dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Sequencing and read mapping. To calculate the optimal economic sequencing depth, two drones (H1 and H2) were sequenced at moderately high depth of coverage (DP; DP ≥ 15), and reads sequentially down-sampled to approximate sequencing at depths of 10, 7, 5 and 3 X. Regression fitting of DP against fraction of genome callable (GX) indicated DP = 5. 3 X ≈ GX = 0. 7, and GX ≥ 0. 8 requires much higher DP (Supplementary Material). We proceeded to assess the suitability of drones sequenced at DP ≈ 6 for characterising population diversity and detecting signatures of selection. Drones were sampled from one population used for honey production (HN; n = 30) and another for royal jelly (RJ; n = 30). The sequence data was supplemented with a reference dataset of diploids (n = 39) published by Harpur et al.
7  , representing four lineages (A, M, C, Y). This reference dataset enabled the fraction of lineage ancestry in HN and RJ populations to be assessed. On average ≥ 91% of reads from both datasets mapped to the reference genome, with ≥ 83% having a mapping quality (MQ) ≥ 20 (Supplementary Table  ST1 ). Mapping duplicates were highest in the reference dataset which is not unexpected given their higher DP (26. 61 ± 2. 04) compared to the drones (7. 1 ± 2. 59). The higher coverage in the reference dataset captured an additional 6% of the reference genome compared to the drones, and resulted in a 10% increase in GX (Supplementary Table  ST1 ). Variant detection in the drone and reference populations. More than twice the number of SNPs were identified on average per individual in the reference dataset than in the drones (Supplementary Table  ST2 ).
Moreover, the numbers of SNPs identified are in agreement with our calculations of SNPs identified in drones sequenced at 5 and 7 X to diploids sequenced at 20 and 30 X (Supplementary Methods SM2). Post-filtering for DP, 10. 44 M SNPs were detected in the reference dataset, whilst in the drones it was 3. 69 M. The higher number of SNPs in the reference dataset compared to the drones (RJ and HN) is not unexpected given the near 4-fold increase in DP and diversity of lineages sampled. The mean number of SNPs detected per drone (~856 K) is marginally lower than that detected in reference bees from the C lineage (~1 M), from which the drones originate, and is consistent with the modelled expectations (see Supplementary Material). Merging both datasets resulted in 11. 24 M unique SNPs, of which 807 K were common to both datasets.
The mean fraction of missing genotypes across all samples was 0. 02. The greatest number of private SNPs (4. 96 M) was detected in lineage A, whilst the least (207 K) was observed in lineage C (Supplementary Fig. SF1 ). As the reference genome was generated from lineage C individuals it is not unsurprising to observe fewer SNPs in bees of the same lineage. For downstream analysis three datasets were constructed, the first (i) comprised all drones (HN and RJ), the second (ii) comprised reference individuals and drones (HN and RJ), whilst the third (iii) was composed of reference individuals and in silico diploids generated by combining the alleles of random pairs of drones within each population. Individuals were genotyped for all SNPs detected and, following quality control and imputation, 2.
53 M SNPs were retained in dataset (i), 3. 12 M SNPs in dataset (ii), and 3. 16 M SNPs in dataset (iii). For each drone population (dataset i), SNPs in which at least 10% of the population carried a non-reference allele were evaluated using Ensembl's variant effect predictor (VEP). This enables the quantification of variants by genomic location (e. g. upstream of a transcript, in coding sequence, etc. ) and consequence (e. g. frameshift, missense mutation, etc. ), enabling one to consider the potential effects on the fitness of mutations. Intergenic variants accounted for the largest number of predicted effects (59. 5%, inclusive of 16. 3% upstream and 15. 8% downstream variants), followed by intron (36. 8%), exon (3. 5%) and splicing (0. 2%) variants (Supplementary Table  ST3 ). The same distribution (to within < 0.
63%) was observed amongst the SNPs private to each population. These results are not unexpected given that exon sequences make up < 12% of the genome, and that mutations within exons and splicing regions are more likely to be deleterious than those in introns and intergenic sequences. Diversity and population structure. Nucleotide diversity (π ) was calculated for each evolutionary lineage and drone population (dataset ii). Diversity was observed to be highest in lineage A (π = 0. 0058), followed by HN bees (π = 0. 0036), lineage Y (π = 0. 0033), RJ bees (π = 0. 0032), M (π = 0. 0029) and C lineage (π = 0. 0024). Plots of autosomal nucleotide diversity in 12 kb bins for HN and RJ bees are available in the Supplementary material (Supplementary Figs SF2 to SF17). Combining the diploid reference dataset of four lineages (A, M, C, Y) with our haploid data, which exhibits systematic homozygosity, could potentially result in misleading relationships in ADMIXTURE analysis.
