dataesr/scientific-paragraphs-categorization huggingface dataset:
Each assay was triplicated on distinct DNA extracts, and technical triplicates were performed. The 16S rRNA gene qPCR datasets are presented in Fig. S1  in the Supplement. These assays confirmed the high number of bacterial cells per compartment (Fig. S1  and Table  S2 ): (1) soils from the infiltration basin (IB) had a median content of 1. 32 × 10 11 16S rRNA gene copies per gram dry weight; (2) sediments from the detention basin (DB) had 1. 83 × 10 11 16S rRNA gene copies per gram dry weight; (3) the runoff waters (WS) had a median content of 4. 75 × 10 8 16S rRNA gene copies per millilitre; (4) the aquifer waters (AQ_wat) had 3. 10 × 10 6 16S rRNA gene copies per millilitre; and (5) the aquifer clay bead biofilms showed 1. 35 × 10 7 16S rRNA gene copies per square centimetre. Sequencing of V5-V6 16S rRNA gene (rrs) PCR products were performed by MR DNA sequencing services (Shallowater, Texas, USA) on an Illumina MiSeq v3.
Bioinformatic analyses All paired-end MiSeq reads were processed using Mothur 1. 40. 4 by following a standard operation protocol (SOP) for MiSeq-based microbial community analysis  (Schloss et al. , 2009; Kozich et al. , 2013) , the so-called MiSeq SOP available at http://www. mothur. org/wiki/MiSeq_SOP (last access: May 2020). Due to the large number of sequences to be processed, the "cluster. split" command was used to assign sequences to OTUs. For the 16S rRNA (rrs) gene sequences, reads were filtered for length (>300 bp), quality score (mean ≥ 25), number of ambiguous bases (= 0) and length of homopolymer runs (<8) using the "trim. seqs" script in Mothur, and singletons were discarded. The 16S rRNA gene sequences passing these quality criteria were aligned to the SILVA reference alignment template (release 128; https:// www.
arb-silva. de, last access: May 2020). Unaligned sequences were removed. Chimeric sequences were identified using the "chimera. uchime" command and removed. Variability in the number of cleaned reads per sample was observed but not correlated with variations in the number of 16S rRNA gene sequences (Table  S2 ). These variations were thus considered to be due to the DNA sequencing process. Therefore, a subsampled dataset (20 624 reads per sample, with the exclusion of samples with total reads below this threshold) was used to mitigate the artifact of sample library sizes. Operational taxonomic units (OTUs) were defined using a 97 % identity cutoff as recommended by several authors in order to collapse sequences into groups that reduce the incidence of sequence errors on the datasets (e. g.
,  Eren et al. , 2013; Johnson et al. , 2019). It is to be noted that amplicon sequence variants (ASVs) could also be used to build contingency tables (e. g. ,  Callahan et al. , 2016; Karstens et al. , 2019). However, exact sequence variants can generate uncertainties when using 16S rRNA gene sequences because of variations among species and strains due to the presence of multiple copies per genome  (Johnson et al. , 2019). Figure  S2  shows the OTU rarefaction curves for the full and the subsampled datasets. The subsampled dataset was used for all downstream analyses except those of the SourceTracker Bayesian approach (see below). OTUs were affiliated to taxonomic groups by comparison with the SILVA reference alignment template if a bootstrap p value over 80 % was obtained. FAPROTAX (Functional Annotation of Prokaryotic Taxa;  Louca et al.
Statistical analyses All statistical analyses were performed in R (v3. 5. 1). For the 16S rRNA gene sequences, alpha-diversity estimates were computed using the function "rarefy" from the "vegan" package  (Oksanen et al. , 2015). Richness (S obs ) was computed as the number of observed OTUs in each sample. The diversity within each individual sample was estimated using the nonparametric Shannon index. To estimate whether the origin of the samples influenced the alpha diversity, an ANOVA test with Tukey post hoc tests was performed. Shared and unique OTUs were depicted in Venn diagrams with the "limma" package  (Ritchie et al. , 2015). Concerning the beta-diversity analyses, a neighbour-joining tree was constructed with a maximum-likelihood approximation method using FastTree  (Price et al.
, 2009). Weighted UniFrac distances were calculated for all pairwised OTU patterns according to  Lozupone et al. (2011)  and used in a principal coordinates analysis (PCoA)  (Anderson and Willis, 2003). Permutation tests of distances (PERMANOVAs)  (Anderson, 2001)  were performed using the vegan package  (Oksanen et al. , 2015)  to establish the significance of the observed groupings.
