dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018). The dataset was also subjected to transposable element (TE) discovery and characterization of long terminal repeat (LTR)retrotransposon (LTR-RT) evolutionary lineages. Most of the TEs were located in intergenic spaces (∼70%), although some overlapped genes. Long terminal repeat retroelements predominated, consisting mainly of Gypsy elements, with overrepresentation of the RLG_peDel (or Tekay lineage according to  Neumann et al. [2019] ), and single elements of P. edulis were obtained for the first time  (Costa et al. , 2019). Additionally,  Sader, Dias, et al. (2019) , who used a Gypsy element sequence as a probe for in situ hybridization, found a dispersed and uniform distribution pattern along P. edulis chromosomes, implying that they were abundant and could have significantly influenced the genome size.
The FISH procedure applied to mitotic chromosomes was essentially as described in  Fonsêca et al. (2010). The hybridization mix consisted of 50% (v/v) formamide, 10% (w/v) dextran sulfate, 2× SSC, and 2 to 5 ng μl -1 of the probes. The slides were denatured for 5 min at 75 ˚C and hybridized for 24 h at 37 ˚C. The final stringency was 76%. The slides were mounted with Vectashield (Vector) containing 2 μg ml -1 4′,6-diamidino-2-phenylindole. Images were captured on an epifluorescence microscope (Leica Microsystems) running Leica QFISH software and a DMLB microscope running Leica Las-AF software. For final processing, the images were uniformly adjusted for brightness and contrast only, using Adobe Photoshop version 10. 0. Three complete metaphase cells were used to measure the short and long arms of the individual chromosomes identified by satDNA probes with the Adobe Photoshop measurement tool.
Finally, the raw data were filtered to obtain high-quality reads (reads with a quality of <0. 75 and a length of <500 bp were discarded). For Illumina short-read sequencing, DNA was extracted by the cetyltrimethylammonium bromide method adapted from  Murray and Thompson (1980)  and used to construct three PCR-free libraries with the Illumina TruSeq kit. Sequencing was run on an Illumina HiSeq platform with the Rapid Run Version 2 (Illumina) protocol to yield 250-bp paired end reads. The Illumina raw reads were trimmed with Trimmomatic software  (Bolger et al. , 2014) , discarding all reads with a PHRED value (a measure for base quality in DNA sequencing) lower than 24. The Plant Genome We also performed one run of Nanopore sequencing to complete the assembly of the mitochondrial genome. MinION libraries were prepared with the Oxford Nanopore 1D Genomic DNA Ligation kit (SQKLSK109) (Oxford Nanopore Technologies) and MinION protocol (version IDI_S1006_v1_revB_18Apr2016), followed by sequencing on a Nanopore MinION platform (MKE_1013_v1_ revAR_11Apr2016).
In addition, taking Populus trichocarpa Torr. & A. Gray ex Hook. and Manihot esculenta Crantz. as references (Supplemental Table  S1 ). RaGOO  (Alonge et al. , 2019)  was used for performing additional reference-guided scaffolding steps (parameters: a minimum unique alignment length of 50,000 bp and a maximum inferred gap size of 10,000 bp). Assembly gaps were closed with GapFiller Version 2. 1. 2 (Nadalin et al. , 2012)  and LR_GapCloser Version 1. 1  (Xu et al. , 2018) , for the trimmed Illumina and PacBio reads, respectively. Benchmarking Universal Single-Copy Orthologs Version 4. 0. 2 (Seppey et al. , 2019)  and the Embryophyta odb10 database were used to assess genome completeness. Genome assembly quality was also assessed on the basis of the LTR Assembly Index, computed according to  Ou et al.
(2018). Repetitive sequences were investigated via the dot-blot method to visualize two sequences and identify closely similar regions after sequence alignment. Default parameters were used for pairwise alignment of the scaffolds run on MUM-mer4  (Marçais et al. , 2018). This approach allowed us to map the centromeric regions. GenomeScope Version 1. 0. 0  (Vurture et al. , 2017)  and Smudgeplot Version 0. 2. 4  (Ranallo-Benavidez et al. , 2020)  were used to estimate the coverage, ploidy, and heterozygosity of the P. organensis genome. The diploid model was used to fit the results and default parameters for analyzing data on 89,918,970 Illumina reads.
