dataesr/scientific-paragraphs-categorization huggingface dataset:
The increasing availability of large genomic datasets provides new opportunities for investigating pathotypes in multiple niches  (Vinatzer et al. , 2014). By comparing the genomes of P. syringae crop pathogens and isolates from environmental reservoirs, it should be possible to identify the genetic basis of disease emergence and the genomic regions that are horizontally transferred between strains, in particular between crop pathogens and their environmental relatives. Therefore, we sequenced the genomes of 107 isolates of crop-pathogenic and environmental P. syringae and analysed them together with 86 publically available P. syringae genomes. We investigated two P. syringae phylogroups  (Berge et al. , 2014)  with contrasting host ranges  (Buell et al. , 2003)  and disease etiology  (Feil et al.
Methods Isolates and sequencing. Genomes of 92 P. syringae isolates from phylogroups 1a and 2d  (Berge et al. , 2014)  were chosen for sequencing, whereby phylogroup 1a was sampled maximizing genetic diversity avoiding multiple crop pathogen strains with identical MLSA sequences, while for phylogroup 2d only one subset was sampled with isolates that were identical at two MLSA loci. These datasets were augmented with 12 genome sequences from the same phylogroups available in public databases, including reference genomes from Pto strain DC3000  (Buell et al. , 2003)  and Psy strain B728a  (Feil et al. , 2005). Most of the isolates sequenced in this study were described previously  (Morris et al. , 2008; Morris et al. , 2010; Monteil et al. , 2012; Monteil et al. , 2014b)  with 36 isolates collected from diseased crops (defined as cultivated lands) and 56 isolates collected from streams and rivers (11 isolates), precipitation (15 isolates), irrigation water (11 isolates) or epilithic biofilms (12 isolates) and leaf litter (seven isolates).
The quality of resulting sequencing reads was examined using FastQC  (Andrews, 2010). TrimGalore  (Krueger, 2015)  was used to trim sequence reads and remove poor-quality data, using command-line options '-q 30 and -paired'. Illumina adapter sequences were removed using CutAdapt  (Martin, 2011). Cleaned sequencing reads were assembled using the de novo assembly algorithm Velvet  (Zerbino & Birney, 2008 ) (version 1. 2. 08). The value of k was optimized by assembling over a range of values and choosing the assembly with maximal N 50. The minimum output contig size was set to 200 bp with the scaffolding option switched off, and all other program settings were left unchanged. The average number of contigs and the standard error in 95 newly sequenced P. syringae genomes was 474AE32 for an average total assembled sequence size of 5 969 322AE18 142 bp.
Sequence reads in FastQ format deposited in the Sequence Read Archive (SRA) are available via BioProject accession PRJNA320409. Core and accessory genome. Analysing core and accessory genome variation, genealogies and recombination patterns, we investigated the evolutionary relationships linking outbreak strains to their relatives in the environment. A reference pan-genome approach  (Méric et al. , 2014)  and geneby-gene alignment  (Sheppard et al. , 2012)  was implemented using BIGSdb open source software  (Jolley & Maiden, 2010). First, a reference gene list was assembled from four publicly available genomes, Pto DC3000  (Buell et al. , 2003)  and Psy B728a  (Feil et al. , 2005) , P. syringae pv. phaseolicola 1448a  (Joardar et al. , 2005)  and the environmental strain CC1557  (Hockett et al.
The software was run with the Jukes-Cantor model of nucleotide evolution and gaps from truncated sequence alignments were considered as missing data. The tree was visualized and annotated using FigTree v1. 4. 2 (http://tree. bio. ed. ac. uk/software/figtree). For the analysis of phylogroups 1a and 2d, trees were reconstructed from alignments including only non-truncated sequences for all core loci (respectively 810 and 2147 loci). Genealogies were inferred using ClonalFrame, a model-based approach for inference of microevolution in bacteria that accounts for recombination events that can disrupt phylogenetic reconstruction  (Didelot & Falush, 2007). This program differentiates mutation and recombination events on each branch of the tree based on the density of polymorphisms. Clusters of polymorphisms are likely to have arisen from recombination, and scattered polymorphisms are likely to have arisen from mutation.
