dataesr/scientific-paragraphs-categorization huggingface dataset:
These rickettsial diseases have shown a marked, continuous increase in incidence since 1970  [13] ; the increase in incidence of scrub typhus has been reported to have started more recently  [14]. Parola et al. [7]  summarized current knowledge on tick-borne rickettsioses, caused by obligate intracellular bacteria belonging to the spotted fever group of the genus Rickettsia, following a geographic approach and incorporating background information on history, epidemiology and diagnostics given from a previous review  [15]. The methodology was to extract information in publication databases (PubMed) with search terms of word combinations such as "ticks", "rickettsia", "rickettsioses", "spotted fever" and "typhus", a common methodology used in review articles. Here, we presented a different approach to assess the disease ecology of rickettsial species by investigating open databases and by using data science methodologies.
First, we explored the dynamics of worldwide epidemics of rickettsial species, using the GIDEON database (Global Infectious Disease and Epidemiology Online Network, www. gideononline. com), which has been used in several recent comparative studies  [16, 17]. Our main aims using this database were to explore the epidemiological trend and changes of rickettsial disease epidemics over the years and to compare this trend with knowledge on emerging rickettsial diseases given by the review of Jones et al. [18]  and more recently by Swei et al. [19]. Second, we investigated the diversity of rickettsial species using a second database, the Enhanced Infectious Disease Database (EID2)  [20]  (https://eid2. liverpool. ac. uk). The purpose was to use a network analysis approach, which has already been shown to be of interest in representing and quantifying transmission ecology of pathogens among different individuals or different host species  [21].
Data Acquisition To explore the epidemics of rickettsial species, we used the GIDEON database. The list of outbreaks of microbial and parasitic diseases per country was extracted from this database, which contains information on the presence and occurrence of epidemics for each country. This dataset has been regularly used in previous comparative studies of pathogen diversity and epidemics  [16, 17]. We used the package 'segmented'  [24]  implemented in the freeware R  [25]  for detecting a breakpoint in the trend of outbreaks of human rickettsial diseases from 1920 to 2016. To explore the emerging rickettsial species, we used the datasets of emerging infectious disease events compiled by Jones et al. [18]  between 1940 and 2004 and Swei et al. [19]  between 1920 and 2016. To explore the diversity of rickettsial species, we used the EID2.
We used bipartite networks where nodes describing hosts interact with nodes describing pathogens. We projected these bipartite networks onto unipartite networks using the 'tnet' package  [27]  implemented in R. A unipartite network represents patterns of relative interactions amongst carriers through the sharing of rickettsial species. Each host within a network plays a different role in rickettsial species sharing relative to all other nodes in the network. The role of each host within the network was examined using its centrality measurement. A central node (a carrier) is the one that is highly connected to other nodes (carriers) and thus is supposed to have a greater transmission potential for rickettsial species. A carrier with high centrality means that this carrier is highly connected to other carriers and thus is likely to have a greater potential of rickettsial transmission to several other carriers.
We calculated the eigenvalue centrality (EC) with the 'evcent' function from the igraph package  [28]  in R. We built two bipartite networks (presence/absence of a link) linking carriers: (1) mammal species, including humans, with all rickettsial species and (2) arthropod vectors with all rickettsial species. We then transformed these bipartite networks where separate nodes from hosts or vectors were connected with nodes of rickettsial species to unipartite networks using the tnet package in R  [25]. We used the function 'cluster_louvain' implemented in the package igraph  [28]  to identify the modularity structure of the unipartite networks. This function is based on a multilevel modularity optimization algorithm  [29]. We also extracted the path lengths among hosts (nodes) for each network.
Outbreaks of Human Rickettsial Diseases The GIDEON database recorded the information on epidemics for the following rickettsial diseases: anaplasmosis, African tick bite fever, human monocytic ehrlichiosis, Japanese spotted fever, rickettsialpox, New World spotted fever, Old World spotted fevers, endemic typhus, epidemic typhus and scrub typhus. The number of worldwide outbreaks recorded by year showed an increasing trend from 1920 to 2016 (Figure  1 ). The start of this change was dated in 1971, with less than five outbreaks per year in average from 1920 to 1970 and with outbreaks reaching around 10 per year around 2015. Trends in outbreaks of rickettsial diseases per year from 1920 to 2016, with significant increase in the number of outbreaks since 1971 (using the package 'segmented'  [24] ).
