dataesr/scientific-paragraphs-categorization huggingface dataset:
Cell proliferation The cell proliferation was determined using Invitrogen™ CellTrace™ (#2161821) according to the manufacturer's instructions. 3 × 10 5 cells were plated in a 6-well plate. The next day, the cells were transfected with either si ZFP36L1 or si control, and after 7 h, they were treated with cell trace violet dye for 20 min. The dye was replaced with (EAGLE, 10% FCS, 0. 1 mM AANE, 1 mM sodium pyruvate, 40 μg/ml gentamicin) media. After 72 h transfection, the cell was trypsinized and centrifuged. The pellet was dissolved in PBS, and cells were used to analyze the cell cycle distribution using flow cytometry (BD. FORTRESSA X20). The data obtained was analyzed using the FlowJo software. The numbers are expressed as mean ± SEM of the percentage of low-proliferating cells, and three independent experiments were performed.
FORTRESSA X20). The data obtained was analyzed using the FlowJo software. The numbers are expressed as mean ± SEM of % of apoptotic cells, and three independent experiments were performed. Transwell assay The transfected TCCSUP cells with either si ZFP36L1 or si control after 72 h were used for the transwell assay. The transfected cells (5 × 10 4 ) were taken in 200 μl (EAGLE, 0. 01% FCS, 0. 1 mM AANE, 1 mM sodium pyruvate, 40 μg/ml gentamicin) media and plated on top of the 8-μm Transwell filter membrane in a 24-well plate. Using a pipette, 600 μl (EAGLE, 10% FCS, 0. 1 mM AANE, 1 mM sodium pyruvate, 40 μg/ml gentamicin) media was added in the lower chamber of the 24-well plate. After 72 h of incubation, the membranes were washed with PBS (1×) and were fixed in 3. 4% formaldehyde by adding 600 μl in the lower chamber and 200 μl in the upper chamber.
Statistical analyses All statistical tests were executed by R/3. 6. 2, including Fisher's exact test for categorical data, a two-sample Mann-Whitney test for continuous data, a log-rank test Kaplan-Meier curve, and Cox proportional hazards regression for hazard ratio (HR) with 95% confidence interval (95% CI)  [49, 50]. Survival analysis was performed by R package survival. Mutual exclusivity analysis was done using one-side Fisher's exact test, where there was an alternative parameter of "less" for mutual exclusivity and "greater" for cooccurrence analysis. For unadjusted comparisons, a two-sided P < 0. 05 was considered statistically significant.
Availability of data and materials RNA sequencing data generated in this paper have been deposited at the NCBI Sequence Read Archive (SRA) hosted by the NIH (SRA accession: PRJNA678814)  [51]. DNA methylation data generated have been submitted to GEO (GEO accession: GSE161651)  [52]. WES sequencing data have been deposited at gated repository  [53] ; WES dataset requires access control, and there is a corresponding Data Access Committee (DAC) who determines access permissions. Data access requests are reviewed by DAC  [53]. Gene expression and DNA methylation data from the TCGA-BLCA cohort were downloaded from UCSC Xena (https://xena. ucsc. edu/), and somatic mutation data and detailed clinicopathological information were obtained from cBioPortal (http://www. cbioportal. org/) under the archive of Bladder Cancer  [33].
6. 2-4. 0. 2  [70] ). Random slopes were identified, and overdispersion and model stability were assessed, using functions kindly provided by Roger Mundry (see below). Boxplots were created in ggplot2 (version 3. 2. 1-3. 3. 2  [71] ) and cumulative incidence plots were created using the package "survminer" (version 0. 4. 6  [72] ). All other packages and functions used are given below. Proportion of times the subjects pressed the button. To analyse the effect of human type, condition, and their interaction, on the proportion of times subjects pressed the button, we fitted a Generalized Linear Mixed Model (GLMM) with a binomial error distribution and a logit link function  [73]. In the model, we included the number of times subjects pressed the button and the number of times they did not press the button, as the response variable using the "cbind" function (see Baayen  [74]  for details).
Condition, human type, and first human experience were manually dummy coded and centred for inclusion as random slopes. Prior to fitting the model, we z-transformed test day order and condition order to a mean of zero and a standard deviation of one to allow for an easier interpretation of results and to ease model convergence. We fitted the model using the function "glmer" from the package "lme4" (version 1. 1-23  [77] ). The model could only be fitted after excluding the correlations among random intercepts and random slopes; therefore, we excluded these correlations to fit the model. As the random slope of the interaction between condition and human type within the random effect of subject was quite redundant with the observation level random effect, we fitted two additional models, one lacking the observation level random effect, and one lacking the random slope of the interaction between condition and human type within the random effect of subject, and we compared these models to determine whether the complete set of random effects was required.
