dataesr/scientific-paragraphs-categorization huggingface dataset:
2-45% and 0. 2-2%, respectively). A remarkable congruence was observed between the quantification based on imaging and metagenomic methods for the larger cyanobacterial diazotrophs (Fig. 6 , Supplementary Figs. S3ab  and S4ab ). Hence, a fully reversible pipeline from images to genomics and genomics to images that allows each to inform the other was developed. The image analysis enables one to quickly identify which parallel metagenomic (or metatranscriptomic) sample(s) should contain a particular diazotroph. For populations like the cyanobacterial diazotrophs which are comparatively less abundant, this approach will reduce search time in genetic analyses. The majority (95%) of the total recruited reads mapping to the nifH database corresponded to 20 taxonomic groups: 5 cyanobacteria, 2 planctomycetes, and 13 proteobacteria.
Boxplots depict the 25-75% quantile range of the dataset without zeros (the corresponding biologically independent seawater samples are indicated in the plot), with the center line depicting the median (50% quantile); whiskers encompass data points within 1. 5× the interquartile range. Source data are provided as a Source data file. populations of Trichodesmium (range of 1-120; ref. 60  ), but below those observed in culture conditions (range 639-697; ref. 60  ). When comparing the polyploidy of Trichodesmium vs Richelia/Calothrix in the 12 samples in which both are detected, the estimate for Trichodesmium is on average 4 times the estimate for Richelia/Calothrix (range 0. 1-15) (Supplementary Data 2). Therefore, Trichodesmium dominance over Richelia/Calothrix is overestimated in the metagenomes due to the higher polyploidy levels of the former.
8 ). Temperature and nutrient availability are common factors which govern diazotroph abundances  8, 20, 63. Iron is also expected to be important due to the high iron requirement of the nitrogenase enzyme  64, 65  , therefore it was unexpected to find a less robust relationship between diazotroph abundances and modeled dissolved iron concentrations (Fig. 8a ). However, we cannot discount inaccuracies of modeled values relative to in situ bioavailable Fe concentrations. We further analyzed abundance and distribution patterns in the deeper depth samples (0-200 m and 200-1000 m, respectively). The higher numbers of N 2 -fixing cyanobacteria detected in the surface (5 m) compared to the deep chlorophyll maximum (DCM; 17-188 m) in both the metagenomic and imaging datasets confirms expected distributions (Figs.
c Taxonomic distribution at deeper resolution. HBD01 to HBD09 (heterotrophic bacterial diazotrophs) corresponds to the metagenome-assembled genomes from <3-µm size fractions  16. Boxplots depict the 25-75% quantile range of the dataset without zeros (the corresponding biologically independent seawater samples are indicated in the plot), with the center line depicting the median (50% quantile); whiskers encompass data points within 1. 5× the interquartile range. Source data are provided as a Source data file. S9 ). The majority of nifH sequences correspond to proteobacteria, however, diazotrophic cyanobacteria were also detected (Supplementary Fig. S9 ). In particular, 44% of total nifH reads in mesopelagic samples at TARA_78 and 6% at TARA_76 (of 0. 2-3 μm size fraction) in SAO corresponded exclusively to UCYN-A (Supplementary Fig.
The lineages grouped into 'Other cyanobacteria' are displayed in the Source data File. The 'OM-RGC. v2' prefix indicates the nifH sequences assembled from the metagenomes of <3-μm size fractions  22  , while HBD01 to HBD09 (heterotrophic bacterial diazotrophs) corresponds to the metagenome-assembled genomes from the same samples  16. Boxplots depict the 25-75% quantile range of the dataset without zeros (the corresponding biologically independent seawater samples are indicated in the plot), with the center line depicting the median (50% quantile); whiskers encompass data points within 1. 5× the interquartile range. Source data are provided as a Source data file. detected (Fig. 9  and Supplementary Fig. S11 ). 9] [80] [81]. On the contrary, only small diameter diazotrophs co-dominated in the SAO; for example, UCYN-A1 and UCYN-A2 were very abundant at stations TARA_76, TARA_78, and TARA_80 (Fig.
22 μm sizefractionated samples from different water layers. A total of 79 nifH sequences in our database mapped with at least 80% similarity to these metagenomes, retrieving a total of 43,161 mapped reads, with the majority of the reads having high identity to proteobacterial nifH sequences. Of the 79 sequences, 15 retrieved only one read. Mapped nifH reads were detected in 92 of the 127 samples (72%), which highlights an unexpected broad distribution of ultrasmall diazotrophs (blue circles in Fig. 10a ; Supplementary Data 1). Notably, when nifH reads were normalized by recA reads, we found that diazotrophs comprise up to 10% of the ultrasmall bacterioplankton, with the highest abundances detected in the AO, and in different water layers (Fig. 10a, b ). This is remarkable considering that the lowest diazotroph abundance in the other size fractions was detected in the AO (Figs.
