dataesr/scientific-paragraphs-categorization huggingface dataset:
We estimate a precision and accuracy of about 4 %. ISOWAT measures the isotopologues with a temporal resolution of 1 s. This translates to a horizontal spatial resolution of about 80 m depending on the aircraft velocity. During the MUSICA campaign, profile flights consisted in a relatively fast ascent followed by a slower descent with intermediate level flights at certain selected altitudes and we achieved an average vertical resolution of about 1. 2 m. The good temporal resolution of ISOWAT allows observing the rather sharp vertical gradients encountered during the flights (see Fig. 1 ). These ISOWAT measurements form an unprecedented dataset of tropospheric water vapour isotopologue profiles (high and well-documented level of accuracy and high vertical resolution). A more detailed report about the aircraft campaign, the ISOWAT measurements, and calibration procedures is currently prepared and will be subject of a separate paper.
Identifying the transport pathways in the water vapour isotopologue data The two lowermost rows of graphs show the respective plots for the FTIR and IASI datasets, which are representative for several years. We work with FTIR data measured during summertime morning hours between 2005 and 2013, where we have a good overlap with the AERONET data. We use IASI-A data measured between October 2007 and the end of July 2013 as well as with a few 2013 IASI-B data (from June/July 2013). All remote sensing datasets reveal the different transport pathways similarly to the in-situ datasets. This qualitative similarity well demonstrates that the MUSICA FTIR and IASI δD remote sensing data add scientifically useful information to the respective H 2 O measurement. A more profound scientific examination of these different transport regimes is out of the scope of this technical validation paper and shall be addressed in an extra paper which is currently in preparation  (González et al.
Conclusions Global remote sensing of tropospheric δD in addition to H 2 O can open up novel opportunities for tropospheric water cycle research. However, the interesting signal of the added value of δD is small. Its detection by remote sensing techniques is a difficult task and prior to a scientific application of the data, their characteristics and uncertainties should be well understood. In this context Schneider et al. (  2012  aircraft instrument ISOWAT, whose extensive pre-, post-, and in-flight calibration modes ensure data of high accuracy and a very high vertical resolution. With this unprecedented dataset and based on the scatter between the coincident ISOWAT and remote sensing observations we can assess the random uncertainty of the NDACC/FTIR δD product to be smaller than 45 ‰ and 15 ‰ for the lower and middle troposphere, respectively.
Instead we use the Izaña and Teide Picarro instruments, whose early morning observations can serve as qualitative reference for the ground-based FTIR data. We are able to demonstrate that the FTIR δD data adds a similar value to the FTIR H 2 O data than the Picarro δD data to the Picarro H 2 O data. Furthermore, we are able to demonstrate that all the instruments involved in our study (ISOWAT, Picarro at Izaña and Teide, NDACC/FTIR, and METOP/IASI) are able to consistently identify two different free tropospheric water vapour transport pathways. Transport from the PBL via vertical mixing can be identified by isotopologue data that approximate a mixing line on the δD-H 2 O surface. Such mixing lines occur in all datasets primarily in the presence of large aerosol amounts, which serve as proxy for large scale vertical mixing over the African continent.
Back Close Full with a strong inversion layer that hinders mixing from the PBL and prevailing moisture transport from higher latitudes. Our study proves that the MUSICA H 2 O and δD remote sensing data are a reliable tool for water vapor isotopologue studies. However, we have to be aware that there is very likely a bias in the δD values. For a better understanding of this bias further research on the spectroscopic line parameters as well as more high quality aircraft profiles and thus further campaigns and validation efforts would be desirable. We would like to propose using the approaches as presented in this paper, in Schneider et al. (  2012 ), and in  Wiegele et al. (2014)  as guideline for best practices when validating water vapour isotopologue remote sensing datasets.
During an aircraft 925 campaign in July 2013 we produce six of such profiles using the dedicated aircraft instrument ISOWAT, whose extensive pre-, post-, and in-flight calibration modes ensure data of high accuracy and a very high vertical resolution. With this unprecedented dataset and based on the scatter between the 930 coincident ISOWAT and remote sensing observations we can assess the random uncertainty of the NDACC/FTIR δD product to be smaller than 45‰ and 15‰ for the lower and middle troposphere, respectively. For the middle tropospheric METOP/IASI δD product we assess a value of 13‰. For the 935 bias assessment we examine systematic differences with respect to ISOWAT and find that there is a systematic positive difference of about 30-70‰, which is similarly observed for the NDACC/FTIR and the METOP/IASI δD product thus suggesting a common error source.
