dataesr/scientific-paragraphs-categorization huggingface dataset:
Species traits Seed dry mass data were collected by our labs and supplemented with the TRY Plant Trait Database  [28]. We could not incorporate variation in seed size due to the lack of coverage for this variable across the network. There is some speculation that large seeds are associated with a capacity to generate large crowns in Pinus pinea  [104]  and Fagus sylvatica  [105]. Our use of species represents a simplification. Mean values per species came from merging our measurements with values in the TRY database. Genus-or family-level means were used where seed mass was missing at the species level. To insure that results are not affected by using genus-and family-level mean, we show the same patterns using only species with species-level seed mass (Supplementary Figure  5  and 6 ). Foliar nitrogen and phosphorus concentration, leaf habit, fruit type, and wood density were obtained from the primary literature, TRY, and the BIEN dataset  [106].
Phylogeny, foliar nutrients, and soil fertility Phylogeny was obtained for 482 species (67% of total) from  [116]. To determine if SSP can be explained by shared ancestry, we estimated the phylogenetic signal using Pagel's λ  [117] , which ranges from 0 (independence over species) to 1 (related species completely resemble each other). Continuous character mapping was used to visualize the phylogenetic signal in fig. 2  using R package phytools  [118]. We explored the linkage between foliar nutrients and species difference in seed production using the phylogenetic regression to account for species relatedness, where the correlation between species increases proportionally with the branch length (i. e. , from the root to the most recent common ancestor) in the phylogeny  [119, 120]. Phylogenetic regression was implemented using R package phylolm  [121]  for 427 species that has foliar nutrients data (89% of the 482 species with phylogeny).
Acknowledgements For access to sites and logistical support we thank the  National Ecological Observatory Network (NEON). The project has been funded continuously since 1992 by  National Science Foundation  grants to J. S. C, most recently  DEB-1754443 , and by the  Belmont Forum  ( 1854976 ),  NASA  ( AIST16-0052 ,  AIST18-0063 ), and the  Programme d'Investissement d'Avenir  under project FORBIC ( 18-MPGA-0004 )(Make Our Planet Great Again). Puerto Rico data were funded by  NSF  grants to M. U. , most recently,  DEB 0963447  and  LTREB 11222325. Data from the  Andes Biodiversity and Ecosystem Research Group  were funded by the  Gordon and Betty Moore Foundation  and  NSF   LTREB 1754647  to M. S. Additional funding to M. Z. came from the  W. Szafer Institute of Botany of the Polish Academy of Sciences  and the  Polish National Science Foundation  ( 2019/33/B/NZ8/0134 ).
M. B. was supported by the  Polish National Science Centre  grant no. 2019/35/D/NZ8/00050 , and  Polish National Agency for Academic Exchange Bekker  programme  PPN/BEK/2020/1/00009/U/00001. J. M. L. was supported by  NSF  grant  DEB 1745496. Jerry Franklin 's data remain accessible through  NSF   LTER DEB-1440409. USDA Forest Service and USGS research was funded by those agencies. We thanked  Walt Koenig  for providing data. We thanked  Christopher W. Woodall  for comments on the manuscript. Any use of trade, firm, or product names does not imply endorsement by the  U. S. Government.
Data availability Seed production data are available at the Duke Data Repository https://doi. org/10. 7924/r4348ph5t [122]. Species traits are downloaded from TRY Plant Trait database at https://www. try-db. org/TryWeb/Home. php [28]. Cation exchange capacity data are obtained at https://soilgrids. org/  [49]. Climate data are extracted from Terraclimate at http://www. climatologylab. org/ [102]  and CHELSA at https://chelsa-climate. org/  [103]. Elevation data are obtained from SRTM at https://srtm. csi. cgiar. org/ and USGS National Elevation Dataset at https://ned. usgs. gov/.
However, the true spatial resolution of the final correlation is limited by the size of the sub-pixel refinement correlation kernel (21 × 21 pixels, minus the effects of a Gaussian windowing function). This means that despite the displacement being calculated for every pixel in the image (1 m/px), spatial variability is well-resolved at approximately 10 m length scales. The combination of the SGM and Bayes EM sub-pixel refinement produces correlation maps comparable to the displacement maps obtained from other correlation softwares a software optimized for motion tracking, such as COSI-Corr  (Leprince et al. , 2007)  and MicMac  (Rosu et al. , 2015). The outcome of the correlation is a pair of high-resolution maps with E-W and N-S displacement. The horizontal displacement maps only reveal a small proportion of the displacemen associated with the extensional dip-slip faulting.
