dataesr/scientific-paragraphs-categorization huggingface dataset:
DNA extraction and molecular identification by 16S rRNA gene sequencing Genomic DNA was extracted using the InstaGeneTM Matrix (Bio-Rad) as described previously  (Lorn et al. , 2021). The V1-V9 regions of 16S rRNA gene were amplified using primers 16S-27F (5 ′ -AGAGTTTGATCMTGGCTCAG-3 ′ ) and 16S-1492R' (5 ′ -TACGGYTA CCTTGTTACGACTT-3 ′ ). DNA sequencing reactions were performed by Eurofins Genomics (Germany) using both forward and reverse primers (16S-SeqF, 5 ′ -AGTAGGGAATCTTCCACA-3 ′ and 16S-SeqR, 5 ′ -CTTGC CACCTACGTATTA-3 ′ ). Taxonomic identification was determined by comparing the sequence of each isolate with those reported in the basic BLAST database (https://blast. ncbi. nlm. nih. gov/Blast. cgi).
Acknowledgments The authors would like to thank  Christine ROJAS  for her technical support. This work was supported in part by an  ANRT-CIFRE (Association Nationale Recherche Technologie-Conventions Industrielles de Formation par la Recherche, France)  fellowship and by the  Regional Council of Bourgogne -Franche Comté , the  "Fonds Européen de Développement Régional (FEDER) ".
ACKNOWLEDGEMENTS The authors thank  Frank Henkler-Stephani  for his valuable contributions as a former member of the JATC review panel. Moreover, we are grateful to  Dolors Carnicer-Pont ,  Olena Tigova  and  Esteve Fernandez , from the  Catalan Institute of Oncology , the leaders of JATC  WP6 , for providing us with relevant EU-CEG data about the use of priority additives. Finally, we thank  Reinskje Talhout  for constructive discussions and critical feedback on the manuscript.
To define whether this finding could be extended to the Lysobacteraceae overall plasmid population and to identify the barriers mostly structuring the plasmid transfer patterns, we analysed the sharing of homologous plasmid genes among the whole set of bacterial genomic sequences available on NCBI databases using a gene-sharing network analysis. After (i) having estimated to what extent plasmids are conserved and shared within the family, we (ii) assessed the influence of the ecological niche and the taxonomy over the patterns of gene sharing and (iii) detected events of gene exchange between plasmids and the chromosome.
Plasmid reference sequences The complete set of plasmid sequences of the family Lysobacteraceae (sensu  [Naushad et al. , 2015] ) were downloaded on November 2017 from the NCBI nonredundant (NR) database (n = 441, obtained from 160 distinct bacterial isolates; i. e. , NCBI biosamples). The family contains 17 genera, but complete plasmids were only available for three of them (Stenotrophomonas, Xanthomonas and Xylella). PlasForest did net identify contigs of plasmid origin among sequences available for other Lysobacteraceae genera. For suspicion of being of chromosome rather than plasmid origin, contigs annotated as plasmids but coding for genes typically chromosome-borne (e. g. , ribosomal protein genes) were discarded, as well as sequences that were net assembled as circular molecules. From the remaining 305 contigs (obtained from 140 bacterial isolates; Table  S1 ), a total of 19,921 genes were predicted using PRODIGAL version 2.
2. 2 Query dataset l Nucl eot id e seq uence extraction f rom existi ng annotations or de nova gene prediction Lysobacteraceae plasmids were obtained and gene sequences extracted. (2) plasmid gene sequences were searched against the entire NR and WGS databases. (3) hits were then clustered and bipartite networks were built based on the sharing of gene clusters between bacterial isolates. AMR, antimicrobial resistance 18,715 unique gene sequences were used as a query for a BLASTN search against the NR and Whole Genome Shotgun (WGS) databases (45,016 and 214,443 biosamples as of November 2017, respectively) using default parameters except for max_target_seqs that was set to 10,000,000. To evaluate the robustness of the gene sharing network to the similarity threshold used, we constituted subsets of the BLASTN hit table with different levels of stringency for the minimum identity (%id) and the length percentage of the matched query gene (%1en).
Six combinations (%id/%1en) of these two filters were used: 90/90, 95/90, 95/95, 98/90, 98/98 and 99. 8/90. For each parameter combination, all NR and WGS contigs matching at least one gene with the minimum identity and coverage defined above were downloaded. Contigs were then classified as plasmid or chromosomal contigs using PLASFOREST  (Pradier et al. , 2021)  with default parameters. Genes were extracted based on NCBI gene annotation when available or conversely after structural annotation using PRODIGAL version 2. 6. 2 as described above. lncluding the query genes, a total of 87,421 genes from 14,576 contigs and 10,112 biosamples were obtained (Table  52 ). Taxonomies, host plants and geographical origins of all phytopathogenic Xanthomonas isolates studied herein were extracted from NCBI metadata and manually curated.
