dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors would like to acknowledge the contribution of the  COST Action   CM1204  ''XUV/X-ray light and fast ions for ultrafast chemistry'' (XLIC). We thank  HZB  for the allocation of synchrotron radiation beamtime. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 312284. We thank the LCLS LF62 collaboration and in particular  Alvaro Sanchez-Gonzalez ,  Thomas Barillot  and Pr ˇemysl Kolorenc ˇ, for providing the experimental Auger electron spectrum for gas-phase glycine.
Characterization TEM images were taken by placing a drop of the particles dispersed in water onto a carbon film-supported copper grid. Samples were studied using a Philips CM200 instrument operating at 200 kV. SEM pictures were prepared using JEOL Scanning Electron Microscope JSM-6490 LV. The XRD data were collected from an X'Pert MPD diffractometer (Panalytical AXS) with a goniometer radius 240 mm, fixed divergence slit module (1/2° divergence slit, 0. 04 rd Sollers slits) and an X'Celerator as a detector. The powder samples were placed on a silicon zerobackground sample holder and the XRD patterns were recorded at room temperature using Cu Kα radiation (λ = 0. 15418 nm). XPS analyses were performed on a Gammadata Scienta (Uppsala, Sweden) SES 200-2 spectrometer under ultra-high vacuum (P < 10 -9 mbar).
The measurements were performed at normal incidence (the sample plane is perpendicular to the emission angle). The spectrometer resolution at the Fermi level is about 0. 4 eV. The depth analyzed extends up to about 8 nm. The monochromatized Al Kα source (1486. 6 eV) was operated at a power of 420 W (30 mA and 14 kV) and the spectra were acquired at a take-off angle of 90° (angle between the sample surface and photoemission direction). During acquisition, the pass energy was set to 500 eV for wide scans and to 100 eV for high-resolution spectra. CASAXPS software (Casa Software Ltd, Teignmouth, UK, http://www. casaxps. com) was used for all peak fitting procedures and the areas of each component were modified according to classical Scofield sensitivity factors. The textural properties of the materials were investigated with a Micromeritics 3Flex Surface Characterization Analyzer instrument using liquid nitrogen  (-196 °C).
5b ). Furthermore, our analysis indicates that 'response to stimulus', 'indole glucosinolate metabolic process', 'defense response' and 'ethylene metabolic process' are activated in Ct-colonized roots under þ P but not À P conditions (cluster 9) (Fig. 5b  and Supplementary Data 9). In contrast, the genes related to 'root cell differentiation' (cluster 8, Fig. 5b ) and phosphate uptake  8  were preferentially activated in Pi-starved Arabidopsis roots during Ct colonization, similar to mycorrhizal symbiont-host interactions 21. To identify key regulatory genes (hub genes) that might orchestrate transcriptional reprogramming in the contrasting directions seen in clusters 8 and 9, we checked which of these genes are often co-regulated in other expression data sets using the ATTED-II gene coexpression database (Fig.
All the assemblies were generated using 'careful' mode in SPAdes to avoid miss-pairing of contigs by scaffolding and for further analyses, contigs o100 bp were removed. To identify and remove potential contaminating sequences, assemblies were aligned to the genomes of A. thaliana, H. sapiens and PhiX (sequencing spike-in control) using MUMmer  36  with default parameter settings. Contigs that aligned with more than 50% of their sequence (coverage; 'COV') and at least 85% sequence identity ('IDY') to any of the tested contaminants were removed from the assemblies. In addition, contigs that aligned with 75-85% identity (and 450% coverage) or with 10-50% coverage (and 485% identity) were also removed, if the judgment of the sequence being non-fungal was confirmed through BLASTN searches in the NCBI nr database (with default settings).
For the Ct0861 assembly, RNA-sequencing data were used for further clean-up. Finally, assembly quality was assessed on the basis of L50/75/90 and N50/75/90 values, percentage of error-free bases estimated with REAPR 37 (version 1. 0. 16, default settings) and gene space coverage estimated with CEGMA  38  (version 2. 0, default settings). Repetitive DNA analysis. We identified repetitive DNA in the genome assemblies using either de novo or homology approaches. For de novo searches, we used PILER and PALS  39  to identify repetitive sequences and classify them into families. The resulting libraries of consensus sequences were then used to scan the genome sequences using RepeatMasker 40 (version 4. 0. 3) to identify individual repetitive elements. For homology-based searches, we used RepeatMasker using a library of all fungal elements in the Repbase database  41.
