dataesr/scientific-paragraphs-categorization huggingface dataset:
This resulted in 68 (=17 × 4) features for each subject (SI Appendix B2, Table  S1 ). The support vector classifier algorithm using these features was trained against a previously published database of 142 EEG recordings (68 MCS and 75 VS/UWS) in 98 different patients  13, 15  with a 20% feature selection (keeping only the 20% markers showing the highest discrimination performance between VS and MCS patients on univariate F-tests in the training dataset, see SI Appendix B2, Table  S1 ) and 5-fold stratified cross-validation with a penalization parameter C, chosen by nested cross-validation among the values = [10 -6 10 -5 10 -4 10 -5 10 -4 10 -3 10 -2 10 -1 ] using a grid-search method. The algorithm was then tested on each participants' resting-state EEG to compute the classifier's prediction of being MCS according to the brain activity both before and after the tDCS session separately.
We used the Platt scaling method to obtain probabilities (between 0 and 1) from the classifier's output. The change induced by tDCS was obtained by subtracting the probability when tested on the resting-state EEG before stimulation from the probability when tested on the resting-state EEG after stimulation. We used the scikit-learn software for machine learning  90. Auditory oddball analyses. For the analysis of EEG during the auditory oddball paradigm, data were further low-pass filtered at 20 Hz and baseline corrected over the first 800 ms (from the beginning of the trial to the onset of the fifth sound). ERP topographies: We first analyzed group-level event-related potentials (ERP) elicited by the auditory oddball paradigm. These were obtained by averaging trials of each condition with the trimmed mean 80% (across trial) over the scalp channels at the subject-level.
Current flow modeling. The segmented tissues output from the DCNN were feed into an open-source software ROAST  96  to model the current-flow in the entire head volume. Electrodes were placed automatically by ROAST at positions F3 and Fp2 on the scalp. Electrodes were modeled as a disc with 25 cm 2 cross-sectional area and 2 mm thickness. Finite element meshing and solving were subsequently performed in ROAST fully automatically. Boundary conditions were set as 2 mA current injected at electrode F3, and 2 mA flowing out of electrode Fp2. Literature tissue conductivity values were used  97. To facilitate the voxel-level correlation analysis that is described in the next section, we registered and warped the electric field magnitude in each subject to the MNI152 standard head (version 2009a  98, 99  ).
This two-step robust non-parametric procedure is able to control for the multiple comparisons over sensors and time by acknowledging the dependence between spatially close electrodes and timepoints. First, a test statistic between the two groups was computed at each sample, i. e. each channel for the EEG markers, each channel-timepoint pair for the ERP and each channels pair for the temporal generalization decoding. Respectively, spatial, spatiotemporal and spatio-spatial clusters were constructed using a connectivity matrix with the inclusion of the test statistic corresponding to a first step statistic p-value of 0. 05. For each cluster identified, the sum of test statistic over sensors included in the cluster (the cluster mass) was computed. The second step relied on the creation of surrogate datasets by 10000 random permutations of the groups' labels to construct the distribution of the cluster mass under the null hypothesis that data from both groups are drawn from the same distribution.
As a result, size of the areas or the integral of the correlation over the area is not an appropriate metric. This motivated us to base the shuffle statistic more directly on the mean correlation coefficient as a measure of the strength of correlation. Softwares. All statistical analyses were performed with python using scipy  105  , mne-python  81  and scikit-learn  90  packages, except for the non-parametric factorial analysis performed in R statistical software with the ARTool 106 package and electric fields analysis performed in Matlab software. Throughout all EEG analyses, from preprocessing to statistical analyses, we tried to comply with the CODIBAS-MEEG Best Practices in Data Analysis and Sharing in Neuroimaging using MEEG initiative  107.
Acknowledgements This work was supported by: 'e(ye) BRAIN, Ivry-sur-Seine, France' , 'Institut National de la Santé et de la Recherche Médicale ' (BH, LN, JS),  Sorbonne Université (LN) , the  James S. McDonnell Foundation  (LN), FRM 2015 (LN),  UNIM (LN) ,  Académie des Sciences-Lamonica Prize   2016  (LN). The research leading to these results has received funding from the  program 'Investissements d'avenir'   ANR-10-IAIHU-06 , 'and by  ANR  grant ' CogniComa ' ( ANR-14-CE-0013-03 ). BH was funded by  'Poste d' Accueil Inserm ' grant.
