dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of exact matches Reference bacterial sequences  (O'Leary et al. , 2016)  were downloaded from the NCBI FTP server on 3 April 2017 together with taxonomy tree files. We identified maximal exact matches using the MUMmer 3. 0  (Delcher et al. , 2002)  software with the maxmatch option, which finds all matches regardless of their uniqueness. Specifically, to find all matches longer than 300 bp between sequences in files 1. faXandX2. fa and save it in the file Res. mumm, we used the following command: mummer -maxmatch -n -b -l 300 1. fa 2. fa > Res. mumm. Further details can be found in the following GitHub repository: https://github. com/mishashe/ HGT  (Sheinman et al. , 2021a , copy archived at swh:1:rev: b32b6ebd11b49349893ec69fc4788cf7ede26003,  Sheinman et al. , 2021b).
Empirical calculation of the MLD for pairs of genera and sets of genera To construct MLDs, we use all contigs longer than 10 5 bp. The MLD of a pair of genera i and j is defined as m ij ðrÞ ¼ M ij ðrÞ ' i ' j ; (3) where M ij ðrÞ is the number of matches of length r between all contigs of genus i and all contigs of genus j. ' x is the total length of the available contigs of genus x. The expected number of matches found in the analysis of a pair of genera scales with the amount of sequence data available for these genera. Normalising by ' i ' j therefore ensures that m ij ðrÞ does not scale with the database size, so that the m ij ðrÞ for different pairs of genera can be compared. In Figure  2 , Figure  5  and where the index i runs over the genera from the first set and the index j runs over the genera from the second set.
Restricted dataset To quantitatively study HGT rate variations, we constructed a smaller, curated dataset to reduce the risk of potential artefacts. The curated dataset encompasses only the exact sequence matches that stem from the comparison of contigs larger than 10 6 bp, since short contigs are more likely to present assembly or species assignment errors, or to originate from plasmid DNA. The resulting dataset comprises 138,273 matches longer than 300 bp. Hence, using the RefSeq database, we analysed all exact sequence matches longer than 300 bp between bacteria from different bacterial families, filtering out all contigs smaller than 10 6 bp. For some organisms we suspect an erroneous taxonomic annotation, due to their high similarity to another species, much higher than what is expected based on their reported taxonomic distances.
For species for which we found very high similarity (i. e. , a large number of long exact matches) to several distant species, we further compared this species to species belonging to its annotated taxa to compute the intra-taxon similarity. When the intra-taxon similarity was smaller than the similarity to distant species, we concluded that the annotation was likely erroneous. We thus manually cleaned the results, removing the comparisons between the species with suspected erroneous annotation and the taxa with which it had large similarities. Using this criterion, we removed from our database the comparison between the following accession numbers and all species of the mentioned taxa:. Accession number NZ_FFHQ01000001. 1 and all Enterococcus.
Environment, Gram, and GC content annotation Ecological annotation of bacterial genera is not well defined, and different members of the same genus can occupy different ecological niches. Nevertheless, using the text mining engine of Google, we annotated some of the genera as predominately marine, gut, and soil (see paragraph 11 in the GitHub repository). Using the same approach we identified Gram-positive, Gram-negative, GC-rich, and GC-poor genera. The results are summarised in Supplementary file 7. Additional information about bacterial genomes (such as Gram classification or lifestyle) were collected from PATRIC database metadata  (Wattam et al. , 2017).
Gene enrichment analyses To assess the enrichment of genes in the set of transferred sequences, we generated a set of control sequences as follows. For each match i present in w i contigs, we randomly sampled without replacement a random sequence with the same length from each of those w i contigs. This way, the control set takes into account the enrichment of certain species in the set of transferred sequences. For the results of Figure  6  and Supplementary file 5, we analysed 12 specialised databases: acquired antibiotic resistant genes (ResFinder database;  Zankari et al. , 2012) , antibacterial biocide and metal resistance genes database (BacMet database;  Pal et al. , 2014) , integrative and conjugative elements (ICEberg database;  Bi et al. , 2012) , virulence factors (VFDB database;  Chen et al.
