dataesr/scientific-paragraphs-categorization huggingface dataset:
80 min, range 0-13 min). 94 of the 130 non-vocal events (72. 3%) were single entries to the dataset (i. e. no more than one event per day per individual). 36 of 130 (27. 7%) travel events were multiple entries from individuals that had been recorded more than once on the same day. 26 of these 36 cases were different travel events with different audiences. The average interval was 79. 19 min (N=16, range 5-280 min). In the shortest case, the focal animal travelled with one individual, engaged in grooming with him for 5 minutes, then initiated another travel bout when a female appeared. Out of the 10 remaining events, 8 appeared to be different travel bouts (separated by an average of 50. 50 minutes, N=4, range 28-78 min) but were recorded with the same audience. Finally, the last two cases occurred consecutively with the same audience and the second case was thus classified as persistence.
Acknowledgements Permission to conduct this research was given by the  Uganda Wildlife Authority (UWA) , the  Ugandan National Council for Science and Technology (UNCST)  and the  National Forestry Authority (NFA). We are grateful to the  Royal Zoological Society of Scotland  for providing core funding for the  Budongo Conservation Field Station. We thank  Stephen Amati  for assistance in the field,  Caroline Mullins  for comments on earlier drafts of the manuscript,  Simon Townsend  and  Cat Hobaiter  for statistical advice, and  Roman Wittig  and  Cathy Crockford  for sharing their social relation data. We thank the editor, the reviewers and  Zanna Clay  for their comments on the manuscript.
At each 24-hour transfer, 1% of the bacterial population was placed in 2 mL of fresh KB, and 20 µL of amplified phage (ca 10 8 viral particles) were added (a control without phage was conducted simultaneously). Bacteria CFUs were counted on solid agar after 48 hours of incubation to estimate population size. Due to non-normality of the data as assessed by a Shapiro test, we used a Kruskal-Wallis test to determine the significance of the between-treatments effects. unbound phage. Observations of c. 50 cells using TEM (Zeis EM10) showed no bound phages after the centrifugation treatment.
have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. No competing interests were disclosed. Competing Interests: Britt Koskella Centre for Ecology & Conservation Biosciences, College of Life & Environmental Sciences, University of Exeter, Exeter, UK F1000Research Exeter, Exeter, UK Approved with reservations: 08 October 2012 08 October 2012 Referee Report: Understanding the response of bacterial populations to bacteriophage viruses is of central importance to predicting microbial dynamics. To do so requires knowledge about both the ecological and evolutionary responses of bacteria to phage in the environment.
funded by a  CNRS-Région Languedoc Roussillon doctoral grant. TB was funded by  NERC. MEH is funded by  ANR  Grant information:  EvolStress  ( ANR-09-BLAN-099-01 ), by the  Ec2Co "Cytrix" program , and the  McDonnell Foundation  ( JSMF 220020294/SCS-Research  Award). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. No competing interests were disclosed. Competing Interests: Britt Koskella Centre for Ecology & Conservation Biosciences, College of Life & Environmental Sciences, University of Exeter, Exeter, UK F1000Research Exeter, Exeter, UK Approved with reservations: 08 October 2012 08 October 2012 Referee Report: Understanding the response of bacterial populations to bacteriophage viruses is of central importance to predicting microbial dynamics. To do so requires knowledge about both the ecological and evolutionary responses of bacteria to phage in the environment.
Male reproductive success Male mating success was estimated by the identity of the male engaged in the first mating. Male fertilisation success was evaluated by the number of offspring sired by each of the four males for all studied females. We genotyped microsatellite loci in up to eight larvae per clutch for the two clutches per female allowed to develop to the second larval stage. We also genotyped all males and females. We used the eight most variable microsatelite loci of those described by Loiseau et al. [48]. Paternity was assessed with PROBMAX software  [49]. We found no difference in paternity pattern between the early and late clutches, which were therefore pooled for data analysis. Coupling percentage paternity with the results of the mating trials allowed distinction between mating success and fertilisation.