Simulations The simulations presented in Fig. 2  were implemented and run using Matlab (MathWorksTM), applying the relevant formula introduced in Supplementary Information §2. The world model included a series of triangulated meshes: a sphere of radius OH = 1 centered at 0 used as a projective plane at infinity; a horizontal ground plane XY; "mountains" based on Matlab's "peaks" function; a model of a city from an online freely available 3-dimensional model; and a small sphere (diameter 0. 03) representing the Moon and projected on the sphere at infinity in the direction OH. The coordinate system was: OH = x, OL = y, OV = z. The parameters of the simulation were varied across the following ranges: elevation of the Moon = [0 2 5 10 15 20  25 30 35 40]  degrees; s = [0. 5: 0. 22 :2. 5]; l = [0.
The absolute area of the Moon in the FoC projective space was computed as: p*(DZ/2)*(DY/2), with DZ, the vertical elliptical length and DY, the horizontal elliptical length of the projected Moon. The area was then expressed as a relative area by dividing area(q) at a given elevation by area(q = 10) at elevation 10 (near the horizon, and corresponding to the lowest elevation for ratings in the Virtual Reality experiment). Rendering of the space for Fig. 2  was performed using Matlab's patch function.
VR setup The experiment was programmed in Unity3d version 2017. 2. 0f3. A scene was created with basic assets (ground plane, cubicles, road planes, mountains, moons) (Fig. 3 ), in order to manipulate perspective cues, based on stereoscopic vision through the Head Mounted Display (HMD), geometrical perspective cues from objects due to their distance, and atmospheric effects (fog). Moon spheres, all of the same diameter (2,500 meter), were placed in the scenes at various elevation and azimuth, but at a constant distance from the location of the participants of 50,000 meters. The experiment used an HTC Vive system for the VR immersion in the scene. Scripts were programmed in C# to control conditions, the positions of moons, ratings, saving the results in a CSV file.
Data analysis All analyses were performed in Matlab (MathWorks TM ). The area of the apparent reference moon disks as a function of elevation q was calculated as A(q) = p*D(X)* D(Y), with D(. ) the diameter (in meters) of the moon. The areas were normalized by that of the target moon at 10° in order to express the areas as a proportion of the lower target moon area, so that A(q) = A(q)/A(q = 10°). The elliptical bias (for block B) was computed as abs(D(Y)/ D(X)). Data were averaged within participants for each elevation (2 samples per condition per elevation). Simulations were performed in order to regress empirical data on data predicted by the model. After considering the empirical range of relative areas (with minimum relative areas of around 0. 6), the space of parameters of the PCM used to generate predicted curves for fitting, ranged as follow: s = [0.
1f, g ), this decrease could be explained by the sensitivity of the spike-field coherence measures to firing rate level  34  , as verified by randomly removing action potential in STN spike trains of the same OFF epoch dataset (Supplementary Fig. 1h-j ). The contribution of other cortical areas (e. g. , somatosensory cortex) in β-oscillations' generation was also excluded by performing large-scale cortical ablation in 6-OHDA rats (Supplementary Fig. 2a, b ). Altogether, these results rule out the contribution of mCx as a major source for the generation and the transmission of β-oscillations in the 6-OHDA rat model of PD. activity could be important for orchestrating abnormal βrhythm  24, 25, 35. We tested the STN contribution to β-oscillations' generation using an opto-inhibitory approach (Fig.
6a, b ). We next assessed if our synthetic β-synchronization shared the same or as many as possible functional properties as parkinsonian β-oscillations. In particular, we focused our analysis on the known STN/GP neurons spike-timing properties during these oscillations (that is the antiphase relationship between STN/prototypic and prototypic/ arkypallidal neurons, as well as the in-phase relationship between STN/arkypallidal neurons  24, 25  ) and the associated firing rate changes (that is the hyperactivity of STN neurons versus the decrease firing of prototypic neurons). Importantly, these fine STN/GP spike-timing features during PD β-oscillations were confirmed in our dataset (Fig. 3d ). On the contrary, artificial ChR2-induced β-oscillations did not reproduce any of the correct STN/GP phase-locking preferences (Fig.
