dataesr/scientific-paragraphs-categorization huggingface dataset:
The ds cDNA was purified using Ampure XP magnetic beads (Beckman Coulter, Inc. ), and 1 ng was used for the Nextera XT DNA sample preparation kit (Illumina). A sequencing library was prepared and sequenced on an Illumina MiSeq with 2-ϫ 150-bp paired-end reads. The total reads  (5, 354, 417)  were mapped to a reference sequence (GenBank accession number JN896331) using BWA (v 0. 7. 5a-r405)  (14) , and visualized in Tablet  (15). A modified SAMtools/vcfutils (16) script was used to generate an intermediate consensus sequence in which any indels relative to the original reference sequence were appropriately called. The intermediate consensus was used as the reference for subsequent iteration of mapping and consensus calling. The total number of assembled viral reads was 387,043 (7. 23% of the total reads).
Despite the low proportion of viral sequence detected within the total data set, coverage of the entire genome was obtained (average read depth of 1,798). The genetic organization of the Ethiopian wolf CDV genome was consistent with those of other CDV genomes, with a complete genome size of 15,690 nucleotides. The genome has closest homology (99%) to a sequence from China (GenBank accession number JN896331) clustering with Asia-1 sequences rather than African CDV sequences, including those from neighboring Tanzania. Accession number(s). The complete genomic sequence of CDV06 has been deposited in GenBank under accession number MF041963.
ACKNOWLEDGMENTS This work was supported by the  European Union   H2020-funded Research Infrastructure Grant "European Virus Archive Global (EVAg)" (H2020 -grant  agreement  653316 ). The  Ethiopian Wolf Conservation Programme  (http://www. ethiopianwolf. org)thanks the  Ethiopian Wildlife Conservation Authority  and the  Bale Mountains National Park  for permission to work in Bale and the  Born Free Foundation , the  Wildlife Conservation Network , and the  U. S. Fish and Wildlife Service  for funding.
Methods All XRD data were first evaluated by comparisons and searches of the International Center for Diffraction Data (ICDD) Powder  Diffraction File using MDI Jade (Materials Data Incorporated, Livermore, CA) software packages. The data were analyzed further via Rietveld refinement methods, using Jade. The Rietveld method involves constructing a model consisting of the crystal structures of all component phases and minimizing the differences between the observed and simulated diffraction patterns by varying components of the model, including scale factors (related to phase abundance) and unit-cell parameters. This method thus provides information on all well-ordered crystalline phases, but is not directly applicable to disordered phases such as clay minerals or X-ray amorphous components.
Cell parameters for well-crystalline minerals present at >∼5 wt. % abundance were refined using structure files from the American Mineralogist Crystal Structure Database. Abundances of phyllosilicate, poorly crystalline, and XRD-amorphous phases were estimated by the FULLPAT full pattern fitting method  (Chipera and Bish, 2002)  using laboratory patterns generated from CheMin IV, a laboratory prototype of the CheMin flight instrument. Major, minor, and some trace element abundances were measured with the APXS located on the end of Curiosity's robotic arm  (Campbell et al. , 2012; Gellert et al. , 2015b). The CheMin drill sample and the dump piles of post-sieved samples are nominally the same material (<150 μm), and, as such, these APXS analyses are the best comparison to CheMin results. Chemical compositions of crystalline components comprising at least ∼5 wt.
Stratigraphic trends in mineralogy and chemistry Depositional and diagenetic environments on modern and ancient Earth are characterized by detailed mineralogical, geochemical, and petrographic analyses and from thorough field campaigns. We recognize that we cannot fully capture the potential mineralogical heterogeneities of these deposits with Curiosity. Bowen et al. (2012) , for example, have documented lateral mineralogical variations at the scales of thin section to outcrop, resulting from changes in water chemistry and sediment composition. Doing field work with a rover on Mars limits our ability to investigate lateral variations so that we can only have data from a 1-dimensional time series. Although we have not investigated lateral variability and we lack petrographic information about the samples measured from Gale crater to discriminate between the depositional or diagenetic nature of minerals, the observed mineralogical and geochemical data in concert with the sedimentological observations provide evidence of stratigraphic trends within the Murray formation that can help elucidate the depositional and diagenetic history of this ancient lacustrine succession.
