dataesr/scientific-paragraphs-categorization huggingface dataset:
8S ribosomal RNA gene; complete sequence; internal transcribed spacer 2' for ITS, and 'matK; trnK; maturase K; trnK gene, intron; and maturase K matK gene' for matK; ii) a list of 285 generic (accepted) names that were sampled in the high-throughput-sequencing dataset; and iii) similarity searches between sequences using MEGABLAST  (Camacho et al. , 2009) , retaining a maximum of 200 hits per search. After removing duplicated sequences and obvious contaminants (defined as sequences with misplaced positions given current taxonomic views), we retained sequences of nrITS and plastid matK for 2,060 species, of which 64% had sequences produced from the same voucher specimen (Table  S2 ).
High-throughput and Sanger sequencing data analyses Illumina libraries were quality-assessed with FastQC software v. 0. 11 (available at https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Paired-end reads were qualityfiltered and adapter-trimmed using TrimGalore! V. 0. 6. 4 available at (https://github. com/FelixKrueger/TrimGalore), using the following parameters: i) -q 30 (minimum Phred score), ii) -length 20 (minimum read length), iii) retaining read pairs that passed the quality thresholds. In-silico retrieval of the Angiosperms353 coding loci was conducted using the pipeline HybPiper v. 2. 1. 6  (Johnson et al. , 2016)  and the same parameters and software described in Pérez-Escobar et al. (2021a; also see Methods S1). Next, low-copy nuclear and Sanger markers were aligned using the software MAFFT v.
fr/packages/pack@newick-utils@1. 6). To visualise the proportion of gene-tree quartets that agreed with the species tree, we produced quartet support pie charts for every branch represented in the species' tree, using the full annotation produced by ASTRAL-III. We also visualized the proportion of gene trees in agreement with each species' tree bipartition using Phyparts v. 1. 0, by labelling as informative any gene-tree bipartition with a LBP > 20%  (Smith et al. , 2015). Following the overall topological congruence amongst the ITS and matK datasets and.
Molecular clock dating analyses and species-level phylogeny assembly Absolute age estimation analysis of orchid phylogenies was conducted in two stages, and by using a Bayesian framework implemented in BEAST v. 2. 6  (Bouckaert et al. , 2019) , as follows: (i) The backbone was inferred by subsampling the Angiosperms353 low copy nuclear gene alignments using SortaDate v. 1. 0  (Smith et al. , 2018). Here, we selected the top 25 low-copy nuclear gene alignments with the lowest root-to-tip variance coefficient (i. e. , clock-likeness). This selection ensured the representation of the entire generic diversity as sampled by the ML high-throughput dataset (i. e. , 285 genera; see Methods S1, and Table  S3 , S4). This data subset was imported in   S4. Finally, the support for the backbone ultrametric trees derived from BEAST was assessed by first computing a maximum credibility consensus tree from the Markov chain Monte Carlo (MCMC) posterior trees using TreeAnnotator v.
(ii) The species-level-ultrametric phylogeny of Orchidaceae was produced by inputting the ITS and matK alignments as unlinked partitions in BEAUTi v. 2. 6  (Bouckaert et al. , 2019) , using the same priors described in (i). Furthermore, the ML consensus phylogram produced in RaxML v. 8. 0 from the ITS-matK supermatrix was used as a starting tree. (iii) To account for phylogenetic uncertainty, we relied on constructing multiple ultrametric species trees through a novel pipeline (Fig. 2 ) as opposed to using a single consensus maximum clade credibility tree. We first randomly sampled 10 posterior trees derived from the BEAST analyses conducted on the genus-level Angiosperms353 and the ITS-matK Sanger species-level datasets. Then, for each genus represented in the Angiosperms353 chronograms, we pruned its counterpart clade sampled on the species-level Sanger chronogram and proceeded to graft it onto the corresponding stem of the Angiosperms353 chronogram.
Spatial analysis of species diversity and speciation rates We queried 29,524 accepted orchid species names obtained from the WCVP through the Our approach focuses on speciation dynamics, as inferred from tip λ rates. This metric represents contemporary rates of speciation for a given lineage and is less prone to bias than net diversification rates (Title & Rabosky, 2019). To obtain tip λ rates, we fitted a timedependent model using BAMM v. 2. 5. 0  (Rabosky et al. , 2013). This was informed by sampling fractions, contrasting accepted species number per genus from the WCVP database with the number of species sampled in our phylogenetic analyses. Here, 54% of the genera sampled attained a species representativeness of 10-50%, with 21% genera including sequences of > 50% of the known species diversity, and 25% sequences representing < 10% of the known species diversity.
