dataesr/scientific-paragraphs-categorization huggingface dataset:
Neuroanatomy For each patient, a preoperative anatomical MRI (T1 contrast) and an image with SEEG electrodes (T1 MRI or CT scan) were obtained in order to position SEEG electrodes. The average number of recording bipolar contacts per patient was 149 AE 31. The electrode contacts were localized and anatomically labeled using the IntrAnat Electrodes software  (Deman et al. , 2018)  compatible with the BrainVisa software  (Rivi ere et al. , 2009). Briefly, the volumetric images acquired before (pre, 3D T1 MRI) and after (post, 3D T1 MRI, T2 MRI or CT) the electrodes implantation were co-registered using a rigid-body transformation computed either by ANTs  (Avants et al. , 2011)  or SPM12  (Ashburner, 2009)  software. The gray and white matter volumes were segmented from the pre-implantation MRI using Morphologist as included in BrainVisa (http://brainvisa.
info). The electrode contact positions were computed in the native and MNI referentials using the spatial normalization of SPM12 software. For each patient, cortical parcels were obtained for different anatomical atlases defined either in the MRI native space (MarsAtlas  (Auzias et al. , 2016) ) or in the MNI space  (Brodmann (1909) , Automated Anatomical Labeling AAL  (Tzourio-Mazoyer et al. , 2002)  and MaxProbMap  (Hammers et al. , 2003) ). Each electrode contact was assigned to the gray or white matter and to specific anatomical parcels by taking the most frequent voxel label in a sphere of 3 mm radius around each contact center. In this study, we mainly describe results obtained with the MarsAtlas parcellation scheme, a surface-based method using the identification of sulci and a set of 41 regions of interest per hemisphere.
SEEG preprocessing The SEEG signals were preprocessed automatically using a pipeline composed of the following steps, supervised at the end for data quality check: detection of each stimulation run from stimulation artifacts in raw iEEG files (i. e. a set a pulses consecutively applied between the same pair of contacts), bad channels detection with a machine learning approach  (Tuyisenge et al. , 2018) , and stimulation artifact correction with a model-based method  (Trebaul et al. , 2016). Briefly, the stimulation artifacts were detected from fast transients in signals and adequate template matching between events to avoid the selection of stimulation-unrelated events. The detection of bad channels was based on a supervised machine-learning model trained on a learning database with channels already classified by experts and using a set of features quantifying the signal variance, spatio-temporal correlation and non-linear properties.
The CCEP values from the electrode contacts were transferred to the brain parcels at the group level. According to the threshold of significance, each contact was declared as responsive or not using a binary variable (1 with a significant response during the first 200 ms; 0 with no significant response during the first 200 ms) and, when responsive, the CCEP features were stored. Then all the contacts in the database stimulated in a given ROI were used to estimate the group response from all the contacts corresponding to the same recording parcels: the CCEP features and binary responses for each contact were transferred at the parcel level by computing the median for the features and averaging the binary response for all the contacts belonging to the same parcel. The maps of connectivity probability for each stimulated ROI correspond to the averaged binary response.
Surrogate data were generated with the following procedure: time series taken during the period before each stimulation run (baseline) were processed in exactly the same way as when computing CCEPs, but with stimulation events randomly distributed during baseline. Connectivity matrices surrogates were then obtained using the exact same number of random stimulation runs. Two hundred surrogate connectivity probability matrices were generated, allowing obtaining of the null distribution of connectivity probability for each parcel pair. The p value of the measured connectivity probability was then obtained for each parcel by comparing the measured value to the surrogate distribution. The group analyses can be performed on any variable of interest. For example, we compared CCEP patterns in response to the stimulation of the white or gray matter, i.
Measurement of distance and conduction velocity between electrode contacts We computed a measure of distance between the stimulating contact and the recording contacts that used the anatomical fibers which can be reconstructed from diffusion tensor imaging (DTI). Because the F-TRACT database does not contain any DTI information, those fibers were obtained from the ARCHI database built from 81 healthy subjects at the Neurospin center  (Guevara et al. , 2012). The fibers were described as sets of points repositioned in the MNI coordinate system after normalization of the anatomy of each subject. The distance between two contacts of each F-TRACT subject was defined as the distance averaged between subjects of the ARCHI database. This distance was defined as the length averaged over all fibers that were close enough to both contacts.