The genomes of crop and environmental isolates in the first cluster were extremely similar to each other (average nucleotide identities ranging from 99. 50 to 99. 97 % within each clade). To confirm these results, sequencing reads were aligned against the Psy B728a genome and SNPs were identified. This approach revealed that some of the crop isolates differed from their most similar environmental relatives by as few as three SNPs per million base pairs (data not shown), suggesting very recent exchanges of P. syringae between cantaloupes and water cycle components. Moreover, ClonalFrame showed that some core genes experienced recent homologous recombination between crop and environmental isolates, which was supported by the Neigh-borNet network shown in Fig. S2b.
Functional predictions using BLAST P of the translated sequences identified strong signatures of a conserved domain specific of the acriflavin resistance protein family (Pfam accession PF00873, E value 4. 03Â10 -143 ; TIGRFAM00915, 8. 04Â10 -81 , >90 % length of sequence). Therefore, this putative membrane protein could be part of an aminoglycoside efflux system involved in either toxin production or resistance processes. This gene has been implicated in multiple antibiotic resistances in human pathogens such as Salmonella Typhimurium  (Piddock, 2006)  but its potential role in conferring resistance to antibiotics and/or other agropesticides in P. syringae is unknown. It remains the case that recombination was found to be most frequent between closely related isolates with little genetic exchange between 1a and 2d isolates, consistent with well-known homology dependence of recombination  (Hanage et al.
Acknowledgements We are grateful to  Haiji Liu  ( Virginia Tech ) for assistance in DNA extraction,  Zhongmeng Bao  ( Cornell University ) for assistance in preparation of some DNA sequencing libraries, and  Caroline Guilbaud  ( INRA ) for assistance in preparing, verifying and shipping strains. We thank  Susan Murray  ( Swansea University ) who helped to run one of the bioinformatics analyses. The computational calculations were done at the  Human Genome Center at the Institute of Medical Science (University of Tokyo, Japan ) and  HPC Wales (UK). C. L. M. received support from  INRA  and the  European Union , in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  AgreenSkills' fellowship  (under grant agreement no. 267196 ). Research in B. A. V. 's laboratory and genome sequencing was funded by the  National Science Foundation of the USA  (grants  IOS-1354215  and  DEB-1241068 ).
All supporting data,code and protocols have been provided within the article or through supplementary data files. confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. Sequencing reads have been submitted to the NCBI Small Read Archive as Bioproject PRJNA320409 with biosample accession numbers SAMN04942971 to SAMN04943055 and SAMN05301579 to SAMN05301583 and can be accessed at the following link: http://www. ncbi. nlm. nih. gov/bioproject?LinkName=sra_bioproject&from_ uid=2500232.
P. aeruginosa PAO1 wild type cells (labeled yellow with YFP) are inoculated in a flow chamber together with an isogenic strain with a pilA mutation (blue BFP labeled). After several days of growth, mushroom structures are formed where the blue non-motile forms the stalks and the yellow wild type cells move to the top of the mushroom structures. The confocal image is treated in Imaris V R software with surface rendering. Mushrooms are 80-100 lm high. For details see  Klausen et al. (2003b). # Mikkel Klausen. (C) Three-dimensional biofilm matrix structure of C. jejuni at 48 h using fluorescence lectin binding analysis (FLBA) and confocal laser scanning microscopy (CLSM) observations. Biofilm was stained simultaneously with fluorescent probes: Syto9 in green for cell detection and two fluorescently labeled lectins Concanavaline-A (ConA) in blue to detect a-D-mannosyl and a-D-glucosyl residues and wheat-germ agglutinin lectin (WGA) in red to detect N-acetylglucosamine (GlcNAc) residues.
Image processing of biofilm images In general, image processing software can be divided into two major categories: (i) programs for making pictures for presentation and (ii) programs for making quantitative measurements of biofilm images. Additional categories include database programs for experiment storage, statistical analysis tools, etc. Some of these are not specifically designed for biofilm research and may have unneeded features or miss features needed for proper biofilm analysis. This section will not attempt to include all programs that can be used for biofilm research but will rather focus on representative examples. What a researcher choses depend on what he or she wants to do. If the aim is to produce pretty, informative images programs in category (i) above should be used. If, on the other hand quantitative measurements are required, e.
g. for making statistical analysis and comparison of biofilm experiments, programs in category (ii) should be selected. Biofilm images are usually recorded with CLSM. Each manufacturer tends to make a proprietary image file format, making interchange of image data and data processing difficult. Recently, a common data format was devised and the major manufacturers of microscopy hardware currently adopt it, at least as an optional export format. The format, Open Microscopy Environment (OME-Tiff), is now standardized and tools exist to convert most proprietary file formats into this  (Linkert et al. , 2010; Rueden & Eliceiri, 2007) , allowing a wider choice of processing software.
