dataesr/scientific-paragraphs-categorization huggingface dataset:
Structure elucidation The bioactive metabolite 1 was isolated from the crude extract by preparative HPLC. unambiguously confirmed that the compound 1 is identical to sporothriolide (1)  (Krohn et al. 1994 ). As by-products of the HPLC fractionation multiple derivatives of 1 were obtained. The first is a metabolite with a molecular formula of C 13 H 20 O 4. The proton NMR of 2 was very similar to the one of 1 except the replacement of the exomethylene by a methyl group and the appearance of an additional methine signal. The data are consistent with the formal hydrogenation of 1 to its 3,7-dihydro analogue. As demonstrated by the observed chemical shifts and coupling constants for H-3 (δ 3. 05, dq, J 3,3a = 10. 1 Hz, J 3,7 = 7. 5 Hz) and H-3a (δ 3. 44, dd, J 3,3a = 10. 1 Hz, J 3a,6a = 6. 0 Hz), the stereo configuration of 2 was assigned as (3R,3aS,6R,6aR), which is the same as those reported for the synthetic dihydrosporothriolide  (Krohn et al.
Acknowledgments Financial support by the  German Academic Exchange Service (DAAD)  and the  Thai Royal Golden Ph. D. Jubilee-Industry program (RGJ)  for a joint TRF-DAAD PPP academic exchange grant to  Kevin D. Hyde ,  Eric Kuhnert ,  Frank Surup  and  Marc Stadler  is gratefully acknowledged. Regis Courtecuisse and  Christian Lechat  are thanked for their continuous support in facilitating field work and finding important specimens during the field trips in the Neotropics and elsewhere. The  field sampling in French Guiana has also benefited from 'Investissements d'Avenir' grants of the ANR  ( CEBA ANR-10-LABX-0025 ,  CNRS Cayenne and Toulouse)  to  Jacques Fournier  and coworkers. We are grateful to  Barbara Schulz  for providing the original sporothriolide producing strain from her collection at  TU Braunschweig.
PCR products were cleaned using AMPure (Beckman Coulter, CA, USA) according to the manufacturer's instructions. DNA concentration was measured on a NanoDrop 1000 spectrophotometer (Thermo Scientific, MA, USA) and the samples were pooled in equimolar amounts. The sample pool was freeze-dried and sent to LGC Genomics (Germany) for adaptor ligation and sequencing on 1/16 th of a plate on a GS FLX Titanium sequencer (Roche, Switzerland). Demultiplexed raw sequence data were deposited in the Sequence Read Archive (http://www. ncbi. nlm. nih. gov/sra) under the accession number SRP042192.
Bioinformatics and taxonomic assignment The raw sequence data were analysed using the SCATA pipeline (http://scata. mykopat. slu. se; [27] ). Sequences were screened for tags and primer sequences, allowing for one mismatch for the primers in addition to degenerate bases. Sequences shorter than 200 bp and those with a mean quality score lower than 20 and containing bases with a score lower than 10 were discarded. The option ''extract high quality region'' was used. Sequences were clustered into operational taxonomic units (OTUs) at a clustering level that was chosen to approximate species level. The sequences passing the quality control were clustered at 1. 5% dissimilarity cut-off in SCATA, using single linkage clustering with default settings (85% alignment, collapse homopolymers to 3 bp, usearch as cluster engine, miss match penalty 1, gap open penalty 0, gap extension 1 and end gap weight 0).
The level of intraspecific variation in the ITS sequence is variable within fungi  [28] , and thus using a single cut-off level will not perfectly reflect biological species. However, we found 1. 5% dissimilarity to be the most appropriate level in this dataset, as higher cut-off levels would group some basidiomycete species into the same OTU. Singletons in the full dataset were removed, as many of them were considered to represent sequencing errors  [29]. In addition, singletons in each sample were removed in an effort to limit the effects of tag switching  [30]. We focused on taxonomically assigning the OTUs represented by at least 10 sequences globally in the dataset (67 OTUs). Some of these could be taxonomically assigned in SCATA by including reference sequences from isolates from the Fungal Biodiversity Center CBS (http://www.
