dataesr/scientific-paragraphs-categorization huggingface dataset:
Genetic diversity, demographic history and molecular dating We used  Dnasp 5. 10 [26]  to compute the number of haplotype (nH), haplotype diversity (H), nucleotide diversity (π) as well as the average number of nucleotide differences (k). Mean genetic distances within and between groups were computed using the p-distance and a pairwise deletion for the gaps/missing data treatment, as implemented in the Mega 5. 10 software  [27]. Demographic history of the haplogroups and the whole dataset was determined with different methods. Firstly, R2  [28] , Fu's Fs  [29]  statistics and their significance were calculated with DnaSP. Ramos-Onsins & Rozas  [28]  recommended using R2 when population sizes are small (~10) and Fs when sample sizes are large (~50). The historical demography of main haplogroups was also estimated based on the cyt b dataset using the skyline plot method (BSP;  [30, 31]  implemented in Beast 1.
8. 0. BSP analyses were performed on each group (including all cyt b sequences) separately with the cyt b partitioned according to codon position and using the HKY + G model as sequence evolution. The likelihood-ratio test performed with tree-puzzle 5. 2  [32]  rejected the strict molecular clock hypothesis (p < 0. 05). BSP analyses were thus conducted using a lognormal-relaxed molecular clock with a substitution rate of 0. 01973 per lineage per million years as estimated by Nabholz et al. [33]  for the Osprey cytochrome b. Although this rate has been obtained for the third codon position we apply it because most substitutions (83. 3 %) are located at this position in our dataset. Analyses were run for 50 million generations, sampled every 1000 generations, after discarding the first 10 % as burn-in.
Historical biogeography reconstruction Probabilistic inference of ancestral range was performed using BioGeoBEARS  [34]  as implemented in R. We performed six different models  [34, 35] , to reconstruct the biogeographic history of Osprey across continents. Each analysis allows for a different subset of biogeographical possibilities, such as dispersal, vicariance and extinction (see  [34, 35] ). Accordingly, we tested the following models: Dispersal-Extinction Cladogenesis Model (DEC), Dispersal-Vicariance Analysis (DIVA), Bayesian inference of historical biogeography for discrete areas (BayArea) and the same models including the founder-event speciation parameter ('j'), DECj, DIVAj and BayAreaj. Finally, statistical fit of the six different models were compared using a model choice procedure by means of the Akaike Information Criterion implemented in the R package BioGeoBEARS  [34, 35].
Phylogenetic relationships For the mitochondrial cyt b dataset (213 individuals = 209 ospreys + 4 outgroups), the best-fit scheme was a partitioning according to codon position with the models HKY + I for position 1 (367 nucleotides), TrN for position 2 (368) and K80 + G for position 3 (368). As the TrN substitution model was not available in MrBayes, the parameter Nst was set to 6. The partitioned ML analysis was performed with 1000 bootstrap replicates using a GTR + G substitution model for each codon position in RaxMl software. The average Bayesian posterior probabilities (pp) and bootstrap values (BP) for supported clades are shown on the tree in Fig. 2. The phylogenetic tree including the totality of individuals (n = 209) is reported in supplementary information (Additional file 4). Comparisons on cyt b sequences of ospreys (n = 146 individuals on 661 bp; see methods) returned 19 mtDNA haplotypes that were used to represent phylogenetic relationships (Fig.
The haplotype diversity (H), nucleotide diversity (π) and other statistics were computed for the four recognized haplogroups and the whole dataset combined (Table  2 ). Haplotype diversity was higher in the largest  The nucleotide diversity showed similar patterns between groups, being very weak in each lineage (range: 0. 00066-0. 00138). Overall, H was 0. 795 and π was 0. 0106 for all Pandion samples. Demographic history of the four phylogroups, as inferred on the basis of Fu's F S and R2 statistics (Table  2 ), indicate that only the AMER group yielded very significant values for both indices, whereas the EUR-AFR lineage showed a significant value for the Fu's FS only. Thus, population expansion can be assumed for the American and possibly for the Western Palearctic groups. By comparison, Bayesian skyline plots (Fig.