The ith column of the connectivity matrix corresponds to the spatial pattern of response to the stimulation of the ith ROI of the parcellation scheme. For example, the map of the insula (Fig. 4A ) shows the highest probability values within the stimulation parcel and then around the stimulation site toward the superior temporal, inferior parietal and frontal, the cingulate cortices and the homotopic parcels. Those were also the significant connections (Fig. 4B ). The dataset containing the connectivity maps of the other areas of the MarsAtlas, Brodmann, AAL and MaxProbMap parcellation schemes is available for download  (David et al. , 2017).
Feature maps Using the insula as an example, we show in Fig. 11  the features maps that are computed for the stimulation of each ROI, obtained when both hemispheres are merged together. The same maps for the other ROIs are available as a public dataset in  (David et al. , 2017). The latency maps (onset and peak latencies: Fig. 11A  and B ) showed first the CCEP propagation first all around the stimulated parcel (insula), reaching the ipsilateral superior temporal, inferior parietal and frontal parcels. Then, the response propagates towards the contralateral middle temporal and medial frontal areas, the ipsilateral cingulate cortex, and the more distant frontal, orbito-frontal and superior parietal parcels. Finally, CCEP components are observed in several distant contralateral parcels but not significantly (Fig.
5 and a range of values between 0. 2 and 1). With that threshold, 25% of the connections with a connectivity probability above 0 were found significant after surrogates testing. At the group level, we chose to pool together all the data and not to process them separately for the different patients. Although using zscores allows the reduction of the inter-subject variability, there is still a 'patient effect' in the data that is not modeled explicitly. Modeling the inter-subject variability was extremely difficult in this study because our database is constituted of a large number of spatially incomplete and different datasets, as the brain of each patient was only very partially explored due to the limited spatial sampling and with stimulation protocols that differed between centers and patients.
Therefore, we opted for a simple approach for our group analyses, considering the mean for connectivity probability and the group median value for all CCEP features to limit the potential influence of outliers. As the F-TRACT database evolves, we expect that the median values of the CCEP feature distribution will converge to robust estimates. For this report, taking the mean or the median of all values between parcels pair without modeling the inter-subject diversity allowed us to maximize the cortex coverage with 217 implantations. We realize, however, that our results were likely biased by patients who underwent a large number of stimulations runs, e. g. 100 stimulations runs vs. 10 in other patients. We developed a probabilistic approach for group analyses because there are many individual variabilities to be expected due to factors of different sorts: instrumental (e.
To improve the physiological value of the forthcoming releases of the F-TRACT atlas by modeling explicitly the impact of epilepsy in connectivity estimates, which are potentially not limited to epileptogenic regions  (Besson et al. , 2017) , we are now working on a new approach that aims at distinguishing physiological CCEP patterns from pathological ones. In addition, with a much higher number of patients processed in our database than for this report, we should be able to generate the F-TRACT atlas for different age ranges in order to avoid the age confound and to evaluate CCEP data can bring relevant information of brain maturation. It is again ongoing work. In contrast to the approach we initially proposed  (David et al. , 2013) , we chose here to cluster our data into parcels defined anatomically by 4 different atlases, and not to confine ourselves to the 3D MNI positions that can be ambiguous for inter-patient matching.
Effect of charge We showed that different charge levels, defined as the product of pulse intensity and its duration, induced different response patterns. Higher charge levels activated more connections, usually more distant from the stimulation site. Networks activated for a low charge level were also usually included in the networks activated for a high charge level. As a result, the connectivity probability values generally increased with charge levels. We also quantified the distance scale of connections easily activated (below 2. 0 cm), as opposed to connections less likely to be activated (more than 4. 0 cm). Including values corresponding to different charge levels in the same atlas adds to the overall statistical power. It also tends to lower probability values for parcels pairs for which we have more data acquired by injecting less charge.
