dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors would like to thank the  French National Meteorology  for providing the BAPMoN/WMO-GAW data and the  French National Forest Research and Development Department of the National Forest Office (ONF)  for supplying CATAENAT/RENECOFOR/ ICP-Forest data. We are grateful to the many MERA, BAPMoN and CATAENAT site operators and to the analytical laboratory staff for supplying qualitative databases. We gratefully acknowledge the helpful discussions we had with Olivier Roustant from the  Laboratoire d'informatique, de mod elisation et d'optimisation des syst emes, Mines Saint-Etienne/CNRS, Saint-Etienne. This work was made possible by financial support from the  French Environmental Agency (ADEME)  and the  French Ministry responsible  for the environment through the  PRIMEQUAL programme  (n  0962C0014 ).
This feature is of particular interest for the production of cofactor Coenzyme-A in this species, since most enzymes from the biosynthetic pathway are missing in all species. Nevertheless, there is little metabolic experimental data available for the three species, which makes the reconstruction of a reliable metabolic model an extremely time-consuming work. Together with the fact that these mycoplasmas are generally grown in complex media, with high serum concentrations, we needed additional experimental data in order to compare the in silico reconstructed metabolic networks with the in vivo metabolic characteristics of the three species. For this reason, we also performed nuclear magnetic resonance spectroscopy (NMR) analyses to detect metabolites consumed and produced in both complex and defined media.
Network reconstruction and refinement We reconstructed the metabolic networks for all available strains isolated from swine so far of M. hyopneumoniae (pathogenic strains 232, 7422, 7448, 168 and non-pathogenic strains J and 168L), M. hyorhinis (HUB-1, GDL-1, SK-76 and ATCC 17981 strain BTS7) and M. flocculare (ATCC 27716 and ATCC 27399). The semiautomated reconstructions were generated by the Pathologic tool from the Pathway Tools software  [41]  using the complete genomes available online. From now on, the species strains will be abbreviated according to Table  1. Pathway Tools automatically associated genes with reactions, based on the annotation names, Gene Ontology (GO) terms and enzyme code (EC) numbers contained in the GenBank files. The software assembled reactions into pathways by comparing them with the reference database, MetaCyc.
The software automatically added missing reactions from the reference database to the model, creating many orphan reactions (reactions that do not have an enzyme associated). In a gapfilling step, we used the Pathway Hole Filler tool, included in the Pathway Tools software, to identify possible candidate genes associated with these orphan reactions. Orphan reactions in the metabolic network were allowed only to allow known functionalities of the organisms or biomass production in the reconstructed models. We incorporated the minimal number of orphan reactions into the models to avoid adding a behavior that was never described. In our work, the refinement of the networks was made in a subsystems approach  [42]  simultaneously on all organisms. As a result of different genomic annotations, slightly different reactions arose throughout the automatic models.
These reactions were checked for consistency, and after validation of only one of them, the duplicates were deleted. Gene-Protein-Reaction (GPR) associations were systematically validated or included based on experimental data, information provided by the Pfam functional domain database  [43] , synteny analysis (gene context) and reciprocal sequence homology (BLASTp) searches, using at first high E-value cutoffs of at least 10 -10  [44]. Whenever no hits were achieved with these parameters, more relaxed parameters were assumed (E-value cutoffs of at least 10 -5 ). Homolog proteins with equivalent functional domains were assigned as isozymes to a particular reaction, while proteins with distinct functional domains were assigned as subunits of a multi-protein complex. Non-metabolic reactions such as DNA polymerization, protein synthesis and RNA synthesis were explicitly deleted from the network but implicitly included in the biomass assembly.
Generic reactions were either specified or excluded from the models. The resulting models were further refined in accordance with a detailed protocol from Palsson and Thiele  [45]. Reactions were computationally balanced for mass and charge, while cofactor usage was determined based on literature data for closely related species  [46] [47] [48]. Reaction directionality was thermodynamically checked and validated. Reaction directionality was determined based on the component contribution method  [49, 50] , which extends the group contrib ution method  [51]  and achieves a significant improvement in the accuracy of the estimations of standard Gibbs energies. An online search and calculation interface called eQuilibrator along with metabolite and reaction thermodynamic databases are available at  [52, 53].
