dataesr/scientific-paragraphs-categorization huggingface dataset:
The control of the time-step is based on the number of iterations required by the Newton method to achieve convergence for the last time iteration. The time-step is reduced, if the number of iterations exceeds a specified threshold, whereas it is increased if the method converges within less iterations. The contribution of the other species is treated explicitly and put as a source term in right hand side of the discretized mass conservation laws  (22). We have also implemented the change of porosity linked to the concentrations of minerals. The second sub-problem consists in solving a singlephase reactive transport problem. In  [57] , we have developed in the DuMu X framework a one-phase multicomponent transport module named 1pmc-react. In this context, we have implemented a new 1pmc module (one-phase, m-component) that is coupled with the calculations of the chemical problem computed with GSL  [61]  through a SIA.
Validation of geochemistry calculations We consider a test case presented in  [18]  which aims to validate the implementation of the activity, chemical equilibrium calculations, mineral dissolution and precipitation reaction and the chosen numerical scheme to deal with geochemistry. There is no flow, no injection and no production. In  [18] , the authors compared their results with those obtained by the geochemistry software ''The Geochemist's Workbench Ò (GWB)''  [64]. GWB models only the geochemistry but not the transport of components in the porous medium. Consequently, as in  [18] , we used a one dimensional grid for an example considering only chemistry with no space dependence. The test deals with the dissolution of the calcite at 25 °C with the reactions presented in Table  2. Reactions (  1 )-(  6 ) are in equilibrium while (  7 ) is a kinetic reaction whose parameters are displayed in Table  3.
Conclusion A mathematical formulation and a finite volume approximation were presented for a system of coupled partial differential and algebraic or ordinary differential equations describing two-phase flow with reactive geochemical transport in the subsurface. The proposed reactive transport software module coupling to the two-phase flow was implemented in the framework of the parallel open-source platform DuMu X. Numerical results are in good agreement with those in  [16, 18]  and provide additional validation of the method. Moreover, this simulation study demonstrates the ability of the model and the method to accurately capture flow behaviour in 3D reservoirs. Due to the strong coupling of the flow and reactive transport equations, a standard approach is to use a fully implicit approach to ensure stability in the solution.
Acknowledgments. This work was partially supported by the  Carnot Institute ,  ISIFoR project (Institute for the Sustainable Engineering of Fossil Resources ) and  CDAPP (Communaute d'Agglomeration de Pau Pyrenees). Their supports are gratefully acknowledged. We also thank the DuMu X and DUNE teams for their help during the development of our reactive transport module and the  CINES (National Computing Center for Higher Education)  to give us access to their computing resources facility. This work was granted access to the HPC resources of CINES under the allocations 2017-A0010610019 and 2018-A0040610019 made by  GENCI. The authors gratefully thank the anonymous referees for their insightful comments and suggestions.
Libraries were then sequenced as described in Supplementary Table  1. Finally, 39,092 BACs (corresponding to a physical coverage of 3. 5× , Supplementary Note 2. 1) were end-sequenced with dye terminator chemistry using an ABI 3730 sequencer (Applied Biosystems) as described previously  50. The sequences can be obtained from GenBank (accession numbers HN154083-HN174138, JS673272-JS676292, JS676293-JS684825 and FO926004-FO981373). We prepared 14 libraries (Supplementary Table  1 ) from 5 different extracted HMW DNA samples with TSLR technology (previously known as Moleculo) according to the Illumina protocol. Briefly, genomic DNA (500 ng) was sheared into fragments of approximately 10 kb in size with g-Tube (Covaris). The fragments were subjected to end repair, A-tailing and adaptor ligation, and the ligated products were size-selected by gel electrophoresis to obtained fragments of 8-10 kb in size, which were quantified by qPCR.
Genome assembly. We first assembled the longest reads together (obtained from 454 and Moleculo libraries) to maximize the separation of the two haplotypes of accession 3P and to overcome the high level of heterozygosity. We used Newbler and Celera  52  as the overlap-layout-consensus (OLC) assemblers. We used Newbler software (version MapAsmResearch-04/19/2010-patch-08/17/2010) with default parameters, with the addition of the -large and -sio options. As Newbler does not accept reads longer than 2 kb, we split Moleculo reads into overlapping 1,999-bp fragments (with overlaps of 1,499 bp) and retained the origin of each fragment for further analysis (see next section). We obtained an assembly (named A1 in Supplementary Table  10 ) of 300,113 contigs with an N50 of 9. 3 kb and a cumulative size of 1.
