dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Xavier Capdevielle ,  Olivier Fabreguettes ,  Martine Martin-Clotté  and  Gilles St Jean  for field and lab assistance, and  Brigitte Lung-Escarmant ,  Thierry Belouard ,  Bernard Boutte ,  Claude Husson ,  Jean-Baptiste Daubrée ,  Margarita Elvira-Recuenco  and  Rosa Raposo-Llobet  for valuable discussions. We thank  Juan Majada  for providing the rooted cuttings used to establish the CLONAPIN collection in Cestas and the experimental unit of  INRA-Pierroton  for trial establishment, height and bud flush measurements. We thank  Hervé Jactel  and  Victor Rebillard  for providing the processionary moth nest data. We acknowledge funding from  IdEx Bordeaux -Chaires  d'installation 2015 (EcoGenPin), the  Spanish National Research Plan (ClonaPin ,  RTA2010-00120-C02-01 ), and the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  773383  ( B4EST ).
Alatina alata sample collection and DNA extraction We collected A. alata material during a spermcasting aggregation in Bonaire, The Netherlands (April 2014, 22:00-01:00) according to the methods in Lewis Ames et al. [7]. We selected a single live spermcasting male medusa from the same cohort as the female medusa used in previously published RNA sequencing studies (Genbank Accession: GEUJ01000000)  [7, 9]. The medusa was divided into 4 longitudinal sections, and one quarter was placed into a 15-mL tube with pure (99%) ethanol, flash-frozen at -180 • C (using a dry shipper), and subsequently transported to the Smithsonian National Museum of Natural History and stored at -20 • C. We extracted genomic DNA using a DNeasy Blood & Tissue Kit (Qiagen: Hilden, Germany), following the manufacturer's protocol.
Cassiopea xamachana sequencing and assembly Library construction and sequencing was performed at Hudson-Alpha Institute for Biotechnology. Four 350-bp paired-end linear libraries with insert sizes of 500 bp were generated with Illumina TruSeq DNA PCR-Free LT Prep Kits (San Diego, CA, USA) and sequenced on the Illumina Hiseq2000. Approximately 634 million reads totaling 117. 6 Gb of high-quality paired-end sequence data were generated. We performed adaptor trimming and quality filtering using Trimmomatic v0. 36  [68]  with default settings, followed by genome size estimation and error correction with Allpaths-LG version 52,488  [69]. We removed mitochondrial reads using FastqSifter v1. 1. 1 (RRID:SCR 017200)  [70]  using the C. xamachana mitochondrial genome as a reference (NCBI NC 01 6466.
We assembled the C. cruxmelitensis mitochondrial genome by capturing contigs from an initial assembly using available staurozoan mitochondrial DNA sequences from NCBI as a reference, following the methods presented in Kayal et al. [78] , using Geneious v9. 0 to generate the final mitochondrial assembly. We checked completeness of the mitochondrial genome using NCBI BLAST against the non-redundant database in addition to a manually generated set of medusozoan genes, annotated transfer RNA (tRNA) genes separately by using tRNAscan-SE  [79]  and Arwen  [80] , and checked the integrity of the assembly by aligning the reads to the completed mitochondrial genome. With the mitochondrial sequences removed, we generated 2 "sub-optimal" assemblies using Platanus v1. 2. 1 with k-mer size of 32 and 45 bp and default settings.
2 Mb of data. We conducted hybrid assembly of Illumina shortreads and PacBio long-reads using MaSuRCA 3. 2. 2 [83]  (which includes an error correction step for paired-end reads), which resulted in an assembly of 291,445 contigs and an N50 of 7,049  bp (NCBI Accession PUGI00000000). We did not perform adapter trimming prior to assembly because the MaSuRCA manual advises against preprocessing of reads, including adapter removal. Nevertheless, we identified considerable adapter contamination in our final assembly. Subsequently, we used a custom script (remove adapters and 200. pl  [74] ) to remove adapters and se-quences shorter than 200 nucleotides. The total length of the assembly was 851. 1 Mb. We recovered 29. 84% (8. 06% complete and 21. 78% partial) of the core eukaryotic genes and 32.
