dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis. For all traits considered, as root length, density of precipitates and statoliths coordinates, the data were analyzed by using a one-way analysis of variance (ANOVA) with the PROC GLM procedure of SAS (v9. 3, SAS Institute Inc, Cary, NC, USA, 2006). The homogeneity of variances was tested using the Levene's test. When the assumption of homogeneity of variance was rejected or when unbalanced data were performed between groups, a Welch's Anova was used instead of the usual Anova to test for differences between group means. Means obtained for the different gravity conditions were separated using a Student-Newman-Keuls multiple comparisons test when a main effect was determined to be significant at a 5% level. Estimation of the least significant displacement of statoliths that could have been detected was done by generating i theoretical samples having same size and standard deviation as experimental samples.
Acknowledgements This work is supported by the  French Space Agency (CNES)  and by the  European Space Agency (ESA). This studies is also supported by the  ANR   Blanc Grap2  ( ANR-13-BSV5-0005-01 ), the  European Research Council  under the  European Union Horizon 2020 Research and Innovation programme  (grant agreement No  647384 ) for HC. We would like to thank  G. Gauquelin-Koch  ( CNES ) for her support,  D. Chaput  (CNES), the  COMAT  and  ESA  teams and  G. Gasset  ( GSBMS ) for the technical assistance and for the preparation of the experiment. We also thank  D. Driss-Ecole ,  E. Badel  and  B. Moulia  for helpful discussions.
Four replicates were prepared by 4 days of dissection with 5-6 persons. rRNA depletion, single stranded-RNA library preparation, multiplexing, and sequencing were performed by Genewiz (New Jersey, USA). Sequencing was performed on the Illumina HiSeq2500 platform, in a 2 Â 125 bp paired-end (PE) configuration in High Output mode (V4 chemistry). Each sample was sequenced on four different flowcell lanes to avoid lane effect. In total, 471,933,074 reads were obtained for eight samples. Raw data is available in NCBI Sequence Read Archive (https://trace. ncbi. nlm. nih. gov/Traces/sra/; last accessed May 22, 2018) with reference number SRP14110.
(2016). The raw fragment counts were converted to counts per million (CPM) using the edgeR  (Robinson et al. 2010 ) R-implemented package (R-Core Team 2017). Expressed genes were filtered based on a CPM > 1 in at least three libraries among the eight analyzed libraries and CPMs were normalized by the edgeR TMM method for Normalization Factor calculation  (Robinson and Oshlack 2010). The mean-variance relationship of the log-CPM was estimated by the voom function  (Law et al. 2014)  and incorporated in the empirical Bayes analysis from the limma package  (Ritchie et al. 2015)  to fit linear models and compare SG vs. AT tissues. Validation of the described differential expression analysis is presented in supplementary fig. S1 , Supplementary Material online. In addition, normalized fragments per kilobase million (FPKM) were calculated with edgeR on the four salivary glands samples and the four alimentary tracts samples separately to obtain whole transcriptomes of each organ.
Mass Spectrometry One microgram of each digested sample was loaded onto a QExactive (ThermoFisher Scientific) high-resolution accurate mass spectrometer connected to a Dionex Ultimate 3000 (RSLCnano) chromatography system. The peptides were separated by a 2-40% gradient of acetonitrile on a Biobasic C18 PicofritTM column (100 mm length, 75 mm ID), using a 120min reverse-phase gradient at a flow rate of 250 nl min À1 with a runtime of 50 and 130 min for saliva and salivary glands, respectively. All data were acquired with the mass spectrometer operating in automatic data dependent switching mode. A full MS scan at 70,000 resolution and a range of 400-1,600 m/z was followed by an MS/MS scan at resolution 17,500 and a range of 200-2,000 m/z, selecting the 15 most intense ions prior to MS/MS. Protein identification of MS/MS data was performed using MaxQuant v1.
5. 6. 5 (www. maxquant. org; last accessed May 22, 2018) following the general procedures and settings outlined in  Hubner et al. (2010). The Andromeda search algorithm  (Cox et al. 2011 ) implemented in the MaxQuant software was used to correlate MS/MS data against the protein reference sequence set of A. pisum obtained from the NCBI (27,984 entries, May 2016) and a contaminant sequence set provided by MaxQuant. The following search parameters were used: first search peptide tolerance of 20 ppm, second search peptide tolerance 4. 5 ppm with cysteine carbamidomethylation as a fixed modification and N-acetylation of protein and oxidation of methionine as variable modifications and a maximum of two missed cleavage sites allowed. False Discovery Rates (FDR) were set to 1% for both peptides and proteins and the FDR was estimated following searches against a target-decoy database.
