dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors thank  O. Martin  for helpful discussions on data analyses and interpretations. This study was supported by the  French INRA  to A. R. , P. M. , L. S. G. , and  J. J. ;  French CNRS  and the  Marie Curie IEF  Grant  SELFADAPT 623486  to S. G. ; and the  University of Paris-Sud  to C. D.
Following transfection, cells were incubated for 48 h and treated for another 12 h with JH III, methoprene, pyriproxyfen, MF or farnesol (all dissolved in ethanol). The cells were then processed with the Dual-Luciferase reporter assay system (Promega). Relative luciferase activity was measured using the Orion II microplate luminometer (Berthold Detection Systems) and data were normalized against Renilla luciferase activity.
Acknowledgments We are grateful to  Thomas G. Wilson ,  Jian Wang , and  Aaron Baumann  for  Met  and gce Drosophila stocks; other strains were provided by the  Bloomington Drosophila Stock Center. We thank  Jinsong Zhu  and  Denise Montell  for the tai expression plasmid. Technical assistance of  Tina Bresser  and comments from  Michael Ashburner ,  Lynn Riddiford ,  Claude Desplan , and  Lloyd D. Graham  are appreciated.
This work was funded by:  Marie Curie International Outgoing Fellowship Award 276569  (http://ec. europa. eu/research/mariecurieactions/about-mca/actions/iof/index_en. htm) to MJ;  Sofja Kovalevskaja Award ,  Alexander von Humboldt Foundation  (http://www. humboldt-foundation. de/web/ skp-2008-en. html)to MU;  CSIRO Advanced Materials Transformational Capability Platform  (http:// www. csiro. au/Organisation-Structure/Flagships/Foodand-Nutrition. aspx) to RJH and MJ. The funders had.
Such shared direction of main morphological variance may occur and have a biological meaning, for instance corresponding to the most frequent standing genetic variation in a population. Here we undertake a case study of the evolution of house mouse molar shape across various islands, based on the real dataset and simulations. We investigated how patterns of main variance influence the depiction of among-group differentiation according to the interpretation of the PCA, bgPCA and CVA. Without arguing about a method performing 'better' than another, it rather emerges that working on the total or between-group variance (PCA and bgPCA) will tend to put the focus on the role of direction of main variance as line of least resistance to evolution. Standardizing by the within-group variance (CVA), by dampening the expression of this line of least resistance, has the potential to reveal other relevant patterns of differentiation that may otherwise be blurred.
As in other rodents, house mice are sensitive to such factors and cases of marked morphological differentiation have been reported for several islands  [29, [37] [38] [39]. Our data set included continental and insular populations from various environmental contexts (Fig  2 ). Phylogenetic relationships were assessed based on data from the literature, and compared to the pattern of morphological differentiation, as provided by the different multivariate methods (PCA, bgPCA, and CVA) applied to the same morphometric dataset.
Phylogenetic analyses A total of 424 mitochondrial (mt) control region sequences of 834 bp were extracted from GenBank: -France: 151  [30, 42, 43] ; Jones et al unpubl. ; -Germany: 71  [30, 42, 43] ; Jones et al unpubl. ; -Italy: 60  [31, 44, 45]  and Jones et al unpubl. ; -Denmark: 23  [45] ; -Orkney: 21  [32, 44, 45]  -Corsica: 1  [46]  -Marion Island: 18  [47]  -and Guillou Island  [47]. These sequences were extracted and aligned using BioEdit  [48]  and MEGA  [49]. The HKY+I substitution model was selected using jmodeltest-2. 1. 4 [50]. Genetic distances were calculated using the software Mr Bayes  [51]  with the following conditions, 25% burn-in and 5 000 000 generations using Mus musculus musculus DQ266060. 1 as an outgroup. Based on these distances, the final tree was visualized using FigTree 1.
A drawback is a high number of variables required to describe the outline (four FCs by harmonics). This method was used to reconstruct an outline with the starting point adjusted along the first axis of the major ellipse. This outline was then analyzed using a Fourier method decomposing the distance of each point to the center of gravity of the outline as a function of the distance along the outline. Each harmonic is weighted by two Fourier Coefficients (FCs) using this method, reducing the dataset required to describe the outline. The zero harmonic, proportional to the outline size, was used as size estimator and to standardize all other FCs so they represent shape variables only. The higher the rank of the subsequent harmonics, the more details they represent on the outline. The shape of a mouse molar is adequately described by the first seven harmonics, i.
