dataesr/scientific-paragraphs-categorization huggingface dataset:
exsecta were never covered by any assembly. However, we never observed a gene covered by a contig with one assembler and not the other. Completeness, computed on a MIRA and a Newbler assembly with comparable parameters (90% minimum identity and 40 bp minimum overlap length, -urt option improving coverage and additional clustering for Newbler), was 81% for MIRA against 84% for Newbler (Table  S4B ). Contiguity, which was computed for each test transcript on assemblies obtained with different combinations of assembly parameters, ranged from 55% to 67. 9% with MIRA and from 72. 0% to 74. 4% with Newbler (Table  S4A ). Thus, contiguity was higher with Newbler 2. 6 and was also more robust to changes in assembly parameters. This approach consisting in selecting a set of known test transcripts to assess the quality of a transcriptome assembly  [32]  therefore allowed us to discriminate between assemblies that were similar in terms of classic metrics (number of contigs, total assembly length, average contig length and number of singletons) and demonstrated its value for choosing among different possible assemblies in other species.
Clustering and mapping. It had been reported that Newbler creates redundant contigs  [37]. This is confirmed in our results: Newbler performs a clustering step, but we still found redundancy in our dataset. When clustering Newbler contigs to avoid redundancy, we reduced the number of contigs from 32,580 to 26,791, while conserving completeness and contiguity values. The additional clustering step we performed was therefore able to reduce redundancy without affecting performance of the assembly on our test transcripts. The mapping strategy against the C. floridanus reference genome did not result in a high-quality assembled dataset, with many reads that did not map to the C. floridanus reference sequences. The distance between the two genera, Camponotus and Formica, probably explains this result: they are likely to have diverged in the late Cretaceous, the most intense period of ant radiation  [9].
Transcriptome Functional Annotation Similarity search annotation. Among the 26,791 contigs and 3,769 singletons initially used for the blastx search against the NCBI nr protein database, 12,668 (41%) sequences had matches, Table  3. Likelihood Ratio Tests for site and branch-site models in some clades of the D9 desaturase gene family. sequences against the predicted protein data sets of seven ant species, we identified 3,052 additional sequences with matches, highlighting the importance of annotated genomic data from closely related taxa. Indeed, ants and wasps diverged during the Jurassic, around 185 My ago, while the ant genera Formica and Camponotus diverged only in the late Cretaceous. In total, 15,720 (51. 4%) sequences were annotated. 14,839 (48. 5%) sequences did not show significant similarity to any protein in the databases and could not be annotated.
The most highly represented GO terms for a molecular function were binding (52. 4%) and catalytic activities (35. 3%) and the most highly represented GO terms for a biological process were metabolic process, cellular process, localisation and biological regulation (Figure  1C ). For GO terms potentially linked to the metabolism of cuticular hydrocarbons and chemoreception, we found 41 sequences that had an odorant binding function and 33 sequences with an olfactory receptor putative activity. To find sequences potentially involved in the metabolism of cuticular hydrocarbons, sequences were annotated with enzyme codes (EC). We could identify an enzyme code to 1,363 sequences. The KEGG database was used to link enzyme codes with metabolic pathways. The following enzymes implicated in the elongation of fatty acids were found: long-chain-enoyl-CoA hydratase, paltiptoyl hydrolase, enoyl-CoA hydratase and 3-hydroxyacyl-CoA dehydrogenase.
exsecta was consistent with findings in other insect species, suggesting that our 454 dataset is a good representation of the F. exsecta transcriptome. Open reading frame prediction. For sequences that had matches against the nr database with an e-value lower than 10e-8, high scoring segments for the same hit protein were joined and extended. This step predicted 12,329 ORFs with a minimum length of 30 bp. For those sequences that didn't meet the e-value cut-off (10e-8) or had no BLAST hit, ESTScan predicted 2,715 (14. 8%) new ORFs. For the remaining sequences, a six-frame translation was carried out and the longest open reading frame was kept. As a result of this stepwise procedure, we could assign 99. 9% of contigs and singletons with an ORF of at least 30 bp. Among these, 28,993 (94. 5%) sequences had an ORF of at least 100 bp.
