dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation through spectroscopy and self-organizing map We use a catalogue of almost 100 000 spectroscopic redshifts to quantify the uncertainties of our z phot estimates. These data were obtained during several campaigns, with different instruments and observing strategies (for a summary, see Table  4 of L16 ). They have been collected and harmonised in a single catalogue by  Salvato et al. (in preparation). Such a wealth of spectroscopic information represents an unequalled benefit of the COSMOS field. The z spec measurements used as a reference are those with the highest reliability, i. e. a confidence level (CL) > 95% (equal to a selection of quality flags 3 and 4 according to the scheme introduced by Le Fèvre et al. 2005). We limit the comparison to sources brighter than [3. 6] = 25, ignoring secure low-z galaxies (those having both z spec and z phot below 2.
These results (Fig. 4 ) summarise the improvement with respect to the photometric redshifts of L16: we reduce the number of catastrophic errors by ∼ 20%, and we also observe a smaller bias at 2. 5 < z < 3. 5 (cf. Fig. 11 of L16 ). The comparison between z spec and z phot is meaningful only if the spectroscopic sample is an unbiased representation of the "parent" photometric sample. Otherwise, we would test the reliability of a subcategory of galaxies only. We introduce a selforganizing map (SOM,  Kohonen 1982)  to show that our spectroscopic catalogue provides a representative sample of the underlying colour and redshift distribution. The algorithm version we use, specifically implemented for astronomical purposes, is the one devised in  Masters et al. (2015). The SOM allows to reduce a high-dimensional dataset in a bi-dimensional grid, without losing essential topological information.
Quiescent galaxies classification We also aim to derive the SMF of passive and active galaxies separately. As shown in the following, the quality of our dataset allows us to extend the classification up to z = 4. Galaxies that stopped their star formation occupy a specific region in the colour-colour diagrams (U -V) vs (V -J), (NUVr) vs (r -K), or (NUVr) vs (r -J), as shown in previous work  (Williams et al. 2009; Arnouts et al. 2013; Ilbert et al. 2013, respectively). We adopted the last one, dubbed hereafter NUVrJ. Compared to (U -V) vs (V -J), which is often referred as UV J, the use of (NUVr) makes NUVrJ more sensitive to recent star formation (on 10 6 -10 8 yr scales  Salim et al. 2005; Arnouts et al. 2007 ). This property results in a better distinction between fully quiescent galaxies (sSFR < 10 -11 yr -1 ) and those with residual star formation (typically with sSFR 10 -10 yr -1 ).
Then, we recompute the stellar mass of each X-ray emitter by means of a 3-component SED fitting code (sed3fit,  Berta et al. 2013 ). 9 This tool relies on the energy balance between dustabsorbed UV stellar continuum and the reprocessed emission in the IR (like MAGPHYS, da Cunha et al. 2008), and also accounts for an additional AGN component from the torus library of Feltre et al. (2012, see also  Fritz et al. 2006 ). X-ray luminous (L x > 10 44 erg s -1 ) AGN are usually hosted in massive (M 10 11 M ) galaxies (e. g. Bundy et al. 2008; Brusa et al. 2009; Hickox et al. 2009). They increase the exponential tail of our SMF at least at z 3, while at higher redshift the number of sources detected by Chandra or XMM is too small (Fig. 10 ). At z > 3, massive galaxies can host as well an active black hole and disregarding its contribution in the SED fitting may cause a stellar mass overestimate of 0.
Acknowledgements. The authors warmly thank the anonymous referee for her/his constructive comments. The authors thank  Shoubaneh Hemmati  and  Hooshang Nayyeri  for providing us with the CANDELS Multiwavelength Catalog in the COSMOS field, and  Andrea Grazian  and  Thibault Garel  for sending their results in a convenient digitalised format. ID thanks  Marta Volonteri ,  Jeremy Blaizot ,  Yohan Dubois ,  Andrea Grazian ,  Roberto Maiolino  for very useful discussions. ID and OI acknowledge funding of the  French Agence Nationale de la Recherche  for the  SAGACE  project. CL acknowledges support from a  Beecroft  fellowship. ID acknowledges the European Union's  Seventh Framework programme  under grant agreement  337595  ( ERC Starting Grant , " CoS-Mass ") AF acknowledges support from the  Swiss National Science Foundation.
