dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS Funding for RAVE has been provided by: the  Anglo-Australian Observatory ; the  Leibniz-Institut fuer Astrophysik Potsdam ; the  Australian National University ; the  Australian Research Council ; the  French National Research Agency ; the  German Research foundation ; the  Istituto Nazionale di Astrofisica at Padova ; The  Johns Hopkins University ; the  National Science Foundation of the USA  ( AST-0908326 ); the W. M. Keck foundation; the  Macquarie University ; the  Netherlands Research School  for Astronomy; the  Natural Sciences and Engineering Research Council of Canada ; the  Slovenian Research Agency ;  Center of Excellence Space. si; the  Swiss National Science Foundation ; the  Science & Technology Facilities Council  of the UK; Opticon; Strasbourg Observatory; and the  Universities of Groningen, Heidelberg and Sydney.
The RAVE web site is at http://www. rave-survey. org. This research made use of the cross-match service, the SIMBAD database and the VizieR catalog access tool provided by  CDS, Strasbourg. This publication makes use of data products from the  Two Micron All Sky Survey , which is a joint project of the  University of Massachusetts  and the  Infrared Processing and Analysis Center/California Institute of Technology , funded by the  National Aeronautics and Space Administration  and the  National Science Foundation.
28 Using qRT-PCR on sorted cells at 6 hpA, we observed that tnfr1 was expressed both by macrophages (mpeg1 + and mpeg1 + tnfa + ) and non-macrophage cells (neg) although to a higher extent for M1-like macrophages (mpeg1 + tnfa + ) (Figure  5g ). To investigate more specifically the role of TNFα/TNFR1 signaling during fin regeneration, morpholino-mediated gene knockdown was used. First, using primers on either side of the splice sites targeted by the tnfr1 morpholino (tnfr1 MO), we provided evidence for the partially altered splicing patterns of tnfr1 in 3 dpf tnfr1 morphants (Supplementary Figure  5 ). Second, to examine the role of tnfr1 in macrophage recruitment, we injected a tnfr1 MO in Tg(mpeg1:mCherry-F) embryos. Although the overall morphology of tnfr1 morphants without amputation as well as their total macrophage population were not significantly affected (data not shown), we observed that macrophages were recruited at 3 hpA.
Larvae were maintained in the dark as MTZ is sensitive to long light exposure. As controls, NTR transgenic larvae were incubated in fish water without MTZ, and wild-type siblings were incubated in zebrafish water with MTZ (5 mM) under the same conditions. Ablation efficiency was assessed by imaging using the MVX10 Olympus microscope (MVPLAPO × 1 objective and XC50 camera, Olympus, France), and by cell counts using FACS LSRFortessa (BD Biosciences, France). Cytometry data were analyzed using the Flowjo software (Tree start, Ashland, OR, USA). For tnfa transcriptional inhibition, larvae were incubated in zebrafish water supplemented with either 0. 35% DMSO (control) or with 35 μM of Pentoxifylline (Sigma-Aldrich) immediately after caudal fin amputation at 3 dpf. Parabiosis experiments. Generation of parabiotic embryos was as previously  29  with the following modifications.
Ringer solution was replaced by embryo water at 24 hpf. FACS purification, mRNA isolation from macrophage subsets and qRT-PCR on sorted cells. mCherry-F + GFP-F -and mCherry-F + GFP-F + cell sorting from 300 Tg(mpeg1:mCherry-F/tnfa:eGFP-F) larvae following amputation, mRNA extraction and qRT-PCR were performed as previously described,  16  and tnfr1 expression was detected using zTNFR1. 5 (see above) and zTNFR1. 3 (5′-CGAGAGCATTCCCATCCTAA-3′). RNA preparation on larva tails and quantitative RT-PCR. To determine the relative expression of tnfa, il1b and il8, total RNA from larva tails (pools of 10 or 18 tails each) was prepared at different time points post amputation. RNA preparation and reverse transcription were as described in ref. 45. QRT-PCR analyses were performed using LC480 software.