A Central American hotspot of orchid speciation In total, 795,735 records were downloaded from the GBIF and RAINBIO repositories. After rigorously filtering out duplicate records and inaccurate distributions sensu the WCVP database (see Methods), 495,755 accessions were retained. Analyses of political country species richness indicated that Ecuador, Colombia, and Papua New Guinea are the top three countries in terms of species-richness. Notably, seven out of ten most species-rich countries are located in the Neotropics. Analyses based on the botanical country species richness (as inferred from the WCVP) yielded similar results (Fig. 5a  [inset], Fig. S10 , Table  S6 ). An analysis of species richness per grid cell derived from the curated GBIF-RAINBIO dataset, showed that Central America (especially Costa Rica) and the northern Andean region (particularly Ecuador and Colombia) have the highest levels of species richness.
These geographical patterns of species richness patterns are thus in agreement with the species richness distributions independently obtained through the WCVP database and support findings of studies conducted at the family level  (Vitt et al. , 2023)  and in the Orchidoideae  (Thompson et al. , 2023). Geographical speciation (λ) patterns in orchids, as informed from tip λ rates, did not always coincide with the areas of highest current species richness. This discrepancy supports, in some cases, the findings of a recent study on global plant diversification rates and species   S7 ). Notably, when considering minimum and maximum values of speciation rates, virtually all ecoregions assessed here presented one-or two-fold variation in their rate (Fig. 5c , inset). Unlike the observed congruence between speciation patterns and species richness throughout most of the Neotropical regions, our study also revealed that southern Australia recovered lower λ tip rates (Fig.
We thank 3 anonymous reviewers and the editor for valuable feedback which helped us improve this paper. This work was funded by grants from the  Calleva Foundation to the Plant and Fungal Trees of Life Project (PAFTOL)  at the  Royal Botanic Gardens ,  Kew. OAPE acknowledges support from the  Sainsbury Orchid fellowship  at the  Royal Botanic Gardens Kew  and the  Swiss Orchid Foundation. The  Comisión Institucional de Biodiversidad of University of Costa Rica  issued the permit for access to genetic resources under the project  B8257. KN acknowledges the  Genomics for Australian Plants consortium  funded by  Bioplatforms Australia  (enabled by  NCRIS) , the  Ian Potter Foundation ,  Royal Botanic Gardens Foundation (Victoria) ,  Royal Botanic Gardens Victoria , the  Royal Botanic Gardens  and  Domain Trust , the  Council of Heads of Australasian Herbaria ,  CSIRO ,  Centre for Australian National Biodiversity Research  and the  Department of Biodiversity, Conservation and Attractions, Western Australia.
recovered the subtribe Bletiinae as paraphyletic (i. e. , Bletia and Chysis as successive sister to Ponerinae, Laeliinae and Pleurothallidinae), a relationship recovered with low support by van den Berg et al. (2005) and Górniak et al. (2010). Unfortunately, voucher information for the data of Zhang et al. (2023) has not been made publicly available (at the time of writing), which limits the extent to which these phylogenomic discrepancies can be evaluated (PRJNA923320 accession code, queried on 25 August 2023 in https://www. ncbi. nlm. nih. gov/, returned zero results). Our results unambiguously place in the orchid tree of life the monospecific genus Cooktownia D. L. Jones (Orchidoideae) and the small genus Claderia (two species; Epidendroideae). These genera are distributed from the western Indian Ocean to the western Pacific and from the south-western Pacific to New Zealand.
we further filtered out records for which the distribution did not match with the species distribution per botanical country (level 3 of the World Geographical Scheme for Recording Plant Distributions; Brummitt, 2001) provided by the WCVP. The original record database sourced from GBIF is available at https://doi. org/10. 15468/dl. v2gwxv, and the filtered geographical records used in all downstream analyses will be available at (forthcoming FigShare link). Estimating net diversification rates (speciation [λ] minus extinction [µ]) in plant lineages GBIF (https://www. gbif. org/) and RAINBIO (https://gdauby. github. io/rainbio/index. html; Dauby et al. , 2016) databases. Our initial step involved accessing the GBIF database through the R-package SPOCC (available at https://github. com/ropensci/spocc).
