dataesr/scientific-paragraphs-categorization huggingface dataset:
The model estimated SOC includes organic C stored in soil and litter pools. We assumed that Rh is the dominant pathway for Global Biogeochemical Cycles 10. 1002/2014GB005021 carbon loss (equation (  1 )) and determined the MRT (equation (  2 )) at annual time step as the ratio of SOC stock to annual Rh, which was used in this study to assess the stability of SOC pool suggested by the MsTMIP multimodel ensemble. C loss ¼ Rh ¼ kÂSOC (1) MRT ¼ 1 k ¼ SOC Rh ( 2 ) where k is the SOC decay rate and MRT is the mean residence time across all the soil C pools and their respective residence times. In reality, the k value is affected by Rh, and other soil C loss ways such as fire emission, soil erosion, disturbance impacts on SOC as well as on dead C pools, and C export to river headstreams. However, except SOC decomposition, no other information is available in MsTMIP data products for identifying the proportions of these terrestrial C losses directly from the soils.
Therefore, we assumed that Rh is the only outgoing SOC flux in all TBMs to calculate SOC residence time. The HWSD data set used in this study is modified by following the methods in Todd-Brown et al. [2013]. Soil organic C for the topsoil and subsoil was calculated by using bulk density and organic carbon content and removing gravel content. The bulk density data from HWSD for Andosol and Histosol was replaced with data from  Batjes [2009]. We examined temporal changes of modeled global SOC stocks as difference between the year 2010 and 1901 due to small interannual variability in SOC. However, the changes of C fluxes (i. e. , NPP and Rh) are quantified as the differences of decadal average value between the 2000s and 1900s given large interannual variability in C fluxes. The consistent design of model experiments made us able to distinguish and quantify the relative contributions of climate, land use, atmospheric CO 2 concentration, and nitrogen deposition to SOC and MRT changes by calculating the differences between experiments (Table  1 ).
We used Pearson's correlation to evaluate relationship between the modeled Rh and climate variables, and the NCAR Command Language (NCL) is used to generate the Taylor diagram. Global Biogeochemical Cycles 10. 1002/2014GB005021 HWSD across the globe. However, compared to the NCSCD database in 1 m depth, neither HWSD nor TBM ensemble could capture the high SOC density (larger than 20,000 g C m À2 ) in high-latitude regions (Figure  2 ). Spatially, SOC estimates from individual models are poorly consistent (grid to grid correlation, r < 0. 4) with HWSD data at half-degree grid level (Figure  3 ). A few of them show similar amplitude of variation as HWSD (i. e. , normalized standard deviation, grid-wise estimates deviated from mean value, is close to 1). However, it should be noticed that HWSD is a statistical model result so it cannot really be used to infer that modeled patterns are wrong since the geostatistical method used in HWSD could also have systematic errors.
Acknowledgments Funding for the  Multi-scale Synthesis and Terrestrial Model Intercomparison  Project (MsTMIP; http://nacp. ornl. gov/MsTMIP. shtml) was provided through  NASA   ROSES grant   NNX10AG01A. Data management support for preparing, documenting, and distributing model driver and output data were performed by the Modeling and Synthesis Thematic Data Center at  Oak Ridge National Laboratory  (http://nacp. ornl. gov),with funding through  NASA   ROSES grant   NNH10AN68I. Finalized MsTMIP data products will be archived at the ORNL DAAC (http://daac. ornl. gov). This is MsTMIP contribution 4. Acknowledgments for specific MsTMIP participating models are as follows. ( 1 ) Biome-BGC. Biome-BGC  code was provided by the  Numerical Terradynamic Simulation Group at University of Montana. The computational facilities were provided by  NASA Earth Exchange at NASA Ames Research Center.
In comparison with other castes (workers, soldiers, primary reproductives), only a few studies focus on determining the genes expressed in neotenic reproductives of termites  [15, 28, 29]. The present study compares the transcriptome content obtained from 13 nymphoid neotenic females of three Reticulitermes species: two West European species, R. grassei and R. lucifugus  [30] , and one North American species, R. flavipes, that has been introduced to France  [26]. Eleven out of the thirteen transcriptomes analyzed in this study (i. e. , those obtained in R. flavipes and R. grassei) were obtained in a previous study  [31]. This dataset was used for SNP detection and population genomics inference only  [32, 33]  and no gene content and functional analysis was performed. Using the same protocol, two additional transcriptomes were generated from nymphoid female neotenics collected in two distinct colonies of R.