A map view of the Red Canyon fault (RCF), the Whits Lake fault and the Little Tepee fault (outlined in Figure  1 ). The large arrows show the movement of the blocks; small gray arrows show the displacement vector for each profile; large gray arrows show the average direction of the rotating vector field along the RCF. Large red and blue arrows are aligned in the direction of regional extension. Blue and red colors represent the subsidence and uplift caused by the 1959 event, respectively. White arrows show the possible direction of extension NE of the RCF and the SE-directed deformation within the microblock between the Whits Lake fault and the Little Tepee fault as determined from the OIC results. OIC-derived offsets are up to twice as large as field and LiDAR measurements (Figure  3b ). Despite the systematically larger offset values obtained with OIC, the general trend of all datasets is comparable.
Acknowledgments L. Andreuttiova was supported by the  Natural Environment Research Council Doctoral Training Partnership  (Grant No. NE/L002485/1 ). J. Hollingsworth  was supported by grants from  BQR ,  Labex , and  CNES. We thank  Sophie Giffard-Roisin  and  Michel Bouchon  for helpful discussion. We would also like to thank two anonymous reviewers who contributed to the quality and clarity of this article.
Data Availability Statement This work uses high resolution historical aerial images available at https://earthexplorer. usgs. gov/. The processing workflow was designed in Ames Stereo Pipeline (https://stereopipeline. readthedocs. io/en/latest/index. html)  (Beyer et al. , 2018). We used COSI-Corr  (Leprince et al. , 2007)  and MATLAB, for the post-processing and data analysis, respectively. The topographic map in Figure  1  is ESRI Shaded Relief available at ArcGIS repository https://server. arcgisonline. com/arcgis/rest/services/World_Shaded_Relief/MapServer via QGIS. The initial locations of the mainshock and aftershocks are from the USGS earthquake catalog https://earthquake. usgs. gov/earthquakes/search/. The 3-D displacement maps are available at https://doi. org/10. 5281/zenodo. 6803257, 3-D displacement profiles from Red Canyon fault used for the analysis can be found at https://doi.
Only 206 epitopes from 79 Listeria antigens are listed in the IEDB database, mainly derived from LLO (69 epitopes) and p60 (41 epitopes) as well as from plcB, gap, mpl, prfA and lmo0209 (3 epitopes each)  58. Of these, 116 epitopes are presented on MHC class I molecules. To extend the antigen knowledge on Listeria we here applied an immunopeptidomics pipeline on two infected human epithelial cell lines. In between more than 15,000 human self-epitopes we identified 68 Listeria epitopes from 42 different bacterial antigens. Along with several known antigens, many novel antigens were detected, often derived from the bacterial periphery. Encoding highly presented antigens as vaccine candidates in mRNA vaccine formulations significantly reduced the bacterial load in liver and spleen in a vaccination-challenge study in mice.
TMT labeling of immunopeptides has previously been shown to extend the comprehensiveness of immunopeptide identification by improving peptide ionizability and fragment ion intensity during LC-MS/MS analysis  60. Following spectral identification with the PEAKS software and filtering for high confident hits with a false discovery rate (FDR) of 1%, in total we detected 15,766 host-and 85 Listeria-derived immunopeptides (Figure  1B  and Supplementary Table  1 ). Assessment of the peptide length distribution resulted in the expected predominance of 9mers among all identified peptides, and also a considerable proportion of 8mer, 10mer, 11mer and 12mer peptides, in line with previous reports (Figure  1C )  56, 61, 62. Submission of 9mer sequences from both HeLa and HCT-116 cells to MixMHCp 2. 1 resulted in the reconstitution of most cell line-specific HLA motifs (Figure  1D )  [63] [64] [65] [66].
High confident detection of Listeria epitopes Next, we compared immunopeptide abundancies between the infected samples and the uninfected controls. We used this quantitative comparison as an additional filtering step to select only high confidence Listeria epitopes. Indeed, bacterial immunopeptides are only expected in the infected samples, however, some of these peptides came with intensity values in the control samples suggesting they are likely false positive identifications (note that with an FDR of 1% we still expect about 160 false positive epitopes in the total dataset) or quantifications (i. e. matching of noise peaks). We therefore only selected Listeria peptides for further analysis that were i) quantified by the PEAKS software in at least two of the infected samples and ii) showed a higher average abundance in the infected samples compared to the uninfected control.