Gene Orthology and Lineage-Specific Gene Ontology We used OrthoFinder v1. 1. 4 [89]  to construct orthologous groups between gene models of A. alatina, C. cruxmelitensis, C. xamachana, N. vectensis, Hydra magnipapillata, and H. sapiens. We also included translated transcriptome assemblies for A. alatina, C. cruxmelitensis, and C. xamachana in these ortholog analyses, as well as an additional transcriptome of the apo-symbiotic polyp stage of C. xamachana, which was assembled using Trinity v2. 4. 0  [90]  with default settings (ENA Project Accession: PRJEB23739). All transcriptomes were translated using TransDecoder 3. 0. 0 [91]  with minimum protein length (-m) set to 50 and all other settings as default. We annotated orthogroups by BLASTing a representative species against the Uniprot/Swiss-Prot database  [92].
Orthogroups with annotations were further mapped to Gene Ontology (GO) terms, and analysed for putative enrichment related to biological function using ClusterProfiler  [93] , against a C. xamachana annotation database generated using AnnotationForge  [94]. Our OrthoFinder analysis generated a total of 80,482 orthogroups for the combined genomic datasets. Using a custom script, we identified 756 Cnidaria-specific orthogroups, another 562 medusozoan-specific orthogroups, and yet another 1,091 Acraspeda-specific orthogroups (Fig. 2 ); genes in each taxonspecific orthogroup were non-overlapping. Of these unique orthogroups, we were able to retrieve Swiss-Prot annotations for 57% of Cnidaria-specific orthogroups, 32% of Medusozoaspecific orthogroups, and 55% of Acraspeda-specific orthogroups (Tables  S1-S3 ).
Unannotated orthogroups may represent taxonomically restricted genes or genes no longer discernable as such due to possibly extensive genetic mutation experienced in evolutionary history. Using this framework, we identified enriched GO terms, uncovering 123 terms corresponding to biological processes that appear to be enriched within Cnidaria: 107 for Medusozoa, and 14 for Acraspeda (adjusted P-value < 0. 01). We used ReViGO to remove redundant GO terms from these initial lists and grouped them further through k-means clustering by Euclidean distance. The optimal number of clusters was predicted using the R package NbClust v3. 0. Our ReViGO analysis reduced the 123 Cnidaria-specific GO terms for biological processes to 59 non-redundant ReViGO terms comprising 5 clusters (Fig. 3 , Table  S1 , Fig.
Unique terms seemingly important to medusozoan biology were those related to wound healing and tissue migration, as well as apoptotic signaling regulation. These terms are possibly associated with unique asexual reductive traits (e. g. , budding, fission, strobilation) seen within the medusozoan lineage. Despite initially identifying 1,091 orthogroups unique to Acraspeda, only 14 GO terms were enriched (highly abundant); this number was further reduced to 8 non-redundant ReViGO terms (Fig. 5 , Table  S3 ). The apparently low enrichment may reflect an under-representation of Acraspeda genes within the reference database. Interestingly, half of the terms were associated with DNA recombination.
Venom Analysis We identified potential venom-encoding genes within the cnidarian transcriptomes using the venomix database (a publicly available curated set of 6,622 venom-related proteins) and associated pipeline  [95]. Additional venom-encoding genes were identified by BLASTing the >6,000 venom-related protein sequences of the venomix database to the Augustus protein pre-  dictions (BLAST v2. 2. 31+ e-value = 10 -6 )  [96]. By combining the results of both approaches, we identified 93 types of venomencoding genes in C. cruxmelitensis, 93 in A. alata, 97 in C. xamachana, 96 in H. magnipapillata, and 91 in N. vectensis. In total, we identified 117 types of putative venom proteins, organized into 32 families, that were present in ≥1 of the 5 cnidarian taxa (Fig. 6 , Table  S4 ). To attempt to reconstruct evolutionary relationships among venom proteins in cnidarians, we added the venomix database to our initial set of input protein sequences and reran our OrthoFinder pipeline.
