dataesr/scientific-paragraphs-categorization huggingface dataset:
Abbreviation, n. d. not detected in the Trifolium root microbiome using high-throughput sequencing a Taxonomy based on Greengenes 16S database  [51]  b Mean bacterial cell number on roots after 25 days in the microcosms in experiment 3 c Highest order of magnitude at which observed OTUs were recorded dominant R. leguminosarum and the less-frequent rhizobia species Bradyrhizobium japonicum, Sinorhizobium sp. , and Mesorhizobium  [27, 28]. For the purpose of the microcosm experiments, we described the root microbiome of Trifolium at a whole-root scale, sampling the entire root system including nodules. For a broader description of legume microbiomes, future work investigating the variation in multiple soil types and comparisons with non-legume plants is needed. Additionally, an indepth spatial assessment of legume root microbiomes would be insightful, e.
4b ) and its classification in the Actinobacteria, a phylum shown to be abundant in plant root microbiomes  [25]. We confirmed that these strains successfully colonized plant roots as suggested by the higher abundances on roots compared to their initial inoculated density to the microcosms (Table  2 ). We could not capture the Microbacterium strain with the community quantification method (Fig. 6b ), and similarly, none of the seven isolates from the reference stock clustered to any OTU in the entire dataset. A first possible explanation is that the Microbacterium is a rare but easily culturable microbiome member. Alternatively, the Microbacterium could be an abundant microbiome member, as indicated by the numerous isolates in the reference stock, but absent in the community profiles because of an observed mismatch in the priming site of the PCR primer 799F.
Sequence processing The raw reads were processed using an in-housedeveloped bioinformatics pipeline, which is available in Additional file 3. Briefly, the raw paired-end reads were quality filtered and trimmed at the 3′-end to 280 bp using PRINSEQ v0. 20. 4  [35]  to improve the merging success and reduce error rate  [36]. The trimmed pairedend reads were merged with FLASH v. 1. 2. 9  [37]. Sequences from individual samples were de-multiplexed according to the forward barcode using Cutadapt v1. 4. 2  [38]. The merged 16S sequences were quality filtered with PRINSEQ and for OTU delineation truncated at a fixed length of 360 bp, sorted by abundance, de-replicated, and clustered to operational taxonomic units (OTU, ≥97% sequence similarity, singletons removed) with UPARSE v8. 0. 1623  [39]. Amplicons were chimera-screened against the GOLD database v.
Statistical analysis of community profiles All analyses were performed using R v3. 1. 2 [43]  and different R packages. The R code and input files required to replicate all analyses and figures is available in Additional file 4, and the approach is outlined in Additional file 1: Supplementary methods. Briefly, the OTU and taxonomy tables were filtered to exclude OTUs classified as eukaryotes, chloroplasts, and mitochondria. The OTU table was rarefied to 20,000 sequences per sample (Additional file 1: Supplementary methods, Figure  S10 ), and the abundance of each OTU was expressed as percentages of the total number of counts in a sample. All statistical analyses were performed on log 2 + 1 transformed data. All P values were adjusted for multiple comparisons with the false discovery rate (FDR) correction using the Benjamini-Hochberg method  [44].
Bacteria reference stock Detailed information regarding isolation, sequencing, and taxonomic assignment of bacteria isolates is available in Additional file 1: Supplementary methods. We isolated root-associated bacteria from two climate chamber experiments and from Trifolium individuals collected from the field site by plating serial dilutions of a root slurry onto flour medium agar  [48]  plates amended with 10 μg mL -1 cycloheximide (to inhibit fungal growth; Sigma Aldrich, St. Louis, MO, USA). DNA extracted from single colony isolates was subjected to PCR using the primers 27F  [49]  and 1401R  [50]  and Sanger sequenced with 1401R as the sequencing primer by Microsynth AG (Balgach, Switzerland). These sequences were used for taxonomy assignment using the RDP classifier against the SILVA (v119)  [41]  database as implemented in QIIME  [42].
UDF6462: ANCILLARY DATA AND INTEGRATED PROPERTIES For our analyses we use a number of ancillary multi wavelength datasets available for UDF6462, including Hubble Space Telescope (HST ) imaging at ∼ kilo parsec resolution, (M /yr) 3. 4±0. 5 I CO(5-4) (Jy km s -1 ) 0. 308±0. 055 L CO(5-4) (10 9 K km s -1 pc -2 ) 1. 65±0. 39 S cont (µJy) 106. 7±26. 2 M gas (10 10 M ) 2. 0 +4. 4 -1. 4 M mol. (10 10 M ) 1. 9±1. 6 infrared (IR) observations from the Spitzer and Herschel satellites and radio maps obtained with the Karl G. Jansky Very Large Array (VLA). We describe in detail these data in the following section. The main physical properties for UDF6462 are provided in Table  1.