Acknowledgements. AG is grateful to  Jean-Jeaques Guilhou ,  Jean-Claude Rossi  and the  INSERM  for professional support and to  Renata Polakowska  for the generous gift of precious BCCP cell line. We thank  Lucía Barbier ,  Tania Lobato ,  Evelyn Andrades ,  Alicia Noriega  and  María Aramburu  for technical assistance and  Natalia Sanz  for critical reading of the MS. To AG: National grants from  Instituto de Salud Carlos III, Fondo de Investigación Sanitaria (ISCIII-FIS/FEDER, Spain) :  PI08/0890 ,  PI11/02070 ,  PI14/00900 ;  Ligue Nationale Contre la Cancer  ( La Ligue ; France). To AT:  ISCIII-FIS PI10/00785. To JP: MINECO grant  SAF2015-66015-R; AES grant ISCIII-RETIC RD12/0036/0009. VC was funded by a fellowship from  La Ligue (France) , PA by  IDIVAL (Spain) ,  RM and IdP by AG lab  and  ISCIII-FIS-FEDER PI11/02070  (Spain).
| SNP calling and filtering Raw sequences were inspected with FastQC  (Andrews, 2010)  for quality controls. Reads were treated with Cutadapt  (Martin, 2011)  to remove potential fragments of Illumina adapters. A 10% mismatch was allowed in the adapter sequence. Stacks 1. 32  (Catchen, Hohenlohe, Bassham, Amores, & Cresko, 2013)  was used to demultiplex reads, identify RAD loci, and call single nucleotide polymorphisms (SNPs). Reads were filtered for overall quality, demultiplexed, and trimmed to 85 bp using process_radtags. One mismatch in the barcode sequence was allowed. BWA-MEM 0. 7. 13  (Li & Durbin, 2009)  was used to map individual sequences against the reference genome of the Great tit  (Laine et al. , 2016)  using default options. The total assembled contigs spans 1. 0 Gb, for an estimated genome size of 1.
2 Gb. We used samtools 0. 1. 19 to build and sort bam files  (Li et al. , 2009). We used pstacks, with a minimum stack depth of 5 (m = 5), the SNP model, and α =. 05. cstacks was used to build the catalogue of loci using n = 3. With sstacks, loci were searched against the catalog of loci. Finally, individuals were genotyped using the stacks's populations module and filtered using VCFtools  (Danecek et al. , 2011). Loci were retained if genotyped in at least 90% of individuals (all individuals from all sites grouped) with individual minimal read depth of 8 ("na" replaced genotypes below a read depth of 8), with a minimum average read depth of 20 across all genotypes, a maximum average read depth of 100 across all genotypes, and a minor allele frequency above 5% (MAF ≥ 0. 05) across all individuals (all individuals from all sites grouped).
We verified that each individual was genotyped for at least 95% of all loci. We filtered the entire dataset for deviations from Hardy-Weinberg equilibrium at the SNP level (HWE, p-value ≥. 01) in ROU. We did not filter specifically the other populations for HWE due to smaller sample sizes, and we considered that most of the significant deviations in every site would also be found in ROU given the high gene flow among the populations. This HWE filtering step was mainly applied to remove sequencing or SNP calling errors as well as paralogous sequences  (Waples, 2015). In turn, this should not interfere with the detection of divergent selection between urban and forest populations as HWE is estimated locally but not across sites. We investigated the average decay of LD in chromosomes 1-20 (the chromosomes on which most of the SNPs (86%) were found), in ZOO (as LD was very similar in all populations), using gdsfmt and SNPRelate  (Zheng et al.
, 2012). Although the data are not phased, this coarse estimate of LD was useful to evaluate the physical distance between outlier SNPs and neighboring genes that may be relevant for our functional analysis and to discuss the proportion of the genome covered by our RADseq analysis. Finally, we calculated identity-by-state IBS among individuals using the R packages gdsfmt and SNPRelate to (i) estimate consistency of SNP calling on the two replicated individuals, and (ii) identify highly related individuals. One individual was removed for each pair of highly related individuals (i. e. , siblings and parent-offspring) to produce a dataset called "no-family-ties. " The rationale for this procedure is that related individuals could artificially increase F st between groups of individuals (see also  Szulkin, Gagnaire, Bierne, & Charmantier, 2016).
| Investigation of genetic diversity, relatedness, and differentiation Genetic diversity was inferred through individual observed heterozygosity (Ho) on the no-family-ties dataset. Ho was estimated using VCFtools. Differences in individual Ho were investigated using ANOVAs in R, with individuals grouped in the six sites. Finer comparisons of Ho between pairs of sites were achieved using a Tukey's HSD test in R. Population structure was investigated via identity-by-state (IBS), fixation index (F st ), a principal component analysis (PCA), and a discriminant principal component analysis (DAPC). We calculated IBS among individuals of the entire dataset to determine whether relatedness among individuals differed between sites and to test whether we could infer dispersal events (via the presence of highly related individuals found in different sites).