(version 20140131). Phylogeny and divergence date estimation. All phylogenetic analyses performed in this study are described in the Supplementary Note 11. For evolutionary divergence date estimation, clustering, protein family selection and phylogenetic analyses were performed with scripts in the Mirlo package (https://github. com/ mthon/mirlo). The phylogeny was calibrated using the penalized-likelihood method implemented in r8s (ref. 42) using one primary and two secondary calibration points (Supplementary Note 11). Short-read alignment and SNP analysis. To compare the genome sequences of Ct isolates, Illumina short reads of the four other isolates were mapped onto the genome assembly of Ct0861 using Bowtie2 (ref. 43) (default settings for paired-end data). Subsequently, duplicate reads were removed using the rmdups function from the SAMtools toolkit  44  (default settings).
On the basis of the mapped genome sequencing reads, single-nucleotide polymorphisms (SNPs) were identified using the mpileup function in SAMtools 44 (version 0. 1. 18; with option -u) The obtained SNP sets were filtered by applying the bcftools script vcfutils. pl varFilter (SAMtools) with adjusted read depth settings according to the respective sequencing read coverage to -d 80 and -D 800 for CBS495 and to -d 40 and -D 400 for CBS130, CBS127 and CBS168. The SNP locations, read coverage for each isolate and locations of conserved regions were visualized using the Circos software package  45  (version 0. 62. 1). In addition, we also calculated SNP densities (SNPs per kb) relative to Ct0861 for each isolate as a function of the genomic location on all Ct0861 contigs larger than 50 kb, using a 10-kb sliding window that moved 1 kb at each step.
For visualization of the SNP densities, these windows were sorted in the increasing order by contig number and position on the contig. To identify windows with a low SNP density, that is, a common haplogroup, between isolates we classified the SNP density in each window as either 'low' or 'high' using a two-state hidden Markov model (HMM). This HMM was created and fitted on the observed 10 kb SNP densities by the expectation-maximization algorithm using functions 'depmix' and 'fit' (R package depmixS4), and subsequently the posterior state sequence (with states 'low' and 'high'), computed via the Viterbi algorithm, was extracted with function 'posterior' (R package depmixS4). Gene annotation. The prediction of Ct and Ci gene models was performed using the MAKER pipeline  46  (version 2. 28) , which integrates different ab initio gene prediction tools together with evidence from EST and protein alignments.
In a first step, for each genome, the pipeline was run using Augustus  47  (with species model Fusarium graminearum) and GeneMark-ES  48  for ab initio gene prediction together with transcript and protein alignment evidence. The resulting gene models from this first run were used as training set for a third ab initio prediction tool, SNAP  49  , and subsequently the annotation pipeline was re-run, this time including all three ab initio prediction tools together with the transcript and protein alignment evidence to yield the final gene models. The alignment evidence was created from BLAST and Exonerate 50 alignments of both protein and transcript sequences of each respective fungus (Ct/Ci) and protein sequences of C. higginsianum and C. graminicola. Ct (isolate 0861) and Ci transcript and protein sequences were obtained from the corresponding RNA-seq data via a transcriptome de novo assembly.
For this purpose, we extracted all RNA-seq read pairs that did not align to the host plant genome from four (Ct) to nine (Ci) in planta samples and combined these with the read pairs from one in vitro sample of the respective fungus. The combined RNA-seq reads were then used as input for Trinity  51  (with default parameter settings for paired-end reads) to assemble transcripts and extract peptide sequences of the best-scoring ORFs (using the Perl script 'transcripts_to_best_scoring_ORFs. pl' provided with the Trinity software). General functional annotations for the predicted gene models were obtained using Blast2GO (ref. 52). To perform Blast2GO searches and ensure stable databases over time for multiple genome annotations, the NCBI nr database was downloaded locally (version: 8 January 2015).
