dataesr/scientific-paragraphs-categorization huggingface dataset:
The differential expression of miR-143, miR-769 and miR-432 detected by microarray was not validated by qRT-PCR. To identify the possible effect of chronological ageing on the validated miRNAs, we re-analyzed the qRT-PCR data according to the anatomical site, representing sun-protected and sun-exposed skin areas. qRT-PCR analyses of the seven selected miRNAs (miR-383, miR-145, miR-6879, miR-34a, miR-663b, miR-3648 and miR-134) revealed that the levels of miR-383 and miR-34a were significantly increased even in the sun-protected skin biopsies in age group #2 and #3 compared with age group #1 suggesting that the expression of these miRNAs were also altered during chronological ageing (Fig. 6 ). Moreover, the expression of miR-383 and miR-34a was increased in skin samples obtained from the sun-exposed skin of old age donors compared to sun-exposed skin of young donors (Fig.
Microarray data is available on NCBI Gene Expression Omnibus (GSE114006). qRT-PCR. Quantification of miRNAs by TaqMan ® qRT-PCR was carried out as described by the manufac- turer (Thermo Fisher Scientific, Stockholm, Sweden). Briefly, RNA was reverse transcribed using the TaqMan ® MicroRNA Reverse Transcription Kit (Thermo Fisher Scientific, Stockholm, Sweden) and miRNA-specific primers (Thermo Fisher Scientific, Stockholm, Sweden). Expression was analyzed using Quantstudio 7 Flex (Thermo Fisher Scientific, Stockholm, Sweden)and expression was normalized against U48 snRNA expression (Thermo Fisher Scientific, Stockholm, Sweden).
Acknowledgements We express our gratitude to all the donors who took part in this study. We also would like to thank the core facility at  Novum, BEA, Bioinformatics  and  Expression Analysis , which is supported by the board of research at the  Karolinska Institute  and the research committee at the Karolinska hospital. We also acknowledge the  Genotype-Tissue Expression (GTEx) Project. The data used for the analyses described in this manuscript were obtained from the 7 th version of GTEx portal (www. gtexportal. org/home/). This study is supported by the research grant from  L'Oréal. CM, FB, AG,  CER , and LB are employed by  L'Oréal.
Data accessibility. The code can be found at https://doi. org/10. 5061/dryad. hk64f  [25]. Authors' contributions. S. P. , who was A. K. 's MSc student when this research was done, developed the software, ran all simulations and prepared the figures and diagrams; A. K. formulated the mathematical model, and analysed and interpreted the results. Both authors participated in writing the paper and gave final approval for publication.
Study of Faustovirus Genes Expression To identify potential splicing events in Faustovirus E12, we used a two-round alignment approach with a spliced-mapper: first, both pre-processed paired reads and singleton were mapped against the genomic sequence of Faustovirus E12 (GenBank accession no. KJ614390. 1) using HISAT2 with minimum and maximum size of introns set to 20 and 5,000 bp  (Kim et al. , 2015). Spliced reads were extracted, and junctions manually validated using the Gene BED To Exon/Intron/Codon BED expander (Galaxy Version 1. 0. 0) (Afgan et al. , 2016)  and the Integrative Genomics Viewer (IGV) tool  (Thorvaldsdottir et al. , 2013). Junctions supported by at least two reads were included as known junctions in the second alignment round. For each time point, reads mapping to the viral genes were quantified and the counts normalized with the geometric method using Cuffnorm (Galaxy Version 2.
2. 1. 1) (Trapnell et al. , 2010). For t = 0 to t = 8 h p. i. , for which two biological replicates were available, both replicates were used as entries for a common normalized count (T0-c to T8H-c). To study the functional profile of the genes expressed during the replicative cycle, a BLASTp  (Altschul et al. , 1997)  search of Faustovirus E12 annotated ORFs was performed against the Nucleo-Cytoplasmic Virus Orthologous Groups (NCVOGs) proteins database 1   (Yutin et al. , 2014). Hits with e-values below 1e-03 were considered significant and assigned to their corresponding NCVOGs. A weighted average of expressed genes in Fragments Per Kilobase of transcript per Million mapped reads (FPKM) was calculated for each functional category at each time point. Proteins of the African swine fever virus (ASFV) identified in the purified particles  (Alejo et al.