The 6GHz flux density of UDF6462 was measured following the Gaussian fitting approach with the PyBDSM software described in  Rujopakarn et al. (2016). To recover extended emission, the fitting was performed on several tapering scales and the one maximizing the signalto-noise ratio (SNR) was chosen as the optimal one for source extraction. With this method we obtained a total flux density of S 6GHz = (3. 9 ± 0. 6) µJy with a SNR=6. 6 and extended over a Gaussian with beam-deconvolved full width at half-maximum (FWHM) of (0. 85 ± 0. 23) × (0. 42 ± 0. 09). This flux density corresponds to a rest-frame luminosity at 1. 4GHz of L 1. 4GHz = 4πD L (1 + z) 1-α S 1. 4GHz = 4πD L (1 + z) 1-α S 6GHz 6 1. 4 α (1) = (1. 35 ± 0. 21) × 10 23 W Hz -1 , (2) when assuming a radio power law S ν ∝ ν -α with α = 0.
2015)  produces observations with a nucleus that is too faint with respect to the real data by roughly a factor × 2. Two facts contribute to this difference: (1) the scatter in the SFR-L IR and L IR -L CO(5-4) relations, and (2) the fact that the SFR map derived from the HST images tends to underestimate dust extinction in the galaxy centre (see Appendix A and discussion in Section 3. 4). To generate simulations that closely reproduce the observed properties of UDF6426, we therefore renormalized the input SFR map to match the observed total line flux over the nuclear region. We processed the simulated data for the extended CO(5-4) emission exactly as the real ALMA observations (channel averaging, uv-plane fitting, etc. ), and found that we were able to recover the intrinsic ∼ 1 size of the input model and its total flux, despite the fact that, just like in the real data, only the central region was clearly visible in the simulated images (see Fig.
Molecular gas mass from CO(5-4) emission We can also use the CO(5-4) data to calculate molecular gas masses, M mol , for the nuclear region and the individual clumps in UDF6462. We stress here that the derivation of the total molecular gas content, to which a more diffuse component than the dense gas traced by CO(5-4) could also contribute, involves two steps that are subject to large uncertainties: the conversion of L CO(5-4) into a CO(1-0) luminosity (for which an excitation correction R 51 must be adopted) and the conversion of CO(1-0) luminosity into molecular mass (for which a specific mass-to-light ratiothe CO-to-H 2 conversion factor, α CO -must be chosen). To highlight the systematics involved in this estimate, we carry out the calculation of M mol for a range of plausible values for R 51 and α CO and show in Fig.
Interaction-driven star formation could explain such a behaviour, but the origin of the enhanced SFE is unclear. Finally, another potential explanation for our data could be a radial dependence of the L CO(5-4) -SFR relation, caused by changes in temperature, radiation field or stellar and gas density. In nearby disc galaxies, high interstellar gas pressure leads to an increase of the dense gas fraction in galaxy centres. As this does not proportionally express itself in form of a higher SFR, variations of the ratio between the intensity of dense gas tracers and IR emission (e. g. , L HCN /L IR ) are observed between the inner and outer regions  (Usero et al. 2015; Bigiel et al. 2016 ). The CO(5-4) peak in the centre of UDF6462 could therefore reflect a more efficient production of the dense gas phase within this region, compared to the less dense intra-clump medium.
A fourth galaxy (labelled Ex1) was not previously detected and we report its 1300 µm continuum flux for the first time here. Near the edge of the field of view of our ALMA observation falls another galaxy (UDF16) from the sample of  Dunlop et al. (2017). At the reduced sensitivity over this region of the ALMA map, this source is not detected in our data. We utilized these four galaxies, plus UDF6462, to correct the astrometric offset between ALMA and HST imaging, which we discovered when analysing the ALMA data. Overlaid on the H-band images in Fig. B1  are the ALMA contours of either the CO(5-4) emission, in the case of UDF6462, or the continuum emission for the other four galaxies. A systematic shift of the HST centroids with respect to the ALMA data is evident in all cases. This shift is mostly in the North-South direction.
