dataesr/scientific-paragraphs-categorization huggingface dataset:
| Data sets and functional laterality measurement A total of 67 participants (33 with RHLD) were included in the present study, all of whom gave written informed consent. All RHLD subjects except one were left-handed (Edinburgh Handedness Inventory [EHI] median = -87. 50), while the controls included 14 left-handed and 20 right-handed participants (EHI median = 76. 39; this composition allowed us to perform post hoc analysis using control groups of different handedness, see below). Summary statistics for language laterality measures and handedness are provided in Table  1 , Figure  2  and Figure  S2. The subjects in this study were recruited from two separate sources, that is, the BIL&GIN data set (France) and the GOAL data set (Belgium). We studied hemispheric lateralization for three language tasks, namely production, reading and listening, using fMRI to calculate Global Hemispheric Functional Laterality Indexes (HFLIs), as described previously.
33 Each participant underwent a slow event-related functional MRI protocol including three runs, one for each language task, presented in a random order. The three runs followed the same structure, alternating execution of the task at the sentence level and at the word fMRI data analysis was performed using the SPM5 software (www. fil. ion. ucl. ac. uk/spm/). Scans of each participant and each run were normalized to our site-specific template, corrected for motion during the run, and then warped into the standard montreal neurological institute (MNI) space using a tri-linear interpolation, with subsequent smoothing using a 6-mm full width at half maximum (FWHM).
| BIL&GIN Gaussian filtering. We then computed for each participant the BOLD signal difference maps and associated t-maps corresponding to the "sentence vs word-list" contrast for the production, reading and listening runs. For each individual and each language task, we computed a HFLI using the LI-toolbox applied to the individual contrast t-map of the considered language task. 34 two-step procedure was then implemented to select RHLD subjects and typically lateralized controls. We first selected the 10 individuals previously identified as strongly right-lateralized in this data set using a stringent criterion based only on language production (HFLI for language production < -50). 3 Then, to identify individuals exhibiting a right-lateralized profile in all three language conditions, but who may have been overlooked in the first-step, we modeled the joint distribution of the three HFLI using a mixture of 3D Gaussian functions and applied a robust consensus clustering approach.
| BIL&GIN Whole genome sequencing (WGS) of the 39 BIL&GIN subjects was performed using Illumina's HiSeq technology by the genomics research organization and service company BGI (Hong Kong/Shenzhen) (https:// emea. illumina. com/systems. html). Thirteen additional subjects of European descent, who were not part of the present study, were also sequenced at the same time, and their data processed together with the 39 through preprocessing and variant calling stages (as some of the processing steps below benefit from being run on the greatest sample size available; a minimum of 30 is recommended  38  ). Sequencing was performed at 20 times average coverage depth, with 90 base pair (bp) paired-end reads for 11 of the RHLD subjects and 14 controls, and 150 bp paired-end reads for six RHLD subjects and eight controls.
Raw reads were cleaned by excluding adapter sequences, reads with low-quality bases for more than 50% of their lengths, and reads with unknown bases for more than 10% of their lengths. Clean reads were mapped onto the human reference genome (hg19) using the software Burrows-Wheeler Aligner. 39 Bam files were sorted using SAMtools v1. 2  40  and polymerase chain reaction (PCR) duplicate reads were marked using Picard v1. 134. Re-alignment around indels (insertion/deletions), and base quality control recalibration was performed using the Genome analysis toolkit software (GATK v3. 5). 41, 42 Genetic variants were called using the HaplotypeCaller (HC) tool of GATK (v3. 5). HC was run separately per sample using the "-ERC GVCF" mode, and then merged together using the GenotypeGVCFs tool, as recommended in the GATK best practices.
| GOAL The genomics company Novogene (Hong Kong/Shenzhen) performed WGS on the 28 samples of the GOAL data set using Illumina's HiSeq Xten technology, and paired-end sequencing with reads of 150 base pairs and 30x sequence depth. The same pipeline as that applied to the BIL&GIN data was used for alignment (build 37), variant calling, annotation and filtering (but updated to SAMtoolsv1. 3. 1, Picard v2. 0. 1, GATK v4. 0. 1. 1 and Gemini v20. 0. 1, as sequencing of the GOAL subjects was performed later). The variant calling and VQSR steps were carried out together with data from 34 European-descent subjects who were not part of the present study, again because these steps benefit from a larger number of subjects. These variants were then normalized using the software tool vt normalize (v0. 5772-60f436c3)  43  and variants belonging to any VQSR sensitivity tier over 99% were excluded.
