dataesr/scientific-paragraphs-categorization huggingface dataset:
Syn-eruptive evolution evidenced from highprecision sampling Following the methodology developed for subaerial eruptions at monitored volcanoes  [Corsaro and Miraglia, 2005 , 2022 , Di Muro et al. , 2014 , Edmonds et al. , 2013 , Gansecki et al. , 2019 , Gurioli et al. , 2018 , Re et al. , 2021] , petrological and geochemical approaches provide an exceptional dataset for a submarine eruption sequence. Here, differential bathymetric datasets and high-precision dredging performed during the MAYOBS campaigns  [Rinnert et al. , 2019]  allowed us to constrain the evolution of magma storage and transfer processes throughout this long-lived voluminous submarine eruption, both in time and space. Phases 1 and 2 of the eruption were first sampled in May and July 2019, more than ten months after the beginning of the eruption, and when the Fani Maoré volcanic cone was already more than 800 m high.
A fractional crystallization model was also run using the Rhyolite-MELTS software  [Ghiorso and Sack, 1995 , Gualda et al. , 2012 , Ghiorso and Gualda, 2015]. We also use the bulk composition of the most primitive sample, DR01-04, as parental melt. Magma storage conditions were set at 1. 0 GPa with 2. 3 wt% H 2 O and an oxygen fugacity at FMQ-1. Temperature decreased from 1250 to 950 °C, with a 10 °C step. Since no orthopyroxene and garnet crystals were found in Comoros lavas, they were excluded from the crystallizing assemblage. Except for P 2 O 5 , our modeling results match well for all major elements with the trend defined by the compositions erupted at the beginning of the sequence (Phase 1) and are consistent with an evolution dominated by fractional crystallization producing the slight variability of Phase 1 (Figure  10 , Supplementary Table  7 ).
Acknowledgements MAYOBS 1 campaign was funded by the  CNRS-INSU TELLUS MAYOTTE program  ( SISMAYOTTE  project). MAYOBS 1, 2, 4, and 15 campaigns were conducted by several French research institutions and laboratories ( IPGP/CNRS/BRGM/IFREMER/IPGS ). All marine operations are performed as part of the MAYOBS set of campaigns (https://doi. org/10. 18142/291 )and we thank the captains and crews of the R/V  Marion Dufresne  ( TAAF/IFREMER/LDA ),  R/V Pourquoi  Pas? (GENAVIR/IFREMER, SHOM),  R/V Beautemps-Beaupré  (SHOM), and  R/V Gauss (FUGRO). The GEOFLAMME oceanographic campaign (CNFH; PI C. Cathalot,  E. Rinnert ,  N. Feuillet ) also funded marine operations that benefited to this study and provided dredge samples. Analyses were funded by the  Service National d'Observation en Volcanologie (SNOV, INSU)  and the  Réseau de Surveillance Volcanologique et Sismologique de Mayotte (REVOSIMA) , a partnership between the  Institut de Physique du Globe de Paris (IPGP) , the  Bureau de Recherches Géologiques et Minières (BRGM) , the  Observatoire Volcanologique du Piton de la Fournaise (OVPF-IPGP) , the  Centre National de la Recherche Scientifique (CNRS) , and the  Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER).
This study contributes to  IdEx Université de Paris   ANR-18-IDEX-0001. Trace element analyses were supported by  IPGP  multidisciplinary program PARI and by  Paris-IdF region SESAME  Grant no. 12015908. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 731070. This is Labex ClerVolc contribution number  567.
Data In the absence of any large coverage survey on rendaku in Japanese dialects, we chose to investigate the spatial variation of rendaku frequency in place names across Japan. We used the database assembled by  Takemura et al. (2019) , which derives from the comprehensive database of postal codes and the corresponding addresses provided by the Japan Post (2014). It contains all place names including any of the six lexemes listed in Table  1  in noninitial position, to the exclusion of place names from Okinawa prefecture and the Amami Islands of Kagoshima, since these areas are traditionally Ryukyuan-speaking areas with peculiar place names  (Shimoji & Pellard, 2010). Each entry consists of the postal code of a location, the corresponding place name in Japanese script, its pronunciation in kana, the prefecture of the location, and an added annotation by  Takemura et al.
