dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Dr. Feng Cong  ( Novartis, Cambridge, MA ) and  Dr. David Bryant  ( Beatson Institute, Glasgow, UK ) for reagents and cell lines. We thank  Dr. Oliver Kent  and  Dr. Helen Burston  for critical reading of the manuscript. This research was funded by the  Canadian Institutes of Health Research Foundation  Grant. A. A. Chandrakumar was supported by the  Queen Elizabeth II Graduate Scholarship in Science and Technology (Sir Edward Dunlop Medical Research Foundation Scholarship)  and  MBP Excellence Ontario Student Opportunity Trust Fund. M. Ikura holds a  Canada Research Chair in Cancer Structural Biology. NMR spectrometers were funded by the  Canada Foundation for Innovation , and the  NMR Core Facility  is supported by the  Princess Margaret Cancer Foundation. The authors declare no competing financial interests.
Nowadays, the use of sulfur dioxide (SO 2 ) during the winemaking process is a controversial societal issue. In order to reduce its use, various alternatives are emerging, in particular bioprotection by adding yeasts, with different impacts on yeast microbiota in early winemaking stages. In this study, quantitative-PCR and metabarcoding high-throughput sequencing (HTS) were combined with MALDI-TOF-MS to monitor yeast population dynamic and diversity in the early stages of red winemaking process without sulfites and with bioprotection by Torulaspora delbrueckii and Metschnikowia pulcherrima addition. By using standard procedures for yeast protein extraction and a laboratory-specific database of wine yeasts, identification at species level of 95% of the isolates was successfully achieved by MALDI-TOF-MS, thus confirming that it is a promising method for wine yeast identification.
The identification of microbiological samples by this method relies on the acquisition of mass fingerprints and subsequent comparison with a Biotyper database. In oenology, several studies have aimed to optimize protein extraction protocols, improve yeast identification by creating specific wine microorganism databases  [28] , or obtain finer identification by the extraction of high mass range moieties  [29]. MALDI-TOF MS Biotyper has been used to differentiate oenological yeast at the genus or species level, such as Saccharomyces  [30] , to identify different groups of Saccharomyces cerevisiae  [31, 32] , and to describe the grape berry microbiota  [33]. However, it has been applied little until now for describing the yeast diversity of a must or wine-related environment, and some wine yeast species have not yet been identified due to their absence from the Biotyper database  [34].
The MALDI-TOF MS Biotyper analysis was used to study the yeast community diversity during the prefermentary stages and to monitor the implantation of both non-Saccharomyces yeasts used as bioprotection. In a first step, the existing Biotyper database was extended to a laboratory-specific database made with 17 additional new species specific to the wine environment (in total, 43 yeast strains). In parallel to MALDI-TOF MS, two complementary approaches were applied: Q-PCR to monitor population levels of targeted species, and HTS metabarcoding to analyze fungi diversity.
1), MTB Compass (Version 3. 1) (Bruker Daltonics, Bremen, Germany), and MALDI-Biotyper TM application (Bruker Daltonics, Bremen, Germany), which allows the similarity of the mass profile of an unknown microorganism to be calculated with the mass profiles in a database. To calibrate the mass spectral data generated by the instrument, the Bruker bacterial test standard (BTS) (Bruker, Germany) was added to each plate as a control. The identification of microbiological samples by this method relies on the acquisition of mass fingerprints and subsequent comparison of the data with the Biotyper database. The spectra were analyzed in an m/z of 2 to 20 kDa  [38]. Results of the pattern-matching process were expressed as proposed by the manufacturer, with scores ranging from 0 to 3. Scores >2. 3 indicated highly probable species identification, score values between 1.
MALDI-TOF MS Oenological Laboratory Specific Database (OLS-DB) The yeast strains chosen to enhance the new laboratory specific database, provided by CRB Oeno (centre de Ressources Biologiques, Unité de Recherche Oenologie, Villenave d'Ornon, France), are listed in Appendix A Table  A1. These strains were previously identified by sequencing of 26S rDNA using NL1-NL4 primers  [37]  (Table  A1 ). An oenological microorganism mass profiles database was created with the MTB Compass Explorer Module (Version 4. 1) and Flex Analysis (Version 3. 4) (Bruker Daltonics, Bremen, Germany) as follows. A complete extraction was carried out for each strain added to the database. The strains were grown and subcloned on YPG medium. For each isolate, yeast protein extraction was carried out in duplicate in order to generate the reference MALDI-TOF MS spectra for a given strain.