Gene Ontology and Secretion Prediction The GO annotation was performed on the whole A. pisum proteome available on NCBI using Blast2GO v2. 5. 0  (Go ¨tz et al. 2008). Associations were realized using a blastp  [BLASTþ v2. 5. 0 (Camacho et al. 2009) ] search against the nonredundant protein database (release 2017-2-4) with the following parameters: e-value ¼ 1eÀ8, max_target_ seqs ¼ 20, soft_making ¼ false, and Interproscan v5. 13. 52. 0  (Jones et al. 2014 ). Assigned GO terms for genes of interest groups were categorized by molecular function (MF), biological process (BP) and cellular component (CC) and GO enrichment was investigated using hypergeometric tests in R. The P-values were adjusted for multiple comparisons using Bonferroni correction. The whole A. pisum proteome was analyzed with SignalP v3.
Orthology Analysis To determine groups of orthologs among the 17 genomes (supplementary table S1, Supplementary Material online), we first kept the longest protein isoforms from each species with an home-made perl script and then ran the OrthoDB_soft_1. 6 (Kriventseva et al. 2015)  using standard parameters. To establish the species phylogeny, 478 groups of conserved single-copy orthologs were extracted and their protein sequences aligned with MAFFT  (Katoh et al. 2005). Alignments were concatenated and used to generate a maximum likelihood tree with RAxML  (Stamatakis 2006)  with default parameters and 1,000 replicates, considering Tetranychus urticae as an outgroup. The phylogeny was then used to establish the level of orthology of each A. pisum gene. The enrichments of orthologous categories among data set were analyzed using hypergeometric test implemented in R.
Single Copy Genes Ortholog groups that were represented by a single gene in the A. pisum genome and in at least one of the other Aphididae genomes were extracted. If alternative transcripts of a gene were present, the longest coding sequence (CDS) for each species was used. Pairs of orthologous CDS (one CDS per species: A. pisum/A. glycines, A. pisum/D. noxia, A. pisum/ M. persicae) were aligned using MAFFT  (Katoh et al. 2005)  within the Hot algorithm from GUIDANCE2  (Sela et al. 2015)  to produce reliable codon-based alignments without poorly aligned regions. From these codon alignments, pairwise dN/ dS values were calculated with PAML v4. 8 using the YN00 program  (Yang and Nielsen 2000; Yang 2007 ). We removed ortholog pairs with dS > 2 or dN > 0. 5 to avoid mutational saturation, as well as pairs with dS ¼ 0.
dN/dS of different gene categories within each species pairs were compared using a Kruskal-Wallis test, and Nemenyi-Tests for multiple comparisons were realized with the R package PMCMR  (Pohlert 2014). Orthologs of characterized salivary effectors were searched in all Aphididae sequences available in Genbank database and in 454-sequenced contigs of Rhopalosiphum padi and Schizaphis graminum assembled for this study (see supplementary material and methods S1, Supplementary Material online), in order to compute more accurate evolutionary rates and to test selection models. For this task, reciprocal blast searches were run with blastn (BLASTþ v2. 5. 0) using A. pisum, M. persicae, A. glycines and D. noxia CDS sequences against the different databases (e-value < 10 À10 ). Then, for each obtained ortholog group, a codon-based alignments was generated with PRANK (Lo ¨ytynoja and Goldman 2005) and cleaned from poorly aligned regions using GUIDANCE2, specifying a minimum alignment quality threshold of 0.
pisum. In SGs, 12,040 genes passed the cut-off value for gene expression (fig. 1A ) and encode proteins that may or may not be secreted in saliva. A protein secretion prediction pipeline was applied to the global gene set of A. pisum (N ¼ 18,601) using SignalP v3 or v4. 1 (Dyrløv  Bendtsen et al. 2004; Petersen et al. 2011) , as well as SecretomeP  (Bendtsen et al. 2004)  To create the candidate SG-up-regulated (or SG-up) effector set, the expression levels of individual RNAs were compared between SGs and AT. After filtering and normalization steps, 12,378 protein coding genes from SGs and AT were retained. Among these genes, 1,989 genes were upregulated in SG tissues, of which 740 genes were predicted to encode secreted proteins. These constituted the candidate SG-up effector set (fig. 1A  and supplementary table  S2 , Supplementary Material online).
