dataesr/scientific-paragraphs-categorization huggingface dataset:
Results PCA analyses were helpful at selecting our genome dataset (Additional file 2: Figure  S1 ). The 95 validated genomes (Table  1 ) were found to contain 1785 unicopy homologous genes, which were multiply-aligned with posterior curation in Aliview  [39]. A total of 297 core-LCBs with at least 5000 bp were found by Progressive-Mauve  [40] , and only five blocks among these lacked significant recombination. The numeric matrix of stretches of indels obtained from SeqState  [41]  (using modified complex coding) followed by binary recoding had 1247 characters. The core-genome saturation plots showed conformity to a straight line (Additional file 3: Figure  S2 ), suggesting lack of conspicuous saturation within our data. Furthermore, the chi-squared compositional test in IQTree  [42]  revealed that no genomes (including those in the outgroup) presented deviant base frequencies.
08) or XCC alone (R 2 = 0. 15), revealing tip-dating was uninformative regarding dating purposes. Given these results, we proceeded with molecular dating using the UCLN model in Beast v1. 10. 4  [49]  without incorporating tip-dating. Dating runs are further summarized in Fig. 5 , with times and AICM values (a posterior simulation analog of the Akaike's information criterion;  [50] ) obtained from the posterior summarized in Table  3  (in order of decreasing fit). The prior distribution using the exponential without data for the root did not overlap the regular run with data, so the 34-taxa LCB dataset was deemed informative for divergence dating. The more complex GTR + I + G model (instead of HKY + I + G) had a substantially higher fit than other scenarios tested, while performing tree search concomitantly with dating had the worst fit (Table  3 ).
Discussion The phylogenetic pattern most common throughout the analyses, [A + A w ], though always with low support except for ML unicopy agreed with a previous work  [14] , while one of the phylogenies (species tree unicopy) agreed upon [A* + A w + A2] (Additional file 4: Figure  S3 ), more in line with Zhang et al. [13]. Gordon et al. [14] , which used a more extensive dataset with regions of recombination removed, argued that the result by Zhang et al. could be explained by the latter authors not having removed recombinant regions. However, Zhang et al. [13]  used ClonalFrame  [44]  in their paper, a method that corrects for the effect of recombination, and yet they obtained the same tree as with ML with all genes. Recombination being disregarded as a possible bias in their case, there might be unnoticed biases in Zhang et al.
syringae  [54] , with global levels of r/m respectively of 2. 0 and 1. 5. As mentioned above, different branches within the evolution of XCC lineages have values larger than those (Fig. 1 ). In a study with species more closely related to XCC (not focused on sampling of pathotypes), Bansal et al. [21]  found an overall r/m = 2. 24, a relatively high value compared to the Pseudomonas datasets mentioned above, but also within the range of some values observed in Fig. 1. Corroborating such findings, a study  [55]  inferred that 10% of the core genome of a dataset comprising different Xanthomonas species were impacted with homologous recombination. In fact, Mhedbi-Hajri et al. [32] , Zhang et al. [13]  and Gordon et al. [14]  had already observed that the impact of recombination has been quite severe on X.
We checked this statement and determined that the region is part of contig 29, which is 42,744 bp long. Approximately 40 kbp of this contig (containing the above region) align with regions in the three sequenced plasmids of Xanthomonas citri pv. citri strain TX160149, an A w genome (Additional file 6: Figure  S5 ). Furthermore, the region in question is also found in plasmids of X. campestris pv. campestris strain CN18 (GenBank Biosample SAMN05791239) and X. campestris pv. campestris strain CN03 (GenBank biosample SAMN02645665), which have as hosts Brassica plants. In any case, assuming all published information regarding X. campestris pv. durantae LMG 696 is correct  [56] , this suggests that in strains LMG 696, TX160149, CN18, and CN03 transient plasmids may be a factor associated with host range.
5; Table  3 ), a feature that is not present in any of the ML trees computed (Fig. 1 ; Additional file 4: Figure  S3 ), nor across the Beast v1. 10. 4 runs (Fig. 5 ; Table  3 ). This is so even after we matched prior distributions for all parameters in Beast2 (as many of them change between the two versions), suggesting the reimplementation of the software may be inducing subtle differences (in at least some datasets) that may have not been acknowledged thus far. This odd XCC overlap feature, together with the fact that time ranges were significantly older compared to all other runs (Fig. 5 ), precluded its time ranges to be included in the final HPDs. All runs performing worse than the above had significantly worse AICM (Table  3 ) when compared to the original run (disregarding the GTR + I + G run), after Burnham and Anderson  [58] , who mention that a ΔAICM value > 10 is sufficient to consider a model unlikely.
