dataesr/scientific-paragraphs-categorization huggingface dataset:
Mean grain-size of the siliciclastic fraction  ����  (Folk and Ward, 1957)  was calculated on the entire dataset with the Rysgran package for R  (Gilbert et al. , 2015; R Core Team, 2018). The sortable silt mean size  ��� , as defined by  McCave et al. (1995; i. e. , the mean of the 10-63 µm grain size range), was also calculated following the same procedure. Furthermore, following  McCave and Hall (2006) , the percentage of sortable silt (SS%) in the total <63 µm fraction was calculated. This percentage, together with the sortable silt mean size, was used as an indication of bottom current velocity  (McCave and Hall, 2006; Toucanne et al. , 2012). It has to be mentioned that the use of  ��� as a proxy for bottom current velocity on cores recovered from CWC mounds may be biased (e. g. Eisele et al.
Acknowledgements We thank the  SNSF (Swiss National Science Foundation ) projects ' Unconventional Carbonate Factories ' and ' 4D-Diagenesis@Mound'  (project numbers  200020_153125  and  200021_149247 ) for funding this research. We also are grateful for the ship time provided by IPEV on the  R/V Marion Dufresne II  within the framework of the  EuroFLEETS GATEWAYS  project (grant agreement  228344 ). We further thank  Tim Collart  for the help he provided with the Rysgran package for R and  Marc Schori  for his help with the ArcGIS software. We further acknowledge the help of  Rene Eichstädter  and  Andrea Schröder-Ritzrau  regarding Uranium-series dating and quality control. The DFG has provided funding for the Uranium-series dating of corals via the project  FR1341/9-1.
2. 2-2. 3 of  Wang et al. (2010). N (D d ; R) = N 0 (R) e -λ(R)D d (3a) N 0 (R) = 4. 9 × 10 7 R -0. 89 (3b) λ (R) = 6. 236 × 10 3 R -0. 4 (3c) The approach of  Croft et al. (2009 Croft et al. ( , 2010 ) is adopted to determine number and mass mean BCS rates by integrating d p , R over the aerosol number and mass size distributions (Eqs. 4-5). In Eqs. (  4 )-(  5 ), the size-dependent particle number distribution n d p ; d p , σ is modelled assuming a log-normal distribution with the geometric median diameter (d p ) and the geometric width (σ ) as parameters. N d p , σ, R and M d p , σ, R are calculated offline for a range of R, d p , and σ using Python 3 (Van Rossum and Drake, 2009) scripts. The interpolation points for R, d p , and σ are generated using R = 10 -1+ 1 7 ×(i-1) mm h -1 for i = 1,.
Finally, log-normal distributions are fit to the bins at T + 1 H (1 h elapsed) and T + 3H (3 h elapsed) by generating random variables (RVs) from the histograms in Python 3 (Van Rossum and Drake, 2009) and fitting a log-normal distribution to the RVs using maximum likelihood estimation. A comparison of BCS applied to a bin aerosol model and to a modal aerosol model is provided in Sect. 5. 2.
S9a ) and SLINN+PH+RC(DM) (Fig. S9c ). The statistical measures of model fit compared to the observations, such as the biases and RMSEs, are equally comparable between the two simulations. The main difference between the simulations becomes apparent when plotting the particle size distributions (PSDs). Figure  10  shows the volume size distributions (VSDs) for an equatorial cross-Atlantic transect, with simulated PSDs from SLINN+PH+RC and SLINN+PH+RC(DM) directly compared to observations from three independent summer (June-August) field campaigns. As a caveat, it is not possible to quantify how representative the observations are of the regional-mean dust PSDs, given that the aircraft campaigns measure a small sample space both spatially and temporally but often remain our only datasets to measure the vertical structure of regional atmospheric aerosol.