International Review Board at INSERM (protocol number: INSERM IRB 14-140). The F-TRACT database nowadays contain data coming from 792 SEEG explorations explored in 25 epilepsy centers (Paris La Piti e-Salpêtri ere Hospital, Rennes University Hospital, Brno Epilepsy Center, Montreal Neurological Institute, Lyon Neurological Hospital, Marseille La Timone Hospital, Yuquan Hospital Epilepsy Center, Canton Sanjiu Brain Hospital Epilepsy Center, Paris Necker Hospital, Helsinki Hospital for Children and Adolescents, Paris Saint-Anne Hospital, Lille University Medical Center, Strasbourg University Hospital, Toulouse University Hospital, Barcelona Hospital del Mar, Bucharest University Emergency Hospital, Milan Niguarda Hospital, Nancy University Hospital, Paris Rothschild Foundation, Valencia La Fe University Hospital Valencia, Rome Bambino Gesù Children's Hospital, London King's College Hospital, Freiburg Medical Center, Paris Bicêtre Hospital).
Acknowledgements The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013) /  ERC  Grant Agreement n. 616268 "F-TRACT". All the computations presented in this paper were performed using the CIMENT infrastructure (https://ciment. ujfgrenoble. fr),which is supported by the  Rhône-Alpes region  (GRANT CPER07_13 CIRA: http://www. ci-ra. org). We thank  Bruno Bzenik  and  Romain Cavagna  for their help on the CIMENT facility, and  Jean-François Mangin  and  Cyril Poupon  from  Neurospin, CEA Saclay , for providing us with the ARCHI DTI database.
96 chip (Fluidigm San Francisco, USA). The qPCR reactions were performed in the BioMark PCR system. Data was analyzed using the BioMark PCR Analysis Software Version 2. 0. mRNA levels were calculated as the fold-change of repression or induction normalized to the control group without myocardial infarction. Immunocytology HL-1 cells transduced with SERCA2a lentivectors or non-transduced cells were fixed with paraformaldehyde (PFA) (4%), washed with PBS, and permeabilized with 0. 1% Triton. Labeling was performed with an antibody against SERCA2a (1:100, MA3-919, Thermo Fischer Scientific, Villebon-sur-Yvette, France) and the Alexa 488 conjugated anti-mouse secondary antibody (Jackson Immunoresearch). Nuclei were visualized with DAPI (1:1000, 32670, Sigma Aldrich).
nih. gov. gate2. inist. fr/ij/) (n=23-35 vessels per fields). Oregon green 488 conjugated wheat germ agglutinin (1:500, W6748, Life Technologies) was used to quantify the area of cardiomyocytes in the septum region. Measurements were performed on transversal heart cryosections by manually tracing the cell contour (round and oval cells with sizes superior to 70 mm 2 ) on whole-heart images acquired with a NanoZoomer digital slide scanner using NDP. view2 Viewing software (Hamamatsu). Sirius red staining was performed on ischemic heart sections, according to standard procedures. Images were acquired with digital slide scanner NanoZoomer (Hamamatsu). Fibrosis was quantified by measuring the area of collagen deposition on the peri-infarct area or distal non-infarct area and pixel density was determined using ImageJ software.
ACKNOWLEDGMENTS Our thanks go to  Y. Barreira ,  C. Pestourie , and  S. Le Gonidec  from the  INSERM US006 platform ANEXPLO Genotoul (Toulouse, France) ,  L. van den Berghe  and  C. Segura  from the  INSERM UMR1037 vectorology plateau (Toulouse) ,  F. Gross  from the  Biotherapy Clinical Investigation Center CIC   BT 1436  (Toulouse),  L. Fontaine  from the  INSERM UMR1048 histology plateau (Toulouse) ,  J. J. Maoret  and  F. Martin  from the  INSERM UMR1048 GeT-TQ plateau of the GeT plateform Genotoul (Toulouse) , and  C. Pécher  and  M. Zanoun  from the  TRI plateform Genotoul (Toulouse). We also thank  W. Claycomb  for providing HL-1 cells and  C. Galès ,  E. Bahraoui , and  C. Müller  for helpful discussion as well as  J. Iacovoni  for English proofreading. This work was supported by the  Association Française contre les Myopathies (AFM-Telethon) , the  Association pour la Recherche sur le Cancer (ARC) , and  Région Midi-Pyrénées , European funding (REFBIO).
Acknowledgment The authors acknowledge the patients and their families for participating in the study. They thank  La Fonation Maladies Rares  et  Retina France. They are indebted to the  INSERM  and  CNRS  and the  Montpellier University  for their institutional supports. They thank the  Region Pays de la Loire, Angers Loire-Métropole, University of Angers , and  University Hospital of Angers  for their support to the  PREMMI  project. They are indebted to  Dr. Lagavulin  for helpful discussions.