It then produces a custom set of species-level trees that reflect branch length variation and support in deep (tribe, subtribe and genus) and recent (species) time as informed by the Angiosperms353 and Sanger datasets. A detailed description of the pipeline is provided in the supplementary Methods S1. Figure 3. Phylogenetic relationships of Orchidaceae. A) Consensus tree-network inferred from 200 bootstrap replicate maximum likelihood (ML) trees derived from the concatenated alignment of 339 low-copy nuclear genes. Circles at nodes represent bipartitions present in > 75% of the bootstrap ML trees (see Figures S1-S6 for a detailed on the consensus treenetwork). Non monophyletic groups are highlighted in bold and grey. Samples sequenced from typological material are highlighted in bold and pink.
B) A palaeoclimatic and tectonic plate reconstruction at 90-80 Ma from Burgener et al. , 2023 showing the possible ancestral range of the orchid MRCA as estimated by Givnish et al. , 2016 and this study. A detailed account of the first and second most likely ancestral areas estimated to the tribe level is provided on TableS5(complete results including annotated trees with the most likely ancestral area at nodes and alternative ancestral areas, likelihoods and probabilities are available at 10. 6084/m9. figshare. 22245940). Geography of speciation and species diversity of Orchidaceae. A) Global patterns of species richness per grid-cell (100 ×100 Km), calculated from a curated database of geographical distribution records; reddish colours indicate higher numbers of species per grid-cell whereas bluish colours indicate lower numbers of species per grid-cell.
Gene expression levels (TPM) correlate well between replicates, with correlation coefficients ranging from 0. 92 to 0. 99, with an average of 0. 96 (Pearson's R), indicating high reproducibility in our data (Figures  S1A  and S1B ). We previously generated a species-wide pan-transcriptomic dataset involving 969 natural isolates from the same strain collection. 13  To evaluate the general gene expression behavior across the diallel hybrid panel against the previous populationlevel data, we calculated the mean expression level (i. e. , abundance) and the mean absolute deviation across samples (i. e. , dispersion) for each of the 6,186 genes in the final dataset. Both metrics showed good agreement between the diallel panel and the natural population, with a correlation coefficient of 0. 79 for abundance and 0.
Highly non-additive genes (A) Neighbor-joining tree based on the genetic diversity of 1,011 yeast isolates. 44  Twenty-six parental isolates selected for the diallel panel are highlighted in red. Representative clades are annotated as in Peter et al. 44  See Table  S1  for detailed origins of the parental isolates. (B) The ecological origin and distribution of the selected 26 parental isolates. (C) Pairwise nucleotide diversity among the parental isolates. Mean divergence is indicated by the red dashed line. (D) Schematics of the diallel crossing design. Homozygous parental diploids are highlighted in red. See Table  S2  for detailed information about the generated hybrids. (E) Schematics of the data acquisition strategy. For each hybrid, the transcript abundance (TPM) are measured for each annotated ORF, as well as allele-specific read counts across all discriminating sites in a given hybrid.
Parental allele counts were extracted from the coverage data at the same sites. (F) Final data metrics and numbers of accessory and core genes included for the subsequent analyses. See Table  S3  for detailed annotations. A D E F G I H J K L M C B Figure 2. Broad-and narrow-sense heritability for genome-wide gene expression traits (A) Distributions of broad-(gray) and narrow-sense heritability (white) estimates based on the diallel hybrid panel for 5,087 gene expression traits. Transcript abundance in white and gray, respectively. See Table  S4  for all of the heritability estimates. (B) Ternary plot showing the percentage of phenotypic variance controlled by additive, non-additive, and residual variance components. Dashed line marks the 50% threshold for each dimension. (C) GSEA for additive, non-additive, and residual variance components, using standard GO BP terms.
13  These introgressed genes are found mainly in specific subpopulations such as the Alpechin clade. 44  In the diallel hybrid panel, two Alpechin isolates were included as parental lines (Table  S1 ), which offered a unique opportunity to examine and compare the regulatory variation among alleles within (S. cerevisiae versus S. cerevisiae) and between species (S. cerevisiae versus S. paradoxus). In total, the regulatory variation for 219 unique introgressed genes were included in our dataset, corresponding to 3,192 gene-trio combinations (between species alleles) (1002 Yeast Genome datafile S2; see STAR Methods). Among the 219 introgressed genes, 202 also showed discriminating sites among their S. cerevisiae counterparts, corresponding to 11,871 gene-trio combinations (within species alleles) (1002 Yeast Genome datafile S2; see STAR Methods).
modest, with several previously defined clades  44  that were not represented here. In particular, the underrepresentation of certain clades could have an impact on the trends we observed, specifically on the regulatory patterns of introgressed genes because our observations were based mainly on two parental lines from the same clade (i. e. , Alpechin isolates). Another limitation that reduced our final sample size was allele imbalance that affected one or multiple chromosomes in some of the hybrids. In our final dataset, only 179 hybrids were included for ASE analyses due to this allele imbalance, possibly reflecting some genome instability events (loss of heterozygosity or aneuploidy), inadvertently selected during the en masse crossing procedure. Finally, because our data were obtained using batch mid-log phase cultures in complete media, our conclusions may be biased when examining other culture conditions or cell states.