2. 6 Transcriptome assembly P. organensis and P. edulis transcriptomes generated by the RNAseq approach were assembled with the Trinity tool Version 2. 11. 0  (Grabherr et al. , 2011)  with the default parameters. Coding regions within transcripts were identified by the TransDecoder pipeline Version 5. 5. 0 (https://github. com/ TransDecoder/TransDecoder).
Transposable element discovery De novo detection of TEs was run in REPET Version 3. 0  (Jouffroy et al. , 2016; Quesneville et al. , 2005) , and the bundled PASTEC tool Version 3. 0  (Hoede et al. , 2014)  used to classify TEs at the order level on the basis of the Wicker hierarchical system  (Wicker et al. , 2007). The elements identified by REPET were manually curated and characterized into superfamilies and evolutionary lineages with DANTE (Domain-based ANnotation of Transposable Elements) Version 1. 0. 1, available on the RepeatExplorer server  (Novák et al. , 2013;  https://repeatexplorer-elixir. cerit-sc. cz/), and the Viridiplantae database Version 3. 0  (Neumann et al. , 2019). Transposable element masking was performed by RepeatMasker Version 4. 0. 7 (http://www. repeatmasker. org) with the "-s -cutoff 260" parameter.
The TEannot pipeline in the REPET package was also used to generate TE annotations in GFF3 file format. To estimate when the LTR-RT lineages were inserted into the P. organensis genome, sequences were used as input to LTR_FINDER Version 1. 0. 7  (Xu & Wang, 2007)  to search for full-length elements and compute the divergence (K) between LTRs. Insertion time (t) was then calculated for each full-length element according to the formula: t = K/2r  (Yin et al. , 2015) , where t is the insertion time in million years ago (mya), K is the number of nucleotide substitutions per site, and r is the nucleotide substitution rate. A value of 1. 5 × 10 -8 was assigned to r, as reported for the Arabidopsis thaliana (L. ) Heyhn. chalcone synthase and alcohol dehydrogenase genes  (Koch et al. , 2000)  used for dating the LTR-RTs in P.
edulis  (Costa et al. , 2019). In order to investigate the incorporation of entire genes or fragments thereof into TEs, the sequences were scanned for conserved domains in the Conserved Domain Database Version 3. 18  (Marchler-Bauer et al. , 2017)  public resource using the Conserved Domain Database search interface (https://www. ncbi. nlm. nih. gov/Structure/cdd/wrpsb. cgi), with default parameters.
Genome annotation For gene prediction, the BRAKER Version 2 pipeline  (Hoff et al. , 2016)  was used for P. organensis, inputting the transcriptome and protein data of very closely related species derived from the Benchmarking Universal Single-Copy Orthologs searches and OrthoDB  (Kriventseva et al. , 2019)  (-etpmode and -softmasking modes). The PASA pipeline Version 2. 4. 1  (Haas et al. , 2003)  produced spliced alignment assemblies based on the RNA-seq data. EVidence Modeler Version 1. 1. 1 (Haas et al. , 2008)  combined with BRAKER2 Version 2. 1. 5 outputs (augustus. ab. initio, augustus. hints, and braker), and the transcript alignments were then used. Finally, two PASA pipeline iterations updated the EVidence Modeler consensus predictions, adding untranslated region annotations and models for the alternatively spliced isoforms.
