dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , ACTRIS (www. actris. eu, last access: 14 September 2022), and international initiatives such as GALION  (WMO, 2007) , could contribute to overcoming current limitations. N d • N i=1 (Mi -Oi) 2 0 to +∞ 0 Fractional Gross Error FGE d = 2 N d • N i=1 | M i -O i M i +O i | 0 to 2 0 Correlation Coefficient CC d = N i=1 (M i -M d )•(O i -O d ) √ N i=1 (M i -M d ) 2 • √ N i=1 (O i -O d ) 2 -1 to 1 1 σM d = 1 N d • N i=1 (M i -M d ) 2 (A2) O d = 1 N d • N i=1 O i (A3) σO d = 1 N d • N i=1 (O i -O d ) 2 (A4) are the mean and the standard deviation of the reanalysis (Eq. A1-A2) and the observed DOD (Eq. A3-A4); N indicates the total number of collocated and concurrent M i -O i pairs. The subscript d denotes the observational dataset used in the calculations. MB captures the average deviation between the two datasets.
CC indicates the extent to which spatial and temporal patterns in the reanalysis match those in the observations, quantifying their correlation and dependence. It ranges between -1 and 1, where -1 means perfect anti-correlation, 0 means no correlation, and 1 indicates perfect correlation. The statistical results obtained by the comparison between the reanalysis and each reference satellite dataset can be aggregated in order to get total average scores by weighting the metrics of Table  A1  by the number of observations N d provided by each dataset, using the equations of Table  A2. Table  A2. Weighted mean of the evaluation metrics obtained by different reference satellite datasets.
Acknowledgements. The authors thank all the Principal Investigators and their staff for establishing and maintaining the  NASA  and PHO-TONS AERONET sites, and the  MODIS  and MISR mission scientists and associated  NASA  personnel for the production of the data used in this study. BSC co-authors acknowledge PRACE ( eDUST ,  eFRAGMENT1  and  eFRAGMENT2 ) and  RES  ( AECT-2019-3-0001 ,  AECT-2020-1-0007 ,  AECT-2020-3-0013 ) for awarding access to MareNostrum at the  BSC  and for providing technical support. Financial support. This research has been supported by the  DustClim  project which is part of  ERA4CS , an  ERA-NET programme  initiated by  JPI Climate  with co-funding by the  European Union 's  Horizon 2020 research and innovation programme  (Grant no  690462 ). The authors acknowledge the  ACTRIS-IMP (Implementation project ), funded by the  European Union 's  Horizon 2020 research and innovation programme  (Grant no  871115 ).
e. , the  WMO Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) Regional Center for Northern Africa , the  Middle East and Europe). The work of  Ralph Kahn  is support in part by the  NASA   Aerosol-Cloud Modeling and Analysis Program  under  Richard Eckman , and the  NASA   Earth Observing System Terra and MISR  projects. Antonis Gkikas  acknowledges support by the  Hellenic Foundation for Research and Innovation  (H. F. R. I. ) under the "2 nd Call for H. F. R. I. Research Projects to support Post-Doctoral Researchers" (Project  Acronym: ATLANTAS , Project Number: 544). The  MIDAS dataset  has been developed in the framework of the  DUST-GLASS  project (grant no. 749461 ;  European Union 's  Horizon 2020 Research and Innovation programme  under the  Marie Skłodowska-Curie Actions).
Data availability. The MONARCH reanalysis dataset  (Di Tomaso et al. , 2021 ) is available at http://hdl. handle. net/21. 12146/c6d4a608-5de3-47f6-a004-67cb1d498d98 (last access: 14 September 2022). The MIDAS dataset  (Gkikas et al. , 2020 ) is available at https://doi. org/10. 5281/zenodo. 424410 AERONET Version 3 data are available from the AERONET web site (https://aeronet. gsfc. nasa. gov, last access: 14 September 2022). The MISR standard data products can be found at the NASA Langley Atmospheric Data Center (ASDC) Distributed Archive Center (DAAC) (https://asdc. larc. nasa. gov, last access: 14 September 2022). AEROIASI data can be provided upon request to the principal investigator of the satellite data: Juan Cuesta, LISA/UPEC, cuesta@lisa. ipsl. fr.
https://doi. org/10. 5194/acp-2022-655 Preprint. Discussion started: 1 November 2022 c Author(s) 2022. CC BY 4. 0 License. dark-water non-spherical AOD retrieval (at 557. 5 nm) provided by the MISR Level 3 Component Global Aerosol Product (MIL3DAE, Version F15_0031) dataset, on a 0. 5°× 0. 5°spatial grids during the period 2000-2016.
mean and the combined standard deviation of the reanalysis (Eq. A5-A6) and the aggregated satellite-based dust products (i. e. , MIDAS+MISR for DOD and MIDAS+AEROIASI for coarse DOD) (Eq. A7-A8). The subscript d denotes the satellite dataset used in the calculations (d = 1 MIDAS and d = 2 MISR for DOD; as well as d = 1 MIDAS and d = 2 AEROIASI for coarse DOD).
