dataesr/scientific-paragraphs-categorization huggingface dataset:
6 years ± 4. 5 SD ). This number respected the sample size required to obtain a 2BIE comparable to previous research with similar task and design (required sample size = 22, for Cohen's d = 0. 824 with power = 0. 95, alpha = 0. 05;  Abassi and Papeo, 2020 ). From behavioral and fMRI data analyses, we excluded one subject for whom average performance in the behavioral task was > 3 SD above the group mean. Thus, the final fMRI analyses included 29 participants, and the final behavioral analyses included 22 participants. This study was approved by the local ethics committee (CPP Sud Est V, CHU de Grenoble).
Behavioral study Sixteen grayscale renderings of single human bodies in profile view and various biomechanically possible poses, were created and edited with Daz3D (Daz Productions, Salt Lake City) and the Image Processing Toolbox of MATLAB (The MathWorks Inc, Natick, Massachusetts). As many bodies were obtained by flipping horizontally each unique body, which yielded to a total of 32 single bodies. Sixteen facing dyads were created from the 32 single bodies, and then they were horizontally flipped to create 16 new dyads, for a total of 32 facing dyads. Non-facing dyads were created by swapping the position of the two bodies in each facing dyad ( i. e. , the body on the left side was moved to the right side and vice versa ). The distance between bodies ( i. e. , the distance between the two closest points of the two bodies in a dyad) was matched across facing and non-facing stimuli (mean facing = 82.
The second block (eight trials per condition) was identical to the actual experiment. Instructions for the familiarization blocks were identical to those of the actual experiment. The experiment lasted ∼40 min. Stimulus presentation and response collection (accuracy and RTs) were controlled with Psychtoolbox  ( Brainard, 1997 )  through MATLAB. Each participant took part in two sessions of the same task. The second session took place with a delay ranging from two weeks to one month after the first one (mean interval 19. 8 days ± 6. 1 SD ). Autistic-Spectrum Quotient measure. After the second behavioral session, subjects completed the 50-items Autism-Spectrum Quotient scale (AQ;  Baron-Cohen et al. , 2001 ) , which measures the degree to which an adult with normal intelligence shows traits associated with the autism spectrum, in the domain of social skills, attention-switching, attention to details, communication, and imagination.
fMRI study fMRI data acquisition. Imaging was conducted on a MAGNETOM Prisma 3T scanner (Siemens Healthcare). T2 * -weighted functional volumes were acquired using a gradient-echo-planar imaging sequence (GRE-EPI; TR/TE = 2000/30 ms, flip angle = 80°, acquisition matrix = 96 × 92, FOV = 210 × 201, 56 transverse slices, slice thickness = 2. 2 mm, no gap, multiband acceleration factor = 2 and phase encoding set to anterior/posterior direction). For the main experiment and the functional localizer task, we acquired eight runs for a total of 1546 frames per participant. Acquisition of high-resolution T1-weighted anatomical images was performed after the third functional run of the main experiment and lasted 8 min (MPRAGE; TR/TE/TI = 3000/3. 7/1100 ms, flip angle = 8°, acquisition matrix = 320 × 280, FOV = 256 × 224 mm, slice thickness = 0.
8 mm, 224 sagittal slices, GRAPPA accelerator factor = 2). Acquisition of two field maps was performed at the beginning of the fMRI session. Main fMRI experiment. The fMRI data acquisition consisted of two parts: the main fMRI experiment, and a functional localizer task, which we describe below. In the main experiment, facing and non-facing dyads in upright or inverted orientation were presented over three runs, each lasting 6. 83 min. Each run consisted of two sequences of 16 blocks (four blocks by condition), for a total of 32 blocks of eight second each. Blocks in the first sequence were presented in a random order, and blocks in the second sequence were presented in the counterbalanced ( i. e. , reversed) order relative to the first sequence. Each block featured eight images of the same condition, presented in a random order.
For all the stimuli, the center of the image overlapped with the center of the screen. Subjects, lying down inside the scanner, viewed the stimuli binocularly ( ∼7°of visual angle) through a mirror above their head. Stimulus presentation, response collection and synchronization with the scanner were controlled with the Psychtoolbox through MATLAB. Functional localizer task. In addition to the six experimental runs, subjects completed a functional localizer task, with stimuli and task adapted from the fLoc package  ( Stigliani et al. , 2015 ). During this task, subjects saw 180 grayscale photographs of the following five object classes: 1) body-stimuli (headless bodies in various views and poses, and body parts); 2) faces (adults and children); 3) places (houses and corridors); 4) inanimate objects (various exemplars of cars and musical instruments); 5) scrambled objects.