Acknowledgements C. Z. would like to acknowledge the nancial support from the  China Scholarship Council (CSC). S. L. gratefully acknowledges  Umweltfreundliche Hocheffiziente Organische Solarzellen (UOS)  for nancial support. A. O. acknowledges the support of the  Cluster of Excellence "Engineering of Advanced Materials ". P. T. and A. M. acknowledge the nancial support from the  Russian Science Foundation  (grant No. 14-13-01031 ). D. A. K. G. and D. I. acknowledge the  Ministry of Education and Science of the Russian Federation  for nancial support (contract No. 14. 587. 21. 0034 (RFMEFI58716X0034) ). J. D. P. is funded by a  doctoral fellowship  grant of the  Colombian Agency COL-CIENCIAS. N. L. acknowledges the nancial support from the  ETI  funding at  FAU Erlangen-Nürnberg , the  DFG  grant:  BR 4031/13-1 , and the  Bavarian Ministry of Economic Affairs and Media, Energy and Technology  by funding the  HI-ERN (IEK11 ) of FZ Jülich.
Background: Organisms typically face infection by diverse pathogens, and hosts are thought to have developed specific responses to each type of pathogen they encounter. The advent of transcriptomics now makes it possible to test this hypothesis and compare host gene expression responses to multiple pathogens at a genome-wide scale. Here, we performed a meta-analysis of multiple published and new transcriptomes using a newly developed bioinformatics approach that filters genes based on their expression profile across datasets. Thereby, we identified common and unique molecular responses of a model host species, the honey bee (Apis mellifera), to its major pathogens and parasites: the Microsporidia Nosema apis and Nosema ceranae, RNA viruses, and the ectoparasitic mite Varroa destructor, which transmits viruses.
Recent advances in genome sequencing technologies and improvements in genome annotation of the honey bee have facilitated fine scale mapping of bee immune responses against multiple pathogens and parasites at the genomic level  [23]. Several studies examining the transcriptional response of honey bees to their primary pathogens, namely Nosema, Varroa and viruses, have already provided considerable insight into the molecular mechanisms mediating host-parasite interactions  [24] [25] [26] [27] [28] [29] , yet these studies have also revealed idiosyncrasies across datasets. Analysis of multiple transcriptome datasets provides not only the opportunity to detect subtle changes in gene expression, but also to highlight commonalities in host responses. Recent studies in mosquitoes and humans have demonstrated the power of meta-analyses to reveal key host responses to multiple pathogen infections  [30] [31] [32].
To comprehensively characterize the interactions between honey bees and their major pathogens and pests, we performed a meta-analysis of the transcriptional responses to Nosema, Varroa and viruses. We collected the gene expression profiles of 7,077 genes across 19 published and new transcriptome datasets of experimentally infected or parasitized honey bees, and utilized statistical and bioinformatics analyses that we newly developed (a 'directed rank product analysis') to perform a synthesis of gene expression patterns from multiple studies and platforms. This resulted in a robust analysis that, (i) identified common genes and pathways regulated in response to different pathogens, (ii) identified genes and pathways uniquely regulated in response to one pathogen in a particular body part or tissue, and (iii) enabled building a gene co-expression network to identify regulatory genes and new gene interactions within the honey bee transcriptome.
Multivariate analysis We performed a multidimensional scaling analysis to visualize the spread of the 19 transcriptome datasets. This showed that gene expression levels vary less within a study than between studies and suggests that gene expression profiles are markedly influenced by experimental design (Additional file 1: Figure  S1 ). Thus, comparisons across studies to find commonly and consistently regulated gene expression patterns are undoubtedly hindered by this large amount of variation, highlighting the importance of performing a metaanalysis with appropriate bioinformatics approaches to obtain robust and reproducible results.
