dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses. Prey mortality in controls without predators was negligible (range 0-2% of initial prey) and prey mortality in controls without predators did not increase with prey density (GLM, F 1,68 = 2. 07, p = 0. 15). The data were thus not corrected for background mortality. We calculated per capita NCM strength as the ratio of dead uneaten prey density over initial prey density, divided by the number of predators. In addition, we also calculated an alternative measure of per capita NCM strength as the ratio of the density of dead uneaten prey over the density of eaten prey, divided by the number of predators. As the results were qualitatively similar, we do not present results for the latter NCM metric. We also tested the goodness of fit of our models using the Hosmer-Lemeshow test and verified that all models fitted the data well (P > 0.
Acknowledgements This study was supported by the  Ministry of Education, Youth, and Sports of the Czech Republic  (projects  CENAKVA -CZ. 1. 05/2. 1. 00/01. 0024and CENAKVA II -LO1205  under the  NPU I program ) and the  Grant Agency of the University of South Bohemia  ( 012/2016/Z ). Work of  D. S. Boukal  and  A. Sentis  was supported by the  Grant Agency of the Czech Republic  ( 14-29857S ). A. Sentis was also supported by the " Development of postdoc positions at University of South Bohemia " project no. CZ. 1. 07/2. 3. 00/30. 0049 ,co-founded by the  European Social Fund  and the state budget of the Czech Republic, by the  French Laboratory of Excellence  project ' TULIP'  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ), and by the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement n.
It consequently appears to be a method of choice for the quantitative analysis of lignification within a whole stem section. The recording of the colorimetric information provided by the staining makes it possible to develop an automated image analysis procedure  [33]. However, the huge amount of data generated increases the difficulty of processing and analysis  [34]. The aim of this work is to present an automated method for the analysis of stained images of stem sections observed with a microscopy slide scanner. The method combines the identification of the different tissue regions that constitute the section based on colorimetric and morphological information, the quantification of the morphometry and the colorimetry of each tissue region. The method is illustrated on a collection of stem sections from several genotypes obtained with contrasted growing conditions.
After staining, sections were rinsed for 24 h with distilled water while stirring continuously. An image of each cross-section was acquired using a slide scanner piloted by the Metafer scanning and imaging platform (MetaSystems GmbH, Altlussheim, Germany). The complete system is composed of an Axio-Imager Z2 Zeiss microscope equipped with a CoolCube 1 camera, a robotic system consisting of a rotating feeder module that delivers samples to the microscope stage, and a computer piloted by Metafer software. Each image was acquired with the 5× objective lens. To reduce computation time and memory space, each picture was converted into a plain TIFF image by choosing the 6× zoom with MetaViewer software. Resulting images had a size of 4000 × 4000 pixels approximately and a resolution of 5. 17 µm per pixel.
Software implementation The whole image processing workflow was developed within the ImageJ/Fiji plateform  [39] , using the Mor-phoLibJ library  [40]. The whole workflow was implemented as an ImageJ/Fiji plugin, freely available on the Internet  [41]. The plugin provides the possibility to finely tune the different parameters used at each step of the workflow. A macro is also provided, making it possible to process a whole batch of images using the same parameters.
Statistical analyses Statistical analyses were performed within the Matlab software (The Mathworks, Natick, MA, USA), using the statistics toolbox and the MatStats library, a collection of functions developed in-house to facilitate the exploration and the analysis of statistical data sets  [42]. Analyses of variance were performed by applying a general linear model to each of the 19 descriptors. Each model took the fixed effects of the genotype, the water treatment and the year, their interactions, the random effect of the sampling block nested to fixed effects, and the random effect of the stem nested to the block into account. The model for a descriptor f is given by the following equation:.
Lignified fraction The tissue fraction corresponding to lignified pith where µ is the constant, G i , T j and Y k are the fixed effects of the genotype, the water treatment and the year, respectively, (GT) ij , (GY) ik , (TY) jk and (GTY) ijk are the fixed effects of the interactions up to the third order, B l (GTK ijk ) is the random effect of the block nested to the genotype, the treatment and the year, S m (GTYB ijkl ) is the random effect of the stem nested to all other effects, and ε ijklm is the residual error term. The "anovan" function of the statistics toolbox of Matlab was used for each of the 19 models. The resulting p values of all the models were concatenated in a data table using the descriptors as rows and the effects as columns.
