dataesr/scientific-paragraphs-categorization huggingface dataset:
8 μm) columns (Waters). The mobile phase solvents were water and acetonitrile, both of which contained 0. 1% (volume/volume) formic acid. Data-independent, ion mobility-enabled, highdefinition mass spectra and tandem mass spectra were acquired in the positive ion mode. [82] [83] [84] [85]  Data acquisition was controlled using MassLynx software (version 4. 1) and tryptic peptide identification and relative quantification using a label-free approach were performed using Progenesis QI for Proteomics software (version 4. 0, Waters). 86  Data were searched against the E. lenta protein database to identify tryptic peptides. 87  Recombinant reductase production and purification Hexa-histidine-tagged reductase constructs were expressed in E. coli Rosetta cells using either the pET28a or pMCSG53 expression vectors, as previously described.
Reductase phylogenetics Genomes for E. lenta, S. wadworthensis, and H. filiformis were downloaded using ncbi-genomedownload (https://github. com/kblin/ncbi-genome-download) (see Table  S12  for genome accessions). We then used anvi'o v7. 1 to convert genome FASTA files into contigs databases (https://anvio. org/m/contigs-db) using the contigs workflow (https://doi. org/10. 1186/s13059-020-02195-w), during which Prodigal v2. 6. 3 identified open reading frames (contigs workflow). 58, 89  Next, we used the EcoPhylo workflow implemented in anvi'o (https://anvio. org/m/ecophylo) in 'tree-mode' to recover reductase genes in genomes using the Pfam model PF00890 and to explain their phylogeny. Briefly, the EcoPhylo workflow (1) used the program hmmsearch in HMMER v3. 3. 2  90  to identify reductases, (2) removed hits that had less than 80% model coverage to minimize the likelihood of false positives due to partial hits, (3) dereplicated resulting sequences using MMseqs2 13.
Reductase active site conservation analyses AlphaFold models of CirA, CirC, CirD, and CrdD were download from Uniprot. 94  Active site amino acids were identified by independently superimposing N-and C-terminal domains of AlphaFold models to the substrate-bound urocanate reductase crystal structure (PDB code 6T87) using PyMOL v2. 5. 1 (http://www. pymol. org/pymol) 'align'. 46  Next, sequences in the monophyletic clade surrounding the experimentally validated sequences were subsetted from the reductase multiple sequence alignment using the program anvi-script-reformat-fasta and alignment positions were sliced in Jalview v2. 11. 2. 5). 95  Finally, the extent of conservancy among the active site-associated residues were visualized with WebLogo 3 (https://weblogo. threeplusone. com/create. cgi).
Acknowledgments We thank  Huaiying Lin  &  Nicholas P. Dylla  for assistance with data analyses and  Laurie Comstock  for helpful feedback. Research reported in this publication was supported by funding from the  National Institutes of Health  ( T32DK007074  to M. A. O. ,  1S10OD020062-01  to A. T. I. , and  K22AI144031  &  R35GM146969  to S. H. L) and the  Searle Scholars Program  (to S. H. L).
See Table S4 for original data. (E) Identified metabolites in mouse feces pre-and post-antibiotic treatment. Area under the curve (AUC) values are presented relative to each metabolite's normalized average (normalized to 1) within the dataset. See Table S5 for original data. *p < 0. 05, **p < 0. 01, *** p < 0. 001. Two-way ANOVA, multiple test vs media alone (A), multiple unpaired t tests (D, E).
3B-D , Table  S9 ), likely reflecting a shared response to environmental drivers. However, the resolution and exact timing of expansion varied among the species. In particular, the recovered demographic history of Q. petraea and F. sylvatica goes back much further in time (i. e. ~32 and ~17 Mya, respectively) than for the other species. This partly reflects their longer generation times, but the depth of the Q. petraea genealogy and its relatively large Ne are also likely a consequence of continuous hybridization with its sister species, Q. robur and Q. pubescens  (22). Notably, the two pine species, P. pinaster and P. sylvestris, showed patterns very distinct from each other, despite similar sampling, mutation rates and generation times, reflecting their different ecological requirements, levels of population   S8 ) or with fastsimcoal2 (diamonds, 2epoch model and one-sample-per-population dataset, see Tables  S6-S7 ).
