dataesr/scientific-paragraphs-categorization huggingface dataset:
Data were assumed to be missing at random, and were imputed for missing candidate predictor variables using mortality risk, other candidate predictor variables, and other baseline demographic variables, with 100 imputations. Evaluation of the score in the validation dataset was done using the same statistical methods as the internal evaluation, with calculation of mortality risk at 2 months, ORs for mortality, and survival curves. Discrimination was assessed using the c-statistic, and calibration with a calibration plot and the Hosmer-Lemeshow test. The study is reported in concordance with TRIPOD guidance for multivariable prediction models (see S2 Appendix)  [33]. Ethical approval for each of the source studies was obtained from the relevant ethics committees in the country of data collection and from the trial sponsors (see S3 Appendix for list of ethics committees).
68 (95% CI 0. 61-0. 74; see S3 Table ), and the Hosmer-Lemeshow statistic had p = 0. 13 (see S5 Fig for the calibration plot). In a sensitivity analysis using multiple imputation for missing data in the validation dataset (n = 644), the c-statistic for the predictive model was 0. 64 (95% CI 0. 60-0. 69), and the Hosmer-Lemeshow statistic had p = 0. 67. ORs for mortality were 5. 3 (95% CI 2. 2-9. 5) for the high-risk group and 2. 1 (95% CI 1. 0-4. 6) for the medium-risk group compared to low-risk patients (p < 0. 001).
The c-statistic for the predictive model in the derivation dataset was 0. 70 (95% CI 0. 63-0. 76), showing moderate discrimination. Calibration of the predictive model was good, as shown by the calibration plot (see S1 Fig) and a Hosmer-Lemeshow statistic p = 0. 78. The clinical risk score for mortality, based on the regression coefficients, is outlined in Fig 2. Observed and predicted mortality risks for the risk score are reported in S2 Fig. Mortality risk groups were defined as low risk (10 points or fewer), medium risk (11 to 20 points), or high risk (more than 20 points) (Fig 3).
The funding for the STAMP trial was provided by the  Joint Global Health Trials Scheme of the UK Department of Health and Social Care , the  Department for International Development , the  Global Challenges Research Fund , the Medical enrolled from October 2015 and  Research Council and Wellcome Trust  ( MR/ M007375/1 ). AG-W has received a  Royal College of Physicians London JMGP Fellowship , ELC has received a  Wellcome Trust Fellowship  ( WT200901/ Z/16/Z ). Funding for LAM-RCT was provided by the  European Developing Clinical Trials Partnership the South African Medical Research Council , and the  South African National Research Foundation  for the  TB Neat  project. The  MSF cohort study at Homa Bay County Hospital ,  Kenya  was funded by  Medecins Sans Frontières. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This method turned out to be extremely useful for routine assays in clinical laboratories as well as for molecular epidemiology, evolutionary and population genetics since it is a fast, robust and cost-effective genotyping method complementary to more traditional fingerprinting techniques. More recently, a new spoligotyping method based on microbeads was proposed for Mycobacterium tuberculosis and Salmonella enterica  [35, 36] , thus further increasing the throughput and the amount of data that can be queried in internet-accessible databases  [37, 38]. CRISPR-based molecular typing did not stay restricted to human pathogens such as Corynebacterium diptheriae, Escherichia coli, Legionella pneumophila, M. tuberculosis, Porphyromonas gingivalis, S. enterica, group A Streptococcus and Yersinia pestis  [39].
Notably, all spacer/repeat arrays were found at the ends of the contigs, suggesting that genome assembly was not complete due to the repetitive character of the sequence or due to other factors. Indeed, scrutiny of the contig ends allowed to identify a short inverted repeat, as typically found at the extremities of an IS element. When analyzing the draft genome sequence of NCPPB 3608, we found these inverted repeats 42 times, always located at the end of contigs, further supporting the hypothesis of an IS element insertion in the CRISPR locus (Additional file 3: Figure  S3 , Additional file 4: Figure  S4  and Additional file 5: Figure  S5 ). BLASTN searches identified similar inverted repeats at the extremities of annotated IS elements in the genome of Ralstonia solanacearum strain Po82 (GenBank accession number CP002820).
