dataesr/scientific-paragraphs-categorization huggingface dataset:
After washes, slides were mounted with Prolong gold Antifade Mountant containing DAPI (Invitrogen). Pictures were taken with TCS SP8 STED 3X nanoscope (Leica). dsRNA foci were detected and quantified with ImageJ  [47]  using an in-house macro (https://github. com/Garcia-BlancoGroup/ Yehetal2022) that processed the background at 50 pixels, adjusted the threshold to remove the noise signal, and quantified particle size larger than 0. 08 μm 2. To test if the anti-dsRNA J2 antibody detected sfRNA, full length sfRNA1 from DENV2 NGC was in vitro-produced as detailed above with the primers V1. 25: TAA TAC GAC TCA CTA TAG GGG AAG TCA GGT CGG ATT AAG C and V1. 26: AGA ACC TGT TGA TTC AAC AGC AC. 5 x 10 5 Huh-7 cells were either mock-infected, infected with DENV2 NGC from ATCC at MOI of 1 for 1 hour, or transfected with folded sfRNA using TransIT-mRNA (Mirus).
Support for this research came from a fellowship from the  McLauglin Family Foundation  to TS, scholarships from the  Fondation pour la Recherche Medicale  ( FRM  project  SPF202110013925 ) to HM, from the  Institut Mediterraneen Hospitalier (IHU, Marseille)  to IMSP and from the  graduate school French Ministry of Higher Education and Research  to FRC and FR, UTMB start-up funds and  Cancer Prevention & Research Institute of Texas  grant  RP200650  to LJK and MKS, the  Ministerio de Ciencia e Innovacion  ( PID2020-115696RB-I00 ) to MAM-R,  Cancer Prevention & Research Institute of Texas  grant  ID RR210018  to GN,  Ministry of Education (Singapore)   Tier3 grant  ( MOE2015-T3-1-003 ) to RMK and JP, a  National Medical Research Council (Singapore)   ZRRF grant  ( ZRRF/007/2017 ) to JP, a  French Agence Nationale de la Recherche  grant ( ANR-20-CE15-0006 ) to JP, and the  Duke-NUS   Signature Research Programme  funded by the  Agency for Science Technology and Research (A � Star Singapore)  and  NIH/NIAID   P01 AI150585  to MAGB.
Premise: Recent phylogenetic studies of the Araceae have confirmed the position of the duckweeds nested within the aroids, and the monophyly of a clade containing all the unisexual flowered aroids plus the bisexual-flowered Calla palustris. The main objective of the present study was to better resolve the deep phylogenetic relationships among the main lineages within the family, particularly the relationships between the eight currently recognized subfamilies. We also aimed to confirm the phylogenetic position of the enigmatic genus Calla in relation to the long-debated evolutionary transition between bisexual and unisexual flowers in the family. Methods: Nuclear DNA sequence data were generated for 128 species across 111 genera (78%) of Araceae using target sequence capture and the Angiosperms 353 universal probe set.
Results: The phylogenomic data confirmed the monophyly of the eight Araceae subfamilies, but the phylogenetic position of subfamily Lasioideae remains uncertain. The genus Calla is included in subfamily Aroideae, which has also been expanded to include Zamioculcadoideae. The tribe Aglaonemateae is newly defined to include the genera Aglaonema and Boycea. Conclusions: Our results strongly suggest that new research on African genera (Callopsis, Nephthytis, and Anubias) and Calla will be important for understanding the early evolution of the Aroideae. Also of particular interest are the phylogenetic positions of the isolated genera Montrichardia, Zantedeschia, and Anchomanes, which remain only moderately supported here.
Smith, 2013; Iles et al. , 2015; Hoffman, 2021). A recent review found that the family was particularly diverse during the late Cretaceous and early Paleocene  (Stockey et al. , 2021). Nauheimer et al. (2012) , using a molecular analysis combined with fossil data and computational modeling, concluded that the early evolution of the family was linked to aquatic or subaquatic environments and that early diversification within the family, which led to the evolution of subfamilial lineages, took place during the Cretaceous and was associated with the fragmentation of the supercontinent Pangaea. Araceae comprises 3667 species assigned to 143 genera  (Boyce and Croat, 2011 onward) , many of which are globally important in horticulture and agriculture. They make up one of the most ecologically diverse families of flowering plants, from the smallest known species (Wolffia globosa (Roxb.
