dataesr/scientific-paragraphs-categorization huggingface dataset:
6 ). The quality and coverage of taxonomic databases continues to improve and we hope to improve our database's coverage by making use of new Catalogue of Life checklists as they become available. Improved software would permit the use of fuzzy searches to reduce the current manual work required to curate taxonomic names. Intersecting our data with datasets of species attributes (Table  2 ) indicates much greater overlap among largescale data resources than might be expected simply based on overall numbers of species. This suggests that the same species are being studied for different purposes, because of either ubiquity, abundance, interest or location. In one sense this is useful, allowing a thorough treatment of certain groups of species, for example by incorporating trait data in analyses.
On the other hand, it highlights the fact that many species are poorly studied in terms of distribution, traits and responses to environmental change. Indeed, many taxonomic groups that matter greatly for ecosystem functions (e. g. , earthworms, fungi) are routinely underrepresented in data compilations  (Cardoso et al. 2011; Norris 2012) , includingdespite our efforts toward representativenessours. The PREDICTS database is a work in progress, but already represents the most comprehensive database of its kind of which we are aware. Associated with this article is a site-level extract of the data: columns are described in Table  S13. The complete database will be made publicly available in 2015, before which we will attempt to improve all aspects of its coverage by targeting underrepresented hotspots, realms, biomes, countries and taxonomic groups.
Figure5. Representativeness of predominant land use and land-use intensity classes. Numbers are the percentage of Sites assigned to each combination of land use and intensity. Numbers in brackets and colors are the differences between these and the proportional estimated total terrestrial area of each combination of land use and land-use intensity for 2005, computed from the HYDE (Hurtt et al. 2011 ) and Global Land Systems datasets (van Asselen and Verburg 2012); no difference is shown for "Urban"/"Light use" because these datasets did not allow us to compute an estimate for this combination. The 12. 15% of Sites that could not be assigned a classification for predominant land use and/or landuse intensity are not shown.
Figure6. Taxonomic coverage. The number of species in our database against the number of described species as estimated by Chapman (2009). Vertebrates are shown in red, arthropods in pink, other animals in gray, plants in green and fungi in blue. The dashed, solid and dotted lines indicate 10, 1 and 0. 1% representation, respectively. Groups with just a single species in the database -Diplura, Mycetozoa, Onychophora, Pauropoda, Phasmida, Siphonaptera, Symphyla and Zorapteraare not shown.
Acknowledgments We thank the following who either contributed or collated data: The  Nature Conservation Foundation ,  E. L. Alcala ,  Paola Bartolommei ,  Yves Basset ,  Bruno Baur ,  Felicity Bedford ,  Roberto B oc ßon ,  Sergio Borges ,  Cibele Bragagnolo ,  Christopher Buddle ,  Rob Bugter ,  Nilton Caceres ,  Nicolette Cagle ,  Zhi Ping Cao ,  Kim Chapman ,  Kristina Cockle ,  Giorgio Colombo ,  Adrian Davis ,  Emily Davis ,  Jeff Dawson ,  Mois es Barbosa de Souza ,  Olivier Deheuvels ,  Jim enez Hern andez Fabiola ,  Aisyah Faruk ,  Roderick Fensham ,  Heleen Fermon ,  Catarina Ferreira ,  Toby Gardner ,  Carly Golodets ,  Wei Bin Gu ,  Doris Gutierrez-Lamus ,  Mike Harfoot ,  Farina Herrmann ,  Peter Hietz ,  Anke Hoffmann ,  Tamera Husseini ,  Juan Carlos Iturrondobeitia ,  Debbie Jewitt ,  M.
The database contains 17,802 unique values of "Best guess binomial". The overlap with species attribute Figure 8. Number of Studies by lowest common taxonomic group. Bars show the number of Studies within each lowest common taxon (so, one Study examined the species Swietenia macrophylla, three Studies examined the species Bombus pascuorum, ten Studies examined multiple species within the genus Bombus, and so on). Colors are as in Figure examined a single species. The six most commonly exam- Arthropoda (4. 67%), Formicidae (4. 67%) and Insecta ined higher taxonomic groups are Tracheophyta (12. 04% (4. 42%). of Studies), Aves (11. 06%), Hymenoptera (7. 86%),.
