dataesr/scientific-paragraphs-categorization huggingface dataset:
Availability of data and materials The mitochondrial DNA sequences supporting the results of this article are available in the GenBank repository, accession numbers: KX857217-KX857259. The Illumina short reads are available from the NCBI sequence read archive, study accession number: SRP089928. Our final SNP dataset, BayeScan output and custom python script (filter. py) are available from the Dryad Digital Repository http://dx. doi. org/10. 5061/dryad. 7c0q8 [47].
The collection dates of the isolates ranged from 1969 to 2012; the oldest isolates were a horse isolate from France in 1969, human isolates from Morocco in 1975 and 1981, and a human isolate from Spain in 1976. Isolates were sampled from various sources: cattle (n ϭ 35), poultry (n ϭ 51), swine (n ϭ 109), a hare (n ϭ 1), a horse (n ϭ 1), and humans (n ϭ 118). The full details for the isolates used in this study are shown in Data Set S1 in the supplemental material. Whole-genome sequencing, de novo assembly, and resistance genes. Isolates were sequenced using either the Illumina HiSeq or the MiSeq platform. Raw sequence data have been submitted to the European Nucleotide Archive (ENA). The raw reads were de novo assembled using the pipeline available from the Center for Genomic Epidemiology (CGE) (www.
The network was generated by connecting proteins in conserved pairs with weight defined as the maximum sequence identity between reciprocal alignments where the sequence identity of alignments was adjusted along query sequences. The core genome was built from the intersection of gene clusters shared by every genome under analysis  (21). SNP identification. Single nucleotide polymorphisms (SNPs) were determined using CSI Phylogeny 1. 1, available from the Center for Genomic Epidemiology (www. genomicepidemiology. org)  (22, 23). Fundamentally, the pipeline consists of various publicly available programs. The paired-end reads were aligned against the reference genome, S. Typhimurium DT104 (GenBank accession number HF937208, genome length 4,933,631 bp)  (15) , using the Burrows-Wheeler aligner (BWA)  (24).
SAMtools  (25)  mpileup commands were used to identify and filter SNPs. The qualified SNPs were selected once they met the following criteria: (i) a minimum coverage (the number of reads mapped to reference positions) of 5, (ii) a minimum distance of 15 bp between each SNP, (iii) a minimum quality score for each SNP at 20, and (iv) the exclusion of all indels. The final qualified SNPs for each genome were concatenated to an alignment by an in-house Python script. The SNP alignments were subjected to maximum-likelihood tree construction using PhyML  (26). Recombination detection. SNP alignments had been detected for significant recombination sites prior to reconstruction of the phylogenetic trees. We used a novel hidden Markov model tool called RecHMM  (27)  to detect the clusters of sequence diversity that mark the recombination events within branches.
All BEAST Monte Carlo Markov chains (MCMC) were run for at least 150 million and up to 300 million steps, with subsampling every 10,000 steps. The trees produced by BEAST were summarized by a single maximum clade credibility (MCC) tree using TreeAnnotator  (30)  with 10% of the MCMC steps discarded as burn-in. Statistical uncertainty was represented by a 95% credible interval (CI) calculated as the 95% highest posterior density (HPD) interval. A final tree was visualized and edited in FigTree (http://tree. bio. ed. ac. uk/software/figtree/). The geographic locations and direction of the transmissions were estimated by discrete phylogeographic analysis using a standard continuous-time Markov chain (CTMC)  (33)  implemented in BEAST. A location-annotated MCC tree was converted to the KML format using phylogeo.
jar, which is relatively equivalent to SPREAD (http://www. phylogeography. org/SPREAD. html). The KML file was then visualized in Google Earth (http://earth. google. com/). The demographic history was reconstructed using the Bayesian skyline plot implemented in Tracer  (30)  by processing the inferred genealogy and effective population size estimated by BEAST at different points along the genealogy time scale. The population size was inferred by the product of the interval size (␥ i ) and i(i Ϫ 1)/2, where i is the number of genealogical lineages in the interval  (34, 35). The effective population size is always smaller than the actual population size as the effective population size exhibits the number of individuals that contribute to offspring to the descendant generation  (35). Nucleotide sequence accession number.