, 2016) , essential genes (DEG database;  Luo et al. , 2014) , toxin-antitoxin systems (TADB database;  Shao et al. , 2011) , peptidases (MEROPS database;  Rawlings et al. , 2012) , bacterial exotoxins for human (DBETH database;  Chakraborty et al. , 2012) , transmembrane proteins (PDBTM database;  Kozma et al. , 2013) , restriction enzymes (REBASE database;  Roberts et al. , 2015) , bacterial small regulatory RNA genes (BSRD database;  Li et al. , 2013) , the transporter classification database (TCDB;  Saier et al. , 2016) , and enzyme classification database (Brenda;  Placzek et al. , 2017). For each set of genes from a database, using the blast toolkit  (Altschul et al. , 1990) , we calculate the total number of unique match-gene hit pairs for the matches (see paragraph 10 in GitHub repository for the exact blast command).
SEED subsystems and GO terms ontological classification To connect identifiers of the SEED subsystems  (Overbeek et al. , 2005)  to accession identifiers of NCBI nr database, two databases were downloaded: nr from NCBI (NCBI Resource Coordinators, 2016) FTP and m5nr from MG-RAST  (Meyer et al. , 2008)  FTP servers (on 17 January 2017). The homology search of proteins of the nr database against m5nr was computed using diamond v0. 9. 14  (Buchfink et al. , 2015). Proteins from the databases were considered to have similar function if they shared 90% of amino acid similarity over the full length. Additional files for SEED subsystems (onto-logy_map. gz, md5_ontology_map. gz, m5nr_v1. ontology. all) were downloaded from MG-RAST FTP on the same date. To annotate exact matches, open reading frames and protein sequences were predicted with Prodigal v2.
6. 3  (Hyatt et al. , 2010)  and queried against nr using diamond. After that subsystems classification was assigned to predicted proteins when possible. To assign GO terms to proteins of the HGT database, we queried them against the PFAM and TIGRFAM databases using the InterProScan v5. 36-75. 0 (Zdobnov and Apweiler, 2001). The scripts used to compute this analysis can be found in paragraph 6 of the GitHub repository. The algorithms of these two systems of ontological classifications are very different. SEED subsystems is based on protein homology search with diamond, where closely related proteins will be classified within the system. Assignment of GO terms is based on HMM profiles search, where more distant relatives of proteins can be recognised. To test for enrichment we conducted the Fisher exact test and a 95% confidence interval was obtained for the enrichment.
Accession number NZ_JOFP01000002. 1 and accession number NZ_FOTX01000001. 1. Accession number NZ_LILA01000001. 1' and all Bacillus. Accession number NZ_KQ961019. 1' and all Klebsiella. Accession number NZ_LMVB01000001. 1' and all Bacillus.
Accession number NZ_JOFP01000002. 1 and accession number NZ_FOTX01000001. 1. Accession number NZ_LILA01000001. 1' and all Bacillus. Accession number NZ_KQ961019. 1' and all Klebsiella. Accession number NZ_LMVB01000001. 1' and all Bacillus. Pairwise comparisons between accession numbers NZ_BDAP01000001. 1, NZ_JNYV01000002. 1, and NZ_JOAF01000003. 1 This resulted in 138,273 unique matches.
Then, adaptors and indexes were ligated and the construction amplified with Illumina-specific primers (eight cycles). Libraryquality was assessed using Fragment Analyzer and libraries were quantified by qPCR using the Kapa Library Quantification Kit (Roche). Sequencing was performed on a NovaSeq S4 lane (Illumina, California, USA), using a paired-end read length of 2 × 150 bp with the Illumina NovaSeq Reagent Kits. (e) Analysis of sex-specific variants based on a male reference genome Pool-seq data were mapped to the male A. ruthenus reference genome (Acipenser_ruthenus. ARUT1. 2. dna. toplevel. fa, which is nearly identical to the current reference genome at NCBI GCF_010645085. 1; electronic supplementary material, table  S1 ) by Minimap2 (with parameters -t 12 -x sr -a;  [38] ) and converted to sorted bam-files by SAMtools  [39].
(h) Phylogenomic reconstruction To build a phylogenetic tree for sturgeons, we downloaded transcriptome assemblies of A. baerii, A. oxyrinchus, Acipenser schrencki, Acipenser sinensis and Acipenser transmontanus from the Public Sturgeon Transcripts Database (http://publicsturgeon. sigenae. org/home. html) and of A. gueldenstaedtii from NCBI (https://www. ncbi. nlm. nih. gov/Traces/wgs/?page= 1&view=tsa&search=acipenser). The transcriptome of Huso huso was de novo assembled using Trinity  [43]  from mixed organs. Annotated genomes of A. ruthenus and Latimeria chalumnae were also downloaded from NCBI (electronic supplementary material, table  S2 ). To identify orthologues between two species, we used the 'Reciprocal best hit (RBH)' method, and A. ruthenus as the hub to retrieve orthologous genes across all species.