4c  and 9 , Supplementary Figs. S8  and S9 ). The majority (84%) of the total recruited reads mapping to the nifH database corresponded to two sequences assembled from the <0. 22 μm size-fractionated metagenomes: OM-RGC. v2. 008173703 and OM-RGC. v2. 008955342. The former has 99% identity to nifH from the epsilon-proteobacterium Arcobacter nitrofigilis DSM7299 83 (hereafter Arcobacter), the second is more close in sequence identity to gamma-proteobacteria. The Acrobacter-like sequence only retrieved reads from surface and DCM, while the gamma-proteobacterium-like retrieved reads from surface, DCM, and mesopelagic (Fig. 10c ). Both sequences also retrieved reads from other sizes fractions (Fig. 7  and Supplementary Fig. S12 ). Members of Arcobacter genus are either symbionts or pathogens  83  , which is inconsistent with >9% of ultrasmall bacterioplankton associated with the <0.
2-1. 6 μm or 0. 2-3 μm; named here 0. 2-1. 6/3 μm size fraction), piconanoplankton (0. 8-5 μm or 0. 8-2000 μm; named here 0. 8-5 μm size fraction), nanoplankton (5-20 μm or 3-20 μm; named here 5-20 μm size fraction), microplankton (20-180 μm), and mesoplankton (180-2000 μm) (Supplementary Fig. S2 )  21. In addition, ultrasmall plankton (<0. 22 μm) were also collected (Supplementary Fig. S2 )  21. The Tara Oceans datasets used in the present work are listed in Supplementary Fig. S2  and specific details about them and their analyses are described below. Read recruitment of marker genes in metagenomes. The use of metagenomes avoids the biases linked to the PCR amplification steps of metabarcoding methods, and thus it is better for quantitative observations. This is especially important for protein-coding gene markers, such as nifH, which display high variability in the third position of most codons, and thus necessitate the use of highly degenerate primers for a broad taxonomic coverage  85.
However, we realize that there are examples of 2-3 nifH copies in heterocyst-forming cyanobacteria such as Anabaena variabilis and Fischerella sp. 88, 89 , or in the firmicutes Clostridium pasteurianum  90  , and that we are not taking into account the polyploidy effect. The metagenomes were aligned against sequence catalogs of marker genes for diazotrophs (nifH) and bacteria (recA) (Supplementary Data 3 and 4). The analysis was carried out using bwa tool version 0. 7. 4  91  using the following parameters: -minReadSize 70 -identity 80 -alignment 80 -complexityPercent 75 -complexityNumber 30. The nifH sequence catalog (hereafter nifH database) was composed of 29,175 publicly available unique sequences from the laboratory of JP Zehr (University of California, Santa Cruz, USA; version April 2014; https://www.
jzehrlab. com). Although the Zehr database has some redundancy (9048 out of the 29,175 total unique sequences are retained when clustered at 95% identity using CDHIT-EST tool  92  ), all non-identical sequences were used to maximize the number of metagenomic mapping reads. Moreover, this database was complemented with additional nifH genes retrieved from the sequenced genomes of Integrated Microbial Genome database (IMG)  93  and from different Tara Oceans datasets: Ocean Microbial Reference Gene Catalog version 2 (OM-RGC-v2  22  ), assemblies  16  and 10 clones (this study). nifH clone libraries were built using DNA extracted from 0. 2 to 1. 6/3 μm size fraction plankton samples as described in Alberti et al. 24. DNA samples from the same oceanic region were pooled together at a concentration of 10 ng/μl and used as template in a nested nifH PCR using degenerate primers (Supplementary Data 3  94  ).
Five PCR products were purified and cloned using the TOPO-TA cloning kit (Invitrogen). Inserts were Sangersequenced bidirectionally using M13 Forward and M13 Reverse vector primers following manufacturer's recommendations. A total of 411 nifH clone sequences were generated, among which 148 showed less than 95% nucleotide identity with any previously published sequence. These 148 sequences were grouped into clusters sharing more than 99% nucleotide identity and represented by 10 consensus sequences (Supplementary Data 3). The recA sequences were obtained from sequenced genomes in IMG  93  and from OM-RGC-v2  22. Homologous sequences were included in the two catalogs as outgroups to minimize false-positive read alignments. These latter sequences were retrieved from IMG, OM-RGC-v2, and the Marine Microbial Eukaryotic Transcriptome Sequencing Project (MMETSP  95  ) using HMMer v3.