Differences in IBS between sites were assessed in R using an ANOVA (see also  Szulkin et al. (2016)  for more details on the method). F st were estimated between the six sites using GenoDive  (Meirmans & van Tienderen, 2004) , which takes into account Weir and Cockerham's optimizations of Wright's theoretical index  (Wright, 1951)  to control for unequal sample sizes  (Weir & Cockerham, 1984). The significance of pairwise F st values was tested through 1,000 permutations in GenoDive. We estimated the F st using both the entire dataset and the no-family-ties dataset. We also estimated a global F st  (Weir & Cockerham, 1984)  between sites for each SNP using SNPRelate and represented with a Manhattan plot. The most likely number of genetic cluster was estimated using the function find. clusters from the R package adegenet  (Jombart, 2008)  on the no-family-ties dataset.
We used a principal component analysis (PCA) using the function snpgdsPCA implemented in the R package SNPRelate to depict genetic structure among the sites, using both the entire dataset and the no-family-ties dataset. Finally, we used a discriminant analysis of principal components (DAPC) implemented | 597 PERRIER Et al. in the R package adegenet to depict genetic structure among the sites, using the no-family-ties dataset. We used the cross-validation procedure from the adegenet package using the function xvalDapc, to identify the optimal number of principal components, reassigning 30% of the individuals, with a training set based on 70% of the individuals and replicating 100 times the procedure for each set of PCs from 10 to 45 by steps of 5, and then from 5 to 15 by steps of 1.
| Effect of geographic distance and of urbanization on genetic diversity, sites differentiation and individual relatedness First, we estimated the correlations between urbanization and observed heterozygosity, at the site level and at the nest level. Second, to infer the effect of geographic distance and of urbanization difference on dispersal and genetic differentiation, we inferred correlations between (i) F st (estimated on the no-family-ties dataset) and geographic distance, (ii) F st and urbanization difference, (iii) IBS (estimated on entire dataset) and geographic distance, and (iv) IBS and urbanization difference. The correlations of either F st or IBS with geographic distance are used to infer potential isolation by distance, whereby dispersal and accumulation of genetic differentiation increase with geographic distance between populations.
The correlations of either F st or IBS with urbanization difference are used to infer potential habitat choice, whereby dispersal and accumulation of genetic differentiation increase with habitat differences. F st is more likely to capture long-term accumulation of allele frequency differences while IBS captures the present time distribution of relatedness and therefore the dispersal events. Third, to investigate simultaneously the influence of latitude, longitude, and urbanization on the genetic distance among individuals (using the entire dataset), we used a redundancy analysis (RDA), which is a constrained version of PCA  (Legendre & Fortin, 2010; Legendre & Legendre, 2012) , implemented in the Vegan R package  (Oksanen et al. , 2007). Using this RDA, we first investigated the portion of the genetic variability that could be explained by a constraining covariance matrix consisting of latitude, longitude, and nest-level urbanization for each individual.
| Search for SNPs, genes, and Gene Ontologies associated with urbanization We In contrast, ZOO individuals closer to forest birds would suggest relatively important response to selection at small spatial scale, or of habitat choice influenced by outlier loci. Five runs (10,000 burn-in, 100,000 iterations) per LFMM test were used to obtain average z-scores, representing the strength of the association between a SNP and an explanatory variable, and associated p-values, assuming a unique genetic cluster. We displayed the Z-scores with Manhattan plots. We estimated q-values from LFMM's p-values, for each of the six tests separately, using the R package fdrtool  (Strimmer, 2008)   high association with urbanization. We reported a table of the SNPs found in the top 10 of each test, to provide more functional details on the putative genes in which these outliers were found.
