dataesr/scientific-paragraphs-categorization huggingface dataset:
A clade with a PP value higher than 0. 95 was considered as well supported. Trees were finally visualized with FigTree v. 1. 4. 0. (http://tree. bio. ed. ac. uk/software/figtree/). Bayesian divergence times estimates. The temporal evolution of Pixibinthus and its close relatives was estimated using the Bayesian MCMC approach implemented in BEAST v. 1. 8. 0 for dataset 2  [58]. DNA loci were combined and partitions were set as in the preliminary Bayesian MCMC analyses (see above). We unlinked all DNA site models to analyze each locus under separate substitution models. An uncorrelated relaxed molecular clock model was selected to allow estimates of independent rate variation across branches following a lognormal distribution. The Birth-Death speciation process was implemented for the tree prior, assuming constant speciation and extinction rates per lineage.
The mean of the branch rates (UCLD. mean) was set to follow a lognormal distribution with a log (mean) set to -4 and a log (standard deviation) set to 1. We started our analysis with a random tree. The MCMC was run for 100 million generations and sampled every 10000th generations. The analysis for each dataset was conducted three times, and was carried out using the CIPRES Science Gateway v. 3. 3  [55]. Convergence of runs and adequate MCMC sampling were checked using Tracer v. 1. 6 [56]. The first 10 × 10 6 generations of each run were manually discarded as burn-in. The remaining trees were summarized using a Maximum Clade Credibility target tree in TreeAnnotator v. 1. 8. 0  [58] , as well as Bayesian posterior probability (PP), median height (= age estimate) and the 95% highest posterior density heights interval (95% HPD) of each node.
Calibration point. Divergence dates were previously estimated to test biogeographical hypotheses in Eneopterinae crickets by Nattier et al. [40]. Age estimates were mainly obtained with several geological calibration points, considering that few fossils or dated trees were available at that time. Since that study, Song et al. [59]  produced a large dated tree for Orthoptera which provided a secondary calibration for the present study in order to avoid the generally less accurate geological calibrations. We choose to use the secondary calibration point which corresponds to the divergence time between the Grylloidea and Gryllotalpoidea superfamilies. This calibration was assigned to the root node of our majority rule consensus tree generated in the preliminary Bayesian MCMC analyses of dataset 2.
It corresponds to the divergence time between our most external outgroup species, Gryllotalpa sp. and all the other Grylloidea species of our dataset 2 sampling. This calibration was set to follow a uniform distribution, and by using a lower bound of 220 Myr and an upper bound of 250 Myr, which correspond to the 95% HPD of this divergence time, with a median value of 233. 99 Myr  [59].
Ensemble species distribution modelling Ensemble Species Distribution Model (ESDM) analyses were carried out using combined R packages, and based on nine algorithms (GLM, GAM, MARS, GBM, CTA, RF, MAXENT, ANN and SVM) in order to test if the contemporary geographical range of Pixibinthus could have been more expanded, and would have been constrained by environmental changes. Dataset was composed of the coordinates of field sampling sites where Pixibinthussonicus has been recorded. Nine GIS ecological layers were used for the modelling analyses. Uncorrelated variables were selected among the 19 bioclimatic variables available in New Caledonia from worldclim database  [62] leading to the selection of seven bioclimatic variables (BIO1, 2, 3, 4, 7, 12 and 15) at 30arcsecond  [62]. We also included altitude from Digital Elevation Model at 10m resolution, and main vegetation units distribution map available at 300m resolution.
All layers were rescaled with a 300 m resolution. We used 30% randomly selected occurrence data as a testing dataset. The consensus model was built by summing algorithms suitability maps weighted with their area under the receiving operating characteristic (ROC) curve (AUC), only for model with an AUC > 0. 75  [63]. To evaluate the relative contribution of each variable to the model Pearson's correlation coefficient between the full model and one without each variable is measured  [64].
Phylogenetic relationships of Pixibinthus The ML and Bayesian MCMC single-locus phylogenetic topologies yield no supported incongruence for both datasets 1 and 2 (with a PP > 0. 95 or BS > 85%; data not shown) and are less resolved than the combined phylogenetic analyses. The ML (data not shown), Bayesian MCMC and BEAST combined analyses provide no supported incongruence between topologies (Fig 8 ; S1 and S2 Figs). Since the Bayesian MCMC combined analysis allows us together to take into account uncertainty only the results of this analysis are discussed hereafter (Fig  8 ). These analyses confirm that the genus Pixibinthus belongs to the Lebinthini tribe (PP = 1; Fig  8 ). The species P. sonicus is monophyletic, since all of the individuals sampled in this study group together with very short branch lengths (PP = 1, see below).