The IS Finder database identified this IS element as ISRso19, which belongs to the IS family IS21. Using the full-length ISRso19 element as a query, we found a single contig in the draft genome of NCPPB 3608 with 72% sequence identity, CCWG01000056. 1, encompasing most of the IS element. Based on sequence information from the X. citri pv. citri and R. solanacearum IS elements, we designed PCR primers to amplify the flanking spacer/repeat units. All five strains that resulted in PCR amplification of a large band of weak intensity (LB302, LB305, LG097, LG115 and NCPPB 3608) were evaluated for the presence of the IS element in the CRISPR locus (Additional file 6: Figure  S6 ). PCR with primer combinations Leader_fw and IS-1_rev and IS-2_ fw and Spacer#18_rev resulted in the amplification of a DNA fragment of approximately 800 bp and 750 bp, respectively, for strains LB302, LB305, LG115 and NCPPB 3608.
Most of the 25 spoligotype patterns likely evolved by the deletion of a single spacer/repeat unit although simultaneous deletion of adjacent spacer/repeat units probably occurred as well, as suggested by the absence of intermediate CRISPR structures (Fig. 2 ). Deletion of spacer/repeat units appeared to be random. In order to decipher the origin of the 37 spacers, the NCBI GenBank was queried for similar sequences using the BLASTN algorithm. As expected, spacers Xcc_23 to Xcc_01 had hits in several genome sequences of X. citri pv. citri, reflecting their high conservation in this pathovar of the species X. citri. Using stringent thresholds (E-value smaller than 0. 1 and at least 90% coverage of the query sequence), we found significant matches between eight spacers and sequences from Xanthomonas-specific bacteriophages, which were however restricted to the 14 unique spacers of strain CFBP 2911 (Table  1 ; Additional file 7: Table  S1 ).
The other six spacers among the 14 unique CFBP 2911 spacers did not have any significant hit. Among the Xanthomonas bacteriophages, we found one that had been shown to cause lytic infections of some strains of X. citri pv. citri (bacteriophage CP1, GenBank accession number AB720063)  [45]. Bacteriophage phi Xc10 (GenBank accession number MF375456) can infect X. citri pv. citri, but also Xanthomonas citri pv. glycines and Xanthomonas campestris pv. campestris. Three bacteriophages, f30-Xaj (GenBank accession number KU595433), f20-Xaj (Gen-Bank accession number KU595432) and XAJ24 (GenBank accession number KU197013), were isolated from walnut trees and have lytic activity against Xanthomonas arboricola pv. juglandis  [46, 47]. All five bacteriophages belong to the order of Causovirales, with CP1 being a member of the Siphoviridae and the others being members of the Podoviridae.
Spacer Xcc_35 was also similar to a virulent bacteriophage for Xylella fastidiosa (bacteriophage Prado; Caudovirales; Podoviridae; GenBank accession number KF626667) with a host range that includes Xanthomonas spp. [48]. Spacer Xcc_31 was also similar to a sequence in the genome of the Ralstonia-related blood disease bacterium R229 (GenBank accession number FR854082), which likely belongs to an integrated prophage and encodes a DNA polymerase A (GenBank accession number CCA83269. 1) (Additional file 7: Table  S1 ). Among the conserved 23 spacers, only four had significant matches in the non-redundant GenBank database, all of which corresponded to sequences from other Xanthomonas species or pathovars (Additional file 7: Table  S1 ). Spacers Xcc_22, Xcc_20 and Xcc_01 were similar to sequences in the X.
citri pv. vignicola strain CFBP 7113. Notably, spacer Xcc_22 matched to locus XcvCFBP7113P_11110, which has been annotated to encode a hypothetical protein. However, BLASTP search of the coding sequence revealed 80% sequence identity with protein I of the Xanthomonas campestris filamentous bacteriophage ΦLf (GenBank accession number AAB88261)  [49]. Spacer Xcc_01 matched to locus XcvCFBP7113P_16810 (annotated as hypothetical protein with similarity to the Pfam domain NinB [PF05772; E-value 8. 2e-30], which corresponds to the DNA recombination protein NinB of bacteriophage lambda) and spacer Xcc_20 matched to the intergenic region between loci XcvCFBP7113P_16630 and XcvCFBP7113P_16635. All these loci belong to a 29-kb region (GenBank accession number CP022270; 3,740,909 to 3,769,866) that likely corresponds to (remnants of) a prophage.
A similar region with 74% sequence identity over the whole length is present in the genomes of the X. citri pv. phaseoli var. fuscans strains (e. g. strain CFBP 6988R, GenBank accession number CP020979, 3,315,711-3,346, 400). Interestingly, spacer Xcc_16 matches to a sequence motif in this region (e. g. locus XcfCFBP6988P_14885 in strain CFBP 6988R, annotated as hypothetical protein). Thus, all spacers that had a hit in the GenBank database derived from bacteriophage or prophage sequences.
