dataesr/scientific-paragraphs-categorization huggingface dataset:
Trials were randomly presented in each block, and blocks were randomly presented across participants. The responses of each participant were later transcribed and coded by two independent coders. They achieve a reliability of Cohen's K = 0. 94. When the two coders disagreed, a third coder listened to the response and decided which of the two proposed code was correct. The three coders were speech-language pathology students. Each response was recorded. The recording started at the beginning of the video and lasted 3 s after the end of the video. Each soundtrack was analysed to detect the syllable onset using a home-made script that detected the beginning of the slope of the amplitude of the envelope of each sound. To ensure a good detection, the experimenter explicitly asked the participants to avoid making mouth noises during the experiment.
Brain signals were recorded using Neuromag Elekta with a total of 306 channels composed of 204 axial gradiometers and 102 magnetometers. Recordings were first preprocessed using signal-space separation through Neuromag software MaxFilter. This allows removing signal coming from outside of the electrode sphere which allows removal of EOG (electrooculography) and ECG (electrocardiography) interference among other sources of noise. Originally, signals were sampled at a rate of 1000 Hz. A zerophase forward-backward high-pass Butterworth filter of order 4 was used to remove frequencies below 0. 05 Hz. Signals were then re-sampled at 250 Hz for further pre-processing stages. An IIR low-pass filter (Chebyshev Type I of order 8) with a cut frequency of 100 Hz was used to avoid aliasing. Before MEG recording, headshape was acquired for each participant using Polhemus.
After the MEG session, an individual anatomical MRI was recorded (Tim-Trio, Siemens; 9 min anatomical T1-weighted MP-RAGE, 176 slices, field of view = 256, voxel size = 1 × 1 × 1 mm 3 ). MEG data were preprocessed, analyzed and visualized using dataHandler software (https :// cenir. icm-insti tute. org/en/servi ce/servi ce-3/), the Brainstorm toolbox  71  and custom Matlab scripts. Analysis. Experiment 1, repetition task. We recorded the participant's vocal response using a microphone. No feedback was provided after each response. The response time was measured as the interval between video onset and start of the syllable repetition from the audio recording on each trial. We also assessed the identification choice made by participants, i. e. , the syllable repeated, on each trial. Experiment 2, pairing task.
For the mean response latency, we measured the interval between video and vocal response onsets, for each type of response of interest. A 3 × 1 repeated measures ANOVA was performed on response times (RTs) with Conditions (congruent, combination and fusion) as a within-subjects factor. All ANOVAs modelled the variables Speaker Gender (female and male), and Consonant Family ('bdg' and 'ptk') as fixed-factors so as to generalize the results obtained to each speaker and each consonant family tested. To compute post-hoc power analyses, we used a post-hoc computation of the likelihood to observe a significant effect given the current samples  72. MEG processing. Using structural data, brain models for each subject were build using Brain Visa Software  73. Individual brain models were mapped to the ICBM-112 brain model template for group-level analysis.
Data analysis was performed with Brainstorm 71 , which is documented and freely available for download online under the GNU general public license. The data considered (trials) started 0. 2 s before the auditory event and until 0. 8 s after the auditory input (i. e. , the first vowel /a/ in /aCa/). We only analysed trials without eye artefacts or jumps in the signal. These artifacts were first removed/reduced using Neuromag software MaxFilter, followed by a manual selection of trials by visual inspection of the MEG signals. We did not conduct the analyses at sensor level as the contribution from different sources is mixed and our aim was to describe the brain network involved in AV speech perception. Nevertheless, we performed two sanity checks on the sensor space. We verified that both the response evoked by visual stimuli and the response evoked by auditory stimuli showed usual activations in the two-dimensional topographical MEG sensor plot  74.
e. , 'ada' and 'ata' responses). Comparing the results of the three classifiers allowed us to assess profile similarity between the classifiers. By comparing the curves of the classifiers, we could determine the presence of information that specifically correspond to combination or that are shared by several conditions. In the decoding procedure, each classifier was trained to associate MEG data with corresponding stimulus conditions (for each trial, the identity of the syllable perceived), irrespective of asynchrony. The amount of relevant information in the MEG signal was evaluated by testing the accuracy of the classifier on a separate set of test data. We performed the analyses at each time point, within 1-ms non-overlapping bins. Decoding analyses were performed on each ROI with a cross-validation procedure where the classifier is trained on a subset of the data, and then the classifier's performance is evaluated on the held-out test data.