Protein sequences of the transcriptome contigs were predicted using Gene-Wise  [44]  by aligning each contig to its orthologous protein in A. ruthenus. Contigs with less than 60% of the protein aligned were discarded. In total, we collected 1017 genes with orthology across all species. For each orthologous gene, the protein sequences were aligned across all species using MAFFT  [45] , and trimmed them, using trimAl  [46]. All 1017 alignments were then combined in a concatenated alignment, consisting of 383506 aligned amino acid sites. Finally, this alignment was transferred to RAxML  [47]  to construct a maximum-likelihood phylogeny. A. ruthenus pools, respectively. Pool-sequencing Illumina reads are available in the Sequence Read Archive (SRA: SRX9341540; SRX9341541), under BioProject reference PRJNA670610.
Acknowledgements. We thank  Marilena Maereanu ,  Katarina Tosic  and  Marian Paraschiv  for help with H. huso catching, propagation and raising of animals;  Asja Vogt ,  Marcus Ebert ,  Jan Hallermann ,  Kevin Bäumler ,  Udo Wolf  for help with A. oxyrinchus or A. ruthenus propagation and raising of animals;  Elodie Dupin-de-Beyssat ,  Camille Eché ,  Antje Tillack ,  Sophia Lambert ,  Alina Heide , and  Petra Kersten  for help in the laboratory,  Lukáš Kratochvíl  for references,  Jörg Plötner  and  Robert Schreiber  for access to the Qiagen robotic workstation, and two anonymous reviewers for their constructive comments on a previous version of this paper.
Observations from the Orbiting Carbon Observatory 2 (OCO-2) satellite have been used to estimate CO 2 fluxes in many regions of the globe and provide new insight into the global carbon cycle. The objective of this study is to infer the relationships between patterns in OCO-2 observations and environmental drivers (e. g. , temperature, precipitation) and therefore inform a process understanding of carbon fluxes using OCO-2. We use a multiple regression and inverse model, and the regression coefficients quantify the relationships between observations from OCO-2 and environmental driver datasets within individual years for 2015-2018 and within seven global biomes. We subsequently compare these inferences to the relationships estimated from 15 terrestrial biosphere models (TBMs) that participated in the TRENDY model inter-comparison.
Using OCO-2, we are able to quantify only a limited number of relationships between patterns in atmospheric CO 2 observations and patterns in environmental driver datasets (i. e. , 10 out of the 42 relationships examined). We further find that the ensemble of TBMs exhibits a large spread in the relationships with these key environmental driver datasets. The largest uncertainty in the models is in the relationship with precipitation, particularly in the tropics, with smaller uncertainties for temperature and photosynthetically active radiation (PAR). Using observations from OCO-2, we find that precipitation is associated with increased CO 2 uptake in all tropical biomes, a result that agrees with half of the TBMs. By contrast, the relationships that we infer from OCO-2 for temperature and PAR are similar to the ensemble mean of the TBMs, though the results differ from many individual TBMs.
e. , for seven global biomes). A further challenge is to use these new global satellite datasets to evaluate and improve process-based estimates of the global carbon cycle provided by terrestrial biospheric models (TBMs). TBMs have become an integral tool for understanding regional-and global-scale carbon dynamics and for predicting future carbon cycling under changing climate. With that said, existing TBMs show large uncertainties in carbon flux estimates at multiple spatial and temporal scales -at regional and seasonal scales (e. g. ,  Peng et al. , 2014; King et al. , 2015) , at global and inter-annual scales (e. g. ,  Piao et al. , 2020) , and in historical and future projections (e. g. ,  Friedlingstein et al. , 2006; Huntzinger et al. , 2017). One approach to inform TBM development is to estimate flux totals using atmospheric observations and compare those totals against TBMs -to inform the magnitude, seasonality, or spatial distribution of fluxes (e.
