dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant agreement No  645884. CABI is an international intergovernmental organisation, and we gratefully acknowledge the core financial support from our member countries (and lead agencies) including the  United Kingdom (Department for International Development), China (Chinese Ministry of Agriculture) ,  Australia (Australian Centre for International Agricultural Research) ,  Canada (Agriculture and Agri-Food Canada) ,  Netherlands  ( Directorate-General for International Cooperation ),and  Switzerland (Swiss Agency for Development and Cooperation ). See https ://www. cabi. org/about-cabi/who-we-work-with/ key-donor s/ for full details. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.
Sourcing organisms Cultivation and biomass production Determination of organism potential Extract production and exploration Rapid identification of new compounds ensuring mitigation against wasted resource on compound rediscovery Purification of characterised compounds Protection of investment and future use Figure 2. Overview of routes to discovery Source Microbial Pipeline Best Legal Frame Research Prototype Practice work Infrastructure partners EMBRC; MIRRI DSMZ; Ex situ -microbial domain In situ -targeted isolation OECD Intellectual CAB Biological Resource centres programmes from the marine (2007); ISO Property International (mBRC) environment and identification Standards Rights (IPR); (CABI) Nagoya Protocol Selected from holdings Baiting and biofilm techniques; Health & Flow cytometry selection safety MIRRI and DSMZ; Institutional EMBRC CABI Methods DSMZ; Biomass generated or DNA Cosmid libraries generated; Genomic Health & University extracted for sequenced genome analysis: selection of Standards safety of St bioactive microorganisms strains for growth Consortium Andrews (USTAN) ELIXIR and EU- USTAN; Biosynthetic gene cluster Cosmids containing them were Nagoya OPENSCREEN analysis Larger clusters incorporated into strains for Protocol complementary Bac library heterologous expression approach EU- DSMZ Fermentation in Broths Bioassay Guided LC- Institutional Health & OPENSCREEN; complementary fractionated fractionation MSMS Methods safety; extracts from media according to profiling Nagoya mBRCs or polarity Protocol commercial sources EU- HZI Comparison OPENSCREEN of datasets Enzyme mining short-chain fatty acids to larger polar and Institutional nonpolar compounds of primary and Methods secondary metabolism HZI Institutional Extracts and up-scaled full chemical biological Institutional Health & OPENSCREEN EU- partners -IP -to/from compounds purification extraction and fermentation, genome reading assisted by characterisation compound of assessment Methods safety Compound bioassay Progression MIRRI - DSMZ Strains cryopreserved and catalogued; OECD IPR; Nagoya mBRCs; EU-OPENSCREEN libraries HZI CABI Related data and strains and compounds catalogued Related extracts and purified compounds stored; EU-OPEN-Standards; (2007); ISO Protocol Legal clarity for use: compliance with regulatory environment SCREEN and MIRRI.
Materials and sampling A data matrix containing three cpDNA regions (matK, ndhF, rbcL) for 396 species, (including eight outgroup species from Bambusoideae, Oryzoideae, Panicoideae), was assembled from GenBank and from own accessions (see Table  S1. 1 in supporting information). Sampling was aimed at being exhaustive at the genus and tribe levels. Sequences for some taxa were obtained de novo by PCR and Sanger sequencing using custom Pooideae-specific primers (Table  S1. 2). Clustal alignments were generated and manual adjusted in BioEdit  (Hall, 1999). The final alignments for matK, ndhF and rbcL were 1605, 899 and 717 nucleotides long, respectively (sum: 3221), and had 2% (8), 14% (54) and 34% (135) of missing sequences, respectively (average: 17%).
Dating Pooideae To obtain age priors for the two deepest nodes of Pooideae, we first performed a separate dating analysis in BEAST of a small Poaceae dataset, consisting of cpDNA (matK, ndhF, rbcL) sequences for 41 taxa sampled from all the major Poaceae lineages, and eight fossil constraints (Appendix 2). Resulting ages (Table  1 ) for the BOP crown and Pooideae stem node were used as normal-distributed priors in the Pooideae dating analysis. The dating analysis was set up in BEAUti v1. 7. 4 and perfomred in BEAST v1. 8  (Drummond & Rambaut, 2007; Drummond, Suchard, Xie, & Rambaut, 2012). The three cpDNA partitions were analysed using unlinked site models, linked clocks, and linked partition trees. Nucleotide substitution model priors were set to GTR + G (four gamma categories) for all partitions, as suggested by JModelTest v.
