dataesr/scientific-paragraphs-categorization huggingface dataset:
1. 5 [23]  and trimmed using the software trimAl applying a gap threshold of 0. 75  [24]. Maximum likelihood trees were build for marker genes from Phylosift, individual and 53 concatenated amino acid sequences annotated with KAAS (16S rRNA, ef1aLike, hsp70, rplA, rplV, rplB, rpsI, rplC, rpsS, rpsG, rplP, rp L25/L23, rplF, rplK, rplE, rp S12/S23, rpsK, rpsH, rp L18P/L5E, rplM, rp L24, psaA, psaB, psaC, psaD, psaE, psaF, psaL, psbA, psbB, psbC, psbD, psbE, psbH, psbL, psbV, rbcS, rbcL, rpoA, rpoB, rpoC1, rpoC2, ilvB, sufB, petJ, chlB, chlN, chlL, atpA, atpD, atpE, clpC, groEL) using RAxML  [25]. The RAxML settings included rapid bootstrap analysis and automatic determination of protein substitution model while the number of distinct starting trees was based on bootstrapping criteria. For these trees, corresponding sequences from 47 reference organisms, selected to cover the genetic diversity within most microalgae lineages, were downloaded from NCBI.
virescens and 39 contigs with a total length of 74,603 bp to G. semen (Table  1 ). In comparison, the size of the chloroplast genome of the marine raphidophyte H. akashiwo ranges between 159,321 and 160,152 bp  [29, 30]  suggesting approximately 50% completeness of the novel freshwater raphidophyte chloroplast genomes. The chloroplast contigs from G. semen and V. virescens had a mean coverage of 613× and 472× respectively with individual contigs having more than 1000× coverage. The majority of the contigs had in both cases a length between 1000-5000 bp and only 3 and 1 contigs respectively in each assembly were longer than 5000 bp. The assembled contigs can be found at NCBI under the accession numbers MK658836 and MK674494. No contigs from the nuclear genome of the two algal species were identified in the other bins, which instead consisted of contigs from various bacterial species.
Annotated genome sequences for multiple species are available and links between genetic mutations and morphological aberrations are curated in accessible online databases such as Flybase (www. flybase. org). Besides the availability of unique genetic toolkits, these resources have made the genus ideal for studies aiming to trace morphological homoplasy between species to their molecular underpinnings (e. g. ,  Wittkopp et al. 2003b; Prud'homme et al. 2006; Kagesawa et al. 2008; Tanaka et al. 2009; Frankel et al. 2012; Signor et al. 2016; Yassin et al. 2016a, b). From two major taxonomic references, we conceptualized 490 morphological characters among 56 drosophilid species. By analyzing the evolution of these traits on a molecularly inferred phylogeny, we were able to quantify the extent of morphological homoplasy in this important clade.
TAXON SAMPLING We selected 56 drosophilid species for which molecular sequences were available in GenBank and full morphological descriptions could be obtained from two major taxonomic books,  Okada's (1968)  Systematic Study of the Early Stages of  Drosophilidae and Bächli et al. 's (2004)  The Drosophilidae (Diptera) of Fennoscandia and Denmark (Table  S1 ). These books represent two of the most explicit standardized and illustrated descriptions for drosophilids juveniles and adults. We selected species from the main drosophilid clades, that is, the subfamily Steganinae with its two tribes the Steganini and the Gitonini and the subfamily Drosophilinae with its two tribes the Colocasiomyini and the Drosophilini, following Yassin's (2013) classification scheme (see O'Grady and DeSalle 2018 for the current status of drosophilids phylogenetics).