Code availability. Due to intellectual property rights restrictions, we cannot provide either the source code or documentation papers for the UM. The Met Office Unified Model is available for use under licence. A number of research organisations and national meteorological services use the UM in collaboration with the Met Office to undertake basic atmospheric process research, produce forecasts, develop the UM code, and build and evaluate Earth system models. For further information on how to apply for a licence, see http://www. metoffice. gov. uk/research/ modelling-systems/unified-model (last access: 4 May 2022; Met Office, 2022). The Slinn+ph+rc BCS scheme is now available on the "trunk" (the Met Office's data repository) and is available for all future UM versions since vn12. 2.
Acknowledgements. The authors would like to thank the  Met Office  and the  UM team  for providing the UM climate model,  Joseph Prospero  and the  University of Miami  for freely providing dust concentration data, all the principal investigators of AERONET for their free provision of aerosol retrieval data, and  Bernadett Weinzierl  for freely providing SALTRACE dust PSDs. Figures were produced using Python 3. 6. 10(https://www. python. org/)and Iris 2. 4. 0 (https://scitools. org. uk/). This work and its contributors (ACJ and AH) were supported by the  UK-China Research & Innovation Partnership Fund  through the  Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. Stephanie Woodward  was supported by the  Met Office Hadley Centre Climate Programme  funded by  BEIS.
Data Analysis (CEDA) at https://doi. org/10. 5285/2e36fe8eb7ee4bd0a0833d3e1edd795a (Jones et al. , 2022b). Python and Fortran scripts used to produce the figures and tables of BCS rates are available from Zenodo at https://doi. org/10. 5281/zenodo. 6617052 (Jones, 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-22-11381-2022-supplement.
6 13. 2 4 4. 01 4. 28 3. 43 6. 55 54 54 53 57 23 0. 005 0. 005 0. 006 0. 003 0. 01 1. 6 1. 58 1. 74 1. 05 4 SLINN+PH+RC(DM) 1190 3. 81 3. 5 49 0. 003 1. 15 LAAKSO(DM) 1219 2. 93 3. 08 55 0. 002 0. 86 lustrate the closeness of fit between the model and observa- tions but do not necessarily show which BCS model is best owing to compensating errors and other caveats (see Sect. 6). Spatial plots of annual-mean values for each of the four observation datasets are shown in Fig. S6. It is clear from all of the observational datasets in Fig. S6that dust is preva-lent over source regions in north and equatorial Africa, the Middle East, and Asia and less prevalent over the Americas, Southern Africa, much of the Pacific Ocean, and the poles. InFigs. 7, 8, 9, 11, and S11we have grouped the observations by region, with associated abbreviations provided in the captions for Tables.
MTases Repertoire in the Genus Xenorhabdus The description of the MTases repertoire among the Xenorhabdus genus was performed by extracting data from the REBASE database  [7]. It includes 12 strains from 9 different Xenorhabdus species. The distribution of MTases genes identified by BlastP analysis among these Xenorhabdus species, ordered in a phylogenetic tree made by Average Nucleotide Identity (ANI) analysis (see the Materials and Methods section for details), is presented in Figure  1. The mean number of MTase encoding genes per genome was 9. 8. Because some MTases have two distinct subunits, the number of MTase encoding genes can be higher than the estimated number of MTases. Taking this into account, the mean number of MTases per genome was 9. 4. However, the distribution varies substantially between the strains studied here, ranging from 3 putative MTases in X.
It allowed the identification of a high number of DNA modification marks (n = 38,077, 42,539, and 38,440 for the strains FR48, FR71, and FR74, respectively) (Table  1 ). As for X. nematophila, the most predominant motif was GATC targeted by Dam, conserved in all three X. kozodoii strains and accounting for >30,000 sites in each genome. Besides GATC, two other motifs were found in two X. kozodoii strains: GACCC in FR71 and FR74, and the partner-motif CATCNNNNNNCTC/GAGNNNNNNGATG in FR48 and FR74. All other identified motifs were found only in one of the tested strains. Only one of the motifs among those identified in the three X. kozodoii strains is defined as a new motif according to the REBASE database (Table  1 ).
