dataesr/scientific-paragraphs-categorization huggingface dataset:
32, 34 Meanwhile, accelerating evidence accumulation through evidence synthetic methods such as NMAs may also help. Rouse et al recently conducted similarly conceived retrospective NMAs for medications for open-angle glaucoma at four to five-year intervals since 1991, in which the most efficacious drug remained to be prostaglandins since 1999. 35 In our analysis by contrast, there were more dramatic changes in the recommendable drugs. Our results also show that the evidence can easily get out of date. It therefore follows that it is highly necessary to update evidence synthesis in a timely manner. Conducting NMAs from scratch is time-consuming. 36, 37 However, if we conduct a prospective NMA according to the pre-specified procedures, we only need to update the dataset regularly. NMA may therefore be the most optimal method to synthesize evidence dynamically.
ACKNOWLEDGEMENT We thank  Professor Ryo Yamada  from the  Center for Genomic Medicine, Kyoto University , for the instructions and advices regarding the use of Shiny package. We also thank  Dr. Thodoris Papakonstantinou  from  Institute of Social and Preventive Medicine, University of Bern , for helping upload the Shiny app to the server and make it open to public. This study was supported in part by  JSPS   Grant-in-Aid for Scientific Research  (Grant Number  17k19808 ) to TAF and by the  National Institute for Health Research (NIHR) Oxford Health Biomedical Research Centre  (grant  BRC-1215-20005 ) to ACi. The views expressed are those of the authors and not necessarily those of the  UK National Health Service , the  NIHR , or the UK Department of Health. ACi is also supported by the  National Institute for Health Research (NIHR) Oxford Cognitive Health Clinical Research Facility , by an  NIHR Research Professorship  (grant  RP-2017-08-ST2-006 ).
The algorithm was implemented in C++ and is available at Github. The purpose of the Xyleme project was to index and analyse the changes on the web where different parts of websites were stored as XML documents. XyDiff also uses the two-pass approach and assigns so-called "XID" to each node. The algorithm supported two "Level 2" edit actions: update and move. It is one of the fastest XML diff algorithms as this research group had to deal with a large number of documents to compare. In order to detect text updates, XyDiff uses LCS (longest common sub-string) with the goal to minimise the edit distance. This approach is good for machine readable delta but not for human readers, while it is difficult to understand the actual changes an author made by analysing the character-based LCS representation.
PERFORMANCE ANALYSES The performance analyses of jats-diff § § are divided into two sub-sections, one on the information extraction capacity and the other on execution performance. This being a state-of-the-art algorithm, our main effort was set on the capacity to detect new edit patterns and change semantics extraction, rather than its implementation performance. § § github. com/milos-cuculovic/jats-diff. 1 7   Sim. index Real-life edits ✓.
This information could be used to validate and enrich the modifications detected by the algorithm. One last idea about a similar topic regarding document comparison semantics and their benefits would be its usage in version control systems (Git, sub-version, etc. ) If those version control systems were to be aware of the programming language grammar they are detecting changes for, using change semantics to interpret some edit actions could be beneficial: for example, one class name change and its impact on other files where this initial class name is called could replace dozens or hundreds of detected modifications with one single edit which initiated all the other changes.
ACKNOWLEDGEMENTS The authors would like to thank  Dr. Shu-Kun Lin  and MDPI for their financial and technical support regarding our study. The authors would also like to thank  Mr. Sasa Simic , IT Engineer from  MDPI , for his work on the "Level 2" change implementation and  Ms. Chams Azouz , student from the  University of Haute-Alsace , for her work on the similarity index calculation implementation.
2 ), which represented more than one-third (33. 4%) of the Kp1 data set. The abundance of this SL (and a few others, such as ST23) reflected the clinical microbiology focus on multidrug-resistant or hypervirulent isolates  (Bowers et al. 2015; Struve et al. 2015; Lam et al. 2018; Wyres, Nguyen, et al. 2020). The phylogenetic structure within other K. pneumoniae phylogroups also revealed a multitude of distinct SLs but no predominant ones, and medically important lineages in these phylogroups are yet to be recognized. Klebsiella pneumoniae strains can recombine large sections of their chromosome  (Chen et al. 2014; Wyres et al. 2015). Large recombination events were detected in 1. 9%  (138/7,198)  genomes (based on their cgMLST profiles) and involved the phylogroups Kp1, Kp2, and Kp4 (supplementary appendix: Detection of hybrids, fig.
