dataesr/scientific-paragraphs-categorization huggingface dataset:
For differential expression analysis, fragment counts were normalized using the 45,682 elements (genes, TE copies and repeats) and DESeq2 (v. 1. 14-1) in the R statistical computing environment (R project v 3. 3. 2). PCA was carried out using the R package FactoMineR (v. 1. 34). Differential expression of genes and TE copies was carried out with DESeq2, requiring a p-value < 0. 05 and a fold-change > 2. Gene heatmaps were made with the heatmap. 2 and hclust R methods. ChIP-seq data analysis. ChIP-seq data were demultiplexed using CASAVA (v1. 8. 2) and bcl2fastq2 (v2. 18. 12). Illumina adapters were removed using cutadapt (1. 12), keeping only reads with a minimal length of ten nucleotides. Paired-end 80 nt reads were mapped (bowtie2. 3. 3) on expected contaminants (mitochondrial genomes, ribosomal DNA, and bacterial genomes).
Acknowledgements We thank  Slimane Ait-Si-Ali  and  Michel Werner  for critical reading of the manuscript. Special thanks to  Slimane Ait-Si-Ali  for advice for tandem affinity purification experiments, to  Maoussi Lhuillier-Akakpo  for her help in setting up the ChIP protocol, and to members of the  Duharcourt lab  for stimulating discussions. This work was supported by the  Agence Nationale de la Recherche  ( ANR-14-CE10-0005-04 'PIGGYPACK' ) to S. D. and O. A. and the salary of E. E. was provided by this grant. Work in the laboratory of S. D. is supported by intramural funding from the  CNRS , the  Fondation de la Recherche Médicale  (Equipe FRM  DEQ20160334868 ), LABEX " Who am I?"  ( ANR-11-LABX-0071_WHOAMI ). A. F. was recipient of Ph. D fellowships from ' Ministère de l'Enseignement Supérieur et de la Recherche' and 'Fondation de la Recherche Médicale' , and of a postdoc transition fellowship from LABEX "Who am I?" supported by the  ANR-11-LABX-0071_WHOAMI  and the  ANR-11-IDEX-0005-02.
C. M. P. is recipient of a  Ph. D fellowship  from  'Ministère de l'Enseignement Supérieur et de la Recherche ''. D. H. is supported by a postdoctoral fellowship from the  Fondation pour la Recherche Medicale  ( SPF20150934266 ). Work in the laboratory of R. M. is supported by a ' Programme Labellise ' grant from the  Fondation ARC pour la Recherche sur le Cancer. This work was supported by " Région Ile-de-France" and "Fondation pour la Recherche Médicale " grants to D. L. We acknowledge the  ImagoSeine facility , member of the  France BioImaging infrastructure supported  by the  ANR-10-INSB-04. The sequencing benefited from the facilities and expertize of the high-throughput sequencing platform of I2BC.
Variability of TTTTA repeat number in control individuals. Analysis of the region where the expansion occurs in 83 European control individuals from two different cohorts showed that it corresponds to a polymorphic microsatellite (short tandem repeat), with the number of TTTTA repeats typically ranging from 9 to 20 (Fig. 2a ; Supplementary Fig. 4a-b ). We never observed larger TTTTA repeats or repeats containing TTTCA motifs in control individuals. A similar number of TTTTA repeats is present in Chimpanzees and Bonobos while this number is reduced in more distant primate species (Supplementary Fig. 4c ). Haplotype analysis reveals an ancient common ancestor. We used available SNP data from the French families (Families 1 and 2) to investigate the possibility of a common haplotype underlying the expansion.