Strains and Growth Conditions The bacterial strains and plasmids used in this study are listed in Table  3. Two different species of the genus Xenorhabdus initially isolated from entomopathogenic nematodes were used in this study: in addition to the X. nematophila F1 laboratory model strain  [60] , we also used 3 different strains of X. Kozodoii, for which their complete genomes were sequenced in the course of this study (see Section 4. 3). E. coli and Xenorhabdus cells were routinely grown in Luria broth (LB) medium with a 180 rpm agitation at 37 • C and 28 • C, respectively. As required, antibiotic concentrations used for bacterial selection were gentamycin (Gm) at 15 µg mL -1. The REBASE database  [7]  was used to identify the putative DNA-methyltransferases (MTase) in 12 genomes from 9 species of Xenorhabdus: X.
bovienii, X. doucetiae, X. griffae, X. hominickii, X. khoisanae, X. kozodoii, X. nematophila, X. poinarii, and X. szentirmaii. For each identified MTase, the distribution of putative orthologs among other strains of Xenorhabdus or in other Gram-negative bacteria was performed on the OmicsBox platform (OmicsBox-Bioinformatics Made Easy, BioBam Bioinformatics, 3 March 2019, https:// www. biobam. com/omicsbox, accessed on 1 September 2022) using Blast2Go software  [64]. We used the BlastP default setting and retrieved the first 100 hits. Only proteins with >70% sequence identity were considered as orthologs. The phylogenetic tree presented in Figure  1  was built using the genome clustering tool of the MaGe Platform (https: //mage. genoscope. cns. fr/, accessed on 1 September 2022). Briefly, the genomic similarity was estimated using Mash [65], a software that computes a distance between two genomes.
The reads were assembled de novo, with the high-quality Hierarchical Genome Assembly Process (HGAP4) from SMRT Link 5. 0. 1, and produced one contig corresponding to the chromosome. The X. nematophila F1 genome can be found at https://mage. genoscope. cns. fr/microscope/mage/index. php (accessed on 1 September 2022). A second, manual step was performed using the unassembled reads to identify plasmids. All unassembled reads with a hit on the chromosome or included in a larger read were excluded, and the remaining reads were aligned against Swissprot bacteria with BLASTX to find proteins with an annotation corresponding to a plasmid. Finally, chromosome and plasmids were polished with Illumina single-end data with BWA 0. 7. 15-r1140 and Pilon v1. 22 software. For X. kozodoii, the sequencing was performed according to PacBio guidelines on a Pacific Biosciences Sequel I instrument (Gentyane, Clermont-Ferrand, France), using a multiplexing step of 5 samples for 1 SMRT-cell.
X. kozodoii assemblies were made with HGAP4 (SMRT Link 5. 1. 0), then the origin of replication was placed using BLAST 2. 7. 1. Finally, the genomes were polished with Blasr and Arrow (SMRT Link 5. 1. 0). For all 4 Xenorhabdus strains, DNA methylation was determined using the ds_modifica-tion_motif_analysis protocol within SMRT Link 5. 1. 0, which uses an in silico kinetic reference and a Welch's-t-test-based kinetic score detection of modified base positions with parameters set as follows: subread/polymerase read length ≥ 500, polymerase read quality ≥ 80, and modification QV ≥ 30. A score of 30 for the modification QV is the default threshold for calling a position as modified, and corresponds to a p-value of 0. 001. Homemade script was used to keep methylated bases for adenine or cytosine with score ≥ 30 and known motifs.