Sampling date 20-June 27-June No. of fields  13 5  No. of samples  26 16  No. of control samples  13 5  No. of fungicide-treated samples  13 11  Wheat developmental stage (DC) 09. 02. 2014;  [31] ) in the clustering. Ko ˜ljalg et al. [31]  introduced the term 'species hypothesis' (SH) in order to facilitate the communication of fungal taxa discovered when clustering DNA sequences at different similarity cut-offs. Stable accession codes for all species hypotheses in the UNITE database have now been introduced  [31] , thus facilitating comparisons among sequencebased studies of fungi. OTUs that did not cluster with any reference sequence in SCATA were blasted against GenBank (http://www. ncbi. nlm. nih. gov/genbank) in order to find suitable reference sequences. The most abundant sequence in each OTU was used for this purpose.
0-10;  [34] ) in R (version 3. 0. 2) As the number of sequences per sample was unequal, the dataset was rarefied to 197 sequences per sample, which was the size of the smallest sample. The rarefaction was performed using the rrarefy function in 'Vegan' (version 2. 0-10;  [34] ). The rarefaction was repeated 1000 times on the sample-by-OTU table and the mean was taken over the 1000 matrices and used for subsequent analysis. Second, we tested the effect of fungicide treatment and geographical area on OTU richness and community evenness (Pielou's evenness index  [35] ) using linear mixed models (LMM). We used the lmer function in the 'lme4' R package  [36]. A model including treatment, geographical area and their interaction, with field and the interaction between field and treatment as random factors was fitted to both OTU richness and evenness.
Significance tests were performed with a Kenward-Roger modification for performing F-tests, the KRmodcomp function in the 'pbkrtest' package  [37]. The LMM analyses were performed both on the full dataset and on a smaller dataset excluding two fields in the Southern area where the control samples were dominated by one single OTU, namely Puccinia striiformis. Third, non-metric multidimensional scaling (NMDS) was used to explore the fungal community composition using the function metaMDS in the 'Vegan' package  [34]  in R. The NMDS was performed using Bray-Curtis dissimilarities with square root transformation and Wisconsin double standardisation. Subsequently, 95% confidence areas were fitted to the ordination using the ordiellipse function. Fourth, we tested the effect of fungicide treatment and geographical area on both community composition and individual OTUs using generalised linear models (GLMs).
First, a model was fitted in order to test the effect of geographical area, treatment and their interaction. The interaction was not significant and a model including field as a block factor and treatment as a fixed factor could be fitted to test the effect of the fungicide treatment in both areas. For these analyses, a GLM was fitted to each OTU using the manyglm function in the 'mvabund' package in R (version 3. 8. 4; [38] ) using a negative binomial probability distribution. The rarefied sample-by-OTU table was input as the response variable. Next, the models were tested using the function anova in 'mvabund', providing both a multivariate test for the whole community and univariate tests for each OTU. The score test was used and the cor. type argument set to shrink to allow for correlated response among OTUs.
Sequence data quality In all, 56% of the 454 reads from the pool of 420 wheat leaves passed the SCATA quality filtering. Singletons made up 1. 7% of the sequences in the filtered dataset (471 global singletons and 324 per-sample singletons) and were removed. The removal of persample singletons resulted in a loss of 30 OTUs represented by 2-5 sequences each. Non-fungal sequences were also removed and these constituted 3. 5% of the dataset, mostly wheat sequences. Per sample, 0-45% non-fungal sequences were removed. The qualitycontrolled dataset contained 44 245 sequences in 42 samples. The number of sequences per sample ranged between 197-2978, with a mean of 1053 sequences per sample.
Taxonomic composition and richness of the fungal community of wheat leaves The fungal community composition in the wheat phyllosphere was characterised using 454 high-throughput sequencing. We found 235 fungal OTUs in the pool of 420 wheat leaves when clustering at 1. 5% dissimilarity level. The rarefaction curves approached saturation (Fig. 2a ) for all conditions, while the species accumulation curves did not (Fig. 2b ). We taxonomically assigned the OTUs containing more than 10 reads in the total dataset (67 OTUs, Table  S3 ). These OTUs accounted for 90-100% of the sequences in the samples and none of the species in the tail were present in more than 10% of the samples. Overall, 45% of the OTUs were identified to species level and the highest taxonomic level of identification was at the order level.