Figure5. Differences in event-related activity between conditions, in two regions of interest, i. e. the STS and the IFG (fusion > congruent conditions in blue, combination > congruent conditions in red, combination > fusion conditions in yellow). The results for the other 4 ROIs are presented in Fig. S4. Stars indicate significant Student t-test values that were estimated in each difference: fusion vs. congruent conditions in blue, combination vs. congruent conditions in red, combination vs. fusion conditions in yellow (P < 0. 05, corrected for multiple comparisons using FDR). Image made using Matlab 2019b.
Acknowledgements We thank  Jean-Luc Schwartz  for support and inspiration. We are grateful to  Alexis Hervais-Adelman  and  Valérian Chambon  for comments and useful discussions about earlier versions of this manuscript, and  Christophe Savariaux  for technical support of the video editing. This work was funded by the  Swiss National Science Foundation  ( SNF 320030_149319  and  SNF 320030_163040  to A-L. G. and  SNF P300P1_167591  to S. B. ), and by the  Fondation pour l' Audition  ( RD-2016-5  to S. B. ).
Data availability The behavioural data are available here: https ://figsh are. com/s/63c77 3e39a 239b7 2c473 and https ://figsh are. com/s/0ca3c c1025 93616 cb3b2 ; The MEG data are available here: https ://figsh are. com/s/a4ba4 83479 05b0c 0790f.
Study Design and Population The Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) multicentric and cross-sectional study included a total sample of 4356 adolescents (51. 6% females), aged 11-19 years  [18]. Data were obtained from 10 European cities located in different geographical points within Europe, during 2006-2007. The HELENA study was designed to obtain reliable and comparable data on nutrition and health-related parameters, applying standardized procedures  [19]. The HELENA study was approved by the Research Ethics Committees of each study site and followed the ethical guidelines of the Declaration of Helsinki 1964 (revision of 2000), good clinical practice, and the legislation about clinical research in humans in each one of the countries involved in the study  [20].
Written informed consent was obtained by the parents/legal guardians of all participants. Blood sampling was performed in one third of the individuals randomly selected from the total sample (N = 1172)  [19]. Inclusion of specific parameters to develop the present study (genomic, adiposity, cardiometabolic risk factors, and dietary data) provided a final number of 605 adolescents (51. 6% female) meeting the selection criteria. Information of selection procedure is displayed in a flow chart (Figure  1 ). The Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) multicentric and crosssectional study included a total sample of 4356 adolescents (51. 6% females), aged 11-19 years  [18]. Data were obtained from 10 European cities located in different geographical points within Europe, during 2006-2007.
Dietary Intake Assessment and Mediterranean Diet Score Dietary habits were determined from a self-administered, computerized, validated 24-h dietary recall called the HELENA Dietary Assessment Tool (HELENA-DIAT)  [25, 26] , a tool validated first in Flemish adolescents  [26]  and then adapted to be used in the 10 cities  [27]. Participants completed the HELENA-DIAT on two non-consecutive days within a time span of two weeks. This method has been used and recommended to assess dietary intake in European children and adolescents  [28]. In order to calculate individual usual dietary intake, the multiple source method (MSM) was used [28]. This method allows correction of dietary data for between and within individuals' variability. We used a Mediterranean diet score (MDS) based on nine single components: vegetables, fruits and nuts, cereals and roots, pulses, fish, monounsaturated/saturated fatty acids ratio, dairy products, meat, and alcohol.
