dataesr/scientific-paragraphs-categorization huggingface dataset:
Further, bacterial taxa belonging to the phylum Proteobacteria, the recently described species Bartonella apis  (Kešnerová et al. 2016) , were found to be significantly higher in abundance in bees performing food processing tasks compared with forager bees, under the DESeq2 test only. Bacteria belonging to the Lactobacillaceae family, (Lactobacillus kunkeei), known to be a dominant crop (foregut) bacterium, also common in hive materials and nectar (Corby-  Harris et al. 2014; Kwong and Moran 2016)  were found to be higher in abundance in foragers compared with bees performing nursing behaviours. We note that very few reads were assigned to L. kunkeei (≪ 0. 05%). Other low abundance taxa (although included in our more stringent dataset, where taxa with < 500 reads were excluded) were also found to be different in abundance between bees performing the task of foraging, compared with bees performing in nest tasks (Table  S3 ).
Acknowledgements We thank  Luciano Scandian  and  Hasan Al Toufalilia  for beekeeping assistance. We are grateful to  Paul Helliwell  for observation colony setup discussions in the initial stages of this project. This work was funded by a  Marie Curie IEF  ( SoDoLS-626555 ) (JCJ), a  NERC ,  NBAF  pilot grant (JCJ and WOHH), the  European Molecular Biology Laboratory ,  EMBL (FH, SF) , an  HFSP   Young investigator grant  ( RGY0077/2016 ) (PE), and the  ERC-StG 'MicroBeeOme' (PE). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creat iveco mmons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Material and methods For this study, we analyzed: 1) whether land cover changes around cities differed significantly from trends at national level; 2) whether different specific conversions of land cover are common for groups of cities occurred over time and finally 3) whether the spatio-temporal supply patterns of ES over the period 1990 -2012 were shared among these Mediterranean cities, depending on data availability. The assessment was carried out in six steps. First, we selected twelve major Mediterranean cities as case studies. We used a systematic approach to define the peri-urban area for each city. Based on time series of available land cover maps (Fig. 1 ), we assessed land cover changes in each peri-urban area and we compared them with national dynamics per country. In addition, we identified the main patterns in land cover changes across all periurban areas.
, 2016)  is a spatial database with a resolution of 100 m and it is available for all European countries for the years  1990, 2000, 2006 and 2012  (Table  1 ). For the North African countries, CLC is available only for 1990. We used GlobCorine Land Cover (GLC)  (Bontemps et al. , 2009)  to include another point in time (  2006 ) for these countries. The GLC land cover map was developed by the European Environmental Agency and European Space Agency attempting to ensure compatibility with CLC (Appendix 1). -Insert Fig. 2  around here -.
This means that the surface of each CLC category (Y x ) is equal to a multiplication of the surface (km 2 ) for each GLC category (X x ) by weighing factors (W x ) (Equation  1 ). Equation 1: For the statistical analysis, the CLC categories were summarized in 7 different CLC groups (Table  3 ). To obtain the urbanization trends in Mediterranean cities, we estimated the total standardized surface by CLC group coming from W p and compared it with the total standardized land cover from the respective Mediterranean parts of countries (Portugal, Spain, France, Italy and Greece; Tunisia and Morocco) over the different periods. Assessments for Europe and North Africa follow the same methods, but were applied separately because the uncertainties and applied methods for the input data are different.
According to sampling distribution of CLC and paired groups, non-parametric Wilcoxon test was suitable for the case of permanent crops, complex cultivation patterns and shrub and/or herbaceous. Based on the assumptions of normal sampling distribution and paired groups, the parametric Twosample t-test was conducted for urban, non-irrigated and irrigated arable land, and forest land cover groups. Appendix 4). The data for the European cities was assessed using Within-class Correspondence Analysis (WCA)  (Benzécri, 1983; Chessel et al. , 2003)  through the "within. coa function" in the R package ade4  (RStudio, 2012). Data for the European and North-African cities was analyzed separately due to the differences in the input data (as discussed in section 2. 3). Changes on ES supply in peri-urban areas over time were estimated conducting a Within-class Principal Component Analysis (WCP)  (Benzécri, 1983; Chessel et al.
