dataesr/scientific-paragraphs-categorization huggingface dataset:
Several tests are routinely conducted in gene therapy trials for this purpose: NAb assays from CSF and blood serum as well as ELISPOT assays to measure T cell responses to the capsid and the transgene. Pre-existing NAbs to the viral capsid could severely impede viral transduction, and many monkeys will naturally present these antibodies before the start of the experiment. The current initiative revealed that fewer than 5% of optogenetic injection experiments included NAb testing in their animals, which could partly explain experimental problems with opsin expression encountered by some investigators. There is currently insufficient data to conclude whether pre-existing NAbs to the viral capsid (detected in the serum) impede viral transduction in the CNS. We encourage investigators to contact their local gene therapy core before initiating their experiment to set up a battery of immunological assays for the animals to accumulate more data on this topic.
Moreover, recent breakthroughs in gene therapy demonstrate the safety and efficacy of delivering transgenes to the human nervous system using the same viral vector technologies used in optogenetics  (Wang et al. , 2019). The way is paved for development of clinical technologies relying on optogenetics to control neural populations and pathways with unprecedented precision. We hope the NHP Optogenetics Open Database will provide a strong basis for those looking to make evidence-based decisions in the development of future pre-clinical and clinical trials.
Acknowledgments This manuscript was inspired by the  PRIME-DE NeuroResource authored by Milham et al. (2018). We would like to thank the  Jazayeri lab  for the initial impetus for this collaboration and the  MIT group (DiCarlo, Desimone, and Jazayeri labs)  for providing the first in-house database of optogenetics experiments in NHPs that was used as a backbone for the current initiative. We are grateful to the  Open Science Framework  for hosting the database. Primary support for the work by M. L. P. and S. Tremblay is provided by the  Charles E. Kaufman Foundation  and the  Canada Banting Fellowship. Primary support for  A. Angelucci 's lab is provided by the  NIH  ( R01 EY026812 ,  R01 EY019743 , and  BRAIN U01 NS099702 ) and the  NSF  ( IOS 1755431 ). Support for L. N. is additionally provided by an  NIH   K99 grant  ( EY029374 ).
G. and A. T. is provided by the  Medical Research Council. Primary support for the work by W. V. and P. F. B. is provided by the  Research Foundation Flanders (FWO-Flanders ), internal  KU Leuven  funds ( C14/17/109 ), and the  European Union   Horizon 2020 Framework Program for Research and Innovation  under grant agreement  785907. Primary support for the work by R. V. and  F. Fabbrini  was provided by  Fonds voor Wetenschappelijk Onderzoek Vlaanderen  and internal  KU Leuven funds  ( C14/17/109 ). Primary support for the work by R. H. W. and J. C. was provided by the intramural research program at the  NIH. Primary support for the work by A. Y. -S. and P. N. S. was provided by an  American Heart Association Western States Affiliate Postdoctoral Fellowship  (to A. Y. -S. ),  Defense Advanced Research Projects Agency (DARPA) Reorganization and Plasticity to Accelerate Injury Recovery  (REPAIR;  N66001-10-C-2010 ), and by the  UCSF Neuroscience Imaging Center.
2. 5. Statistical Analysis The data were analyzed using the SPSS statistical software (version 21, IBM incorporation, USA). Data from the clinical sample were presented according to their mean and standard deviation from the mean. Means were compared with the non-parametric Mann-Whitney-Wilcoxon test. Quantitative data were analyzed using the Spearman test. We performed a ratio comparison with the non-parametric chi square test for diagnostic label analysis and metaphorical analysis and we calculated odd ratios. Two-sided P values <0. 05 were considered significant. For each rater, a nonparametric receiver operating characteristic (ROC) curve was based on DSA scores in order to assess the sensitivity and specificity of the diagnostic procedure, assisted by DSA. ROC analysis was made for each of the 3 DSA (original DSA, weighted DSA and simplified DSA).
Identification and Analysis of Counterparts of the Intracytoplasmic Domain of P2X7 Available EST indices and genome databases were mined using TBLASTN and human or rat P2X7 intracytoplasmic sequences as queries. Searches in EST databases were mainly performed at http://www. ncbi. nlm. nih. gov/. Blast queries on complete genomes were sent to http://www. ensembl. org, http://www. ncbi. nlm. nih. gov/ and http://reefgenomics. org/blast. When relevant genomic regions were identified, potential exons were identified by comparison with known sequences, consensus nucleotide sequences were translated and ORF were compared to predicted protein models. Multiple alignments were performed using Clustal Omega (https://www. ebi. ac. uk/Tools/msa/clustalo/), to analyse the conservation of key residues identified previously in the P2X7 ballast domain.