Heuristic rules were used to improve the directionality assignment. We analyzed the network topology to identify compounds that were only produced or consumed in the network, the so-called dead-end metabolites (DEM). Whenever a DEM was found, we either (i) added or validated an orphan reaction to reconnect it to the rest of the network, or (ii) removed the reaction from the model when both substrate(s) and product(s) were disconnected from the network and/or did not affect the overall metabolism. Transporters were predicted at first from genome annotation using the Transport Identification Parser, from Pathway Tools  [54]. Transporters were predicted mainly based on sequence similarity to other known transporters, since experimental data for the three mycoplasmas were not available. Specific transport reactions and exchange reactions required for production of biomass components were manually added to the final version of the networks.
NMR analysis Sample preparation was as follows: 60 μL of a mixture containing 1. 25MKH 2 PO 4 phosphate buffer (pH=7,4) in D 2 O with 2 mM NaN 3 and 0. 1 % trimethylsilyl propionate (TMSP) was added to 540 μL supernatant samples. Both solutions were mixed thoroughly and 550 μL were then transferred to 5 mm NMR tubes and sorted in 96-tubes racks. All NMR experiments were carried out on a Bruker 800 MHz NMR spectrometer equipped with a 5 mm TXI probe and a SampleJet autosampler, enabling highthroughput data acquisition for large collections of samples. The temperature was controlled at 27 °C throughout the experiments, and the samples were kept refrigerated at 4 °C during a waiting time of less than 24 h in the autosampler, before the NMR analysis. Standard 1 H 1D NMR pulse sequence nuclear Overhauser effect spectroscopy (NOESY) with z-gradient and Carr-Purcell-Meiboom-Gill (CPMG) with water presaturation (Bruker pulse program noesygppr1d and cpmgpr1d) were applied on each sample to obtain the corresponding metabolic profiles.
A total of 128 transient free induction decays (FID) were collected for each experiment with a spectral width of 20 ppm. The relaxation delay was set to 4 s. The NOESY mixing time was set to 10 ms. The total acquisition time of each sample was 12 min 34 s. The CPMG spin-echo delay was set to 300 ms, for a total filter of 77 ms, allowing an efficient attenuation of the lipid and protein NMR signals. The 90°p ulse length was automatically calibrated for each sample at around 10 μs. Data processing: All FIDs were multiplied by an exponential function corresponding to a 0. 3 Hz linebroadening factor, prior Fourier transformation. 1H-NMR spectra were manually phased and referenced to the TSP signal (δ= -0. 016 ppm at pH 7. 4) using Topspin 3. 1 (Bruker GmbH, Rheinstetten, Germany). Extraction of a data matrix for multivariate statistical analysis from the 1 H NMR profiles was done using the Statistics toolbox of AMIX (Bruker Biospin).
Spectra were integrated from 0. 3 to 10 ppm at a step of 0. 01 ppm but excluding the regions of residual water at 4. 68-4. 88 ppm. No normalization of the intensity was performed. The resulting data matrix contains 947 NMR variables. Multivariate data analysis: Principal component analysis (PCA) and hierarchical clustering analysis (HCA) were performed using SIMCA-P 13 (Umetrics, Umea, Sweden) with scaling based on the Pareto method. Metabolites identification and quantification: Metabolite identification was achieved by comparing spectra with databases such as HMDB  [66]. Identification of the metabolites was further verified with homonuclear and heteronuclear 2D NMR experiments such as 1 H -1 3C HSQC, 1 H-1 H TOCSY and J-resolved experiments. Absolute quantification of the metabolites was performed using Chenomx NMR Suite (Chenomx Inc.
Model reconstruction and refinement Based on the published genomes of 6 strains of M. hyopneumoniae, 4 strains of M. hyorhinis and 2 strains of M. flocculare, we reconstructed 16 genome-scale metabolic models: one model for each strain separately, one for each species and a pan-reconstruction for all three species (Table  1 ). The semi-automated reconstructions were manually refined according to the description given in the "Methods" section. The Pan-swine Mycoplasma network (representing the merge of all strains, and called pan-network) was initially composed by 829 reactions. Duplicate reactions arose from the fact that Pathway Tools is based only on the annotations contained in the GenBank files. We removed generic and duplicate reactions from the models and replaced them with the specific and validated ones.