Then, sequences were dereplicated before being clustered using SWARM  [42]  with a local clustering threshold with a distance of 3. This single-link method is robust and independent of the sequence at which it is begun. Chimeras were removed with vsearch  [43] : chimeras are sequences formed from two or more biological sequences joined together. The resulting sequences were filtered to remove singletons, using Filter phiX (contaminant databank). Taxonomic assignment of operational taxonomic units (OTUs) corresponding to 18S rDNA sequences was performed using silva132 18S  [44]  as the reference database. Sequences were filtered on BLAST with a percentage of identity (97%) and percentage of coverage (95%). An affiliation postprocess allowed inclusive amplicon ambiguities to be resolved and OTUs aggregated based on alignment metrics.
Statistical Analysis The α-diversity was calculated by using R package phyloseq  [45]  from the OTU matrix generated by FROGS as input  [41]. "Observed" concerns the number of OTUs; "Chao1" estimate the number of unobserved species from those observed one or two times. Shannon and Inverse Simpson are quantitative index; it takes into account the abundance of each OTUs. The data were assumed to be normally distributed (Shapiro-Wilks normality test, p > 0. 05) and the variance homogeneity was verified (Leven test, p > 0. 05). The data were then analyzed by single-factor variance analysis (ANOVA, p < 0. 05) and the normal distribution of the residual data was verified (Shapiro-Wilks normality test, p > 0. 05). All tests were carried out using the R Studio program.
Comparative Analysis of Species Identification by MALDI-TOF MS Biotyper Database and 26S rDNA Sequencing In a first study, 623 yeast clones isolated from the prefermentary stages and beginning of alcoholic fermentation were first identified using 26S rDNA sequencing. All the yeast isolates were identified successfully by sequencing at species level, resulting in 17 yeast species among traditional oenological ones (Figure  1 ). A majority of the yeast isolates (66%) were identified correctly at species level (score > 2) by the MALDI-TOF MS with Biotyper database and according to the 26S rDNA sequencing identification, whereas 6% were identified only at genus level (score between 1. 8-2). Finally, 28% of yeast isolates were not identified (176 clones out of 623) using the MALDI-TOF MS Biotyper database, probably because the colony extraction and/or identification scores were not correct.
Although the S. cerevisiae species is well represented in the Biotyper database (13 strains), only 54% of isolates identified by 26S rDNA sequencing (191/353 clones) were correctly identified using the MALDI-TOF MS Biotyper database (score >2) (Figure  1 ). For the 162 S. cerevisiae isolates not correctly identified by the MALDI-TOF MS Biotyper database, the scores were lower than 1. 8 (134 isolates), probably due to the non-wine origin of the S. cerevisiae in the Biotyper database. For H. uvarum, representing 20% of the isolates by 26S rDNA sequencing, 90% of total H. uvarum isolates were identified correctly by the MALDI-TOF MS Biotyper database that contains eight strains.
base and 26S rDNA Sequencing In a first study, 623 yeast clones isolated from the prefermentary stages and beginning of alcoholic fermentation were first identified using 26S rDNA sequencing. All the yeast isolates were identified successfully by sequencing at species level, resulting in 17 yeast species among traditional oenological ones (Figure  1 ). A majority of the yeast isolates (66%) were identified correctly at species level (score > 2) by the MALDI-TOF MS with Biotyper database and according to the 26S rDNA sequencing identification, whereas 6% were identified only at genus level (score between 1. 8-2). Finally, 28% of yeast isolates were not identified (176 clones out of 623) using the MALDI-TOF MS Biotyper database, probably because the colony extraction and/or identification scores were not correct.
Oenological Laboratory Specific Database of MALDI-TOF MS The oenological laboratory specific Biotyper database was created with yeast species and strains specific to the wine environment. Yeast strains originating from must and wine were provided by CRB OENO. For all isolates, species identification was validated by 26S rDNA sequencing, and the sequence and match information on BLAST are presented as Table  A1. To implement the database, different situations were considered: (i) when strains of a given species were present in the Bruker database and its identification result was <2. 0, only one or two strains isolated from the wine environment were approved in the database (as for example for Hanseniaspora uvarum or Lachancea thermotolerans), (ii) when species were not present in the database (Shizosaccharomyces japonicus or Starmerella bacillaris), only one or two strains were added, leading to an identification score >2.