The cyan line represents a Rayleigh distillation curve for an initial atmospheric temperature of 25• C, a relative humidity at the surface of 80 %, and an averaged thermodynamic profile. Rayleigh distillation means that all water that condenses during the transport pathway is immediately removed from the airmass. There is a strong correlation between the ISOWAT water vapour isotopologue data and the aerosol proxy: (1) when vertical mixing over Africa is clearly discernable in the aerosol data, the ISOWAT δD data group around a mixing line. (2) When the aerosol data reveal no significant vertical mixing over Africa the δD data approximate towards a Rayleigh distillation line (blue dots and cyan line in the top graphs of Fig. 16). These different transport pathways are also well observed in the in-situ isotopologue datasets as collected by the two Picarros at Izaña and Teide.
AMTD AMTD Acknowledgements. This study has been conducted in the framework of the project  MUSICA  which is funded by the  European Research Council  under the European Community's  Seventh Framework Programme  (FP7/2007-2013)/ ERC  Grant agreement number  256961. E. Sepúlveda is supported by the  Ministerio de Economía and Competitividad of Spain  for the project  NOVIA  ( CGL2012-37505 ). The aircraft campaign has been co-funded by the project  MUSICA  and the  Spanish national project AMISOC  ( CGL2011-24891 ). We are grateful to  INTA Aerial Platforms , a branch of the  Spanish ICTS program , and the  Spanish Air Force  for their efforts in maintaining and operating the aircraft. The Izaña aerosol in-situ measurements are part of the project  POLLINDUST  ( CGL2011-26259 ) funded by the  Minister of Economy and Competitiveness of Spain.
Nevertheless, this study also met with some sharp criticisms based on methodological grounds  (Nickle and Naskrecki, 1997; Desutter-Grandcolas, 2003). Desutter-Grandcolas (2003)  coded 85 morphological characters from 12 ensiferan terminals (three emblematic genera and nine families), which resulted in a well-resolved phylogeny, but this taxon sampling did not allow generalizations to be made about ensiferan evolution because polymorphism was included in the data matrix. Jost and Shaw (2006)  then reconstructed the phylogeny of Ensifera based on three ribosomal loci and 51 terminals, which resulted in a radically different phylogenetic relationship from previous hypotheses, but the dataset used in this work was later shown to be highly sensitive to different analytical methods  (Legendre et al.
, 2010). Zhou et al. (2014)  presented a mtgenome phylogeny of Ensifera based on a newly sequenced mtgenome of Prophalangopsidae (representing Hagloidea) and available ensiferan mtgenomes, but the study lacked several key taxa from basal ensiferans to make a significant impact. As for Caelifera,  Flook and Rowell (1997)  proposed the    first phylogeny of the suborder based on 32 ingroup taxa representing all major lineages and two mitochondrial ribosomal genes, but this dataset was not sufficient enough to resolve the basal relationships. Flook et al. (2000)  studied the phylogenetic relationships among the lower caeliferans based on a slightly more expanded taxon sampling and redefined some superfamily concepts. Leavitt et al. (2013)  conducted a thorough analysis of mtgenome data across major caeliferan superfamilies, but their taxon sampling was more biased toward Acridoidea than other lineages.
, 2014). We were not able to obtain DNA-grade samples for four families, which were Dericorythidae, Euschmidtiidae, Lathiceridae, and Mastacideidae. For outgroup taxa, we included four polyneopteran species, Grylloblatta sculleni (Grylloblattodea), Tamolanica tamolana (Mantodea), Timema californicum (Phasmatodea), and Sclerophasma paresisense (Mantophasmatodea). For 69 terminals, which represented key taxa for understanding higher-level relationships, we included complete mtgenome data, ten of which were newly sequenced for this study. The remaining mtgenomes were either previously generated by us  (Fenn et al. , 2007 (Fenn et al. , , 2008;; Sheffield et al. , 2010; Leavitt et al. , 2013)  or obtained from GenBank (Table  2 ). For all taxa, we generated complete 18S and 28S ribosomal RNA sequences and two conserved nuclear protein-coding genes, histone 3 and wingless, all of which have been frequently used for higher-level insect phylogenetics  (Flook et al.