This analysis provides axes maximizing the between-group to within-group variance ratio. It corresponds to an eigenanalysis of BW -1. Note that CVA presents computational problems when number of variables is larger than number of cases. It may require a reduction of dimensionality of the data  [21]. This is not an issue here because using Fourier methods allows for a thresholding of the number of variables before statistical analyses. Multivariate analyses were performed under R  [55]  using the package ade4  [56].
Comparison between patterns of differentiation provided by the different multivariate methods The representations of differentiation provided by the different multivariate methods applied to the same dataset (molar shape) were compared as follow. The scores of the group means on axes of a given analysis provide a configuration that can be compared to the configuration of group means as provided by another method. Such configurations can be compared using a Procrustes superimposition (Protest)  [58]. This provides a measure of Procrustes distance between the two configurations (D) and a coefficient of correlation R. The probability that the configurations are more related than random is assessed using permutations. As a way to assess how some multivariate methods applied to morphometric datasets may or may not favor an evolutionary hypothesis, the patterns of shape differences were compared to the genetic differentiation.
In order to do so, the matrix of genetic distance was converted into a set of multivariate axes by using a Principal Coordinate Analysis (PCoA)  [59]. Based on the genetic data available in the literature, groups as close as possible as the ones used for morphometric analyses were identified. Their means on the PCoA axes were computed, providing a configuration that was compared to the configurations provided using the morphometric analyses using a Protest. pcoa and protest are R functions from the package 'vegan'  [60].
Impact of oriented within group variance on the output of multivariate analyses: simulations In order to assess the effect of an 'anisotropic' within-group variance (i. e. oriented along a preferential direction shared among groups) on the output of PCA, bgPCA and CVA, simulations were performed. 999 datasets were built from the same seven populations and the same individuals per population observed in the real dataset. For each simulated dataset, the seven populations kept their observed means. However, the variance of each variable within each population was modified to follow a same variance. This was done for each population separately by randomizing the simulated values from a multinormal distribution with the following parameters: the 14 observed means characteristic of the given population, and as variance the average of the 7 geometric means of the 14 variances per population.
This was repeated independently for each of the 7 populations. PCA, bgPCA and CVA were performed on these simulated datasets. Three pairwise Protests were computed to compare the among-group configuration obtained on four axes of each analysis (PCA vs. bgPCA, PCA vs. CVA, bgPCA vs. CVA). For each comparison, the 999 Protest distances from the simulated datasets generated a simulated distribution function. The observed distances between analyses from the real dataset were compared to this simulated function. The script in R is provided as supplementary file (S1 Script).
Phylogenetics: Independent colonization of the different archipelago A total of 424 mt DNA control region sequences of 834 bp were obtained from GenBank. The populations from the four archipelagos are found on different branches of the phylogenetic tree, suggesting that Orkney, Guillou Island, Marion Island and Corsica were colonized by different founder populations from Europe (Fig  3 ). Eday, is oblique to the first axis (42. 3% of between-group variance) and second axis (35. 1%). No clear pattern emerges on axes 3 and 4 (bgPC3 = 8. 0%, bgPC4 = 6. 9%; data not shown).
For the CVA cases, simulated datasets displayed configurations closer by 4 to 5 times compared to those provided by the PCA and the bgPCA than the original configuration. This is in agreement with the fact that the occurrence of a marked direction of main variance Pmax, parallel among groups, strongly impacts the pattern of differentiation as revealed by a CVA. Table  2. Relationships between genetic and morphometric patterns of differentiation between groups. Patterns of genetic differentiation are based on coordinates of group means on the first four axes of a PCoA on the genetic distances. 14 groups: continental samples are averaged by country, Piana excluded; 18 groups: genetic data duplicated for continental samples of a same country; Piana included and considered as genetically identical to Corsica.
Patterns of morphometric distances are based on the first four axes of a PCA, bgPCA and CVA on the same dataset (Fourier coefficients describing the molar shape outline). Coordinates of group means on the multivariate axes correspond to configurations compared using a Protest. It delivers a coefficient of determination (R 2 ), a Procrustes distance between configurations (D Procrustes ), and a probability P that the observed configurations are less correlated than permuted configurations (999 permutations). In italics P < 0. 05, in bold P < 0. 001. The CVA assumes isotropic variation. In the case of anisotropic variance oriented along similar Pmax, this assumption is not fulfilled.