981, n observations = 25, n raters = 2, P < 0. 001). Latency to approach the human. To analyse differences in latency to approach the two different humans (helpful and unhelpful), we fitted a Cox proportional hazards regression model  [85, 86]. We used the (start, stop] style of the model  [87]  and entered the response with a start time of zero for all subjects, the latency to reach proximity to the human, and status (i. e. whether they reached proximity to the human or not) using the "Surv" function in the package "survival" (version 3. 1-8  [88, 89] ). We included fixed effects of human type, test day order, and first human experience with human type being the fixed effect of interest. As only one frailty term could be included in the model, we fitted two models, one with subject as the frailty term and one with human as the frailty term.
We compared the log-likelihood and model complexity of the two models and selected the model with human as the frailty term for further inference (log-likelihoods: model with subject: -106. 1143 [df = 7. 21]; model with human: -109. 16779 [df = 4. 75]; see S1 Appendix for final full model). We fitted the model using the function "coxph" in the package "survival" (version 3. 1-8  [88, 89] ). Prior to fitting the model, we z-transformed test day order to a mean of zero and a standard deviation of one to allow for an easier interpretation of results. We tested the assumption of proportional hazards using the "cox. zph" function from the package "survival" (version 3. 1-8  [88, 89] ) and by visual inspection of scaled Schoenfeld residuals using the "ggcoxzph" function in the package "survminer" (version 0.
4. 7  [90] ; see S2 Fig). Based on assessment of both, the assumption of proportional hazards was upheld. We assessed model stability by visual inspection of plots of dfbeta residuals using the function "ggcoxdiagnostics" from the package "survminer" (version 0. 4. 6  [72] ; see S3 Fig). Model stability was deemed acceptable. As an overall test of the effect of human type, we conducted a full-null model comparison  [82] , as above. The sample for this model included a total of 41 observations across 21 subjects and 12 humans. Interobserver reliability for latency to approach the test human was excellent (ICC = 1, n observations = 8, n raters = 2, P < 0. 001). Duration of proximity to the human. To determine whether human type had an influence on the proportion of time subjects spent in proximity to the human in the interaction session, we fitted a GLMM with a beta error distribution and a logit link function  [91] [92] [93].
As such beta models cannot handle zeroes and ones, and our dataset comprised at least one zero, we transformed the response using the following formula (where "x" is the variable to be transformed; see Smithson and Verkuilen  [93] ): x' ¼ x: length x ð Þ À 1 ð Þ þ 0:5 length x ð Þ We included the fixed effect of human type as the primary term of interest. To control for their possible effects, we also included test day order and first human experience as fixed effects. We included random intercept effects of subject and human. No random slopes were identifiable for this model; therefore, no random slopes were included. We fitted the model using the function "glmmTMB" from the package "glmmTMB" (version 1. 0. 0  [94] ). Prior to fitting the model, we z-transformed test day order to a mean of zero and a standard deviation of one to allow for an easier interpretation of results and to ease model convergence.
Acknowledgments We would like to thank the dogs and their owners for participation in this study. We are also very grateful to all those who participated in the study as helpful and unhelpful human partners. We thank  Roger Mundry  for statistical support. We also thank  Karin Bayer ,  Jennifer Bentlage ,  Karoline Bu ¨rger ,  Aleksandar Orlic ,  Brigitte Pavlik , and  Christina Szaga-Doktor  for administrative support, as well as  Peter Fu  ¨reder and  Wolfgang Berger  for technical support.