Mann-Whitney , one tail was performed to test significance for Figures 4c andd,5c, f and g,6j and n and Supplementary Figures S4c,S4d, S4e and S6f. ANOVA using Kruskal-Wallis with a Dunn's post-test was performed to test significance for Figures 3c ande using GraphPad Prism 5 Software. Acknowledgements. This work was supported by  Inserm  and grants from ' Région Languedoc-Roussillon ,  Chercheur d'Avenir ' no. DGA3/DESR 2012/Q209 , from  French National Research Agency 'Zebraflam ' no. ANR-10-MIDI-009  and from the  European Community 's  Seventh Framework Program  ( FP7-PEOPLE-2011-ITN ) under the  Marie-Curie Initial Training Network FishForPharma  (Grant Agreement No. PITN-GA-2011-289209 ). We thank the  MRI facility  for their assistance,  J-P. Levraud  ( Institut Pasteur, France ) for trans-shipping Tg(mpeg:Gal4;UAS:NTR-mCherry) line,  A.
Raw spectra were processed using Mascot through Proteome Discoverer (version 1. 4, Thermo Scientific) and further analyzed in myProMS  (Poullet et al. , 2007). Data was searched against a Swissprot fasta database containing S. cerevisiae sequences. Only proteins found in two experiments and not in the control IPs were considered candidates. Co-immunoprecipitation 6. 10 8 cells were harvested, washed one time with PBS, and lyzed in 1. 5 ml lysis buffer (20 mM HEPES/KOH pH7. 5; 150 mM NaCl; 0. 5% Triton X-100; 10% Glycerol; 1 mM MgCl2; 2 mM EDTA; 1 mM PMSF; 1X Complete Mini EDTA-Free (Roche); 1X PhosSTOP (Roche); 125 U/mL benzonase (Sigma)) and glass beads three times for 30 s in a Fastprep instrument (MP Biomedicals, Santa Ana, CA). The lysate was cleared by centrifugation at 13,000 g for 5 min.
The screens were scanned using Typhoon FLA 9500 (GE Healthcare) and quantified by Image Quant software. Helicase assays were conducted as described before  (Nakagawa and Kolodner, 2002b). 1 nM DNA substrate (in molecules) was used in all experiments. Reactions were assembled on ice, initiated by adding Mer3 and incubated for 30 min at 30 ˚C. The reactions were then stopped with 5 ml of stop solution containing 150 mM EDTA, 2% SDS, 30% glycerol, 0. 25% bromophenol blue and 1 ml Proteinase K (14-22 mg/ml, Roche) for 15 min at 30 ˚C. The products were separated by electrophoresis on 10% polyacrylamide gel (acrylamide: bisacrylamide 19:1, Biorad) at room temperature. Gels were dried and quantified as above. The nuclease assays were performed as described before  (Ranjha et al. , 2014). The products were analyzed on 1% agarose gels, post-stained with Gel Red and imaged on a UV illuminator.
We used the sequences from SGD for S288C (S288C_reference_genome_R64-1-1_20110203) and the sequence made by Scott Keeney's lab (http://cbio. mskcc. org/public/SK1_MvO/) for SK1. Next, sequencing reads from octad were aligned on the S288C and SK1 reference sequences using the BWA software  (Li and Durbin, 2009)  to genotype SNPs and deduce recombination events. Only perfectly matching reads to the reference sequences were taken into account. The average sequencing depth was 70X. The SNP genotype was considered as valid when there was one type of reads only (S288C or SK1) and if the number of reads was comprised between 15 and 150, or when there was two types of reads if the most abundant was comprised between 40 and 150 and the less abundant was below 5. Deduction of strand.
ACKNOWLEDGMENTS The authors are grateful to  Ewold Verhagen  and  Ruben Maas  for critical reading of the manuscript,  Henk-Jan Boluijt ,  Mohamed Tachikirt ,  Marco Seynen , and the mechanical workshop at AMOLF for help with custom parts of the experimental setup. This work is part of the research programme of The  Netherlands Organisation for Scientific Research (NWO). The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/ 2007-2013)/ ERC  Grant Agreement No. 337328 , " Nano-EnabledPV ". H. L. and Y. Z. acknowledge the support of  Leverhulme Trust. H. L. would like to thank  The Royal Society  for funding his  University Research Fellowship.
Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration-specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ∼50 × 50-km cells across the entire vegetated land surface.