Acknowledgments Eleanor Jones is thanked for providing access to original mtDNA data from a currently submitted manuscript. Guila Ganem ,  Jean-Pierre Quéré ,  Jean-Louis Chapuis  and  Benoit Pisanu  are deeply acknowledged for going to the field to sample wild mice, a role without which such study could not exist. We also thank the stimulating and constructive comments of the reviewers,  Phillip Mitteroecker  and an anonymous reviewer, as well as the very helpful contribution of the editor  Alistair Evans  for improving our final text. This is contribution ISEM  2015-082.
Samples and sequence data Twenty-nine DNA samples (five size fractions) were sequenced using Illumina HiSeq technology (one sample (TARA_030/DCM/180-2000 μm) did not yield enough DNA to be processed). For two samples (TARA_007/SUR and DCM/5-20 μm), DNA yield was low, therefore, DNA was whole genome amplified before sequencing. Sequence data from these two samples were used only in part of the present study (they were not included in quantitative analyses). The metagenome samples yielded 4714 million reads. For assembly, 3160 million reads were used to generate 4. 2 Gbp of assembled sequences in five million metagenomic contigs (Supplementary Table  S1 ), with the use of either SOAPdenovo v1. 4/v1. 5  (Luo et al. , 2012)  or Velvet v1. 0. 15  (Zerbino and Birney, 2008). Only contigs ⩾ 500 bp were included in these assemblies.
Domain identification in metagenomes Metagenomic ORFs were first masked using the SEG low-complexity sequence filter  (Wootton, 1994). Using position-specific iterated basic local alignment search tool  (Altschul et al. , 1990 (Altschul et al. , , 1997)) , we performed sequence similarity searches from all position-specific score matrices in the Conserved Domain Database (CDD) of the National Center for Biotechnology Information (NCBI)  (Marchler-Bauer et al. , 2013)  to the translated and filtered ORFs. Then the best CDD profile (E-valueo10 -5 ) was assigned to each ORF. To compile a list of retrotransposonand retrovirus-related sequence domains recorded in CDD, the definition lines of CDD profiles were searched for relevant keywords (for example, 'retrotransposon', 'retrovirus', 'retroviral', 'RNase', 'integrase').
Classification of the RTs We built seven HMM profiles (HMMER/hmmbuild  (Finn et al. , 2011) ) based on multiple sequence alignments of diverse groups of RT sequences retrieved from the Gypsy database (http://gydb. org; (Llorens et al. , 2011) ) as well as from  (Gladyshev and Arkhipova, 2011). Searches with these seven profiles plus 51 HMM pre-compiled profiles from Gypsy database were carried out against the metagenomic/metatranscriptomic ORFs (using hmmsearch, E-valueo10 -5 ). Identified metagenomic sequences were compared by BLASTP (E-valueo10 -5 ) with the reference RT sequences (Gypsy database and  (Gladyshev and Arkhipova, 2011) ) and the best BLAST hit was used for RT classification (for example, LINEs, Gypsy, Copia and so on). The longest RT-like ORF from the Tara Oceans metagenomes (AHX23DCM1GGMM11BCE.
ASY1CTG52) and its close homologs (cryptophyte Dualen-1_GCr and Dualen-5_CCu) that we identified in the Marine Microbial Eukaryote Transcriptome Sequencing Project (http://camera. crbs. ucsd. edu/mmetsp/, (Keeling et al. , 2014) , as of May 2014) were submitted to Repbase (http://www. girinst. org/repbase/; (Jurka et al. , 2005) ). Relative gene abundances in the metagenomic and metatranscriptomic data sets were estimated by their 'average coverage' defined by the cumulative sizes of reads mapped on the contigs divided by contig sizes (see Supplementary Methods and Results for detail).
Criteria for environmental clades To delineate 'environmental clades' that were only comprised of environmental sequences, we used the following three criteria: (i) the clade contained at least three environmental sequences, (ii) there was no known RT sequences (that is, RT sequences of known taxonomic origin from the public databases such as NCBI non-redundant database (NR)) within the clade and (iii) the bootstrap branch support for the clade was ⩾ 80%. Prior to applying the second criterion, we first identified candidates for environmental clades fulfilling criteria (i) and (iii). We then performed BLASTP searches from the RT domains of the environmental sequences of the candidate clades against three databases: NR, our RT reference database (see above) and themselves. Most of the members of the initial candidate clades had BLASTP scores with other members of the same environmental clade higher than with any other known sequences, but several environmental sequences had best hits to known RT sequences.