Software packages for qualitative presentation of biofilm (confocal) images Currently, there are numerous software packages available that will allow processing of confocal image stacks to make 2-D data representations, or virtual 3-D representations such as animations. Probably the most used software package currently in the biofilm community is Imaris V R by Swiss company Bitplane AG (http://www. bitplane. ch). This commercial package was designed for taking confocal images and generates pseudo-3-D images with shadow projection, iso-surface presentation, cross-sections and 3-D animations (Figure  10(B),  and S1 ). However, it is rather costly which is why some research groups prefer to use alternative public domain software packages. Among these, is the popular image processing software ImageJ  (Abr amoff et al.
, 2004 ), an open system to which independent software developers can make plugins for particular purposes. Currently, more than 325 macros and 500 plugins are available at the ImageJ website  (Schneider et al. , 2012). Several of these deal with 3-D image processing and CLSM, as the free McMaster Biophotonics Facility (MBF) plugin collection, which can do most of what Imaris can although requiring more effort from the user. Another very popular free software (for noncommercial purposes) is the daime image processing package  (Daims et al. , 2006) , which can do both visualization of image data and some analytical processing. Contrary to the ImageJ plugins, this package was created with biofilm images in mind and can do both their visualization and analysis.
Software packages for quantitative analysis of biofilm (confocal) images To compare biofilm images, a quantitative measurement of the images, preferentially followed by statistical analysis, can remove the unintentional possibly biased interpretation of the researcher  (Kuhn, 1970). Various objective parameters can be used to quantitatively describe biofilms, ranging from the obvious like biomass, biofilm (maximum) height to subtler ones like roughness coefficient and fractal dimension. Common to all is that raw data is the confocal microscopy stack images, a range of individual images (slices) recorded from focal planes in specified positions. Each slice can be represented by one image if only one color is recorded, or several images, one for each channel, if more fluorescence colors are used.
The biofilm is represented by pixels having a fluorescence signal above a certain value, the threshold. All gray values below the threshold are regarded as noise. Several analysis packages are available, each with advantages and drawbacks. One of the most widely used is the Comstat package, originally made as a MATLAB V R script  (Heydorn et al. , 2000b)  and later rewritten as a plugin (Comstat2) to ImageJ  (Vorregaard, 2008). Comstat2 is freely available from http://www. comstat. dk. Similar packages are the MATLAB script PHLIP  (Mueller et al. , 2006)  and ISA3D which is a compiled MATLAB script  (Beyenal et al. , 2004). Daime is the fourth alternative  (Daims et al. , 2006) , designed for presentation of the data and not dedicated to but capable of extracting quantitative data. It is also possible to use more general purpose software, e.
Harmonized vocabulary for data interchange The MIABiE consortium has prepared a set of guidelines for documenting biofilms experiments and data, namely the minimum information that should be reported to guarantee the interpretability and independent verification of experimental results, and their integration with knowledge coming from other fields  (Lourenco et al. , 2014). This practical and semantically structured vocabulary defines the fundamental concepts about experiment design, biofilm recovery, biofilm formation and biofilm characterization  (Sousa et al. , 2014). It is being used by biofilms-centered databases, such as BiofOmics and Morphocol (http://morphocol. org), and bioinformatics tools, such as the Biofilms Experiment Workbench (BEW) (see below).
Databases The implementation of publicly accessible databases is vital to disseminate results and promote data interchange. BiofOmics (http://biofomics. org) stands as the first database providing public Web access to biofilms experiments  (Lourenco et al. , 2012). Experiments are indexed by organism, method of analysis and tested conditions. Complementary, two recent resources tackle antibiofilm research, in particular the study of antimicrobial peptides against biofilms. The Biofilm-active antimicrobial peptides (BaAMPS) provides useful physicochemical data on peptides specifically tested against microbial biofilms (http://www. baamps. it/) (Di  Luca et al. , 2015). In turn, the Antimicrobial Combination Network represents synergistic and antagonistic effects of peptide-drug combinations (http://sing.
