dataesr/scientific-paragraphs-categorization huggingface dataset:
The overall analysis ended with 1,037 unique raw formulas-when removing isotopic forms and adduct ions (Supplementary Table  S2 )-among which 1,033 were more accumulated in WI-Asn compared to WI-CH and four were less accumulated in WI-Asn than in WI-CH (Figure  3A ). They were mainly phytochemicals (42%) and lipid-like (26%). From the 1,037 obtained raw formulas, 546 compounds could be identified in databanks. Among them, the Top20 most accumulated annotated compounds in WI-Asn samples were predicted to be phytochemicals (60%), lipids (20%), and carbohydrates (20%) (Supplementary Table  S3A ). The metabolome of WI-Asn was also different from that of WI-CCh (Figure  2B ). The comparison between these two sample sets highlighted 1,162 m/z significantly regulated, of which 1,121 were common to the comparison Asn versus CH (Supplementary Table  S2 ).
Sodium arsenite induced an increase in woody tissue autofluorescence In order to assess whether a sodium arsenite treatment could improve the defensive state of woody tissues by increasing the impregnation of cell walls with phenolic compounds, we then observed the autofluorescence (under UV and blue excitation) of the samples. Initially, the fresh samples were observed macroscopically under epifluorescence. Figure  5  shows next to each zone observed in bright-field optical microscopy, the combined image of the color spectra (bright field), UV excitation  (excitation, 359-371; emission, 397 nm) , and blue excitation  (excitation, 455-495 nm; emission, 505-555 nm). At this resolution, no marked autofluorescence was detected in healthy tissues of asymptomatic (Figure  5A ) and symptomatic vines (data not shown).
Acknowledgments We thank  DImaCell Imaging Center  ( INRAE, UniversiteB ourgogne Franche-Comte ) and especially  Christine Arnould  for allowing us to carry out stereomicroscopic analyses,  Aline Bonnotte  for supporting us in conducting scanning electron microscopy experiments (with or without X-ray detector), the RCEN platform and especially  Frederic Herbst  for giving us access to the scanning electron microscope with an X-ray detector, and  Alessandro Spagnolo  and  Jean-Francois Guise  for their help in uprooting and cutting the plants. The authors thank also the  Lyceé VitiCampus  for the availability of the experimental plot to carry out the treatment.
Furthermore, quantifying PNC and DL across a clade of several thousand species avoids the pitfalls surrounding the potential nonrepresentativity of results when studying small clades. We deploy large, high-resolution, global-scale taxonomic, phylogenomic, and geographic distribution datasets based on expertcurated plant material collected during targeted fieldwork in key tropical biodiversity hotspots over the past two decades, and leaf tissue and species occurrence data from specimens sampled across the world's herbaria. Using these newly generated phylogenomic, taxonomic, and occurrence datasets, we assess (i) whether turnover is primarily structured by DL or PNC, and which environmental factors determine PNC; (ii) what levels of PNC versus niche shifting and DL versus long-distance dispersal through time characterize a species-rich clade that has fully colonized the lowland tropics; (iii) whether patterns and drivers of turnover are similar across continents, implying general processes shaping turnover, or whether phylogenetic turnover is primarily driven by taxon and area-specific idiosyncrasies; and (iv) whether the tempo of lineage diversification through time is associated with global Cenozoic climate change.
Notably, across almost all potential extinction rates, we find a marked increase in speciation rate in the Late Eocene or at the Eocene-Oligocene transition (c. 34 Ma ago; fig. S49 ). This acceleration in speciation coincides with the transition from "Warmhouse" to "Coolhouse" conditions, the most prominent Cenozoic climate transition that coincided with the formation of ice sheets in Antarctica  (44). This acceleration in speciation also coincides with the evolutionary transition from typically large tree and liana genera of African and Asian humid forests to a series of dryadapted lineages in the core Mimosoid clade, originating in the Oligocene (34 to 23 Ma ago; Fig. 1 ) and distributed across the tropics as a whole. This prominent transition from slower to faster diversification thus appears to be associated with environmental change, specifically with cooling and global expansion of dry habitats in the Oligocene and Miocene  (45).
