dataesr/scientific-paragraphs-categorization huggingface dataset:
Of course, extra information coming from other structure-and/or sequence-based algorithms should be used at each step in conjunction with our algorithm, if possible, to single out interesting sites. As a final observation, we note that our choice to attribute an equal weight to the three indicators in constructing the combined score S i in eq. (  9 ) is arbitrary. It would be interesting to inquire whether there exists an optimal combination of weights For example, one may imagine to use standard opti- mization techniques  77, 78  or genetic algorithms  79  to efficiently determine an optimal set of weights, by training our algorithm on the CSA and other databases. 9 ).
ACKNOWLEDGMENTS P. L. W. is a founding member of the EAPCRI, received project funding from  Myconostica ,  Luminex , and  Renishaw Diagnostics , was sponsored by  Myconostica ,  MSD , and  Gilead Sciences  to attend international meetings, was on a speaker's bureau for Gilead Sciences, and provided consultancy for  Renishaw Diagnostics Limited. R. A. B. is a founding member of the EAPCRI, received an educational grant and  scientific fellowship award  from  Gilead Sciences  and  Pfizer , is a member of the advisory board and speaker's bureau for  Gilead Sciences, MSD, Astellas , and  Pfizer , and was sponsored by  Gilead Sciences and Pfizer  to attend international meetings. J. L. is a founding member of the EAPCRI, received an educational grant and  scientific fellowship award  from  Pfizer , and was sponsored by  Astellas  to attend international meetings.
The model runs on an annual basis, using subbasins averaging 180 km 2 , and was developed to estimate nitrogen and phosphorus load to surface water at the European scale. It was calibrated against monitored data for 1985-2005 for total nitrogen and total phosphorus for all of Europe (Bouraoui et al 2011, Grizzetti et al 2012). The calibration provided satisfactory results (see paragraph 3. 1). For this study we extracted the values relative to the Seine river basin at Poses (figure  1 ). In the GREEN model, the Seine river basin at Poses is discretized into 351 sub-basins. Total nitrogen retention values (annual loads) were considered to compare the models (table  S1 ).
SWAT model The SWAT model is a physically based model of water and nutrient processes at the river basin scale  (Arnold et al 1998). The model is based on the Hydrologic Response Unit (HRU) concept, where a HRU represents the sum of areas with an identical unique slope, soil and land use combination in a sub-basin. The responses of each HRU in terms of water, sediment, and nutrient losses are calculated by the model, then aggregated at the sub-basin level and routed to the catchment outlet through the channel network. SWAT simulates nitrogen cycling and losses in various forms. Nitrogen pathways include surface runoff, sediment, drainage and ground water (table  S2 ). The spatial discretization used in the SWAT model was 45 sub-basins and 373 HRUs (figure  1 ) (table  S1 ). We developed a map for the land use based on the spatial information of the Corine Land Cover data base and the information on agricultural practices by PRA small agricultural regions (PRA, petites régions agricoles) available from INRA (Institut National de la Recherche Agronomique)  (Mignolet et al 2007 , Billen et al 2013).
We established the major crop rotations per PRA region and we randomly distributed them (respecting the share per region) within the agricultural land reported by Corine Land Cover. The soil map was available from the European Soil Database  (ESDB 2004 , Panagos 2006 , Panagos et al 2012). Climate data were taken from SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie, Météo-France), establishing nine weather stations for the simulation (their location is shown in figure  1 ). For point sources from wastewater treatment discharges, we used the data developed by Passy et al (2013) (table  S3 ). The SWAT model was calibrated over five years (from 2001 to 2005) and validated on the previous period of six  years (1995-2000)  at the Poses gauging station (figure  1 ).
The model performance was also evaluated at two upstream stations, corresponding to two major tributaries of the Seine, the Marne (at Gournay-sur-Marne) and the Yonne (at Courlon-sur-Yonne) (figure  1 ). Water flow (Q), nitrate (NO 3 ) and phosphate (PO 4 ) concentrations were simulated using daily values. Annual and monthly loads of NO 3 and PO 4 were calculated using the flow-weighted concentrations according to  Maotar and Meybeck (2005, Method 5 ). For the computation of water nitrogen retention, annual loads of total nitrogen simulated by SWAT were used (but for the model calibration only data on NO 3 and PO 4 were available).