Along with the available NCBI functional annotations, all these genes were functionally annotated using a BLASTP search against the Swiss-Prot and Trembl databases  (O'Donovan et al. , 2002) , the Pathogen Host Interaction database using the available online tools (PHI-base;  Urban et al. , 2019)  and the CARD database using the resistance gene identifier tool  (Alcock et al. , 2019). Annotations were then manually curated and merged. To make manual curation possible given the very large number of genes that were included, we reduced the functional annotations to five categories most relevant to the plasmid nature of the genes and the plant pathogenic nature of the bacterial isolates we focused on: conjugation apparatus, plasmid stabilization, pathogenicity, antibiotics and heavy metal resistance.
Bipartite network of gene sharing Bipartite networks were then constructed using the R package igraph  (Csardi & Nepusz, 2006). Two classes of nodes coexist in such networks: bacterial isolates and gene clusters. Whereas no direct link exists between two nodes of the same class, isolates sharing a gene cluster are connected to this same gene cluster node and conversely a gene cluster is connected to all the isolates in which it was detected. To reduce the redundancy of the data set due to unbalanced sequencing efforts and to limit the inclusion of vertical inheritance rather than horizontal transfer, isolates from the same species and containing the exact same gene clusters were merged. This made our network conservative in that it globally represents the minimum number of plasmid gene movements that must have occurred to lead to the current plasmid gene distribution.
Groups of genes shared between plasmids and chromosomes We then searched for chromosomes carrying multiple genes from our query plasmid genes. We started by identifying contigs of chromosomal origin. Because of the relative unfinished nature of the genomic project available on the WG5 and NR databases and to avoid over-estimation of the sequence sizes due to improper scaffolding (e. g. , chromosomal scaffolds that would contain plasmid contigs), ail sequences were split at every occurrence of long N sequences (indicative of a contig junction). We then searched for instances where more than 10 genes from a given query plasmid were found within a single contig of more than 1000 genes identified as chromosome using PLASFOREST. We restricted our analysis to these RICHARD ET AL. events of sharing implying a fair number of genes (here 10) in very large contigs to (i) limit the inclusion of small spurious gene sharing events, (ii) avoid the annotation of plasmid sequences as chromosomal (i.
3. 1 The Lysobacteraceae plasmid database with pathogenicity. Among these, 28 plasmids carried transcription activator-like effector (TALE) genes that code for proteins that manipulate the expression of hast plant genes ta promote bacterial colonization  (Bach et al. , 2009). Overall, our plasmid gene query database mostly comprised genes associated with (i) intrinsic plasmid functions such as partitioning, conjugation, replication and maintenance, or mobile genetic element functions such as transposition, (ii) resistance against anthropic pesticides (copper, streptomycin) used in agriculture, and (iii) host-pathogen interactions.
Plasmid genes are readily shared within the family Lysobacteraceae Gene sharing relationships between our set of query plasmid genes and the whole of the sequences of the NR and WGS NCBI databases were defined between replicons (plasmids or chromosomes) carrying one or more identical gene clusters (see Methods and Figure  1 ). First, it must be borne in mind that these results are inherently limited and biased by the properties of the databases. Either database composition biases, database redundancies or the quality of the sequences (degree of completion of the genome) would leave strong imprints in the network. For example, nonpathogenic Lysobacteraceae species are poorly represented in NCBI databases. As such, based on a few genomes, some genera possibly artefactually appear plasmid-free.
3. 3 From partial to full plasmid sharing ln the global network, most of the gene sharing events involved partial plasmid segments and a few genes (Figure  3 ), indicative of plasmid mosaicism, a common feature that has been documented in the genus Xanthomonas  (Gochez et al. , 2018; Richard et al. , 2017). Several of the Lysobacteraceae plasmids in our database were apparent chimeras relative ta each other (see the module over the diagonal on Figure  51b ). Only a fraction of the isolates shared full or almost full (> 90%) plasmids. Ta visualize plasmid sharing rather than gene sharing, networks representing the sharing of almost full plasmids were computed (Figure  4 ). lmportantly, whereas the existence of a link implied the sharing of > 90% of the plasmid genes, we had no indication that they were actually carried on a single plasmid or if a similar gene content was borne on multiple plasmids.