Metabolomics of swine mycoplasma NMR analysis of the culture media was performed in order to detect possible differences in the metabolism of the three species. All data measured in defined and complex media can be seen in Additional file 1: Tables  S6a  and S6b. The major differences in both complex and defined media were related to the metabolism of pyruvate (Fig. 6 ): M. hyorhinis produced higher quantities of pyruvate at the end of 48 h in complex medium. As a result, pyruvate conversion to acetate was detected in low quantities for this species in both media. M. hyopneumoniae and M. flocculare, on the other hand, produced higher amounts of acetate (this was even more pronounced for the growth of M. hyopneumoniae in complex medium). Low amounts Fig. 5  Cultivation curves in defined and complex media by species.
Selected species and strains, abbreviation, NCBI accession numbers and experimental availability of strains Strains available only in Brazil are protected strains which are property of the government and cannot be used outside authorised laboratories. Strains available in France were purchased from the ATCC repository; these strains were also available for testing in the laboratory in Brazil b : M. hyorhinis strain GDL-1 was retrieved from a contaminated cell line and, to our knowledge it has never been reported neither as pathogenic nor as an attenuated strain c : M. hyorhinis strain ATCC 17981 genome is available online but not published in any paper up to now. (source: http://genomeportal. jgi. doe. gov/Mychy1/Mychy1. info. html).
Acknowledgements This work was supported by grants  CAPES-COFECUB 782/13. MGF's doctoral fellowship was funded by the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7 /2007-2013) / ERC  grant agreement no. [247073]10. SGM was the recipient of a CAPES doctoral fellowship. The metabolomic analysis was carried out in the  Centre de RMN à Très Hauts Champs (CRMN-ISA-Lyon)  with financial support from the  TGIR-RMN-THC   FR3050 CNRS.
repeatmasker. org/) [87]. Repeat families with 10 or more elements identified by RepeatScout were annotated and classified into categories based on the presence of protein domains (BLAST  [88] ). Structural features including Long Terminal Repeats (LTRs) and Terminal Inverted Repeats (TIRs) were verified using the EMBOSS  [89]  software package. Sequences with no known proteins or structural features were grouped into the unclassified category. Identification and annotation of protein-coding genes were performed using the JGI Annotation Pipeline, which takes multiple inputs (scaffolds, ESTs, and known genes), runs several analytical tools for gene prediction and annotation, and deposits the results in the JGI fungal genome portal MycoCosm (http://jgi. doe. gov/fungi) [90]  for further analysis and manual curation.
Several gene-prediction programs falling into three general categories were used to annotate the repeat-masked assembly as described by Ohm et al. [19]. The resulting set of putative genes was then filtered for the best models based on EST and similarity support to produce a nonredundant representative set. This representative set of filtered gene models from the automated annotation pipeline was subject to further analysis and manual curation as described by Goodwin et al. for Z. tritici  [17]  and by Ohm et al. [82]  for more recently sequenced species. Measures of model quality included proportions of the models complete with start and stop codons (88% of models), those that were consistent with ESTs (30% of models) and those supported by similarity with proteins from the NCBI NR database (74% of models) as summarized in S8 Table.
Functional annotations for all predicted gene models were made using SignalP  [91] , TMHMM  [92] , InterProScan  [93] , and BLASTp  [88]  against the nr, SwissProt (http://www. expasy. org/sprot/), KEGG  [94]  and KOG  [95]  databases as described by Ohm et al. [19]. Multigene families were predicted with the Markov clustering algorithm (MCL)  [96]  to cluster the proteins using BLASTp alignment scores between proteins as a similarity metric. Functional annotations are summarized in S9 Table. Manual curation of the automated annotations was performed using the web-based interactive editing tools of the JGI Genome Portal to assess predicted gene structures, assign gene functions, and report supporting evidence. Gene models predicted by the JGI annotation pipeline were also analyzed using the program Blast2GO  [97]  with an E-value of < 10 -6.