Functional images were preprocessed and analyzed using MATLAB, in combination with SPM12  ( Friston et al. , 2007 ) , the CoSMoMVPA toolbox  ( Oosterhof et al. , 2016 ) , the Mars-BaR toolbox  ( Brett et al. , 2002 )  and the MatlabTFCE toolbox  ( Thornton, 2016 ). The first four volumes of each run were discarded, taking into account initial scanner gradient stabilization  ( Soares et al. , 2016 ). Preprocessing of the remaining volumes involved despiking, slice time correction, geometric distortions correction using field maps, spatial realignment and motion correction using the first volume of each run as reference. The maximum displacement after despiking was 0. 74 mm on the x -axis (mean max = 0. 30, SD = 0. 18), 2. 31 mm on the y -axis (mean max = 0. 65, SD = 0. 52) and 3. 29 mm on the z -axis (mean max = 1.
Behavioral study Visual categorization task. Separately for the two sessions of the task, we computed the mean proportion of correct responses (accuracy) and the median RTs for each condition, for each subject. Median RTs were computed over trials with accurate response and RTs within 3 SD from the individual median value (discarded RTs in session 1: 11. 64% ± 7. 40 SD ; in session 2: 8. 59% ± 6. 03 SD ). All responses were included in the accuracy analysis. Accuracy and RTs data were analyzed with 2 Category (body, chair) x 3 Stimulus (facing dyads, non-facing dyads, single bodies) x 2 Orientation (upright, inverted) repeated-measures ANOVAs. Critical comparisons were tested using pairwise t tests. Analyses were repeated values from each session separately. Test-retest reliability. For each subject, we computed the 2BIE on accuracy values [(Upright-inverted) facing -(Upright-inverted) non-facing ] for each of the two sessions.
al. , 2002 ). ROIs analyses. Using data recorded during runs with dyads in the main fMRI experiment (N = 29), we analyzed in each ROI the effects of seeing upright or inverted facing and non-facing dyads. In particular, we performed a repeated-measures ANOVA with factors: 5 ROI (EBA, FBA, FFA, PPA and EVC) x 2 Stimulus (facing and non-facing dyads) x 2 Orientation (upright and inverted). Next, pairwise t -tests were used to investigate, in each ROI, the two signatures of configural processing: 1) larger response to facing than to non-facing (upright) dyads (  = 0. 05 one-tail), and 2) larger difference between upright and inverted stimuli for facing, than for non-facing dyads -i. e. , the neural 2BIE (  = 0. 05, one-tail). Brain-behavior correlation (in ROIs). We investigated whether the behavioral 2BIE, taken as a signature of the configural processing of dyads, could predict the response to dyadic stimuli in visual cortex.
This analysis was repeated using data from the behavioral sessions 1 and 2. Brain-behavior correlation (in the whole-brain). For the 22 participants who participated in both the behavioral and the fMRI sessions, we used the behavioral-2BIE from the first behavioral session and we measured a neural 2BIE for each voxel in the brain. To do so, we selected all the voxels across the brain with a positive -weight for all conditions of body dyads and, for the selected voxels, we extracted the -weights for each condition. Then, separately for facing and non-facing dyads, we computed for each voxel the normalized inversion effects ([upright stimuli -inverted stimuli] / (upright stimuli + inverted stimuli]) and defined the neural-2BIEs as the difference in the normalize inversion effect between facing and non-facing dyads.
For all participants, for each voxel, we computed the Pearson correlation between the neural-2BIE and the behavioral-2BIE. For this analysis, the statistical significance of the correlation was determined using a voxelwise threshold of p ≤ 0. 05 (one-tailed). The statistical map of Pearson correlation coefficients (Fisher-transformed) was corrected for multiple comparisons using a permutation testing approach  ( Nichols and Holmes, 2002 )  combined with threshold free cluster enhancement transformations (TFCE,  Smith and Nichols, 2009 )  through the MatlabTFCE toolbox (permutation number = 5000, H = 2, E = 0. 5, dh = 0. 1, Connectivity = 26, onetailed). The same analysis was repeated using values of the behavioral-2BIE based on sessions 1 and 2 of the behavioral task, separately.