Elevated net diversification rates were sustained through this Coolhouse period of global aridification  (45)  until the Middle to Late Miocene (c. 13 to 8 Ma ago; fig. S49 ), notably by a set of arid radiations, especially of Acacia with >1000 species in Australia, and diversification along the mainly dryadapted backbones of the core Mimosoid and ingoid clades (Fig. 1  and fig. S48 ). More recent Mimosoid evolutionary radiations are ecologically diverse and geographically scattered, including both wet forest lineages such as Archidendron (Southeast Asian rain forests) and the Jupunba and Inga clades (both neotropical and predominantly in Amazonian rain forests) (Fig. 1  and fig. S48 ), as well as the mostly arid synchronous radiations of the three largest clades of Madagascan Mimosoids in the past c.
Phylogenetic inference To construct a phylogeny that is robustly supported and densely sampled in terms of taxa, we combined a newly generated Hybseq backbone phylogeny with a set of 15 newly generated, taxonomically enhanced, or previously published species-level phylogenies for particular subclades. The backbone phylogeny was constructed from DNA sequence data generated via targeted enrichment of 997 nuclear genes selected specifically for phylogenomics of Mimosoid legumes [Mimobaits  (24) , https://github. com/erikkoenen/ mimobaits]. For the backbone, data were generated for 420 species representing 158 of the 163 genera in subfamily Caesalpinioideae, which contains the Mimosoid clade, and sampling taxa spanning the root nodes of each of the species-level subclades. This large phylogenomic dataset ensures that the backbone phylogeny is as robustly supported as possible and facilitates highprecision time calibration using a subset of informative and clocklike genes.
The 15 species-level phylogenies (hereafter referred to as subtrees) were constructed using a range of DNA sequence data types (Hybseq, RADseq, and traditional Sanger-sequenced loci), either newly generated or from published data, and appropriately rooted. Densely sampled ultrametric subtrees for these 15 subclades were grafted onto the time-constrained backbone phylogeny. We refer to the resulting time-calibrated phylogeny as a metachronogram, based on the earlier idea of meta-trees  (52, 53) , as implemented by Spriggs et al. (54)  to build a phylogeny for grasses. One potential limitation of the metachronogram approach is that combining different data types and different clock models for the backbone and subtrees could affect subsequent diversification rate analyses, but these impacts are likely to be minor and manifest only at finer scales.
(24). To generate the bait set, a custom pipeline was used to select putatively single-copy genes from transcriptomes of four genera (Albizia julibrissin, Entada abyssinica, Microlobius foetidus, and three species of Inga) spanning the Mimosoid clade  (24). During the capture reactions, 25 libraries were pooled on the basis of approximate evolutionary distances between taxa. Sequencing was performed on Illumina HiSeq 4000, resulting in 150-base pair (bp) paired-end reads. In addition to these newly generated sequences, we also used the raw reads of the same genes from 122 Caesalpinioideae taxa previously generated by Koenen et al. (24)  and 18 Inga species generated by Nicholls et al. (56). Together, this resulted in a dataset of 424 taxa, covering 161 of the 163 genera of subfamily Caesalpinioideae, including 99 of 100 Mimosoid genera (table  S1 ).
Using these cleaned alignments, final gene trees were inferred using RaxML with the GTRGAMMA model and 200 rapid bootstrap replicates. On the basis of Koenen et al. (24)  and visual inspection of the resulting gene trees, all trees with a root-to-tip length variance over 0. 009 were excluded from the dataset to remove outlier gene trees that may be affected by alignment or orthology inference issues. Following Mirarab  (61) , branches with bootstrap support lower than 10% were collapsed. Gene trees were used to infer three separate species trees with ASTRAL-III 5. 7. 3 (fig. S3 ) (62): one using just the single-copy genes, one using the single-copy genes plus the multicopy genes treated as single-copy (i. e. , without paralogs), and one using the single-copy genes plus the multicopy genes with paralogs.
Time calibration The ASTRAL single-copy gene topology of the Caesalpinioideae Hybseq backbone phylogeny was time-scaled in BEAST v. 1. 8. 4 (69, 70) , i. e. , the topology was constrained, while BEAST was used to calculate the branch lengths, using a fixed local clock model with six local clocks to account for discrete branch length variation observed across the tree. The fixed local clock is a good compromise between relaxing the clock to account for rate heterogeneity and having the data influence the branch lengths and divergence times. In other words, we are not "over-relaxing" the clock such that branch lengths and divergence times are mostly estimated from the priors rather than the molecular data  (71, 72). From the Hybseq dataset, 100 gene alignments were selected using SortaDate (73) as follows: (i) All genes with less bipartition correspondence to the ASTRAL topology than the median were discarded; (ii) all genes with total tree length shorter than the median were discarded; and (iii) the 100 most clock-like genes, i.