, 1999; Whiting, 2002; Whiting et al. , 2003; Svenson and Whiting, 2004; Jost and Shaw, 2006). For the 26 taxa for which we obtained mtgenome sequences from GenBank, we were not able to generate the other four genes due to an obvious lack of access to specimens. The DNA-grade tissue samples used for this study were either collected by the authors or provided by collaborators. They were preserved in 100% ethanol and vouchered to the À80 °C cryofacility in the Insect Genomic Collection at Brigham Young University (BYU-IGC). We followed standard protocols for DNA extraction, polymer chain reaction (PCR),   S1. sequencing, and primer walking for mtgenome sequencing, which we described in detail elsewhere  (Sheffield et al. , 2010; Leavitt et al. , 2013; Mugleston et al. , 2013). All newly generated mtgenomes were first uploaded as raw fasta files to MITOS  (Bernt et al.
, 2013)  to identify open reading frames (ORFs) and tRNAs. The initial MITOS annotation was used as a guideline to delimit gene boundaries and start and stop codons of each protein-coding gene were manually identified in Sequencher 4. 8 (GeneCode), following the recommendation by  Cameron (2014a). The annotations for the 10 newly sequenced mtgenomes are presented in Table  S1. The DNA sequence data generated for this study are deposited to GenBank with accession numbers: KM657331-KM657340 (mtgenomes) and KM853171-KM853857 (nuclear genes).
Phylogenetic analyses We employed several alignment strategies for different loci in our dataset. For both mitochondrial and nuclear protein-coding genes, we aligned based on the conservation of reading frames by first translating into amino acids in MEGA 5  (Tamura et al. , 2011) , aligning individually in MUSCLE  (Edgar, 2004)  using default parameters, and back-translating to nucleotides. The tRNA sequences were individually aligned in MUSCLE using default parameters. Mitochondrial ribosomal RNA (16S and 12S) genes were first aligned using E-INS-i strategy in MAFFT  (Katoh et al. , 2005). Then, we used predicted secondary structures of 16S and 12S of Locusta migratoria in RNAfold Web-Server (http://rna. tbi. univie. ac. at/cgi-bin/RNAfold. cgi) as input constraint files to perform secondary structure-based alignment in RNAsalsa  (Stocsits et al.
The first analysis was based on the complete mtgenome data (16 758 aligned bp) from 69 taxa to establish deep relationships within Orthoptera, which was also used for a divergence time estimate analysis. The second analysis was based on the total evidence dataset (21 025 aligned bp and 258 taxa), which represented a taxon-rich analysis, but had heterogeneously missing data. For 189 of 258 taxa, we did not have mtgenome data, and for 26 taxa, we did not have nuclear gene data. The rationale behind this second analysis was that the mtgenome data would establish robust "backbone" relationships, while the taxa without mtgenome data would still be able to form accurate phylogenetic relationships based on four nuclear genes. We used parsimony and maximum likelihood (ML) inference methods to reconstruct the phylogeny of Orthoptera.
xsede. org) through CIPRES Science Gateway  (Miller et al. , 2011). Nodal support was evaluated using 5000 replications of rapid bootstrapping implemented in RAxML. The resulting trees were visualized using Fig-Tree  (Rambaut, 2006 (Rambaut, -2009) )  and iTOL  (Letunic and Bork, 2011). Our aligned datasets and the resulting trees were deposited to Dryad (doi: 10. 5061/ dryad. 96r8b).
Divergence time estimate analysis In order to estimate timing and rates of divergence across major orthopteran lineages using abundant fossil records, we performed a divergence time estimate analysis using BEAST v. 1. 8 (Drummond et al. , 2012). For this analysis, we used the 69-taxa dataset based on the complete mtgenome data using the partitioning scheme and the models of nucleotide evolution recommended by PartitionFinder. We created an xml file in BEAUti  (Drummond et al. , 2012) , specifying the starting tree, fossil priors, monophyly constraints, and parameters for molecular clock models. We used Yule process as a tree prior and exponential distribution as a distribution prior for fossil calibration points, which is shown to be suitable for modelling fossil calibrations  (Ho, 2007). The ML phylogram generated from RAx-ML was transformed into an ultrametric chronogram using non-parametric rate smoothing (NPRS) in Tree-Edit  (Rambaut and Charleston, 2001) , and the base of the ingroup was re-scaled to the minimum age of the earliest definitive fossil Orthoptera (299 MYA) to scale the entire chronogram.