Molecular Modeling of the Structure of P2X7 Homologs Protein structure homology-modeling was performed using the SWISS-MODEL program, accessible via the ExPASy web server (https://swissmodel. expasy. org/), using as template the structure of the rat P2X7 receptor obtained by single-particle cryoelectron microscopy [PDB ID: 6u9v  (33) ]. The relevant domains of the models were extracted and compared using Pymol (available at https://pymol. org/2/).
P2X7 C-Terminal Region Comprises a Conserved Zn-Coordinating Cysteine Based Domain All human P2X sequences contain a typical P2X motif (Interpro IPR001429) encoded by exons 1-10 (Figures  S1A, B ). In this region, exon junctions are highly conserved between the different P2X. In contrast, sequences of P2X C-terminal regions are very variable, with various number of exons and different positions of exon junctions. Thus, exon 12 is not related between P2X, and only P2X5 and P2X7 have a 13th exon: a short one for P2X5 encoding 13 aa, and a long one for P2X7 encoding a 170 aa peptide with multiple conserved cysteines which were shown to be in a tetrahedral geometry likely coordinating a dinuclear Zn ion complex [Figure S1B  (33) ]. We then compared P2X7 sequences across vertebrates, from fin fish to mammals.
found in an echinoderm, the sea urchin Strongylocentrotus purpuratus, and in the acorn worm Saccoglossus. All Zncoordinating cysteines found in P2X7 and nanor-like sequences were conserved in these proteins (Figure  5 ). In protostomes, no such domain could be detected from C. elegans or Drosophila databases. However, ZCD was found in proteins from the oyster Crassostrea gigas and from the limpet Lottia gigantea, indicating that it is present in mollusks. Among Ecdysozoa, the motif was found in arthropods-in the mite Ixodes scapularis and in a true bug (Myzus persicae)-as well as in nematods Haemonchus contortus and Ancylostoma caninum (Figure  5 ). Surprisingly, it may be absent from holometabole insects since we could not find it in dipterans (flyes), hymenopterans (ants, bees and wasps) or coleopterans (beetles).
Our data therefore suggest that the structure of this region in the sponge ZCD may be conserved and may also bind a GDP. Linkage were determined from Ensembl genome assemblies at Ensembl Metazoa (http://metazoa. ensembl. org/index. html) and at https://www. ncbi. nlm. nih. gov/genome/?term=saccoglossus for Saccoglossus kowalewsky, using orthology/paralogy relationships and blast analyses. Homologous genes are indicated by the same color in different species (ZCD containing genes are in green), and gene IDs are associated to each symbol. For Ensembl genes, the prefix for each species is given below the species name (e. g. , Aqu2. 1 for the sponge Amphimedon queenslandica). For the snail Lottia, where some paralogs were distant from the reference ZCD containing genes, the coordinates on the scaffolds were added below symbols.
In the case of the hρ1 GABAAR, the GABA control and coronaridine congener containing test solutions were applied for 5 s. A washout period of 90 s followed each recording to ensure a return to baseline current readings was achieved before proceeding to the next recording. Receptor currents were obtained using a patch clamp amplifier (Axopatch 200A, Axon Instruments, Foster City, CA, USA) equipped with a CV201A headstage, whereas a pClamp10. 0 (Molecular Devices, Sunnyvale, CA, USA) on an inverted fluorescence microscope equipped with a FITC fluorescence cube was also used to record hρ1 GABAAR currents. The GABAAR and GlyR currents were low-pass filtered at 5 kHz, monitored on an oscilloscope and a chart recorder (Gould TA240), sampled at 30 KHz using a Digidata 1440A and stored on a computer (pClamp 10.
Acknowledgements This research was supported by grants from the  Texas Alzheimer's Research and Care Consortium Investigator Grant Program (354528)  (to R-Q. H. ),  Institute for Aging and Alzheimer's Disease Research  ( RI6146 , to R-Q. H. ; and  RI6068 , to E. B. G. ), and  Oklahoma State University Center for Health Sciences  (to H. R. A. ). The authors thank  Dr. Kuehne  ( University of Vermont, VT, USA ) for his gift of (+)-catharanthine hydrochloride. The authors are grateful to  Nikki Sabourin-Gibbs ,  Rouen University Hospital , for the writing assistance and review of the manuscript in English.
