dataesr/scientific-paragraphs-categorization huggingface dataset:
Olfactometry GC effluent was carried to the sniffing port using a deactivated and uncoated fused silica capillary column, heated to 200 C. The sniffing port was supplied with humidified air at 40 C with a flow of 600 mL. min À1. Olfactometry analyses were conducted by 8 experienced judges. Each judge performed one olfactometric analysis for each wine. Therefore, a total of eight GC-MS-O analyses were carried out for each wine. Judges were asked to express their perceptions via the olfactometric software interface  [9] , representing an aroma wheel made of 56 descriptors and designed for wine analysis. Characteristics of the perceptions were recorded throughout each judge's analysis and data were directly obtained from the olfactometric software. Odorant zones detected by at least 3 out of 8 judges were reported with their Linear Retention Index at apex and their associated odor descriptors.
Odorant compounds identification The identification of compounds corresponding to each odorant zone was performed by comparing Linear Retention Index (LRI) and mass spectra of detected compounds with those of the databases (Wiley 6. 0, NIST and in-house databases), by injection of the standard compounds when available, and by comparison of the odor perceived with those referenced in databases (in house database and The good scents company database  [10] ). The list of odor-active compounds is reported in Table  4. Compounds non-identified were named after their apex indices number.
Acknowledgments The authors would like to thank the panelists involved in the sensory descriptive analysis and in the olfactometric task. They also thank  Aurore Boissy ,  Erell Bourhis ,  Caroline Chiffoleau ,  Solenn Jourdren ,  Catherine Fillonneau ,  Laurent Lethuaut ,  InterLoire (Interprofession des vins du Val de Loire ) and the  BIVC (Bureau Interprofessionnel des Vins du Centre-Loire)  for their assistance at different stages of the project, and  Henry Freulon  for administrative support.
the odor-active Specifications table Subject area Food science More specific Wine flavor research subject area Type of data Microsoft Excel Worksheet containing 8 sheets: (1) Information, (2) Experimental factors, (3) List sensory descriptors, (4) Sensory descriptive analysis, (5) List VOC, (6) VOC quantification, (7) List GC-MS-O and (8) GC-MS-O How data was acquired 49100 Angers, France -GCO data were obtained at ONIRIS, Nantes-Atlantic College of Veterinary Medicine and Food Science, UMR GEPEA CNRS 6144, BP 82225, F-44307, Nantes, France Data accessibility The raw data, provided as a Microsoft Excel Worksheet, are available on the Zenodo open-access research data repository [1], http://doi. org/10. 5281/zenodo. 1213610 Related research Roche, A. , Perrot, N. , Chabin, T. , Villi ere, A.
INTRODUCTION The Neotropical ecozone has been defined as the region from central Mexico to southern Brazil  (Morrone, 2014). The Neotropics hold the highest terrestrial biodiversity on earth  (Antonelli & Sanmartín, 2011)  and harbor all major tropical biomes: lowland rain forests, seasonally dry forests, mid-elevation montane forests, savannas, high elevation grasslands and deserts  (Hughes et al. , 2012). A recent estimate of tree species based on a pantropical tree inventory database suggested that the number of tree species in the Neotropics was as many as in the Indo-Pacific region and almost triple the counterparts in continental Africa  (Slik et al. , 2015). Several hypotheses have been proposed for the origins and evolution of Neotropical biodiversity during the past two decades. These hypotheses can be coarsely classified as biotic (e.
bioinformatics. babraham. ac. uk/projects/fastqc/). Reference-guided assembly then proceeded with HybPiper  (Johnson et al. , 2016) , using the new reference described above. Briefly, the program works as follows: reads are sorted by gene based on the reference. Local de novo assemblies are carried out using SPAdes  (Bankevich et al. , 2012) , and coding DNA sequences (CDS) are predicted using Exonerate  (Slater & Birney, 2005). When a gene is assembled into several disconnected contigs-common in degraded samples where the fragments (and therefore effective read length) are very short-HybPiper scaffolds these short contigs in the correct order based on the reference. In the event that multiple genes are assembled for a single target, HybPiper distinguishes orthologs from paralogs using a combination of alignment length and identity relative to the reference.