We do this in several ways-without defining the PFT of each grid cell and using 4 or 14 PFTs; each model's predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means. M odeling global climate and the carbon cycle with Earth sys- tem models (ESMs) requires maps of plant traits that play key roles in leaf-and ecosystem-level metabolic processes  (1) (2) (3) (4). Multiple traits are critical to both photosynthesis and respiration, foremost leaf nitrogen concentration (Nm ) and specific leaf area (SLA)  (5) (6) (7).
Each of these methods has limitations-for example, trait-environment relationships do not well explain observed trait spatial patterns  (1, 25) , while species-based approaches limit the scope of extrapolation to only areas with well-measured species abundance. More critically, the first two global methodologies emphasized estimating a single trait value per PFT at every location, whereas both ground-based  (5, 14)  and remotely sensed  (26)  observations suggest that at ecosystem or landscape scales traits would be better represented by distributions. Here, we use an updated version of the largest global database of plant traits  (14)  coupled with modern Bayesian spatial statistical modeling techniques  (27)  to capture local and global variability in plant traits. This combination allows the representation of trait variation both within pixels on a gridded land surface and across global environmental gradients.
This is reinforced by recent modeling studies that have begun to incorporate distributions of traits at regional  (29, 30)  and global  (31)  scales. It has been shown that using trait distributions leads to different estimates of carbon dynamics  (32)  and that higher-order moments of trait distributions contribute to sustaining multiple ecosystem functions  (33). While species-level mapping  (21, 23, 24)  does capture trait distributions, it has been limited geographically and restricted to subsets of functional groups. Even the largest plant trait database offers only partial coverage across the globe in terms of site-level measurements. Hence, gap-filling approaches need to be adopted to extrapolate trait values at regions with no data coverage. Here, we overcome data limitations through PFT classification, trait-environment relationships, and additional location information to develop a suite of models capable of estimating trait distributions across the entire vegetated globe.
The greatest decrease in RMSPE occurs between the PFT-free grouping (a single category for all plants) and the broad (4-PFT) grouping across each of the models tested. If our trait data were perfectly predicted by environment, there would be no usefulness to including PFTs in mapping traits. That this is not so implies that the broad PFTs, based primarily on growth form and leaf type, offer superior predictive skill than environmental covariates on their own  (19). However, the extra information in the narrow (14-PFT) grouping does further improve the fit and produces the most accurate predicted trait surface. Global Maps. We selected two sets of maps to describe, in broad strokes, how trait distributions vary across the land surface: the narrow 14-PFT spatial model and its categorical counterpart.
2A ) and more strongly highlights the gradient of Pm from the tropics to the Arctic (16) (Fig. 4A ). The most consistent estimates between the categorical and spatial models are in the boreal regions dominated by needleleaved trees; the measurements in this region are relatively sparse, which may have limited the ability of the spatial model to capture differences. On the other hand, broad-leaved trees span a wide range of environments, but a large portion of the measurements come from the tropics (66%), where there is a limited range of values among the climate covariates and therefore little variation with which to estimate a correlation. The grasses and shrubs have the largest SDs of the four broad PFTs (SI Appendix, Table  S4 ) and dominate wide swaths of the land surface, but have fewer measurements-shrubs are the least measured of the broad PFTs in the database, and this appears to reduce the accuracy of the categorical model more than that of the spatial model (Table  1 ).
Complementary work has retrieved leaf trait maps from a global carbon cycle model fused with Earth observations  (38) , providing another method that could be used for direct comparison against the trait maps produced here. While the methodology outlined in our analysis brings the possibility of a PFT-free land surface closer, we remain several steps away from being able to make such maps as accurately as we do using PFT characterizations for trait prediction. Several actions can bring us closer to that goal. First, incorporation of additional information (such as phylogenetic relatedness and trait-trait covariance) will likely improve trait maps, even using existing observations. Second, as the current level of observations is extremely sparse in some regions and sparse in most regions, expanded trait databases will also aid in development of PFT-free trait maps.