Acknowledgements This work has received support from  European Union   FP7 projects OPERAs  (Contract No. 308393 , to APGN and WC),  EU BON  (Contract No. 308454 , to IG and WC) and  ECOPOTENTIAL  project (Contract No. 641762 , to IG). The authors acknowledge  Labex OT-Med  ( ANR-11-LABX-0061 ) funded by the  French Government Investissements d'Avenir program  of the  French National Research Agency (ANR)  through the A* MIDEX  project ( ANR-11-IDEX-0001-02 ). Special thanks for valuable advice go to  Berta Martín-López ,  Violeta Hevia-Martín ,  Claude Napoleone ,  Marina Cantabrana  and  Benjamin Mary. We also thank three anonymous reviewers for their constructive comments.
To collect information from an OSN, the sampler issues an Application Programming Interface (API) query for a particular user, which returns its one-hop neighborhood and the contents published. Though some OSNs (for instance Twitter) allow access to the complete database with additional expense, we focus here on the typical case when a sampler can get information only about the neighbors of a particular user by means of API queries. There are several ways to collect representative samples in a network. One straightforward way is to collect independent samples via uniform node or edge sampling. However, uniform sampling is not efficient because we do not know the user ID space beforehand. Consequently, the sampler wastes many samples issuing invalid IDs resulting in an inefficient and costly data collection method.
Datasets We use the following datasets. All the datasets are publicly available. 3   (9) ν RT (D m(B) (S n )) = m(B) k=1 ξ k -1 t=1 f (X (k) t ) d X (k) t + i∈I n g(i) d S n m(B) k=1 ξ k -1 t=1 1 d X (k) t + n d S n , m(B) := max    k : k � j=1 ξ j ≤ B   . NRMSE := √ MSE/ν(G), where MSE = E ν (n) (G) -ν(G) 2. 3 See public repositories SNAP (https://snap. stanford. edu/data/) and KONECT (http://konect. uni-koblenz. de/).
DNA extraction from liver samples was performed as described elsewhere  [32]. The same procedure was used for fibroblasts and monocytes, except that the cells were lysed by the direct addition of lysis buffer and proteinase K to the cell pellet. The genotyping of two Montbéliarde bulls was performed by LABOGENA (France) on semen and blood DNA from the same individuals using the commercially available BovineSNP50 v2 BeadChip (Illumina). Genotypes were determined using the Genotyping Module of GenomeStudio software (Illumina). For each animal, copy number variations (CNVs) were searched for and compared between tissues. The Log R Ratio (LRR, normalized measurement of total signal intensity) and B Allele Frequency (BAF, measurement of the allelic intensity ratio) were used to infer copy number changes in the genome.
In silico analyses A script developed in house was used to extract the coordinates of all the CCGG sites present in the genomes of different species (cattle, sheep, horse, pig, mouse and human; Fig. 1e ). The CCGG sites were then annotated relative to different gene features, CpG density and (See figure on previous page. ) Fig. 1  Global DNA methylation level measured by LUMA is low in bovine sperm. a Global DNA methylation level in bovine sperm and PBMCs. Each colored dot represents one individual. The black dots and horizontal bars indicate the means ± standard deviations. The difference between cell types is highly significant (p < 2. 2e-16, Welch's t-test). b Global DNA methylation level of sperm in four bovine breeds. The effect of the breed on CCGG methylation is significant (p < 0. 05, one-way analysis of variance for independent samples).
The CCGG sites were annotated relative to gene features, CpG density and overlapping repeats. For each genomic feature examined, species with extreme values are indicated (min. and max. ). In the bovine genome, CCGG sites are particularly enriched in CpG islands and satellites, and are within the ranges of other species for other genomic features. SD: standard deviation repeats by means of a pipeline developed in house (https://github. com/FAANG/faang-methylation/tree/ master/RRBS-toolkit/Annotation) and using the genome annotation features indicated in Additional file 1: Table  S1. The following criteria were applied: promoter-TSS, -1000 to + 100 bp relative to the transcription start site (TSS); TTS: -100 to + 1000 bp relative to the transcription termination site (TTS); shore, up to 2000 bp from a CpG island (CGI); and shelve up to 2000 bp from a shore.