Analyses of bacterial community coalescence The SourceTracker computer package  (Knights et al. , 2011)  was used to investigate community coalescence. Source-Tracker is a Bayesian approach built to estimate the most probable proportion of user-defined "source" DNA reads in a given "sink" community. In the present analysis, various scenarios of community coalescence were investigated such as the coalescence of bacterial taxa from the watershed runoff waters and sediments from the detention and infiltration basins with those of the downstream-SIS aquifer water samples or of recent biofilms developing on clay beads incubated in the aquifer. SourceTracker was run with the default parameters (rarefaction depth = 1000 reads from the original cleaned dataset of 16S rRNA gene reads; Fig. S2a ; burn-in: 100, restart: 10) to identify sources explaining the OTU patterns observed among the aquifer samples (waters and clay bead biofilms, n = 12).
16S rRNA gene-inferred bacterial taxa undergoing coalescence in the aquifer To identify the bacterial taxa involved in the coalescence process, OTUs of the 16S rRNA gene dataset were allocated to taxonomic groups using the SILVA reference alignment template. These taxonomic allocations indicated that (1) 14 genera were only recorded in the aquifer samples, (2) 421 genera were only recorded in the upper-surface compartments of the watershed and (3) 219 were recorded among aboveground and aquifer compartments (Table  S6 ). The following bacterial genera were exclusively associated with the aquifer bacterial communities: Turicella, Fritschea, Metachlamydia, Macrococcus, Anaerococcus, Finegoldia, Abiotrophia, Dialister, Leptospirillum, Omnitrophus, Campylobacter, Sulfurimonas, Haemophilus and Nitratireductor.
Sequences that could not be attributed to one of the tested sources were grouped under the term unknown. ploration of the bacterial species undergoing a coalescence with the aquifer microbiome. A total of 19 129 tpm OTUs were recorded among the samples (from datasets resampled to reach 4636 reads per sample). As expected, these tpm reads were mainly assigned to Proteobacteria (WS = 92 % of total reads, DB = 87 %, IB = 76 %, AQ_wat = 83 % and AQ_wat = 85 %), but some reads could also be attributed to Bacteroidetes, Nitrospirae and Cyanobacteria (Table  S7 ). These taxonomic allocations allowed for the identification of 24 bacterial genera and 91 species, whose distributions are summarized in Tables  S7  and S8. The tpm sequences were mainly allocated to Pseudomonas (WS = 36 % of the reads, DB = 27 %, IB = 7 %, AQ_wat = 51 % and AQ_bio = 48 %), Aeromonas (WS = 1 % of the reads, DB = 3 %, IB <0.
Only OTU00573 was found to be high in number (867 reads), but this contaminant did not have an impact on the coalescence analysis because of its absence in the underground datasets (Table  S10 ). Other contaminant OTUs did not represent more than 10 times the ones observed in the field samples for identical OTUs, a criterion used to distinguish significant contaminants  (Lukasik et al. , 2017).
, 1958). Despite this filtering effect, infiltration induces significant changes in the diversity of groundwater bacterial communities. Both water and biofilm aquifer samples recovered downstream of the SIS had higher bacterial richness than those collected upstream. It is to be noted that soils of the infiltration basin showed higher bacterial diversity than those of the sediments of the detention basin and runoffs. This is most likely related to a development of plantassociated bacteria in this compartment. Indeed, the infiltration basin was covered by several plant species of Magnoliophyta like Rumex sp. which can disseminate rapidly through rhizomes  (Bedell et al. , 2013)  and generate multiple ecological niches for bacteria. The SourceTracker Bayesian probabilistic approach based on 16S rRNA gene metabarcoding datasets  (Knights et al.
Boulch et al. (2019) complex Microbial community function analysis) Explore website (http://macadam. toulouse. inra. fr/,last access: May 2020) according to LeBoulch et al. (2019). For the tpm gene sequences, chimeric sequences and primers, barcodes were removed, and the dataset was limited to sequences of a minimum length of 210 bp (average length = 215 bp). These reads were aligned against the tpm.
Acknowledgements. We thank the  Labex IMU (Intelligence des Mondes Urbains) , the  Greater Lyon Urban Community , the  School of Integrated Watershed Sciences H2O'LYON , the  Lyon Urban School  and  CNRS  for their support in the development of interdisciplinary research initiatives such as these. Authors also thank the  OTHU network  for technical assistance and financial support and the reviewers of this paper for their constructive comments. Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant nos. ANR-16-CE32-0006 ,  ANR-17-CE04-0010 ,  ANR 17-EURE-0018  and  ANR-17-CONV-0004 ); the  Agence Nationale de Sécurité Sanitaire de l'Alimentation, de l'Environnement et du Travail  (Iouqmer; grant no. EST 2016/1/120 ); and the  Centre National de la Recherche Scientifique  ( MITI Urbamic  project grant).