Byrne et al. (2020b)  assimilated in situ and GOSAT observations of atmospheric CO 2 and an inverse model framework and found contrasting environmental sensitivities of IAV in CO 2 fluxes between western and eastern temperate North America. The goal of this study is to use atmospheric CO 2 observations from OCO-2 to quantify the relationships between spatiotemporal patterns in CO 2 fluxes and patterns in environmental driver datasets. We conduct this analysis for years 2015-2018 and focus on relationships that manifest across an individual year and individual biome. We specifically quantify the relationships using a top-down regression framework and a geostatistical inverse model (GIM). We then compare the relationships inferred using OCO-2 observations against those inferred from 15 state-of-the-art TBMs from the TRENDY model comparison project (v8, https: //sites.
Overview We quantify the relationships between CO 2 observations from OCO-2 and environmental driver datasets for different regions of the globe using a top-down regression framework and a GIM. We cannot directly observe the relationships between CO 2 fluxes and environmental driver datasets. With that said, an overarching idea of this study is that these relationships manifest in atmospheric CO 2 observations, and we can quantify at least some of these relationships using observations from OCO-2 and a weighted multiple regression. The coefficients estimated as part of the regression relate patterns in atmospheric CO 2 observations to patterns in the environmental driver datasets. As part of this analysis, we also explore differences in the estimated environmental relationships (i. e. , regression coefficients) among different years and different biomes.
To this end, we estimate separate regression coefficients for each of seven different global biomes, and we estimate separate coefficients for each individual year of the study period  (2015) (2016) (2017) (2018). Hence, each coefficient estimated here represents the relationship between OCO-2 observations and an environmental driver dataset across an entire year and a global biome. Miller and Michalak (2020)  explored when and where current OCO-2 observations can be use to detect variability in surface CO 2 fluxes, and the authors argue that, in most seasons, the satellite can be used to constrain fluxes from seven large biome-based regions -hence the choice of the seven biomes used in this study (Fig. 1 ). We first conduct this analysis using CO 2 observations from OCO-2. We then conduct a parallel analysis using the outputs of 15 terrestrial biosphere models (TBMs) from the TRENDY model inter-comparison project (v8).
The multiple regression used in this study has the following mathematical form (e. g. ,  Fang and Michalak, 2015) :  where z (n × 1) is a vector of real or synthetic CO 2 observations from OCO-2, X (m × p) is a matrix of environmental driver datasets (described in Sect. 2. 2), and β (p × 1) represents the regression coefficients that are estimated as part of the regression. Each column of X represents a different environmental driver dataset for a specific biome in a specific year. Note that we estimate all of the coefficients for the different environmental drivers and different biomes simultaneously in the regression model. In addition, ζ (m×1) represents patterns in the fluxes that cannot be described by the environmental driver datasets, and these values are unknown. This component of the fluxes is also commonly referred to as the stochastic component and is discussed in Sect.
2. 5. h() is an atmospheric transport model (described later in this section) that relates surface CO 2 fluxes (Xβ + ζ ) to the atmospheric CO 2 observations, and (n × 1) is a vector of errors in the OCO-2 observations and/or in the atmospheric model. The statistical properties of these errors are estimated before running the regression (described in Sect. 2. 4). Note that this framework assumes linear relationships between the environmental driver datasets and the OCO-2 observations. Numerous existing studies have used linear models to approximate relationships with environmental driver datasets. For example, studies have used linear models to compare the relationships between CO 2 fluxes and environmental driver datasets in TBMs (e. g. ,  Huntzinger et al. , 2011) , to infer these relationships using eddy flux observa-tions (e.
g. ,  Mueller et al. , 2010; Yadav et al. , 2010) , and to infer relationships between atmospheric CO 2 observations and environmental driver datasets (e. g. ,  Gourdji et al. , 2012; Fang et al. , 2014; Fang and Michalak, 2015; Piao et al. , 2013 Piao et al. , , 2017;; Rödenbeck et al. , 2018). z = h(Xβ + ζ ) + , (1) The equations above require an atmospheric transport model (h()). We use the forward GEOS-Chem model (version v9-02; http://www. geos-chem. org, last access: 28 October 2020) in this study, and we further use wind fields from the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) to drive atmospheric transport within GEOS-Chem  (Gelaro et al. , 2017). The GEOS-Chem simulations used here have a global spatial resolution of 4 • latitude by 5 • longitude and therefore are best able to capture broad, regional spatial patterns in atmospheric CO 2.
