dataesr/scientific-paragraphs-categorization huggingface dataset:
Task-induced image acquisition and analysis The functional volumes were acquired as T2*-weighted echo-planar images (EPI) (TR = 2 s; TE = 35 ms; flip angle = 80°; 31 axial slices; field of view, 240 × 240 mm 2 ; isotropic voxel size, 3. 75 × 3. 75 × 3. 75 mm 3 ). In the three  runs, 192, 194 and 194  T2*-weighted volumes were acquired for the Word-List production, listening and reading tasks, respectively. For each participant, the pre-processing of T2*-weighted echo-planar EPI images included (1) the T2*-FFE volume rigid registration to the T1-MRI; (2) the T1-MRI segmentation into three brain tissue classes: grey matter, white matter and cerebrospinal fluid; and (3) the T1-MRI scans normalization to the BIL&GIN template, which included 301 volunteers from the BIL&GIN database (aligned to the MNI space), using the SPM12 "normalize" procedure with otherwise default parameters.
For each of the three fMRI runs, data were then corrected for slice timing differences and, to correct for subject motion during the runs, all the T2*-weighted volumes were realigned using a six-parameter rigid-body registration. The EPI-BOLD scans were then rigidly registered to the structural T2*-FFE image. The combination of all registration matrices allowed for warping the EPI-BOLD functional scans to the standard space with a single trilinear interpolation. The initial analysis included the application of a 6-mm full-width at half-maximum Gaussian filter to each functional volume of each run. Global linear modelling [statistical parametric mapping (SPM), http://www. fil. ion. ucl. ac. uk/spm/] was then used to process the task-related fMRI data. For each participant, BOLD variations corresponding to each Word-List task versus the cross-change detection task belonging to the same run were computed [Word-List production (PROD), Word-List reading (READ), and Word-List listening (LISN)].
Only one subcortical area, the pallidum Fig. 2  Regions of the AICHA atlas significantly activated in the three tasks (top row), significantly asymmetrical in the three tasks (middle row), and significantly conjointly activated and conjointly asymmetrical in the three tasks (bottom row). The hROIs are projected on the white matter surface of the BIL&GIN template with Surf Ice (https ://www. nitrc. org/proje cts/surfi ce/) software. Leftward asymmetrical hROIs, as well as conjointly leftward activated and conjointly asym-metrical hROIs, are presented on the left hemisphere, and rightward asymmetrical hROIs are presented on the right hemisphere. The hROIs considered activated or asymmetrical per hemisphere by task were selected according to a p value < 0. 05; the statistical threshold applied to the conjunction of asymmetry and activation for a given task was p < 0.
Resting-state image acquisition (rs-fMRI) and processing Spontaneous brain activity was monitored for 8 min (240 volumes) using the same imaging sequence (T2*-weighted echo-planar images) as that used during the language task. Immediately prior to rs-fMRI scanning, the participants were instructed to "keep their eyes closed, to relax, to refrain from moving, to stay awake and to let their thoughts come and go"  (Mazoyer et al. 2016 ). After pre-processing identical to that used for task-induced fMRI acquisition, time series of white matter and cerebrospinal fluid (individual average time series of voxels that belonged to each tissue class) and temporal linear trends were removed from the rs-fMRI data series using regression analysis. Additionally, rs-fMRI data were temporally filtered using a least squares linear-phase finite impulse response filter design bandpass (0.
Acknowledgements We thank  David Bartel  for providing the WT and dcr1Δ strains of N. castellii. We also would like to thank  Patricia Legoix -N é,  Virginie Raynal ,  Benoît Albaud , and  Sylvain Baulande  from the  Next Generation Sequencing (NGS) platform of Institut Curie ;  Choumouss Kamoun  from the  bioinformatics platform of Institut Curie ;  Camille Gautier  and  Marc Descrimes  for preliminary bioinformatics analyses and tools development;  Myriam Ruault  ( UMR3664 ,  Institut Curie ) for assistance in microscopy analyses;  Herv é Vennin-Rendos  for his contribution in the construction of the pAM376 vector;  Eve Samani  for her enthusiastic participation in validation and characterization of yeast strains; and  Ines A Drinnenberg  for critical reading of the manuscript. We are grateful to all members of the lab for discussions.
