dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016; Sirohi et al. , 2016 ). Here we focus on the structure of the mature form of serotype 1 of DENV, with PDB code 4CCT  (Kostyuchenko et al. , 2013) , and of the equivalent mature form of ZIKV, as given by one of the recently published structures, with PDB code 5IZ7  (Kostyuchenko et al. , 2016). Those two structures were shown to be very similar, with only small differences that will be discussed in light of their dynamics. A cartoon representation of ZIKV is given in Figure  1A. The DENV capsid shows the same architecture. The PDB file for 4CCT only contains Cα atoms. For consistency, we used Cα only representations of 4CCT and 5IZ7. We isolated from those two files all the Cα atoms of the viral capsid. For both viruses, we considered E protein in four different environments: isolated, MONO, (corresponding to chain A in the asymmetric unit of 4CCT and chain B of the asymmetric unit of 5IZ7), within the asymmetric unit, UNIT, within a raft, RAFT, and within the whole capsid structure, FULL.
Strain P4 was isolated from clinical mastitis in the UK  [24]  and has become largely accepted as a prototypical mastitis strain for E. coli mastitis research. Strain K71 was isolated from cow shed  [15]  and does not cause inflammation in the mammary gland in mice  [14]  or cows (S. Blum, personnal communication). This strain was included in the analysis along with 13 E. coli strains from other pathovars chosen to represent the different phylogroups described in the E. coli species (Table  1 ). The Escherichia fergusonii strain ATCC 35469 was chosen as an outlier in phylogenetic analyses. Genomic DNA sequences were downloaded from GenBank or described in previous reports  [14, 25]. Strains P4 (CIRMBP-993), B41 (CIRMBP-546), D6-113. 11 (CIRMBP-549) and D6-117. 07 (2IM-260) can be obtained upon request at the International Centre for Microbial Ressources -Bacterial Pathogens (CIRM-BP http://www6.
General genomic features Genome annotation was performed by AGMIAL, an integrated bacterial genome annotation system  [33]. Prediction of coding sequences used SHOW, the self-training gene detection software based on hidden Markov models  [33]. PlasmidFinder  [34]  was used to detect plasmid-associated nucleotide sequences. Plasmidassociated genes were next detected using a basic local TBLASTN search (identity cut-off 80%; HSP score coverage cut-off 80%)  [35]. For this, a database containing all previously published E. coli plasmid sequences (retrieved from the ENA database) was used to query the gene nucleotide sequence dataset of each strain. A similar approach was used to detect phage-associated genes using the phage sequences found in the ENA public database at the date of analysis.
Comparison to E. coli pangenome We retrieved all the E. coli genes recorded in the Genoscope genomic databases (data kindly provided by David Roche, Genoscope). TBLASTN was used to build the list and detect homologous genes within the E. coli genomic data available at the date of the comparison (identity cut-off 80%; HSP score coverage cut-off 80%). The resulting pangenomic dataset was used to query the genomes of the 5 mastitis-associated E. coli and the 14 strains retained for comparison (Table  1 ) by TBLASTN (identity cut-off 80%, HSP score coverage cut-off 80%). This yielded a list of genes shared by all of the E. coli 19 strains whose sequences were concatenated for further phylogenetic analyses. The data relative to the genes only found in a subset of the 19 strains were treated as presence/absence binary bits.
MLST analyses The strains under analysis were first compared on the basis of Wirth et al. 's MLST scheme  [38]. For the sake of comparison we retrieved the allelic profiles of the 83 ECOR strains from the MLST database managed by the University of Warwick (http://mlst. warwick. ac. uk/mlst/ dbs/Ecoli/documents/primersColi_html). The sequences at each locus were extracted from the 5 mastitis strains genomic using BLASTN. An additional set of 40 mastitis-associated strains were characterized by direct sequencing of the seven MLST loci as described elsewhere  [38]. These strains are available upon request to the International Centre for Microbial Resources (CIRM-BP; http://www6. inra. fr/cirm_eng/Pathogenic-Bacteria). Salmonella enterica subsp. enterica ser. Typhi was used as an outlier to root the tree (data taken from  [38] ).
Sequences were concatenated and aligned using the Run-mummer3 software included in the MUMmer3. 0 program suite  [39]. Genetic relationships were inferred using a GTR model with the CAT approximation of rate heterogeneity option implemented in RAxML v8. 1 (Stamanakis 200). Node support was assessed using the rapid bootstrap procedure (100 bootstraps). Tree visualization was done using Dendroscope 3. 2. 10 [40]. The expected distribution of the mastitis strains among the 5 phylogroups defined by Selander et al. [41]  was estimated using the frequency of each phylogroup within ECOR collection  (45[35%] , 40[31%], 10[8%], 19[14%] for A, B1, B2, D and E group respectively). The observed distribution was compared using a parametric Chi 2 test.