2. 1 with gathering threshold option (http://hmmer. org/). The outgroups for recA consisted of sequences coding for the RecA Pfam domain (PF00154), including RADA and RADB in Archaea, RAD51, and DCM1 in eukaryotes, and UvsX in viruses  96. Outgroups for nifH consisted of sequences coding for the Pfam domain Fer4_NifH (PF00142), including a subunit of the pigment biosynthesis complexes protochlorophyllide reductase and chlorophyllide reductase  97. Phylogenetic analysis of recruited metagenomic reads. To support the taxonomic affiliation of metagenomic reads recruited by nifH sequences from 'spheroid bodies', we carried out a phylogenetic reconstruction in the following way. The translated metagenomic reads were aligned against a NifH reference alignment using the option --add of MAFFT version 6 with the G-INS-I strategy  98.
22-1. 6/3 μm. For biovolume estimations of single-cell free-living diazotrophs, we assumed an average cell biovolume of 1 μm 3 based on the cell dimensions reported in literature for cultured NCDs  [102] [103] [104] [105] [106] [107]. The free-living bacterial densities were retrieved from the last version of the publicly available flow cytometry Tara Oceans dataset (https://doi. org/10. 17632/ p9r9wttjkm. 2), which were generated in the following way (see also refs. 23, 108 ). Briefly, three 1-mL seawater samples (pre-filtered through 200 μm mesh) were collected from the three layers (surface, DCM, and mesopelagic) using the vertical profile sampling system (CTD-rosette). Cold 25% glutaraldehyde (final concentration of 0. 125%) was amended to each sample for 10 min, which was held in the dark prior to flash freezing in liquid nitrogen, and long-term storage at -80 °C until analyses.
The two other subsamples were not stained and used to enumerate phototrophic pico-phytoplankton, including Prochlorococcus and Synechococcus. The latter were discriminated by gating on side scatter, chlorophyll content (FL3, red fluorescence), and phycoerythrin (FL2, orange fluorescence). A minimum of 30,000 events and on average 90,000 events were acquired for each subsample. Total abundance of prokaryotic cells was based on the abundances of heterotrophic prokaryotes and phototrophic prokaryotes. Detection of diazotrophs in the confocal laser-scanning microscopy dataset. We analyzed the quantitative microscopy images publicly available on the Ecotaxa web platform  109. These images were generated using environmental High Content Fluorescence Microscopy (eHCFM) 30 on (i) 61 samples collected at 48 different stations using a microplankton net (20 μm mesh size) and filtering the cod end volume through a 180 μm sieve; (ii) 75 samples collected at 51 stations using a nanoplankton net (5 μ mesh size) and filtering the coded volume through a 20 μm sieve (Supplementary Fig.
We used the confocal microscopy data to quantify DDAs and free filaments of Trichodesmium for abundance and biovolume. Image classification and annotation were carried out using the Ecotaxa web platform  109  in the following way. We first manually searched for the target taxa and curated an initial training set in a few samples where molecular methods detected high abundances (i. e. , high metagenomic read abundance of nifH), obtaining 55 images for DDAs and 39 for Trichodesmium filaments (Supplementary Data 5). This training set was then used for automating the classification of the whole dataset by means of supervised machine learning (random forest) based on a collection of 480 numeric 2D/3D features  30. The predictions were, in turn, manually curated and used as a new training set, repeating this step iteratively until no new images appeared.
21 × 10 9 nifH copies/nifH ng). Hence, the estimate of the nifH copies (C) per liter of a given biological group in the original seawater is provided by C = DNA extracted * (1. 21 × 10 9 copies nifH/ng nifH) * reads nifHtaxon /reads total /(V seawater /(3 L * n)). Underwater Vision Profiler dataset and analysis. The Underwater Vision Profiler 5 (UVP5, Hydroptics, France)  33  is an underwater imager mounted on the Rosette Vertical Sampling System. This system illuminates precisely calibrated volumes of water and captures images during the descent (5-20 images s -1 ). The UVP5 was operated in situ and was designed to detect and count objects of >100 μm in length and to identify those of >600 μm in size. In the current work, we used this method for the quantification of Trichodesmium colony abundance and biovolume by mining the images publicly available on the Ecotaxa web platform  109.
