dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion and perspectives Two major outcomes are obtained in our study. First, the results showed that TLS and SfM-photogrammetry are relevant techniques to monitor morphological changes in fluvial environments, especially for large river side channels. SfMphotogrammetry is recommended for its cost and the flexibility of the acquisition, especially in dynamic side channels showing important and rapid changes. Producing and comparing complete 3-D models by combining terrestrial point clouds with bathymetric data provide a reliable way to identify and assess accurately morphological changes following floods at different spatial scales. Second, we demonstrate the importance of combining geodetic tools with geomorphical methods to develop straightforward morphodynamic indicators able to capture morphodynamic adjustments and processes.
Acknowledgments This research was funded by the  European Community  ( LIFE08 NAT/F/00471 ), the  City of Strasbourg , the  French National Center for Scientific Research (CNRS) , the  ZAEU (Zone Atelier Environnementale Urbaine -LTER) , the  Water Rhin-Meuse Agency , the  DREAL Alsace, the "Région Alsace", the "Département du Bas-Rhin" and the company "Électricité de France ". The authors would like to thank  Stoil Chapkanski ,  Alexandre Hubeny ,  Léa Keiser  and  Jordane Serouilou  for field assistance as well as  Etienne Supper  and  Julie Roy  for fieldwork and data processing. We also thank the two anonymous reviewers who provided very constructive comments that greatly contributed to improve the manuscript.
Acknowledgments This project was supported by the  French national Institute for Research for Development (IRD). SEM observations and TOC analyses were performed on the  ALYSES facility (IRD-UPMC ) which was supported by grants from the  Région Ile de France. We thank the staff of the  Institut des Régions Arides of Médenine  for their support, and especially  Saad Sefraki  for his valuable help in the field. Finally, we also acknowledge the technical assistance of  Féthiyé Cetin  and  Magloire Mandeng-Yogo.
Characters of specimens were measured using a graded eyepiece (Leica, see above). Chant and McMurtry's (1994, 2007)  concepts of the taxonomy of the family Phytoseiidae for identification and the world catalogue database of  Demite et al. (2014 Demite et al. ( , 2021) )  for distribution and information on descriptions and redescriptions were used. World distribution indicated for each species is the world distribution prior to tthis study. The setal nomenclature system adopted was that of  Lindquist & Evans (1965)  and  Lindquist (1994)  as adapted by  Rowell et al. (1978)  and  Chant & YoshidaShaul (1989)  for the dorsal surface and by  Chant & YoshidaShaul (1991)  for the ventral surface. Pore (= solenostome) and poroid (= lyrifissure) notations are that of  AthiasHenriot (1975). Macrosetal notation (Sge = genual macroseta; Sti = tibial macroseta; St = tarsal macroseta) are that of  Muma and Denmark (1970).
Acknowledgements Thanks are first due to the Department to which the senior author belong for funds that have granted travels and accommodations to him in Grande Comore Island: UMR CBGP (Internal call for proposals 2018). A grateful thanks to  UR Hortsys  and to the head  Dr Fabrice Le Bellec  that have allowed  RoseMy Payet  to join the senior author for collections and partly granted field collecting trips. Field collections had been made with authorizations of the government of Union des Comores by letters of the head of  INRAPE, Union des Comores  (ref. n°18/193/INRAPE/DG and n°18/210/INRAPE/DG, respectively).
As a source of auxin, we tested two likely scenarios (i) a uniform source of auxin along the epidermis (Model A) or (ii) flow of auxin from lateral root cap (LRC) and QC into epidermis  (Petersson et al, 2009; Tian et al, 2013) (Model B). In addition, other potential scenarios were tested by introducing independent auxin inputs in epidermis (Models C and D), and however, these models showed a poor performance compared to model B when faced with experimental measurements and thus were less favorable (see  Materials and Methods and Appendix Fig S7F). In our model, PIN2 polarity and auxin distribution as well as cell length and number of cell division resulted purely from predictions of the model. To test the model, we compared experimental To comprehend a necessity for these two essential components in our model, we closely inspected the relation between auxin activity levels and PIN2 fluorescence in our experimental dataset in roots supplemented with NO À 3 or NH þ 4.
Cell elongation and cell length analysis Cell elongation was measured after 12 h exposure to either to ammonium or nitrate manually with the software Fiji (v1. 52). For cell length analysis, confocal microscopic images of propidium iodide-stained PIN2::PIN2-GFP, PIN2::PIN2S439A-GFP, PIN2:: PIN2S439D-GFP, Col-0, and eir1-4 roots were used, and the length of each cell in different cell files (epidermis and cortex) was measured manually using Fiji (v1. 52).