3d , e and Supplementary Fig. 5g ) suggesting that this opposition of phase is likely driven by prototypic neurons collateral inhibition onto arkypallidal cells. These firing relationships between STN/GP neurons were also confirmed using longer (2 s) continuous light stimulation in the ChR2 dataset (Supplementary Fig. 6c-e ). Finally, considering firing rate changes induced by the ChR2 or the ArchT β-patterning, we found that, in both cases, artificial βoscillations could not faithfully reproduce the rate changes associated with parkinsonian β-oscillations (i. e. , STN hyperactivity and prototypic neurons hypoactivity  24, 25  ). Indeed, here ChR2 opto-patterning of STN neurons at β-frequency reproduced STN increase firing (Fig. 3f  and Supplementary Table  3 ) but also induced hyperactivity of prototypic cells (Fig.
3-10 kHz, LFP: 0. 1 Hz-10 kHz; model 1700, A-M Systems). For ECoG recording, the signal was amplified 1000-fold and filtered with the same differential AC amplifier (0. 1 Hz-5 kHz). Finally, all recorded signals were digitalized at 20 kHz, by using the Power1401-3 connected to a computer equipped with Spike2 software (Cambridge Electronic Design). To facilitate the identification of STN during the recording sessions, a concentric bipolar electrode (SNEX-100, Rhodes Medical Instruments) connected to an isolated stimulator (DS3 Isolated Current Stimulator, Digitimer), was inserted in the mCx (coordinates: AP, +3. 5 mm rostral to bregma, ML, +3. 5 mm from midline, DV, -1. 8 mm from cortical surface). STN was identified by checking online the STN short-latency triphasic response to mCx electrical stimulation.
The electrode was connected to an isolated stimulator (DS3 Isolated Current Stimulator, Digitimer) and an electrolytic lesion was performed by applying a continuous current (3 mA during 20 s) in two stereotaxic coordinates separated by 300 µm in the rostro-caudal axis. Ipsilateral ECoG recording was performed before and after STN lesion. Juxtacellular stimulation. In this study, we performed juxtacellular labeling to identify the location/cell-type of the recorded unit in STN or in GP. Briefly, the recorded unit was stimulated by a current pulse of 250 ms long (50% duty cycle, 1-10 nA) sent through the recording electrode by axoClamp 2B (in bridge mode; Molecular Devices). Only recording with at least 250 stimulations were included in this study. Data processing and analysis. We performed simultaneous recording of the mCx ECoG and STN unit activity at a sampling rate of 20 kHz during epoch containing β-synchronization.
All data processing (spike sorting and ECoG filtering) were realized offline with Spike2 software (Cambridge Electronic Design). The ECoG signal was high-pass filtered at 0. 5 Hz (DC remove) and low-pass filtered at 500 Hz following by a down-sampling at the same frequency (to avoid aliasing effect) for further field analysis. The frequency resolution was set at around 1 Hz (FFT size = 500/512 Hz) to analyze the root mean square ECoG power spectrum or to calculate the coherence between spike train and ECoG (using 'coher' script, freely available on CED website). For generating peri-event spectrograms, we computed in Matlab ® R2016a (MathWorks, Natick, MA, USA) using the ft_freqanalysis function (time/ frequency slide windows: 50 ms/0. 071 Hz, cycle number: 7) of the Fieldtrip toolbox  73. Peri-stimulus time histograms or PSTH (width: 6 s, offset: 2 s, bin size: 50 ms) of each spike trains were generated using spike2 and a custom Matlab ® script.
The linear phase histograms were then transferred to Matlab ® for further analysis using circular statistics toolbox  74. To determine if a spike activity was significantly modulated by the β-oscillations measured in the ECoG, we performed the Rayleigh test for circular uniformity using the 'circ_rtest' function which evaluates if the spike unit activity is uniformly distributed across the ECoG β-oscillation cycles (H0, p > 0. 05). If H0 was rejected, neuron was considered as significantly entrained by β-oscillations. The mean angle and vector length for each spike train were also measured by using the 'circ_mean' and 'circ_r' function, respectively. Finally, the mean angles of entrained neurons were compared (OFF vs. ON epoch or between groups) by using the Watson-Williams F test ('circ_wwtest' function, circular statistics toolbox).