Interestingly, data from one farm (D10) where eradication was presumably unsuccessful showed that isolates found posteradication were not the same lineage as the isolates found prior to eradication. Salmonella genomic island 1 and resistance genes. All of the isolates in the susceptible-resistant clusters contained small fragments or partial sequences of the 43-kb Salmonella genomic island 1 (SGI1) (GenBank accession number AF261825)  (49, 50) , but none of them harbored the 13-kb SGI1 multidrug resistance region (51) (Fig. 5 ). The phylogenetic tree based on the SNPs of SGI1 of all of the DT104 isolates and other Salmonella and Proteus mirabilis genomes that carry SGI1 are shown in Fig. S10  in the supplemental material. The tree showed that the SGI1 sequences of the DT104 isolates were very similar, and they were similar to SGI1 sequences from other Salmonella and P.
ACKNOWLEDGMENTS This study was supported by the  Center for Genomic Epidemiology  ( 09-067103/DSF ) (www. genomicepidemiology. org). D. J. W. is a Sir Henry Dale Fellow, jointly funded by the  Wellcome Trust  and the  Royal Society  (grant  101237/Z/13/Z ). We acknowledge the institutes that provided the DT104 isolates used in this study: (i) Servicio Enterobacterias,  Departamento Bacteriología ,  INEI-ANLIS Dr. Carlos G. Malbrán ,  Buenos Aires , Argentina; (ii)  Austrian Agency for Health and Food Safety (AGES) ,  NRC Salmonella, Austria; (iii) Food Safety and Animal Health Division, Alberta Agriculture and Rural Development, Canada ; (iv)  Federal Institute for Risk Assessment (BfR), Department of Biological Safety, National Reference Laboratory for Salmonella (NRL-Salm ), Germany; (v) National Reference Laboratory Salmonella,  Department of Agriculture, Food and the Marine Laboratories, Backweston Campus, Kildare, Ireland; (vi) Government Central Laboratories, Jerusalem, Israel; (vii) Surveillance Epidémiologique, Laboratoire National de Santé, Luxembourg; (viii) Central Veterinary Institute (CVI ), part of  Wageningen UR, Lelystad , The Netherlands; (ix) Enteric Reference Laboratory and Leptospira Reference Laboratory,  ESR (Institute of Environmental Science and Research Ltd.
Methods Phylogenetic analysis. Sequences from six nuclear genes and six mitochondrial genes were used to construct the phylogenetic tree of the Cyprinodontiformes. The nuclear genes used comprised of enc1, pomgnt2, snx33, myh6, rag1 and x-src. The mitochondrial genes used included CYTB, COX1, ND1, ND2, 12S-rRNA and 16S-rRNA. CYTB was divided by codon position into separate alignments. Sequences for all genes for species assigned to the order Cyprinodontiformes were downloaded from Genbank (accessed September 2014) as well as sequences for seven outgroup species. We reduced the dataset to the longest sequence per gene per species. We then selected one species per genus that possessed sequences for the highest number of genes from our chosen set. The final data set included sequences from 85% of recognized Cyprinodontiform genera representing 94% of species.
Additional reconstructions using stochastic character mapping were performed with the make. simmap function in the phytools R package 34 v0. 4-31. This method fits a continuous-time reversible Markov model for the evolution of the selected trait. It then simulates stochastic character histories using that model and the tip states on the tree  34. We simulated 1,000 character histories per trait, which produced results (Supplementary Figs  5  and 6 ) biased towards those traits associated with increase diversification rates as expected  14. These results indicated a single origin of viviparity in Anablepidae. Finally, we performed ASR using the Binary State Speciation and Extinction model  35, 36  on viviparity and annualism independently, which produced identical results to the combined MuSSE reconstruction.
There is one well-known instance of variation in annualism in the genera shown on the phylogenetic tree, that is, in the genus Fundulopanchax 8. In the analyses above, Fundulopanchax was scored as annual. However, we also reran all BAMM and MuSSE analyses, scoring this genus as non-annual, and it did not affect the results or plots of net diversification (Supplementary Fig. 12 ). Diversification rates. We used BAMM v2. 2. 0 (ref. 13) to estimate rates of speciation and extinction across the phylogeny of Cyprinodontiformes. BAMM allows variation in evolutionary rates through time and among lineages, relaxing the assumption that diversification rates must be time homogenous as in MEDUSA  37. BAMM allows for the incorporation of incomplete taxonomic sampling at the backbone and clade level for which our tree contains 85% sampling of backbone taxa and varying proportions of species present sampling fractions per clade (Supplementary Data 2).