ACKNOWLEDGEMENTS We thank the anonymous Referee for their constructive comments. AC ( ORCID 0000-0003-4578-514X ) acknowledges support from the  UK Science and Technology Facilities Council (STFC)  consolidated grant  ST/L000652/1. MTS acknowledges support from a  Royal Society   Leverhulme Trust Senior Research Fellowship  ( LT150041 ). We acknowledge financial support from the  Swiss National Science Foundation  (AC, project  PBEZP2 137312 ), from the  Agence Nationale de la Recherche  (AC, ElF, contract no. ANR-12-JS05-0008-01 ) and from the  E. C. through an  ERC grant  (AC and FB, StG-257720). GEM acknowledges support from the  ERC   Consolidator Grant funding scheme  (project  ConTExt , grant number No. 648179 ) and a research grant ( 13160 ) from  Villum Fonden. WR is supported by a  CUniverse Grant  (CUAASC) from  Chulalongkorn University  and  JSPS KAK-ENHI  grant no.
JP15K17604. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2013. 1. 01271. S. ALMA is a partnership of  European Southern Observatory (ESO , representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  NSC  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. The research leading to these results has received funding from the  European Commission Seventh Framework Programme  ( FP/2007(FP/ -2013 ) ) under grant agreement no. 283393  ( RadioNet3 ).
Results Single-cell RNA-seq and data processing. To investigate how Polycomb repression relates to stochasticity in gene expression, we profiled single-cell transcriptomes of mouse OS25 ESCs cultured in serum and leukemia-inhibitory factor (LIF), previously used to map RNAPII phosphorylation and H2Aub1  5. Single-cell RNA-seq was performed using the Fluidigm C1 system, applying the SMARTer kit to obtain cDNA and the Nextera XT kit for Illumina library preparation. Libraries from 96 cells were pooled and sequenced on four lanes of an Illumina HiSeq2000 (Fig. 1 ; please refer to Methods for details). Next, we performed quality-control analysis for each individual cell data set and removed poor-quality data based on two criteria (as described before in ref. 11  ). Cells were removed if: (1) the total number of reads mapping to exons for the cell was lower than half a million and (2) the percentage of reads mapping to mitochondrial-encoded RNAs was higher than 10%.
1 ). Comparison of our average single-cell expression profiles with the bulk gene expression (mRNA-seq) profiles from Brookes et al. 5  yields a high correlation (Spearman's rho = 0. 87, Supplementary Fig. 1F ), suggesting that the chromatin and RNAPII data reflect cells in the same biological state as the single-cell RNA-seq data. Next, we analyzed gene expression variation within the singlecell data. First, we quantified cell-to-cell variation at each mean expression level using the coefficient of variation (Supplementary Fig. 2A ). Cell-to-cell variation can arise either due to stochastic gene expression itself, or technical noise or confounding expression heterogeneity due to biological processes such as the cell cycle. Fig. 1  Summary of methodology. OS25 mESCs were cultured and characterized by single-cell RNA-seq using the Fluidigm C1 system, applying the SMARTer kit to obtain cDNA and the Nextera XT kit for Illumina library preparation.
4H ), suggesting a 2D spatial arrangement of these genes as Active-PRCa-PRCr (as visualized in Fig. 5a ). We next asked whether the linear genomic position effects of PRCs are reflected in the three-dimensional (3D) genome organization in ESCs. Recently, Schoenfelder et al. 27  found that PRC1 acts as a major regulator of ESC genome architecture by organizing genes into 3D interaction networks. They generated mESC Promoter Capture Hi-C (CHi-C) data  28  , and analyzed it using the GOTHiC (Genome Organization Through Hi-C) Bioconductor package (http://www. bioconductor. org/packages/ release/bioc/html/GOTHiC. html). This yielded a strong enrichment for long-range contacts between promoters bound by PRCs. We applied the same approach to this data set using our gene list. We found that there is a strong enrichment for long-range promoter-promoter contacts for both PRCa and PRCr genes (Fig.
