dataesr/scientific-paragraphs-categorization huggingface dataset:
Total, transverse and longitudinal velocity SFs In order to analyse the scale-dependent distribution of horizontal KE, the computation of the total second order velocity SF (VSF) D U U will be deduced from the longitudinal D and transverse D ⊥⊥ second order VSF, isotropically averaged over r, so that: These longitudinal and transverse VSFs are computed using a similar definition as in  Babiano et al. (1985) : D U U (r) = D (r) + D ⊥⊥ (r). (4 D (r) = δU (x, r) • r 2 x r 2 , ( 5 ) D ⊥⊥ (r) = δU (x, r) × r 2 x r 2. ( 6 ) This definition is particularly useful because it does not depend on the coordinate system used and therefore does not require to rotate velocity components prior to the calculation of each increment δU. The longitudinal and transverse VSFs will be computed on zonal and meridional velocities from the SADCP dataset presented in section 2.
We gathered data from different observing systems around New Caledonia, including SADCP, TSG, ARGO, which made this study purely based on in situ observations. The analyses of SFs computed on these data showed that the dynamical regime at work in the 3-100 km range depends on depths, regions, seasons and scales. For each dependence type, we dedicate a section in which our findings are summarised and discussed. We eventually give some perspectives arising from this study.
Perspectives Overall, our results suggest that the structure function analysis is a powerful tool that provides physically consistent results. Amongst the benefits of the SF analysis, the capacity to be directly computed on uneven samples is clearly an advantage compared to Fourier techniques. In order to provide more insights into the turbulent regimes at work, we tried to investigate the direction of the KE cascade using third-order structure functions as done in  Balwada et al. (2016)  but our dataset revealed to be too insufficient to compute accurate higher order statistics. We believe that more ADCP observations would solve this problem and provide better statistics in order to infer the direction of the energy cascades from SFs. A larger SADCP database would also allow disentangling seasonal and local variations in order to provide information on the seasonality of SFs and turbulent regimes.
Acknowledgements. The Ssalto/Duacs altimeter products were produced and distributed by the  Copernicus Marine and Environment Monitoring Service (CMEMS)  (http://www. marine. copernicus. eu). The authors acknowledge Collect Localisation Satellite for the M2 internal tide maps estimated from altimetry by  Ray  and Zaron (2015). The authors are grateful to all who contributed to the acquisition, processing and distribution of SADCP data: to the PIs of the cruises, the engineers, and the crew. They wish to thank in particular the R/V Alis crew, and  E. Firing ,  J. Hummon  and  P. Caldwell  for maintaining the  Joint Archive for Shipboard ADCP (JASADCP , http: //ilikai. soest. hawaii. edu/sadcp/main-inv. html). The ADCP dataset produced after quality control at the  Laboraboire d'Études en Géophysique et Océanographie Spatiale  is available on request.
In tuber rotting assays, D. dianthicola isolates were more aggressive than Pectobacterium isolates. The complete genome sequence of D. dianthicola LAR. 16. 03. LID was obtained and compared with other D. dianthicola genomes from public databases. Overall, this study highlighted the ecological context from which some Dickeya and Pectobacterium species emerged in Morocco, and reported the first complete genome of a D. dianthicola strain isolated in Morocco that will be suitable for further epidemiological studies.
All the gapA PCR products obtained with gapA-F/gapA-R primers (Table  S1 ) were sequenced using Sanger technology (GATC Biotech, Konstanz, Germany). The sequences were trimmed using the CLC genomic workbench (V10. 1. 1, Aarhus, Denmark) and aligned using ClustalW. The phylogenetic analysis of the gapA gene was performed as follows: the evolutionary distances were computed using the maximum composite likelihood method (Mega7 software, Pennsylvania State University Park, PA, USA) with 1000 bootstrap. The obtained sequences were deposited in GenBank (Table  S2 ).
Genome Assembly Assembly of the Illumina reads was performed using the CLC Genomics Workbench v10. 1. 1 software (CLCInc, Aarhus, Denmark). After quality (quality score threshold 0. 05) and length (above 40 nucleotides) trimming of the reads, contigs were generated by de novo assembly (CLC parameters: automatic determination of the word and bubble sizes with no scaffolding). The draft genome sequences of each strain were deposited at NCBI and annotated using the NCBI Prokaryotic Genome Annotation Pipeline. Statistics of all the ten draft genomes are presented in Table  1. Assembly of the Nanopore reads was performed as follows. Fast5s from Nanopore sequencing were obtained with MinKNOW version 1. 10. 23 and were basecalled with ONT Albacore Sequencing Pipeline Software version 2. 1. 10 and reads passing the internal test were used for subsequent analysis.
