dataesr/scientific-paragraphs-categorization huggingface dataset:
2) At the coarse-grained analysis (green area), we tested for the effect of climate on species pools CWM. 3) We tested the interaction between climate and local conditions in determining bromeliad CWM (blue-gray area). climatic conditions because few bioclimatic zones had both open and closed canopy, and consequently canopy cover would be confounded with bioclimatic zone. All multivariate analyses were performed using the vegan package  (Oksanen et al. 2017). Mixed effect models were performed using 'lme4' R package  (Bates et al. 2015 ) and all analyses were done using the R programming language (R Core Team). The code for the sub-sampling and statistical analysis, as well as the adaptation of the PERMANOVA can be found in: <https://github. com/lmguzman/ Climate_invertebrate_traits>.
An important conclusion of our analysis is that there are cross-scale interactions between environmental drivers of trait composition. That is, the effect of local conditions depends on the regional climate. Specifically, the effect of either detrital amount or water volume depended on temporal variation in precipitation and temperature at the field site. This may reflect the ability of large detrital-filled bromeliads to buffer the effects of climate variation on drought prevalence  (Srivastava et al. 2020a ). Studies of functional traits that use coarse-grained data such as range maps or remote sensing data cannot test for such cross-scale effects of the response of the community to local and climatic conditions. However, there is a growing number of fine-grained datasets with a complete census of the community for which interactions between local conditions and larger scale environmental constraints can be tested.
Such datasets are particularly well represented by plants  (Blonder 2018 , Bruelheide et al. 2018 ), but also freshwater invertebrates  (Aspin et al. 2019) , fish  (Winemiller et al. 2015) , intertidal organisms  (Menge et al. 1999 ) and marine coastal fishes  (Hemingson and Bellwood 2018). The challenge for analyzing cross-scale effects in these studies is not the large-scale climatic data, but rather the finegrained environmental data that matters for resource acquisition, competition, predation and facilitation. Fine-grained microenvironmental data, only some of which was available in our study, is likely to be critical in determining the relative importance of environmental filtering and biotic interactions as well as the degree of context dependence  (Blonder 2018). A particular advantage of our study was that we were able to measure the entire aquatic macroinvertebrate community at a fine scale at multiple locations across a wide biogeographic range, which then were assembled into a large database, through the support of the French data synthesis centre, CESAB.
The randomized sub-sampling procedure was used to control for uneven sampling effort between sites, and gave robust estimates of variance explained between sites. Although sub-sampling reduces statistical power, we gained confidence in our among site comparisons. Overall, we found that climate explained more variation in invertebrate trait composition than did local conditions, and that the two scales interactively determine the functional traits of bromeliad macroinvertebrate communities across their Neotropical range. Our ability to contrast the effects of climatic versus local conditions hinged on the compilation of multiple fine-grained datasets. We argue that in addition to sampling communities at local scales, ecologists should aggregate studies that span large ranges in climate variation in order to fully understand trait filtering at local, regional and global scales.
Funding -This research is a product of the FunctionalWebs group funded by the synthesis center CESAB of the  French Foundation for Research on Biodiversity  (FRB; <www. fondationbiodiversite. fr>). We acknowledge the support provided by The  Natural Sciences and Engineering Research Council of Canada (CGS-D)  to LMG and ( Discovery Grant ) to DSS, by  UBC Four Year Fellowships  to LMG, by the  Agence Nationale de la Recherche  through an  Investissement d'Avenir grant  ( Labex CEBA ,  ANR-10-LABX-25-01 ) to CL and RC, by a  BPE-FAPESP  grant no. 2016/01209-9  to GQR, by  CNPq-Brazil  research grants to VDP (no. 307689/2014-0 ) and VFF (no. 312770/2014-6 ), by  CONICET  Proyecto  P-UE 22920160100043  and  UNR AGR-290  to IMB, and by grants from the  Royal Society of Edinburgh , the  Carnegie Trust for the Univ.
Open field The general exploratory locomotion and anxiety in a novel environment were tested during 15 min long sessions in the open field arena (72 × 72 × 36 cm) located in a test room and lit by a 600 lux for background lighting, according to published protocol  92. The open maze was divided by lines into sixteen squares (18 x 18 cm). Each mouse was placed in the center of the arena and allowed to freely move while being video recorded. The recorded data were analyzed offline with EthoVision XT software system (Noldus Information Technology). The time spent in the center (four central quadrants) vs. the perimeter (12 peripheral quadrants) was used to measure anxiety, while the total distance traversed in the arena and average moving speed (mean velocity) was used to evaluate locomotor activity.