Measurements of temperature were recorded at the time of sampling using the vertical profile sampling system (CTD-rosette) (https://doi. org/10. 1594/PANGAEA. 836319 and https://doi. org/10. 1594/PANGAEA. 836321). Dissolved nutrients (NO - 3 , PO 4 ) were analyzed according to previous methods  110, 111  with detection limits of 0. 05 and 0. 02 μmol L -1 , respectively. Iron levels were derived from a global ocean biogeochemical model  112. The datasets are listed in Supplementary Data 6. Plotting and statistical analysis. Graphical analyses were carried out in R language (http://www. r-project. org/) using ggplot2  113  and treemaps were generated with treemap. Maps were generated with borders function in ggplot2  113  and geom_point function for bubbles or scatterpie  114  package for pie charts.
The trends between diazotroph abundance and latitude were displayed with generalized additive models using the geom_smooth function of ggplot2 113 Spearman's rho correlation coefficients and p-values were calculated using the cor. test function of the stats package. Metric scaling (NMDS) analysis to visualize Bray-Curtis distances was carried out with the metaMDS command in the R package vegan  115  , and the influence of environmental variables on sample ordination was evaluated with the function envfit in the same R package, whereas the pie charts were plotted with scatterpie 114 package. Hierarchical agglomerative clustering of samples using average linkage was performed with the function hclust of the R package stats.
Acknowledgements We would like to thank all colleagues from the  Tara Oceans consortium  as well as the  Tara Ocean Foundation  for their inspirational vision. We also acknowledge the current and past members of the  laboratory of JP Zehr  for the curation and public access to the nifH database. We are grateful to  Lionel Guidi ,  Stéphane Pesant , and  Julie Poulain  for providing details about Tara Oceans sampling,  Luca Santangeli  from  EMBL Heidelberg  for confocal microscopy image acquisition and  Luis Pedro Coelho  (currently at  Fudan University ) for image processing,  Benjamin Blanc  from  Laboratoire d'Océanographie de Villefranche-sur-mer  for help with UVP5 image processing and annotation,  Josep Gasol  from  Institut de Ciències del Mar-CSIC  for expertise and assistance with flow cytometry,  Daniel Lundin  from  Linnaeus University  for his analytical support and advice,  Tom Delmont  from  Genoscope  for useful discussions,  Flora Vincent  from  Weizmann Institute of Science  for her help with Ecotaxa resource,  Eric Bapteste ,  Philippe Lopez  and  Romain Lannes  from  Sorbonne Université  for their advice with ultrasmall bacteria analysis.
This work has been supported by the  FFEM -French Facility for Global Environment ,  French Government 'Investissements d'Avenir'  programs  OCEANOMICS  ( ANR-11-BTBR-0008 ),  FRANCE GENOMIQUE  ( ANR-10-INBS-09-08 ),  MEMO LIFE  ( ANR-10-LABX-54 ), and  PSL Research University  ( ANR-11-IDEX-0001-02 ). R. A. F. acknowledges funding from  Knut and Alice Wallenberg foundation. C. B. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Diatomic; grant agreement No. 835067 ) and  Agence Nationale de la Recherche  " Phytomet " ( ANR-16-CE01-0008 ) projects. F. M. C. -C. acknowledges funding from the  European Union 's  Horizon 2020 research program  under the  Marie Sklodowska-Curie  grant agreement No. 749380  ( UCYN2PLAST ).
Data availability The authors declare that all the data supporting the findings of this study are publicly available in the following repositories and in the supplementary information files of this paper. The contextual data are available in Pangaea (www. pangaea. de)  21 with the identifier https://doi. org/10. 1594/PANGAEA. 875582. Flow cytometry data  23, 108  are available in Mendeley Data (https://data. mendeley. com) with the identifier https://doi. org/10. 17632/p9r9wttjkm. 2. The confocal microscopy  30  and UVP5  33  images annotated as diazotrophs in the current work and their corresponding metadata were submitted to the EMBL-EBI repository BioStudies (www. ebi. ac. uk/biostudies) under accession S-BSST529; while the whole image databases and their metadata are available at Ecotaxa (confocal microscopy of 5-20 μm sized-fractionated samples: https://ecotaxa.