Selection of plant tissue was based on the best available material, considering that an ideal sample extraction should contain 100-200 ng of DNA with fragment sizes ≥350 bp, following manufacturer recommendations regarding library preparations (New England BioLabs, Ipswich, Massachusetts, USA). Material was selected from, in order of preference, (1) fresh leaf tissue dried in silica gel, which generally gave optimal results; (2) DNA aliquots from the bank at the Royal Botanic Gardens, Kew (https://dnabank. science. kew. org/homepage. html); and (3) plant tissue material extracted from herbarium specimens, which yielded DNA of variable quality. In addition, we took advantage of public data for three samples available in the Sequence Read Archive (SRA; https://www. ncbi. nlm. nih. gov/sra; Appendix S1).
Sequence data processing Raw sequence reads (FASTQ files) were trimmed using Trimmomatic  (Bolger et al. , 2014)  with leading-and trailing-end trim thresholds of 20 and 10, respectively, and a sliding window trimming threshold of 20, averaged over 4 bp (LEADING: 20; TRAILING: 10; SLIDING WINDOW:4:20). Trimmed reads <40 bp were then removed  (MINLEN: 40). Paired reads and combined unpaired reads were used to recover Angiosperms353 target genes  (Johnson et al. , 2019;  https://github. com/mossmatters/Angiosperms353) using HybPiper version 1. 3. 1  (Johnson et al. , 2016). The HybPiper BLASTN option was used to map the reads to each of the target genes. The reads assigned to each target gene were then assembled de novo using SPAdes  (Bankevich et al. , 2012)  and coding sequences (hereafter "genes") were extracted using exonerate  (Slater and Birney, 2005).
Noncoding sequences (i. e. , introns and UTRs) flanking the exons were recovered and combined with the exon sequences into so-called supercontigs using the HybPiper intronerate. py script. Sequences from all taxa were combined for each gene and supercontig data set. Each dataset was aligned separately using MAFFT version 7  (Katoh and Standley, 2013) , with accuracy-oriented methods (--localpair; --maxiterate 1000) and the option to generate reverse complement sequences to align them with the remaining sequences based on 6-mer counting (--adjustdirectionaccurately). Single locus alignments were subsequently trimmed using phyutility (https://github. com/blackrim/phyutility) to remove nucleotide sites missing in ≥80% of the taxa (-clean 0. 8). Recovery heatmaps (Appendix S3) and statistics (Appendices S1 and S4) were generated using scripts from HybPiper.
Sequence data from the three samples obtained from the SRA were processed using the approach described in  Baker et al. (2022). GetOrganelle version 1. 7. 5  (Jin et al. , 2020)  was used to assemble the plastid genome from raw reads of targetcapture samples. Additional plastid genomes were retrieved from the National Center for Biotechnology Information (NCBI). These supplementary plastid genomes came either from Organelle Genome Resources (https://ftp. ncbi. nlm. nih. gov/refseq/release/plastid/) and GenBank (https://www. ncbi. nlm. nih. gov/nuccore/) or from the Sequence Read Archive (https://www. ncbi. nlm. nih. gov/sra), in which case plastid genomes were also assembled from raw reads using GetOrganelle. Plastid genomes were then queried against a reference set of the 76 most common plastid genes with BLASTN  (Camacho et al.
, 2016)  and model selection (-m MFP). Branches with support values <10%  (Mirarab, 2019 [preprint] ) were collapsed in each gene tree using Newick Utilities version 1. 6  (Junier and Zdobnov, 2010). Genes with <25% of the taxonomic sample and with alignments <100 bp were excluded. A first coalescent analysis was performed using ASTRAL-III  (Mirarab and Warnow, 2015)   Gene trees were evaluated using TreeShrink  (Mai and Mirarab, 2018)  to identify outlier taxa that increased the diameter of each gene tree (i. e. , maximum distance between any two tips of the tree) by >20%, using centroid re-rooting (-b 20 -c). The second and final coalescent analysis was conducted on this new set of gene trees, using the same method as described above. A quartet-based polytomy test, used to evaluate gene tree discordance in the dataset and identify potential hard polytomies, was conducted using ASTRAL-III as described by  Sayyari and Mirarab (2018).