MOLECULAR PHYLOGENETIC TREE In order to define the phylogenetic relationships of species that were not included in  Yassin's (2013)  family-wide phylogenetic revision, we obtained molecular sequences from GenBank for the 56 taxa for one mitochondrial (COII) and four nuclear genes (28S rRNA, Adh, Amyrel, and Gpdh; Table  S1 ). For each gene, DNA sequences were aligned using the Muscle program  (Edgar 2004)  with default parameters as implemented by the MEGA7 software package  (Kumar et al. 2016 ). The alignments were then concatenated in a single nexus file (Supporting Information S1). MEGA7 was also used to infer the best DNA substitution model for each gene. For the five genes, the GTR+G+I model had the lowest Akaike Information Criterion (AIC) value and thus was chosen for phylogenetic inference.
Relaxed clock models had better marginal likelihood values (TK02 = -42,657 and IGR = -42,658) than the strict  (-42,684)  and no clock  (-42,712). Consequently, we chose the unpartitioned, relaxed clock under the TK02 model. For the trees to be used in subsequent homoplasy analyses in this paper, we performed two simultaneous runs of 2,000,000 generations with sampling every 100 generations for the unpartitioned matrix under the TK02 relaxed clock model (Supporting Information S1). However, we stopped the runs after 1,004,000 generations since an average SD of 0. 006 was attained. We then estimated the consensus tree after a burning period of 25% of the 20,086 sampled trees (Fig. 1 ). In subsequent analyses, we rerooted the consensus and sampled trees by considering the subfamily Steganinae a sister outgroup to the remaining species following the general consensus in drosophilid systematics  (Yassin 2013; O'Grady and DeSalle 2018).
We then used the NBClust package in R  (Charrad et al. 2014)  to estimate the optimal number of clusters for each character using Euclidean distance and Ward's method. Values belonging to the same cluster were then attributed the same code in the data matrix. Figure  2A-D  shows an example for the coding of a single numerical character (wing length). (2) Verbal descriptions: Qualities such as "large," "fusiform," "concave," "divergent," and so on were directly coded when only few distinct values were given (e. g. , "concave" vs. "convex"). When illustrations were present, we used ImageJ package  (Abramoff et al. 2004 ) to estimate lengths, angles, and areas for characters for which qualities were slightly ambiguous (e. g. , "elliptical" vs. "ovoid," "subequal" vs. "slightly equal," etc. ).
We six possible changes for character spaces in a pair of species depending on the degree of sameness, similarity, and difference of the ancestral and present states in the two species (Fig. 3 ). We then used PAUP * to infer under the ACCTRAN and DELTRAN models the ancestral states at each internal node. Using a customized perl script we obtained for each taxon for each trait its current state, its original state, and the internal node at which the state changed (Supporting Information S4). For each trait, we conducted all possible pairwise comparisons between species with no missing data and estimated the frequency of each change category (Supporting Information S4). Unlike parallelism and convergence, reversal does not imply comparisons between taxa, but a comparison between the state present in a taxon with the states present in its ancestors, and consequently this may lead to any of the possible between-species mechanisms shown in Figure  3.
Using a perl script (Supporting Information S5), we compared for each trait the current state of each taxon with that of its ancestors and counted incidences were a reversal (e. g. , 0 → 1 → 0) was found. Because the path of each taxon to the root had to be manually entered in the script from PAUP * node labeled cladogram, we conducted these analyses on a single tree, that is, the consensus Bayesian phylogeny.
HOW MANY MORPHOLOGICAL CHARACTERS ARE THERE? In order to draw general conclusions about the extent of homoplasy, one should be aware about how representative is the studied sample of characters. Information on the total number of morphological traits is lacking, not only because their study often requires profound learning and expertise but also because of the lack of a standard conceptualization approach. To the best of our knowledge, our study represents the largest morphological dataset to be analyzed for the prevalence of homoplasy in an insect. Various Drosophila evolutionary developmental studies have investigated some of the traits analyzed here (Table  1 ). Morphological phylogenetic studies have analyzed 18  (Throckmorton 1962  as reanalyzed by  Grimaldi 1990) , 217  (Grimaldi 1990) , 68  (Hu and Toda 2001) , and 37 traits  (Yassin 2013 ).
