dataesr/scientific-paragraphs-categorization huggingface dataset:
16S rRNA gene and ITS sequence processing and bioinformatics analysis A total of 28,903,483 and 28,532,432 paired bacterial and fungal reads respectively in. fastq format were merged using the "-fastq_mergepairs" command in USEARCH (v9. 2. 64). Merged reads were filtered for quality control using the "-fastq_filter" command in USEARCH, with a maximum expected error rate of 0. 01. Merged reads were trimmed to 450 bp and shorter reads were discarded. Filtered reads were subjected to OTU clustering at 97% sequence identity using the UPARSE  [63]  algorithm within USEARCH, and taxonomic information was provided for representative sequences of bacterial OTUs using the "assign_taxonomy. py" command in QIIME (v1. 9) against the SILVA database (128 release). Fungal OTUs were interrogated against a previously curated fungal database designed for skin microbiome surveying  [64].
Chimera detection was performed using UCHIME2  [65]  within USEARCH under highconfidence mode. OTUs in taxonomic lineages present in greater than 5% of negative controls were deemed potential contaminants  [19]  and were removed from the dataset. In addition, chimeric, chloroplast, and mitochondria OTUs were also removed. Following quality control and removing undesirable reads, a total of 9,656, 916 and 14,649,172 bacterial and fungal reads were retained, respectively. We verified that there was no bias in our experimental procedures by sequencing gDNA of artificial mixes of 10 bacterial and two fungal species (ZymoBIOMICS Microbial Community Standard, Zymo Research, Irvine, CA, USA), as well as in-house wholecell controls containing mixture of varying proportions of known bacterial or fungal species (six different bacterial and five different fungal mixture controls were tested).
The expected relative abundances were obtained in all the controls (data not shown). Unless specified, the bioinformatics tools and methods employed applied to both and fungal sequences. Differential abundance analysis was performed using DeSeq2 available as the QIIME (v1. 9) script "differen-tial_abundance. py. " OTUs with fewer than 100 reads and present in < 25% of samples in each city (when comparing between sites within a city) or each skin site (when the comparison between cities within a site) were not included in analysis. Taxa with DeSeq2 log 2 fold change ≥ |2| and false discovery rate (FDR)adjusted p ≤ 0. 05 were deemed significantly and differentially abundant. Oligotype analysis (pipeline v2. 1) was performed for OTUs classified as Propionibacterium, Staphylococcus, and Corynebacterium.
Specifically, all quality-filtered reads (not only representative reads) clustered into OTUs of the three genera were subjected to entropy analysis and oligotyping, with minimum substantive abundance criteria (-M) of 1000, 100, and 500 for Propionibacterium, Staphylococcus, and Corynebacterium, respectively. Within-sample Shannon diversity was estimated using the breakaway (v4. 0) package in R v3. 5. 1  [66]. The Good's estimator of coverage was > 90% for all but one sample for both bacterial and fungal communities (Additional file 18: Table  S11 ), indicating that the sequencing depth adopted was sufficient to capture the microbial diversity and richness. Betadiversity analyses were performed to assess the community membership (unweighted UniFrac and Binary Jaccard distances for bacterial and normalized fungal datasets, respectively) and community composition (weighted UniFrac distance and Bray-Curtis dissimilarity for bacterial and normalized fungal datasets, respectively).
Among the OTUs, 74 bacterial and 69 fungal taxa were selected after filtering for relative abundance. The PAH measurements were log-transformed to fit the Gaussian distribution. A total of 202 individuals with PAH and OTU data were included in the analyses. sCCA (a regularized version of the canonical correlation analysis used to study the relationship between two datasets while selecting only significant correlations  [68] ) was performed to select the OTU (bacteria or fungi) and PAH descriptors that were active in the between blocks relationships. The sparsity parameters of sCCA were selected with a permutation scheme using the CCA. permute function from the PMA (v1. 0. 9) package in R  [68, 69]. The cross-correlation between selected PAHs and OTU relative abundance (bacteria or fungi) was visualized using a heatmap representation.