Taxonomic annotation Metagenomic and metatranscriptomic RT-like ORFs were compared using BLASTP to reference databases (UniProt (release of May 2014) and Marine Microbial Eukaryote Transcriptome Sequencing Project). When the best hit reference sequence showed ⩾ 60% amino-acid sequence identity against a query sequence (E-valueo10 -8 ), the taxonomic annotation of the reference sequence was transferred to the query sequence. This percent identity threshold was determined according to the distribution of sequence similarities among known Copia and Gypsy RT sequences to maximize the accuracy of taxonomic  annotation at the second level of the hierarchy of the NCBI taxonomic classification such as Stramenopile and Viridiplantae (Supplementary Figure  S1 ).
Detrended correspondence analysis To estimate the biotic (that is, organismal) and abiotic (that is, physicochemical) variables explaining patterns of RT distribution across samples, we used a detrended correspondence analysis (DCA) and the envfit function of the R package vegan  (Oksanen et al. , 2007). Abiotic variables were available via PANGAEA (Tara Oceans Consortium, Coordinators; Tara Oceans Expedition, Participants, 2014;  Chaffron et al. , 2014a,b)  through the sample codes in Supplementary Table  S4. Biotic composition of samples were derived from the 18S-V9 rDNA barcodes described in  de Vargas et al. (2015) , Supplementary Table  S5 ). Vector fitting (envfit) with 1000 permutations was used to estimate the significance of correlations between the ecological variables and the ordination by DCA (P-valueso0.
Bacterial and viral RT transcripts were detected in all size fractions Metatranscriptomic data were screened for the presence of transcriptionally active RT-like ORFs using HMM profiles. The search resulted in the identification of 4 258 RT-like ORFs. Relative abundances of transcriptionally active RTs among all transcripts varied from 0. 002% (St TARA_007/ DCM/180-2000 μm) to 0. 15% (St TARA_007/DCM/ 20-180 μm) (Figure  3 ). Taxonomic annotation of these transcribed RTs indicated that many originated from eukaryotes (Supplementary Figure  S7 ). However, metatranscriptomic RTs showed a distinct classification pattern to that observed in metagenomes. First, the relative abundance of LINE and BEL RTs decreased in the metatranscriptomes relative to the metagenomes, whereas the opposite was true for Gypsy (detected in 22 samples) and Copia (detected in all samples) RTs (Figure  3 ).
Second, the relative abundances of bacterial RTs increased in the metatranscriptomic data for all fraction sizes. Group II intron RTs, which were mainly detected in pico-and piconano-fractions of metagenomes, were detected in 20 metatranscriptomes, including those from larger size fractions. Retron-type RT transcripts were detected in the metatranscriptomes from all the analyzed size fractions from Station 7. RT sequences similar to DGRs were identified in samples from Station 30. Third, the frequency of retrovirus RTs was much higher than in the metagenomes (Figure  3  and Supplementary Figure  S7 ). These viral RTs were observed in various size fractions (22 samples). In the St TARA_023/SUR/20-180 μm sample, retroviral RTs were the most dominant type of transcribed RTs (Figure  3 ). Putative taxonomies were assigned for 2050 out of 4258 metatranscriptomic RT-like sequences using BLAST.
, (2009)  examined the abundance of RTs in metagenomic data for size fractions under 20 μm (that is, 0. 1-0. 8 μm, 0. 22-0. 8 μm, 0. 8-3. 0 μm, 3. 0-20. 0 μm). They found a positive correlation between the frequency of RTs and the size of organisms. Our results, extending the coverage of organism sizes up to 2000 μm, revealed a similar trend (Figure  2 ) probably owing to an increased tolerance for transposon proliferation in large genomes of larger organisms. It should be noted that the taxonomic assignment of environmental RT sequences analyzed in the present study should be periodically revisited in the future, as more eukaryotic genomes, presently underrepresented, are added to reference databases. RTs are known in prokaryotes but are less abundant than in eukaryotes. Well-described prokaryotic RTs are found in group II introns, retrons and DGRs  (Simon and Zimmerly, 2008).
(2010)  found that Class II transposases were the most abundant and ubiquitous genes in nature. However, in our list of highly abundant sequence domains (Supplementary Table  S6 ), we found only a few such domains (for example, Tn3 transposase DDE domain (pfam01526) ranked 78th (180-2000 μm fraction), and the putative transposase OrfB (PHA02517) ranked 74th (5-20 μm fraction)). This may be due to the fact that bacterial, archaeal and viral sequences were overrepresented in the data analyzed by  Aziz et al. , 2010. Alternatively, the Class II transposases may be underrepresented in the CDD database; Aziz et al. re-annotated all TEs in their study. More precise and systematic quantitative comparisons of the relative abundances of Class I and Class II TEs (enriched in different size fractions) will require other types of data than those used in these and our studies, because size-fractionated samples are not suitable for such a comparison.