Model performance The goodness of fitness of the simulations was computed by the coefficient of efficiency E (Nash and Sutcliffe 1970), which takes values between one and minus infinite (with one indicating perfect agreement between observations and model simulation, values near zero meaning that the model is equivalent to using the mean of the data, and negative values indicating poor agreement between observations and model simulation), and the coefficient of determination R2  (Bennett et al 2013). For the RiverStrahler model, the performance of the calibration reported by Passy et al (2013) is quantified by the Bravais-Pearson coefficient of correlation. To quantify and compare the error of the models, we computed the mean absolute relative error (MARE)  (Bennett et al 2013) , using as observations the data used in the respective calibrations (because the models were calibrated using different data sets).
Modelling performance The performance of the calibration/validation of the three models is summarized in table  S4. It is important to note that the models were calibrated in different contexts and using different data sets. The GREEN model was calibrated at the European scale using annual values of total nitrogen loads: the model performed satisfactorily (E = 0. 87 for the basins in the North Sea and Atlantic Sea region), considering the temporal and spatial scale of the application and the data availability. However, the model was not calibrated specifically for the Seine river basin (table  S4 ). On the other hand, the RiverStrahler model was developed and applied specifically to the Seine river basin, and the processes of nutrient cycling mediated by the biota are described based on laboratory experiments and field observations in the region.
This suggests that an unknown process, selective or neutral, is opposing this universal mutational bias by favouring the fixation of G/C alleles Previously, an analysis of a large number of E. coli genomes had suggested a possible role of gBGC, based on the link between GC-content, recombination and the organization of the chromosome in this species  [24]. However Hildebrand et al. [23]  observed that the excess of G/C!A/T mutations was still present after removing datasets with evidence of recombination. Moreover they found no correlation between GC-content and recombination rate across bacterial species. They therefore concluded that this force could not be gBGC and hence that selection was driving an increase of genomic GC in Bacteria. The nature of this selective advantage remains however mysterious, though various hypotheses have been proposed  [25, 26].
Here we argue that the analyses performed by Hildebrand et al. [23]  are not conclusive regarding the gBGC hypothesis, and we present evidence that variations in GC-content observed in Bacteria are influenced by gBGC. One pervasive signature of gBGC is that genomic regions undergoing high recombination rates will also acquire a high GC-content  [6]. We thus studied the relationship between recombination and GC-content in 20 groups of Bacteria and one group of Archaea. This dataset covers a wide range of clades representative of the bacterial diversity. To avoid problems inherent to comparisons of recombination rates among species (such as differences in polymorphism, genome samples, population size, mutation rates, an other life history factors), we examined the intragenomic variability for both recombination and GC-content.
A universal relationship between recombination and GC% in Bacteria In Bacteria, recombination occurs in the form of gene conversion (i. e. unidirectional transfer of genetic material from a donor sequence towards a homologous recipient sequence). To detect past gene conversion events in bacterial species, it is necessary to compare closely related genomes. We therefore selected in the database of homologous gene families HOGENOM (release 6)  [27]  all groups of closely related species or strains encompassing at least 6 sequenced genomes. This dataset contains 20 bacterial groups and one archaeal group. For each gene family represented in these groups, we computed i) the average GC-content at different positions of codons and ii) the index of recombination provided by PHI  [28]  based on alignments of standardized length (see methods for details).
In Eukaryotes, a general relationship between various estimates of recombination rate and the GC% of genes has been documented and provides indirect evidence for gBGC. Our first goal was to test this prediction in Bacteria and Archaea. To exclude a potential effect of the number of genes in the alignment on our estimates of recombination (because alignments with more sequences are expected to give more power to detect recombination), we focused on single-copy genes of the core genome (i. e. genes that are present in only one copy and found in each genome of a group). In 7 of the 21 groups, the proportion of single-copy genes of the core genome with evidence for recombination was very low (<2% of all gene alignments tested), suggesting that these species are clonal or nearly so (Table  1 ; shaded datasets in Fig.