SNP Discovery and Analysis With default parameter values, 34,701 SNPs were predicted. As SNP calling is subject to two main sources of error: sequencing errors (more than 1% in 454) and incorrect mapping of reads to the reference, we applied a stringent filtering procedure (see Material and Methods) and after filtering, a total of 13,567 sites (39%) were considered as high-quality SNPs and thus were kept. In this F. exsecta transcriptome dataset, 4,747 (17. 7%) contigs had at least one SNP. The mean number of SNPs per contig was 0. 5. There were 0. 5 SNPs per 1,000 bp on average. We found a positive correlation between the number of SNPs and the length of the contig (y = 0. 0048x, r = 0. 91, p-value,0. 05) (Figure  S2 ). Some of the most polymorphic genes had precise GO annotations: a fatty acid synthase, a deoxythymidylate kinase, a galactosyltransferase, a chaperone for superoxide dismutase and a heat shock protein.
exsecta, formed a hymenopteran-specific clade with high bootstrap support. The moderately supported clade with sequences orthologous to CG9747 showed a large polytomy at its base, and included a well-supported and large ant-specific clade with important expansions in several ant species. This clade is an important candidate for involvement in the evolution of chemical signaling function after the ant radiation. F. exsecta showed only one duplication in this clade, but recent duplication could have been missed as very close sequences can be mistaken as allelic differences. Discrepancies between our desaturase phylogeny and the ones previously found by  [3]  and  [5]  could have a basis in methodological differences. Although we used very similar methods (alignment and phylogenetic programs), our dataset comprised more Hymenoptera species than in the previous studies and we used a different substitution model from  [3].
This model was not yet implemented in the version of RAxML that they used, and proved to be the best evolutionary model for our dataset. However, with a more stringent curation of the alignment and the same evolutionary model as in  [3] , we retrieved the same major differences (clades A and B not supported). This suggests that increasing the number of sequences and species might have modified the reconstruction and revealed clades whose support was not robust. Selection analyses. As we found several robust clades in the phylogenetic analysis, we used these clades to analyse patterns of desaturase evolution in social Hymenoptera. We reconstructed a phylogeny of social Hymenoptera desaturase sequences and selected nine groups, either large clades with good bootstrap support, or clades constituted by species-specific multiple duplications (Figures  3  and 4 ) on which selection analyses were performed.
Conclusions The transcriptome dataset we obtained with 454 sequencing on a pooled sample of workers from 70 F. exsecta Finnish colonies represents the first large-scale genomic data available for the ant genus Formica. In addition, this study provides some general guidelines for de novo transcriptome assembly that should be useful for future transcriptome sequencing projects. Having sequenced a normalised cDNA library, our attention in this study focused on sequence variation. Deeper sequencing experiments and specific transcriptomics studies will be needed to complement this first large-scale dataset, in order to gain insights into patterns of gene expression in Formica ants. One important goal has been achieved here: this first transcriptome reference sequence provides sequence and polymorphism data that will allow researchers working on Formica ants to develop studies to tackle the genetic basis of eusocial phenotypes.
In particular, it will be useful for many genetic applications such as SNP genotyping, quantitative trait locus (QTL) analysis or genetic mapping which will be key to identify key genetic changes underlying important adaptations studied in F. exsecta and other Formica ant species, such as polymorphic social organisation and social parasitism. The D9 desaturase gene family is an important multigene family involved in chemical communication in insects and which could play a major role in the evolution of sociality in social insects. Our analysis of these candidate genes in the F. exsecta transcriptome dataset identified a number of D9 desaturase genes but did not reveal any large gene family expansion in this species. Wholegenome sequencing data would be needed to confirm this result. However, the few expressed desaturase genes identified in F.
More generally, the large desaturase gene expansions revealed by our large-scale phylogenetic analysis in social Hymenoptera and the potential signature of positive selection on these genes suggest a very dynamic evolution of these genes in social insect genomes which could facilitate adaptation to sociality. Taking advantage of this transcriptome dataset, it will also be possible to apply these candidate gene approaches to other phenotypes studied in Formica ants, using findings in other ant taxa to provide predictions. For example, in the ant Solenopsis invicta, the number of queens is determined by a ''social chromosome''  [86] , a large non-recombining region that includes the single gene, Gp-9, which is believed to code for an odorant-binding protein  [87]  but occurs widely throughout the body  [88].