In addition, orthologs from other aphids were retrieved from public databases (supplementary table S4, Supplementary Material online) and used to estimate dN/dS ratios under different site-models of selection. The global dN/dS for each gene showed a range from 0. 06298 (Armet) to 0. 64653 (Mp1) (table 1 and supplementary table  S5 , Supplementary Material online). Differences were found between insect-or arthropod-conserved effector genes like Armet, Mif1 and Mp10, which seem to have evolved under strong negative selection (dN/dS < 0. 12), and the aphidspecific, fast-evolving effector genes (dN/dS > 0. 40, in the top 5% of values obtained from pairwise comparisons). Despite these faster evolutionary rates, the examination of null and alternative models of codon substitutions [M1 vs. M2 and M7 vs.
It is notable that the other aphid genomes comprise just one ACE of clade 4 (fig. 6A ). All clade-4 ACE proteins except ACE10 are determined as functional peptidases, based on the presence of the M2 peptidase HEXXH domain (IPR001548) (fig. 6A ). Positive selection was inferred in three branches of A B FIG. 4. -Evolutionary analysis of the Aphididae gene family. (A) Codon-based maximum-likelihood tree to compute dN/dS under the branchsite (BS) model of codon Numbers below branches indicate bootstrap support (percentage of the 1,000 replicates) when >40. DNOX, MPER, AGLY, and APIS sequences from Diuraphis noxia, Myzus persicae, Aphis glycines, and Acyrthosiphon pisum, respectively. Branches where positive selection affected certain sites are in bold. (B) Estimated evolutionary parameters for these branches (numbered as on the tree).
7 ). Genes of this clade generally encode proteins with a signal peptide followed by a M1 peptidase domain (IPR014782) and an ERAP1-C domain (IPR024571). However, some have lost one of the two domains, and the vast majority (apart from D. noxia apN-3, A. pisum apN-1 and A. pisum apN-5) possesses the HEXXH active site ensuring the peptidase function (fig. 8A ). Traces of positive selection were detected by the branchsite model in seven branches of apN clade 4 (fig. 8B  and C ): the branch leading to D. noxia gene apN-1 (branch #1), the ancestral branches #3, #5, #7, and those leading to A. pisum apN-3 (#2), apN-8 (#4), and apN-12 (#6) genes. Along these branches, sites under positive selection (with BEB > 0. 75) were mainly located in the M1 peptidase and ERAP1-C domains, and in the uncharacterized part between the signal peptide sequence and M1 peptidase domain for branches #1, #2, and #3 (supplementary figs.
While pioneering studies have improved our understanding of the diversity of tick-associated microorganisms, a profusion of bacterial species has been found in ticks  (Carpi et al. , 2011; Nakao et al. , 2013; Zhang et al. , 2014; Van Treuren et al. , 2015; Sui et al. , 2017; Swei and Kwan, 2017). The generation of large datasets in next generation sequencing projects faces the challenge of data analysis to answer relevant questions such as how these microbial communities are organized. It is thus paramount to develop methods to capture the microbial communitylevel patterns  (Kautz et al. , 2013). Network analysis (or graph theory) offers the methodological framework to produce simple indexes that ideally define the associations, but not the trophic interactions, of each microorganism in the structure of the microbiome.
This study is aimed to build a network-based framework for analyzing co-occurrence patterns of microorganisms in Ixodes ricinus ticks and one of its main hosts, the vole Myodes glareolus. The focus is neither on the description of the ticks or vole microbiomes or the differences in bacterial composition between tick instars. The proposed framework is a proof of concept that uses a large dataset of bacterial genera of both ticks and voles recorded at two different ecosystems. We intended to provide a foundation for analyzing hierarchical levels of complexity in the tick microbiome, phylogenetic relationships among bacteria of ticks and voles, yielding an example with immediate application to similar cases.