Last, in order to obtain a common representation of individuals of the two blocks, a hierarchical multi-blocks analysis (MAXVAR-A) was performed using the RGCCA (v2. 1. 2) package in R  [70]. To identify specific correlations between OTU relative abundances and PAH exposure levels while controlling for host covariates, MaAsLin2  [71]  was performed separately on cheek and scalp samples. For both skin sites, age group, cohort city, and exposure levels to PAHs were included as covariates. In addition, for cheek analysis, host clinical parameters including acne onset, pore severity, clinical assessment of wrinkle grade, oiliness, and shininess on foreheads and cheeks, facial spot intensity, and frequency of pigment spots were also included as covariates. For scalp sites, scalp oiliness, hair volume, hair root dryness and greasiness, adherent dandruff scores, consumption of tap and purified water, and clinical assessment of dandruff status were included in analysis.
Acknowledgements We thank  Hedwig Cheung  for her assistance with preparing samples for sequencing,  Zhiyong Shen  for FindFungi analysis, and staff of  SeqMatic LLC  for their expertise in sequencing. We appreciate the technical assistance provided by  Ronson Wu  and  Juan Villada. We also acknowledge  Dr. Namita Misra  for her help in interpreting the functions involved in microbial-host interaction and  Dr. Jun Liu  for coordinating the project. This work was supported by a grant from  L'Oréal Research & Innovation. PB, FG, RJB, SM, JL, NB, CC, and LB are employees of  L'Oréal Research and Innovation.
To achieve this, a well supported phylogeny of the double coconut and its relatives is required, as well as supporting data from key characters and environmental variables. Borasseae and the syncarpous clade have never been studied phylogenetically beyond genuslevel and synthetic analyses  (Baker et al. , 2009; Faurby et al. , 2016). Seed size data are incomplete for palms, notwithstanding a recent trait dataset for the family  (Kissling et al. , 2019). These knowledge gaps must be filled in order to understand how Borasseae achieved their current distributions and seed sizes, and to clarify the origin of the double coconut. Here, we present a macroevolutionary study of the biogeography and possible drivers that led to the origin of the double coconut. First, we inferred a new phylogeny of tribe Borasseae (including the double coconut) in the broader context of the syncarpous clade based on novel DNA sequence data from two nuclear genes and five plastid regions.
Second, we completed molecular dating and ancestral range inferences to establish a biogeographic framework and examine the frequency and age of oceanic dispersals. Thirdly, we generated a novel seed trait dataset from which ancestral seed sizes were estimated and their variation correlated with changes in key variables (plant size, architecture, insularity and habitat). We then discussed our results in the context of the six hypotheses outlined above. In this way, we lay the foundations for future ecological and evolutionary research to address one of the most remarkable phenomena in the plant kingdom.
Molecular dating and biogeography Ages of divergence were estimated using BEAST 1. 8. 4  (Drummond et al. , 2012). We used a GTR + G model with four rate categories and an uncorrelated lognormal relaxed clock to model the nucleotide substitution rate. A birth-death model with incomplete sampling  (Stadler, 2009)  was used as the speciation prior after that path-sampling estimations of marginal likelihoods and Bayes factors calculations showed strong evidence  (Kass & Raftery, 1995)  for this prior over a Yule prior. The inference was run independently two times for 50 million generations, resulting in effective sampling sizes > 200 for all parameters and convergence towards the same posterior distributions (visualised using TRACER v. 1. 7 software;  Rambaut et al. , 2018). A burn-in fraction of 25% of the trees was discarded before reporting median posterior ages on the maximum clade credibility tree using  TREE ANNOTATOR v.
One occurrence per grid cell of the MODIS land cover dataset  (Friedl et al. , 2010; Channan et al. , 2014)  was then sampled for each species, and the habitat of each occurrence was defined as closed if the land cover was a type of forest (values 1-6) or otherwise open (values 7-14). Species with more than 50% of occurrences falling in a given habitat (closed or open) were classified as occurring in this habitat, whereas three species with exactly 50% of occurrences in each habitat were classified as occurring in a closed habitat, based on the literature (Table  S2 ). Ó 2020 The Authors New Phytologist Ó 2020 New Phytologist Trust  New Phytologist (2020) 228: 1134-1148  www. newphytologist. com.