Male reproductive success During the first hour of the experiment, 93% of females engaged in copulation (see Table  S1 , and Dataset S1 in the supporting information for details). We analysed the traits of the males only for these females. Invasive males tend to be the first to copulate. In our experimental design, native females were presented with three native males and one invasive male each, so 25% of first copulations would be expected to be with the invasive male if mate choice were random. Conversely, invasive females were presented with one native male and three invasive males, so 75% of first copulations would be expected to be with invasive males in a context of random choice. We found that 40% of native females and 85% of invasive females first copulated with an invasive male (Figure  1 ), regardless of the population of the male (see Table  S1  in Table  2.
Acknowledgments We thank  Denis Bourguet ,  Cathleen Thomas ,  Stefan Toepfer ,  Julie Turgeon , and  G. Prinsloo  for sampling efforts, and  Wang Su ,  Tamsin Majerus ,  Wolf Blackenhorn  and two anonymous reviewers for helpful comments. All molecular data were generated at the molecular genetic analysis technical facilities of the  Environment and Biodiversity IFR 119 at Montpellier (France).
The Ensembl gene annotation pipeline created gene models using Uniprot protein alignments, limited coelacanth cDNA data, RNA-seq data generated from L. chalumnae muscle (18 Gb of paired end reads were assembled by Trinity 14 , Supplementary Figure  2 ) as well as orthology with other vertebrates. This pipeline produced 19,033 protein coding genes containing 21,817 transcripts. The MAKER pipeline used the L. chalumnae Ensembl gene set, Uniprot protein alignments, and L. chalumnae (muscle) and L. menadoensis (liver and testis)  15  RNA-seq to create gene models, yielding 29,237 protein coding gene annotations. In addition, 2,894 short non-coding RNAs, 1,214 lncRNAs and more than 24,000 conserved RNA secondary structures were identified (Supplementary Note 2, Supplementary Tables  3 4 , Supplementary Dataset 1-3, Supplementary Figure  3 ).
How slowly evolving is the coelacanth? The morphological resemblance of the modern coelacanth to its fossil ancestors has resulted in it being nicknamed 'the living fossil' 1. This invites the question: Is the genome of the coelacanth as slowly evolving as its outward appearance suggests? Earlier work found that a few gene families, such as Hox and protocadherins, showed comparatively slower proteincoding evolution in coelacanth than in other vertebrate lineages  8, 10. To address this question, we examined several types of genomic changes in the coelacanth compared to other vertebrates. Protein-coding gene evolution was examined using the 251 concatenated protein phylogenomics dataset (Figure  1 ). Pair-wise distances between taxa were calculated from the branch lengths of the tree using the Two-Cluster test proposed by Takezaki et al.
20  to test for equality of average substitution rates. Then, for each of the following species and species clusters (coelacanth, lungfish, chicken and mammals), we ascertained their respective mean distance to an outgroup consisting of three cartilaginous fishes (elephant shark, little skate and spotted catshark). Finally, we tested whether there was any significant difference in distance to the outgroup of cartilaginous fish for every pair of species and species clusters, using a Z-statistic. When these distances to the outgroup of cartilaginous fish were compared, we found that the coelacanth proteins tested were significantly more slowly evolving (0. 890 substitutions/site) than the lungfish (1. 05 substitutions/site), chicken (1. 09 substitutions/site) and mammalian (1. 21 substitutions/site) orthologues (Supplementary Dataset 5), in all cases with p-values <10 -6.
Additionally, as can be seen in Figure  1 , the substitution rate in coelacanth is approximately half that in tetrapods since the two lineages diverged. A Tajima relative rate test  21  confirmed the coelacanth's significantly slower rate of protein evolution (Supplementary Dataset 6). Secondly, we examined the abundance of transposable elements (TEs) in the coelacanth genome. Theoretically, TEs might contribute most significantly to the evolution of a species by generating templates for exaptation to form novel regulatory elements and exons, and by acting as substrates for genomic rearrangement  22. We found that the coelacanth genome contains a wide variety of TE superfamilies and has a relatively high TE content (25%); this number is likely an underestimate due to the draft nature of the assembly (Supplementary Note 5, Supplementary Tables  7 8 9 10 ).
