dataesr/scientific-paragraphs-categorization huggingface dataset:
013 ); the  International Max Planck Research School for Astronomy and Astrophysics at the Universities of Bonn and Cologne ;  DFG research grant  " Jet physics on horizon scales and beyond " (grant No. FR 4069/2-1 ); Joint Princeton/Flatiron and Joint Columbia/ Flatiron Postdoctoral Fellowships, with research at the  Flatiron Institute  supported by the  Simons Foundation ; the  Japanese Government  (Monbukagakusho: MEXT) Scholarship; the  Japan Society for the Promotion of Science (JSPS)   Grant-in-Aid for JSPS Research Fellowship  ( JP17J08829 ); the  Key Research Program of Frontier Sciences, Chinese Academy of Sciences  (CAS, grants  QYZDJ-SSW-SLH057 ,  QYZDJ-SSW-SYS008 ,  ZDBS-LY-SLH011 ); the  Leverhulme Trust Early Career Research Fellowship ; the  Max-Planck-Gesellschaft (MPG) ; the  Max Planck Partner Group of the MPG  and the CAS; the  MEXT/ JSPS KAKENHI  (grants  18KK0090 ,  JP21H01137 ,  JP18H03721 ,  18K03709 ,  18H01245 , 25120007); the  Malaysian Fundamental Research Grant Scheme  ( FRGS) FRGS/1/2019/STG02/UM/ 02/6 ; the  MIT International Science and Technology Initiatives (MISTI) Funds; the  Ministry of Science and Technology (MOST) of Taiwan  ( 103-2119-M-001-010-MY2 ,  105-2112-M-001-025-MY3 ,  105-2119-M-001-042 ,  106-2112-M-001-011 ,  106-2119-M-001-013 ,  106-2119-M-001-027 ,  106-2923-M-001-005 ,  107-2119-M-001-017 ,  107-2119-M-001-020 ,  107-2119-M-001-041 ,  107-2119-M-110-005 ,  107-2923-M-001-009 ,  108-2112-M-001-048 ,  108-2112-M-001-051 ,  108-2923-M-001-002 ,  109-2112-M-001-025 ,  109-2124-M-001-005 ,  109-2923-M-001-001 ,  110-2112-M-003-007-MY2 ,  110-2112-M-001-033 ,  110-2124-M-001-007 , and  110-2923-M-001-001 ); the  Ministry of Education (MoE) of Taiwan Yushan Young Scholar Program ; the  Physics Division, National Center for Theoretical Sciences of Taiwan ; the  National Aeronautics and Space Administration  ( NASA, Fermi Guest Investigator  grant  80NSSC20K1567 ,  NASA   Astrophysics Theory Program  grant  80NSSC20K0527 ,  NASA   NuSTAR award   80NSSC20K0645 ); the  National Institute of Natural Sciences Grant  and the  NSERC   Alexander Graham Bell Canada Graduate Scholarships-Doctoral Program ); the  National Youth Thousand Talents Program of China ; the  National Research Foundation of Korea  (the Global PhD Fellowship Grant: grants  NRF-2015H1A2A1033752 ,  2015-R1D1A1A01056807 ; the  Korea Research Fellowship Program :  NRF-2015H1D3A1066561 ,  Basic Research Support Grant   2019R1F1A1059721 ); the  Netherlands Organization for Scientific Research (NWO)   VICI award  (grant  639.