Acknowledgments: The computations were enabled by resources in project  SNIC 2017/7-328 ,  SNIC 2020/ 15-107  and  SNIC 2021/5-540  provided by the  Swedish National Infrastructure for Computing (SNIC)  at UPPMAX, partially funded by the  Swedish Research Council  through grant agreement no. 2018-05973. CSC -IT Center for Science, Finland ,  Genotoul Bioinformatics platform Toulouse Occitanie  and the  Genetic Diversity Centre at ETH Zurich, Switzerland , are acknowledged for generous computational and storage resources, and GBFOR, INRAE, Forest Genetics and Biomass Facility and  Natural Resources Institute Finland (LUKE)  for sample collection. Funding: European Union's  Horizon 2020 Research and Innovation Programme  grant agreement no. 676876   Academy of Finland  grant nr. 287431  (TP)  Swiss Secretariat for Education, Research and Innovation (SERI)  contract no.
ddRADseq data processing Raw reads were visually explored with FastQC  (Andrews, 2010)  to ensure data quality and absence of adapters. Then, reads for each individual were demultiplexed and processed with ipyrad v. 0. 9. 84  (Eaton and Overcast, 2020) , discarding sites with Phred score < 33, and reads with more than three missing sites. Consensus sequences with less than six reads or more than two alleles were also discarded. We also filtered by maximum read depth as well as by maximum sharing of heterozygous sites to detect and filter out potential paralogous loci. We created three different datasets: one to explore population structure analyses and gene flow containing all individuals of Cerastes together with E. omanensis (as outgroup); a second to explore more in-depth population structure analyses at the genus level containing only samples of the genus Cerastes; and a third dataset to explore intraspecific relationships with only C.
cerastes samples. Then, for each dataset three independent ipyrad runs were performed using different clustering parameters for both filtered and consensus sequences (step 3 and 6 in ipyrad, respectively): 0. 85, 0. 89 and 0. 92, selecting the clustering that maximized the number of samples without significantly reducing the number of loci retained (0. 85 with the outgroup and 0. 89 for both datasets without the outgroup). Then, following recommendations from O'Leary et al. (  2018 ), we used Radiator  (Gosselin et al. , 2017) , Plink2  (Chang et al. , 2015)  and VCFr  (Knaus and Grünwald, 2017)  implemented in a custom script (https://g ithub. com/BernatBurriel/Post_processing_filtering) from Burriel-Carranza et al. (2023a) to filter iteratively and alternatively low-quality samples and loci.
For all datasets, values of missing data allowance ranged from 98 % to 60 % of missing genotype call rate and missing data per individual, decreasing 2 % between iterations. Subsequently, we applied a hard threshold of missing genotype call rate of 60 %. We removed non-biallelic SNPs, applied a minor allele frequency filter (maf < 0. 05) and removed monomorphic sites. For the different analyses, we retrieved loci, haplotype and SNP data (with or without an outgroup, depending on the analyses' requirements): loci and haplotype datasets were generated with a second round of ipyrad after removing all individuals that did not pass the previously explained iterative filters and retaining only loci that were at least in 60 % of all individuals. The SNP dataset (with the outgroup) was used for the introgression analysis, where all previously filtered SNPs were kept.
Population structure analyses We performed a Principal Component Analysis (PCA) with the unlinked SNP dataset (a total of 2,856 uSNPS) containing only Cerastes samples using Plink v1. 9  (Chang et al. , 2015). Then, we used ADMIX-TURE  (Alexander et al. , 2009)  to analyze the same dataset to detect population ancestry from K = 2 to K = 5. A total of 20 replicates for each K were calculated, selecting the best K value after 20 cross validations. We also used fineRADstructure  (Malinsky et al. , 2018)  on the haplotype dataset to explore population structure within the three species, setting the minimum number of samples per loci to four. This analysis is optimal to detect different levels of structure within and between populations and to explore co-ancestry patterns for non-model organisms  (Malinsky et al.
, 2018). We also performed a Mantel test with the Cerastes cerastes samples (SNP dataset containing only C. cerastes samples) to explore if geographic distances explained genetic divergence within this species (isolation-by-distance, IBD). Lastly, we calculated genetic diversity and pairwise F st values with GenoDive v. 3. 0  (Meirmans, 2020)  with 10,000 permutations at the species and subspecies level, using the SNP dataset. Visualization of results from these analyses was performed with R v. 3. 6. 3 (R Core Team, 2021) and the R package ggplot2  (Wickham, 2016).