Rank product analysis Previous comparative analyses of honey bee immune responses across transcriptome datasets simply involved determining if there was a significant overlap in the differentially expressed gene lists from different studies  [24, 25, 27, 28]. However, the significant variation in expression levels between studies (Additional file 1: Figure  S1 ) undoubtedly reduces the power of such comparisons. Thus, we employed a rank product analysis to identify sets of genes that are significantly differentially expressed across the 19 transcriptomes datasets we collected. The rank product analysis is a non-parametric statistic that identifies genes that are consistently highly ranked in a number of datasets, based on the gene expression fold changes. In total, we found 344 genes with significant differential expression across datasets, categorized by (i) 56 genes with significant increased expression (i.
e. upregulated) across datasets, (ii) 109 with significant decreased expression (i. e. down-regulated) across datasets and (iii) 179 genes with significant differential expression (i. e. differentially-regulated), up-regulated in some studies, down in others (Fig. 1 ; Additional file 1: Figures  S2  and S3 ; Additional file 2: Tables  ST1-ST3 ). Note that using this rank product analysis, a gene may be statistically significantly up-regulated across all 19 datasets but still be down-regulated in one or more datasets (and vice-versa for significantly down-regulated genes). In fact, subsets of up-regulated genes (45 of 56 genes) and down-regulated genes (83 of 109 genes) were also categorized as differentially-regulated (up-and downregulated across datasets; see Additional file 1: Figure  S3 ).
Gene evolutionary rate We compared the evolutionary rate, obtained from the database OrthoDB and measured as the average of protein sequence identities from pairwise alignments across 12 bee genomes  [33] , of genes that were significantly differentially expressed across the transcriptome datasets and genes that were not differentially expressed. Genes showing significant differential expression across the transcriptome datasets exhibited a higher evolutionary rate than non-differentially expressed genes (Kruskal-Wallis χ 2 = 103. 1476; df = 3; p < 0. 001; Fig. 2 ), suggesting rapid evolution of genes responding to pathogen infection. All three categories of differentially expressed genes showed significantly higher evolutionary rates than nondifferentially expressed genes (Dunn's test with Benjamini-Hochberg corrected p-values: differentiallyregulated vs.
The functional analysis of 104 Fig. 1  Heat maps illustrating the expression levels (relative ranks) of the 344 significantly regulated genes across the 19 transcriptome datasets. Genes are categorized as 56 up-regulated genes (top left), 109 down-regulated genes (bottom left), and 179 differentially regulated (up and down) genes (right). Orange shows increased expression and blue decreased expression after pathogen infection. Top classification is N for Nosema infection, N/V for Nosema and RNA virus co-infection, V for virus, and M for Varroa mite ('Varroa plus virus'). Numbers at the bottom correspond to dataset numbers in Table  2. Each row represents the differential expression of the same gene across all 19 datasets. In each category, genes are ordered following the arithmetic means of their ranks displayed in the right column of the heat map.
Note the presence of genes showing decreased expression in some datasets although found as statistically up-regulated across datasets, and vice-versa genes with increased expression upon Nosema infection revealed overrepresentation of genes encoding enzymes and proteins involved in metabolic processes, catalytic activities, and transporter activity (Additional file 2: Table  ST6 ), while genes related to cell components were overrepresented among 88 down-regulated genes after Nosema infection (Additional file 2: Table  ST7 ). To explore the specific response of honey bees to viruses, we examined the transcriptome datasets of honey bees experimentally infected by RNA viruses or parasitized by Varroa mites and, thus, by viruses. We justify merging Varroa and virus datasets with the idea that the impact of Varroa may stem largely from damage to the cuticle during feeding as well as from transmitted viruses, thus suggesting little immediate impact of sole Varroa parasitism on immune gene expression of the host  [34].
Gene co-expression network Our directed rank product method enabled us to detect genes with similar and opposite expression profiles across the 19 transcriptome datasets and to build a gene co-expression network. We found a total of 16,110 significant inter-gene connections, with nearly half of the 7,077 genes (N = 3,589) interconnected within one major module, while 2,931 genes remained unconnected and 557 genes were placed within small modules of 2 to 11 genes. The majority (98%) of inter-gene connections were observed within the major module, with 12,694 positive (i. e. similar expression profiles) and 3,087 negative (i. e. opposite expression profiles) inter-gene connections (Fig. 3a ). Notably, 320 out of the 344 significantly differentially expressed genes from the rank product analysis were identified within the major module, illustrating the tight interconnectivity of the expression regulation of these genes.