(2019)  as to whether the place name exhibits rendaku or not. The database contains a total of 7,725 different place names (Table  1 ). Since the original database does not provide the geographical coordinates of the locations, the CSV address matching service of the Center for Spatial Information Science of the University of Tokyo 4 was used to retrieve them. The geocoding process was successful for 7,470 entries, that is, 96. 7% of the 7,725 entries, and the 255 (3. 3%) remaining ones were discarded. The lexemes chosen by  Takemura et al. (2019)  are commonly used in place names across Japan, and all of them undergo rendaku in at least some compounds. However, inspecting Table  1  reveals disparities in the rendaku frequency of the different lexemes (χ 2 (5, N = 7725) = 191. 73, p <. 001, V =.
Map 1 indicates the location of the place names in the final version of our database. The spatial distribution of presence versus absence of rendaku is difficult to interpret by simply mapping the data, partly due to the large number of data points, even if we examine separately place names with and without rendaku and use hexagonal bins (Map 2) and even if we further map separately the different lexemes (Map 3). 5 A statistical analysis is thus needed in order to assess the existence of clusters.
Notes 1 One of the first applications of spatial statistics to linguistic data is  Lee and Kretzschmar (1993). See  Grieve (2018)  for an overview. 2 The consonant h alternates with b due to a sound change *p > h in the history of Japanese. 3 See  Vance (2015)  for an introduction to the topic, and  Vance and Irwin (2016)  for a thorough review of the issues and the relevant literature. 4 https://geocode. csis. u-tokyo. ac. jp/. 5 Additional geospatial data for mapping was obtained from the GADM database  (v. 4. 1, Global Administrative Areas, 2018). 6 Formally, with x i and x j the value of the variable x of presence (x = 1) vs. absence (x = 0) of rendaku at respectively locations i and j (i ≠ j), J BB ¼ 1 2 P P w ij x i x j , J WW ¼ 1 2 P P w ij 1 À x i ð Þ 1 À x j À Á , and J BW ¼ 1 2 P P w ij x i À x j À Á 2.
, 2021). Here, we aimed to provide insights into the France 2022 diphtheria emergence by reporting on its epidemiology and by placing the involved isolates in the global genomic context of C. diphtheriae populations. We introduce DIPHTOSCAN, a genotyping tool designed for rapid and standardized genomic analyses of Corynebacteria of the C. diphtheriae species complex (CdSC), and illustrate its use by analyzing the 101 C. diphtheriae isolates (including 56 tox-negative ones) collected in 2022 in France (henceforth, the France-2022 dataset). We provide context of this emergence by analyzing 1249 other C. diphtheriae genomes of diverse geographic and temporal origins, including 379 newly sequenced isolates collected by the French national surveillance laboratory, mostly between 2018 and 2021. We uncovered novel insights into the global population structure of C.
Clinical isolates inclusion and global genomic sequence dataset To investigate the epidemiology of diphtheria in France, we included all cases of C. diphtheriae infections detected by the French surveillance in 2022. Among 144 isolates received by the National Reference Center, there were 101 deduplicated isolates when retaining only one from each patient. These were isolated in metropolitan France as well as in Mayotte, La Reunion and French Guiana (France-2022 dataset, Table  S1 ). Note that metropolitan France comprises mainland France and Corsica, as well as nearby islands in the Atlantic Ocean, the English Channel (French: la Manche), and the Mediterranean Sea. All isolates collected in 2022 from metropolitan France were from mainland France. Overseas France is the collective name for all the French territories outside Europe.
In addition, a total of 1,249 comparative genomes were included (Table  S1 ). First, we sequenced for the present study 379 additional isolates, including 320 collected prospectively between 2008 and 2021 by the French National Reference Center (NRC), 34 historical clinical isolates mostly from metropolitan France and 19 isolates from Algeria  (Benamrouche et al. , 2016). These new genomes were sequenced to complement the 226 previous genomes from C. diphtheriae from the French diphtheria surveillance system  (Hennart et al. , 2020; Guglielmini et al. , 2021) , including 43 isolates from Yemen  (Badell et al. , 2021). Together, these represent 599 produced by the NRC for Corynebacteria of the diphtheriae complex (non-2022 French NRC dataset, Table  S1 ). Nearly four-fifths (531; 88. 6%) of these isolates were prospectively collected between 2008 and 2021 from French metropolitan and overseas territories, 54 isolates (9.