The identification of haplotypes influencing traits of agronomic interest, with well-defined effects across environments, is of key importance to develop varieties adapted to their context of use. It requires advanced crossing schemes, multi-environment characterization and relevant statistical tools. Here we present a sorghum multi-reference back-cross nested association mapping (BCNAM) population composed of 3901 lines produced by crossing 24 diverse parents to three elite parents from West and Central Africa (WCA-BCNAM). The population was characterized in environments contrasting for photoperiod, rainfall, temperature, and soil fertility. To analyse this multi-parental and multi-environment design, we developed a new methodology for QTL detection and parental effect estimation. In addition, envirotyping data were mobilized to determine the influence of specific environmental covariables on the genetic effects, which allowed spatial projections of the QTL effects.
D. Genetic diversity Figure  2A  illustrates the genetic diversity covered by the parental lines of the WCA-BCNAM population compared to a panel representative of the global sorghum diversity (Methods S1). We compared the number of common polymorphic SNPs of our population, of the US-NAM  (Bouchet et al. 2017 ) and the global diversity panel. Overall, the WCA-BCNAM parents covered 90. 2% of the global sorghum genetic diversity considered for the analysis (192K SNP) which offer a slightly better coverage (11 %) than the parents from the sorghum US-NAM which already captured 79. 2% of the considered diversity. Principal component analysis of the WCA-BCNAM genetic data (Figures 2 B, C and D) detected three distinct groups corresponding to the three recurrent parents (Figure  S2 ). Clear subdivisions of the populations according to the donor parent race and some specific divergences from this general pattern were observed especially for the populations involving the Hafijeka Guinea Margaritiferum accession.
R. Phenotypic data The Grinkan and Kenin-Keni populations were partly phenotyped in 2012 and partly in 2013 (Table  2 , Figure  S1 ). We considered each population within a year as a separate population (GR2012, GR2013, KK2012, KK2013). Each population was phenotyped at a combination of two locations (Sotuba and Cinzana, Figure  5A ) and two sowing dates (Sowing 1: end of June, Sowing 2: three to four weeks later, Table  S8 ), which gave a total of four environments (SB1, SB2, CZ1, CZ2; Figure  S8 ). The Sotuba environment is characterized by around 900 mm/year of precipitation and lower temperatures while the Cinzana environment is characterized by lower precipitation (600-700 mm/year) and warmer temperature. In both environments, the second sowing date had a lower level of precipitation and humidity and higher temperatures (Figure  S7 , Table  S9 ).
In each environment, the progenies of Grinkan and Kenin-Keni populations were laid out as augmented design  (Kempton, 1984)  with the three recurrent parents used as checks. The Lata3 population was entirely phenotyped in 2013 in three environments defined by two levels of phosphorus fertilization (Low-P and high-P) at the Samanko station and standard conditions at Kolombada station (Figure  5A ). In each environment, the genotypes were laid out as an alpha-lattice design  (Kempton, 1984)  with two replications  (Diallo et al. , 2019). We measured eight traits listed with crop ontology (CO) code. Flag leaf appearance (FLAG, CO_324:0000631) was the number of days after sowing when half of the plot had their ligulated flag leaves visible. For the QTL analysis FLAG data were converted into degree day.
S. environmental covariables -EC We complemented the data by 15 daily observed ECs (Table  S11 ) to evaluate the environmental influence on plant adaptation of the Grinkan and Kenin-Keni populations. With four environments (SB1, SB2, CZ1, CZ2) we could observe environmental gradients, which was not possible for Lata3 data because the low-P and high-P trials were performed at the same time and location which reduce the number of environments to two. The ECs were divided in three categories. The ECs values came from weather stations at the locations complemented by Nasapower satellite observations (Spark 2018) and transformation using the R package EnvRtype  (Costa-Neto et al. , 2021). We projected the genetic effects beyond the tested environments, using compiled environmental data from various sources extended with EnvRtype for a grid of 60 points between 12.