Use of long reads to simplify the contig graph. Once the initial Newbler assembly was obtained, we used long-range information from Moleculo reads to simplify the contig graph. The Newbler output file "454ContigGraph. txt" describes the contig graph, in which the nodes are contigs and the edges are links between two contigs spanned by a read. Contigs were generally fragmented due to the presence of repeat or heterozygous regions. We extracted links between the contigs created from different parts of single long reads. Finally, a file containing all the links was generated (in DE format) and used as input for the string graph assembler (SGA) scaffolding module  53. We obtained an assembly (named A2 in Supplementary Table  10 ) composed of 198,695 contigs with a N50 of 16. 2 kb and a cumulative size of 1.
45 Gb (including 11. 19% ambiguous bases). Choice of the final assembly. The choice of the final assembly was based on the metrics of the two assemblies obtained with Celera and Newbler (assemblies C2 and A3) and comparisons with high-quality BACs (see Supplementary Note 2. 1. 3 and examples in Supplementary Fig. 9 ). We chose the Newbler assembly because it better discriminated between the two haplotypes. Gap filling. The scaffold gaps of the A3 assembly were closed with GapCloser software  56  and Illumina paired-end reads. As input, we used 95× coverage (of the haploid genome) of overlapping paired-end reads and 95× coverage (of the haploid genome) of a standard paired-end library (400-600-bp fragments). We obtained an assembly (named A4 in Supplementary Table  10 ) consisting of 9,025 scaffolds with an N50 of 821 kb and a cumulative size of 1.
NATurE PlANTS Predicted genes were then aligned against the NCBI NR database with BLAST-p. We kept the best match for each predicted protein and used the corresponding taxon. The 198 scaffolds containing > 50% bacterial genes for the assigned proteins were considered to be putative contaminants and were removed from the assembly file (assembly A5 in Supplementary Table  10 ). Single-haplotype assembly. We used the Haplomerger v. 1 pipeline  58  to reconstruct allelic relationships in the released polymorphic diploid assembly and to reconstruct a reference haploid assembly. The diploid genome was first soft-masked with the following programs: TRF  59  to mask tandem repeats; RepeatMasker  60  to mask simple repeats, low-complexity and Viridiplantae-specific TEs; DUST  61  to mask low-complexity sequences; and RepeatScout 62 to mask unknown TEs.
The EuGene pipeline was trained on a set of 342 genomic and full-coding complementary DNA pairs for which coding sequences were confirmed by protein evidence. One-third of the dataset was used for training the following ab initio gene structure prediction software: Eugene_IMM  74  , which is based on probabilistic models for discriminating between coding and non-coding sequences; SpliceMachine  75  , which was used to predict coding sequence (CDS) start and intron splicing sites; and FGENESH, an ab initio gene finder (http://linux1. softberry. com/berry. phtml), which was used with Populus trichocarpa parameters. Another one-third of the dataset was used to optimize the EuGene parameters. The final one-third of the training dataset was used to calculate the accuracy of EuGene predictions. Sensitivity values of 85.
8% and 75. 2%, and specificity values of 87. 7% and 74. 6%, for exons and genes, respectively, were estimated. We refined alignments with nucleotide similarity-based methods (Blat and Sim4) using transcript contigs from Q. robur and Quercus petraea  76. We ensured that alignment quality was high by respecting the following criteria: 100% coverage and 98% identity for alignments with contigs shorter than 300 bp; < 98% coverage and 98% identity for alignments with contig lengths between 300 and 500 bp; < 95% coverage and an identity of 98% for alignments with contigs longer than 500 bp; and < 95% identity for all other cases. We also used BLAST-x 2. 2. 29+ to match protein sequences with sequences in protein databases, such as SwissProt, and databases built for species phylogenetically related to oak, such as P.
persica v. 1. 39, Vitis vinifera v. 1. 45, P. trichocarpa v. 2. 10, Eucalyptus grandis v. 2. 01 and Arabidopsis thaliana v. 1. 67. We filtered out predicted genes overlapping TEs identified with the REPET package (see previous section), but retained TEs in introns and untranslated regions. The results of the various analyses were combined in EuGene to predict the final gene models. Predicted genes of < 100 nucleotides in length were automatically filtered out by EuGene. We initially predicted 77,043 protein-coding genes from the diploid version (V2) of the Q. robur genome sequence. In total, 2,067 genes from different gene families were manually curated by experts (Supplementary Note 3. 5). From the 77,043 predicted genes, 43,240 were entirely recovered in the haplome, including 1,176 of the manually curated genes.