Positive selection sites estimated under M8 model by BEB approach with PPs. 95% are listed. doi:10. 1371/journal. pone. 0068200. t003 including 8,269 unique hits, a figure in the range of those obtained for other non-model species. The most represented taxa for the best hit of each match were two hymenopteran species: Apis mellifera with 7,438 hits and Nasonia vitripennis with 3,001 hits. The annotation of the ant genomes is still ongoing so new gene annotations are released on the Hymenoptera database before being released on NCBI. When blasting the remaining 17,891.
Positive selection sites estimated under M8 model by BEB approach with PPs. 95% are listed. doi:10. 1371/journal. pone. 0068200. t003 including 8,269 unique hits, a figure in the range of those obtained for other non-model species. The most represented taxa for the best hit of each match were two hymenopteran species: Apis mellifera with 7,438 hits and Nasonia vitripennis with 3,001 hits. The annotation of the ant genomes is still ongoing so new gene annotations are released on the Hymenoptera database before being released on NCBI. When blasting the remaining 17,891 Clades 2Dl b Estimated parameters under M8 Sites under positive selection c n a M1a vs M2a M8 vs M8a A vs A null clade 4 8 ns 8. 66* - p1 = 0. 007 (v = 7. 53) 260 V clade 5 21 8. 81* 11. 5** - p1 = 0. 055 (v = 1. 76) 178 K, 295 G, 300 N, 304 M clade 5a 21 10.
Lava flow hazard maps can be a very powerful instrument in identifying operational guidelines for mitigation during eruptive emergencies  36. Moreover, knowing the areas that are the most exposed to inundation by a lava flow is useful for environmental preservation and to encourage better land use planning. Clearly the hazard map does not provide a deterministic forecast of future eruptions, but a useful prediction based on probabilistic modeling of historical eruptive activity of the volcano. Presently our study shows that diligence is required in developing accurate lava flow hazard maps in densely populated areas. Improved management of volcanic threats requires real-time access to differing geophysical datasets, i. e. volcanic monitoring data, to achieve continuous updating of short-term hazard maps in the case of unrest, and production of a risk map evaluating the expected damage that an eruptive event may cause to life, health, property and environment in a given area in a certain time period.
Methods Spatiotemporal probability of future vent opening. The different typologies of available geological and structural datasets were accounted for by converting them in separate probability density functions (PDFs), giving each a relevance value (weight), and finally combining all the PDFs and weights in a non-homogeneous Poisson process. For the flank eruptions at Etna, we considered four datasets: the eruptive fissures, divided into recent (post-1600) and older (pre-1600) fissures, the dykes and the main faults. We defined a 500-m spaced grid of potential vents v i (x,y) covering an area 32. 5 km E-W and 36 km N-S (Fig. 1 ), over which we calculated separate PDFs using the formula for the Gaussian kernel: l xyt ~XN i~1 exp { d 2 i 2h 2 Ã exp kt i ð Þ ð1Þ where N is the total number of volcanic structures considered in the calculation, d i is the distance between the potential vent v i (x,y) and the ith volcanic structure, t i is the elapsed time since the eruption at the i-th eruptive fissure, k is a constant, and h is the smoothing factor.
The PDFs for pre-1600 and post-1600 eruptive fissures, dykes and faults, after being normalized so that their integral across the whole area is unity, were aggregated in a linear combination using weights that express the importance and the quality of each dataset in evaluating the distribution. The best weights were computed from back analysis exploiting the location of the most recent flank eruptions since 1983, and found to be 0. 15, 0. 6, 0. 15 and 0. 1, respectively, for pre-1600 and post-1600 eruptive fissures, dykes and faults  27. Finally the spatiotemporal probability map for future opening was calculated as: p a v i ,Dt ð Þ~1{ exp {L xyt DxDyDt À Á ð2Þ where L xyt is the linear combination of the four PDFs rescaled with the recurrence rate for the next 50 years 6 (0. 231), which was calculated on the basis of the frequency of flank eruptions occurring since 1610.
