dataesr/scientific-paragraphs-categorization huggingface dataset:
As discussed in Section 3. 2. 1, differences can also be observed in the seasonal cycle of DMS aq with respect to those of atmospheric MSA and nssSO 4. The two components nssCa and nssMg in Figure  4  exhibit an anomaly in the latitude range -45° to -60°. Although being abundant in atmospheric dust, these two components likely do not originate there from dust emissions since their high levels of nssCa and nssMg remain when coastal and island data are eliminated from the dataset. The -45° to -60° latitude belt represents the so-called storm track area, where strong emissions of sea-salt aerosols by the bubble bursting mechanism take place. In this latitude range both, averages and medians of nssCa and mean nssMg-data reach absolute maxima (see Figure  4 ). High values of nssCa in marine aerosols have been reported from many laboratory and field studies  (Hoffman and Duce, 1977; Weisel et al.
These DMS a distributions could contribute to the spatiotemporal distributions of particulate biogenic sulfur-in the marine atmosphere over the Southern Ocean. Information on the potential aerosol source regions may be inferred from the mapped distributions of airmass back trajectories from aerosol sampling sites during given sampling times. The maps comprised 14x14 geocells of equal projected size on a stereographical projection of the Southern Hemisphere centered on the South Pole. Hourly hits of back trajectories were counted in each of these geocells. Assuming that the highest values of any aerosol data were most clearly connected to an aerosol source region we summed up trajectory hits in maps for all component values above the respective global 75 th percentile. As references we constructed corresponding maps for all component values up to the respective 25th percentile.
Data cleaning and overview of host phenotypes Under one diet, outliers were identified based on feed efficiency (REI, FCR), energy intake (ADEI), growth rate (ADG) and body composition (MD, BFT) traits. The animal was removed from the diet data subset when one of the phenotypes was outside the range [l À 3 SD; l + 3 SD], where l is the mean phenotype. Eight animals were filtered out under a concentrate diet (out of 277 lambs), and three were filtered out under a mixed diet (out of 166 animals). Linear regressions were fitted with the lm function included in R software (version 4. 1. 2) (R Core Team, 2021). All traits recorded under a concentrate diet (Trait C ) were regressed as follows (Eq. (2)): Trait C ¼ l Trait C þ b 1;C line þ b 2;C age þ b 3;C suckling þ b 4;C year þ b 5;C penjyear þ e C ð2Þ where l Trait C denotes the trait mean.
b 1;C to b 5;C respectively stand for the effects of the line, age, suckling, year and pen nested in the year. Finally, e C denotes the model residuals. Traits recorded under a mixed diet were regressed similarly (Eq. (3)): Trait M ¼ l Trait M þ b 1;M line þ b 2;M age þ b 3;M year þ b 4;M periodjyear þ b 5;M penjperiodjyear þ e M ð3Þ where l Trait M denotes the trait mean. b 1;M to b 5;M respectively stand for the effects of the line, age, year, period nested in the year, and pen nested in the period and the year. e M denotes the model residuals. Then, regression solutions (Eqs. (2) and (3)) were used to compute least square means per line and host trait with the emmeans R package (version 1. 7. 3) (Lenth, 2022). Comparison of traits between RFI lines were carried out with the Tukey test. To account for multiple testing, P-values were corrected per diet with Benjamini-Hochberg procedure (Benjamini and Hochberg, 1995) (seven traits under a concentrate diet, six under a mixed diet).
Sequencing data cleaning and overview For data cleaning and subsequent analyses (steps 2-6, Fig. 2 ), four distinct compositional datasets were considered: one per diet (concentrate or mixed diet) and amplified gene (16S or 18S). Then, sequencing data were filtered at the sample and OTU levels. Samples with low sequencing depths were associated with low OTU richness and small variations between the number of observed OTUs (Supplementary Fig. S1 ). Thus, samples with a depth smaller than 7 500 reads were discarded (step 2, Fig. 2 ). OTU filtering was Fig. 2. Workflow from lamb microbiota data handling, to statistical learning and inference. Abbreviations: OTU: operational taxonomic unit; PCA: principal component analysis; RFI: residual feed intake; sPLSDA: sparse partial least squares discriminant analysis; sPLSR: sparse partial least squares regression.