Information sharing dynamics can be described as a temporal network Single unit recordings of neuronal activity were acquired simultaneously from the CA1 region in the Hippocampus and in the mEC (Figures 1. A and S1) for 16 rats under anaesthesia (18 recording for 16 rats). Following  12  , we constructed time-resolved weighted networks of functional connectivity, adopting a sliding window approach. Within each 10s long time-window, we took connection weights (functional links) between pairs of neurons (network nodes) to be proportional to the amount of shared information 41 between their firing rates (see Methods). We then slide the time window by 1s, in order to achieve a 90% overlap between consecutive windows. In Figure  1. B, we represent the temporal network construction procedure. Based on the data segments in each of three windows centered at times t a , t b and t c , we extract a N × N matrix for each time window, where N is the number of neurons and in which the element (i, j) corresponds to the shared information between nodes i and j.
These network states repertoires are largely global state-specific and are robustly identified using both weighted and unweighted network characterizations. We extracted here functional connectivity networks evaluating information sharing -i. e. , time-lagged mutual information-between the firing of pairs of single units. This choice was motivated by the fact that mutual information is relatively simpler to evaluate than other more sophisticated and explicitly directed measures of information transfer  48  , which require larger amounts of data to be properly estimated. Using a simpler measure was thus better compliant with our need to estimate connectivity within short windows, to give rise to a temporal network description. Although mutual information is a symmetric measure, the introduction of a time-lag makes our metric "pseudo-directed" because information cannot causally propagate from the future to the past.
Animal surgery We used in this work a portion of the data (12 of 18 experiments) initially published by  34  , which includes local field potentials (LFPs) and single-unit recordings obtained from the dorsomedial Enthorinal Cortex of anesthetized rats. Seven additional simultaneous recordings in both mEC and dorsal HPC under anesthesia were included in this study, previously analyzed by  12. Details of anatomical locations and numbers of cells included can be found in Supplementary Figure  S1. All experiments were in accordance with experimental guidelines approved by the Rutgers University (Institutional Animal Care and Use Committee) and Aix-Marseille University Animal Care and Use Committee. We performed experiments on 12 maleSprague-Dawley rats (250 to 400 g; Hilltop Laboratory Animals) and 7 male Wistar Han IGS rats (250 to 400 g; Charles Rivers Laboratories).
Data collection and spike sorting Extracellular signal recorded from the silicon probes was amplified (1000x), bandpass-filtered (1 Hz to 5 kHz), and acquired continuously at 20 kHz with a 64-channel DataMax System (RC Electronics ora 258-channel Amplipex) or at 32 kHz with a 64-channel DigitalLynx (NeuraLynx at 16-bit resolution). We preprocessed raw data using a customdeveloped suite of programs  73. After recording, the signalswere downsampled to 1250 Hz for the LFP analysis. Spike sorting wasperformed automatically using KLUSTAKWIK (http://klustakwik. sourceforge. net 74 ]), followed by manual adjustment of the clusters, with the help of autocorrelogram, cross-correlogram (CCG), and spike waveform similarity matrix (KLUSTERS software package;https://klusta. readthedocs. io/en/latest/ 75 ) After spike sorting, we plotted the spike features of units as a function of time and discarded the units with signs of significant drift over the period of recording.
We determined the statistical significance of putative inhibition or excitation (trough or peak in the [+2 : 5] ms interval, respectively) using well-established nonparametric test and criteria used for identifying monosynaptic excitations or inhibitions  34  , in which each spike of each neuron was jittered randomly and independently on a uniform interval of [-5 : 5] ms a thousand times to form 1000 surrogate datasets and from which the global maximum and minimum bands at 99% acceptance levels were constructed.
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 713750. Also, it has been carried out with the financial support of the  Regional Council of Provence-Alpes-Côte d'Azur  and with the financial support of the  A*MIDEX  ( ANR-11-IDEX-0001-02 ), funded by the  Investissements d' Avenir  project funded by the  French Government , managed by the  French National Research Agency (ANR). CB and WC were supported by the M- GATE  project (European Union's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement no. 765549 ). PQ acknowledges support from  FRM, FFRE , and  CURE Epilepsy Taking Flight Award. DB and AB acknowledge support by the  CNRS "Mission pour l'Interdisciplinarité" INFINITI program (BrainTime).