JM was funded by a  DOC fellowship of the Austrian Academy of Sciences  (O ¨AW) (https:// www. oeaw. ac. at/en/austrian-academy-of-sciences/ ) at the  Konrad Lorenz Institute of Ethology, University of Veterinary Medicine, Vienna. JM and FR were funded by the  Austrian Science Fund (FWF)  (https://www. fwf. ac. at/de/) number  W1262-B29. MM was funded by the  Austrian Science Fund (FWF)  number  P30704  (https://www. fwf. ac. at/de/). The funders of this study did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
6, and bubbled with room air. After dissection, the preparations were allowed to equilibrate for 15 min. under an initial tension of 1. 5 g. Different concentrations of ACh were then added to the preparation (1, 3, 10, 30, and 100 µM), and the maximum contraction was observed before washing and subsequent application of the next concentration of acetylcholine. Responses for each concentration were expressed in grams (g), produced by each individual flap preparation. The effect of Carvacrol (300 µM) on the acetylcholine dose-response plots was determined. Contractions were monitored on a PC computer, using a BioSmart interface, and eLAB software (ElUnit, Belgrade). The system allows real time recording, displaying, and analysis of experimental data. Sigmoid dose response curves for each individual flap preparation at each concentration of the antagonist were described by the Hill equation.
Statistical analyses The results of contraction assay are expressed as means ± S. E. in grams (g) of contractions. Sigmoid concentration dose-responses were described by the equation as follows: % response = 1/1+[EC50/Xa]nH, where the median effective concentration (EC50) is the concentration of the agonist (Xa) producing 50 % of the maximum response and nH is the Hill coefficient (slope). GraphPad Prism ® Software, (San Diego, CA. ) was used to estimate the constants EC50 and nH, by non-linear regression for each preparation. We determined the mean contraction responses to each concentration of acetylcholine. Whole cell current electrophysiology responses were analyzed using the pCLAMP 10. 4 package (Molecular Devices). EC50 and IC50 values were determined using non-linear regression on normalized data (100 µM ACh as maximal response) using GraphPad Prism ® software.
TIRF imaging 3 days old etiolated seedlings were used for hypocotyl epidermal cells observations. TIRF-VAEM imaging were made using an ilas2 TIRF microscope (Roper Scientific) with 100x Apo NA 1. 46 Oil objective and a Prime 95B camera (Photometrics, https://www. photometrics. com/) and 1. 5 coverslips were used (VWR 61-0136). Time lapses were acquired during 300 time-points for 300s (acquisition time 500ms). Spot density were measured using Spot_detector ImageJ macro (Bayle et al. 2017). Particle identification and tracking was performed using ImageJ plugin Trackmate (https://research. pasteur. fr/fr/software/trackmate/) using following parameters: Particle min size 0. 5, threshold 50, particles with contrast >0. 04 were filtered. For tracks filtering 0 gap, min distance between two spots 0.
The same approach was used to deduce the localization of the mutated version of SAC9. We then computed a two-sided Tukey Post hoc tests (function "glht" in multicomp) to specifically compare each pair of conditions. To assess the effect of the inhibitor PAO on anomalous mCIT-TUBBYc intracellular compartments, we manually counted their number per cell. We tested PAO effect and the effect of treatment duration (30 min and 2 h) on the number of marked intracellular compartments using a generalized linear mixedeffect model (Poisson family) to account for image ID as a random factor. Two sided Post-hoc tests were performed using the package lsmeans. We compared the number of FM4-64 positive compartments in Col-0 and sac9-3 -/-using a generalized linear mixed model (Poisson family) to account for image ID (id est root) as a random factor.
3. 6. 1, (R Core Team, 2019), using R studio interface and the packages ggplot2  (Wickham 2016) , lme4  (Bates et al. 2014) , car  (Fox and Weisberg 2011) , multcomp  (Hothorn et al. 2008 ) and lsmeans  (Lenth and Lenth 2018). To compared TPLATE-GFP density between Col-0 and sac9-3 -/-we used a two-sided non-parametric Kruskal Wallis rank-sum tests for each replicate and obtained each time a statistical difference (p. value < 0,05 between the two genotypes). Graphs were obtained with R and R-studio software, and customized with Inkscape (https://inkscape. org). C, Quantification of primary root length in sac9-3 -/-homozygous mutants expressing mCIT-SAC9 and TdTOM-SAC9, mCIT-SAC9 C459A under the control of the native promoter (SAC9prom). Wild-type (Col-0) seedlings and two independent mutant alleles of SAC9, sac9-1 -/-and sac9-3 -/-, are used as controls.