We therefore disregard dating times returned by those tests as well, though we acknowledge that some of them returned HPDs overlapping the most likely models (Fig. 5 ; Table  3 ). We mention particularly the test for faster rate, in which the upper rate bound of the prior was higher by two orders of magnitude (1e-7 in the original run, to 1e-5 s/s/l/y after  [59] ); for this dataset, dates were very recent (as the posterior on rates abounded to the faster values), and as mentioned, the AICM value is substantially worse (Fig. 5 ; Table  3 ); indeed, the closer relative to Xanthomonas in the Duchene et al. dataset  [59]  is Pseudomonas aeruginosa, still quite distant from our target taxa (and yet with rates lower than 1e-6 s/s/l/y). We therefore suggest caution when considering priors (and interpreting posteriors) embracing such high rates.
HPDs of XCC origin and divergence were more recent when recombining regions were removed from the dataset, but its model likelihood is not comparable to the other runs. This suggests that comparing dates between recombining vs. non-recombining datasets may be interesting to provide conservative time estimates; we therefore embraced such date estimates in the reported HPDs (Table  3 ). In a previous study  [32] , the authors reconstructed the phylogeny of the more inclusive X. axonopodis group using seven housekeeping genes, clarifying the relationship among the six groups proposed earlier  [63]  (groups 9. 1-9. 6). XCC clustered within group 9. 5  [21] , with the time to the most recent common ancestor of this group (tMRCA) being also a conservative upper bound for the tMRCA of XCP (including XCC) regarding comparisons to our dates.
With such focal points in mind, we interrogated a vetted dataset of 95 XCC genomes with the largest taxonomic inclusion (ingroup and outgroup) to date. By carrying a thorough phylogenomic investigation (better sampling, use of different genomic regions, use of parametric and non-parametric phylogenetic methods, impact of population structure), we confirmed the presence of an [A + A w ] clade as observed in a previous study  [14]. Important clues obtained here led to the hypothesis that evolution of XCC pathotypes operated by retention of ancestral polymorphisms and recombination, likely blurring part of the phylogenetic signal. Fig. 7  Schematic view of the main results regarding evolution of X. citri subsp. citri. The lineage originated ~16. 0-46. 0 thousand years ago (kya), with an associated event of host switch from Fabaceae to Rutaceae, within the Indian Subcontinent.
A* and A2 likely share a great portion of ancestral polymorphism, whereas A and A w had a larger impact from recombination ("Rec") on their genetic varibility (especially in A, the generalist pathotype) prior to each respective diversification. Colors of the most common haplotypes in each lineage are the same as in previous figures (except for A2, which due to its high genetic similarity to A* according to BAPS v6. 0, is also shown in blue). Dotted lines correspond to minor genetic contributions from given haplotypes (as detected in BAPS v6. 0) or inferred from the ML-unicopy phylogeny. Fabaceae images obtained and modified from the Encyclopedia of Life database: "Clitoria ternatea" (https://eol. org/pages/47317701; copyright: Vinoth Kumar Rajalingam; license: cc-by-nc-4. 0), and "Cajanus cajan (L.
Genome sequencing and assembly The new genomes were sequenced using the Illumina HiScanSQ plataform. An average of ~20 M (2 × 100 bp) reads for each genome was generated (Additional file 1: Table  S1 ). The raw reads were trimmed with seqyclean software (https://bitbucket. org/izhbannikov/seqyclean), using minimum phred value of 23, minimum read length of 30 bp, and removing custom Illumina TruSeq adapters. Genome assembly was carried out with SPAdes v3. 8. 1  [73]  with default parameters. Potential plasmid derived scaffolds were identified with plasmidSPAdes v3. 8. 1  [74].