Libraries from 96 cells were pooled and sequenced on four lanes on Illumina HiSeq2000 using 100 bp paired-end protocol. Mapping Reads. For each cell, paired-end reads were mapped to the M. musculus genome (GRCm38) using Genomic Short-read Nucleotide Alignment Program (GSNAP) with default parameters  52. Next, uniquely mapped reads to the genome were counted using htseq-count (http://www-huber. embl. de/users/anders/HTSeq/) and normalized with size factors using DESeq  53. Classification of genes based on ChIP-seq profiles. To integrate ChIP-seq data with single-cell RNA-seq, we mapped 18,860 UCSC known gene IDs from Brookes et al. 5  to Ensembl IDs using BioMart  54. Then, we categorized the genes based on Brookes et al. classification: (1) "Active" genes (n = 4732) are defined as those without PRC marks (H3K27me3 or H2Aub1) but with active RNAPII (S5pS7pS2p), (2) "PRCa" (n = 1263) genes are marked by PRCs (H3K27me3 or H3K27me3 plus H2Aub1) and active RNAPII, (3) "PRCr" genes (n = 954) have both PRC marks (H3K27me3 and H2Aub1), unproductive RNAPII (S5p only and not recognized by antibody 8WG16) and not expressed in bulk mRNA data by Brookes et al.
We focus henceforth on these genes in analysis of burst size and frequency. We should note that our kinetic analyses do not account for technical noise as our data do not contain external spike-in molecules (the only way to incorporate technical noise in our kinetic model). Therefore, we addressed this point by focusing on moderately or highly expressed genes with an expression cutoff of 10. The assumption is that technical noise for these genes is small enough to estimate kinetic parameters accurately. We should also note that our results are robust to changes in selection of expression cutoff (Supplementary Fig. 3A ). Controlling for expression levels in kinetic models. To control for expression levels for PRCa and active gene sets, we extracted expression-matched sets of active and PRCa genes using "matching" function in R "arm" package with default settings.
The expected squared CV or the expected residual was approximated by using a running median. Calculation of bimodality index. Bimodality index is a measure to identify and rank bimodal signatures from gene expression data, and was calculated according to definition introduced by Wang et al. 20. The distribution of a gene with bimodal expression is assumed to be described as a mixture of two normal distributions with equal standard deviation. Proportions of observations in two components were estimated using R package "mclust". Identifying noisy and stable genes across mouse chromosomes. To investigate the position effects of noise in mRNA expression using DM values, we first sorted all expressed genes (n = 11,861) in descending the order according to their DM values and chose the top 20% as "noisy" genes and the bottom 20% as "stable" genes.
27. Short-range intrachromosomal contacts were excluded by filtering contacts separated by <10 Mb. To measure the enrichment of contacts within a set of promoters, 100 random promoter sets were generated with comparable pair-wise distance distributions to the experimental set. Contact enrichment was derived by dividing the number of contacts in the experimental set by the average number of contacts in the control sets. For each experimental set, we calculated the contact enrichment using three independent control sets and showed the mean contact enrichment and the standard deviation. Contact enrichment differences were evaluated using one-tailed t-tests. Gene ontology and KEGG pathway analyses. Annotation of KEGG pathways  58  and their associated genes was retrieved using Bioconductor Package KEGGREST.
Acknowledgements We are grateful to  Neil Brockdorff  for the kind gift of Ring1A/B dKO cells. We acknowledge funding from the  Wellcome Trust Strategic Award on Mouse Gastrulation , the  Lister Institute , and the  Helmholtz Association. E. T. T. acknowledges an EMBO short-term fellowship ( ASTF 336-2015 ). J. K. K. acknowledges  DGIST Start-up Fund  (2017010073) of the  Ministry of Science, ICT and Future Planning.
EXPERIMENTS AND RESULTS The proposed method aims to re-extract high quality tie points by using information from the result of first photogrammetric processing to improve the precision of pose estimation by a second bundle block adjustment. To evaluate the performance of the method, we use two different real-case studies. For each case, a classical photogrammetric processing operates firstly to get input data for the proposed method. Then, improved tie points are extracted by the proposed method and a second bundle block adjustment is performed on these new tie points. The precision of pose estimation will be calculated with ground control points. Tie point projection error will be evaluated as well. All photogrammetric processings are realized with MicMac  [Deseilligny and Clery, 2011]  -a free open source project for photogrammetry.
2001). We then determined the extent of both protected areas and remaining natural habitat within each ecoregion. To designate the protected area network, we used the World Database of Protected Areas (UNEP-WCMC 2016), which is inclusive of International Union of Conservation of Nature  (IUCN)  categories I to VI  (Dudley 2008) , as well as many community conservancies, aboriginal ownership, and private lands without an IUCN category. To assess habitat, we used tree-cover maps in forested ecoregions  (Hansen et al. 2013 ) and excluded globally significant patterns of human land use and populations (anthropogenic biomes, or "Anthromes") in nonforested ecoregions  (Ellis et al. 2010 ; detailed methods in supplemental appendix S2). We conducted this analysis across all 846 terrestrial ecoregions distributed among the Earth's 14 terrestrial biomes (supplemental appendix S1).