To test this, we compared ADMIXTURE analyses using a mixed haploid-diploid dataset with those obtained with a diploid-only dataset by combining in silico alleles from pairs of drones to create diploid individuals. The optimal number of ancestral backgrounds (K) was inferred from the cross-validation (CV) error estimation (Supplementary Fig. SF18 ). Following 10 replicates for 1 ≤ K ≤ 6 the optimal was K = 5 in the haploid-diploid dataset (ii; Fig. 1a ; Supplementary Table  ST4 ), whilst it was K = 4 in the diploid-only dataset (iii; Fig. 1b ; Supplementary Table  ST5 ). The ancestral backgrounds identified coincide with the four lineages (A, M, C, Y) in the reference dataset. A fifth background dominated by RJ bees is present in the haploid-diploid dataset (ii), and emerges at K = 5 in the diploid dataset (iii), whilst at K = 6 both analyses are comparable.
Unrooted phylogenetic trees constructed from the haploid-diploid (ii; Fig. 1c ) and diploid datasets (iii; Fig. 1d ) broadly support the ADMIXTURE results. The F ST values at the optimal K between populations in the diploid dataset (iii) ranged from 0. 381 when comparing A to C, to 0. 61 when comparing Y to M (Supplementary Table  ST6 ), whilst those in the haploid-diploid dataset (ii) ranged from 0. 139 when comparing C to RJ, to 0. 625 when comparing Y to M (Supplementary Table  ST7 ). Mitochondrial sequences extracted from the whole-genome alignments were used to construct an unrooted phylogenetic tree (Supplementary Fig. SF19 ) and haplotype network (Fig. 2 ). These results broadly support the ADMIXTURE results with the exception of two HN bees (HN9, HN19) falling in the O lineage and two M lineage bees (SRR957059, SRR957060) in the C lineage.
Multiple alignment of these sequences, together with 68 references downloaded from GenBank representing each of the major lineages, supports the clustering of HN9 and HN19 with bees possessing an O lineage mitotype (Supplementary Fig. SF20 ). A method was also developed to retrieve the tRNA Leu -cox2 sequence from the whole-genome sequence data, so as to reconstruct the mitotype by referencing 226 sequences downloaded from GenBank. The efficacy of the approach was validated against the mitotype sequences of the same bees that were Sanger sequenced for the region (Supplementary Fig. SF21 ). Median-joining haplotype networks (Fig. 3 ; Supplementary Fig. SF22 ) arising from the subsequent alignment of these sequences also supports the earlier analyses.
In summary, the data suggests HN and RJ bees originate from the C lineage, from which the majority of domestic strains are derived  5  , with a fraction of admixture with the M lineage being evident in some individuals. Analysis of the mitochondrial genome revealed the unexpected placement of four bees. Two of these were HN drones (HN9, HN19), which Sanger sequencing confirmed to possess O lineage mitotypes, whilst the other two bees were from the M lineage in the reference dataset. ADMIXTURE analyses indicated the HN drones to originate predominantly from the C lineage, with minor admixture from the M lineage. In the ADXMITURE results, the two HN drones found to possess O lineage mitotypes revealed a Y lineage ancestry fraction of 0. 11-0. 12 at K = 4, switching at K = 5 to an A lineage ancestry fraction of 0.
Consequently, the network is based on only 16 segregating sites. Similarly, the absence of duplications of the Q element in some sequences would result in these sites being masked also. These issues can be mitigated by re-coding sequences. For instance a recent study coded Q elements as present or absent to ensure they were retained in the analysis  25. A network is provided in the supplement (Supplementary Fig. SF22 ), in which sequences were reduced to 3 nucleotides by re-coding 'C' as 'G' , and then re-coding gaps as 'C'. The results indicate that despite the complex genetic architecture of A. mellifera mitotypes, comprising point mutations, indels and duplications, it remains possible to retrieve mitotype sequences from whole-genome sequence data. This facilitates their analysis in context with the extensive mitotype sequence data available in GenBank, providing further insight into the mitochondrial history of the sequenced specimen.