ACKNOWLEDGMENTS This work was supported by  NIH  grant no. R01  ( GM147040-01 ),  European Research Council (ERC)   Consolidator  grant no. 772505  (to J. S. ), and  French National Research Agency (ANR)   Young Investigator  grant no. ANR-22-CE12-0023-01  (to J. H. ). It is also part of the  Integrative Molecular and Cellular   Biology (IMCBio) , as part of the  Interdisciplinary Thematic Institutes (ITI) 2021 -to-2028 program of the  University of Strasbourg ,  CNRS , and  INSERM , supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ). J. S. is a Fellow of the  University of Strasbourg Institute for Advanced Study  and a member of the  Institut Universitaire de France.
Yet, we have previously demonstrated that the VGCCcomponent of the Ca 2+ influx decreases with the distance from the soma and that the Na v 1. 2-mediated Ca 2+ component is dominant in the distal part of the AIS  [24]. Thus, we hypothesised that the change in the AP waveform had negligible effect on the VGCC-component of the Ca 2+ influx and that the Ca 2+ transient increase produced by AaH-II delivery was due to an increase in the Na v 1. 2-mediated Ca 2+ component. To assess this hypothesis, we reanalysed the Ca 2+ imaging dataset distinguishing from the distal AIS (30-40 µm from the soma) and proximal AIS (5-15 µm from the soma). In the example of Fig. 5A , AaH-II local delivery caused an increase in Ca 2+ influx in the distal AIS, but not in the proximal AIS. As shown in Fig. 5B , in control condition, the peak of the distal Ca 2+ current occurred before the peak of the proximal Ca 2+ current.
This work was supported by the  Agence Nationale de la Recherche  through three grants ( ANR-21-CE18-0042 -Nav12RESCUE ;  Labex Ion Channels Science   and Therapeutics: program  number  ANR-11-LABX-0015  and  National Infrastructure France   Life Imaging "Noeud Grenoblois ") and by the  Fédération pour la Recherche sur le Cerveau  ( FRC -Grant Espoir en tête ,  Rotary France ). We are indebted to  Fondation Leducq  and the  FEDER  resources for financing the automated patch-clamp setup. Conflict of interest statement: M. D. W. is a founder of  Smartox Biotechnology. All other authors declare.
Establishing a machine learning algorithm to classify binary interactions To provide a universal and unbiased approach to score quantitative PPI data from various assays, we aimed to implement a support vector machine (SVM) learning algorithm, which is commonly used for binary classifications  (Chang & Lin, 2011). For datasets that contain a high degree of mislabeling, it has been shown that a multi-adaptive sampling approach can be used to iteratively update the labeling class (positive or negative) of the training data and thereby improve the performance of the SVM learning algorithm  (Yang et al, 2017). We rationalized that this approach could be applied to analyze quantitative PPI datasets, in which frequently, a positive reference set interaction is labeled positive, but will not score positive in a certain assay or tagging configuration and should therefore be relabeled as negative (for this specific assay or tagging orientation) during the training of the SVM.
Classifying binary interactions within multiprotein complexes To further generalize the overall applicability of the maSVM algorithm to more complex PPI datasets, we screened proteins that are part of well-characterized multiprotein complexes where the same protein is tested against a number of interacting and non-interacting proteins using two quantitative assays: LuTHy and mN2H. To this end, we selected three human complexes based on the following criteria: 1) they consist of at least four subunits; 2) at least one 3D structure is available in PDB  (Berman et al, 2000) ; and 3) at least 80% of cloned open reading frames (ORFs) encoding the reported subunits are available in the human ORFeome 8. 1 collection  (Yang et al, 2011). This resulted in a list of 24 distinct protein complexes (Supplementary Table  2 ), among which three structurally diverse candidates with well-characterized biological functions were selected: 1) the LAMTOR complex, also termed "Ragulator" complex  (Araujo et al, 2017) , which regulates MAP kinases and mTOR activity and consists of seven subunits (LAMTOR1, LAMTOR2, LAMTOR3, LAMTOR4, LAMTOR5, RRAGA and RRAGC); 2) the MIS12 complex that connects the kinetochore to microtubules  (Petrovic et al, 2016) , and is made of five subunits (CENPC1, DSN1, MIS12, NSL1 and PMF1); and 3) the BRISC complex, a large deubiquitinating machinery  (Rabl et al, 2019)  consisting of five proteins (ABRAXAS2, BABAM1, BABAM2, BRCC3 and SHMT2) (Figure  2A ).