Computerized data operation and analysis BEW is the first software tool dedicated to biofilms data operation and analysis  (Perez-Rodriguez et al. , 2015). It supports structured and standardized documentation of experiments, statistical assessment of various analytical results, on-demand and Web-publishable experiment reporting, experiment publication/ retrieval in/from public databases, and comparison of results between laboratories. A novel general-purpose data representation format, the Biofilms Markup Language (BML), has been formalized for effectively promoting data interchange across resources and software tools. BEW is publicly and freely available at http://sing. ei. uvigo. es/bew.
Acknowledgements The authors would like to thank for permission to use illustrations:  Dr. Thierry Bernardi ,  Biofilm Control ,  Saint-Beauzire , France (Figure 3),  Dr. Bryan Warwood ,  Biosurface Technologies Corp, Bozeman, MT  (Figures 4 and5),  Prof. Andrew McBain ,  University of Manchester, UK (Figure 9 ),  Dr. Hana Turo  nov a,  University of Chemistry and Technology Prague, Czech Republic (Figure 10(C) ),  Dr. Pierluigi Aldo DiCiccio ,  Universit a Degli Studi Di Parma, Parma, Italy  ( Figure 11(A) ),  Professor Thomas Bjarnsholt ,  University of Copenhagen  ( Figure 10 (B,D)), and  Professor Alokmay Datta ,  Saha Institute of Nuclear Physics, Kolkata, India  ( Figure 12 ). The authors are grateful to  Prof. Gordon Ramage  for critical reading of the manuscript.
The magnitude of noises is about 10 -15% of the useful signal. Simulation time is increased to 20 seconds. 3) Why do the authors use a (so small) sampling time (2 µ s)? Sampling time depends on the type of the inverter the power system uses and influences the accuracy of model calculation. In our realistic simulations we verify the efficiency with three-phase IGBT switches which are based on a Universal Bridge Block of Matlab. 2 µs is the typical value for similar test schemes -please see, for instance, PMSM with speed regulation typing 'ac6 example' in Matlab command line. 4) Why do the authors use the same gains for the observers I (the new one) and II? Since the authors vary their gains as the simulation changes and I and II are different observers (there is no sense in considering the same gains), did the authors choose the best gains for II? The difference between both observers is in the parameter estimator only, in Observer I it is a DREM while for Observer II it is a standard gradient.
Differences in rates of branching were chosen to produce phylogenies while keeping species richness constant. For each type of tree, we simulated 10 trees each of 10, 20, 40, and 80 species to examine the predictive power of ES1 and ES2. Thus, 40 simulated phylogenies of each tree type were created, totaling 160 phylogenies. We explored other types of tree simulations (e. g. , more extreme branching rate of different time periods and different mean rates of branching and number of time steps), but all led to the same conclusions as those based on the four tree types presented here. Simulated phylogenies, their LTT plots, and the phylogenetic metrics measured on them are deposited in the Dryad Digital Repository: http://dx. doi. org/10. 5061/dryad. jd150 (Yguel et al. 2016). We described the LTT by fitting a curve, because this reduces a complex pattern to a few standardized parameters that can easily be interpreted and compared between branching patterns of communities of different species richness and mean age.
To find a 2 and a 3 , we solved the equation Z(t) p 0 1 a 2 X 1 a 3 Y, where Z(t) is N(t) 2 [ln(species richness)=t max ]t, X is t(t 2 t max ) and Y is t(t 2 2 t max 2 ). We used the same method for the second-order polynomial. All these and subsequent analyses were performed in R (R Core Team 2014; see supporting file S1 [supporting files available online] for code to create LTT plots and extract the third-order polynomial parameters). The third-order polynomials generally fit better than the second-order polynomials across all simulations, even penalizing for the number of predictor variables (R 2 and adjusted R 2 between 94. 9% and 99. 2%; supporting file S2). We note that fits of greater than thirdorder polynomials impeded simple descriptions of curve shape (see below) and thereby straightforward interpretation in terms of ancient versus recent diversification.
incorrect classifications). We compared the rate of good classification with those obtained on the basis of FDAs using either of the commonly used phylogenetic metrics: Faith's PD, mean pairwise distance (MPD), and mean nearest taxon distance (MNTD; see  Webb 2002). These metrics were measured using the R package picante (v1. 6-2; Kembel et al. 2010). Admittedly, PD, MPD, and MNTD were not intended to measure branching patterns. We hence additionally used Pybus and Harvey's gamma (2000) and Colless's index of phylogenetic tree imbalance  (Heard 1992 ; see also  Heard and Cox 2007). Strictly, Colless's index does not measure changes in diversification through time, but the method is nevertheless established in the literature as an approach to describe patterns of diversification (e. g. ,  Heard and Cox 2007).