Animals were starting randomly from each of the four cardinal points at the edge of the pool and the sequence of the start points was randomized over days. Mice were tested for retention in a 18-days delay probe trial and two extinction tests: the first 2 hours after probe trial and the second 2 hours after the first. For the probe trial, the platform was removed; the mice were introduced in the pool from the North-East (a start point never used during acquisition) and allowed a 60seconds swimming time to explore the pool. Data were collected and computed by a videotracking system (SMART; AnyMaze software). For the visible platform and training trials following parameters were used: the distance traveled and the latency time before reaching the platform and the average swimming speed. For the probe trial and extinction tests the time (in secondes) spent in the target quadrant (i.
Image processing and data analysis All image analyses were performed in Matlab (Math Works) using custom-written routines  97. In brief, full frame images were corrected for potential x and y brain displacement, and regions of interests (ROIs) were semi-automatically selected based on the maximum and mean projections of all frames. Fluorescence signals of all pixels within a selected ROI were averaged, the intensity traces were low pass filtered at 10Hz. Contamination from neuropil signals was accounted for, as described previously  97  , using the following equation: F ROI_comp = F ROI -0. 7 x F neuropil + 0. 7 x median (F neuropil ) F ROI_comp stands for neuropil-compensated fluorescence of the ROI, F ROI and F neuropil represent the initial fluorescence signal of the ROI and the signal from the neuropil, respectively.
M2 microscope equipped with a Plan-Apochromat 20x/0. 8 objective, high performance B/W camera (Orca Flash4, Hamamatsu) and run by the Zeiss Zen2 software. Images were quantified using the ImageJ freeware. First, the user defined ROIs corresponding to the cytoplasm and nucleus or several PV positive cells at several Z positions. Then a homemade macro was used to calculate the ratio, in the green channel, of the cytoplasm intensity divided by the nucleus one.
Sections were then mounted on slides with Prolong Diamond (Life Technologies) before confocal microscopy. Confocal images were acquired on a Leica SP8 Falcon microscope using 63X (NA 1. 4) with a zoom power of 3. Images were acquired at a 2048x2048 pixel image resolution, yielding a pixel size of 30. 05nm. To quantify the density of synaptic markers, images were acquired in the molecular layer 1/2 of the primary motor cortex area, using the same parameters for all genotypes. Images were acquired from top to bottom with a Z step size of 500nm. Images were deconvoluted using Huygens Professional software (Scientific Volume Imaging). Images were then analyzed as described  100. Briefly, stacks were analyzed using the built-in particle analysis function in Fiji  101. The size of the particles was defined according to previously published studies  72, 102.
Structural MRI scans All data were acquired on a dedicated small bore animal scanner (Biospec 117/16, Bruker, Ettlingen, Germany) equipped with a cryogenically cooled two-element surface (MRI CryoProbeTM, Bruker BioSpec, Ettlingen, Germany) transmit/receive coil. Anatomical brain images were acquired in coronal slice orientation (30 slices) applying a gradient-echo (FLASH) sequence with acquisition parameters as: TE/TR 2. 95/400 ms (TE = echo time, TR = repetition time), matrix 30 x 340 x 340, resolution 250 x 50 x 50 mm 3 ). Volumetric tissue analysis followed previously established semi-automatic procedures  103, 104. Briefly, data processing was performed by the inhouse developed software package Tissue Classification Software (TCS). For optimized visualization, the acquisition matrix of 340x340 voxels was transformed into a 768 × 768 grid by nearest neighbour interpolation.
Funding This work was funded by  Agence Nationale de la Recherche  ( ANR-16-CE92-0031  to ALB and  LD ,  ANR-16-CE16-0015  to LD), by  Fondation pour la recherche médicale (FRM ,  DEQ20180339179 ),  Axa Research Funds (rare diseases  award  2019 , to LD),  Fondation Thierry Latran  (HypmotALS, to LD),  MNDA  ( Dupuis/Apr16/852-791  to LD),  ALSA  ( 2235 ,  3209  and  8075  to LD and CLT),  Target ALS  (to CLT),  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's  Excellence Strategy  within the framework of the  Munich Cluster for Systems Neurology -EXC 2145 SyNergy -ID 390857198  (SL), the  DFG, Emmy Noether Programme (SL) , the  Deutsche Gesellschaft für Muskelkranke (SL)  and the  Graduate School for Systemic Neurosciences GSN-LMU  (ZG, VK). CLT is the recipient of the  Araminta Broch-Healey Endowed Chair in ALS.