Results For the parsimony analysis of the complete mtgenome data (Fig. 4a ), we found a single most parsimonious tree (L = 130 483; CI = 0. 199; RI = 0. 365). We failed to recover monophyly of Orthoptera as well as Stenopelmatoidea, Eumastacoidea, and Acridoidea, although we recovered monophyletic suborders. Nodal supports were generally poor across all backbone nodes. For the ML analysis of the same data (Fig. 4b ), we recovered robust monophyly for Orthoptera as well as each suborder and all of the superfamilies except Stenopelmatoidea. The parsimony analysis of the total evidence data yielded three most parsimonious trees (L = 148 589; CI = 0. 204; RI = 0. 414). A strict consensus tree (not shown) recovered monophyly of Orthoptera, Ensifera, and Caelifera, as well as Schizodactyloidea, Grylloidea, Hagloidea, Rhaphidophoroidea, Tettigonioidea, Tridactyloidea, Tetrigoidea, Proscopioidea, and Eumastacoidea.
Ommexechidae showed a close relationship with Romaleidae in both analyses, but Romaleidae did not form a monophyletic group in the total evidence analysis. The monophyly of Acrididae was supported in the mtgenome analysis, but was not supported in the total evidence analysis due to paraphyly with Romaleidae. When we compared our topology with the previous phylogenetic hypotheses using the SH and the AU test (Table  4 ), we found that the caeliferan relationship was not statistically different from that of  Flook et al. (1999 Flook et al. ( , 2000)) , but the ensiferan relationship was significantly different from both that of  Desutter-Grandcolas (2003)  and  Jost and Shaw (2006). This pattern was found in both the mtgenome and the total evidence analyses. The topology deduced from the mtgenome data did not statistically differ from the total evidence topology.
COII ATP8 tRNA-Lys tRNA-Asp CENOZOIC Fig. 6. A divergence time-estimate analysis of Orthoptera based on mtgenome data and nine fossil calibration points using BEAST. White and yellow circles on nodes indicate posterior probability values over 90 and 95 respectively. Green bars represent the node age 95% credible intervals and the numbers next to the nodes are the estimated node ages. Terminals are colour coded to show superfamily-level relationships. Superfamily names used in this figure follow the currently recognized 15 superfamilies according to the Orthoptera Species File  (Eades et al. , 2014). Asterisk denotes paraphyletic groups. Also shown is the evolution of tRNA gene rearrangement, which took place in the common ancestor of Tetrigoidea and Acridomorpha. the group, and it may be reasonable to elevate some of the gryllid subfamilies to the family level, given more thorough analyses with more taxon sampling in the future.
, 2013)  showed that leaf-like wings have evolved multiple times within the family. Among the herbivorous katydids, the diet habits vary across florivory, granivory, and folivory  (Gwynne, 2001) , which suggests that the availability of diverse angiosperms could have promoted the diversification of katydids. Although katydids appear to show a cosmopolitan distribution, only three groups, Conocephalinae, Phaneropterinae, and Pseudophyllinae, are truly cosmopolitan, while the other lineages have more restricted distributions  (Kevan, 1982; Eades et al. , 2014). This suggests that the numerous vicariance events that followed the break-up first of Pangaea, and later of Gondwana and Laurasia, may have played an important role in the divergence of major katydid lineages. This biogeographical pattern also seems to explain the distribution of crickets.
Mitochondrial genome evolution A typical metazoan mtgenome consists of 37 genes (13 protein-coding, two ribosomal RNAs and 22 transfer RNAs) and a variable size of AT-rich control region  (Clary and Wolstenhome, 1985; Boore, 1999) , and has been shown to accurately resolve phylogenetic relationships over broad evolutionary periods across many insect groups  (Cameron, 2014b). In this study, we show that the mtgenome data are very useful in resolving the phylogeny of Orthoptera. We also demonstrate that not only the sequence data of the mtgenome, but also its gene arrangements, contain important phylogenetic information. Flook et al. (1995)  sequenced the first orthopteran mtgenome from the migratory locust, Locusta migratoria, and noticed that two tRNA genes (tRNA-Lys and tRNA-Asp) between COII and ATP8 were reversed from the ancestral insect arrangement.
Acknowledgements We thank the  Insect Genomic Collection of M. L. Bean Museum at Brigham Young University  for providing a large number of DNA-grade tissue samples used for this study. Additional valuable specimens were provided by:  Corinna Bazelet , Battal C ß iplak, Wei-an  Deng ,  Mario Elgueta ,  David Gray ,  Claudia Hemp ,  Taewoo Kim ,  Michel Lecoq ,  Norman Penny ,  Michael Picker ,  Tony Robillard ,  Lauren Spearman ,  Kate Umbers , and  Amy Vandergast. Fieldwork  to  South Africa  and Australia was conducted by the senior author under permit numbers  CRO 177/08CR  and  CRO 178/08CR  ( Eastern Cape ) and a license number  SF007010  ( Western Australia ). A large number of students have contributed to molecular data generation throughout the duration of this project:  Gabriella Alava ,  Beka Buckman ,  Reshley Dalisay ,  Corina DeLeon ,  Dan Fenn ,  Kevin Hiatt ,  Dana Jensen ,  Jessica Jensen ,  Daniel Kaonohi ,  James Leavitt ,  Nathan Mahler ,  Matthew Moulton ,  Joey Mugleston , and  Keone Young.