List of taxa investigated in the molecular study, with voucher information, and country and locality origin of samples, taxonomical classification. molecular dataset. The phylogeny-based taxonomies of Chintauan-Marquier et al. [37]  for Gryllidea and Robillard & Desutter-Grandcolas [36]  for Eneopterinae were used; superfamilies and families are indicated for all taxa, and subfamilies and tribes for ingroup taxa only. Abbreviations used for subfamilies: ENEO = Eneopterinae, GRYL = Gryllinae; Tribes: Ene = Eneopterini, Eur = Eurepini, Gry = Gryllini, Leb = Lebinthini, Nis = Nisitrini, Xen = Xenogryllini. N/A corresponds to non-available data.
Acknowledgments We would like to thank  Frédéric Rigault & Edouard Bourget  ( IMBE/IRD Noumea ),  Léa Colmas , and  Alexia Omont  who provided help for field work, habitat characterization and for mapping our sampled localities. We also thank  Céline Gomez  ( IMBE/IRD Noumea ) and  Sylvain Schmitt  (AMAP/IAC) who performed Ensemble Species Distribution Modelling. We are also grateful to  Eric Vidal  ( IMBE/IRD Noumea ) for interesting discussion and logistic support. We thank the  New Caledonian Direction de l'Environnement de la Province Sud , for collecting permits. We thank the  Grand Observatoire du Pacifique Sud (GOPS)  for its contribution to funding through the project entitled ' Bioacoustique des grillons de Nouvelle-Calédonie ' in the context of their call for proposal 2013. We also thank the  Labex BCDIV (Diversités biologiques et culturelles)  for funding a part of this study, under the project entitled " A diachronic study of biodiversity and a test of biogeographic scenarios in New Caledonia as based on field inventories of fossil and present-day insects ".
This study was partly funded by a PhD grant, 'Bourse d'encouragement à la recherche universitaire, attributed to JA by the  Government of New Caledonia. The bioacoustic study was supported by a grant from the  Grand Observatoire du Pacifique Sud :  AAP GOPS 2013 /  ' Bioacoustique des grillons de Nouvelle-Calédonie '. The laboratory procedures were realized through the two research programs:  Action Transversale du Muséum (ATM)  ' Biodiversité actuelle et fossile. Crises, stress, restaurations et panchronisme: le message systématique', and  ATM 'Barcode'. The Labex BCDIV (Diversités biologiques et culturelles) funded a part of this study, under the project entitled " A diachronic study of biodiversity and a test of biogeographic scenarios in New Caledonia as based on field inventories of fossil and present-day insects ".
affinis nitida (Chopard, 1925) GRYLLOIDEA / Trigonidiidae Bullita sp. GRYLLOIDEA / Trigonidiidae Cardiodactylus enkraussi Otte, 2007 GRYLLOIDEA / Gryllidae Cardiodactylus guttulus (Matsumura, 1913) GRYLLOIDEA / Gryllidae Cardiodactylus novaeguineae (Haan, 1842) GRYLLOIDEA / Gryllidae Cardiodactylus novaeguineae (Haan, 1842) NC GRYLLOIDEA / Gryllidae Cardiodactylus tankara Robillard, 2009 GRYLLOIDEA / Gryllidae Cearacesa sp. GRYLLOIDEA / Gryllidae Diatrypa sp. GRYLLOIDEA / Gryllidae Ectecous sp. GRYLLOIDEA / Phalangopsidae Eneoptera guyanensis Chopard, 1931 GRYLLOIDEA / Gryllidae Eurepini sp. GRYLLOIDEA / Gryllidae Fryerius sp. GRYLLOIDEA / Gryllidae doi:10. 1371/journal. pone. 0150920. t001 Table 2. Genbank accession numbers of studied taxa.