Using this process, we identified 124 orthogroups encoding venom genes in the 5 cnidarian genomes and the human genome (Fig. 7 ). Of the 124 venom orthogroups, few were found to be specific to any 1 cnidarian lineage, with 5 orthogroups present across all cnidarians, 2 spanning medusozoans, and 1 shared between Acraspeda. Most of the proteins in the venomix database were identified first in bilaterian animals, and properly curated based on extensive supporting data, whereas putative toxins identified in non-model cnidarians often lack robust evidence to support annotations, precluding their entry into curated databases; hence the limited number of proteins returned in our homology search. However, we were successful in identifying 9 cnidarian-specific toxin proteins  [4, [97] [98] [99] [100].
Hydra magnipapillata genome. Protein annotations were retrieved from Swiss-Prot. Table  S3. Orthogroups specific to Acraspeda identified using OrthoFinder and annotated by a representative gene from the Hydra magnipapillata genome. Protein annotations were retrieved from Swiss-Prot. Table  S4 : Venom-encoding gene repertoire of 5 cnidarian genomes identified via the venomix database and pipeline. Venom genes are categories by families (column 1). Both genomic and transcriptomic data were used, with transcriptomic isoforms counted as a single venom-encoding gene.
Funding The sequencing of Alatina alata was funded by  Iridian Genomes. A. O. acknowledges funding from an  NSF   Dimensions Grant  ( OCE 1442206 ); C. L. A. acknowledges funding from The  University of Maryland Eugenie Clark Scholarship  and a fellowship from  Oak Ridge Institute for Science and Education (ORISE) ; startup funds from the  University of Florida DSP Research Strategic Initiatives  # 00114464  and  University of Florida Office of the Provost Programs  to J. F. R.
2. 2 [ 3 Figure 4 : 2234 Figure 4: Gene ontology biological processes over-enriched within Medusozoa-specific orthogroups visualized using ReViGO. Over-representation analysis was performed with ClusterProfiler, with a P-adjusted cutoff of 0. 01. Color indicates log10-transformed P-adjusted value. Terms are plotted within an x-y semantic space, in which similar terms are clustered within closer proximities. Color indicates P-value and circle size indicates frequency of GO term in the Cassiopea database.
Acknowledgements The authors would like to thank  Mark Vreysen ,  Kostas Bourtzis ,  Andrew Parker ,  Nicole Culbert ,  Thierry Baldet  and  Haoues Alout  for comments on early versions of this paper and  Lyse Primault ,  Geoffrey Gimonneau  and  Fabrice Chandre  for providing egg viability data. We also thank  Randy Gaugler  and  Devi Shankar Suman  for discussions regarding their venereal transfer experiment. Financial support was provided by the  Institute Carnot Santé Animale  ( IVEMA  project) and the  REVOLINC (Revolutionising Insect Control) ERC Consolidator Grant. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  682387 ).
Extraction of Multicellular Topological Dynamics in the Arabidopsis SAM Live imaging of four independent wild-type Arabidopsis SAMs carrying a plasma membrane targeted YFP marker  (Yang et al. , 2016)  was performed at 11-h intervals (Figure  1A ). Every cell in the first 4 layers of the SAM central and peripheral zones was segmented in 3D and converted into polygonal meshes at each the 0 h (T0), 11 h (T1), and 22 h (T2) time points using the image analysis software MorphoGraphX (de Reuille et al. , 2015) (Figure  S1 ). In the instances where cells divided, lineage was established by manually performing registration between the cell meshes. Networks describing cellular connectivity in the SAM at each time point were also extracted as previously described  (Jackson et al. , 2017b; Montenegro-Johnson et al.
Spatial Distributions of Geometric and Topological Features in the Arabidopsis SAM To examine the spatial relationships between the geometric and topological properties of cells in the SAM, we analyzed cell size and growth using the image analysis software MorphoGraphX  (de Reuille et al. , 2015). These quantitative data are false colored onto the individual cells of a 3D-segmented SAM sample across each of the top 3 cell layers (L1-L3) (Figure  2 ). Both cell volume and area are heterogeneous across each of the cell layers, with cells in L1 being smaller than those in the underlying layers  (Figures 2A and 2B). The distribution of cell growth, representing the change in cell volume across the 11-h time frame of this experiment, showed a similar broad distribution (Figure  2C )  (Kierzkowski et al.