The fungal community in the present study consisted of almost equal proportions of ascomycetes (54%) and basidiomycetes (46%). The most common orders in the dataset were Sporidiobolales, Tremellales, Capnodiales and Pleosporales (Fig. 3 ). We identified a 'core' fungal community of six OTUs that was found across all treatments and sites. In fact, these six most abundant OTUs were found in all samples except two, including all fields. Of these six, five were identified as basidiomycete yeasts (OTU_0_Sporobolomyces_roseus, OTU_3_Dioszegia_fristingensis, OTU_4_Cryptococcus_tephrensis, OTU_12_Cryptococcus_sp and OTU_9_Dioszegia_hungarica), and one as the ascomycete OTU_5_Cladosporium_spp. The phyllosphere fungal community of wheat has often been described as consisting of 'pink' yeasts (Sporobolomyces and Rhodotorula producing carotenoid pigments), 'white' yeasts (Cryptococcus) and ascomycete saprotrophs such as Cladosporium and Alternaria  [17, 18].
Leucosporidium golubevii is a yeast discovered in freshwater  [46] , and has been reported from the phyllosphere of balsam poplar  [47]. In addition, the relative abundance of OTU_16_Phaeosphaeria_juncophila (p,0. 01) was higher in fungicide-treated leaves (Fig. 6 ). Phaeosphaeria juncophila was first isolated from the rush Juncus articulatus, but little is known about its ecology. The fungicide sensitivity of phyllosphere fungi has mostly been investigated with fungicides that are no longer used or have been prohibited in Sweden, except for some sterol biosynthesis inhibitors (SBI). SBI fungicides have been shown to have no or Species hypothesis accession codes in the UNITE 1 database version are indicated when available. Functional assignment as 'pathogen' is only used for taxa known to be pathogenic on wheat.
The antagonistic potential of biological control agents is frequently strain-specific, depending on the mechanism of antagonism. From the sequence data, we cannot make inferences about the antagonistic capacity of an OTU. Hence, it is unknown whether the reduction in the relative abundance of A. pullulans in fungicide-treated leaves has an impact on the antagonistic capacity of the fungal community. Several OTUs were identified as common wheat pathogens in the dataset: OTU_14_Mycosphaerella_graminicola, OTU_13_ Blumeria_graminis, OTU_1_Puccinia_striiformis, OTU_47_ Phaeosphaeria_nodorum (Parastagonospora nodorum), OTU_20_ Monographella_spp and OTU_224_Pyrenophora_tritici-repentis. Surprisingly, there was no significant effect of fungicide treatment on the relative abundance of any of these OTUs.
454 sequencing is a semi-quantitative method only allowing quantification of the relative abundance of different OTUs  [25]. Thus, we were unable to determine whether the fungicide treatment had an effect on absolute abundance of the pathogens. In soil, DNA from dead fungal mycelia has been shown to degrade rapidly  [54] , but data on the rate of DNA degradation in the phyllosphere are lacking. It is possible that DNA from fungi killed by the fungicides might have been detected by the PCR. The difference in community composition between fungicide-treated and untreated samples may therefore be underestimated. However, the large difference in the relative abundance of P. striiformis between fungicide-treated and untreated samples does not support this hypothesis. Fungicide use was significantly correlated with changes in the relative abundance of certain fungal taxa (Fig.
2b ). The community evenness tended to be lower in the Southern area (Fig. 5b , Table  4 ), but there was no significant difference (p. 0. 05) when samples dominated with P. striiformis had been removed (Fig. S3b , Table  S4 ). The variation in community composition among fields was high, as field was a significant factor in the GLM analysis (Table  3 ). In addition, most of the OTUs (155 out of 235) only occurred in one sample in the dataset. For OTU richness, the variable field explained one third of the random variation, while for evenness, field did not explain any of the random variation (Table  4 ). At the order level, Sporidiobolales had a significantly higher relative abundance in the Southern area (p,0. 001), while Pleosporales (p,0. 01), Helotiales (p,0. 05) and the unassigned group of OTUs (p,0.
Supporting Information Figure  S1  Neighbour-joining tree of the most abundant ascomycete ITS2 sequences in the dataset. The most abundant sequence in each operational taxonomic unit (OTU_x) is included together with publicly available reference sequences and selected environmental sequences. OTUs marked with an asterisk were taxonomically assigned in SCATA. Species hypothesis accession codes in the UNITE database are given when available. Dotted lines represent branches with bootstrap values lower than 70%. Sporobolomyces roseus is included as an outgroup. (EPS) Figure  S2  Neighbour-joining tree of the most abundant basidiomycete ITS2 sequences in the dataset. The most abundant sequence in each operational taxonomic unit (OTU_x) is included together with publicly available reference sequences and selected environmental sequences.