Vertebrate adaptation to land As the sequenced genome closest to our most recent aquatic ancestor, the coelacanth provides a unique opportunity to identify genomic changes that were associated with the successful adaptation of vertebrates to an important new environment -land. Over the 400 MY interval that vertebrates have lived on land, genes that are unnecessary for existence in their new environment would have been eliminated. To understand this aspect of the water-to-land transition, we surveyed the Latimeria genome annotations to identify genes that were present in the last common ancestor of all bony fish (including coelacanth) but that are missing from tetrapod genomes. More than 50 such genes including components of the Fgf signaling, TGF-beta/Bmp signaling, and Wnt signaling pathways, as well as many transcription factor genes, were inferred to be lost based on the coelacanth data (Supplementary Dataset 7, Supplementary Figure  9 ).
Previous studies of genes lost in this transition could only compare teleost fish to tetrapods, meaning that differences in gene content could have been due to loss in the tetrapod or in the lobe-finned fish lineages. We were able to confirm that four genes previously shown to be absent in tetrapods (Actinodin genes  29  , Fgf24 30 , Asip2 31 ), were indeed present and intact in Latimeria, supporting their loss in the tetrapod lineage. We functionally annotated the >50 genes lost in tetrapods using zebrafish data (gene expression, knock-downs and knock-outs). Many genes were classified in important developmental categories (Supplementary Dataset 7): Fin development (13 genes), otolith and ear development (8 genes), kidney development (7 genes), trunk/somite/tail development (11 genes), eye (13 genes), and brain development (23 genes).
With the availability of gene sequences from coelacanth and lungfish, it became possible to test this hypothesis. We used a branch-site model in the HYPHY package  38  , which estimates dN/dS (ω) values among different branches and among different sites (codons) across a multiple species sequence alignment. For the rate-limiting enzyme of the hepatic urea cycle, carbamoyl phosphate synthase I (CPS1), only one branch of the tree shows a strong signature of selection (p = 0. 02), namely the branch leading to tetrapods and the branch leading to amniotes (Figure  3 ); no other enzymes in this cycle showed a signature of selection. Conversely, mitochondrial arginase (ARG2), which produces extrahepatic urea as a byproduct of arginine metabolism but which is not involved in the production of urea for nitrogenous waste disposal, did not show any evidence of selection in vertebrates (Supplementary Figure  16 ).
Acknowledgments Acquisition and storage of Latimeria chalumnae samples was supported by grants from the  African Coelacanth Ecosystem Programme  of the  South African National Department of Science and Technology. Generation of the Latimeria chalumnae and Protopterus annectens sequence by  Broad Institute of MIT  and  Harvard  was supported by grants from the  National Human Genome Research Institute (NHGRI). KLT is the recipient of a  EURYI award  from the  ESF. We would also like to thank the  Genomics Sequencing Platform of the Broad Institute  for sequencing the L. chalumnae genome and L. chalumnae and P. annectens transcriptomes,  Said Ahamada ,  Robin Stobbs  and the  Association pour le Protection de Gombesa (APG)  for their help in obtaining coelacanth samples,  Yu Zhao  for the use of data from Rana chensinensis, and  Leslie Gaffney ,  Catherine Hamilton  and  John Westlund  for assistance with figure preparation.
Experiment 3: Brain transcriptomics of Nosema ceranaeor Varroa destructorparasitized bees The brain transcriptome modifications induced by parasitism were determined in bees originating from two different colonies using digital gene expression (DGE) analysis. Two DGE-tag libraries were generated for each experimental group, control, Nosema-infected and Varroa-infested. For each library more than 100,000,000 tags were produced that were then narrowed down to around 9,300 unique gene hits for the honey bee (Additional file 3: Table  S4 ). These distinct tags and their genomic count are available from NCBI Gene Expression Omnibus (GEO) database with the accession number: GSE41109. The number of genes whose expression was affected in the bee brain by Nosema-and Varroa-parasitism was markedly different.