COI sequencing and phylogenetic inference We sequenced a COI fragment for a total of 24 individuals for the three species within the genus Cerastes (information regarding the specimens used can be found in Table  S1 ). Primers and PCR amplification conditions were implemented according to  Nagy et al. (2012). PCR products were purified and Sanger sequenced by Genewiz (UK). Then, we performed a multiple sequence alignment with MAFFT  (Katoh and Standley, 2013)  for the 24 samples together with two previously sequenced samples (13693 and 12953, accession number ON943580 and ON943571, respectively, from Velo-Antón et al. , 2022) as well as two outgroups (two individuals of Echis carinatus, accession numbers MG699965 and MG699966, respectively). We used BEAST2 v. 2. 6. 4  (Bouckaert et al. , 2019)  to reconstruct a Bayesian Inference (BI) timecalibrated tree.
Phylogenomic reconstructions We reconstructed the phylogenomic history of the three species of Cerastes using three different phylogenetic methods. First, with the concatenated loci dataset (531,260 bp, including the outgroup) we reconstructed a Maximum Likelihood (ML) tree. For that, we used RaxML-NG  (Kozlov et al. , 2019) , with the GTR + I + G model with a total of 100 starting trees (50 random and 50 parsimony) and performing 1,000 bootstraps for branch support. Later, we filtered the loci dataset to maintain only loci without missing data, obtaining a total of 627 loci and 40,831 bp (including the outgroup). With those, we used BEAST2 v. 2. 6. 4  (Bouckaert et al. , 2019)  to obtain a time-calibrated tree. We used the same calibration point as described for the mitochondrial phylogeny.
e. , effective sample size > 200) was confirmed with Tracer v. 1. 7. 2  (Rambaut et al. , 2018). Posterior distributions were then combined with LogCombiner v. 2. 6. 3 (BEAST2 v. 2. 6. 4;  Bouckaert et al. , 2019)  discarding 10 % of the posterior trees as burnin and the resulting trees were visualized with Densi-Tree v2. 2. 6  (Bouckaert, 2010). Alternative prior combinations produced highly concordant results. We produced a second species tree under the same conditions but including two specimens of C. c. hoofieni to the same dataset (obtaining 6,379 SNPs) to study possible different evolutionary histories of the two C. cerastes subspecies.
Niche overlap analyses In order to investigate the potential role of environmental adaptation in the diversification of the genus, we performed pairwise niche overlap tests between the major clades within the genus, resulting from our ddRADseq inferences, considering two geographic scales and two types of variables. The global scale analyses included a total of 990 records at a spatial resolution of 5 arc minutes (~10x10km; 561C. cerastes, 213C. gasperettii and 216C. vipera) spanning across the whole distribution of the three species (Fig. S2 ), and two distinct sets of low correlated variables (R < 0. 72; Table  S2 ): (1) a climatic dataset, which included seven precipitation-and temperature-related variables downloaded from WorldClim v. 2. 1 (https://www. worldclim. org), and (2) a landcover dataset, which comprised three percentages of landcover variables, which resulted from calculating the number of 10 arc seconds resolution pixels of three landcover categories representative of the Saharan and Arabian deserts (from the ESA GlobCover ver 2.
2,  Bicheron et al. , 2008)  in 5 arc minutes resolution pixels, using the function aggregate from ArcGIS v. 10. 5  (ESRI, 2016). Occurrence data for the global scale analysis were gathered from distinct sources, including fieldwork conducted by the authors for the period 2001-2021 (n = 241), bibliographic references (n = 180; e. g. ,  Werner et al. , 1999; Brito et al. , 2011; García-Cardenete et al. , 2017) , museum collections (n = 106; e. g. , NHM-London, MNHN-Paris) and the Global Biodiversity Information Facility, GBIF (n = 463; https://www. gbif. org, 2021, https://doi. org/10. 15468/dl. p6x8td). Due to the underrepresentation of the landcover heterogeneity for the Saharan and Arabian deserts as available in the ESA GlobCover dataset, we also approached niche overlap for the Saharan species at a regional scale of the Atlantic Sahara (i.
e. , Mauritania and Western Sahara), where a detailed landcover map was recently derived  (Campos and Brito, 2018). Consequently, for this regional scale analysis, we considered a subset of 170 records (118 for C. cerastes and 52 for C. vipera), available at a higher accuracy (i. e. , 30 arc seconds, ~1 × 1 km), since 69 % were gathered during fieldwork campaigns (the remaining from bibliography, 19 % and GBIF, 12 %). In addition, we used seven percentages of landcover variables (see Table  S2 ), which resulted from using the function aggregate from ArcGIS over seven landcover categories or groups of categories with 1 arc second resolution pixel (see Table  S2 ) to new rasters with 30 arc seconds resolution pixel. For the three approaches (global-climate, global-landcover and regional-landcover), occurrence data were used to extract the corresponding variable values and then run three distinct PCA.