Topological Analyses of Cellular Connectivity Graphs Topological properites for each cell were calculated using the NetworkX package for Python  (Schult and Swart, 2008). In order to make different cell connectivity networks comparable, the centrality measurements were normalised by 2/((n -1)(n -2)) where n is the number of nodes in a network (i. e. the number of cells)  (Schult and Swart, 2008). Cellular networks were not normalised by network density, as all networks were subject to physical embedding within Euclidean space, resulting in similar densities. Degree, betweenness centrality and random walk centrality were calculated for each cell. Whilst degree and random walk centrality in the cell networks were normally distributed, betweenness centrality of cells had an approximately log-normal distribution.
In order to perform equivalent statistical analyses with all topological measurements, betweenness centrality was log 10 transformed, resulting in a normal distribution. Statistical Analyses t-tests, linear regressions to determine correlations (R 2 ), earth mover's distance calculations (Wasserstein distance) and Kolmogorov-Smirnov tests were performed using the SciPy package for Python  (Jones et al. , 2014). ANOVA and post-hoc Tukey's HSD tests were performed using SPSS. Logistic regression was carried out using the glm2, car and ROCR packages in R  (Marschner, 2011; Fox and Weisberg, 2011; Sing et al. , 2005). Data were treated as one complete set and fitted to a logistic regression model to classify non-dividing and dividing cells in T0. A large likelihood ratio (LR c 2 ) indicates a measurement's usefulness in making the correct prediction, and it's incorporation into the model.
ACKNOWLEDGMENTS M. D. B. J. was supported by  BBSRC   DTP BB/M01116X/1  MIBTP, G. W. B. by  BBSRC  grants  BB/L010232/1  and  BB/J017604/1 , H. X. and G. W. B. by  BBSRC  grant  BB/N009754/1 , and G. W. B. and S. D. -N. by  Leverhulme  grant  MIBTP RPG-2016-049. I. G. J. was supported by a  Birmingham Fellowship. S. S. and R. S. S. were funded by  DFG Plant MorphoDymanics Research Unit   FOR2581 , and D. K. by  NSERC   Discovery Grant   RN000758. We thank  Miltos Tsiantis  for hosting some experiments by D. K. in his lab with funding from  DFG   Sonderforschungsbereich 680. We thank  Theo Greaney ,  Hagley Road High School, Birmingham, UK , for assistance annotating 3D datasets.
Consequently, these matDKO/CAST embryos will inherit no maternal DNA methylation  [9]  but are able to sufficiently establish DNA methylation post-fertilization  [23]. Allelic gene expression was evaluated in E7. 5 epiblast and ExE of all hybrid crosses (Fig. 1a, b ; Additional file 1: Figure  S3 ). Details of biological replicates and datasets generated for this study are summarized in Additional file 2: Table  S1.
5 epiblast and ExE, and gene expression in E7. 5 epiblast, E7. 5 ExE, and E12. 5 placenta  [15]  (Fig. 2b ; Additional file 1: Figure  S4 ). In epiblast, 85. 2% (23/27) of informative genes associated with an imprinted H3K4me3 peak (Additional file 1: Figure  S4 ) showed a significant allelic bias in at least one dataset (Additional file 2: Table  S3 ). In ExE, 94. 4% (51/54) of informative genes associated with an imprinted H3K4me3 peak (Fig. 2b ) showed a significant allelic bias in at least one dataset (Additional file 2: Table  S2 ). Thus, imprinted H3K4me3 peaks are strongly predictive of mono-allelic H3K36me3 and gene expression of the nearest genes, as demonstrated by the Peg3 gene (Fig. 2c ; Additional file 1: Figure  S4 ).