Annotation of the genomes We considered the inclusion of 107 genomes (XCC plus outgroup) available at least as contigs and/or scaffolds available in GenBank as of July 2018. This list included all publicly available XCC genomes, plus the six newly sequenced genomes by our group. We annotated all genomes with DFAST  [75]  using an augmented database of complete Xanthomonas citri (and outgroup) complete genomes. The six genomes that we sequenced were further reannotated with the NCBI Prokaryotic Genome Automatic Annotation Pipeline  [76]  and submitted to GenBank, with accession numbers given in Table  1.
Unicopy gene families The protein-coding genes of the 95 genomes were input into Get_Homologues  [77]  for gene family clustering using the OMCL option ('-M'). This setup first produces all-vs-all BLASTp  [78]  comparisons between predicted protein products, and then runs OrthoMCL  [79]. We used thresholds of 80% for both coverage and identity. After the homologous gene families were clustered, the compare_clusters. pl script (within Get_Homologues) was run to obtain the set of genes of single copy present in all 95 genomes, which in principle can be considered to be enriched with vertical phylogenetic signal  [80]. Multiple sequence alignment (MSA) of each core-genome single copy gene family was performed by Muscle  [81]. Each MSA was then manually checked in Aliview  [39] , and whenever local regions were suspected of misalignment we used the software's "realign selected block" option (again using Muscle, within Aliview), to minimize impact of alignment biases in downstream analyses.
Locally Colinear blocks (LCBs) We also employed core-LCBs for phylogenomic analyses due to their multiple alignments being independent of annotation biases (if a gene is missing from the reference genomes then it may be left out in other genomes too, and contrarily a wrongly inferred gene annotation can also be perpetuated across genomes), while at the same time allowing larger segments (as 5000 pb is much larger than the average bacterial gene length of ~1000 bp) therefore bringing more power to some of the analyses (such as inference of gene trees; see below). We identified LCBs ≥5000 bp using Pro-gressiveMauve  [40] , which automatically aligned each of them. The core-LCBs (from here on, simply LCBs) were obtained from this larger LCB set by running stripSubsetLCBs (http://darlinglab. org/mauve/snapshots/2015/2015-01-09/linux-x64/).
Phylogenomics and network estimation Saturation plots were obtained with genetic distances calculated using the F84 substitution model  [83]  in DAMBE  [84] , to assess possible saturation effects that could compromise phylogenetic estimation and dating analyses  [85]. Phylogenetic inferences of the unicopy dataset were done by Maximum Likelihood (ML) and Maximum Parsimony (MP), to test for methodological biases. ML analyses and UFBoot branch support  [86]  were obtained in IQTree  [42]. During substitution model assessment each model was tested for rate variation across sites with a discretized gamma distribution of rates and/or proportion of invariant sites, and alternatively with 2 to 5 rateacross-sites mixture matrices. MP with the unicopy set was run in MPBoot  [87] , with branch support obtained by 1000 UFBoot pseudoreplicates.
LCBs were analyzed either separated (as "gene trees" for a consensus network), concatenated, or analyzed under a species tree paradigm. ML trees were inferred by either using the whole LCBs concatenated, or else by removing blocks with signs of recombination (see below for details). A consensus network based on LCB gene trees was employed in SplitsTree4  [88]  with a threshold of 0. 05 (i. e. , with tree splits appearing in at least 5% of the gene trees) to assess qualitatively the amount of reticulations leading to pathotype lineages (because some reticulations may be indicative of HGT). Only three genomes from each pathotype were maintained for this analysis (plus all putative outgroup genomes, totaling 34 genomes) to avoid excess of detection of recombination events in terminal branches of the ingroup, harming the network's interpretation unnecessarily (as such events are not the main focus of the present study); this dataset is referred to as the 34-set.
Rooting trees The ML unicopy tree was rooted by the MAD algorithm  [93] , which finds the branch minimizing deviations from the midpoint criterion (i. e. , the idea that assumes that the middle of the path between any two OTUs should coincide with their last common ancestor) across all possible root positions and all OTU pairs of the unrooted tree, being more accurate than other known rooting procedures  [93]. This rooted unicopy ML-based tree was then fixed for recombination, dating, and biogeographic analyses. The phylograms obtained from different datasets and phylogenetic methods above were rooted by the same method.