Faustovirus E12 Gene Expression The transcriptome sequencing of Faustovirus E12-infected V. vermiformis resulted in 8,909,144 read pairs distributed over nine time points with two biological replicates corresponding to t = 0 min, 15 min, 90 min, 3 h, 6 h, and 8 h, and one replicate for t = 11, 17, and 20 h. After quality control, pre-processing and double-round mapping, reads corresponding to Faustovirus E12 represented <1% of the total number of generated reads, yet covering 93. 5% of the genome positions with at least one read in at least one dataset. Single-base-resolution coverage maps across the genome for datasets of both cycles are reported in Figure 2 and Supplementary Figure  1. We observed a gradual increase in genome coverage during the replication cycle, illustrating an active transcription process starting early after infection.
From 8 h p. i. , the majority (50-73%) of the transcripts corresponded to proteins involved in virion structure and morphogenesis (Figure  3 ). Table  1  lists Faustovirus E12 genes predicted to encode for homolog proteins to those detected  The transcripts corresponding to the proteins of Faustovirus E12 with a homolog in African swine fever virus (ASFV) are indicated with a "+" when detected in the corresponding dataset. in ASFV purified particles proteome and their expression in late time points. All proteins forming the core shell in ASFV have their homologs in Faustovirus E12 expressed starting from 6 h p. i. : the 220 kDa polyprotein, the 62 kDa polyprotein and the protease necessary for their cleavage into their corresponding products. Other proteins found in the nucleoid of ASFV with their homolog predicted genes being expressed in Faustovirus E12 include all RNA polymerase subunits and RNA modification enzymes, transcription factors and DNA repair enzymes.
Faustovirus E12 Major Capsid Protein Transcription In order to study the transcription of the MCP, we used both the gene prediction results of GeneWise, and the junctionreads after the first-round alignment. Junction-reads confirming the positions of predicted exons were added to the validated junctions file for the second-round alignment. The complete MCP transcript appears composed of 13 exons delineating 12 introns. Nine of the 13 exon-intron boundaries are supported by detected junction-reads. The mean intron length is 1,273 bp, with minimum and maximum lengths of 396 and 3,256 bp, respectively, and a mean G + C content of 35. 2%. Exons forming the MCP coding transcript are significantly shorter (p = 0. 0007, unpaired t-test) with length varying from 13 to 527 bp for a mean length of 149 bp and a mean G + C content of 43.
12 average coverage versus 16. 89 in intronic regions of the same sample. To get a closer view on the mechanisms involved in the expression of the MCP gene, exon-intron boundaries were examined for conserved splice-sites that would suggest the presence of spliceosome-processed introns. Moreover, the intronic sequences were searched against the Rfam database for conserved motifs. Through this approach, five group I self-splicing introns were identified, and two introns were shown to contain an inserted ORF encoding a GIY-YIG homing endonuclease (Figure  5A ). All the MCP gene exon-intron boundaries show noncanonical splice-sites with five types of donor-acceptor associations, among which AA/TG was the most represented (Figure  5B ).
, 2002; Suárez et al. , 2010). At t = 20 h p. i. as described in the developmental cycle of Faustovirus E12, most amoebae are lysed  (Reteno et al. , 2015)  or appear at different stages of the replicative cycle of Faustovirus E12. This shows in our data with a mix of early and late transcribed genes in this dataset. Although our data confirm the expression of most of Faustovirus E12 predicted protein-encoding genes, the low abundance of viral reads doesn't allow further interpretation. With the high abundance of amoebal rRNA and mRNA in the total RNA extract, and in the absence of the V. vermiformis complete genome sequence from international sequence databases, the reads that could not be aligned against the Faustovirus E12 genome could not be unequivocally attributed to this amoeba. The possibility of using a ribodepletion strategy should be explored for future transcriptomic studies targeting giant viruses of amoebae.
Gene splicing was first described by two teams in Adenovirus 2 in 1977  (Berget et al. , 1977; Chow et al. , 1977). Subsequently, it has proved extensive in eukaryotes and as a central mechanism in gene regulation and protein diversity generation  (Kelemen et al. , 2013). The presence of introns has been suggested to increase gene expression by controlling the DNA accessibility or through the regulatory effect of some introns on the RNA polymerase  (Hir et al. , 2003). In Faustovirus E12, splicing was detected in the MCP gene, a high abundance protein encoding gene, with most spliced reads corresponding to this. This could reinforce the hypothesis that splicing plays a role in increasing gene expression  (Alejo et al. , 2018). However, the low abundance of viral reads in our datasets was a limit to the high confidence identification of other spliced genes.
