dataesr/scientific-paragraphs-categorization huggingface dataset:
Briefly, after assembly of paired-end reads, we assigned reads to their respective samples (with 0 and 2 mismatches allowed on tag and primer sites, respectively) and excluded reads with low assembly scores or that contained ambiguous bases (i. e. , "N"). We dereplicated strictly identical reads using the "obiuniq" algorithm and removed singletons (i. e. , one single occurrence over the entire dataset, which likely indicates degraded sequences). We used the "obiclean" algorithm to detect and remove potential PCR/sequencing errors (Boyer et  al. 2015). We then clustered the remaining sequences into Operational Taxonomic Units (OTUs) based on their similarity using the "sumaclust" algorithm (https://git. metabarcoding. org/obitools/sumatra/wikis/home), with a 97% similarity threshold. Taxonomic assignations were obtained using "ecotag" with the MOTHUR version of the SILVA bacterial database (release 102).
We excluded 241 OTUs from the analyses because they were more abundant in the PCR-specific controls than in the samples, and we excluded an additional 15 OTUs because they were more abundant in the sampling-specific controls than in the samples. The number of sequences did not differ between males and females (4942 ± 288 sequences vs. 4942 ± 264 sequences respectively; F 1,40 = 0. 01, P = 0. 93) and between subordinates and adults (5100 ± 187 sequences vs. 4676 ± 410 sequences; F 1,41 = 1. 14, P = 0. 29). OTUs sequences have been deposited in GenBank (accession numbers: KY630752-KY631487; Supplementary Table  S2 ). Data handling and statistical analyses. For statistical analyses of our chemical data, we excluded peaks that were present in only one sample. Also, because we could not control for the amount of secretion collected, regardless of sampling method, we did not rely on the absolute abundance of chromatogram peaks; rather, we quantified each peak as the proportion of the peak size relative to the total area of the chromatogram  67.
For odorant samples obtained from the anal pouch, we first verified that repeat samples from the same individual produced consistent chromatograms (see Fig. 2 ) and then retained only the most-recently obtained sample in our statistical analyses to avoid pseudoreplication. We used a subset of the chemical data (derived from anal-pouch samples) in our correlative analyses against the bacterial dataset. This is because most organic compounds produced by bacteria are in the range of 100-250 D  24  and, although some bacteria are known to metabolize cholesterol into coprostanol  68  , sterol biosynthesis is mainly viewed as an eukaryotic process  69. We therefore restricted our analyses of chemical and bacterial covariation to those chemical compounds that had lower retention time than that of nonadecane (C 19 H 40 , molecular weight: 268 D).
In addition, because pure anal-gland secretions were sampled from subordinates only, we restricted the comparison between pure secretions and mixtures to subordinate meerkats. We performed all statistical analyses using the R statistical software  70  and express values as mean ± standard error (SE) throughout. To determine if the chemical composition or the bacterial communities present in the meerkat's anal pouch related to the animal's sex and social class (i. e. , subordinate versus dominant), to the two-way interaction between these two factors, or to the animal's group membership, we used a PERMANOVA with 5000 permutations i. e. nonparametric multivariate analysis of variance, Adonis function, VEGAN package in R  71  , based on Bray-Curtis distance for relative abundance data and Jaccard distance for presence/absence data.
Whenever we observed differences between the sexes or social classes in either chemical composition or bacterial community, we conducted similarity percentage analyses (SIMPER procedure in the VEGAN package in R) to elucidate the contribution of specific chemical compounds or OTUs. We used linear models to determine if OTU richness and chemical compound richness were related to the animal's sex, social class, the two-way interaction between these two factors, or to the animal's group membership. We evaluated covariance between chemical compounds and bacterial assemblages in mixed paste using Mantel tests (VEGAN package in R). When the covariance was significant, we used the "bv. step" procedure in R equivalent to the BVSTEP procedure in BEST in the software PRIMER  72  to select the combination of OTUs that best explained the chemical composition of odorants.
Acknowledgements We thank  T. Clutton-Brock  and  M. Manser  for providing access to the animals and their records at the  Kalahari Meerkat Project (KMP). We thank  A. Szabo ,  N. Thavarajah , and  J. Samson  for their help in the field, and  L. Zinger  for her help in bioinformatic analyses. We are grateful to the  Kotze  family and other farmers neighbouring the Kuruman River Reserve for graciously allowing us to work on their land and to the  Northern Cape  for permission to conduct the research. This study, which is part of the  Laboratoire d'Excellence (LABEX)  entitled  TULIP  ( ANR-10-LABX-41 ), was supported by a  Fondation Fyssen  postdoctoral grant (to S. L. ), and research grants from the  "Agence Nationale de la Recherche " (No. ANR-13-PDOC-0002  to S. L. ) and the  National Science Foundation  ( IOS-1021633  to C.