The other C lineage populations were sampled from the Kmetijski Inštitut Slovenije (Agricultural Institute of Slovenia) and from the Institute for Bee Research Hohen Neuendorf in Germany. The M lineage population was sampled from Ouessant, an island in Brittany (France), where it has remained in isolation for around 30 years. One drone per colony was sampled at the pupae/nymph stage or at the larval stage if no male pupae/nymphs were available at the time of sampling. Further details on DNA extraction and treatment are provided in the Supplementary material. Sequencing was performed on Illumina HiSeq 2000 and 2500 platforms, with 20 samples per lane, following the manufacturer's protocols for library preparations (2 × 100 bp, or 2 × 125 bp). Sequencing reads of 39 A. mellifera workers were downloaded from the European nucleotide archive (ENA; study accession: PRJNA216922) to act as a reference dataset  7.
Mapping and variant detection. Sequencing reads were mapped to Amel4. 5 using BWA-MEM v0. 7. 9a;49 , duplicates marked with Picard (v1. 88; http://picard. sourceforge. net), and local realignment and base quality score recalibration (BQSR) performed using GATK v3. 3-0;50. SNPs were called in each drone independently and consolidated into a single set of master sites, from which all individuals were genotyped (see Supplementary Material). A filtering step in Plink (v1. 9; https://www. cog-genomics. org/plink2) retained SNPs on chromosomes 1 to 16 with minor allele frequency (MAF) ≥ 0. 05 and genotyping call rate ≥ 0. 9. Missing genotypes were imputed using BEAGLE v4;51. Minor alterations were implemented to the pipeline to facilitate analysis of the diploid sequence data downloaded from the ENA, specifically this required the retention of heterozygous SNPs.
Further details are provided in the Supplementary material. To quantify variants by consequence, the VCF files for drones from the populations selected for honey (HN) production and those selected for royal jelly (RJ) were merged to create a single VCF file for each population. SNPs with a non-reference allele count (AC) > 3 (equivalent to 10% of the population) were analysed using the Perl version (v78) of Ensembl's Variant Effect Predictor 52 and the Amel4. 5 variation database (apis_mellifera_ core_25_78_45). Where reported, the effects of amino acid substitutions on protein function were assessed using the default parameters of PROVEAN  53. Population structure and relationships. For each evolutionary lineage (A, C, M, Y) and population (HN, RJ), nucleotide diversity (π ) was calculated in 5 kb windows with 1 kb step size using VCFtools v0.
1. 12a;54 , and averaged to generate a genome-wide π for each lineage and population. To assess population structure using ADMIXTURE v1. 23;55 , diploid individuals were first constructed from the drones by combining all SNPs using both alleles from randomly selected pairs of drones. The diploid drone and reference datasets were merged and filtered to retain SNPs on chromosomes 1 to 16 with minor allele frequency (MAF) ≥ 0. 05 and genotyping call rate ≥ 0. 9. ADMIXTURE was run unsupervised assuming 1 ≤ K ≤ 6 with 10 bootstrap replicates. The optimal K was inferred from the minimum observed cross-validation error, and Q estimates plotted in R. The SNP data used for the ADXMITURE analysis was imported into R via the GenABEL package  56  , and an unrooted phylogenetic tree constructed using the complete linkage agglomeration method and plotted using the APE package  57.
For comparison, this analysis was also performed using a combined dataset of reference individuals and haploid drones. Mitochondrial analyses. Consensus sequences were recovered for each sample for the mitochondrial genome from BAM files using SAMTOOLS and BCFTOOLS, and aligned with ClustalW  58. The alignment was analysed with jmodeltest2  59, 60  to identify the best-fit model of nucleotide substitution, including models with a proportion of invariable sites and unequal base frequencies. The results from the GTR+ I+ G model identified by the Akaike Information Criterion (AIC) were used to configure MrBayes 61 : (a, c, g, t) = (0. 43, 0. 10, 0. 05, 0. 42), R(a, b, c, d, e, f) = (2. 140, 27. 536, 2. 3000, 0. 940, 46. 066, 1. 000), which was run for 2 M generations and the results plotted using FigTree v1.