In addition, a local b2gdb mysql database was generated (version 201402) and connected to the Blast2GO java tool. For each genome annotation, BLASTP was performed against the local NCBI nr database ( À e 1E À 3, -v 10 À b 10) and tabular BLAST output was loaded into Blast2GO using graphical java interface. Further analyses were performed according to the Blast2GO user manual. MCL analysis. Gene families and clusters of orthologous genes were inferred using OrthoMCL 53 (version 2. 0) with standard parameters and granularity 1. 5 for the MCL clustering step. Functional enrichment and overrepresentation analyses were performed using a Fisher's exact test, adjusting for FDR. For each gene family inferred with orthoMCL, a multiple sequence alignment of the protein sequences was obtained using Clustal Omega  54  and an HMM model was generated with the hhmake program of the HHSuite toolkit  55.
Sequences from the fungal database fuNOG  56  were similarly aligned and HMM models generated. To annotate whole gene families, the hhsearch program was used to obtain matches between the gene family and the fuNOG HMMs and only hits with a probability equal to or higher than 0. 99 were considered. To annotate whole gene families, the hhsearch program was used to obtain matches between the gene family and the fuNOG HMMs and only hits with a probability Z0. 99 were considered. Ancestral genome reconstruction. Gene families inferred with OrthoMCL were used to reconstruct the ancestral genomes of each Colletotrichum lineage. GLOOME  57  (maximum-likelihood approach) was used to infer ancestral gene gains and losses (GGLs) and to reconstruct the ancestral GGLs of gene families on the species tree of Ct0861 and the other five genomes available for this genus.
The average d N /d S ratio was calculated for each protein family and a one-sided Fisher's test (FDR corrected) was performed to identify protein families with a significant enrichment of synonymous mutations per synonymous site versus non-synonymous mutations per non-synonymous site. Annotation of specific gene categories. Secretomes of all species were predicted using WoLF-PSORT 61 with default settings. Colletotrichum CSEPs were defined as extracellular proteins with no significant BLAST homology (E-value o1 Â 10 À 3 ) to sequences outside the genus Colletotrichum in the UniProt database (SwissProt and TrEMBL components). To identify secreted proteases, sequences of predicted extracellular proteins were subjected to a MEROPS Batch BLAST analysis  62. Membrane transporters were identified and classified through BLAST searches against the Transporter Collection Database (http://www.
Libraries were subjected to paired-end sequencing (100 bp reads) using the Illumina HiSeq2500 Sequencing System. To make sure the sequenced reads were of sufficiently high quality, an initial quality check was performed using the FastQC suite (http:// www. bioinformatics. babraham. ac. uk/projects/fastqc/). Subsequently, the RNA-seq reads were mapped to the assembled and annotated genomes of either Ct 0861 or Ci, and in parallel to the annotated genome of the host plant A. thaliana (TAIR10) using Tophat2 (ref. 64; a ¼ 10, g ¼ 10, r ¼ 100, mate-std-dev ¼ 40). The mapped RNA-seq reads were then transformed into a fragment count per gene per sample using the htseq-count script (s ¼ reverse, t ¼ exon) in the package HTSeq  65. The complete RNA-Seq data presented by Hiruma et al. 8  and in this manuscript have been deposited under the GEO series accession number GSE70094.
Statistical analysis of differential gene expression. All statistical analyses of plant and fungal gene expression were performed in R (codes are available upon request). For the analyses of plant gene expression, genes with less than 100 mapped fragments in total (that is, across all the analysed samples) were rated as 'not expressed' and therefore excluded. For analyses of fungal gene expression, we excluded genes that were not sufficiently expressed in the in planta samples, that is, genes with less than 100 (Ct, 24 samples) or less than 50 (Ci, 6 samples) mapped fragments across all the analysed samples. Subsequently, the count data for all expressed genes was TMM-normalized and log-transformed using the functions 'calcNormFactors' (R package EdgeR 66 ) and 'voom' (R package limma 67 ) to yield log 2 counts per million (log 2 cpm).
To analyse the aspects of differential gene expression in Ct0861, Ci and their host plant Arabidopsis, we fitted for each analysis a distinct linear model to the respective log 2 -transformed count data using the function lmFit (R package limma  67  ) and subsequently performed moderated t-tests for specific comparisons of interest. Resulting P values were adjusted for false discoveries due to multiple hypotheses testing via the Benjamini-Hochberg procedure (FDR). To extract genes with significant expression differences, a cutoff of FDRo0. 05 and |log 2 FC|Z1 was applied. Heatmaps of gene expression profiles were generated with the Genesis expression analysis package  68  and interactive Tree Of Life 69 was used to visualize CSEP gene expression data. To derive Arabidopsis, Ct and Ci gene expression profiles during the time-course experiment, log 2 expression ratios were calculated between the normalized number of reads detected for a given gene at a given developmental stage and the geometrical mean of the number of reads calculated across all developmental stages.