Low-coverage sequencing (6-10×) allowed retrieving one to four reads displaying the expansion per individual (Supplementary Fig. 6 ). Detected expansions typically spanned 4-6. 5 kb and comprised between 791 and 1035 repeats in total (Fig. 3a-c , Table  1 ). However, we observed a substantial variability in reads covering the expansion in the same individual (Fig. 3c, d , Supplementary Data 3, a-i). Four reads incompletely covering the expansion were sequenced in individual 2-IV-9, two of them spanning a variable TTTCA stretch that was alone up to 5 kb (Supplementary Data 3, j-m). To confirm that the observed variability possibly reflects somatic mosaicism and not an artifact introduced by the sequencing procedure, we used molecular combing (Fiber FISH) to analyze very long, single-stretched DNA fibers in an unbiased fashion in blood cells from nine members of Families 1 and 2 and one healthy control.
Phenotypic variability and genotype-phenotype correlations. We used data from blood cells to explore further the relationship between the repeat number of each motif and the age at onset of epilepsy and tremor. We observed an inverse correlation between the age at seizure onset and the length of the expansion, mainly driven by the size of the TTTCA repeats (Fig. 5c ). On the contrary, no significant correlation was observed between the age at tremor onset and any parts of the expansion (Fig. 5d ). Accordingly, the two individuals with the largest expansions (2-IV-9, 2-V-9) were amongst the most severely affected individuals. Both started to have generalized seizures at 17-18 years of age. Individual 2-IV-9 had a moderate, asymmetric myoclonic tremor affecting the upper limbs (the right side being more affected than the left side) when last examined at age 60 years despite treatment with sodium valproate (VPA) and clobazam (CLB), and he also showed non-specific gait difficulties.
-I V -6 _ 1 1 -I V -6 _ 2 1 -I V -6 _ 3 1 -I V -6 _ 4 1 -I V -8 _ 1 1 -I V -9 _ 1 1 -I V -1 1 _ 1 1 -I V -1 1 _ 2 2 -I V -1 6 1-IV-6_1 1-IV-6_2 1-IV-6_3 1-IV-6_4 1-IV-8_1 1-IV-9_1 1-IV-11_1 1-IV-11_2 2 Expansions do not alter MARCH6 expression in blood and skin. Finally, we investigated whether expansions affect the expression of MARCH6 in blood cells and fibroblasts of affected family members. MARCH6 is a ubiquitously expressed gene encoding an E3 ubiquitin ligase that mediates the degradation of misfolded or damaged proteins in the endoplasmic reticulum  20, 21. At least three isoforms, resulting from alternative splicing of exons 2-4 have been detected in several tissues, but the full-length (NM_005885. 3) is the predominant isoform (Fig. 6a , GTEx database). RNA-seq had previously failed to detect any difference in MARCH6 expression in lymphoblasts of patients and control individuals (Supplementary Fig.
After normalization and quality control, qualified libraries were sequenced on a HiSeqX5 platform from Illumina (Illumina Inc. , CA, USA), as paired-end 150-bp reads. One lane of HiSeqX5 flow cell was used for each sample, in order to reach an average sequencing depth of 30X per sample. Sequence quality parameters were assessed throughout the sequencing run and standard bioinformatics analysis of sequencing data was based on the Illumina pipeline to generate FASTQ files for each sample. Reads were then aligned on the human genome (GRCh37) and decoy (Heng Li's hs37d5 genome for 1000 genomes project [ftp://ftp. ncbi. nlm. nih. gov/1000genomes/ftp/technical/reference/phase2_reference_assembly_sequence/hs37d5. fa. gz] was performed using BWA software (mem+default option [https://github. com/ lh3/bwa]).