043. 513 ) and  Spinoza Prize SPI   78-409 ; the  New Scientific Frontiers  with  Precision Radio Interferometry Fellowship  awarded by the  South African Radio Astronomy Observatory (SARAO) , which is a facility of the  National Research Foundation (NRF) , an agency of the  Department of Science and Technology (DST) of South Africa ; the  Onsala Space Observatory (OSO)  national infrastructure, for the provisioning of its facilities/observational support (OSO receives funding through the  Swedish Research Council  under grant  2017-00648 ); the  Perimeter Institute for Theoretical Physics  (research at  Perimeter Institute  is supported by the  Government of Canada  through the  Department of Innovation, Science and Economic Development  and by the  Province of Ontario  through the  Ministry of Research, Innovation and Science) ; the  Spanish Ministerio de Ciencia e Innovaci -n (grants  PGC2018-098915-B-C21 ,  AYA2016-80889-P ,  PID2019-108995GB-C21 ,  PID2020-117404GB-C21 ); the  State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa " award for the  Instituto de Astrofísica de Andalucía  ( SEV-2017-0709 ); the  Toray Science Foundation ; the  Consejería de Economía, Conocimiento ,  Empresas y Universidad of the Junta de Andalucía  (grant  P18-FR-1769 ), the  Consejo Superior de Investigaciones Científicas  (grant  2019AEP112 ); the  M2FIN-DERS  project, which has received funding by the  European Research Council (ERC)  under the  European Unionʼs Horizon 2020 Research and Innovation Programme  (grant agreement No.
101018682 ); the  US Department of Energy (USDOE)  through the  Los Alamos National Laboratory  (operated by  Triad National Security, LLC , for the  National Nuclear Security Administration of the USDOE  (contract  89233218CNA000001 ); the  European Unionʼs   Horizon 2020 research and innovation program  under grant agreement No. 730562  RadioNet;  Shanghai Pilot Program for Basic Research, Chinese Academy of Science ,  Shanghai Branch  ( JCYJ-SHFY-2021-013 );  ALMA North America Development Fund ; the  Academia Sinica ; Chandra  DD7-18089X  and  TM6-17006X ; and the  GenT Program (Generalitat Valenciana)  Project  CIDEGENT/2018/021. This work used the  Extreme Science and Engineering Discovery Environment (XSEDE) , supported by  NSF  grant  ACI-1548562 , and CyVerse, supported by  NSF  grants  DBI-0735191 ,  DBI-1265383 , and  DBI-1743442.
001-A  awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , for  NASA , under contract  NAS5-26555. Hector Olivares and  Gibwa Musoke  were supported by  Virtual Institute of Accretion  (VIA) postdoctoral fellowships from the  Netherlands Research School for Astronomy (NOVA). APEX is a collaboration between the  Max-Planck-Institut für Radioastronomie (Germany) ,  ESO , and the  Onsala Space Observatory (Sweden). The SMA is a joint project between the SAO and ASIAA and is funded by the  Smithsonian Institution  and the  Academia Sinica. The JCMT is operated by the  East Asian Observatory  on behalf of the NAOJ, ASIAA, and  KASI , as well as the  Ministry of Finance of China, Chinese Academy of Sciences , and the  National Key R&D Program  (No.
2017YFA0402700 ) of China. Additional funding support for the JCMT is provided by the  Science and Technologies Facility Council (UK)  and participating universities in the UK and Canada. Simulations were performed in part on the SuperMUC cluster at the LRZ in Garching, on the LOEWE cluster in CSC in Frankfurt, on the HazelHen cluster at the HLRS in Stuttgart, and on the Pi2. 0 and  Siyuan Mark -I at  Shanghai Jiao Tong University. The computer resources of the  Finnish IT Center for Science (CSC)  and the  Finnish Computing Competence Infrastructure (FCCI)  project are acknowledged. Junghwan Oh  was supported by the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education  ( NRF-2021R1A6A3A01086420 ). We thank  Martin Shepherd  for the addition of extra features in the Difmap software that were used for the CLEAN imaging results presented in this paper.
The computing cluster of Shanghai VLBI correlator supported by the  Special Fund for Astronomy  from the  Ministry of Finance  in China is acknowledged. The LMT is a project operated by the  Instituto Nacional de Astrífisica, Óptica, y Electrínica (Mexico)  and the  University of Massachusetts at Amherst (USA). The IRAM 30 m telescope on Pico Veleta, Spain, is operated by IRAM and supported by  CNRS  ( Centre National de la Recherche Scientifique, France ),  MPG  ( Max-Planck-Gesellschaft, Germany ), and  IGN (Instituto Geogrífico Nacional, Spain). The SMT is operated by the  Arizona Radio Observatory , a part of the  Steward Observatory of the University of Arizona , with financial support of operations from the  State of Arizona  and financial support for instrumentation development from the  NSF.