Metachronogram As indicated above, to generate a densely sampled time-calibrated phylogeny, we grafted a set of densely sampled subtrees, i. e. , phylogenies of subclades and genera, onto the Hybseq time-calibrated backbone phylogeny described above. Because the backbone tree and the grafted subtrees were derived from data sets that used different markers, both the backbone and the subtrees were estimated as ultrametric trees to make the branch lengths compatible. The subtrees were then rescaled according to the corresponding node in the time-calibrated backbone onto which it was subsequently inserted. A Python script to carry out this procedure is available at https://github. com/erikkoenen/metachronogram and can be used to produce similar metachronograms for other clades where a backbone and subtree phylogenies with (potentially) nonoverlapping markers are available.
Mimosoid clade To assemble an accurate quality-controlled species occurrence dataset, we first compiled a comprehensive taxonomic checklist of accepted names with partial synonymy for all species and infraspecific taxa in the Mimosoid clade. For each genus or clade, the most recent taxonomic monograph or revision (when available) was used, and more recently described taxa were added using the International Plant Names Index (www. ipni. org). This checklist was used to download species occurrence records from the Global Biodiversity Facility (GBIF; www. gbif. org), the Latin American Seasonally Dry Tropical Forest Floristic Network (DryFlor; www. dryflor. info), and the Southwestern Environmental Information Network (http://swbiodiversity. org/seinet), as well as various other taxon-or region-specific sources.
Extensive data cleaning was performed: We updated synonymous names using the checklist and removed records not based on vouchered herbarium specimens (except for DryFlor records based on plot data or checklists), records located in the sea or on country or major area centroids, cultivated records, and records located outside native distribution ranges as delimited in the primary taxonomic literature. For some taxa, precompiled occurrence datasets were used. For example, for the vast majority of the largest Mimosoid genus, the predominantly Australian genus Acacia that comprises c. 1000 species, we did not assemble a custom checklist and occurrence data but instead relied on the occurrence dataset of González-Orozco et al. (77)  with minor updates [see Appendix 1 (data S1) for details]. Literature used to assemble the taxonomic checklist and perform quality control of the occurrence data, as well as GBIF DOIs, taxon-specific notes, and other sources of occurrence data, are in Appendix 1 (data S1).
Non-Mimosoid Caesalpinioideae For the optimizations of climate and geography across the metachronogram, the 78 outgroup taxa across Caesalpinioideae outside the Mimosoid clade in the phylogenomic backbone were included to mitigate against the tendency to reconstruct intermediate trait values across deeper ancestral nodes in quantitative trait optimizations and hence improve the accuracy of ancestral area and climate reconstructions across the early nodes of the Mimosoid clade. We assembled an occurrence dataset for the 78 non-Mimosoid Caesalpinioideae taxa present in the phylogeny, using published  (20, 78)  and newly assembled occurrence data in the same way as for the Mimosoid clade (see above). For each outgroup taxon, literature, GBIF DOIs, data sources, and notes are in Appendix 1 (data S1).
Abundance of Mimosoids across biomes We estimated the fraction of Mimosoids across biomes and continents using several datasets. The fraction of Mimosoid species among all Amazonian tree species was estimated in two ways: using appendix S1 of ter Steege et al. (79)  and dataset S01 of Cardoso et al. (80). For the fraction of Mimosoid species among neotropical dry forest trees, the occurrence dataset of DryFlor (downloaded from www. dryflor. info/data/datasets) (81) was used. The fraction of Mimosoid species among African savanna trees was assessed using appendix S1 of Fayolle et al. (82). Last, the fraction of Mimosoid species among all native Australian Angiosperms was calculated using a species checklist from the Australasian Virtual Herbarium (https://avh. chah. org. au/).
Phylogenetic turnover Phylogenetic turnover across the global tropics was quantified using the phylogenetic version of Simpson's pairwise dissimilarity index  (83) (84) (85). Also known as (phylo)beta-sim or βsim (83, 86), Simpson's dissimilarity index quantifies the "true" turnover component of Sørensen's dissimilarity index  (83, 84)  and has the important advantage of not being influenced by differences in species richness between sites  (83, 84, 86, 87). The "phylo. beta. pair" function of the betapart R package (88) was used to calculate pairwise phylogenetic turnover between all half-by-half degree longitude/latitude grid cells with at least three (metachronogram) or five (genus-level tree) taxa located at the most 33°latitude north or south of the equator, i. e. , restricting assessment of phylogenetic turnover patterns to the global tropics and subtropics.
