiquimod upregulates the expression of Toll-like receptors 7, 11, and 12, following interconversion from bradyzoïtes to tachyzoïtes. Consequently, MyD88 pathway is activated, resulting in the induction of the immune response to control reactivated Toxoplasma foci. This study positions Imiquimod as a potent drug against toxoplasmosis and elucidates its mechanism of action particularly against chronic toxoplasmosis, which is the most prevalent form of the disease., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Hamie, Najm, Deleuze-Masquefa, Bonnet, Dubremetz, El Sabban and El Hajj.)

The malignant transformation of melanocytes causes several thousand deaths each year, making melanoma an important public health concern. Melanoma is the most aggressive skin cancer, which incidence has regularly increased over the past decades. We described here the preparation of new compounds based on the 1-(3,4-dihydroxyphenyl)imidazo[1,2-a]quinoxaline structure. Different positions of the quinoxaline moiety were screened to introduce novel substituents in order to study their influence on the biological activity. Several alkylamino or alkyloxy groups were also considered to replace the methylamine of our first generation of Imiqualines. Imidazo[1,2-a]pyrazine derivatives were also designed as potential minimal structure. The investigation on A375 melanoma cells displayed interesting in vitro low nanomolar cytotoxic activity. Among them, 9d (EAPB02303) is particularly remarkable since it is 20 times more potent than vemurafenib, the reference clinical therapy used on BRAF mutant melanoma. Contrary to the first generation, EAPB02303 does not inhibit tubulin polymerization, as confirmed by an in vitro assay and a molecular modelisation study. The mechanism of action for EAPB02303 highlighted by a transcriptomic analysis is clearly different from a panel of 12 well-known anticancer drugs. In vivoEAPB02303 treatment reduced tumor size and weight of the A375 human melanoma xenografts in a dose-dependent manner, correlated with a low mitotic index but not with necrosis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Masson SAS. All rights reserved.)

Introduction: E-reputation is the reputation of an entity on the Internet. This phenomenon lies within numerous domains, including the medical sector. The primary objective of this study was to describe the general practitioners' interest for their e-reputation and the e-reputation data available in the PACA region. The secondary objectives were to identify the determinants of the doctors' interest and feeling of control of their e-reputation., Methods: The study was descriptive. The final sample was made of 240 physicians chosen randomly.The data collection has been done with a self-administered questionnaire and with a direct data collection on the Internet., Results: The questionnaire answer rate was 64,2%. 51,3% of respondent physicians had no interest for their e-reputation, 9,7% had a strong or very strong interest. Physicians established for less than 25 years and physicians aged under 50 had more interest. 79,9% of respondent physicians considered to have no control of their online reputation (median value = 1). 71,6% out of the 240 interrogated physicians had at least one evaluation. The average number of evaluation per physician was 3,65 (standard deviation = 3,92). Physicians aged over 50 and physicians practising in urban area received a higher number of evaluations. 44,8% of physicians would like to be further informed on the way to answer opinions on the Internet., Discussion: The physician's interest for their e-reputation was low. Their control was weak. They are however impacted by this phenomenon and are asking for information.

Heterocyclic compounds hold a huge and recognized place in the field of medicinal chemistry thanks to their multiple biological activities. Their synthetic pathways allow their easy and rapid access due to different bond-forming methodologies and provide a huge amount of multi-functionalized compounds for drug delivery. The syntheses of heterocyclic compounds are today well known for the majority, described and reviewed in an extensive literature. In this review, we choose to gather and classify available information concerning the biological activities of quinoxaline-based compounds annulated at bond a containing one and more nitrogen atoms in the fused azole ring., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Lee, Goëau, Bonnet and Joly.)

Fluorescent tools have revolutionized our capability to visualize, probe, study, and understand the biological cellular properties, processes and dynamics, enabling researchers to improve their knowledge for example in cancer field. In this paper, we use the peculiar properties of our Imiqualines derivatives to study their cellular penetration and distribution in a human melanoma cell line A375 using confocal microscopy. Preliminary results on colocalization with the potent protein target c-Kit of our lead are also described.

Coffee is a beverage enjoyed by millions of people worldwide and an important commodity for millions of people. Beside the two cultivated species (Coffea arabica and Coffea canephora), the 139 wild coffee species/taxa belonging to the Coffea genus are largely unknown to coffee scientists and breeders although these species may be crucial for future coffee crop development to face climate changes. Here we present the Wild Coffee Species database (WCSdb) hosted by Pl@ntNet platform (http://publish.plantnet-project.org/project/wildcofdb&#95;en), providing information for 141 coffee species/taxa, for which 84 contain a photo gallery and 82 contain sequencing data (genotyping-by-sequencing, chloroplast or whole genome sequences). The objective of this database is to better understand and characterize the species (identification, morphology, biochemical compounds, genetic diversity and sequence data) in order to better protect and promote them., Database Url: http://publish.plantnet-project.org/project/wildcofdb&#95;en., (© Crown copyright 2020.)