The Pearson correlation was very high (r = 0. 97 ± 0. 02) between technical replicates and high (r = 0. 93 ± 0. 02) between biological replicates (e. g. , the same subsets from different donors), indicating good reproducibility across measurements and donors (Table  S1 ). In total, we identified 42,723 non-redundant peptide sequences corresponding to 4,196 protein groups (Table  S2 ; Table  S3 ). Requiring a protein to be expressed in at least two donors for each subset, we identified 2,351, 2,197, 2,009, and 1,883 proteins in pDCs, BDCA3+ mDCs, CD1c+ mDCs, and CD16+ cells, respectively, and 2,823 proteins overall (Figures S2A-S2D; Tables  S2  and S3 ). Next, using the CORUM database of protein complexes, we inspected the identification of components of protein complexes essential for cell homeostasis (e.
Together, the expression patterns of established marker proteins demonstrate the ability of our approach to discern the distinct identity of each subset. We also obtained quantitative information using the LFQ algorithm in MaxQuant  (Cox et al. , 2014). First, we used this information to compare protein expression in the three main populations of blood DC-like cells (i. e. , pDCs, CD1c+ mDCs, and CD16+ monocytes), excluding the BDCA3+ mDC samples. We calculated average expression differences between any two subsets and visualized these in volcano plots (Figure  1A ; see Table  S4  for complete statistical analysis). It should be noted that here and in the remainder of the manuscript, only proteins expressed in at least two donors in one of the cell types being compared are included. Pairwise comparisons further highlighted subset identity, showing specific expression of CD11c (ITGAX/ITGB2) in myeloid cells and overexpression in the pDCs of several proteins with a reported pDC-specific expression and function (e.
Next, we assessed how our protein data related to mRNA data. We merged our protein data with a previously published and publicly available microarray dataset of the same subsets  (Lindstedt et al. , 2005). For one of the donors that was used for MS analysis, we also acquired sufficient material to perform RNA sequencing on the same sample (Table  S5 ). We first assessed the overall correlation between microarray-derived RNA data and proteome data for each cell type and found this to be low, similar to our previous observations (Figure  1D ; r = 0. 28-0. 31) (Buschow et al. , 2010). The correlation between RNA-sequencing data and proteome data for the matched donor was slightly better (0. 37-0. 45). Cross-correlation of this RNA-sequencing dataset to the protein data of the two other donors, however, produced a similar correlation, indicating that the different RNA analysis method was mostly responsible for the improved correlation (Table  S6 ).
To make a more in-depth comparison of the RNA data to our proteome dataset possible, we transformed both microarray-derived RNA and protein expression data to relative expression levels for each dataset separately (Z scores, mean to 0, variance to 1). From the merged dataset, we yielded 742 DEPs for which also RNA data were available. Again, we used the merged RNA and protein data for these 742 DEPs as input for hierarchical clustering (Figure  1E ; Table  S6 ). The combined protein and RNA samples grouped the distinct subsets from the two datasets together, indicating that, despite moderate correlation between absolute expression levels, there was a good correlation between RNA and protein expression patterns (Figure  1E ). Importantly, the clustering was determined neither by biological variation between donors nor by technical variation between omics technologies.
Proteins included in signatures based on level I and level II evidence behaved consistently in RNA and protein datasets. Thus, these IL3RA (CD123) 2 3 7 CLEC4C (BDCA2) 0 2 4 TLR7 0 4 7 NRP1 (BDCA4) 1 0 1 CD1c + CD1C 2 2 2 BDCA3 + IDO1 8 5 1 0 TLR3 3 0 0 THBD a (BDCA3) 1 0 2 CLEC9A a 0 1 0 CD16 + FCGR3A (CD16) 3 3 3 Shown here are the numbers of peptides identified and quantified for each marker protein for each subset. Shown markers were uniquely identified in the indicated subset. and S2  and Tables S1, S2 , S3, S4, S5, and S6. proteins are, thus, likely mostly regulated at the transcriptional level. Importantly, for these proteins, proteomics data confirmed RNA-identified differences between subsets. Many established markers were present in these two groups: e. g. , IL3RA, CLEC4C, TLR9, CD1c, and FCGR3B (CD16).
Finally, proteins that were put forward as differentially expressed by RNA data (differentially expressed genes; DEGs), and that were also present in our protein dataset but were not confirmed as differentially expressed by proteomics, are listed in Table  S8. Together, we confirmed differential or unique expression of 253 proteins between the three subsets previously observed by RNA expression (pDCs, 109 higher/65 lower; CD1c+ mDCs, 17 higher/1 lower; CD16+ monocytes, 34 higher/27 lower) that could also be derived from the transcriptome data (levels I and II; Figure  3 ; Table  S7 , including lower confidence DEPs). In addition, 143 proteins were found to be differently/uniquely expressed between subsets based on proteomics data only (levels III and IV), which hold yet-unappreciated differences between DC subsets (pDCs, 75 higher/14 lower; CD1c+ mDCs, 10 higher/8 lower; CD16+ monocytes, 10 higher/26 lower).