Temperate regions were excluded because Mimosoids show high tropical niche conservatism (25): Only 31 taxa (i. e. , 0. 83% of all taxa in the occurrence dataset), belonging to Neltuma (13 taxa), Mimosa (six taxa), Desmanthus (three taxa), Strombocarpa, Albizia, Prosopidastrum, Senegalia (each two taxa), and Calliandra (one taxon), have over half their occurrence points north or south of 33°latitude in North or South America, Africa, the Middle East, or Asia, and these climatic and geographic outliers are likely to strongly bias explanations of patterns of phylogenetic turnover in these regions. The only exception to this is Australia, where 288 taxa of Acacia and one taxon of Paraserianthes (i. e. , 7. 70% of all Australian taxa) have over half of their occurrence points south of 33°latitude, and phylogenetic turnover was therefore quantified across all of Australia, including Tasmania.
Predictor variables Geographic (great circle) distances between centers of grid cells were calculated using the "RdistEarth" function of the fields package (89), or internally in the "gdm" function of the gdm package  (90). Climatic distances between grid cells were calculated using all 19 Bioclim variables of CHELSA  (91) , cloud cover, and the intraannual SD of cloud cover downloaded from EarthEnv. org (92) , plus DSL, i. e. , the number of consecutive months with rainfall <100 mm/month  (20). Predictor variables were aggregated to a half degree resolution using the "aggregate" function of the raster package  (93).
Phylogenetic regionalization Phyloregionalization or bioregionalization analysis provides a way to visualize and map patterns of spatial phylogenetic turnover purely based on the phylogeny and occurrence dataset, independent from climatic or other types of data  (77, 86, 104). We identified the best clustering algorithm for phyloregionalization by correlating cophenetic distance matrices with the corresponding phylogenetic distance matrices  (86, 87, 105). However, as has been reported before  (87, 105) , in some continents the best-scoring algorithm, unweighted pair group method with arithmetic mean (UPGMA), yielded highly unbalanced results, resulting in one very large phyloregion consisting of almost all grid cells, and multiple smaller phyloregions of just one or very few cells. We therefore selected the Ward clustering algorithm instead  (87).
Optimization of precipitation Precipitation was optimized as a continuous variable across the MCC tree of the metachronogram using the "contMap" function of the phytools package  (67). Two independent optimizations were performed, using the median values per species of MAP (Bio12) and DSL (the number of consecutive months with rainfall < 100 mm). To increase the accuracy of the optimization especially at deeper levels in the Mimosoid tree, data for the 78 non-Mimosoid Caesalpinioideae outgroup taxa in the phylogeny were included in the analyses. While the outgroup sampling is less dense than sampling within the Mimosoid clade, the 78 outgroup taxa provide a reasonable representation of the climatic and geographic distributions of the non-Mimosoid Caesalpinioideae and include taxa belonging to 59 of the 63 non-Mimosoid Caesalpinioideae genera.
Rainfall category thresholds correspond to the climatic space distinguishing lineages with wet and dry affinities. Wet lineages are hypothesized to not occur below 1200 MAP and dry lineages not above 1800 MAP  (17). Dry tropical forest/succulent biome vegetation predominantly receives <1200 MAP  (20, 33, 101)  and is absent from regions with >1800 MAP  (101) , whereas 1800 (to 2000) MAP is the lower bound of tropical rain forest vegetation  (17, 108). Similarly, tropical rain forests experience fewer than 4 months with <100-mm rainfall per year  (108) , whereas more than half of all areas of succulent biome have a DSL of at least 8 months  (20). Phylogenetic signal Phylogenetic signal, expressed as Pagel's lambda  (34) , was calculated using the "phylosig" function in the phytools package  (67) , the MCC tree of the metachronogram, and median values of all Mimosoid species of MAP (Bio12) and DSL.