Dating Poaceae and monocots using new fossils Recently, three fossils of grasses or putative grasses  (Poinar Jr. et al. , 2015; Prasad et al. , 2011; Wu et al. , 2017)  have been discovered indicating an older age of Poaceae than previously assumed. To estimate the impact of these fossils on the age of monocots and Poaceae, we performed an additional set of dating analyses on an independent 4-locus dataset for 46 angiosperm species (Table  S1. 3), including 29 grasses, nine non-grass monocots and one outgroup. Sequences for seven markers , i. e. atpA from the mitochondrome (mtDNA), atpB, matK, ndhF and rbcL from cpDNA, and phyB and topo6 from the nuclear genome (nDNA), were downloaded from GenBank and aligned with Clustal. For node dating we identified 11 angiosperm fossils (Table  S1. 4; Barba-Montoya, dos Reis, Schneider,  Donoghue, & Yang, 2018; Iles et al.
Geo-referenced records and climatic data Geo-referenced records for Pooideae and outgroup taxa were downloaded from the Global Biodiversity Information Facility (GBIF; www. gbif. org) using rgbif package in R  (Chamberlain, 2017). To exclude unreliable records we discarded coordinates with less than three decimals and employed additional filtering implemented in the SpeciesGeoCoder package  (Töpel et al. , 2016)  in R. Taxa with synonymous names were merged using taxonomic information from GBIF. For each of the filtered geo-referenced records, 19 Bioclim variables were downloaded from the WorldClim database (http://www. worldclim. org/) in a 2. 5 arc-minutes resolution using the 'raster' package in R. After excluding the lower and upper 5% of each Bioclim variable and taxon we calculated mean and standard deviation, which were used in downstream phylogenetic analyses.
ASR of climatic space To assess the phylogenetic information contained in each Bioclim variable we determined the phylogenetic signal represented by Pagel's lambda (λ)  (Pagel, 1999)  using phylosig function of the R package phytools  (Revell, 2012). For the Bioclim variable with the strongest phyolgenetic signal (BIO3, isothermality) we reconstructed ancestral states as continuous trait. To investigate the Pooideae most recent common ancestor's (MRCA) exposure to frost and prolonged cold, we scored BIO6 (minimum temperature of the coldest month) and BIO11 (mean temperature of the coldest quarter) as binary traits. In case of BIO6, we scored a single binary trait, measuring exposure to frost as either "yes" (BIO6 <0) or "no" (BIO6 ≥0). To measure adaptation to winter severity, we explored which temperatures of BIO11 comprised the strongest phylogenetic signal.
We found that the number of months below a mean BIO11 temperature of 2°C and 3°C exhibited the two strongest phylogenetic signals (data not shown). Based on these findings we scored BIO11 as three binary traits, whether BIO11 was below ("yes"), or not below ("no") 2 °C, 3 °C and 4 °C, respectively. To test if the binary traits were distributed non-randomly along the phylogeny we estimated Fritz & Purvis' (2010) D using the phylo. d function in the R package caper  (Orme, 2013 ; Table  S1. 5). The continuous and binary ASRs were performed with BEAST using a separate data partition on the previously obtained timetree. We supplied the MC timetree as starting tree and turned off all tree operators. The respective trait was assumed to evolve under a symmetrical Brownian motion (continuous ASR) or an asymmetrical discrete model (binary ASR), and under the same clock as the nucleotide partitions.
strongly contradict previous estimates for young origins of both Poaceae and the internal BOP clade, indicating instead crown ages for Poaceae and subfamily Oryzoideae of at least 101 Ma and 66 Ma, respectively. Including versus excluding the three old calibrations in an angiosperm-wide, four-locus and 46-taxon dataset nearly doubled the age for Poaceae (crown node 101-103 Ma vs. 56-71 Ma) and considerably increased the ages for other internal Poaceae lineages (Fig. S3. 1, Tables 2, S1. 6).