Microbiota preprocessing steps are numbered from 1 to 6. Microbiota compositions were studied at the OTU level. Main variation factors of compositions were identified with PCAs. Two kinds of predictive models were used: sPLSDA and sPLSR. Accuracies were compared with corrected t-tests. RFI lines were selected for a decreased or an increased estimated breeding value of RFI. Host traits included feed efficiency, feed intake, growth and body composition traits. performed by removing OTUs with a prevalence lower than 20% under the considered diet (step 3, Fig. 2 ). The numbers of sequencing reads and OTUs before and after data cleaning are reported in Supplementary Table  S2. To give an overview of microbiota compositions, the sequence of OTU seeds and descriptive statistics are available in Supplementary Table  S3  (16S data) and Supplementary Table  S4  (18S data).
Phyla mean relative abundances were also computed after regrouping OTUs by phyla with the phyloseq R package  (McMurdie and Holmes, 2013). Then, at the OTU level, unsupervised learning was used to identify the main variation patterns driving microorganisms' abundances (analysis A, Fig. 2 ). Prior to principal component analyses (PCAs), several preprocessing steps were carried out to account for compositionality and sequencing effects. Null abundances of OTUs were imputed with the Geometric Bayesian Multiplicative replacement procedure (step 4, Fig. 2 )  (Martín-Fernández et al. , 2015). Then, a Centered LogRatio (CLR) transformation was carried out with OTU abundances (step 5, Fig. 2 ). CLR coordinate values were adjusted univariately for sequencing effects with a robust MM regression  (Maechler et al.
CLR X ð Þ i ¼ b 0;i þ b 1;i seq depth þ b 2;i seq batch þ b 3;i seq batchjplate þ e i ð4Þ where CLR X ð Þ i stands for the CLR values of the i th OTU. Then, b 0;i stands for the intercept. For the i th OTU, b 1;i to b 3;i represent the effects of sequencing depth, sequencing batches (n = 2) and sequencing plates nested in batches (n = 5), respectively. Finally, e i stands for residuals. Residuals were used as adjusted CLR values to define the principal components (PCs) with the mixOmics R package (version 6. 18. 1)  (Rohart et al. , 2017).
Comparing prediction accuracies from different sets of predictors To predict lamb lines or traits, three sets of predictors were considered: (i) The first set included either 16S-or 18S-adjusted CLR values. (ii) The second set included systematic effects: suckling method (concentrate diet only), phenotyping period (mixed diet only), year, pen, age and final BW. (iii) Finally, the third set of predictors included the systematic effects and CLR values of 16S or 18S OTUs. Models fitted on different sets of predictors were compared per predicted trait and data subset. Differences between model accuracies were tested with corrected t-tests  (Bouckaert and Frank, 2004 ) (Analysis C, Fig. 2 ). Then, P-values were adjusted with Benjamini-Hochberg procedure per predicted trait and data subset.
Predicting host traits Host traits were predicted with sPLSR by using fixed effects and BW and/or microbiota data. Pearson correlations between actual traits and predictions are reported in Fig. 5 , and other metrics are provided in Supplementary Table  S5. Predictions from microbiota data only -Under a concentrate diet, when host feed efficiency was predicted from 16S (Fig. 5A ) or 18S (Fig. 5B ) data, the average correlations between actual traits and predictions were almost null for REI C (0. 11 for 16S compared to 0. 06 for 18S data) or low to moderate for FCR C (0. 35 for 16S, 0. 16 for 18S). Under a mixed diet, predicting REI M led to low average correlations (0. 35 with 16S, 0. 17 with 18S) (Fig. 5C, D ). Predicting each component of feed efficiency also led to varying average Pearson correlations, depending on the diet and the sequencing dataset.
Acknowledgements We wish to acknowledge our  P3R  colleagues (https://doi. org/10. 15454/1. 5483259352597417E12 )for caring for animals and their help in collecting samples. We thank  M-L Chemit  for the estimation of feed dietary values, DNA extraction and amplification. We acknowledge the help of Géraldine Pascal in bioinformatics, to process sequences files with Frogs tools. We are grateful to the  Genotoul bioinformatics platform (Toulouse, France) and Sigenae group  for providing computing and storage resources (Galaxy instance https://vm-galaxy-prod. toulouse. inra. fr).