Systolic blood pressure and heart rate Systolic arterial pressure (SAP) and heart rate were measured in conscious animals using a computerized tailcuff plethysmography system, a noninvasive blood pressure acquisition system (Visitech Blood Pressure Analysis System, model BP-200, USA). The rats were adapted to the pre-warmed tail-cuff apparatus for three consecutive days to acclimatize them and to avoid stress during the experimental procedures. Each session consisted of 15 cycles of measurements; the first 5 cycles were acclimation cycles and the data were not used, whereas the following cycles were used for the calculation of mean values of hemodynamic parameters.
For loading control, we used mouse antiglyceraldehyde 3-phosphate dehydrogenase (GAPDH, 36 kDa) monoclonal antibody (10R-G109a; Fitzgerald Industries, USA). The membranes were incubated with secondary goat anti-mouse IgG HRP-labeled antibody (cat #31430; Thermo Scientific, Inc. , USA) or with secondary goat anti-rabbit IgG HRP-labeled antibody (cat #31460; Thermo Scientific, Inc. ) at 1:10000 dilution for 1 h at room temperature, followed by incubation with chemiluminescent kit Supersignal (Thermo Scientific, Inc. ) and exposition to X-ray film. Biological samples were analyzed in a minimum of three technical replicates. The X-ray film bands densitometry analysis was performed with the software Image Studio Lite version 4. 0 (LI-COR Biotechnology -UK Ltd, United Kingdom) and standardized by the GAPDH density.
Study Design and Setting This retrospective pilot study took place between 2014 and 2017 at Bordeaux university hospital. This work conforms in terms of the protection of personal health data and the protection of privacy to the framework provided by Article 65-2 of the Data Protection Act as amended and the General Regulation on the protection of personal data. Data were collected from patient CSFs sent to the biochemistry laboratory for haem pigments detection (suspected SAH) by spectrophotometry. Net bilirubin absorbance (NBA) and net oxyhaemoglobin absorbance (NOA) were calculated as recommended by the UK guidelines. We systematically added the detection test of haem pigments by PTS and the determination CSF total protein concentration, in real time. Only the spectrophotometry results were communicated to the clinician.
Sensitivity, specificity, positive predict value (PPV) and negative predictive value (NPV) of Multistix® urine test strips to support SAH were calculated using haemoglobin and bilirubin detection by spectrophotometry as the reference method (UK guidelines). Lab conclusions were compared to the final clinical SAH diagnosis. Graph Pad software was used for statistical analysis. Descriptive data were expressed as mean+/-SEM (Standard Error of the Mean) and mean of differences with 95% confidence intervals. D'agostino & Pearson normality test determined normal distribution of the values. The twotailed unpaired t-test were performed to compare means of two groups and calculate p values. p values less than 0. 05 were considered statistically significant.
This result explained the lack of sensitivity of PTSb. Together, these data suggest that PTSh was more sensitive than spectrophotometry and that spectrophotometry was more sensitive than PTSb. Because both bilirubin and haemoglobin presences are required to ascertain biological SAH as to UK algorithm guidelines by spectrum, we next considered positive pigments test strip-combined haemoglobin-bilirubin results (PTShb) when bilirubin and haemoglobin were detected. The PTShb was negative when haemoglobin and bilirubin were undetectable. The PTShb was reported as inconclusive when only haemoglobin or bilirubin was present. So, we interpreted the haem pigments results between PTShb and spectrophotometry using the UK guidelines. Then, as our main goal was to evaluate the ability of the PTShb result to rule out SAH (high sensitivity and NPV), we defined those inconclusive results as positive for the 2 tests (PTShb and spectrophotometry).
, 2011; Betancourt, 2014b). Based on simulating artificial dataset conditioned on the sampled parameters, several likelihood-free inference methods have been proposed to perform inference when likelihood computations are prohibitively expensive  (Papamakarios and Murray, 2016; Meeds and Welling, 2014, 2015; Gutmann et al. , 2016). It is well-known that gradient-free sampling algorithms such as  Metropolis-Hastings (Metropolis et al. , 1953; Hastings, 1970) , Gibbs sampling  (Geman and Geman, 1984; Gilks et al. , 1995)  and slice-sampling  (Neal, 2003; Bishop, 2006)  generally fail to explore the parameter space efficiently when applied to large-scale inverse problems  (Girolami and Calderhead, 2011; Hoffman and Gelman, 2014) , as often encountered in the application of whole-brain imaging for clinical diagnoses.