Functional annotation was performed with OmicsBox Version 1. 4. 10 (https://www. biobam. com/omicsbox/), InterProScan Version 5. 46-81. 0 (Jones et al. , 2014) , and EggNOG-mapper Version 1. 0. 3  (Huerta-Cepas et al. , 2017)  with the EggNOG database Version 5. 0  (Huerta-Cepas et al. , 2019). The UniPro-tKB/TrEMBL (The UniProt Consortium, 2017) Viridiplantae database was used for protein function assignment. Gene ontology (GO) term enrichment analysis across different sets of genes was run with the MCScanX algorithm  (Wang et al. , 2012) , which classifies genes as singletons and dispersed, proximal, tandem, and segmental or wholegenome duplication (WGD) duplicates according to the copy number and genomic distribution. The following criteria were applied: (a) singletons were single-copy genes, (b) adjacent genes were tandem, (c) genes separated by 10 or fewer genes were proximal, (d) genes separated by more than 10 genes were dispersed, and (e) the anchor genes in collinear blocks were WGD genes.
We then identified over-represented GO terms in these gene sets with GOATOOLS software  (Klopfenstein et al. , 2018) , with a corrected p value of <. 01 as the threshold for determining significant overrepresentation. In addition, orthogroups were defined to assess common protein-coding genes, and all gene sequences of Malpighiales species (P. organensis, P. trichocarpa, Salix purpurea L. , Ricinus communis L. , and M. esculenta) were used as input to OrthoVenn2  (Xu et al. , 2019) , set to the default parameters. To identify the genes potentially involved in self-incompatibility, BlastP searches using Brassicaceae reference sequences of the S-locus and partial P. edulis sequences  (Madureira et al. , 2014)  were run to recognize homologs in the predicted P. organensis protein dataset. Sequences were identified on the basis of similarity and a domain structure resembling S-locus glycoproteins (SLG) and S-locus receptor kinases (SRK).
Annotation of noncoding RNA All noncoding RNA classes were scanned with a query set from the Rfam database and cmsearch software in Infernal toolkit Version 1. 1. 2 (Nawrocki & Eddy, 2013). The following tools were used for class-specific annotation of ncRNAs: (a) tRNAscan-SE Version 2. 0  (Lowe & Chan, 2016; Lowe & Eddy, 1997)  with default parameters to identify tRNA genes, (b) the RNAmmer Version 1. 2  (Lagesen et al. , 2007)  algorithm with the default parameters to identify rRNA genes (5. 8S, 18S, and 28S), (c) nucleotide BLAST  (Altschul et al. , 1990)  against miRBase release 22. 1  (Kozomara & Griffiths-Jones, 2014)  with dust parameters and an e-value of 0. 00001. Filtering was based on identity and a query coverage of at least 90%. The Plant Genome.
Whole-genome duplication analysis Whole-genome duplication events were predicted according to the distribution of synonymous substitutions per synonymous site (K) among paralogs. First, an all-by-all BlastP was run with the default parameters to scan all potential homologous gene pairs within any genome (P. organensis, P. trichocarpa, S. purpurea, M. esculenta, and R. communis; see Supplemental Table  S1 ). The DupGen_finder pipeline  (Qiao et al. , 2019)  was then run to identify WGD-derived gene pairs on the basis of the MCScanX algorithm  (Wang et al. , 2012)  bundled with the pipeline, and to calculate the K  (Qiao et al. , 2019)  (see https://github. com/qiao-xin/Scripts_for_GB). To assess the genome similarities among related Malpighiales species we used the entirely sequenced and well-annotated genomes of P.
trichocarpa, S. purpurea (both Salicaceae), and M. esculenta and R. communis (both Euphorbiaceae) (Supplemental Table  S1 ). MCScan was run with the default parameters according to the instructions [https://github. com/tanghaibao/jcvi/wiki/MCscan-(Pythonversion)]. Searching each genome for homologous groups of protein-coding genes was based on the best five hits with the best nonself match in each target genome that met an e-value threshold of 0. 00001. For constructing the matrix of average nucleotide identity (ANI) across coding regions, information was extracted from the BLAST output using Custom Perl scripts and the Malpighiales genomes available in Phytozome database Version 13  (Goodstein et al. , 2012 ) (P. trichocarpa, Populus deltoides, S. purpurea, M. esculenta, R. communis, and Linum usitatissimum L.