ORF detection Complete and 5'-or 3'-truncated open reading frames (ORF) were detected using Prodigal software for metagenomic data  [38]  using standard genetic code. ORF with a stretch of N (undetermined nucleotides) inside the sequence were not discarded. When several ORF were detected on the same contig, only the longest was kept since it would more likely correspond to a true protein. The software Cd-hit  [39]  was used to remove ORF redundancy from our dataset by detecting sequences showing 100% identity in homologous regions.
Orthology prediction BLAST-based pairwise orthologs relationships between R. grassei-R. flavipes, R. grassei-R. lucifugus, and R. flavipes-R. lucifugus species pairs were first assessed by InParanoid 4. 1 program using default parameters  [40]. Non-redundant translated contigs of the 3 species displaying length > 100 bases were used for that purpose. MultiParanoid  [41]  was then used to analyze the orthology relationship of gene clusters between the 3 species.
Taxonomic assignation BLAST results from the annotation step were parsed to retrieve sequence identifier (GI) and NCBI taxonomic identifier (TaxID) from the NCBI database (ftp://ftp. ncbi. nih. gov/pub/ taxonomy). Each contig was assigned to 5 taxonomic ranks (i. e. , superkingdom, kingdom, phylum, genus and species). Contigs were assigned to Bacteria when the indicated superkingdom was 'Bacteria'; they were assigned to Termites when the superkingdom was 'Eukaryota' and the kingdom was 'Metazoa'; they finally were assigned to protists when the superkingdom was 'Eukaryota' and when the kingdom was not 'Fungi, Viridiplantae and/or metazoa'. For protists, downstream taxonomic ranks (phylum, genus and species) were inspected manually to verify that each of them corresponded to a protist taxon.
Functional annotation Amino acid homologies of the non-redundant predicted ORFs were analyzed using BLASTp program  [42]  against the Genbank nr database (March 1, 2011). The first BLAST hits were kept and the minimum E-value was set at 0. 001. HSP length cut off was 33 and the lower capacity filer was enabled. GI identifiers from BLAST results were used to retrieve UniProt IDs from the PIR Protein Sequence Database, the latter served to associate GO terms with our predicted ORFs. In addition, known protein signatures were detected using the software InterProScan  [43]  based on InterPro collection databases. Annotation steps were performed with Blast2go software V. 2. 2. 6  [44]  using default parameters. GO terms analysis was performed on the three termite species independently. Based on the KEGG metabolism pathways database V.
64. 0  [45, 46]  implemented in Blast2go, GO terms of biological processes were used to retrieve contigs belonging to the starch and sucrose metabolism process (map00500). GO results were displayed at GO level 2. A GO level referred to the hierarchical structure of the Gene Ontology as the number of GO terms between a given term and the Root Term of the ontology. Contigs assigned to a function related to the starch and sucrose metabolism process were further analyzed for the presence of hallmarks of Carbohydrate-Active Enzymes (CAZ) domains using CAZymes Analysis Toolkit (CAT)  [47]  with the CAZy database updated on 09/20/2013  [48]. In this software, the assignment method is based on both similarity search on proteins sequences and the presence of Pfam conserved domains (option 'Pfam rules based annotation').
Sequencing, assembly and annotation of transcriptomes The numbers of illumina reads obtained for all of the 11 termite samples are indicated in Table  1. For each termite species, reads from conspecific individuals were pooled together before assembly (N = 9 for R. grassei, N = 2 for R. flavipes and R. lucifugus). The obtained species-level assemblies were comparable among species and exhibited high N50 values (Table  2 ). In total, 64,328, 65,814 and 79,404 non-redundant ORF were predicted in R. flavipes, R. lucifugus and R. grassei, respectively. As expected with non-model organisms lacking complete and well-annotated genome sequences, only a third of ORF showed a significant BLAST hit in the nr protein database. After the functional annotation performed with Blast2Go program, only a small fraction (6-8%) of the initial ORF set could be assigned with one or more GO terms (Table  2 ).