Blank samples were systematically included as control PCR reactions. No nucleic acid from any of our blanks was amplified indicating no detectable contamination during sample processing. Amplicons were then sent to the sequencing facilities at Rush University Genomics and Microbiome Core Facility (Chicago, IL, USA), where the amplification of the V3-V4 regions of the 16S cDNA was performed using the 341F (CCTACGGGNGGCWGCAG) and 785R (GACTACHVGGGTATCTAATCC) primer set  (Klindworth et al. 2013). The 16S amplicons were sequenced on a MiSeq platform (Illumina) (paired end 2Â 300 bp). Unfortunately, several sample tubes imploded during air freight resulting in several missing samples. 16S rRNA raw read sequences were deposited to the Short Read Archive under Bioproject number PRJNA934342 (accession number are ranged from SRR23445189 to SRR23445253.
Analysis of sequencing data The raw sequencing reads were processed using the DADA2 R package (v1. 20)  (Callahan et al. 2016). The paired-end reads were quality-trimmed (maxEE = c [2,2] for both experiments) and only reads > 200 bp were retained. From a mean of 54,173 AE 18,456 sequences per sample, 60% AE 6% were retained after sequence pre-processing (quality control, merging and chimaera removal). The taxonomic assignment of the amplicon sequence variants (ASVs) was performed using the SILVA_138. 1 database  (Quast et al. 2013)  and functions "assignTaxonomy" and "addSpecies" from the DADA2 R package. Assignment of a sequence to the species level was done only at 100% sequence identity. Both α-diversity and β-diversity were characterized by R packages phyloseq v1. 36 (Mcmurdie and Holmes) and vegan v2.
Acknowledgments We thank the crew and officers of the  N/O Le Pourquoi Pas? (Flotte Océanographique Française)  for their professional help during the EMSO-LO cruise (Chief scientist  Dominique Lefèvre ). This work benefited from services provided by the  EMSO_ERIC Virtual Organization , supported by the national resource providers of the  EGI Federation  ( EMSO-France CNRS-IFREMER)  for sustainability of the moored research infrastructure. Part of the equipment used in this work was funded by the  European Regional Development Fund (ERDF). The authors also thank the  OMICS ,  SAM ,  PACEM , and  CULTURE-MIO  platforms for sample analyses. We thank  Vincent Garel  for the cropping and assembling of the pictures of particles (Fig. 3) and  Fabrizio D'Ortenzio  for Chlorophyll data. This work was partly supported by the  French National program LEFE (Les Enveloppes Fluides et l'Environnement, INSU-CNRS) , through the  PARTY  project ( remineralization des PARticules marines et Transfert vers les abYsses ) awarded to FACLM and from own resources (PROTISVALOR) allocated to C.
T. This manuscript is a contribution of the  ANR APERO  (project number  ANR-21-CE01-0027 ). H. P. G. was supported by the  Zooflux and Pycnotrap  projects ( GR1540/19-1  and  GR1540/37-1 ) of the  German Science Foundation (DFG). Submitted 14 March 2023 Revised 25 August 2023 Accepted 20 October 2023 Associate editor:  Gordon T. Taylor.
We analyzed the data in three successive steps, broadly following the first three hypotheses on the effects of (i) forest type, (ii) neighborhood diversity, and (iii) environmental variability in plot and plant characteristics. This was done in order to account for the fact that forest types, neighborhood diversity, and plot and plant characteristics were not completely independent of each other. Furthermore, we tested the fourth hypothesis regarding modulations of tree diversity and composition effects by feeding guild and tree ontogeny through all three successive steps. Response variables were log+1 transformed when this improved the model fit for all three successive steps (see Table  1  for full model specifications). First, we tested for the effects of the three forest types on leaf damage in a very basic model.
Lastly, we replaced NDiv with plot and plant characteristics to determine whether looking at plots in finer detail than just forest type/diversity yielded models with a better fit. In addition to nutrient capacity and soil PCs of the preceding model, we included plot-level Douglas fir and Norway spruce APA as measures of tree proportions since, even though tree proportions were correlated with forest type (and therefore not entered in the first type of models), they varied among mixtures. Furthermore, we added canopy openness and sampled sapling or mature tree height as factors not strictly attributed to forest type and checked for interactions of plot and plant characteristics with nutrient capacity (Table  1 ). Owing to a lack of replicates for the sapling data (some sites lacked European beech saplings), we had to exclude one of the predictors in the sapling damage models.
We estimated all models with the nlme R package  (Pinheiro et al. , 2020)  and subsequently performed a stepwise selection procedure based on AIC c  (Burnham & Anderson, 2010)  in order to acquire models with the fewest number of predictors and the lowest global AIC c. Afterwards, we confirmed that multicollinearity between variables was low using VIF (≤5), calculated with the car R package  (Fox & Weisberg, 2018). Lastly, we used the Tukey's honestly significant difference (HSD) post hoc test and p-values adjusted with the Holm method using the multcomp R package  (Hothorn et al. , 2008)  to test for significant differences (p < 0. 05) in leaf damage and leaf forest types and developmental stage (Appendix S1: Table  S2 ). All analyses and figures were made in R version 4. 0. 2 (http://www.