For Saccharomyces cerevisiae, the scores with wine isolates were not correct (<2. 0), and five additional S. cerevisiae strains originating from a wine environment were therefore added, and the resulting identification scores were found to be greater than 2 using these two databases. For Pichia guilliermondii, 14 strains were already present in the Biotyper database, while the identification results were >2. 0 for wine isolates; only one strain of Pichia guilliermondii related to the wine environment was added. Considering the genetic and phenotypic diversity of Brettanomyces bruxellensis  [46] , 15 strains representative of the genetic groups of the species were added. Finally, 17 distinct species and 43 different strains were added to the existing database (Table  1 ). Merlot N. (Vitis vinifera L.
Five isolates did not grow after subcloning and only eleven isolates were isolated from one sample, and this sample was therefore not considered for further analyses. Some isolates  (26)  were not identified as their profiles did not find a match in the database and, finally, 657 isolates (95%) were identified successfully at species level by MALDI-TOF MS, among which were ten different species (Figure  5 ). Metschnikowia pulcherrima, Torulaspora delbrueckii, Aureobasidium pullulans, and Hanseniaspora uvarum were previously reported as OTUs by HTS, whereas Kluyveromyces lactis, Saccharomyces cerevisiae, Whickerhamomyces anomalus, and Lachancea thermotolerans were identified among the "Others". Candida guillermondii and Cryptococcus flavescens were not Levels of the total yeast population by plating method on LT medium are given in Table  A4.
Some isolates  (26)  were not identified as their profiles did not find a match in the database and, finally, 657 isolates (95%) were identified successfully at species level by MALDI-TOF MS, among which were ten different species (Figure  5 ). Metschnikowia pulcherrima, Torulaspora delbrueckii, Aureobasidium pullulans, and Hanseniaspora uvarum were previously reported as OTUs by HTS, whereas Kluyveromyces lactis, Saccharomyces cerevisiae, Whickerhamomyces anomalus, and Lachancea thermotolerans were identified among the "Others". Candida guillermondii and Cryptococcus flavescens were not identified by HTS. As for the Q-PCR analysis, only Metschnikowia pulcherrima was detected with SO 2 or without SO 2 (0) treatment as part of the indigenous population, with a higher percentage without SO 2 at vatting and 24 h of fermentation (24 h).
Discussion Until now, yeast diversity analysis by culture-dependent techniques has been performed using 5. 8S-ITS-RFLP analysis and/or 26S rDNA D1/D2 domain sequencing  [4, 6, 15, [49] [50] [51]. This approach based on PCR analysis and DNA sequencing is time-consuming. Recently, MALDI-TOF MS has been demonstrated to be a rapid and cost-effective tool for the identification of wine yeast at the species level  [26, 29, 33]. In this study, species identification by MALDI-TOF MS was validated for 66% of wine yeast isolated at the prefermentary or beginning of alcoholic fermentation stages, in comparison with 26S rDNA sequencing. These first results revealed that it was necessary to enrich the MALDI-TOF MS Biotyper database not only with missing wine yeast species, such as Starmerella bacillaris, but also to add wine strains for some species, such as S.
cerevisiae or H. uvarum, to improve identification. Different authors  [28, 52]  have already highlighted the importance of enriching the yeast database from standard spectra of isolates originated from the oenological environment. In the present study, the Biotyper database was extended by an oenological laboratory-specific database (43 new additional strains corresponding to 17 different species specific to the wine environment). Gutièrrez et al. (2017)  [28] reported the successful identification of 95. 4% of yeast isolates after optimization of the preanalytical steps and the development of an in-house MS database. By using standard procedures for colony extraction (without optimization of the preanalytical steps) and an oenological laboratory specific extended data base, we were able to obtain the same identification rate with 95% of the isolates successfully identified.
METHODS We retrospectively collected data from all patients treated for advanced or metastatic bladder or upper urinary tract cancer with a VH or non-UC, in seven European hospitals between March 2005 and April 2020 using local databases. Patients with VH were included in the analysis only if they had a variant histopathologic growth pattern as the major component (i. e. > 50%). Patients were excluded if they did not receive systemic treatment for advanced or metastatic disease. Patients were considered as advanced disease if they had positive lymph nodes, absence of distant metastasis and had been treated as in the metastatic setting. For each patient, data was collected from their medical records and included: age, gender, date of diagnostic, ECOG performance status, primitive tumor location, stage, histology, location of metastasis, presence of surgery or not, hemoglobin, and treatment regimen.