Random spike removing in mCx experiment. Our mCx optogenetic inhibition was associated with a reduction in the firing rate of STN neurons and a consequent decrease in the coherence value measured between STN spike and ECoG reduction in the functional connectivity. To determine the influence of change in firing rate on the spike-ECoG coherence value we performed random spike removal on our STN spike timestamps using the 'randsample' Matlab ® function. We analyzed the effect of a 20 or 50% decrease in STN firing rate and performed coherence analysis in Spike2 as previously described. Effect of optogenetic stimulation across different β-power. To analyze the effect of the optogenetic stimulation across the β-power distribution, we compared the root mean square power of every individual β-oscillations period as measured through the AUC within the 12-30 Hz frequency band for each stimulation epoch (OFF laser vs.
ON laser) for each neuron. The β-AUC was calculated in Matlab ® through an interpolation method ('interp1' function) of the power spectrum in the 12-30 Hz frequency band. A β-AUC difference (β-AUC above interpolated line) -(β-AUC below interpolated line) was calculated for every 2 s long stimulation epoch (OFF and ON laser stimulation). Finally, the cumulative distribution was computed with the 'cdfplot' function (Matlab ® ). Beta bursts detection. The β bursts were extracted from the ECoG signal recorded above the mCx and according to a threshold detection method (See Supplementary Fig. 1a ). The signal was first down sampled at 500 Hz and band-pass filtered in the β-band (see 'Data processing and analysis'). The filtered EcoG signal was then imported into Matlab and the envelope of the β signal was calculated using the hilbert function.
For each rat, we averaged the different STN ROI values and correlated this measure to the percentage of opto-inhibition as determined by our opto-mapping strategy. Statistical analysis. The statistical analyses were two-tailed statistical tests with a risk α set at 0. 05 and were performed in SigmaPlot 12 (Systat Software). For independent samples, we applied the normality (Shapiro-Wilk test) and equal variance tests. A t-test was used if the distributions were normal and the group variances were equal. Otherwise, the Mann-Whitney signed rank test was used. In a same way, Kruskal-Wallis one-way ANOVA was used instead of one-way ANOVA, when normality distributions and variance homogeneity were not verified. ANOVA was followed by Student-Newman-Keuls post hoc test. For dependant sample, the paired t-test was used excepts if the normality distribution test failed (Shapiro-Wilk test, p < 0.
Acknowledgements This work was supported by grants from the  French Agence Nationale de la Recherche  ( ANR-14-CE13-0024-01  and  ANR-15-CE37-0006 ), from the  CNRS PEPS Idex Bordeaux  ( UB101 CR-2014R ), and the  LABEX BRAIN   ANR-10-LABX-43. B. C. was supported by a fellowship from  French Ministry (Higher Education, Research and Innovation) , from  France Parkinson  nonprofit organization ( UB320 CR-3219R ), and from the  LABEX BRAIN  ( UB106 CR-3219R ). We are grateful to  T. Nguyen  and  H. Orignac  for technical assistance,  Drs D. Hansel , and  P. Magill  for insightful scientific discussions;  Drs M. Deffains ,  J. Baufreton , and  A. McDonald  for comments on the paper.
Treatment Consistent with the described data showing NLRP3inflammasome activation, both probands showed complete remission with the antieIL-1 receptor antagonist anakinra (100 mg/day) administered by subcutaneous injections. One week after anakinra administration, both patients reported a significant clinical and biological improvement. A complete resolution of the urticarial skin rash, fever, and systemic inflammation (C-reactive protein levels <5 mg/liter) was observed. Probands I and II are still under treatment today, having received the treatment for the past 2 and 3 years, respectively. During the follow-up period, kidney function tests were found to be normal in both probands. However, the treatment has so far no effect on hearing loss.
, 2016). The position of the target oligos is available on request. We used a custom targeted capture which contains the coding and the untranslated regions of the following genes: ADA2, CARD14, IL1RN, IL36RN, LACC2, LPIN2, MEFV, MVK, NLRC4, NLRP12, NLRP3, NOD2, PLCG2, PSMB8, PSTPIP1, RBCK1, TMEM173, TNFAIP3, TNFRSF11A, and TNFRSF1A. Sequence data were analyzed using an in-house double pipeline with sequence reads in fastq format aligned to the reference human genome (hg19) using BWA and Bowtie2. Duplicate sequence reads were removed (by Picard and SAMtools, respectively). Variant calling was performed using GATK and SAMtools, respectively. Additional screening of insertion-deletions was performed by the Pindel software. Variant calls in VCF format were then annotated through ANNOVAR. All results obtained by this targeted sequencing were confirmed using Sanger sequencing.