To quantify the fluorescence signal in each cell per selected root tissue (epidermis and cortex) first, we positioned a segmented line over the nuclei in the corresponding tissues with the ROI manager tool of the software Fiji (v1. 52)  (Appendix Fig S2C). Next, we analyzed the fluorescence plot profiles of the different lines with the peak analyzer function of the software Origin (OriginLab Corporation) to find local maxima along the lines, which represented the fluorescence value of the nuclei in the tissues. In case of R2D2, auxin distribution plots were derived by reciprocal mean values of the normalized n3xVenus/ntdTomato ratio. Relative auxin level data in each cell per tissue were graphed after data interpolation using the Origin built-in algorithm for smoothing. 3D SIM and polar density analysis of PIN2-GFP Live Arabidopsis seedlings, which were incubated on either nitrate or ammonium amended medium for 6-8 h, were mounted on to coverslips as previously described by Johnson and Vert  (Johnson & Vert, 2017)  with the coverslips additionally fixed to the slide with nail polish.
Cells in the elongation zone of the root epidermis were imaged using an OMX BLAZE v4 3D SIM (Applied Precision), as described  (Hille et al, 2018). Briefly, a 60× 1. 42 NA Oil Immersion objective and a 100 mW 488 laser was used to make optical sections in the Z dimension, in order to capture the totality of the lateral polar domain of the subject cell. Each Z-section image is based on 15 images generated from three different angles and five different SIM patterns and reconstructed using SOFTWORX software (Applied Precision). A maximum projection of the Z-stack was used for analysis. Images were made binary and subjected to watershed segmentation using Fiji  (Schindelin et al, 2012). PIN2 spots were then detected using TrackMate  (Tinevez et al, 2017). The number of PIN2 spots was calculated in regions of interest (0.
The samples were incubated overnight to allow the radioactivity to evenly diffuse into the whole volume of the scintillation cocktail. Finally, the radioactivity ª 2021 The Authors The EMBO Journal 40: e106862 | 2021 was measured with a scintillation counter (Hidex 300XL), with each sample counted for 100 s, three times. Three samples with only the scintillation solution were used as background controls. As an additional background control, another batch of samples were prepared the same way as described above except [3H]-IAA containing droplets were placed not on the root meristem but next to the seedlings. Data shown on the figure were calculated against the background.
GUS (β-Glucuronidase) staining CycB1::GUS expression was analyzed in seedling roots 7 DAG, 12 HAT to ammonium or nitrate-containing media. Seedlings were incubated for 2 h in 37°C in staining buffer containing 1 mM ferricyanide, 150 mM sodium phosphate buffer (pH 7), and 1 mg/ml of X-Gluc dissolved in DMSO. Seedlings were cleared using subsequent incubation at room temperature in a series of ethanol dilutions from 60 to 10% then mounted on slides with 5% ethanol-50% glycerol mounting solution. The pattern of the GUS histochemical staining was analyzed by an Olympus BX53 microscope and Olympus DP26 digital camera, controlled by cellSense Entry software.
Data distribution, skewness, and kurtosis were checked with the fitdistrplus R package (Delignette-Muller & Dutang, 2015), and Gamma distribution was chosen for setting up a regression analysis based on generalized linear models (GLMs). Besides the main effects of the variables, several models were virtually possible when the interactions between some or all the variables were considered. First, a simple model including only main effects was generated and residual vs. fitted values evaluated prior to analysis of deviance. Second, a model including main effects and all possible interactions between variables was built. The analysis of the interactions of the fit model was carried out with the phia R package  (Rosario-Martinez et al, 2015) , showing a possible but not strong interaction between tissue and treatment factors.
This insight was used to generate a third model. The performance of the second and third model was then compared by repeated k-fold cross-validation with the caret R package, and the second model was selected according to RMSE and R2 measures. After analysis of deviance, post hoc pairwise comparisons were conducted with estimated marginal means (EMMs) using the emmeans R package  (Lenth et al, 2020)   (Appendix Fig S9A and B). A recursive partitioning analysis was performed, and a decision tree was generated with the partykit R package  (Hothorn & Zeileis, 2015)  to confirm the results showed by regression analysis and to visualize the role of different variables on cell length distribution  (Appendix Fig S9C).