4. 2 (http://tree. bio. ed. ac. uk/software/figtree). Several drones (HN n = 5; RJ n = 9; Supplementary Figs SF20-SF21) were mitotyped by sequencing the mitochondrial tRNA Leu -cox2 intergenic region, amplified using primers E2 and H2  38. Sanger sequences were processed using FinchTV (http://www. geospiza. com) and aligned with MAFFT  62. MrBayes was used to generate a consensus tree which was subsequently plotted using FigTree. A haplotype median-joining network was generated using PopArt. To analyse the whole-genome sequence data with reference to historical mitotypes generated by traditional Sanger sequencing of the tRNA Leu -cox2 region, reference sequences for 226 mitotypes were downloaded from GenBank. Sequences were trimmed to start from the 3′ end of tRNA Leu 'CTTTTATTAAA' and terminate at the 5′ end of COX2 ' ATTTCCACA'.
For each sample, reads spanning the tRNA Leu -cox2 genomic region (NC_001566. 1:3355-4295) were recovered from the BAM file and converted to FASTQ using SAMtools. These reads were then mapped to each reference mitotype and the error rate (e = PF_HQ_ERROR_RATE + PF_ INDEL_RATE) and alignment quality (q = PF_HQ_ALIGNED_Q20_BASES/PF_ALIGNED_BASES) calculated with Picard. Where e > 0 & q > 0. 5 the accession number, sequence length and e to 6 decimal places were recorded. Requiring e > 0 is necessary to avoid accepting a perfect alignment to a very short mitotype where for example an alignment of 99% identity is present for a longer mitotype. Requiring q > 0. 5 filters out alignments in which the majority of base quality scores are poor. The alignment with the lowest error rate, and longest sequence length in the event of identical mismatch rates for multiple sequences, was considered the best match.
Signatures of selection. Tests for signatures of selection were conducted using hapFLK  27. Additional drones were included to represent "progenitor" bees from lineage M, sampled from Ouessant (OU, n = 30), to act as an outgroup for rooting of trees in hapFLK. Additional drones were also included from pure populations representative of lineage C, sampled from Berlin (BE, n = 12) and Slovenia (SL, n = 18), to provide a contrast to the selected HN and RJ populations. The additional drones were sampled, sequenced and processed using the same procedure outlined above, and subsequently genotyped for the SNPs identified in the HN and RJ populations. The hapFLK analysis was first conducted on the genome-wide dataset of SNPs to generate a kinship matrix from pairwise Reynolds' distances between populations using lineage M as the outgroup.
The population under selection was identified from the eigenvector and loadings of the Q i,j matrix (for further details refer to Supplementary methods of Fareillo et al. 27  ). ST17 ) were identified using the g:ORth tool and submitted for profiling using the g:GOSt tool of g:Profiler  63. D. melanogaster orthologs were chosen for analysis over A. mellifera genes due to the more comprehensive reference databases available for the former. Due to the absence of orthologs for some of the genes identified, an analysis of GO term information content was conducted using the IT-GOM tool in DaGO-Fun 31 on GO terms of A. mellifera genes identified in each population. Two approaches were applied, an annotation approach which included inferred electronic annotations, and the GO-universal topology approach.
Acknowledgements We thank the beekeepers from  GPGR (Groupement des Producteurs de Gelée Royale), Altigoo-Apiculture (Tarn, France) ,  l′ Association Conservatoire de l′ Abeille Noire Bretonne ;  Bruno Lumineau  ( Bénac, France ),  Dr. Ales Gregorc  from the  Kmetijski Inštitut Slovenije  ( Agricultural Institute of Slovenia ) and  Kaspar Bienefeld  from the  Institute for Bee Research Hohen Neuendorf  for providing us the drone samples. This work was performed in collaboration with the  GeT platform, Toulouse (France) , a partner of the National Infrastructure France Génomique, thanks to support by the  Commissariat aux Grands Invetissements  ( ANR-10-INBS-0009 ). Bioinformatics analyses were performed on the GenoToul Bioinfo computer cluster. We wish to thank  Betrand Servin  ( GenPhySE, INRA ) for discussions on haplotype analysis.
Lensing of the CMB The CMB fluctuations measured by Planck provide a slightly perturbed image of the last-scattering surface, due to the effects of gravitational lensing by large-scale structure. Lensing slightly washes out the acoustic peaks of the CMB power spectrum, an effect we see in the Planck data at high significance. Lensing also introduces distinctive non-Gaussian features into the CMB maps, which allow us to map and make statistical measurements of the gravitational potentials, and the associated matter. These are studied in detail in Planck Collaboration XV  (2015). The lensing signal is consistent with the basic, 6-parameter, ΛCDM model that best fits the temperature data. This gives us a very strong consistency check on the gravitational instability paradigm and the growth of structure over more than two decades in expansion factor.