The virus is closely Figure  5  Expression levels show similar directionality for 20 brain genes commonly affected by Nosema and Varroa parasites. For the total number of genes changed by parasitism by Varroa (N = 455 genes) or Nosema (N = 57 genes), a statistically significant number of genes occur in both lists (N = 21) with 20 genes expressed in the same direction (Exact hypergeometric probability test: P < 0. 0001). Color scale for the heatmap (red to green) indicates log2 transcription ratios where red color indicates underexpression of the gene in the parasitized bee and green color indicates overexpression. For each gene, the accession number and annotation are indicated. associated with Varroa infestation  [56]  and thus it was not unusual that it occurred in higher levels in Varroainfested brains.
Bees and parasites This experiment was performed using hives of a hybrid of Apis mellifera mellifera and A. m. ligustica located at the Institut National de la Recherche Agronomique in Avignon, France. Nosema-treated bees were individually fed 2 uL of 50% sugar solution with a mixture of freshly extracted spores of Nosema ceranae at a concentration of 50,000 spores/uL. The presence of N. ceranae was confirmed by PCR analysis  [73]. Guts were dissected at the end of the experiments and no spores were found in the control bees (data not shown). Varroa-parasitized bees were obtained following a similar procedure described in  [74]. Colonies that were not treated with miticide were used and the queen was caged to stop egglaying so that Varroa mites had no cells to parasitize. Meanwhile, the queen from a different colony was transferred into a queen-excluder for 7 days with an empty frame.
Analysis and mapping of DGE tags Image analyses and base-calling were conducted using the HiSeq Control Software (HCS 1. 4. 5. 0) and RTA component (RTA 1. 12. 4. 0). Extraction of 16 bp tags (reads were trimmed for adaptor sequence) and tag counting were performed using home-made Perl script. Sequences were first aligned (using the Illumina's sequencing analysis software, CASAVA 1. 8) to transcripts of the Apis mellifera genome version 4. 5 downloaded from NCBI. Only those 16 bp tags that were perfect matches were retained. Those tags that could not be aligned to transcripts were re-aligned to the complete Apis mellifera genome (version 4. 5). Mapping was also performed on sequences of honey bee virus genomes (Chronic bee paralysis virus RNA 1: GenBank EU122229, Chronic bee paralysis virus RNA 2: EU122230, Sacbrood virus: AF092924, Deformed wing virus: AJ489744, Black queen cell virus: AF183905, Acute bee paralysis virus: AF150629, Kashmir bee virus: AY275710, Varroa destructor virus 1: AY251269 and Israel acute paralysis virus: EF219380).
The package DESeq from Bioconductor was used to conduct the analysis  [79]. Genes (i. e. tags that matched a transcript) and tags that were only aligned to the genome were analyzed separately. Tags that occur less than one time in a million, in two or more samples, were filtered from the analysis. DESeq estimates variance-mean dependence in count data from DGE-tag libraries and tests for differential expression based on a model using the negative binomial distribution. Genes were considered to be differentially expressed between two treatments at an adjusted P-value < 0. 05. The P-value was adjusted for multiple testing with the Benjamini-Hochberg procedure, which controls for false discovery rate.
Because surveillance systems are usually restrained by budget considerations or structural aspects and may have been designed before source attribution became a potential objective, the data collected may not fully meet the ideal requirements cited above. The French integrated surveillance system for Salmonella covers the whole food-chain from the breeding farms to the human cases. Many actors contribute to this "mosaic" surveillance system  (David et al. , 2011). Salmonellosis cases are monitored by the National Reference Centre for Salmonella (NRC, Institut Pasteur, a collection of human strains sent on a voluntary basis by private and public medical laboratories), and the National Public Health Institute (InVS, mandatory declaration of enteric disease outbreaks). Regarding food-animal reservoirs of Salmonella, two parallel systems gather data on the contamination level in the different commodities at the national level.