We proceeded with a visual inspection of the raw location data to ensure there were no mistakes in the classification using time series of NSD, i. e. the squared distance between the first location and all the subsequent locations of an individual trajectory  41. We also checked that migrant individuals monitored for more than two seasons came back to their initial range after migration by estimating the range overlap (using VI) between seasonal range i and seasonal range i + 2. We considered that individuals came back to their initial range when the VI index was >0. For all individuals, we measured the linear displacement between consecutive seasonal geometric centres (spring: from winter to summer, autumn: from summer to winter). For residents only, we also used the individual range overlap (VI index) value to estimate the amplitude of seasonal range overlap, as complementary analyses presented in Supplementary Material.
We retained the model with the lowest AICc value reflecting the best trade-off between complexity and precision. When the difference in AICc between two models (ΔAICc) was less than 2, models were considered as equivalents. We provided marginal and conditional R² for models with ΔAICc < 2 to indicate model fit quality  47. We reported the correlation coefficient between explanatory variables in Supplementary Material (Tables  S3  and Table   S11 ). Analyses were performed using the package "adehabitatHR"  48  , "sp"  49  and "nlme"  50  from R software  51.
Acknowledgements We thank the agents from the  Hunters' Federations of Ain, Doubs and Jura , the wildlife rangers from  Bern, Fribourg and Vaud  states for capture and handling of red deer. We are grateful to  Simon Chamaillé-Jammes  and  François Klein  for their advice on an earlier version of the manuscript. This study was funded by  Office National de la Chasse et de la Faune Sauvage  and the  European program  "Interreg". We also are grateful to the  Federal Office for the Environment  for its financial support. We thank  Catherine Carter  for improving the English.
g. Gobbi et al. , 1991; Solomon, 1999; Ronsmans et al. , 2016)  and further enhances the depletion of ozone. HNO 3 has been measured by a variety of instruments over the last few decades, of which the MLS (on the UARS, then the Aura satellite) provided the most complete data set. MLS measurements began in 1991 and allowed for the extensive analyses of seasonal and interannual variability, as well as the vertical distribution of HNO 3  (Santee et al. , 1999 (Santee et al. , , 2004)) , however with a coarse horizontal resolution. Other instruments have also measured HNO 3 in the atmosphere, such as MIPAS (ENVISAT,  Piccolo and Dudhia, 2007) , ACE-FTS (SCISAT,  Wang et al. , 2007)  and SMR 1  (Odin,  Urban et al. , 2009) , although few of these data have been used for geophysical analyses in terms of chemical and physical processes influencing HNO 3 , mostly due to the limited horizontal sampling of these instruments.
Identifying the factors driving its spatial and temporal variability could consequently help to characterize its behaviour in stratospheric chemistry, and hence its interactions with O 3. The Infrared Atmospheric Sounding Interferometer (IASI) on-board the Metop satellites has been, and still is, providing global measurements of the HNO 3 total column, which are used here to investigate HNO 3 spatial and temporal variability. The data set used (Sect. 2) consists of a time series of HNO 3 columns retrieved from IASI/Metop A measurements over the period 2008-2016; measurements were taken twice daily and with global coverage. This unprecedented spatial and temporal sampling of the high latitudes allows for an indepth monitoring of the atmospheric state, in particular during the polar winter  (Wespes et al.
IASI HNO 3 data The HNO 3 columns used here were retrieved from measurements taken by the IASI instrument on-board the Metop A satellite. IASI measures the upwelling infrared radiation from the Earth's surface and the atmosphere in the 645-2760 cm -1 spectral range at nadir and off-nadir along a broad swath (2200 km). The level 1C data set used for the retrieval consists of measurements taken twice daily (at 09:30 and 21:00, equatorial crossing time) at a 0. 5 cm -1 apodized spectral resolution and with a low radiometric noise (0. 2 K in the HNO 3 atmospheric window)  (Clerbaux et al. , 2009; Hilton et al. , 2012). The ground field of view of the instrument consists of four elliptical pixels (2 by 2) yielding a horizontal footprint (single pixel) that varies from 113 km 2 (12 km diameter) at nadir to 400 km 2 at the end of the swath.