Patients were excluded if they were younger than ten years of age, or if they had an alternate final diagnosis other than SE or psychogenic status, or due to lacking or incomplete description in the medical record. Physicians at the end of the hospital stay, using clinical evolution and medical investigations, established psychogenic status and SE diagnoses. Reviewing of the records of patients explored by video-EEG monitoring for a suspicion of SE during a period of two years (from January, 1-2012 to December, 31-2013) led to the inclusion of 37 patients with SE and 3 patients with psychogenic status. To increase psychogenic status population, a further search was performed into the video-EEG monitoring database using the same inclusion criteria. Only eight more patients were found and added in the psychogenic status group, leading to 11 patients.
Data acquisition This study relied on the computerized medical records to collect the descriptive terms. This included final reports of emergency room, SAMU digital notes, ICU, medical unit and neurology unit. In order to ensure reproducibility, we grouped together some terms that are available in the supplemental data 2. We collected patient's demographic data, context of seizures, investigations and application of medical procedures, admission units, and total number of hospitalization date (supplementary appendix 3).
Furthermore, the presence of chronic AED therapy may misdirect the diagnosis to drug-resistant epilepsy  (26). Several limitations of this study should be underlined. First, it was difficult to estimate the number of subjects required to provide sufficient power since this study tackles an unprecedented topic. Based on estimates of prevalence  (27) , we expected to have one patient with psychogenic status out of four to six patients with epilepsy among our target population with "suspicion of SE". However, after reviewing all patients receiving video-EEG monitoring for "suspected SE" during a 2-year period in our institution, only three patients were included and eight patients were added from our video-EEG monitoring database provided they met the inclusion criteria. This could suggest that the prevalence of psychogenic status in the population of patients referred to video-EEG monitoring for "suspected SE" was lower than anticipated (8%).
siRNA experiments For human syndecan-1 (accession number NM_002997) knock-down experiments, siRNA ( 1214 GGAGGAAUUCUAUGCCUGA) was synthesised (Proligo, Sigma-Aldrich). For human MMP-9 and MMP-14 knock-down experiments, specific siRNAs were used (MMP-9 siRNAs, Santa-Cruz Biotechnology and MMP-14 siRNAs, Horizon Discovery, Cambridge, Royaume-Uni). Cell transfection was performed using Lipofectamine 3000 (Invitrogen, Cergy Pontoise, France) with 100 nM duplex siRNA according to the manufacturer's recommendations. In all experiments, cells were analysed 48 h after transfection, and viability was assessed by flow cytometry analysis using the anti-syndecan-1 Mi15 mAb. A firefly luciferase GL2 sequence (CGUACGCGGAAUACUUCGA) was used as a negative control (100 nM) and resulted in no reduction of syndecan-1 or MMP expression.
Gelatin zymography and western blot analysis Conditioned media (40 µl) or 40 µg of cell lysate were loaded on an 8% SDS-polyacrylamide gel containing 0. 8 mg/ml gelatin (BioRad Co. , Ltd. ). The gel was run in Tris/glycine buffer and then incubated in a 2. 5% Triton X-100 solution for 40 min to remove SDS. To detect MMP-9 activity, the gel was incubated in reaction buffer containing 50 M Tris-HCl, 0. 15 M NaCl, and 10 mM CaCl2 (pH 7. 8) overnight at 37°C. The gelatinolytic activity was visualised by staining gels in PageBlue Protein Solution (Thermo Fisher Scientific). The results were analysed using ImageJ software. MMP-9 was activated with 2 mM APMA in 0. 1 N NaOH at 37°C for 1 h prior to SDS-PAGE analysis. For western blot analysis of MMP-9, rabbit pAb 444236 (Merck Millipore) and mAb MMP14 EP1264Y (Abcam) were used.
After washing and drying, images of the well were captured with a Camera Fusion FX (Vilber Lourmat, Marne La Vallée, France). Non-cellularised surfaces were quantified using Adobe Photoshop CS3 Extended (version 10. 0) software, and the wounded areas are presented as a percentage of the initial gap surface. Data are expressed as the means ± SD of at least six experiments. Alternatively, experiments were carried out on glass coverslips, and the slides were processed for immunofluorescence experiments as described above, except that the cells were permeabilised with 0. 1% Triton X-100 for 10 min and the blocking agent was PBS 1% BSA.