Nucleic Acid Manipulations The extraction of plasmid DNA from E. coli was performed using the GenElute™HP Plasmid miniprep purification kit as recommended by the manufacturer (Sigma, Saint-Quentin-Fallavier, France). Chromosomal DNA was extracted from bacterial cells using the QIAamp DNA Mini kit (Qiagen, Courtaboeuf, France). Restriction enzymes and T4 DNA ligase were used as recommended by the manufacturer (New England Biolabs, Evry, France and Promega La Farlede, France, respectively). Oligonucleotide Primer sequence was designed using the Primer3 software (Untergasser et al. , 2012). They were synthesized by IDT (Leuven, Belgium) and are listed in Table  S1. PCR was performed in a T100 thermal cycler (Biorad, Marnes-la-Coquette, France) using the iProof high-fidelity DNA polymerase (Biorad).
qPCR analyses were performed in 1. 5 µL using SensiFAST SYBR No-ROX kit (Bioline), 0. 5 µL of cDNA synthesis mixture (diluted 1:20), and 1 µM specific primers for the studied genes (Table  S1 ). The enzyme was activated by heating for 2 min at 95 • C. All qPCRs were performed in three technical replicates, with 45 cycles of 95 • C for 5 s and 61 • C for 30 s, and were monitored in the LightCycler 480 system (Roche). Melting curves were analyzed for each reaction, and each curve contained a single peak. For standard curves, the amounts of PCR products generated were determined with serially diluted genomic DNA from X. nematophila. To display the MTase-encoding genes level of expression in the WT strain of X. nematophila, the relative transcript level of each tested gene versus the mreB housekeeping gene, as calculated with LightCycler 480 software (Roche), is represented as histograms (Figure  3 ).
The data shown are the medians of experimental triplicates, and error bars represent the statistical standard deviations. To display the comparison in the level of expression of 9 selected genes between the dam-overexpressing strain and the control strain (Figure  9 ), the data were analyzed with the REST software 2009 [71] using the pairwise fixed randomization test with 5000 permutations. Data are presented as a ratio with respect to the reference housekeeping gene recA, as previously described  [70].
Data Availability Statement: The datasets generated and analyzed during the current study are available as follows: EBI Genomic data (genomes, reads, annotations) are deposited under the project code PRJEB55110 (https://www. ncbi. nlm. nih. gov/bioproject/PRJEB55110 (which will be publicly released soon). Epigenomic data are available at: https://doi. org/10. 57745/VLYY4R (for XnF1 strain), https://doi. org/10. 57745/IW6GGB (for XkFR48 strain), https://doi. org/10. 57745/OXGHDO (for XkFR71 strain), https://doi. org/10. 57745/FVW8SY (for XkFR74 strain). A browser has also been generated to easily explore the detailed methylomic data of XnF1 strain: https://doi. org/10. 25794 /YW7M-SQ69.
The raw sequences were deposited at SRA under accession PRJNA800346. Bioinformatics analysis. FastQC was used to check quality and no relevant contamination hit was found after the alignment against E. coli, Yeast and PhiX. Reads mapping and variants calling were performed using Nextflow v20. 10. 0 and Sarek pipeline v2. 6. 1. Genome Analysis Toolkit (GATK) best practices were followed as implemented in the Sarek pipeline. In this project, the following steps were performed: aligning the reads with BWA v0. 7. 17-r1188 against the mouse genome assembly C57BL_6NJ_v1 (GCA_001632555. 1), marking duplicate reads (Mark-Duplicates), base quality recalibration (BQSR) and calling germline small variants (Haplotype-Caller in GVCF mode) with GATK v4. 1. 7. 0 on merged bam by sample. Finally quality control is performed with MultiQC v1.