To retrieve HNO 3 atmospheric concentrations, we use the level 1C measurements available in near real-time at Université Libre de Bruxelles (ULB) and retrieved by the Fast Optimal Retrievals on Layers for IASI (FORLI) software, which uses the optimal estimation method  (Rodgers, 2000). A complete description of the FORLI method can be found in  Hurtmans et al. (2012)  and a summary of the retrieval parameters specific to HNO 3 in  Ronsmans et al. (2016). The retrieval initially yields HNO 3 vertical profiles on 41 levels (from 0 to 40 km altitude) but with limited vertical sensitivity. The characterization of the retrieved profiles conducted by  Ronsmans et al. (2016)  showed that the degrees of freedom for signal (DOFS) range from 0. 9 to 1. 2 at all latitudes. Because of this lack of vertical sensitivity, the HNO 3 total column is the most representative quantity for the IASI measurements and is exploited here for the investigation of HNO 3 time evolution.
Variables used for the regression Given the strong relationship between the O 3 and HNO 3 chemistry and variability  (Solomon, 1999; Neuman et al. , 2001; Santee et al. , 2005; Popp et al. , 2009)  and the novelty of applying such a regression study in an HNO 3 dataset, we consider the major and well known drivers of total O 3 variability here, namely a linear trend, harmonic terms for the annual variability and geophysical proxies for the solar cycle, the QBO, the ENSO phenomenon and the Arctic (AO) and Antarctic oscillations (AAO) for the Northern and Southern hemispheres, respectively. Considering the short length of the time series, however, the linear trend did not yield any significant result and, recalling that the aim of the paper is not to derive long term trends, this aspect will not be discussed further.
More generally, this study shows that the IASI data allow a good analysis and understanding of the HNO 3 variability in the atmosphere. The measurements are made with exceptional spatial and temporal sampling, which allows a detailed analysis of the polar regions throughout the entire year. The amount of data allows for a thorough monitoring of the processes regulating the HNO 3 variability, such as denitrification processes in the southern polar regions, or seasonal variability in tropical regions. The IASI HNO 3 time series will soon be extended with the launch of Metop C in September 2018, which should further improve the regression model. As shown here by the still significant residuals at some periods and locations, other factors could also probably be included to acquire a full and coherent representation of the HNO 3 total columns variability.
Acknowledgements. IASI was developed and built under the responsibility of the " Centre National d'Etudes Spatiales" (CNES, France). It is flown on board the Metop satellites as part of the  EUMETSAT Polar System. The research was funded by the  F. R. S. -FNRS , the  Belgian State Federal Office for Scientific, Technical and Cultural Affairs (Prodex arrangement 4000111403 IASI. FLOW)  and  EUMETSAT  through the  Satellite Application Facility on Atmospheric Composition Monitoring (ACSAF). The authors would like to thank  Ingo Wohltmann  for the VPSC proxy and for useful discussions. Gaétane Ronsmans  is grateful to the " Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture" of Belgium  for a PhD grant (Boursier FRIA). Cathy Clerbaux is grateful to  CNES  for financial support.
Libraries were pooled in equimolar amounts using the Qubit and Fragment Analyser data, and size selected on a Pippin prep using a range of 300-600 bps. The quantity and quality of each pool was assessed on a Bioanalyser (Agilent Technologies) and subsequently by qPCR using the Illumina Library Quantification Kit from Kapa on a Roche Light Cycler LC480011, according to manufacturer's instructions. The amplicon pool was sequenced on an Illumina MiSeq with 15% PhiX spiked in. All sequences have been deposited in NCBI's Sequence Read Archive and metadata for analyses are also available here (SRA PRJEB23224).
Primary DNA processing and characterisation of microbial communities Raw amplicon sequences were processed with the LotuS pipeline  (Hildebrand et al. 2014). For this we used the command line options in LotuS: "-simBasedTaxo 2-refDB beetax" to use the Lambda aligner  (Hauswedell et al. 2014)  to match OTU seed sequences against a specialized reference database (see below), "-p miSeq derepMin 8:1, 4:2, 3:3" to use miSeq optimized parameters and to dereplicate only sequences occurring at least 8 times in one sample, 4 times in 2, or 3 times in 3 separate samples. For read quality filtering we used LotuS miSeq defaults, trimming reads to 220 bp and rejecting reads with an accumulated error < 1, requiring unique reads to be present at least 8 times in one sample. In total 14,477,902 reads passed quality filtering and were subsequently clustered with UPARSE  (Edgar 2013).