Phylogenomic reconstruction and molecular dating Sequences were aligned with MACSE  (Ranwez et al. , 2011) , gene by gene (-fs 30 -stop 50). We reconstructed phylogenetic relationships using both a concatenated maximum likelihood (ML) and a coalescent approach. Milicia exceisa and Artocarpus venenosus were specified as the most external outgroups based on recent phylogenetic analyses of Moraceae  (Clement & Weiblen, 2009; Zerega et al. , 2010; Chung et al. , 2017; Gardner et al. , 2017; Zhang et al. , 2018). Sequences were concatenated into a supermatrix and partitioned according to genes using the fasta_merge. py script from HybPiper; a ML tree was reconstructed with RAxML v8. 2. 10  (Stamatakis, 2014)  as implemented on CIPRES  (Miller et al. , 2010)  1000 rapid bootstrap replicates. GTRGAMMA was chosen as the substitution model.
9 package  (Yang, 2007). MCMCTree has been used to estimate divergence times with other similar phylogenomic datasets (e. g. ,  Foster et al. , 2017) , where other programs such as BEAST2  (Bouckaert et al. , 2014)  would take too long to converge. The tree topology was fixed, using the best-scoring tree from the RAxML analysis, and all calibrations were implemented as uniform priors with soft boundaries (2. 5% on both sides)  (Ho & Phillips, 2009). We set the minimum age of the fossil and as the minimum boundary and the younger bound of the root (73 Ma) as the maximum boundary of the uniform distribution for each fossil calibration. The birth-death process was used as the tree prior. For the MCMCTree analysis, we used the supermatrix, analyzed as a single partition under the GTR substitution model.
We first ran baseml in PAML v4. 9 with a strict clock model to estimate the rough mean of parameters such as the shape parameter for the overall rate and the transition/transversion rate ratio. Two steps are needed for divergence time estimation by approximate likelihood in MCMCTree. We first estimated the gradient and Hessian, and then used them to estimate the divergence times. We set the prior in both steps according to the estimates of baseml. In both steps, we ran the process for 38. 5 million generations, with the first 10% of the chain length discarded as burnin, sampling a total of 10,000 generations at a frequency of once very 3,500 generations. Two independent runs with the same settings were conducted to confirm the convergence of the MCMC. To check the influence of the prior on the estimation, we used another prior setting (program defaults), followed the same steps as above, then ran the chain for 22 million generations, sampling a total of 10,000 generations at a frequency of once every 2,000 generations.
Divergence times of Dorstenieae and Dorstenia The crown-group age of tribe Dorstenieae was estimated in the  and that of Dorstenia in the Paleocene (50. 8-67. 3 Ma) (Table  2 , Figure  S2 ). The stem and crowngroup ages of the Neotropical Dorstenia clade were dated in the Eocene to early Oligocene  respectively). Brosimum s. l. , the other Neotropical clade in Dorstenieae, was estimated to date from the late Eocene to early Miocene (stem node: 19. 6-41. 5 Ma; crown node: 14. 9-31. 1 Ma). Runs with the prior only showed different results to those with data, indicating the data had a significant impact on the posterior (results not shown). Results of the two different prior settings were similar to each other (Table  2 , Figure  S2a, b ). Divergence times estimated with the PL approach were all compatible with those from the Bayesian approach (i.
Biogeographic history of Dorstenieae We analyzed the data set with both the dispersal-extinction-cladogenesis (DEC) and DEC+J (DEC with founder-event speciation) models. The time-stratified model fit the data significantly better than the time-constant model in all analyses conducted (Table  S2 ), therefore we focus mainly on the results from the time-stratified analyses here, unless otherwise mentioned. In addition, the DEC+J model had a lower value of Akaike information criterion (AIC) than that of DEC (Table  S2 ). The ancestral distribution area of Dorstenieae was estimated to be the combined area of continental Africa, Madagascar, and Southeast Asia and Oceania (CDF) with both the DEC and DEC+J models (Figure  3 , Figure  S3c ). The ancestral area for both the stem and crown-group nodes of Dorstenia s.