To determine significant enrichments of repair occupancy on the genome, we developed a computational method for single-cell domain calling. We modified a multi-scale representation of genomic signals (MSR) approach (13) that unbiasedly identifies enrichments of variable sizes (Fig. S2 , Methods). Upon damage induction, specific repair signal accumulates at DSB sites (Fig. 1B, S1C-E ), which compares well with publicly available population-based ChIP-seq datasets (Fig. S1E-G ). Measured across all cells and DSB sites, we calculated the proportion of cells in which each DSB was repaired by the different target proteins (referred to as "repair frequency"). Our collective dataset indicates superior sensitivity compared to previous reports (Fig. 1C ), allowing for the study of a broader range of infrequently captured DSB sites.
We reasoned that, if repair proteins spread according to these topology-driven rules of variability and boundary anchoring, loci with stronger Hi-C contacts should more frequently show coordinated occupancy of repair proteins across single cells. Conversely, repair signal should be independent for loci with weak (or few) Hi-C contacts. Coordination is thus a measure of how frequently a repair protein occupies a DSB locus, measured across all cells: it should reflect the diversity of topological configurations commonly observable in a given Hi-C window. Indeed, repair protein coordination corresponds to features in Hi-C data that are related to cohesin-mediated loop extrusion, suggesting that the repair machinery spreads within an ample variety of preferentially anchored singlecell loops (Fig.
For a detailed justification and explanation of the Sørensen-Dice multi-way similarity metric, see  (51, 57, 58). We briefly describe the way the metric was used here, below. The multiple-site similarity index for any number of T AsiSI clusters can be formulated as where a i is the number of cells in which cluster A i is repaired, a ij the number of cells that share repair of cluster A i and A j , and a ijk the number of cells that share repair of cluster A i , A j and A k , and so on. In case of T = 2 the outcome would reflect the definition of the original pairwise Sørensen-Dice similarity index. We wrote a Python implementation of the betapart R package  (52)  to compute the multiple-site Sørensen similarity index on a subset of matrix M defined as: where c is a vector of AsiSI cluster indices of any size in M.
Acknowledgments We thank members of the  Kind and Legube groups  for useful feedback. Specifically, we acknowledge  Koos Rooijers  for developing computational methods and performing bioinformatic analyses. Purified pA-MNase and m6 A-Tracer protein were respective gifts from  Alexander van Oudenaarden  and  Bas van Steensel. We thank  Reinier van der Linden  at the  Hubrecht FACS Facility  and  Anko de Graaff  at the  Hubrecht Imaging Center  for excellent technical support and training. High-throughput sequencing and computational resources were provided by the  Utrecht Sequencing Facility (USEQ)  and  High-Performance Computing Facility.
DamID and Dam&T samples DamID data contains reads that result from DpnI-restriction activity. Briefly, raw sequencing data was processed by removing reads that contained contaminants (resulting from library preparation), using cutadapt (v2. 0). Samples were then demultiplexed (using an in-house python script), and for every single sample one (or two, in the case of paired-end data) files were obtained. Demultiplexed data were aligned using hisat2 (v2. 1. 0) to human reference genome hg19/GRCh37, supplemented with the ERCC spike-in sequences. Since DpnI cleaves GAˇTC, we prefixed each read in silico with a 'GA' dimer to improve alignment rates. Aligned reads were counted per genomic GATC position and a vector of counts per chromosome was stored. For DamID, reads not aligning at GATC positions were discarded.
Identifying contacting hubs The identification of hubs of contacting AsiSI clusters builds on the pair identification described above, but is elaborated by walking through Hi-C matrix M 2 (Figure  S4G ), testing all theoretical combinations of hubs with defined sizes for Hi-C contacts. The hub identification function for any number of k AsiSI clusters included in the hub can be written as where n is the total number of AsiSI clusters included in M 2. All theoretical combinations of hubs were called as contacting hub if all AsiSI clusters within H contact each other (Hi-C logFC ≥0. 2). Our hub identification function written in Python is available on GitHub/ Zenodo.
Metagenomics can be used to monitor the spread of antibiotic resistance genes (ARGs). ARGs found in databases such as ResFinder and CARD primarily originate from culturable and pathogenic bacteria. However, ARGs composing the resistome of the human gut microbiota or the environment remain understudied. Functional metagenomics is based on phenotypic gene selection and can identify ARGs from non-culturable bacteria with a potentially low identity shared with known ARGs. In 2016, the ResFinderFG v1. 0 database was created to collect ARGs from functional metagenomics studies. Here, we present the database second version, ResFinderFG v2. 0. Functional metagenomics studies were analyzed and DNA sequences described were retrieved, deduplicated and annotated. Sequences were curated to include only ARG sequences.
