dataesr/scientific-paragraphs-categorization huggingface dataset:
904, H6N5F4 at m/z 2612. 945, H6N5L1E1 at m/z 2620. 925, H7N6E1 at m/z 2712. 973, H7N6L2 at m/z 2940. 016, H9N8 at m/z 3124. 111, H7N6L4F1 at m/z 3632. 243) as calibrants (minimum five used), followed by peak picking in mMass, with cut-off signal-to-noise (S/N) 3. The peaklist was manually revised and analyzed in GlycoWorkbench 2. 1 stable build 146 (http://www. eurocarbdb. org/) using the Glyco-Peakfinder tool (http://www. eurocarbdb. org/ms-tools/) for generation of a glycan compositions list. Our novel in-house software, developed for automated data processing, MassyTools version 0. 1. 8. 0  [39] , was used for calibration using a 3 rd degree function and targeted data extraction of the area under the curve for each individual mass spectrum. To prevent over-estimation of overlapping glycan species, only the first three isotopes were extracted and the area under the curve was corrected based on the theoretical isotopic pattern.
The quality of the data was assessed using several quality parameters calculated within the software. Only good quality spectra (total intensity > 1 × 10 5 and fraction of analyte area with S/N > 9 is more than 50%) as well as analytes (S/N > 6, ppm < 20, quality score > 0. 10) were included for analyses. Raw data after pre-processing is provided in the Supplementary Tables. Finally, the corrected area-under-the-curve values were rescaled to a total relative intensity of 100% for each spectrum. Selected glycan compositions were confirmed by MS/MS and a final peak list as well as MS/MS data is given in Supplementary Table  S2. MS/MS spectra were manually interpreted and fragment ions annotated using GlycoWorkbench 2. 1 according to the nomenclature of  Domon and Costello [40]. Averages of direct traits per cell line were used to build a principle component analysis model in SIMCA Version 13.
0 (Umetrics AB, Umea, Sweden), with seven random cross-validation (CV) groups. For increased robustness, derived glycan traits such as galactosylation, fucosylation, sialylation, and others were calculated in SPSS Version 23 (IBM Corp, Armonk, NY). The formulas for calculation are given in Supplementary Table  S4  and the average relative abundances are given in Supplementary Table  S3. Due to non-normally distributed data, a two-tailed Mann-Whitney test was performed in Rstudio statistical software environment (Version 0. 99. 892, Kent, OH, USA, http://www. r-project. org/) with the significance level α = 0. 05 to assess differences in N-glycosylation between CDX1 high and low expressing cells. Bonferroni correction was applied to p-values to adjust for multiple testing (Supplementary Table  S3 ).
Gene Expression Microarrays and Data Analysis Total RNA was extracted by using the RNeasy mini kit according to the manufacturer's instructions. Twenty micrograms of RNA of each sample were sent to the Molecular Biology Core Facility of the Paterson Institute for Cancer Research, Manchester, UK, for gene expression microarray analysis using the Human genome U133+2 chips, following the manufacturer's instructions (Affymetrix, High Wycombe, UK). Microarray data were analyzed using Partek Genomics Suite software. The data were log2-transformed and RMA-normalized (with GC correction) using quantile normalization with Median Polish for Probeset summarization as optional settings in the software. Glycosyltransferase genes (Supplementary Table  S5A ) as well as glycan-related genes (Supplementary Table  S5B ) were selected for analysis and differentially expressed genes were identified from a t-test comparing mean-expression levels of the 8 CDX1 high vs.
8 CDX1 low cell lines. The significant level was adjusted for multiple testing. Fold changes were calculated for CDX1 high and CDX1 low cell lines and for significantly different expressed glycosyltransferases, data was visualized as boxplots in Rstudio showing the median and interquartile range. To evaluate the correlation between relative abundances of N-glycans traits based on mass spectrometry data and glycosyltransferase gene expression in the 16 investigated cell lines, linear regression analysis was performed in GraphPad Prism Version 6 (GraphPad Software, Inc. , La Jolla, CA).
Materials and methods This study was approved by local institutional IRB/ethical review boards of all participating centres, and written informed consent was obtained prior to genetic testing from all the families involved. Clinical details were obtained through medical file review and clinical examination. Genomic DNA was extracted from peripheral blood samples according to standard procedures. Whole exome sequencing (WES) was performed as described elsewhere  (Mencacci et al. , 2016) , and our bioinformatics filtering strategy included screening for only exonic and donor/acceptor splicing variants. In accordance with the pedigree and phenotype, priority was given to rare variants [50. 01% in public databases, including 1000 Genomes project, NHLBI Exome Variant Server, Complete Genomics 69, and Exome Aggregation Consortium (ExAC v0.