5 , Fig. 6 , step 3 Fig. 7 ). As a supporting case, cyt b data suggest that Japan was likely colonized after Russia and not directly from Australia. Such a scenario would explain the current distribution of Pandion across all continents and the genetic divergence found among the four lineages. This hypothesis is corroborated by the p-distances which displayed lower values between AMER and IND-AUS (p = 0. 020) than those recorded between AMER and EUR-AFR (0. 026). This result indicates that the EUR-AFR group did not originate directly from the AMER group, as also suggested by the lowest likelihood values yielded by the alternative biogeographical reconstruction (scenario "S2" in which EUR-AFR would have been colonized from AMER; Table  3 ).
Acknowledgements We thank all the colleagues who kindly provided Osprey samples:  Pertti Saurola ,  Harri Koskinen  and  Juhani Koivu  (Finland),  Urmas Sellis  (Estonia),  Aigars Kalvāns  (Latvia),  Miroslav V. Babushkin  and  Vladimir V. Ivanovsky  (Russia),  Martin Haase  and  Daniel Schmidt  (Germany),  Rolf Wahl ,  Franҫois Gossmann  and  Eric Pasquet (France) ,  Luis Palma  and  Ursula Hofle Hansen  (Portugal),  Manuel Siverio  ( Canary islands ),  Pedro López-Suárez  ( Cape Verde ),  Houssine Nibani  (Morocco),  Rafel Triay Bagur  (Balearic islands). Special thanks to  Yula Kapetanakos  and  Alan Poole (Cornell lab of Ornithology, Ithaca, USA)  for collecting and performing free analyses of 10 samples from the  USA. Many thanks are also addressed to the following museums and birds' collection curators who allowed us to gather samples from their collection:  Mark Adams  ( Natural History Museum of London; UK ),  Steven van der Mije  and  Bechy Desjardins  ( Netherlands Centre for Biodiversity Naturalis of Leiden ),  Isao Nishiumi  ( National Museum of Nature and Science of Amakubo; Japan ),  Mikhail Kalyakin ,  Pavel Tomkovitch ,  Yaroslav Red'kin  ( Zoological Museum of Moscow; Russia ),  Guillermo Delgado  ( Museo de la Naturaleza y el Hombre of Tenerife Island; Spain ) and  Enrico Borgo  ( Museo Civico di Storia Naturale Giacomo Doria di Genova ; Italy).
We also thank the  Portuguese Navy  and in particular the contra-almirante  José Luís Branco Seabra de Melo (Head of Cabinet of the Navy Chief of Staff)  and the capitão-de-mar-e-guerra  José Jaime Gonçalves Ribeiro  (Director of the  Vasco da Gama Aquarium)  for providing historical samples of Portuguese ospreys. All extractions from museum specimens were performed using the facilities of the platform " ADN dégradé" (Labex CeMEB , Montpellier, France) coordinated by  Christelle Tougard. Sequencing was supported by the project  SEQMED  attributed to CM by the " Bibliothèque du Vivant "  sequencing project  ( CNRS-INRA-MNHN ). Alan Poole provided valuable comments and improved the English of earlier drafts. This study was funded thanks to a grant from the  Foundation Prince Albert II de Monaco  and the  Associazone italiana delle Fondation Prince Albert II de Monaco ONLUS.
-However, the importance of the barotropic or baroclinic pressure gradient differs among models. In the model showing a weaker correlation between the Iceland-Scotland overflow strength and the AMO index, the wind stress in the Nordic seas also influences the overflow strength. -Our study demonstrates that palaeo-climate simulations provide a useful tool to understand mechanisms and large-scale connections associated with localised and rather sparse palaeo-observations. With respect to palaeo-climate simulations, the simulations of the last millennium performed within the framework of the CMIP5 and PMIP3 projects provide an excellent database for future studies.
Acknowledgements. This work was supported by the  European Community 's 7th framework programme ( FP7/2007 ( FP7/ -2013 ) ) under grant agreement no. GA212643  (THOR: Thermohaline circulation -at risk?,  2008-2012 ). K. Lohmann also received funding through the  Cluster of Excellence "CLISAP"  funded by the  German Science Foundation (DFG). D. Matei  was supported by the  Federal Ministry for Education and Research (BMBF) NORTH ATLANTIC  and RACE projects. The authors wish to thank  Davide Zanchettin  for a critical review of the manuscript prior to submission. Fruitful discussions with other colleagues at the  Max Planck Institute for Meteorology  are also acknowledged. The authors greatly acknowledge helpful comments by  Flavio Lehner  and one anonymous reviewer, which improved the manuscript a lot.
