dataesr/scientific-paragraphs-categorization huggingface dataset:
Declarations Monitoring visits at the investigator's site prior to the start and during the course of the study will help to follow up the progress of the clinical study, to assure utmost accuracy of the data and to detect possible errors at an early time point. The Sponsor organizes professional independent monitoring for the study. All original data including all patient les, progress notes and copies of laboratory and medical test results will be available for monitoring. The monitor will review all or a part of the eCRFs and written informed consents. The accuracy of the data will be veri ed by reviewing the above referenced documents. The investigator's site will collaborate with the Appletree CI group. Data monitoring: Interim analysis Interim analyses will include pre-test comparisons (healthy controls versus FMS) on the main and secondary outcomes after completion of the rst MORE group as well as exploratory pre-post comparisons on the main outcomes to control whether methodological adjustments have to be made.
Methods A systematic literature review (SLR) was conducted to identify relevant assessments by HTA agencies or NITAGs of HPV GNV that included HNC data. A detailed protocol was developed before conducting the SLR describing the methods, which followed the requirements of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines  [25]  and the Cochrane Handbook for Systematic Reviews of Interventions  [26]. Two main types of data sources were searched. First, searches were conducted in the MEDLINE Ò and EMBASE Ò databases using the Ovid SP Ò search platform. Searches were predefined in the SLR protocol and are detailed in full in Supplemental Table  1. The study design limits applied to the searches were based on CADTH validated filters. [27]  Second, searches were conducted directly on the websites of HTA agencies and NITAGs of 32 countries that were known to have issued gender-neutral HPV vaccination recommendations (see list of countries in Table  1 ).
Publications included The electronic database searches were performed on MEDLINE Ò and EMBASE Ò on August 8, 2019 and, upon deduplication, identified 205 unique records for review. Of those, twenty-seven were selected for full text review after screening the titles and abstracts against the eligibility criteria. During the full text assessment, ten records were excluded, resulting in the final inclusion of seventeen reports by HTA agencies and NITAGs. The PRISMA diagram in Fig. 1  shows the SLR process. The main characteristics of the seventeen assessments included in this SLR can be found in Table  2. Sixteen of seventeen assessments were published between 2014 and August 2019. The majority of the assessments (12/17) were conducted by European countries. A world map highlighting the countries with available reports is presented in Fig.
Incidence and prevalence data Incidence data on HNC were included in thirteen reports and, of these, seven also included prevalence data. The methods used to identify the existence of HPV-related HNC varied between studies. Out of the thirteen reports with HNC incidence data, nine used local data retrieved through country-specific registries or data-bases. [15, [35] [36] [37] [38] [39] [40] [41] [42]  Prevalence data were mainly retrieved from meta-analyses and published reports, rather than country-specific registries and databases. Eight assessments provided incidence data. Five reports described OPC incidence rates by gender, and the other three reports described incidence of HNC by site. The reports were in consensus that incidence of OPC was higher in men. One report described trends through time by cancer site and sex.
[37, 43]  The incidences of other subsites such as mouth, lip, base of tongue, tonsils, pharynx, and larynx were less frequently reported. Reported trends in HNC incidence data indicated a significant increase in the last two decades. [14, 44, 45]  Two of the assessments reported that the incidence of OPC in males have almost doubled in Europe and the USA in the past 20 years, a trend that is reported in other high-income countries. [45, 46]  Compared to females, males were reported to have higher incidence rates of HNC in eight reports. [14, 31, 32, 44, [47] [48] [49] [50]  In Sweden, OPC rates were highest in males who were 60-69 years old  [49]. Prevalence data were reported heterogeneously, with some of the reports presenting raw data and others presenting prevalence rates. Three out of the seven reports that included prevalence data used a published meta-analysis.
Attributable fraction of HPV Attributable fraction (AF) of HPV refers to the proportion of cases caused by HPV infection. [57]  AF of HPV in HNC was reported in more than 80% (14/17) of the assessments, and was determined by testing methods including p16 INK4a and E6/E7 mRNA. Most reports (12/14) described HPV AF in head and neck based on different studies, highlighting the high heterogeneity of data in this area. In addition, the majority (12/14) of reported data were not country-specific. When country-specific data were unavailable, reasonable estimates were used to describe HPV AF. For example, European estimates were used for Belgium and Italy. Nine reports provided the distribution of HPV type in specific HNC sites (oropharynx, oral cavity, pharynx, nasopharynx, hypopharynx) based on available literature.