3  Comparison of phylogenetic analyses based on CRISPR data (a) and AFLP data (b) for 56 strains of X. citri pv. citri. AFLP data were taken from previous work  [17]. AFLP and CRISPR data were converted into a binary array according to the presence or absence of each marker (except for the 14 unique spacers of strain CFBP 2911) and clustering was inferred using the UPGMA method. Different colors of characters indicate different clusters and the same strains are represented by the same color in both panels identification of A w strains, as in several cases, A and A w strains were found to share identical patterns. Distinction between these strains was only possible by additional evidence. For instance, the presence of an IS element could distinguish some A w strains (LB302, LB305, LG115) from A strains (CFBP 2852, JW160-1, LB100-1).
Correlations among different DNA-based typing methods Correlation analyses of AFLP versus CRISPR or minisatellite-based typing (MLVA-31) data revealed a fairly good congruence between these methods. We found more AFLP haplotypes (49 haplotypes) and MLVA-31 haplotypes (37 haplotypes) than CRISPR spoligotypes (25 haplotypes). Hence, the AFLP method appears to better resolve the genetic diversity among strains of X. citri pv. citri than the two other methods but suffers from technical limitations making interlaboratory comparisons difficult to achieve, a characteristic that precludes a wide use for epidemiosurveillance  [17]. In general, strains belonging to a certain spoligotype clade in the CRISPR tree also cluster together in the AFLP tree (Fig. 3 ). Exceptions were two strains, LH001-3 (spoligotype 23) and LG117 (spoligotype 25), with exceptionally small numbers of spacers, 12 and 8, respectively, which might explain their misplacement in comparison to the AFLP, MLVA-31 and SNP analyses  [17] [18] [19].
Distinguishing pathotypes A and A* Interestingly, pathotype A and pathotype A* strains of our dataset with different citrus host range can be distinguished from each other by the presence or absence of spacer Xcc_06, which corresponds to the first deletion event in the evolution of pathotype A* spoligotypes. Knowledge of pathotype is of importance for disease management and has consequences for regulation measures. However, conventional determination of pathotypes is laborious, as it requires assaying citrus plants. Moreover, some PCR-based techniques failed to accurately identify pathotype A* strains  [53, 54]. Apart from whole genome sequence data, the most straightforward method for distinguishing pathotype A* from another X. citri pv. citri pathotype is currently MLVA-31 (or its derivative MLVA-12) targeting minisatellites  [18, 19].
Origin of spacers CRISPR arrays represent a signature of the long history of interactions between bacteria and bacteriophages or other extrachromosomal genetic elements. To understand the evolution of CRISPR loci, it is of interest to know from where the spacer sequences derive. To elucidate their origin, we performed BLASTN searches against the NCBI GenBank. In addition to the hits in the CRISPR loci of completely sequenced X. citri pv. citri strains, we found significant hits between spacer sequences and five Xanthomonas bacteriophages, a finding that supports the principal mechanism of CRISPR immune system in bacteria. Homologies with Xanthomonas bacteriophage CP1 (GenBank accession number AB720063) were found for spacers Xcc_ 36, Xcc_28 and Xcc_25 (Additional file 7: Table  S1 ). Four bacteriophages (CP1, CP2, CP115 and CP122) have been used for classification of X.
citri pv. citri strains based on their sensitivity to phage for quarantine purposes  [55, 56]. Strains from X. citri pv. citri were variable in their sensitivity to bacteriophages CP1 and CP2  [55, 57]. The studies of genomic analysis of bacteriophage CP1 and CP2 have reported that the CP1 DNA sequence was detected in the genome sequence of X. campestris bacteriophage phiL7 (GenBank accession number EU717894), X. oryzae bacteriophage OP1 (GenBank accession number AP008979) and Xanthomonas bacteriophage Xp10 (GenBank accession number AY299121)  [45]. In addition, a sequence in the genomic contig of the Ralstonia-related blood disease bacterium R229 (GenBank accession number FR854082) was related to spacer Xcc_31; this sequence encodes a DNAdependent DNA polymerase with homology to DNA polymerases of the Xanthomonas-specific bacteriophages phiL7, OP1 and Xp10  [58] [59] [60].