Seed data and ancestral state estimations To obtain robust and comprehensive data on seed size variation in Borasseae and other syncarpous tribes, we analysed specimens in the collections of the Royal Botanic Gardens, Kew (herbarium and the Millennium Seed Bank), and data from the literature. Borasseae fruits contain one to three seeds, and each seed is surrounded by an endocarp, which can be very thick (Fig. 1b ). These endocarps protect the seed when it is dispersed, and thus the seed plus its endocarp, hereafter termed the pyrene, is the relevant unit to consider for questions related to dispersal. Up to 10 collections per species were sampled, from which the length of up to 10 fruits and of their pyrenes and seeds were measured using a set of digital callipers. Table  S3  summarises the number of collections and fruits, pyrenes and seeds measured per species.
When possible, we calculated for each collection the ratios of fruit length and pyrene length, fruit length and seed length, and pyrene length and seed length. To increase our seed and pyrene datasets, we then used those ratios to estimate seed or pyrene length from other collections of the same species that had only fruits and/or pyrenes available to measure (Table  S3 ). Fruit, pyrene and seed width and height were also collected and used to estimate volumes, but as our results were qualitatively identical to these obtained using only lengths (not shown) and as most studies on seed size rely only on length, we only used the latter in our final analyses. Ancestral plant heights, pyrene lengths and seed lengths were estimated using the fastAnc function in the package CONTMAP  (Revell, 2012 (Revell, , 2013)) , which assumes trait evolution under a Brownian motion model.
This model had the smallest AICc when fitted to our data, compared with the other models available in the GEIGER package  (Harmon et al. , 2008). The effects of plant size, inflorescence branching order (low, high), insularity (island, continent), and habitat (open, closed) on seed or pyrene size (log-transformed to achieve normality and equal variances) were tested by fitting linear models with all variables and their interactions. A stepwise model reduction procedure was used to remove nonsignificant terms and the model with the lowest corrected AIC was selected using the AICCMODAVG R package  (Mazerolle, 2019). The procedure was repeated with models including phylogenetic structure, using the phylogenetic generalised least squares (pgls) function from the CAPER R package  (Orme et al. , 2018).
All analyses were performed in R 3. 5 (R Core Team, 2018), using the packages APE  (Popescu et al. , 2012) , GGPLOT2  (Wickham, 2016) , GGPUBR (https://rpkgs. datanovia. c om/ggpubr/), GGREPEL (http://github. com/slowkow/ggrepel), GRIDEXTRA (https://cran. r-project. org/web/packages/gridExtra/in dex. html), PHYTOOLS  (Revell, 2012) , and RCOLORBREWER (https://CRAN. R-project. org/package=RColorBrewer). Analyses were repeated without Lodoicea to assess its influence on the results.
Acknowledgements We thank  Laura Kelly ,  Barbara Pickersgill  and  John Dransfield  for comments on the manuscript, and  Oscar Alejandro P erez-Escobar  for improving Fig. 1. Samples and sequence data were kindly provided by  Conny Asmussen-Lange ,  John Dowe ,  Himmah Rustiami ,  Carl Lewis  and the  Montgomery Botanical Center. Field work was completed with the support of  Caleb Obunyali ,  Muthama Muasya ,  Quentin Luke  and the  National Museums of Kenya (Kenya) ;  Rolland Ranaivojaona , H el ene Ralimanana,  Solofo Rakotoarisoa ,  Adam Britt  and the  Parc Botanique et Zoologique de Tsimbazaza (Madagascar) ;  Amad e Ou edraogo ,  Sita Guinko ,  Joseph Boussim ,  Paulin Ouôbo  and the  Universit e de Ouagadougou (Burkina Faso). This research was funded by a  NERC  PhD scholarship ( NER/S/A/2001/ 06315 ), the  Garfield Weston Foundation postdoctoral fellowship, VILLUM FONDEN  (grant  00025354 ), the  South Florida Palm Society , the  Central Florida Palm Society , the  International Palm Society , the  Montgomery Botanical Center , the  Arthur Hosier Travel Award Scheme  and the  School of Plant (now Biological) Sciences of the University of Reading.