Acknowledgments We thank  Sabina Mollenhauer ,  Anne Reboul  and two anonymous referees for their help at different stages of this work. Supported by the  Freie Universität Berlin , the  Deutsche Forschungsgemeinschaft (Excellence Cluster Languages of Emotion) , the  Medical Research Council (UK)  ( MC_US_A060_0034 ,  U1055. 04. 003. 00001. 01 )and the  Engineering and Physical Sciences Research Council (UK)  (BABEL grant).
Possible role of paraspeckles in response to proteasome inhibition In a search for cellular functions altered by enlarged paraspeckle formation in response to proteasome inhibition, we used MEFs prepared from Neat1 -/-knockout mice (Neat1 tm1. 1Shna ;  Nakagawa et al. , 2011). First, we confirmed that Neat1 lncRNA was also upregulated by MG132 treatment in MEFs (Figure  10A ). In particular, Neat1_2 is significantly increased after 3 and 6 h of MG132 treatment. Next we monitored the growth of Neat1 -/-MEFs or Neat1 +/+ MEFs, treated either with DMSO (control) or MG132 over 48 h. Cell growth was measured using the xCELLigence system (Roche), which derives a "cell index" correlated with the number of cells still attached to the dish (Figure  10B ). These data show that primary MEFs from both Neat1 -/-and Neat1 +/+ mice die with MG132 treatment, in parallel with elevated NEAT1 binding within elongated paraspeckles.
Electron microscopic studies Conventional ultrastructural microscopy after Epon embedding was as in  Souquere et al. (2009). Thin sections were analyzed with a FEI Tecnai Spirit, and digital images were taken with a SIS MegaviewIII charge-coupled device camera. Paraspeckle frequency was established by counting paraspeckle sections within 10 squares of 200mesh EM grids for samples of control (5. 6 ± 2. 8 paraspeckles/square) and MG132-treated HeLa cells (19 ± 6. 1 paraspeckles/square) with similar cell densities (cell pellets). When coupled to the 50% increase of the mean length of the paraspeckles (as in Figure  1C ), this indicates a 5. 1-fold increase of paraspeckle surface after 17-h, 5 μM MG132 treatment. Immuno-electron microscopic studies were as in  Souquere et al. (2010). Data were obtained on paraformaldehydefixed HeLa cells except for anti-PML immunodetection, which was carried out on glutaraldehyde-fixed HeLa cells.
FluoView FV10-ASW1. 7 software (Olympus) was used for image acquisition and processing. All overlaid images were transferred as high-resolution JPEG files. Figures were prewarmed MG132, bortezomib, or DMSO (diluted in culture medium) to each well as appropriate, with each drug treatment on each cell type being carried out in triplicate. The plate was immediately inserted into the xCELLigence apparatus inside an incubator, and electrical impedance was measured across the bottom of each well in real time every 15 min for 40 h according to the manufacturer's recommendations.
ACKNOWLEDGMENTS We thank the members of the  Hirose, Fox, and Pierron laboratories. G. V. was supported by an  Erasmus exchange program  with the  University of Naples (Naples, Italy). This research was supported by the  Funding Program for Next Generation World-Leading Researchers  of the  Japan Society for the Promotion of Science  (to T. H. ) and by grants from the  New Energy and Industrial Technology Development Organization  (to T. H. ), the  Takeda Science Foundation  (to T. H. ), the  National Health and Medical Research Council of Australia  (to A. H. F. ), the  Medical Research Foundation of Royal Perth Hospital  (to A. H. F), and the  Centre National de la Recherche Scientifique  and the  Association pour la Recherche sur le Cancer  (to G. P. ).
Several points have to be addressed in the following years of the project. We plan to refine the approach clearly defining the boundaries of the layers and adding to each one of the layers the suitable techniques for data reasoning. An important point here is that of developing the approach in such a way to be adapted to the needs of the particular organisation that should be able to also feed in a contextual way the necessary data to effectively exploit the approach. Also we plan to integrate better our results to those coming from projects with related aims, as FLOSSMetrics (http://flossmetrics. org/), QualiPSO project (http://qualipso. org/), QualOSS (http://www. qualoss. eu/) and OSSMETER (http://www. ossmeter. eu/).