Highthroughput sequencing was performed by the  NGS platform of Institut Curie , supported by the grants  ANR-10-EQPX-03  and  ANR10-INBS-09-08  from the  Agence Nationale de la Recherche  (investissements d'avenir) and by the  Cancerop ôle Ile-de-France. Antonin Morillon's lab  is supported by the  "DNAlife" grant  ( ANR-15-CE12-0007 ) from the  Agence Nationale de la Recherche , and by the  "EpincRNA" (starting) and "DARK " (consolidator) grants from the  European Research Council.
Apical cell curvature Apical cell contours were drawn manually from confocal images of meridional plans of apical cells immersed in NSW. Similar procedure was followed for tobacco pollen tubes from photos given by Gleb Grebnev (B. Kost's lab, Erlangen Univ. , Germany). We devised a Python 3 script to compute the average contour for a series of images and used it on Ectocarpus (n = 17; S2 Fig) and tobacco pollen tubes. The program starts with a hand-drawn contour for each cell, from which it computes a smoothed cubic spline curve. A set of equidistant points (we used a point-to-point distance of 50 nm) are extracted from the spline, and the meridional curvature κ s is computed at each point (S2 Fig). To obtain average symmetrical curvatures, a pair of windows starting from the tip point and sliding in both directions was used (window width = 200 nm, sliding step = 50 nm).
Orthogonality of tip growth The protocol was adapted from  [30]  and is described in detail in  [31]. Young sporophyte filaments grown in glass-bottom petri dishes were covered with sonicated 0. 1% (w:v NSW) of FluoSpheres amine, 0. 2 μm, red (F8763, Molecular Probes), washed with NSW and mounted under a TCS SP5 AOBS inverted confocal microscope (Leica) controlled by the LASAF v2. 2. 1 software (Leica). The growth of 25 apical cells growing parallel to the glass surface was monitored, and bright-field and fluorescent pictures of median planes for each apical cell were acquired at several time points. Cell wall contours were hand-drawn on time-lapse images using GIMP, together with their respective indicator points. The position of the extreme tip (s = 0) was fixed for each meridional contour, and the drawing of cell contours and microsphere positions were aligned during the time course by using steady microspheres attached on fixed positions.
Modeling and simulations Modeling is described in S1 Text. The simulation program performed a simple simulation with graphic output or an array of simulations within a range of F and σ y values. The input was a list of cell wall point coordinates and values from, for instance, computations of average contours (ad hoc generated data were also used for simulations starting with geometrically designed profiles). For each point, the stress was computed from turgor, curvature, and cell wall thickness values. Then, using F and σ y , the strain rate and the normal velocity were computed. The velocity and displacement direction (normal to the cell wall) gave the new position of the point, calibrated for a tip growth of 1 nm at each step. After computing new positions for all points, the program designed a cubic spline (without smoothing) from which a new sample of points was extracted, thus keeping a constant distance between points throughout the simulation.
Robustness To assess the robustness of the results, we performed a bootstrap analysis. Three thousand samples were constructed by drawing with replacement 17 cell contours and 15 cell wall TEM images out of their respective datasets. For each sample, the average contour and the cell wall gradient were computed as explained above. The stress σ e and expected strain rate _ ε � were computed as functions of the meridional abscissa s. To test consistency with the model, the (σ e ; _ ε � ) points were fitted a Lockhart equation by adjusting values F and σ y and computing the Pearson correlation coefficient (r 2 ) for the increasing part of the function, i. e. , σ e > σ y ; _ ε � > 0. S1 Movie. Tip-growth simulation in Ectocarpus. The cell wall, discretized using one point every 50 nm, was subjected to strain computed according to the viscoplastic model.
Acknowledgments We thank  Benedikt Kost  ( Erlangen University, Germany ) for the photo of the tobacco pollen tubes in bright field,  Gleb Grebnev  ( Erlangen University, Germany ) for the tobacco pollen tube stained with  FM4-64 ,  Aude Le Bail  ( Erlangen University, Germany ) for photos of Physcomitrella filaments,  Florian Frugier  ( Institute of Plant Sciences-Paris Saclay University, France ) for the photo of the Medicago root hair, and  Markus Braun  ( DLR, Germany ) for the photo of the Chara rhizoid. We are greatly indebted to  Mark Hildebrand  ( Scripps, CA, USA ) for access to the atomic force microscope at the  SCRIPPS Institution of Oceanography. We also thank  Florian Frugier  ( Institute of Plant Sciences-Paris Saclay University, France ) and  Sylvie Mazan  ( OBS Banyuls/mer, France ) for critical reading of the manuscript.
