dataesr/scientific-paragraphs-categorization huggingface dataset:
Subjects We recruited a total sample of 70 participants at the Department of Neurology, Paracelsus Medical University Salzburg, Austria, from May 2012 to December 2015 within a larger study focused on memory disorders. After exclusion of participants who did not undergo both EEG-examinations (two TLEr, one TLEl, three HC) and whose EEG was of poor quality (one SCC, one TLEl, two HC) 60 participants remained for this analysis. Poor quality of the EEG was defined as less than 4 s in at least one of the two recordings after excluding segments of 500 ms according to the automatic data inspection (see Section 2. 4). The same data was used in a recent study on difference in test-retest reliability between patient populations  (Höller et al. , 2017). The data stems from a study in which we were interested in memory impairments, so that we report standard mini-mental state examinations in patients recruited from the memory clinic (MCI and SCC subgroups) and the Montreal cognitive assessment results for HCs, which was employed in order to have a sensitive measure that should disclose clinically relevant memory problems in the healthy subgroup.
Data registration EEG was recorded in a quiet room. Participants were instructed to close their eyes and stay awake. Recordings lasted for 2-3 min. We used a BrainCap with a 10-20 system and a BrainAmp (Brain Products GmbH, Germany) 16-bit ADC amplifier. The sampling rate was 500 Hz. Of the 32 recorded channels, one was used to monitor the lower vertical electrooculogram and one was used to measure electrocardiographic activity. Two were positioned at the earlobes for re-referencing purposes to remove the bias of the original reference, which was placed at FCz. Data analysis was conducted for data collected from the remaining 27 electrodes F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T7, T8, P7, P8, Fz, Cz, Pz, FC1, FC2, CP1, CP2, FC5, FC6, CP5, CP6, TP9, and TP10. Impedances were kept below 10 k.
Data preparation Data was pre-processed with Brain Vision Analyzer (Version 1. 05. 0005, Brain Products GmbH). In order to re-reference all channels, a new reference was built by averaging the signal of both earlobe electrodes. Butterworth Zero Phase Filters were used for a high-pass filter from 1 Hz (time constant 0. 1592 s, 48 dB/oct) and an additional notch filter (50 Hz) was applied. An automatic artifact detection was carried out. Please note that the automation of this procedure ensures objectivity, which means at the same time that it is reproducible. Nevertheless, the nature and number of artifacts surely depends on the specific recording and participant. Maximal allowed voltage step per sampling point was 50 µV (values which exceeded this threshold were marked within a range of ±100 ms); maximal allowed absolute difference on an interval of 200 ms was 200 µV and lowest allowed absolute difference during an interval of 100 ms was 0.
Data discontinuity and data length variation Feature extraction was performed in 5 variants, illustrated in Figure  1:  1. Complete data: over the whole dataset (all 123. 5 s); thus, the data was continuous but included artifacts. 2. Artifact-free data: data was segmented into 500 ms segments (250 sampling points); if the segment overlapped with a marked artifact, it was excluded from further analysis for all channels; the remaining segments were concatenated; thus, the data was discontinuous without artifacts. 3. Length variation: the estimation of the features was repeated by increasingly cutting 500 ms at the beginning of the data at each repetition; thus, the data was continuous with artifacts and we varied data length. 4. 0. 5 s cut-outs: data was segmented into 500 ms segments (250 sampling points); in loops the estimation of the features was repeated by increasingly leaving out every second segment; thus data was discontinuous with artifacts, we varied length and discontinuity.
Feature Extraction We estimated a set of measures of interaction between all of the 27 selected channels. The estimation was performed for each of the participants. The measures were calculated based on the multivariate autoregressive model with the functions mvfreqz. m and mvar. m from the BioSig toolbox  (Schlögl and Brunner, 2008)  with model order from 1 to a maximum of 250, that is, equaling the length of the segmented data and enabling us to model at least one oscillation for each of the examined frequencies. In order to estimate the multivariate autoregressive model we used partial correlation estimation with unbiased covariance estimates  (Marple, 1987) , which was found to be the most accurate estimation method according to  Schlögl (2006). The model was then transformed from the time-domain into the z-domain and the f -domain, which accordingly yield two transfer functions.