Taxonomic assignment Table  3  shows the taxonomic distribution of the contigs displaying a significant BLAST hit of their coding sequence against the nr protein database. In total, 41,287 contigs were assigned. Most of them (94. 9%) were assigned to the termite genome, whereas the remaining contigs were assigned to diverse lineages of microorganisms (i. e. protists, bacteria, archeae, virus) (3. 9%), fungi (0. 9%) or plants (0. 4%). Contigs assigned to fungi and plants most likely represent environmental contaminations since no endosymbiotic association has been described so far between these organisms and Reticulitermes termites. Most contigs assigned to microorganisms were probably expressed by diverse microorganisms living in the hindgut of Reticulitermes termites. These well-known microbial communities are composed of protists (two main lineages: Parabasalia and Oxymonadida), Bacteria (the most abundant: Spirochaetes, Bacteroidetes, Firmicutes and Elusimicrobia), methanogenic Archeae (Methanobacteriaceae family) and bacteriophage virus infecting Spirochaetes  [3, 55, 56].
Several reasons could explain the low proportion of microbial contigs in our dataset. First, contigs assigned to prokaryotes were particularly scarce (< 1% for the 3 termite species), probably because the cDNA library protocol underwent an enrichment of mRNA based on the existence of poly-A tails, which are mostly absent in prokaryotic transcripts. Second, as expected with non-model organisms, which lack complete and well-annotated genome sequences, expressed genes in gut microbial communities of termites have not been fully characterized yet. Therefore, the genomic database is likely incomplete and could thus result in an underestimation of microbial genes. Third, all transcriptomes were generated from nymphoid neotenic females. Like other castes or developmental stages in subterranean termites, reproductive castes do not necessarily feed on the wood themselves and instead are fed by workers who provide them with nutrient-rich salivary trophallactic transfers  [57].
Contigs putatively associated with wood-degradation enzymes Transcriptome annotation was used to retrieve contigs associated with functions belonging to the starch and sucrose metabolism. Some of these may be involved in cellulose degradation   ). These three categories of enzymes work synergistically to efficiently degrade chains of cellulose. Whereas EGs and BGs are quite common in microorganisms, animals and plants, CBHs are apparently more rare and appear to be restricted to bacteria, fungi and protists  [59, 60]. In our dataset, 9 contigs were assigned to putative EGs and 11 contigs were assigned to putative BGs (Fig  6 ). However, we found no evidence for the presence of genes encoding CBHs. Using CAZymes Analysis Toolkit  [47] , 9 families of putative genes of Carbohydrate-Active Enzymes (CAZy) were detected in the 3 transcriptomes (Table  4 ): 5 glycoside hydrolases (GH1, GH9, GH13, GH37, GH45), 2 glycoside transferases (i.
e. , GT3, GT35) and 2 carbohydrate-binding modules families (i. e. , CBM6, CBM48). Genes of a same GH family are usually considered to share not only structural motifs and the catalytic machinery, but also an evolutionary origin  [61]. Among the putative GH genes detected in our analyses, GH1 represents a single family of BGs, whereas GH9 and GH45 are two families of EGs. Previous studies show GH1 and GH9 to be mostly expressed by the genome of termites either in their salivary glands and/or in the hindgut  [62]. However, genes encoding GH45 can be expressed by both the hosts and their symbiotic protists or prokaryotes living in the hindgut. In addition, CBMs are usually considered to be expressed by microorganisms only (Watanabe & Tokuda 2010). Therefore, the detection of putative genes encoding GH45 and CBM in our dataset suggests that the gut microbial community of neotenics may play a role in the synthesis of the enzymatic system involved in the degradation of cellulose.
Conclusion Comparison of a set of assembled transcriptomes of nymphoid neotenic reproductives was performed from 13 colonies belonging to 3 related termite species based on high throughput  Illumina sequencing. Intraspecific variation was addressed by pooling two to nine individuals per species. As expected with non-model organisms, a large fraction of contigs had no detectable homologs in the public database. The majority of recovered transcripts had a termite origin, although transcripts from microorganisms provided evidence for the presence of an active gut microbiome in this non-wood feeding life stage. These transcripts were indeed over-represented in starch and sucrose metabolism pathways, and some of them are likely to encode enzymes involved in cellulose degradation.