0%) were collected between 1990 and 2007 from France and Algeria and 14 (2. 3%) isolates collected between 1951 and 1987 from metropolitan France. Second, we included publicly-available genomes from NCBI, mostly previously published and isolated in South Africa  (du Plessis et al. , 2017 ), Germany-Switzerland (Meinel et al. , 2016) , Germany  (Dangel et al. , 2018; Berger et al. , 2019) , Canada  (Chorlton et al. , 2019) , Austria  (Schaeffer et al. , 2020) , the USA  (Williams et al. , 2020; Xiaoli et al. , 2020) , Spain  (Hoefer et al. , 2020) , India  (Will et al. , 2021)  and Australia  (Timms et al. , 2018). Altogether, this represents a dataset of 579 genomes (non-French public dataset, Table  S1 ). Further, we sequenced 6 ribotype reference strains  (Grimont et al. , 2004). Together with 65 previously sequenced  (Hennart et al.
, 2020) , this represents a dataset of 71 genomes of ribotype reference strains (Table  S1 ). From the global set of 1,249 genomes (non-2022 French NRC + non-French public dataset + ribotype datasets), we created a non-redundant subset of genomes by randomly selecting one genome per genomic cluster (threshold: 25 cgMLST mismatches; see below), isolation year and city (if city was unavailable, the country was used instead); this deduplicated subset comprised 976 genomes (hereafter, the global dataset).
Merging of the Oxford and Pasteur MLST databases Two C. diphtheriae databases using the BIGSdb framework were originally designed separately for distinct purposes: while Oxford's PubMLST database mainly offered 7-gene MLST  (Bolt et al. , 2010) , the Pasteur database was used for the Corynebacterium cgMLST typing  (Guglielmini et al. , 2021). To facilitate the use of these resources and avoid redundancy in the curation of the two independent genomic libraries, a merging of the databases was decided in agreement with PubMLST administrators. In order to merge the data available in the two databases, we proceeded as per BIGSdb dual design: isolates genomes and provenance data were imported into the "isolates" database, whereas allelic definitions of MLST were imported into the "seqdef" database.
Regarding the isolates database, we first downloaded Oxford's PubMLST C. diphtheriae database. To avoid isolate entries duplication, we identified common isolates between the two databases, and filtered duplicate isolates before import into the Pasteur database. In total, 684 out of 934 (73. 2%) isolates from the Oxford database were imported. To facilitate the tracing of isolates and their possible previous existence in Oxford's database, isolates identification numbers (BIGSdb-Pasteur ID number) of isolates from the Oxford database were numbered from 1,520 to 2,003. We also collated them into a public project collection called "Oxford" (project ID 13). Regarding the sequence and profiles definition database, we imported MLST alleles and profiles into an initially void MLST scheme container within the BIGSdb-Pasteur database.
MLST analysis was performed on all isolates of the BIGSdb-Pasteur database, including the ones imported from Oxford, which were therefore assigned the same MLST genotype as previously in the Oxford database. At the end of the merging process, all isolates and MLST data from PubMLST's C. diphtheriae database were available into the BIGSdb-Pasteur C. diphtheriae species complex database (https://bigsdb. pasteur. fr/diphtheria/), and Oxford's PubMLST C. diphtheriae database was shut down. As of September 22 nd , 2022, the database resulting from the merged datasets comprised 1,478 public isolates records with 794 associated genomes, and 2,392 isolates in total when considering private entries. The number of entries varied across species: C. diphtheriae (n = 1,291; 87. 4%) and C. ulcerans (n = 131; 8.
cgMLST and nomenclature of sublineages The MLST and cgMLST genotypes (cgST) were defined using the Institut Pasteur C. diphtheriae species complex database at https://bigsdb. pasteur. fr/diphtheria. A core genome MLST (cgMLST) scheme comprising 1,305 loci  (Guglielmini et al. , 2021)  was employed to define the alleles and cgST of the 1,249 genomic sequences using BIGSdb (https://bigsdb. pasteur. fr/diphtheria). Using the 1,249-genomes dataset, the mean number of missing alleles per profile was 12 (0. 9%) and almost all (n=1,242; 99. 4%) genomes had a cgMLST profile with fewer than 65 (5%) missing alleles. A cgST number was defined for all but one cgMLST profiles (one genome had 219 missing alleles, whereas the admissible threshold is 10%, i. e. , 130 missing alleles). Genomes were classified using the single-linkage cluster-profile.
Development of the DIPHTOSCAN pipeline To develop DIPHTOSCAN, we combined code from Kleborate v2. 2. 0  (Lam et al. , 2021) , AMRfinderPlus  (Feldgarden et al. , 2021)  and BIGSdb  (Jolley & Maiden, 2010)  with some modifications. The structures of DIPHTOSCAN and its custom database are presented in Figure  S3  and Figure  S4. A custom code was created for DIPHTOSCAN initiation, interpretation and for displaying results. The C. diphtheriae specific genes (genomic markers, AMR determinants and virulence factors) for which the genomes are screened by DIPHTOSCAN (Figure  S4 ) are provided in a custom database similar in its structure to the AMRFinderPlus database (https://ftp. ncbi. nlm. nih. gov/pathogen/Antimicrobial_resistance/); this database can be further enriched with novel features in the future.