With even less stringent criteria there are also matches with Xanthomonas-specific bacteriophages for spacers Xcc_34 (bacteriophage CP1), Xcc_ 32 (bacteriophage CP1), Xcc_11 (Streptomyces phage Yaboi), and Xcc_2 (Microbacterium phage Memento-Mori) (data not shown). However, as demonstrated in Additional file 7: Table  S1 , relaxing the threshold results in an increased number of matches in genomes of diverse bacteria. Therefore, we cannot conclude that these are bona fide homologs the sequences of which have been altered with the long time since these spacers were acquired, or if these are merely false positives. Only four of the 23 older spacers matched to sequences in GenBank that did not correspond to the CRISPR arrays of X. citri pv. citri. In all four cases, homology to sequences from integrated prophages or from a filamentous bacteriophage was observed.
citri pv. citri strain that we have analysed (Fig. 2 ). This finding can be explained by three scenarios. The first explanation that these 14 new spacers were deleted in all X. citri pv. citri strains but CFBP 2911 is very unlikely because CFBP 2911 does not represent an ancestral clade at the root of X. citri pv. citri phylogeny  [18]. Second, it is possible but unlikely as well that none of the 56 strains except for CFBP 2911 was challenged by alien DNA elements, such as bacteriophages or plasmids, since they had acquired spacer Xcc_23. We favor the third hypothesis that the CRISPR immunity system was mutationally inactivated in its ability to acquire new spacers in the ancestor of all 56 X. citri pv. citri strains in our dataset, yet the CRISPR/Cas system was maintained during evolution as a mechanism of protection against bacteriophage infection.
For further insight it would be interesting to analyze more strains from the same region as CFBP 2911 (i. e. Pakistan) by assuming that they might have undergone the same evolutionary event(s). In addition, we found two cases of IS element insertions in CRISPR loci of X. citri pv. citri. One insertion occurred in the repeat between spacers Xcc_20 and Xcc_21 (LB302, LB305, LG115 and NCPPB 3608,) and another insertion had occurred in spacer Xcc_18 (LG097) (Fig. 2 , Additional file 3: Figure  S3 , Additional file 4: Figure  S4  and Additional file 5: Figure  S5 ). The first four strains originated from India (LG115, NCPPB 3608) and Florida (LB302, LB305). Notably, these strains were all assigned to pathotype A w and genetic lineage 3 based on minisatellite typing  [19]. Interestingly, the spacer Xcc_14 was deleted from strains, LB302, LB305 and LG115 whereas NCPPB 3608, probably representing the ancestral spoligotype of our dataset, had all 23 spacers.
Genealogy of CRISPR spoligotypes Since the CRISPR array of all strains originated from a conserved array of 23 spacers, one can use this information to establish an evolutionary trajectory among the observed spoligotypes. To building such an evolutionary pathway one could assume to minimize the number of mutational events that are necessary to connect all spoligotypes with each other. Yet, without additional information it is impossible to be absolutely certain about a given scenario because several alternatives might exist with a similar number of postulated mutation (deletion) events. Here, we took advantage of the availability of genome sequence data for 42 out of the 56 X. citri pv. citri strains, which were used to build a robust phylogenetic tree based on whole genome alignment upon removal of regions with signs of recombination  [18].
These data provided information about the evolutionary relationships among 21 spoligotypes. Only spoligotypes 7, 13, 20, 21, and 23 were not covered by full genome data. In these cases, information was taken from global studies using AFLP and MLVA data  [17, 19]. Based on these phylogenetic datasets, which can be considered as evolutionary neutral, we were able to manually build trees for all observed spoligotypes, with one tree representing pathotype A and another tree representing pathotype A* strains (Figs. 4  and 5 ). Future work including strains representing a larger temporal scale, e. g. from herbarium specimen  [52] , together with approaches to build time-calibrated phylogenies will help to assess the speed of the molecular CRISPR clock  [66]. The phylogenetic trees for pathotype A and A* strains demonstrate the utility and power of spoligotyping in order to assess the genealogy of bacterial strains.
Conclusions This study provides the necessary information to set up a spoligotyping scheme and a spoligotyping database for X. citri pv. citri, similar to the well-established spoligotyping scheme for M. tuberculosis  [37]. It demonstrated the advantages and disadvantages of a CRISPR-based typing method. In order to facilitate future work and comparisons we have deposited all CRISPR typing data in the MLVAbank under the name "Xanthomonas_citri_ CRISPR" (http://www. biopred. net/MLVA/) [68]. In accordance with previous studies  [28, 42] , we confirmed that CRISPR-based typing can be an efficient and robust method to study the evolution of bacterial isolates and to resolve the phylogenetic relationship among strains. We confirmed that CRISPR loci can differ among strains due to bacteriophage exposure, IS element insertion and intralocus recombination leading to loss of spacer/repeat units, thus giving a valuable typing tool as well  [42].