Therefore, we treated all inter-complex pairs as negative controls, similar to protein pairs from a random reference set (e. g. hsRRS-v2). We observed that each individual LuTHy and mN2H fusion construct showed a broad distribution of interaction scores for intra-and inter-complex pairs, with a high variability between individual constructs (Figure  EV5A-H ). To compensate for different background signals between constructs in the downstream analysis, we median-normalized outputs from all constructs and performed a robust scaler normalization  (Pedregosa et al, 2011)  for constructs with a higher interquartile range (IQR) than the IQR of the entire dataset (see methods for details; Figure  EV5A-H ). We then trained the maSVM algorithm with the normalized LuTHy-BRET, LuTHy-LuC and mN2H data, iteratively sampling from the intra-complex pairs as positive reference interactions and the inter-complex protein pairs as negative reference interactions.
Identifying high-confidence PPI targets for SARS-CoV-2 To apply the maSVM algorithm to an independent test-case scenario, we experimentally assessed all possible pairwise combinations between SARS-CoV-2 proteins using LuTHy (Figure  3A ). Since SARS-CoV-2 interactions potentially include homo-and heterodimer, as well as subunit-subunit interactions of larger multiprotein complexes, we trained the maSVM algorithm to generate models representing both the hsPRS-v2/hsRRS-v2 and the multiprotein complex protein pairs (Figure  3B ). As described above, we median-normalized all constructs of the training set and the SARS-CoV-2 test set and performed a robust scaler normalization for constructs with a higher IQR than the IQR of the entire dataset (see methods; Figure  EV5, 6 ). Next, we used the normalized training set to train 50 maSVM models for LuTHy-BRET and LuTHy-LuC, in order to predict the classification probabilities of the 350 SARS-CoV-2 protein pairs in the test set (2,548 configurations, Figure  3C-F ).
In total, 26, 56 and 143 protein pairs were classified by the algorithm to interact with >95%, >75% or >50% probability, respectively (Figure  3G, H,  Figure  EV7A , Supplementary Table  3 ). Among the high-confidence PPIs (>95% interaction probability), we could confirm the structurally resolved interactions between NSP8 and NSP12 (PDB: 6YYT, 7EIZ), NSP10 and NSP16 (PDB: 6WVN, 6W4H) (Rosas-Lemus et al, 2020), NSP10 and NSP14 (PDB: 7DIY, 7EIZ)  (Lin et al, 2021; Yan et al, 2021) ; and the homodimerization of NSP8 (PDB: 7EIZ)  (Yan et al, 2021) , ORF3a (PDB: 6XDC) (Kern et al, 2021), the nucleocapsid protein (N) (PDB: 6VYO), and the well-established homodimerization of the spike protein (S) (PDB: 6VYB for example)  (Walls et al, 2020). We also detected the NSP7 homodimerization, which was previously described in two independent studies  (Yin et al, 2020; Wilamowski et al, 2021).
DISCUSSION Targeting PPIs offers great opportunities to tackle various diseases, but it remains a great challenge to reliably identify and modulate protein complexes. To improve comparisons between binary PPI datasets generated in different experiments and laboratories and confidently prioritize potential targets for PPI drug discovery, we have utilized a maSVM learning algorithm  (Chang & Lin, 2011; Yang et al, 2017)  to coherently score interactions of quantitative PPI datasets. Traditional approaches involve i) selecting a cutoff of maximal specificity, i. e. at which none of the random pairs used as negative controls are scored positive  (Choi et al, 2019) , ii) ROC analyses with selected false-positive rates of ~5%  (Yao et al, 2020) , ~3%  (Trepte et al, 2015) , or ~1-2%  (Trepte et al, 2018; Cassonnet et al, 2011) , and iii) Gaussian distributions  (Taipale et al, 2012).