Optimization of geography Geography was optimized across the MCC tree of the metachronogram, including non-Mimosoid Caesalpinioideae outgroups (see above), using BioGeoBEARS (109). To do so, all taxa in the metachronogram were assigned to one or more of eight areas using their distribution data: North America, South America, Africa, Madagascar, Asia, Australia, Oceania, and the European Mediterranean. The BioGeoBEARS R package  (109) (110) (111)  was used to fit six different models: DEC, DEC+J, DIVALIKE, DIVALIKE+J, BAYAR-EALIKE, and BAYAREALIKE+J. After removing nodes representing non-Mimosoid Caesalpinioideae outgroup taxa from the BioGeoBEARS output of each model, the most probable ancestral range of each internal node was determined as the combination of all the most probable areas that together have a probability >50%.
Lineage diversification dynamics through time To explore scenarios of lineage turnover through time in relation to Cenozoic climate cooling that led to an increase in dry habitats across the planet (45), we ran a set of analyses with BAMM (Bayesian Analysis of Macroevolutionary Mixtures)  (112) , using the MCC tree of the metachronogram set with the outgroup removed except for Erythrophleum and Pachyelasma (the sister group of the Mimosoid clade) to include the stem lineage of the Mimosoid clade. This method has been criticized  (113, 114) , but the author of the program has responded to these criticisms  (115, 116). More generally, any diversification rate estimation method suffers from a lack of power to estimate extinction rates  (117) , meaning that speciation rates are not identifiable from phylogenies  (118).
To account for this limitation, we ran analyses across a wide range of fixed extinction rates to assess how speciation rates would vary through time under various levels of turnover while making use of the powerful way in which BAMM can take unsampled diversity into account by assigning sampling fractions to genera or clades, which we estimated on the basis of our Mimosoid checklist and taxonomic expertise. Priors were set using the setBAMMpriors option of the BAMMtools R package  (119) , and extinction rates were fixed across different analyses at 0. 05 lineages per million years and ranging from 0. 5 to 3. 5 with intervals of 0. 5. Speciation rates and rate shifts (or, more accurately, shifts to different speciation rate regimes as the model also includes time-variable speciation rates) were left as free parameters to be estimated during the analysis.
Acknowledgments We thank the following:  B. Adhikari ,  A. Forrest ,  D. Lorence ,  B. Marazzi ,  É. de Souza ,  M. Thulin , the G, K, JRAU, L, MEL, NY, FHO, P, RB, WAG, and Z herbaria, the  Millennium Seed Bank, Kew , the  South African National Botanical Institute , the  Direction de Environment, New Caledonia , and the  National Tropical Botanical Garden, Hawaii, USA , for provision of leaf or DNA samples;.
A major challenge for achieving accurate phylogenetic analysis of teleost genomes is their high number of duplicate (paralogous) gene copies. Many of these paralogs are inherited from a whole genome duplication (WGD) in their last common ancestor  (20) , and are known to mislead phylogenetic reconstructions  (11). To mitigate the effect of paralog inclusion, we applied a WGDtailored pipeline leveraging gene sequences and synteny conservation (supplementary materials, Methods section) to select 955 high-confidence 1-to-1 orthologous genes across all the 25 genomes we analyzed. This list represents by far the largest molecular dataset considered for teleost phylogeny reconstruction, both in terms of included Elopomorpha genomes and of total alignment size (see fig. S1 ). We then performed phylogenetic reconstructions of these 955 individual gene trees using summary analyses with ASTRAL (Fig.
Acknowledgments: We thank  Yoann Guilloux ,  Fabien Quendo  and  Aaron J. Adams  for their help in providing fish samples. We would also like to thank the leaders of the oceanography cruises and the crew of the  RV Atalante, France  and  ORI, Taiwan  in organizing the survey and helping to collect the deep-sea fish samples under the  TDSB-TFDeepEvo joint Program.
Funding: This work was supported by the  Agence Nationale de la Recherche, France (ANR)  on the  GenoFish  project,  2016-2021  (grant No. ANR-16-CE12-003 ) to HRC. , CB. , JB. , JHP. , M. R. C and YG, and by  France Génomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  ANR  (grant No. ANR-10-INBS-09 ) to CD. Part of the fellowship to E. P. was supported by funds from the  European Union   Horizon 2020 research and innovation program  under Grant Agreement No  817923  ( AQUA-FAANG ). M. R. -R. was supported by the  Swiss National Science Foundation  grant  31003A_173048. J. H. P was supported by the  National Institute of Health  under grant agreement No  R01OD011116.