Acknowledgements We thank  Johanne Kerr  and  Sylvain Desbiens  (both  Parc de Miguasha, Quebec, Canada ) for loaning the specimens of Cheirolepis canadensis and for giving permission to section them. We are grateful to  Vincent Rommevaux  ( MNHN, Paris ) for his technical assistance with ground sections and to  Omar Boudouma  ( Institut des Sciences de la Terre de Paris, Université Pierre et Marie Curie, Paris, France ) for his assistance using the SEM facility at the  Service commun de Microscopie à Balayage  of the ISTEP. We also thank  Alessandro Minelli  ( University of Padova, Italy ) and  Patrick Laurenti  ( Université Paris 7-Denis Diderot, France ) for constructive discussions and papers that they sent us,  Gloria Arratia  ( University of Kansas, Lawrence, USA ) for useful comments on the draft, and the Editor,  Stephen Brusatte  ( University of Edinburgh, UK ), for his efficient handling of the draft.
Methods We collated the biological information compiled from three databases of expression quantitative trait loci (eQTLs) involved in cells of the immune system. We derived a list of single nucleotide polymorphisms (SNPs) that are functional in that they correlate with differential expression of genes in at least two of the databases. We tested the association of those SNPs with AIDS progression in two cohorts, GRIV and ACS. Tests on permuted phenotypes of the GRIV and ACS cohorts or on randomised sets of equivalent SNPs allowed us to assess the statistical robustness of this method and to estimate the true positive rate.
Increasingly popular, expression quantitative trait loci (eQTLs) quantify the relationship between genetic polymorphisms and gene transcription  [22, 23]. It has been proposed that eQTL are of the utmost important in the development of pathological traits  [24, 25]. In the present work, we have developed a novel, general-purpose pipeline based on the use of several eQTL databases as filters to preselect so-called functional SNPs. We used the databases "GHS_Express" by  [26] , "Gene Expression Analysis Based on Imputed Genotype" by  [27] , and "Genevar" by the Sanger Institute  [28] [29] [30]. These databases correlate each SNP with gene expression in a specific cell line. We preselected the SNPs exhibiting the most significant p-values in each database, and to further warrant a prominent functional impact on gene expression, we selected the ones concurrently found in two or three databases.
This restricted set of candidate functional SNPs was tested for genetic associations with AIDS using the GRIV cohort of extreme progression to AIDS  [4, [11] [12] [13] [14]  and using the Amsterdam Cohort Studies (ACS) cohort for replication  [31]. As an added benefit, the eQTL databases allow us to directly associate gene expression levels with AIDS progression. This unique approach selects a few hundred candidate SNPs to be tested for genetic association with AIDS, on the sole basis of their functional impact on gene expression, and it also differs from past candidate gene studies  [32]  since there was no gene pre-selection based on specific knowledge of AIDS biology. In this article, we describe how this approach was successful in unravelling novel, statistically-significant associations with biological activities particularly relevant to HIV-1 infection.
Functional SNPs We determined the modes of the associations between SNPs and gene transcription levels as described in the Methods section and found: 16,000 additive associations, 2173 recessive associations, 2050 dominant associations, 1133 overdominant associations, 3491 additive-or-dominant associations and 682 additive-or-recessive associations. Ambiguous modes were considered dominant for the remainder of the study, given the overwhelming majority of associations is additive. Using the threshold p = 10 -4 and the additive mode for eQTLs, we obtained a set of 1788 SNP/gene pairs corresponding to 1706 distinct SNPs and 567 distinct genes. The set of SNP/ gene pairs is made of 73 pairs common to all three databases, 33 pairs common to Cambien and Genevar only, 527 pairs common to Dixon and Genevar only and 1155 pairs common to Cambien and Dixon only.
Linkage disequilibrium between those SNPs was removed. Pairs of SNPs with more than r 2 ! 0. 2 were identified using the Broad Institute's 'SNAP Pairwise LD'. (See S2 File for details). Such pairs define a 'linkage disequilibirum graph', where SNPs are vertices and edges connect SNPs in linkage disequilibrium. The connected components of this graph are calculated and a representative SNP is chosen for each connected component (for convenience's sake, we chose the SNP with the lowest ID in dbSNP). Generally, though not always, consistent groups of genes are regulated among the SNPs within a connected component. We obtain a set of 655 independent SNPs (see S1 Table ). Schematic summary of our methodology. The data from three databases are integrated to provide us with functional SNPs likely to be associated with changes in gene transcription in the tissue of interest.