DISCUSSION This study describes an elaborate proteome analysis of humanblood-derived DC subsets and provides DC subset-specific protein signatures. This dataset holds unique information on the differences between DC subsets and reveals which differences, previously identified using mRNA, are really present at the protein level. Previously, Luber and colleagues analyzed the proteome of murine DC subsets  (Luber et al. , 2010) , but large-scale proteomics of human DCs was thus far restricted to in-vitrogenerated monocyte-derived DCs (moDCs) and CD1c+ mDCs  (Buschow et al. , 2010; Schlatzer et al. , 2012). The latter study reported 725 proteins expressed in resting and TLR-stimulated CD1c+ mDCs together, of which the majority (75%) was also identified, in the present analysis, in resting CD1c+ mDCs, along with a further 1,500 other proteins.
The present dataset thus represents, to the best of our knowledge, the most complete quantitative proteome analysis of human DC subsets and provides a unique side-by-side comparison of these cells from the same donors. (A-E ) Signatures were used as input for STRING PPI analysis (confidence level, 0. 4) to visualize possible connections between proteins. (See Table  S9. ) Proteins indicated in blue were included based on protein and RNA data, and red proteins were pointed out by protein data only (see also Experimental Procedures and Table  S7 ). Important biological functions of sections of each network are indicated (based on FA analysis). See also Table  S9. We report on nearly 400 differentially expressed proteins between the three main blood DC-like subsets. In addition, despite the presence of CD1c+ mDCs in the BDCA3+ mDC sample, we identify over 60 proteins differentially expressed between mDC subsets, of which we subsequently validated four by flow cytometry.
In contrast, TLR7 activation in monocytes induced IL-1b and IL-18, rather than type I IFN  (Chattergoon et al. , 2014; Dreux et al. , 2012). Thus, the differentiation program involving IRF7 that allows pDCs to secrete large amounts of Type I IFN may downregulate inflammasome pathway constituents, including caspase-1. Although this causal relation still awaits further experimental confirmation, this could switch the pDC response to TLR stimulation/NF-kB activation away from IL-1b and toward type I IFNs. A switch between type I IFNs and IL-1b could serve to prevent excessive damaging inflammation during antiviral responses. Taken together, the proteome dataset that we describe provides a rich resource to solidly establish the phenotypic and functional capacities of human DC subsets and to decipher the contribution of each subset to the initiation of immune responses.
MS Data Processing Proteins were identified and quantified from raw mass spectrometric files using MaxQuant software, version 1. 3. 0. 5  (Cox and Mann, 2008). A database search was performed in the Andromeda search engine  (Cox et al. , 2011)  against the Human Uniprot database (86,749 entries, June 2012). The protein abundance was determined by MaxLFQ, as described by  Cox et al. (2014). The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the identifier PRIDE: PXD004678  (Vizcaı ´no et al. , 2016). (http://proteomecentral. proteomexchange. org). See Supplemental Experimental Procedures for details.
Statistical Analysis of Protein and RNA Data See the Supplemental Experimental Procedures for details. Statistical analysis was performed in the R programming environment. Data were visualized using GENE-E software (http://www. broadinstitute. org/cancer/software/ GENE-E) and BioLayout Express3D (version 3. 3)  (Theocharidis et al. , 2009). For PPI analysis, we used the STRING PPI web tool (version 10; http:// string-db. org/), and FA analysis was done using the DAVID web tool (https:// david. ncifcrf. gov/). Western Blotting and ELISA These were performed according to standard procedures. See Supplemental Experimental Procedures for details and antibodies used. In Vitro DC Activation Isolated DC-like subsets were resuspended in X-VIVO 15 (Cambrex) containing 2% pooled human serum (Sanquin).