Development and Implementation of a cgMLST-Based LIN Code System Following the principle of the LIN code system, initially proposed based on the ANI similarity  (Marakeby et al. 2014) , we defined a cgMLST-based LIN (cgLIN) code approach. As LIN coding is performed sequentially, we first explored the impact on the resulting partitioning of cgMLST profiles, of the order in which genomes are assigned. We confirmed that the number of partitions (hence their content too) varied according to input order (supplementary fig. S10 , Supplementary Material online). However, we established that the order of genomes determined by the traversal of a Minimum Spanning tree (MStree)  (Prim 1957 ) naturally induces a LIN encoding order that is optimal, that is, most parsimonious with respect to the number of identifiers generated at each position of the code (see supplementary appendix, Supplementary Material online).
Using this MStree traversal strategy, we defined cgLIN codes for the 7,060 nonhybrid genomes (as a first step), resulting in 4,889 distinct cgLIN codes. Furthermore, cgLIN codes can be displayed in the form of a prefix tree (fig. 5 ), which largely reflects the phylogenetic relationships among genomes. In addition, cgLIN code prefixes can be used to label particular phylogenetic lineages  (Vinatzer et al. 2017). For example, a single cgLIN code prefix defined each phylogroup (e. g. , Kp1: prefix 0_0; Kp2: prefix 2_0; fig. 5 ). Likewise, a full one-to-one correspondence between prefixes and SLs was observed, and almost all (99. 4%) CGs also had a unique prefix (supplementary tables S4 and S5, Supplementary Material online; fig. 6 ).
MBE In contrast, the incorporation of hybrid genomes into the cgLIN code database left the cgLIN codes of the 7,060 previous genomes entirely unaffected; there were no merging of groups, as per the design of the system. In particular, the seven phylogroup prefixes corresponding to species and subspecies remained unaffected (supplementary fig. S11 , Supplementary Material online); instead, additional prefixes were created for the hybrid genomes (supplementary tables S1 and S2, Supplementary Material online).
Implementation of the Genomic Taxonomy in a Publicly Accessible Database The MLSL nomenclature was incorporated into the Institut Pasteur K. pneumoniae MLST and whole-genome MLST databases (https://bigsdb. pasteur. fr/klebsiella) under the classification scheme functionality developed in BIGSdb version 1. 21. 0. In brief, the cgMLST profile of every isolate with fewer than 30 missing scgMLSTv2 alleles was assigned to a core genome sequence type (cgST), and these were next grouped into single linkage partitions for each of the 10 classification levels. For SLs and CGs, a custom classification group field (named SL or CG within the system) was additionally populated with identifiers inherited from 7-gene MLST. All cgMLST profiles and classification identifiers are publicly available. To allow users to identify K.
pneumoniae isolates easily, a profile matching functionality was developed, enabling them to search for cgMLST profiles related to a query genome sequence. This was implemented on the website sequence query page (https://bigsdb. readthedocs. io/e n/{PI}latest/administration. html#scheme-profile-clusteri ng-setting-up-classification-schemes). This functionality returns the classification identifiers (including MLST-inherited CG and SL identifiers) of the cgMLST profile that is most closely related to the query genomic sequence, along with its number of mismatches compared with the closest profile. cgLIN code functionality was also incorporated into BIGSdb version v1. 34. 0 (https://bigsdb. readthedocs. io/en/ latest/administration. html#setting-up-lincode-definitions -for-cgmlst-schemes). In particular, cgST profiles can be queried by full cgLIN code or any prefix, and a nomenclature can be attached to LINcode prefixes of interest (e.
In addition, the ANI metric is nonreciprocal and highly dependent on comparison implementations and parameters. These shortfalls may yield imprecision and nonreproducibility that are particularly impactful for comparisons between very similar genomes. We, therefore, adapted the LIN code concept to cgMLST-based similarity (i. e. , one-complement of the allelic mismatch proportions) to classify strains into a cgLIN code system. This strategy leverages the benefits of cgMLST and introduces more Klebsiella Strain Taxonomy • https://doi. org/10. 1093/molbev/msac135 MBE intuitive shallow-level classification thresholds. The multilevel similarity information embedded in MLSL and cgLIN "barcodes" provides a human-readable snapshot of strain relationships, as nearly identical genomes have identical barcodes up to a position near the right end.