Porechop 0. 2. 1 (https://github. com/rrwick/Porechop) was used for adaptor trimming. Illumina paired-end reads were processed with trim_galore 0. 4. 0 (https://github. com/FelixKrueger/TrimGalore), to trim adaptor sequences. Nanopore reads were assembled using Canu 1. 7  [48]  with the "genomeSize = 5 m" and "minReadLength = 3000" options. For Nanopore-only assembly, one output contig was obtained, then polished three times using Pilon 1. 22 (https://github. com/broadinstitute/pilon), with the "-mindepth 25" option. The contig was finally circularized using Circlator 1. 5. 1 (https: //github. com/sanger-pathogens/circlator).
edu/ani/, Atlanta, GA, USA). The in-silico DNA-DNA hybridization (isDDH) was evaluated using genome sequence-based species delineation (http://ggdc. dsmz. de/, Braunschweig, Germany) (Table  2 ). The genome map of the D. dianthicola strain LAR. 16. 03. LID was generated using CGView Server  [48]. Synteny analysis of the complete genomes of D. dianthicola LAR. 16. 03. LID, D. dianthicola ME23 and D. dianthicola RNS049 was performed using the MAUVE software  [49]. Paired end reads for the strain LAR. 16. 03. LID were mapped against the two complete genome sequences of D. dianthicola strains ME23 and RNS049 with threshold set as 0. 8 of identity on 0. 5 of read length using CLC Genomics Workbench version 10. 1. 1 software. The mappings were used for detection of variations (SNPs and InDels) using the basic variant calling tool from the CLC genomic workbench version 10.
We tested whether a correlation between these different parameters and the diversity of pathogens (the combination of Dickeya and Pectobacterium species) existed. Statistical analysis with the SAS (Statistical Analysis System, version 9. 00, SAS Institute, 2002, Cary, NC, USA) software (Qui2 test with p < 0. 05) revealed Important information was also collected from the farmers regarding the potato variety, origin of the seed tubers (local production or importation), irrigation mode (surface water from a dam or underground water from wells) and geography (the sampled regions). We tested whether a correlation between these different parameters and the diversity of pathogens (the combination of Dickeya and Pectobacterium species) existed. Statistical analysis with the SAS (Statistical Analysis System, version 9.
The alignment of the concatenated genes fusA, rpoD, rpoS, acnA, purA, recA, mdh, mtlD, groEL, secY, glyA, gapA and rplB was generated using ClustalW; the evolutionary history was inferred using the Neighbor-Joining method and the evolutionary distances were computed using the Maximum Composite Likelihood method. Phylogenetic analyses were conducted using MEGA7 software. The name of the Moroccan isolates is underlined. Genomic data were also used to calculate ANI and isDDH values. Most of the Moroccan strains exhibited an ANI value higher than 95%, and an isDDH value higher than 70% with the closest type strains, confirming their taxonomic assignation. Strains belonging to the P. versatile clade showed an isDDH lower than 70%, but an ANI value higher than 95% with the strain SCC1. Recently, another study confirmed the classification of the strains S4.
16. 03. 3I (= CFBP8660), S4. 16. 03. 3F (= CFBP8659) and SCC1 into the P. versatile species  [7]. The alignment of the concatenated genes fusA, rpoD, rpoS, acnA, purA, recA, mdh, mtlD, groEL, secY, glyA, gapA and rplB was generated using ClustalW; the evolutionary history was inferred using the Neighbor-Joining method and the evolutionary distances were computed using the Maximum Composite Likelihood method. Phylogenetic analyses were conducted using MEGA7 software. The name of the Moroccan isolates is underlined.