rubrum, n=3; Sargocentron cornutum, n=1; Neoniphon sammara, n=2) and seven adults (S. punctatissimum, n=3; S. rubrum, n=2; M. kuntee, n=1; Ostichthys sp. , n=1). The adult dataset was completed with previously published transcriptomic (S. rubrum, n=1; Sargocentron spiniferum, n=1; Sargocentron diadema, n=1; N. sammara, n=3; M. berndti, n=4; Myripristis jacobus, n=2, Myripristis murdjan, n=1) and genomic (M. jacobus, n=1) data  (Malmstrøm et al. , 2017; Musilova et al. , 2019; de Busserolles et al. , 2021) , resulting in a total dataset of 35 retinal transcriptomes and one genome, spanning 12 species. Briefly, initial retinal tissue digestion was conducted using Proteinase K (20 mg ml -1 , 15-30 min digest at 55°C; New England Biolabs). Total RNA was extracted and isolated from the retinas using the Monarch Total RNA Miniprep Kit (New England Biolabs) and genomic DNA was removed using RNase-free DNase (Monarch).
Opsin gene mining, phylogenetic reconstruction and expression analyses All cytochrome c oxidase subunit I (COI) and opsin gene extractions and expression analyses were conducted in Geneious Prime v. 2021. 1. 1 (Biomatters Ltd). Initially, COI genes were extracted from de novo assembled transcriptomes for species identification by mapping to species-specific references from GenBank (https://www. ncbi. nlm. nih. gov/genbank/) with medium sensitivity settings. Opsin gene extractions were performed by mapping assembled transcriptomes to published opsin coding sequences (CDS) of the dusky dottyback (Pseudochromis fuscus)  (Cortesi et al. , 2016)  with customised sensitivity settings (fine tuning, none; maximum gap per read, 15%; word length, 14; maximum mismatches per read, 40%; maximum gap size, 50 bp; index word length, 12; paired reads must both map).
Contigs mapped to COI and opsin references were scored for similarity against publicly available sequences using BLASTn (NCBI, Bethesda, MD; https://blast. ncbi. nlm. nih. gov/Blast. cgi). One of the limitations of de novo assembly of highly similar genes (such as opsin gene paralogs) using short-read transcripts is that it can produce erroneous (chimeric) sequences or fail to reconstruct lowly expressed transcripts. Thus, a second approach was also employed using a manual extraction method from back-mapped reads to verify the initially extracted opsin genes, as per de  Busserolles et al. (2017). During manual gene extraction, filtered paired reads were mapped against P. fuscus reference opsin CDS (with previously stated customised sensitivity settings) in Geneious Prime. Matching reads were connected by following single nucleotide polymorphisms (SNPs) across genes with continual visual inspection for ambiguity and were extracted as paired mates to mitigate sequence gaps.
The consensus of an assembly of these extracted reads was used as the reference for low sensitivity (high accuracy, 100% identity threshold) mapping. Partial CDS extractions were cyclically mapped using the low sensitivity approach to prolong and subsequently remap reads until a complete CDS was obtained. To confirm the identity of each gene, full coding sequences were preliminarily checked on BLASTn and then used in conjunction with a reference dataset obtained from GenBank (www. ncbi. nlm. nih. gov/genbank/) and Ensembl (www. ensembl. org/) to reconstruct the opsin gene phylogeny (de  Busserolles et al. , 2017 Busserolles et al. , , 2021)). All opsin gene sequences were aligned using the MUSCLE plugin v. 3. 8. 425  (Edgar, 2004)  in Geneious Prime. MrBayes v. 3. 2. 6  (Ronquist et al. , 2012)  on CIPRES  (Miller et al.