When launching DIPHTOSCAN, the AMRFinderPlus and custom databases are merged. We used the functions of species determination, MLST genotyping, and full CDS prediction from Kleborate. All the functionalities are presented in Figure  S2. To facilitate readability and downstream analyses, the output of DIPHTOSCAN is generated in a tab-delimited format. The execution time of DIPHTOSCAN increases linearly with the number of input genomes. Roughly, 40 seconds are needed to scan a single genome with 1 cpu. DIPHTOSCAN computations can be parallelized, as AMRFinderPlus and JolyTree use parallelization.
, 2018)  using the global dataset; 100% concordance was achieved. MLST profiles and sequence types (ST) were defined using the international MLST scheme for C. diphtheriae and C. ulcerans. DIPHTOSCAN defines these genotypes for genomic sequences using the analogous script from Kleborate. In order to use an up-to-date version of the MLST nomenclature, which is regularly updated, the MLST profiles and alleles are downloaded at the start of the pipeline before genotyping the genomes. The download_alleles. py script from BIGSdb is used for this purpose (https://github. com/kjolley/BIGSdb/tree/develop/scripts/rest_examples).
Biovar-associated markers detection The three main biovars of C. diphtheriae can be distinguished based on isolate abilities to reduce nitrate and to metabolize glycogen. Previously, a strong concordance was found between the biovar and the presence in the genome of several genomic markers including spuA, which codes for a putative alpha-1,6glycosidase, and the narKGHJI operon for nitrate reductase  (Sangal et al. , 2014; Santos et al. , 2018; Hennart et al. , 2020). We therefore included in the custom DIPHTOSCAN query database the spuA marker and its adjacent genes (DIP0351; DIP0353; DIP0354; DIP0357=spuA), which are strongly associated with biovar Gravis, and the narIJHGK cluster, which is typically absent or partly disrupted, mainly due to mutations in the narG  (Hennart et al. , 2020)  or narI  (Sangal et al.
Detection of antibiotic resistance genes Antibiotic resistant genes were identified using AMRfinderPlus, with the database found at: https://ftp. ncbi. nlm. nih. gov/pathogen/Antimicrobial_resistance/. Features are detected by using the BLAST family of tools, with identity and coverage defined for each family of antibiotics (fam. tab). A few genes particularly relevant for the CdSC were added to this database: pbp2m  (Forde et al. , 2020; Hennart et al. , 2020)  and mutation points of rpoB (WP_004566675. 1) and gyrA (WP_010933942. 1). AMRfinderPlus v3. 11. 2 is used within DIPHTOSCAN with no modifications.
Detection of virulence genes from the C. diphtheriae species complex A custom database of virulence features of C. diphtheriae and related species was compiled from literature for the purposes of this work. We included in the custom query database, a panel of genetic features for which published experimental evidence of their clinical relevance exists in C. diphtheriae or closely related species (i. e. , increased virulence in animal models, or decreased antimicrobial susceptibility in vitro) (Table  S2 ). These target genes are the following: tox, SpaA-, SpaD-, and SpaH-type pili gene clusters, DIP0733 (67-72p), the genes DIP1281 and DIP1621 that code for proteins of the NlpC/P60 family, DIP0543 (nanH),  DIP1546 and DIP2093 (Ott, 2018)  and pld (phospholipase). A second panel of genetic features with no experimental evidence but with strong suspicion for a role in virulence, based on homology with genes from other pathogens, was also included for broader screening of virulence features (Table  S2 ).
For DtxR sequence variation, dtxR was detected using BLASTn with DIPHTOSCAN, and the translation into amino acid, alignment and visualization of mutations were performed using seaView. Virulence genes were identified using the method of AMRfinderPlus but based on our custom database of virulence features. The virulence genes are detected by BLASTn with thresholds of minimum 80% identity and 50% coverage. Based on the output of AMRfinderPlus, the gene completion and allele similarity is reported as described above for the tox gene following the Kleborate convention.