Population genetic analysis Because the three pathotypes may have diverged from each other relatively recently  [32]  and populations may still bear high levels of mixing, two population-level analyses were conducted: (I) we used the population genetic-based BAPS v6. 0  [51]  with unicopy SNPs to assess the actual number of structured populations within XCC (found automatically by the software) and to infer the degree of admixture in each of them, assuming a model with linkage between SNPs; BAPS attributes individuals to populations in a Bayesian way by determining the maximal set of individuals resembling each other genetically as much as possible in each of them, while concomitantly updating the inference of the number of populations  [51] ; and (II) a complimentary way not assuming any model of population subdivision was also employed with the unicopy SNPs, employing DAPC in the R adegenet package  [94].
Recombination Four recombination assessment methods were employed with LCBs. Three of them were used to detect blocks across the 95 genomes showing significant signs of recombination (PHI, NSS and MaxCHI) in the PhiPack package  [95]  with a significance level of 0. 05. Genes bearing any significant signs of recombination were removed for a second-round ML phylogenetic reconstruction, to test for the effect of recombining regions in the estimated tree. The fourth recombination method employed was ClonalFra-meML  [44] , to estimate the strength of recombination throughout the tree for the 34-set, considering the LCBs with at least 5000 bp (only the subset including all outgroup plus three genomes from each pathotype was employed for this analysis). Given an inferred topology, it calculates the contribution of recombination relative to single-point mutations (r/m), doing this for each branch.
Dating Regarding dating analyses, we employed the same core LCB-based 34-taxa dataset used for network estimation. The rooted ML unicopy tree was fixed throughout most dating analyses. A test for the best molecular clock type (strict or relaxed) was carried in the R package treedater 0. 2. 0  [47]. Subsequently, we tested whether including tip-dating would be informative using TempEst  [48]. The best clock type was then set up in BEAST v1. 10. 4  [49]  for the remaining analyses, using as default setup ("original run") HKY + I + G for the substitution model (easier to converge on most analyses), a birth-death prior (BD) on node times, an exponential time for the root (following bounds specified below), with rates following the literature (also mentioned below), and without removing recombining regions.
We then compared the effect of different parameter/data scenarios against the original run: (I) a MCMC run without data, to test for data informativeness regarding dating (II) tree search (instead of MLfixed topology); (III) removal of recombining regions inferred by ClonalFrameML; (IV) employing a coalescent skyline model (with five points) instead of a BD tree prior; (V) a Uniform distribution on the root age (instead of Exponential); (VI) BEAST v2. 5. 2  [96]  to compare the effect of a different implementation of the same software; (VII) a faster overall rate of evolution (obtained from  [59] ); and (VIII) a more complex GTR + I + G substitution model. Tip-dating was employed according to isolation dates in Table  1 , or by assuming a uniform distribution on dates between [0, 104] ya in the case of genomes for which isolation dates were unavailable.
In Brazil, a striking proportion of 68. 1 to 75% of the population with positive serological results reporting symptoms contracted a chronic form of the disease  [13, 16]. However, the epidemiological features, genomic diversity, and transmission dynamics of recent CHIKV outbreaks in this country remain poorly understood. Inferences that are based only on clinical-epidemiological notifications are complicated by underreporting of cases by the national reporting system  [17] , mostly due to the co-circulation and co-infection with viruses that cause overlapping symptoms, such as Zika and dengue viruses  [18] [19] [20]. Moreover, CHIKV serological tests may cross-react with other alphaviruses, such as Mayaro virus, that circulate in the north and centre-west regions of Brazil  [21, 22]. In this context, it is challenging to use only clinical-epidemiological and serological data to evaluate the true extent of the disease.
Moreover, the Amazon region has recently been highlighted as a region with high transmission potential of vector-borne diseases  [4]  and, more generally, a region with high potential for virus zoonoses and emergence  [28]. Due to its connectivity and potential impact on global epidemiology of vector-borne and zoonotic virus from the Amazon basin, it is important to improve genomic pathogen surveillance in Roraima. By August 2018, the public health laboratory of Boa Vista (capital city of Roraima state) had reported 5,928 CHIKV cases, 3,795 of which were laboratory-confirmed. Here we a use combination of on-site portable virus genome sequencing, and epidemiological analysis of case count and web search data to describe the circulation, genetic diversity, epidemic potential and attack rates of a large CHIKV outbreak in Boa Vista.