Success of the targeted enrichment strategy with herbarium specimens Most (99%) of the samples in this study were from herbarium specimens (Table  S1 ). Some of them were collected more than 40 years ago and the amount of sample collections from the herbarium was typically limited (around 3 to 20 mg) due to destructive sampling policies. Three samples (Dorstenia aristeguietae, D. choconiana, and D. prorepens) were filtered by HybPiper because of the low matching of reads to the reference. We excluded another fifteen taxa for the low number of genes recovered (less than 30, Table  S1 ). The lowest amount of DNA used among the final 98 taxa retained in our dataset was 25. 1 ng (Dorstenia brasiliensis). Samples that were excluded ranged from being over 100 years old to 11 years old, while samples that were included were collected as long ago as 1923.
, 2013) , Inga (Fabaceae,  Hart et al. , 2016) , and Annonaceae. Our results also suggest that the baits, which were originally designed for tribes Artocarpeae and Moreae in the same family  (Gardner et al. , 2016) , worked well in Dorstenieae. In addition to the phylogenetic markers developed from 333 inferred single-copy exons for Moraceae, we retrieved approximately another 200 genes in this study for Dorstenieae. Those assembled untargeted genes had a mean identity to at least one target gene of 84%, and a mean alignment length to at least one target gene of 87% (as a percentage of the untargeted genes). These Moraceae specific baits worked well throughout the entire Moraceae family  (Zerega & Gardner, 2019). Five untargeted genes were included in our final 102-gene data set. The most genes retrieved for one taxon was 515 from the outgroup sample Ficus macrophylla, suggesting a high probability that these baits would also work well to explore relationships within Ficeae (Ficus), the largest tribe in Moraceae  (Couvreur et al.
Divergence times of Dorstenieae and its genera Runs with or without data showed different results suggesting the data were informative. Runs with different prior settings showed similar estimates, suggesting that our results are robust to various assumptions on rate variation. The crown-group age of Dorstenieae was estimated in the Upper Cretaceous (65. 8-79. 8 Ma; Table  2 ), which overlaps with former studies that used fewer genes  (Zerega et al. , 2005; Gardner et al. , 2017; Zhang et al. , 2018). The crown-group age of Dorstenia was estimated in the Paleocene (50. 8-67. 3 Ma), which is younger than in  Misiewicz and Zerega (2012). Although the estimated stem-group node of Neotropical Dorstenia clade in our study was younger than that in  Misiewicz and Zerega (2012) , the crown-group node of the same clade fell within a similar range in both studies.
ACKNOWLEDGEMENTS We thank the  Field Museum (F) , the  Missouri Botanical Garden (MO) , the  New York Botanical Garden (NY) , and the  Muséum national d'Histoire naturelle (P)  for sharing herbarium material;  Tracy Misiewicz  and  Caroline Storer  for assistance with DNA extraction;  Charles Foster  for providing helpful advice on running MCMCTree; and  Santiago Ramirez-Barahona  for information on fossil calibrations. This work was supported by a  China Scholarship Council (CSC)  PhD grant (grant No. 201506140077 ) and a research grant from the  International Association for Plant Taxonomy (IAPT)  to Q. Z. Sequencing costs were covered by  Agence Nationale de la Recherche  grant  ANR-12-JVS7-0015-01  to H. S.
Cyanobacteria cells represent a sustainable resource for biotechnology due to their photosynthetic, N-fixation, and autotrophic capacities  [17, 18, 24]. Due to the current increase in their pharmaceutical value and in their application prospects for use in medicine or biotechnology, the exploration of uncovered cyanobacterial taxa constitutes a promising strategy to efficiently explore the chemical diversity of their bioactive compounds. The present review globally and systematically describes current knowledge on the biological activities described for cyanobacterial natural products, and, thanks to the construction of a specific and freely available molecular database, regroups all information described so far concerning the chemical structures, the producing organisms, and the various bioactivities of all the different cyanobacterial metabolite families.
Methods for Dataset Construction A database was constructed using different search engines, notably PubMed and Google Scholar. The keywords used were "cyanobacteria," "metabolite" or "natural product," "beneficial" and "activity," or "biological properties. " The database was first based on reviews and further completed with recent publications dealing with the isolation of new compounds from cyanobacteria. The main entries into the database were the names of the metabolites. To avoid bias in the counting of metabolites, we stored all the data for each molecule and its variants as a "family. " In fact, there are still no molecular classification references for a natural product description. As discussed by Janssen  [25] , there is no standardized naming system along cyanobacterial metabolites, as in natural product discovery in general, that could induce an underestimation of the real diversity of natural products and to hide the potential link between their chemical structures, biosynthetic pathways, and evolution routes.