Following a three-dimensional hypervolume approach, the first three components of each PCA were used to describe the environmental niche of each major clade (i. e. , total volume and unique part, intersection and union with the other species' volume) as obtained from ddRAD data analyses and perform pairwise niche-overlap tests between them (see Martínez-Freiría et al. , 2020 for a similar procedure). Analyses were performed in R with the 'hypervolume' package  (Blonder et al. , 2014) , using a Silverman bandwidth estimator and a set of 999 random points to sample the kernel density to build hypervolumes. To quantify environmental niche overlap, the Sørenson index (K) and the Overlap Index (OI) were used (see  Blonder et al. , 2014; Simó-Riudalbas et al. , 2018). These statistics quantify predicted niche similarity, and they both range from 0 (no overlap) to 1 (identical niche).
Introgression analyses To study ancestral introgression, we ran the ABBA-BABA test implemented in Dsuite  (Malinsky et al. , 2021)  with the SNP dataset (32,254 SNPs), including the outgroup, using the genomic tree (see results). To do so, we split our samples per species (and subspecies when possible) and calculated the D-statistics using the genomic topology in which Cerastes gasperetti is sister to all other congeneric Cerastes species. Patterson's D-statistic uses asymmetry in gene tree topologies to quantify introgression between either of two lineages which share a common ancestor (P1 and P2) and a third lineage (P3) that diverged from the common ancestor of P1 and P2. A fourth individual (P4) is needed to inform the ancestral state for each comparison  (Malinsky et al. , 2021). In here, we set E.
Population structure analyses Population genomic results of the unlinked SNP dataset (2,856 uSNPs) showed an unexpected result (Fig. 2 ). PC1 split C. gasperettii from both C. cerastes and C. vipera individuals whilst PC2 separated C. cerastes from C. vipera (Fig. 2A ). The C. c. hoofieni individuals clustered together with the rest of African C. cerastes (Fig. 2A ). PC3 sorted C. cerastes following their geographical locations (northwestern Africa, northeastern Africa and Arabia). The best supported scenario in Admixture recovered the three different species for K = 3 (Fig. 2B. For K = 4, the samples of C. c. hoofieni formed a unique cluster, with the two geographically closer African individuals (92 and 12953; Table  S1 ) sharing a limited amount of co-ancestry (Fig. 2B ). FineRADstructure analyses showed four different clusters representing the different species and with C.
49 < K < 0. 81; 0. 70 < OI < 0. 84; Table  S5 ). Statistical tests were all significant (p < 0. 05), with the exception of the test of K index for C. cerastes vs C. vipera in the globallandcover dataset (Table  S5 ). Overall, niche overlap analyses show that the three species exhibit non-equivalent but highly overlapping niches.
Introgression analyses We used the filtered SNP dataset containing 32,265 SNPs that were present in at least 60 % of the samples to study introgression between the different clades with the genomic tree (i. e. , C. gasperettii being sister to the remaining Cerastes species). Both D-statistics and f4-ratio analyses showed a significant excess of shared alleles between C. vipera and C. c. cerastes individuals (Table  1  and Fig. 5 ). Moreover, f-branch analyses supported D-statistics and f4-ratio and suggested an introgression of around 10 % of the sampled sites between C. vipera and C. c. cerastes (Fig. 5 ). Ancestral introgression was not found between C. gasperettii and C. c. hoofieni, both occurring in Arabia (Table  1  and Fig. 5 ).
Insights into the systematics of Cerastes cerastes This study provides novel genetic and genomic information for the subspecies C. c. hoofieni. Moreover, our genome-wide data does not recognize C. c. mutila as an independent genetic unit, as it did not exhibit any genetic differences compared to other individuals of C. c. cerastes (Figs. 2-3 and Fig. S4 ). This finding further strengthens the previous discussion on the validity of C. c. mutila as a subspecies  (Martínez del Mármol et al. , 2019). Conversely, our relatively small dataset seems to support the recognition of C. c. hoofieni as a subspecies within C. cerastes (Figs. 2-3 and Fig. S4 ), with C. c. cerastes being paraphyletic, as we found isolation by distance to be affecting the genetic pattern of population structure for the species (Fig.