Funding This work has benefited from  French Government Grants (LabEx Saclay Plant Sciences-SPS , ref. ANR-10-LABX-0040 - SPS , and  ANR-11-BTBR-0006 BIOMASS FOR THE FUTURE ), managed by the  French National Research Agency  under an " Investments for the Future" programme (ref. ANR-11-IDEX-0003-02 ).
16S rRNA Gene Sequence Analysis The 16S rRNA gene sequences of strains LMG 28154 T and LMG 28155 were determined as described by  Vandamme et al. (2007). Sequence assembly was performed using the BioNumerics v7 software. EzBioCloud was used to identify the nearest neighbor taxa with validly published names  (Yoon et al. , 2017). The 16S rRNA sequences of two strains without taxonomic standing, i. e. , Burkholderia sp. TSV85 (GenBank accession GCA_001523725. 1) and Burkholderia sp. TSV86 (GenBank accession GCA_001522865. 1), were retrieved from their whole genome sequences as preliminary analysis of genome sequences indicated a close phylogenetic relationship to strain LMG 28154 T. The 16S rRNA sequences of strains LMG 28154 T , LMG 28155, TSV85, TSV86 and Burkholderia representatives (1011-1610 bp) were aligned against the SILVA SSU reference database using SINA v1.
Genome Sequencing, Assembly and Annotation Strain LMG 28154 T was grown for 48 h on chocolate agar (Oxoid) and genomic DNA was prepared by the CTAB and phenol:chloroform purification method  (Currie et al. , 2007). Paired-end 100 bp libraries were sequenced on an Illumina HiSeq2000 sequencer  (Macrogen Inc. , Geumcheongu, Seoul, South Korea). Sequencing reads were qualityfiltered and assembled as described earlier  (Carlier et al. , 2016). Briefly, the reads were prepared for assembly using the adapter trimming function of the Trimmomatic software  (Bolger et al. , 2014)  and sequencing reads with a Phred score below 20 were discarded. The trimmed reads were used for de novo assembly using SPAdes v3. 0 assembler with k-mer lengths of  21, 33, 55, 77, and 89 (Bankevich et al. , 2012). The QUAST program was used to generate the summary statistics of the assembly (N50, maximum contig length, GC)  (Gurevich et al.
, 2013). Trimmed reads were mapped back to the assembled contigs using the SMALT software 5 and mapping information was extracted using the Samtools software suite. Contigs <500 bp and with <35× coverage were discarded. Final contigs were checked for contamination against common contaminants (e. g. , PhiX 174 genome sequence) and submitted for annotation to the RAST online service  (Aziz et al. , 2008)  with gene prediction option enabled. Annotated contig sequences were manually curated in the Artemis software  (Carver et al. , 2008). Annotated contigs were deposited in the European Nucleotide Archive 6 with the accession numbers FXAN01000001-FXAN01000135. Raw sequencing reads are available under accession ERR1923794. Ortholog computations were done with the Orthomcl v1. 4 software  (Li et al.
, 2003) , using the NCBI Blastp software  (Altschul et al. , 1997 ) with e-value cut-off of 1. 0 × 10 -6 and a percentage identity cut-off of 50%. Assignment of Clusters of Orthologous Genes (COG) functional categories was done by searching protein sequences in the COG database  (Galperin et al. , 2015)  using the NCBI rpsblast software with an e-value cut-off of 10 -3. COG accessions were searched in the STRING v10 database  (von Mering et al. , 2005)  and interaction networks were drawn by selecting neighborhood and co-expression interaction sources. Genes belonging to each COG category were counted and the distributions were compared using a Pearson's chi-squared test run with the R software package  (R Core Team, 2014). Putative pseudogenes were predicted as described earlier  (Pinto-Carbo et al.
, 2016). Briefly, predicted genes were searched against a database of Burkholderia orthologs calculated as described above and intergenic regions using BLASTx  (Altschul et al. , 1990)  against a custom database of protein sequences predicted from Burkholderia genomes. Only hits with >50% identity and with an e-value < 10 -6 were considered. Hit regions were aligned with the best-scoring subject protein sequence using 5 http://www. sanger. ac. uk/science/tools/smalt-0 6 http://www. ebi. ac. uk/ena the tfasty program of the FASTA software suite v3. 6 in order to find the boundaries of the pseudogenes  (Pearson, 2000). Hits with <80% of the ORF length intact (either due to frameshift or early stop codon) were considered putative pseudogenes.