Taxonomy shapes plasmid gene transfer The network was built using plasmid genes from the family Lysobacteraceae as queries. lt therefore delineated the extent ta which close homologues of the plasmid genes from this family are present within other bacterial taxa. Our approach retrieved 41% of the Lysobacteraceae and 84% of the Xanthomonas genomes available in the WGS and NR databases from NCBI (see Table  52  for details). lndeed, 39% of the isolates of the network belonged ta the family Lysobacteraceae. Conversely, beside the absence of full plasmid sharing events beyond the family Lysobacteraceae, inspection of the full bipartite network (Figure  2 ) demonstrated that interfamily gene sharing was rare and restricted ta a small number of genes. ln a k-mer-based plasmid network study, Xanthomonas aise appeared as an isolated genus  (Acman et al.
ACKNOWLEDGEMENTS The  European Union  (ERDF contract  GURDT 12016-1731-0006632 ),  Conseil Régional de La Réunion ,  ANSES  and  CIRAD  provided financial support. D. R. acknowledges a PhD grant from  ANSES  and  CIRAD. This work was supported by  Agropolis Fondation (Labex Agro -Montpellier, E-SPACE  project no. 1504-004 ). Analyses were performed using the SouthGreen HPC server.
, 2014;rised 19,921 genes encoded on 305 plasmid contigs from 140 bacterial isolates (i. e. , NCBI biosamples) (Table51and Figure51). lsolatesbelongedtothree genera and 19 species, mostly Xanthomonas citri (49%), Xyfella fastidiosa (15%) and Xanthomonas campestris (9%). The remaining 16 species represented 5% or less of the database each. Plasmid size was very variable, with small (<10 kb) to intermediate size plasmids (<100 kb) being the most represented (20% and 73%, respectively). Nevertheless, 22 plasmids larger than 100kb were present and carried 29% of the analysed gene pool. lsolatescarriedfromone plasmid (53 isolates) to nine plasmids (three Xa. campestris isolates) with a mean number of 2. 9 plasmids per isolate. mercury,silverand/orzinc. Although heavy metals other than copper are of little use in agriculture(Hobman & Crossman, 2015), their presence can probably be explained by the frequent colocalization of genetic systems conferring resistance against different metals on a single plasmid(Gullberg et al.
Statistical Analysis The antimicrobial activity of nanolotion, Quench, and Dermazin cream was compared for all of the four strains (Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, and Candida albicans) separately by applying one-way ANOVA (analysis of variance) and a post hoc test, i. e. , LSD (least significant difference) using SPSS (Statistical Package for the Social Sciences).
To determine which specific groups differed from each other, post hoc test LSD was applied using SPSS, in which pairwise comparison of groups was carried out. Post hoc test LSD was then applied; in the case of E. coli, the p-value obtained for nanolotion and Quench was 0. 018, for nanolotion and Dermazin it was 0. 001, and for Quench and Dermazin the p-value came out to be 0. 006, which indicates significant differences between nanolotion and Quench, nanolotion and Dermazin, and Quench and Dermazin, respectively. ZOI values as well as p-values indicate that the nanolotion has a superior antibacterial activity against E. coli as compared to the commercialized creams, i. e. , Quench and Dermazin. The p-values obtained for nanolotion and Quench, nanolotion and Dermazin, and Quench and Dermazin were 0.
For alternative nitrogen sources, ammonium sulfate and CSM were replaced by the alternative nitrogen source at growth yield limiting concentrations (30 mg N/L). Cultivation using the Scan-o-matic platform  82  was performed in thermostatic cabinets at 30°C and constant, high humidity. Cells in each colony were counted at 20 min intervals using high-quality desktop scanners and Scan-o-matic 2. 2 (https://github. com/Scan-o-Matic/scanomatic), with exact pixel intensity calibration and transformation into cell counts. Population size growth curves were established, smoothed and transformed into population doubling times (d) and total cell yields (y), as described  82. To account for spatial variation within and between plates, fixed genotypes (the diploid hybrid DBVPG6756xYPS128) were included in every 4th of the 1536 colony positions on each plate, and their log2 d and y measures were used to normalize the corresponding d and y measures of adjacent colonies.