Emerging Rickettsial Diseases The lists of emerging infectious diseases gathered by Jones et al. [18]  and Swei et al. [19]  indicated several rickettsial agents that have emerged from 1920 to 2016 (Table  1 ). Some likely causes of emergence were demography, war and famines. Causes of greater importance include global travels and trade and land use changes, with few mentions of the effects of human susceptibility to infection or climate change (Table  1 ). Table  1. Emerging rickettsioses (from the supplementary materials of Jones et al. [18]  and Swei et al. [19] ) from 1920 to 2016 (with the exception of novel rickettsial characterization). From the EID2 database, we obtained records describing rickettsial species screened from the following carriers: arthropods (Figure  2A ), domestic animals, wild mammals and humans (Figure  2B ).
The geographical distribution of rickettsial species according to data extracted from EID2 showed a highly biased pattern, with high species richness in the US, Europe, China and Japan and low species richness in the tropical regions with the exception of Thailand (Figure  3 ). The observed pattern of rickettsial species richness is likely explained by the bias in screening effort, which is reflected in both the number of publications and the number of DNA sequences deposited in the NCBI nucleotide database (Figure  3 ), with the greatest numbers again recorded for US, Europe, China and Japan (along with the notable exception of Thailand). The low screening effort of rickettsial species in South America, Africa and Southeast Asia may explain the low number of carrier species from these regions in the EID2 database (Figure  2 ).
The geographical distribution of rickettsial species according to data extracted from EID2 showed a highly biased pattern, with high species richness in the US, Europe, China and Japan and low species richness in the tropical regions with the exception of Thailand (Figure  3 ). The observed pattern of rickettsial species richness is likely explained by the bias in screening effort, which is reflected in both the number of publications and the number of DNA sequences deposited in the NCBI nucleotide database (Figure  3 ), with the greatest numbers again recorded for US, Europe, China and Japan (along with the notable exception of Thailand). Using the presence information linking each carrier species with their rickettsial species, we obtained bipartite networks and unipartite projections in which each node was a carrier species, either arthropod or mammal species.
List of arthropod species is given in Figure  2. Data were extracted from the EID2 database  [20]. Composite panels of modules identified using (A) bipartite network and (B) subgroups identified by unipartite network subgroups (differentiated by colors) of shared rickettsial species among mammal species and humans. The links among nodes (node = species) of the unipartite network depict shared rickettsial species/mammals and humans (vertices were placed according to the Fruchterman-Reingold algorithm) with thickness of links proportional to number of rickettsial species shared and size of vertices proportional to the degree centrality of carriers (nodes). List of mammal species is given in Figure  2. Data were extracted from the EID2 database  [20]. In the case of arthropod carriers, the bipartite network identified eight modules (Figure  4A ), such as the one grouping the carriers Rhipicephalus turanicus, R.
New Approaches for the Study of Rickettsial Diseases The epidemiology of rickettsial zoonoses is mostly investigated by disease or by group of rickettsial species supposed to share similar epidemiological features. Published reviews mostly listed rickettsial species with their vectors, reservoirs and geographical distribution but could hardly capture a whole epidemiological ecology. We showed here that the application of data science, as a first approach used in disease ecology, may give useful tools to describe the global pattern of the epidemiology of rickettsial species. First, we confirmed previous studies that emphasized the increasing incidence in rickettsial diseases at the onset of 1970  [13]. Using the GIDEON database and the records on outbreaks of rickettsial diseases, it was even possible to date the start of this increase of global outbreaks from 1971 (Figure  1 ).
Second, network analysis of association between rickettsial species and their carriers (vectors or reservoirs) extracted from the EID2 database appears to be a useful statistical tool. Investigation of the composition of modules bipartite networks or subgroups of unipartite networks of shared rickettsial species not only highlighted the importance of several carriers (vectors or reservoirs) but also exhibited their connections in the whole network of shared rickettsial species. Some arthropod species were identified in most of the bipartite modules and unipartite subgroups and noted for their centrality values in the unipartite network. These species were R. turanicus, R. sanguineus, A. hebraeum, A. americanum, H. longicornis, H. concinna I. persulcatus, I. ricinus and C. felis. These arthropod species harbor a significant number of shared rickettsial species and play a key role not only as vectors but also as bridges exposing various domestic animals and humans to various rickettsial diseases.