Univariate test of the single-body inversion effect (BIE). To assess the recruitment of configural processing for single bodies, we tested whether the difference in the response to upright vs. inverted bodies was statistically larger than a general effect of inversion that would be observed for any (non-body) stimulus. For each of the above ROIs, data recorded during the runs of the main fMRI experiment involving single bodies and objects were analyzed with a repeated-measures ANOVA with factors, 2 Category (body and object) and 2 Orientation (upright and inverted). Next, pairwise t -tests were used to investigate, in each ROI, the inversion effect for single bodies. In particular, we first computed the inversion effect (stronger response to upright than inverted stimuli) separately for bodies and objects (  = 0.
Cavitation and cellular death dynamics regarding cellular water stress: nonlinear regressions and sigmoid parameters The images obtained with the optical method for determining the leaf vulnerability curve to cavitation were reused to determine the level of embolism over the decrease in RWC Leaf. Using the RWC Leaf data collected as described previously and a linear regression in phases, we calculated the RWC Leaf for each individual and associated each image with a RWC Leaf value. We then plotted the RWC Leaf against PEP for six to eight individuals per species. The RWC 50 (the RWC Leaf value at which 50% of the xylem cavitation events had been observed) was determined by fitting a sigmoid using Eqn 5  (Pammenter & Vander Willigen, 1998) , where a is the slope of the curve at the inflexion point: PEP ¼ 100 1 þ e a RWC Leaf À RWC 50 ð Þ ð Þ Eqn 5 By analogy with the P 12 and P 88 points, RWC 12 and RWC 88 representing the RWC Leaf value associated with 12% and 88% loss of hydraulic conductance were computed using the FITPLC R package  (Duursma & Choat, 2017)  replacing the Ψ Leaf inputs with the RWC Leaf values (Fig.
ÀHillslope Eqn 6 where EL 50 represents the RWC Leaf value at which 50% of the cells are dead, max and min represent the maximum and minimum EL value of the dataset, and Hillslope represents the slope of the regression at the inflexion point. From this NLS fitting was extracted the critical level of RWC (RWC crit ) below which the cells start to incur damage from water stress. By analogy with the air entry pressure defined by  Meinzer et al. (2009)  and Torres-Ruiz et al. (2017), the RWC crit was determined at the x-intercept of the tangent through the midpoint (EL 50 ) of the NLS regression curve and corresponded to 12% of cell damage (Fig. 1 ). The slope of the regression was used to determine the 'Mortality Belt' (Fig. 1 ), that is the range in which the decrease in RWC Leaf provokes the maximum cell damage.
3 μm. The complete tomographic scan included 2048 projections, 50 ms each, for a 180°rotation. Samples were exposed for 75 s to the X-ray beam. Tomographic reconstructions were performed using PYHST2 software  (Mirone et al. , 2014)  employing the method of  Paganin (2006). Each leaf scan led to a volume of 2048 images. For each species, three images were extracted from the volumes of five (E. viminalis; L. nobilis) or six (P. tremula × alba) samples and were analysed using FIJI software  (Schindelin et al. , 2012). The different leaf tissues, that is epidermis, palisade parenchyma and spongy parenchyma, were outlined in three to five slices per stack using the freehand selection tools, and their area determined in μm 2 using the measuring tool of FIJI  (Schindelin et al. , 2012). At the same time of the scan, three leaves located next to the scanned leaf were used to measure Ψ Leaf with a Scholander-type pressure chamber (Precis 2000, Gradignan, France).
Measurements of leaf light transmittance During the experiments allowing to generate the vulnerability curves to cavitation, obvious colour changes occurred. In the 8bit image stacks, these changes could be detected as a darkening of the leaf tissue, and thus the stack of images was used to monitor changes in greyscale  (Brodribb et al. , 2021). For this, using FIJI software  (Schindelin et al. , 2012)  and the command 'measure stack' from the OSOV Toolbox, we measured the grey mean value for each image of the stack. The loss in light transmittance was then calculated as the percentage of maximum. A second noticeable change in leaf colour (i. e. blackening), more intense than the darkening observed along the dehydration, was noticed after full embolization of the vessels at very low RWC Leaf and started at the edges of the leaf.