Acknowledgments We would like to thank  Simon Dupont  and  Sylvain Guyot  for termite sampling,  Vincent Ranwez  for help with the NCBI taxonomy database and  M. Rivera  for English revisions. Analyses largely benefited from the ISEM computing cluster platform with the help of  Khalid Belkhir. We are also grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees  for providing computing and storage resources. This does not alter our adherence to PLOS ONE policies on sharing data and Materials.
coli (EHEC), enteropathogenic E. coli (EPEC), enterotoxigenic E. coli (ETEC), extra-intestinal pathogenic E. coli (ExPEC), and uropathogenic E. coli (UPEC). Mammary pathogenic E. coli (MPEC) has been proposed as a new E. coli pathotype  [4]. Distinct subspecies or phylogroups of E. coli have long been acknowledged  [7] [8] [9] [10]  and strains of the different phylogroups appear to differ in their ecological niches, and disease characteristics. At present, there is no published E. coli phylogeny that includes MPEC isolates in a comparative dataset that encompasses representative isolates of the various E. coli pathovars. The only published sequence based phylogeny regarding MPEC that we are aware of, uses MLST data and reports a single most parsimonious tree of a set of 165 MP trees and includes only one non-MPEC isolate in the analysis  [11].
We determined the genome sequences of the four MPEC strains isolated from transient (ECA-O157 and ECA-727) and persistent (ECC-1470 and ECC-Z) bovine mastitis. Roche/454 pyrosequencing, involving single or paired-end reads from the FLX sequencer, was used to determine the sequence of the four MPEC genomes. The genome sequence for ECA-727 did not involve paired end data. The sequences were assembled (De novo assembly with Newbler Software) into 1173, 539, 994, and 24 contigs for ECA-0157, ECA-727, ECC-1470, and ECC-Z, respectively. Genome annotation for the four MPEC strains was done by the National Center for Biotechnology Information (NCBI) Prokaryotic Genomes Automatic Annotation Pipeline (PGAAP) at http://www. ncbi. nlm. nih. gov/genomes/static/Pipeline. html. The whole genome shotgun projects for ECA-0157, ECA-727, ECC-1470, and ECC-Z have been deposited at DDBJ/EMBL/GenBank under the accession numbers AHHK00000000, NZ_AHHL00000000, NZ_AHHM00000000, and AHHN00000000, respectively.
We compared these four MPEC genome sequences to 56 complete E. coli genome sequences representing a combination of commensal isolates and the following ten pathotypes or pathovars: ABU (Asymptomatic Bacteriuria), AIEC, APEC, EAEC, EHEC, EPEC, ETEC, ExPEC, NMEC (Neonatal Meningitis), and UPEC. Also included were nine Shigella sp. isolates representing four different species. All of these 65 sequences were obtained from NCBI; their strain names, accession numbers, genome lengths and respective pathotypes are presented in S1 Table in the supporting information.
Recombination for genes in these clusters was assessed using a combination of three methods: (i) the Pairwise Homoplasy Index (PHI), (ii) the Neighbor Similarity Score (NSS), and (iii) Maximum χ 2. We excluded any cluster that showed evidence of recombination for any two of the three methods. Using this approach, we excluded 546 clusters (438 remained). We utilized two approaches to phylogenetic reconstruction. The first was a gene tree consensus approach. Here, phylogenetic trees for each MCL gene cluster alignment were obtained using maximum likelihood (PhyML v. 3. 0), and a consensus of these gene trees obtained using the Triple Construction Method as implemented in the program Triplec  [23]. This procedure is based on the observation that the most probable three-taxon tree consistently matches the species tree  [24].
Gene Ontology (GO) terms were assigned to all genomes using Blast2GO v. 2. 5. 0  [29]. Relative enrichment (over representation) of GO terms among lineages was assessed using Fisher exact tests. The test was performed using the Gossip statistical package  [30]  implemented within Blast2GO. The false discovery rate (FDR) procedure of Benjamini and Hochberg  [31]  was used to correct for multiple hypothesis testing (FDR = 0. 05).
Therefore, Clermont determinations of MPEC as typically phylogroup A may not have been accurate. In the present study, we used a set of 438 orthologous loci to infer the evolutionary history of 60 E. coli strains, and nine strains of Shigella. This involved data for 56 complete genomes of E. coli taken from GenBank representing all the major phylotypes (with the exception of group C for which there were no complete genome sequences at the time this study was conducted) and pathovars, plus draft genome sequences of our four MPEC isolates. The comparative genome sequence data were first screened for putative recombinant loci, which were excluded in the phylogenetic reconstructions and therefore these orthologous loci should represent our best source of data to estimate the true evolutionary history of these set of isolates.