Region Bretagne (ARED "ECTOTIP"). Received by HR. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. France BioImaging  (grant number  ANR-10-INBS-04-01 ). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Labex "Saclay Plant Science  (grant number  ANR-11-IDEX-0003-02 ). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
METHODS Piezo1 channel modelling Structural data were obtained from the mouse cryo-EM structure PDB: 6B3R  28. Missing residues were added with MODELLER (v 9. 19)  47, 48  and the loop refinement tool was used to remove a knot in one chain between residues 1490-1498. The best loop was selected out of 10 candidates according to the discrete optimized protein energy method  49. The final Piezo1 mouse model does not comprises the first N-terminal 576 residues and residues 718-781, 1366-1492, 1579-1654, 1808-1951. Therefore, each chain is composed by five non-overlapping fragments: residues 577-717, 782-1365, 1493-1578, 1655-1807 and 1952-2547. The Piezo1 model obtained was further energy minimized in vacuum with GROMACS 5. 0. 7  50  prior simulations.
Quantification of dome depth To obtain the dome depth, the simulation trajectory was fitted to the protein coordinates (reference structure). The coordinates of the CG phosphate particles in each frame of the fitted trajectory are extracted by a Python script. The phosphate particles are then used to separate the bilayer leaflets using a branching network algorithm. Briefly, this method starts with a single phosphate particle, and identifies the other phosphate particles which are within a cut-off distance (2 nm) of the starting particle. The cut-off distance is selected to be smaller than the separation between the bilayer leaflets. Phosphate particles identified in this way are grouped into the same leaflet as the starting residue. This process iterates repeatedly until no more new particles can be added, and the remaining particles are assumed to be part of the other leaflet.
For each frame, the average z-coordinate of beads contained in each bin was calculated and stored in a matrix. The matrices of all frames are averaged to create the final height map, which is plotted using the PyPlot library. The code used is documented at: https://github. com/jiehanchong/membrane-depth-analysis. Isolation of endothelium from mesenteric artery Endothelium was freshly isolated from secondorder branches of mouse mesenteric arteries as described previously  19. Briefly, dissected second-order mesenteric arteries were enzymatically digested in dissociation solution (126 mM NaCl, 6 mM KCl, 10 mM Glucose, 11 mM HEPES, 1. 2 mM MgCl2, 0. 05 mM CaCl2, with pH titrated to 7. 2) containing 1 mg. mL -1 collagenase Type IA (Sigma-Aldrich, Dorset, UK) for 14 min at 37 °C and then triturated gently to release endothelium on a glass coverslips for recordings on the same day.
ACKNOWLEDGEMENTS The research was supported by a  British Heart Foundation   Intermediate Research Fellowship  to JS, Wellcome Trust Investigator and  British Heart Foundation  programme grants to DJB, an  Academy of Medical Sciences  and  Wellcome Trust   Springboard Award  to AK and INSERM funding to MR,  ANS  and NA. AJH was supported by a  BBSRC   PhD Studentship  and JC by a  MRC Clinical Research Training fellowship. We thank  Katsuhiko Muraki  and  Mark Kearney  for helpful feedback on the manuscript.
Recruitment of additional PFs might also depend on the intensity of stimulation. However, the lack of correlation between the charge of the first EPSCs, PPR, PC1 or PC2 and the stimulation current intensity indicated that STP profiles were not affected by this parameter (Figure  2 -figure supplement 2C). Choosing intensities just above the threshold could also induce failure of fiber recruitment rather than presynaptic mechanisms. However, the classification of inputs using PCA transformation followed by k-mean clustering analysis was weakly affected when EPSCs at the first stimulus were excluded from the dataset (Figure  2 -figure supplement 3). Hence, putative experimental errors due to unexpected changes in PF excitability did not influence the overall classification of inputs. Among the four groups identified, only C1 connections exhibited depression of glutamate release after the second pulse, while C2 and C3 connections exhibited facilitation (Figure  2D ).