MADS-box genes in P. organensis By using the gene prediction pipeline combined with protein predictions output by Augustus Version 3. 2. 3 (Keller et al. , 2011) , we isolated the candidate P. organensis homologs in MADS-box genes with the BlastP tool  (Altschul et al. , 1997)  and A. thaliana sequences as baits. The MADS-box sequences from A. thaliana, P. trichocarpa, and Vitis vinifera L. were obtained from the PlantTFDB database  (Jin et al. , 2017). Amino acid sequences were used for aligning the sequences on the basis of the L-INS-i algorithm in MAFFT Version 7  (Katoh & Standley, 2013). The alignments were recurrently inspected, then misaligned or nonaligned P. organensis sequences were examined further and either discarded or manually curated with Bioedit  (Hall, 1999). The final alignment was fitted to the model with ModelFinder (Kalyaanamoorthy et al.
Chloroplast and mitochondrial DNA assembly and annotation GeSeq (Annotation of Organellar Genomes)  (Tillich et al. , 2017)  with the default settings was used to predict P. organensis genes and identify sequence features, including chloroplast DNA protein-coding gene sequences, rRNA, and tRNA, followed by manual correction for start and stop codons and intron positions in GenomeView software. All tRNA genes were further confirmed in tRNAscan-SE  (Lowe & Chan, 2016)  and the ARAGORN online search server  (Laslett & Canback, 2004). Pseudogenes were classified on the basis of the nucleotide losses or the presence of internal stop codons. Finally, the circular chloroplast genome map was constructed in OGDRAW  (Greiner et al. , 2019). PacBio long reads were assembled in Canu Version 2. 1. 1  (Koren et al.
For mitogenome annotation, the P. edulis genome (MT140634)  (Yang & Wang, 2020)  was used as the reference, following the protocol described for chloroplast DNA annotation. Gene sequences, intron-exon boundaries, and trans-spliced events were manually curated by comparisons with annotated orthologs available in the plant mitochondrial genome database at NCBI (www. ncbi. nlm. nih. gov/genome/organelle). All other genetic elements were detected by manual BlastX and nucleotide BLAST searches against GeneBank, identifying the repeats in Blast2seq. Maps were constructed with the Circos Version 0. 69-9 visualization tool  (Krzywinski et al. , 2009)  and manually adjusted with Inkscape (https://inkscape. org/).
Approximately 21,000 genes were annotated for protein function, gene ontology assignment and InterPro domain. Annotation of noncoding and structural RNA genes revealed 492 tRNAs, at least 122 rRNAs (110 5. 8S and six each of 18S and 28S rRNAs) and 83 miRNA genes. We investigated the presence of genes potentially involved in the self-incompatibility (SI) determining locus. Brassica sequences were used as a reference because of their wellknown system and their use in a previous investigation of P. edulis  (Madureira et al. , 2014). In total, 54 sequences were recovered from the P. organensis dataset, similar to SRK and SLG  (Takayama & Isogai, 2005 ) (Supplemental Table  S8 ). They included 14 SRKs (B_lectin-SLG-PAN_APPLE-TM-KD) and four SLGs (B_lectin-SLG-PAN_APPLE) with a typical domain architecture  (Xing et al.
Whole-genome duplication analysis The distribution of pairwise synonymous substitution rates across the corresponding duplicated genes (paralogs) in conserved genomic blocks was studied in P. organensis and the Malpighiales P. trichocarpa, S. purpurea (both Salicaceae), and M. esculenta (Euphorbiaceae). These are related species with entirely sequenced and well-annotated genomes (Supplemental Table  S1 ). Synonymous nucleotide substitution ratebased analysis  (Cai et al. , 2019)  identified WGDs in all species (Figure  4a-d ). The Ks distribution were also examined in the transcriptomic data from P. organensis (subgenus Decaloba) and a combination of three plants of the sour passionfruit (P. edulis, subgenus Passiflora)  (Cai et al. , 2019). These species share the ancient genome triplication event (γ whole-genome triplication), exhibiting a second peak that corresponds to a second shared WGD event (Figure  4e ,f) beginning in the Eocene, according to  Cai et al.