Effect of Artifacts First, we compared test-retest reliability calculated over the whole dataset (complete data, that is, 123. 5 s) with the artifact-free data, that is, the dataset when artifacts were excluded. The complete data variant included artifacts but could be assumed to be continuous, while the artifact-free data variant is discontinuous because of the excluded artifacts. For this purpose, measures were averaged over frequency ranges before calculation of reliabilities. As we can see from Figure  2 , for most measures, excluding artifacts increased test-retest reliability. This effect was very strong for spectrum and coherence. For measures such as direct directed transfer function and full frequency directed transfer function test-retest reliability was high in both cases, with and without artifacts.
, 2015) , and the effect of the reference on reliability needs to be determined in future studies. Moreover, the subsamples drawn from neurological populations were quite small, so that conclusions for the individual populations are limited. In addition, the group of healthy controls was drawn from students, as well as elderly people that attended the University 50+ programme, an offer of the University of Salzburg for people with the desire to attend University classes for selected topics of interest. It is possible that this interest in learning and the high education of the control group caused a general bias. It is of interest that even with a very reliable measure such as the full frequency directed transfer function and a long data segment, there are participants with a very low reliability.
ACKNOWLEDGMENTS We thank  Marlene Höhne  for proof-reading of the manuscript,  Julia Höfler ,  Judith Dobesberger ,  Susanne Grinzinger ,  Markus Leitinger ,  Giorgi Kuchukhidze ,  Alexandra Rohracher ,  Wolfgang Staffen , and  Martin Scherr  for supporting recruitment, the team in the  Center for High Performance Computing, Salzburg  for the possibility to use the Doppler cluster in order to perform the time-consuming calculations, and  Dr. Peter Zinterhof  for the very helpful support in using the cluster.
Survivals -according to the groups defined by the explanatory variables-were compared using the log rank test. The effect extent was calculated through the hazard ratio (HR) base on a univariate Cox regression model, verifying the proportionality assumption of the model. Variables studied in univariate analysis were used in a multivariate Cox analysis. Analyses were two-tailed. Type I error level was 0. 05, except in case of multiple testing. In this situation, a correction was performed, based on the Dunn-Sidak's method: α' = 1-(1-α) 1/k , k being the number of tests. Analysis of multiple correspondences was processed. Analyses were processed using the Stata 11 software (StataCorp LP, Tewas, USA) and the R 3. 2. 2 software (R Core Team. R Foundation for Statistical Computing, Vienna, Austria) with ADE4 and FactoClass packages.
Acknowledgements We kindly thanks  Anne Atlan ,  Benjamin Audit ,  Eric Badel ,  Hugues Chaté ,  Yves Couder ,  Catherine Coutand ,  Thierry Fourcaud ,  Xavier Leoncini  and  Agnès Schermann , for stimulating and helpful discussions. This work was granted access to the  HPC resources of Aix-Marseille Université , funded by the project  Equip@Meso  ( ANR-10-EQPX-29-01 ) of the  programme 'Investissements d'Avenir ' supervised by the  ANR.
Data A total of 246 surficial sediment samples were realized on the sandy seabed of the bay, between 13 February 2013 and 22 August 2013, at a mean sampling interval of 500 m. Most of the areas were reached by foot and samples were collected by hand in the first 4 cm. For the water areas (tidal channels, the western border of the intertidal domain), samples were collected with an Ekman grab from a zodiac. Grain-size analyses were performed using an LS230 laser granulometer (© Beckman Coulter) on the fine fractions (<2 mm) and a sieving column for the coarsest fractions (>2 mm). A calcimetric analysis (Bernard calcimeter) was performed on 64 samples, distributed according to a 1 km grid. All results have been entered into the Granush® software of SHOM  (Pedreros, 1996)  to calculate sedimentological parameters (e.
These data are referenced into the French altimeter system IGN69. They cover the intertidal area with a vertical accuracy of ±0. 15 m for the February-March 2011 data and ±0. 08 m for the other surveys (28 September 2011 to 11 April 2013), except in water areas in which the LiDAR signal does not penetrate (e. g. bottom of tidal channels, hunting ponds in salt marshes, lowest areas of the intertidal zone, subtidal areas). These topographic LiDAR data have been used to perform the mapping of the morphology and the dynamics of the bay.
Cartography and computer graphics Maps were realized from the sediment and topographic data by means of ArcGIS10 (© ESRI) and Adobe® Illus-trator® CS6 Tryout (© Adobe Systems Inc. ) softwares. The sedimentary facies map (Figure  1 ), established according to the classification of  Folk (1965) , was built as a shapefile of polygons drawn by hand. All other sedimentary maps (mean grain size on Main Map; mud and fine sand contents, sorting, CaCO 3 content on Figure  1 ) correspond to rasters created using kriging interpolation proposed in ArcGIS10 (© ESRI), with a resolution of 1 m. On the Sediment Map (Main map), sedimentary zones were delimitated on the basis of the distribution of mean grain size, which is a synthetic sediment parameter, and content in fine sands, which is the most represented sedimentary class in the bay (Figure  1 ).