Due to the multicentric design of the HELENA study, congregating participants from 10 European cities, the researchers have been provided with large datasets from diversely distributed populations across Europe. Finally, the study is focused on adolescents, a population age that is understudied and where early detection plays a key role in the development of obesity and metabolic syndrome. Little has been found in the literature using a similar approach, where most similar studies are available on adult populations. As MetS and excess of adiposity may occur at any stage from childhood to adulthood, early detection and diagnosis is fundamental to elaborate on health prevention programs among youth to effectively reduce the risk of cardiovascular diseases and T2DM  [52, 53]. At the same time, it has been previously shown that greater adherence to MD was associated with a significant improvement in overall health status among youth, suggesting the implementation of a MD dietary pattern for primary prevention of major chronic diseases  [54].
Supplementary Materials: The following are available online at http://www. mdpi. com/2072-6643/12/12/3841/s1, Table  S1 : Mediterranean diet score items specified in g/day displayed in sex-specific median intake in HELENA participants with dietary information available, Table  S2 : Main characteristics of the 21 single nucleotide polymorphisms (SNPs) included in the obesity genetic risk score (obesity-GRS), Table  S3 : Comparative analysis of the 21 single nucleotide polymorphisms (SNPs) included in the obesity genetic risk score (obesity-GRS) by sex, Figure  S1 : Interaction models between HOMA, diastolic blood pressure (DBP) and mean arterial pressure (MAP) and Mediterranean Diet (MD) according to the obesity genetic risk score (obesity-GRS) modulation in female. Obesity-GRS values  (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) (28)   Funding: Miguel Seral-Cortes, the corresponding author, has received funding from the Iberus Talent Pre-doctoral fellowships 2018, under the European Union's H2020 research and innovation programme under Marie Sklodowska-Curie grant agreement No 801586.
Pilar De Miguel-Etayo was supported by ISCIII-CB15/00043 (Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición, CIBERObn). This work was part of the HELENA Study (http://www. helenastudy. com/). We gratefully acknowledge financial support of the European Community Sixth RTD Framework Programme (contract FOOD-CT-2005-007034). The data for this study were gathered under the auspices of the HELENA project, and further analysis was additionally supported by the Spanish Ministry of Economy and Competitiveness (Grants RYC-2010-05957 and RYC-2011-09011), the Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERObn).
Acknowledgments: The autors acknowledge the  Laboratoire d'Analyse Génomique Centre de Ressources Biologiques  ( LAG-CRB) BB-0033-00071   Institut Pasteur de Lille, F-59000 Lille, France  for managing human samples. We are grateful for the support provided by school boards, headmasters, teachers, school staff and communities, and for the effort of all study nurses and our data managers.
Gravity field estimation The Bennu gravity field measurement carried out by the OSIRIS-REx mission involved several teams each using distinct combinations of software tools and data processing techniques. The Radio Science teams were based at the University of Colorado (CU) in the Colorado Center for Astrodynamics Research and at the Jet Propulsion Laboratory (JPL). The navigation and flight dynamics teams were represented by the KinetX Inc. team in Simi Valley, CA and in residence at Lockheed Martin's Waterton Campus in Denver and by an independent Goddard Space Flight Center team. The gravity estimates and other fitting data from each team were compared against each other and found to converge to the same values within the expected errors. The specific values quoted in the paper are from the JPL spacecraft and particle estimates.
Spacecraft gravity field solution To model the OSIRIS-REx spacecraft trajectory about Bennu, we used JPL's Mission Analysis, Operations, and Navigation Toolkit Environment  (60). For gravity estimate purposes, we considered the Orbital B phase of the mission, particularly the data arc between the M5B maneuver on 28 June 2019 and the M1C maneuver on 6 August 2019. This interval is well suited for gravity estimation because it was maneuver free and conducted in close proximity to Bennu (~1-km orbit radius). The Sun-Earth-Bennu angle was approximately 40° at the start of this period. The spacecraft was tracked at least 8 hours per day throughout this period. Among the DSN radiometric data (61), we processed two-way X/X Doppler using a 60-s compression rate. The Doppler measurements were corrected for ionospheric and tropospheric effects and weighted on the basis of the internal noise level of each pass, with an uncertainty floor set to 2.
