dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Mairi C Noverr  for the gift of the CHN1 strain. This work was supported by  National Institutes of Health (NIH)  grants  R01 AI093808  (TMH),  R01 AI139632  (TMH),  R21 AI105617  (TMH),  R21 AI156157  (TMH),  Burroughs Wellcome Fund Investigator in the Pathogenesis of Infectious Diseases Award (TMH) ,  Ludwig Center for Cancer Immunotherapy (TMH) ,  NIH   P30 CA008748  (to MSKCC),  NIH   R01 AI123163  (AYK),  K24 AI150992  (AYK),  Roberta I. and  Normal L. Pollock Fund (AYK) ,  NIH   R01 AI41893  (RJB),  NIH   R01 AI081704  (RJB),  NIH   R21 AI144651  (RJB),  NIH   R21 AI139592  (IVE),  NIH NIGMS IDeA  award.
Data availability Strains and plasmids are available upon request. Whole genome sequencing data for SC5314, 529L, CHN2 are available at NCBI SRA as BioProject PRJNA730828. The raw sequence reads for SC5314 and 529L have been previously published on NCBI under BioProject PRJNA193498 (37) for SC5314, and under accession numbers SRX276261 and SRX276262 for 529L  (57). 16S raw reads are available on NCBI under BioProject numbers PRJNA734639 and PRJNA735873.
Subject Omics: Proteomics Specific subject area Plant cell wall proteomics Type of data Figure  1 : Short separation by 1D-E of proteins extracted from the cell walls of internodes and leaves of B. distachyon How data were acquired The data have been acquired by mass spectrometry, using a LTQ XL ion trap instrument (Thermo Electron, Thermo Fisher Scientific Inc, Courtaboeuf, France). The data have been managed with the X!Tandem Piledriver software (2015. 04.
Data Description The data provide a cell wall proteomic approach to contribute to the understanding of the role of the cell wall proteins during growth in a monocot model plant, Brachypodium distachyon. They complement a previous study on the same material, but using a different strategy for protein separation and data analysis  [1]. The raw data include information about protein identification in stem internodes and leaves, at two developmental stages, i. e. during active growth and in mature organs. All the LC-MS/MS proteomics data have been deposited at PROTICdb ( http://moulon. inra. fr/protic/wallomics ). The data regarding the proteins predicted to be secreted have been deposited at WallProtDB ( http://www. polebio. lrsv. ups-tlse. fr/WallProtDB ).
The parameters used for protein identification are described at PROTICdb ( http://moulon. inra. fr/protic/brachypodium _ cell _ wall _ proteome ). Briefly, peptide ions were analyzed using Xcalibur 2. 5. 0 (Thermo Fisher Scientific Inc) with the following data-dependent acquisition steps: full MS scan (mass-to-charge ratio (m/z) 350 to 1300, centroid mode); and MS/MS (qz = 0. 25, activation time = 30 ms, collision energy = 35%; centroid mode). The second step was repeated for the three major ions detected in the first one. Dynamic exclusion was set to 30 s. The B. distachyon genomic data were downloaded from (i) the Phytozome version V13 ( https://phytozome. jgi. doe. gov/pz/portal. html#!info?alias=Org _ Bdistachyon ) and (ii) the version 3. 2 of the B. distachyon genome ( https://genome.
jgi. doe. gov/ ). Raw data were converted using ReAdW 4. 3. 1 before the database search. The X!Tandem software (Release 2015. 04. 01. 1) was used for protein identification  [5]  and the X!Tandem Pipeline (Release 0. 4. 2)  [6]  was used for MS data processing. Only proteins identified with at least two different peptides in the same biological replicate and found in at least two biological replicates were validated.
Bioinformatics annotation of proteins The sub-cellular localization as well as the function of the identified proteins were predicted using the ProtAnnDB tool ( http://www. polebio. lrsv. ups-tlse. fr/ProtAnnDB/ )  [7]. A protein was considered as a cell wall protein if no more than one trans-membrane domain was found, there was no predicted intracellular retention signal and if it was predicted as secreted by two bioinformatics programs, as described  [8]. Whenever possible, a literature survey was performed to look for experimental data to check the predictions. Functional annotation was done by combining available experimental data to bioinformatics predictions and is available at PROTICdb ( http://moulon. inra. fr/protic/brachypodium _ cell _ wall _ proteome ) and at WallProtDB for cell wall proteins ( http://www.