The other sediment parameters have been synthetized for each zone and presented on the Interpretative Map. The morphology map (Main map) presents the topography of the bay and the localization of the crests of sedimentary bars and hydraulic dunes for the 1 April 2013, as well as a morphological zonation, based on DEMs (resolution: 1 m) created from all the six airborne LiDAR topographic data sets in the 2011-2013 period, using a conversion from points to raster. Since the LiDAR beam does not penetrate water, data in the tidal channels and the lowest parts of the tidal flats were removed from DEMs. Nevertheless, tidal channels can be easily localized in these blind areas as sinuous and straight features, meandering between the entrance and the bottom of the bay. Hydraulic dunes crests were automatically determined using ParamDunes software of SHOM  (Hoche, Garlan, & Thomas, 2006).
The ParamDune software uses an algorithm based on the principle of watershed delimitation, which allows to detect crest and feet of hydraulic dunes, and then to calculate dune morphometric parameters. Sedimentary bars were digitized by hand using ArcGIS10 (© ESRI). The morphological zonation has been delimitated qualitatively according to the dominant morphological feature observed on the six topographic data sets acquired during the 2011-2013 period. Five types of morphology have been considered and mapped: tidal channels, sedimentary bars, hydraulic dunes, salt marshes and flat high-altitude beaches. They have been recognized: (i) in a qualitative way from the texture observed on the LIDAR data (rough due to vegetation for salt marshes, smooth for high-altitude beaches, wavy for sedimentary bars), and (ii) in a quantitative way for hydraulic dunes using the ParamDune software.
Channels (sector M 2 ; Main map) allow the Somme river to flow from Saint-Valéry-sur-Somme, located in the bottom of the bay, to the ebb delta, and facilitate the penetration of the tide from the west. Sedimentary bars (sector M 3 ; Main map) are located along the northern and southern coasts of the bay (west of the Pointe de St-Quentin and the Pointe du Hourdel), exposed to waves and where they are connected to coastal barriers. They are up to 1 km long and 3 m high, with a spacing of about 200 m and are generally parallel to the coast (Figure  2 ). Hydraulic dunes (sector M 4 ; Main map) are the most represented tidal bedforms on the intertidal area (Main map and Figure  2 ). Automatic detection realized by ParamDunes software  (Hoche et al. , 2006)  identified 4189 dunes on the 1 April 2013 topographic DEM.
Software Granush® (SHOM) was used to calculate the sedimentological parameters and weight percentages of the different sedimentary classes, and to define the sedimentary typology according to the classification of  Folk (1965). 'ParamDunes' software (SHOM) was used for automatic digitizing of the crests of hydraulic dunes, based on the DEM of 1 April 2013 LiDAR topography. Sedimentological and morphological information was integrated into ArcGIS10 (© ESRI) to create all maps, including shapefiles, rasters, DEMs and DODs. Finally, Adobe® Illustrator® CS6 Tryout (© Adobe Systems Inc. ) was used to design the final version of maps and figures.
Acknowledgements The authors thank the colleagues from the  M2C Laboratory  who helped collecting the sediment data and the operational team CIRCLE who acquired and processed the LIDAR data. The Bay of Somme is a site of the SNO DYNALIT, from which they received support. The paper and the map benefited greatly from reviews by  J. Abraham ,  B. J. Todd  and  F. Mascioli , and helpful suggestions from editors  M. J. Smith  and  T. Piacentini.
Detailed neuron models We used two spatially extended neuron models described in  [7] , a simple model of a cylindrical soma and a cylindrical 50 μm long axon (diameter 1 μm) available on ModelDB  [40]  and a morphologically detailed model based on a reconstruction of a cortical pyramidal cell (personal communication of Prof. Yuguo Yu). We also used a multicompartmental model of a pyramidal cell described in  [12] , also available on ModelDB. These models were implemented in Neuron 7. 4  [41]  with time step 1 μs and analyzed in Python. In Fig  3 , the extracellular field was computed with the standard line source method  [42, 43] , with extracellular conductivity σ = 0. 3 S. m-1 , and simulated with the NeuronEAP Python library  [44].