Acknowledgments We thank  Ellen Van Obberghen-Schilling  for her critical reading of the manuscript. This work was supported by the  French Agence Nationale de la Recherche  ( ANR-08-PCVI-0031  and  ANR-13-RPIB-0003-01 ),  Ligue Nationale Contre le Cancer , and by  CNRS  in the framework of the  GDR GAG  ( GDR 3739 ). AM received her PhD student salary from  Region Rhône-Alpes  ( ARC 2 ). MM and CG received their respective PhD and Master student salaries from  ANR-13-RPIB-0003-01. We acknowledge the contribution of the PLATIM platform and the  AniRA -Cytometry platform of SFR Biosciences Gerland-Lyon Sud  ( UMS344/US8 ).
Functional Localizer of Visually-Responsive Electrodes A visual functional localizer task was performed on a different day than the tactile stimulation. Stimuli of the visual functional localizer task were generated in Matlab and consisted of a unilateral dart-board pattern avoiding a central circular region (0. 4° of visual angle) and displaced by 20° of polar angle from the vertical meridian. The stimulus radius was 10° of visual angle. The spokes of the dart-board pattern moved in opposite radial directions, each stimulus lasted for 0. 5 s, and left (ipsi-lateral to the implanted electrodes) and right (contra-lateral) visual field stimulation were alternated with an interleaving 3 s baseline (grey screen), see Fig. 1. Both hemifields were stimulated to detect brain regions in visual cortex responding to visual stimulation in the entire visual field.
ECoG Data Analysis ECoG data were analysed using Matlab. Data quality for each electrode was evaluated by neurologists and one electrode showing artefacts located in the temporal lobe was removed from the analysis. For both the visual localizer and tactile stimulation, data were re-referenced to the common average of all remaining electrodes. Common average reference (CAR) has been proven to be effective in removing common noise across electrodes in ECoG datasets and to provide similar results as referencing to a silent electrode, which was located at the mastoid in this participant  (Hermes et al. 2014; Liu et al. 2015; Biasiucci et al. 2019). For the tactile stimulation, power spectral density was computed per electrode for tactile stimulation and baseline epochs, using Welch's periodogram averaging method (1 Hz sampling and 1 s window).
Functional Localization of Visual Cortex Electrodes: pRF Mapping Visual stimuli for the purpose of estimating the visual response properties of a population of neurons and to obtain retinotopic maps for individual electrodes (known as pRF mapping  (Dumoulin and Wandell 2008; Kay et al. 2019 )) were generated in Matlab and consisted of a bar stimulus covering 16. 6° of visual angle. A grayscale contrast pattern was viewed through a bar aperture that swept across the visual field eight times in twenty-eight 0. 85 s steps, which also included 8 blanks each. Each bar stimulus was displayed for 0. 5 s followed by a 0. 35 s blank period showing a grey mean luminance image. The participant completed two runs of the task during which she fixated on a cross located in the center of the screen and was instructed to press a button every time the fixation changed color (from green to red or red to green).
Fixation cross color changes were created independently from the stimulus sequence and occurred at randomly chosen intervals ranging between 1 and 5 s. Stimuli were displayed on a 1920 × 1080 pixel NED MultiSync E221 LCD monitor positioned at 75 cm distance from the participant's eyes. After common average referencing, the recorded ECoG data for each electrode were epoched into separate trials time-locked to the onset of each bar stimulus after which Welch periodograms were computed for the stimulus on period (0-0. 5 s) using a 0. 5 s window and 1 Hz sampling using Matlab R2018b. The obtained power density estimates were averaged across 30-200 Hz (avoiding the line noise frequency of 50 Hz and harmonics) using the geometric average in order to whiten the spectrum and to avoid bias towards the lower frequencies  (Hermes et al.
2017) , resulting in a single power estimate for each bar position in the stimulus sequence. The pRF models were computed as described previously  (Dumoulin and Wandell 2008) , using the Compressive Spatial Summation (CSS) variant  (Kay et al. 2019). Briefly, this involved (1) converting the stimulus into a sequence of binary contrast apertures, (2) projecting the contrast apertures onto the best-fitting 2D isotropic Gaussian pRF, and (3) passing the output through a static nonlinearity (power function) to predict the response. The pRF models were fit to each electrode's ECoG responses separately by minimizing the difference between the predicted response and the observed response, using nonlinear optimization as implemented in the analyzePRF toolbox in Matlab  (Kay et al. 2019). Before fitting the model, data were averaged across the two runs.