Core genome phylogenetic analyses The comparison of the pangenomic dataset (see above) to the genomes in the analysis yielded a subset of 1976 genes common to all 19 strains. Homologous gene sequences were retrieved from the Escherichia fergusoniii genome dataset using a similar BLAST comparison and used as outgroups in further phylogenetic analyses. Each of the 1976 genes was first treated separately. The best evolutionary model was inferred for each of them under the GTR+gamma+I hypothesis using PhyML 20111216  [42]. Second, a global composite model was next computed using the mean value of the relevant parameters weighted by gene sizes. Concatenated data were analyzed using this composite model and a quartet puzzling procedure implemented in TreePuzzle 5. 2 to estimate branch support  [43].
Incongruences between species and individual gene trees The concatenated sequences of the 1976 common genes were analyzed in order to detect localized incongruence of phylogenetic signature. The topology found using the whole concatenated dataset was used as reference. The likelihood of this particular topology was estimated using TreePuzzle 5. 2 under HKY model for any 5kb sliding window (spaced by 250bp) on the concatenated dataset. The likelihoods and the numbers of polymorphic sites were used respectively as dependent and explicative variables in a regression analysis performed on R v. 2. 15. 1. The deviation between observed and estimated likelihood was summarized in a score that took negative values for an observed likelihood lower than estimated. The sizes of incongruent regions were estimated relative to the positions of flanking genes on the E.
Core genome based phylogeny The genome repertoire of E. coli strains can be separated into two sets: a core genome including genes present in all strains and an accessory genome that confers specificity to each E. coli strain. As a first step, we analyzed the core genome of the mastitis isolates: the pangenomic data for the E. coli species retrieved from the Genoscope public database was compared to our dataset including the 5 genomes of mastitis strains and 14 published E. coli genomes. The concatenation of the 1976 genes shared by all the 20 genomes under scrutiny yielded a 1 875 936 bp sequence for each strain. A strongly supported phylogeny was found by comparing these sequences (Fig  2 ). The inferred phylogenetic tree is in accordance with the MLST-based approach: P4, D6-117. 07, VL2732 and VL2874 were clustered with phylogroup A strains within a single monophyletic clade.
Accessory genome analysis We then attempted to analyze the relatedness of mastitis isolates in terms of accessory genome. A hierarchical clustering was performed after analysis of the presence/absence of each gene of the pangenomic dataset in each of the strains included in our analysis. One should note that very similar clustering patterns were observed using the common genes phylogenetic approach and accessory genes presence/absence. E. fergusonii, the B2 strains cluster and the strains representing the F and D phylogenetic group (respectively I1I39 and UMN026) were in basal positions (Fig  3 ). A larger cluster included the remaining E. coli with the E (Sakai) and B1 (IAI1 and SE11) strains was located basally. The mastitis E. coli were clustered within the same phylogenetic group as determined above (A for P4, D6-117.
07, VL2732 and VL2874; E for D6-113. 11) and strain K71 clustered with group B1. These results do not argue for a large-scale acquisition of mastitis related-specific genes. We then tried to identify MPEC specific genes by orthologous gene clustering. This analysis yielded a total of 88 clusters that included genes taken from 2 or more of the 5 mastitis strains. For 59 of these clusters we nevertheless found a homologous gene in the pangenomic dataset (see below) which means that they were already found in a previously studied E. coli strain. These clusters were removed from the analysis. We then kept a total of 29 clusters that included exclusively genes taken from respectively 2 of the 5 mastitis strains. A final BLASTP search was performed by querying public databases in order to detect homologous genes.
This step allowed to detect close homology for all except for two of the genes included within the putative mastitis-related clusters (identity percentage > 90%; coverage > 90%) (S2 Table ). As a second approach to identify MPEC-specific genes, we attempted to detect strain specific genes in each of the mastitis strains. This was first done by comparing their genomic data to an E. coli pangenomic dataset. The TBLASTN search yielded a total of 675, 590, 365, 350 and 595 putative orphan genes (respectively found in D6-113. 11, D6-117. 07, VL2874, VL2732 and P4). Short sequences (<200 pb) with no assigned function were considered as potential pseudogenes and removed from the analysis. The pangenomic dataset used for comparison included a single sequence for each gene. This first step was thus likely to produce false orphans genes for highly polymorphic gene loci and was thus refined using the 19 E.
coli genomes for comparison. This second comparison allowed detection of homologous genes for all except for 90, 47, 75, 98 and 92 genes (respectively found in D6-113. 11, D6-117. 07, VL2874, VL2732 and P4). A final BLASTP search was performed by querying public protein sequence databases in order to broaden the homologous gene search among all previously published E. coli genetic data. This allowed to find a close homologous gene (identity percentage > 90%; coverage > 90%) for all except 1, 12, 8, and 15 genes for D6-113. 11, VL2874, VL2732 and P4, respectively (S3 Table ). These 36 genes are therefore the only genes that could be considered as mastitis isolate specific, although they are not present in every mastitis isolate. Finally, a gene-candidate analysis was performed to assess the presence/absence of 302 candidate virulence genes in the 19 E.