Environmental driver datasets We estimate the relationships between OCO-2 observations (either real or synthetic) and environmental driver datasets drawn from commonly used meteorological reanalysis. We specifically consider the following driver datasets as predictor variables in the multiple regression: 2 m air temperature, precipitation, photosynthetically active radiation (PAR), downwelling shortwave radiation, and specific humidity. We also include a nonlinear function of 2 m air temperature as an environmental driver dataset in the regression (referred to hereafter as scaled temperature; plotted in Fig. S1  and described in detail in Supplement Sect. S3). Numerous existing studies show that the relationship between temperature and photosynthesis has a different sign depending upon the temperature range; at sufficiently warm temperatures, an increase in temperature yields a decrease in photosynthesis (e.
g. ,  Baldocchi et al. , 2017). The scaled temperature function considered here can account for those differences, and we find that this function yields a better model-data fit in the regression analysis than using temperature alone. The scaled temperature function used here is from the Vegetation Photosynthesis and Respiration Model (VPRM)  (Mahadevan et al. , 2008)  and describes the nonlinear relationship between temperature and photosynthesis  (Raich et al. , 1991). The function is shaped like an upside-down parabola (shown in Fig. S1 ). Furthermore, this type of nonlinear temperature function has been commonly used in existing TBMs (e. g. ,  Heskel et al. , 2016; Luus et al. , 2017; Dayalu et al. , 2018; Chen et al. , 2019). The environmental driver datasets described above are drawn from the Climatic Research Unit (CRU) and Japanese reanalysis (JRA) meteorology product (CRUJRA;  Harris, 2019).
We use environmental driver data from CRUJRA because it is the same product used to generate the TRENDY model estimates. All flux outputs from TRENDY are provided at a monthly temporal resolution, so we input monthly meteorological variables from CRUJRA into the regression framework. Furthermore, we regrid the environmental driver datasets to a 4 • latitude by 5 • longitude spatial resolution before inputting these datasets into the regression. This spatial resolution matches the resolution of the atmospheric transport simulations used in this study (described in Sect. 2. 1). The regression coefficients therefore quantify the relationships between OCO-2 observations and patterns in environmental driver datasets that manifest at this spatial and temporal resolution. We subsequently rerun the regression analysis using environmental driver datasets drawn from a second meteorological product.
Estimates of environmental driver data like temperature or precipitation can vary among meteorological models, and these differences among models are a source of uncertainty in the estimated regression coefficients. Hence, the use of a second meteorological product can at least partially account for these uncertainties. We specifically rerun the regression analysis using environmental driver datasets drawn from MERRA-2. We choose MERRA-2 because it is a commonly used, global reanalysis product from the NASA Global Modeling and Assimilation Office (GMAO). Furthermore, we use wind fields from MERRA-2 to drive all atmospheric transport model simulations in this study (described in Sect. 2. 1), so the use of MERRA-2 for the environmental driver datasets in the regression creates consistency with the wind fields in the atmospheric model simulations that support the regression.
Note that we do not include any remote sensing indices (e. g. , solar-induced chlorophyll fluorescence or leaf area in-dex) in the present study. Rather, the focus of this study is to explore environmental drivers of CO 2 fluxes and not remote sensing proxies for CO 2 fluxes. Also note that we standardize (i. e. , normalize) each of the environmental driver datasets within each biome and each year before running the regression, as has been done in several previous GIM studies (e. g. ,  Gourdji et al. , 2012; Fang and Michalak, 2015). This step means that all of the estimated regression coefficients (β) have the same units, are independent of the original units on the environmental driver data, and can be directly compared to one another.
Model selection We use model selection to decide which environmental driver datasets to include in the analysis of the OCO-2 observations and in the analysis of each TBM using synthetic observations. Model selection ensures that the environmental driver datasets in the regression (X) do not overfit the available OCO-2 data (z). The inclusion of additional environmental driver datasets or columns in X will always improve the model-data fit in the regression, but the inclusion of too many driver datasets in X can overfit the regression to available OCO-2 data and result in unrealistic coefficients (β) (e. g. ,  Zucchini, 2000). In addition, model selection indicates which relationships with environmental drivers we can confidently constrain and which we cannot given current OCO-2 observations (e.