Funding: RB was supported by  Agence Nationale de la Recherche  ( ANR-14-CE13-0003 , http://www. agence-nationale-recherche. fr/) and the  European Research Council  ( StG 240132 , https://erc. europa. eu/). MT was supported by  Ecole des Neurosciences de Paris  (ENP, http://www. parisneuroscience. fr/)and  DIM Cerveau et Pensee  (http://dimcerveaupensee. fr/). BF was supported by the  European Union 's  Horizon 2020 research programme  through a  Marie Sklodowska-Curie fellowship  ( AUDICON-655137 , https://ec. europa. eu/programmes/horizon2020/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
SMRT sequencing library preparation Sample indexes for patient tracking were added to the LR-PCR fragments using an additional five-cycle PCR with the previous LR-PCR conditions. Barcoded pools were then purified with AMPure XP Beads, and pooled equimolar according to their size. Molar concentration was verified on a Bioanalyzer 12000 chip (Agilent, California, USA). For each barcoded pool, a SMRT-bell library was prepared according to the PacBio's 5-or 10-Kb Template Preparation procedures. Pooled amplicons were sequenced on five SMRT cells on the PacBio RSII system with the P6 sequencing chemistry. Data collected from 360-min movie time was preprocessed using the standard primary analysis tools (Fig. 1 ). F I G U R E 1 Flowchart of the applied analytical approach for the identification of potentially pathogenic variants and VUCS in ADPKD patient samples.
Key processes in the workflow describe details and thresholds used for (1) sequencing of pooled LR-PCR amplified fragments with PacBio RSII and postprocessing of reads including alignments and read quality filters; (2) identification of variants using two independent strategies including the reconstruction of allelic sequences, and small variant calling using Quiver; (3) standardization of variant nomenclature to represent a correct HGVS description and facilitate the comparison between datasets; (4) enrichment of variant annotations with VEP (including effect prediction, ClinVar, SIFT, PolyPhen, 1000 Genomes Project, dbSNP, and SwissProt annotations among others), and selection of high-confidence variants; (5) identification of potentially pathogenic variants and VUCS based on their confidence, effect prediction, and population frequencies.
Sequence alignment and variant calling We used the RS_Resequencing protocol from SMRT Analysis Suite v. 2. 3 to align long reads against the human reference genome, downloaded from the Genome Reference Consortium version 37 patch 13 (GRCh37. p13). Samples were demultiplexed into individual files (H5 and BAM formats) using known barcode sequences and a minimum barcode identity score of 22 (Fig. 1 ). Alignments were filtered to contain mapped reads with a mapping quality threshold of 30 Phred score using Samtools v. 1. 2 (Supp. Table  S2 ). Read coverage and targeted PCR statistics for LR-PCR amplicons were computed with BedTools v. 2. 25. 0, and PicardTools v. 1. 1. 40 (Supp. Tables  S2-S4 ). Variant calling was performed using Quiver (allowing for diploid calling and maximum coverage of 10,000).
ADPKD VARIANT NOMENCLATURE AND GENOTYPING Variant descriptions were standardized to concord with HGVS guidelines (den Dunnen et al. , 2016), using the Mutalyzer Name Checker tool  (Wildeman, van Ophuizen, Dunnen, & Taschner, 2008). Genomic HGVS descriptions were converted to coding notations using the Position Converter from Mutalyzer  (Wildeman et al. , 2008). Only changes in RefSeq-annotated canonical transcripts for PKD1 (NM_001009944. 2) and PKD2 (NM_000297. 3) were further analyzed. HGVS descriptions of deletion-insertions (delins) were manually inspected to avoid variant redundancies and undesired clustering of neighboring independent events (Fig. 1 ; Supp. Table  S5 ). Then, standardized variants were annotated using the Variant Effect Predictor (VEP), from Ensembl tools v. 83  (McLaren et al.
g. , missense, in-frame indels, frameshifts, and splice-site acceptor or donor variants); (2) population frequency in the 1000 genomes project <1%; (3) unique occurrence (1/19) (∼5%) in the patient cohort since, in ADPKD, no single disease-causing variant accounts for more than 2% of affected families  (Harris & Rossetti, 2010) , or more than 1. 7% of ADPKD reported cases in the ADPKD database (PKDB) (http://pkdb. mayo. edu/; accessed version 3. 1)  (Gout et al. , 2007)  (Fig. 1 ).