ACKNOWLEDGMENTS The authors thank  John-Paul Jukes  for critical reading of the manuscript. This work was supported by a grant from the  Dutch Cancer Society  ( KUN2009-4402 ), a  Radboudumc  RIMLS PhD grant, and grants from the  Netherlands Organisation for Scientific Research  ( 951. 03. 002  and  822. 02. 017 ). J. T. received grant  NWO-Veni 863. 13. 024. A. B. v. S. received grant  NWO-Vidi 864. 11. 006. I. J. M. d. V. received grant  NWO-Vici 918. 14. 655. C. G. F. received the  NWO Spinoza  grant and  ERC Adv  grant  PATHFINDER (269019 ).
Techniques for 3D mapping are maturing. At the same time the need for 3D data is increasing. This has pushed national (and regional) mapping agencies (NMAs) to consider extending their traditional task of providing topographic data into the third dimension. To show how research results in 3D mapping obtained over the past twenty years have been adopted by practice, this paper presents the ongoing work on 3D mapping within seven NMAs, all member of the 3D Special Interest Group of European Spatial Data Research (EuroSDR). The paper shows that some NMAs are still in the initial (experimental) phase of 3D mapping, while others have already built solid databases to maintain 2. 5D and 3D topographic data covering their whole country.
INTRODUCTION Techniques for collecting, reconstructing and maintaining 3D information have matured and many applications make use of 3D data ranging from flooding and noise simulations to energy and urban heat island calculation. Consequently, there is an increasing need for up-to-date, countrywide 3D data. Several National and Regional Mapping Agencies (NMAs) in Europe are making the step from 2D to 3D mapping to produce such data or are considering to make this step. To join experiences and pursue the research agenda for 3D mapping, twelve European National Mapping Agencies have organised themselves in a 3D Special Interest Group within the organisation "European Spatial Data Research" (i. e. EuroSDR 3D SIG). This paper summarises the progresses on 3D national mapping made by a selection of these NMAs and the remaining issues that they face.
3D MAPPING AT ICGC The Institut Cartogràfic i Geològic de Catalunya (ICGC) is the regional mapping agency of the Government of Catalonia. From its creation in late 1982, the ICGC activities in the cartographic production have been focused continuously in the improvement of the production workflows. The production of topographic data includes the scales 1:1. 000 (CT-1M) in urban areas, and 1:5. 000 (BT-5M) and 1:25. 000 (BT-25M) covering all the country. The data are collected mainly using photogrammetric systems, completed in urban areas by data surveyed on the field, and updated in a period of 5 years and more frequently if there are important changes in infrastructures. Data are stored in an ORACLE database using 2. 5D data models, where each vertex is defined by X, Y and Z coordinates. Data at 1:1.
000 and 1:5. 000 scales include a rich collection of altimetric data, including break lines, that allows to derive a digital terrain model (DTM) and a digital surface model (DSM). These elevation models are widely used in orthophoto rectification and in the contour and shadow relief generation ensuring a complete coherence between planimetric and altimetric information. LoD1 city models are automatically derived from 1:1. 000 scale and 1:5. 000. Since 2002 LiDAR systems are an additional source to obtain elevation data, with a full coverage of Catalonia available with 0,5points/m2, and a partial coverage at higher densities along the coast and over other specific areas. In 2014 an oblique camera was bought to obtain more accurate 3D information on urban areas. The ICGC products, including data, metadata and specifications, are distributed free-of-charge according the Creative Commons licence CC-BY.
Improvement of the topographic model at 1:1. 000 scale The first line of the 3D work in progress is focused in the enrichment of the CT-1M data model, adding detail of the roofs for obtaining LoD2 city models (see Figure  1 ) and the topographic information under bridges and other constructions. In the current topographic model, buildings include flat polygons derived from linear boundaries, where each vertex of the boundary is collected in the coordinates X,Y, Z at the real height, and a centroid to indicate the highest or the more significant value of the roof. In the new model it is planned to collect the roofs with all the 3D details, including the overhangs. After some tests on Tridicon software for obtaining inclined roofs automatically, based on oblique image correlation and building boundaries, the results are promising although manual tasks will be required to refine them.
Main limitations of Tridicon software are related to its pattern recognition method using predefined types of buildings, which in most cases are not well adapted to the irregular buildings and constructions of the territory. As it is in use in the BT-5M and the BT-25M databases, this model will include for each feature instance an identifier unique and persistent along its whole life-cycle, and its characterization by temporal, metadata, and thematic attributes. The life-cycle information will describe temporal characteristics to make feature versioning possible; the metadata attributes will contain the information related to quality and lineage (survey, stereoplotting, field collection, external databases or other); and, finally, features will have a set of thematic attributes to characterize them.