[6, 14, 30, 32, 33, 44, 49, 50, 58]  The most common site reported on was the oropharynx (11/17). [6, 44, 45, [47] [48] [49] [50] [58] [59] [60] [61]  The tests used to assess AF were HPV DNA PCR and immunohistochemistry for the biomarkers E6/E7 mRNA and p16 INK4a. [8, 51, 62]  Several HPV genotypes were reported. HPV16 was cited as the genotype responsible for HNC in seven reports. [32, 34, 45, 49, 50, 63, 64]  However, in some reports additional HPV genotypes were reported (e. g. genotype  6, 11, 13, 16, 18, 31, 33, 35, 39, 45, 51, and 52). [14, 33, 44]  Table  4  presents the HPV AF data collected and used by the respective reports.
Economic data The included HTA and NITAG assessments considered economic data relating to HPV HNC from different perspectives. Nine reports included an incremental cost-effectiveness ratio (ICER), integrating HNC in the base case or sensitivity analyses, for an HPV GNV program in their country. [14, 30, 31, 34, 44, 45, 49, 59, 61]  Five reports used cost-effectiveness results from published models or economic studies, while three others provided results from novel economic models created by the HTA agencies and/or NITAGs during the assessment process  [14, 30, 49] , or adapted pre-existing models to their situation  [65, 66]. The reported ICER from the analyses without HNC ranged from €13,541 to €180,823 per QALY. As shown in Table  5 , inclusion of HNC in addition to cervical, vaginal, vulvar, and anal cancer tended to reduce the ICER associated with an HPV GNV program, which ranged from €7,600 to €37,917 (400,000 SEK) per QALY.
Here, we targeted the photosynthetic gene psbO from metagenomes to circumvent these limitations: the method is PCR-free, and the gene is universally and exclusively present in photosynthetic prokaryotes and eukaryotes, mainly in one copy per genome. We applied and validated this new strategy with the Tara Oceans datasets, and showed improved correlations with flow cytometry and microscopy than when based on rRNA genes. Furthermore, we revealed unexpected features of the ecology of these organisms, such as the high abundance of picocyanobacterial aggregates and symbionts in the ocean, and the decrease in relative abundance of phototrophs towards the larger size classes of marine dinoflagellates. To facilitate the incorporation of psbO in molecular-based surveys, we compiled a curated database of >18,000 unique sequences.
The fraction of the obtained sequencing reads corresponding to a given taxon is then used as a proxy for its relative abundance. Most studies have so far focused on taxonomically informative fragments of the hypervariable regions of the 16S (prokaryote and chloroplast) or 18S (eukaryotic nuclear) rRNA genes that are by far the most represented in reference databases  (Guillou et al. , 2013; Pawlowski et al. , 2012; Quast et al. , 2013). These markers are occasionally targeted in both DNA and RNA to exclude inactive microbes and as proxies of metabolic activities  (Campbell, Yu, Heidelberg, & Kirchman, 2011; Logares et al. , 2012) , but more recent studies have indicated severe limitations of this concept and only mRNA can be considered as an indicator of the metabolic state  (Blazewicz, Barnard, Daly, & Firestone, 2013).
, 2012; Louca, Doebeli, & Parfrey, 2018; Starke, Pylro, & Morais, 2020). In protists, this correction is even more challenging due to intraspecies variation in 18S rRNA gene copy number. For example, it varies almost 10-fold among 14 different strains of the haptophyte Emiliana huxleyi  (Gong and Marchetti 2019). In addition, there are major difficulties for generating a comprehensive database of 18S rRNA copy numbers  (Gong & Marchetti, 2019). Finally, functional traits such as photosynthesis cannot be inferred solely from rRNA genes or other housekeeping markers, whereby their knowledge is limited to a restricted number of taxa known from experts and the literature. Indeed, while photosynthesis occurs in almost all cyanobacteria (except a few symbiotic lineages that have lost it  (Nakayama et al.
We applied and validated this new strategy with the Tara Oceans datasets (Table  I ). We quantified the biases in taxon abundance estimates using rRNA gene markers as compared to optical approaches (flow cytometry, microscopy), and we compared these patterns with those obtained by our proposed method. We also searched for psbO within metatranscriptomes to analyse its potential use as a proxy of photosynthetic activity and/or biovolume (due to the higher transcript level requirements in larger cells). Besides finding a more relevant marker gene for phytoplankton, we also propose its combination with single-copy housekeeping genes (e. g. , recA for bacteria and genes encoding ribosomal proteins in eukaryotes) to estimate the fraction of photosynthetic members in the whole community or in a given taxon.