A site/fragment was considered to belong to a CGI (respective shore and shelve) if an overlap of at least 75% was observed between the site/fragment and the CGI (respective shore and shelve). A site/fragment was considered as being overlapped by a repetitive element whatever the extent of this overlapping. In Additional file 1: Tables  S2  and S3 , in silico reduced representation (RR) genomes digested by MspI restriction enzyme were produced for different species and using different size selection criteria by means of another pipeline developed in house (https://github. com/ FAANG/faang-methylation/tree/master/RRBS-toolkit/ RR_genome). The RR genome fragments were then annotated as explained above.
1c -d that were collected on the same bulls and rams, and (ii) two fibroblast samples and two livers that were collected on the same animals. Two independent amplifications of the same fibroblast cultures were used for MeDIP and RRBS. The sperm and blood DNA samples used for genotyping were the same as used for LUMA, Fig. 1c-d. PBMCs: peripheral blood mononuclear cells quantified using Pyromark Q24 software. Each sample was assayed in duplicate. The global methylation percentage per sample was then calculated as follows: Methylation% ¼ 100-Average signal obtained with HpaII after EcoRI normalization Average signal obtained with MspI after EcoRI normalization Ã 100 The conditions were compared using non-parametric tests suited to small samples (permutation tests for two independent samples or for two paired samples according to the situation, with Monte-Carlo sampling of 100,000 permutations) or using t-test and analysis of variance when appropriated (larger samples with a normal distribution).
ccb. jhu. edu/pub/data/assembly/Bos_taurus/Bos_taurus_UMD_3. 0/annotation/; accessed Aug. 2010). The microarray design and hybridization protocol, as well as more details on the data analysis, can be found in  [35]. The identification of regions of interest containing clusters of probes enriched in at least one tissue, the identification of differentially methylated regions (DMRs) among these regions of interest, as well as the calculation of mean percentages of enriched probes (Pr) in each tissue for each region r (DMR or region of interest), are detailed in the Additional file 1: Supplementary methods. Three Pr were obtained per region: Pr sperm , Pr liver and Pr fibro- blasts. The scatterplot shown in Fig. 3d  illustrates the Pr liver -Pr sperm and Pr fibroblasts -Pr sperm differences for the regions of interest (in black) and for the DMRs specific to the comparison between sperm and somatic cells (in red).
Positive values for both Pr liver -Pr sperm and Pr fibroblasts -Pr sperm indicated that the two somatic cell types were more methylated than sperm in the region considered, while negative values for both Pr liver -Pr sperm and Pr fibroblasts -Pr sperm indicated the contrary. Similarly, an opposite sign for the values of Pr liver -Pr sperm and Pr fibroblasts -Pr sperm reflected the contrasting behaviors of liver and fibroblasts. Among the DMRs, those undermethylated in sperm were selected based on a positive value for both Pr liver -Pr sperm and Pr fibroblasts -Pr sperm and were annotated as explained regarding the in silico analyses, together with the 27,684 regions of interest. Genes containing DMRs were subjected to DAVID analysis (Database for Annotation, Visualization and Integrated Discovery;  [36] ) using genes containing the 27,684 regions of interest as the background.
Reduced representation bisulfite sequencing (RRBS) and data analysis RRBS libraries were prepared as described elsewhere  [37, 38]. Briefly, 200 ng of genomic DNA were digested by MspI (Thermo Scientific), end-repaired and ligated to 55 bp Illumina adapters for paired-end sequencing. Size selection by gel excision was performed in order to select fragments ranging from 150 to 400 bp (genomic fragments of 40-290 bp + adapters). The DNA was then purified using the MinElute gel extraction kit (Qiagen) and then bisulfite-converted twice consecutively with the EpiTect bisulfite kit (Qiagen), following the manufacturer's instructions for DNA extracted from FFPE tissues. Converted DNA was amplified with Pfu Turbo Cx hotstart DNA polymerase (Agilent) using 14 PCR cycles for sperm and fibroblasts and 12 cycles for monocytes.