Several long polar fimbriae genes have been identified in the E. coli species, which were named here lpfA_O104 (a. k. a. lpfA141), lpfA_O111 (a. k. a. lpfA154), lpfA_O157_1 and lpfA_O157_2 based on the serogroup of the strain from which the sequences were extracted. None of these genes were found in phylogroup A strains P4, D6-117. 07, VL2732 and VL2874. Only lpfA_O111 and lpfA_O157_1 were found in strain D6-113. 11 while only lpfA_O111 was found in strain K71. Third, type 6 secretion systems (T6SS) have been identified in other mastitis isolates  [16]. The presence of five different types of T6SS was analyzed using the SecReT6 database  [52]. The T6SS subtype i2 from strain 536 was not observed in any of the mastitis isolates. Only one or two genes homologous to T6SS subtype i1 from strain 536 were observed in strains VL2874 and P4, respectively.
Blum, Personnal communication). One hypothesis is therefore that the ferric citrate system is required for growth in milk and that, given the high concentration of citrate in milk  [75, 76] , there is no need for other accessory iron acquisition genes. In addition to the candidate gene-based approach, we attempted a bottom-up search of candidate virulence factors. A careful comparison to an E. coli pangenomic dataset allowed us to identify 36 putative novel ORFs only detected in single mastitis strains. This may be compared to the number of specific genes found within different groups of pathogenic E. coli (respectively 16 for ExPEC pathotypes strains; 28 for invasive diarrhea strain group,  [29] ). The functional annotation process showed that several of the genes belong to large families whose representatives are commonly involved in mechanisms of E.
Although several small clusters were apparently associated with mastitis strains this likely arose from the initial choice of strains used for comparison. A broader BLAST search indeed allowed us to find close homologous genes in previously published E. coli datasets. The function of the genes included in the remaining clusters were largely unknown (23/29 genes). Among the 6 remaining genes 3 of them were likely mobile elements associated genes with unknown function; the 3 other genes respectively coding for a transcriptional regulator belonging to the AraC family, an oxygen-sensing protein (DosP) and a ClpK-like protein that may be involved in heat resistance  [80]. Several phenotypic traits are expected to be associated with mammary infection strains. This include neutrophil killing evasion  [9, 66]  that may imply several avoidance strategies based on oxygen sensing  [81].
Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy.
Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.
, 2014; Bier et al. , 2015). Thus, to assess the importance of microorganisms in explaining ecosystem processes, we used statistical modeling to evaluate the value of environmental variables and microbial community structure both alone and in conjunction for explaining rates of C and N cycling processes within global datasets. Although the literature reporting environmental variables, microbial community data, and biogeochemical processes in a single study is sparse  (Rocca et al. , 2014) , we collected and re-analyzed 82 datasets spanning a multitude of diverse environments. We hypothesized that while the environment is a strong driver of most ecosystem processes, microbial community structure adds to our understanding of processes under certain circumstances, and we discuss patterns across ecological, biogeochemical, and phylogenetic subsets of data.
MATERIALS AND METHODS We completed an exhaustive literature review for studies measuring environmental variables, microbial community structure, and biogeochemical processes across a range of ecosystems (Supplementary Table  S1 ). We then constructed statistical models for three predictor sets and their additive combinations -environmental data, microbial community data, and microbial biomass measurements (Supplementary Figure  S1 ). Because the measurement of microbial biomass (defined as microbial C and/or N content) was not a criterion for dataset selection, only 28 datasets included biomass measurements. To incorporate differences in microbial community structure into our models, we used ordination-based techniques to condense multivariate community data into vectors that we included as predictors in our models (Supplementary Methods).
The Shannon diversity index of each sample was also included as a measure of alpha diversity. In total, these taxonomic metrics are referred to as 'community diversity' when applied to 16S rRNA genes or PLFAs and 'functional diversity' when applied to functional genes. Functional gene abundances were included as measures of total gene abundance per gram of soil when possible (n = 17), although five datasets only listed gene abundances as normalized relative to 16S rRNA gene abundance. Within each type of data, methodology to collect environmental, microbial, and process data, as well as variables collected, inevitably varied. To minimize potential error introduced by these differences, we re-analyzed data within each dataset using a multimodel inference approach with the 'MuMIn' package  (Barton, 2011)  in R software (R Core Team, 2014) and subsequently compared results from our analyses.