All expressed genes fell into four out of the five main classes  (Bowmaker, 2008) , while the only sws1 sequence was derived from a genome  (Musilova et al. , 2019). Numbers in brackets are number of rh2 paralogs. Black circles denote Bayesian posterior probabilities >0. 8. The scale bar denotes substitutions per site. rh1, rhodopsin-like middle-wavelength sensitive 1 (rod opsin); rh2, rhodopsinlike middle-wavelength sensitive 2; sws1, shortwavelength-sensitive 1; sws2, short-wavelength-sensitive 2; lws, long-wavelength-sensitive; va opsin, vertebrate ancient opsin (outgroup). GenBank accession numbers are provided in Table  S2. map to multiple paralogs. The proportional expression of rod versus cone opsin genes was calculated as the number of reads mapped to either rh1 or all cone genes divided by the number of reads mapped to all genes, adjusted to account for differing gene lengths.
4 n=3). To control for diel fluctuations in visual gene expression, all individuals were euthanised at 08:30 h  (Yourick et al. , 2019). All analyses were conducted using the genomics virtual laboratory on the Galaxy platform. Firstly, the quality of the sequencing reads was assessed using FastQC (v. 0. 72) and raw reads were subsequently trimmed and filtered using Trimmomatic (v. 0. 36. 6), as described previously. Given that a high-quality reference genome was not available for the species sequenced, a reference transcriptome was de novo assembled using Trinity (v. 2. 8. 4) from the combined reads of one paired-end library from each life stage (with settings described above). A mapping-based estimation of transcript abundance was then obtained using Salmon quant v. 0. 14. 1. 2 (using default settings except specifying paired-end, stranded reads (SF), discarding orphan quasi, validating mappings and mimicking Bowtie)  (Patro et al.
, 2017). Differentially expressed transcripts were identified using DEseq2 v. 2. 11. 40. 6 (using default settings with the following changes: setting input data to transcripts per kilobase million (TPM) values generated by Salmon, outputting normalised counts table and no independent filtering)  (Love et al. , 2014). The DEseq2 result file was filtered (Filter v. 1. 1. 0) on the adjusted P-value column (≤0. 05) to obtain DEGs. Separate lists of up-and down-regulated DEGs were obtained by filtering for positive and negative values in the log fold change column of the DEseq2 result file. These lists were re-formatted (using FASTA-to-TABULAR v. 1. 1. 1, Cut v. 1. 0. 2, Sort v. 1. 1. 1, Join v. 1. 1. 2 and TABULAR-to-FASTA v. 1. 1. 1 tools) to generate simple gene ID-to-sequence lists of DEGs in FASTA format.
The top BLAST hit from the UniProtKB/Swiss-Prot database (The UniProt Consortium, 2018) was obtained using NCBI BLAST+ blastx v. 0. 3. 3 (using default settings but selecting blastx analysis) and Blast top hit descriptions v. 0. 0. 9, and was used to annotate DEG sequences  (Altschul et al. , 1997; Camacho et al. , 2009; Cock et al. , 2013 Cock et al. , , 2015)). For the top 15 DEGs, QuickGO  (Binns et al. , 2009)  and UniProtKB were used to manually search for gene ontology (GO) and function. For lists of DEGs upregulated at each life stage, PANTHER 17. 0 (http://www. pantherdb. org/; Mi et al. , 2021)  was used via The Gene Ontology Resource website to perform a GO statistical overrepresentation analysis for biological processes  (Ashburner et al. , 2000; Gene Ontology Consortium, 2021).
Acknowledgements We acknowledge the Dingaal, Ngurrumungu and Thanhil peoples as traditional owners of the lands and waters of the Lizard Island region from where specimens were collected. We also acknowledge the traditional owners of the land on which the  University of Queensland  is situated, the  Turrbal/Jagera people. We would like to thank the staff at  Lizard Island Research Station (LIRS) ,  Dr Anne Hoggett  and  Dr Lyle Vail , as well as the staff at the  Centre of Island Research and Environmental Observatory (CRIOBE)  for support during field work. We also thank  Cairns Marine  for supplying animals. We thank  Janette Edson  from the  QBI Genomics Facility  and staff at  Novogene Co. , Ltd  for library preparation and transcriptome sequencing. Finally, we would like to thank  Professor Mark McCormick  and his team for generously lending us their light traps and assisting with some animal collections.