The "selection model" sketched above predicts that the frequency of all optimal codons (both GC-ending and AU-ending) should increase with recombination. In contrast, a model incorporating the effect of gBGC predicts that GC-ending codons (not specifically optimal codons) should be enriched in recombining regions, and that AU-ending codons (and Total number of genes in the core-genome, as well as the number of core genes classified as recombinant and non-recombinant based on PHI analysis and unclassified ones (genes with insufficient signal to test for recombination, excluded from comparison tests) are indicated. The mean proportion of GC (all positions of codons) and GC3 (third position of codons) of core genes are shown for each dataset. doi:10. 1371/journal. pgen. 1004941. t001 possibly AU-ending optimal codons if gBGC is strong enough to override selection on codon usage) should display the opposite pattern.
01). This is possibly because the prediction of recombination status of intergenic regions is indirect (based on the status of flanking genes) and therefore less accurate than that of coding regions. However, the number of cases where the difference in GC-content between the two classes is positive (11 out of 14 comparisons) is significantly higher than expected by chance (Chi-squared test: p = 0. 03). 1 ). doi:10. 1371/journal. pgen. 1004941. g001 Figure  2. Effect of recombination on codon usage of core genes. Difference in frequency of optimal (fop) or non-optimal (fnop) codons (as determined by RP method) in recombining and non-recombining genes in each dataset for AU-ending (redish colors) and GC-ending (blueish colors) codons. The recombination status of genes was determined as in Fig.
1 , only datasets with more than 10% recombining genes are shown. A positive difference indicates that recombining genes are enriched in a category of codons, while a negative difference indicate depletion. Stars indicate significance of a Student's t-test between recombining and non recombining genes. Colored boxes on the right of dataset names indicate the numbers of AU-ending and GCending optimal or non-optimal codons used by the taxon (detailed in S2 Table ). Symbols and dataset abbreviations as in Fig. 1 ; shading is only used to distinguish between datasets. It should be noticed that variations in fopGC and fnopAU (resp. fopAU and fnopGC) are not totally independent (typically, for all amino-acids encoded by two synonymous codons, if the optimal codon is GC ending, the non-optimal is ATending).
Thus, the observed correlation is probably an underestimate. To try to get more robust estimates of rho, we binned the dataset into 20 groups of genes according to their GC3, and we computed the correlation between the average GC3 and the average rho of each bin. Using this approach, we observed a strong correlation between the GC3 and recombination (R 2 = 0. 60; Fig. 3A ). For a comparison, we performed a similar analysis in humans: we randomly selected 600 human genes (which corresponds to the average number of genes analyzed in our bacterial data sets), binned the data set into 20 groups of genes according to their GC3, and we computed the correlation between the average GC3 and the average recombination rate as obtained from population-wide surveys  [42]. The average correlation (computed after repeating the random sampling 1,000 times) is R 2 = 0.
If optimal codons tended to be GCrich, recombination could drive GC% higher by favoring the adaptation of genes to better For Hsap, recombination rate is expressed as cM• Mb-1; a subset of 600 genes out of the 16,346 human genes is shown as a representative of 1,000 random samples (mean R 2 is 55%, see Main Text). For Spyo, recombination rate is expressed as the value of rho parameter in ClonalOrigin  [41]  inferences, which is scaled by arbitrary coalescent time units; a subset of 437 genes out of 478 core genes was used, after removal of the 41 genes showing no convergence of the rho estimate (correlation on the full 478 core genes yields a R 2 of 31%, see S1 Text). (B) Correlation between GC3 and P REC , the proportion of genes detected as recombinant by PHI test  [28]  in the class, for all 14 bacterial datasets showing sufficient evidence of recombination (Table  1 ).