Data availability. The 16S rRNA gene sequences are available at the European Nucleotide Archive (https://www. ebi. ac. uk/ena, Cournoyer, 2020) using the following accession numbers: PR-JEB33510 (IB), PRJEB21348 (DB), PRJEB29925 (AQ) and PR-JEB33507 (WS). The tpm gene sequences are available using the accession number PRJEB33622. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/hess-24-4257-2020-supplement. Author contributions. BC coordinated and designed the experiments. YC, VRN, TW, FMB, RB, LM, RM, FV, EB, DB, JV and BC performed the experiments and contributed to the analysis of the datasets. YC and BC prepared the paper with contributions from all co-authors.
Boulch et al. (2019) complex Microbial community function analysis) Explore website (http://macadam. toulouse. inra. fr/,last access: May 2020) according to LeBoulch et al. (2019). For the tpm gene sequences, chimeric sequences and primers, barcodes were removed, and the dataset was limited to sequences of a minimum length of 210 bp (average length = 215 bp). These reads were aligned against the tpm database (BD_TPM_Mar18_v1. unique_770seq). Unaligned sequences were removed. The number of sequences per sample was then subsampled (4636 sequences per sam- ple, with the exclusion of samples with total reads be- low this threshold). Operational taxonomic units (OTUs) were defined at a 100 % identity cutoff. The BD_TPM_ Mar18_v1. unique_770seq database (http://www. graie. org/ othu/donnees, last access: May 2020) was used to clas- sify the sequences using the Wang text-based Bayesian classifier (Wang et al.
, 2007) and a p bootstrap value above 80 %. Local BLAST analyses were performed on the BD_TPM_Mar18_v1. unique_770seq database using the NCBI (National Center for Biotechnology Information) BLAST (Basic Local Alignment Search Tool, version X) program to check the quality of the taxonomic affiliations. functional inferences were performed on the MACADAM (MetAboliC https://doi. org/10. 5194/hess-24-4257-2020Hydrol. Earth Syst. Sci. , 24, 4257-4273, 2020.
6 % 12. 6 - - - -36. 4 % 18. 3 7. 0 % 15. 7 AQ_bio_up3 44. 0 % 6. 6 - - - -48. 1 % 8. 1 7. 8 % 10. 8 * Two main groups of analyses are shown from subsampled datasets set at 1000 reads:.
Wind velocity: The wind velocity derived from the low and medium resolution data of the rotational line 12 CO J = 2 ! 1 in the ground vibrational state -with J specifying the rotational quantum number -is listed in Table  S1  (see also Sect. S2). For each target, the integrated flux is extracted for a range of apertures. The extraction aperture yielding the largest integrated flux of the CO line is used to measure the extent of the blue and red wing (in km s 1 ) with respect to the v LSR (for intensities greater than 3 times the rms noise). Listed in Table  S1  is the maximum velocity obtained from the blue and red wings, and for the observations at low and medium spatial resolution. The uncertainty in the velocity measure is taken from the spectral resolution of the data, being ⇠1. 25 km s 1.
85v wind (SiO); see Table  S2. The uncertainty in the derived fraction is ⇠0. 11, owing to the velocity resolution of ⇠1. 25 km s 1. -Step 3: SiO morpho-dynamical information: The SiO data were then analyzed using stereograms, moment1-maps, and position-velocity (PV) diagrams (see Sect. S7). Stereograms and moment1-maps are used to identify possible positional offsets between blue-and redshifted emission, hence yielding information on the velocity vector field (v) and rotation (Table  S2 ). As examples, see the data for S Pav, T Mic, U Del, and ⇡ 1 Gru (Fig. S10 , Fig. S14 , Fig. S18 , and Fig. S43 ). PV diagrams are constructed for two orthogonal slits so that the asymmetry between the two PV diagrams is maximized to unravel the morpho-kinematical behavior (85) (Table  S2 ). A rotating Keplerian disk or an EDE with constant radial velocity appear in one (or both) of the PV diagram(s) as a 'butterfly'-like signature (85).