ResFinderFG v2. 0 was then compared to other databases for their relative sensitivity in searches for ARGs in subcatalogs from the Global Microbial Gene Catalog (GMGC). Fifty publications were considered, for a total of 23'764 ARGs identified from different environments. After deduplication, annotation and curation, 3'913 ARGs were included. New ARGs included are mainly glycopeptides/cycloserine or beta-lactams resistance genes identified mostly in human-associated samples. Results of GMGC gene subcatalogs annotation showed that ResFinderFG v2. 0 detected comparable or higher ARG numbers than those detected with other databases. Most of the unigene hits obtained were database-specific and ResFinderFG v2. 0 specific unigene hits included among others: glycopeptides/cycloserine, sulofnamides/trimethoprim resistance genes and beta-lactamases encoding genes.
Identifying ARGs and assessing this risk is essential to better understand and putatively find means to prevent their dissemination in pathogenic bacteria. To identify ARGs, culture-based methods, PCR, qPCR  6  , genomic and metagenomic sequencing have been used. Metagenomics makes it possible to sequence all the DNA from a sample and, thanks to sequences comparison with specific databases, allows ARG identification in an environment or host. Several AMR public reference databases  7  exist such as CARD  8  or ResFinder  9. However, since the detection is matching newly obtained sequences to ARG sequences in databases, only sequences that are similar to previously-described ones will be detected with an acceptable degree of confidence. Therefore, unknown ARGs, or ARGs sharing a low identity with ARGs included in the chosen database may not be detected.
Although culturable and/or pathogenic bacteria only represent a small fraction of microbial diversity, their genes make up the vast majority of the ARGs present in existing databases. In order to detect new ARGs or low sequence similarity percentage ARGs, functional metagenomics has been used  10. This technique is based on phenotypic detection by expressing exogenous DNA in an antibiotic-susceptible host. Using functional metagenomics, ARGs sharing low amino acid identity to their closest homologue in NCBI  11  , or even not previously classified as ARGs  12  , could be detected in human  [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]  , animal  [22] [23] [24] [25] [26] [27] [28] [29]  , wastewater  [30] [31] [32] [33] [34] [35] [36] [37]  and other environmental samples  5, 11,. Despi e being a laborious technique, genes described by functional metagenomics are mainly absent in classical ARG databases.
Two databases listing specifically functionally identified ARGs were created: ResFinderFG v1. 0  61  and FARME DB  62. ResFinderFG v1. 0 (https://cge. food. dtu. dk/services/ResFinderFG-1. 0/) was based on the data coming from 4 publications, while FARME DB includes data from 30 publications, mainly reporting environmental genes which were not necessarily cured to include only ARGs sequences  63. Here, we report a new version of the ResFinderFG database, ResFinderFG v2. 0, providing wellcurated data from functional metagenomics publications available until 2021 that include environmental and host-associated samples.
Construction of ResFinderFG v. 2. 0 To retrieve publications using functional metagenomics for the identification of antibiotic resistance genes, the 4 publications used to construct ResFinderFG v1. 0 were first considered. Then, all the publications which were cited by these 4 publications and all the publications that cited one of these publications were collected. In addition, publications found with the following terms on PubMed: "functional metagenomics" AND "antibiotic resistance", were added to this pool. After filtering out all the reviews, publications were screened one by one to check whether functional metagenomics was actually used to study antibiotic resistance and whether insert sequences described were available. Database construction and curation was then performed as follows (Figure  1 ).
Accession numbers describing insert DNA sequences functionally selected using antibiotics were included and DNA sequences were retrieved using Batch Entrez. CD-HIT  64  was used to remove redundant DNA sequences and annotation of the remaining was done using PROKKA v. 1. 14  65. To specifically select insert DNA sequences with ARG annotations, a representative pool of ARG annotations was obtained by applying the PROKKA annotation process to the ResFinder v4. 0 database. Resulting annotations were used as a reference to specifically select insert DNA sequences containing an ARG. Accession number of the remaining inserts were used to retrieve information on the insert DNA sequences, such as the origin of the sample and the antibiotic used for selection. Additional filtering steps were added to check the antibiotic used for selection and ARG annotation link, minimum gene size with at list the median amino acid (aa) size of antibiotic resistance determinant (ARD) from the same ARD family (260 aa for beta-lactamase, 378 for tetracycline efflux genes, 641 aa for tetracycline resistance ribosomal protection genes, 178 aa for chloramphenicol acetyltransferase, 247 aa for methyltransferase genes and 158 aa for dihydrofolate reductase genes) and the presence of one unique annotation corresponding to an ARG on the insert DNA sequence.