Duplicate sequences were removed from bam files using Sambamba tools [http://lomereiter. github. io/sambamba/docs/sambamba-view. html]. An additional realignment step was performed on the bam file using GATK (Realig-nerTargetCreator/IndelRealigner). Coverage analyses were generated using an inhouse pipeline based on metrics generated by Bedtools programs [http://code. google. com/p/bedtools/]. Variant calling was performed using four tools: Uni-fiedGenotyper and HaplotypeCaller from GATK, Platypus [http://www. well. ox. ac. uk/platypus], and Samtools [http///www. htslib. org/]. Results generated by these four programs were assembled in a VCF file. Annotation of the VCF file was carried out and annotated using snpEff and snpSift [http://snpeff. sourceforge. net and http://snpeff. sourceforge.
net/SnpSift. html] based on data available in the Ensembl [http://www. ensembl. org/index. html] and dbNSFP [https://sites. google. com/site/jpopgen/dbNSFP] databases. In an additional step, we screened genome data for tandem repeat expansions using four different bioinformatics pipelines: ExpansionHunter v2. 5. 5  15  , STRetch  18  , TRhist  19  , and exSTRa  17, 26. We also used ExpansionHunter v2. 5. 5 to assess the number of TTTTA repeats present in available genome data of 53 control individuals (Supplementary Fig. 4b ). By convention, the repeat motif searched for is indicated as the first non-redundant repeat motif using alphabetical order of nucleotides (e. g. , AAAAT for TTTTA and AAATG for TTTCA). 27 A sequencing. In parallel to genome sequencing, RNA-seq and small RNA-seq was performed for 10 individuals: three affected individuals (1-III-16, 1-IV-9, and 1-IV-11) and one healthy spouse (1-III-24) from Family 1, three affected individuals (5-III-2, 5-III-3, and 5-IV-4) from Family 5 and three healthy controls (Ctrl1-3).
The last step was an acrylamide gel purification of 140-145 nt amplified cDNA constructs (corresponding to cDNA inserts+120 nt adapters). Final libraries were checked for quality and quantified using capillary electrophoresis. The libraries were sequenced on Illumina HiSeq 4000 sequencer as single-read 50-base reads following Illumina's instructions. Image analysis and base calling were performed using RTA 2. 7. 3 and bcl2fastq 2. 17. 1. 14. Adapters were trimmed from total reads with FASTX_Toolkit [http://hannonlab. cshl. edu/fastx_toolkit/]. Non-coding RNA profiling was performed by the ncPRO-seq 1. 6. 5 analysis pipeline  37  with the annotation from miRBase release 21  38  and Rfam v11 database  36. The DESeq2 package v1. 16. 1  31  was used to normalize and to assess miRNA differential expression between patients and controls.
Sanger sequencing was used to determine the number of TTTTA repeats present on non-pathogenic alleles in 30 healthy blood donors (Supplementary Fig. 4a ) and FAME3 patients. After amplification of the corresponding region using primers FAME3-P2F and FAME3-P1R, forward and reverse sequence reactions were performed with the Big Dye Terminator Cycle Sequencing Ready Reaction Kit (Applied Biosystems) using the same primers. G50-purified sequence products were run on an ABI 3130xl DNA Analyzer (Applied Biosystems) and sequence data were analyzed with Geneious (Biomatters). Haplotype analysis. SNP genotypes were available for individuals of Families 1 and 2  25. A core haplotype was defined based on sharing amongst affected individuals from both French families. Haplotype dating was performed using a published method  39  , and website developed in the Bahlo lab [https://shiny.
The entire coverslip was scanned on a FiberVision® automated scanner (Genomic Vision). Image analysis and signals measurement was performed using FiberStudio® software (Genomic Vision). We measured each part of the signals (B: blue, R: red, G: green) for all alleles present on one coverslip (healthy control and individuals 1-IV-6, 1-IV-9, 1-IV-11, 2IV-18, 2-V-9, 2-V-20, 2-V-22) or two coverslips (individuals 1-IV-8 and 2-IV-9). For alleles without red staining, the unstained part between the blue and green signals was referred to as Y on images. For alleles with red staining (pathogenic alleles), Y refers to the unstained part between the blue and red signals; unstained parts were also detected between the red and green signals or in-between two red signals and referred to as W. Distribution of non-pathogenic versus pathogenic alleles was assessed by comparing data of the control individual to those of affected patients (Supplementary Fig.
