dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the  Genotoul platform  for sequencing. T. Giraud acknowledges the ANR  Grant  ANR-12-ADAP-0009  and the  ERC   Starting Grant GenomeFun 309403. We thank all the strain collectors (Table S1, Supporting information). We thank  Michael Hood  for strains and help all along the project. We thank  Gilles Deparis  and  Laetitia Giraud  for technical help and  Nicolas Bierne ,  Christophe Lemaire  and  Christelle Fra€  ısse for help with analyses. © 2016 The Authors. Molecular Ecology Published by  John Wiley & Sons Ltd.
linkage mapping Salmo trutta salmonids RAD markers chromosomal rearrangements recombination rate A renewed interest for linkage mapping studies has recently occurred thanks to the simplified procedures for generating large genotype datasets in nonmodel organisms  (Slate et al. 2008; Stapley et al. 2010; Ekblom and Galindo 2011). High-density linkage maps have been particularly developed in teleost fishes because of their high number of species, economic importance for aquaculture (e. g. ,  Sauvage et al. 2012; Zhao et al. 2013; Wang et al. 2015; Liu et al. 2016; Kumar and Kocour 2017) , phylogenetic position within vertebrates  (Amores et al. 2011; Rondeau et al. 2014; Wang et al. 2015; Kuang et al. 2016) , and broad interests for eco-evolutionary and conservation/management issues  (Everett et al.
Genotyping pipeline Bioinformatic analyses followed the same procedure as in  Leitwein et al. (2016). Briefly, raw reads that passed an Illumina purity filter and were of sufficient FastQC quality (. 28) were demultiplexed, cleaned, and trimmed with process_radtags. pl implemented in Stacks v1. 35  (Catchen et al. 2013). The Atlantic salmon genome (GenBank accession number: GCA_000233375. 4_ICSASG_v2; Lien et al. 2016 ) was used to determine individual genotypes at RAD markers using a reference mapping approach. First, reads were aligned to the S. salar genome with the BWA-mem program  (Li and Durbin 2010). Alignment result files were processed into pstacks to build loci and call haplotypes for each individual (using m = 3 and a = 0. 05). Individuals with a minimum mean coverage depth under 7• were removed, resulting in a data set of 147 offspring in FAMILY 1 and 149 offspring in FAMILY 2.
(2015a)  for details]. We used the subset of markers that were found heterozygous only in the female parent to phase progeny haplotypes within each family. We only focused on female informative sites because the lower recombination rate in male salmonids makes them less adequate for inferring centromere locations  (Limborg et al. 2015a). Moreover, lower male recombination rates have been reported in brown trout  (Gharbi et al. 2006 ). The phased genotypes data sets were used to estimate RFm for intervals between markers along each LG. Finally, we plotted the RFm calculated from each LG extremity to determine the chromosome type and the approximate location of the centromere for metacentric chromosomes.
Data availability Supplemental Material, Table  S1  contains RAD markers IDs, consensus sequences, mapping position on the brown trout linkage map, physical position on the Atlantic salmon genome, and estimation of local recombination rate. Table  S2  contains correspondence between the microsatellite and the new RAD linkage map. Figure  S1  shows RFm plots obtained for each female haploid data set. Figure  S2  shows Map-Comp Oxford plot comparing the brown trout to the Atlantic salmon linkage map, with markers paired through the Atlantic salmon genome. Figure  S3  shows distribution of female and male marker positions along each LG for FAMILY 1. File S1 is an archive containing the. loc and. map files from both families. Demultiplexed sequence reads aligned against the Atlantic salmon genome (in Bam file format) are available at NCBI Short Read Archive under accession number PRJNA371687.
(Li and Durbin 2010)6)6)d to our new high-density linkage map using the MAPCOMP pipeline (available at https://github. com/enormandeau/mapcomp/)(Sutherlandetal. 2016). The microsatellite markers' primer sequences retrieved fromGharbi et al. (2006), as well as the five microsatellites used inLeitwein et al. (2016)and the lactate dehydrogenase (LDH-C1) gene fromMcMeel et al. (2001)were aligned with BWA_aln(Li and Durbin 2010)to the Atlantic salmon reference genome. Only single hit markers with a quality score (MAPQ) above 10 were retained. The 3977 RAD markers integrated in the high-density S. trutta linkage map (see Results) were also aligned to the S. salar reference genome with BWA_mem. Microsatellite and LDH markers were anchored onto the RAD linkage map by identifying the closest RAD locus located on the same contig or scaffold.