Enrichment is quantitated as read count normalized to library size, correcting for peak length. b Heatmap showing allelic bias (log 2 (pat/mat)) for E6. 5 ExE H3K4me3 at H3K4me3 peaks identified in E6. 5 ExE. Allelic bias (log 2 (pat/mat)) for E6. 5 ExE H3K36me3, E7. 5 ExE gene expression, and E12. 5 placenta (P) gene expression is shown for associated nearby genes (Additional file 2: Table  S2 ). Reciprocal hybrids are denoted as B/C (B6/CAST), C/B (CAST/B6), F/C (FvB/CAST), and C/F (CAST/FvB). White boxes indicate where there was insufficient data (ChIP-seq < 20 SNP-spanning reads in all replicates, RNA-seq < 5 SNP-spanning reads in all replicates). ChIP-seq data was quantitated is as in a, RNA-seq data was quantitated as read count over exons. H3K4me3 peaks were excluded if there was no gene within 10 kb or the associated gene was uninformative in all datasets.
H3K4me3 peaks overlapping more than one gene promoter are duplicated in the H3K4me3 column. Novel imprinted genes are marked with an asterisk. c Screenshot of allelic enrichment for H3K4me3 and H3K36me3 in E6. 5 ExE and gene expression in E7. 5 ExE for B6/CAST and CAST/B6 at the known imprinted gene Peg3. Box indicates the location of the maternal gDMR. ChIP-seq data is quantitated using enrichment normalized RPKM for autosomal 1-kb running windows with a 100-bp step (scales in square brackets); paternal (blue) and maternal (red) enrichments are shown on mirrored axes. Gene expression is quantitated as log 2 (RPKM) for 500-bp running windows with a 50-bp step subset is non-canonically imprinted. To evaluate these questions, we identified all ERVK LTRs that fell within ExE H3K4me3 peaks that were active promoters in extraembryonic tissues (N = 40), which included the 8 noncanonical imprinted ERVK LTRs and 32 ERVK LTRs without imprinted expression (Additional file 2: Table  S6 ).
Epigenetic regulation of non-canonical imprints in postimplantation embryos It has been shown that non-canonical imprinting in the early embryo is mediated by the inheritance of maternal H3K27me3 from the oocyte  [21]. Therefore, we were (See figure on previous page. ) Fig. 5  A non-canonically imprinted ERVK LTR drives imprinted expression of Gab1 in placenta. a Screenshot of allelic gene expression, H3K4me3, H3K36me3, and H3K27me3 in B6/CAST and CAST/B6 ExE. ChIP-seq data is quantitated using enrichment normalized RPKM for 1-kb running windows with a 100-bp step (scales in square brackets); paternal (blue) and maternal (red) enrichments are shown on mirrored axes. RNA-seq data is quantitated as RPKM for 1-kb running windows with a 100-bp step. The box denotes the location of the non-canonical imprinted H3K4me3 peak associated with the known non-canonical imprinted gene Gab1.
b Screenshot of allelic gene expression, H3K4me3, H3K36me3, and H3K27me3 in matDKO/CAST ExE, quantitated as in a. c Screenshot of H3K4me3, H3K27me3, and DNA methylation in GV oocytes. One-kilobase running windows with a 100-bp step were used; ChIP-seq data was quantitated as RPKM (scales in square brackets). d Screenshot showing allelic gene expression in F/CAST and CAST/F E12. 5 placenta across the Gab1 locus. The box depicts the non-canonical imprinted paternal H3K4me3 peak containing an imprinted transcriptionally active ERVK LTR element (RLTR15). RNA-seq data is quantitated as log 2 RPKM for 1000-bp running windows with a 100-bp step. e Read count for maternal (red) and paternal (blue) transcription is shown for the non-canonically imprinted RLTR15 and exon 1 of the Gab1 gene in E12. 5 and E16.