See, for example, the PV diagrams of 12 CO in ⇡ 1 Gru shown in Fig. S44. The outcomes of each of these three steps are described below and summarized in Table  S2. This stepwise procedure allows the characterization and classification of 13 out of the 14 ATOMIUM AGB stars. U Her seems an outsider: the CO channel maps show pronounced asymmetric arcs although they seem not to be linked to a spiral-like structure. -Step 1: Based on the CO channel maps, we discern a spiral-like structure in five targets (R Aql, W Aql, GY Aql, IRC-10529, and IRC+10011; Fig. S46 , Fig. S50 , Fig. S54 , Fig. S58 , and Fig. S62 ). The CO data of both R Hya and ⇡ 1 Gru show a clear 'bipolar' signature. For R Hya (⇡ 1 Gru) the blue-shifted emission is east (north) with respect to the central star, while the red-shifted emission is west (south) (see the CO channel maps in Fig.
5 M ) of > ⇠ 1. This dependence of the multiplicity fraction on initial mass is also evident in the Gaia DR2 data  (133). As a result, i) the ATOMIUM data complemented with the additional ALMA data presented in Sect. S4. 1 (Table  S3 ) and Sect. S9, ii) the outcome of theoretical models for binary systems summarized in Sect. S4. 1 and Sect. S8, and iii) the population statistics here described all indicate that binary interaction is the dominant wind shaping mechanism for these AGB stars. Our conclusion applies to all AGB stars for which observations indicate a mass-loss rate greater than ⇠1⇥10 7 M yr 1 -i. e. , those with a mass-loss rate exceeding the nuclear burning rate (4) so the mass loss determines the further stellar evolution. A high fraction of these targets will have an initial mass greater than ⇠1.
S7 ALMA ATOMIUM observational results In this section, we present all the data used in our morphological characterization of the ATOM-IUM AGB stars. These include: channel maps of the 12 CO J = 2 ! 1 line at medium and low spatial resolution; stereograms of the 28 SiO J = 5 ! 4 line; and moment1-maps and positionvelocity diagrams of the 12 CO J = 2 ! 1 and 28 SiO J = 5 ! 4 lines. The minimum and maximum velocity offset with respect to the local standard-of-rest velocity are determined from the line profile, so that the integrated line intensities are greater than 3 times the rms noise (see Table  S6 ). We set the minimum intensity in the color scale is set to ( 1) ⇥ rms. The maximum of the color scale is chosen to better visualize the weaker features, and is set to ⇠0. 5 times the maximum intensity of the data.
INSU co-funded by CEA and CNES, F. D. C. is supported by the EPSRC iCASE studentship programme, Intel Corporation and Cray Inc. , J. M. C. P. acknowledges support from the UK STFC grant ST/P00041X/1, J. Y. acknowledges support from the UK STFC grant ST/R001049/1, M. V. d. S. acknowledges support from the FWO through grant 12X6419N, T. D. acknowledges support from the FWO through grants 12N9917N & 12N9920N, M. M. acknowledges support from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant agreement No. 665501 with the FWO ([PEGASUS] 2 Marie Curie fellowship 12U2717N, A. B. acknowledges support from the "Programme National de Physique Stellaire" (PNPS), I. M. acknowledges funding by the UK STFC grant ST/P000649/1, EDB acknowledges financial support from the Swedish National Space Agency, I.
E. M. acknowledges support from the FWO and the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 665501, S. E. acknowledges funding from the UK STFC as part of the consolidated grant ST/P000649/1 to the Jodrell Bank Centre for Astrophysics at the University of Manchester, P. K. acknowledges support from the French PNPS of CNRS/INSU, C. A. G. acknowledges support from NSF grant AST-1615847, T. J. M. is grateful to the STFC for support under grant ST/P000321/1, A. A. Z. was supported by the STFC under grants ST/T000414/1 and ST/P000649/1, M. D. G. thanks the STFC for support under consolidated grant ST/P000649/1 to the JBCA; Author contributions: L. D. is principal investigator (PI) of the ALMA Large Program ATOMIUM, leads the ATOMIUM team and supervises the ATOMIUM project, developed the methodology, analyzed the results, wrote the draft manuscript, contributed to.
E. M. acknowledges support from the FWO and the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 665501, S. E. acknowledges funding from the UK STFC as part of the consolidated grant ST/P000649/1 to the Jodrell Bank Centre for Astrophysics at the University of Manchester, P. K. acknowledges support from the French PNPS of CNRS/INSU, C. A. G. acknowledges support from NSF grant AST-1615847, T. J. M. is grateful to the STFC for support under grant ST/P000321/1, A. A. Z. was supported by the STFC under grants ST/T000414/1 and ST/P000649/1, M. D. G. thanks the STFC for support under consolidated grant ST/P000649/1 to the JBCA; Author contributions: L. D. is principal investigator (PI) of the ALMA Large Program ATOMIUM, leads the ATOMIUM team and supervises the ATOMIUM project, developed the methodology, analyzed the results, wrote the draft manuscript, contributed to (PNPS) of CNRS/ 81.