Furthermore, Listeria peptide sequences were searched against the human database with all possible leucine/isoleucine permutations since leucine and isoleucine residues are virtually indistinguishable by mass spectrometry due to their identical mass. In addition, also sequences found in the human database were removed from the bacterial immunopeptide list. These filtering steps resulted in 68 high confident Listeria peptides originating from 42 bacterial proteins (Figure  2A , Supplementary Table  2 ). 58 of these peptides were predicted by NetMHCpan EL 4. 1 to bind to at least one of the HLA alleles expressed on HeLa or HCT-116 cells (Figure  2B ) with most peptides binding to HLA-A alleles (Supplementary Figure  2 ), further supporting their high confident detection  67. Moreover, we synthesized 24 of the Listeria-derived immunopeptides to compare their experimentally recorded MS2 spectrum with the spectrum of their synthetic counterpart.
Listeria antigens are often derived from the bacterial periphery According to their predicted subcellular localization  78  , the majority of the detected Listeria antigens is located either extracellularly or at the bacterial surface (Figure  3A ). This makes these antigens more easily accessible to the host antigen processing and presentation machinery, likely explaining their overrepresentation compared to bacterial cytoplasmic antigens, as suggested previously  55, [79] [80] [81] [82]. Similarly, the cluster of orthologous groups (COG) annotation revealed cell wall/membrane biogenesis as most common COG term for identified Listeria antigens (Figure  3B ). Mapping physical and functional associations between the identified Listeria antigens in the STRING database  83  yielded nine clusters of associated proteins, with the two largest clusters separating again peripheral (virulence) proteins from cytoplasmic proteins (Supplementary Figure  4 ).
7) APC (Figure  5 ) or with antibody CD8a (53-6. 7) FITC and TCRbeta (H57-597)APC (Supplementary Figure  6 ). Cells were then fixed and permeabilized with BDCytoFix/CytoPerm solution (BD), intracellular staining using a IFN-(XMG1. 2) PE antibody (Biolegend, San Diego, CA, USA) was performed in Cytoperm buffer for 30 min at RT. After additional washing steps, samples were measured by a MACSQuant 18 (Figure  5 ) or 16 (Supplementary Figure  6 ) flow cytometer and analysed by FlowJo® software (BD company). Three spleen samples from lmon_0149 vaccinated mice (Figure  5 ) and two spleen samples from PBS vaccinated mice that showed a cell viability lower than 35% were excluded from the analysis. The gating strategy is outlined in Supplementary Figure  6.
DATA AVAILABILITY The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE 132 partner repository with the dataset identifier PXD031451. Data supporting the findings of this manuscript are available within the article, the Supplementary Information and the Source Data files or are available from the corresponding authors upon reasonable request. A reporting summary for this article is available as a Supplementary information file. Source data are provided with this paper. clustering  64, 65. To this end, the tool assumes that all peptides are of length equal to core length, are naturally aligned and that defined positions at the beginning (first three amino acids) and end (last two positions) can be found in the peptide sequence, which is typically the case for HLA-I ligands.
ACKNOWLEDGEMENTS We are grateful to  Prof. Pascale Cossart  for sharing the Listeria monocytogenes EGD strain. We thank  Jarne Pauwels  and  Jonas Erzeel  for technical assistance and the VIB Proteomics Core for LC-MS/MS analysis. R. L. M. is supported by the  Flemish Institute for Biotechnology (VIB)  and a faculty interim fellowship from the  UGent Faculty of Medicine and Health Sciences. R. V. is funded by a  UGent   BOF postdoc grant  ( BOF20/PDO/041 ) and A. G. is supported by a PhD fellowship from the  Higher Education Commission (HEC) Pakistan. F. I. acknowledges support from Odysseus grant  G0F8616N  from the  Research Foundation Flanders (FWO)  and D. E. is funded by a  FWO   PhD fellowship  for fundamental research. F. I. , L. M. , B. V. and I. L. acknowledge support from  Ghent University Concerted Research Action  grant  BOF21/GOA/033.
7 was used to calculate spectral correlation including spectrum_utils version 0. 3. 5 and pyteomics version 4. 5. 2128,129. Spectrum processing was performed by annotation of fragment ion peaks for a, b and y ions including singly and doubly charged ions, followed by removal of precursor peaks for up to two isotopes and removal of low intensity (<5% of the maximum) peaks. All steps used a 50 ppm mass error tolerance. Pearson correlations were calculated on the intensities of all annotated fragment ions per spectrum. The code including an example peptide can be found on GitHub (https://github. com/RalfG/2022- of 120,000 (at 200 m/z), was followed by tandem MS scans in the orbitrap (resolution 50,000 at 200 m/z) of the most intense ions fulfilling predefined selection criteria (AGC target 7.