In contrast to single linkage clustering partitions, one important limitation of LIN codes is that preexisting classification identifiers (e. g. , ST258) cannot be mapped onto individual LIN code identifiers, because these are attributed with reference to the upper levels and are set to 0 for each downstream level  (Marakeby et al. 2014). However, cgLIN code prefixes may represent useful labels for particular lineages. The cgMLST-based nomenclatures have some limitations. First, a comparison of allele numbers rather than SNPs implies the loss of information. In turn, this approach is advantageous to estimate evolutionary relationships of closely related genomes that diverged following homologous recombination events, which are common among strains within bacterial species  (Feil 2004; Vos and Didelot 2009).
Second, MLST-based distances saturate more rapidly than SNP distances (once a locus is affected, even by a single mutation, further mutations at this locus will change the allele but will not increase the allelic distance), and are therefore mostly meaningful within bacterial species. Third, in contrast to the original ANI-based LIN code approach  (Tian et al. 2020) , cgMLST-based LIN codes require prior development of cgMLST schemes, which are larger, hence more powerful for strain resolution, for single species. Therefore, the advantages of cgLIN codes for population genomics and epidemiological questions come at the expense of universality. Still, the dual MLSL and cgLIN code approach proposed here is in principle applicable to all bacterial species (or closely related groups thereof) for which large representative sets of genomes are available.
The cgLIN code algorithms were incorporated into BIGSdb and should be readily portable to other existing cgMLST platforms such as EnteroBase. However, the use of genetic thresholds for an entire group of organisms may not always be meaningful, depending on the population genetic structure. Whereas K. pneumoniae shows strong structuring with neat peaks and valleys of pairwise genetic distances, other species may have a more fuzzy structure. In the latter cases, the approach will still be applicable, but even optimally defined thresholds may be less relevant biologically.
Conclusions A unified nomenclature of pathogen genotypes is required to facilitate communication in the "One Health" and "Global Health" perspectives. K. pneumoniae represents a rapidly growing public health threat, and the availability of a common language to designate its emerging SLs is therefore highly timely. The proposed unified taxonomy of K. pneumoniae strains will facilitate advances on the biology of its SLs across niches, time, and space, and will endow surveillance networks with the capacity to efficiently monitor and control the emergence of SLs of high public health relevance. Here, we propose a dual barcoding approach to bacterial strain taxonomy, which combines the complementary advantages of stability provided by the cgLIN codes, with an unstable, but human-readable MLSL nomenclature rooted in the popular 7-gene MLST nomenclature.
Definition of an Updated Core Genome MLST (scgMLSTv2) Genotyping Scheme We previously defined a cgMLST (using strict synteny criteria, hence name scgMLSTv1) scheme of 634 highly syntenic genes  (Bialek-Davenet et al. 2014). Here, we updated the scgMLSTv1 scheme, with the following improvements. First, two loci (KP1_2104 and aceB = KP1_0253) were removed because they were absent or truncated in multiple strains, based on 751 high-quality assemblies available in the BIGSdb-Pasteur Klebsiella database on October 16, 2017 (project id 11 at https:// bigsdb. pasteur. fr/cgi-bin/bigsdb/bigsdb. pl?db=pubmlst_ klebsiella_isolates). Second, the remaining 632 loci templates were modified so that they would include the start and stop codons of the corresponding coding sequence (CDS). This was not the case for all CDSs of the scgMLSTv1 scheme, as some loci corresponded to internal portions of CDSs.
Definition of a Genomic Sequence Data set of 7,060 Isolates with cgMLST Profiles The KpSC comprises seven phylogroups that have been given taxonomic status in the prokaryotic nomenclature: K. pneumoniae subsp. pneumoniae (Kp1, also known as K. pneumoniae sensu stricto), K. quasipneumoniae subsp. quasipneumoniae (Kp2), K. variicola subsp. variicola (Kp3), K. quasipneumoniae subsp. similipneumoniae (Kp4), K. variicola subsp. tropica (Kp5), "K. quasivariicola" Hennart et al. • https://doi. org/10. 1093/molbev/msac135 MBE (Kp6) and K. africana (Kp7)  (Rodrigues et al. 2019). We retrieved all KpSC genomes from the GenBank assembly repository on March 15, 2019, corresponding to 8,125 assemblies. We then chose high-quality assemblies by excluding draft genomes: (1) containing more than 1,000 contigs of size >200 nt; (2) for which the ANI values (estimated using FastANI v1.