Chikungunya virus case count time series The Roraima State Central Laboratory (LACEN-RR) is responsible for the differential diagnosis of suspected arbovirus cases presenting to Roraima's public health units. Between Jan 2014 and September 2018, LACEN-RR notified 5,928 CHIKV cases in Boa Vista alone, 3,795 of these laboratory-confirmed, to the National Reportable Disease Information System (SINAN). Case count time series are available from Github (https://github. com/arbospread/chikamazon). We follow the Brazilian Ministry of Health's guidelines and define a notified CHIKV case as a suspected case characterized by (i) acute onset of fever >38. 5˚C, (ii) severe arthralgia and/or arthritis not explained by other medical conditions, and (iii) residing or having visited epidemic areas within 15 days before onset of symptoms.
0 instrument (Life Technologies). PCR products for samples yielding sufficient material were barcoded and pooled in an equimolar fashion using the Native Barcoding Kit (Oxford Nanopore Technologies, Oxford, UK). Sequencing libraries were generated from the barcoded products using the Genomic DNA Sequencing Kit SQK-MAP007/SQK-LSK108 (Oxford Nanopore Technologies). Libraries were loaded onto a R9/R9. 4 flow cell and sequencing data were collected for up to 48hr. Consensus genome sequences were produced by alignment of two-direction reads to a CHIKV virus reference genome (GenBank Accession number: N11602) as previously described in  [33]. Positions with�20× genome coverage were used to produce consensus alleles, while regions with lower coverage, and those in primer-binding regions were masked with N characters.
Collation of CHIKV-ECSA complete genome datasets Genotyping was first conducted using the phylogenetic arbovirus subtyping tool available at http://www. krisp. org. za/tools. php. Complete and near complete sequences were retrieved from GenBank on June 2017  [34]. Two complete or near-complete CHIKV genome datasets were generated. Dataset 1 included ECSA-PreAm (ECSA sampled outside the Americas) and ECSA-Br (ECSA sequences sampled in the Americas) sequences. This dataset contained 36 complete genomes from the ECSA genotype, including  7   [36] , no evidence of recombination was found in both datasets.
Maximum likelihood analysis and temporal signal estimation Maximum likelihood (ML) phylogenetic analyses were performed for each dataset using RAxML v8  [37]. We used a GTR nucleotide substitution model with 4 gamma categories (GTR+4Γ). In order to investigate the evolutionary temporal signal in each dataset, we regressed root-to-tip genetic distances against sample collection dates using TempEst  [38]. For both datasets we obtained a strong linear correlation (dataset 1: r 2 = 0. 93; dataset 2: r 2 = 0. 84) suggesting these alignments contain sufficient temporal information to justify a molecular clock approach. However, for dataset 1, the Angola/M2022/1962 strain was positioned substantially above the regression line. Previous investigations have suggested this strain may have been the result of contamination or high passage in cell culture  [9] , so this sequence was removed from subsequent analyses.
In less than 48 hours genome sequence data was obtained for all selected isolates and in less than 72 hours preliminary results were shared with local public health officials and the Brazilian Ministry of Health. A mean genome coverage of 86% (20x) per base pair was obtained for the sequenced data; mean coverage increased to 90% when focusing on samples with Ct<26 (Fig  2A ). Coverage of individual sequences and epidemiological information for each sequenced isolate can be found in Table  1. Identification of virus genotypes was conducted using phylogenetic analysis of full-length genome datasets (manual classification) and using an online phylogenetic analysis tool (automated classification). Both approaches identified the ECSA genotype as the dominant genotype circulating in both Roraima and Manaus between 2015 and 2017.
The time lag between the phylogenetic estimate of the date of introduction of a virus lineage and the date of the first confirmed case in a given region, enables us to identify surveillance gaps between the arrival and discovery of a virus in that region  [71]. We used genomic data collected over a 3-year period to estimate the genetic history of the CHIKV-ECSA-Br lineage. We estimate that the CHIKV-ECSA-Br lineage arrived in Roraima around July 2016, whilst the first confirmed CHIKV cases in Roraima occurred earlier, in August 2014. That the discovery date anticipates the estimated date of introduction can be explained by initial introduction(s) of the Asian linage (from the north of Brazil or from other south American regions) resulting in only limited onwards transmission, followed by the replacement of the Asian lineages by an epidemiological successful ECSA lineage.