dataesr/scientific-paragraphs-categorization huggingface dataset:
HQ and MHQ MAGs were not significantly different from WGS genomes in terms of gene density (Supplementary Table  1 ). A phylogeny of these genomes was established using domain-specific marker genes of the Genome Taxonomy Database (GTDB)  34  , highlighting a total of 107 phyla (with unclassified) including highly represented phyla in marine environments, such as Proteobacteria, Bacteroidetes, Actinobacteria, and Cyanobacteria 35 (Fig. 1a ). Within prokaryotic genomes, the number of genes in most high-level functional categories has been shown to scale as a power-law to the total number of genes in a genome  36. A potential explanation for these observed scaling laws among microbial genomes is a conserved average duplication rates for the evolutionary process within each functional category.
Abiotic factors shaping genome community composition and activity Next, we mapped Tara Oceans metagenomics and metatranscriptomics sequencing reads from surface (SRF) and deep chlorophyl maximum (DCM) samples (N=118) onto our genome collection (see methods) to generate a comprehensive global ocean abundance and expression profiling of microbial communities in relationship with abiotic environmental factors (see Supplementary Table  3 ). Average mapping rates were 16. 0% and 12. 3% for metagenomes and metatranscriptomes, respectively (Fig. 2a  and Extended Data Fig. 3 ). Using the same Tara Oceans dataset, gene and transcript abundances have previously been shown to be highly correlated  41. Here, we observed an overall relatively good concordance between genome-wide abundance and expression (Spearman rho=0.
A database of species-level marine prokaryotic genomes A database of genomes from marine prokaryotes was assembled using several specialised databases as well as genomes reconstructed within specific studies. These databases included whole-genome sequences from marine prokaryote isolates (WGS), single-amplified genomes (SAGs), and metagenomic-assembled genomes (MAGs). The main database source for our genome collection was the Marine Metagenomic Portal 79 through the use of the databases MarRef v4. 0 (N=943, mostly high-quality WGS)  79  , MarDB v4. 0 (N=12,963)  79  , and aquatic representative genomes from the ProGenomes database v1. 0 32 (N=566). This collection of well-documented genomes was complemented by 5,319 MAGs assembled from four distinct studies, namely: Parks et al. Note that depending on their study of origin, included MAGs may have been reconstructed using different assembling and binning methods.
Genomes that did not meet at least the MQ threshold were tagged as low-quality (LQ) and discarded from the database (N=1,127). Quality estimates were used in the de-replication process that was performed using dRep v2. 2. 3  83  (see dReplication in ecosysmic repository). dRep uses average nucleotide identity (ANI) and filters out redundant genomes via a 2-step clustering strategy: a fast coarse-grained clustering by MASH ANI (threshold used: 90% ANI over 60% of the genomes), followed by a slow fine-grained clustering through NUCMER ANI in clusters identified in the previous step only (threshold used: 95% ANI over 60% of the genomes). This process yielded 7,658 non-redundant species-level genomes with an average nucleotide identity below 95%, a threshold previously reported to delineate species level for prokaryotes  84.
These genomes were assigned taxonomic information using GTDB-TK v0. 3. 2  85  (see SnakeGTDBTk in ecosysmic repository), which also allowed us to place our genomes within a phylogenetic tree using iTOL v5  86. Since GTDB-Tk reconstructs two independent trees for Archaea and Bacteria, we linked them at the root using a distance of 0. 122  87  , as recommended by the authors and tool maintainers (https://github. com/Ecogenomics/GTDBTk/issues/209).
Genomic scaling laws analysis We used scaling laws as a framework to characterise the functional content of our genomic database (WGS, SAGs, MAGs). These genomic scaling laws were also used as a tool to properly identify enriched or depleted functional and metabolic potentials within specific groups of genomes (e. g. , origin, co-active or not) by taking into account genome size and identifying enriched/depleted potential in proportion of the observed genome size. EggNOG provides 25 high-level categories and a KEGG Orthology (KO) equivalent for each Cluster of Orthologous Group (COG) annotation. The KO database also provides a 4-level hierarchy of (unnamed) functional categories. We were able to group our 23,224 KO identified in our catalogue into 54 high-level categories (level 2 in the hierarchy that presented for us the best compromise between specificity and tractability of the metabolic functions).