Average Nucleotide Identity (ANI) Values Calculation of ANI values was done on a whole genome sequence database of Burkholderia species (type strains if genome sequence was available at the time of analysis, otherwise representative genome sequence as listed by NCBI GenBank): B. thailandensis E264 T (GenBank accession GCA_000012365. 1), B. pseudomallei K96243 (GenBank accession GCA_000011545. 1), B. oklahomensis C6786 T (GenBank accession GCA_000170375. 1), B. mallei ATCC 23344 T (GenBank accession GCA_000011705. 1), B. humptydooensis MSMB43 T (GenBank accession GCA_001513745. 1), Burkholderia sp. TSV85 (GenBank accession GCA_001523725. 1) and Burkholderia sp. TSV86 (GenBank accession GCA_ 001522865. 1). The draft assembly data of strain LMG 28154 T in FASTA format was uploaded to the JSpeciesWS website  (Richter and Rossello-Mora, 2009).
Whole Genome Phylogeny Representative genomes from individual Burkholderia, Paraburkholderia, Caballeronia and Robbsia species were downloaded from the NCBI RefSeq database (accessed Feb. 2017). In addition, genomes from strains LMG 28154 T , TSV85 and TSV86 were added to the database. Nucleotide and amino acid sequences of annotated CDS were extracted and searched against a database of 40 nearly universal, single copy gene markers using the FetchMG program with the -v flag to retain only the best hits  (Sunagawa et al. , 2013). Twenty-three COGs present in all genomes were extracted, aligned with Clustal Omega and the resulting alignments were concatenated using the AlignIO utility of Biopython  (Cock et al. , 2009). The concatenated alignment was trimmed with TrimAl  (Capella-Gutierrez et al.
G+C Content and Evolutionary Rates Analysis Overall mol% G+C was calculated from the final set of genome contigs using the Quast software  (Gurevich et al. , 2013). Intra-genome %G+C distributions were calculated on non-overlapping 1-kb fragments using ad hoc Python scripts. Extreme values were discarded for clarity, and the median and quartile values, as well as the estimated size of the genomes were plotted together with the phylogenetic tree using the iTol v3 web server  (Letunic and Bork, 2016). Putative orthologs between the genomes of strain LMG 28154 T , B. thailandensis E264 T , B. pseudomallei K96243, B. oklahomensis C6786 T , Burkholderia sp. TSV86 and B. glumae BGR1 (Genbank accession GCA_000022645. 2) were calculated using the Orthomcl software as described above. Single copy orthologs from each genome were selected and the protein sequences were aligned using MUSCLE with standard settings  (Edgar, 2004)  and back-translated into nucleotide alignments using T-Coffee  (Notredame et al.
, 2000). Poorly aligned regions and gaps were trimmed using the TrimAl software  (Capella-Gutierrez et al. , 2009)  and overall G+C content as well as G+C content for each codon position were calculated using ad hoc Python 2. 7. 3 scripts (scripts available upon request). Statistical tests were performed with the R software package version 3. 1. 0 (R Core Team, 2014), including the pgirmess package for non-parametric Kruskal-Wallis tests  (Giraudoux, 2013). Statistical tests for recombination were conducted on the trimmed nucleotide alignments for each single copy orthologous groups using the PhiPack software  (Bruen et al. , 2006)  and the p-values were calculated with 1000 permutations and a 100 bp window. Alignments which failed the permutation test were discarded and a p-value < 0. 05 was considered as evidence of recombination.