Acknowledgements This research was supported by  La Région Auvergne-Rhône-Alpes  ' Pack Ambition International 2020' through the project  ' ThirsTree '  20-006175-01 ,  20-006175-02  and the  Agence Nationale de la Recherche , grant/award no. ANR-18-CE20-0005 , 'Hydrauleaks'.
Ultimately, the vertical dimension of movement is as, if not more, important as horizontal movement for most aquatic species, with depth use influencing survival, fitness, and resilience to ongoing climatic impacts  (2). While the horizontal distributions and movement patterns of marine megafauna (large fishes, cetaceans, pinnipeds, etc. ) have been examined at a diverse range of temporal and spatial scales, facilitating global-scale comparative studies  (3, 4) , these studies in the vertical plane are limited. This is understandable given the very large size of datasets that would be needed for analyzing both horizontal and vertical space use at the global scale within a single study. These comparative studies, however, are particularly relevant to establish baselines of habitat use and generate hypotheses for threatened megafauna and taxa experiencing global population declines, such as sharks, rays, and skates (hereafter "elasmobranchs")  (5).
Elasmobranchs are often tracked through a combination of biotelemetry and biologging devices. These electronic tags can record the depths encountered by tagged individuals, and, depending on the tag type, data are either directly downloaded once a tag is physically recovered (archival tags or recovered pop-up satellite archival tags; hereafter "archival tags") or remotely transmitted via satellite when an individual surfaces or the tag detaches and floats to the surface at a preprogrammed date ("satellite tags")  (15). For satellite tags, depth data can be transmitted as coarse time series and/or in a summarized binned histogram, where the sampling rate and histogram bin sizes are selected by the user a priori, whereas archival tags typically record high-resolution, continuous time-series data.
Data synthesis We compiled 96,169 days of vertical movement data from 989 individuals, encompassing 38 species that were representatives of 14 families of elasmobranch. Most depth data were obtained from tag deployments on representatives of the families Lamnidae and Carcharhinidae, accounting for 43. 7 and 21. 7% of the data days and 37. 4 and 28. 9% of the individuals, respectively. The number of tagged individuals varied across species, ranging from 1 (Munk's pygmy devil ray, Mobula munkiana; pelagic stingray, Pteroplatytrygon violacea; and Cuban dogfish, Squalus cubensis) to 187 (white shark, Carcharodon carcharias) (Table  1 ). The number of days of data recorded by the tags per individual ranged from 7 to 784, with a mean (± SD) of 98. 4 ± 80. 9 data days per tag deployment across all species.
Calculations of mean depth, median depth, and mean skewness were only available for species where time-series datasets were available. NA, not applicable. Tagging locations were distributed across 17 of the 31 marine biogeographic realms defined by Costello et al. (22)  (Fig. 1 ). One-quarter (25. 0%) of all tag deployments were in the North Pacific marine biogeographic realm, followed by the Tropical Western Atlantic (15. 3%), and the Tropical Indo-Pacific and Coastal Indian Ocean (9. 6%) (Fig. 1  and table  S3 ). Tag detachment locations were available for 845 individuals and spanned 21 different marine biogeographic realms, with 70. 3% of the tags detaching within the same marine biogeographic realm they were deployed in.
Vertical habitat use and reference metrics A whale shark, Rhincodon typus, recorded the deepest depth in our dataset with a maximum depth of 1896 m, which approached the physical limit of the pressure sensors of electronic tags. Depths greater than 200 and 1000 m were attained by 31 and 13 of the 38 species, respectively (Table  1 ). A total of 26 species spent over 95% of their time within the top 250 m of the water column (Fig. 2 ), while 4 species spent less than 50% of their time in the top 250 m (Fig. 2 ; table  S4 ). Only 8 species spent more than 25% of their time in the top 5 m (a zone reflecting surface-oriented behaviors and where animals are potentially detected by aerial surveys), and 13 species spent less than 5% of their time within this zone (Fig. 2  and table  S4 ). Coastal species spent the highest proportion of time in the top 100 m of the water column (98.