X-ray crystallographic studies. Crystals of Ni1 and Ni3 were obtained by laying diethyl ether onto methanol at room temperature. With graphite-monochromated Mo Kα radiation (λ = 0. 71073 Ǻ), cell parameters were obtained by global refinement of the positions of all collected reflections. Intensity was corrected for Lorentz and polarization effects and empirical absorption. The structures were solved by direct methods and refined by full-matrix least squares on F2. All hydrogen atoms were placed in calculated positions. Structure solution and refinement were performed by using the SHELXL-97 package  [27]. Detail of the X-ray structure determinations and refinement is provided in Table  1. CCDC 935117 and 935118 contain the supplementary crystallographic data for this paper. These data can be obtained free of charge from the Cambridge Crystallographic Data Centre via www.
Phylogenetic Analysis and Estimation of Divergence Time We inferred phylogenetic relationships and divergence-times using a relaxed Bayesian molecular clock with an Uncorrelated Lognormal model (BEAST version 1. 6;  [25] ) on a cytb dataset comprising 96 unique haplotypes (Z. vivipara of our study plus deposited sequences of Z. vivipara spp. , Podarcis peloponnesiaca, Podarcis cretensis and Lacerta viridis as outgroup (GenBank accession numbers: Z. vivipara: AY714882-AY714929, Podarcis peloponnesiaca: AY896117-AY896123, Podarcis cretensis: AF486191-AF4861220 and Lacerta viridis: EU116514). JModelTest version 1. 0. 1  [26]  was used to select the appropriate model of evolution for cytb gene under the Akaike Information Criterion AIC  [27]. The GTR model of nucleotide substitution with gamma rate heterogeneity among sites and, as a prior, a Yule pure birth model of speciation to estimate the time of divergence between Z.
Trees were summarized as maximum clade credibility trees using the TreeAnnotator program which forms part of the BEAST package, and visualized using FigTree version 1. 3. 1 (http://tree. bio. ed. ac. uk/software/figtree). In each case, the first 10% of samples was discarded to avoid sampling the burn-in phase. A Bayesian Skyline Plot was also constructed with the software BEAST version 1. 6  [25]  using the GTR+G evolutionary model, a log-normal relaxed molecular clock with a mean substitution rate of 7. 8610e-9 per site per year and visualized with Tracer version 1. 5  [25]. The evolutionary rate was calculated on the basis of the P. peloponnesiaca and P. cretensis divergence time. This analysis was run multiple times to check for convergence with 50 million iterations and samples drawn every 5000 MCMC steps, after a discarded burn-in of 5 million steps.
To confirm BEAST results and to get Bayesian posterior probability values of the tree we also applied MrBayes version 3. 1. 2 [32]. We ran the same dataset with 10 million generations (after this number of generations the standard deviation of split frequencies had reduced to less than 1%) with a sampling frequency of 1000, to be sure that a good sample of the posterior distribution had been obtained. The first 2500 sampled trees were discarded as 'burn-in' and posterior probabilities were calculated and reported on a 50% majority rule consensus tree of the remaining 7501 trees in the sample. The GTR+G evolutionary model was used. Moreover, a Maximum Likelihood analysis was performed with PAUP* version 4. 0  [33]  using tree-bisectionreconnection (TBR) branch swapping with 1000 rearrangements and 100 bootstrap replicates.
Additionally, we performed phylogenetic analyses based on three nuclear genes (C-mos, ACM4, Mc1r) on a subset of 92 samples of the entire dataset (64 samples from eastern Alps and 28 from the whole distributional range of the species), selected to include all the major cytb mtDNA clades. We performed phylogenetic tree reconstructions for each single nuclear gene and for a concatenated sequence of 1598 bp. We first applied PartitionFinder version 1. 1. 1 [34]  in order to test the best partition scheme for codon positions and different single gene models of molecular evolution using the Bayesian information criterion (BIC). Four partitions were identified in nuclear sequences: ACM4 and C-mos 1 st position (GTR+I+G), C-mos 2 nd position (JC), Cmos 3 rd position and Mc1r 3 rd position (GTR) and Mc1r 1 st and 2 nd positions (HKY+I).
These partitions and models were applied for performing phylogenetic reconstruction for each single nuclear gene and for the concatenated sequence using MrBayes version 3. 1. 2 [32]  for Bayesian analyses with the same settings as for mtDNA; for Maximum Likelihood analyses we used RAxML (version 7. 4. 2, [35] ) and each partition was run under GTR+G model. For nuclear phylogenetic reconstructions, we used Atlantolacerta andreanskyi as outgroup (accession numbers: JX485363, JX462052, JX461870), being the phylogenetically closest lacertid lizard with all three nuclear genes available  [21]. We chosen not to concatenate mtDNA and nuclear genes, since we focused cytb analysis on the estimation of divergence time between Z. v. carniolica and Z. v. vivipara taking advantage of a calibrated external node dated on the divergence time between P.