obs-vlfr. fr/ prj/3365; confocal microscopy of 20-180 μm sized-fractionated samples: https://ecotaxa. obs-vlfr. fr/prj/2274; UVP5: https://ecotaxa. obs-vlfr. fr/prj/579). Tara Oceans metagenomes  22, 24, 25  are archived at ENA under the accession numbers: PRJEB1787, PRJEB1788, PRJEB4352, PRJEB4419, PRJEB9691, PRJEB9740, and PRJEB9742. The nifH and recA catalogs were compiled from Integrated Microbial Genomes (IMG, https://img. jgi. doe. gov/); Marine Microbial Eukaryotic Transcriptome Sequencing Project (MMETSP; https://github. com/dib-lab/dib-MMETSP); OM-Reference Gene Catalog version 2 (OM-RGC-v2, https://www. ocean-microbiome. org/); Tara Oceans assemblies (Supplementary Table  8  from ref. 16  ); and the nifH database (version April 2014) curated and hosted at Zehr Lab, University of California, Santa Cruz, CA, USA (https://www.
Results. The high resolution continuum and line emission maps reveal multiple fragments with subsolar masses within the inner ∼1000 AU of VLA 3. Furthermore, the velocity field of the inner disk observed at 843 µm shows a similar behavior to that of the larger scale velocity field studied in the CORE project at 1. 37 mm. Conclusions. We present the first observational evidence for disk fragmentation towards AFGL 2591-VLA 3, a source that was thought to be a single high-mass core. While the fragments themselves are low-mass, the rotation of the disk is dominated by the protostar with a mass of 10. 3±1. 8 M. These data also show that NOEMA Band 4 can obtain the highest currently achievable spatial resolution at (sub-)mm wavelengths in observations of strong northern sources.
2018b). In order to address the open questions in high-mass star formation studies, namely the underlying physics and chemistry during formation and fragmentation of disks along with the infall and outflow processes, we observed 20 high-mass star forming regions (HMSFRs) at 1. 37 mm within the framework of the IRAM NOEMA large program "CORE" 2  (Beuther et al. 2018). The CORE sample consists of northern HMSFRs that are otherwise mostly or entirely inaccessible to the southern hemisphere observatories (e. g. ALMA). The program provides a unique understanding of the physical and chemical properties of the observed regions both with detailed case studies (e. g. Ahmadi et al. 2018; Gieser et al. 2019; Cesaroni et al. 2019; Bosco et al. 2019; Mottram et al. 2020; Olguin et al. 2020 ) and statistical studies of larger samples  (Beuther et al.
2012). Additionally, multiple outflow directions were observed in a previous CORE study using  CO, SiO, and SO emission. This is indicative of either a disk wind or fragmentation below 1400 AU that the 1. 37 mm observations did not resolve, with each fragment powering individual outflows  (Gieser et al. 2019). In this paper, we present a new set of high spatial resolution NOEMA observations as an extension of the CORE large program carried out at 843 µm towards VLA 3. In the spectral setup, we included highly excited molecular lines in order to be able to probe the gas kinematics in the immediate vicinity of the protostar. The data reveal fragmentation at scales of ∼800 AU. The structure of this study is as follows: we describe the observations and data calibration in Section 2 and present the analysis of the continuum and line emission maps in Section 3.
Two quasars, 2013+370 and 2037+511, were used for amplitude and phase calibration. The quasar 3C84 was used for bandpass calibration, while the absolute flux calibration was done using a bright star (MWC349). The uncertainty in the flux calibration is ∼20%. The total on-source time was 7. 9 hours with precipitable water vapor (pwv) < 2 mm. The data reduction was performed using the Continuum and Line Interferometer Calibration 3  (CLIC), part of the GILDAS software developed by the Institut de Radioastronomie Millimétrique (IRAM). We extracted the 843 µm continuum emission from the line-free channels in the two low-resolution spectral units (L01 and L02) of PolyFiX. For the lines observed with 2 MHz resolution, the continuum emission was subtracted prior to imaging. Continuum subtraction for the lines covered by the high-spectral resolution windows was done using the uv_baseline method of the MAPPING package within GILDAS.
We cleaned and restored each individual map using the MAPPING package of GILDAS where we used the Clark algorithm. The continuum emission was, firstly, cleaned with no clean-box. Once we had obtained a clean continuum map, we used a clean-box around the detected emission from the source and a robust parameter of 0. 1 to achieve the highest possible spatial resolution. We interactively cleaned and re-set the size of the clean box after each set of iterations until the cleaned flux as a function of the identified clean components converged. Selfcalibration had been shown to improve the 1. 3 mm images of the full CORE sample  (Beuther et al. 2018). However, because of the limited signal-to-noise ratio and only 5 antennas being available in the array for Band 4 observations, it did not enhance this dataset.