YO-PRO-1 penetrates also into apoptotic cells  85, 86. At each indicated aging time point, 5 µL of cells were transferred into 100 µL of staining solution (Phosphate Buffer Saline + 3 µM propidium iodide (Sigma-Aldrich) + 200 nM YP (Invitrogen)) in a 96 well plate and incubated for 5 min in the dark at 30°C. Samples were analysed on a FACS-Calibur flow cytometer (Becton Dickinson) using a High Throughput Sampler (Becton Dickinson) device to process 96 well plates and detect fluorescence with FL-1 (YP; 488/10 nm excitation, 530/30 nm emission) and FL-3 (PI; 488/10 nm excitation, 670 nm emission) channels. Experiments were run in a single replicate and data were normalized to 4 internal controls distributed in each plate. 10,000 cells per sample were analysed.
Loss-of-function variants: We used SnpEff 88 (v. 4. 1) and SIFT  89  (Sorting Intolerant from Tolerant; v. 5. 2. 2) to predict single base variant effects. An in-house script was used to estimate small frameshifting indels of multiples other than three. Likely losses of gene function were called when such frameshifts, premature stop codons, or loss of either the predicted start or stop codon were found. Assuming loss-of-function variants to be completely dominated by functional variants, we called gene loss-of-function only when such variants were homozygotic.
Acknowledgements The authors thank  Folkert van Werven ,  Gilles Fisher  and  Bertrand Llorente  for the helpful discussions and  Barak Cohen  for the kind gift of the SPS2::GFP tagged strains. This work was supported by  Agence Nationale de la Recherche  ( ANR-11-LABX-0028-01 ,  ANR-13-BSV6-0006-01 ,  ANR-15-IDEX-01 ,  ANR-16-CE12-0019  and  ANR-18-CE12-0004 ) and the  Swedish Research Council  ( 2014-6547 ,  2014-4605 ,  2018-03638  and  2018-03453 ).
Landmark and shape analysis We analyzed a total of  27   A configuration of landmark coordinates was defined to capture the overall shape of the right Meckel's cartilage resulting in four landmarks and 21 semilandmarks (Fig. 2 ), which were digitised using the software tpsDIG2 v. 2. 18  [46]. The coordinates were then subjected to a generalised Procrustes analysis (GPA) to remove differences in size, position, and orientation  [47]. The GPA was performed allowing the semilandmarks to slide to minimise the bending energy  [48]. The aligned coordinates were then used for a principal component analysis (PCA) to assess the shape variation for all specimens. The shape variations from the PCA were interpreted by using thin-plate spline deformation grids, which allow observing the shape changes from the mean along the main axes of variation.
To determine the number of PCs to be analysed, we used the function get-MeaningfulPCs implemented in the Morpho R package  [49]. With this method only the first two PCs (accounting for 83. 2% of the variance) were deemed meaningful. Another PCA was performed with the shape variables and considering the centroid size to construct a sizeshape space  [13]. To assess the differences in the mean shapes, while accounting for the size as a covariate and the developmental stage and their taxonomic assignment, we implemented an ANOVA using residuals permutation (999 permutations). Additionally, a phenotypic trajectory analysis was performed on the shape coordinates to estimate changes between species in the path distances, their orientation (measured as angles differences) and the shape changes as the differences in Procrustes distances  [50].
Acknowledgements We are thankful to the staff at  Haus des Meeres Aqua Terra Zoo, Vienna, Austria; Ozeaneum Aquarium, Stralsund, Germany , the  Musée Océanographique de Monaco, principauté de Monaco ; and the  University of Montpellier ,  France. Dr. John J. Cawley  ( University of Vienna ) for kindly proofreading the English manuscript.
b, Illustration of the reconstructed AMKs (left), with a six-colour code (with light and dark shades), that evolved into the modern genomes (right) of Acorus, Spirodela polyrhiza, Colocasia esculenta and Dioscorea (alata and rotundata) as well as the AGK (pre-ρ and post-ρ grass ancestors), oil palm (pre-p and post-p ancestors) and pineapple as defined in Murat et al. 13  (green panel). Polyploidization events are shown as red dots on the tree branches. The evolution of the number of chromosomes from the AMK to the extant species is shown in green circles together with inferred chromosomal rearrangements (fissions and fusions) on the tree branches. c, Illustration of the synteny between the AMK and modern monocot species with conserved genes linked with lines between chromosomes using the colour code from b.