| Stratification and inbreeding Within the BIL&GIN and GOAL data sets separately, population structure was assessed by calling genotypes from the sequence data for selected sets of common variants (BIL&GIN: 77 553 variants, GOAL: 41 273 variants) spanning the autosomes. These were highconfidence single-nucleotide polymorphism (SNP) sites identified by the 1000 Genomes Project, 1000G_phase1. snps. high_confidence. hg19. vcf. gz with minor allele frequencies (MAFs) > 10% in each data set,  38  and had been pruned to be in low linkage disequilibrium (LD) with one another using the program PLINK (v1. 9) (maximum LD r-square 0. 2). 44, 45 Multidimensional scaling was used to visualize the major dimensions of genome-wide variability (Figure  S1 ). None of the first five dimensions was associated with the RHLD vs control distinction in either of the data sets (all |T| < 1, P >.
33). Inbreeding was assessed with the F coefficient estimate within each data set using PLINK (v1. 9). 45 The measure was not associated with the RHLD vs control distinction in either data set (both |T| < 1, P >. 39). Note that common genetic variants were only used for the purposes of assessing population stratification and inbreeding within the data sets, whereas the rest of the study was focused on rare genetic variation, which has the potential to involve highly penetrant effects.
gs. washington. edu/EVS/, http://www. internationalgenome. org/home). Within the BIL&GIN and GOAL data sets separately, any variants present in at least 19 participants (case or control) were excluded as they are likely to be platform-specific errors or else common variants not previously detected by other sequencing platforms or protocols, and would necessarily be present in at least two control subjects in BIL&-GIN or three controls in GOAL (hence unlikely to be high-penetrance mutations for RHLD).
| Monogenic mutation models Recessive: Here, we considered only homozygous or compound heterozygous mutations as potentially trait-causal. For screening purposes, compound heterozygosity was assigned when a given gene had at least two different mutations, although allelic phase information was not usually available due to the limited sequence read lengths. Variants were excluded when they had MAF ≥ 10% on the basis of on-line population databases (see above). At 10% MAF, assuming Hardy-Weinberg equilibrium, the variant would be present in homozygous form at 1% in the population, that is, roughly equal to the RHLD frequency in the population. In the case that 50% penetrance might arise from L-R randomization, as has been observed for mutations which cause situs inversus with PCD,  51.
| Gene-level testing The BIL&GIN and GOAL data sets were combined for subsequent analysis. We first verified that the total number of mutated genes per subject did not differ significantly between RHLD and control subjects, under either the dominant or recessive model (t tests, all P >. 10). Significance for single-gene analysis was then assessed separately for individual genes and models (recessive or dominant), using the one-tailed Fisher's exact test for a 2 × 2 contingency table, for the categories "mutated" and "not mutated" in 33 RHLD subjects and 34 controls. The minimum number of mutated RHLD subjects to achieve a nominally significant P value (ie, less than. 05) was 5, that is, if a gene would be mutated in five out of the 33 RHLD subjects and none of the 34 controls, that gene would show a nominally significant P value of association with RHLD, as a putative majorgenetic effect (P value =.
| Association with handedness within the UK Biobank Because the large majority of people with RHLD are left-handed, any monogenic contributions to RHLD would likely also be strongly penetrant for left-handedness. We checked whether a specific mutation of interest in the gene TCTN1, rs188817098, which we initially considered a potential candidate for causing RHLD in some subjects (see Results), is also associated with handedness the UK Biobank cohort data. There were 330 474 subjects (32 367 left-handed) available for this analysis. In this data set, rs188817098 had been directly genotyped and was in Hardy Weinberg equilibrium (P = 1), and the minor allele C had a frequency of 0. 001305. Handedness (UK biobank field ID: 1707. 0. 0) was self-reported and coded for the present purposes as "left-handed" or "right-handed", as described elsewhere.