The Food Directory of the French Agriculture Ministry (DGAL) coordinates sampling plans at the farm level within the framework of European and national regulation-based surveillance. Those plans are designed so that surveillance is representative of national production and constitute an active surveillance of food-animal sources. The Salmonella network (SN), hosted by the French Agency for Food, Environmental and Occupational Health and Safety (Anses), gathers non-human Salmonella strains sent on a voluntary basis by public and private veterinary laboratories spread all over the national territories, ensuring a passive surveillance of Salmonella in the food-animal sources  (Danan et al. , 2011; David et al. , 2011). We thus had the opportunity to compare two surveillance datasets of food-animal sources of Salmonella.
In each dataset, the sources contamination data were linked to human cases data described above. The first dataset, referred to as the active dataset, comprised data that were similar to the data used in the original attribution approach (representative prevalence data). The second dataset, referred to as the passive dataset, had some weaknesses (unknown representativeness, no access to prevalences but only types repartition in a commodity) but covered supplementary food-animal sources. In this paper, using our previous proposal of an adapted parameterization for the Bayesian microbial subtyping attribution model  (Hald et al. , 2004; David et al. , 2012) , we assessed the impact of the deviation from the required data quality on source attribution results globally and more particularly the impact of the sources contamination indicator used (prevalence in the active dataset versus proportion, that is, repartition of Salmonella types in a given commodity, in the passive dataset) and the number of sources included.
Data Two databases were used in the analyses, associating the human data with either passive or active surveillance data for the contamination of the sources. In the active dataset, source contamination data meet the requirements of the original model and this dataset constitutes our reference for attribution results. In the passive dataset, source contamination data had unknown representativeness and were measured as types proportions and not prevalences, so that this dataset does not meet the data requirements. However, it covers a broader range of sources. The targeted study period was 2005, but some data on sources contamination in the active surveillance dataset were retrieved from 2004 to 2007 according to the commodity. In the following paper, we will use "type" to designate both serotype (non Enteritidis and Typhimurium Salmonella) and subtype (for S.
Human cases The human cases data were retrieved from the NRC database for the year 2005. The cases included in the study correspond to confirmed non-Typhi salmonellosis cases for which the strain, or an analysis report, has been sent on a voluntary basis to the NRC by a public or private microbiology laboratory, together with epidemiological information. Travel-related cases and outbreak-related cases were excluded, based on the travel information available in the NRC database, and on the mandatory declarations of outbreaks centralized by InVS. The cases registered in the overseas "départements" and territories were excluded as well. For each outbreak, one case was kept in the dataset to represent it in the model  (Hald et al. , 2004)  This avoids (i) overestimation of the role of the source of the outbreak type when this type is specific to that source and (ii) underestimation of the role of the source of the outbreak type when this type occurs in several other sources.
2,1,2,1 Active dataset In the frame of European regulations, national prevalence studies of one year in length have been conducted from 2004 to 2007 in the main animal commodities either at the farm  (layers in 20042005, broilers in 20052006, turkeys in 20062007)  or at the slaughterhouse (pigs in 20062007) level. For each food-animal species, a random sample of flocks or carcasses representative of the national production has been tested for Salmonella: 519 layer flocks (faeces and environmental samples), 371 broiler flocks (faeces samples), 331 turkey flocks (faeces samples) and 1,166 pig carcasses (ileo-caecal lymph nodes). A flock or carcass was considered positive if at least one sample of Salmonella was positive, and all isolates were serotyped. The estimated prevalences were adjusted for the flock size for layers, broilers and turkeys.
The flock size was known for layers and we used the barn capacity as an indicator of the flock size for broilers and turkeys. The weights were calculated as follow:   i i i F F  where F i is either the flock size (layers) or the barn capacity (broilers and turkeys). More details on the sampling and analyses methodologies can be found in the corresponding European Food Safety Agency reports  (Anonymous, 2007a (Anonymous, , 2007b (Anonymous, , 2008a (Anonymous, , 2008b)). The 2005 data on cattle were gathered within the framework of the national surveillance plan of antimicrobial resistance in indicator and zoonotic bacteria in cattle, a national annual monitoring plan conducted by the French Ministry of Agriculture aimed at assessing the prevalence of resistant bacteria in this commodity.