The method detection limit (MDL) was 5 pg of Hg. 25  Details on RM analysis are described in section S2 of the Supporting Information, and a data overview is given in section S3 of the Supporting Information. In total, eight blank membranes were collected from the jars that housed PES prior to deployment of the new samples. The mean Hg content on PES blanks was 13 ± 8 pg (1σ; n = 8). The limit of detection (LOD) was 25 pg (3σ of blank). The lowest RM on PES was 333 pg, collected from June 4 to 11, 2019. The blank percentage compared to the median RM was 2% (ranged from 1 to 6%), and all samples were above the LOD. 2. 3. 2. RMAS Membrane Total Hg Measurements. The RMAS was used to collect RM on CEM and nylon membranes. The sampling procedures for CEM and nylon membranes and their blank treatments were identical.
2. 4. RM Compounds. Upstream nylon membranes were thermally desorbed to characterize and quantify RM compounds. The thermal desorption profiles for different RM compounds (section S4 of the Supporting Information) were compared to pure GOM compounds for which profiles have been developed, including HgO, HgBr 2 , HgCl 2 , HgN 2 O 6 , HgSO 4 , and elemental Hg, as well as methylmercury chloride directly added to membranes. 20, 21, 36 Thermal desorption profiles have been used to identify Hg compounds in not only ambient air, but also other matrices. Exemplarily, Biester and Scholz 55 developed profiles for solids that have peaks at similar locations as ours for similar compounds. Individual RM compounds were identified from peak deconvolution of thermal desorption profiles. 19, 20, 24, 32 Each profile was considered to follow a Gaussian distribution (section S3 of the Supporting Information) and was deconvoluted on the basis of the curve-fitting function in MATLAB, version 2018a.
For details on the thermal desorption and peak deconvolution procedures, see the study by Luippold et al. 32  2. 5. Back-Trajectory Modeling. The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT, version 4. 2. 0), developed by the National Oceanic and Atmospheric Administration (NOAA),  56  was driven with 3 hourly meteorological input data from the Global Data Analysis System (GDAS; 1°latitude-longitude 360 × 181 grid) to identify the potential source regions of RM compounds. The model was run in backward mode for 10 days every 2 h throughout the CEM and nylon membrane sampling periods at Zeppelin (474 m asl). In total, ∼84 backward trajectories were calculated for each sampling period. The spatial (horizontal and vertical) residence times of the air masses were calculated.
33  and was subsequently applied by Huang et al. 36  Here, the code was modified according to the description in section S6 of the Supporting Information and is publicly accessible (https://github. com/JiaoyanHuang/Dry_Depo_ multi_res_model). The meteorological model input parameters were derived from hourly ERA5 data, the fifth generation ECMWF atmospheric reanalysis of the global climate. 57  The meteorological data were representative for a 31 km grid covering the area around Zeppelin. Snow depth was determined at two measurement stations, representing a lower and upper limit of snow depth in the area. The first station, representing low snow depth, was located on a measurement field just south of the Ny-Ålesund village, and the second station, Bayelva, was located about 3 km outside the village and represented the upper limit of snow depth.
58  Third, we multiplied the RM compound-specific dry deposition velocities (calculated by the model) with the relative percentage of the RM compound concentrations measured by Tekran or from CEM analysis. 2. 7. Uncertainty in RM Dry Deposition Modeling. The uncertainty of the modeled RM dry deposition was assessed using the Monte Carlo simulation approach (Crystal Ball) and is reported as the 95% confidence interval of 10 000 simulations. The probability distributions of the key input parameters for the model were obtained using the "BatchFit" function in Crystal Ball based on the dataset for each sampling period. Normal distribution and log-normal distribution were chosen for distribution fitting, and the p values for the significance levels of the fitted distributions were all <0. 05. The uncertainty analysis, however, does not include the issues of (1) lower sorption efficiency (ca.
10  A weak positive relationship between Hg-N and the time that the air mass passed over land without snow cover (r 2 = 0. 2; p = 0. 06; Figure  S4  of the Supporting Information) indicated an anthropogenic source of N compounds (i. e. , NO 2 ) from more densely populated areas further south or, more specifically, springtime transport of plumes from Asian industries or Siberian wildfires. 77 Emitted NO 2 leads to enhanced GEM oxidation and increased Hg-N concentrations downwind. Although more data are required (multi-seasonal sampling) to establish significant relationships between RM compounds and exposure time of air masses to different surface types, our conclusions are well-illustrated by comparing surface exposure during weeks 7 and 8. A maximum of Hg-N compounds was measured during week 7 (70% of total RM), when exposure time of air masses over land (43%) and the residence time in the FT (89%) were long.