Sample Collection All of the materials used in this study were collected during several expeditions conducted by the Museum National d'Histoire Naturelle of Paris (MNHN-www. expeditions. mnhn. fr) and the Holford Laboratory. The data set includes 1275 specimens collected from 25 localities with a focus on the Indo-Pacific province (Supplementary Table  S1  available on Dryad at http://dx. doi. org/10. 5061/dryad. j008j5h). Samples were collected from 0 m to approximately 800 m in depth and specifically fixed for molecular analysis in the field. Live specimens were anesthetized using magnesium chloride (MgCl 2 ) isotonic with seawater, and a piece of tissue was cut from the foot and fixed in 95% ethanol. Specimens collected after 2012 were processed with a microwave oven to facilitate removal of soft tissue from the shell  (Galindo et al.
1. 6 (Posada 2008). Best-scoring maximum likelihood (ML) trees were estimated using RAxML  (Stamatakis 2006 (Stamatakis , 2014)). Each gene, and each codon position within the COI gene, was considered as independent, each following its best-fit substitution model. Robustness of the nodes was assessed using the thorough bootstrapping algorithm  (Felsenstein 1985)  with 1000 replicates. Phylogenies were jointly estimated using the Bayesian Markov Chain Monte Carlo method implemented in BEAST version 1. 8. 4  (Drummond and Rambaut 2007). The program BEAUti version 1. 8. 4  (Drummond and Rambaut 2007)  was used to generate the file used in BEAST. A birth-death process speciation prior was implemented and the substitution models identified in jModelTest2 version 2. 1. 6 were applied to each gene independently.
The resulting range of depth can, therefore, be considered as a reliable but a minimal value. This algorithm was implemented in an in-house Python script to quickly analyze large data sets of species occurrences (Supplementary Fig. S1  available on Dryad). For trait-dependent diversification analyses, depth was converted into a binary categorical trait with the two states "shallow" and "deep" using a 100-m threshold. The use of this depth threshold value roughly corresponds on average to the end of the photic zone and is in agreement with previous publications on marine gastropods, and represents a zone for which it is generally observed a drop in the number of collected samples due to technical limitations  (Bouchet et al. 2008 (Bouchet et al. , 2009)).
First, we calculated the S Chao1 for the Indo-Pacific subset of our Terebridae data set, because it corresponds both to a biodiversity hotspot for molluscan fauna and to the most densely sampled area, obtaining the estimated Indo-Pacific diversity. We then calculated the ratio of the estimated Indo-Pacific diversity to the sampled Indo-Pacific diversity, a measure of how well our sampling reflects the real diversity for that specific area. Assuming that the effectiveness of our sample is the same worldwide (which is reasonable given that both diversity and sampling effort are lower outside the Indo-Pacific), we applied the same ratio to the total number of Terebridae species described in WoRMS  (WoRMS Editorial Board 2018). Finally, we added to the estimate the number of newly delimited species from this study, to derive the total estimated Terebridae biodiversity.
Diversification Rates through Time and Across Clades Macroevolutionary dynamics of diversification were modeled across the Terebridae phylogeny (after outgroup removal) using the software Bayesian Analysis of Macroevolutionary Mixtures (BAMM) v. 2. 5. 0  (Rabosky et al. 2013; Rabosky et al. 2014 ) on the Maximum Clade Credibility tree obtained in BEAST. BAMM explores models of lineage diversification implementing a Metropolis Coupled Markov Chain Monte Carlo (MC3) to improve the efficiency in simulating the posterior probability distribution. Ten million generations of reversible jump Markov Chain Monte Carlo sampling were run, drawing samples from the posterior every 10,000 generations. Priors were chosen using the setBAMMpriors command in the R package BAMMtools  (Rabosky et al. 2014) , except for the prior probability of rate shift, which has been shown to affect BAMM results  (Moore et al.
2016; Rabosky et al. 2017). For this prior, we tested values ranging from 0. 1 to 50 and we chose the value leading to the highest ESS values for LogLikelihood and NumberOfShifts (Supplementary Table  S2  available on Dryad). We accounted for incomplete taxon sampling using a sampling fraction of 26%, estimated using a total Terebridae diversity value obtained as described above. We processed the output data using BAMMtools to obtain summary statistics after removing a 10% burn-in, and to plot diversification rate through time. BAMM was used both to estimate diversification rates through time and among/within clades, and to define diversification rates for continuous traits (depth and size) using the same parameters. To corroborate BAMM results we used the timedependent diversification approach implemented in the R package RPANDA  (Morlon et al.