Genes were tagged as 'unreliable' if their coding sequences were < 500-bp long (corresponding to 166 amino acids), transcript coverage was < 90% or the genes were not curated manually. Based on these criteria, 13,575 genes were tagged as unreliable, and the remaining genes were tagged as 'regular' (28,484 genes) or 'manual' (1,176 genes). We then performed a manual analysis of the 43,240 candidate gene models, guided first by an OrthoMCL run of the 16 genome sequences used in the evolutionary analysis (see the section "Oak karyotype evolution and genome organization"), in which we filtered out genes from OrthoMCL clusters associated with the following criteria: domains identified as plant mobile element domains (PMD domain) or TE domains (for example, transposases or GAG, a structural protein for virus-like particles within which reverse transcription takes place); and similarity to TE proteins, based on BLAST analyses against KEGG library results.
5. 13-52. 0  77 webservice for domain and motif searches. This included all the manually curated genes, 78% of the regular set and 17% of the unreliable set. Subcellular targeting signals and transmembrane domains were predicted with SignalP, TargetP and TMHMM  78  and InterProScan. We also carried out similarity searches with BLAST-x V2. 2. 29+ against PDB, Swissprot and KEGG  79  , and rpsBLAST (14 June 2009) searches for conserved domains against the CDD database 80 and KOG  81. We also used the BLASTKoala webservice (http://www. kegg. jp/blastkoala/, January 2016) to associate KEGG orthology groups, and E2P2 to identify the associated enzyme codes when relevant (https://dpb. carnegiescience. edu/labs/rhee-lab/ software, v. 3. 0). We assigned 'definitions' to the predicted proteins as proposed by Phytozome 82 and D.
M. Goodstein (personal communication). We used the annotation from the most accurate analysis as input: EC number (E2P2), KEGG orthology group (KO; KEGGKOALA), PANTHER (InterProScan), KOG (conserved domain database for eukaryotic organisms) and PFAM (InterProScan). We then calculated the multiplicity (M) of annotations across the entire genome, both as single (for example, KOG0157, PF0064 and PF0005) and same-type compound keys (for example, PF0064//PF0005). Mixed compound keys were not considered (for example, KOG0157//PF0064). Weighting (W) factors were applied to protein definitions to give priority to the most informative annotations as follows: EC = 1, KO = 1. 1, PANTHER = 2, KOG = 3, PFAM = 4. The final protein definition corresponds to the least frequent description (minimum M × W value) from this analysis.
Estimation of heterozygosity of the reference genotype 3P. All the short Illumina paired-end reads used to produce the 3P oak reference genome were mapped against the haplome assembly with bowtie2  83  , using standard parameters for the "fast end-to-end" mode. Duplicated mapped reads were removed with Picard (http://broadinstitute. github. io/picard/). SAMtools/bcftools  84  were used to call variants. We then used a combination of custom-made scripts (available at http:// www. oakgenome. fr) to calculate coverage and estimated allele frequency from the "DP4" tag of the. vcf file. We discarded all SNPs with a minor allele frequency value < 0. 25 and all insertions and deletions; the proportion of heterozygous sites on the chromosomes was then calculated with a sliding window approach. For each window, this proportion was weighted by the N% and the fraction covered, defined here as the proportion of bases within a window satisfying the same sequence depth criteria as used for SNP calling.
