dataesr/scientific-paragraphs-categorization huggingface dataset:
RESULTS In this section we first report statistics about our participants and group (section 5. 1), before detailing how participants perceived their avatars in the immersive virtual store (section 5. 2). section 5. 3 is dedicated to an analysis of how participants evaluated the products proposed in our immersive virtual store. section 5. 4 reports scores of the embodiment and presence questionnaires while section 5. 5 investigates participants' shopping behavior. We computed Kolmogorov-Smirnov Z values to assess data normality (see details in the Supplementary Material). Our data follow a normal distribution except for questions M2 (about tastiness of the products bought by participants) and M4 (products sustainability) of the M questionnaire. Note that to compare our groups, we systematically performed analyses of variance (ANOVAs using Bonferroni corrected alpha values) using a confidence interval of 95%.
Shopping Behavior Previous work showed a negative correlation between products bought and thus money spent in (real) supermarkets and BMI, see  Lear et al. (2013). Unlike them, we did not detect any significant difference in our immersive virtual supermarket. This could obviously be due to the slightly different buying processes in virtual and real stores we mentioned previously. However, we performed additional statistical tests in order to understand our participants' behavior during the shopping task in the immersive virtual store. In order to do so, we computed correlations and regressions between shopping data and different aspects of our groups of participants. First of all, we investigated the relationship between participants' BMI and their shopping behavior (number of products bought).
Apparatus Stimuli were presented using MATLAB (Mathworks) and the Psychtoolbox extensions  (Brainard, 1997; Pelli, 1997; Kleiner et al. , 2007)  on a 32 Display++ LCD monitor (Cambridge Research Systems, Rochester, United Kingdom) with a refresh rate of 120 Hz and resolution 1920×1080, with a gray background, mean luminance 60 cd/m 2. Participants sat 57 cm from the screen with their chin on a chin rest.
Stimuli Four gray-scale faces (two male and two female), with cropped hair and neutral expressions, were created with Daz software 1. Observers were presented with two versions of each face, one rotated 15 • to the right and the other 15 • to the left, an example is shown in Figure  2. To control for any effect of stimulus asymmetry, on half the trials, a rightward oriented face was presented by vertically flipping the leftward oriented face and similarly, a leftward oriented face was presented by vertically flipping a rightward oriented face. Eye deviation was manipulated by replacing the original eyes with realistic counterparts that could be moved according to precise angular coordinates. The whole-head stimuli were presented to fit in approximately 400×400 pixels, subtending approximately 14×14 degrees of visual angle, and are the same as used in  Balsdon and Clifford (2017).
Analysis Data processing was conducted in the same manner as Experiment 1 for the single-interval l/r task and the two-interval task. A slightly more complex approach was required for the single-interval y/n task: The proportion of 'direct' responses at each gaze deviation was fit with the difference from two logistic functions (one that would correspond to increasing 'leftward gaze' responses with more leftward gaze, and one that would correspond to increasing 'rightward gaze' responses with more rightward gaze, had the participants been asked to identify the direction of indirect gaze), as shown in Figure  4. The logistic functions were constrained to have the same slope and be equidistant from the peak of the proportion of direct responses. Four parameters could therefore describe the proportion of direct responses in both head orientations: the peak of the proportion of direct responses in each head orientation (two parameters), the distance between the means of the two logistic functions, and the slope of the logistic functions.
Gene Expression Cells were lysed in RLT Plus buffer (QIAGEN) and RNA was extracted using the RNeasy Plus Mini Kit (QIAGEN). After reverse transcription with the Reverse Transcription Kit (Fluidigm), cDNA preamplification was carried out as recommended by the supplier (Fluidigm). Finally, samples and primers (Delta Gene assays from Fluidigm) were loaded on a chip according to manufacturer's instructions and the chip was prepared with the IFC controller MX. Data were acquired with the Biomark system (Fluidigm). Results were normalized to endogenous control GAPDH and expressed using the DC T method.
RNA Sequencing RNA sequencing data were generated on the Illumina HiSeq platform and read aligned to human hg19 genome. Statistical analysis was performed for genes in R (R version 3. 4. 3). Genes with low counts were filtered out according to the rule of one count per million (CPM) in at least one sample. Library sizes were scaled using trimmed mean of M values normalization and log-transformed into CPM (EdgeR package version 3. 20. 8) (Robinson et al. , 2010). Differential expression was computed with the limma-trend approach  (Ritchie et al. , 2015)  by fitting the compared samples into one linear model. Moderated F test was applied and post-hoc classification performed. The adjusted p value was computed by the Benjamini-Hochberg method, controlling for FDR. The library construction, sequencing, and statistical analysis were performed by the Genomic Technologies Facility of the University of Lausanne.