OTUs marked with an asterisk were taxonomically assigned in SCATA. Species hypothesis accession codes in the UNITE database are given when available. Dotted lines represent branches with bootstrap values lower than 70%. Rhizopus oryzae and Rhizopus microsporus are included to form an outgroup. (EPS) Figure  S3  Richness of operational taxonomic units (OTUs) and community evenness in the full dataset. Boxplots with interquartile ranges of a) OTU richness and b) community evenness grouped by treatment (fungicide-treated and control samples) and geographical area. Horizontal lines represent medians and dots mean values. Also samples from fields infected with yellow rust (Puccinia striiformis) in the Southern area (fields 15 and 16, Table  S1 ) were included. F-tests with Kenward-Roger approximation showed a significant effect of geographical area on OTU richness (p,0.
Acknowledgments We are grateful to the  Plant Protection Centres of the Swedish Board of Agriculture ,  Cecilia Lerenius  and  Tove Nilsson  (Skara) and  Gunilla Berg  (Alnarp), for help with field sampling and data collection. We would also like to thank  Maria Jonsson  for technical assistance and  Mikael Brandstro ¨m-Durling  for demultiplexing of sequence data.
Genome Screening The EVEs from the isopod crustacean A. vulgare were identified from data generated as part of the ongoing A. vulgare genome project in our laboratory. Briefly, total genomic DNA was extracted from a single A. vulgare individual. A paired-end library with approximately 370 bp inserts was prepared and sequenced on an Illumina HiSeq2000. Reads were filtered with FastQC and assembled using the SOAP de novo software version 1. 05. The best assembly (obtained with a k-mer size of 49) was composed of approximately 3. 5 million scaffolds and contigs totaling approximately 1. 5 Gb (at 40Â average coverage). An in-house pipeline of in silico analyses was developed to search for EVEs in the A. vulgare genome sequences. We first constructed a comprehensive library of all nonretroviral virus nucleotide sequences available in public databases (GenBank and EMBL), including genomes from small RNA and DNA viruses, as well as large dsDNA viruses, often not considered in paleovirology screenings.
This library was used as a query to perform TBLASTX searches  (Altschul et al. 1997 ) (e value 1) to screen for A. vulgare genome sequences exhibiting similarity to virus sequences. This analysis aimed at selecting a subset of A. vulgare genome sequences that matched with viral sequences before further in-depth analyses. Then, we performed reciprocal BLASTX searches  (Altschul et al. 1997 ) using the selected subset of A. vulgare genome sequences as queries to screen for homologous coding sequences in the whole set of nonredundant protein sequences of the National Center for Biotechnology Information (NCBI) database. Armadillidium vulgare genome sequences were considered of viral origin if they unambiguously matched viral proteins in the reciprocal best hits (e value 0. 001). From these sequences, putative viral open reading frames were inferred through a combination of automated alignments, using the exonerate program  (Slater and Birney 2005)  and manual editing, based on the most closely related exogenous viral sequences in the nonredundant protein database.
Phylogenetic Analyses Using ClustalOmega  (Sievers et al. 2011 ) and manual edition, multiple amino acid (aa) alignments were performed for each inferred A. vulgare EVE peptide, including closely related exogenous and endogenous viral proteins resulting from the reciprocal BLASTX analysis and closely related proteins of representative virus species recognized by the International Committee on Taxonomy of Viruses (ICTV;  King et al. 2011 ). In addition to A. vulgare EVEs, we included several previously unknown EVEs uncovered in other taxa as a result of the reciprocal BLASTX. These EVEs correspond to proteins of viral origin that have been annotated as host genes and are therefore present in the nonredundant protein database of NCBI because they are devoid of nonsense mutation. Maximum likelihood (ML) inferences were performed on each multiple aa alignment using RAxML  (Stamatakis 2006)  with the substitution model and parameters WAG + G + I.
EVE Diversity in the A. vulgare Genome To identify EVEs in whole-genome sequences of the isopod crustacean A. vulgare, we first performed a TBLASTX search using all complete viral genomes publicly available in GenBank and EMBL (January 2014) as queries (n = 2,048). We then used all hits resulting from this search (n = 10,727) as queries to carry out a reciprocal BLASTX on the nonredundant protein database of the NCBI. This approach yielded a total of 54 A. vulgare genome sequences of unambiguous viral origin, ranging from 42 to 588 aa in length (average = 173 aa) and showing 46-78% aa similarity (average = 58%) to their most closely related exogenous viral protein sequences (fig. 1  and supplementary table  S1 , Supplementary Material online). The 54 EVEs were assigned to four different families (Bunyaviridae, Circoviridae, Parvoviridae, and Totiviridae) and one order (Mononegavirales), representing three of the seven types of viral genomes (-ssRNA, dsRNA, and ssDNA).