Immunoblots HEK cells were transfected with Nfasc155 constructs for 24 h, then the cells were washed in PBS and solubilized on ice for 15min in 1% Triton TM X-100, 140 mM NaCl, 20 mM Tris-HCl, pH 7. 4 containing protease inhibitors. Proteins (50 mg) were loaded on 7. 5% SDS-PAGE gels, transferred, and immunoblotted with a mouse antibody against  Myc (1:2000)  or a mouse antibody against -tubulin (1:2000; MABT205; Merck). Immunoreactivity was revealed using peroxidase-coupled secondary antibodies (1:5000; Jackson ImmunoResearch) and BM chemiluminescence kit (Roche). The integrated densities of each protein band were measured with ImageLab software (Bio-Rad).
Molecular modelling Wild-type and mutated NFASC isoforms were built from structure PDB ID 3P3Y (Supplementary material). Each protein was placed in a cubic box and minimized. A water layer of 0. 8 nm and Na + ions to neutralize the system were added, and a second minimization was performed. In all cases we used AMBER99SB-ILDN (Lindorff-Larsen et al. , 2010) force field and simple point charge water. In all systems we performed NVT [constant number of atoms, volume and temperature (canonical ensemble)] and NPT [constant number of atoms, pressure and temperature (isothermal-isobaric ensemble)] equilibrations for 100 ps, followed by 360 ns NPT production run at 300 K. The temperature was controlled with a modified Berendsen thermostat  (Bussi et al. , 2007) , and the pressure with an isotropic Parrinello-Rahman, at 100 kPa.
Co-expression network analysis Co-expression network analysis was used to investigate the function of NFASC across the human central and peripheral nervous system. This analysis was performed by using GTEx V6 gene expression data  (Carithers et al. , 2015)  to generate co-expression networks for each of the 13 brain tissues and tibial nerve RNA-seq data included within the GTEx study. The raw FPKM (fragments per kilobase of transcript per million mapped reads) values were corrected for known batch effects, age at death, sex and post-mortem interval, as well as unknown effects. The unknown effects were detected with the surrogate variable analysis (SVA) R Package  (Leek and Storey, 2007)  and correction was performed using ComBat  (Johnson et al. , 2007). The resulting residuals were used to create a signed network using the blockwiseConsensusModules R function from the WGCNA R package  (Langfelder and Horvath, 2008)  for each of the 14 tissues.
Next, the modules obtained in each of the 14 networks were assigned to cell types using the userListEnrichment R function implemented in the WGCNA R package, which measures enrichment between module-assigned genes and defined brain-related lists using a hypergeometric test. The same approach was used to annotate modules with Gene Ontology, REACTOME  (Fabregat et al. , 2018)  and KEGG  (Kanehisa et al. , 2016)  terms.
Bars represent mean and SEM. Molecular weight markers are shown on the left (in kDa). N. S. = not significant. (J-O) N2A cells were transfected with GFP (green) in combination with CNTN1 and CASPR1 and were incubated for 2 h with cells transfected with tandem tomato (magenta) and wild-type Nfasc155 (J), p. P705T (M) or pP939Ter (O). As negative control, N2A cells transfected with Nfasc155 (magenta) were incubated with cells transfected with GFP. (P) The numbers of green and magenta cell aggregates (dashed line circles in J-O) per visualization field were quantified in each condition (n = 4 experiments for each condition). p. R370P, p. P705T, p. after demyelinating phenomena. Those data indicate that the frameshift variant in Patient 3, which induces a complete lack of Nfasc155 expression, does not provoke a complete disruption of the node.
funding (WT093205 MA and WT104033AIA). Grunt funding was provided by the MRC (MR/S01165X/1, MR/S005021/1, G0601943), The National Institute for Health Research University College London Hospitals Biomedical Research Centre, Rosetree Trust, Ataxia UK, MSA Trust, Brain Research UK, Sparks GOSH Charity, Muscular Dystrophy UK (MDUK), Muscular Dystrophy Association (MDA USA). We also acknowledge the CINECA Awards N. HP10CRVL7F, 2017, for the availability of high-performance computing resources and support, as well as the funding from Radboudumc and RIMLS Nijmegen (Hypatia tenure track fellowship), the "Deutsche Forschungsgemeinschaft" (DFG CRC1140 KIDGEM) and the European Research Council (ERC StG TREATCilia, grant No 716344). The biochemistry work was supported by the Association Francaise contre les Myopathies (grant#21532; J.