Thus, such a valuable classification of cyanobacterial metabolites is still needed, notably in the current context of genomic and metabolomic development. In our database, metabolites were grouped and classified based on different criterion, initially selected by different authors  [13, 15, 25]. First, they were classified according to their biosynthetic pathways based on the genomic data reviewed by Dittmann et al. (e. g. , microcystins, cryptophycins, and aeruginosins)  [15]. Secondly, when biosynthetic information was not available, metabolites were classified, according to their structural homology, as proposed by Boudreau et al. ,  [26] , Janssen  [25] , and Chlipala et al. [13] , supposing that they might be sharing at least a similar, if not the same, biosynthetic route (e. g. , kulolide-family, aerucylamides, and cyanopeptolins).
The data collected were then classified depending on the chemical class of the compound, the chemical structure, and the strain producing the metabolites with all the taxonomic information (species, genus, family, and order), in accordance with  Komarek et al. (2014)   [27]. In addition, we compiled the demonstrated activities for the purified compounds. Fourteen classes of activity were mostly tested through the literature: lethality (against brine shrimp, and other small invertebrates), neurotoxicity, hepatotoxicity, dermal toxicity, cytotoxicity, anti-inflammatory activity, antioxidant activity, antiviral, antibacterial, antifungal, antialgal, antiprotozoal, serine protease inhibition, and other types of enzyme inhibition. Additionally, 670 publications were analyzed, dating from the 1970s until today (April 2019).
It is interesting that, except for these four orders, the others (i. e. , Pleurocapsales, Chroococcidiopsales, Gloeobacterales, and Spirulinales) remain weakly represented in the database: less than five families of metabolite have been reported so far for all of them. Some metabolites have been isolated from cyanobacterial assemblages without accurate identification of the producer organisms. For these cases, the authors identified the genera of the two dominant cyanobacteria of the assemblage but could not accurately determine which one of them produces which molecule  [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40]. Tidgewell et al. (2010)   [9]  also identified the prevalence of marine cyanobacterial products within Oscillatoriales and Nostocales with 58% and 24% of the isolated molecules, respectively.
Within Oscillatoriales, members of the genus Lyngbya, and, notably, Lyngbya majuscula produce the highest number of metabolites. This benthic genus is widely spread through the tropical marine ecosystem and has been widely studied because of its toxicity and implication in many dermatitis cases around the world  [41, 42]. A number of studies have been conducted on the Lyngbya genus, and a high number of new metabolites have been described. In fact, Lyngbya is, to date, the most productive genus of bioactive cyanobacterial compounds (Figure  2B ). Recent studies showed that Lyngbya is polyphyletic  [27, 43]  and using polyphasic approaches, Lyngbya has been split into four new genera: Moorea  [44] , Okeania  [45] , Limnoraphis  [46] , and Microseira  [47]. Some marine   [13, 25, 26]. We observed a progressive stabilization of the number of compound families in the database that supports the postulation of the exhaustiveness of the present database.
3%, producing 122 families of compounds), followed by Nostocaceae and Microcoleaceae (17. 2% and 10. 9% for 69 and 48 molecule families, respectively) (Figure  2A ). At the genus level (Figure  2B ), Lyngbya-Moorea exhibits the highest number of isolated compounds (85 families of metabolites representing 20. 6%), in accordance with the perceived richness of production for the Lyngbya genus due to its polyphyletic status  [48]. Nostoc is the second most prolific genus of bioactive compound families with 50 isolated families so far (12. 1% of the total number of families of metabolites). The other most important genera are Anabaena, Oscillatoria, and Microcystis (with 32, 31, and 27 families of molecules, respectively, representing 7. 8%, 7. 5%, and 6. 6%) (Figure  2B ). When looking at the habitats of these cyanobacteria, a large number of compounds were isolated from marine environments (148 families of metabolite in the database, which means 53% of the families of metabolites) in comparison to the number of strains isolated from freshwater environments (77 families of metabolites, 27.