Statistical significance: estimation of the sensitivity To demonstrate that the number of significant hits really arises from associations between polymorphisms and the phenotypes, we carried out two sets of randomisations: (1) phenotype randomisation to show that the choice of the functional SNPs is itself not sufficient to warrant significant hits, (2) SNP randomisation to show that neither is the spectrum of phenotypes is. Those randomisations come with the added benefit that we can estimate the false discovery rate and its distribution (hence, of the sensitivity of our method). We carried out 1000 phenotype randomisations and 36 SNP randomisations (12 of which are sampled from the eQTL databases). The results are shown in Fig  2. The phenotype randomisations allow us to estimate two useful, complementary measures from the distribution: the fraction of expected false positives ('sensitivity') and the likelihood of Table  1.
An association between MRPS7's expression and rs2242229 is also reported in the Geuvadis database (p = 3. 4×10 -23 )  [23]. The identification of DNAJB12 as a potentially important gene is reminiscent of the observation that the HSP40/DNAJ family proteins play a role in infection of various viruses. Urano and colleagues identified DNAJ/HSP40B6 as a potential regulator of HIV-1 replication  [40]. It is an interesting finding considering the attention that another chaperone, namely HSP90, has recently attracted: HSP90 could promote infectiousness of HIV by controlling HIV reactivation from latency  [41]  and several inhibitors of HSP90 are currently in clinical development  [42]. This is consistent with our observation that a lower expression of DNAJB12, which is part of Table  2. List of SNP/gene pairs associated with AIDS progression.
The genetic association is linked back to its association with gene expression levels to provide an association between transcription levels (we use the word 'regulation' for convenience's sake) and AIDS progression. the HSP90 pathway  [43] , correlates with slow or non-progression towards AIDS. DNAJB12 also interacts with many proteins known to be associated with HIV infection: among the twelve protein-protein interactions reported by InnateDB  [44] , eight proteins (DSTN, EGFR, HSPA8, MME, MYC, SGTA and UBC) are also found in the HIV-1 Human Interaction Database  [45]  (see S1 File). The association of low expression of chaperone-coding gene CCT8 with slow-and non-progression also points to a role of chaperones in promoting HIV infection; also of interest is the observation that CCT8 is one of the proteins to be differentially regulated in synaptosomal isolates from HIV/gp120 transgenic mice  [46] ; an association between CCT8's expression and rs2205418 is also reported in the Geuvadis database (p = 1.
Discussion Genomewide association studies have successfully allowed the confident discovery of many factors involved in human diseases. However, they haven't yet told the whole story. In AIDS, they have mostly yielded associations in the HLA region and in relation with the CCR5 region  [12]. Still, there obviously is a gap between our current ability to detect genetic associations and our capacity to predict the risk based on genetics alone; the 'missing heritability' hints that there remain difficult-to-identify genetic markers  [15, 51]. Larger cohorts are used to overcome this  [17]  but another route consists in integrating data from various sources. This study provides a successful implementation of the second solution. The rationale behind our work is that the integration of eQTL data provides us with a reliable SNP/gene map, not without similarities with the concept of 'eSNP' (genetic variants directly associated with higher or lower transcript expression levels), which is more likely to point towards functional and causal factors.
Using three gene expression databases (GHS_Express, Gene Expression Analysis Based on Imputed Genotype and Genevar), we could identify polymorphisms more likely to play a functional role. Our approach is substantiated by the number of SNP/gene pairs confirmed in the GTEx and Geuvadis DBs. The randomisations involving SNPs sampled from the eQTL databases highlight the value of integrating data from several databases. We looked for genetic associations in GRIV and replicated those in ACS and found eight independent SNPs significantly associated with slow or non-progression to AIDS. An important facet of this study was the evaluation of the statistical robustness of our findings, which confidently supports a significant of positive associations. eQTL databases were not only essential in the preparation of a carefully-selected set of functional SNPs, they also were instrumental in identifying candidate genes whose expression profiles could be more directly associated with the AIDS progression phenotypes: ENTPD4, CCT8, SLC35B4, MRPS7, MRPS10, DNAJB12, ZFP57, HCG27 and GUCA1B.