Funding This research was supported by the  Australian Research Council (ARC)  ( DE180100949  awarded to F. d. B. ) and the  Queensland Brain Institute (QBI). F. d. B. , F. C. and N. J. M. were supported by the  ARC  ( DE180100949 ,  DE200100620  and  FL140100197 , respectively); L. G. F. was supported by the  Australian Government  and the  University of Queensland (UQ)  ( Research Training Program Stipend ) and  QBI  ( Research Higher Degree Top Up Scholarship ); D. L. was supported by the  Institute of Coral Reefs of the Pacific (IRCP). Open Access funding provided by  The University of Queensland. Deposited in PMC for immediate release.
Acknowledgments This research was supported by  Australian Research Council  grants  LP210100173 ,  LP170100863  and  DE210100084  and supported by the  Australian Government's National Collaborative Research Infrastructure Strategy (NCRIS) , with access to computational resources provided by the  National Computational Infrastructure (NCI)  through the  National Computational Merit Allocation Scheme  and through the  Sydney Informatics Hub HPC Allocation Scheme , which is supported by the  Deputy Vice-Chancellor (Research) ,  University of Sydney  and  ARC  grant  LE190100021.
Code Availability The codes used for this paper are available on the public EarthByte and GPlates github sites. This includes absolute plate motion model optimisation code at https://github. com/EarthByte/optAPM, CitcomS software for mantle convection modelling at https://github. com/EarthByte/citcoms, and GPlates/pyGPlates at https://github. com/gplates for viewing and manipulating plate reconstructions.
gadMor1. 73. dna. toplevel. fa, Ensembl), zebrafish (Danio_rerio. Zv9. 66. dna. toplevel. fa, Ensembl), silver (Coho) salmon (Okis_V2, GCA_002021735. 2, Ensembl), European (common) carp (Cypcar_WagV4. 0, GCA_905221575. 1, Ensembl), sea bass (seabass_V1. 0, GCA_000689215. 1, Ensembl), large yellow croaker (L_crocea_2. 0, GCA_000972845. 2, Ensembl), swamp eel (M_albus_1. 0, GCA_001952655. 1, Ensembl), mangrove killifish (ASM164957v1, GCA_001649575. 1, Ensembl), spotted gar (http://www. ncbi. nlm. nih. gov/assembly/GCF_ 000242695. 1/, Genbank Assembly, accessed on 30 January 2015) and African coelacanth (Latimeria_chalumnae. LatCha1. 72. dna_toplevel. fa, Ensembl).
TE Annotation TE libraries were established by a combination of both automatic and manual annotations. Manual annotations corresponded to TBLASTN searches against genomes (downloaded or on the NCBI website https://www. ncbi. nlm. nih. gov/genome/, accessed on 15 September 2021) using TE proteins from the PIF-Harbinger and PIF-ISL2EU superfamilies as queries, using default parameters without any filtering for low complexity regions. TE sequences were also retrieved from the Repbase database (http://www. girinst. org, accessed on 15 September 2021)  [30]. Automatic annotation was performed using the Re-peatModeler software (Smit, AFA, Hubley, R. , http://www. repeatmasker. org, accessed on 15 September 2021) with default parameters. For the coelacanth, we used and reannotated the library from Amemiya et al.
TE Genome Masking, Copy Number and Genome Coverage TE genome masking, copy number and genome coverage estimations were performed according to  Chalopin et al. (2015)   [9]. Briefly, RepeatMasker version 3. 3. 0 (Smit, AFA, Hubley, R, and Green, P. RepeatMasker Open-3. 0. 1996-2010; http://www. repeatmasker. org, accessed on 15 September 2021) with "-a" and "-lib" default parameters was locally used to mask genomes. Copy number and genome coverage were calculated on RepeatMasker outfiles (. out) with custom scripts (Supplementary Data S3). In order to eliminate very short and too divergent sequences, data were filtered to include only elements longer than 80 nucleotides and sharing more than 80% of identity with the reference sequences from the species-specific library  [9].