Data analysis Analysis were performed with home-made python routines (WinPython 3. 3. 5, Python Software Fundation) based on custom scripts. All statistical analyses were performed using SciPy plugin (https:// scipy. org/) (Dorgans, 2019a; Dorgans, 2019b; Dorgans, 2019c; Dorgans, 2019d ; see key resource table; copies archived at https://github. com/elifesciences-publications/eLife2018-STP-GC-MLI). Error bars represent ± SEMs of data distribution. Student's t-test was used in the case of a normal distribution of data, Mann-Whitney Rank Sum Test (MWRST) was used in other cases. One way ANOVA with post hoc Tukey tests were used for multiple comparisons. The levels of significance are indicated as ns (not significant) when p>0. 05, * when p<0. 05, ** when p<0. 01 and *** when p<0. 001.
Principal component analysis PCA is a linear transformation algorithm that examines the main sources of variability inside a dataset composed of multiple observations in order to classify the dataset. PCA analyses covariance between the n variables of a dataset and transforms an original dataset in eigenvalues around a small number of dimensions representing the principal components. The first two Principal Components (PC1 and PC2) which explain the highest source of variance from the original dataset are represented in a scatter plot. We used PCA in order to classify STP in our datasets and extract the most relevant inter-individual differences. PCA were computed using the python-based sklearn plugin. Input variables were normalized and centered using Vector Space Model (VSM) that linearly scales the observations between 0 and 1  (Salton et al.
Data processing For STP analyses using minimal stimulation protocols, data was collected by estimating EPSC charges at any stimulus number from 7 (or more) consecutive trains at 100 Hz elicited every minutes. Failures were arbitrarily detected as signals below a threshold of 3 x s noise , where s noise is the standard deviation of the amplitude of the noise calculated on a 300 ms fixed temporal window preceding the stimulation. PCA transformations (Figures 2, 5 and 7) were performed on the median charge value of each EPSC from the 100 Hz train pulse for each synapse (n = 96). The charge of EPSCs evoked at unitary GC-MLI synapses by photostimulation was measured in a minimal number of 7 successive recordings. To calculate the average charge, values were binned (bin width = 5 ms) from the stimulation onset to 100 ms post-stimulus for each sweep (n = 1080) and PCA transformation was applied using the charge value for unitary dataset (Figures 6 and 7 n = 89).
The delay of MLI peak frequency was estimated from the stimulation onset. In rare cases, photostimulating GCs could evoke neurotransmitter release at more than one GC-MLI synaptic contact. To optimize GC-MLI STP dataset, post-hoc monitoring of EPSCs evoked by GC activation was systematically performed using SpAcAn (Spontaneous Activity Analysis), a collection of IGOR pro-functions (WaveMetrics) (https://www. wavemetrics. com/project/SpAcAn). When EPSCs displayed important kinetic variability, the recordings (both loose-patch and whole-cell recordings) were systematically discarded. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
3 nm ± 15. 2, n = 108; Syn II KO 432. 2 nm ± 13. 2, n = 81). (C) Scatter plot of the number of docked synaptic vesicles (SVs) versus the active zone (AZ) length from dataset obtained in B. Genetic deletion of Syn II led to a specific loss of GC bouton endowed with both a long active zone (>800 nm) and high number of docked synaptic vesicles (>15 synaptic vesicles). DOI: https://doi. org/10. 7554/eLife. 41586. 013.
Acknowledgements This work was supported by the  Centre National pour la Recherche Scientifique , the  Universitede Strasbourg , the  Agence Nationale pour la Recherche Grant  ( ANR-2015CeMod ) and by the  Fondation pour la Recherche Medicale  to PI (#  DEQ20140329514 ). KD was funded by a fellowship from the  Ministere de la Recherche. We thank  Dr. Sophie Reibel-Foisset  and the staff of the animal facility (Chronobiotron, UMS 3415  CNRS and Strasbourg University ) for technical assistance. We thank  Pr. Fabio Benfenati  ( Italian Institute of Technology, University of Genova, Genova, Italy ) for the gift of synapsin triple knock-out mice. We thank  Dr. Frank Pfrieger  for critical reading of the manuscript.