5a  and 6a ). Although there was a subtle bias of H3K27me3 towards the maternal allele in ExE at non-canonical imprinted H3K4me3 peaks (p = 0. 02), when we identified regions with imprinted H3K27me3 in ExE using the EdgeR statistical approach (Additional file 1: Figure  S9 ), only one non-canonically imprinted H3K4me3 peak was associated with imprinted H3K27me3. Furthermore, we found that the vast majority of imprinted H3K27me3 in post-implantation ExE was localized to two large imprinting clusters (Kcnq1/Kcnq1ot1 and Igf2r/Airn) and entirely dependent on maternal gDMRs (Additional file 1: Figure  S9  and S10 ). To determine whether another repressive epigenetic mark replaced maternal H3K27me3 in the post-implantation embryo, we assessed the allelic DNA methylation. We generated high coverage bisulphite sequencing data from ExE and epiblast of E7.
Ultra low-input native chromatin immunoprecipitation Ultra low-input native ChIP-seq was done as previously described  [22]. ChIP-seq libraries for H3K4me3, H3K27me3, H3K36me3, and 10% inputs were generated for replicates of pooled E6. 5 embryos: B6/CAST Epi (N = 2) and ExE (N = 2), CAST/B6 Epi (N = 2) and ExE (N = 2), and matDKO/CAST Epi (N = 2) and ExE (N = 2). Prior to immunoprecipitation with antibody-bound beads, each chromatin sample (200 μL) was divided into 5 1 for each antibody (54 μL), 1 10% input (20 μL), and 1 10% input for bisulphite sequencing (20 μL) (Additional file 1: Figure  S1 ). For each immunoprecipitation, 250 ng of anti-H3K4me3 (Diagenode K02921004), 125 ng of anti-H3K27me3 (Millipore 07-449), and 250 ng of anti-H3K36me3 (Diagenode C15410192) were used. Library preparation was done using the MicroPlex Library Preparation kit v2 (Diagenode), as per the manufacturer's recommendations, and libraries amplified using 15 amplification cycles.
Public datasets The following public datasets were used in this manuscript: stranded total RNA-seq from FvB x CAST reciprocal hybrids for E12. 5 placenta, E12. 5 visceral endoderm, E12. 5 liver, E16. 5 brain, E16. 5 heart, and E16. 5 liver  [15] ; H3K27me3 ChIP-seq from B6 x PWK hybrid embryos for early and late 2-cell embryos, 8-cell embryos, and E3. 5 blastocyst inner cell mass (ICM)  [31] ; H3K4me3 ChIP-seq from B6 x PWK hybrid embryos for early and late 2-cell embryos, 8-cell embryos, and E3. 5 blastocyst inner cell mass (ICM)  [44] ; RNA-seq and bisulphite-seq from C57BL/6 pre-and post-implantation embryos  [30] ; bisulphite-seq  [28] , total RNA-seq  [27] , and H3K4me3 and H3K27me3 ChIP-seq  [22]  data from C57BL/6 GV oocytes. All raw data files were obtained from Gene Expression Omnibus or the DNA data bank of Japan and were mapped and evaluated using the following pipelines.
Allelic mapping of sequencing data RNA-seq data was subjected to trimming with Trim Galore (v0. 4. 5) and aligned using HISAT2 (v2. 1. 0, guided by gene models from Ensembl annotation release 70, options: --dta -sp 1000,1000). ChIP-seq and input material was also trimmed with Trim Galore and aligned using Bowtie2 (v2. 3. 2, options -X 1200). In addition to adapter and quality trimming, bisulfite sequencing data had the first 9 bp of both read 1 and read 2 removed to reduce biases arising from the 9 N oligo pull-down reaction (Trim Galore options: --clip_r1 9 --clip_r2 0 --paired). The trimmed PBAT data was then aligned using Bismark (v0. 19. 0, options: --pbat)  [45]. Following alignments, all sequencing data was then split allele-specifically using SNPsplit (v0. 3. 3) [46]. In brief, sequencing reads were mapped to a Mus musculus (GRCm38)-derived genome, where SNPs between hybrid strains (C57BL6 and CAST/EiJ, or FvB and CAST/EiJ, or C57BL6 and PWK) had been masked by the ambiguity nucleobase N (N-masked genome).