Bead-Based Immunoassays After stimulation of astrocytes for 5 days, supernatants were harvested, centrifuged for 10 min at 2,000 3 g and stored at À20 C until use. All cytokine and growth factor concentrations were measured using multiplex bead-based immunoassays (Affymetrix and R&D Systems, detailed in Supplemental Information) according to the manufacturer's instructions. Data were acquired with Bio-Plex 200 reader (Bio-Rad) and analyzed using the Bio-Plex Manager software (version 5. 0).
Statistical Analysis and Heatmap Representations Statistical analyses were performed with GraphPad Prism software version 7. 03. Paired non-parametric Wilcoxon test was performed to compare groups two by two. The differences among three groups or more were tested using the paired non-parametric Friedman test, followed by post-hoc Dunn's multiple comparison test if significant. A p value <0. 05 was considered significant. Principalcomponent analyses were performed using the prcomp module of RStudio software version 0. 99. 902. For heatmap representations (Figures  5  and 6 ), zero was set as minimum and the highest value was set as maximum after exclusion of outliers (represented in dark red on the graphs) determined by ROUT method. C. M. performed the experiments. L. E. K. and A. L. P. provided the technique for reprogramming erythroblasts into hiPSCs and revised the manuscript.
ACKNOWLEDGMENTS The authors thank  Geraldine Le Goff  and  Alexandra Garcia , as well as the  ARSEP , for helping in enrolling patients and obtaining blood samples. Dr. Florence Niel-Bu  ¨tschi and  Mrs Anne-Claude Magnin , from the  Constitutional genetics laboratory of the University Hospital of Lausanne  (CHUV) performed karyotype analyses. Images were acquired at the  Cellular Imaging Facility of the University of Lausanne. This work was supported by grants from the  Swiss National Science Foundation  ( 320030_159997 ) and the  Swiss MS Society ; and partially supported by the  French National Health Institute (INSERM) , and the  AFM-Telethon.
Materials and methods Students were recruited from six universities: University of Oxford, UK (51°45' N, 1°15' W); University of Groningen, The Netherlands (53°13' N, 6°33' E); LMU, Munich, Germany (48°8' N, 11°3 4' E); University of Western Australia, Perth, Australia (31°57' S, 115°51' E); Monash University, Melbourne, Australia (37°48' S, 144°57' E) and University of Auckland, New Zealand (36°50' S, 174°44' E). Students were asked to complete the online version of the MCTQ twice, in May and October of 2010, to control for seasonal influences. Overall, 13 299 individuals completed the MCTQ online. Over half of the participants were excluded from the analysis (see section on 'Data processing'). 6 441 students (mean age 21. 5 ± 2. 2 years, 67. 5% female, see Table  1  for group demographics) were included in the analysis.
timeanddate. com/worldclock). "Daily irradiances" for the collection periods, for both May and October 2010, were obtained from three sources on an hourly basis. The data for Oxford, Groningen and Munich were provided by Dr Lucien Wald (MINES, ParisTech), obtained from Meteosat satellite images and converted to data maps of solar radiation using the Heliosat-2 method  (Rigollier et al. 2004 ). The data for Perth and Melbourne were obtained from the Australian Bureau of Meteorology, again derived from satellite images processed by the Australian Bureau of Meteorology. Finally, the data for Auckland were obtained from the New Zealand Meteorological office based on readings from its ground station in Auckland. For all daily irradiance, the data represent light intensity experienced at ground level, taking into account weather conditions, either through processing of satellite data or as data taken at ground level.
In a sample of approximately 200,000 individuals from primarily Central Europe and North America, spending more time outside was associated with an earlier chronotype  (Roenneberg and Merrow 2007; Roenneberg et al. 2015). However, when age was taken into consideration, 15-20 year olds did not show a significant correlation between time spent outside and chronotype, and 20-25 year olds had only a weak correlation  (Roenneberg et al. 2015). Our sample of over 6000 students (17-26 years) falls across these age ranges and also differs from the Roenneberg sample (MCTQ database) in several important aspects. A key difference is the work status of the populations studied: all individuals within our sample are university students. However the general population sample from the MCTQ database would have included individuals who were studying at school or university, or working.