ACKNOWLEDGMENTS We thank  E. Ravel  and people from the  Fédération Départementale de la Pêche de l'Hérault (France)  and the Babeau hatchery for their help at several stages of this project. We also thank  Y. Anselmetti  for his kind advice on bioinformatics computing,  M. Limborg  for his helpful scripts and advice, and  B. Sutherland  for his suggestions with Map-Comp. This project largely benefited from the  Montpellier Bioinformatics Biodiversity  cluster computing platform. Presequencing steps necessary for the production of SNP data were performed at the Genotyping and Sequencing facility of the  LabEx CeMEB  ( Centre Méditerranéen pour l'Environnement et la Biodiversité, Montpellier ), and RAD sequencing was performed at  Montpellier GenomiX  facility (Montpellier, France; http://www.
De novo genome assembly Various sets of the longest MinION 2D reads, which refer to various theoretical genome coverage (10•, 15•, 20•, and all the 2D reads, i. e. 25•) taking 15 Mb as genome size estimate (Supplemental Material, Table  S1  in File S1), were subjected to four assemblers: ABruijn (v0. 3b)  (Lin et al. 2016) , Canu (v1. 1)  (Berlin et al. 2015) , miniasm (v0. 2-r137-dirty)  (Li 2016) , and SMARTdenovo (https://github. com/ruanjue/smartdenovo). ABruijn and miniasm were run with default parameters, while "genomeSize=13 m, minReadLength=2500, mhapSensitivity=high, corMhapSensitivity=high, and corOutCoverage=500" was set for Canu and "-c 1 -k 14 -J 2500 -e zmo" for SMARTdenovo. After the assembly step, we polished each set of contigs with Pilon (v1. 18)  (Walker et al. 2014) , using 100• of Illumina 2 • 100 bp paired-end reads.
RESULTS AND DISCUSSION Three D. bruxellensis isolates (UMY321, UMY315, and 133) were sequenced in this study (Table  1 ). These strains were determined to be diploid based on flow cytometry analysis and were all isolated from wine or grape must in Italy or South Africa. The genome of the UMY321 isolate was sequenced using a combination of Nanopore long-read and Illumina short-read sequencing data to obtain a highquality assembly. By contrast, the UM315 and 133 isolates were only sequenced using a short-read strategy. In addition, these genomes were compared to previously genome sequences of six other D. bruxellensis isolates (Table  1 )  (Curtin et al. 2012; Pi skur et al. 2012; Borneman et al. 2014; Crauwels et al. 2014; Olsen et al. 2015).
De novo genome assembly construction and comparison For the UMY321 isolate, a total of three MinION Mk1 runs were performed with the R7. 3 chemistry using 2D library types with 8 kb mean fragmentation size. A total of 115,559 reads representing a cumulative size of 1. 15 Gb were generated, among which 41,686 2D reads showed an average quality greater than nine (2D pass reads). We focused on these 2D pass reads representing a total of 376. 8 Mb, with the longest read being 70,058 bp (mean = 9033 bp and median = 8676 bp) (Figure  S1 ). Four subsets of our 2D pass reads (10 Using these various datasets and assemblies, the objective was to define the best assembler and the minimal coverage needed. Hence, we computed the standard contiguity metrics for all assemblies to evaluate their quality, which is related to both the assembler and the dataset (Figure  1  and Table  S2  in File S1).
First, we observed that, considering the results by assembler, the number of scaffolds obtained with the 10• dataset is much higher compared to the other datasets, which suggests that a 10• coverage of MinION reads is too low to obtain a good quality assembly. By assembler, the results obtained for the higher coverages are comparable. Using Canu, the number of scaffolds is much higher and N90 as well as N50 are much lower, producing the less connected assemblies (Figure  1  and Table  S2 in File S1 ). The contiguity metrics associated with the assemblies constructed with SMARTdenovo, ABruijn, and miniasm were closely related, and it seemed difficult to select a single best assembly on the sole basis of these measurements, especially since good contiguity metrics are not necessarily associated with assembly completeness.