The test evaluates the null hypothesis that a given branch is a polytomy. For the plastid data, sequences from each gene were aligned independently in MAFFT version 7. 4  (Katoh and Standley, 2013) , using the adjustdirection and auto options, then trimmed with Trimal version 1. 3  (Capella-Gutiérrez et al. , 2009)  with the gappyout method. The tree was estimated using IQ-TREE version 2. 1. 3 (Minh et al. , 2020)  with independent GTR+G models for each gene and 1000 ultra-fast bootstraps. Simplified versions of the nuclear and plastid trees were compared visually through a tanglegram produced in R with the package phytools  (Revell, 2012).
Sequence recovery The final nuclear data set comprised 142 accessions, including 128 Araceae representing 111 genera, and 13 samples used as outgroup taxa (Acorus gramineus and 12 samples from other Alismatales families; Appendix S1). For Araceae accessions, 3. 75 million read pairs were sequenced on average, ranging from 0. 23 to 57. 6 million, while this number was slightly lower for the outgroup taxa (3. 01 million read pairs on average, ranging from 0. 28 to 20. 3 million). On the other hand, the percentage of reads on target was lower for Araceae than for the outgroup taxa, on average 0. 64% and 2. 74%, respectively. The mean number of genes (including flanking regions) with sequences is lower for Araceae than for the outgroup taxa (173 and 222, respectively, although the range varies considerably, 10-347 for Araceae and 58-309 for the outgroup taxa), as reflected also in the number of genes recovered with a 50% target length threshold (mean of 96 for Araceae and 142 for outgroup taxa; Appendix S1).
kew. org/), hence the different values reported for Araceae between the two comparisons presented here. Even though the Tree of Life Explorer data was compiled using a different approach for sequence recovery than the one used here  (Baker et al. , 2022) , it is evident that sequence recovery for the Angiosperms353 probes is much lower in Alismatales than in the rest of angiosperms. The plastid data set contained 95 samples, 87 of which are Araceae (64 from targeted enrichment sequence data, 23 from public repositories), and covered 79 Araceae genera (Appendix S2). Not all samples processed using targeted enrichment have produced enough off-target reads to allow an adequate recovery of the plastid genome, hence the lower number of genera in the plastid data set. The average length of the plastid genome recovered for all 95 samples is 106,082 bp and 107,630 bp for Araceae samples, ranging from 30,527 bp to 176,835 bp.
DISCUSSION The variability of gene number recovery in Araceae and Alismatales is lower than in monocots and angiosperms in general (Figure  4 ). Within Araceae, the number of genes recovered ranges from 10 to 347 in our data set, but the variation is even greater when comparing the length of the genes recovered at 50% of the target file, which averages 96 per sample. Nevertheless, the Angiosperms353 probe set has been efficient for producing a well-resolved phylogenetic tree for Araceae. However, it is disappointing to see the probe kit performing poorly in comparison with results obtained for other angiosperm groups. The limited representation of Alismatales (four species representing four families) and Araceae (only one species, Pistia stratiotes L. ) in the OneKP data set, which was used for the design of the Angiosperms353 probes  (Johnson et al.
, 2019) , might partly explain the low gene numbers recovered for Araceae and Alismatales compared to other angiosperms groups. Out of the four samples of Alismatales in OneKP, only two (Pistia stratiotes and Posidonia australis Hook. f. , Posidoniaceae) contribute with probe sequences to the Angiosperms353 set (50 and 184, respectively), which represents only 0. 3% of the 75,451 probes found in the set. Ideally, future versions of the Angiosperms353 probes should try to mitigate this gap. This phylogenetic analysis, based on a large sample of genera (111) and sequence data (130-347 genes and associated introns), has strengthened some of the findings of recent molecular phylogenetic research on the family and provided clear and well-supported evidence for key relationships within it. For the first time, all the major clades, including all subfamilies, are fully supported, as are most of the relationships between them.
CONCLUSIONS The present phylogenomic data provide significant strengthening of the phylogenetic backbone of Araceae, which is now highly supported throughout. Using this robust framework, the classification of monophyletic subfamilies can be set on a firmer foundation. The expanded delimitation of subfamily Aroideae, including the bisexualflowered genus Calla, provides a sounder basis for future research on the evolution of a phenotypic syndrome that combines unisexual flowers, highly adapted spathes, zoned spadices, rapid anthesis, inaperturate pollen with little or no sporopollenin, and enhanced chemical defense in the tissues. Our results also strongly suggest that new research on African genera will be of particular significance to understanding the evolution of the Aroideae as a whole, since Callopsis, Nephthytis, and Anubias all display significantly different relationships from those recovered in previous, plastid-based studies; similarly, the proximity of Stylochaeton and Zamioculcadeae emerges as much less convincing.