The libraries were then purified using Agencourt Ampure beads (Beckman-Coulter) and sequenced on an Illumina HiSeq2500 sequencer to produce 75 bp paired-end reads (Integragen SA, France). RRBS sequences were analyzed using an integrated pipeline combining scripts developed in house in Python, R and Shell, together with external tools (https:// github. com/FAANG/faang-methylation/tree/master/ RRBS-toolkit/). Details about the analysis and the identification of differentially methylated cytosines (DMCs) are provided in the Additional file 1: Supplementary methods. For each tissue, the mean methylation percentage was calculated (mean of the methylation percentages obtained in the two biological replicates) as well as the difference between two tissues (Additional file 2, column L). The scatterplot shown in Fig.
3e  illustrates the differences between monocytes and sperm and between fibroblasts and sperm for the 1,580,644 CpGs covered by 5 to 500 uniquely mapped reads (CpGs 5-500) in all six samples (in black) and for the DMCs specific to the comparison between sperm and somatic cells (in red). Among these DMCs, those undermethylated in sperm were selected based on a positive value for both differences. Together with the CpGs 5-500, they were then annotated relative to gene features, CGIs and repeats as explained for the in silico analyses. Genes containing DMCs were subjected to DAVID analysis using genes containing the 1,580,644 CpGs 5-500 as the background. To better characterize repetitive elements, an artificial genome containing the consensus sequence of each bovine repeat was constituted from the Repbase database  [39].
The primers used to amplify each region are listed in Additional file 1: Table  S4 , together with the hybridization temperatures and MgCl 2 concentrations. The reverse primers were 5′-biotinylated. After denaturation and purification, the biotinylated antisense strand of PCR product was used as a template for pyrosequencing with 0. 3 μM pyrosequencing primer, using the Pyromark Q24 device and Pyromark Gold Q96 reagents (Qiagen). The pyrosequencing primers are listed in Additional file 1: Table  S5. Each CpG was assayed in duplicate, and inconsistent duplicates (more than 5% difference) were repeated. The methylation percentage per CpG was then obtained by calculating the mean of all replicates that passed quality control by the Pyromark Q24 software. The statistical analysis was performed on the mean percentage per CpG using permutation tests as explained for LUMA.
Hypomethylation of four regions is confirmed by bisulfite-pyrosequencing Four regions were selected for validation, based on their hypomethylation in sperm, the presence of both DMRs obtained by MeDIP analysis and DMCs obtained by RRBS analysis whenever possible, and their position relative to genes involved in sperm functions. Figure  6a  shows the detailed localization of these regions together with their coverage and individual methylation in both MeDIP and RRBS. LSM4, which contained a hypo-DMR, is a gene involved in RNA processing  [45]. methylation of these genes associate to male infertility  [46, 47]. BTSAT4 was the most frequently represented bovine satellite in our RRBS data and displayed undermethylation in sperm (Additional file 4). Overall, the four regions represented 42 analyzed CpGs.
Acknowledgements We would like to thank  Evolution, Umotest, Awe, GEN'IATEST, OSON, Capgenes, LNCR ,  Michèle Magistrini  and  Rachel Lévy  for providing semen samples;  Véronique Duranthon  and  Delphine Dubé  for providing fibroblasts and  Chaneze Mehalla  for help with the pyrosequencing of  DDX4  and  SYCP3. We are grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees (Bioinfo Genotoul)  for providing computing and storage resources.
Funding This work was supported by grants from the  French National Research Agency  (grant  ANR-13-LAB3-0008-01  ' SeQuaMol'  and grant  ANR-11-INBS -0003  in the framework of the ' Investing for the Future' program ). SeQuaMol funding allowed the collection of large number of animal semen samples and molecular analyses (MeDIP-chip, RRBS, bisulfite conversion, pyrosequencing). The grant  ANR-11-INBS -0003  supported molecular analyses such as LUMA. The funders had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. JPP (phD student) was supported by the  French Ministry of Higher Education and Research  and was a fellow of the ABIES doctoral school. HAA was supported by the  PremUp foundation.