We identified the top 5% most interconnected genes from all 7,077 genes of this study, which represents 209 hub genes with at least 34 connections to other genes (Additional file 1: Figure  S5 ). Notably, 52 hub genes were significantly differentially expressed across the 19 transcriptome datasets (Fig. 3b ; Additional file 2: Table  ST12 ), and differentially expressed genes were significantly more connected than non-differentially expressed genes (Kruskal-Wallis χ 2 = 445. 9856, df = 3, p-value < 0. 001; Additional file 1: Figure  S6 ). To identify novel candidate genes involved in immune regulation, we collected all interactions involving canonical immune genes and generated an immune network composed of 26 modules containing at least 2 interconnected genes. The major module of this immune network contained 281 genes, including 25 immune genes from all immune pathways (Fig.
General expression patterns Multivariate analysis indicated that there was a wide between-study variation, likely due to a combination of factors, including variation in technology, experimental approach (e. g. , RNA extraction method, infection dose), tissues examined, method of analysis, and host and pathogen genotypes. These substantial differences may explain the limited overlap observed previously between differentially expressed gene lists from different studies   3  Gene co-expression network. a Main module of the gene co-expression network, representing 3,589 interconnected genes. Red nodes show genes significantly regulated across the 19 transcriptome datasets, and black nodes show non-significantly regulated genes. Square nodes show the most connected (hub) genes. Grey edges illustrate positive correlation between two gene expression profiles while blue edges show negative correlations.
A file available at https://idata. idiv. de/DDM/Data/ShowData/35 provides the possibility of navigating within the network. b Scatter plot representing the total number of connections (x-axis) over the number of connections to significantly regulated genes across the 19 transcriptome datasets for the most (top 5%, N = 209) connected genes (i. e. hub genes). Red triangles show significantly regulated hub genes, while black dots show non-significantly regulated hub genes. Two hub genes with high connectivity to significantly regulated genes are shown: a kynurenine/alpha-aminoadipate aminotransferase (LOC724239), and a L-lactate dehydrogenase (LOC411188). c Main module from the coexpression network of the immune genes of the honey bee. Coloured nodes represent immune genes from the Toll (purple), JAK/STAT (brown), apoptosis (green), RNAi (blue) and Imd (pink) pathways (see immune genes list in the Additional file 2: Table  ST13 ).
Oval nodes show genes with low connectivity, squares show genes with high connectivity (hub genes, with at least 34 connections). Genes significantly regulated across the 19 transcriptome datasets have a red outline. Black edges represent positive co-expression and blue edges are negative co-expression. In insets, the expression profiles across the 19 transcriptome datasets (black lines) of the four immune hub genes (i. e. highly connected immune genes), accompanied by expression profiles of genes with which they are connected. Orange profiles display similar profiles (positive connections, i. e. black lines in the network) and blue reflect opposite profiles (negative connections, i. e. blue lines in the network). The y-axis displays the relative ranks of differential expression level, from up-regulated (value towards 1) to down-regulated (value towards 0) Fig.
4  Diagram of the canonical innate immune response of the honey bee. Gene names in colour-filled boxes show evidence of significant regulation after infection by Nosema (yellow), or infection by RNA viruses and/or infestation by Varroa mites (light blue) or all pathogens (grey). Orange lines surrounding a box show increased expression and blue surrounding lines indicate decreased expression after pathogen infection -mixed orange and blue lines show genes found differentially-regulated, either up-or down-regulated across the datasets. Notably, the AMP defensin-1 exhibited increased expression in most of the datasets, but a decreased expression in the abdominal tissues of honey bees infected by Nosema. Therefore, a mixed background and outline colour are displayed. Green surrounding lines show genes found non-significantly regulated in this analysis.