S3 ). This extraction method allowed for the annotation of a large part of the lipidome using the most recent tools in molecular networking by LC-HRMS/MS and the comprehensive database LIPID MAPS and the Metabolomics Workbench (https://www. metabolomicswork bench. org/). Lipids were initially annotated by comparison with available public databases using the Global Natural Product Social Molecular Networking platform. Positive identities were assigned to metabolites based on cosine scores higher than 0. 7 in the molecular networks (MN). The annotation of some lipids were expanded from the GNPS platform using the LIPID MAPS classification by combining structural similarities obtained from the molecular network with accurate masses and fragment similarity  32. A list of 14 clusters was identified using HRMS and MS/MS data available for various lipids.
While the diacylated derivatives were the major DGCC in Millepora cf. platyphylla and Porites spp. , the monacylated derivatives were highly present in Pocillopora spp. (Fig. S7 ). Two large clusters of platelet activating factor (PAF) and lyso-PAF were also identified in all three species, but varied in their relative abundance among host genera (Fig. S8 )  15, 34. Lyso-PAF C16 and C18 were also present in all genera from both islands, but the bioactive PAF C16 and C18 were only detected in specimens of Pocillopora spp. Interestingly, a putatively new sulfolipid cluster prevalent on island 06 was detected using HRMS and MS/MS data in M. cf. platyphylla. Further comparison with well-known lipids led to the tentative identification of a betaine lipid derived from cysteine. However, the identity of these compounds will need to be confirmed by isolation and further characterisation by  1  H NMR (Fig.
Dimethylsulfoniopropionate (DMSP) which is often associated with heat stress has been widely reported in various hard corals such as Porites, Pocillopora, Acropora and Stylophora based on LC-MS or GC-MS  40, 44. However, our 1 H NMR analysis did not detect DMSP in any of our studied coral holobionts. Whether this is due to the absence of DMSP in the corals or a lack of sensitivity of NMR remains to be further tested. A similar level of confidence could not be attained for our MS data as standards for lipids are not widely available commercially, especially in a pure form. Nevertheless, the annotation of entire clusters of lipids was possible by comparison with MS/MS data available in online databases and annotation based on similarity through molecular networking and LIPID MAPS. This allowed for the putative annotation of more than 210 lipids.
Solvents were removed under vacuum using a centrifugal evaporator and the extracts were weighed. To assess instrument variability, data quality control quality (QC) samples were prepared, one per genus, and one with a mixture of the three genera. After optimization, QC samples were prepared by mixing 83 μL of a re-suspended replicate 2 of each colony for the 3 genera (3 QC / mix of 12 extracts), with 30 μL of the re-suspended replicate 1 of all the 36 extracts from the 3 genera (1 QC / 36 extracts). Blank samples were prepared using the same procedure for biological samples, but without the coral holobiont fragment. All the extracts were stored at -80 °C until analyses. Samples used for method optimization were excluded from all statistical analyses. Sample preparation for LC-MS, analytical methods and data acquisition.
The organic extracts analysed by LC-MS were prepared by adding 1 volume of ACN/ MeOH (50:50) according to weight to reach a final concentration of 1 mg/mL for all samples. The supernatant was filtered on a 0. 22 μm (ø 13 mm) filter and transferred to analytical vials. The data acquisition was divided into four analytical batches: one by genus, and one for the mix of all three genera. Samples were randomized, and then organized in order of injection: extraction blanks, priming QC, randomized samples, QC, and extraction blanks. One QC was injected after analysis of every 8 vials. Up to 6 QC samples, 3 blanks, and 2 solvent injections were used to equilibrate the analytical system, and therefore subsequently removed from the data prior to data analysis. Principal Component Analysis (PCA) was performed to assess the technical variability (measured by replicate analysis of a pooled intrastudy QC sample) and biological variability as part of a quality control process.