We performed multiple BAMM runs of 30 million generations, sampling every 6,000 generations and checked convergence and stationarity using the CODA package v0. 16-1 (ref. 38) in R. effective sample size (ESS) of all parameters were 4200. The process was repeated three times to ensure convergence of separate runs. We then calculated the mean of the marginal posterior density of speciation, extinction and net diversification rates at all points on each branch of the summary tree. Credible shift sets, Bayes factor calculations and cumulative shift probabilities were obtained with the R package BAMMTools v2. 02 (ref. 39). Speciation and extinction rates over time were calculated for each of the three trait groups (non-annual viviparous, annual oviparous and NAO) using all samples (minus burn-in) from the posterior distribution of a single run.
MuSSE was implemented using the R package Diversitree  15. We fit a maximum likelihood (ML) MuSSE model with 12 parameters as a starting point to estimate parameters using a Bayesian framework. We again accounted for incomplete sampling using a sampling fraction for each state. We then ran a MCMC chain of 10,000 generations using the full model and an exponential prior 1/(2r), where r is the character independent diversification rate. We removed 10% burn-in and then summarized the MCMC samples to assess variation in state-dependent speciation, extinction and net diversification rates (Supplementary Fig. 10 ). We calculated statistical significance between the character-state groups using the credible intervals of differences among posterior distributions of the state-dependent speciation, extinction and net diversification rates (Supplementary Fig.
Analyses of these datasets revealed characteristics of gene architecture, novel repetitive introns and deviations in splice donor sequence. We also analyzed the predicted proteome of polar Clade E2 isolate CCMP2099 and generated genomic information for a more basal Class II prasinophyte by growing and sequencing the transcriptome of Dolichomastix tenuilepis. Our comparative studies identified proteins that are likely involved in scale formation and features of the land plant ancestor as well as essential components of photosynthesis. Among these is the presence of a bacterial-like peptidoglycan pathway that has been retained in lineages from across the Archaeplastida supergroup (Fig. 1 ), but selectively lost in multiple independent events. Our studies highlight the complementarity of two evolutionary distinct green algal groups, represented by Micromonas and Chlamydomonas, for investigating plant systems and provide new insights into the development of the green lineage.
The vast majority (9,870) of CCMP1545 ). The total number of genes with no evidence: 25 (CCMP1545) and 37 (RCC299). Note that for RCC299 the lower number of gene models supported by LC-MS/MS data  (2, 306)  than in CCMP1545  (8, 432)  is due to generation of fewer peptides protein-encoding gene models had supporting evidence, while 25 did not (Fig. 2c ). Ninety-eight percent of genes were confirmed using RNA-Seq, and 96 % were supported by at least two types of evidence. Of the 21 predicted proteins with only Interproscan evidence, 11 were related Sel-1 like repeat (SLR) proteins. For RCC299, 97 % of models were confirmed with RNA-Seq and 94 % were supported by at least two evidence types (Fig. 2d ), while 37 gene models had no evidence. Twelve predicted proteins from mixed families of unknown function (two with zinc finger predictions) had only Interproscan support.
The significant expansion in M. pusilla (CCMP1545) provides an example of expansion that may relate to basic biological differences between these two isolates. Collectively, the differences observed here and in prior studies  [6, 8, 10]  clearly support Clade A (RCC299) and Clade D (CCMP1545) as being different species. Therefore, here we name RCC299 Micromonas commoda based on molecular diagnoses and the protocols of the International Code of Nomenclature for Algae, Fungi and Plants. The species name refers to the fact that RCC299 is easy to grow in an axenic state in the laboratory. This naming will avoid confusion in the literature  [36, 37]  where Clade A strains such as RCC299 or its close relatives (e. g. , Clade C isolate Mp-Lac38) are incorrectly termed Micromonas pusilla. Etymology -The specific epithet commoda refers to the 'ease and convenience' of culturing and propagating this species which grows well in artificial seawater when amended according to K or L1  [38]  medium recipes and in other standard marine algal media.
g. , M. pusilla, Class III and VII prasinophytes) to differential PG-pathway losses between some Micromonas species, other Class II prasinophytes and chlorophyte algae (Fig. 5b ). These findings led us to investigate PG biosynthesis in a broader sampling of Archaeplastida lineages. We characterized PG pathway gene complements in the Viridiplantae by searching protein families in the Phytozome database, version 11  [62]. The mosses Physcomitrella patens, Sphagnum fallax, and Selaginella moellendorffii contained the complete pathway, including PBP, as expected based on prior literature on P. patens  [60, 63]. Each gene in the pathway was represented by a single Phytozome gene family, except PBP, which was not represented (see methods; Additional file 1: Table  S5 ). Surprisingly, we also identified a full complement of PG pathway genes except PBP in taxa distributed throughout the streptophytes (Fig.