Common host response The common response to multiple pathogens identified in our meta-analysis is characterized by the increased expression of several immune genes, including all canonical AMPs, genes encoding cuticular proteins (LOC552685, CPR14 and LOC100577229), which likely respond to tissue damage by pathogens, and heat-shock related proteins (LOC410087, LOC724367), which may serve as markers of stress during infection response  [36]. However, substantially more genes showed decreased expression across transcriptome datasets in response to pathogen infections. Among them we found genes encoding enzymes involved in carbohydrate metabolism, four cytochrome oxidase P450s, the egg yolk protein precursor vitellogenin (Vg), the major royal jelly proteins Mrjp1 and Mrjp9 and two transcriptional repressors hairy and knirps.
An additional 179 genes showed significant differential expression but were inconsistently up-and downregulated across the 19 transcriptome datasets. Among them we found many cytochrome oxidase P450s and immune genes, including the recognition receptors PGRP-S1, PGRP-S2, B-gluc1, SP12 and Serpin-5, and the antimicrobial Lys-2, the gene encoding caspase-like, involved in apoptosis, and ninjurin-1-like, a transmembrane protein induced by the Toll immune pathway and involved in non-apoptotic cell death in Drosophila  [38]. Other genes with potential immune activity also showed significantly variable regulation, such as the GMC oxidoreductases 3 and 7, the apidermins Apd-2 and Apd-3 and the hemolymph apolipophorin-III-like protein. Functional analysis of the 344 significantly regulated genes showed an overrepresentation of genes associated with extracellular regions and response to biotic stimulus (e.
However, common host responses have also been described in other models, such as in mosquitoes and humans  [30] [31] [32]. In humans, this general response to pathogens has been suggested to constitute an 'alarm signal' , which may be triggered by different cell types, to maximize the detection and the response of infection  [31]. The role of the common host response in honey bees remains to be determined. Genes involved in this common host response (including several immune genes such as hymenoptaecin, def-2, PGRP-S1, B-gluc1) showed higher evolutionary rate among bees than genes which did not show significant expression differences across the transcriptome datasets. Notably, B-gluc1 has recently been shown to be evolving under positive selection in the honey bee  [8]. Recent analysis suggested that sociality and the increased pathogen pressure in colonies densely packed with worker bees may be a cause of such rapid evolution in bumble bee immune genes  [39].
Specific response to pathogens To characterize the specific response of the honey bee to its major pathogens, we identified genes whose expression profile across transcriptome datasets is most similar to a theoretical expression profile, i. e. genes that are up or down-regulated in response to a pathogen type, Microsporidia or Varroa/virus. This method, named here 'directed rank product analysis' , combines the identification of genes following a specific expression pattern by subtraction of a gene's differential expression scores (i. e. here, relative ranks), within the statistical framework of the rank product analysis. This method takes advantage of the rank product analysis so that it can detect biologically relevant gene expression changes from heterogeneous datasets obtained from different platforms, microarrays and sequencing  [41].
3. 7E-02 This table shows the overrepresented GOslim terms for all regulated genes (344 genes) and the categories; up-regulated, down-regulated and differentially-regulated. Note that no overrepresented GO term was obtained for up-regulated genes. Gene lists corresponding to these GO terms are available in Additional file 2: Tables ST1-ST3 action of the RNAi pathway may be transient, and thus not always captured by transcriptome analysis. Upregulation of these genes may be detectable only during the early stages of viral infection, which would explain an inconsistent effect in our dataset, since host transcriptomes were measured at different times postinfection. We also found the increased expression of genes encoding a transcription factor (LOC727085), translation factors (EF1-alpha, LOC726500) and posttranscriptional modification proteins (LOC412975, LOC724690), which may illustrate a general transcriptome dysregulation following infection by viruses  [53].
Gene co-expression network In addition to identifying shared and unique responses to pathogens, our large dataset enabled the exploration of gene co-expression and the identification of new regulatory genes. Among the most interconnected (hub) genes, we found several genes encoding ribosomal proteins and NADH dehydrogenase enzymes. But most importantly, we identified two genes with many inter-gene connections with other differentially expressed genes: a kynurenine/alpha-aminoadipate aminotransferase gene (LOC724239) and an L-lactate dehydrogenase gene (LOC411188), exhibiting 97 and 76 connections, respectively. The expression of both genes were significantly increased in most transcriptome datasets, and connected to a large proportion of genes that exhibited reduced expression (65 and 50% of negative interactions, respectively).