3 × 10 3 , including, 2-5 positively charged precursors, peptide match off, exclude isotopes on, dynamic exclusion time 60 s). The higher-energy collisional dissociation (HCD) was set to 38% normalized collision energy, and the polydimethylcyclosiloxane background ion at 445. 12003 Da was used for internal calibration (lock mass). infected against uninfected samples employing permutation-based multiparameter correction at an FDR of 5%. Heat maps were constructed by z-scoring Listeria-derived peptide log 2 intensities before hierarchical clustering. the script can be found online at Binder (https://mybinder. org/v2/gh/RalfG/2022-listeria-spectrum-Calculation listeria-spectrum-similarity) and Zenodo (https://doi. org/10. 5281/zenodo. 5948475). A runnable version of similarity/HEAD?labpath=2022-listeria-spectrum-similarity.
5 Da, fixed first mass 100 m/z, spectrum data type: centroid, intensity threshold 8. 3 × 10 3 , exclusion of unassigned, 4-8, >8 positively charged precursors, peptide match off, exclude isotopes on, dynamic exclusion time 12 s). The higher-energy collisional dissociation was set to 28% normalized collision energy, and the polydimethylcyclosiloxane background ion at 445. 12003 Da was used for internal calibration (lock mass). long micropillar array column (µPAC, PharmaFluidics) with C18-endcapped functionality. Peptides were eluted by a non-linear gradient from 1 to 55% MS solvent B over 87 min, starting at a flow rate of 750 nl/min switching to 300 nl/min after 15 min, followed by a 13-min washing phase plateauing at 99% MS solvent B. Mass spectrometry raw data were searched with PEAKS Studio X (Bioinformatics Solutions Inc, Waterloo, Canada) against a database of the human sequences in Uniprot SwissProt (version January 2019, 20,413 entries) merged with Listeria monocytogenes EGD sequences from TrEMBL (version April 2019, 2,847 entries) 124.
Databases were merged using dbtoolkit 2. 0 (version 4. 2. 5) 125. The peptide length was restricted to 8-30 amino acids, and unspecific digestion was chosen as digest mode. Methionine oxidation and Nterminal acetylation were set as variable modifications, and mass error tolerances were set to 10 ppm and 0. 02 Da for parent and fragment ions, respectively. For indicating potential contaminant peptides, the MaxQuant contaminant database (MQ version 1. 6. 3. 4) was enabled 126. False discovery rate (FDR) estimation was carried out using the decoy-fusion approach. Identified peptide sequences were filtered at the PSM level for an FDR of 1% or better prior to label-free or TMT-10plex quantification in PEAKS Studio. Quantification results were not filtered and TMT-labeled peptide quantifications not normalized before export as csv files for further processing using the Perseus software platform 127.
Molecular networking has also proven to efficiently assign the origin of molecules detected on human skin (skin cells, microbes, environment, and lifestyles)  [147, 156, 159]. Since the development of molecular networking, several complementary bioinformatic tools have been developed to enhance quantitative metabolomics analysis, expand molecular annotations through a chemical tree-based approach and in silico annotation tools, as well as to re-analyze public metabolomics data using formatted metadata to compare metabolites between groups of samples from different datasets  [160] [161] [162] [163]. Additional bioinformatic advances have improved functional interrogations of the human microbiome including genome mining tools, such as antiSMASH, SMURF, and PRISM that has greatly facilitated the prediction of biosynthetic gene clusters (BGCs).
Further development of computational algorithms applied to metagenomic sequencing data of the human microbiome has led to the discovery of small molecule-producing BGCs, such as Type II Polyketides that appear to be widely encoded in the oral, gut, and skin microbiome  [164] [165] [166] [167]. Advances in algorithms that pair metagenomic and metabolomic datasets, such as NRPquest, NRPminer, and MetaMiner, have allowed more accurate identification of bioactive metabolites produced by BGCs from different environments  [168] [169] [170]. Examples include the discovery of known and unknown post-translationally modified peptides (RiPPs) from lichen and human microbiomes, and the identification of Non-Ribosomal Peptides (NRPs), such as lugdunin from Staphylococcus skin isolates and surugamides from soil datasets  [168, 169].