For blood samples, no globin depletion procedure was applied, and RNA purification was performed as reported in Maroilley et al. 13. One µg of total RNA was reverse transcribed for 90 min at 37 °C in a 20 µl volume containing 0. 25 mM dNTP of OligodT, 25 U of MuMLV reverse transcriptase in 4 μl 5X MuMLV buffer (Eurogentec, Liège, Belgium). After heat-inactivation at 93 °C for 5 min, generated cDNA was stored at -80 °C until use. Finally, reverse-transcribed RNA samples of MLNs, IPPs, JPPs and PB from the four pigs chosen for transcriptome analysis were sequenced using three lanes in an Illumina HiSeq 2000 with the Illumina TruSeq Stranded mRNA Library Prep Kit. On average, 47 million reads (100 bp paired-end) were obtained per sample. The raw reads are available at NCBI's SRA repository (Bioproject PRLNA416857; accessions SAMN07966348 to SAMN07966363).
Differential expression analyses of RNA-Seq data. After quality control using the FastQC tool, sequencing reads were filtered with the Trimmomatic tool (v. 0. 32)  14  by trimming leading and trailing bases with phred qualities less than 3 and dropping reads shorter than 36 bases long and those with average phred qualities per base less than 15. Reads were subsequently aligned on the reference genome Sscrofa10. 2 using TopHat2 (v2. 0. 14), with the default parameters and the gene annotation available for Sus scrofa in the Ensembl 85 release database  15. Based on the read mapping with TopHat2, gene expression was quantified by obtaining read counts with the HTSeq-count software (v. 0. 6. 1p1)  16 with a default parameter that discards all reads that map to multiple locations. Expression levels of antisense transcripts were also analyzed by using the strand read information.
We quantified antisense transcription according to the number of reads mapping to the corresponding gene reference sequence on the opposite strand. Therefore, we did not use the annotation of antisense transcripts available in Ensembl 89 but only the annotation of reference genes with their genomic position. Read counts were further analyzed using the edgeR R/Bioconductor package (v. 3. 12. 1)  17. We retained genes as expressed in a tissue when the count per million (CPM) sense reads was greater than one for at least two animals. Similarly, a gene was found to have an antisense transcription if the antisense read CPM was greater than one for at least two animals. Read counts were then normalized according to the total number of reads of each sample using the Trimmed Mean of M-values normalization method (TMM)  18  implemented in edgeR (Supplementary Tables  S1  and S2 ).
The sense and antisense expression data structures were explored with multi-dimensional scaling (MDS) plots. The correlation between sense and antisense transcription across tissues was estimated by Spearman correlation using the normalized read counts. The differentially expressed (DE) genes between GALT tissues were detected by fitting a negative binomial generalized linear model (GLM) in edgeR. In order to take into account intra-individual variability, the model included covariates for both tissue and individual. A likelihood ratio test was performed to identify DE genes among each pair of tissues, and P-values were corrected for multiple testing using the Benjamini-Hochberg control of the False Discovery Rate (FDR < 0. 05). Finally, smear plots of log-fold expression changes versus log-concentration were produced using the "plotSmear" function in the edgeR package.
24. 0) and re-aligned reads on the masked genome with the same STAR 2-pass protocol. Using the dataset from this second alignment, counts of reads covering each allele at selected SNPs were obtained using ASEReadCounter of GATK v3. 7 with parameters ensuring an adequate coverage and quality (read depth greater than 10, mapping quality greater than 10, base quality greater than 2). We then adapted ASEReadCounter output for the format required by QuASAR v. 0. 1  22  and used the associated statistical method to genotype individuals from multiple RNA-Seq samples (fitAseNullMulti) and determine for each animal the probability of each possible genotype according to the alleles found in each tissue (with a minimum read coverage equal to five). Finally, we performed binomial tests on allelic read counts to detect potential allelic imbalance for the SNPs found to be heterozygous in each pig.
Functional enrichment analysis. The Sus scrofa Ensembl gene IDs (release 89 corresponding to Sscrofa10. 2 assembly) annotated from DE and ASE analyses were submitted to Biomart in order to identify lists of human orthologous genes for subsequent functional enrichment analyses. The annotation was further updated using the Ensembl 91 release (Sscrofa11. 1 assembly). For 869 genes still lacking GeneName HUGO Genome Nomenclature Committee (HGNC) annotation, we aligned the protein sequences corresponding to the pig gene Ensembl ID against the human RefSeq protein database using BLASTP  23. The GOrilla tool  24  was used for enrichment analyses of expressed genes in each tissue. For DE genes, we used the Ingenuity Pathway Analysis tool (IPA; http://www/ingenuity. com) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway mapper  25.