Materials and Methods Data. The TRY database (www. try-db. org) (  14 ) provided all data for leaf traits and the categorical traits to aggregate PFTs (TRY-Categorical Traits Dataset, https://www. try-db. org/TryWeb/Data. php#3, January 2016) used in the analysis. The TRY data may be requested from the TRY database custodians. See SI Appendix, section S10 for a complete list of the original publications associated with this subset of TRY. The extract from TRY used here has just under 45,000 measurements of individuals from 3,680 species with measurements of at least one of SLA, leaf nitrogen per dry leaf mass (Nm), and/or leaf phosphorus per leaf dry mass (Pm). The number of individual measurements varies from 32,315 for SLA on 2,953 species to 19,282 for Nm on 3,053 species down to 8,052 for Pm on 1,810 species; see SI Appendix, Table  S4  for the number of unique measurements and species found in all categorizations used in the analysis.
We finally selected 5 predictors-mean annual temperature (MAT), total annual radiation (RAD), moisture index (precipitation/evapotranspiration) (MI), percentage of hydrogen (aqueous) (pH), and percentage of clay content (CLY). Remote-sensing data products, such as Normalized Difference Vegetation Index  (52) , are not used as covariates, to allow for inference outside of the historical observation period through perturbations of environmental covariates. We used environment-trait relationships to obtain predictions of trait values  (1, 16-18, 37, 43)  in a linear regression setup. The formal details of the initial model are as follows. We denote log-transformed trait values at a geographical location s as y trait (s). This set of five predictors at a location s is denoted by the vector x(s) = (x 1 (s), x 2 (s),.
As mentioned earlier, we have PFT classifications for each observation of the dataset used here and satellite estimates of PFT abundance at all pixels. The global regression approaches described above ignore this information and can yield biased predictions at locations dominated by PFTs poorly represented in the data, such as shrubs. Hence, we also incorporate the PFT information in these regression models by allowing the trait-environment relationship to vary between different PFTs. Finally, the PFT-specific distributions from the Bayesian models were weighted by the satellite-based PFT abundances to create a landscape-scale trait distribution, thereby enabling straightforward comparison between all three categorizations of PFT. Details of the PFT-based Bayesian models are provided in SI Appendix, section S2.
This study has been supported by the TRY initiative on plant traits (www. try-db. org). The TRY database is hosted at the Max Planck Institute for Biogeochemistry (Jena, Germany) and supported by DIVERSITAS/Future Earth, the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, and the EU H2020 project BACI (Grant 640176). B. B. acknowledges a Natural Environment Research Council (NERC) independent research fellowship NE/M019160/1. J. P. acknowledges the financial support from the European Research Council Synergy Grant ERC-SyG-2013-610028 IMBALANCE-P, the Spanish Government Grant CGL2013-48074-P, and the Catalan Government Grant SGR 2014-274. B. B. -L. was supported by the Earth System Modeling program of the US Department of Energy, Office of Science, Office of Biological and Environmental Research.
K. K. acknowledges the contribution of the Wageningen University and Research Investment theme Resilience for the project Resilient Forest (KB-29-009-003). P. M. acknowledges support from ARC Grant FT110100457 and NERC Grant NE/F002149/1. W. H. acknowledges support from the National Natural Science Foundation of China (Grant 41473068) and the "Light of West China" Program of the Chinese Academy of Sciences.
The grassland is characterized by an abundance of positive net microbial fluxes and the model proves to be a promising tool capable of capturing the day-to-day variability in microbial fluxes with a relatively small bias and sufficient accuracy. PLAnET is still in its infancy and will benefit from future campaigns extending the available training dataset as well as the inclusion of ever more complex and critical phenomena triggering the emission of microbial aerosol (such as rainfall). The model itself is also adaptable as an emission module for dispersion and chemical transport models, allowing further exploration of the impact of landcover-driven microbial aerosols on the atmosphere and climate.
e. , the still air chamber), and diameter of the collection probe (0. 08 m) was at least 40 % larger than the nozzle diameter  (Marple and Olson, 2011). Data from literature indicate a sampling efficiency ranging from 80 to 100 % for mildew spores  (Schwarzbach, 1979). Given the Burkard sampler's high flow rate, sampling happens at a super-isomean-velocity compared with external wind speed. The sampling efficiency is therefore expected to decrease for larger particles proportionally with the ratio between external wind speed and the Burkard's sampling speed  (Brockmann, 2011). A series of 10 min samplings with the Burkard samplers kept at the same height were performed to evaluate the minimum resolvable gradient (MRG) following Eq. (  1 ) below  (Edwards et al. , 2005; Fritsche et al. , 2008).