To infer the functions of genes driving early monocot evolution, we built a chronogram based on 28 representative angiosperm species with fossil constraints, which predicted that the MRCA of monocots dates back to approximately 169. 76 Ma, consistent with the TimeTree database (92. 5-188. 0 Ma)  38  (Supplementary Fig. 10a  and Supplementary Table  7 ). Applying the Dollo-Parsimony approach, we found that 77 and 964 orthologous groups (OGs) were acquired and lost in the AMK, respectively (Supplementary Fig. 10a  and Supplementary Table  7 ). The 77 OGs acquired in the ancestral monocot were enriched in Gene Ontology (GO) terms such as transporter activity, plasma membrane vacuole, membrane, cell communication, transport and response to external stimulus (Supplementary Fig. 11  and Supplementary Table  8 ), whereas the 964 OGs lost in the ancestral monocots were enriched in GO terms such as intracellular, Golgi apparatus, mitochondrion and cytoplasm (Supplementary Fig.
12  and Supplementary Table  9 ). For example, OG0010560 which contains WOX1 involved in cotyledonary primordia development, was completely lost in monocots (Supplementary Table  9 ). In addition, by setting a P value threshold of 0. 05 for gene family expansion and contraction in CAFE software analysis, we extracted 41 OGs with significant expansion and 1,278 OGs with significant contraction in monocots (Supplementary Fig. 10b  and Supplementary Table  7 ). The 41 OGs that were expanded in the ancestral monocot were enriched in GO terms such as metabolic process, cellular process and response to stress (Supplementary Fig. 13 ), whereas the 1,278 OGs contracted in the ancestral monocot were enriched in GO terms such as signal transduction and cell communication (Supplementary Fig. 14 ). For example, OG0000057, a disease resistance protein (TIR-NBS-LRR class) family, was contracted in the ancestral monocot (Supplementary Table  10 ), whereas OG0000047, which belongs to leucine-rich repeat protein kinases containing bacterium defence-related members, including IOS1 and FRK1, was significantly expanded in the ancestral monocot (Supplementary Table  11 ).
Methods Plant material, genome sequencing and RNA-seq of Acorus. Acorus tatarinowii (NCBI Taxonomy ID: 123564) was collected from Shennongjia Nature Reserve (Hubei, China). DNA from leaves was extracted using Plant DNA Isolation Reagent (TIANGEN). For genome size estimation, genomic DNA was sheared into 250-280 bp fragments with the NEBNext Ultra DNA Library Prep Kit for Illumina (NEB) based on the manufacturer's protocol. Paired-end reads (150 bp for each end) were sequenced on the Illumina HiSeq 4000 platform. DNA libraries were constructed based on the PacBio library preparation protocol and further sequenced on the PacBio Sequel platform (Pacific Biosciences) with the Sequel II Binding Kit 1. 0, Sequel II Sequencing Kit 1. 0 and Sequel II SMRT Cell 8M at Frasergen. Subread data was obtained via SMRT LINK 7.
0 Fluorometer (Life Technologies) and Agilent 2100 Bioanalyzer (Agilent Technologies), respectively. Qualified RNAs of each sample (3 μg) were then used to construct the Illumina sequencing library according to the recommendations of the NEBNext Ultra RNA Library Prep Kit for Illumina (NEB). The libraries were sequenced on the Illumina HiSeq 2500 platform at Novogene with 150 bp paired-end reads. Chromosomal-level assembly of Acorus tatarinowii. The genome size and heterozygosity of Acorus were estimated by jellyfish  72  and genomic charactor estimator  73  using k-mer frequency distribution (k-mer = 17 as the default) based on Illumina reads with default settings. For genome assembly, Nextdenovo software v. 2. 5. 0 was applied for PacBio read correction with the following parameters: read_cutoff = 1k; seed_cutoff = 40,150; blocksize = 1g; pa_correction = 2; seed_ cutfiles = 2; sort_options = -m 4g -t 10 -k 50; minimap2_options_raw = -x ava-pb -t 8; correction_options = -p 15.
The corrected PacBio reads were further trimmed and assembled by Canu v. 2. 2 with the trimming parameters 'genomeSize = 400m; correctedErrorRate = 0. 12; corMaxEvidenceErate = 0. 15; minReadLength = 1,000; minOverlapLength = 500; merylThreads = 40' and the assembling parameters 'genomeSize = 400m; maxThreads = 60; correctedErrorRate = 0. 035' (https:// github. com/Nextomics/NextDenovo). After mapping PacBio reads on the polished contigs, redundant contigs were removed by purge_haplotigs based on read coverage. The Hi-C sequencing reads were aligned to the final contigs by BWA-MEM  74. Finally, scaffolding of these contigs into pseudochromosomes was performed with LACHESIS  75. Juicer was applied to construct high-resolution contact maps of chromosomes, and JuiceBox v. 2. 1. 10 was further used to visually correct the assembly errors, including the orientation, order and internal misassembly of contigs  76.