This log 2 ratio is referred as the 'Relative Expression Index'. The Cytoscape plug-in ClueGO þ CluePedia 70 was used to construct GO term enrichment networks and to visualize functionally grouped terms among significantly regulated genes. Significant enrichments were determined using the hypergeometric test and Bonferroni step-down corrected P values are represented. Co-regulated genes that were also co-expressed in other Arabidopsis expression data sets were identified using ATTED-II (http://atted. jp/) and co-expression networks were generated using Cytoscape 71 (version 3. 1. 1). RT-qPCR analysis. First-strand cDNA was synthesized from 1 mg DNase-treated total RNA using the iScript cDNA synthesis kit (Bio-Rad) and PCR amplification was performed using the iQ5 real-time PCR detection system (Bio-Rad).
Acknowledgements This work was primarily supported by the  Max Planck Society  (S. H. , B. K. , K. H. , R. G. -O, E. V. L. v. T. , E. K, P. S. -L and R. J. O. ), the  Agence Nationale de la Recherche  grant  ANR-12-CHEX-0008-01  (R. J. O. ), the  European Research Council  advanced grant (ROOTMICROBIOTA) (P. S. -L), the  'Cluster of Excellence on Plant Sciences' program  (funded by the  Deutsche Forschungsgemeinschaft; R. G. -O, E. K. , A. C. M, P. S. -L). Other funding sources included the  German Center for Infection Research, DZIF  (P. C. M. ) and the  Japan Society for the Promotion of Science  (Post-doctoral Fellowship for Research Abroad; K. H. ). We are grateful to  Lisa Vaillancourt  ( University of Kentucky ) and  Pedro Crous  ( CBS-KNAW, Utrecht ) for providing fungal cultures. We thank the staff of the  Max Planck Genome Centre Cologne  for their expertise in generating the sequencing data.
Taxon sampling Representatives of all known African perennial-tubered Dioscorea (Dioscoreaceae) were sampled (Table  1 ). These included five pachycaul species, three Cape species, three species of the D. buchananii subclade (as defined by Wilkin and Muasya  [8] ), two species of the southern African members of the CL clade, and two species from the Enantiophyllum clade as well as two species from the East Africa subclade. We also included representatives from all known Dioscorea lineages  [6] : four from the Mediterranean clade, D. tentaculigera and D. prazeri from South-East Asia and two New World taxa (D. brachybotrya and D. galeottiana), respectively belonging to the New World I (NWI) and II clades (NWII); and Tacca and Stenomeris (Dioscoreaceae) were selected as outgroups. Voucher specimen information and GenBank accession numbers are listed in Table  1  and trace files and sequences are available on the Barcode of Life Data System (BOLD; www.
Phylogenetic analyses: parsimony and Bayesian approaches Maximum parsimony (MP) using PAUP* version 4. 0b1  [30]  was performed on the individual and combined datasets. Tree searches were conducted using 1,000 replicates of random taxon addition, retaining 10 trees at each step, with tree-bisection-reconnection (TBR) branch swapping and MulTrees in effect (saving multiple equally parsimonious trees). Support for clades in all analyses was estimated using bootstrap analysis  [31]  with 1000 replicates, simple sequence addition, TBR swapping, with MulTrees in effect but saving a maximum of 10 trees per replicate. Delayed transformation character optimization (DELTRAN) was used to calculate branch lengths, due to reported errors http://paup. sc. fsu. edu/paupfaq/paupans. html with accelerated transformation optimization (ACCTRAN) in PAUP v.
Mozambique and South Africa Forest, woodlands found among rocks and ravine Tuberous plants with annual to persistent stems reaching up to 12 m height       Bootstrap values are provided in Fig. 3. All data sets were analyzed separately, and the individual bootstrap consensus trees examined by eye to identify topological conflicts, i. e. moderate to high support for different placement of taxa. In order to test for significant conflicts between the independent DNA data matrices, a partition homogeneity test was performed  [32] [33] [34]. The Incongruence Length Difference (ILD) test of Farris et al. [32]  implemented in PAUP* 4. 0 b10  [30]  was performed through 1000 random-order-entry replicates to estimate if the six datasets were significantly different from random partitions of the same size.