Cytb A 385 bp portion of the mtDNA cytb gene was examined in our 191 Italian samples. A total of 28 polymorphic sites, all of which were parsimony-informative, and 11 haplotypes were identified. Five of these were new haplotypes, since no match was found with any previous published haplotype (Table  S2 ). No deletions or insertions were observed in our dataset. Mean nucleotide percentage composition was T, 35. 5; C, 25. 3; A, 26. 7; G, 12. 4; the estimated transition/transversion ratio was 8. 36. We reconstructed a phylogenetic tree using Italian samples from this study and deposited sequences from different clades and origins. The phylogenetic tree obtained using BEAST, ML and Bayesian inference (Figure  2 ), with Lacerta viridis as the outgroup, was topologically similar to others reported in the literature  [12, 13].
In contrast, two out of five ACM4 alleles (ACM4_4 and ACM4_6) are shared among individuals of Z. v. carniolica and individuals of clades B (Z. v. louislantzi), D (Z. v. vivipara), E (Z. v. vivipara) and F (Z. v. vivipara). Similarly, phylogenetic trees obtained from each single nuclear gene suggested a highly supported (bootstrap and Bayesian posterior probability higher than 97% and 0. 97, respectively) monophyly of Z. v. carniolica in C-mos and Mc1r (Figure  S3 ), but not in ACM4. Phased alleles along with their accession numbers were listed in Table  S2. After concatenating the three genes, we obtained a 1598 bp sequence. Thirty-two variants out of the 92 sequences were identified. The tree topology obtained with different methods was concordant so that only the Maximum Clade Credibility Tree is presented in Figure  5.
Acknowledgments Barbara Crestanello  and  Alexis Marchesini  are warmly thanked for their help during sampling, molecular and data analyses. Map of sampling sites and editing of figures have been made possible with the help of  Clara Tattoni  and  Andrea Gandolfi. Thanks are due to  Samuele Ghielmi  for comments and information on sampling sites. Giorgio Bertorelle ,  Gabriele Gentile ,  Jim P. Arntzen  and  Sean Hoban  provided us with helpful suggestions that significantly improved an earlier version of the manuscript. We also thank  Dan Sargent  for the final linguistic revision. Finally, we would like to thank the contribution of two anonymous reviewers that helped us significantly improve the manuscript.
© 2014 Michelet et al. ; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4. 0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
Search in RASS data As detailed in Planck Collaboration Int. IV (  2013 ), the validation follow-up with XMM-Newton has shown the importance of the RASS data to assess the reliability of the Planck sources. In particular, Planck Collaboration Int. IV (2013) showed that a large fraction of Planck clusters are detectable in RASS maps, but this depends on the region of the sky and on the ratio Y 500 /S X which exhibits a large scatter (see later in Fig. 31  the case of the PSZ sources). We therefore exploit the RASS data to consolidate the identification with clusters from optical catalogues (see below Sect. 4. 3. 2) and to assess the reliability of the Planck SZ candidates. We first perform a cross-match with the RASS bright source catalogue (BSC,  Voges et al. 1999 ) and the faint source catalogue (FSC,  Voges et al.
2000)  within a 5 ′ radius of the position of each of the Planck SZ detections. We then perform a reanalysis of the RASS data following the methodology and prescriptions given by  Böhringer et al. (2000 Böhringer et al. ( , 2004) )  and  Reiprich & Böhringer (2002). We compute count-rate growth curves in order to check for the extension of the signal. We estimate the source flux from both the growth curve (when adequate) and from a fixed 5 ′ aperture radius with respect to the surrounding background (after PS subtraction). We then derive the associated S/N in RASS, (S/N) RASS. For this, we make use of the RASS hard-band, [0. 5-2] keV, data that maximize the S/N of the detections. We furthermore computed the source density map of the BSC and FSC catalogues and the associated probability that a Planck cluster candidate will be associated with a B/FSC source within a radius of 5 ′.