5b , Additional file 1: Tables  S5  and S6 ). Most of these protein sequences are derived from automated gene predictions, therefore we verified expression using the predicted protein sequence as a tblastn query against EST databases (Additional file 1: Table  S6 ). Evidence for expression was found for all genes, although not necessarily from all species (possibly because available EST data in GenBank is sparse for many streptophytes). Representative orthologs of each PG pathway protein have predicted chloroplast transit peptides (Additional file 1: Table  S7 ) and AtMURE has been shown to localize to the plastid, where it is thought to function in chloroplast biogenesis  [64]. For the vascular seed plants that did not contain the full pathway (including A. thaliana, see e. g. , Fig. 5b ), four genes were usually present: MURE (Phytozome gene family #63996886), MRAY (#63772898) and MURG (#63987572), as well as DDL (#63770242).
Most of these taxa have one or two orthologs and the mosses Sphagnum fallax and P. patens have three. We named this bi-domain protein "Peptidoglycan Pathway Associated Streptophyte Protein (PPASP)" because it was not detected in the NCBI nr database (except in streptophytes with the nearly complete pathway) or in the glaucophyte and prasinophytes studied here. Phylogenetic analyses of MURE (as a representative of the four-gene subset), MURA and PPASP showed branching patterns that follow known relationships among plants (Additional file 2: Figure  S4 ). The presence/absence patterns of PBP and PPASP across the Archaeplastida suggest that PPASP evolved or was acquired around the time that streptophytes diverged from the ancestor shared with chlorophyte and prasinophyte algae. PPASP would then have been differentially lost in taxa from multiple plant lineages, alongside other PG-pathway components.
Briefly, SEQUEST input files were created using in-house parent isotope correction algorithm DeconMSn (  [89] , omics. pnl. gov), a parent mass tolerance of ±3 Da was employed to capture remaining de-isotoping errors, a static modification was applied to all Cysteine residues to reflect alkylation with iodoacetamide (+57. 0215 Da), and no proteolytic enzyme was specified. The output from this analysis was then rescored using the MSGF spectral probability algorithm  [90]. A subset of the full dataset was analyzed using a targetdecoy approach, whereby all sequences are combined with their reverse complement in a single search file, allowing false discovery rates (FDR) to be assessed at given quality level cutoffs  [91]. It was observed that an MSGF spectral probability E-value ≤ 10 -10 provided an FDR of <1 %, and this value was used to filter subsequent data.
tenuilepis and also included an assembly step, as described  [24]. Like the libraries generated for CCMP1545 and RCC299 a poly-A selection step was employed. We used all predicted proteins as blastp queries against the NCBI nr database and against a database of translated prasinophyte transcriptomes  [39]  in order to identify potential contamination in the assemblies. CCMP2099 had no hits to bacteria that were superior to those to prasinophytes, and of 10,965 proteins unique to D. tenuilepis, 132 had best blast hits (E-value cutoff 10 -10 ) with non-photosynthetic bacteria suggesting the bulk of data comes from the alga rather than potential bacterial contaminants in the culture. Likewise, only one full-length 18S rRNA gene sequence each was present in the CCMP2099 and the D. tenuilepis transcriptomes (and these matched the published 18S sequences) as were single variants of cytochrome c and actin in each.
6. 1) with parameters -dna -mod zoops -nmotifs 6 -minsites 10 -maxsize 200000. Introner element positions were determined using fimo  [97]  (part of the meme suite) at -output-pthresh 1e-10 with the seven selected meme motifs on the CCMP1545 genome. The motifs detect different parts of the introner elements, therefore hits were concatenated if they occurred within 50 nt of each other. Splice donor consensus sequences and logos for IE containing and non-IE containing introns were also created using meme on a random subsample of 500 sequences for each group using the fasta-subsample script in the meme suite. Meme parameters were -dna -mod oops -nmotifs 1 -w 12. For IE identification in RCC299 predicted introns (from gene models described in this paper) were clustered as described for CCMP1545, resulting in a single group of 41 introns.