Blast2GO assigns GO terms based on the BLAST definitions. Comparisons between groups of genes for enrichment of GO terms were done by using Fisher's exact test implemented in the Blast2GO program. Potential secreted proteins were identified with a python script made to run all gene models through SignalP 3. 0  [98]  and subsequently filtered for proteins that had no transmembrane domains, no signal anchor motifs, were fewer than 300 amino acids in length and had at least four cysteine residues. The gene models that fulfilled these criteria were considered as potential SSPs. For re-sequencing of isolate CIRAD 139A, a paired-end library was made using the standard Illumina library prep protocol with NEB reagents. Average insert size of the library was 272 base pairs. Sequencing was done on an Illumina GAIIx in one lane of a 54-cycle paired-end run using 36-cycle version 5 SBS Kits.
Repetitive element analysis For each repetitive element family, a subset of elements with lengths within 50% of the longest element was aligned using clustalX  [99]. These alignments were submitted to RIPCAL  [28]  to determine the dinucleotide bias observed in repetitive elements. RIPCAL estimates 'RIP dominance' for each dinucleotide containing a cytosine. It is the ratio of a given dinucleotide (e. g. , CA) to the sum of the other three dinucleotides (CG/CC/CT). To test for isochores in the P. fijiensis genome, a contiguous stretch of sequence with an arbitrarily chosen average GC content of less than 45% was categorized as an AT block. Custom python scripts were used to calculate the percent GC across the genome, to generate AT blocks and to calculate the average percent GC across the AT blocks and those fewer than 500 bp apart were merged into blocks of at least 1 kb in length, which were retained and analyzed for their composition and distribution of repetitive sequences and genes.
To estimate and compare the amount of RIP between the AT-rich blocks and the rest of the genome, a custom python script was written using a 500-bp sliding window with a step size of 100 bp. The amount of RIP was calculated as an index (CpA+TpG)/(ApC+GpT) and estimated separately for each of the AT-rich versus AT-poor regions in the genome. The RIP index measures the depletion of the RIP targets CpA and TpG; thus, lower values of (CpA+TpG)/(ApC +GpT) are indicative of a higher degree of RIP  [28]. To estimate the ages of transposon insertions, LTR retrotransposons were identified and annotated using the LTRharvest  [100]  and LTRdigest  [101]  modules in GenomeTools  [102]. LTR sequences from these elements were aligned using ClustalW  [99]  and manually curated to estimate the numbers of mutations that had accumulated over time.
All transition mutations were ignored in this analysis to remove the bias caused by RIP. Age of the LTR retrotransposons was calculated using the average rate of 1. 09 × 10 -9 substitutions/site/year as proposed for fungal sequences  [103]. To test whether the transposons in the P. fijiensis genome were unique, a comparison was made to transposable elements (TEs) in the genome of C. fulvum, the only other sequenced fungus in the Capnodiales with an expanded genome. RepeatMasker  [85]  was used to mask the C. fulvum genome using the repeat database from the P. fijiensis genome. The resulting file was parsed using the 80:80 rule of Wicker et al. [27] , i. e. , 80% identity across 80% length to identify repeats in common between the two genomes. Another run was done at a 70:70 cutoff to allow for greater divergence generated by RIP.
Phylogenetic analysis Phylogenetic analysis showing the placement of species within the Capnodiales with expanded genomes was done using Internal Transcribed Spacer regions (ITS). DNA sequences were downloaded from GenBank with the following accession numbers: AF181692 for Z. tritici, EU514233 for P. eumusae, EU514265 for P. musae and EU514248 for P. fijiensis or obtained from genome data available at the Fungal Genome portal at JGI. Sequence alignment was done using MUSCLE  [112]  and the phylogenetic tree was generated with MEGA 6. 0  [113]  using a Maximum Likelihood statistical method and the Tamura 3-parameter substitution model. Support for the nodes of the tree was estimated by bootstrapping with 1000 replications.