ACKNOWLEDGEMENTS We are grateful to the  SiCE group  ( RDP, Lyon, France ),  Dr Yohann Boutté  ( LBM, Bordeaux, France ),  Dr Fabrice Besnard  and  Dr Nicolas Doll  ( RDP, Lyon, France ),  Hugo Ducuing  ( INMG, Lyon, France ),  Sébastien This  ( CIRI, Lyon, France ) and  Augustin Le Bouquin  ( IGFL, Lyon, France ),  Dr Sophie Piquerez  ( IPS2, Paris-Saclay, France ) for comments and discussions. We thank  Justin Berger ,  Patrice Bolland , and  Alexis Lacroix  from our plant facility, and  Claire Lionnet  from the imaging platform. We are also grateful to  Dr Daniël van Damme  ( VIB, Ghent, Belgium ) for sharing with us T-PLATE-GFP and for discussions. We would like to thanks  Pr Dr Erika Isono  ( University of Konstanz, Konstanz, Germany ) for sharing SH3P2-sGFP transgenic line with us. We are also grateful to  E.
Russinova  ( VIB, Ghent, Belgium ) for kindly providing us ES9-17;  S. Bednarek  for sharing markers and for discussions. This work was supported by two Seed Fund ENS LYON-2016 and LYON-2019 (to MCC), a  Junior Investigator grant   ANR-16-CE13-0021  (to MCC),  ERC  no. 3363360-APPL under FP/2007-2013 (to YJ). MD and AL were funded by  Ph. D. fellowships from the  French Ministry of Research and Higher Education.
Following this clearing protocol, we exploit the high-resolution optical sectioning of Two-Photon Fluorescence Microscopy to perform 3D mesoscopic imaging of human brain samples. To analyze the large amount of generated data, we apply different software tools to process the images in an automated fashion and extract reliable and quantitative information. A custom-made software called ZetaStitcher is used to stitch together the 3D tiles acquired with the TPFM microscope. Then a 2. 5D Machine Learning method based on a Convolutional Neural Network is used to automatically segment the neurons in 3D to extract quantitative data (both cell counting and cell volume reconstruction). The volumetric assessment, together with cell counting, obtained with this approach allows us to extract morphological information such as cell density, cell localization, cell average volume, and cell volumetric density, and discriminate the layer organization of the cerebral cortex.
Volumetric image stitching To obtain a single file view of the sample imaged with the TPFM, the acquired stacks were fused together using the ZetaStitcher tool  [28]. This software can take advantage of the overlap between neighboring stacks to correct the mechanical error of the imaging platform. Indeed, mesoscopic reconstruction with TPFM can take several days, and temperature changes and mounting medium evaporation can lead to some micron-scale distortion. The software is based on two steps: an alignment process followed by image fusion. As a first step, a 2D cross-correlation map is evaluated at several depths for every pair of adjacent 3D stacks, moving each stack relative to its neighbor. The final position of all stacks is determined by applying a global optimization algorithm to the displacements of the individual pairs.
This approach allows us to retrieve a three-dimensional vectorial reconstruction of the segmented objects in the entire z-stack, although limited by a grouping effect that sometimes emerges after the instance segmentation step: neurons that are too close to each other are sometimes identified as a single unit. All the 2. 5D computations have been performed on a standard Linux-based workstation equipped with the ALIQUIS software ecosystem  [33] , using Google TensorFlow as the CNN backend  [34]. The ground truth was annotated by two distinct operators on LAIRA, a web-based collaborative application  [35]. By following an Active Learning paradigm  [36]  the network was incrementally trained against a number of positive and negative samples originating from the four specimens to improve inter-specimen statistical representatives: the final training dataset is composed of 112 images of 512 × 512 px, corresponding to 450 × 450 µm, for a total of 7312 manually annotated neurons (1180 from the first annotation without Active Learning).
14 independent images (1505 neurons) were used to validate the CNN and an additional 14 images (1208 neurons) to test it. Model regularization is provided in the form of dropout layers, each with a dropout factor of 0. 5. The manually annotated ground truth used to train the neural network is also made available for download on the EBRAINS platform  [29]  in Ximage format  [37]  (see Supplement 1 and Dataset 1  [38] ).
Data analysis The physical boundaries of the neuronal soma were stored in the form of triangular meshes in Alembic  [39]  binary file format, which is suitable for rendering and for further analysis. These files were then processed with Python scripts. For volumetric evaluation, thresholds were applied to remove segmentation artifacts: volumes lower than 400 µm 3 and higher than 12000 µm 3 were discarded. To visualize neuronal count, density and mean volume distributions of the analyzed samples, we plotted histograms using 3D moving bins of 100 × 100 × 100 µm as shown in Fig. 4. The centroid of the identified meshes was used to assign each object to their corresponding histogram bin. Then, using the masks that were manually drawn for each cortical layer (Fig. S11  in Supplement 1), we extracted cell counts and densities as shown in Table  2.