The state of the model produced from this calibration is therefore sensitive to inconsistencies between the different datasets. The resulting transient artefacts are usually dissipated during a relaxation period where the model drifts from the observations. Because historic remote sensing data collections are spatially incomplete as well as temporally sparse, most distributed maps are mosaicked, stacked or averaged to maximise the spatial coverage at the expense of the temporal information  (Mouginot et al. , 2012). However, in the last few years, the development of space-borne ice sheet observations has entered a new era with the launch of new satellite missions, considerably increasing the spatial and temporal resolution of surface observations. Because they require linearised versions of the forecast model and of the observation operator, extending the existing variational methods implies important numerical developments (e.
Initial ensemble For atmosphere and ocean models, the initial state is usually sampled from a climatology, either observed or from a model run. This method can not be used for the parameters and the initial ensemble must reflect the background and the estimation of its uncertainty, available a priori before the assimilation. Following previous studies  (Gudmundsson and Raymond, 2008; Pralong and Gudmundsson, 2011; Bonan et al. , 2014; Brinkerhoff et al. , 2016) , we assume that the initial distributions for b and C are Gaussian with a given mean and a prescribed covariance model. Furthermore we assume no cross-correlation between the initial b, C and z s , and we draw the initial ensembles independently. For b and C, the initial samples are drawn using the R package gstat  (Pebesma and Wesseling, 1998).
As is classical in geostatistics, the covariance model is prescribed using a variogram γ (d) that is half the variance of the difference between field values as a function of their separation d. It is usually defined by two parameters, the sill s that defines the semi-variance at large distances and the range r a which, for asymptotic functions, is defined as the distance where the γ (r a ) = 0. 95s. The package gstat allows one to directly draw simulations, i. e. random realisations of the field, from the prescribed spatial moments  (Pebesma and Wesseling, 1998). For the bed we use an exponential function, γ (d) = s(1 -e -3d ra ), (22) with r a = 50 km and s = 4000 m 2. We also add a nugget model defined by γ (d) = 0 d = 0 nug d > 0 , (23) with nug = 200 m 2. This model is meant to represent the bed measurement error.
Acknowledgements. The author thanks  Gael Durand ,  Olivier Gagliardini  and  Jérémie Mouginot  for valuable comments on the first drafts of the manuscript. Financial support. This research has been supported by the  French National Research Agency (ANR)  through the  TROIS-AS  project (grant no. ANR-15-CE01-0005-01 ). Review statement. This paper was edited by  Carlos Martin  and reviewed by  Dan Goldberg  and two anonymous referees.
Code availability. Elmer/Ice code is publicly available through GitHub (https://github. com/ElmerCSC/elmerfem, last access: 25 June 2018,  Gagliardini et al. , 2013). PDAF is distributed under the GNU General Public License, version 3, and is available at http://pdaf. awi. de (last access: 25 June 2018; Nerger et al. , 2005b). www. the-cryosphere. net/14/811/2020/ The Cryosphere, 14, 811-832, 2020.
In this study, our objectives were to develop a genome-wide high-density Pyrus SNP array that is informative for both the genetic characterization of the diverse NCGR collection, as well as GWAS and QTL mapping analysis. Among the various technologies available, SNP arrays have the advantage of providing complete and reliable genotypic data without requiring a preliminary complex bioinformatics processing, as required for GBS methods  [44] [45] [46]. SNP arrays at various densities have been developed for many plant species (poplar  [47] , pea  [48] , maize  [46] , chickpea  [44] , to cite some of the most recent), including several Rosaceae (e. g. , apple  [45, 49] , rose  [50] , strawberry  [51] ). Here, we designed a high-efficiency Applied Biosystems™ Axiom™ Pear 70 K Genotyping Array and validated it by genotyping almost the entire NCGR collection and two F1 pear populations.
Selection of 700 K SNPs for the first "draft" axiom Array and assessment of their performance We successfully annotated 989,566 newly discovered SNPs using the 'Bartlett' v1. 0 gene predictions and the software SnpEff  [52]. Among these, 84,509 were non-synonymous mutations with HIGH and MODER-ATE impact, and 100,191 SNPs were classified as LOW impact. HIGH impact variants cause changes in start or stop codons, or hit splice sites, therefore they have a disruptive impact in the protein, MODERATE are non-disruptive mutations that might change protein effectiveness, while LOW impact variants are synonymous mutations or non-synonymous that are assumed to be mostly harmless (Additional file 2). The HIGH and MODERATE impact SNPs were prioritized on the array and, in addition to 93% of the LOW impact SNPs, they were successfully tiled (Table  1 ).