, 2017). The Virtual Brain (TVB;  (Sanz Leon et al. , 2013) ) is an open-access computational framework written in Python to reproduce and evaluate the personalized configurations of the brain by using individual subject data. This neuroinformatics platform integrates brain computational modeling and multimodal neuroimaging data to systematically simulate the individual's spatiotemporal brain activity. However, there is currently no specific workflow for automatic model inversion and data fitting validation in preparation for TVB. More recently,  Jirsa et al. (2017)   (Jirsa et al. , 2017). However, the inverse problem of such large-scale brain network models is a challenging task due to the intrinsic non-linear dynamics of each brain network node as well as the related large number of model parameters and the observation as commonly encountered in brain-imaging setting.
In this note, we use TVB (see https://www. thevirtualbrain. org) for brain network simulations, and Stan (see https://mc-stan. org) as well as PyMC3 (see https://docs. pymc. io) for inverting the simulated whole-brain model. In what follows, we show step by step how to build the BVEP model for a particular patient in order to fit the constructed brain model against insilico data and validate our inference. The accuracy and the reliability of the estimations are validated by several convergence diagnostics and posterior behavior analysis.
Individual patient data For this study, we selected two patients: a 23 year-old female with drugresistant occipital lobe epilepsy (patient 1), and a 24 year-old female with drug-resistant temporo-frontal lobe epilepsy (patient 2). The patients underwent standard clinical evaluation, details of which were described in a previous study  (Proix et al. , 2017). The evaluation included non-invasive T1-weighted imaging (MPRAGE sequence, repetition time = 1900 ms, echo time = 2. 19 ms, 1. 0 x 1. 0 x 1. 0 mm, 208 slices) and diffusion MRI images (DTI-MR sequence, angular gradient set of 64 directions, repetition time = 10. 7 s, echo time = 95 ms, 2. 0 x 2. 0 x 2. 0 mm, 70 slices, b-weighting of 1000 s mm -2 ). The images were acquired on a Siemens Magnetom Verio 3T MR-scanner.
Network anatomy The structural connectome was built with a reconstruction pipeline using generally available neuroimaging software (see Fig. S1 ). The current version of the pipeline evolved from a previously described version  (Proix et al. , 2017). First, the command recon-all from Freesurfer package  (Fischl, 2012)  in version v6. 0. 0 was used to reconstruct and parcellate the brain anatomy from T1-weighted images. Then, the T1-weighted images were coregistered with the diffusion weighted images by the linear registration tool flirt  (Jenkinson et al. , 2002)  from FSL package in version 6. 0 using the correlation ratio cost function with 12 degrees of freedom. The MRtrix package in version 0. 3. 15 was then used for the tractography. The fibre orientation distributions were estimated from DWI using spherical deconvolution  (Tournier et al.
Synthetic data sets and model inversion In order to validate the inference using BVEP, we take advantage of and MEG with a wide range of clinical applications from Alzheimer disease  (Zimmermann et al. , 2018) , chronic stroke  (Falcon et al. , 2016)  to human focal epilepsy  (Jirsa et al. , 2017). In this study, we used TVB to reconstruct the personalized brain network model. In order to validate the inference on spatial epileptogenicity, we simulated epileptic seizures for two patients: one simulation with the seizure spread to all brain nodes specified as PZ (patient 1), and another with the seizure spread to some of the brain nodes specified as PZ (patient 2). These data sets were generated using two different structural connectivity matrices and distinct spatial map of epileptogenicity.
Discussion This technical note presents a probabilistic framework namely the Bayesian Virtual Epileptic Patient (BVEP) to infer the spatial map of epileptogenicity for developing a personalized large-scale brain model of epilepsy spread (cf. To demonstrate the potential functionality of the BVEP in prediction of seizure initiation and propagation, we simulated simple and complex seizure spread using different spatial maps of epileptogenicity (cf. Fig. S2 ). We have used these synthetic data for fitting, since given the ground-truth of model parameters, we can use the standard error metrics such as confusion matrix, posterior shrinkages and posterior z-scores to validate the accuracy of the estimations, thus to evaluate the performance of the proposed approach. Our results demonstrated that in both synthetic data sets, by inverting the large-scale brain network model with the help of PPLs (Stan/PyMC3), we can achieve a remarkable similarity between the simulated and the predicted seizure activity regarding the initiation, propagation and termination.