Visualization of model predictions The computer simulation representing the dynamic auxin flow through the root tissues was created using the version of VV (Vertex-Vertex) programming language and in the L-system-based modeling software L-studio  (Karwowski & Prusinkiewicz, 2004). The model simulates a cross section of the plant root focusing on the cortical and epidermal tissues. Plant cells are visualized as foursided polygons representing the cell walls. For the sake of simplicity, cell membranes and the extracellular space shared by adjacent cells are not rendered. Only the first ~20 cells (counting from the QC) are visualized, mirroring the available experimental measurements. Meristematic and elongating cells are distinguished with different cell wall coloring, blue for meristem and yellow for elongation zone, respectively.
Statistical inference and parameters estimation Data analysis and plotting were performed using the R language environment for statistical computing (R Core Team, 2017) and the plotting package ggplot  (Ginestet, 2011). Parameters estimation of the previously described models was carried out with the RStan  (Carpenter et al, 2017)  and brms (B€ urkner, 2017) packages, which implement a modified version of Hamiltonian Monte Carlo sampling algorithm to approximate the parameters posterior distribution. Model comparison was performed using the loo package  (Vehtari et al, 2017)  to carry out Pareto smoothed importance-sampling leave-one-out cross-validation (PSIS-LOO) for posterior predictive performance estimation.
Acknowledgements We acknowledge  Gergely Moln  ar for critical reading of the manuscript,  Alexander Johnson  for language editing and  Yulija Salanenka  for technical assistance. Work in the Benkov a laboratory was supported by the  Austrian Science Fund  ( FWF01_I1774S ) to K € O, RA and EB. Work in the  Benkova laboratory  was supported by the  Austrian Science Fund  ( FWF01_I1774S ) to KO, RA and EB and by the  DOC   Fellowship Programme  of the  Austrian Academy of Sciences  ( 25008 ) to C. A. Work in the Wabnik laboratory was supported by the  Programa de Atracci on de Talento 2017 (Comunidad de Madrid ,  2017-T1/BIO-5654  to K. W. ),  Severo Ochoa Programme for Centres of Excellence in R&D  from the  Agencia Estatal de Investigaci on of Spain  (grant  SEV-2016-0672  ( 2017-2021 ) to K.
Unfortunately, uninformed intervention in wildlife rescue rises questions concerning the risk of zoonotic disease transmission and the harmonisation of recommendations regarding wildlife handling. The European hedgehog (Erinaceus europaeus Linnaeus, 1758) is a nocturnal insectivorous mammal with a wide distribution throughout western and central Europe  [8]. In several regions of its range, the decline of the hedgehog population has been described  [9, 10]. In 1979, this situation led their inclusion in Appendix III of the Bern Convention on the Conservation of European Wildlife and Natural Habitats. In France, hedgehogs have been designated as a species of conservation concern since 1981  [11]. The presence of T. erinacei in European hedgehogs has already been reported  [12, 13] , but, so far, information regarding its prevalence is based on a small dataset.
Animals and Sampling All hedgehogs arriving at Chuv-FS Alfort in 2016 were sampled and held complete information regarding origin, age, sex, date of arrival and weight (Supplementary Data Table  S1 ). Before sampling, the animals were submitted to an extensive clinical examination, including the detection of ectoparasites and skin lesions suggesting dermatophyte infection, such as spine loss, crusty skin and erythema  [18]. Clinical examination was performed under anaesthesia by isoflurane inhalation (Vetflurane ® Virbac, Carros-France). The animals were sampled by scrubbing the whole skin surface with a 5-cm 2 autoclavesterilised carpet square  [19]. Animals were resampled on a monthly basis until their release. Additionally, the skin scraping test was performed on animals with skin lesions by rubbing off a layer of cells until the capillary oozed with the edge of a blunt scalpel blade.
PCR product purification followed the protocol of Réblová et al. [25]. Automated sequencing was performed with Macrogen Sequencing Service (Amsterdam, The Netherlands) using both terminal primers. Editing of the PCR products sequences was performed using BioEdit Sequence Alignment Editor Software  [26]. The sequences were compared with those derived from the ex-type and reference strains, which are deposited in the GenBank database of the National Center for Biotechnology Information (NCBI) using the BLAST algorithm. Furthermore, 16 dermatophyte isolates showing morphological features not compatible with T. erinacei were further analysed by mass spectrometry (MALDI-TOF MS). Protein extract samples were obtained, as previously recommended by L'Ollivier et al. , for the dermatophytes  [27] , with slight modifications.