e. , present and/or active) within a sample when at least 30% of its genome was horizontally covered (also called breadth). Overall, we were able to compute abundances for 107 samples, and activities for only 71 samples. To lower spurious correlations, abundance and activity data points for unobserved genomes were discarded and genomes with less than 10 observations across our samples were removed. This was done independently for abundance (N=1,232 genomes observed in at least 10/71 samples) and activity (N=902 genomes observed in at least 10/71 samples). Finally, the inherent compositional nature of the sequencing datasets was taken into account using centred log-ratio (CLR) transformation and the adaptive pseudo-count implemented in FlashWeave. Both abundance and activity matrices were used as input to FlashWeave using parameters "normalize=true, "n_obs_min=10, max_k=3, heterogenous=true" (see the FlashWeave documentation for more information about these parameters).
We limited the community metabolic analyses to MHQ+HQ genomes in order to lower the risk of predicting spurious interactions in communities of lower-quality genomes and metabolic models. SMETANA was run in both global and detailed modes with the solver IBM CPLEX v12. 10, using in each mode the default media provided by the package (which is a complete media for global analysis, and a community-specific minimal media for detailed analysis). A set of inorganic compounds were excluded from the analysis as explicitly recommended by one of the package author (https://github. com/cdanielmachado/smetana/issues/20#issuecomment-827389107). Other parameters used were "--flavor bigg --solver CPLEX --molweight". The "community smetana score" reported in the main text is obtained by summing all smetana scores predicted for a given community.
In order to compare communities of different sizes, this score was normalised by dividing the "smetana score" by the total number of potential genome-genome interactions, i. e. N x (N-1) / 2 (with N the size of the community). We referred to this new score in the main text as "normalised smetana score". In order to classify the different metabolites in the SMETANA database into metabolite categories (e. g. , amino acids, carboxylates), we first mapped the metabolite identifiers to the MetaNetX database (available at: https://www. metanetx. org/cgi-bin/mnxget/mnxref/chem_xref. tsv). From this mapping, we extracted MetaCyc identifiers to subsequently obtain their ontologies (available at: https://metacyc. org/groups/export?id=biocyc14-14708-3818508891&tsv-type=FRAMES).
Statistical analyses All statistical tests and analyses were performed using scipy. stats Python module v1. 7. 3. All figures were generated using Python v3. 7. 12 and R v4. 2. 2. We used statannotations v0. 4. 4 (https://github. com/trevismd/statannotations) to append statistical significance to all boxplots. Stars are used to define significance level as follow: **** for P ≤ 10 -4 , *** for 10 -4 < P ≤ 10 -3 , ** for 10 -3 < P ≤ 10 -2 , * for 10 -2 < P ≤ 5x10 -2 , and finally ns for P > 5x10 -2. All data analysis sub-packages were installed in the same environment using Conda v22. 11. 1, the versions of which are detailed in the yaml file located in each repository cited above.
Acknowledgements Tara Oceans (which includes both the Tara Oceans and Tara  Oceans Polar Circle  expeditions) would not exist without the leadership of the  Tara Ocean Foundation  and the continuous support of 23 institutes (http://oceans. taraexpeditions. org). We wish to thank the commitment of the following sponsors:  CNRS  (in particular  Groupement de Recherche  GDR3280 and the  Research Federation  for the study of  Global Ocean Systems Ecology and Evolution , FR2022/Tara  Oceans-GOSEE ),  European Molecular Biology Laboratory (EMBL) ,  Genoscope/CEA , The  French Ministry of Research , and the  French Government  ' Investissements d'Avenir' programmes   OCEANOMICS  ( ANR-11-BTBR-0008 ),  FRANCE GENOMIQUE  ( ANR-10-INBS-09-08 ), the  CNRS MITI  through the  interdisciplinary program Modélisation du Vivant  ( GOBITMAP grant  to SC), the  RFI   ATLANSTIC2020  ( ECOSYSMIC grant  to SC), and the  H2020  project  AtlantECO  (award number  862923 ).
Acknowledgments We thank  Jean-Michel Claverie  for his comments on the manuscript and discussions all along the project. The data were collected at the  Cryo-EM Swedish National Facility  funded by the  Knut  and  Alice Wallenberg ,  Family Erling Persson  and  Kempe Foundations, SciLifeLab, Stockholm University , and  Umeå University  under the expert assistance of  Matthieu Coinçon. The preliminary electron microscopy experiments were performed on the  PiCSL-FBI core facility  ( Aïcha Aouane, IBDM, AMU-Marseille) , a member of the  France-BioImaging  national research infrastructure, sample freezing conditions and cryo-EM preliminary acquisitions were performed at the AFMB microscopy platform and on the IMM imaging platform ( Dr. Artemis Kosta ). We thank the  PACA Bioinfo platform  for computing support.