Allele distributions, calculations (mean, median, and standard deviation), graphs (box plots), and statistical analyses were performed using in-house R scripts. Real-time quantitative reverse transcription PCR (qRT-PCR). MARCH6 isoforms and their corresponding expression were assessed using Alamut 2. 11 (Interactive Biosoftware) and the GTEx database [https://gtexportal. org/home/]. Blood samples from 12 expansion carriers (1-IV-6, 1-IV-9, 1-IV-11, and all nine expansion carriers from Family 2) and 10 non-carrier individuals were collected in Paxgene® Blood RNA tubes (PreAnalytiX, Qiagen). Total RNA was isolated using the PAXgene® Blood RNA Kit (Qiagen) and RNA integrity was verified on a 2100 Bioanalyzer (Agilent). In parallel, fibroblasts from four expansion carriers (1-IV-6, 1-IV-8, 1-IV-9, 1-IV-11) and four unrelated healthy individuals were cultured in AmnioMAX (Thermo Fisher Scientific), and total RNA was isolated using the RNeasy mini kit (Qiagen).
included in this study have not consented to have their genome data publicly released. The source data underlying Figs. 1, 2b, 3c, 4f, 5a-d and 6b-e, as well as Supplementary Figs. 4a, 7, 8 and 12a-b are provided as a Source Data file. Raw images (molecular combing experiments) and raw nanopore data (corresponding to reads included in this study) are available from the corresponding author, upon request. The MARCH6 expansion has been deposited in ClinVar under the accession SCV000924549. RNA-seq and small RNA-seq data have been deposited in the ArrayExpress database at EMBL-EBI (www. ebi. ac. uk/arrayexpress) under accession numbers E-MATB-8300 and E-MTAB-8301. Received: 19 March 2019; Accepted: 23 September 2019;.
Acknowledgements We thank the families for their participation in this study,  Agnès Rastetter  ( ICM, Paris, France ) for RNA extraction, and  Emmanuelle Apartis  ( Hôpital Saint-Antoine, Paris, France ) for electrophysiological assessment of Family 1. DNA extraction and cell culture of lymphoblasts have been performed at the DNA and cell bank of  ICM (Paris, France ). RNA-seq has been performed on the GenomEast platform of  IGBMC, Illkirch, France. WGS has been performed by the  Centre National de Recherche en Génomique Humaine (CNRGH) Institut de Biologie François Jacob, Evry, France. We thank  Jean-Louis Mandel  and  Nicolas Charlet-Berguerand  ( IGBMC, Strasbourg, France ),  Cécile Cazeneuve  ( Hôpital Pitié-Salpêtrière, Paris, France ),  Charles Marcaillou  ( Integragen, Evry, France ) and  Isabel Silveira  ( Porto, Portugal ) for valuable discussions.
This study has been financially supported by three different grants from the  Fondation Maladies rares  to C. D. (2009, 2010, 2016),  Assistance Publique des Hôpitaux de Paris (APHP) ,  INSERM , the " Investissements d'Avenir" programme   ANR-10-IAIHU-06  ( IHU-A-ICM ),  University Duisburg-Essen  and  University Hospital Essen. M. B. was supported by an  Australian National Health and Medical Research Council (NHMRC)   Program Grant  ( GNT1054618 ) and an  NHMRC   Senior Research Fellowship  ( GNT1102971 ). This work was also supported by the  Victorian Government's Operational Infrastructure Support Program  and the  NHMRC Independent Research Institute Infrastructure Support Scheme (IRIISS). Laura Canafoglia: Member  of the European Reference Network on Rare and Complex epilepsies, ERN EpiCARE.