Information theory-analyzed reconfigurations of leaf metabolomes by insect feeding follow OD theory predictions To test the reformulated predictions of the defense theories in the axes of information theory descriptors, we reared larvae of either the specialist (Ms) or a generalist (Sl) herbivore on leaves of rosettestage N. attenuata plants (Fig. 2A ). Using MS/MS analysis, we retrieved 599 nonredundant MS/MS spectra from methanolic extracts of leaf tissues collected after caterpillar feeding (data file S1). Visualizing reconfigurations of the information content in MS/MS profiles using the RDPI, Hj and j indices revealed intriguing patterns (Fig. 2B ). A general trend was the time-dependent increases in all degrees of metabolic reorganizations as described by the information descriptors as caterpillars continuously fed on leaves: 72 hours after herbivore feeding, RDPI was strongly enhanced; Hj was significantly decreased compared with undamaged controls as a result of an increase in specialization of the metabolic profile as quantified by the j index.
2A )  (34). Using the MS/MS analytical and computational pipeline, we retrieved 443 MS/MS spectra (data file S1), which largely overlapped with those previously assembled from the direct feeding experiment. An information theory analysis of this MS/MS dataset revealed that the reprogramming of the leaf specialized metabolome by simulated herbivory exhibited OS-specific elicitations (Fig. 2C ). In particular, OS Ms elicited a stronger increase in metabolome specialization at 4 hours than did the OS Sl treatment. Notably, metabolic kinetics visualized in a two-dimensional space using Hj and j as coordinates were consistent with a directional increase over time of metabolome specialization in response to simulated herbivory treatments as compared to the direct herbivore feeding experiment dataset (Fig.
2D ). In parallel, we quantified levels of amino acids, organic acids, and sugars (data file S2) to investigate whether this directional increase in metabolome specialization in response to simulated herbivory resulted from reconfigurations of central carbon metabolism (fig. S2 ). To better interpret this pattern, we further monitored metabolic accumulation kinetics of the previously discussed phenolamide and 17-HGL-DTG pathways. The herbivore OS-specific induction translated into differential reconfiguration patterns within phenolamide metabolism (fig. S3 ). Phenolamides containing coumaroyl and caffeoyl moieties were preferentially induced by OS Sl elicitation, while OS Ms triggered a specific induction of feruloyl conjugates. For the 17-HGL-DTG pathway, an OS differential elicitation effect was detected for the downstream malonylated and dimalonylated products (fig.
An early priming of transcriptome specialization underlies metabolome specialization We next investigated OS-elicited transcriptome plasticity using a time-course microarray dataset that simulated herbivory using OS Ms treatments to leaves of rosette-stage N. attenuata plants; the sampling kinetics largely overlapped with those used in the present metabolomics study  (35). Compared with metabolome reconfigurations in which metabolic plasticity particularly increased over time, we observed a transient burst of transcription in leaves induced by Ms, in which transcriptome inducibility (RDPI) and specialization (j) were strongly enhanced at 1 hour, whereas diversity (Hj) was strongly decreased at this time point, followed by a relaxation of transcriptome specialization (fig. S4 ). Metabolic gene families such as P450, glycosyltransferases, and BAHD acyltransferases involved in the assembly of specialized metabolites from building blocks derived from primary metabolism followed the early high specialization pattern described above.
Exploring MS/MS herbivore-specific associations using MS/MS structural analysis To pinpoint specialized metabolites with significant contributions to herbivore species-specific responses and whose production was fine-tuned by ET signaling, we used a previously developed structural MS/MS approach. This approach relies on a biclustering method for the de novo inference of metabolic families from MS/MS fragment [normalized dot product (NDP)] and neutral loss (NL)-based similarity scoring. The MS/MS dataset constructed from the analysis of ET transgenic lines resulted in 585 MS/MSs (data file S1), which was resolved by biclustering into seven main MS/MS modules (M) (Fig. 4A ). Some of these modules are densely populated with previously characterized specialized metabolites: for instance, M1, M2, M3, M4, and M7 were enriched with various phenolic derivatives (M1), flavonoid glycosides (M2), acyl sugars (M3 and M4), and 17-HGL-DTGs (M7).