Particle gravity field solution We derived a particle-based gravitational field, truncated at degree and order 9, by simultaneously estimating the gravitational field for a number of well-observed particles. This served the dual purposes of constraining the gravitational field of Bennu for follow-on geophysical studies and facilitating more reliable orbit estimation for the many particles that did not have a solid gravitational signal, i. e. , those having few detections or short data arcs or both. The detection, tracking, and modeling of the ejected particles are described in  (20) , the underlying dataset for which is available in  (20). The final gravity field estimate  (20)  was based on tracking particles with more than 30 detections and with observational arcs either more than 6 hours in length or covering more than 80% of the particle lifetime.
of 16 estimation activities. C. M. H. , V. E. H. , P. M. , and K. J. W. provided supporting discussion. M. C. N. and D. S. L. provided scientific leadership and oversight. Competing interests: The authors declare that they have no competing interests. Data and materials availability: The DSN spacecraft tracking data are available via the Planetary Data System (PDS) at https://sbn. psi. edu/pds/ resource/orex/rs. html. The optical images used for spacecraft and particle tracking are available via the PDS in the TAGCAMS bundle at https://sbn. psi. edu/pds/resource/orex/ tagcams. html. Data are delivered to the PDS according to the schedule in the OSIRIS-REx Data Management Plan, available in the OSIRIS-REx mission bundle at https://sbnarchive. psi. edu/ pds4/orex/orex. mission/document/. Observational and derived data for the particle-based gravity field are available in  (72).
RT 2 profiler PCR array Equal volumes of RNA from biological triplicates were pooled for each condition. Two hundred to 500 ng of RNA were transcribed with the RT 2 First Strand Kit (SA Biosciences, Qiagen). Synthetized cDNA was subjected to a PCR array specific for the human antiviral response (RT 2 Profiler PCR array-PAHS-122Z, SA Biosciences, Qiagen), according to the manufacturer's instructions. Data were normalized using the HPRT1 house-keeping gene and analyzed with the -2ΔΔCt method for relative quantification. According to the manufacturer's instructions, an arbitrary cut-off of 3 was applied to determine significant differences. The analysis was performed using the Qiagen Data analysis center (http://www. qiagen. com/fr/shop/genes-and-pathways/data-analysis-centeroverview-page/).
49 m software. For automated quantification (cells immunostained with antibodies directed against HuC/ HuD, TBEV-E3 and OLIG2, neurites immunostained with an antibody against βIII-tubulinand TUNELstained cells), images were acquired using the Cellomics ArrayScan automated microscope (Thermofisher Scientific) and analyzed using "Colocalization" or "Neuronal profiling" bio-applications on HCS Studio Cell Analysis Software V6. 6. 0 (Thermofisher Scientific). In all experiments, an average of 1200 (manual quantification) or 5000 (automated quantification) cells per well were enumerated. The digitized images shown were adjusted for brightness and contrast using ImageJ, without further alteration.
Nuclei were counterstained with DAPI. Scale bars = 20 μm. b Manual enumeration of GFAP-positive cells using ImageJ software. Normalization was performed relative to non-infected GFAP-positive cells at 24 hpi. c Cells immunostained with OLIG2 antibody were enumerated automatically. Normalization was performed relative to non-infected OLIG2-positive cells at 24 hpi. The results are expressed as the mean ± SD and are representative of two (oligodendrocytes) and three (astrocytes) independent experiments performed in triplicate. Statistical analysis was performed using a two-tailed unpaired t test with Graphpad Prism V6. 0. 1. ns, non-significant (p > 0. 05); *p < 0. 05 developing a stronger antiviral response to TBEV than human neurons. To validate the PCR array data and to gain further insight into the kinetics of expression of antiviral genes in each cell types, we performed RT-qPCR at 7, 24, and 72 hpi for IFN-β, (Fig.
b-k RT-qPCR analyses of selected antiviral response genes in En-As (red) and En-N (green). l-p Basal expression of antiviral genes. l, m Scatterplots of basal expression levels of antiviral response genes in enriched neurons (En-N) compared with that of unsorted cells (Uns-C) (l, k) or enriched astrocytes (En-As) (m). Analysis was performed using an antiviral response PCR array. n-p RT-qPCR analysis of the basal expression of IFIH1/MDA5 (n), DDX58/RIG-I (o), and OAS2 (p) genes in En-N and En-As. Gene expression was normalized to HPRT1 and the -2ΔΔCt method was used for relative quantification (normalization to non-infected En-N, at 7hpi for b-k). The results are expressed as the mean ± SD. Data are representative of two independent experiments performed in triplicate (b-k, n-p) and one experiment performed with pooled triplicates (l, m).