Their medium was then refreshed by complete culture medium (33˚C). The supernatant (conditioned medium, CM) was collected after 72 h as describe above and stored at -80˚C until later use on Co cells. For this, Co colonocytes were plated in 8-chambered slide (Lab-tek1 Chamber slide TM system, Nunc, Rochester, USA) or in 6-well plates and treated with CM from nF cells (CM-nF), mF cells (CM-mF) or Co cells (CM-Co) at 6, 24 and 48 h. Co cells in 6-well plates were then trypsinized (t = 72 h), suspended in culture medium and counted with a luna TM cell counter. Co cells in 8-chambered slide were fixed/permeabilized with ice-cold 100% methanol for immunofluorescence (IF) assay. Coculture with K02288, a BMP type I receptor inhibitor. nF or mF fibroblasts were seeded at a density of 10 5 cells per well in 6-well plates and cultured alone during 24 h.
Suspensions of Co cells (1. 2 x10 5 cells per insert) were then seeded onto Thincert™ inserts with 0. 4 μm pore size, and cultured in the presence or absence of fibroblasts on the bottom of the well. Treatment with K02288 (Sigma Aldrich; 250-500 nM) was performed at the basolateral pole of the colonocytes at t = 6 h. Control cells were treated with an equal volume of dimethyl sulfoxide (DMSO) vehicle. The medium was replaced and the treatment repeated at 24 h and 48 h. At t = 72 h, Co epithelial cells were trypsinized, suspended in culture medium, and counted with a luna TM cell counter. Experiments were repeated at least four times in triplicate. Treatment with BMP signaling agonist SB4. For experiments with SB4 (2-[[(4-Bromophenyl)methyl]thio]benzoxazole; Sigma Aldrich), Co cells were seeded in 6-well plates with 1.
Images were acquired using the Leica Microsystems fluorescence microscope (LEICA DM2000 Led) and Leica DFC7000T camera using a 40x objective. The images were processed using the Leica Advanced Application Suite software (Version 2018. 1, Leica Microsystems: Wetzlar, DEU). Five different zones in each well of the culture chamber were counted (with an average of one hundred cells per zone) and an average value was calculated for each cell passage. The experiments were replicated a minimum of four times.
Real time cell analysis The method has been extensively described in a previous study  [25]. Briefly, a real-time cell analyzer (RTCA iCELLigence™ instrument, ACEA Biosciences, Inc, CA, USA) was used to evaluate the proliferation of fibroblasts. Cell index (CI) impedance measurements were performed according to the instructions of the supplier. For this purpose, cells were re-suspended in media and subsequently adjusted to 10 5 cells/well or 2. 10 5 cells/well in E-plate 8. The instrument was placed in a standard CO 2 cell culture incubator at 33˚C where it transmits data wirelessly to the Control Unit (an iPad housed outside the incubator). The rate of cell growth was determined by calculating the slope of the line between two given time points (0-24 h).
2 μm Nitrocellulose Transfer Packs (Bio-Rad) using the Transblot-Blot1 SD Semi-Dry Transfer Cell (Bio-Rad) at constant 25V for 7 min. Membranes were then incubated with primary antibodies overnight (4˚C) (S2 Table ). Bound antibodies were detected by using the WesternBreeze Immunodetection Chemiluminescent System (Invitrogen, Fisher Scientific, Illkirch, France). The optical density of bands was visualized with the Image System (ChemiDoc, Biorad) and analyzed with Quantity oneimage analysis software (Bio-Rad Laboratories).
Rt-qPCR Total RNA was isolated from nF or mF cells using the MiniRNeasy Kit (Qiagen, Hilden, Germany). RNA samples (1 μg) were then reverse-transcribed with the iScript™ Reverse Transcription Supermix (Bio-Rad) for real-time quantitative polymerase chain reaction (qPCR) analyses. The primers for Sybr Green assays are presented in S3 Table. Amplifications were performed on a ViiA 7 Real-Time PCR System (Applied Biosystems). The qPCR data were normalized to the level of the Hypoxanthine-Guanine Phosphoribosyltransferase (Hprt1) messenger RNA (mRNA) and analyzed by the LinRegPCR v. 11 software.