Acknowledgements This work was supported by grants  PGC2018-098290-B-I00  ( MCIU / AEI/ FEDER, UE) ,  Spain ,  PID2021-128901NB-I00  ( MCIN/AEI/ 10. 13039/501100011033  and by  ERDF ,  A way   of making Europe) ,  Spain , and grant  2021-SGR-00751  from the  Departament de Recerca i Universitats  from the  Generalitat de Catalunya, Spain  to SC. GM-R is supported by an  FPI  grant from the  Ministerio de Ciencia, Innovación y Universidades, Spain  ( PRE2019-088729 ), BB-C is supported by  FPU  grant from  Ministerio de Ciencia, Innovación y Universidades, Spain  ( FPU18/04742 ), AT is supported by  "la Caixa" doctoral fellowship   program  ( LCF/BQ/DR20/11790007 ), HT-C is supported by a " Juan de la Cierva   -Formación" postdoctoral fellowship  ( FJC2021-046832-I ) funded by  MCIN/AEI/10.
13039/501100011033  and by the  European Union NextGenerationEU/PRTR , GV-A was supported by the  FCT  ( CEECIND/00937/2018 ) and recently by a  Ramón y Cajal research  grant ( Ref. RYC-2019-026959-I/AEI/10. 13039/501100011033 ), J Š was supported by the  Czech Science Foundation (GA ČR)  under grant number  22-12757S  and by the  Charles University Research Centre  under grant number  204069  and FM-F and JCB are supported by  FCT -Fundação para a Ciencia e Tecnologia de Portugal  ( DL57/2016/CP1440/CT0010 ,  CEECINST/00014/2018/CP1512/CT0001 , respectively). We thank two anonymous reviewers for improving the manuscript.
Orchids constitute one of the most spectacular radiations of flowering plants. However, their geographical origin, historical spread across the globe, and hotspots of speciation remain uncertain due to the lack of a broad phylogenomic framework. We present a new Orchidaceae phylogeny based on high-throughput and Sanger sequencing datasets, covering all five subfamilies, 17/22 tribes, 40/49 subtribes, 285/736 genera, and ~7% (1,921) of the currently 29,524 accepted species. We then use it to infer geographic range evolution, diversity, and speciation patterns by adding curated geographical distribution data through the World Checklist of Vascular Plants. Orchid's most recent common ancestor is traced back to the Late Cretaceous in Laurasia. The modern Southeast Asian range of subfamily Apostasioideae is interpreted as relictual, matching the history of numerous clades that went extinct at higher latitudes following the global climate cooled during the Oligocene.
Our spatial information derives from herbarium-vouchered, georeferenced occurrence data, sourced from the Global Biodiversity Information Facility (GBIF; https://www. gbif. org) and the RAINBIO mega-database  (Dauby et al. , 2016). This dataset was subsequently vetted using the spatial distribution data of the World Checklist of Vascular Plants (WCVP; Govaerts et al. , 2021). Based on these datasets, we: i) infer the evolutionary and biogeographical history of the orchid family, ii) test the hypothesis of an Australasian origin for the most recent common ancestor of living orchids, and iii) revisit the hypothesis that Southeast Asia is the region with the highest current diversification of orchids.
Taxon sampling, DNA library preparation and sequencing Our phylogenomic framework was assembled using a high-throughput-sequencing dataset, complemented with Sanger data for single genes. The high-throughput-sequencing dataset focused on the genes targeted by the Angiosperms353 probe set  (Johnson et al. , 2019; Baker et al. , 2022)  and includes 448 orchid species, representing 285/736 genera, 40/49 subtribes, 17/22 tribes, and 5/5 subfamilies (in the classification of  Chase et al. , 2015). These data were generated by the Plant and Fungal Trees of Life Project  (Baker et al. , 2022)  at the Royal Botanic Gardens, Kew, and the Genomics for Australian Plants Consortium (https://www. genomicsforaustralianplants. com/). Nineteen monocot species were included as outgroup taxa, with Dioscorea caucasica (Dioscoreales) used as a rooting terminal, for a total of 467 species in the high-throughput-sequencing dataset.
Illumina sequencing reads were newly produced for 377 samples (vs Pérez-Escobar et al. , 2021) from expertly curated specimens housed in the Kew (K) and Australian National herbaria (CANB), including 23 species sampled from type material and silica-gel dried tissue (Table  S1 ). Detailed protocols for DNA extraction and total genomic DNA libraries are provided in Methods S1. The Sanger sequencing dataset was assembled through the SuperCRUNCH pipeline  (Portik et al. , 2020). This pipeline was executed using an initial set of 24,172 sequences of the nuclear ribosomal ITS and the plastid matK markers -two commonly sequenced, informative loci for Orchidaceae and other angiosperms  (Grace et al. , 2021)  -obtained from GenBank (https://www. ncbi. nlm. nih. gov/nuccore). The following parameters were used: i) search terms of 'ITS; ITS1; ITS2; internal transcribed spacer 1; partial sequence; 5.