Data collection Depth data were collated from either archived time series from recovered archival tags or transmitted time series and/or timeat-depth binned histogram data from satellite tags, for 1038 individuals across 38 species of elasmobranch tagged between 2000 and 2019. Deployment locations and, where possible, end-of-track positions were also reported for each tag deployment. All data were quality-and suitability-checked before inclusion in the study (see the "Data processing" section below). All animal handling procedures were approved by the respective ethical review committees at the data owners' institutions. Elasmobranchs were tagged throughout the Arctic, Atlantic, Indian, and Pacific Oceans (Fig. 1 ), and tags were deployed on animals captured using either baited lines, during commercial fishing operations (longlining or purse seine) or while the animals were free-swimming.
e. , fork length) via established length-length conversions (table  S2 ) before being referenced against published length-at-maturity data (table  S1 ). Where length data were available, but the sex was not specified, we conservatively assessed maturity status on the basis that the individual was female (i. e. , longer to mature). Where length data were lacking (n = 53), maturity was defined as "unknown. " All data processing, analysis, and visualization took place in the R Statistical Environment.
Data processing Data from tagged individuals providing less than 7 days of depth data were excluded. Data were trimmed to the time at liberty by identifying the point of initial submersion (i. e. , tag deployment), and the point at which a constant depth was recorded for >24 hours (i. e. , tag detached from animal and floating at the surface and/or mortality whereby a constant depth was observed). The first day of data (24 hours) was removed from each dataset to reduce biases due to potential atypical behaviors associated with capture and tag deployment  (69). Each tag dataset was also checked for drifting pressure sensors (i. e. , a consistent and unnatural shift in the minimum depths being recorded), and where drift was apparent, the dataset was either cut to the period before drift [n = 5; e.
g. ,  (34) ] or removed from the database if the remaining period was less than 7 days (n = 1), resulting in a final dataset of 989 usable tags. Data were also queried for values >0 m [i. e. , above the surface, which can occur in instances of exposure of tag at the air-water interface such as when an individual breaches  (70) ] and where present, converted to zero values. To account for the influence of biogeography on vertical habitat use within and among species, we assigned marine biogeographic realms [as defined in  (22) ] to each individual on the basis of the start and end points of the tag deployment (see table  S3  for breakdown by species). Beyond latitude and longitude, these realm assignments account for influence of environmental heterogeneity and land barriers on species distributions and movements  (22).
Additional positional information for individual sharks was not included because of the variable accuracy of light-based geolocation [error can be in the order of hundreds of kilometers;  (71) ] and the frequency of geolocation data collection among datasets, which varies with tag type and time spent in the photic zone  (72). SST (°C) at the location of tag deployment was obtained from the National Oceanic and Atmospheric Administration's Multi-Scale Ultra-High Resolution level 4 analysis on a 0. 01° spatial resolution and averaged across the 7 days following deployment to correspond with INLA models (see the "Data analysis" section below).
Data analysis Vertical habitat use and reference metrics For each species, we calculated the mean and total data days (i. e. , tracking days with vertical data available, this may not represent the Downloaded from https://www. science. org at IFREMER-Centre de Documentation de la mer on March 29, 2023 total days of a tag deployment because of transmission gaps in uplinks to satellites) and the absolute maximum and mean maximum depths across all individuals. Each dataset was summarized into the percentage of time that a species occupied a depth bin. For time-series data, bin sizes were set at the top 5, 10, 50, 100, and 250 m. Where possible, these depth bins were also used to summarize histogram data, combining existing bins where necessary (e. g. , combining 0-to 5-m and 5-to 10-m bins into a single 0-to 10-m bin).
For individuals with time-series data available, we calculated the mean depth, mean skewness, and median depth and then averaged these for each species and determined the associated SD and/or IQR. This approach weighted each individual equally (i. e. , did not bias to individuals with longer tracking durations). Given relatively high skewness values in many species, median rather than mean depth values were used in further visualization and analysis. To visualize core vertical habitat use, hourly median depths were calculated for each individual. These were then combined for each species and overall (grand) medians and IQRs were calculated. The full distributions of the data were visualized using violin plots for (i) species with >1000 days of combined time-series data available and (ii) all species with time-series data.
Regional variability We also investigated intraspecific variation in vertical movements across the major FAO fishing areas. For species tagged in multiple FAO areas, we visualized vertical metrics (median depth, maximum depth, % top 5 m, % top 100 m, and % top 250 m) by deployment area, and statistical comparisons were made when sample sizes within FAO areas consisted of at least five individuals of a given species. Nonparametric Kruskal-Wallis tests were used to test for differences among FAO areas, given the data heteroscedasticity, with significance accepted at P < 0. 05.