25   (Madec, 2008)  and LIM2  (Fichefet and Morales Maqueda, 1997). In our study, we use the first ORAS5 member (out of five members), which is the control (unperturbed) member, initialized using the ECMWF Reanalysis -40 years (ERA40) from 1958 to 1979. The atmospheric forcing is ERA-Interim from 1979 to 2015, and the ECMWF operational numerical weather prediction model thereafter. ORAS5 assimilates sea-surface temperature (SST), sea-ice concentration and ocean temperature and salinity from different products that are described in  Zuo et al. (2019). Data are available from 1979, and we extract monthly mean ocean potential temperature, zonal and meridional ocean velocities, as well as net downward heat flux. In our study, monthly data covering the period 1988-2017 are used for all model and reanalysis datasets.
Code and data availability. ORAS5 reanalysis data (Zuo et al. , 2019)  were retrieved from the portal of the Center for Earth System Research and Sustainability (CEN) from the University of Hamburg (https://www. cen. uni-hamburg. de/icdc/data/ocean/easy-init-ocean/ecmwf-oras5. html). Model data are available on the Earth System Grid Federation (ESGF) nodes (https://esgf-node. llnl. gov/search/cmip6) and references of the different datasets are provided in Table  1. The Python scripts to produce the outputs and figures of this article are available on Zenodo: https://zenodo. org/record/7358097  (Docquier, 2022).
Although current observations lack enough sensitivity to discriminate between these two scenarios, higher sensitivity surv e ys could be fruitful to this goal on a 1 -10 Myr age range. When compared with the available ALMA (Atacama Large Millimeter/submillimeter Array) Band 7 data, both viscous and magnetic wind models are compatible with the observationally inferred dust radii in Lupus, Chamaeleon I, and Upper Sco. Furthermore, in the driftdominated regime, the size-luminosity correlation is reproduced in Lupus, both in Band 7 and 3, while in Upper Sco a different slope than in the data is predicted. Sub-structures (potentially undetected) can explain several outliers with large observed sizes. Higher angular-resolution observations will be helpful to test our predictions in the case of more compact discs, expected in both frameworks, particularly at the age of Upper Sco.
N U M E R I C A L M E T H O D S A N D O B S E RVAT I O N S 1D simulations of gas and dust evolution are performed using the code developed by  Booth et al. ( 2017 ). The magnetic wind models of  Tabone et al. ( 2022b )  are used for gas, while for dust we employ the simplified treatment of grain growth developed by  Birnstiel et al. ( 2012 ). The outputs of our simulations are then post-processed to produce synthetic (sub-)millimetre dust observations, to be compared with real data. Hereafter, our numerical methods and observational sample are described.
Code ar chitectur e The code implements by default a routine solving the viscous evolution equation  (Lynden-Bell & Pringle 1974 ). This is updated to take into account the effect of magnetic winds as in the master equation of Tabone et al. ( 2022b , see equation 10, which is written here in the Keplerian case): ∂ ∂t = 3 R ∂ ∂R R 1 / 2 ∂ ∂R α SS g c 2 s K R 1 / 2 + 3 2 R ∂ ∂R α DW g c 2 s K - 3 α DW g c 2 s 4( λ -1) R 2 K , ( 1 ) where we call R the cylindrical radius and t the time variable. The first term in equation (  1 ) describes how the total disc surface density varies as a consequence of viscous-diffusion processes. Here, g is the gas surface density, and viscosity, ν = α SS c 2 s / K , is written according to the  Shakura & Sunyaev ( 1973 )  prescription. α SS labels the angular momentum transport efficiency due to viscosity, K is the Keplerian angular velocity, computed for a solar mass star, and c s is the locally isothermal sound speed, which is determined as c s ∝ T 1 /2 , where T is the disc temperature.
To first order, α DW is proportional to the disc magnetization, generally denoted by the inverse of the thermal to magnetic pressure ratio in the disc midplane, β 0  (Tabone et al. 2022b  ). The third term identifies the massloss rate in the wind, whose impact on disc evolution is described by the magnetic lever arm parameter, λ, defined as the ratio of the angular momentum carried away by the wind along a streamline and at its base  (Blandford & Payne 1982 ). Equation (  1 ) is solved using finite-differences methods on a grid made up of 4096 cells equally spaced in R 1/2 , between R in = 10 -2 au and R out = 10 4 au. In Appendix A, we show that our code exactly reproduces the analytical self-similar solutions of  Tabone et al. ( 2022b ). As initial condition, we consider a tapered power-law profile (Lynden-Bell & Pringle 1974 ): ( R, t = 0) = M 0 2 πR 0 R exp - R R 0 , ( 2 ) where M 0 = 0.