Alive patients without progression were censored at the date of their last follow-up. OS was defined as time between the first-line treatment initiation and death from any cause. Patients known to be alive were censored at the date of their last follow-up. Tumor responses were classified according to the Response Evaluation Criteria in Solid Tumors criteria 1. 1. ORR was defined as the sum of complete and partial responses. All analyses were made with RStudio software. p-value ≤0. 05 was considered as statistically significant.
NaTUrE MIcroBIology twenty-first century is even more limited, and these issues are partly explained by the fact that it has not been possible to culture TPA outside of a rabbit until recently  22. In this multi-centre collaborative study, we used direct whole genome sequencing (WGS) to generate a global view of contemporary syphilis from patients in Africa, Asia, the Caribbean, South America and Australia sampled between 1951 and 2019. Our dataset also includes a detailed analysis of the 'within-country' variation seen in TPA genomes in North America and Europe. We present evidence of globally spanning transmission networks with identical strains found in dispersed countries, indicating that, based on our data, TPA is genetically homogenous. Furthermore, we show that this genetic homogeneity is the result of a rapid and global expansion of TPA sublineages occurring within the last 30 years following a population bottleneck.
Describing the global population structure of T. p. pallidum We performed targeted sequence-capture whole genome sequencing on residual genomic DNA extracted from diagnostic swabs taken from TPA PCR-positive syphilis patients and on TPA strains previously isolated in rabbits. We combined these data with 133 previously published genomes  3, 4, [18] [19] [20] [23] [24] [25] [26]. After  ssessment for genome coverage and quality, we had a total of 726 genomes with >25% of genome positions at >5X coverage (mean 82%, range 25-97%), which is sufficient for primary lineage classification. This dataset included 577 new genomes sequenced directly from clinical samples and 16 new genomes sequenced from samples passaged in rabbits. Our dataset includes 23 countries (range 1-355 genomes per country; Fig.
1a, c ), including previously poorly or unsampled regions such as Africa (South Africa (n = 1) and Zimbabwe (n = 18)), Scandinavia (Sweden, n = 7), Central Europe (Hungary, n = 20), Central Asia (Tuva Republic, Russia, n = 10) and Australia (n = 5), as well as substantially increasing the sampling from North America (Canada, n = 157; the USA, n = 86) and Western Europe (Spain, n = 5; Belgium, n = 1; Ireland, n = 4; the UK, n = 355). We also included previously published genomes from South America (Argentina, n = 1), the Caribbean (Cuba, n = 3; Haiti, n = 1) and elsewhere. Due to a lack of long-term archived samples, 96. 0% (n = 697) of samples were collected from 2000 onwards (Fig. 1b, e ). Samples collected before 2000 (n = 29) were passaged in the in vivo rabbit model (Supplementary Data 1), whereas most samples collected from 2000 onwards (89.
Fine-scale analysis of SS14-and Nichols-lineage phylogenies To answer finer-scale evolutionary questions, we filtered our dataset to focus on the 528 genomes with >75% genome sites at >5X (genome length 1,139,569 bp, mean % sites 92. 9%, range 75. 1-96. 9%) and a mean coverage of 111X (range 11X-727X). This filtered dataset comprised 401 new and 127 published genomes (Supplementary Data 1), but excluded the only sample from Belgium, leaving 22 countries in the analysis. After excluding 19 regions of recombination and genomic uncertainty due to gene paralogy or repetitive regions  3, 4, 6  , we used Gubbins  27  to infer a further 19 regions of putative recombination (see Methods and Supplementary Data 2 for details). We refer to the resulting masked sequence alignment as the core genome and used it to infer a whole genome maximum likelihood phylogeny using IQ-Tree 28 (Supplementary Fig.