Vector and Reservoirs Carriers The review of Swei et al. [19]  synthesized the existing literature of emerging vector-borne zoonotic diseases and showed that a great number of 131 emerging vector-borne diseases from the years 1940 to 2018 were rickettsial diseases. Ixodidae ticks (Ixodes, Dermacentor, Amblyomma spp. ) were recorded to transmit 37 (40%) emerging vector-borne diseases, which are mainly caused by Rickettsiaceae bacteria. The authors also found that the highest number of vector-borne diseases emerged in North America (27%) followed by Europe (21%) and Asia (20%). A similar geographical pattern was found using the EID2 database (Figure  2 ). Interestingly, the authors recorded the most commonly cited drivers for emergence in each reviewed reference and found that land use change was the first invoked factor for 26%, followed by international trade and commerce for 11%, while climate and weather-related factors accounted for 10%.
Climate Change Although the reviews of Jones et al. [18]  and Swei et al. [19]  questioned the importance of climate factors and climate changes in the epidemiology of rickettsial diseases,  Parola et al. [38]  provided evidence of a warming-mediated increase in the aggressiveness of the tick R. sanguineus, leading to an increase of human attacks, associated with clusters of cases of spotted fever caused by R. conorii and R. massiliae in France and Italy in 2007. Using the EID2 database, McIntyre et al. [39] investigated the climate sensitivity of important human and domestic animal pathogens in Europe. The pathogens were selected using a prioritization method based on the H-index of the diseases  [40]. Unfortunately, among the 3628 pathogen species, only 157 were selected, with only one rickettsial species, Anaplasma phagocytophilum.
Current Limitations A first limitation is that many of the Rickettsiales listed in EID2 database have no experimental or epidemiological or clinical/veterinary support for their pathogenicity. A second limitation is that our approach necessitates continuously updating databases (EID2, GIDEON, etc. ), taking into account changes in taxonomy (valid species, synonymy, etc. ); such databases are not updated in real time. For example, and only for Southeast Asia, Low et al. [5]  summarized the newly discovered regional rickettsial species, including Rickettsia thailandii, Candidatus R. sepangensis, Candidatus R. johorensis, Candidatus R. laoensis, C. Rickettsia mahosotii, C. Rickettsia khammouanensis and C. Anaplasma pangolinii. The last limitation is the lack of associated ecological data that would help to contextualize the patterns depicted by the network analyses.
Funding: This research was funded by  French ANR  grant  numberANR-17-CE35-0003-01. The APC was waived. Acknowledgments: This study is part of the  French ANR  project  FutureHealthSEA  ( ANR-17-CE35-0003-01 ). S. M. is supported by the  Thailand International Cooperation Agency (TICA). We thank the organizers of the APC2. We thank two anonymous reviewers for their helpful comments.
The spectra generated were analysed by Biotyper 3. 0 software, which is regularly incremented with the local URMS database (https://www. mediterranee-infection. com/urms-data-base). Misidentification with MALDI-TOF MS led to amplification of the 16S rRNA gene using the primer pair fD1 and rP2 (Eurogentec, Angers, France) and then sequencing using the Big Dye® Terminator v1. 1 Cycle Sequencing Kit and 3500xLGenetic Analyzer capillary sequencer (Thermofisher, Saint-Aubin, France), as previously reported  [20]. All 16S rRNA nucleotide sequences were assembled and edited using CodonCode Aligner software (http://www. codoncode. com). Once a consensus sequence is obtained, it is submitted to the NCBI nucleotide database (https://www. ncbi. nlm. nih. gov/ nucleotide/) and a comparative analysis of nucleotides by BLASTn (https://blast.