We then pooled DNA from individual samples to obtain an equimolar solution with a final concentration of 570 ng µ l -1. We used this pool of DNA to prepare a pairedend genomic library with a Paired-End DNA Sample Preparation Kit (Illumina). This library was sequenced on 10 lanes of a HiSeq2000 sequencer (Illumina) (2× 100-bp paired-end reads), generating 1,732,899,595 paired-end reads (331 Gb, that is, ~400× haploid genome coverage). Raw reads were trimmed to remove low-quality bases, as described in the "Sequence processing" section. All reads were then mapped against the oak haplome assembly with bowtie2 83 , using standard parameters for the "sensitive end-to-end" mode. Potential PCR duplicates were removed using Picard (http:// broadinstitute. github. io/picard/). Samtools 84 and PoPoolation2  90  were then used to call SNPs with counts of at least 10 for the alternate allele and a depth between 50 and 1,000× at the position concerned.
We obtained 284-fold (L1), 250. 5-fold (L2) and 264. 9-fold (L3) haploid genome coverage for these samples. For each of the three branches (L1, L2 and L3), reads were mapped against the reference genome sequence with BWA-MEM 92 using the default parameters, except for minimum seed length (k = 79). After sorting, PCR duplicates were removed with Picard (http://broadinstitute. github. io/picard/). We searched for somatic mutations using MuTect (a program developed for the detection of somatic point mutations in heterogeneous cancer samples  11  ) to compare the three libraries (six pairwise combinations; Supplementary Table  20 ). This frequency-dependent detection approach was considered to be particularly well suited to identify somatic mutations in plants. Because considering sequencing error (that is, false positives) is essential for detecting mutations and is vital for drawing valid conclusions, particularly with respect to the detection of somatic mutations within a single individual, we addressed this concern and took all possible actions to minimize it.
02 to 0. 05. Embryos resulting from the self-pollination of 3P were expected to be homozygous for the reference allele, and most outcrossed embryos were expected to be heterozygous. We observed a mean heterozygosity of 0. 54 over the 12 control loci. In the absence of selfing and based on allele frequencies estimated in the pool of 20 individuals, mean heterozygosity would have been close to 0. 96, thus suggesting a relatively high rate of selfing (44%). Unamplified loci (2/21 SNPs; Supplementary Table  6 ) were excluded from the analysis. The overall rate of missing data was high (39% for missing somatic mutations and 54% for control SNPs), so all polar plots from Typer Viewer software of the MassArray iPLEX assay were inspected visually to check that genotyping calls were accurate. Oak karyotype evolution and genome organization.
We used two previously defined parameters  93  to increase the stringency and significance of BLAST sequence alignment by either parsing BLAST results and rebuilding HSPs or using pairwise sequence alignments to identify accurate paralogous relationships within oak (25,808 gene models; Supplementary Data Set 1). Orthologous relationships between oak and grape (26,346 genes on 19 chromosomes  15  ), peach (28,086 genes on 8 chromosomes  16  ) and cocoa (23,529 genes on 10 chromosomes 17 ) were also determined. We estimated the sequence divergence of paralogues and orthologues from the K s calculated with the PAML 4 package  94  for oak-peach, grape-grape, peach-peach, cocoa-cocoa and oak-oak gene pairs. Dot plot representations of synteny and paralogy were obtained with the R package ggplot2 (http://ggplot2.
Gene family expansion and contraction in oak. A classification of groups of orthologous sequences (orthogroups, also referred to here as gene families or clusters) was developed for 16 eudicot plant species: all the predicted oak proteins (corresponding to 25,808 gene models) and the proteins catalogued from 15 other eudicot species (Supplementary Table  21 , Supplementary Note 5. 1. 2). The other eudicot species were Arabidopsis lyrata, A. thaliana, Citrus clementina, Carica papaya, E. grandis, Fragaria vesca, Glycine max, Malus domestica, P. persica, P. trichocarpa, Ricinus communis, Solanum tuberosum, Theobroma cacao, V. vinifera (genomes available from https://phytozome. jgi. doe. gov) and Citrullus lanatus (genome available from http://www. icugi. org/cgi-bin/ICuGI/index. cgi). These 15 plant genomes were selected on the basis of the following criteria: availability of genome sequences and gene models from public databases; assembly quality (N50 length of assembled fragments) and the number of predicted genes; classification (order, family and genus), the main goal being to cover the entire range of eudicots.