Sequencing analysis Fluorescent sequencing was performed using the Big Dye-terminator cycle sequencing kit and the ABI Prism 3130xl Genetic Analyzer (Applied Biosystems). Sequences were analyzed with sequencing analysis 5. 3 software (Applied Biosystems), identified using V-quest IMGT (http://www. imgt. org/IMGT_vquest/vquest?livret=0&Option= humanIg) and confirmed with Ig-BLAST sequence similarity searching tool (http://www. ncbi. nlm. nih. gov/igblast/).
Sequencing analysis Among the 15 amplified IG rearrangements, only 8 IGH and 3 IGK-Kde rearrangements were successfully sequenced. Negative results of sequencing or insufficient DNA specimens did not allow data analysis. The sequenced N-region confirmed the occurrence of VH-JH and VK-Kde rearrangements as determined by multiplex PCR (Table  3 ). Unfortunately, only the sequence of RSS-Kde rearrangement was not possible. The number of nucleotides in VH-JH or VK-Kde junctional region was identified (Table  3 ). The overall mean number of insertion was 6. 9 (range: 0-24) and the overall mean number of deletions was 10. 6 (range: 0-26). The N-region allowed the ASO construction in 8 IGH and only 2 IGK-Kde junctional regions.
Bioinformatics The quality of reads generated was checked using FastQC (available online at: www. bioinformatics. babraham. ac. uk/ projects/fastqc/). The reads were trimmed to attain optimum quality using the dynamic trim function of SolexaQA  [22]  and FASTX-Toolkit (http://hannonlab. cshl. edu/fastx_toolkit/). A de novo assembly of the reads was done using Trinity (http://trinityrnaseq. github. io/) based on the de Bruijn graph algorithm. The resulting contigs from de novo assembly were analysed by BLASTn against a virus database to detect all viral sequences present in the data. In the Virfind online analysis pipeline, the sequences were trimmed (default setting ~5 bases). De novo assembly was performed using Trinity (v. 2. 0. 2) but where the average sequence length was ≤80 nucleotides, de novo sequence assembly was done by Velvet with kmer = 15, 19, 23, 27, to generate sequences ≥90 nucleotides.
Where the average sequence length ≤ 40 nucleotides, de novo sequence assembly was done by Velvet with kmer = 11, 13, to generate sequences ≥90 nucleotides. Assembled contigs were subjected to a BLASTn search in NCBI at the default e-value (0. 01) to generate annotated sequences. All sequences not detected as 'virus' by BLASTn were subjected to a BLASTx search against all GenBank virus proteins with the e-value at the default (0. 01). For the maize samples, libraries were de-multiplexed allowing one error within the index sequence and de novo assembly done with Trinity using a custom script.
Phylogenetic and sequence analysis of ALPV and BSRV-like genomes Multiple sequence alignments were done using MUSCLE  [24]  in Molecular Evolutionary Genetics Analysis version 6. 0. (MEGA 6)  [25]. The evolutionary history was inferred using either the Neighbor-Joining method  [26]. ORF Finder (https://www. ncbi. nlm. nih. gov/orffinder/) was used for ORF prediction. To annotate the proteins encoded by the ORFs, an online search was conducted in the NCBI conserved domain database (https://www. ncbi. nlm. nih. gov/ Structure/cdd/cdd. shtml) for the location of conserved domains.
Recombination analyses Analyses to detect recombination was done using RDP4 software package. Alignments and analyses were performed for ORF 1 and ORF 2 separately. Default parameters were used for the RDP  [28] , GENCONV  [29] , MAXCHI  [30] , 3Seq  [31] , SiScan  [32]  and CHIMAERA  [33]  methods selected for the analysis. A Bonferroni corrected p value of 0. 05 was considered significant. Evidence of recombination was accepted if it was supported in at least three different methods with a p value >10 -6.