Thereby p a specifies the probability of activation of each potential vent v i (x, y), i. e. the probability that at least one eruption will occur in the unit area DxDy (500 m 3 500 m) within the Dt time period considered (50 years). For summit eruptive activity at Etna, one dataset including the four summit craters (CC, NEC, SEC and NSEC) was considered. The grid of potential vents was defined on a circular region with a spatial resolution of 100 meters (dashed area of Fig. 4a ). For the spatial analysis, we considered the areas covered by CC, NEC, SEC and NSEC, on which we identified four centroids that were used to calculate the PDF with the Gaussian kernel for point patterns. The best estimate for the smoothing parameter h (300 m) was found by minimizing the LSCV score on the centroid locations.
Rod and Frame Test The Computer Rod and Frame (CRAF) test used in these investigations was a modified version of the program described by Bagust et al. [14]. The program was written in Visual Basic, and  ran on a portable laptop computer. The subject was presented with a square white frame on a plain black background. Within the frame a single white line could be rotated around its centre in either clockwise or counter clockwise directions by depressing the right or left mouse buttons respectively. The rod rotated in steps of 0. 5u for each mouse key press. The Rod and Frame display was presented to the subjects on a 17 inch flat screen monitor. A black cylinder 13 inches diameter and 18 inches long placed in front of the screen formed an optical tunnel. One aspect of the cylinder abutted the screen and the other opening allowed the subject to rest their chin inside the tube.
Time In addition to measuring the errors from vertical, the computer Rod and Frame program also recorded the time taken to align the rod in each presentation. The minimum mean time was 5. 5 s (Table  5 ). This represents the shortest time in which it was possible to rotate the rod from its starting position to vertical. Values below this might suggest that the subject was terminating the session, without trying to align the rod accurately. These data contain no evidence of that occurring. Comparison between the alignment times recorded at the three different ages revealed statistically significant differences between the three sets of data, but the direction of these changes was not consistent.
'Deep' watertables were considered to have a depth > 3 m at the start of the growing season and a depth >3. 9 m at veraison. All other sites were characterised as having a 'shallow' watertable. The agreement between the categorical soil and watertable depths, as obtained by using a contingency table analysis (JMP 8. 0. 1, SAS Institute Inc. , Cary, NC, USA), was not strong (κ = 0. 36, n = 41).
Vines with access to subsurface moisture (watertables) should experience relatively little change compared to vines that lack watertable access at the end of the season (whether or not the watertable is absent or intermittent during the season)  (Guix-Hébrard et al. , 2007). For the 2008 time-series, the temporal mean (μ) and standard deviation (σ)o fET and NDVI were calculated across the 5 seasonal layers at both resolutions (μET 15 , μET 90 , μNDVI 15 , μNDVI 90 , σET 15 , σET 90, σNDVI 15 and σNDVI 90 ). This provided some general statistics on the ET and NDVI observation at each pixel, i. e. whether the observation at a location was high or low, variable or constant over time etc. In addition, information on the temporal trend in the ET and NDVI responses were derived. Since ET may be affected by short term climatic variations, the data was scaled before the temporal trend analysis was performed.
Digital terrain attributes There were two digital elevation models available for the survey area. A larger pixel size DEM captured at 3″ (~80 m pixel 1  ) by the Shuttle Radar Topographic Mission (SRTM) (obtained from http:// srtm. csi. cgiar. org) and a smaller (10 m) pixel size DEM obtained from an aerial LIDAR survey  (Bailly et al. , 2008). The DEMs were analysed using CLASS Spatial Analyst 2. 0. 1  (Teng et al. , 2005; Teng et al. , 2008)  to extract primary, secondary and tertiary digital terrain attributes. The CLASS Spatial Analyst software is designed as a hydrological tool to operate on catchment areas. Defining catchment boundaries helps to eliminate some of the boundary effect issues in secondary and tertiary terrain analysis. The programme also calculates Multi-resolution Valley Bottom Flatness (MrVBF)  (Gallant and Dowling, 2003).