60; Fig. 3A ). To investigate the amplitude of this relation in the other bacterial species studied here, we used P REC (an index based on the proportion of genes in a bin that are detected as recombinant by PHI), which provides a good estimate of the average recombination rate in a bin, and is much easier to compute than ClonalOrigin's rho. We observed a significant correlation for 11 of the 14 species, and these significant correlations were positive in all cases, with R 2 values comprised between 0. 24 and 0. 68 (on average R 2 = 0. 43; Fig. 3B ). Thus, in many bacteria, the average recombination rate in a bin is a good predictor of its average GC-content. We performed an analogous analysis in human genes using jackknife sampling of the dataset to scale it to the size of bacterial datasets.
pylori could hence be that b 0 is null in this species, in compensation of a very high rate of recombination. Although the MMR pathway is a good candidate as a molecular source of gBGC in Bacteria, the association of gBGC with MutSL genes is not straightforward. These genes are absent from three of our genome datasets, C. jejuni, H. pylori and Bifidobacterium longum, resulting from ancestral losses in Delta-Proteobacteria and Actinobacteridae, respectively  [52]. In H. pylori, we indeed find no evidence of gBGC while the genomes are recombining at high frequency  [50, 51]. In C. jejuni and B. longum, however, we observe patterns similar to the other bacterial datasets that are in support of the existence of gBGC, indicating that it does not depend on the presence of a typical MutSL complex.
The existence of gBGC in Bacteria and Eukaryotes however suggests that it may have been present in the last universal common ancestor of all cellular life forms (LUCA). Unfortunately, the only archaeal dataset matching our criteria was a group of Sulfolobus sp. genomes for which our analysis showed few evidence of recombination (Table  1 ), in agreement with the previously described isolation of endemic clades in this group  [36]. We do not claim that gBGC is the unique determinant of base composition in bacterial genome: in fact there is evidence that mutation patterns vary significantly among species  [22] , and these variations are expected to contribute to differences in genome base composition. However, the model we propose provides a simple explanation for several important results of comparative bacterial genomics.
Genomic datasets We used the HOGENOM database  [27]  to select sets of genome sequences comprising at least six closely related strains or species. The selection of closely related genomes was based on a genomic distance derived from the HOGENOM6 database. For each pair of genomes present in the database, we computed the best identity score, as obtained using BLAST, within each family of homologous proteins as defined in HOGENOM, and averaged these scores to obtain a global similarity score s. We then took 1-s as a distance and selected groups of genomes with at least 6 members and a distance lower than 0. 15. This criterion left 21 groups of species representing a variety of bacterial and archeal species (Table  1 ). For each gene family, CDSs were extracted using ACNUC Python API  [56]  and re-aligned with MUSCLE  [57]  using default parameters.
Detection of recombining genes Detection of recombination based on multiple alignments is expected to be sensitive to both the number of sequences aligned, and the total length of the alignment. For this reason, we used only the universal unicopy genes of each species, and selected the 900 central positions of each nucleotide alignment (remaining positions as well as genes shorter than this threshold were ignored in our analysis). We used the software Phipack implementing the PHI test  [28]  (parameters: window size of 100bp, p-value computed from 1,000 permutations) to test if a gene family alignment contained evidence for recombination. Only families for which the site permutation test could be performed were considered, i. e. where the phylogenetic signal was sufficient to accept or reject the hypothesis of recombination.
As Geneconv differs from the other methods on the nature of the reported evidence, it was not considered when defining a consensus classification. Individual tests and their consensus yielded quite different classifications of gene families, but all led to qualitatively very similar results (see S1 Fig. ,  S3 Fig. and S4 Fig. ). To test for the presence of gBGC, we performed Student's t-tests comparing the mean GC-content (at all or at each separate codon position) of recombinant versus non-recombinant core gene family sets. All alignments as well as the results of the various tests for recombination are available at ftp://pbil. univ-lyon1. fr/pub/datasets/DAUBIN/ Lassalle_PloS_Genet_2015.
Estimation of recombination rate We used ClonalOrigin  [41]  to estimate the recombination rate on full-length core gene family alignments of 900bp or more. As ClonalOrigin inferences are highly demanding in computation time and power, we only performed this analysis on the moderately sized dataset of S. pyogenes genomes (Spyo). The program was used with default parameter weights, running the MCMC with 1,000,000 burn-in generations and 500,000 generations sampled every 1,000 (command line was: 'warg -a 1,1,0. 1,1,1,1,1,1,0,0,0 -x 1000000 -y 500000 -z 1000'). Families for which this task could not be finished in less than 1 month computation were discarded, yielding a total dataset of 478 gene families (out of 496 core !900bp-long ones). A number of genes also exhibited very high variance in their estimates of rho and their exclusion yielded better correlations with GC3.
e. ending in A/U or G/C. As there is a debate on whether this method is appropriate to define optimal codons  [38] [39] [40] , we also used an alternative definition and took optimal codons datasets from a previous exhaustive survey of  Hershberg and Petrov [38]  (HCB method). A set of optimal codons was selected when determined in Hershberg and Petrov  [38]  study for several strains with a strong consensus, i. e. when >60% documented strains agreed on the preferred codon and remaining strains preferred a codon with the same composition (A/U or G/C) at the third position (found in all datasets but A. baumanii) (S3 Table ).