54 We performed association analysis of rs188817098 with handedness using the program BOLT-LMM (v2. 3) which uses linear mixed effects regression under an additive genetic model. 55 The top 40 principal components capturing genetic diversity in the genome-wide genotype data, calculated using fastPCA  56  and provided by the UK biobank,  57  were included as covariates to control for population structure, as well as sex, age, genotyping array, and assessment center. The UK Biobank data were obtained as part of research application 16 066, with Clyde Francks as the principal applicant. The data collection for the UK Biobank has been described elsewhere. 58 Informed consent was obtained by the UK Biobank for all participants. 6 | RESULTS.
| Monogenic mutational models We focused on mutations in the 33 RHLD cases which are known to be relatively rare in the general population on the basis of large-scale genetic databases and predicted to disruptively affect protein sequence, while not being mutated in a set of 34 control subjects (see Methods). As noted above, a given gene would need to be a monogenic cause for at least 10 or 11 of the 33 RHLD cases in this study, and not mutated in controls, to be detected at a significant level after multiple testing correction. There were no genes which met this threshold, under either the dominant or recessive models. Under a recessive model, no gene was even nominally significant (ie, showed unadjusted P <. 05), which could have arisen from being mutated in as few as five RHLD cases and no controls.
In the dominant model, TCTN1 was the only nominally significant gene (P =. 0267 before multiple testing correction), with five RHLD cases and no controls having heterozygous mutations (Table  2 ). TCTN1 encodes a member of a family of secreted and transmembrane proteins and is a component of the tectonic-like complex, which forms a barrier between the ciliary axoneme and the basal body. 59  Rs188817098 was also associated with handedness in the UK biobank data set (P =. 034), with the minor allele C (frequency 0. 001305) associated with left-handedness (odd ratio = 1. 24). However, this modest effect does not seem compatible with a role of this variant as a highly penetrant cause of RHLD and left-handedness.
As noted in the introduction, non-leftward language dominance has previously been shown to have a heritability of roughly 30%, although the trait definition in that study included ambilateral individuals in addition to RHLD. 10  As RHLD is mostly found in left-handed people,  3  and comprises roughly 10% of the left-handed population, then any highly penetrant genetic effects on RHLD would presumably also be strongly associated with left-handedness. One individual gene, TCTN1, carried rare, protein-altering mutations in five RHLD cases and no controls. Three of these cases carried the same rare variant, and the very large UK Biobank data set, comprising hundreds of thousands of participants, allowed us to test this rare variant for association with left-handedness. (No functional imaging measures of language laterality were available in the UK Biobank to study RHLD in that data set.
Given that common variants of the microtubule-associated gene MAP2 have recently been associated with left-handedness by large-scale GWAS,  30, 31  our findings here in relation to RHLD may be broadly concordant, insofar as they also implicate the cytoskeleton in the developmental origins of human brain laterality. The possible link of RHLD to actin cytoskeleton genes will need to be replicated in larger independent data sets. Within the present study, we combined the BIL&GIN and GOAL data sets to maximize the power to detect genetic effects on RHLD, although the functional tasks used to define RHLD differed between these two data sets: hemispheric dominance was defined using a contrast at the sentence level in BIL&GIN, and a word-level contrast in GOAL (see Methods). However, we are not aware of a large-scale data collection in existence, or currently underway, in which a harmonized phenotypic measure of RHLD will become available and which would be wellpowered for GWAS.
it is theoretically possible that a single causal variant in a gene could have up to 14% population frequency under a recessive model and Hardy-Weinberg equilibrium, and still be consistent with a trait frequency of 1%, if it was the only variant involved and caused all cases of the trait. However, allelic and genetic heterogeneity are typical for monogenic traits. Therefore a MAF threshold of 10% under a recessive model is an inclusive rather than strict filter. Variants not present or with no MAF information in the population databases were retained. There were on average 43 recessively mutated genes per subject for the BIL&GIN-90 bp protocol (range: 31-61), 64 per subject for the BIL&GIN-150 bp protocol (range: 55-77), and 45 per subject for the GOAL data set (range:33-64). Integrative Genome Viewer (IGV v2.