However, our results show that the most critically Note: The ecoregion data can be found in supplemental tables S1 and S2. (1) Half Protected: 50% or more of the total ecoregion area is protected. (2) Nature Could Reach Half: Less than 50% of the total ecoregion area is protected, but the sum of the total ecoregion protected and unprotected natural habitat remaining is 50% or more. (3) Nature Could Recover: The sum of the amount of natural habitat remaining and the amount of the ecoregion that is protected is less than 50% but more than 20%. (4) Nature Imperiled: The sum of the amount of natural habitat remaining and the amount of the ecoregion that is protected is less than or equal to 20%. endangered biome-as is determined by the proportion of Nature Imperiled ecoregions that constitute each-is the tropical dry forests, whereas two nonforested biomes are nearly as endangered: (1) tropical and subtropical grasslands, savannas, and shrublands and (2) Mediterranean forests, woodlands, and scrub.
" We contend that for the climate deal to succeed, we need a Global Deal for Nature (box 1). NNH provides a baseline from which we can monitor progress as the environmental data sets are increasingly dynamic, annually updated, and freely available and serve as a scorecard to underpin a Global Deal for Nature and assist the CBD in measuring progress. Finally, NNH could help provide government, lenders, citizens, and industry guidance about where to site extractive industries and develop large infrastructure projects. Providing clear implementation guidelines can help address weaknesses associated with NNH. For example, insisting that NNH be empirically derived for each of the world's ecoregions is important. However, in trying to erect a simple, science-based target that nonscientists can understand-50% protected by 2050-the approach runs the risk of giving the misimpression that 50% is the "right" target for each ecoregion.
Acknowledgments We dedicate this article to the inspirational efforts of  Edward O. Wilson  and his lifelong efforts to save biodiversity. We thank  Linus Blomqvist ,  Julia John ,  Naomi Kingston ,  James McNamara ,  Robin Naidoo ,  Stuart Pimm ,  Taylor Robb-McCord ,  John Robinson , and  Chris Stadler  for their comments on the manuscript. We acknowledge  Tom Allnutt ,  Nicola Bergh ,  Phil Clarke ,  Jon Fisher ,  Jane Glavan ,  Glen Griffith ,  Stephen Holness ,  Larry Kopold ,  Peter Stranger ,  Abdul Wali Al Khulaidi ,  James Omernik ,  Jon Oetting ,  Kate Parr ,  William J Platt ,  Amanda Recino ,  Mathieu Rouget ,  Michael Rutherford ,  Leonardo Sotomayor ,  Aleks Terauds ,  Joseph W Veldman , and  Crammy Wanyama  for their review of ecoregion maps and mapping. Abu Dhabi Global Environmental Data Initiative and  Saudi Wildlife Cambridge ,  United Kingdom.
However, for the measurements of NR-PM 1 species in ambient air, a good agreement is commonly observed between data from these two instruments  (Crenn et al. , 2015; Fröhlich et al. , 2015). Individual aerosol particles were analyzed using a field emission gun scanning electron microscope (FEG SEM, JEOL JSM 7100F TTLS) coupled with an energy-dispersive X-ray (EDX) system (X-Max Large Area Analytical Silicon Drift Detector, Oxford Instrument). Aluminum foils used for sampling of aerosol particles (sampling described in Sect. 2. 2) were mounted directly on the sample holder by double-sided conductive carbon tape. SEM-EDX acquisitions were performed under high vacuum at 5 keV accelerating voltage. Micrographs were acquired by a secondary electron detector at 4-6 mm working distance (WD). The samples were first scanned under low magnification to obtain the overall distribution of the particles and afterwards closely examined at magnifications from 10 000 to 50 000×.
Aerosol age and origin influence the filter-loading effect The influence of the aerosol age was indirectly investigated by using the PSCF. We divided the dataset into two subsets distinguished by k 6 being bigger or smaller than the campaign average of 0. 002. The analysis was carried out for the summer period at Paris and Payerne (Fig. 9 ). For both sites, k 6 > 0. 002 and k 6 < 0. 002 occurred around 60 and 40 % of the time, respectively. There are sufficient data in both subsets to provide comparable results. In Paris, the compensation parameter k is very well correlated with aerosol age: high values indicate local sources, while values close to 0 indicate long-range transport (Fig. 9 ). High k 6 values are associated with a local pattern of air pollution, since the highest PSCF values are located near the station.