All samples were analyzed using a Vanquish Liquid Chromatography system using a Kinetex ® 1. 7 μm Phenyl-Hexyl 100 LC column 100 × 2. 1 mm, coupled with an electrospray Q Exactive Plus Orbitrap mass spectrometer with a HESI source. The gradient used for the chromatographic separation consisted of two solvents, solvent A (0. 1% formic acid in LC-MS grade water) and solvent B (0. 1% formic acid in acetonitrile LC-MS grade). The flow rate was set at 0. 40 mL/ min with the following gradient: t = 0. 0, 35% B; t = 2. 0, 35% B; t = 10. 0, 100% B; t = 13. 0, 100% B; t = 14. 0, 35% B; t = 17. 0, 35% B; all changes were linear. The column temperature was set at 30 °C and the injection volume was 5 μL. Data were analysed in positive and negative ionization mode within the mass range of 120-1500 m/z at resolution 35,000.
Ion source parameters were set as follows: sheath gas = 45, aux gas = 15, sweep gas = 0, spray voltage = 3. 2 kV, capillary temp. = 320 °C, aux gas heater temp. = 250 °C. The raw data obtained in positive and negative modes were converted into mzXML files with the Proteowizard v3. 0 19202 software. Data processing with W4M was performed in 7 steps: identification of peaks for each sample, clustering of similar peaks across all samples, retention time correction, re-clustering of similar peaks across samples, peak integration followed by annotation, removal of ions from blanks and background, statistical analysis and finally data visualization  50. A step verifying the quality of the generated matrix was performed at the end of the treatment. Among these steps, some fundamental parameters are listed as examples for the negative ionization mode: the interval of m/z value for peak picking was set to 0.
The variables of interest with VIP scores greater than 1. 5 from the metabolomics study were statistically processed with the Rstudio v2021. 09. 2 software by a non-parametric Wilcoxon test (agricolae package) to compare their distribution between populations. In order to obtain relevant annotations on the VIPs, the MS and MS/MS spectra of the VIP were compared with the following databases: DRUGBANK, PubChem and MoNA (MassBank of North America). In addition, all VIPs were submitted to a putative annotation research study on the Compound Discoverer 2. 1 software (Thermo Fisher Scientific). The molecular networks were generated in the positive and negative modes via the mzXML files, using the online workflow on the Global Natural Products Social molecular networking web-platform (GNPS)  55. MS/MS spectra were window filtered by choosing only the top 6 peaks in the +/-50 Da window throughout the spectrum.
For some analyses, data were clustered with MSCluster with a parent mass tolerance of 0. 02 Da and a MS/MS fragment ion tolerance of 0. 02 Da to create consensus spectra. Edges between two nodes were included in the network if each of the nodes appeared in each other's respective top 10 most similar nodes. The library spectra were filtered in the same manner as the input data. All matches between network and library spectra were required to indicate a cosine score above 0. 7 and at least six matched peaks. The generated similarity networks were compared with a set of databases proposed by GNPS. mzXML files were directly processed using the MZmine 2. 40. 1  56. The process was performed in both positive and negative ionisation modes. Chromatogram building was achieved using a minimum height of 2,000, and m/z tolerance of 0.
The generated data were compared to a set of databases proposed by Mzmine2 and MetGem 1. 2. 2 (LipidBlast, MASSBANK-EU, MetaboBASE, etc)  57. All spectra similarity networks generated with GNPS were imported into Cytoscape v3. 7. 2 for processing. For each mode (positive and negative ionization) all the metadata were merged to obtain a unique network of similarity. For each ionisation mode, all spectral similarity networks were cleaned when redundancy was observed. In such cases, each MS and MS/MS spectrum was compared among each other to validate that it corresponded to the same metabolite. Following this, the pie charts representing the proportion of each variable in the different samples were used to generate a consensus and an average value. The connections to the other nodes were represented by a mean cosine score derived from the two values obtained with GNPS.
Sample preparation for NMR, analytical conditions and data acquisition. NMR samples were prepared according to a published procedure  58. Briefly, the frozen hydroalcoholic extracts were thawed at RT and data acquisition was performed immediately after NMR tube preparation. Factors that impact the spectra quality and comparability between samples include pH variation, differences in ionic strength and peak broadening due to the presence of paramagnetic and other metal cations. In order to reduce the chemical shift variation, the pH consistency was controlled by adjusting the sample using a KH 2 PO 4 /K 2 HPO 4 buffer. We screened ionic strength with three phosphate buffer concentrations (25, 50 and 100 mM) with pH 7. 2-7. 8, and presence (5-15 mM), or absence of EDTA. Optimal results were obtained using the following conditions: extract concentration = 15 mg/ 650 μL, phosphate buffer concentration = 50 mM, no addition of EDTA, pH adjusted to 7.