Search for phytoplankton marker genes To estimate cell-based relative abundances of the major marine phytoplankton groups, we searched for genes present in all photosynthetic organisms (both prokaryotes and eukaryotes) and with low copy-number variability among taxa. To fulfil the latter requirement, we first excluded the plastid-encoded genes to avoid the variations in number of chloroplasts per cell and in number of chloroplast genomes per organelle. We did this by retrieving sequences from the KEGG (M. Kanehisa, 2000)  database that are assigned to the photosynthetic electron transport chain, the Calvin Cycle and chlorophyll biosynthesis to be used as queries for sequence similarity searches against >4,100 plastid genomes available at NCBI (https://www. ncbi. nlm. nih. gov/genome/organelle/).
For this, BLAST version 2. 2. 31 ("tBLASTn" program) searches were conducted with an e-value cutoff of 1e-20  (Camacho et al. , 2009). To retain only core photosynthetic genes, i. e. , those present in all phototrophs, we then made an equivalent BLAST search against cyanobacterial and eukaryotic nuclear genomes from the IMG  (Chen et al. , 2019)  and PhycoCosm  (Grigoriev et al. , 2021)  databases and from the polyA-derived transcriptomes of the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP)  (Keeling et al. , 2014). To minimize false-negative cases, only completely sequenced genomes were considered for establishing the gene absence. This survey was also used for determining gene copy number variation. This survey resulted in a list of five genes that are core, nuclear-encoded and present in low-copy numbers (Table  II ).
For selecting a gene marker of phytoplankton among them, we carried out a deeper sequence analysis to detect nonphotosynthetic homologs and to see if the phylogeny reflects the evolutionary history of cyanobacteria and endosymbiosis. We first performed a sequence similarity search using HMMer version 3. 2. 1 with gathering threshold option (http://hmmer. org/) for the corresponding Pfam domain against the translated sequenced genomes and transcriptomes from PhycoCosm and MMETSP as well as in the whole IMG database (including viruses, archaea, bacteria and non-photosynthetic eukaryotes). The Pfams used in the search were: MSP (PF01716) for PsbO, Rieske (PF00355) for PetC, PRK (PF00485) for phosphoribulokinase, UbiA (PF01040) for chlorophyll-a synthase, and NAD_binding_1 (PF00175) for ferredoxin:NADP + reductase.
Analysis of Tara Oceans datasets Tara Oceans expeditions between 2009 and 2013 performed a worldwide sampling of plankton in the upper layers of the ocean  (Sunagawa et al. , 2020). To capture the whole size spectrum of plankton, a combination of filter membranes with different pore sizes (size-fractionation) was used to separate organisms by body size  (Pesant et al. 2015). There is an inverse logarithmic relationship between plankton size and abundance  (Belgrano, Allen, Enquist, & Gillooly, 2002; Pesant et al. , 2015) , so small size fractions represent the numerically dominant organisms in terms of cell abundance (albeit not necessarily in terms of total biovolume or biomass). Thus, the protocols consisted in the filtering of higher seawater volumes for the larger size fractions  (Pesant et al.
, 2015). Five major organismal size fractions were collected: picoplankton (0. 2-3 μm size fraction), piconanoplankton (0. 8-5 μm size fraction), nanoplankton (5-20 μm size fraction), microplankton (20 to 180 μm), and mesoplankton (180 to 2000 μm). These plankton samples were leveraged to generate different molecular and optical datasets that were analysed in the current work (Table  I ). We exclusively used the datasets corresponding to surface samples (5 m depth).
psbO-based community data To use the metagenomic and metatranscriptomic read abundances of psbO as a proxy of phytoplankton relative cell abundance and 'activity', respectively, we carried out a HMMer search as stated in the previous section against the two Tara Oceans gene catalogues: the Ocean Microbial Reference Gene Catalog version 2 (OM-RGC. v2) covering prokaryotic and eukaryotic picoplankton (<3 µm), and the Marine Atlas of Tara Oceans Unigenes version 1 (MATOU. v1) covering eukaryotic plankton ranging from 0. 8 to 2000 μm (Table  I ). The metagenomic and metatranscriptomic reads are already mapped onto both catalogs, thus we retrieved these values for those sequences obtained by our HMMer search. For the taxonomic assignment of psbO unigenes, we performed a phylogenetic placement of the translated sequences on the PsbO protein reference phylogenetic tree described in the previous section.