Inflation The release of the 2013 Planck data and findings had an enormous impact upon the inflationary community, and the Planck 2015 results continue to stress the importance of this window into the early Universe. Planck Collaboration XX (2015) presents our constraints on inflationary models. The Planck data are consistent with a purely adiabatic, power-law spectrum of initial fluctuations, whose spectral index (n s = 0. 9677 ± 0. 006) is significantly different from unity. The addition of polarization data has significantly improved the constraints on any isocurvature modes, which are now constrained at the percent level. Despite a detailed search, and study of several models, we see no statistically significant evidence for departures from a power law. The combination of Planck data with the BICEP2/Keck Array data provide a strong upper limit on the tensor-to-scalar ratio, and disfavour all monomial models (V(φ) ∝ φ 2p ) with p ≥ 1.
On large-angular scales, a dipolar power asymmetry is investigated through several approaches, and we address the subject of a posteriori correction. Our ability to include results based on polarization data is limited by two factors. First, CMB polarization maps have been high-pass filtered to mitigate residual large-scale systematic errors in the HFI channels, thus eliminating structure in the maps on angular scales larger than about 10 •. Second, an observed noise mismatch between the simulations and the data prevents robust conclusions from being reached based on the nulll-hypothesis approach adopted throughout the paper. Nevertheless, we perform the first examination of polarization data via a stacking analysis, in which the stacking of the data themselves necessarily acts to lower the effect of the noise mismatch.
Future surveys, particularly at 5-20 GHz, will greatly improve the separation, since the predicted brightness between the two models disagrees substantially in that range. In polarization, synchrotron emission completely dominates on angular scales larger than 1 • and frequencies up to 44 GHz. We combine Planck and WMAP data to make the highest signalto-noise ratio map yet of the intensity of the all-sky polarized synchrotron emission at frequencies above a few gigahertz, where Faraday rotation and depolarization are negligible (Figs. 21 and Fig. 22 ). Most of the high-latitude polarized emission is associated with distinct large-scale loops and spurs, and we re-discuss their structure following the earlier study of  Vidal et al. (2014)  based on WMAP observations. We argue that nearly all the emission at -90 • < l < 40 • is part of the Loop I structure, and show that the emission extends much further in to the southern Galactic hemisphere than previously recognized, giving Loop I an ovoid rather than circu-Planck Collaboration: The Planck mission lar outline.
g. ,  Martin 2007; Draine & Fraisse 2009). This estimate provides new empirical validation of many of the common underlying assumptions of the models, but is not very discriminating among them. The second ratio, R P/p , is higher than model predictions by a factor of about 2. 5. A comparable difference between data and model is observed for I/τ V (Planck Collaboration Int. XXIX 2014). To address this, changes will be needed in the optical properties of the large dust grains contributing to the submm emission and polarization. The spectral dependence in the submm is also important for constraining dust models. In Planck Collaboration Int. XXII (2015) the Planck and WMAP data are combined to characterize the frequency dependence of emission that is spatially correlated with dust emission at 353 GHz, for both intensity and polarization, in a consistent manner.
9 , similar to the scaling found earlier for power spectra of I (Miville-  Deschênes et al. 2007). The frequency dependence of the power spectra for polarized thermal dust emission is consistent with that found for the modified blackbody emission in Planck Collaboration Int. XXII  (2015). A systematic difference is discovered between the amplitudes of the Galactic B-and E-modes, such that C BB /C EE = 0. 5. There is additional information coming from the dust T E and T B spectra. These general properties apply at intermediate and high Galactic latitude in regions with low dust column density. The data show that there are no windows in the sky where primordial CMB B-mode polarization can be measured without subtraction of polarized dust emission.
Acknowledgements. Planck is a project of the  European Space Agency  in cooperation with the scientific community, which started in 1993. ESA led the project, developed the satellite, integrated the payload into it, and launched and operated the satellite. Two Consortia, comprising around 100 scientific institutes within Europe, the  USA , and  Canada , and funded by agencies from the participating countries, developed and operated the scientific instruments LFI and HFI. The Consortia are also responsible for scientific processing of the acquired data. The Consortia are led by the Principal Investigators: J. -L. Puget in France for HFI (funded principally by  CNES  and  CNRS/INSU-IN2P3 ) and  N. Mandolesi in Italy for LFI  (funded principally via ASI). NASA's  US Planck Project , based at  JPL  and involving scientists at many US institutions, contributes significantly to the efforts of these two Consortia.