Genome characteristics Genomic DNA extraction was performed with the EZ1 biorobot using the EZ1 DNA tissue kit (Qiagen, Hilden, Germany), and sequencing was performed on the MiSeq instrument (Illumina Inc. , San Diego, CA, USA) using the Nextera Mate Pair and Nextera XT Paired End (Illumina) sample preparation kit, as previously described  [20]. The genomic assembly was carried out using the three following softwares: Velvet  [23] , Spades  [24]  and Soap Denovo  [25]. MiSeq and Trimmomatic  [26]  softwares were used for trimmed or untrimmed sequences. To reduce assembly gaps, GapCloser software  [27]  was used. Best assembly was determined using different criteria, such as the number of scaffolds, N50 or number of N. Scaffolds were deleted when their nucleotide number was <800 bp and their depth value < 25% of the mean depths.
Genome annotation of these two species was performed as described elsewhere  [28]. In addition, the Genome-to-Genome Distance Calculator web server available online (http://ggdc. dsmz. de) made it possible to assess the similarity between the genomes being compared and to replace the DNA-DNA hybridization (DDH) with a digital DDH (dDDH)  [29]. Average nucleotide identity analysis was also evaluated using the OAT software  [30].
Results Strain identification and phylogenetic analysis Attempts to identify the strains cultivated on blood agar by mass spectrometry failed, indicating that these isolates were not known from the MALDI-TOF database. Therefore, their generated spectra were added to the local database. 16S rDNA-based similarity analysis of strain Marseille-P4857 and strain Marseille-P4512 against GenBank exhibited highest nucleotide sequence similarities of 95. 12% with Megasphaera micronuciformis strain AIP 412. 00 (accession number NR_025230. 1) and 96. 78% with Anaerococcus tetradius strain CCUG 46590 (accession number NR_041941. 1), being respectively the two phylogenetically closest species. As these similarity values were below the 98. 65% threshold recommended for the delimitation of new bacterial species  [29, 31] , strain Marseille-P4857 and strain Marseille-P4512 were considered potentially new species within the phylum Firmicutes.
Anaerococcus vaginimassiliensis sp. nov. , is a strict anaerobic bacterium that grows preferentially at 37°C. It has catalase activity, but not oxidase. Colonies are white with regular boundaries and have a mean diameter of 2 mm. The A. vaginimassiliensis is able to ferment glycerol, xylose, D-galac- tose, D-glucose, D-fructose, methyl αD-glucopyranoside, Nacetyl-glucosamine, amygdalin, arbutin, esculin ferric citrate, salicin, trehalose, cellobiose, maltose, lactose, xylitol, gentiobiose and potassium 5-ketogluconate. Alkaline phosphatase, leucine arylamidase and acid phosphatase are positive. The major fatty acids were C 16:0 (42%), C 18:1n9 (25%) and C 18:2n6 (19%). The genome size of A. vaginimassiliensis strain Marseille-P4512 is 1. 83 Mbp with 33. 1 mol% G + C content. The 16S rRNA and draft genome sequences of strain Marseille-P4512, are available in GenBank database under accession numbers LT934505 and UZAS00000000, respectively.
MBD4 Targeted Sequencing Germline DNA of 1099 UM patients from the UM consecutive series (before removal of the 6 a forementioned patients) and DNA of a series of 192 M3 UM tumors were screened for MBD4 variants by pooled MBD4 targeted sequencing. Details on the sequencing strategy and bioinformatics pipeline are described in the Supplementary Methods (available online). Deconvolution of the identified pooled DNA samples with an MBD4 variant was carried out by Sanger sequencing. Identified MBD4 variants (Supplementary Table  1 , available online) were defined following the recommendations of the Human Genome Variation Society (http://varnomen. hgvs. org/) and numbered based on the MBD4 (MIM: 603574) cDNA and protein sequences (GenBank accession numbers NM_003925. 2 and NP_003916. 1, respectively).
Incidence of Germline MBD4 Deleterious Mutations in UM Taken together, we thus identified 8 LoF germline variants in MBD4 among the 1093 consecutive UM case patients, including p. Arg468Trp with deleterious effect on MBD4 glycosylase activity (Table  1 ). These account for a statistically significant 9. 15fold increase in deleterious variant frequency compared with the general population, even when restricting ourselves to truncating and splicing MBD4 LoFs as defined by GnomAD (7 LoFs of 2186 observed alleles in UM, representing a variant allele frequency [VAF] of 0. 0032 vs 88 LoFs out of a median of 251 450 alleles in the GnomAD v2. 1 general population; VAF ¼ 3. 50 Â 10 -4 ; Fisher exact test P ¼ 2. 00 Â 10 -5 ). To circumvent the imbalanced dataset, a "matching" subsampling approach was used, giving a similar P value (1.