3. 55) was used to visualize the possible compound heterozygous mutations, and genes carrying these were discarded when both mutations were definitely present on the same allele (ie, "in phase") on a given sequence read. Dominant: Here, we considered heterozygous or homozygous mutations as potentially trait-causative. Variants were excluded as potentially causative when they had population MAF ≥ 1% in the population databases, on a similar logic as for the recessive model above, but appropriate for allelic dominance and the frequency of RHLD in the population (roughly 1%). Variants not present or with no MAF information in the population databases were retained. There were on average 196 genes per subject for the BIL&GIN-90 bp protocol (range: 154-215), 240 per subject for the BIL&GIN-150 bp protocol (range: 208-268), and 262 per subject for the GOAL data set (range: 229-300).
1  and animation file downloadable from https://data. mendeley. com/datasets/y747bdb42h/1). Crucially, these responses are also characterized by state-dependent changes in timeefrequency features  [19]  and overall spatiotemporal complexity both at early and late latencies  [17]  that can be fully replicated by recording the cortical responses to single-pulse intracranial electrical stimulation  [20, 21] , which is not associated with any auditory or somatosensory response. Having recognized the heterogeneity of TEPs' quality reported in the current literature, future methodological studies should consider open data sharing and direct comparisons across laboratories in order to appraise the current state of the field and propose constructive exchanges to jointly define standard procedures.
In this vein, we share a dataset (see link in the supplementary material) from the original study reported in Fig. 1A   [11] , as a first opening toward the implementation of a TMS-EEG data repository. Future methodological work should certainly also include the use of realistic sham conditions (scalp and auditory stimulation) to reassess the contribution of sensory ERPs on TEPs absolute amplitude and topography in cases where the effectiveness of cortical   2019 )  [1]  are directly compared with the group average TEPs from a study that the authors take as methodological reference  [11]. Black traces represent grand averages of TEPs recorded at the EEG channel closest to the TMS coil from Figure  4  panel A of  [11] , whereas blue traces represent TEPs recorded at all EEG channels from upper panels of Figures  4  and 5  in Ref.
These factors encompass coil design, E-field orientation with respect to axons, the 3D morphology and cytoarchitectonic of the underlying cortical tissue in individual brains. In this respect, TMSeEEG is not a magic wand but more akin to an ultrasound probe that requires skilful handling in order to recover a strong signal of interest amidst layers of noise. The first duty of an ultrasound operator is to aim and orient the probe so as to recover on her/his monitor a robust echo from the target structure; when basic signal-to-noise criteria are met, only then can the actual measurement start. We all trust ultrasound for key decisions in life-threatening cases, even though we know that the same probe would only recover noise if placed on our skin by a blinded operator. Just like the ultrasound community, the TMSeEEG community is now ready to recognize this fact, to share and compare datasets, to develop standard criteria, and to focus on the development of shared real-time TEPs visualization tools and artifact-rejection algorithms [e.
iPSCs generation and human brain endothelial cells differentiation. The ED-iPSC line has been provided through the Harvard Stem Cell Institute iPS Core Facility. For the SP-iPSC line, Placenta from fibroblasts explants (Serena Pavoni, PhD, CEA Fontenay aux Roses/SupBiotech, France) was reprogrammed using the Sendai virus method until colonies began to adopt an iPSC-like morphology 15 days after transduction with OCT4, KLF4, SOX2 and C-myc (data not shown). After 6 to 10 passages on mouse embryonic fibroblasts (MEFs), iPSC lines were adapted to feeder-free conditions (Matrigel) and regularly verified for their pluripotency state. iPSC lines expressed TRA-1-60 and SSEA4 markers (Fig. 1A ). We showed the expression of the endogenous pluripotency marker genes OCT4, SOX2, REX1 and NANOG (Fig. 1B ) while no expression was shown for those genes in the primary cells.
human brain but high permeability in vitro. This could be explain by the fact that the concentration of erlotinib used in vitro induced inhibition of P-gp/BCRP expressed in the in vitro iPSC-hBBB model. Although this study needs to be extended to a larger set of radioligands with known PK parameters in human brain, our data suggest that the in vitro iPSC-hBBB model will be useful in drug screening strategies. In this context, we evaluated the permeability of a larger panel of marketed drugs across the in vitro iPSC-hBBB model. A robust and validated LC-MS/MS method was used to evaluate the ability of the in vitro iPSC-hBBB model to discriminate between CNS and non CNS drugs (Table  1 ). The mean P app value for the marketed non-CNS compounds from the apical to basal compartment was about 2.