, 2010)  is performed. For C. diphtheriae, additional genotype categorizations can be performed using the BIGSdb-Pasteur database tool: cgST, genomic cluster and sublineage assignment  (Guglielmini et al. , 2021). Next, the detection of antimicrobial resistance determinants (mutations in core genes and horizontally acquired genes) and virulence factors is performed. DIPHTOSCAN also includes a prediction of the functionality or disruption of the tox gene, the most important virulence factor of CdSC isolates. DIPHTOSCAN next searches for genomic markers associated with biovars Gravis, Mitis and Belfanti, a biochemical-based classification that was initiated in the 1930s  (Anderson et al. , 1931; McLeod, 1943)  and which is still in use for C. diphtheriae strain characterization. IntegronFinder2  (Néron et al.
Genetic diversity of C. diphtheriae isolates from France, 2022 The C. diphtheriae isolates belonging to the France-2022 dataset were sequenced and their genomic sequences were analyzed using DIPHTOSCAN. Sublineage classification of the isolates showed that the France-2022 dataset comprised 41 distinct sublineages (defined using the 500 cgMLST mismatch threshold). The nomenclature of these sublineages was established using an inheritance rule that captures their majority MLST denomination, where possible  (Guglielmini et al. , 2021; Hennart et al. , 2022) , resulting in a strong concordance of sublineage denominations with the classical MLST identifiers (Figure  1 ). There were 51 different STs, as 9 sublineages comprised two or more closely related STs; in 7 of 9 cases, they only differed by a single locus.
The global phylogenetic framework of C. diphtheriae We investigated the global diversity of C. diphtheriae to provide context to the France-2022 emerging genotypes. A dataset of 1,249 comparative C. diphtheriae genomes were sequenced or gathered from previous studies (see Methods). cgMLST grouped these isolates into 245 sublineages. The 7-gene MLST analysis revealed 364 distinct STs. Almost all (360; 98. 6%) STs corresponded one-to-one with the sublineage level, i. e. , all isolates of these STs belonged to the same sublineage. However, 72 sublineages (29. 4%) comprised at least two STs. Of the 123 novel sublineages uncovered here, 114 sublineages were given an identifier inherited from the 7-gene MLST nomenclature (whereas 9 were attributed an arbitrary number, see Methods). There were 576 genomic clusters, many of which comprised previously documented epidemiological clusters of related isolates.
For example, GC456 comprised 43 isolates from a Vancouver inner city outbreak  (Chorlton et al. , 2019). Whereas 47 GCs had between 5 and 27 isolates (Table  S1 ; Figure  S5A ), the 529 remaining ones had only 1 and 4 isolates. 106 (43. 3%) of the 245 sublineages comprised at least two genomic clusters. To eliminate the population bias introduced by multiple sampling of outbreak strains, we created a non-redundant subset by randomly selecting one genome per genomic cluster, isolation year and city (if city was unavailable, the country was used instead) and with the same resistance genes profile and tox status (see column 'Dataset' in Table  S1 ). These 976 deduplicated genomes (hereafter, the global dataset) define the background population of C. diphtheriae. Within the global dataset, 35 sublineages were represented 7 times of more (Figure  2 ).
The two predominant sublineages were SL8 (n=61) and SL5 (n=48); their main 7-gene MLST sequence types were ST8 and ST5, previously noted to be predominant in the ex-USSR 1990s outbreak. The most represented tox-positive sublineages in the global dataset were SL8, SL453, SL486, SL377 and SL91, and SL50 was a predominant NTTB sublineage (Figure  2 ). Of the 10 sublineages with tox-positive isolates observed in France-2022, 7 were found in the global dataset; of which 5 were among the 35 frequent global sublineages. Besides, 9 tox-negative sublineages from France-2022 were also frequent in the global dataset (Figure  2 ). Of the common France-2022 sublineages, SL377, SL384 and SL297 were also common in the global dataset (Figure  2 ), and their toxigenicity and resistance features matched those observed in the global dataset.
In contrast, SL698 (metropolitan France) and SL824 (Indian Ocean) were uniquely common in the France-2022 dataset (Figure  S5B ). The phylogenetic structure of C. diphtheriae revealed a star-like phylogeny with multiple deeplybranching sublineages as previously reported  (Berger et al. , 2019; Seth-Smith & Egli, 2019; Hennart et al. , 2020; Guglielmini et al. , 2021)  (Figure  3 ). Sublineages appeared to be grouped according to biovars Gravis (and its spuA marker gene) and Mitis as previously noted  (Hennart et al. , 2020) , as they formed two main lineages named Gravis (green branches) and Mitis (purple), defined by the presence of the spuA gene (Table  S1 ). cgMLST-defined sublineages were highly concordant with the phylogeny and often comprised more than one 7-gene ST (Figure  3 ; Table  S1 ).