Whole-genome comparisons and synteny analyses Two tools, Circos  [115]  and MUMmer  [116] , were used for structural analysis of the P. fijiensis genome. A nucleotide-based similarity search was done between the masked P. fijiensis and Z. tritici genomes and visualized using Circos  [115] , whereas protein comparisons between the masked genomes were done using Promer  [116]. Proteins with greater than 60% identity were reported. Proteins in P. fijiensis and Z. tritici with at least 50% amino-acid identity and match length were grouped as orthologs using OrthoMCL  [117]  and synteny blocks were determined using Orthocluster  [118]. The Z. tritici protein dataset also was compared to two other phylogenetically distant Dothideomycetes in the order Pleosporales, P. nodorum and P. tritici-repentis.
Gene diversity within each population (H S ) in total and by locus was calculated using GenAlEx 6. 4  [123]. Total diversity over the entire sample (H T ), mean gene diversity within populations (H S ), genetic differentiation among populations (G ST ) and the corrected, standardized measure of genetic differentiation (G" ST ) were calculated using GENODIVE Beta version 2. 0  [124]. In all cases, H T and H S refer to the unbiased estimates as developed by Nei  [125]. Pairwise estimates of G" ST and of Jost's differentiation (D)  [126]  also were calculated with GENODIVE. Multilocus haplotype diversity was calculated with multilocus (http://www. bio. ic. ac. uk/evolve/ software/).
Acknowledgments This research paper is dedicated to the memory of  Mr. Romano Orlich MSc -deceased December 24, 2015-previous president of the  National Banana Cooperation of Costa Rica (Corbana) , who greatly motivated our interest in banana research. We acknowledge the MusaForever strategic alliance between  Wageningen University and Research (WUR)  and  Embrapa , the company of technological innovation linked to the  Ministry of Agriculture, Livestock and Food Supply (Mapa) in Brazil  for facilitating the exchange of CFF, SG and MS. We acknowledge the  International Transit Center of Bioversity International at the Catholic University of Leuven, Belgium  for providing Calcutta IV and CIRAD930 germplasm and  Rahan Meristem Ltd. (Rosh Hanikra, Israel)  for continuous support by providing numerous Cavendish Grand Naine tissue culture plants.
The work conducted by the  U. S. Department of Energy Joint Genome Institute  is supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. This work was supported in part by  USDA CRIS  projects  3602-22000-015-00D  and  3602-22000-017-00D. Work at Plant Research International was partly funded by The  Dutch Dioraphte VSM  grant  14. 03. 01. 00. UNALMED and CIB work was supported by the  Colombian Administrative department of Science, Technology and Innovation -Colciencias  contracts no  0031-2013  and No  018-2015 , and by the  National University of Colombia at Medellín. CDT's research work was supported by scholarship 187781 from the  National Council of Science and Technology (CONACyT)  from  Mexico. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Such a review has been the objective of the APPRAISAL EU FP7 project (http://www. appraisal-fp7. eu) with the main goal to identify methodologies and their limitations and to propose possible key areas to be addressed by research and innovation on the basis of this review. To this end, a structured online database has been developed in collaboration with experts involved in the design of AQP. The following topics were considered: (1) synergies among national, regional and local approaches, including emission abatement policies; (2) air quality assessment, including modelling and measurements; (3) health impact assessment approaches; (4) source apportionment; and (5) uncertainty and robustness, including Quality Assurance/Quality Control (QA/QC). The current work relies on the APPRAISAL database which currently totals 59 contributions from 13 MS.
Two groups were distinguished to refine the analysis: the stakeholders involved in the design of "air quality plans" (AQP) and groups involved in "research projects" (RP). While AQP, which represent 60% of the database information coming from 10 MS, is representative of current practices in the decision process, RP (30% of the database contributions, coming form 7 MS) are usually assumed to be based on the most updated methods. Countries represent the study area in 20% of cases, regions in 25% and agglomeration or urban level in 30% of the cases. The remaining percentage refers to other types of focus which could not be classified in these categories. Note that the database (http://servizi. appraisal-fp7. eu/appraisal/faces/ pages/public/query. xhtml) is still open to contributions. An example of the current status (September 2015) is presented in Fig.