2016). This approach enables both speciation and extinction to change through time, whereas in BAMM the extinction rates are assumed to be constant, thus allowing scenarios in which diversification rates are negative  (Morlon et al. 2011). For the whole Terebridae tree (with a 26% sampling fraction), we tested with RPANDA six nested diversification models: 1) a Yule model, with a constant speciation rate and null extinction, 2) a constant birth-death model, with constant speciation and extinction rates, 3) a variable speciation rate model without extinction, 4) a variable speciation rate model with constant extinction, 5) a rate-constant speciation and variable extinction rate model, and 6) a model in which both speciation and extinction rates vary  (Legendre and Condamine 2018). To select the best-fitting model, ML score of each model and the resulting corrected Akaike information criterion (AICc) were compared (Supplementary Table  S3  available on Dryad).
Continuous traits were transformed into categorical two-state traits using appropriate thresholds as described above. We applied the Binary State Speciation and Extinction model (BiSSE;  Maddison et al. 2007 ) for the four two-states data sets, accounting for state-specific incomplete taxon sampling, estimated based on our data as detailed in the Supplementary Materials available on Dryad. The BiSSE model has six distinct parameters: two speciation rates, two extinction rates, and two transition rates (i. e. anagenetic change) between the trait states. Analyses were performed using the R package diversitree  (Fitzjohn 2012 ) on the MCC tree obtained from BEAST, using the functions make. bisse to construct the likelihood functions for each model based on the data, and the functions constrain and find.
Evolutionary Modeling To test whether shifts in larval development are associated with selective constraints on the evolution of shell size and bathymetric distribution, and if depth shifts are associated with selective constraint on shell size evolution, we fitted two Brownian Motion (BM) models and five different Ornstein-Uhlenbeck (OU) models using the R package OUwie  (Beaulieu 2016)  to 100 trees reconstructed with stochastic character mapping of the trait "larval development" and the trait "depth" (coded as discrete) using the make. simmap function available in the R package phytools. For the parametrization of make. simmap, we used the estimated ancestral state, and a transition matrix with equal rates estimated from our empirical data with a MCMC search, and we performed 100 replicates then summarized in a consensus tree, to account for the inherent stochasticity of the process.
Species Diversity Identifies Potential Cryptic Lineages A data set of 1275 samples was used to reconstruct the molecular phylogeny of the Terebridae family (Fig. 2  and Supplementary Table  S1  available on Dryad). Among them, 130 species were confidently identified because their shell matched a described species and corresponded to a unique lineage in the independent gene trees. Some names previously synonymized with others were elevated at the species level (marked with an * in Fig. 2 ;  Fedosov et al. 2019 ). In addition, 69 new species were identified based on morphological grounds and/or correspondence to divergent lineages in the independent gene trees, with genetic distances among species equivalent or even higher to genetic distances recovered among already described species (K2P genetic distances > 2.
9 and 1. 0. VOL. 69 comprise pseudo-cryptic species. Our findings suggest that a considerable fraction of the Terebridae diversity still requires formal description. Three species complexes comprised pairs of lineages with allopatric distribution, and in three clusters comprising three or more divergent lineages (P. textilis, Tabellaria fenestrata and Punctoterebra trismacaria) where at least one of them does not overlap in distribution with others. In addition, our data suggest difference in bathymetric distribution in at least four putative species complexes: Terebra cumingii, Myurella burchi, P. trismacaria, and P. orientalis. However, such differences do not exist between sister-lineages, suggesting that the lineages within a species complex actually correspond to different species  (Puillandre et al.
Dated Terebridae Molecular Phylogeny Recovers New Sister Clade A multigene approach was applied using COI (1161 samples), 16S (717 samples), 12S (817 samples), and 28S (263 samples) genes. Analyses of each individual gene were performed using RAxML and no supported conflicts were found between the four separately generated gene trees (Supplementary Figs. S2-S5  available on Dryad). The four genes were combined to produce a consensus tree (Fig. 2 ). Only samples with ≥ 2 genes successfully sequenced were used in the combined gene data set, a total of 898 samples. Even though the species representation doubled and the number of samples tripled from the previous reported terebrid molecular phylogenies, the overall topology of the terebrid tree is largely consistent with the previous study and the family has remained monophyletic as described in the first molecular phylogeny of the group  (Holford et al.