Families included in this study have not consented to have their genome data publicly released. The source data underlying Figs. 1, 2b, 3c, 4f, 5a-d and 6b-e, as well as Supplementary Figs. 4a, 7, 8 and 12a-b are provided as a Source Data file. Raw images (molecular combing experiments) and raw nanopore data (corresponding to reads included in this study) are available from the corresponding author, upon request. The MARCH6 expansion has been deposited in ClinVar under the accession SCV000924549. RNA-seq and small RNA-seq data have been deposited in the ArrayExpress database at EMBL-EBI (www. ebi. ac. uk/arrayexpress) under accession numbers E-MATB-8300 and E-MTAB-8301. Received: 19 March 2019; Accepted: 23 September 2019;.
Computational methods EROT videos were processed using a macro written in ImageJ (NIH, Bethesda, MD, USA). The background was subtracted using a median filter, each frame was converted to binary image and an ellipse was fitted to a cell. The change of the ellipse axis angle was exported to MATLAB (Mathworks, Natick, MA, USA) and filtered using moving average filter to remove the noise. The rotational velocity of a cell was calculated as an average change of the angle between each frame in a 15 s video sequence. Eq 3 was used to fit the single-and double-shell models to the electrotorational velocity spectrum. The radius of each cell, the frequency of the rotational electric field, and the conductivity of the extracellular medium were measured, reducing the number of unknown parameters. Furthermore, the permittivity of the aqueous extracellular medium is well known in literature and was also fixed.
Curve fitting was performed using the Levenberg-Marquardt algorithm through the curve_fit() function in the Scipy (v1. 1. 0) module in Python 3. 7 to minimize the sum of square residuals (Eq 10) between the experimental data and Eq 3. A Markov Chain Monte-Carlo (MCMC) algorithm was also used to fit the models to the EROT spectra for each cell type, enabling the goodness of fit to be explored by varying the parameter spaces for each model. Briefly, a sample of a bounded parameter space is used to fit the model, which was then used to evaluate the objective function, Eq 10. Based on this evaluation, an additional sample will be used to re-evaluate the model and Eq 10. This process is repeated, with each iteration depending only on the results of the previous iteration and will generate a series of samples from the parameter space that enable the convergence of the objective function and create probability distributions for each parameters.
The emcee module  [39]  was used to generate parameter estimates using an Affine Invariant Markov Chain Monte-Carlo Ensemble sampler with 20 walkers at 5 temperatures, each taking 10000 steps. No burn-in was used during data analysis due to the large number of steps and multiple temperatures ensuring relatively rapid convergence. The root mean square intensity of the EROT-inducing electric field was calculated using a three-dimensional simulation created using the finite element environment FEniCS (v2018. 1. 0) [40]. Gmsh (v3. 0. 1) was used for spatial discretization into linear triangular elements  [41]. Statistical methods. Following parameter estimation, statistical differences between each cell type were characterized using a one-way analysis of variance. If significance was found, statistical differences between the parameters for each group were compared using a an unpaired t-test.
The authors would like to acknowledge the generous financial support provided by the  LabeX LaSIPS  ( ANR-10-LABX-0040-LaSIPS ) managed by the  French National Research Agency  under the " Investissements d'avenir" program  ( nANR-11-IDEX-0003-02 ) and  COST TD1104 , www. electroporation. net. The authors would like to thank the  NSF CAREER  Award  CBET-1055913 , the  NSF   IGERT DGE-09661 , and the  NIH   R01-CA213423  for their gracious support of this work. The authors would also like to acknowledge project support from  ENS Paris Saclay and Virginia Tech.
Air masses trajectory analysis In order to better understand the hydrological conditions during sampling and origin of the previous rainfall, an air mass back trajectory model was used. The program for 'Hybrid Single Particle Lagrangian Integrated Trajectory Model' (HYSPLIT) developed by National Oceanic and Atmospheric Administration (NOAA) has been run for the sampling dates of main stations. This model is run for the air masses back trajectories upto five days to check if any short term severe cyclonic event (generally the case during the winter monsoon over Indian subcontinent) has impacted the hydrological systems in the Western Ghats prior to sampling.