Multimodel inference is a broad regression-based, model-averaging statistical approach designed to reduce errors in model selection, and this method has the advantage of standardizing our approach across studies while accounting for a lack of a priori system-specific knowledge  (Burnham and Anderson, 2002). This statistical approach has been used in other small scale studies with similar objectives  (Powell et al. , 2015). We validated the accuracy of MuMIn in our dataset by comparing models for several datasets with expert-built regression models, which yielded comparable results (more details available in Supplementary Methods). Using the 'dredge' command in the 'MuMIn' package to fit and evaluate the explanatory power of all possible combinations of variables within a predictor set on a process rate, we selected a set of best fit models for each predictor set consisting of all models with a delta AICc value no more than four greater than the model with the lowest AICc value  (Burnham and Anderson, 2004).
We generated an averaged final model from this model set using Akaike's weights, implemented with the 'model. avg' command in R (Supplementary Methods). Final models from different predictor sets were compared for statistically significant differences at a delta AICc value of four to provide conservative estimates of model improvements  (Burnham and Anderson, 2004). Models were also evaluated for ecologically relevant improvement, defined by an increase in adjusted R 2 value greater than 10% of the environmental model adjusted R 2 value. This criterion was implemented to remove artifacts from datasets in which statistically different models, based on AICc values, yielded similar adjusted R 2 values. Only models that showed both statistical and ecological improvement were considered to be improved.
Finally, model results were synthesized across studies within ecologically relevant subsets of data. We examined results within the full dataset and within biogeochemical process [C mineralization (referred to here as 'respiration'), nitrification, denitrification, N mineralization], microbial data (PLFA, tRFLP, ARISA, DGGE, qPCR, next generation sequencing) and environment types (natural soil, sediment, agricultural soil) with sufficient replication (n ≥ 12). We report the mean adjusted R 2 of models and the increase in adjusted R 2 value as measures of effect size as well as the percent of models statistically improved by the addition of microbial data relative to models constructed with only environmental parameters. Differences among the explanatory power of models with environmental, microbial, or both environmental and microbial data were assessed by comparing the distribution of model adjusted R 2 values within each predictor set using unpaired one-sided Mann-Whitney U-tests for non-parametric data.
We also examined correlations between predictor sets to determine the extent to which environmental variables explained variation in microbial community structure and biomass and to which biomass explained variation in microbial community structure. We analyzed these relationships with redundancy analysis (RDA), utilizing the 'ordistep' function in the 'vegan' package  (Oksanen et al. , 2013)  in R to automate forward model selection, and we report the average R 2 values of correlations between predictor sets as a measure of effect size (Supplementary Methods).
RESULTS Overall, models based on environmental variables explained significantly more variation in processes than models based on microbial community structure (n = 82, average adjusted R 2 0. 56 vs. 0. 31, Mann-Whitney U, p < 0. 0001, Figure  1 ). On average, models with both environmental and microbial predictors explained more variation in processes than environmental models (n = 82, average adjusted R 2 0. 65 vs. 0. 56, Mann-Whitney U, p = 0. 046); however, only 29% of datasets were significantly improved by adding information on microbial community structure, by an average of 0. 08 increase in adjusted R 2 within all models. Data on microbial community structure from ARISA, tRFLP, qPCR, and next generation technology did not differ in their explanatory power of process rates, but DGGE and PLFA had significantly lower explanatory power than other metrics (Supplementary Figure  S2A ).
All microbial data types showed weak correlations to environmental variables but gene abundance data displayed a trend for higher correlation (n = 22, average adjusted R 2 0. 36) than community (n = 55, average adjusted R 2 0. 20) or functional diversity (n = 5, average adjusted R 2 0. 21) metrics (Supplementary Figure  S2B ). We also examined each process individually for those functions for which we had the most data: nitrification (n = 14), denitrification (n = 17), N mineralization (n = 12), and respiration (n = 26). Overall, variation in N mineralization and denitrification rates was less well-explained than other processes by any predictor set (Figure  2 ). Only 17% of datasets examining N mineralization were improved by microbial community dataan average increase in adjusted R 2 of 0.
008 within all N mineralization datasets -and the average explanatory power of microbial community structure alone on N mineralization rates was 0. 21, compared to 0. 31 in the full dataset. Within N mineralization studies that assayed microbial biomass, we found that microbial biomass correlated with environmental variables (n = 5, average adjusted R 2 0. 58) but not with process rates; none of the N mineralization models were improved with the addition of data on biomass. No denitrification studies examined microbial biomass or PLFAs. However, despite low replication, it is notable that in three of four studies, models of denitrification were improved with the addition of 16S rRNA gene diversity data (0. 13 average increase in adjusted R 2 ), while only three of eleven models were improved with data on functional gene abundance (0.