It is possible, therefore, that imposed work schedules could result in more morning vs. evening light exposure in the general MCTQ population. In addition, the data for the current study was collected exclusively in May and October, whilst the general population sample was collected all year round, which might also have an impact upon the timing of light exposure. The timing of light exposure has a differential effect upon circadian phase: early light exposure advances the cycle whilst light late in the internal day delays circadian phase  (Czeisler et al. 1989; Khalsa et al. 2003 ). In our student population, we found that longer time spent outside the later chronotype, which would suggest that our population was exposed to more phase delaying evening light than phase advancing morning light.
Serial dilutions of each studied transcript were used to determine the amplification efficiency of each target and housekeeping gene (TBP or RPLP0). Data were obtained by analysis with BioRad MFX Software 2. 0 and are presented as the fold-change in target gene expression normalized to the internal control gene. The average threshold cycle (CT) was calculated for both the target gene and housekeeping genes, and ΔCT was determined as [the mean of the triplicate CT values for the target gene]-[the mean of the triplicate CT values for TBP or RPLP0]. ΔΔCT represented the difference between the paired samples, as calculated by the formula ΔΔCT = (ΔCT of sample-ΔCT of control).
Acknowledgments We are very thankful to Francoise Raynaud,  Dolorès Jouy ,  Emmanuel Comoy ,  Jerome Delmotte ,  Elodie Sanfins  and  Laure Caruana  in our laboratory,  Ouerdia Arkoun  ( Sup'Biotech ) for the statistical analysis support provided, and  Charlotte Leuxe  ( CEA ) for the contribution to the MSD analysis. This work was supported by the  French national PIA2 program  under contract No. P112331-3422142  ( 3DNS  project) and the  EU Joint Programme Neurodegenerative Diseases Research  (JPND; 3DMiniBrain).
4] for at least 30 min at room temperature (RT). Membranes were incubated overnight with primary antibodies diluted in TBS containing 0,1% Tween 20 (TBS-T) and 5% non-fat dry milk. The antibodies and their final concentration used were: anti-pro-BDNF 1:500 (Millipore), rabbit anti-BDNF 1:1000 (Abcam), rabbit anti-TrkB 1:1000 (Abcam), anti-pTrkB (Abcam), rabbit anti-PSD95 1:1000 (Cell Signaling), mouse anti-pERK 1,2 1:500 (Santa Cruz), rabbit anti-p75NTR 1:20000 (Abcam), rabbit anti-pERK5 1:1000 (Millipore), and rabbit anti-actin 1:2000 (Sigma). Specie-specific HRP conjugated IgG (1:10000; Vector Laboratories) were employed as secondary antibodies. The obtained chemiluminescent signals were visualized by ECL SuperSignal West Pico PLUS (Thermo scientific) and normalized to actin by using ImageJ software.
z-stack imaging series were acquired and stored for further analysis. Dendrites were considered "proximal" when within 50 μm from the soma. Distances from the neuronal body were measured with the Zeiss proprietary ruler tool. 3-dimensional reconstructions were obtained from stacked images and analyzed with the Fiji 3D Object Counter 94 function. Once images were corrected for artifacts (i. e. : fluorescent particles unrelated to the analyzed neuron/region) and background subtracted, the plugin counted the number of the selected 3D particles present in the stack and automatically provided the volume and the surface area of each object. Spine lengths and diameters were, instead, evaluated with a semi-automated approach with the Reconstruct software  95.
Acknowledgements The authors are in debt with  Angela Maria Vergnano  for the helpful discussions and the critical revision of the manuscript. The authors thank  Carlo Corona  for helping with the gelatin zymography as well as  Annalisa Nespoli  and  Manuela Iezzi  for helping with brain cryosectioning. SLS is supported by research grants from the  Italian Department of Education  ( PRIN 2011 ;  2010M2JARJ_005 ) and the  Italian Department of Health  ( RF-2013-02358785  and  NET-2011-02346784-1 ).