Overall, we have found in the existing literature and in published datasets compelling biological grounds for the possible implication of the genes identified in this study in progression to AIDS. Interestingly, for six out of those eight genes, down-regulation is associated with non-progression, which makes them appealing targets to combat HIV infections. Importantly, our study revealed a number of yet uninvestigated candidate genes, which can further our understanding of AIDS infection and AIDS progression as well as facilitate the discovery of new drugs. Researchers working on other diseases could easily apply this method to their own genomewide datasets. The set of 655 functional SNPs is provided as S1 Table and is available from http://www. griv. org/functSNPs/.
Sets of functional SNPs In this work, we investigated associations between gene expression and AIDS progression. The associations were sought through the selection of 'functional SNPs', known to be associated with changes in gene transcription levels; informally, we can define a functional SNP as a SNP likely to have a direct biological action through gene expression. We achieved this by integrating the data obtained from three separate mRNA-expression databases: GHS_Expression (hereafter referred to as the 'Cambien' database)  [26] , Gene Expression Analysis Based on Imputed Genotype (hereafter referred to as the 'Dixon database)  [27]  and Genevar  [28] [29] [30]. (See S2 File for details). Data integration. Formally, a SNP s is considered functional if it meets the following criterion: there exists a gene g such that the genotype of s consistently and significantly (reported pvalue is less than 10 -4 ) correlates with the transcription levels of the gene g in at least two of the three databases Cambien, Dixon and Genevar.
This selection warrants high confidence in the selection. The mode (additive) must be coherent throughout the datasets and the correlation must be consistent including across different probes within a single dataset. The set of functional SNPs is a list of elements ({S, s}, g, m, d, D), where {S, s} is a biallelic SNP (defined as a major/minor pair of alleles), g is a gene, m is the mode in which the allele s is associated with an altered expression of the gene g, d is the direction of regulation (+ or-) and D the datasets where the regulation is observed (Cambien, Dixon or Genevar). The threshold p < 10 -4 was partly constrained by the fact that the Cambien database only reports associations for which the p-value is less than 10 -4 already. Had the data been complete, other choices of threshold could have been made, provided the significance of the results could be ascertained using the randomisations described below.
The project was funded by  Agence Nationale de Recherche sur le SIDA et les Hépatites  (fellowship to PR) by Peptinov and by the  Fondation pour la Recherche Médicale  (to JN, grant number  ING20140129444 ). The funder  Crucell Holland BV  provided support in the form of salaries for authors HS and DvM, but did not have any additional role in the study design, data collection and analysis,.
Phylogeny The determination of the almost complete 16S rRNA gene sequence of strain L21-RPul-D2 T was already described in  [7]  and deposited in the GenBank/EMBL/DDBJ databases under the accession number KC665949. Phylogenetic trees based on almost complete 16S rRNA gene sequences with a minimum length of 1300 nucleotides were reconstructed using distance matrix (neighbor-joining), parsimony and maximum-likelihood programs included in the ARB package  [14]. The dataset of aligned and almost complete 16S rRNA gene sequences was based on the ARB SILVA database release 111 (July 2012)  [15]. Based on the comparative sequence analysis of 16S rRNA genes strain L21-Rpul-D2 T was most closely related to Spirochaeta species. The genus Spirochaeta comprises currently more than 20 species with validly published names  [16].
Accordingly, the 16S rRNA gene sequence identity values among type strains of the genus Spirochaeta are in the range of 99. 6 to 82%, which is typically found among strains related at family or order level, but not within a genus. Several recent taxonomic studies have identified a 16S rRNA gene identity value of 95% or above for strains belonging to one genus  [8, 9, 19]. This indicates that the genus Spirochaeta is currently only loosely defined on the molecular level and comprises several misclassified species. In reconstructed phylogenetic trees based on 16S rRNA gene sequences strain L21-Rpul-D2 T was affiliated with high bootstrap support to a clade of Spirochaeta species isolated from alkaline or saline environments (Figure  1 ). A BLAST search against the nr/nt database including 16S rRNA gene sequences of non-cultured bacteria resulted in several hits with identity values above 97%.