6%) (Figure  2B ). However, this difference might be at least partly due to the high number of compounds isolated from the marine species Lyngbya majuscula-Moorea producens (49 families of molecules, 18. 8% of the families in the database) and to the existence of various research programs focused on marine species (e. g. , the Panama International Cooperative Biodiversity Group, ICBG). Overall, we observed that diversity at the genus level is important, as illustrated by the 90 different genera present in the database. Moreover, 65 different genera have been reported to produce less than four molecules (Figure  2B ). We also noticed that five molecules were isolated from Lyngbya/Schizothrix assemblages and five others from unidentified strains of cyanobacteria (Figure  2B ). Thus, at the genus level, the diversity of producers is large with a high number of genera studied (90 different genera).
Nevertheless, these genera generally belong to the same orders (e. g. , Oscillatoriales, Nostocales, Synechococcales, and Chroococcales) while some orders were not studied. For example, among the Pleurocapsales order, only four genera have been reported to produce metabolites. As a result, the covered diversity appears not to be exhaustive and can still be increased. According to  Shih et al. (2013)   [49] , the genomic potential of cyanobacteria to produce secondary metabolites is high with more than 70% of the studied strains presenting non-ribosomal peptide synthase (NRPS) or polyketide synthase (PKS) gene clusters in their genomes. In particular, they identified one strain belonging to the Fischerella genus (Fischerella sp. PCC 9339) that exhibits 22 NRPS/PKS clusters in its genome. On the contrary, only five compound families have been isolated from the genus Fischerella so far and are listed on the present database.
The "Other" category concerns other taxonomical families that produce less than two compound families. (B) The histogram shows the number of compound families for each genus. The "Other" category corresponds to genera producing less than four compound families. * indicates cyanobacterial assemblages whom the real metabolite producer is still undetermined. The boxes indicate the environmental origins for the corresponding genera. For both charts, the colors correspond to the taxonomical order of each genus or family. In addition, the best producer genus, Lyngbya-Moorea, remains rarely studied at the genomic level: four genomes are available in the Genbank database and another three are available on the Microscope platform  [50]. Considering the number of compounds isolated from the Lyngbya-Moorea genus (85 compound families), most of the links between the identified molecules and the responsible biosynthetic gene clusters remain to be characterized.
We also compared our collected data with those reported by Dittman et al. (2015)  [15]  in order to determine when the isolated molecule families are linked with a specific and identified biosynthetic gene cluster. This review showed that less than 20% of the molecule families from the database are associated with specific identified biosynthetic gene clusters. Thus, the biosynthetic pathways of a large majority of compounds is still unknown as well as the regulation mechanisms controlling their production. Therefore, these observations highlight part of the remaining possibilities for the discovery of new molecules, gene production, and biosynthetic pathways.
" However, their respective IC50 values appear to be very different: 26. 3 nM against HeLa S3 human cervical cancer cells for odoamide and 135 µ M against Vero cells for scytoscalarol, which represents a concentration difference of 500 times between their respective inhibition potentials. Furthermore, tests can be realized against several cell lines and strains presenting different sensitivity responses, which limit the comparison between results. With 10 chemical classes and 14 types of bioactivity, the cyanobacterial metabolites are diverse and highly active. However, half of the families of metabolites listed in the database are peptides or peptide derivatives. This could be due to the importance of the peptide biosynthetic pathway (NRPS, PKS, and RiPPs) or the extraction methods used, which might favor peptide extraction.
Furthermore, tests can be realized against several cell lines and strains presenting different sensitivity responses, which limit the comparison between results. With 10 chemical classes and 14 types of bioactivity, the cyanobacterial metabolites are diverse and highly active. However, half of the families of metabolites listed in the database are peptides or peptide derivatives. This could be due to the importance of the peptide biosynthetic pathway (NRPS, PKS, and RiPPs) or the extraction methods used, which might favor peptide extraction. We did not observe a link between chemical classes and activities, but this observation must be considered carefully with regard to the low number of investigated molecules in some classes (i. e. , polyketides, polysaccharides, and terpenes). The most frequently detected activity for cyanobacterial metabolites is cytotoxicity (42% of the metabolite families), whereas antioxidant or anti-inflammatory activities were detected for only 1.