Natural variation in JA bursts underlie intraspecific variations in herbivory-induced metabolome specialization We further extended the analysis from a single N. attenuata genotype to natural populations in which intense intraspecific variations in herbivory-induced JA levels and specialized metabolites levels have been previously described  (26). Using this dataset that covers 43 accessions consisting of 123 individual N. attenuata plants derived from seeds collected at different native habitats in Utah, Nevada, Arizona, and California (fig. S7 ), we calculated metabolome diversity (here referred to as population-level  diversity) and specialization shaped by OS Ms elicitation. Consistent with the previous study, we observed a wide range of metabolic variations along Hj and j axes, indicating that accessions differ markedly in the plasticity of their metabolic responses to herbivory (fig.
However, as far as we know, this is the first application to describe plants' metabolic information space and address ecological questions regarding the function of temporal metabolic changes in response to environmental cues. In particular, the power of the present approach lies in its ability to compare patterns within and among plant species to examine how herbivory has shaped plant metabolism at different scales in the evolutionary hierarchy, from microevolutionary patterns within species to among-species macroevolutionary patterns. Principal components analysis (PCA) that transforms multivariable dataset into a reduced dimensionality space such that the main trends in the data are interpretable is frequently adopted as an exploratory technique to parse datasets such as deconvoluted metabolomes.
However, dimensionality reduction loses part of the information content in the dataset, and PCA provides no quantitative information about traits that are particularly germane for ecological theory, such as: How does herbivory reconfigure diversity (e. g. , richness, distribution, and abundance) in specialized metabolites? Which metabolites are predictors for a given herbivory-induced state? Decomposing the information content of leaf specialized metabolite profiles in terms of specificity, diversity, and inducibility revealed that herbivore feeding activated idiosyncratic metabolic rearrangements. Unexpectedly, we observed that the resulting metabolic landscapes, as described by the implemented information theory indices, largely overlapped after attack by the two herbivores, Sl, a nocturnal feeding generalist, and Ms, a Solanaceae specialist, despite their distinct feeding behaviors and concentrations of fatty acid-amino acid conjugate (FAC) elicitors in their OS  (31).
Microarray and RNA sequencing data analysis Microarray data were originally published in  (35)  and deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus database (accession no. GSE30287). Data corresponding to leaves elicited by W + OS Ms treatments and undamaged controls were extracted for the present study. Raw intensities were log 2 , and baseline was transformed and normalized to their 75th percentile using the R software package, before statistical analysis. The raw RNA sequencing (RNA-seq) data of Nicotiana species were retrieved from the NCBI short reads archive (SRA) under the project number PRJNA301787, which was reported by Zhou et al. (39)  and processed as described in  (56). Raw data corresponding to W + W, W+ OS Ms , and W + OS Sl treatments of Nicotiana species were selected for the present study analysis and processed as follows: raw RNA-seq reads were first converted to FASTQ format.
MS/MS analyses were conducted using nitrogen as collision gas and involved independent measurements at the following four different collision-induced dissociation voltages: 20, 30, 40, and 50 eV. The quadrupole was operated throughout the measurement with the largest mass isolation window, from m/z 50 to 1500. This mass range was automatically activated by the operating software of the instrument when the precursor m/z and the isolation width are experimentally set to 200 and 0 Da, respectively. Mass fragments were scanned as in the single MS mode. Mass calibration was performed using sodium formate (50 ml of isopropanol, 200 l of formic acid, and 1 ml of 1 M NaOH in water). Data files were calibrated postrun on the average spectrum from a given time segment, using the Bruker high-precision calibration algorithm.
Deconvolution of compound-specific MS/MS MS/MS assembly was achieved via correlational analysis between MS1 and MS/MS mass signals for low and high collision energies and newly implemented rules. The correlation analysis for precursor-toproduct assignment for was implemented using an R script and rules were implemented using a C# script (https://github. com/MPI-DL/ indiscriminant-MS-MS-assembly-pipeline). To reduce false-positive errors resulting from spurious correlations from background noise due to the fact that some m/z features are only detected in a few samples, we applied "fill peaks" function of R package XCMS (use for background noise correction) to replace "NA" (not detected peak) intensities. When the fill peaks function is used, there still were many "0" intensity values in the dataset that affect the calculation of correlations; we then compared data processing results obtained with and without the fill peaks function and calculated a background noise value from the average correction estimates, and these 0 intensity values were then replaced with the calculated background value.