Results Out of the 462 wild hedgehogs rescued during 2016, 412 were included in the present study. The other 50 animals were excluded because three or more epidemiological variables were missing. When data such as age (n = 3), sex (n = 81) or origin (n = 38) were not available during clinical examination, the animal with the missing data was excluded from the corresponding epidemiological analysis. The origin of animals was mainly from the Ile-de-France region (349/374; 93. 3%), but some hedgehogs also came from neighbouring regions such as Centre-Val de Loire, Normandie, Hauts-de-France and Grande-Est (Figure  1 ). One hedgehog came from as far as the Pays-de-la-Loire region. The number of hedgehogs arriving at the centre fluctuates during the year (Figure  2 ). Few arrivals occur during the winter season (n = 10); later, the numbers increase with time, reaching a peak during the summer season.
1  A and B). To determine whether absence of LLS detection in the supernatant is due to its very low abundance, we performed an immunoprecipitation assay to concentrate the supernatant of the LLS + -FLAG strain. These data confirm the total absence of LLS in the supernatant and its presence in the membrane and cytoplasmic fraction (Fig. 1B ). In the cytoplasm, we detect a band of ∼6 kDa corresponding to the expected molecular mass of a monomeric tagged LLS prepeptide, but we also detect a higher-molecular mass Western blot analysis was performed on a strain expressing LLS + (negative control) and a strain expressing LLS + -FLAG (FLAG at the C terminus). Proteins were fractionated into four compartments: supernatant (SN), cell wall (CW), membrane (M), and cytoplasm (CY). InlA, ActA, EF-Tu, and InlC were used as controls for fractionation.
Equivalent amounts of each fraction, corresponding to 100 μL of bacterial culture, were separated by sodium dodecyl sulfatepolyacrylamide gel electrophoresis (SDS-PAGE) and submitted to immunodetection using the indicated antibodies. Data from one representative experiment out of the three performed are shown. Pre-LLS FLAG, unmodified LLS peptide. (B) LLS SN and M fractions were immunoprecipitated on a strain expressing LLS + (negative control) and a strain expressing LLS + -FLAG by using magnetic beads coupled to anti-FLAG antibodies. Equivalent amounts of each fraction, corresponding to 2. 5 mL of bacterial culture, were separated by SDS-PAGE and submitted to immunodetection using the anti-FLAG antibody. Input (In) was from the membrane fraction. Data from one representative experiment out of the three performed are shown.
Cocultures were prepared as indicated previously. The dye DiBAC 4 (3) was added to the BHI medium (final concentration 2. 5 μM) when the cocultures were prepared. Nisin was used as a positive control (final concentration 5 μM). After 3, 4, and 6 h of coculture, bacteria were washed once and resuspended in PBS, mounted on a glass coverslip with Fluoromount-G mounting medium (Interchim), and dried in the dark at 37 °C for 30 min. Slides were observed with an AxioObserver. Z1 inverted microscope (Carl Zeiss) equipped with a highspeed CSU-X1 spinning-disk confocal system (Yokogawa) and an Evolve EM-CCD camera (Photometrics). Images were acquired through a Plan-Apochromat 100× oil objective, using MetaMorph software (version 7. 7. 9. 0). Stacks of 20 images were acquired every 200 nm in the z axis.
Three-dimensional reconstruction was performed on stacks with IMARIS software (Bitplane; Oxford Instruments). Icy was used for image processing and analysis. For each condition tested, at least 150 cells were marked as regions of interest (ROIs) manually by using only one image of the Z stacks (better focused image). To quantify the effects of depolarization, ROI mean pixel intensities of the GFP channel were obtained and normalized to the background mean pixel intensities of the GFP channel. Statistics. The Shapiro-Wilk test was used to test the normal distribution of datasets. Normally distributed data with equal group variances were expressed as means ± SEM. Statistical tests were performed using Prism 8. 0 (GraphPad Software) and differences were evaluated by an unpaired Student's t test or unpaired multiple t tests as indicated.