The samples were treated with the SWITCH/TDE clearing method and immunolabeled with the Neuron-specific Nuclear protein (NeuN) to detect neurons and DAPI for nuclear staining (sample area of ≈ 1 × 1 cm 2 and depth of ≈ 500 µm). Imaging was performed with a custom-made Two-Photon Fluorescence Microscope designed to perform mesoscopic reconstruction with a resolution of (0. 88 × 0. 88 × 2) µm, see Fig. 2  and Fig. S3  in Supplement 1. After the acquisition, adjacent stacks were aligned and merged together by evaluating the cross-correlation of the overlapping areas using a custom-made stitching software called ZetaStitcher  [28].
S5, S6, in Supplement 1 and Visualization 1). The statistical assessment of the 2. 5D performance was determined by analyzing four representative stacks of 100 × 100 × 100 µm, one for each specimen, and a representative stack of 100 × 100 × 450 µm to determine the accuracy along with the full depth (Fig. S7  in Supplement 1). The total number of neurons found by the 2. 5D method is 442 over the 474 neurons annotated in the GT, and the volumetric evaluation of the neurons is characterized by a precision and a recall rate respectively, equal to (91 ± 7) % and (96 ± 3) %. To verify the stability of the 2. 5D approach along the tissue depth, we plotted the z-profiles for cell counts, mean volume, and volumetric density across different regions of the cortex along 10 different The four datasets acquired with the TPFM were processed with the 2.
The optical sectioning and the high-resolution optical investigation made possible by TPFM, in combination with the tissue clearing technique, allowed imaging of the 3D organization of whole neurons lowering visual artifacts. Volumetric imaging of samples generates a large amount of data (from tens of GB to tens of TB) that needs to be processed in an automated fashion to extract reliable and quantitative information. The software tools exploited in this study made it possible to analyze such big data. As a first step, we stitched together the 3D tiles acquired using the TPFM microscope. Adjacent tiles were aligned and merged by evaluating the cross-correlation of the overlapping areas using a custom-made software called Zetastitcher. Once stitched, we performed an automatic cell analysis using a 2.
Acknowledgments. We thank  Raffaele Decaro  from the  University of Padova  for providing a control human brain tissue specimen analyzed in this study. We express our gratitude to the donor involved in the body donation program of the  Association des dons du corps du Centre Ouest, Tours , who made this study possible by generously donating his body to science. Bioretics srl, a company specialized in Machine Learning solutions for Computer Vision, is a subcontractor of LENS in the framework of the  BRAIN Initiative Cell Census Network  (n. 1U01MH117023-01 ). The content of this work is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This research was supported by the  EBRAINS research infrastructure , funded from the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant  Agreement No.
Genome segments may be subject to different selection pressures, as nicely observed with EWSMV RNA2. This segment was lost during viral infection after serial mechanical passaging on N. benthamiana  [17] , suggesting that this segment is likely required for vector transmission and dispensable from the plant host. In the case of the better-studied virus in the group, RSV, population structure analysis point to strong purification selection and evidence of recombination  [264]. Emaraviruses are the most diverse of the group of the plant-infecting bunyavirids, given that their genome has between 5 and 10 segments and some proteins have no orthologs in the databases. This diversity has led to deliberation on the exact number of segments carried by each virus, and there are cases where emaravirus genomes have been revisited and expanded  [22, 109].
Acknowledgements We are grateful to  Roy Gereau  for translating Achille Richard's Latin text. We wish to thank  Sylvia Mota de Oliveira  and  Nicolas Fumeaux  for information about original specimens at the  National Herbarium Nederland, Naturalis Biodiversity Center, Leiden, and at the Herbarium of the Conservatoire et Jardin botaniques de Genève , respectively. We acknowledge the  Conservatoire et Jardin botaniques de Genève  and the  Muséum national d'Histoire naturelle de Paris , for granting permission to publish the images of type specimens of Patabea tenuiflora and Ronabea morindoides, respectively. We are also grateful to  Hervé Galliffet  for allowing us to reproduce his excellent photographs of Appunia morindoides. It would not be feasible for these distant coauthors to collaborate and develop this work without the invaluable JSTOR Global Plants database [https://plants.