The 10 stimulation scores were added, giving a total score between 0 and 10 for each rat. Next, the stimulation was applied with a rough (pig bristle) paint-brush, using the same protocol. Dynamic mechanical allodynia or hyperesthesia were defined as a significant increase in the total score using the smooth or the rough brush, respectively. Statistical analysis of the behavioural data. Behavioural data from electronic Von Frey and Pinch tests were examined using two-way analysis of variance (ANOVA) followed by a Student Newman-Keuls test for two-by-two comparisons. Behavioural data from paint-brush tests were examined with a Kruskal-Wallis test followed by a Wilcoxon matches pairs test and a Mann-Whitney U test for two-by-two comparisons. Data were expressed as means 6 SEM, and the levels of significance were set at: * p,0.
The reported p-values correspond to the control of the type I error risk of each individual gene (no adjustment for multiple testing). The data have been deposited in NCBIs Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/) and assigned Series accession number GSE53897. Real-time quantitative PCR. Quantitative real-time PCR was performed using the fluorescent binding-dye SYBR Green (Absolute Blue SYBR Green Rox mix, ABgene, Thermo Scientific) on a real-time PCR machine (LightCycler Technology, Roche) with primer sets designed at the Roche website (www. roche-applied-science. com). Each qPCR assay was conducted in triplicate, using cDNA derived from 50 ng total RNA from each animal. Each sample well contained 4 ml of cDNA (5 ng/ml), with a primer concentration of 0. 5 mM plus 5 ml of SYBR Green.
Each qPCR assay was conducted with one candidate housekeeping gene for normalisation (actin, beta: Actb). The following primers were used: Gabbr2, 59-AAGACCTTTGAAACACTCTGCAC-39 (forward) and 59-CCAAAGGCAGTTGTGTAGCC-39 (reverse); Actb, 59-CCCGCGAGTACAACCTTCT-39 (forward) and 59-CGTCATCCATGGCGAACT-39 (reverse). qPCR was performed on all samples (saline-saline; saline-oxycodone; salinemorphine; vincristine-saline; vincristine-oxycodone; vincristinemorphine). All fluorescence data were processed using a post-PCR data analysis software program (LightCycler relative Quantification Software version 1. 0; Roche), providing an automatic calculation of a normalized ratio. Saline samples were used for normalisation. The ratios of each gene of interest were compared among samples, using an unpaired t-test.
An analysis of staining intensity and size diameter distribution was performed using Image J software (http://rsb. info. nih. gov/ij/). The background staining intensity was subtracted from each image, and results were expressed as the mean intensity of immunoreactivity 6 SEM. The mean diameter, percentage of positively stained cells, and mean intensity of GABA B2 staining in DRG neurons were compared between all groups by one-way ANOVA using SigmaStat software, followed by the Student Newman-Keuls test to detect differences between different groups. GABAB2 receptor labelling in the spinal dorsal horn. Free floating serial sections from the lumbar segment L4-L5 were incubated in rabbit anti-GABA B2 receptor antibody (1:300; ref. 52248, Abcam, Cambridge, UK). This was either performed alone for quantification, or in combination with the following antibodies for double or triple staining: 1) IB4, a FITCconjugated isolectine  (   [27] ).
Functionality and localisation of GABA B2 receptors To be functional, GABA B receptors must be organised as heterodimers comprising one GABA B1 and one GABA B2 subunit (for review see  [31] ). Using double immunostaining, we effectively observed their colocalisation in small and medium sized DRG neurons (Figure  S3A2C ) and in superficial layers of the dorsal horn (Figure  S3D2F ), suggesting that functional GABA B receptors are expressed at these levels. In addition, we observed that Table  1. Cont. Data are illustrated as follows: 1) Gene accession number; 2) Gene symbol; 3) Mean expression of the genes for the six different groups; 4) results of variance analysis between vincristine and control: p-value; 5) results of Student's t-test between morphine and oxycodone in vincristine-treated rats: p-value and expression difference (morphine -oxycodone); 6) results of Student's t-test between morphine and oxycodone in control rats: p-value and expression difference (morphine -oxycodone).