The possibility cannot be excluded that some of the moderately supported nodes cited above may be a consequence of missing data rather than sampling gaps. It appears that the number of recovered genes with the Angiosperms353 probe set was significantly lower for Araceae genera (and Alismatales) than for other angiosperms. Obtaining sequences from a higher number of genes, either through an updated version of Angiosperms353 or using a Araceaespecific probe set, may strengthen some of the relationships still in debate.
ACKNOWLEDGMENTS We gratefully acknowledge assistance from colleagues at the  Royal Botanic Gardens ,  Kew: M. Sellaro  ( Tropical Nursery );  D. Devey  and  R. Cowan  (laboratory support);  T. Fulcher ,  I. Kahandawala , and  R. Woods  ( DNA Bank ); and  B. Gallego  and  E. Toledo  (bioinformatics support). We are equally grateful to colleagues who provided plant material:  M. Christenhusz  ( RBG Kew ),  F. Pautz ,  A. Bour ,  G. Ferry ,  M. Remy ,  C. Drechsler , and  C. Denjean  ( Conservatoire et Jardins Botaniques de Nancy, Université de Nancy I, France );  D. Scherberich  ( Jardin Botanique de la Ville de Lyon, France );  P. Boyce  ( Centro Studi Erbario Tropicale, University of Florence ); and staff of  Missouri Botanical Garden, USA. We are grateful to the anonymous reviewers who helped improve the manuscript.
Finally, we use ecological data sets, describing pollination interactions between plants and insects species  [59] [60] [61]. These data sets cover a wide range of system sizes and of interaction size distributions (see Methods and Supplementary Note 1 in the SI, for a detailed description of each data set). In the following, we give results on the music-review, email-EU, housecommittees, and congress-bills data sets, and we refer to the SI for the other data sets. Figure  2  shows the results of the hyper-core decomposition on two data sets. The relative size n (k,m) of the (k, m)-hyper-cores exhibits distinct behaviors as a function of k and m, identifying structural differences between data. In some cases, the decrease with k is rather smooth (Fig. 2a  and Supplementary Figures  2  and 3  in the SI), showing that most shells are populated.
e. cliques not completely contained in a larger clique, and promote them to a hyperedge status. Finally, we consider hypergraphs built from ecological data sets provided by the Web of life: ecological networks database  [59]. The data are in the form of bipartite graphs, where the nodes represent insect species or plants and the links connecting them represent a pollination relationship. Starting from these bipartite graphs we built two types of projected hypergraphs, obtained respectively by considering insect species as nodes and hyperedges connecting species that pollinate the same plant, or by considering plants as nodes and hyperedges connecting plants that are pollinated by the same insect species. Here we use two bipartite networks: M PL 015  [59, 61]  and M PL 062  [59, 60] , yielding the hypergraphs M PL 015 ins and M PL 062 ins with insects as nodes, and the hypergraphs M PL 015 pl and M PL 062 pl with plants as nodes.
a-e: congress-bills data set with unanimity rule. f-j: email-EU data set with union rule. Committed nodes are selected through random seeding (a,f ), top k-coreness (b,g), top s-coreness (c,h), top frequency-based Rw hyper-coreness (d,i) and top size-independent R hyper-coreness (e,j) strategies. With the top R hyper-coreness strategy, a fraction p = 1. 51 × 10 -2 in the congress-bills data set with unanimity rule is enough to allow the minority takeover over a range of β values whose extension is ∆β 0. 5. This cannot be achieved with the other strategies, for which below p = 2. 8 × 10 -2 only ∆β ∼ 0. 4 can be reached (see panels a-e). In the email-EU data set with the union rule, a fraction p = 4. 1 × 10 -3 is enough to obtain the minority dominance over ∆β 0. 5 when seeded according to the top size-independent R hyper-coreness strategy.
DATA AVAILABILITY STATEMENT The data that support the findings of this study are publicly available. The SocioPatterns data sets at http://www. sociopatterns. org/; the Contacts among Utah's School-age Population data sets at https:// royalsocietypublishing. org/doi/suppl/10. 1098/rsif. 2015. 0279; the online and political interactions data sets at https://www. cs. cornell. edu/ ~arb/data/; the Web of life ecological data sets at https://www. web-of-life. es.