Considering these results, we decided to map the Illumina paired-end reads back on the generated assemblies with BWA  (Li and Durbin 2009). Among all the assemblies, the proportion of unmapped reads ranged from 1. 12 to 11% (Figure  1  and Table  S2  in File S1). Surprisingly, the assemblies constructed with miniasm were less complete, as. 5% of the reads did not map back, compared to ,1. 5% for the ABruijn and SMARTdenovo assemblies. By comparing standard metrics and the proportion of unmapped reads, the most accurate assembly was obtained with the 20• 2D reads dataset combined with the SMARTdenovo assembler. This assembly is composed of nine scaffolds, i. e. , very close to the estimated number of chromosomes, which appears to vary between four and nine among different strains of this species (Hellborg and Pi skur 2009), for a complete assembly size of 12.
ACKNOWLEDGMENTS We are grateful to  Warren Albertin  and  Isabelle Masneuf-Pomarede  for fruitful discussions and invaluable advice. We thank  Jure Piskur  and  Anna Schifferdecker  for generously providing the 133,  UMY315  and  UMY321  Dekkera bruxellensis isolates. We also thank the  BioImage platform (IBMP-CNRS, Strasbourg, France)  for their support. This work was supported by  France Génomique  (grant  ANR-10-INBS-09-08 ), and the  Agence Nationale de la Recherche  (grant  ANR-16-CE12-0019  to J. S. ). T. F. and J. -S. G. are supported by a grant from the  French "Ministère de l'Enseignement Supérieur et de la.
The cell wall structure and composition is very important in these species because they determine the properties of the extracted raw materials. Flax, for instance, has been used for several thousands of years to make ropes and linen tissues and more recently, for the production of environmentally friendly fiber-based composite materials  [8, 9]. To optimize the use of these materials in the future, targeted engineering of the cell wall to improve product quality will require a thorough understanding of the biosynthetic pathways leading to the production of the main cell wall polymers. Over the past years, significant efforts including genome sequencing  [10] , development of molecular tools  [11] [12] [13]  and databases  [14, 15]  have allowed us not only to improve our understanding of flax fiber cell wall biology, but also to identify the different gene models potentially associated with the biosynthesis of different cell wall polymers.
Identification of genes related to phenylpropanoid biosynthesis in flax The whole genome shotgun assembly of flax  [10]  was first surveyed to identify the genes potentially related to phenylpropanoid biosynthesis. BLAST analyses were carried out to search against the 88,420 scaffolds including 43,471 gene models. A total of 69 genes were identified and organized in gene families containing between 3 and 15 members (Table  1 ) and the presence of the corresponding transcripts for each gene was checked among the different public EST databases. Only Lus4CL8, Lus4CL9 and Lus-CCoAOMT5 have no corresponding ESTs (E value <e-50) and the expression of LusHCT1, LusCCR3, LusCCR8, LusCAD8 and LusCAD11 was also undetectable when using whole genome microarrays  [22]. In addition to these 8 genes, no expression data were obtained for LusCCR9, LusCCR11, LusCCR12 and LusCAD9 using EST-based microarrays  [11, 23].
Based on previous studies reporting predictive and experimental data on the cleavage of LAC4 and LAC17 transcripts in Arabidopsis  [20]  and potentially 29 laccase transcripts in Populus  [21]  by miR397, we first searched for a flax orthologous miRNA precursor sequence in the EST and genome databases as previously described  [44]. The c3244 EST from the genolin database  [11]  and scaf-fold1999 extracted from the phytozome genomic database  [45]  both contained the pre-miRNA sequence (Fig. 5 ). The deduced mature lus-miR397 sequence was first used to search for targets among the flax laccases and among these, 11 genes were predicted with penalty mismatches included between 0 and 2. 5 within the duplex. The amplification and sequencing of the 5′-end of the degraded transcripts by RLM-RACE allowed us to experimentally validate the cleavage of LusLAC1, LusLAC5, LusLAC9, LusLAC38, LusLAC39 and LusLAC44 among the predicted targets (Fig.