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., Competing Interests: The authors declare no competing interests., (© 2020 The Authors.)

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., (Copyright © 2020 Bonnet, Le Gal, Calderon, Delhaes, Quinio, Robert-Gangneux, Ramel and Nevez.)

Background: Chronic neuropathic perineal pain syndrome is a collective term that encompasses several diagnoses. In patients where the neuropathic pain syndrome is caused by pudendal or cluneal nerve entrapment, surgical release can be proposed if other measures have failed. The aim of this study is to evaluate the clinical outcome of patients suffering from pudendal and/or cluneal nerve entrapment at 1 year after this minimal invasive surgery, which is based on the open trans gluteal approach who has proven its efficacy compared to medical treatment in a randomized control trial., Methods: Patients eligible for inclusion had chronic perineal neuropathic pain for at least 3 months in the area served by the pudendal and/or cluneal nerve, refractory to conservative measurements. Patients met all five of the Nantes criteria., Results: Fifteen patients underwent the ENTRAMI technique. At 1 year after surgery, overall reduction of the average maximal Numeric Pain rating Scale (NPRS-score) was from 9 (range, 7-10) at baseline to 5 (range, 0-10; P-value <.05). At 1 year 73% of patients declared to have a "good treatment response" (patient global impression of change [PGIC] >30%) and optimal treatment response (PGIC ≥90%) was found in 40% (P-value <.05). No complications were recorded., Conclusion: This study clearly shows that the technique is feasible with promising long-term results in a difficult to manage patient group., (© 2020 Wiley Periodicals LLC.)

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., (Copyright © 2020 Davis, Champ, Park, Breckheimer, Lyra, Xie, Joly, Tarapore, Ellison and Bonnet.)

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., (© 2020 Champ et al. Applications in Plant Sciences is published by Wiley Periodicals, LLC. on behalf of the Botanical Society of America.)

Superstructures or self-assembled nanoparticles open the development of new materials with improved and/or novel properties. Here, we present nickel fluoride (NiF 2 ) self-assemblies by successive preparatory methods. Originally, the self-assemblies were obtained by exploiting the water-in-oil microemulsion technique as a result of auto-organization of hydrated NiF 2 (NiF 2 ·4H 2 O) nanoparticles. The nanostructuration of NiF 2 ·4H 2 O nanoparticles was confirmed by X-ray diffraction (XRD) and transmission electron microscopy (TEM) data. The size and shape of NiF 2 ·4H 2 O nanoparticles and their subsequent self-assemblies varied slightly as a function of water-to-surfactant and water-to-oil ratios. Scanning electron microscopy (SEM) and TEM characterizations revealed that the nanoparticles are organized into a succession of self-assemblies: from individual nanoparticles assembled into layers to truncated bipyramids, which further auto-organized themselves into almond-shaped superstructures. Anhydrous NiF 2 was achieved by heating NiF 2 ·4H 2 O self-assemblies under the dynamic flow of molecular fluorine (F 2 ) at a moderate temperature (350 °C). Preservation of self-assemblies during the transformation from NiF 2 ·4H 2 O to NiF 2 is successfully achieved. The obtained materials have a specific surface area (SSA) of about 30 m 2 /g, more than 60% of that of bulk NiF 2 . The lithium-ion (Li + ) storage capacities and the mechanism of the nanostructured samples were tested and compared with the bulk material by galvanostatic cycling and X-ray absorption spectroscopy (XAS). The nanostructured samples show higher capacities (∼650 mAh/g) than the theoretical (554 mAh/g) first discharge capacity due to the concomitant redox conversion mechanism of NiF 2 and solid-electrolyte interphase (SEI) formation. The nanostructuration by self-assembly appears to positively influence the lithium diffusion in comparison to the bulk material. Finally, the magnetic properties of nanostructured NiF 2 · x H 2 O ( x = 0 or 4) have been measured and appear to be very similar to those of the corresponding bulk materials, without any visible size reduction effect. The hydrated samples NiF 2 ·4H 2 O show an antiferromagnetic ordering at T N = 3.8 K, whereas the dehydrated ones (NiF 2 ) present a canted antiferromagnetic ordering at T N = 74 K.

Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens-preserved plant material curated in natural history collections-but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Institute of Biological Sciences.)

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., (© 2020 The Authors. Applications in Plant Sciences is published by Wiley Periodicals, LLC on behalf of the Botanical Society of America.)