Only orthologous groups without statistical evidence of recombination (p-value > 0. 05) were used for the calculation of evolutionary rates. Rates of synonymous (dS) and non-synonymous (dN) substitutions were estimated for the gene families with strictly one ortholog in each of the eight species. Protein sequences for each ortholog cluster were aligned with MUSCLE and subsequent nucleotide codon alignments were generated and trimmed using Pal2nal perl script  (Suyama et al. , 2006). Pairwise dN/dS values were estimated with the yn00 module of the PAML v4. 4 package  (Yang, 2007)  using the  Yang and Nielsen (2000)  method. Pairs of orthologs with insufficient levels of divergence (dS < 0. 1) or near saturation (dS > 1) were excluded from the analyses. Pairwise ratios of non-synonymous to synonymous (dN/dS) mutations were extracted from the PAML output using ad hoc Python scripts and plotted in R.
Nonparametric Wilcoxon signed rank tests were performed to test whether dN/dS values were higher for genes in the novel species compared to free-living Burkholderia (null hypothesis). Codon frequencies were calculated on sets of orthologous genes that did not show evidence of recombination (n = 2890) with the Cusp program of the Emboss toolkit  (Rice et al. , 2000). Only fourfold degenerate codons were considered for analysis. Statistical analyses were done in R. For the calculation of substitution bias in intergenic regions, we selected a random set of 17 intergenic regions which were conserved in strains LMG 28154 T , TSV85 and TSV86, and did not contain mis-annotated CDS as evaluated by BLASTx against the NCBI nr database (accessed March 2017). Sequences were aligned using MUSCLE and the alignments were visualized in the CLC Main Workbench v.
Instead, predicted proteins FIGURE 2 | Genomic phylogeny and characteristics of the genus Burkholderia. The phylogenetic tree was built using an alignment of 23 conserved single copy COGs and an approximate maximum-likelihood approach (see Materials and Methods for details). The %G+C distribution was calculated on non-overlapping 1 kb segments for each genome and plotted as box plots with extreme values removed for clarity. Total genome size (bar plot) is approximated by size of the assembly and coding content (dark gray) corresponds to the sum of the length of all annotated CDS features, discounting predicted pseudogenes. Shimodaira-Hasegawa-like local support values as given by the program Fasttree are indicated. of the gene cluster show high similarity, ranging from 45 to 70% identity, to a syntenic gene cluster of B.
We did not find any statistically significant increase in average %G+C at the third codon position (which we showed above displayed the largest effect) in orthologous sets which showed evidence of recombination (Student's t-test p = 0. 387, data not shown). This is in accordance with the recent results of  Lassalle et al. (2015)  who did not find evidence that recombination was affecting %G+C of core genes of some species of the B. cepacia complex and the B. pseudomallei complex, in contrast to most bacterial species. Our method likely underestimated the number of genes subject to recombination because of the small genome dataset and our focus on single copy orthologs conserved in all genomes. However, because recombination is rare (<2%) in the core genome of species of the B. pseudomallei complex  (Lassalle et al.
mallei, B. glumae and B. gladioli strains (Supplementary Figure  S4 ). These species all have average genomic %G+C > 68. 5%, indicating that loss of the PolX function is unlikely to be solely responsible for the altered %G+C of strain LMG 28154 T. However, BSIN_0972, which encodes a putative DNA-3-methyladenine glycosylase 1 of the TagI family, is a pseudogene in strain LMG 28154 T (and also in TSV86). TagI proteins catalyze the excision of alkylated adenine and guanidine bases from DNA as part of the BER pathway  (Bjelland and Seeberg, 1987). Instead, BSIN_4945 encodes a protein of the same superfamily (COG2818) with 47% identity to the product of BSIN_0972, but this gene has no close homologs in Burkholderia species outside of strains TSV85 and TSV86 (Supplementary Figure  S4 ). This gene was possibly acquired via horizontal gene transfer, since the closest homologs in the NCBI nr database belong to bacteria only distantly related to Burkholderia (closest hit in the genome of Cohnella sp.
MALDI-TOF MS was performed as described previously (De Bel et al. , 2011)  using a Microflex LT MALDI-TOF MS instrument (flexControl version 3. 4, MALDI Biotyper Compass Explorer 4. 1) with the reference database version 6. 0. 0. 0 (6,903 database entries) (Bruker Daltonik).