Selection of candidates for lignin engineering in flax The success of targeted engineering of cell wall lignin genes to improve the quality of different flax-derived products depends upon the correct identification of individual family gene members involved in the trait to be modified. Some genes potentially involved in lignin biosynthesis have already been partially characterized in flax. The first flax lignin cDNA sequence was deposited in the GenBank database in 2005 and the corresponding CCoAOMT gene functionally characterized  [30]. It was chosen because it is responsible for the synthesis of feruloyl CoA used for the formation of both G-and S-lignin units and, as confirmed by down-regulation, because it plays a central role in maintaining SCW integrity by regulating the quantity and the S/ G proportion in the non-condensed lignin fraction.
All the samples were triplicates and always frozen immediately in liquid nitrogen before storage at -80 °C prior to RNA extraction. For in situ hybridization, the median stem, leaves and 2-cm root fragments were collected and fixed as described below. In silico identification and analysis of phenylpropanoid genes in L. usitatissimum The flax phenylpropanoid protein and gene sequences were identified in the annotated v1. 0 of the Linum usitatissimum genome hosted on the Phytozome website  [45]. The entire database derived from the published genome sequences  [10]  was queried with the names of the gene families used as keywords and also by BLASTp interrogation with previously lignin-related identified sequences from Arabidopsis thaliana  [78]  and Populus trichocarpa  [25]. When it was necessary, unknown portions of some sequences were replaced by the corresponding sequences found in public EST databases and truncated sequences were discarded.
For each gene model, the presence of a corresponding EST was searched in the dbEST  [79]  and genolin  [11]  databases. The intron/exon structures were also retrieved and the identities of the protein sequences compared. The k-means medians clustering function from the TIGR MultiExperiment Viewer (TM4 Mev v4. 8. 1) was used to cluster the genes according to their expression profiles. The distance metric was Pearson correlation and the default parameters were used for the k-means calculation.
Phylogenetic analyses For each gene family, the protein sequences were aligned with ClustalW and the phylogenetic analyses obtained with the MEGA 6. 06  [80]  software for performed by maximum likelihood method based on the JTT matrixbased model  [81]. Consensus trees were generated with 1000 bootstrap replicates. HT-RT-qPCR using a 96. 96 dynamic array Total RNA was extracted from the different organs and tissues using the TriReagent method (Molecular Research Center). RNA integrity and concentration were evaluated with RNA Standard Sens Chips in the Experion automated capillary electrophoresis system (Bio-Rad). cDNA was then synthesized using the High Capacity RNA-to-cDNA Kit (Applied Biosystems) according to the manufacturer's instructions. The large scale quantitative RT-PCR was performed with a BioMark HD System using a Fluidigm 96.
miRNA target prediction and experimental validation The sequence of the lus-miR397 precursor was identified among the known flax ESTs using stringent rules described elsewhere  [44]. The laccase transcript models were analysed with psRNATarget  [85]  to predict the targets that can potentially match with the microRNA without any gaps. When referring to the miRNA sequence, there were not more than one mismatch between the nucleotides number 1 and 9, none between 10 and 11, and no more than 2 consecutive mismatches after position 11. The experimental validation of the laccase targets was carried out by using a modified protocol of the RNA ligation mediated (RLM) RACE included in the GeneRacer kit (Invitrogen). RNA was extracted from a mix of flax organs and the cleaved transcripts (without 5′-cap) were selectively ligated to a 5′-RNA adaptor.
Acknowledgements We are indebted to the  Research Federation FRABio (Univ. Lille, CNRS ,  FR 3688 ,  FRABio, Biochimie Structurale et Fonctionnelle des Assemblages Biomoléculaires ) for providing the scientific and technical environment conducive to achieving this work. JLR gratefully acknowledges the french Ministère de l'Enseignement Supérieur et de la Recherche  for financial support.
To this end, the potential of lidar (Light detection and ranging), which is a technology based on the emission and reception of laser pulses, has been widely acknowledged  [21, 22] , in particular that of airborne laser scanner (ALS). Lidar technology provides an opportunity to build variables describing aspects of the forest structure which differ from those observed, or measured, during field surveys. Lidar data set over the Lowland area. None of the authors were involved in the collection of field data sets and confidentiality agreements were signed with IGN and AgroParisTech that explicitly stipulate that authors are not allowed to disseminate field data sets. All interested researchers are invited to proceed in the same way the authors did to access the data sets used in this study: Nicolas Paparoditis, the scientific director of IGN can be contacted regarding IGN data sets (nicolas.