After this step, 235 profiles were excluded they had more than 30 missing alleles. The resulting data set comprised 36 taxonomic references of Kp1-Kp7  (Rodrigues et al. 2019 ) that, together with eight additional genomes of phylogroup Kp7, were considered as a reference data set of the KpSC taxa ("K. quasivariicola" reference strain KPN1705, SB6096, was excluded because it had more than 30 missing alleles). Besides these 44 reference genomes, 7,154 GenBank genomes were retained, resulting in a total data set of 7,198 genomes (supplementary tables S1 and S2, Supplementary Material online). For some analyses, 138 genomes were set aside, defined as "hybrids" between phylogroups (see below), resulting in a 7,060-genome data set (supplementary fig. S2 , Supplementary Material online). We estimated within-outbreak variation using previously published outbreak sets (supplementary tables S6 and S7, Supplementary Material online).
Genome assemblies based on 454 sequencing technology, which are prone to frameshifts, were also excluded for novel allele definitions. No genome assemblies based on IonTorrent sequencing technology were found. In order to speed the scanning process, we used the fast scan option (-e -f) of the BIGSdb autotag. pl script (https:// bigsdb. readthedocs. io/en/latest/offline_tools. html). This option limits the BLASTN search to a few exemplar alleles, which are used as query to find the genomic region corresponding to the locus. In a second step, a direct database lookup of the region was performed to identify the exact allele.
Definition of MLST STs and cgST Classical 7-gene MLST loci have been defined previously  (Diancourt et al. 2005)  as internal portions of the seven protein-coding genes gapA, infB, mdh, pgi, phoE, rpoB, and tonB. Novel alleles were defined in the Institut Pasteur Klebsiella MLST and whole-genome MLST database https://bigsdb. pasteur. fr/klebsiella. In 7-locus MLST, the combination of the seven allelic numbers determines the isolate profile, and each unique profile is attributed an ST number. Incomplete MLST profiles with one (or more) missing gene(s) are recorded in the isolates database, but in these cases, no ST number can be attributed and the profiles are therefore not defined in the sequence definition database. The 7-locus MLST genes were not included in the scgMLSTv2 scheme. Similar to ST identifiers used for unique 7-gene MLST allelic combinations, each distinct cgMLST profile can be assigned a unique identifier; however, when using draft genomes, cgMLST data can be partly incomplete due to de novo assembly shortcomings or missing loci.
1 ). A core genome multiple sequence alignment (cg-MSA) of 7,060 cgMLST profiles free of evidence for interphylogroup "hybridization" (see below) was constructed. The gene sequences were retrieved based on allele number in the sequence definition database, individual gene sequences were aligned with MAFFT v7. 467 (missing alleles were converted into gaps), and the multiple sequence alignments were concatenated. IQ-TREE v2. 0. 6 was used to infer a phylogenetic tree with the GTR + G model (fig. 2 ). Locus-by-locus recombination analyses were computed with the PHI test  (Bruen et al. 2006 ) using PhiPack v1. 0. Kleborate v2. 0. 4  (Lam et al. 2021 ) was employed to identify acquired antimicrobial resistance and virulence genes in genomic sequences, based on CARD v3. 0. 8 database, with identity >80% and coverage >90%.
e. , proportion of alleles labeled by the predominant phylogroup) was derived. The distributions of the phylogroup homogeneity indices allowed determining hybrid genomes (supplementary fig. S12 , Supplementary Material online). The exclusion of such hybrid genomes resulted in a genomic data set of 7,060 isolates deemed as having a majority of alleles inherited from within a single phylogroup. Of the 44 reference genomes, one (SB1124, of phylogroup Kp2) was defined as having a hybrid origin: 414 alleles were attributed to Kp2, whereas 150 alleles originated from non-KpSC species; as a result, 73. 4% of SB1124 alleles were part of the majority phylogroup, which was below the defined threshold of 78%. The quantification of recombination breakpoints was performed based on the position of cgMLST loci on the NTUH-K2044 reference genome (NC_012731), counting the number of recombination breakpoints in each successive 500 kb fragment along the reference genome.