Computational approach to the identification of novel ncRNAs containing Telo-box and Site II regulatory elements To systematically identify novel non-coding RNA (ncRNA) genes directed by TeloSII cis-regulatory elements in Arabidopsis, we assembled a customized pipeline (Fig. 1 ). We considered a region as a candidate promoter if both the Telo-box and Site II elements (TeloSII) were found (regardless of their order and distance) in a sequence window of 1 kb as suggested earlier by Gaspin et al. [27]. The next step in prediction involved testing adjacent regions. Based on the features of known snoRNAs obtained from previous studies  [20] [21] [22] [23] [24] , we scanned 500 nt downstream of TeloSII for existing gene annotations. For that purpose, we used annotations contained in the Arabidopsis TAIR10 database  [31]  and information contained in both the microRNA database (miRBase)  [32]  and the Plant Long ncRNA database (PLncDB)  [33].
Among the remaining loci, 160 mapped to known ncRNA genes. These correspond to 82 loci encoding 184 known snoRNAs characterized in previous studies  [20] [21] [22] [23] [24] 27]  and 18 genes encoding miRNAs annotated in miRBase  [32]. In addition, 60 TeloSII loci mapped to genes expressing long intergenic ncRNAs (lincRNAs) that were previously identified  [38, 39]  and recorded in PLncDB  [33]. These results confirmed that our approach of using TeloSII as an indicator could effectively detect functional ncRNAs and allowed the identification of 287 potential novel ncRNA candidates. In the next step, we estimated the protein-coding potential of the 287ncRNA candidates. To this end, we adopted ESTScan  [40] , a program used to detect protein coding regions in ESTs. This step revealed that 166 of the 287 candidate loci had protein coding capability.
Characterization of novel snoRNAs The remaining 121 ncRNA candidates were analyzed using SnoReport, an algorithm that predicts both C/D and H/ ACA box snoRNA/scaRNAs  [41] , and were subsequently processed with Rfam tools  [34]  to detect any RNA family signature in the remaining predictions. This process identified 68 TeloSII loci associated with snoRNA-like gene candidates (Fig. 1 ). A detailed survey of these genomic regions using SnoReport showed that most of these predictions corresponded to polycistronic genes encoding two or more snoRNAs, thereby corresponding well to the organization of plant snoRNA genes  [25]. We further restricted our selection to those loci whose expression was supported by cDNA, EST, or RNA-Seq data available in the TAIR database. This restriction resulted in the designation of 12 polycistronic/monocistronic TeloSII gene loci for further detailed analyses (Table  1 ).
The 12 TeloSII loci mapped onto ten polycistronic and two monocistronic snoRNA genes. Overall, these loci encode 26 novel snoRNAs, including three H/ACA box and 23 C/D box snoRNAs (Table  1 ). We noted that six of them (marked with triangles and asterisks in Table  1 ) had already been described as lincRNAs or intermediate-sized RNAs and shown to be expressed, albeit without functional assignment  [38, 39]. As in the case of most previously annotated Arabidopsis snoRNAs  [20] , multiple copies of ten of the predicted novel snoRNAs can be found in the Arabidopsis genome by using BLASTN (e-value < 1e-5; similarity > 80 %). However, not all of the snoRNA copies are associated with TeloSII elements. For example, there are 12 copies of cluster 2 in the genome, but only one copy has TeloSII elements (Table  1 ).
Comparative analysis with BLASTN and Phytozome database (version 10)  [42]  revealed that many of the predicted snoRNA sequences showed a high level of conservation among the closely related species Arabidopsis lyrata, Capsella rubella and Brassica rapa (Table  1 ). No similar sequences were identified in the genomes of more distant dicotyledonous (Medicago truncatula) or monocotyledonous (Oryza sativa) plant genomes. However, the absence of conservation in the distant species must be taken with caution. The plant snoRNAs exhibit varying degrees of divergence among species according to their targets. Some are well conserved among distant species like Arabidopsis and rice, while others are restricted to closely related ones. However, snoRNAs with different sequences can still be functional orthologs, targeting the same rRNA residue for modification in the two distant species  [23, 25].