Rs-fMRI Resting state fMRI datasets were preprocessed using an existing pipeline for removal of unwanted confounds from the time series  (Zerbi et al. 2015) , with modifications  (Sethi et al. 2017). After artifact removal (see Supplementary Material) and despiking  (Patel et al. 2014) , datasets were band-pass filtered (0. 01-0. 3 Hz), coregistered to the skull-stripped T2-weighted images and normalized to the AMBMC template (www. imaging. org. au/AMBMC) using ANTs v2. 1 (picsl. upenn. edu/ANTS).
Development of Functional Connectivity Networks We first employed a data-driven independent component analysis (ICA) approach across all animals (124 rs-fMRI datasets) and identified 16 meaningful resting-state networks (RSNs), which were named according to the ontological area of their maximum Z-score intensity (Fig. 1 ). For each of these RSN, we estimated a surrogate measure coupling strength at the voxel level using a dual regression approach  (Filippini et al. 2009 ). We found an overall genotype effect: Fmr1 -/y mice differed from their wildtype littermates mainly in lateral striatum and auditory/associative cortical networks. The only differences between CNTNAP2 -/-and control littermates were found in small clusters in the ventral striatum and ventral hippocampus (Fig. 2A-C ). Next, we derived an index of coupling strength at the network level by averaging the Z-scores from the group-mean RSN masks (threshold at the 75th percentile, Fig.
This age-dependent effect was also found in our structural data, in which a reduction of fiber coherence in the cingulum was observed in adult CNTNAP2 mice compared with wildtype littermates. Anatomically, this single observation is particularly interesting, as it strengthens the idea that structural and functional deficits in these mice primarily affect the limbic system. Furthermore, in CNTNAP2 -/-mice we found a reduction in the axonal inner diameter in the corpus callosum, suggesting the presence of an underlying white-matter pathology. Taken together, these data strongly indicate a role for CNTNAP2 in shaping connectivity, particularly between areas of the limbic system, which may have important implications for the appearance of ASD-like behavioral features later in life  (Penagarikano and Geschwind 2012).
2014). At the methodological level, it is important to consider that our results were collected in a lightly anesthetized state. As previously demonstrated in rats  (Bettinardi et al. 2015)  and monkeys  (Barttfeld et al. 2015) , modulating the depth of anesthesia correlates with changes in BOLD variability and reduces the overall connectivity strength. Thus, data acquired with any level of anesthesia cannot be fully generalized to the awake state, although it may have the positive effect of removing confounding BOLD fluctuation due to the environment or unconstrained cognitive shifts while awake. In this study, we took several measures to ensure that the levels of anesthesia were kept as consistent and low as possible, that is, we used mechanical ventilation to maintain the same tidal volume and blood oxygenation throughout the experiment, combined with continuous intravenous infusion of medetomidine and low dose of isoflurane (0.
Figure1. Selected brain networks. From a group-level independent component analysis of all our 124 resting-state fMRI datasets we selected 16 meaningful networks, many of which bear a striking resemblance to analogous human networks underscoring the high translational value of rs-fMRI across different species. Brain networks are overlaid with an anatomical template in a graphical 3D representations (A) and split according to their macroscopic ontology (B).
Genetically, the disease is also highly heterogeneous, with more than 100 defective genes reported today. 3 4 n our laboratory, before the advent of next-generation sequencing (NGS), the screening strategy was based on Sanger sequencing of candidate genes, except for the PMP22 duplication, following the recommendations of the French Network of Molecular Diagnosis Laboratories for NeuroMuscular Diseases (http://www. anpgm. fr/), who had set up decision trees based on data from the literature. The screening of one specific gene was determined by clinical, genealogical and electrophysiological criteria. For all patients, the 1. 5 Mb duplication at chromosome 17p11. 2 encompassing the PMP22 gene, the most frequent cause of CMT, was first achieved by multiplex ligation-dependent probe amplification (MLPA).