Complete Genome of D. Dianthicola LAR. 16. 03. LID The genome of D. dianthicola LAR. 16. 03. LID was the first complete genome of a D. dianthicola strain collected in Morocco, and the third D. dianthicola genome in the NCBI database that already hosted those of strains ME23 and RNS04. 9 (Figure  4 ). Phylogenetic relationships between all D. dianthicola genomes available in NCBI were determined using MLSA. In the phylogenetic tree, the two Moroccan strains LAR. 16. 03. LID and S4. 16. 03. P2. 4 appeared to be highly related. This could be explained by the close isolate locations, that were two potato fields separated only by a road. The two Moroccan D. dianthicola strains clustered with D. dianthicola strain ME23, which has been recently collected in a potato field in USA (Figure  4 ). The genomic relationship between D.
dianthicola ME23 and D. dianthicola LAR. 16. 03. LID was confirmed using SNP/InDels calling. The SNP and InDel number in the LAR. 16. 03. LID genome reached 12,259 and 16,335, using the ME23 and D. dianthicola RNS04. 9 genomes as a reference, respectively. The LAR. 16. 03. LID genome was annotated using the NCBI Prokaryotic Genome Annotation Pipeline. A graphical genome map is provided in Figure  5. The D. dianthicola LAR. 16. 03. LID genome exhibited a high synteny with that of D. dianthicola strains  The genome of D. dianthicola LAR. 16. 03. LID was the first complete genome of a D. dianthicola strain collected in Morocco, and the third D. dianthicola genome in the NCBI database that already hosted those of strains ME23 and RNS04. 9 (Figure  4 ). Phylogenetic relationships between all D.
The three D. dianthicola genomes hosted three or four CRISPR loci (Figure  6  and Table  S5 ). The genes fusA, rpoD, leuS, rpoS, purA, infB, gyrB, recA, groEL, secY, glyA, gapA, rplB, dnaX and gyrA were concatenated. The alignment was generated using ClustalW; the evolutionary history was inferred using the Neighbor-Joining method and the evolutionary distances were computed using the Maximum Composite Likelihood method. Phylogenetic analyses were conducted using MEGA7 software. The Moroccan D. dianthicola isolates are indicated in bold face. The genes fusA, rpoD, leuS, rpoS, purA, infB, gyrB, recA, groEL, secY, glyA, gapA, rplB, dnaX and gyrA were concatenated. The alignment was generated using ClustalW; the evolutionary history was inferred using the Neighbor-Joining method and the evolutionary distances were computed using the Maximum Composite Likelihood method.
Phylogenetic analyses were conducted using MEGA7 software. The Moroccan D. dianthicola isolates are indicated in bold face. The D. dianthicola LAR. 16. 03. LID genome exhibited an arsenal of virulence genes similar to that described for D. dianthicola RNS049  [39]. All the pectinase-encoding genes described in the model strain D. dadantii 3937  [54]  were conserved in the three D. dianthicola genomes, with the noticeable exceptions of the lacking pehK gene (which encodes a predicted polygalacturonase) and the presence The D. dianthicola LAR. 16. 03. LID genome exhibited an arsenal of virulence genes similar to that described for D. dianthicola RNS049  [39]. All the pectinase-encoding genes described in the model strain D. dadantii 3937  [54]  were conserved in the three D. dianthicola genomes, with the noticeable exceptions of the lacking pehK gene (which encodes a predicted polygalacturonase) and the presence of a truncated form of the pelA gene (which encodes Pectate lyase A).
Aside macerating enzymes determinants, other genes implicated in different stages of the host infection were conserved in the three D. dianthicola strains, including those involved in the resistance to oxidative stress, acidic pH (cfa, asr) and antimicrobial peptides (arnB-T, sapABCDF), synthesis of cell envelope components (such as bscABCD and wza-wzb-wzc), and siderophore synthesis and uptake (acsF-A and cbrABCDE for achromobactin and fct-cbsCEBA for chrysobactin)  [54]. We strengthened this analysis using the resistance gene identifier (RGI) and AntiSMASH softwares. We searched for genes involved in the resistance to different families of antimicrobial compounds. No differences were observed between the three D. dianthicola genomes (Table  S6 ). The AntiSMASH analysis identified many secondary metabolite biogenesis clusters in D.
Conclusions This study revealed a wide diversity of Pectobacterium and Dickeya pathogens in northern Morocco, including P. polaris and P. versatile, that are reported for the first time in this country. In tuber maceration assays, the tested isolates of the emerging pathogen D. dianthicola were more aggressive than the Pectobacterium isolates. This feature should alert stakeholders to the threat that this pathogen poses to potato tuber production in northern Morocco. The nucletotide sequence data of the Dickeya and Pectobacterium Moroccan isolates, including a complete genome of D. dianthicola, 10 draft genomes and 119 partial sequences of the gapA gene, were deposited in a public database (NCBI GenBank).