This has been shown to be related to watertable depths in Australian conditions  (Ransley et al. , 2007). The CLASS Spatial Analyst was run according to the user guide  (Teng et al. , 2005)  for both DEMs to derive slope (S), aspect (A), curvature (C), planar curvature (PlanC), profile curvature (ProfC), flow derivatives, the compound topographic wetness index (CTI) and MrVBF covariates at both 10 and 80 m resolutions. For the calculation of flow derivatives and the CTI, the D∞ option of the software was used. Recent work has shown that the effectiveness of DTAs depends on the neighbourhood size used in the calculation as well as on the DEM pixel size  (Roecker and Thompson, 2010). However, the effect of neighbourhood size is not included in this analysis to avoid too many effects in the analysis.
The neighbourhood used is the default settings from within the CLASS Spatial Analyst. As for the imagery-derived covariates, a numeric subscript is used to differentiate between the same terrain covariate at different resolutions e. g. MrVBF 10 and MrVBF 80. Again, the vector of covariates associated with a particular DEM are denoted in bold i. e. DEM 10 and DEM 80. There were several DTAs generated by the CLASS software that were not included for subsequent analysis. The flow direction and accumulation were omitted as they are strongly correlated to the CTI, which is derived from the flow attributes. Elevation was also omitted. As a covariate, elevation is an absolute value. However, it is the relative location, not the absolute value, of the elevation in the landscape that is important for understanding the pedology and hydrology at a site.
The minimum number of individuals in a node was set at 5, which corresponded to 12% of the dataset. Considering a lower number for minimum individuals may produce unrepresentative nodes. Covariates remained within the algorithm at all times, thus the same covariate could be selected at several splitting iterations within the regression tree. For categorical analyses, the negative loglikelihood (-lnLLH) can be used in a similar manner to the sum of squares for continuous covariates to determine a goodness of fit for a model (denoted as r 2 ). This is a measure of the amount of total uncertainty that can be attributed to the model and is calculated as 1 -[(-lnLLH Model) / (-lnLLH for Corrected Total)] (SAS Institute Inc. , 2007). The -lnLLH for Corrected Total is derived by fitting a model where the probabilities are estimated by fixed rates for each response level.
This provides the background uncertainty for a model with no effects and is analogous to the Total Sum of Squares (Model SS + Residual SS) in ANOVA with continuous data. A leave-one-out cross validation (LOOCV) was performed for each model. The LOOCV was also assessed using an r 2 value based on the -lnLLHs. All analysis was performed using the Partition platform in JMP 8. 0. 1 (SAS Institute Inc. , Cary, NC, USA).
Overall modelling results A summary of the results of running Models 1-7 on the soil and watertable depth responses is given in Table  1. Fits (r 2 ) are given for both the overall model (fitted with all the data and no validation) and the LOOCV. Also recorded are the number of splits in the regression tree and the covariates selected by the algorithm. In general the selected covariates are arranged in the order selected by the partitioning algorithm; however repetitions of covariate selections are not indicated. Given that a covariate could be selected at multiple splits in the regression tree, the number of selected covariates does not always equal the number of splits. For example, Model 1 s of soil depth has 5 splits but only 3 covariates as Curve 10 and Slope 10 are repeated. Model fitness was assessed primarily from the LOOCV fits.
In the best watertable depth model (Model 7 w , Fig. 4b ) a low μET 15 identifies points with a deep watertable (poor vine access to ground water) in areas where shallow watertables are expected (high MrVBF). Similarly, in the last split of the best soil depth model (Fig. 4a ), the mNDVI 15 response is differentiating soil depth on points that are not located on the valley floor. In these areas (slopes and hill crests), locations with a deep soil profile can be determined by a positive trend in the NDVI response (mNDVI 15 > 0. 008) around veraison (relative to other locations in the catchment). The relevance of the ET 15 covariates indicates an advantage to the downscaling of the ET data from 90 to 15 m pixels. The original larger pixel imagery (ET 90 ) is known to contain 'mixed' pixels. Soil sampling was only performed within vineyards; however the response over the 90 m pixels is often 'mixed' (multiple landuses).