For Electrode 2, we additionally compared the anatomical location to probabilistic maps of visual topographic areas  (Wang et al. 2015 ) and a whole brain 1 3 neuroanatomical parcellation  (Glasser et al. 2016) , which further demonstrated overlap of Electrode 2 with TO2 and parts of the MT + complex, respectively (Fig. 5 ). Second, we estimated visual population Receptive Fields for all four electrodes responding to the tactile stimulation based on the independent pRF dataset (see Methods). Only electrodes 1 and 2 exhibited reliable pRF estimates as obtained from the fits of high frequency band power time courses (median R 2 = 0. 94, 95% confidence interval = [0. 91, 0. 96] and R 2 = 0. 22 CI = [0. 14, 0. 31], respectively; see Methods). The estimated pRF for electrode 1 was located precisely on the vertical meridian and-consistent with an early visual response profile-was relatively small and highly stable across bootstrapped model fits (median eccentricity 5.
Acknowledgements This work was supported by the  Netherlands Organization for Scientific Research (NWO) ,  Vidi  Grant number  13339  (N. P. ), the  European Research Council (ERC) Advanced  'iConnect' project, number  320708  (N. F. R. ), the  National Institute of Mental Health of the National Institutes of Health  under award number  R01MH111417  (N. P. ), and the  Swiss National Science Foundation  ( 169206  to M. M. M. ). The authors would like to thank  Alessio Fracasso  for collecting the visual task data,  Sara Stampacchia  for useful discussion and  Frans S. S. Leijten ,  Cyrille H. Ferrier ,  Tineke Gebbink  and the clinical neurophysiology team for the experimental environment and their help in collecting the data, and  Jonathan Winawer  for help with relating electrode locations to various brain atlases.
The assignment of the other bands is still a matter of debate  [85, 94, [96] [97] [98] [99]. The best relationship between Raman parameters and thermal maturity in diagenesis is generally found for the full width at half maximum (FWHM) of the G and D bands, the position of the G and D band, the D/G area, and D/G intensity ratio (see  [87]  for a review). Micro-Raman spectroscopic analyses were performed on whole-rock powders using a Witec-Rise Micro-Raman spectrometer, calibrated against the 520. 74 cm -1 band of silica. Data were collected over the first order Raman spectrum (700-2300 cm -1 )  [88] , using a 600 grooves/mm grating and CCD detector. A green laser (λ = 532 nm) with a power of 75 mW was used as light source, while the optical filters adjusted the laser power at <0. 4 mW. To reduce the fluorescence, the Raman backscattering was recorded after an integration time of 30 s for four repetitions  [85] ; then between 10 and 30 measurements per sample were performed in order to ensure reproducibility.
In this work we adopted the automatic approach proposed by Schito and Corrado (2018) that requires a minimum data manipulation. In this method, D and G bands are fitted separately, applying a one-band asymmetrical Gaussian deconvolution for each spectral region, with an asymmetry of 65% and 60% for the D and G bands, respectively. We determined position, intensity, width, and integrated area of the D and G bands. The applicability range is between 0. 3 and 1. 5 R o %  [100]. The Raman spectral parameters are used to calculate the Ro equivalent % (R o % eq. ) through a parametric equation resulting from a multi-linear regression based on a correlation between Raman parameters and vitrinite reflectance (R o %)  [100].
Thermal Modeling Thermal history and burial of the northwestern side of the Maghrebian flyschs basin were performed using the Basin Mod 2-D software package by Platte River (2020). Assumptions used for the modeling are: (1) decompaction of the burial curves according to Sclater and Christie  [101] ; (2) thrusting duration is considered instantaneous  [102] ; (3) a sediment-water-interface temperature of 12 • C and a surface temperature of 20 • C are assumed; (4) burial and thermal models were constrained by organic thermal indicators (vitrinite reflectance R o % and R o % eq. [100] ), and are carried out using the LLNL Easy%Ro method based on the kinetic model of vitrinite maturation of  [103]  and  [104] ; (5) heat flow is fixed at 55 mW/m 2 [105,106]; (6) thicknesses, lithology, and ages of sediments are from  [18, 57, 61, 62] , and have been used to constrain the pseudo-wells perpendicularly to the main stratification while respecting the samples positions;  (7)  finally, the sea-level variations are neglected, as the thermal evolution is mainly controlled by sediments thickness  [107].