FRAP imaging and data analyses A promUBQ10::H2B-RFP marker  [96]  was introgressed in 3h1-mutant plants by crossing. Both wild-type and triple-mutant segregants were analyzed. Measurements were done on the root tips of 2-week-old seedlings grown as previously described. One sample was prepared at a time: the root was excised and delicately mounted (i. e. , without squashing) in 0. 5× MS between the slide and coverslip (precleaned with EtOH), sealed with transparent nail polish, and let to equilibrate upside down for 10 min on the microscope platform before measurements. The imaging chamber was set at a constant temperature of 20 °C (higher/fluctuating temperatures induce nuclei juggling). Bleaching and imaging were done using an APO PL × 40 oil immersion objective, NA 1. 3, over a single plane capturing an optical section of ~2 μm encompassing a single nucleus (pinhole opening to 5 AU) with a 256 × 256 pixels image format, threefold zoom factor.
Spatial autocorrelation analysis delivers a mathematical model of chromatin density distribution for each ROI with respect to the physical length scales within which signal patterns (i. e. , local objects of similar intensities) are repeated in a regular pattern (periodicity)  [45]. We used a user-friendly graphical interface developed in Matlab for batch processing of multiple ROIs available at https://github. com/barouxlab/ChromDensity-Nano and described in details previously  [46].
RNA-seq and differential gene expression analyses RNA was isolated using modified TRIzol method  [102]  in three biological replicates for wt and 3h1 from 3week-old seedlings pooled and frozen in liquid nitrogen. Ribosomal RNA was removed using RiboMinus Plant Kit (Thermo Fisher), and ERCC RNA Spike-In Mix 1 (Thermo Fisher) was added. Libraries were prepared with Ion Total RNA-Seq Kit v2 and Ion Xpress RNA-Seq Barcode 1-16 Kit according to the user guide. Sequencing template was generated with Ion PI™ Template OT2 200 Kit v3 on Ion OneTouch™ 2 System. Sequencing was performed on Ion PI™ chip v2 and Ion Proton™ sequencer using Ion PI™ Sequencing 200 Kit v2 (all Ion Torrent kits and software are trademarks of Thermo Fisher). Base calling and adapter trimming were performed automatically by Torrent Suite software.
Residual rRNA and ERCC reads were identified and filtered out using bbsplit and filterbyname scripts from BBTools suite  [103]. Reads were aligned to TAIR10 genome using TMAP 5. 0. 13. with soft clipping from both ends and returning all the mappings with the best score. Other settings were set according to Torrent Suite defaults. Unaligned reads were aligned with BBMap (Brian Bushnell). Quantitation to ARAPORT11 transcripts was performed in Partek Flow (Partek Inc. ) using Partek E/M (expectation-maximization) algorithm. This allows inclusion of multi-mapping reads into quantitation. The algorithm quantitates unique alignments first and then iteratively divides the value of each multi-mapping read between possible loci based on the quantitation value in their close vicinity. The quantitation was performed on gene level (one value per gene).
This approach was developed to reduce the occurrence of false-positive and false-negative rate of differentially expressed gene calls, especially in low replication studies and genes with low expression, where high or low dispersion of quantitated values can result from sampling effects. Pearson correlation between replicate samples is shown in Additional file 1: Figure  S10B. To divide Arabidopsis genes into quintiles of expression, the quantified values from Partek E/M algorithm (described above) were normalized using reads per kilobase of million (RPKM) mapped reads method  [107]. The expression values were averaged across replicates. The genes with zero values of expression in each genotype were sorted into a separate group. The remaining genes were divided into quintiles based on the expression levels separately for wt and 3h1 datasets.
MNase-seq data analysis Base calling and adapter trimming were performed automatically by Torrent Suite software. Reads were aligned with TMAP 5. 0. 13. Soft clipping was turned off, end repair was allowed, and all alignments for multi-mapping reads were reported. Other settings were set according to the Torrent Suite defaults. Multi-mapping read positions were resolved using MMR  [108]  with default settings. Peak calling was performed on reads reaching terminal adapter with length range between 147 and 220 nt using a set of custom-made Python scripts. First read centers were piled up, and then the highest coverage positions were selected using greedy algorithm. Ends of the longest read used to define a position were used as peak boundaries. A peak was called only if its boundaries were not overlapping those of the neighboring peak.
NRLs were defined by calculating peak-to-peak distances from peak calling results. The frequency of distances was calculated as a percentage of all measurements and binned into three groups. Histograms were plotted in Microsoft Excel. Quantile-normalized wiggle occupancy files were generated with DANPOS2  [109]  using the dpos function with default settings. To avoid shifting of read positions (automatic procedure for single-end reads), the program was fed with "fake 75-nt paired-end" bed files, generated from both ends of alignments of fully sequenced reads. To create fake fragments, the genomic interval of each mapped read (single-end) was divided into two 75-nt intervals on opposite strands that were used to feed the Danpos algorithm as if those were paired-end data. This did not changed the informative, real read data content but enabled to circumvent a limitation of Danpos imposing read shifting for single-end reads, which is not appropriate for single-end sequencing of full fragments after MNase digestion.