The algorithm was run iteratively through MATLAB's GlobalSearch function in order to avoid finding a set of parameters satisfying only a local minimum. To avoid mathematically sound, but nonrealistic solutions, GlobalSearch was looking for minima only within a bounded parameter space. The upper and lower bounds of the parameter space are shown in Table  1. The percentage of leaf area exposed to turbulence was arbitrarily estimated to correspond to 5 % of the average leaf area density of the grassland that was set at 94 g m -2  (Sims and Singh, 1978). The latter assumption was needed to scale the measurements of  Hirano and Upper (1986)  and  Wilson and Lindow (1994)  in CFU per gram to the units needed by the model (CFU m -2 ). The calibrated model was then run on the data collected in 2015 in order to assess its performance on a dataset not used for training.
Acknowledgements. Part of this work was funded thanks to the  AirFors  project ( PIAPP-GA-2011-286079 ), part of the  Seventh Framework European Programme. The authors wish to thank  Alessandro Zaldei  for his contribution to the preparation of the instrumentation for the 2015 campaign, as well as the technical and engineering staff from the  INRA Unit of Plant Pathology  ( Jean-Marc Bastien ,  Joël Beraud ,  Jean-Francois Bourgeay ,  Frédéric Pascal and Michel Pascal)  for their support during the field campaigns. A special thanks goes to  André Chanzy  ( INRA ) for his precious help on Montfavet meteorological data. Edited by: Alex Huffman Reviewed by: three anonymous referees.
Data availability. The model used in this text is available in the MathWorks repository as indicated in Sect. 5. All the other publicly available data employed (such as MODIS and GFS data) are correctly referenced in the text. As for the authors' microbial concentration and flux data, they are still undergoing processes for future publications and are therefore not eligible to be released publicly just yet. The Supplement related to this article is available online at https://doi. org/10. 5194/acp-17-14919-2017supplement.
Our observations suggest that the foramen is always present, though it is not always easy to observe (Figure  4 ). Therefore, we have scored both as present. All these changes in scoring, and others not commented here for lack of space, are documented in SOM 1, a Mesquite Nexus file incorporating the data matrix and several trees, which can be accessed in the HAL open archive (https://hal. archivesouvertes. fr/) through this link: https://hal. archives-ouvertes. fr/ hal-01618314. Note that the Nexus format can also be read by MacClade 3. 0  (Maddison and Maddison, 1992)  and PAUP 4. 0. This specimen is an almost complete skull and part of the postcranial skeleton (not shown) housed in the Fossil Vertebrate Collection of Instituto de Geociencias, São Paulo University. Scale bar: 10 mm. a, angular; ax, axis; cev, cervical vertebra; d, dentary; f, frontal; j, jugal; l, lacrimal; ltf, lower temporal fenestra; mx, maxila; n, nasal; p, parietal; pf, postfrontal; po, postorbital; pp, postparietal; prf, prefrontal; q, quadrate; qj, quadratojugal; sa, surangular; sm, septomaxilla; sp, splenial; sq, squamosal; st, supratemporal.
Exhaustive Taxonomic Sample The search yielded two most parsimonious tree requiring 383 steps, with a CI of 0. 5666 and with a retention index of 0. 6605 (Figure  5 ). All lengths reported here were computed in Mesquite 3. 1, by distinguishing between partial uncertainty and polymorphism; under the default settings PAUP 4 considers all these as uncertainty, though settings can be changed to interpret these data as in Mesquite. This introduces only a difference in tree lengths between both programs, though the shortest trees in one program remain the shortest ones in the other. In these trees, mesosaurs appear as the sister-group of all other sauropsids, as they were in  Laurin and Reisz (1995). However, sauropsid phylogeny differs strongly from the topology recovered by  Laurin and Reisz (1995)  and most recent analyses because parareptiles appear to be nested within diapsids, as the sister-group of younginiforms (more crownward diapsids are not included in our taxonomic sample).