paparoditis@ign. fr) and both information and contact for the geolocalised EcoPlant database can be found with related publications at the following address: http://www6. nancy. inra. fr/foret-boislerfob/Les-equipes/EcologieForestiere/Outils-etbases-de-donnees/EcoPlantto For Lidar data over the Mountainous area the scientific direction of IGN can also be contacted. For the Lowland Lidar data set the consortium agreement stipulates that data can be provided for research purpose with all the owners' consent (or the agreement would be considered established in case of no answer within a month). They will become available with no agreement from 2019. Data can be asked to either FCBA (francis. de. morogues@fcba. fr), Irstea (sylvie. durrieu@irstea. fr), ONF (anne. jolly@onf. fr) or IGN (nicolas.
Lopatin et al. [27]  also used a partial least squares path model (PLS-PM) algorithm based on ALS data to predict plant richness from variables related to topography, and to both crown cover and tree height in Mediterranean forests. Complementing ALS variables with environmental variables has improved the predictive power of floristic biodiversity models  [28, 29]. To move further in this direction would entail using additional non-biotic variables and a modelling framework that can better address the complexity of biodiversity models. Biodiversity modelling could therefore be improved by combining the significant advances in Bayesian modelling and input variables enriched with ALS data that would provide more appropriate and reliable structure measurements. The aim of our study was to demonstrate the utility of ALS variables when attempting to highlight and better understand the influence of forest structure on understory biodiversity.
ALS data Data were collected at both sites using small-footprint ALSs. Only a partial sub-area of each site was covered by the ALS survey. Specifications on the ALS data are given in Table  1. Data pre-processing was performed for each study area by the data providers: Sintegra (France) and the French National Institute of Geographic and Forest Information (IGN, France) for the Riegl and Optech data, respectively. Ground points were classified following the TIN-iterative algorithm  [32]  in order to produce a digital terrain model (DTM). Next, first return points were extracted from the data to produce a digital surface model (DSM). Both the DTM and DSM had a 1 m resolution. For each acquisition, aboveground heights were calculated by subtracting the ground elevation given by the DTM from each corresponding ALS elevation point; thereby removing topographic effects from the ALS point clouds.
Field inventories Field data were collected on 789 circular plots within the Lowland site. For the 48 plots (9m radius) within the sub-area covered by ALS data, field data were obtained from the EcoPlant database  [33] , and for the remaining 741 plots (15 m radius), data came from the IGN database (http://inventaire-forestier. ign. fr/spip/spip. php?article707). At the Mountain site, data were obtained from the IGN for 1,155 circular plots. 171 (15 m radius) were surveyed within the sub-area covered by ALS data, and the remaining 984 (15 m radius) plots were located outside the sub-area. At both study sites, field data were collected from 2008 to 2012. Field plots covered in ice and snow during the inventories were excluded from the dataset. Plot centre positions were measured using a differential global positioning system (DGPS).
Soil characteristics, i. e soil pH (Reaction) and soil water capacity (SWC), were derived from the mean Ellenberg values of the understory species at both sites. Temperatures (T mean ; ˚C) and global solar radiation (Solrad; MJ/m 2 /day) were based on May to September average values. Monthly values were obtained from the French National Meteorological Service (Meteo-France). Solrad was estimated from temperature data using the equation in  [34]. The spatial resolution of the meteorological data was 1 km 2. Topography was described by three variables, i. e. Type of Topographical Situation (TTS), Slope, and Aspect. TTS was defined according to the French National Forest Inventory documentation as follows: 0-flat terrain; 1-summit   [17] ). Flat topographies (0, 6, and 8) were not distinguished in the dataset.
Methods In our study, we focused on ground-layer floristic abundance and richness. We considered abundance for the eight most representative species at each study site, excluding woody species. We also considered species richness for three ecological groups based on light preference, i. e. shade-tolerant, mid-light preferring, and heliophilous. We developed Bayesian models to link species richness and abundance with both environmental and ALS variables. In accordance with Austin and Van Niel  [28] , we included seven environmental abiotic variables in our models: Reaction, SWC, T mean , Solrad, TTS, Slope and Aspect, as well as one tree stand variable, C tot , as an indicator of forest structure. As in Austin and Van Niel  [28] , we included a single biotic variable. Indeed, using Bayesian models requires a high ratio between reference data and explanative factors to provide reliable and interpretable results.