Figure2. Phylogeny of the Moroccan strains based on MLSA. The phylogenetic trees were generated separately (A) for Pectobacterium and (B) for Dickeya strains. The alignment of the concatenated genes fusA, rpoD, rpoS, acnA, purA, recA, mdh, mtlD, groEL, secY, glyA, gapA and rplB was generated using ClustalW; the evolutionary history was inferred using the Neighbor-Joining method and the evolutionary distances were computed using the Maximum Composite Likelihood method. Phylogenetic analyses were conducted using MEGA7 software. The name of the Moroccan isolates is underlined.
Acknowledgments:  Yves Dessaux  and  Peter Mergaert  ( I2BC ) are thanked for their advice during the preparation of the manuscript of which an uncorrected preprint was deposited (04 Feb, 2020) in Research Square database (https://www. researchsquare. com/article/rs-13222/v1; DOI 10. 21203/rs. 2. 22648/v1 ) before a submission to Microorganisms Journal.
Funding: This work was supported by The  French National Agency for Research  ( ANR-15-CE21-0003 ) and a cooperative project between France and  Morocco  ( PRAD 14-02 ,  Campus France  n  • 30229ZK ). Research in the I2BC was supported by the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ).
Morphological Data Species were scored for 102 morphological characters (Appendix S1). Seventy-eight were extracted from earlier phylogenetic studies  (Weiblen 2000 , Clement and Weiblen 2009 , Chantarasuwan et al. 2015)  and sometimes redefined, while 24 characters were used for the first time. Whenever possible we cross-validated our observations and accounted for polymorphism using descriptions available in the literature  (Corner 1938 , Corner 1967 , Corner 1969b , Corner 1969a , Corner 1970 , Corner 1978a , Corner 1978b , Berg and Wiebes 1992 , Berg and Corner 2005 , Berg 2009 , Berg et al. 2011 ) and conspecific specimens from other localities. Data were analysed using Maximum Parsimony (MP) as implemented in PAUP* version 4. 0a  (Swofford 2003). We used a heuristic search with 5000 random addition sequences (RAS) to obtain an initial tree and "tree bisection and reconnection (TBR)" as branch swapping option, with reconnection limit set to 100.
The 583 loci obtained in this step (mergeR1 dataset) were used as a starting point to assemble paired reads into longer RAD loci. The pipeline, scripts and parameters used for the assembly of paired reads are available from https://github. com/acruaud/radseq_ficus_2020. Briefly, for each sample, forward reads used to build the 583 cstacks loci of the mergeR1 data set as well as corresponding reverse reads were retrieved from original fastq files with custom scripts and assembled with Trinity  (Haas et al. 2013). Contigs were aligned to the reference genome of F. carica assembly GCA_002002945. 1 with Lastz Release 1. 02. 00  (Harris 2007). Homology between cstacks loci and reference genome and homology between sample contigs within cstacks loci were tested as follows. For each cstacks locus, the genome scaffold with the highest number of alignment hits was considered as likely to contain the RAD locus.
When contigs aligned with different parts of the same scaffold, the genome region that showed the highest identity with the sample contigs (as estimated with Geneious 11. 1. 4: https://www. geneious. com) was considered as the most likely RAD locus. Cstacks loci for which the sample of F. carica JRAS06927_0001 included in the RAD library was not properly aligned with the reference genome (hard or soft clipped unaligned ends > 10 bp) or for which the majority of contigs did not align with the same genome region as F. carica JRAS06927_0001 were removed. Finally, loci for which at least one sample contig appeared more than once were discarded. When several contigs were retained per sample (e. g when forward and reverse reads did not overlap; or in case of polyploidy or sequencing mistake) a consensus was built and the IUPAC code was used without considering any threshold, if, for a given position, different nucleotides were present.
Retrieval of RAD loci in genome of outgroup species. As expected for a RAD experiment  (Rubin et al. 2012 , Gautier et al. 2013) , outgroups included in the library had a high level of missing data (ca 70%). To decrease missing data, we mined RAD loci in available outgroup genomes. Aside from F. carica, only two genomes of Moraceae were available on NCBI when we performed this study: Morus notabilis (assembly GCA_000414095. 2) and Artocarpus camansi (assembly GCA_002024485. 1). Pipeline, scripts and parameters used for the retrieval of RAD loci in genome of outgroups are available from https://github. com/acruaud/radseq_ficus_2020. Briefly, the 530 RAD loci extracted from the genome of F. carica in the previous step were aligned with the two outgroups genomes using Lastz. Alignment results were parsed with Samtools  (Li et al.