H. acknowledges support from the FWO Flemish Fund of Scientific Research under grant G086217N, F. H. acknowledges support from the "Programme National de Physique Stellaire" Homan, T. Danilovich, L. Decin, A. de Koter, J. Nuth, M. Van de Sande, ALMA detection of a tentative nearly edge-on rotating disk around the nearby AGB star R Doradus. Astron. Astrophys. 614,A113 (2018). 80. V. Bujarrabal, A. Castro-Carrizo, J. Alcolea, H. Van Winckel, Detection of Keplerian dynamics in a disk around the post-AGB star AC Herculis. Astron. Astrophys. 575,L7 (2015). 84. H. J. G. L. M. Lamers, J. P. Cassinelli, Introduction to Stellar Winds (Cambridge University Press, 1999). 85. W. Homan, J. Boulangier, L. Decin, A. de Koter, Simplified models of circumstellar morphologies for interpreting high-resolution data.
This phenomenal progress was made possible by the development of new high-throughput genome sequencing technologies which enable scientists to obtain high quality cp genome sequences in a more convenient and relatively inexpensive way. As a result, around 3,500 chloroplast genomes have been deposited in the database of the National Center for Biotechnology Information (NCBI). It has been recently proposed that the whole chloroplast genome sequence should be used as a universal super-barcode in the identification of plant species. This approach may overcome the limitations of the traditional two-locus barcode based mainly on sequence variation within two plastome regions (rbcL and matK) that is not always sufficient for species discrimination  40. In Colobanthus, the development of cpDNA markers has so far been restricted to the following loci: matK, trnK (C.
masonae, C. affinis  41  ), matK, trnK, trnL, trnL-trnF, trnF, rps16 (C. muscoides; unpublished data, available in NCBI) and ndhF (C. brevisepalus  42  ). Complete chloroplast genome sequences are available only for C. quitensis  25  and C. apetalus  26. The published data indicate that the Colobanthus cp genome is typical for angiosperms in terms of size (151,276 bp for C. quitensis and 151,228 bp for C. apetalus) and composition (112 genes). This genome has a conserved quadripartite circular structure with a large single copy (LSC) region, a small single copy (SSC) region and two copies of inverted repeat (IR) regions  43, 44. The complete sequences of the cp genome of C. quitensis and C. apetalus provide molecular data that are essential for advanced genomic studies. However, the genomic data for other members of the genus Colobanthus are still limited.
Results Organization of chloroplast genomes. Six Colobanthus species were sequenced to produce 1,986,760-7,849,218 raw reads (150 bp for average read length) which were mapped separately to the reference genome of C. quitensis. A total of 210,882-414,160 reads were ultimately mapped with 202. 7× to 396. 5 × coverage (Table  1 ). The six Colobanthus cp genome sequences were deposited in GenBank under the following accession numbers:    1 ). The overall GC content was 36. 61-36. 66% and it was nearly identical in all Colobanthus cp genomes (Table  1 ). All of the analyzed Colobanthus cp genomes contained an identical set of 112 genes composed of 73 protein-coding genes, 30 tRNA genes, four rRNA genes and five conserved chloroplast ORFs (ycf1, ycf2, ycf3, ycf4, ycf68) (Table  2 ). Fifty-eight protein-coding genes, 22 tRNA genes and 2 conserved chloroplast ORFs (ycf3 and ycf4) are located in LSC, whereas the SSC region contained 11 protein-coding genes and one tRNA gene.
The total number of codons for all protein-coding genes in the cp genomes of six Colobanthus species ranged from 25,159 to 26,162. The most and least abundant codons (excluding these associated with the initiation and termination of translation) were ATT (4. 33%) and TGC (0. 25%), respectively (Supplementary Table  S1  online). Furthermore, leucine appeared as the dominant amino acid (10. 7%), whereas cysteine was less frequently encountered (1. 2%). Since the data for codon usage were not available for the previously published plastomes of C. quitensis and C. apetalus, these species were included in the analysis. Both species shared the same pattern of codon usage and amino acid frequency. The boundaries between IR and SSC/LSC regions in all Colobanthus cp genomes were identified (Fig. 2 ).