MATERIALS AND METHODS A literature review was carried out on the basis of keywords in ISI Web of Knowledge, using the "All Databases" option, with the following formula: "enchyt * or potworm * and pesticid * or herbicid * or fungicid * or molluscicid * or nematicid * or insecticid * " in Topics. In total, 2,741 publications were found. In a first step, publications were sorted and classified according to title, keywords and abstract. This selection revealed very different numbers for the specific parts of the review. For the individual and population levels, 226 papers were selected, while less than 15 publications were interesting for the assessment of pesticide effects on enchytraeids at the community level. In parallel, those authors which have been identified as relevant for our topic, were checked again.
, 2012). Microarrays are so far the only available tools to assess pesticide effects on enchytraeids at this level of organization. However, they have the disadavantage of targeting only certain genes present on the microarray, i. e. , they do not allow to screen the genome without an a priori selection of the genes that are expressed. Moreover, microarrays are biased due to signal saturation  (Zhao et al. , 2014). To screen the genome expression without a priori selection and to quantify the level of expression of the differentially expressed genes, toxicogenomic approaches should be used, i. e. , differential transcriptome analysis. However, so far these methods have not been standardized. It seems that there is a potential of using gene expression in risk assessment  (Novais et al. , 2012b) , especially since a database containing genomic information for E.
Field Approach In order to survey enchytraeids in the field, soil samples are taken with a corer (diameter usually between 5 and 7. 5 cm). Theses samples are separately placed onto sieves hanging in plastic bowls filled with water, and the enchytraeids are driven via wet-extraction from the soil. This procedure has been internationally standardized (International Organization for Standardization, 2007). Species identification is only possible with living specimens, which limits the number of samples that can be handled in parallel. Species determination via genetic methods (DNA barcoding and meta-barcoding) is a promising alternative  (Orgiazzi et al. , 2015)  since the establishment of DNA sequencing as a cheap routine laboratory procedure. However, there is still a problem with the interpretation of the results, because the number of reliable data sets combining genetic and morphological information is small.
, 2011; Peijnenburg et al. , 2012) ? -Which endpoints are the most sensitive, relevant, robust and practical ones? Reproduction surely comes first but is timeconsuming. Could genetic markers take over this role while being quicker to perform  (Amorim et al. , 2011) ? -What about the trait concept-can we identify morphological/ecological groups which may be more relevant than individual species? -Is it possible to overcome taxonomic burdens by building up a database containing enchytraeid gene sequences so that enchytraeid diversity could be an "easy" endpoint in field studies or monitoring approaches? In a regulatory context, further questions have to be answered (most of them are important for other soil organisms too; Van Gestel, 2012): -How could bioavailability be included in ERA schemes without losing protectivity? -Should the information gained in sub-individual studies be involved in regulatory risk assessment and if yes, how? -How can enchytraeids be established in higher-tier tests such as TMEs or field tests-and would it be useful to standardize gnotobiotic test systems? -How can relevant protection goals for enchytraeids be defined, both for their functional roles and for their diversity (EFSA Panel on Plant Protection Products and their Residues (PPR) et al.
Normalized double-differential cross sections for top quark pair (tt) production are measured in pp collisions at a centre-of-mass energy of 8 TeV with the CMS experiment at the LHC. The analyzed data correspond to an integrated luminosity of 19. 7 fb -1. The measurement is performed in the dilepton e ± µ ∓ final state. The tt cross section is determined as a function of various pairs of observables characterizing the kinematics of the top quark and tt system. The data are compared to calculations using perturbative quantum chromodynamics at next-to-leading and approximate next-to-next-to-leading orders. They are also compared to predictions of Monte Carlo event generators that complement fixed-order computations with parton showers, hadronization, and multiple-parton interactions. Overall agreement is observed with the predictions, which is improved when the latest global sets of proton parton distribution functions are used.
In total, the double-differential tt cross section is measured as a function of six different pairs of kinematic variables. These measurements provide a sensitive test of the standard model by probing the details of the tt production dynamics. The double-differential measurement is expected to impose stronger constraints on the gluon distribution than single-differential measurements owing to the improved resolution of the momentum fractions carried by the two incoming partons. The analysis uses the data recorded at √ s = 8 TeV by the CMS experiment in 2012, corresponding to an integrated luminosity of 19. 7 ± 0. 5 fb -1. The measurement is performed using the e ± µ ∓ decay mode (eµ) of tt, requiring two oppositely charged leptons and at least two jets. The analysis largely follows the procedures of the single-differential tt cross section measurement  [15].