Using these "fake fragments," we could thus switch off the read shifting function in Danpos for further data processing. Wig files were converted to BigWig format using UCSC wigToBigWig  [110]  and used in deepTools  [111]  for plotting. Nucleosome occupancy was calculated as the number of reads, adjusted to a length of 80 nt summed over bins of 10 nt, smoothed by 20-nt running window, and quintile normalized.
Acknowledgements We are indebted to  Ueli Grossniklaus  ( UG; University of Zurich ) for the valuable advice and insightful discussions, for sharing laboratory facilities, and for the financial support; to  Chris Bowler  and  Vincent Colot  ( Ecole Normale Supérieure Paris ) for the constant support and helpful discussions to FB; to  Valeria Gagliardini ,  Christof Eichenberger ,  Arturo Bolaños ,  Daniela Guthörl , and  Peter Kopf  ( University of Zurich ) for the general lab support; to  Daniel Prata  ( University of Zurich ) for the assistance in plant growth and selection; to  Stefan Wyder  ( University Research Priority Program "Evolution in Action ",  University of Zurich ) for the advice on the statistical tests; to  Lea Wolff  ( FB Lab, Ecole Normale Supérieure Paris -IBENS CNRS 8197 Paris ) for the assistance in Western blot experiments; to  Dominique Bergman  ( Stanford University ) for the advice on stomata analysis; to  Matthieu Ingouff  ( IRD Montpellier ) for sharing the DynaMET lines; to  Alexis Maizel  and  Daniel von Wangenheim  ( University of Heidelberg ) for the pUBQ10:H2B-RFP line; to  Mariamawit Ashenafi  ( University of Zurich ) for the technical assistance in FISH and immunostaining; and to  Marek Kalinowski  for the scripts to analyze chromatin state distributions.
Funding This work was supported by the  University of Zurich , grants of the  Swiss National Science Foundation  to CB ( SNF 310013A_149974 ) and  UG  ( 310030B_160336 ), grants of  SystemsX. chthrough an iPhD project to CB ( 2014/235 ) and the  RTD  project  MecanX  to UG and CR, fellowships from the  "Forschungskredit" of the University of Zurich  and the  COFUND Program PLANT FELLOWS  ( 2010-267243 ) of the European Union's  7th Framework Program  to KR, support from the  Polish National Science Centre  to ML ( 2011/01/N/NZ2/04849 ), a grant of the  Ministry of Sciences and Higher Education  to AJ ( MNiSW/PO4A/03928 ), the " Investissements d'Avenir" program  launched by the  French government  and implemented by the " Agence Nationale de la Recherche (ANR) "  to FB  ( ANR-10-LABX-54   MEMOLIFE ,  ANR-10-IDEX-0001-02 PSL*Research University ,  ANR-18-CE13-0004-01 Chromalight ), support to FB lab by the  EPIPLANT Groupement de Recherche , a  PhD fellowship  from the  Université Paris-Sud Doctoral School in Plant Sciences  to GT, and  COST Action   CA16212 INDEPTH.
Availability of data and materials The data for MNase-seq and RNA-seq discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  [113]  and are accessible through GEO Series accession number GSE113558 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE113558)  [114]. The image data used for cytological quantifications and FRAP analyses are available at https://figshare. com/articles/3h1_vs_WT_nuclei/8223977  [115]. The Matlab-script to analyze chromatin density distribution is freely available at https://www. github. com/barouxlab/ChromDensityNano.
Prior to phase retrieval, all sets of radiographs are scaled to the same magnification and mutually aligned. We determine a first estimate of the amplitude and phase using the approach proposed by Paganin et al. [50] , and extended to multiple distances. Here, we assume a homogenous ratio δ/β = 174 for silicon at an X-ray energy of 17 keV. This first estimate provides only a blurred version of the phase map. The map is recursively improved using 15 iterations of a non-linear least squares optimization. The phase retrieval was carried out with ESRF inhouse software using the GNU Octave programming environment (www. octave. org) and the public domain image analysis program ImageJ (see http://rsbweb. nih. gov/ij). Secondly, a standard tomographic reconstruction  [41]  based on the filtered back-projection algorithm  [42]  and implemented in the ESRF software PyHST2  [44]  allows us to obtain the distribution of the refractive index decrement δ(x, y, z).