We performed PCAs, using the function snpgdsPCA implemented in the R package SNPRelate, followed by linear models in R, to investigate the proportion of variance in individuals' scores of urbanization at the nest and at the site explained by the genetic distances between individuals when considering several outlier lists of SNPs with q-values <. 05, as well as the entire set of SNPs. This procedure was achieved to inspect the power of gene-urbanization associations. Either the PC1 or PC2 or both were considered to explain individual nest-or sitelevel urbanization, using a linear model in R. The PCA, realized with the list obtained from tests D, E, and F, was furthermore used to visually inspect the genetic distance of ZOO with other sites. To assess to which extent genomic associations with urbanizations were polygenic, we inferred, using linear models, the proportion of variance in nest-level urbanization that was explained by PCAs using an increasing number of SNPs, from 1 to 49,969, sorted by decreasing Z-score in test A.
| Genetic diversity, relatedness, and differentiation There was a general pattern of reduced diversity in individuals from the four most urbanized city sites, in particular MNO, compared to ZOO and to ROU. The observed genomewide observed heterozygosity (Ho) was different among sites (ANOVA: F = 11. 6, p-value =. 000866, Table  1 ). Ho was lower in MNO (the most urbanized site) than in ROU and in ZOO. Ho was lower in GRA than in ROU. Pairwise IBS between individuals ranged from 0. 73 to 0. 89 and was in average of 0. 75 (Figure  2a, b ). Average IBS values were not significantly different between the sites, using either the entire dataset (ANOVA: F = 1. 99, p-value =. 08) or the no-family-ties-dataset (ANOVA: F = 1. 62, p-value =. 15). The F st ranged from 0. 004 to 0. 009, for an average of 0.
59; r 2 BE >. 58; r 2 CF =. 75, p <. 001). In turn, the As expected, the PCAs using outlier SNPs revealed patterns of genetic structure linked to urbanization (Figure  7 ), in contrast with the PCA using the entire SNP dataset that showed very little genetic structure linked to urbanization (Figure  3b  and d ) but rather a geographic structure (Figure  3a  and c ). The first axis of the PCA based on the entire set of SNPs explained 3% of the variation in urbanization score at the nest (Table  4 ). Taken together, the first and second axes of this PCA explained 6% of the variation in urbanization score at the nest. Similar results were obtained for the site-level urbanization scores, the first axis of the PCA explained 4% of the variation    8a ) with the increase in the number of SNPs added to the model, by decreasing Z-score from test A.
The increase in r 2 was particularly sharp, with the 45 first SNPs (q-values <. 05, Figure  8b ) explaining 66% of the variation in urbanization at the nest, 80% being reached with 123 SNPs and 85% with 240 SNPs. F I G U R E 2 (a) After a plateau, the proportion of variance explained in urbanization decreased down to 3% explained with the entire dataset of SNPs (Figure  8a ). In contrast, there was little effect of the number of SNPs on the percentage of variation explained when SNPs were added randomly to the PCAs (Figure  8a ,b, gray circles). SNPs from the entire dataset were found in 5276 genes. 266 genes were identified among the 661 SNPs found at least once with a q-value <. 05 (Appendix S6). None of the seven SNPs below 0. 05 q-values at the intersection of the six tests were found in genes.
used latent factor mixed model (LFMM; Frichot, Schoville, Bouchard, & Francois, 2013)  on the entire dataset to identify SNPs potentially under divergent selection along the urbanization gradient. As urbanization may act as a selection force at different spatial scale on great tits, we implemented three tests using fine-to largescale incorporation of urbanization. We first used the fine-scale nestlevel urbanization values (50 m radius from their nest) as explanatory variable (referred as test A). In test B, we used site-specific average urbanization. In test C, we used a binary test comparing ROU (coded as 0) and the city sites (coded as 1). These three tests were performed again, excluding the ZOO (referred as tests D, E, F). This was motivated by the fact that birds in the Zoo may experience similar selection pressures as ROU birds (as similar low urbanization values were observed), while being in the perimeter of the city.
ACKNOWLEDGEMENTS This project was funded by the  European Research Council  (Starting grant  ERC-2013-StG-337365-SHE  to AC) and the  OSU-OREME. We thank everyone who assisted in the fieldwork since 2011, in particular  Samuel Perret ,  Marcel Lambrechts , the  City Council of Montpellier  and the  Zoo de Lunaret (Montpellier)  for logistical support,  Annick Lucas  for laboratory work, and  Pierre-Alexandre Gagnaire ,  Boris Delahaie , and  Veronika Laine  for discussions. We also thank  Nicolas Bierne  and three anonymous reviewers for their constructive comments on previous versions of this manuscript.