Data and materials availability: Genome sequences of the mutants of mimivirus reunion have been deposited to NCBI (accession numbers: KO_qu_946: OQ700912; KO_qu143: OQ700913, 2KO: OQ700914). 3D reconstruction maps and the corresponding PDB have been deposited to EMDB (Deposition number Cl1: 8ORS, EMD-17131; Cl2: 8ORH, EMD-17125). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (42) partner repository with the dataset identifier PXD041298. AlphaFold predictions were performed using HPC/AI resources from GENCI-IDRIS (Grant 2022-AD011013526).
Xcalibur 4. 4 0 (Thermo Scientific). Peptides and proteins were identified using Mascot (version 2. 8. 0, Matrix Science) through concomitant searches against homemade A. castellanii protein sequence database, homemade virus-specific protein sequence databases, and a homemade database containing the sequences of classical contaminant proteins found in proteomic analyses (human keratins, trypsin. ). Trypsin/P was chosen as the enzyme and two missed cleavages were allowed. Precursor and fragment mass error tolerances were set at respectively at 10 and 20 ppm. Peptide modifications allowed during the search were: Carbamidomethyl (C, fixed), Acetyl (Protein N-term, variable) and Oxidation (M, variable). The Proline software version 2. 2. 0 (39) was used for the compilation, grouping, and filtering of the results: conservation of rank 1 peptides, peptide score ≥ 25, peptide length ≥ 6 amino acids, peptide-spectrum-match identification false discovery rate < 1% (40), and minimum of 1 specific peptide per identified protein group.
Proline was then used to perform a MS1-based quantification of the identified protein groups. Intensity-based absolute quantification (iBAQ) (41) values were calculated for each protein group from the MS intensities of razor and specific peptides. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 832601). Proteomic experiments were partly supported by ProFI (ANR-10-INBS-08-01) and GRAL, a program from the Chemistry Biology Health (CBH) Graduate School of University Grenoble Alpes (ANR-17-EURE-0003). France-BioImaging national research infrastructure (ANR-10-INBS-04). CDC gratefully acknowledges STARPLUS 2020 (project no. 21 UNINA EPIG 042) from the University of Napoli for financial support.
Active bromine (e. g. , Br2, BrCl, BrO, HOBr) promotes atmospheric ozone destruction and mercury removal. Here we report a previously unidentified participant in active-Br chemistry, cyanogen bromide (BrCN), measured during the NASA Atmospheric Tomography (ATom) mission. BrCN was confined to polar boundary layers, often appearing at concentrations higher than other Br compounds. The chemistry of BrCN determines whether it promotes or inhibits ozone and mercury 25 removal. This dataset provides evidence that much of the BrCN was from atmospheric Br chemistry involving surface reactions with reduced nitrogen compounds. Since gas phase loss processes are known to be relatively slow, surface reactions must also be the major loss processes, with vertical profiles implying a BrCN atmospheric lifetime in the range 1-10 days.
BrCN Formation As noted above, formation of BrCN has been demonstrated in a number of chemical systems both abiotic and from marine biota. The bulk of the evidence in ATom-2,3&4 datasets is that BrCN formation occurs only in polar regions when there is active-Br chemistry occurring. Conversely, neither of the ATom-3&4 deployments observed instances where there 440 was substantial O3 destruction and no measurable BrCN present. The destruction of O3, i. e. O3 substantially below what is in the lowest 1-2km of the atmosphere above, is one tell-tale sign of active-Br chemistry. Another sign of active-Br chemistry is the presence of elevated CHBr3 concentrations relative to the closely-related biogenic compound CH2Br2. These markers could be considered evidence that BrCN was produced from active-Br chemistry.