High-Throughput Sequencing Data Analyses and Taxonomic Classification of OTUs We use rigorous experimental procedures for the direct estimation of biases from the data produced by 16S rRNA amplicon sequencing  (Galan et al. , 2016). MiSeq datasets were processed with mothur v1. 34  (Schloss et al. , 2009)  and with the MiSeq standard operating procedure  (Kozich et al. , 2013). This allowed us to: (1) construct contigs of paired-end read 1 and read 2 using the make. contig command; (2) remove the reads with poor quality of assembly (>275 bp); (3) align unique sequences on the SILVA SSU Reference alignment v119  (Quast et al. , 2013) ; (4) remove the misaligned, non-specific (eukaryotic) and chimeric reads (uchime program); (5) regroup the reads into Operational Taxonomic Units (OTUs) with a 3% divergence threshold; and (6) classify the OTUs using the Bayesian classifier included in mothur (bootstrap cutoff = 80%) and the Silva taxonomic file.
As mentioned above, we made PCR replicates for each rodent sample and several controls were included in each sequencing run. Therefore, we had more PCR products than rodent samples. We obtained a table with the number of reads for each OTU (rows) and each PCR product (columns). The stepwise trimming process described in  Galan et al. (2016)  was then applied to clean the raw dataset from bacterial contaminations and false assignation of reads to samples. Sample replicates were checked for consistency and samples were considered to be positive for a given OTU only if both PCR replicates represent positive results. Sample replicates positive for given OTU were combined by adding read numbers for each OTU. The completion of bacteria catalogs for each sample was evaluated using cumulative curves (taxa number vs.
Phylogenetic Analysis The 16S rDNA reads length resulting from our High-Throughput Sequencing (HTS) were too small (251-bp) to build a reliable phylogenetic tree. To overcome this issue, 16S rDNA nucleotide sequences of 126 bacteria species (one bacteria species representing each bacterial genus, available as Tables  S2, S3 , Figure  S1 ) were obtained from GenBank. This approach is based on the reasonable assumption that bacteria species within a genus are more related between them than to any other bacteria of other genera. Sequences were aligned using MAFFT  (Katoh and Standley, 2013). The evolutionary history was inferred using the Neighbor-Joining method  (Saitou and Nei, 1987) , implemented in MEGA 6. Reliability of internal branches was assessed using the bootstrapping method (1,000 replicates).
The chart shows "spirals" to display the communities of the genera of the microorganisms, sorted according to its Betweenness Centrality. Table  S1  | The set of complete raw data used in this study, for each individual vole or tick, in either forest or ecotone. This is the dataset used for all calculations and to build the networks. The table includes information about the carrier (tick or vole), the sex of the carrier (M: male; F: female), the landscape (forest or ecotone), and the number of OTUs and reads obtained for each individual sample. Table  S2  | 16S rDNA nucleotide sequences of the genera of 126 bacteria detected in either ticks or voles, in "fasta" format, and used to build the phylogenetic tree of detected microorganisms. Table  S3  | Genetic distances of the genera of bacteria detected in this study, obtained from the sequences detailed in Table  S1.
The accession number of each sequence in GenBank is included together with the genus of bacteria. Table  S4  | Data about all the genera of bacteria (including higher taxonomic ranks, like Family and Order) detected in voles or ticks. This table includes the percentage of times that each genus appeared in a carrier (voles or ticks), and the number of reads of each genus found in either voles or ticks, in either forest or ecotone. In example, the genus Midichloria was detected in more than 80% of ticks but less than 1% of voles. Table  S5  | General data about the indexes of the four networks generated for voles or ticks, in either forest or ecotone. Included are the genera of bacteria, and for each genus the values of Weight, Betweenness Centrality, Closeness Centrality, Eigenvector Centrality, PageRank and Clustering coefficient.
Acknowledgments The authors acknowledge the contributions of  Kim Hoek  and  Mae Newton-Foot  for help with primer design,  Andrew Whitelaw  for laboratory support,  Kim Tutt  for guidance on the animal inoculation protocol,  Marco Botha  for creating the map used in Figure 1, and  Kristine Schwartz  and the students of Alfred Botha's biology class for help with sample collection. This study was funded by the  Fonds Wetenschappelijk Onderzoek-Vlaanderen  (G. 0514. 14N ). I. S. S. was supported by an  Association of Medical Microbiologists and Infectious.
Evolution of CA cropping systems on 10 year time Figure  2  shows the evolution of the major CA cropping systems adopted by local farmers between 2003 and 2013 on the oldest monitored plots on tanety (upland) and baiboho (colluvial soils) (77 plots). The major CA systems are those originally based on one associated plant, i. e. Brachiaria spp, Stylosanthes guianensis, Vicia villosa (vetch), Dolichos lablab (dolic) and dead mulch. Various modifications of these systems have been observed: introduction of peanut in the crop rotation (observed as well in the 3000 plots database of the BV-lac project), shift from recommended cover crops to other associated crops (dolic replaced by cowpeas for instance), re-introduction of ploughing regularly, replacement of mulch from cover crops by straw mulch (from rice from instance).