2009)  to select among the genome regions on which a single RAD locus matched. We considered that the genome region with the highest similarity was the most likely to be homologous with the query RAD locus. Putative RAD loci were extracted from the genome with custom scripts and aligned with the contigs of forward and reverse reads produced in the previous step using MAFFT v7. 245 (-linsi option). The final dataset (mergeR1R2) was composed of 530 loci, 71 ingroup species (70 included in the RAD library plus the genome of F. carica) and five outgroup species (Antiaris toxicaria, Artocarpus sp. and Morus alba that were included in the RAD library plus the genomes of Artocarpus camansi and Morus notabilis). Summary statistics for data sets and samples were calculated using AMAS  (Borowiec 2016).
Data set cleaning. To reduce inference bias due to possible misalignment or default of homology, TreeShrink  (Mai and Mirarab 2018)  was used to detect and remove abnormally long branches in individual gene trees of the mergeR1R2 dataset. As suggested in the manual, the value of b was determined by a preliminary analysis on a subset of loci and set to 20. Four rounds (two with and two without the outgroups) were performed, to ensure a proper cleaning. Following  Tan et al. (2015)  we only performed a light filtering of alignment positions that contained gaps to reduce signal loss. Sites with more than 75% gaps were removed from the locus alignments using the program seqtools implemented in the package PASTA  (Mirarab et al. 2014).
Exploration of potential bias. To explore potential sources of bias, a correlation analysis between locus properties was performed with the R package Performance Analytics  (Peterson and Carl 2018). 12 Then, we explored a possible impact of heterogeneity of evolutionary rates between taxa with three different methods: i) the LS 3 approach (Rivera-Rivera and Montoya-Burgos 2016, Rivera-Rivera and Montoya-Burgos 2019) ii) a custom approach based on iterative principal component analysis (PCA) of long branch (LB) heterogeneity scores of taxa  (Struck 2014)  in individual gene trees and iii) different rooting approaches : midpoint rooting, minimal ancestor deviation (MAD) approach  (Tria et al. 2017 ) and minimum variance rooting (MinVar)  (Mai et al. 2017). We evaluated a possible impact of base composition heterogeneity among taxa and markers using i) incremental removal of the most GC-biased loci and ii) the custom iterative PCA of GC content of taxa in individual gene trees.
For the LS 3 approach we defined four clades of interest which corresponded to the four highly supported clades recovered in the phylogenetic trees inferred from the mergeR1R2 data set: Clade1= sect. Pharmacosycea; Clade2=subg. Urostigma, Clade3=sect. Oreosycea, Clade4= "gynodioecious clade". The LS 3 algorithm was then used to find a subsample of sequences in each locus that evolve at a homogeneous rate across all clades of interests. The minTaxa parameter was set to 1. We developed a custom iterative PCA approach in R (R Core Team 2018) to analyse LB heterogeneity scores and GC content of the RAD loci. The PCA consisted in the eigenanalysis of the matrix of the correlations between loci and yielded a set of principal axes corresponding to linear combinations of these variables  (Manly and Alberto 2017).
We used the scores of the taxa along the axes to detect a possible non-random distribution of taxa on the reduced space of the PCA. Depending on the studied properties different groups were The locus showing the highest correlation with the first axis of this new PCA was removed and so on. Only loci for which no structure could be observed were retained for phylogenetic analysis (i. e loci for which Wilcoxon tests were non-significant, indicating no difference between the groups along the first axis of the PCA). This approach was implemented to homogenize taxa properties as much as possible prior to phylogenetic inference. PCA were performed with the R package ade4  (Dray and Dufour 2007). The R script and a tutorial to perform iterative PCAs is available from https://github. com/acruaud/radseq_ficus_2020.
Phylogenetic inference. Gene trees were inferred with a maximum likelihood (ML) approach as implemented in raxmlHPC-PTHREADS-AVX version 8. 2. 4. A rapid bootstrap search (100 replicates) followed by a thorough ML search (-m GTRGAMMA) was performed. Phylogenetic analyses of the concatenated data set were performed with supermatrix (ML) and coalescent-based summary methods. For the ML approach, we used raxmlHPC-PTHREADS-AVX version 8. 2. 4 and IQTREE v1. 6. 7  (Nguyen et al. 2015). Data sets were analysed without partitioning. A rapid bootstrap search (100 replicates) followed by a thorough ML search (-m GTRGAMMA) was implemented for the RAxML approach. IQTREE analysis employed an ML search with the best-fit substitution model automatically selected and branch supports were assessed with ultrafast bootstrap  (Minh et al.