Sequence Alignment and Phylogenetic Analysis Nucleotide and amino-acid sequences were aligned using MAFFT  [32]. Phylogenetic trees were built using maximum likelihood calculation with the PhyML software  [33]  using the LG model, which is optimized for amino-acid alignment  [34]  (invariable sites optimized, tree searching operation with best of NNI and SPR, bootstraps with 100 replicates). Phylogenetic analysis was also performed with the MrBayes package  [35]  using the mixed model (estimated by the protest-3 software  [36] ), 500,000 generations of Bayesian inferences for the tree with consensus sequences and 5,000,000 for the tree with transposase copies to reach average standard deviation of split frequencies <0. 02 (burn-in: 25%).
TE Expression Analysis in Spotted Gar, Zebrafish, Cod and Medaka TE expression was studied by comparing TE consensus protein sequences against the PhyloFish database, which provides qualitative non-normalized expression tendencies  [37]. Results were analyzed using the RNAbrowse interface (http://phylofish. sigenae. org/ index. html, accessed on 15 January 2022)  [38]. Expression was given at the copy level in PhyloFish and summarized for all superfamilies.
Within both transposase and Myb-like molecular phylogenies, some sequences from divergent fish species appeared to be more related than expected from species relationships. This was particularly the case for Harbinger-1_OK from the salmonid Oncorhynchus kisutch (silver salmon) and Harbinger-1_CC from the cyprinid Cyprinus carpio (European carp), which were very close in the phylogenies (Figure  4  and Figure  S2 ). Harbinger-1_OK-related sequences were detected in other salmonids, including the Atlantic salmon Salmo salar, and in the Northern pike Esox lucius from the related family Esocidae (ca. 80 million years divergence) (data not shown). In contrast, besides C. carpio, no sequence closely related to Harbinger-1_OK was found in the ten other cyprinid fish genus that were tested (data not shown).
Harbinger-like Transposons Are Expressed in Fish Despite the broad distribution of Harbinger transposons, studies on their activity remain scarce  [27] [28] [29]. To the best of our knowledge, no proof of activity has been reported so far for ISL2EU transposons. Using the PhyloFish database, which allows investigating sequence expression thanks to multiple fish transcriptome datasets from multiple organs, Harbinger-like transposon expression was detected in numerous fish species. Particularly, expression datasets were accessible for four of the species studied in Figure  1 : spotted gar, zebrafish, cod and medaka. Expression of Harbinger and ISL2EU transposons was found in all four species (except for ISL2EU transposons, which are absent from spotted gar) (Figure  6 ). Harbinger and ISL2EU were particularly highly expressed in organs such as brain and gills, and in testis and embryos in some species.
We further investigated expression in the medaka, the species with the highest genome coverage of Harbinger transposons in our analysis (Figure  1 ). We focused on medaka gonads, since transposition activity in germ cells allows transmission of new insertions and expansion of TE families. Analysis of RNAseq data showed expression of both Harbinger and ISL2EU transposons in both male and female gonads, each family showing similar expression levels (Figure  7 ). Transposon expression was testis-biased, i. e. , higher in male than in female gonads. Expression analysis of Harbinger and ISL2EU transposons in spotted gar, zebrafish, cod and medaka using the PhyloFish database  [37]. Expression identified in PhyloFish database is indicated with +. Absence of "+" indicates that the element was not detected in the genome.
Harbinger-Like Transposons Are Transcriptionally Active in Teleost Fish We report here the expression of Harbinger-like elements in teleost fish. Using the PhyloFish database  [37] , we detected the expression of these transposons in spotted gar, zebrafish, cod and medaka (Figure  6 ). Harbinger and ISL2EU transposons were mainly expressed in the same tissues in these species, suggesting common mechanisms of activation and repression. Moreover, in the medaka, both Harbinger and ISL2EU transposons showed testis-biased expression in gonads (Figure  7 ), as reported for other transposable elements in other organisms  [45, 46]. TEs are repressed in genomes, limiting their potential deleterious effects through insertional mutagenesis. This repression can occur through the prevention of transcription (generally with epigenetic marks, such as DNA methylation or histone modifications) or post-transcriptionally (with piRNAs, for example)  [47] [48] [49] [50].