D A T A AVA I L A B I L I T Y S T A T E M E N T The Whole Genome Shotgun project has been deposited at DNA Data Bank of Japan, European Nucleotide Archive, and GenBank under accession JAEPBF000000000. The genome version presented in this paper is JAEPBF010000000. Passiflora organensis genome annotations are available at https: //genomevolution. org/coge/GenomeInfo. pl?gid=59125.
Microsynteny and comparative analysis of Passiflora organensis and other Malpighiales, showing different patterns of gene retention and loss in different segments of the same genome, and across the genome segments of each species. (a, b) Exclusive and shared microsyntenic segments in the genomes of P. organensis and Manihot esculenta, and in P. organensis and Populus trichocarpa: (a) mycrosinteny between P. organensis and M. esculenta; (b) mycrosinteny between P. organensis and P. trichocarpa. (c) Shared microsyntenic segments in the genomes of P. organensis, M. esculenta, and P. trichocarpa. (d) Average nucleotide identity (ANI) and neighbor-joining tree based on the coding regions of all sequenced Malpighiales genomes available in public databases; Arabidopsis thaliana was the outgroup of floral organs [reviewed by.
F Microsynteny and comparative analysis of Passiflora organensis and other Malpighiales, showing different patterns of gene retention and loss in different segments of the same genome, and across the genome segments of each species. (a, b) Exclusive and shared microsyntenic segments in the genomes of P. organensis and Manihot esculenta, and in P. organensis and Populus trichocarpa: (a) mycrosinteny between P. organensis and M. esculenta; (b) mycrosinteny between P. organensis and P. trichocarpa. (c) Shared microsyntenic segments in the genomes of P. organensis, M. esculenta, and P. trichocarpa. (d) Average nucleotide identity (ANI) and neighbor-joining tree based on the coding regions of all sequenced Malpighiales genomes available in public databases; Arabidopsis thaliana was the outgroup of floral organs [reviewed by.
Geodetic Network and Data Processing The GPS data used in this work come primarily from continuous GPS sites, some of them installed within the COCONet project  (Braun et al. , 2012). Additional campaign measurements are available on some of the islands, mostly in Martinique and Guadeloupe. The 74 velocities used here are a subset of the 445 stations that we routinely process that cover the entire Caribbean region. A list of all stations and GPS velocities is available in the supporting information. The data processing procedure is the same as used in  Symithe et al. (2015)  and is only briefly summarized hereafter. We use the GAMIT-GLOBK software package  (Herring et al. , 2010)  to process the double-difference phase measurements using precise orbits and clocks from the International Global Navigation Satellite Systems (GNSS) Service (IGS), Earth orientation parameters from the International Earth Rotation Service (IERS) and to produce loosely constrained daily solutions.
Data Availability Statement This work uses data services provided by the UNAVCO Facility with support from the U. S. National Science Foundation (NSF) and National Aeronautics and Space Administration (NASA) under NSF Cooperative Agreement EAR-0735156. Permanent and episodic GPS within the OVSG, OVSM, and SRC footprint in the Lesser Antilles have been funded by CNRS-INSU-ACI programs originally, then through three FED-ER European Community programs (CPER-PO and Interreg IV and V Caraïbe projects) cofunded by the French Ministry of Research, the French Ministry of Environment, the Guadeloupe Regional Council, and the IPGP. All data used in this work are openly available from the IGS (http://www. igs. org/), UNAVCO (www. unavco. org), and IPGP data center (http://volobsis. ipgp. fr/) archives.