Since the X-ray energy of 17 keV is far above any absorption edge of the materials under investigation, we obtain the electron density distribution ρ e (x, y, z) from the well-known expression  [45]  ρ e (x, y, z) = 2π r e λ 2 δ(x, y, z) with r e the classical electron radius. The resulting structure was rendered with open-source software ParaView (see www. paraview. org).
ACKNOWLEDGMENTS This work was supported by the " Stirring of light!" program  of the " Nederlandse Organisatie voor Wetenschappelijk Onderzoek" (NWO) , by the  NWO-domain "Toegepaste en Technische Wetenschappen" (TTW)  nr. 11985 , the  Shell-NWO/FOM programme "Computational Sciences for Energy Research" (CSER) , the  MESA + Institute for Nanotechnology (Applied Nanophotonics (ANP) ), and by the 2014  Descartes-Huygens Prize  of the  French Academy of Sciences  to W. L. V. We thank  ESRF  for granting beamtime through experiments  HS-2520  and  CH-5092. We thank  Leon Woldering ,  Hannie van den Broek ,  Willem Tjerkstra ,  Simon Huisman ,  Rajesh Nair ,  Elena Pavlenko ,  Mehdi Aas , and the  MESA+ Nanolab  and the  ESRF  staff for help, and  Arie den Boef  (ASML),  Jean-Michel Gérard (Grenoble) ,  Hans Hilgenkamp ,  Detlef Lohse ,  Allard Mosk  (Utrecht),  Pepijn Pinkse , and  Hasan Yilmaz  ( Yale ) for fruitful discussions.
The six combinations were randomly assigned to the different containers. The chemical composition of the tap water was analyzed at the beginning of the experiment using spectrophotometric techniques (WTW kit and Photolab S12) (mean value: pH = 8. 47; conductivity: 618 µS/cm; [O 2 ] = 10. 47 ± 0. 24 mg. L -1 ; [NO 2 -] = 0. 13 ± 0. 02 mg. L -1 ; [NO 3 -] = 14. 82 ± 0. 51 mg. L -1 ; [NH 4 + ] = 0. 11 ± 0. 02 mg. L -1 ; [PO 4 3-] = 0. 15 ± 0. 02 mg. L -1 ). During the experiment, the water was not aerated, no nutrients were added, and the water levels in the containers were maintained regularly with tap water. The light intensity was natural and the temperature was measured every half minute with three sensors (HOBO TidbiT Water Temperature Data Logger). The water temperature in the glasshouse (means ± SD: 20.
Leaf area of M. spicatum was not calculated as its leaves are small, thin and highly dissected. DMC (g. g -1 ) was assessed for each pool of three shoots per species, using the dry mass of the pool divided by the fresh mass of the pool of individuals. The LDMC (mg. g -1 ), calculated as dry mass of the leaf divided by its fresh mass, was measured on three leaves attached to each planted shoot (upper part of the plant shoot). SLA (mm 2. mg -  ) which is correlated to relative growth rate and investment in structural tissue was calculated as the ratio of fresh leaf area to leaf dry mass (mm 2 mg -1 ). Data of SLA and LDMC were averaged per stem.
1). We chose non-informative priors for each of the model parameters. Model parameters were estimated by Markov chain Monte Carlo sampling (MCMC) with the rags 4. 3. 0 library in R 3. 4. 4. We ran four independent chains of 50,000 iterations with a burn-in period of 20,000. Quality of model fit was assessed using Gelman-Rubin diagnostics. Untransformed means and standard errors were used in the figures to facilitate interpretation. Several points appeared to have a significant impact on the conclusions of the statistical analyses. Outliers detected using an approach based on the Cook distance were removed from analyses (see Supplementary Materials for more details on the procedure). When data points were removed, we present the output of our models with and without these data points in the Results section.
ACKNOWLEDGMENTS We thank the staff of the  Unité Éxpérimentale d'Écologie et d'Écotoxicologie Aquatique  ( Institut National de la Recherche Agronomique, Rennes, France ), namely  Didier Azam ,  Martine Ollitrault ,  Alphonse Quéméneur ,  Maïra Coke ,  Antoine Gallard ,  Bernard Joseph ,  Cédric Lacoste , and  Julien Tremblay  for preparing the experimental design and their assistance during the experiments. We thank  Anne Tréguier  for field assistance, as well as  Nolwenn Renaud  and  Laurent Martinez  for technical assistance. We are grateful to  Ulrich Brose  for providing a review of the paper and  Benjamin Rosenbaum  for advice on Bayesian modelization. We also thank the reviewers for their helpful comments on the manuscript.