ARG detection in several GMGC gene subcatalogs using ResFinderFG v. 2. 0 and other databases ABRicate (default parameters) was used to detect ARGs in GMGC human gut (Figure  3 ), soil (Supplementary Figure  1a. ) and aquatic (marine and freshwater) subcatalogs (Supplementary Figure  1b. ). Using ResFinderFG v2. 0, 3'025, 211 and 129 unigene hits were obtained analyzing human gut, soil and aquatic subcatalogs respectively. The 3 most frequently detected ARG families in all gene catalogs were glycopeptides/cycloserine resistance genes (20. 9 to 39. 7% of detected ARGs), sulfonamides/trimethoprim resistance genes (21. 8 to 58. 1% of detected ARGs) and beta-lactamases encoding genes (7. 9 to 25. 6% of detected ARGs). Phenicols (up to 6. 0% of detected ARGs), aminoglycosides (up to 5. 3%), cyclines (up to 6.
2%) and macrolides/ lincosamides/streptogramins resistance genes (up to 0. 03%) were also detected. Also, ResFinderFG v2. 0 provides habitat information on where a given ARG was first identified by functional metagenomics. A majority of ARGs identified in the gut subcatalog (90. 2%) were indeed initially identified in the human gut by functional metagenomics (supplementary table  2 ). In the soil gene subcatalog, 62. 6% of ARGs detected were also genes identified initially in soil with functional metagenomics. However, ARGs detected in the aquatic gene subcatalog were primarily first identified by functional metagenomics in soil. ResFinderFG v2. 0 identified a comparable or even greater number of ARGs compared to other databases. We observed that the most frequently observed ARG family depended on the database used.
In the human gut gene subcatalog, glycopeptides/cycloserine resistance gene was the most frequent ARG family found by ResFinderFG v2. 0 (39. 7% of all unigene hits obtained with ResFinderFG v2. 0). In contrast, the beta-lactamase family was the top ARG family with ARG-ANNOT (21. 2%). NCBI and ResFinder detected mostly tetracycline resistance genes (20. 4 and 23. 8% respectively). Finally, multidrug efflux pump unigene hits were the most frequent using CARD (39. 4%). ResFinderFG v2. 0 was the database with the highest fraction of database-specific hits, with 89. 1% of specific unigene hits composed mainly by glycopeptides/cycloserine resistance genes (D-alanine-D-alanine ligase ; supplementary table  3 ) and sulfonamides/trimethoprim resistance genes (dihydrofolate reductase). By comparison, CARD had 73.
7% of specific unigene hits, mostly composed by gene encoding multidrug efflux pumps. Of note, 16. 2% of unique CARD specific multidrug efflux pump unigene hits found in the human gut were regulatory genes (supplementary Table  3 ). Between 2. 6 and 4. 2% of all unigene hits depending on the gene subcatalog analyzed were shared by all the databases used. Beta-lactamases -encoding genes were the most prevalent among them (ranging from 38. 1 to 51. 3% of the shared unigene hits), followed by, phenicols, aminoglycosides and tetracyclines resistance genes. However, 25. 1, 23. 2 and 46. 3% of betalactamases, aminoglycosides and phenicols resistance genes respectively, were only detected using ResFinderFG v2. 0 (Figure  3 ; supplementary Figure  1 ).
Discussion ResFinderFG v2. 0 contains 3'913 ARGs which were described with functional metagenomics in 50 publications. Here, we showed that using ResFinderFG v2. 0 enabled us to describe the resistome with ARGs that were not detected by other databases. Notably, ResFinderFG v2. 0 permitted a better description of sulfonamides/trimethoprim, glycopeptides/cycloserine resistant genes and beta-lactamase encoding genes. Exhaustive description of ARG content in the environment can be complicated since most ARG databases are biased towards ARGs coming from culturable and/or pathogenic bacteria. One way to detect genes that are not described in such databases, or that are too different from described genes, is to use functional metagenomics: a laborious and low throughput method that was used by only a few research groups  10  which allows phenotypic identification rather than sequence-based identification of ARGs.