Method details Experimental design Each participant underwent two scanning sessions separated by two hours, during which they were subject to the short-term monocular deprivation procedure described below. Just before each scanning section, their binocular rivalry was measured psychophysically. One (male) participant was excluded because of strong eye dominance tested with binocular rivalry before the deprivation. This left 19 participants (8 females and 11 males) whose complete datasets were entered all analyses. Sample size was set to enable testing for correlations between neuroimaging and psychophysical data. Previous work  (Lunghi et al. , 2015b ) reveals a correlation between MR spectroscopy data and binocular rivalry measures r = 0. 62 (or higher), which implies a minimum of 17 participants to detect a significant correlation at 0.
Binocular rivalry Binocular rivalry was measured in two short sessions (each comprising two runs of 3 min each), immediately before the Pre-and Post-deprivation MR sessions, in a quiet space adjacent to the MR control room. Visual stimuli were created in MATLAB running on a laptop (Dell) using PsychToolbox  (Brainard, 1997) , and displayed on a 15-inch monitor (BenQ). Like in  (Lunghi et al. , 2015b) , observers viewed the visual stimuli presented on the monitor at a distance of 57 cm through anaglyph redblue goggles (right lens blue, left lens red). Responses were recorded with the computer keyboard by continuous alternate keypresses. Visual stimuli were two oblique orthogonal red and blue gratings (orientation:±45 ˚, size: 3 ˚, spatial frequency: 2 cpd, contrast 50%), surrounded by a white smoothed circle, presented on a black uniform background in central vision.
GLM analysis of fMRI data General Linear Model analysis was performed with in-house MATLAB software (D  'Souza et al. , 2016). We assumed that fMRI timecourses result from the linear combination of N predictors: boxcar functions representing stimulus presence/absence (one per stimulus type) convolved by a standard hemodynamic response function (see Equation  2 ), plus two nuisance variables (a linear trend and a constant). We modeled the hemodynamic response function as a gamma function h(t): h t ð Þ ¼ tÀd t À Á nÀ1 ð Þ e À tÀd t ð Þ t n À 1 ð Þ! (2) with parameters n = 3, t = 1. 5 s, and d = 2. 25 s  (Boynton et al. , 1996). Beta weights of the stimuli predictors were taken as estimates of the BOLD response amplitude and normalized by the predictor amplitude to obtain a measure that directly corresponds to % signal change; beta weights were also scaled by an error measure to obtain t-values, following the same procedure as in  (Friston et al.
Population receptive field mapping The pRFs of the selected voxels were estimated with custom software in MATLAB, implementing a method related to that described by  Dumoulin and Wandell (Dumoulin & Wandell, 2008)  and provided as supplementary material. We modeled the pRF with a 1D Gaussian function defined over eccentricity, with parameters " and s as mean and standard deviation respectively, and representing the aggregate receptive field of all neurons imaged within the vertex area. We defined the stimulus as a binary matrix S representing the presence of visual stimulation over space (here, eccentricity between 0 and 10 deg with 40 steps per deg) for each of 6 ring stimuli. We used the results of our GLM analysis to estimate the vertex response to each of our 6 rings (as t-values; using beta values yields very similar results).
We assumed that each vertex response is the linear sum over frequency of the overlap between the pSFT of the voxel and the input stimulus, which is mathematically equivalent to the matrix multiplication between the stimulus and the pSFT. Like for pRFs, we estimated the best-fit sparameter of each vertex pSFT with a two-step procedure: a coarse-grid search followed by the simplex search. We used the matrix multiplication of the pSFT and the stimulus to predict the response to our five bandpass noise stimuli for a large set of initial svalues (between 1 and 1,000 in 100 logarithmic steps); for each vertex, we measured the correlation (our goodness-of-fit index) between the predicted response and the observed t-values. If the highest correlation was <. 5, the voxel was discarded, otherwise the parameter yielding the highest correlation were used to initialize a nonlinear search procedure (MATLAB simplex algorithm), which manipulateds to maximize goodness-of-fit, with the constraint that scould not be smaller than.