Statistical analysis The results were analyzed using software GraphPad Prism 9. 3. 0 for Windows. Different responses were analyzed by one-way ANOVA and student's t-test, respectively. When ANOVA showed a statistically significant effect (P<0. 05), comparison among data was done using Tukey's HSD Post-hoc test. Results are expressed as mean ± SEM and P<0. 05 was considered significant. The percentage of Ki67-positive nF fibroblasts was about 5. 3% when cultured in CM-nF, whereas only 3. 0% of nF fibroblasts cells were positive for Ki67 when in CMC-nF/Co (S6B Fig). In contrast, CMC-mF/Co only tended to decrease the number of proliferating mF-type fibroblasts.
These data support our observation that the dialogue between nF fibroblasts and Co cells resulted in increased BMP4 expression in nF fibroblasts and activation of the BMP pathway in colonocytes, as evidenced by increased Smad1/5 phosphorylation in these cells. We also showed that K02288 prevented the inhibitory effect of the coculture conditioned medium on Co cells growth, signifying the involvement of type 1 BMP receptors. Interestingly, the CM of nF fibroblasts in indirect coculture had no effect on the growth of Co cells, demonstrating the importance of the dialogue between the two cell populations. Together these data suggest that nF fibroblasts controlled epithelial growth via BMP4-Smad1/5 signaling following an information exchange with epithelial cells. The mechanism by which Co cells interact with fibroblasts to induce BMP4 production is a part of the dialogue that we have not yet elucidated.
Genome selection and mining of olfactory receptor genes Using BUSCO (v5. 2. 2)  [37] , highly complete genomes (in terms of coding sequences) of 185 ray-finned fishes were selected, including 178 teleost genomes with a BUSCO score > 90% and seven non-teleost genomes with BUSCO score ranging from 81% to 93% (Additional file 2: Supplementary Data 1). A time-calibrated phylogenetic tree of ray-finned fishes was downloaded from https:// fisht reeofl ife. org  [2]  and pruned using the R package ape (v5. 0)  [38]  to the 185 species included in our study. Single-exon genes that code for OR receptors were mined following methods described by  Policarpo et al. (2021). TAAR, OlfC, and ORA genes, which consist of several exons, were identified following  [39] , with slight modifications. In brief, a TBLASTN  [40]  was performed using known TAAR, OlfC, or ORA sequences as queries with a threshold e-value < 1e-10 to select regions containing putative TAAR, OlfC, or ORA genes.
Nonoverlapping hit regions were extracted and extended 5000 bp upstream and downstream using SAMtools  [41]. For each extended non-overlapping hit region, the protein with the best TBLASTN match was aligned to the DNA sequence using EXONERATE (v2. 2)  [42] , and the resulting protein-coding sequence was used as query for a BLASTX against a custom database of OR, TAAR, OlfC, ORA, and other G protein-coupled receptors (GPCRs). Protein-coding sequences that best matched TAAR, OlfC, or ORA receptors were retained and manually curated. Each protein-coding sequence was translated and aligned to known olfactory receptors and other GPCR genes with MAFFT (v7. 487)  [43] , and maximum likelihood trees were computed with IQ-TREE (v1. 6. 12) [44]. Only protein-coding sequences that clustered with known olfactory receptors by visual inspection using iTOL  [45]  were retained as olfactory receptor genes.
When several identical sequences were retrieved in a genome, only one was kept using CD-HIT  [46]. Retrieved coding sequences were classified as (1) 'gene' if complete and without loss-of-function mutation (premature stop codon or frameshift), (2) 'pseudogene' if with at least one loss-of-function mutation, (3) 'truncated' if incomplete and without loss-of-function mutation, and (4) 'edge' if incomplete and less than 30 bp from a contig border. We assessed the quality of our mining pipeline by comparing the olfactory gene repertoires we identified with those published by other authors for four teleost species. We systematically found more genes than previous studies; in particular, in P. senegalus  [47] , we identified three times more OR genes (Additional file 3: Supplementary Data 2).