Results Statistics for MP analysis for the six plastid markers and combined dataset are presented in Table  2. Of all the genes used, matK and rpl32-trnL had a significantly higher number of variable sites (27. 85 % and 25. 17 % respectively) compared to the other regions than display percentages below 10 % (see Table  2 ). The number of potentially informative characters is higher for matK (12. 07 %) than rpl32-trnL (10. 01 %), however contribution to total of parsimony informative character (PIC) is lower for matK (26. 35 %) than for rpl32-trnL (30. 84 %; Table  2 ).
Maximum parsimony analyses MP analysis of each of the six regions resulted in trees that were similar in topology (Additional files 1, 2, 3, 4, 5 and 6), and were thus combined and treated as a single dataset. ILD test results provide support for congruence (p > 0. 05). The psaA-ycf3 region is significantly different from rbcLa, matK, trnL-F, trnH-psbA, and rpl32-trnL and probably caused by the psaA-ycf3 sequence of D. galeottiana. However, the observed congruence between the trees obtained for each region separately and the ILD results (Table  3 ) support combining these regions. The statistics for the MP analysis for the combined data is presented in Table  2. From the heuristic search, we found 72 most parsimonious trees of which one is presented in the supplementary Additional file 7. The combined MP tree is largely congruent with that obtained from Bayesian analysis and therefore bootstrap values recovered in the MP analysis are plotted onto the Bayesian consensus tree (Fig.
Acknowledgements We thank the  Government of Canada through Genome Canada  and the  Ontario Genomics Institute  ( 2008-OGI-ICI-03 ), the  International Development Research Centre (IDRC) ,  Canada  and the  Central Analytical Facility of the Faculty of Science, University of Johannesburg (Spectrau) , the  Spanish Aragon Government  and the  European Social Fund  ( PC, Bioflora research grant ), and the  South African National Research Foundation  for financial support towards fieldwork and sequencing. Plant samples were collected with permission of land owners and respective authorities, with special thanks for assistance going to  Charles Stirton ,  Graham Grieves ,  Geoffrey Mwachala ,  John Burrows ,  Sebsebe Demissew , the late Tony Abbotts. The  Royal Botanic Gardens, Kew ,  DNA Bank , in particularly Felix Forest,  Laszlo Csiba  and  Rhina Duque-Thues , for DNA aliquots and  Bezeng S.
Key steps in the life cycle of a virus, such as the fusion event as the virus infects a host cell and its maturation process, relate to an intricate interplay between the structure and the dynamics of its constituent proteins, especially those that define its capsid, much akin to an envelope that protects its genomic material. We present a comprehensive, comparative analysis of such interplay for the capsids of two viruses from the flaviviridae family, Dengue (DENV) and Zika (ZIKV). We use for that purpose our own software suite, DD-NMA, which is based on normal mode analysis. We describe the elements of DD-NMA that are relevant to the analysis of large systems, such as virus capsids. In particular, we introduce our implementation of simplified elastic networks and justify their parametrization.
MATERIALS AND METHODS We have used our own software package, DD-NMA, to perform all the analyses discussed in the Results section. In the following, we highlight some of the elements of DD-NMA that are relevant to the analysis of large systems. We note that DD-NMA is available as a web-based service at http://lorentz. dynstr. pasteur. fr/suny/index. php?id0=delaunaynma#welcome.
RESULTS AND DISCUSSION DENV and ZIKV are both members of the flaviviridae family. DENV serotype 1 and ZIKV (which are the focus of this study) share 53% sequence identity  (Kostyuchenko et al. , 2016). Their particles share a common fold, with their capsids having icosahedral symmetry. Those capsids are formed of 60 asymmetrical units, with each unit containing three copies of E protein (495 and 504 residues in DENV and ZIKV, respectively) and three copies of the membrane protein M (74 and 75 residues in DENV and ZIKV, respectively). The high resolution cryo-EM structures of all four serotypes of DENV, as well as the structure of one strain of ZIKV, are available in the Protein Data Bank  (Zhang et al. , 2012; Kostyuchenko et al. , 2013 Kostyuchenko et al. , , 2014;; Fibriansah et al. , 2015; Kostyuchenko et al.