Statistics Data from individual participants (mean binocular rivalry phase durations or mean BOLD responses/ pRF/pST across V1 or V2 vertices) were analyzed with a repeated measure ANOVA approach, after checking that distributions do not systematically deviate from normality by means of the Jarque-Bera test for composite normality (MATLAB jbtest function, p-values given in the relevant figures). F statistics are reported with associated degrees of freedom and p-values in the Results section, in the form: F(df,df err ) = value; p = value. Post-hoc paired t-tests comparing conditions follow the ANOVA results, in the form: t(df)= value, p = value. Associations between variables are assessed with Pearson product-moment correlation coefficient, reported in the form: r(n) = value, p = value. Aggregate subject data (i.
Acknowledgments This research was supported by the  European Research Council  under the European Union's  Seventh Framework Programme  ( FPT/2007-2013)  under grant agreement number  338866  (PB, CL, and MCM) and under  ERA-NET  project ' Neuro-DREAM ' (CL, and MCM) and by the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  Marie Sklodowska-Curie  grant agreement number  641805  (JWK) and by the  Italian Ministry of University and Research  under the project  PRIN2015  (MCM) and by  Fondazione Roma  under the  Grants for Biomedical Research: Retinitis Pigmentosa (RP)-Call for proposals 2013-'Cortical Plasticity in Retinitis Pigmentosa: an Integrated Study from Animal Models  to Humans'. The authors would like to thank  Mauro Costagli  for help with the data acquisition, and  David C Burr  for his comments on the manuscript.
Both NMDA2/3 and AKDF-Oca are represented by sequences from only one species, further sequencing of non-bilateral species will be required to fully demonstrate their existence. Furthermore, the evolution of mGluRs has generated a sister group to classes I, II and III, class IV. We have also identified classes of non-bilaterian mGluRs orthologous to I-II-III-IV. We propose that the classification of these two families of GluRs, key to the physiology of the nervous system, has to be updated to include our findings. Continued on next page genomic and, when available, transcriptomic databases. Subject sequences not identified in the first tear and having an E-value below 0. 05 were selected as candidate homologs. These were re-blasted using BLASTX against the NCBI 'non-redundant protein sequences' database.
002 and 1. 000. In Maximum-likelihood analysis the starting tree was estimated using a neighbor-joining method and branch support was obtained after 1000 iterations of ultrafast bootstrapping  (Hoang et al. , 2018). Gene/protein names were given based on their position in the tree. Phylogenetic trees were rendered using FigTree (http://tree. bio. ed. ac. uk/software/figtree/). Phylogenetic calculations were performed at the IBB -UAB heterogeneous computer cluster 'Celler' and at the CIPRES science gateway (RRID: SCR_ 008439)  (Miller et al. , 2010).
B. belcheri glutamate receptor sequences were aligned with the genomic sequence of B. lanceolatum, and high identity fragments were used to design primers. All primers were 20-25 base pair long, had GC content over 40-45% and a Tm between 60-65 ˚C. Primers were designed to obtain amplicons between 140-270 base pairs. Values of normalized expression were statistically analyzed using GraphPad Prism5. No outliers were identified and no data points were excluded. Comparisons between whole body and nerve chord expression levels were done with Student's T-Test for unpaired samples or the Welch variant of the Student's T-Test for samples with different variance. For multiple comparisons between the expression levels of genes belonging to the same class one-way ANOVA analysis was performed using Tukey's Post-Hoc test.
Cell line All expression experiments were done with a mycoplasma-free HEK293T cell line kindly provided by Prof. F. Ciruela (Universitat de Barcelona) and purchased from the American Type Culture Collection (ATCC, CRL-3216, RRID: CVCL_0063). The ATCC has confirmed the identity of HEK293T by STR profiling  (STR Profile; CSF1PO: 11, 12; D13S317: 12, 14; D16S539: 9, 13; D5S818: 8, 9; D7S820: 11; TH01: 7, 9. 3; TPOX: 11; vWA: 16, 19 ; Amelogenin: X). After the purchase of the cell line, mycoplasma tests are performed in the laboratory on every new defrosted aliquot. The kit used for mycoplasma detection is PlasmoTest (Invivogen, code: rep-pt1). Expression of GluE1 and GluE7 in HEK293T cells and analysis of plasma membrane trafficking HEK293T cells were maintained in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% FBS and 1% Antibotic-Antimycotic (Gibco) in a humidified incubator at 5% CO 2 air and 37 ˚C.