ACKNOWLEDGMENTS. We thank  Mélodie Duval ,  Filipe Carvalho ,  Alessandro Pagliuso ,  Richard Wheeler ,  Nika Pende ,  Ivo Gomperts Boneca , and  Shaynoor Dramsi  for useful advice, discussions, and technical support;  Fabrice Agou ,  Agnès Zettor , and  Sara Consalvi  from the  Chemogenomic and Biological Screening Platform at Institut Pasteur for technological  and technical support; the members of the  Cytometry and Biomarkers Platform (UTECHS CB) at Institut Pasteur  for providing training; and the  Centre de Ressources Biologiques de l'Institut Pasteur  for providing strains. This work received financial support from  Institut Pasteur ,  Institut Carnot Pasteur Microbes et Santé  ( PasteurInnov Grant   LLS-Bact ),  Région Ile-de-France (DIM-MALINF) , and  Fondation pour la Recherche Médicale  (Grant  FDT201904008152 ) (to J.
Main Text The fruit fly Drosophila melanogaster has a long and fruitful history in biological research, dating back to the first experiments of Thomas Hunt Morgan in the early 20th century  (Morgan, 1910). Drosophila has been at the basis of many key discoveries in genetics, development, neurobiology, circadian rhythms, stem cell biology, and aging  (Bellen et al. , 2010). The highly collaborative nature of the Drosophila community contributed to many of these successes, and led to the development of essential research resources, including a high-quality genome  (Adams et al. , 2000) , a large collection of mutant lines and a large variety of genetic and molecular tools, as well as important databases such as Flybase  (Larkin et al. , 2021) , FlyMine  (Lyne et al. , 2007) , FlyLight  (Jenett et al.
, 2021). However, these data have been generated by different laboratories on different genetic backgrounds, with different dissociation protocols and sequencing platforms, hindering systematic comparison of gene expression across cells and tissues. Here, we present a single cell transcriptomic atlas of the entire adult Drosophila, separately analyzing male vs female samples, using a uniform genotype and a unified single-nucleus RNAseq (snRNA-seq) platform  (McLaughlin et al. , 2021)  with two sequencing strategies: droplet-based 10x Chromium and plate-based Smart-seq2. The resulting Tabula Drosophilae, the first comprehensive dataset within the Fly Cell Atlas consortium, contains over 580k cells, resulting in more than 250 distinct cell types that were annotated by over 100 experts from 40 international Drosophila research laboratories.
In addition, we sequenced two more rare cell types using Smart-seq2: insulin-producing cells (IPCs, Dilp-GAL4) and corpora cardiaca cells (CCs, Akh-GAL4). In the second strategy, we sorted nuclei from the entire head and body of sexed wild-type flies and profiled them on 24 10x Chromium lanes, aiming to detect cell types that may not be covered by the selected tissues. The libraries were sequenced using Illumina NovaSeq 6000 in 4 batches, for a total of 38 billion reads. In total, we obtained 580k high-quality cells: 570k from 10x Genomics of 63 runs and 10k from Smart-seq2 of 35 384-well plates (Fig. 1A ). To analyze the 10x Genomics data in a reproducible manner, we used an automated computational pipeline called VSN (https://doi. org/10. 5281/zenodo. 3703108; see Methods), which takes the raw sequencing data as input, performs filtering, normalization, doublet removal, batch effect correction, dimensionality reduction, and clustering, and produces LoomX formatted files with expression data, embeddings and clusterings (Fig.
1B  and fig. S1 ). As we observed a presumed artefactual cluster of cells showing expression of nearly all genes, we added an additional preprocessing and filtering step that models and then subtracts ambient RNA from the gene expression values  (Yang et al. , 2020) , resulting in computational removal of this cluster. However, since adjusting the gene expression values per cell can also introduce other biases (e. g. , overcorrection, removal of non-doublet cells), we retained both a Relaxed dataset of 570k cells, and a Stringent dataset of 510k cells (see Methods and Fig. 1C ). In the analyses below, we focus on the Stringent dataset, unless otherwise mentioned (e. g. , Fig. 2C ). Cells from 10x Genomics and Smart-seq2 were well aligned after batch correction using Harmony  (Korsunsky et al.
S21, 22). Figure  1. Overview of the Fly Cell Atlas (A) Experimental platform of single-nucleus RNA-seq (snRNA-seq) using 10x Genomics and Smart-seq2. (B) Data analysis pipeline and data visualization using SCope  (Davie et al. , 2018)  and ASAP  (David et al. , 2020). (C) Two versions of 10x datasets have been generated: a Relaxed and a Stringent version. tSNE colors based on gene expression: grh (epithelia, red), Mhc (muscle, green) and Syt1 (neuron, blue). Red arrow highlights artefactual cluster with co-expression of all three markers in the Relaxed dataset.