Another limitation to the study is the bias for a European population in our cohort, which reflects the higher incidence of the disease in this population  (30). Interestingly, 5 recurrent MBD4 germline deleterious mutations were identified when taking together the LoF variants from our UM cohort and those found in public databases and reports of other cancer types: c. 1 ), suggesting founder mutations. Furthermore, the observation of different tumor types associated with the same MBD4 germline mutation suggests a more global role of MBD4 in cancer predisposition. The peculiar UM proneness in MBD4-mutant carriers (13 out of 23 carriers; Table  1 ) remains unexplained, but the fact that the frequent M3 in UM inactivates wild-type copies of both BAP1 and MBD4 suppressor genes may at least in part explain the frequent inactivation of MBD4 in UM.
In summary, we described here a novel autosomaldominant syndrome that is caused by germline mutations of MBD4, characterized by a high RR of developing hypermutated UM and possibly other malignancies. Tumors arising in such a context are associated with a CpG>TpG mutator phenotype and have clinical relevance because they may respond to immunecheckpoint inhibitors. Cancer (SiRIC) de l'Institut Curie. The Institut Curie ICGex NGS platform is funded by the EQUIPEX "investissements d'avenir" program (ANR-10-EQPX-03) and ANR10-INBS-09-08 from the Agence Nationale de le Recherche.
MBD4 germline deleterious variants in UM and in other malignancies a ¼ acute myeloid leukemia; GBM ¼ glioblastoma; M3 ¼ monosomy 3; NA ¼ not available; ND ¼ not determined; NFE ¼ non-Finnish European; UM or UVM ¼ uveal melanoma; -¼ no value given because of the absence of the variant in dbSNP and/or in the GnomAD NFE population. b NFE population of the Genome Aggregation Database (GnomAD v2. 1. 1). c Variant found in more than 1 nonrelated patient. d Inactive: absence of glycosylase activity of the recombinant protein carrying the variant.
confidence interval; LoF ¼ loss-of-function (deleterious) variants; NFE ¼ non-Finnish European; RR ¼ relative risk; UM ¼ uveal melanoma; -¼ no value given here because the relative risk, confidence interval, and statistic tests are presented between the UM consecutive series and each GnomAD subpopulation in the rows below. b NFE population subset of the Genome Aggregation Database (GnomAD v2. 1. 1). c For all GnomAD populations described, refers to the median number of allele count. d RR here is calculated by dividing the LoF frequency in the UM consecutive series by the LoF frequency in the corresponding GnomAD population subset. e Confidence interval of the relative risk is calculated as previously described (15).
aCI¼ confidence interval; LoF ¼ loss-of-function (deleterious) variants; NFE ¼ non-Finnish European; RR ¼ relative risk; UM ¼ uveal melanoma; -¼ no value given here because the relative risk, confidence interval, and statistic tests are presented between the UM consecutive series and each GnomAD subpopulation in the rows below. b NFE population subset of the Genome Aggregation Database (GnomAD v2. 1. 1). c For all GnomAD populations described, refers to the median number of allele count. d RR here is calculated by dividing the LoF frequency in the UM consecutive series by the LoF frequency in the corresponding GnomAD population subset. e Confidence interval of the relative risk is calculated as previously described (15).
Acknowledgements This study was supported by the  National Key R&D Program of China  (No. 2019YFA0706900 ), the  National Natural Science Foundation of China  (No. U1732161  and  91753120 ), the  Science and Technological Fund of Anhui Province for Outstanding Youth  (No. 1808085J04 ), and the  Innovative Program Development Foundation of Hefei Center Physical Science and Technology  (No. 2017FXCX002 ). The  PHC program Xu Guangqi  (No. 43357ZD ) is acknowledged for providing travel funds to J. S. Z. , C. M. and V. B. between Heifei and Saclay. This work was supported by the  French Infrastructure for Integrated Structural Biology (FRISBI ;  ANR-10-INSB-05 ). We thank  F. Beau  for housing the liquid scintillation counting ( CEA/Joliot Institute, France ). We thank the  Instrument Analysis Center of University of Science and Technology of China  for assistance on characterizations.