Acknowledgements We are grateful to  Dr Irina R Arkhipova  for providing some reference RT sequences used in this study. Computational resources were provided by the  Bioinformatics Center  and the  Supercomputer System, Institute for Chemical Research, Kyoto University. We thank the PACA-bioinfo platform for computing services. This article is contribution number  31  of the Tara Oceans Expedition 2009-2013. We thank the commitment of the following people and sponsors who made the Tara Oceans Expedition 2009-2012 possible: CNRS in particular  Francoise Gaill  and the  Groupement de Recherche   GDR3280 ,  European Molecular Biology Laboratory  (EMBL),  Génoscope/CEA , the  French Government 'Investissements d'Avenir' programmes OCEANOMICS  ( ANR-11-BTBR-0008 ),  FRANCE GENOMIQUE  ( ANR-10-INBS-09-08 ),  MEMO LIFE  ( ANR-10-LABX-54 ),  PSL* Research University  ( ANR-11-IDEX-0001-02 ), the  ANR  projects  FRANCE GÉNOMIQUE  ( ANR-10-INBS-09-08 ),  POSEIDON  ( ANR-09-BLAN-0348 ),  PROMETHEUS  ( ANR-09-GENM-031 ),  TARA-GIRUS  ( ANR-09-PCS-GENM-218 ),  PHYTBACK  ( ANR-2010-1709-01 ),  SAMOSA/ANR-13-ADAP-0010 ,  EU FP7  (MicroB3/No.
sRNA mapping and data analyses For the analysis of dsRNA-feeding associated siRNAs trimmed sequence data sets were mapped with the Burrows-Wheeler Alignment tool (36) using default settings. Reads were first aligned to a set of bacterial sequences, as well as to mitochondrial and ribosomal DNA sequences, tRNA and snoRNA, according to the recent P. tetraurelia genome annotation (ParameciumDB, http://paramecium. cgm. cnrsgif. fr/), allowing one mismatch per read. Then, cleared data sets were aligned to the reference genome of strain 51  (37, 38) , without mismatch. Read counts of target siRNAs were normalized to the total number of sequence reads obtained per sample, expressed as reads per million (rpm). For determination of siRNA levels mapping within the dsRNA trigger region, the average of 22 and 24 nt normalized sRNA counts was defined as a threshold level to be passed for unambiguous siRNA accumulation.
Among those regions producing predominantly 23 nt siRNAs, only clusters were considered that showed a minimum siRNA coverage of 60× and a minimum size of 250 bp. For identification of siRNAs produced from bacterial sequences, trimmed reads were matched without mismatch to E. coli strain W3110 (AP009048. 1), first on its 16S (ECK3750:JWR0082:b3756) and 23S (ECK2587:JWR0052:b2589) rRNA, then on the 4227 protein coding genes (http://bacteria. ensembl. org/ escherichia coli str k 12 substr w3110/Info/Index). The LacZ gene was removed from the sequences prior to mapping. Representations of siRNA distributions were made with R scripts, ggbio and R samtools packages  (39, 40). Raw sequencing data for ND169-and ICL7a-dsRNA feeding to wild type and mutants and aligned reads of RDR3and PTIWI08/14-dsRNA feeding to wild type have been submitted to the NCBI Sequence Read Archive (SRA).
The Paramecium RNAi machinery can process singlestranded bacterial RNA As a preliminary test to determine whether the uptake of RNA from food vacuoles and its processing by RNAi is restricted to dsRNA, the reads were mapped to E. coli 16S and 23S rRNA sequences as well as to an E. coli transcriptome data set without pre-mapping to other sequences, allowing no mismatch. In WT samples, variable quantities of sRNAs corresponding to the sense rRNA and mRNA transcripts were identified (Figure  5 , top). Variations are likely due to differences in cell starvation and washes prior to RNA extraction (see Materials and Methods), leading to variable densities of food bacteria. The size distribution of both sense rRNA-derived and sense mRNA-derived sR-NAs did not show any specific pattern, consistent with most of these molecules representing rRNA and mRNA fragments being degraded.