Data were as the average ± SD from three monolayers. Monolayers were validated for sucrose permeability from A to B when the P app was below 8 × 10 -6 cm. s -1. The propranolol permeability was also tested using the same protocol with an apical tested dose about 1 µCi/mL (48 nM) of [ 3 H]propranolol in transfer buffer. Monolayers were validated for propranolol permeability from A to B when the P app was under 16 × 10 -6 cm. s -1. For the activity of efflux pumps and the asymmetry of transporters distribution of the endothelial cells, we measured the efflux of permeability studies. After checking the cell monolayer integrity, the monolayers were transferred to new plates. The buffer (150 mM NaCl, 5. 2 mM KCl, 2. 2 mM CaCl2, 0. 2 mM MgCl2, 6 mM NaHCO3, 2. 8 mM glucose and 5 mM Hepes) was added: 1.
5 mL to the basal chamber (B) and 0. 5 mL to the apical chamber (A). Experiments were performed in triplicate for each compound. The tested compounds (5-10 μM) were introduced in the donor chamber (either the apical or the basal compartment). After 60 min, aliquots were removed from the acceptor and basal chambers for drug-concentration determination by LC-MS/MS. For LC-MS/MS a sample aliquot (transfer buffer) was diluted by half with acetonitrile and then, 10 μL of this solution was injected into the analytical equipment made up a Liquid Chromatography system coupled to a triple quadrupole mass spectrometer Thermofisher TS Quantum Discovery or Waters Quattro Premier XE equipped with a electrospray ionization source. System control and data processing were carried out using Xcalibur or Masslynx software.
Study participants Participants were recruited consecutively from May 2015 to December 2016, from eight private sleep centers in France. We used a definition of a couple that relies on the following three criteria  [18] :  (1)   Before starting CPAP, patients benefited from a standardized one hour educational program including a 10-min video about OSA definitions, symptoms and the benefits of CPAP usage  [22]. Objective CPAP adherence data were downloaded at day 120 from the CPAP device's software. Couples and quality of relationship characterization: Measure of partner's involvement At CPAP initiation, without the presence of their wife/husband/partner, patients evaluated their subjective view of their marital relationship using the Quality of Marriage Index (QMI)  [23]. This was completed by additional items about the relationship including duration, family context and sleeping arrangements.
ACKNOWLEDGEMENTS We thank  Dr. Bernard Douay  for data collection and study monitoring and  Dr Alison Foote  ( Grenoble Alpes University Hospital ) for critically editing the manuscript. manuscript: TG, SB, RT, EG, JLP, Critically revising the manuscript: FJ, CV, PMB, DG, AP, JJG, JK, LK, JLP had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis All authors have read and approved the submitted manuscript.
Data analysis Image analysis was performed by using ImageJ and MATLAB (Mathworks). ROIs covering individual target and non-target cell somata were manually selected in ImageJ based on both red (ReaChR-dTomato) and green (GCaMP6s) channels. The time-lapse fluorescent signal of GCaMP6s from all ROIs was exported in MATLAB. Image frames from ROIs that are affected by the photostimulation artefacts were removed in analysis. For each ROI, the relative percentage change of GCaMP6s fluorescence was computed as ΔF/F=(F-F 0 )/F 0 , where F 0 was the average raw fluorescence signal 3-0. 2 s before photostimulation started. A cell was considered activated if the average ΔF/F 1 s after the last illumination onset was significantly larger compared to that 1 s before the first illumination onset (right-tailed paired t-test with a significance level of 0.