Statistic analyses for phylosymbiosis detection The bacterial OTU table was rarefied at 5,750 sequences and converted into Bray-Curtis dissimilarity distances. To test the effect of host species identity on Harpagifer GMM composition, a permutational multivariate analysis of variance (PERMANOVA) was performed using VEGAN (v2. 6-2) and PAIRWISEADONIS (v0. 4) R packages, respectively. A set of 14 environmental abiotic variables presumed important in influencing microbiome structure, was extracted for each sampling locality from the Bio-ORACLE database (60) (Supplementary Figure  1 ). All the environmental variables, standardized to a mean of zero and a standard deviation of one, were analyzed using a principal component analysis (PCA). The scores of the samples on the first two principal components (capturing 91% the variability), were transformed into Euclidean distance using the vegdist function of the R package VEGAN (v2.
Core microbiome definition and neutral model fitting To identify bacterial taxa that are common to the eight Harpagifer populations, a core microbiome was defined at the OTU level, based on a minimum prevalence criterion >40% across all gut mucosa samples from our dataset  (23). The relationship between the prevalence and abundance of core microbiome OTUs was compared to the neutral community model proposed by Sloan et al. (64)  and formalized in R by Burns et al. (65). Well-predicted OTUs (i. e. with abundance and prevalence comprised within the 95% confidence limits of the model) are supposed to be driven by stochastic factors (e. g. dispersal and ecological drift), while OTUs that deviated from the 95% confidence interval of the neutral model predictions are more likely influenced by deterministic factors (e.
Reduced core microbiome in Harpagifer spp. gut mucosa A reduced core microbiome comprising 17 OTUs was detected across all Harpagifer species studied (Figure  3 ). This core microbiome represented in average 22. 5% ± 2. 9% of the relative abundance in gut mucosa samples, and spanned 9 bacterial classes belonging mostly to the Gammaproteobacteria phylum (Table  3 ). Prevalence of core OTUs ranged from 40% up to 96% of the gut mucosa samples. Two core OTUs were particularly abundant (OTU2 and OTU4, representing 12. 6% and 6. 9% of the total relative abundance in the whole GMM dataset, respectively), while the 15 others had relative abundance <0. 9% (Table  3 ). The fitting of GMM composition to the neutral model was relatively low, explaining no more than 20% of the gut microbiome variance of Harpagifer (m=0.
Acknowledgments The authors thank the  French Polar Institute  project 1044  PROTEKER  for field access, and the  University of Wisconsin Biotechnology Center DNA Sequencing Facility  (Research Resource Identifier -RRID: SCR_017759 ) for providing MiSeq® sequencing facilities and services. The bioinformatic analyses were partially supported by the infrastructure of the  National Laboratory for High Performance Computing Chile  (NLHPC). This research was partially supported by  ANID  Grant  ACE210006. We would like to express our gratitude to  Zambra Lopez ,  Francisco Bahamondes  and  Javier Cárcamo  for their contributions in field sampling. We also thank  Sebastián Rosenfeld  and  Natalie Ulloa  for their graphical contributions to this study.
In this paper we introduce a spectral surface reflectivity climatology based on observations made by the TROPOMI instrument on board the Sentinel-5P satellite. The database contains the directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface for 21 wavelength bands ranging from 328 nm to 2314 nm and for each calendar month. The spatial resolution of the database grid is 0. 125 • × 0. 125 •. A recently developed cloud shadow detection technique is implemented to avoid dark scenes due to cloud shadow. In the database, the anisotropy of the surface reflection is described using a third-order parameterisation of the viewing angle dependence. The viewing angle dependence of the DLER is analysed globally and for a selection of surface type regions. The dependence is found to agree with the viewing angle dependence found in the GOME-2 surface DLER database.
Differences exist, related to the actual solar position. On average, the viewing angle dependence in TROPOMI DLER is weaker than for GOME-2 DLER, but still important. Validation of the new database was first performed by comparison of the non-directional TROPOMI surface LER with heritage LER databases based on GOME-1, OMI, SCIAMACHY, and GOME-2 data. Agreement was found within 0. 002-0. 02 in the UV-VIS (below 500 nm), up to 0. 003 in the NIR (670-772 nm), and below 0. 001 in the SWIR (2314 nm). These performance numbers are dominated by the performance over ocean, but they are in most cases also representative for land surfaces. For the validation of the directional TROPOMI surface DLER we made use of comparison with MODIS surface BRDF for a selection of surface type regions. In all cases the DLER performed significantly better than the traditional LER and we found good agreement with MODIS surface BRDF.