For parallelization of the task, a set of 50 unigenes were translated and the PsbO specific Pfam PF01716 region was retrieved for the analysis in the following way. First, they were aligned against the reference alignment described in the previous section using the option --add of MAFFT version 6 with the G-INS-I strategy  (Katoh and Toh 2008). The resulting alignment was used for building a phylogeny with PhyML version 3. 0 as described above  (Guindon et al. , 2010). The sequences were classified using the APE library in R  (Paradis & Schliep, 2019)  according to their grouping in monophyletic branches of statistical support >0. 7 with reference sequences of the same taxonomic group. Due to challenges of assembling eukaryotic genomes from complex metagenomes, the MATOU-v1 catalog only contains sequences assembled from poly-A-tailed RNA  (Alberti et al.
, 2017; Carradec et al. , 2018) , which biases against prokaryotic sequences. To determine the structure of the whole phytoplankton community (including both cyanobacteria and eukaryotic phytoplankton), we aligned all the metagenomic reads from Tara Oceans to a curated database of psbO sequences (described below; see also Table  I ). The analysis was carried out using the bwa tool version 0. 7. 4 (H. Li & Durbin, 2009)  with the following parameters: -minReadSize 70 -identity 80 -alignment 80 -complexityPercent 75 -complexityNumber 30. Abundance values were expressed in rpkm (reads per kilobase covered per million of mapped reads). In general, the rpkm values for the different taxa under study were converted to percentage of (either total or eukaryotic) phytoplankton. However, for a specific analysis the psbO rpkm values were normalized by those values form single-copy housekeeping genes: by bacterial recA  (Sunagawa et al.
rRNA gene-based community data We used two different datasets generated by Tara Oceans for "traditional" DNAbased methods: 16S rRNA gene miTags (size fraction 0. 2-3 µm) and 18S rRNA gene (V9 region) metabarcoding (sizes fractions 0. 8-5, 5,20, 20-180, 180-2000 µm) (Table  I ). We extracted the relative abundances for the 726 Operational Taxonomic Units (OTUs) assigned to picophytoplankton (cyanobacteria and chloroplasts) from the 16S miTags and the 31,930 OTUs assigned to eukaryotic phytoplankton from the V9-18S metabarcoding data. The read abundances were expressed as relative abundance (%) in relation to the picophytoplankton community for 16S miTags, and in relation to eukaryotic phytoplankton for V9-18S metabarcoding. The assignations of the 16S and V9-18S rRNA sequences to phytoplankton were based on literature and expert information and included photosynthetic dinoflagellates and chrysophytes when their taxonomic resolution was sufficient to match known photosynthetic lineages.
A full description of the 18S taxonomic classification procedure is at http://taraoceans. sb-roscoff. fr/EukDiv/ and the last version of the trait reference database used in the current work is available at https:// zenodo. org/record/3768510#. YM4ny3UzbuE. In the case of 16S miTags, the taxonomic assignment was improved by building a phylogenetic tree with the 16S miTags sequences and a curated set of references from NCBI and MMETSP. Sequences were aligned using MAFFT v7. 0  (Katoh & Standley, 2013)  with --auto setting option and then trimmed using trimal with the -gt 0. 5 and -gt 0. 8 settings, and the resulted alignment was used for tree building using RAxML v8  (Stamatakis, 2014)  (100 bootstrap replicates, GTRCAT substitution model).
Optical-based community data We also used quantitative optical data generated by Tara Oceans (Table  I ), where cell abundance is assumed to be more accurate and less biased, and additional features such as biovolume can be determined. The datasets cover: flow cytometry for picoplankton, confocal microscopy for 5-20 μm size fraction, and light microscopy for 20-180 µm size fraction. Flow cytometry counts were determined on 1 ml replicated seawater samples filtered through 200 μm that were fixed with cold 25% glutaraldehyde (final concentration 0. 125%) and stored at -80°C until analysis. Details about the procedure can be found in  (Gasol & Morán, 2015; Hingamp et al. , 2013; Pierella Karlusich et al. , 2021). The cell biovolume was calculated using the equation of  (Calvo-Díaz & Morán, 2006)  on the bead-standardized side scatter of the populations and considering cells to be spherical.