It is at this middle elevation that the wettest places are found in Karnataka (Hulikal at 13° 43' N and Agumbe at 13° 30' N) and in Kerala (Vythiri on the edge of the Wayanad plateau at 11° 33' N and Walakkad in the Silent Valley at 11° 03' N). Tropical coastal areas are typically characterized by rain water with δ 18 O of about -3 ± 1 ‰ (GNIP database) and the groundwater of several tropical coasts and islands reflect similar mean δ 18 O values  (Lambs et al. , 2016 (Lambs et al. , , 2018)). This is also the case for the west coast of South India, with similar groundwater stable isotope ratios at Udupi (δ 18 O = -3. 09 ± 0. 05 ‰, n=34;  Tripti et al. , 2016). This δ 18 O value corresponds with the rainfall mean weighted values in the nearest GNIP station, Kozhikode (δ 18 O = -2. 97 ± 0. 73 ‰;  Warrier et al.
Continental and seasonal effects As stated in the previous section, precipitation (rain water) along the tropical coasts display δ 18 O value of about -3 ± 1 ‰ (GNIP database) and represents relative depletion of heavy isotopes than that of the seawater (δ 18 O = 0 ‰, by definition). The two GNIP stations in South India, Kozhikode and Tirunelveli (Figure  1. b. ) display very similar weighted mean δ 18 O values of about -2. 97 and -2. 79 ‰ respectively. In our earlier studies  (Lambs et al. , 2011; Tripti et al. , 2013 Tripti et al. , , 2016)) , it was reported that there is a homogeneity in the groundwater isotopic signature from coastal plain up to the moderate elevation (< 1500 m asl) of the Ghats. The mean value of δ 18 O in water samples was around -3. 15 ± 0. 63 ‰, close to the GNIP rainwater δ 18 O value (-2.
Statistical analysis Multiple linear regression analysis was carried out for determining the extent to which the elevation effect, continental effect and rainfall amount effect influence the δ 18 O values. When running the statistical test for the whole set of points (n = 51), the elevation effect appears to be the most significant explicative parameter, and the influence of two other effects remain insignificant. If we split the dataset into two groups, like in Figure  4 , for the first 42 points with elevation lower than 2050 m asl (Table  4 ), there is neither a strong nor a significant contribution from one specific parameter. It is a global contribution which enables observation of the δ 18 O values to decrease from -2 ‰ along the eastern coast of Arabian Sea to -4. 0‰ more inland at 1800 m asl in the Western Ghats.
e. , 2000 m asl) for the humid tropical mountains, and the isotopic elevation effect is observed only in the mountains of elevation greater than 2000 m asl of the Western Ghats. The findings of this study are significant as it has implications on the studies involving past climate and paleoelevation of the tropical mountain belt using records preserved in paleoarchives. This will help in improving the understanding of isotopic signatures of paleowater, tree ring, speleothem and other geological records found in South India. With less GNIP observations stationed in South India, the present study (though the observation is mainly from surface and subsurface water) adds to the global database, as it provides the new evidence on the isotopic elevation effect along the highlands of tropical mountain rainforest ecosystem of India.
Acknowledgements This work was funded by the  International Unit of Toulouse III University (Toulouse, France). We thank  Dr. Balakrishna K  and  Manipal Academy of Higher Education  (earlier  Manipal University ), Manipal for the Structured PhD fellowship of TM during November 2013,  Dr. Ajitkumar MP  for assistance during the field work at  Kannur , and the Associate Editor and three anonymous reviewers for their critical comments and constructive suggestions on the earlier version of manuscript. We are thankful to  NOAA Air Resources Laboratory  for accessibility to HYSPLIT Trajectory model. We also acknowledge  Indian National Science Academy (INSA), Government of India for the DST-INSPIRE Faculty  award to TM and sponsoring the present research on the Western Ghats. In Memoriam We dedicate this work to our esteemed colleague,  Dr.