Funding This study was financially supported by the  French National Institute for Agriculture, Food and the Environment (INRAE)  and  DIM MalInf  ( Ile de France ). MF was financially supported by a Ph. D. fellowship from  INRA  and the  Paris Institute of Technology for Life, Food, and Environmental Sciences (AgroParisTech). GG was financially supported by Laboratoire d' Excellence Integrative Biology of Emerging Infectious Diseases  grant  ANR-10-LABX-62-IBEID. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data analysis To investigate whether the two indices were significantly different, a paired Student's t-test was used. The RMP and LHC should be inversely proportional to each other, as one describes the proportion of the femoral head that is not covered by the acetabulum (RMP) and the other describes the proportion of the femoral head that is covered by the acetabulum (LHC). In order to conduct the paired Student's t-test, 1-LHC was calculated. Pearson's rank correlation coefficient was used to assess the strength of correlation between the RMP and LHC. SPSS v26 (IBM Corp. , Armonk, NY, USA) was used for the statistical analyses. The inter-and intra-assessor repeatabilities of the LHC measurements were investigated using intraclass correlation coefficients (3,1) (two-way mixed, single measures).
SPSS v26 was used to compute the intra-assessor intraclass correlation coefficient (3,1) (two-way mixed, single measures) using 90 measurements (3 assessors×10 images×3 repeats). The inter-assessor intraclass correlation coefficient was calculated using the first of the repeat images from each assessor (a total of 30 measurements, 10 per assessor). To investigate potential bias between sessions, the mean of the two measurements from the first session for each assessor was deducted from the second session measurements. The means and standard errors of the mean (SEM) differences were calculated to allow calculation of the upper and lower bias limits. To aid the establishment of the potential clinical utility of LHC, the minimal detectable difference (MDD) was calculated. The MDD is the smallest change in two measurements that can confidently (95% confidence intervals [CIs]) be taken as a true difference.
ACKNOWLEDGEMENTS Thanks to  Dr Anne McNee ,  Dr Robert Eckersley , and  Professor Lionel Tarassenko  for their guidance and advice. This work was funded by  Action Medical Research (UK) , a registered charity in England and Wales (208701) and in Scotland ( SC039284 ), and supported by the  Wellcome/Engineering and Physical Sciences Research Council Centre for Medical Engineering  ( WT 203148/ Z/16/Z ).
For inclusion in this study, a model must have both completed at least one palaeoclimate simulation and provided the required output fields for at least 30 years for both this simulation and the pre-industrial control (see Sect. 2. 3 for details). The resulting 32 models are listed in Table  1 ; combined, they contain over 35 000 years of monthly ENSO information. Further information about the CMIP5 models is provided in Table  9. A. 1 of  Flato et al. (2014). The CMIP6 models used in this study are described in more detail in the Supplement and also online on the PMIP4 website https://pmip4. lsce. ipsl. fr/doku. php/database:participants (last access: 8 July 2020).
Simulations This study uses simulations consisting of seven different experiments. Four of the experiments are part of the CMIP effort and form part of the "DECK" set of core simulations  (Eyring et al. , 2016a) : the pre-industrial and historical and two idealised future warming scenarios. The study also includes three past climate experiments from the PMIP database; the mid-Holocene and Last Glacial Maximum were included in both PMIP3/CMIP5 and PMIP4/CMIP6, whereas the last interglacial was only included in PMIP4/CMIP6. The mean state and ENSO variability of the models are evaluated in simulations with prescribed historical forcings, known as historical simulations  (Hoesly et al. , 2018; Meinshausen et al. , 2017; van Marle et al. , 2017). The specification of the historical simulation differs slightly between CMIP5 and CMIP6, most notably by the CMIP6 simulations being extended until 2015 CE.