Computational analysis After capturing enrichment and sequencing, raw data were converted to Fastq files, aligned to the reference sequence of the human genome (University of California Santa Cruz (UCSC) hg19/GRCh37), and annotated, using the Ion Proton platform integrated workflow (Thermo Fisher Scientific, USA). The obtained variant call format and binary alignment map (BAM) files were used for variant search as described below. In a diagnosis settings, we realised a targeted analysis of the NGS data, focusing on a list of 81 IPN genes (see online supplementary table  1 ). The data were filtered, using an 'in-house' tool for variant annotation and Filtering Variant Annotation and Filter Tool (https:// varaft. eu/ download. php) 5 : (1) variants with allele frequencies <1% in the Exome Aggregation Consortium (ExAC) data set (http:// exac.
broadinstitute. org/) were removed, (2) the remaining variants were filtered based on their type and genomic localisation, thus, synonymous, intronic, variants in intergenic, 3' and 5' Untranslated Region (UTR) regions were discarded. In order to predict the deleterious effect of the identified sequence variations, different bioinformatics tools were applied, such as MutationTaster (http://www. mutationtaster. org/),  6  Sorting Intolerant From Tolerant (SIFT) (http:// sift. bii. a-star. edu. sg/),  7  PolyPhen-2 (http:// genetics. bwh. harvard. edu/ pph2/)  8  and Universal Mutation Database (UMD) predictor (http:// umd-predictor. eu/). 9 n order to check whether variants had already been inventoried and classified, we looked up in Online Mendelian Inheritance in Man (OMIM) (https://www. ncbi.
sanger sequencing Variants found by NGS were confirmed and segregated by Sanger sequencing. In our previous sequential gene by gene analysis, the complete coding sequence of genes were PCR amplified and sequenced by fluorescent Sanger sequencing. In both situations, genomic and cDNA sequences of the genes were obtained from the UCSC Genomic Browser, February 2009, human reference sequence (GRCh37). Primers used for PCR amplification were designed using Primer3 software (http:// frodo. wi. mit. edu) to amplify the region surrounding the candidate DNA variations. PCR products were purified by mixing with a volume ratio (1/8) (36 uL) of AMPure beads (Beckman Coulter, USA) according to the manufacturer's instructions and both strands were sequenced using the Big Dye Terminator V. 1. 1 Cycle Sequencing Kit (Applied Biosystems).
Sequence reactions were purified on Sephadex G50 (Amersham Pharmacia Biotech, Foster City, California, USA) and capillary electrophoresis was performed on Genetic Analyser ABI3500XL (Life Technologies, USA). Electrophoregrams were analysed on the Sequence Analysis Software V. 5. 2 (Applied Biosystems) and aligned with the reference sequence using Sequencher V. 5. 4. 6. results Among the 123 patients, 28 (23%) had CMT type 1, 64 (52%) had CMT type 2, 11 (9%) had dHMN, 9 (7%) had HSAN, 8 (6,5%) had intermediary CMT and 3 (2,5%) had CMT without clinical information. Table  1  sums up the phenotypic features of the patients for whom we identified a mutation or a candidate variant (table  1A  for patients with CMT1, table 1B for patients with CMT2 and table  1C  for patients with others forms of IPN).
Among these 52  +, presence; -, absence; AD, autosomal-dominant; AR, autosomal recessive; CMAP, compound muscle action potential; CMT1, Charcot-Marie-Tooth disease type 1; ENMG, Electroneuromyogramm; F, female; IPN, inherited peripheral neuropathy; MNCV, motor nerve conduction velocity; NA, not available; NR, not recordable; M, male; SPO, sporadic. Open access new variants, we were able to confirm the pathogenicity of 25, based on: (1) their low frequency in the ExAC database, (2) compatible phenotype, (3) segregation in the family study and (4) in silico pathogeneicity prediction. However, we were unable to establish the pathogenicity in 11 patients requiring further explorations at either clinical or genetic levels (see table 2A,B). Among the 28 patients affected with demyelinating forms, 16 (57%) had a pathogenic variant, 3 (11%) had a potentially pathogenic variant and 9 (32%) remained negative.
Among the remaining 43 negative cases, 17 were analysed by targeted analysis of 81 genes from NGS data and for 10 of these 17 patients, we identified the molecular defect in a gene unexplored by Sanger sequencing. Between 2015 and 2017, a pathogenic variant was found in 49 patients (40%) by targeted analysis of 81 genes from NGS data. During the same period, the PMP22 duplication was identified by MLPA in 67 patients. In conclusion, in our laboratory, we were able to reach a diagnosis in 23% of patients using Sanger sequencing and in 40% of patients when using targeted NGS of 81 IPN genes. When including PMP22 duplication, 63% of patients with CMT1 were able to get a diagnosis with Sanger analysis against 87% with NGS. In parallel, we were able to make a diagnosis for 17% of CMT2 patients with Sanger analysis against 36% with NGS.