Moreover, the CRISPR-based typing method is easier to perform and more reproducible than AFLP and rep-PCR methods since it can be performed with a simple conventional PCR approach and results in robust binary data. Genotyping-based surveillance is informative for assessing the geographical expansion of plant pathogenic bacteria, their prevalence, and to identify new strains, especially in the case of regulated pathogens such as X. citri pv. citri. We therefore consider our new typing method as a valuable tool for further studies and conclude that, if complete genome sequence data cannot be made available, a combined use of minisatellite and CRISPR-based typing, two techniques combining overall fairly good phylogenetic signals, discriminatory power and portability, should be preferred for placing strains associated with new outbreaks in the global diversity of X.
Bioinformatic and statistical analyses The obtained DNA sequences were edited and assembled using the CAP 3 program  [69] , using default parameters. CRISPR spacers and repeats were identified using CRISPRCasFinder (https://crisprcas. i2 bc. paris-saclay. fr/CrisprCasFinder/Index) [25]  (Table  1 ). CRISPR spacers and repeats were annotated on the X. citri pv. citri sequences using Artemis Release 17. 0. 1 (http://www. sanger. ac. uk/science/tools/artemis/ )  [70]. To compare the CRISPR loci of the 56 X. citri pv. citri strains, we represented the spacers by different colors, an approach that was called "spacers crawling" in a similar study on E. amylovora  [42]. To elucidate the origin of spacer sequences, we performed BLASTN searches in the non-redundant NCBI database (https://blast. ncbi.
nlm. nih. gov/Blast. cgi), using the following parameters: E-value threshold 0. 1, word size 7, default mismatch and gap penalties (i. e. match/mismatch scores: 2, -3; gap costs: 5 for existence, 2 for extension), no filter for low complexity regions. Only hits with at least 90% coverage were retrieved. Hits in eukaryotic organisms were excluded. For IS element identification, the IS-Finder database (https://www-is. biotoul. fr/) was used. In order to compare the resolution and discriminatory power of CRISPR typing with other typing methods, MLVA-14 (microsatellite), MLVA-31 (minisatellite) and AFLP genotyping data were retrieved from our previous studies  [17, 19, 21]. The discriminatory power of MLVA-14, MLVA-31, AFLP and CRISPR was calculated based on Hunter's index (D)  [71]. Distance matrices of each genotyping method were compared with a Mantel test using the 'CADM.
Comparison of distance tree analysis between AFLP and CRISPR typing AFLP data used for comparison were derived from a previous study  [17]. In order to produce a distance tree for 56 strains, a presence/absence matrix was produced based on the distribution of the polymorphism of fragment for AFLP and the distribution of CRISPR spacers for CRISPR typing, respectively. These matrices were analyzed with the Den-droUPGMA program (http://genomes. urv. es/UPGMA/ ) using the Dice similarity coefficient and the UPGMA (Unweighted Pair Group Method with Arithmetic Mean) method  [73]. The FigTree program was used to visualize the distance tree map (version 1. 4. 2; http://tree. bio. ed. ac. uk/software/figtree/). CRISPR repeats. CRISPR spacers are represented by green diamonds. Spacers are numbered from 1 to 23, starting with the terminator proximal spacer, which presumably represents the oldest spacer.
The UVR doses are extracted from UVI using Equation  1 : 𝑈𝑉𝑑 ∆𝑡 𝑈𝑉𝑖𝑛𝑑𝑒𝑥 40 100 , ( 1 ) where UVd is the daily dose and ∆t the interval time between two measurements. The UVI uncertainty has recently been estimated to be 5%  [10]. This instrument is affiliated with the Network for the Detection of Atmospheric Composition Change (NDACC). A parametric and sensitivity study has been done by  Lamy et al. , 2018 [12]  on Tropospheric Ultraviolet and Visible Model (TUV) at Saint-Denis. Ground-based and satellite data was used for clear sky UVI modelling and a relative difference of 0. 5% was found with UVI from the Bentham DTMc300 (Bentham Instruments Ltd, Berkshire, UK).
Climatology Analysis The climatological monthly mean of the total daily UVR dose was computed. Since daily total cumulative UVR dose in SED units was used, days with incomplete data were removed from the datasets. The monthly climatology mean, standard deviation, and box diagrams were computed. Then, the UVR intensity was analyzed by investigating the UVI threshold frequency using the UVI categories of low, moderate, high, very high, and extreme over the 10-year study period. In addition, the half-month maximum was used, following the World Health Organization (WHO) UVR exposure categories  [17].