This work was supported by  French National Research Agency (ANR)  grant  Ancestrome  ( ANR-10-BINF-01-01 ) and  EcoGenome  ( BLAN-08-0090 ). (http://www. agence-nationale-recherche. fr/). FL received a doctoral scholarship from  Ecole Normale Supérieure de Lyon  (http://www. ens-lyon. eu/) and is supported by  ERC  grant  BIG_IDEA 260801  (http://erc. europa. eu). The funders had no role in.
Results Phylogeny, species monophyly, and dating. Bayesian phylogenetic estimations yielded resolved trees indicating that all three genera and most species are monophyletic with high support (Supplementary Text S1 and Supplementary Fig. S2 ). The only marked exception was Troides haliphron that was recovered as paraphyletic, suggesting that two molecular lineages with different evolutionary trajectories may be recognised (T. h. haliphron plus T. h. purabu occurring in South Sulawesi, and T. h. socrates plus T. h. naias in Sumbawa to Alor islands, Supplementary Fig. S2 ). This pattern was independently recovered using different molecular datasets and as a result we provisionally recognize two species within T. haliphron. The first putative haliphron species would include the subspecies in the Sulawesi region, i.
e. T. h. haliphron (Southwest Sulawesi), T. h. purabu (Batuata Island), T. h. pistor (Bonerate, Jampea, Kalaotoa, and Madu islands), T. h. pallens (Selayar Island) and T. h. eleonorae (Buton Island). The second putative haliphron species would comprise the two subspecies T. h. naias and T. h. socrates, occurring in the Lesser Sunda Islands (Alor, Flores, Solor, Sumba, Sumbawa, and Wetar islands), and possibly T. h. bellwoni (Lucipara Islands, Moluccas). We assembled a species-level dataset comprising 4,395 nucleotides (five genes) for 34 birdwing species and 31 outgroups that was used to conduct simultaneous inferences of species-level phylogenetics and divergence time estimates. The species-level phylogenetic tree yielded by the BEAST analysis was fully resolved and presented robust node supports (Fig.
Methods Taxon sampling and molecular dataset. About 90% of birdwing species were included (Supplementary Table  S5 ). This sampling encompasses all described genera and subgenera, including 182 specimens of birdwing butterflies representing all 14 known Ornithoptera species, 17 of 21 Troides species, and the two species of Trogonoptera. Only four species (Troides darsius, T. dohertyi, T. minos and T. plateni) were not sampled. Most specimens come from museum collections and permits were obtained by the first author for species included in CITES or IUCN Red lists. Based on the most comprehensive phylogeny of Papilionidae 41 , we also included representatives from all remaining Troidini genera in order to implement secondary calibrations. Thirty-one species of Troidini (from Atrophaneura, Battus, Cressida, Euryades, Pachliopta and Pharmacophagus) were added to the dataset for that purpose.
Three mitochondrial genes (cytochrome oxidase I, NADH dehydrogenase 5, and 16S rRNA) and two nuclear protein-coding genes (elongation factor-1a and wingless) were used. DNA sequences were either downloaded from GenBank  21, 23, 41, 42  or newly obtained using standard molecular protocols  42. Taxon sampling and GenBank accession numbers are given in Supplementary Table  S5. Phylogenetics and dating. Phylogenetic relationships were reconstructed under Bayesian inference (Supplementary Text S2). Gene trees were then used as a blueprint to generate a species-level dataset (with one specimen per species). Based on this reduced dataset we estimated divergence times with an uncorrelated lognormal Bayesian relaxed-clock approach. We used secondary calibrations based on a fossil-calibrated phylogeny of Papilionidae  41.