Short-read loss of power for known PKD1 pathogenic variants in WGS and WES Previously known pathogenic variants for PKD1 gene were obtained from PKDB. Only variants that were classified as "definitely pathogenic" were selected for further analysis. Large deletions (few hundred bp to several Kbp long) were excluded from the analysis as they are not usually detected with common variant calling algorithms. For the genomic position of each pathogenic variant, sequencing depth was extracted from nine publicly available WGS and WES datasets  (Sun et al. , 2015). In addition, we included the sequencing depth of nine randomly selected libraries from the study of  Rossetti et al. (2012).
Sensitive detection of ADPKD small variants PKD1 is known to be a highly polymorphic gene with many variants reported in addition to the disease-causing or pathogenic variants  (Gout et al. , 2007). Hence, the required sensitivity to resolve PKD1 was achieved by the combination of variant calling using Quiver and the reconstruction of amplified allelic sequences. Overall, we identified 1,683 variants (404 SNVs) across 19 ADPKD patients, from which 177 variants (119 SNVs) were located in coding or splice-site regions (Fig. 2C ). Variants were distributed along PKD1 (Supp. Fig. S3A ) including regions with large SDs and high GC content. The mismatch rate of PacBio data was empirically assessed based on average frequency of mismatches at each position. We observed an average of 1. 2% mismatch rate across the entire PKD1 gene (Supp.
Comparative analysis between SMRT-Seq and the ADPKD diagnostic assay The evaluation of 167 coding or splice-site variants identified by standard ADPKD diagnostic assay showed that 159 out of 167 were correctly detected by PacBio (Supp. Fig. S3C ). The overall observed sensitivity and specificity in detecting coding variants was of 95. 2% (159/167) and 88. 8% (159/179), respectively. Eight variants were solely detected by Sanger (Supp. Fig. S3A : crosses), from which, despite the high sequencing depth, the majority (6/8) had low number of reads supporting the presence of these variants in PacBio sequencing data with variant frequency below the applied frequency thresholds (Supp. Fig. S3B : yellow dots). The remaining 2/8 variants (Supp. Fig. S3B : red dots) constitute a pathogenic insertion (c.
Loss of PKD1 diagnostic power in short-read (Illumina) NGS The potential loss of diagnostic power when resolving PKD1 by shortread NGS was evaluated based on 797 pathogenic variants that were previously reported and validated, and are publicly available in PKDB. The repetitive nature of PKD1 gene hampers proper alignment of short Illumina NGS reads (Supp. Fig. S5 ). Over 12% of the reported pathogenic variants would have been missed in WGS and WES data purely due to poor sequencing depth (Supp. Fig. S6 ). In comparison, other short-read approaches based on LR-PCR enrichment show lower percentage (1. 3%) of reported pathogenic variants that would have been missed because of low sequencing depth. However, this approach required very high sequencing depth, which can be appreciated from the observed high variability in coverage ranging from <8× to >30,000× (Supp.
This provides a cleaner dataset for variant calling, free of chimeric (0. 85%) and pseudogene (5. 6%; Supp. Table  S2 ) reads that are introduced by the LR-PCR amplification. Finally, by using this approach, we identified 20 out of 21 (95. 2%) PKD1 disease-causing variants diagnosed by Sanger sequencing or MLPA, providing a correct diagnosis for 18 out of 19 ADPKD patients (94. 7%) with at least one pathogenic variant in PKD1. In comparison to current ADPKD diagnostic assays, based on Sanger sequencing and MLPA, we show that direct long-read sequencing can aid in resolving PKD1 for ADPKD diagnostics. Longer sequencing reads discriminate between PKD1 and pseudogenes (Fig. 2A ), and improve the mapping quality of PKD1 (Fig. 2B ). The improved mappability reduced the interference of homologous sequences, high GC content, or repetitive elements for ADPKD diagnosis  (Qi et al.
In recent years, several attempts have been made to replace the standard ADPKD diagnostics by NGS approaches that would improve the screening of PKD1 gene  (Eisenberger et al. , 2015; Mallawaarachchi et al. , 2016; Qi et al. , 2013; Rossetti et al. , 2012; Tan et al. , 2014; Trujillano et al. , 2014). These screenings were based on Additional information of each variant including SIFT classification, and 1000G frequencies among other annotations can be obtained from the VCF files uploaded to EGA with accession number EGAS00001002106. TA analyzing WGS or WES data  (Mallawaarachchi et al. , 2016; Qi et al. , 2013) , on the enrichment of PKD1 using LR-PCR  (Rossetti et al. , 2012; Tan et al. , 2014) , or the hybridization capture of PKD1  (Eisenberger et al. , 2015; Trujillano et al. , 2014).