A new lineage of insect-dwelling Rhizaria As reported in Záhonová et al. [18] , the non-canonical code of Blastocrithidia spp. (Trypanosomatida) was discovered accidentally by one of us noticing that a transcriptome shotgun assembly (TSA) from the heteropteran Lygus hesperus  [25]  is contaminated by trypanosomatid sequences including in-frame termination codons. Surprisingly enough, looking for contigs in the TSA data from L. hesperus exhibiting in-frame termination codons revealed not only those with obvious trypanosomatid affinities, but also some that could not be readily assigned to any particular eukaryotic taxon when compared by blastx against the NCBI non-redundant protein sequence database. Therefore, we probed the TSA data from L. hesperus with a set of conserved proteins employed in previous phylogenomic studies of the global eukaryote phylogeny (see Methods for details) and detected 71 orthologs that clustered neither with metazoans nor with trypanosomatids in individual single-protein trees (Additional file 1: Table  S1A -C).
The resulting tree showed it branching with maximum support within Rhizaria, specifically within Filosa as a sister lineage to G. vulgaris (Additional file 2: Figure  S1 ). However, only very few representatives of Filosa could be included in the phylogenomic analysis due to an extremely low number of sequenced genomes or transcriptomes of this diverse group. Therefore, we sought to determine the phylogenetic position of the rhizarian exLh within Filosa using the 18S ribosomal RNA (rRNA) gene, the most broadly sampled phylogenetic marker for rhizarian phylogeny. Searching the L. hesperus TSA sequences revealed two contigs that proved to be chimeric sequences consisting from artificially merged parts of a different origin, including a 3' segment of an 18S rRNA dissimilar to any 18S rRNA sequence in the GenBank database (and very different from the L.
The presence of sequences from a rhizarian species in the L. hesperus TSA may be explained by an accidental contamination or an error in data handling in the sequencing center. Alternatively, it may reflect a specific physical connection between L. hesperus and the rhizarian, most likely with the latter being a symbiont of the former. To distinguish between these possibilities, we obtained several individuals of L. hesperus captured in the wild (Kern County, CA, USA), isolated DNA from whole individuals, and carried out a PCR reaction using specific primers designed on the basis of the assembled 18S rRNA sequence of the rhizarian exLh. One of the reactions (with a template consisting of DNA pooled from five L. hesperus individuals) yielded a product of the expected size of 1200 bp, and sequencing revealed that, except for two substitutions, it was identical to the respective region of the 18S rRNA sequence obtained from the published transcriptomic data from L.
No conflicting signal concerning the identity of the nucleotides corresponding to any of these UAG codons was apparent (each in-frame UAG was supported by more than one read, with the read variability lower than 4. 50%) (Additional file 1: Table  S1D ). As an alternative to the hyperconserved position-based inference of the UAG codon meaning, we devised a phylogeny-informed MLbased method that unselectively considers all UAG positions (see Methods for details). Briefly, we first inferred an organismal phylogeny using a smaller dataset of eight conserved proteins (to save computation time), with the respective genes from the rhizarian exLh containing 71 in-frame UAG codons, represented as an undetermined amino acid (X) in the alignment. We then prepared 20 modifications of the dataset, each with a different amino acid considered at positions corresponding to the inframe UAG codons in the genes from the rhizarian exLh.
Then, we calculated the best ML tree for each of the 20 datasets, using the same substitution model and the tree from the initial dataset as a constraint. The dataset where UAG was translated as leucine showed the highest likelihood score, and the conditional probability that UAG encodes leucine in the rhizarian exLh (calculated conditional upon UAG encoding one amino acid at all positions) is virtually 1. 0, whereas it is negligible for any other amino acid (Table  1 ). Based on these results, we conclude that UAG is the seventh codon for leucine in the rhizarian exLh and does not serve as a stop codon in this organism. This is not without precedent, as the stop-to-leucine UAG reassignment was previously reported from mitochondrial genomes of several green algae of the order Sphaeropleales  [22, 28]  and of the chytrid fungus Spizellomyces punctatus and its relatives  [21].