Positive gamma suggests that the branching rate increases through time (i. e. , diversification is rather recent); negative gamma suggests that it decreases (i. e. , diversification is rather ancient). Colless's index is 0 for a perfectly balanced phylogeny in which all lineages diversify at the same rate, and it is 1 for a maximally imbalanced phylogeny. Gamma and Colless's indices were measured using the R package ape (v3. 0-11) and apTreeshape (v1. 4-5), respectively. Colless's index was standardized by 2=[(n 2 1)(n 2 2)] as in  Heard (1992)  with n as the number of terminal taxa. We recall that we created the same branching patterns for each level of species richness, and hence standardization of parameters for species richness was not needed. Finally, we used ANOVA to test the effect of phylogeny shape on ES1 and ES2.
To allow for comparisons with  Cadotte et al. (2009)  for the same system, we analyzed the data using the phylogeny successfully applied by  Cadotte et al. (2009) , hereafter referred to as "Cadotte phylogeny. " This Cadotte phylogeny was obtained using maximum likelihood analysis of DNA sequences from four genes: matK, rbcl, ITS1, and 5. 8s. We transformed it into an ultrametric phylogeny as done in  Cadotte et al. (2009)  using the R package ape (v3. 2). This function estimates the node ages of a tree using a semiparametric method based on penalized likelihood (Sanderson 2002). The branch lengths of this phylogeny from  Cadotte et al. (2009)  are derived from mean numbers of substitutions (i. e. , per site) between 0 (root) and 1 (present) and used as a proxy of time. We also used a more recent phylogeny from Zanne et al.
The ES1 and ES2 were often considered combined, because they do not represent two distinct variables measured independently but parameters of a single function. Together, ES1 and ES2 describe the bivariate relationship between time and branching in the same phylogeny of the same species within the same community. We always found the residuals of our simple and multiple regression analyses to approach normality and homoscedasticity and hence used a Gaussian error distribution. All phylogenetic metrics measured on the basis of simulated phylogenies and on the basis of data from  Cadotte et al. 2009  are deposited in the Dryad Digital Repository: http://dx. doi. org/10. 5061/dryad. jd150 (Yguel et al. 2016).
Acknowledgments This work was a product of the working group sCoMuCra (Communities, Museums, and  Cradles of Biological Diversity) , including a postdoctoral fund to B. Y. , both funded by the  Synthesis Centre of the German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig  ( DFG FZT 118 ). B. Y. also benefited from a postdoctoral fund from the  French national research agency LabEx  ( ANR-10-LABX-0003-BCDiv ), in the context of the " Investissements d'avenir "  ANR-11-IDEX-0004-02. We acknowledge valuable comments from  M. Cadotte  (who also kindly provided data),  M. Leibold ,  P. Venail ,  J. Wiens , and an anonymous referee. Author participation: All authors worked on the concept and the first design of the analyses. H. J. , B. Y. , and S. P. designed the analyses, and B.
4 and 2. 0 TeV are explored in a final state containing one charged lepton and jets (referred to as lepton+jets in the following), while the all-hadronic final state is used for very high H + masses, 1. 5 to 3. 0 TeV, with a jet substructure technique to reconstruct the top-quark decay products in one single large-radius jet. The two s-channel analyses are reinterpretations of recent searches for W → tb in ATLAS  [22, 23]. Based on dedicated simulations of the H + → tb signal and a reinterpretation of the data, upper limits are derived for the s-channel production of a charged scalar particle decaying to tb. The paper is organised as follows. Section 2 describes briefly the ATLAS detector, then summarises the data and the samples of simulated events used for the analyses. Section 3 describes the reconstruction of objects in ATLAS.