Detection of dicistroviruses RNA sequences in aphid and maize samples RNA isolated from aphids sampled in bean plots and isolated from maize leaves was subjected to Illumina sequencing. Two sequence analysis pipelines were used to analyse the sequence data from the pooled and individual aphid samples (see Materials and Methods). Results from analysis of the assembled sequences revealed the presence of three known or putative dicistroviruses: ALPV, RhPV, and a BSRV-like virus in both pooled and individual aphid samples. The samples where these viruses were detected are shown in Table  1. Three aphid samples (P7, P9 and S45) and sample T2F3S4 from maize had ALPV sequences approximately 9. 6 Kb in length (Table  1 ). This genome size was comparable to other full-length ALPV genomes in GenBank (Table  2 ).
Sequences from samples P7 and P9 were mapped against an ALPV genome from GenBank (reference number NC004365) in CLC workbench version 5. 1. to get deeper sequence coverage (Fig. 2 ). The upper panel in Fig. 2  shows diagrammatically the arrangement of putative functional domains in the proteins encoded by the genome but showing only those predicted with highest confidence. These two sequences from aphid samples (P7 and P9) were renamed KE (Kenya) Aphid P7 and KE Aphid P9 while sample T2F3S4 from maize was renamed KE Maize in further analyses described in this article. ALPV sequences were also detected in two other maize samples (T1F4S3 and T1F5S2) but these were partial genomes and were not used in subsequent analyses. The ALPV sequences from these samples are available in the additional materials (Additional file 7: Text file S4).
One maize sample, T2F3S4, contained a 6816 bp sequence annotated as RhPV (Additional file 2: Text file S1). Additionally, there were shorter (~550 bp) assembled sequences annotated as RhPV from aphid samples (Additional file 8: Text file S5). By comparison, the full-length RhPV genomes available in GenBank are 10Kb in length. For the purposes of this study, RhPV sequences were not analysed further except by confirming the presence of the viral RNAs using RT-PCR and sequencing of the DNA products. Eleven samples had sequences ranging from 9640 to 10,281 nucleotides long annotated as 'Big Sioux River virus' following de novo assembly (Table  1 ). The variation in the length of the genomic RNA sequences was mainly due to partial sequence assembly of the 3′-untranslated region. Of the 11 sequences, sample S37 (GenBank number KY933255) was the longest assembly at 10,281 nucleotides long including a 15 nucleotide polyA tail and is most likely the most complete of the BSRV-like genomes assembled.
Analysis of ALPV sequences To predict the domains encoded by the ORFs, analysis of the ORF sequences was conducted in the NCBI conserved domain database. Results revealed the predicted regions coding for the helicase and RdRp domains in ORF1 and four capsid proteins in ORF2. The position of the protease domain was determined by searching for the conserved protein sequence 'VVV QNR GSY TYH AVT FFG DCG SIL IAS NAA ITQ KIM GMH IAG ITH MNKG' for this region  [36]  at residues 1395-1443 of the ORF1 protein sequence when translated from the genomic RNA. A schematic of the ALPV genome and the mapping depth is shown in Fig. 2.
Phylogenetic and sequence comparisons between different ALPV sequences Three ALPV genomic RNA sequences from this study and six others from GenBank were used for phylogenetic analysis (Table  2 ). Results revealed two main clades (Fig. 3a ). The isolates from our study (KE Aphid P7, KE Aphid P9 and KE Maize) were in one of the major clades, grouping with samples from China, South Africa, and East Timor. The two isolates from the USA and Spain formed the other clade. Analyses of nucleotide sequence similarity revealed genetic variation within the subclades and between the clades detected in the phylogenetic analysis. The KE Aphid P7 and KE Aphid P9 ALPV isolates shared 98. 9% nucleotide sequence identity with each other and 93% similarity to the isolate from Israel when whole genomes were compared.
Sequence and phylogenetic analyses that BSRV-like isolates may belong to a new dicistrovirus species From BLAST analyses, the 11 nucleotide sequences from this study had 87% similarity to isolate BSRV1 (GenBank number: JF423195. 1), which is 1473 nucleotides long and encodes part of the non-structural polyprotein (ORF1) of BSRV. A BLAST analysis comparing ORF2 sequences from our BSRV-like sequences and an ORF2 sequence from GenBank, BSRV4 (GenBank number: JF423198. 1) revealed 77% and 86% similarity at nucleotide and protein sequence comparisons respectively. The nucleotide similarity among the BSRV-like viruses was 98-99%. Comparison of the 11 BSRV-like sequences to two ALPV (KE Aphid P9, KE Aphid P7) and three RhPV sequences from GenBank revealed 57-60% similarity to ALPV and 70-72% to RhPV.