2 , Supplementary Fig. 1  and Extended Data Figs. 2 3 4 ). Sublineage 6 diverged from other genomes very close to the common ancestor of TPA, and due to low total diversity in the dataset can appear on either side of the root (Nichols or SS14) depending on the phylogenetic approach used; for the purposes of this analysis, we classified sublineage 6 strains as Nichols lineage since they are more distantly related to the recent contemporary SS14-lineage expansions. From Fig. 2 , it is apparent that the phylogeny of the SS14 lineage is dominated by SS14 sublineage 1 (n = 365), composed of closely related genomes present in 18 countries and 6 continental groupings (Asia, Caribbean, Europe, North America, Oceania and South America). The oldest example of sublineage 1 was collected in 1981 (TPA_USL-SEA-81-3, Seattle, USA) and the most recent samples were from 2019 (Fig.
NaTUrE MIcroBIology dataset of 520 samples and 883 variable sites. We inferred a median molecular clock rate of 1. 28 × 10 -7 substitutions per site per year (95% highest posterior density 1. 07 × 10 -7 -1. 48 × 10 -7 ), which is equivalent to one substitution per genome every 6. 9 years, consistent with recent analyses  4, 6. Within the global TPA phylogeny (Fig. 3a ), we observed several patterns of genomic dispersal. The first reflects the separation of the Nichols and SS14 lineages (median date in our analysis 1534, 95% highest posterior density 1430-1621), which is the subject of much previous analysis  3, 4, 6. These data also showed that the common ancestor of these lineages was separated from recent samples by long phylogenetic branches and an absence of older ancestral nodes, suggesting unsampled historical diversity and that most contemporary TPA descended from much more recent ancestral nodes.
We previously dated the common ancestors for clonal expansions of 9 SS14 sublineages between the 1980s and early 2000s  4. With this expanded dataset, we focused on the major clonally expanded sublineages 1, 2, 8 and 14, each having at least 15 samples. Next, we used Bayesian Skyline analysis to determine the relative genetic diversity over different time periods in the phylogeny (Fig. 3b ), and found a very sharp decline during the 1990s and 2000s, followed by an equally sharp rise that continued until the present. To test the statistical support for this expansion, we extracted the proportion of trees in the posterior distribution supporting a >2-fold population expansion above the population mean (68. 6%, 9,263/13,503 trees) and plotted the distribution of expansion start dates (Fig. 3c ) (median date 2011).
4d ). In particular, Canada, the UK and the USA with the highest sampling (Fig. 4b ), showed the most zero pairwise interactions with other countries (Fig. 4d ). Therefore, we cannot rule out similar transmission events occurring between other countries. We compared pairwise SNP distances with geographical distance between country centroids. Although we found a moderate correlation for Nichols lineage (Pearson's correlation 0. 49, P < 0. 001, 9,620 comparisons), correlation was lower for the SS14 lineage (0. 31, P < 0. 001, 181,476 comparisons) and for the four largest multi-country sublineages (sublineage 1: 0. 09, P < 0. 001, 133,225 comparisons; sublineage 2: 0. 43, P < 0. 001, 1,024 comparisons; sublineage 8: 0. 27, P < 0. 001, 225 comparisons; sublineage 14: 0. 08, P < 0. 001, 3,025 comparisons) (Extended Data Fig.
8 ). Hence, overall this indicates weak geographical structure for TPA. To understand these observations more fully, we focused on British Columbia (Canada) and England, both of which have experienced a recent rise in syphilis incidence (Extended Data Fig. 9a ), and for which we had a large number of samples. Included were 84 high-quality BC genomes collected by the BC Centre for Disease Control between 2000 and 2018. From England, we had 240 high quality genomes from samples collected by the National Reference Laboratory at Public Health England (n = 198) and four non-referring laboratories (n = 42) between 2012 and 2018. In BC, SS14 sublineage 1 dominated throughout the 18 year survey period, representing 82% of all BC genomes (Extended Data Fig. 9b ). In addition, isolated cases of SS14 sublineage 2 were seen in 2000 and 2012, as well as a single Nichols-lineage sample (singleton) in 2002 (Extended Data Fig.
Discussion Previous attempts to understand the origins of the original syphilis pandemic  3, 6, 21  , as well as the dynamics of the current one  3, 4  , have been constrained by the technical difficulty in sequencing TPA genomes, as well as relatively small datasets with limited geographical diversity and sampling biases. In our study, we assembled the most comprehensive collection of syphilis samples from around the world to date, including samples from both the twentieth and twenty-first centuries. Despite this, we still find that the TPA population consists of just two deep-branching lineages, the SS14 and the Nichols lineage, with no outlying lineages. We showed that these lineages are both globally distributed and, where we have densely sampled, we found the relative proportions of each to be consistent.