We reconstructed the phylogeny using both a concatenated dataset that included all 438 of these nonrecombinant orthologs (16,062 SNPs) (Fig. 1 ), and a consensus of the set of gene trees (Fig. 2 ). Both resulting trees were highly congruent and in agreement with earlier E. coli phylogenies (e. g. [36] ), provided convincing support for the monophyly of the various phylogroups. Both the concatenated and the consensus trees did not support MPEC monophyly or the monophyly of either transient or persistent phenotypes. Three these isolates (ECA-727, ECC-Z, and ECA-0157) originated from within the predominately commensal clade, referred to as phylogroup A. This classification is in accordance with our previous work, based on triplex PCR typing  [5]. In the consensus phylogeny, ECC-Z, and ECA-727 were imbedded within a clade of six other exclusively commensal isolates.
Since our isolate sample is limited, combined with the relatively low sequence coverage for two of our strains, we suggest there is little to be gained here by cataloging the presence and absence of virulence loci. Of somewhat greater possible interest, since our study does represent one of the first comparative genomic datasets involving MPEC strains, are the genes that are uniquely present in all four MPEC strains, or at least three of the four MPEC strains, and/or loci that are uniquely present in each of the transient or persistent strains (presented in S2 and S3 Table  respectively ). was only one hypothetical protein unique to all four MPEC strains. Loci uniquely present in at least 3 MPEC strains included glutamate decarboxylase, multidrug efflux system protein MdtE, RhsC core protein with extension, a sensor protein, phosphoenolpyruvate synthase, an oxidoreductase, transcription-repair coupling factor, putative hydrolase, carboxymethylenebutenolidase and a few hypothetical proteins (S2 Table ).
Statistical analysis Distributions of respondents according to their age and sex as well as standardized residuals (which are standardized differences between observed and theoretical effectives) were calculated using the R Software version 3. 1. 0 (R Core Team 2014) and two packages specialized in data analysis: the ade4 package  (Dray et al. 2007 ) and the FactoMineR package  (Husson et al. 2014). Multiple correspondence analyses (MCA) were performed with questions 1 to 10 except questions 5 and 6. In this type of analysis, associations between variables are uncovered by calculating the chi-square distance between different categories of the variables and between the individuals (or respondents). Data are represented as points in a Euclidean space to visualize associations between variables.
Acknowledgements The authors thank the following colleagues for helpful discussions:  W Verbeke  from  Ghent University, Belgium , as well as P Mainsant,  J-D Daudin ,  I Cassar-Malek ,  D Rémond ,  M Doreau ,  P Sans ,  D Bauchart ,  J Agabriel  and  B Picard  from  INRA, France.
The temperature at which this transition occurs is the midpoint temperature. Interestingly, if ambient temperature is higher than the membrane midpoint temperature, no phase transition occurs upon drying and rewetting, reducing cell death. However, when drying takes place at an ambient temperature below the midpoint temperature, cell death due to membrane transition is increased. A consequence of a change in membrane midpoint temperature can be seen in the change in vibration frequency (Figure  3B ). When the midpoint temperature is lowered one expects the temperature at which phase transition occurs to be lower resulting in a wider window of ambient temperatures higher then the midpoint temperature, increasing survival. Survival data indicates that this is what happens after desiccation of Sinorhizobium meliloti 1021.
The Culturable Fraction (CFU) Fraction 1: The CFU fraction is the smallest of the three recognized fractions of rewetted Sinorhizobium meliloti 1021 after desiccation. This fraction is so small [3. 1%,  Vriezen et al. , 2012]  that it falls within the error of measurement of the MC fraction (+10. 6%). Thus, even a doubling in the fraction of culturable cells would not be reflected in a significant change in dead and living cells. Even though, it is the culturable fraction that counts in formulations of rhizobia since non-growing but living cells do not contribute to nodule formation  [Basaglia et al. , 2007]. Therefore, understanding the conditions and cellular responses increasing this fraction remain of crucial importance. Even though no data exist on how osmotic stress and temperature affect the appearance of VBNC and MC fractions, more is known about these conditions in relation to desiccation survival.