Among the BSRV-like sequences the similarity was 98-99% (Fig. 4 ). In phylogenetic analyses, the BSRV-like sequences clustered in a clade containing members of the genus Cripavirus (Fig. 5 ). This demarcation confirmed that the BSRV-like isolates detected in our samples were distinct viruses unrelated to ALPV or RhPV and were likely to be a novel BSRV-like dicistrovirus. The sequencing of BSRV-like RNA revealed two ORF. The 5′ proximal ORF 1 was predicted to encode a translation  of 2007 amino acids while ORF2 was predicted to encode a product of 753 amino acids (Fig. 6 ). Analysis of the genomic RNA sequence for ORF1 in the NCBI conserved protein database predicted regions with similarity to helicase and RdRp domains. The ORF2 sequence was predicted to encode a precursor for three structural proteins (Fig.
Secondly, there may have been a seasonal effect on aphid diversity. Literature on the seasonal variation of aphids, in East Africa and Kenya specifically, is scant but past surveys conducted in Kenyan bean farms found that A. fabae and A. gossypii were the most prevalent aphids over the April to June long rainy season  [37, 38]  but no information was previously available for the October to November short rainy season. Viral metagenomic analysis of aphids and maize leaf samples detected three dicistroviruses: ALPV, RhPV, and a novel BSRV-like virus. ALPV and RhPV have been detected in R. padi, A. fabae and maize in South Africa  [11, 12] , but not previously in East or Central Africa. The discovery of BSRV-like viruses in A. fabae is, to our knowledge, the first report from Africa. Phylogenetic comparison of the ALPV isolates from our study with those identified by others and deposited in GenBank revealed two main clades.
Interspecies comparisons revealed a clear demarcation between the different species. The sequences were aligned using MUSCLE and the arrangement of the samples in the matrix is based on phylogenetic clustering of neighbour-joining tree constructed from the multiple sequence alignment. RhPV sequences (marked with two asterisks) used in this analysis were from GenBank while the ALPV sequences used (marked with a single asterisk) were from this study obtained in Kenya from and A. fabae showed genetic variation, indicating that there could be more than one lineage of ALPV. However, the Kenyan isolates did not meet the divergence thresholds for reclassification as separate species. Recombination is a powerful driver of viral speciation, especially in viruses with monopartite genomes  [41, 42]. For viruses with multipartite genomes, recombination can occur more in one of the genomic RNA components though the overall rate of recombination is infrequent  [43, 44].
Our sequence analyses have revealed the first evidence of recombination among for ALPV. The results point to the likelihood that the three isolates (KE Aphid P7, KE Aphid P9 and Israel) are separate strains from isolate KE There is currently no full genome for BSRV in GenBank and, therefore, our isolates may be BSRV or could be a BSRV-like, novel dicistrovirus species infecting A. fabae. The BSRV-like sequences from Kenya cluster phylogenetically with viruses of the genus Cripavirus. From previous reports, BSRV appears to be a multi-host pathogen; having been previously detected in honeybees, mosquitoes and soybean aphids  [16] [17] [18]. All discoveries of BSRV have been through metagenomic studies and hence BSRV is yet to be formally classified by the International Committee on Taxonomy of Viruses (ICTV), which until recently has required evidence of biological properties such as pathogenicity, host range and epidemiology.
Acknowledgements We thank all the teams at  BecA-ILRI Hub  for technical and institutional support for the  Illumina MiSeq sequencing. We would like to thank the staff of the  Segolip Unit at the BecA-ILRI Hub in Nairobi  for help with Sanger sequencing. We thank  David Karanja , the  National Bean Programme coordinator at Kenya Agricultural and Livestock Research Organization , for his help and support in accessing smallholder farms in Katumani and Kaiti. We are grateful to  Mr. Moses Njiriri  and  Mrs. Jacinta Nthenya Muia  for allowing us to collect aphid samples from their farms at Ndeiya and  Oloirien in Kiambu  and Kajiado counties, respectively. We thank  Dr. Andrew Firth ,  Dr. Nina Lukhovitskaya , and  Professor Sir David C. Baulcombe  for useful discussions. Funding Work was funded by a grant from the  Sustainable Crop Production Research for International Development programme  funded by the  UK Biotechnology and Biological Sciences Research Council (BBSRC)  with co-funding from the  UK Department for International Development , the  Bill & Melinda Gates Foundation , the  Department of Biotechnology of India's Ministry of Science and Technology , and the  Indian Council of Agricultural Research  ( BB/ J011762/1 ) and a  Global Challenges Research Fund   Foundation Award  ( BB/ P023223/1 ).