10 5 FIG. 6. 56 FIG. 5. Comparison of seeding strategies for committed minorities in a naming-game process. The stationary fraction n * A of nodes supporting only the name A is shown as a function of the fraction of committed nodes p and the agreement probability β. a-e: congress-bills data set with unanimity rule. f-j: email-EU data set with union rule. Committed nodes are selected through random seeding (a,f ), top k-coreness (b,g), top s-coreness (c,h), top frequency-based Rw hyper-coreness (d,i) and top size-independent R hyper-coreness (e,j) strategies. With the top R hyper-coreness strategy, a fraction p = 1. 51 × 10 -2 in the congress-bills data set with unanimity rule is enough to allow the minority takeover over a range of β values whose extension is ∆β 0. 5. This cannot be achieved with the other strategies, for which below p = 2.
We test these predictions with both experimental and computational methods. For experimental tests, we run a pre-registered online experiment about movie preferences (N = 230). For computational tests, we leverage two large cultural datasets, namely the Internet Movie Database (N = 9424 movies) and the Movie Personality Dataset (N = 3. 5 million participants), and use machine-learning algorithms (i. e. , random forest and topic modeling). In all, consistent with how the human preference for spatial exploration adaptively varies, we provide empirical evidence that imaginary worlds appeal more to more explorative people, people higher in Openness-to-experience, younger individuals, males, and individuals living in more affluent environments. We discuss the implications of these findings for our understanding of the cultural evolution of narrative fiction and, more broadly, the evolution of human exploratory preferences.
35%. In parallel, we combine natural language processing techniques (i. e. , Sbert Transformer) and topic modeling methods to project those 9424 movies into a semantic latent space, embedding the summary plots: the closer movie summaries are in their meaning and content, the closer movies will be into this space. Seven clusters naturally emerged, and we extracted the most specific n-grams to describe their content (see Fig. 2 ). Combining both algorithmic methods, we show that at least one cluster which has emerged embeds more specifically movies with imaginary worlds, as identified by the random-forest algorithm. First, we find a significant relationship between being associated with a specific cluster and being detected as a movie with an imaginary world (X 2 (6, N = 9424) = 576. 754, p < 0.
We will use its tagging of movies with imaginary worlds in the next study. Study 2: Demographic and psychological characteristics of individuals who 'like' movies with imaginary worlds on Facebook. We now turn to specific predictions about the variability of the fascination for imaginary worlds in stories, that we derived from the adaptive sources of variability of human environmental curiosity (see "Introduction"). We predicted that people higher in Openness-to-experience, younger people, males, and people living in affluent local environments would be more likely to enjoy fictional stories set in imaginary worlds. We used the Movie Personality Dataset (MPD) which aggregates averaged personality (i. e. , Big Five) and demographic traits (i. e. , sex, age) from the Facebook myPersonality Database (N = 3.
5 million  81  ). We couple this dataset with the outcome of the random-forest algorithm which efficiently identifies movies as being set in an imaginary world or not (see "Study 1"). First, we find that, as predicted, movies with imaginary worlds on Facebook are liked by an audience that is, on average, higher in Openness-to-experience than movies with no imaginary worlds (ß = 0. 12, p < 0. 01, CI [0. 02, 0. 22], Cohen's d = 0. 24; Fig. 4 ). In other words, approximately 60% of movies with imaginary worlds have higher aggregated scores of Openness-to-experience than the mean of Openness-to-experience of movies with no imaginary world. Although we had no specific prediction derived from our hypothesis, we report the correlations between the four other traits of the Big Five and the liking of imaginary worlds (Agreeableness: ß = 0.
Observational analyses of large cultural datasets showed that people who 'like' movies with imaginary worlds on Facebook are overall higher in Openness-to-experience, younger, and more likely to be males (when controlling for the two other variables). This dataset can be biased as it aggregates personality scores of people who decided to create a Facebook account and 'like' movies on Facebook. Therefore, we replicated such findings with experimental methods: we provided consistent evidence that participants who report enjoying imaginary worlds in movies, novels, and video games are overall higher on a scale of exploratory preferences, higher in Openness-to-experience, younger, higher in systemizing, and more likely to be males. We did not find an association between the socioeconomic status of the participants and their reported preference for imaginary worlds, although participants higher in socio-economic status scored significantly higher on the scale of exploratory preferences.