The TROPOMI surface DLER database is a clear improvement on previous surface albedo databases and can be used as input not only for satellite retrievals from TROPOMI observations, but also for retrievals from observations from other polarorbiting satellite instruments provided that their equator crossing time is close to that of TROPOMI. The algorithm that is introduced in this paper can be used for the retrieval of surface reflectivity climatologies from other polar satellite missions as well, including OLCI on the Sentinel-3 satellites, Sentinel-5 and 3MI on the MetOp-SG-A1 satellite to be launched in 2025, and the future CO2M mission.
Introduction The reflectivity of the Earth's surface is important input for many satellite retrievals of atmospheric composition. Examples of retrievals for which this is the case are the retrievals of ozone (O 3 ), nitrogen dioxide (NO 2 ), methane (CH 4 ), formaldehyde (CH 2 O), bromine oxide (BrO), water vapour (H 2 O), carbon monoxide (CO) and carbon dioxide (CO 2 ), and of cloud and aerosol information. In many retrieval codes, the surface is described as a Lambertian reflector, meaning that the surface reflection is assumed to be isotropic. This is a simplified approach which may be justified in some cases, but certainly not in all. For instance,  Lorente et al. (2018)  show that relying on traditional Lambertian surface reflection databases can result in errors of a factor of two in the assumed surface reflection for vegetated surfaces -at least for certain viewing geometries -due to their lack of directional dependence.
Generally speaking, it is better to describe the surface reflection as a function of incidence and reflection angles, using a bi-directional reflectance distribution function (BRDF)  (Nicodemus et al. , 1992; Schaepman-Strub et al. , 2006). The MODIS surface BRDF database  (Gao et al. , 2005) , an established product, is available for land surfaces and is widely used. However, inserting a BRDF where the radiative transfer code expects a Lambertian surface reflection would lead to errors, especially for the shorter wavelengths  (Tilstra et al. , 2021, Sect. 3. 3). Apart from that, the MODIS BRDF wavelength bands are not positioned spectrally such that they can support all atmospheric retrievals mentioned above. Recently, a number of new databases have appeared that provide Lambertian surface albedos which include a directional dependence of the albedo values.
The geometry-dependent surface Lambertian-equivalent reflectivity (GLER)  (Qin et al. , 2019)  uses MODIS surface BRDF information and converts it to a Lambertian surface albedo at 466 nm for satellite footprints of the OMI instrument, respecting the viewing and solar directions of the OMI observations. This is done for the land-covered footprints. For the water-covered footprints model calculations are used  (Fasnacht et al. , 2019). The geometry-dependent effective Lambertian-equivalent reflectivity (GE_LER) database  (Loyola et al. , 2020 ) also provides Lambertian surface albedos with a directional dependence. The surface albedo in this case is derived from level-1 data from GOME-2 or TROPOMI. The GE_LER provides daily maps of the surface properties for land, ocean, and snow/ice in one database.
The directionally dependent Lambertian-equivalent reflectivity (DLER) derived from GOME-2  (Tilstra et al. , 2021 ) is a monthly surface reflectivity climatology derived from GOME-2 level-1 observations. This database also provides the directional dependence of the surface reflectivity but via a parameterisation of the viewing angle dependence. That is, the provided directional surface albedo is not only available for a specific satellite footprint or for a specific satellite instrument, but for any observation from any satellite instrument provided that the overpass time of the satellite instrument is close to that of the GOME-2 orbit (09:30 LT). Because the surface albedos provided by the GLER, GE_LER, and DLER databases are Lambertian (despite their directional dependence), those albedos can be used as input for radiative transfer codes that rely on Lambertian surface reflection.
The addition of directionality is in all cases a large improvement on traditional non-directional surface albedo databases based on, for instance, TOMS  (Herman and Celarier, 1997) , GOME  (Koelemeijer et al. , 2003) , OMI  (Kleipool et al. , 2008) , and SCIAMACHY  (Tilstra et al. , 2017). However, a specific TROPOMI DLER database exploiting the small footprint size of TROPOMI did not exist, and the existing GOME-2 DLER database cannot be used as input for TROPOMI retrieval algorithms because of the different overpass time of the GOME-2 orbit. This paper introduces a new surface albedo database based on measurements performed by the Tropospheric Monitoring Instrument (TROPOMI)  (Veefkind et al. , 2012). The database contains the TROPOMI surface DLER retrieved for 21 wavelength bands ranging from 328 to 2314 nm on a two-dimensional grid of the Earth's surface with a spatial resolution of 0.