Chimeric OTUs were filtered with uchime  (Edgar et al. 2011 ) against a specialized reference 16S database (http:// drive 5. com/uchim e/rdp_gold. fa). High-quality paired seed sequences for each de novo OTU were subsequently extracted, then merged with FLASH  (Magoč and Salzberg 2011). These seed sequences were matched with lambda  (Hauswedell et al. 2014 ) against a custom 16S rRNA gene database with all major known bacterial taxa associated with the honey bee gut (developed by P. Engel). For the taxonomy assignments the LotuS least common ancestor algorithm was used to assign a taxonomic identity based on the alignments to known bee taxa. OTUs were summed to genus, family, class, and phylum level per sample, according to their taxonomic classification. Additionally, we aligned all sequences against the Greengenes and Silva SSU databases using lambda  (Hauswedell et al.
Statistical analyses and comparisons of microbial communities All downstream analyses, unless otherwise specified, were performed in R with the packages ape, ggplot2, phyloseq, phangorn, sgof and vegan  (Paradis et al. 2004   for analyses are available at https ://githu b. com/fruci ano/ Mater ial_Publi shed_Paper s/tree/maste r/Jones _et_al-Insec tes_Socia ux). Additionally, unless otherwise specified, all the analyses were performed on samples rarefied to the smallest number of sequences per individual observed. As the results of our analyses could depend on the specific rarefied sample used, we repeated the rarefaction procedure five times. For each of the rarefied matrices, we then computed matrices of pairwise dissimilarity among individuals (Bray-Curtis, UniFrac distances). Finally, the dissimilarity matrix obtained from the first rarefied dataset was compared with each of the dissimilarity matrices obtained from the other rarefied datasets by computing their correlation and testing its significance with a Mantel test  (Mantel 1967 ).
As PERMANOVA can suffer from lower power or higher type I error in the case of differences in dispersion between groups (Anderson and Walsh 2013), we also tested for differences in dispersion in vegan. This test of differences in dispersion, which is based on  Anderson 2005 , was performed on each of the dissimilarity measures used (Bray-Curtis, weighted UniFrac, unweighted UniFrac). We also computed the Shannon diversity index, a commonly used metric where both taxon richness and evenness of OTUs in each sample is accounted for in each individual with the "diversity" function in vegan and tested for differences between behaviours using ANOVA. To test which OTUs were differentially represented between honey bees performing different tasks, we used two different procedures. First we used the procedure suggested by McMurdie and Holmes 2014 on a dataset of non-rarefied samples (excluding taxa with < 500 reads to reduce false positives due to small sample sizes).
This procedure overcomes the need for rarefaction and uses the method implemented in the package DESeq2  (Love et al. 2014) , which is more commonly used to detect differential gene expression in RNAseq data. The DESeq2 method fits a model based on negative binomial distribution to test for differences in gene expression (in this case read counts) between two a priori defined groups. We controlled for false discovery rate using the Benjamini and Hochberg procedure (Benjamini and Hochberg 1995). The procedure based on DESeq2 shows higher sensitivity on smaller datasets (< 20 samples per group), but tends towards a higher false discovery rate with more samples, very uneven (> 10×) library sizes or compositional effects  (Weiss et al. 2017 ). Because of these potential limitations, we also performed an analysis of composition of microbiomes (ANCOM)  (Mandal et al.
Bacterial sequences and classification We obtained a total of 14,477,902 16S rRNA V4 region sequences from the 73 sampled bees assigned to the different behavioural task categories (see Table  S1  for sampling details). After quality filtering, the number of sequences obtained per sample ranged from 101,846 to 344,963 reads which clustered in a total of 357 different OTUs (one sample with a low read number (35,604) was excluded for all further analyses). The main bacterial taxa previously found to dominate the gut community of honey bees were represented in high proportions in the samples studied here (Fig. 2 ). We were able to assign 90% of the sequence reads down to species level using a custom honey bee bacterial database, and find that the major previously identified taxa or strains were present in our data (Neisseriaceae, Snodgrassella alvi; Orbaceae, Gilliamella apicola and Frischella perrara; Lactobacillaceae, Firm-4 and Firm-5 species groups (genus Lactobacillus) and Lactobacillus kunkeei); Bifidobacteriaceae; Bifidobacterium asteroides;.