Sleep restriction procedure The sleep restriction was done using a shaker apparatus (Plexiglas cylinder, diameter 30 cm, 45 cm height) that prevent sleep occurrence by transient up and down movements (20-40 ms, 1 cm height). A sequence of stimulation was built by randomizing the number of stimulations (3-5), the delay between 2 stimulations (100-200 ms). Each sequence of stimulations was randomly administered (10-30 s interval) for 20 consecutive hours. The stimulation schedule was controlled using dedicated software (Viewpoint s , Lyon, France). This sleep restriction protocol was previously validated using electrophysiological recordings and polysomnographic analysis  (Chauveau et al. , 2014).
Statistics Statistical analysis was performed using Statistica 7. 1 software. Comparisons to the chance level (50% alternation for the T-maze experiment) were carried out using one-sample t-test. Data were expressed as mean7s. e. m. Comparisons between D1 and D2 behavioral tests were carried out using paired t-tests. Comparisons among groups were carried out using a two-way factorial analysis of variance (ANOVA) with sleep restriction (SR effect), a NPS treatment (NPS effect) effects and interaction. If necessary, the ANOVA tests were followed by Newman-Keuls post-hoc tests for multiple comparisons. The significance level was set at po0. 05. Correlations between behavioral performance and Fos expression in single brain area (simple correlation) were done using Spearman correlation tests. C-fos activation between brain areas (neural network activity; multiple correlations; Supplementary Tables  2 3 4 5 ) were done using Spearman correlation tests with the significant threshold adjusted with Bonferonni correction at po0.
Acknowledgement This research was supported by the  Service de Santé des Armées  and funded by a grant ( n1 PDH-1-SMO-2-0504 ) from  Direction Générale de l'Armement DGA, France). This work has benefited from the facilities and expertise of the imagery platform from IRBA and animal room and experimental lab from  NeuroPSI institute (Université Paris-Saclay ).
A549 lung adenocarcinoma cells and Madin-Darby Canine Kidney (MDCK) cells were cultured in DMEM (Corning Inc. , Manassas, VA) supplemented with 10% FBS (Atlas Biologicals, Fort Collins, CO). Influenza A/WSN/33 (H1N1) virus stocks were grown in MDCK cells, and titered by plaque assay on MDCK cells  [7]. The genome of our stock of WSN was sequenced and compared to those recorded in the Influenza Research Database. Two point mutations were identified in our stock, which are not present in the deposited sequences. In segment 6 (NA), a G to U change at position 158 of the ORF results in a Ser to Ile change at residue 53. In segment 7 (M), an A to G change at position 502 of the ORF results in a Thr to Ala change at residue 168. This latter change is known  [30]  and does not alter virus growth characteristics in cultured MDCK cells.
Acknowledgements The authors thank  Jerry Kudenov  ( UAA ) for helpful discussions, and  Adolfo García-Sastre  ( ISMMS ) for A/WSN/33 virus. We appreciate the work of the  Emory University Robert P. Apkarian Integrated Electron Microscopy Core  for processing of EM samples. We also thank  Dr. Martina Ralle  ( Oregon Health & Science University ) for ICP-MS analyses.
Methods: IICR computation It is possible to estimate the IICR by simulating independent T 2 values and using them to estimate the IICR at various time points t i , as follows  (Mazet et al. 2016) : d IICRðt i Þ ¼ 1 À d F T 2 ðt i Þ d f T 2 t i ð Þ ð1Þ where d F T 2 t i ð Þ is the estimated or empirical cumulative dis- tribution function of T 2 and d f T 2 t i ð Þ is an estimated approx- imation of its density around t i. We can thus predict the IICR for any model of interest for which T 2 values can be simulated. It can then be compared to the PSMC results by simulating genomic data under the same demographic model and sampling scheme. In the next section, we identify several models commonly used in population genetics and provide the IICR curves for them. In all cases data were simulated using the ms software  (Hudson 2002).
For each scenario 10 6 independent T 2 values were simulated and the IICR computed and plotted with a python script available at: https://github. com/willyrv/ IICREstimator. Details of the models and all ms commands are provided in the Supplementary files. In most cases the figures were scaled so that the deme size was N = 500, and assuming a generation time of 25 years. Finally, for the scaling with human genomic data we used a mutation rate of 2. 5 × 10 -8 , as in  Li and Durbin (2011). The exact values of these parameters are to some extent of limited importance as we are mainly interested in general principles.