An extensive sampling was conducted for both species in South Africa. Thereafter, an integrative approach including population genetic (microsatellite and mtDNA), morphological, and behavioural data in addition to site-based habitat and climatic variables were used to evaluate potential factors (e. g. , geographic, behavioural, ecological) and mechanisms (e. g. , selection, drift) affecting divergence. Specifically, the genetic data set was used to quantify the extent of population and species divergence and diversity, as geographic-based genetic variation is a prerequisite for many scenarios of speciation  (Gavrilets et al. , 2000)  and reproductive isolation is known to correlate with genetic distance  (Christie & Strauss, 2018; Coyne & Orr, 1989). Although microsatellites are presumed to be neutrally evolving loci and thus cannot be used to inform on specific adaptations, they nevertheless have utility in providing an initial assessment of the relative importance of neutral or selective forces driving divergence  (Geue et al.
, 2016; Orsini et al. , 2013). Correlations between neutral loci and environmental variables that cannot be accounted for by geographic distance alone may suggest divergent selection  (Geue et al. , 2016; Nosil, 2009). The morphological data set was used to quantify interspecific differentiation and to examine associations with habitat or environmental traits which could indicate a role for local adaptation. Behavioural data in the form of biotremulations (via drumming) were examined due to their role in mate recognition and potentially choice  (Cocroft et al. , 2008; Rodríguez, 2019). Climate, altitude and habitat (e. g. , vegetation type) data were included to examine ecological associations between either population or species-level divergences with such parameters. A number of hypotheses were examined in the current study.
Amplification conditions were: 94°C for 2 min; two cycles of 94°C for 30 s, 55°C for 30 s, 72°C for 1 min; followed by two cycles each at annealing temperature in 2°C decrements from 55°C (53-45°C); 72°C for 5 min. Products were purified using ExoSAP-IT (Thermo Fisher) and sequenced on an ABI 3130xl Genetic Analyser (Applied Biosystems). Sequences were edited using codoncode aligner v. 4. 2. 7 (CodonCode Corporation).
| Population genetics To obtain base-line information on within-and between-species genetic diversity, differentiation and structure, a suite of methods were employed using the three microsatellite data sets (17 loci Kbi, 11 loci Kbo and 12 loci in common to both species). For all three data sets, the partitioning of genetic variation was assessed by means of F st (in fstat), and a principal component analysis (PCA), neighbour joining tree (NJ) and ancestry proportions analysis in r 3. 4. 2. (R Core Team, 2017). The packages adegenet v. 2. 1. 1  (Dray & Dufour, 2007; Jombart, 2008)  and ape v. 5. 3 (Paradis & Schliep, 2018)  were used to generate the PCA and NJ, whilst ancestry proportions were estimated in the package lea v. 2. 0. 0 based on sparse nonnegative matrix factorization and least-squares optimisation  (Frichot & François, 2015; Frichot et al.
The noneffective dispersal rate (m ij ) between sites was estimated using the Bayesian method in Bimr v. 1. 0  (Faubet & Gaggiotti, 2008)  under default settings. Three independent runs were conducted. The lowest loglikelihood was used to select the best run. For the data set of 12 loci in common only (i. e. , assessing between species divergence), analysis of molecular variance (AMOVA) was examined in arlequin v. 3. 5. 2. 2  (Excoffier & Lischer, 2010) , allelic frequencies were calculated and plotted in r using adegenet and ade4 v. 1. 7-13  (Dray & Dufour, 2007) , and the potential for hybrid taxa was assessed. Statistical evidence for hybrid individuals was examined using parallelnewhybrids  (Wringe et al. , 2017)  which calls the newhyBrids software v. 1. 1b (Anderson & Thompson, 2002).