5×10 14 M ⊙ and 5. 7×10 14 M ⊙ , and Q SDSS,dat = 3 otherwise. The cluster-finder algorithm outputs the position of the counter-part (Brightest Cluster Galaxy (BCG) and barycentre) and the estimated photometric redshift. When spectroscopic data are available for the brightest selected galaxy a spectroscopic redshift is also reported. The outputs of the cluster-finder algorithm are compared to those obtained by  (Li & White 2014, in prep)  from different method based on the analysis of the full photometric-redshift probability distribution function  (Cunha et al. 2009). In this approach, the position and redshift in the SDSS data that maximizes the S/N are considered as the best estimates for the counter-parts of the Planck SZ detections.
Search in WISE data WISE provides an all-sky survey at 3. 4, 4. 6, 12, and 22 µm (W1, W2, W3, W4) with an angular resolution of 6. 1 to 12. 0 arcsec in the four bands. We search for counter-parts of the Planck SZ detections in the WISE source catalogue in two ways. On the one hand, we run an adaptive matched filter cluster finder developed by  (Aussel et al. 2014, in prep) , similar to the one described by  Kepner et al. (1999) , using the cluster members' luminosity function of  Lin et al. (2012). The background counts were determined from the neighbouring square degree in the vicinity of the Planck cluster candidate, excluding regions of fifteen arcmin centred on candidate positions. On the other hand, we use a method developed by  (Aghanim & Fromenteau 2014, in prep)  based on a search for overdensities of bright (W1 ≤ 17) and red (W1-W2 > 0) sources within a 5 ′ radius circle centred on the position of Planck detections with respect to a background computed in a 15 ′ radius area.
Aghanim & Fromenteau (2014, in prep)  find that a goodquality association between a Planck SZ-detection and a counter-part overdensity in WISE data is reached when there are at least ten galaxies above 2 σ in the 5 ′ search region, and when the corresponding fraction of galaxies is at least 30% of the total number of galaxies retained in the 15 ′ circle. Performing the search for counter-parts of an ensemble of random positions on the sky, we compute the purity of the detections, i. e. , the probability of a Planck candidate having a real counter-part in the WISE data as opposed to a chance association. The quality criterion for the association between Planck detection and WISE overdensity is high, Q WISE = 1, for a purity larger than 90%. When it lies between 90% and 80% the association of Planck SZ-detections and WISE overdensities is assigned a lower quality criterion Q WISE = 2.
DSS images Finally for each Planck cluster candidate, the second Digitized Sky Survey 7  (DSS) database was queried for a field of 5 ′ × 5 prime centred at the position of the Planck SZ detections in the r and ir bands. The DSS images were used for visual inspection. 8 lusters and rich groups out to z ≃ 0. 3 to 0. 4 can easily be identified in these plates as an obvious concentration of galaxies. This qualitative information was thus used: (i) to consolidate some identifications of Planck SZ detections with previously-known clusters; (ii) to optimize our strategy for the follow-up observations of Planck candidates (see Sect. 5); and (iii) to qualitatively assess the reliability or significance of the Planck SZ detections.
Identifications from NED and SIMBAD The information provided from querying NED and SIMBAD databases is mainly redundant with cross-checks with cluster catalogues. However, it lets us avoid missing a few associations. We therefore performed a systematic query in SIMBAD and NED with an adopted search radius set to 5 ′. Similarly to the association with clusters in optical catalogues, the positional association is consolidated using the results of the search in RASS data. Furthermore, the Planck-candidates solely matching NED or SIMBAD entries were inspected and the identification was confirmed or discarded using the information from WISE counter-parts and the DSS images.
Planck clusters associated with known clusters A total of 683 out of 1227 SZ detections in the Planck catalogue, i. e. , 55. 7%, are associated with previously-known clusters from X-ray, optical, or SZ catalogues, or with clusters found in the NED or SIMBAD databases. We give the number of clusters identified in each category and we discuss notable cases of known clusters that are not included in the Planck SZ catalogue. The validation follows the order of association with MCXC clusters, then Abell and Zwicky clusters, then SDSS clusters, then SZ clusters, and finally clusters from NED/SIMBAD.
Identification with clusters from NED or SIMBAD As expected only a small number of clusters are identified from querying the databases, supplying identifiers for thirteen SZ Planck detections. This is because the information in NED and SIMBAD is redundant with that in the X-ray, optical, or SZ catalogues used for the external validation. The thirteen clusters found solely from querying the databases are found in the RASS survey but not in dedicated cluster catalogues, and thus not included in the MCXC; they are found in serendipitous Chandra surveys, or they are part of miscellaneous cluster catalogues.