SWOT analysis on CA sustainability The SWOT analysis results display pros and cons of CA and key answers to the main constraints (Tab. 3) as perceived by farmers. Although 55% of the producers have confirmed that they do perceive a yield increase (under strong impact of the presence of BV-lac project extensionists since many years), this is difficult to confirm with regard to existing project databases and surveys. McDowall et al. (2011)  showed in this same area that the CA yield increase over 5 years was limited to 10 to 15% when Bruelle (2014) observed a 20% increase (low input systems with no chemical fertilization). The most commonly shared feeling by farmers is that yields in CA are maintained over the years compared to declining yields in traditional systems. The most significant result for farmers is the doubling of global production over 10 years with the disappearance of the fallow period.
Transition period In line with the indicative timelines for submitting molecular datasets that require the generation of plant material (EFSA, 2014), the recommendations described in this explanatory note will be applicable for GM plant applications submitted 24 months after its publication. However, applicants are recommended to take into account the elements described in this document for all applications submitted after its publication. A transition period of two months applies to those elements for which only the provision of information is recommended, such as: • a description of how the plant material was collected including the number of analysed plants • a description of all the experimental protocols including those for method validation • information on all the critical methodological parameters and any associated limitations and method deviations • the provision and description of representative standard curves (see Section 4.
Required information The determination of NEP levels in GM plant tissues requires the use of an appropriate analytical method to detect and quantify the NEP(s). The NEP analytical methods discussed in this document are antibody-based methods such as ELISA and immunoblotting/western blot analysis as well as mass spectrometry (MS) methods, such as liquid chromatography-tandem mass spectrometry (LC-MS/MS) (e. g. Andreasson et al. , 2015; Crespo et al. , 2008; Grothaus et al. , 2006; Kruve et al. , 2015a,b; Liebler and Zimmerman, 2013; Pan et al. , 2009; Schmidt and Alarcon, 2011; Settlage et al. , 2017). These methods are the most commonly used approaches for NEP quantification reported in dossiers submitted for GM market authorisation in the EU. More extensive reference is made to the ELISA methodology because the majority of datasets on NEP quantification are produced by this experimental approach.
Information on data reporting and data analysis methods • A description of all data analysis methods including the number of replicates for all the steps and the model fitting method(s) to produce standard curves. • Representative standard curves should be included and sufficiently described. • A description of how the protein concentrations used for the standard curve (e. g. ng/ml) were used to deduce the values for NEP levels in the analysed tissues (e. g. μg/g tissue). • Data should be clearly summarised (i. e. tabulated) for each NEP measured in the different tissues of the GM plant. NEP levels should be calculated taking into account the determined extraction efficiency for each tissue and these calculations should be clearly described. • Besides those cases for which it may not be technically possible (e.
Capillary migrations were performed on a 3730XL Bioanalyzer (Applied Biosystem, USA), using 4 µl of each sample, 10. 8 µl of Hi-Di formamide and 0. 2 µl of GS 1,200 LIZ size marker (Thermo Fisher scientific, USA). The fluorograms were analyzed with the software Genemapper 4. 0. The signals comprised between 100-1000 bp were selected, binned to windows of 5 bp and transformed into relative fluorescence intensities (RFI) using a previously published method  [27].
Immune gene expression Five individuals per family per plant watering condition were assessed for immune gene expression. Typically, immune responses are tested with an immune challenge serving as positive control. Due to data limitation, this was not possible; hence immune gene expression is assessed only in response to plant water stress. However, previous work shows upregulation of most of the tested genes under bacterial challenge  [29]. The qPCR was performed on individual carcasses dissected as described above. RNA was extracted by homogenization of the samples in 1ml of Trisure (Bioline-Germany; Appendix 1). The qPCR targeted seven immune genes following methods by  [29] : Lysozyme C, prophenoloxidase, attacin, peptidoglycan recognition protein LC, β-1,3-glucan recognition protein, serpin 3a and pelle, as well as two housekeeping genes: mitochondrial ribosomal protein L37 and S24.