Three independent runs of 100,000 sweeps with Jeffrey's priors and a burnin of 50,000 were used. The results were validated using a simulated data set of parental, F1, F2 and backcross individuals made using hyBridlaB v. 1. 0 (Nielsen et al. , 2006)  and by jackknifing over loci. Using the two individual species' data sets (i. e. , assessing within species population divergence), standard measures of molecular diversity were calculated in fstat and arlequin and the relationship between genetic and geographic distances were examined (i. e. , isolation by distance, IBD) was assessed using 10,000 permutations in ecodist v. 2. 0. 1 (Goslee and Urban 2007). In addition to the microsatellite data, mtDNA was analysed to uncover phylogenetic and phylogeographic structure and to date divergences. Therefore, a dated Bayesian gene tree was constructed for the newly generated mtDNA sequences in combination with previously published data, using Beast v.
| Morphology Morphology data were collected from individuals at all sites to uncover site and species based differences which could be implicated in population divergence. Thirty-five body length measurements with discriminatory power in interspecific relationships  (Klass et al. , 2003)  were measured on a subset of individuals. The eight most informative measures were retained for the remainder of the samples (n = 341; Table  S3 ), following preliminary analyses (ANOVA) with F I G U R E 1 (a).
| Drivers of divergence The potential processes driving divergence were investigated using a simple correlative approach and landscape genetics. Associations between morphological, biotremulation, genetic and geographic distance matrices were assessed using a mantel-based analyses using Moran spectral randomization  (Crabot et al. , 2019; Wagner & Dray, 2015). This approach was found to efficiently correct for spurious correlations whilst conserving the ability to detect true correlations when present, thus avoiding spatial autocorrelation and inflated type 1 error rates  (Crabot et al. , 2019). Reynolds genetic distance between sites was calculated in adegenet before using the function msr. mantelrtest in adespatial  (Dray et al. , 2012). Factors which may be facilitating or restricting gene flow were further investigated using general dissimilarity modelling (GDM) using the r package gdm v.
| Biotremulation Kbi were recorded from all sites (n = 16), whilst fewer Kbo individuals were recorded from 10 sites (Table  S2 ). Males and females of both species were always analysed separately due to their distinctive biotremulation signals. Using the full data set of parameters for males, and the pulse repetition time of females, substantial overlap was evident between the two species for signals of both sexes (Figure  5 ). Between species differentiation in the male signals was driven by the longer intervals between pulse trains in Kbo males (longer PTR & IPI) and by the faster signalling rate in Kbo females (lower PRT). Signals of hybrid individuals were not intermediate between the species. Male hybrids had a typical call type for males of Kbi (Figure  5a ); female Kbi hybrids had a higher PRT than Kbo, with almost no overlap in values (Figure  5b ).
Data evaluation The impact of herbivores, N, and water on leaf quality was evaluated by 3-factorial analyses including interactions, using ANOVA followed by Tukey's honest significance test (HSD) or Kruskal-Wallis test followed by Dunn's test, depending on data distribution (checked by Shapiro-Wilk and Bartlett tests). The performance of leaf chewers was evaluated similarly by 2-factorial analyses. The performance of sap feeders was evaluated by generalized Poisson-log distribution models, followed by "General Linear Hypotheses" (GLHT) for multiple comparisons. Parasitism data were evaluated in the same way as sap feeder performance. Herbivore nutritional value was evaluated by linear models. Correlations were analyzed using Spearman's rank coefficients. Regarding plant VOC, peak areas were treated as suggested by  Hervé et al.
Acknowledgments We are very grateful to  Lionel Salvy ,  Sylvain Benhamou ,  Lucie Monticelli ,  Edwige Amiens ,  Christiane Métay-Merrien ,  Benoit Industri ,  Laurent Lapeyre ,  Bénédicte Héral ,  Michael Bitterlich ,  Leif Nett , and  Florian Bolzau  for lending a helping hand and sharing their expertise during the presented experiment and data evaluation. We sincerely thank  Céline Roy  and  François-Xavier Gray  of the  European Research Institute of Natural Ingredients (ERINI, Grasse, France)  for providing the opportunity to analyze secondary metabolites as well as  Doriane Bancel  and  Patricia Laugier  of the  INRAE Avignon  for their help during carbon and nitrogen analysis. The presented experiments received financial support from the project " Sustainable Tomato Production" (STomP ) and the region PACA,  France.
CB has received the support of the  EU  in the framework of the  Marie-Curie   FP7 COFUND People Program , through the award of an  AgreenSkills+ fellowship  under grant agreement  n°609398. CRediT authorship contribution statement CB, AVL, and TM designed the experiment with the supporting expertise of JLB, SA, RB, XF, and ND. PH and MRC likewise contributed to planning and conducting the experiment together with CB, AVL, ET, RB, and PB. Insects were reared and provided by  PB, ET ,  MRC ,  CB , and PH. Plants were reared, provided and tended to by PB, CB, and PH. CB, pH, AVL, and ET analyzed the plant nutritional and defense compounds as well as volatiles. JLB, SA and XF provided facilities for these analyses. CB analyzed the data, CB, TM, and  AVL  interpreted and discussed the results, CB and AVL led the writing of the manuscript.