Ancillary information necessary to process GPS data, such as precise satellite orbits and antenna phase center models, is openly available from the IGS (http://www. igs. org). Global SINEX files used here are publicly available at MIT (acc. igs. org/reprocess. html). The software used to process the GPS data (GAMIT-GLOBK) is openly available at MIT (http:// geoweb. mit. edu/gg/). The codes that were used to obtain the geodetic coupling models are on Zenodo (Jolivet, 2020) but for the upto-date codes we refer readers to the versions of CSI (https://github. com/jolivetr/ csi) and AlTar (https://github. com/AlTarFramework/altar) on GitHub. Figures were produced using the Generic Mapping Tools software package  (Wessel & Smith, 1998).
Since the number of TDs per unit time decrease with distance from the Sun (e. g. ,  Tsurutani & Smith 1979; Lepping & Behannon 1986) ,  Neugebauer et al. (1986)  suggested that the KHI may have destroyed TDs as the KHI growth rate becomes larger with decreasing Alfvén speed. Despite all these postulations, the KHI has not been observed in situ in the solar wind to our knowledge. Here we report unambiguous in-situ KHI detection in Solar Orbiter observations (SolO;  Müller et al. 2020)  during its cruise phase. SolO   (Parker 1958 ) calculated using a constant velocity of 300 km s -1. This figure was obtained using the 3DView software  (Génot et al. 2018). is an ESA mission, launched on February 10, 2020, aimed to study the Sun and inner heliosphere from out-of-ecliptic vantage points. The cruise phase started in June 2020 with the in-situ instruments operated nominally after a successful commissioning.
On July 23 -24, 2020, SolO was at the distance 0. 69 AU from the Sun. From July 23 at 12:00 UT to July 24 at 12:00 UT, SolO was moving from the distance r = 1. 03 × 10 8 km (0. 68 AU) to r = 1. 04 × 10 8 km (0. 70 AU) from the Sun. Fig. 1  shows the projected position of SolO onto the equatorial plane with the solar wind Parker spiral (orange lines)  (Parker 1958 ) calculated using a constant solar wind speed of 300 km s -1. This figure is obtained using the 3DView software  (Génot et al. 2018)  available at http://3dview. cdpp. eu/. The radial speed of SolO was V R = 11. 3 km s -1 throughout this interval. Fig. 2  shows the global context on July 23, 2020 from 18 to 24 UT of the solar observations with the SolO position (blue cross) projected back to the Sun's surface. This figure is obtained from the Magnetic Connectivity Tool  (Rouillard et al.
We transform the magnetic field from the RT N coordinates to LMN coordinates as follows. First, the current sheet normal N is obtained from the cross-product of the asymptotic 10-s averaged magnetic fields just outside the current sheet interval. Second, the M component is obtained from N × L 1 , where L 1 is the maximum variance direction obtained from the MVA technique  (Sonnerup & Cahill 1967)    V L component in Fig. 5e. Note that the velocity data have lower cadence (4 s) than the magnetic field data (1 s), thus the velocity is sampled only at a few points in the vicinity of the current sheet interval. The velocity peak is marked by a grey dashed line. This ion jet has a magnitude of ∆V = 13 km s -1. Although there is only one velocity measurement associated with the peak inside the jet, one should note that the operation cycle of the PAS instrument is such that each sample is made over only 1 s every 4 s.
Acknowledgements. Work at IRAP was supported by the  Centre National de la Recherche Scientifique (CNRS, France) , the  Centre National d'Etudes Spatiales (CNES, France) , and the  Université Paul Sabatier (UPS). Solar Orbiter data are publicly available at http://soar. esac. esa. int/soar/. We acknowledge science teams of the Connectivity Tool (http://connect-tool. irap. omp. eu/) and 3DView (http://3dview. irap. omp. eu/) at IRAP and CNES. Y. Y. is supported by grant No. 11902138  from the  National Natural Science Foundation of China. The computing resources were provided by the  Center for Computational Science and Engineering of Southern University of Science and Technology. D. R. is supported by grant  RTA6280002  from  Thailand Science Research and Innovation. J. E. S is supported by the  Royal Society University Research Fellowship   URF\R1\201286.