Identification of Genetic Discontinuities in the KpSC Population Structure The tool MSTclust v0. 21b (https://gitlab. pasteur. fr/GIPhy/ MSTclust) was used to perform the single linkage clustering of cgMLST profiles from their pairwise allelic mismatch dissimilarities, as well as to assess the efficiency of the resulting profile partitioning (for details, see supplementary appendix: Minimum Spanning tree-based clustering of cgMLST profiles, Supplementary material). Briefly, for each threshold t (= 0-629 allelic mismatches), the clustering consistency was assessed using the silhouette metrics S t  (Rousseeuw 1987) , whereas its robustness to profile subsampling biases was assessed using a dedicated metrics W t based on the adjusted Wallace coefficients  (Wallace 1983; Severiano et al. 2011).
Both consistency (S t ) and stability (W t ) coefficients converge to 1 when the threshold t leads to a clustering that is consistent with the "natural" grouping and is robust to subsampling biases, respectively. The adjusted Rand index R t  (Hubert and Arabie 1985; Carrico et al. 2006)   Simpson's diversity index was computed using the www. comparingpartitions. info website  (Carrico et al. 2006 ). The clade compatibility index of STs or other groups was calculated using the ETE Python library (http://etetoolkit. org/{PI}docs/latest/tutorial/tutorial_trees. html#checkingthe-monophyly-of-attributes-within-a-tree), in order to define whether their constitutive genomes formed a monophyletic, paraphyletic, or polyphyletic group within the recombination-purged sequence-based phylogeny of the core genome.
Classification of cgMLST Profiles into Clonal Groups and SLs The classification scheme functionality was implemented within BIGSdb v1. 14. 0 and relies on single linkage clustering. Briefly, cgSTs were defined in the sequence definitions ("seqdef") database as distinct profiles with fewer than 30 missing alleles over the scgMLST scheme, and their pairwise cgMLST distance was computed as the number of distinct alleles. To account for missing data, a relative threshold was used for clustering: the number of allelic mismatches was multiplied by the proportion of loci for which an allele was called in both strains. Hence, in order to be grouped, the number of matching alleles must exceed: (the number of loci called in both strains × (total locidefined threshold))/total loci. cgSTs and their corresponding SL, CG, and other levels partition identifiers are stored in the seqdef database and are publicly available.
Here, classification schemes were defined in the Klebsiella seqdef database on the top of the scgMLSTv2 scheme, and host single linkage clustering group identifiers at the 10 defined cgMLST allelic mismatch thresholds (see Results). For classification groups defined using 43 and 190 allelic mismatch thresholds, scheme fields were defined and populated with the identifiers defined by inheritance from 7-gene MLST ST identifiers (see supplementary appendix: Nomenclature inheritance algorithm, Supplementary Material online).
These codes, which we refer to as cgLIN codes, are composed of a set of p positions, each corresponding to a pairwise genome similarity threshold s p. These similarity thresholds are sorted in ascending order (i. e. , s p < s p+1 ), the first positions of the code (on the left side) thus corresponding to low levels of similarity. Following the initial proposal, the codes are assigned as follows (supplementary fig. S13 , Supplementary Material online): (step 1) the code is initialized with the first strain being assigned the value "0" at all positions; (step 2) the encoding rule for a new genome i is based on the closest genome j already encoded as follows, from the similarity s ij ∊ ] s p-1 , s p ]: i) identical to code j up to and including position p -1; ii) for the position p: maximum value observed at this position (among the subset of codes sharing the same prefix at the position p -1) incremented by 1; iii) "0" to all downstream positions, from p + 1 included.
For example, cgLIN codes 0_0_22_12_0_1_0_0_0_0 and 4_0_3_0_0_0_0_0_0_0 would denote two strains belonging to distinct species (as they differ by their first number in the code). cgLIN codes 0_0_105_6_0_0_75_1_1_0 and 0_0_105_6_0 _0_75_1_0_0 correspond to strains from Kp1 (prefix 0_0) that differ by only 2 loci; they are identical up to the second bin, corresponding to 2 locus mismatches (supplementary fig. S13 , Supplementary Material online); note that 0 and 1 mismatches are both included in the last bin: genomes have an identical identifier when having 0 difference, and a different identifier when having 1 mismatch (supplementary fig. S14 , Supplementary Material online). The impact of genome input order on the number of cgLIN code partitions at a given threshold was defined using the 7,060 high-quality, nonhybrid cgMLST profiles, which were encoded 500 times with random input orders (see details in the supplementary appendix: Impact of strains input order on LIN codes, and use of Prim's algorithm, Supplementary Material online).