Core materials Core MD02-2508, the main core investigated in this study was retrieved ∼ 90 km off the Baja California coast (23 • 27. 91 N, 111 • 35. 74 W) during the R/V Marion Dufresne MD126 MONA (IMAGES VII) campaign in 2002 (Fig. 1a ). This 40. 42 m core was piston-cored (giant corer Calypso) at a depth of 606 m b. s. l. and covers the last 80 kyr  (Blanchet et al. , 2007). At this location and depth, the coring site lies in the margin of the ENP OMZ core, where the current mean annual [O 2 ] is about 0. 13 mL L -1 according to the World Ocean Atlas 2013 dataset  (Garcia et al. , 2014;  http://www. nodc. noaa. gov/OC5/woa13/, last access: 4 May 2021). Several core tops, recovered along transects through the WNP, ENP, EEP, ESP, SWACM, and AS OMZs using a piston corer, multi-corer, and CASQ corer, were also investigated.
This allowed the sampling throughout an oxygenation gradient of 0. 03 to 2. 88 mL L -1 , where bottom water concentrations were estimated using the World Ocean Atlas 2013  (Garcia et al. , 2014)  regarding the ENP, AS, and WNP core   1. This figure was generated by using the Ocean Data View software  (Schlitzer, 2020)  and the World Ocean Atlas 2013 dataset  (Garcia et al. , 2014). sites and the World Ocean Atlas 2009  (Garcia et al. , 2010)  regarding the EEP core sites (originally investigated by  Bandy and Arnal, 1957 and Betancur and Martinez, 2003 , and recently by  Patarroyo and Martinez, 2021) , and CTDO measurements were made concerning the ESP and SWACM (investigated by  Leiter and Altenbach, 2010)  core sites. Core top location, water depth, and modern [O 2 ] values are detailed in Table  1.
(2015 , regarding MD01-2415 , dataset of benthic foraminiferal counts is available in their supplementary material). These samples were collected during the R/V Sonne cruise Leg SO201-2 in 2009 and the 2001 WEPAMA cruise of the R/V Marion Dufresne, respectively. Regarding the EEP OMZ, census counting and oxygen information from 17 core tops (see list in Table  1 ), recovered from the Panama Basin, originally investigated by  Bandy and Ar-nal (1957)  and  Betancur and Martinez (2003) , was recovered from the supplementary material shared by  Patarroyo and Martinez (2021). Regarding the SWACM samples, as no census data are provided in  Leiter and Altenbach (2010) , we used the relative abundance visible on their Figs. 3  and 5. As the authors divided their samples into 150-250 and > 250 µm size fractions and provided only benthic foraminiferal relative abundances for each fraction of each sample without giving the total number of tests per sample, we were not able to gather both fractions to investigate the > 150 µm fraction.
(2017a). Depending on the abundance of benthic foraminifera, different aliquots were investigated to pick about 300 specimens per sample. We decided to mainly focus on coarse-fraction data (> 150 µm) as this is the size fraction commonly used in benthic foraminiferal studies and in order to propose a calibration adapted to most of the studies. However, we also decided to include fine-fraction data in our calibration by using the > 63 µm counting when available (for 10 out of the 45 core tops; see Table  2 ). According to this, only 17 out of the 63 shared core tops from census counting from  Patarroyo and Martinez (2021)  were used (the ones originally investigated by  Bandy and Arnal, 1957, and Betancur and Martinez, 2003) , as the other core tops were investigated for the fine fraction alone.
1. 52e;  Schneider et al. , 2012 ; http://imagej. nih. gov/ij/ last access: 4 May 2021) and was automatically performed by using the MorFo_. ijm (Morphometrics on Foraminifera) Java macro developed for this study (available at https://github. com/microfossil/ ImageJ-LabView-Scripts, last access: 4 May 2021, and in the Supplement). Once installed (simply put the MorFo_. ijm file into the plugin directory of your ImageJ software) and selected, the MorFo plugin will automatically perform the following: 1. It will ask the user for the input folder where original images are located and create three output subfolders for resized images, processed images, and results in the designated input folder. It also asks the user for a known distance in pixels and a known distance in micrometres in order to generate calculated measurements in micrometres.