Modelization of the NLRP3 structure The three-dimensional structure of the human NLRP3 protein has been modeled as follows: Using the HHpred software, we found that the three-dimensional structure of the NACHT, NAD, and leucine-rich repeat domains of Nod2 (5IRN, Oryctolagus cuniculus)  (Maekawa et al. , 2016)  has been identified as the closest to human NLRP3 protein. Using the Modeller software, the predicted structure of human NLRP3 has been obtained. Using the Pymol software, the amino acids involved in mosaic NLRP3 mutations have been highlighted.
ACKNOWLEDGMENTS The authors are grateful to the affected persons and their families whose cooperation made this study possible. The authors would like to thank  Pastelle Metura  and the  M2 Bioinformatics group of Paris University  for their help in developing a three-dimensional NLRP3 model. This study was funded by the  Agence Nationale de la Recherche , France  ANR-17-CE17-0021-01. FA was supported by  Alquds University in Palestine  and from the  "Fondation pour la Recherche Medicale", France  ( FDT20130928419 ). EA was supported by  An-Najah University in Palestine. CLo was supported by  INSERM, France  (Poste d'accueil,  00552404 ).
Nonhuman primate neuroimaging is on the cusp of a transformation, much in the same way its human counterpart was in 2010, when the Human Connectome Project was launched to accelerate progress. Inspired by an open data-sharing initiative, the global community recently met and, in this article, breaks through obstacles to define its ambitions. Nonhuman primate (NHP) neuroimaging carries tremendous translational promise for biomedicine  (Phillips et al. , 2014; Roelfsema and Treue, 2014). However, progress has been slow, as researchers face not only the many challenges that human neuroimaging has overcome but also unique obstacles that require consensus solutions. To date, the approach has remained largely piecemeal and single-lab driven, causing most NHP researchers to struggle to amass datasets consisting of even 10 to 20 subjects, whereas their human-imaging counterparts now aim for thousands.
Such phantoms would be created and made freely available as a 3D-printed model of a given species' brain filled with a contrast agent with known relaxation times to standardize signal-to-noise assessment across sites. Phantoms could be created for any of the primate species (apes, marmosets, baboons, macaques). Importantly, working on good quality data acquisition beats any ). National differences in NHP research and NHP welfare regulations are particularly problematic for efforts to collaborate internationally. The community agreed that addressing this challenge going forward will benefit from additional transparency when sharing their datasets, including identification of the relevant regulatory body and reference to their published standards. Additionally, it will be important to increase the collection and sharing alongside MRI data of objective and evidence-based measures of animal health status as metadata, which can also provide scientific insights (e.
Crediting and Intellectual Property. In NHP research, where substantial costs and efforts are required for training or maintenance of a single individual, investigators hold real concerns about not being appropriately credited or being ''scooped'' analytically with one's own data. Recent years have witnessed an increasing acceptance of ''data descriptors'' or ''data papers'' on resource-sharing infrastructures (Neuroimaging Informatics Tools and Resources Clearinghouse [NITRC], Zenodo) as a publication-based means of crediting data generators and encouraging sharing. Digital object identifiers (DOI) assigned to datasets can further assure the rapid identification, crediting, and tracking of datasets. However, such efforts need to be recognized by the institutions and used in promotion reviews (e.
g. , Declaration on Research Assessment, https://sfdora. org). This situation is problematic for the advancement of open science and must be addressed by a coordinated effort involving both institutions and funders recognizing the importance of data generation and sharing. These realities often drive investigators to hold back their newest data from sharing initiatives, instead sharing only those datasets that have already yielded publications. GCW participants converged on a solution that moving forward, in addition to fully open sharing options, a ''collaboration seeking'' sharing option will be added with the following terms: (1) early sharing encouraged, but an investigator can accept or reject access requests to these data; (2) the investigative team may receive co-authorship credit on the publication (to be negotiated by the dataset holders and proposed collaborator); and (3) upon publication of the first manuscript, the data status will switch to open sharing.