Terebrid Diversification Rates Increase Over Time We examined terebrid diversification rates as a function of time and across the six individual Clades A-F delineated in our phylogenic reconstruction (Fig. 2 ). Using a realistic sampling fraction of 26%, BAMM analysis supported a model that indicated a steady rate of terebrid diversification over time, with a 0. 97 posterior probability. Both posterior probabilities and Bayes factors were remarkably lower for alternative models with one or two rate shifts (Supplementary Table  S2  available on Dryad). The credible shifts plot depicts a single evolutionary regime for the Terebridae regardless of the value attributed to the prior probability of a rate shift (Fig. 3A  and Supplementary Table  S2  available on Dryad). The rate-through-time BAMM plot supports a scenario of a slow increase of diversification for the whole Terebridae (Fig.
Evolution Rate Shifts in Depth and Shell Size Despite the absence of across-clade heterogeneity in diversification rates, the most supported configurations recovered by BAMM analysis for continuous traits displayed evidence of shifts in evolutionary rates of terebrid traits. Specifically, for shell size, we recovered two likely evolutionary rate shifts: one for the single species Myurella pertusa belonging to clade E1 and the other for Clades B and C, corresponding to the Terebra and Oxymeris genera (Supplementary Fig. S6  available on Dryad). Shell size appeared to have undergone a fast divergence at the beginning of the Terebridae  The rate-through-time plot for depth distribution emphasizes a constant, very low evolutionary rate at the beginning of Terebridae evolutionary history, followed by a steep increase at ca.
Phylogenetic signal and PD analysis with regard to the presence or absence of a VG were carried out on a subset of 51 species. The strong phylogenetic signal (D = t1. 08) obtained for the VG indicates that the trait is phylogenetically conserved, indicating that members of a same clade tend to share same trait state. Through a PD analysis, negative SES values and low quantiles were obtained both for the MNTD and for the MPD of the species without a VG, indicating that their phylogenetic distance is smaller than expected (Supplementary Table  S6  available on Dryad). These results confirm the conservatism of the trait identified by the phylogenetic signal, and highlight that the loss of a VG happened in phylogenetically clustered terminal taxa, and that when the VG is lost in the ancestor, the reversal is extremely unlikely.
Distribution and PD of Terebrid Larval Ecology We examined the protoconch in a total of 638 intact terebrid adult specimens belonging to 116 species. In our data set, multispiral (m) protoconchs had between three and five whorls, and paucispiral (p) protoconchs had a maximum of 2. 25 whorls. A number of specimens displayed an intermediate protoconch, with 2. 5 whorls and a general appearance compatible either with a lecithotrophic larva with a longer dispersive stage, or a short-lived planktotrophic larva. In those cases, instead of using only whorl numbers, the shell was attributed to one of the two developmental types based on protoconch characteristics, where a small nucleus and an evident boundary between protoconch and teleoconch were considered indicative of a planktotrophic development.
Of the 199 species examined in the study, 72% are planktotrophic and 28% are lecithotrophic (Fig. 2  and Supplementary Fig. S9  available on Dryad). Phylogenetic signal was quite strong for larval development (D = t0. 21), whereas PD analysis recovered negative SES values and low quantiles for MNTD of the lecithotrophic community only. The values obtained for MPD were negative with low quantiles for the planktotrophic community, and positive with high quantiles for the lecithotrophic community (Supplementary Table  S6  available on Dryad). The negative MNTD values for the lecithotrophic community indicate that the phylogenetic distance among lecithotrophic species is smaller than expected, and that this clustering can be detected closer to the tips of the phylogeny. Therefore, lecithotrophy appears to be a trait shared by closely related species, indicating that it has evolved before separation of the species-level lineages and supporting the current view that reversal to planktotrophy is an unlikely event.
Evolutionary Modeling of Traits Establishes Larval Development and Shell Size Relationship We identified an evolutionary link between larval ecology and shell size in the Terebridae using OUwie analyses. Specifically, the rate of shell size evolution is more than five times higher in planktotrophic species ( 2 = 83. 15±0. 23) than in lecithotrophic species ( 2 = 15. 1±0. 51), whereas the strength of pull toward a shell size optimum is about three times higher for lecithotrophic species ( = 0. 30±0. 043) than for planktotrophic ones ( = 0. 67 ±0. 01). This finding is based on the best-fitting model for the Terebridae adult shell size across the species included in our data set, which is the OUMVA, according to the Akaike weights, with a delta AICc >5 with respect to the second best-fitting model OUMA (Supplementary Table  S7  available on Dryad).