The 20-180 µm size fraction is also available, but the curated taxonomic annotation is limited to symbiotic (Richelia, Calothrix) and colony-forming (Trichodesmium) nitrogen-fixing cyanobacteria  (Pierella Karlusich et al. 2021) , which were also used in the current work. For light microscopy, three ml of each sample (from 20-180 µm size fractions) were placed in an Utermöhl chamber with a drop of calcofluor dye (1:100,000) which stains cellulose, thus allowing to better detect and identify dinoflagellates. Cells falling in 2 or 4 transects of the chamber were identified and enumerated using an inverted light microscope (Carl Zeiss Axiophot200) at 400x magnification. To be compared with the molecular data, the optical data were expressed as relative abundance (%). In the case of flow cytometry, as % over the total number of cells counted as picophytoplankton (Prochlocococcus + Synechococcus + eukaryotic picophytoplankton).
psbO database generation We compiled, curated and annotated a database of >18,000 unique psbO sequences covering cyanobacteria, photosynthetic protists, macroalgae and land plants (Figure  S1 ). It includes sequences retrieved from IMG, NCBI, MMETSP and other sequenced genomes and transcriptomes from cultured isolates, as well as from the environmental sequence catalogs from Global Ocean Sampling  (Rusch et al. , 2007)  and Tara Oceans  (Carradec et al. , 2018; Delmont et al. , 2020 Delmont et al. , , 2021;; Salazar et al. , 2019). The database can be downloaded from the EMBL-EBI repository BioStudies (www. ebi. ac. uk/biostudies) under accession S-BSST659. We expect to maintain it updated to facilitate its incorporation in molecular-based surveys.
Plotting and statistical analysis Graphical analyses were carried out in R language (http://www. r-project. org/) using ggplot2  (Wickham, 2016)  and treemaps were generated with treemap. Maps were generated with borders function in ggplot2 and geom_point function for bubbles or scatterpie package for pie charts  (Yu, 2018 ). Spearman's rho correlation coefficients and p-values were calculated using the cor. test function of the stats package. Shannon diversity indexes were calculated using the vegan package  (Oksanen et al. 2020). Intra-and inter-specific genetic distances were calculated in MEGAX  (Kumar, Stecher, Li, Knyaz, & Tamura, 2018)  using the Maximum Composite Likelihood model.
Marine phytoplankton community structure based on psbO shows remarkable differences with the traditional molecular approaches The abundance and diversity of phytoplankton was first examined in Tara Oceans samples by focusing on the traditional marker genes coding for the small subunit of rRNA (16S for prokaryotes and plastids, 18S for eukaryotes) in the different size-fractionated samples. We focused exclusively on the phytoplankton signal of these datasets, despite the uncertainties in assigning photosynthesis capacity in groups such as dinoflagellates and chrysophytes (this is evaluated in one of the next sections). Based on 16S miTag read abundance among picophytoplankton (0. 2-3 µm), the picocyanobacteria Prochlorococcus and Synechococcus were prevalent, while ~60% of the average read abundance was attributed to eukaryotic photosynthetic taxa such as haptophytes, chlorophytes, pelagophytes, dictyochophytes, chrysophytes, cryptophytes and diatoms (Figure  2A ).
2-3 µm), along with dictyochophytes and chrysophytes (Figure  2A ). In the larger size fractions, haptophytes, chlorophytes and pelagophytes clearly dominated the eukaryotic phytoplankton in the 0. 8-5 μm size fraction, whereas diatoms and dinoflagellates were more abundant in the three larger size ranges (5-20 μm, 20-180 μm, 180-2000 μm), although haptophytes, chlorophytes and pelagophytes were also detected in large quantities (Figure  2B ). The potential cyanobacteria present in these large size fractions are presented later in another section due to the need to bypass the sequences assembled from poly-A-tailed RNA for analysing prokaryotes (see Methods and Table  I ). We noted some differences in psbO read counts between metagenomic and metatranscriptomic datasets. In the case of picophytoplankton, Prochlorococcus was enriched in metagenomes in comparison to (total RNA) metatranscriptomes (likely due to low transcript content needs for their low cell volume), the opposite occurred for pelagophytes and haptophytes, whereas no major changes were observed for Synechococcus and chlorophytes (Figures  2A  and S8A ).