GO enrichment analysis. All GO term enrichment analyses were performed using R 3. 3. 1 software 102 and the topGO 2. 22. 0 package  103. The weight01 algorithm  103  and Fisher's exact test were used to detect significant enrichment in GO terms in the various test sets. As stated by the authors of topGO, the P value of a GO term is conditioned on the neighbouring terms. The tests are therefore not independent, and the multiple testing theory does not directly apply. P values should therefore be interpreted as corrected or not affected by multiple testing. Fold-enrichment was defined as illustrated below: • At the gene level, if 52/9,189 (that is, 0. 56%) of input genes are involved in "chitinase activity" and the background level is 60/25,808 genes (that is, 0. 23%) associated with chitinase activity, the fold-enrichment is approximately 0.
56%/0. 23% = 2. 43 for this molecular function. • At the orthogroup level, if 6/126 (that is, 4. 76%) of input orthogroups are involved in "protein serine/threonine kinase activity" and the background level is 50/36,844 orthogroups (that is, 0. 136%) associated with protein serine/threonine kinase activity, the fold-enrichment is approximately 4. 76%/0. 136% = 35 for this molecular function. The first example corresponds to the fold-enrichment calculations performed for TDGs, LDGs, singleton genes and orthogroups expanded in oak. The second corresponds to the fold-enrichment calculation for orthogroups expanded in woody perennials. Web resources. We set up several tools and a browser based on the international open-source project Generic Model Organism Database (http://www. gmod. org) to provide us with access to both structural information and functional annotation (Supplementary Note 6).
For JBrowse, tracks have been generated from the reference genome using data generated for EuGene prediction. Reporting summary. Further information on experimental design is available in the Nature Research Reporting Summary linked to this article. Code availability. The source code for the prediction of miRNA is available as a workflow at https://forgemia. inra. fr/genotoul-bioinfo/ngspipelines/tree/master/ workflows/srnaseq. Custom-made scripts for the estimation of heterozygosity of the reference genotype 3P are available at the oak genome website (http://www. oakgenome. fr/?page_id= 587).
Software and code Policy information about availability of computer code Data collection 1/ sequencing data collection: we used Roche/454 (non operating anymore) and Illumina sequencing machines that included their own computer packages for DNA sequence collection. 2/ genotyping data collection: Allele calling from the MassArray iPLEX assay was processed in Typer Viewer v 4. 0. 26. 75 software (Agena Bioscience). Data analysis custom-made scripts were made available through public web sites. This is clearly stated in the Ms. We relied on R for statistical analyses. The following public or commercial software were used: AMAS / Augustus / BLAST / Bowtie2 / bcftools / BWA-MEM / BLAT / bedtools / BioNJ / Celera assembler / COANCESTRY / CAFE version 3.
Acknowledgements The main sources of funding were the  ANR  ( GENOAK 2022-BSV6-009-02 ),  INRA  and  CEA. T. L. was supported by fellowships from the  ANR  and a  European Research Council   Advanced Grant  ( ERC TREEPEACE FP7-339728 ). I. Lesur  and T. A. were supported by fellowships from the  ANR. N. F. received funding from the  ANR  ( ARBRE ANR-22-LABX-0002-02 ) and the  ERC. J. C. was supported by the  Swedish Foundation for Strategic Research.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability The oak haploid genome assembly and corresponding annotation have been deposited in the European Nucleotide Archive under project accession code PRJEB19898. Other sequence release data are indicated in Supplementary tables 1, 13, 14 and 19 and Supplementary Data Set 10. We also invite readers to download data stored at the URLs indicated in section 6 (Web resources) as well as in the oakgenome web site: http://www. oakgenome.
31  isolated a novel insecticidal protein, Tma12, from the fern Tectaria macrodonta. Transgenic cottons carrying Tma12 exhibit outstanding resistance to whitefly, yet show no decrease in yields, demonstrating tremendous agricultural potential. Tma12 has a high similarity to chitinbinding proteins (Pfam PF03067), but its evolutionary origin is unknown. Here, we found a Tma12 homologue to be present in the Salvinia genome (henceforth ScTma12), as well as in a few 1,000 Plants (1KP)  32  fern transcriptomes, but not in Azolla or any other publicly available plant genomes. Phylogenetic analyses position the fern Tma12 sequences together with bacterial sequences, and are most closely related to the chitin-binding proteins from Chloroflexi (Fig. 4 ). We investigated whether this insecticidal protein was more likely a result of horizontal gene transfer (HGT) from bacteria to ferns or produced by fern-associated microorganisms.