Protein clustering and gene family assignment Refseq protein sets for O. tauri and O. lucimarinus, were downloaded from Genbank. O. RCC809 proteins were obtained from JGI (http://genome. jgi. doe. gov/OstRCC809_2/ OstRCC809_2. download. html) and B. prasinos Bban7 proteins from https://bioinformatics. psb. ugent. be/gdb/bathycoccus/ RELEASE_15jul2011. M. sp. CCMP2099 and D. tenuilepis proteins were based on translation of short read contigs  [24]  using ESTScan v3. 0. 3  [98]  with default settings and a training set consisting of all RefSeq mRNA entries under the Bacillariophyta classification. Peptides shorter than 30 aa were removed from the results. For creating multiple species ortholog sets, we used OrthoMCL  [34]  according to the user manual, with the Evalue cutoff set to 10 -15. PercentMatchCutoff was set to 60 in the comparison of RCC299 and CCMP1545.
The meme suite program fimo  [97]  was used to match the two motifs against all Phytozome version 9. 1 proteins using -output-pthresh 1e-10. Proteins were retained only when they contained both domains, resulting in a total of 14 proteins from 10 organisms, all of which contained the full PG pathway. This gene family is present as #63760547 in Phytozome 11, containing 18 genes in 13 organisms, all containing the full PG pathway. Fimo was run separately against databases of Micromonas, D. tenuilepis, and red algal proteins  [39] , but no matches were found.
Phylogenetic reconstructions Archaeplastida plastid sequences used in [103] were amended with additional prasinophyte and streptophyte homologs from GenBank, the Chloroplast Genome Database (CpBase, http://chloroplast. ocean. washington. edu/) and the Marine Microbial Eukaryote Transcriptome Sequencing Project  [39]. Sixteen conserved plastid-encoded proteins were present in the partial chloroplast genome sequence from CCMP1545 (rpoC2, rpo1B, rpoC1, psaA, psbA, psaB, psbF, psbL, rpl14, rpl16, rps11, rps18, rps19, rps3, rps7, and rps8) and this subset was used to infer relationships across a total of 45 Archaeplastida taxa. First, protein sequences were aligned with MAFFT [104], the alignments were refined with the ED program implemented in MUST and regions of unambiguous alignment eliminated [105], as were positions having gaps.
Absent proteins were treated as missing data. The individual protein alignments were then concatenated to a single alignment comprising 5,286 amino acid positions and analyzed using Maximum Likelihood (ML) methods (under the cpREV + G model and 5 rate categories, [106]) in PhyML  [107]. Node support was computed with 1000 bootstrap replicates. The tree was rooted with the glaucophyte Cyanophora paradoxa as outgroup for display purposes. For peptidoglycan protein reconstructions, amino acid sequences were retrieved from Phytozome 11 for streptophytes and MMETSP for prasinophytes and the glaucophyte. Besides the PPASP dataset, 2 examples of PG pathway proteins present throughout the Viridiplantae (MURE) or distributed in only some clades (MURA) were used to illustrate their evolutionary histories.
Data deposition The ribosomal RNA operon sequence from RCC299 was deposited under the accession KU612123. RNA-Seq data has been deposited in the SRA under BioProject accessions PRJNA309330 (CCMP1545) and PRJNA309331 (RCC299). LC-MS/MS peptide data has been deposited in the MASSIVE database under accession MSV000079483. The new gene model sets can be downloaded at http://genome. jgi. doe. gov/Micromonas_pusilla/ and http://genome. jgi. doe. gov/ Micromonas_commoda/.
Acknowledgements We thank  D McRose ,  S Yan  and  M Cuvelier  for assistance with growing algae. We thank  N Turland  for guidance on the International Code of Nomenclature (http://www. iapt-taxon. org/nomen/main. php) protocol for species naming and  N Simon  for proof reading it. We are deeply grateful to  V Jimenez  for leading manual annotation efforts and  J-H Lee ,  C-J Choi ,  J Guo ,  M Gutowska ,  C Poirier  and  S Wilken  for contributions. We also thank the anonymous reviewers for comments on the manuscript. Electron.
About half of the 23 B. prasinos and 24 D. tenuilepis neuraminidases belonged to families shared between the two species, but none were shared with naked prasinophytes. No neuraminidases were found in RCC299, O. tauri, and O. RCC809, one neuraminidase was present in CCMP1545. O. lucimarinus contained four other neuraminidases. Blastp of the B. prasinos proteins against the NCBI nr database revealed just single neuraminidase proteins in the Trebouxiophyceae Chlorella variabilis, Coccomyxa subellipsoidea C-169, Auxenochlorella protothecoides, and Helicosporidium sp. ATCC 50920. Hence, these proteins provide a second example of enrichment that is potentially related to scale formation. Compared organisms CCMP1545 CCMP1545 RCC299 CCMP1545 O. lucimarinus (pairs) RCC299 CCMP2099 CCMP2099 O. lucimarinus O.