melanogaster and the morphological differences between wild species  (Sturtevant 1921). Patterson and Stone (1952)  argued that resemblance between differences and D. melanogaster mutants might involve homologous or quite different loci, which seems to be the case as unraveled by recent evo-devo studies (e. g. ,  Frankel et al. 2012 Frankel et al. , 2016b;; Yassin et al. 2016a; Yassin et al. 2016b ). On the online database Flybase, the effect of genetic mutations on different anatomical structures is curated, and for each gene in D. melanogaster links to its orthologous genes (when present) in other Drosophila species are also provided. However, data on the evolutionary differences in these structures are still scattered in the taxonomic literature for the nearly 4,500 drosophilid species.
The matrix provided here represents a first attempt to curate these disperse data and future research should complement missing data, adding more taxa and enriching Flybase resources with structures evolution. This endeavor would significantly promote the identification of the genetic underpinnings of morphological changes leading to better character state delimitation. Given the prevalence of the "deep homology" of developmental pathways in animals, such knowledge would ultimately lead to a better understanding of the molecular basis of morphological homoplasy in Drosophila and beyond.
Dust emission Two kinds of calculations are required to arrive at the dust emission spectra, which depend on whether the medium considered is optically thin or thick. In the case of an optically thin medium, the dust emission spectrum is computed with the DustEM 5  code described in  Compiègne et al. (2011). DustEM is a numerical tool, which computes the dust emission and extinction as a function of the grain size distribution and optical properties for a given radiation field. In the case of an optically thick medium, we use the 3D Monte Carlo radiative transfer model CRT  (Continuum Radiative Transfer, Juvela & Padoan 2003; Juvela 2005) , which is coupled to DustEM  (Ysard et al. 2012). We first assume a filamentary cloud externally heated by the standard interstellar radiation field (ISRF) as defined by  Mathis et al.
Optically thin medium Using the DustEM code, the equilibrium temperatures of the grains presented in Table  1  and their SEDs as flux densities have been calculated and are shown in Figs. 3  and 4 , respectively. As described in Sect. 2, the temperatures and spectra are computed for the standard ISRF with G 0 = 1 for various values of the size distribution centroid: a 0 = 0. 1 µm, 1 µm, 10 µm, 100 µm, 1 mm, and 1 cm. First of all, pure a-C:H grains have a far different behaviour from the other grain types. Indeed, Fig. 1  shows that their opti-10 -1 10 0 10 1 10 2 10 3 a 0 (µm)   1 , illuminated by the ISRF with G 0 = 1. The same colour code is used as in Fig. 2. cal properties are quite different with an absorption efficiency always inferior to the scattering efficiency, regardless of the grain size.
Acknowledgements. We would like to thank  A. Dutrey ,  A. Maury , and  S. Guilloteau  for fruitful discussions. We also thank  M. Juvela  and  V. Ossenkopf  for making their softwares publicly available (for radiative transfer and dust optical properties calculations, respectively). Finally, we thank our anonymous referee whose careful reading and interesting comments helped to clarify and improve the paper. I. Jimenez-Serra  acknowledges the support from the  STFC  through an  Ernest Rutherford fellowship  (grant number  ST/L004801 ), from the  FEDER  funding under grant  ESP2017-86582-C4-1-R , and from the  Spanish State Research Agency (AEI)  through project number  MDM-2017-0737   Unidad de Excelencia Mara de Maeztu-Centro de Astrobiologa (INTA-CSIC ). A. P. Jones,  L. Verstraete , and  N.
Methods. To this end we used fully coupled radiation-hydrodynamics RAMSES-CUDATON simulations of the epoch of reionization. Results. We find that reducing the speed of light by a factor 5 (20, 100) leads to overestimating the post-reionization average volume-weighted neutral hydrogen fraction by a similar factor ∼5 (20, 100) with respect to full speed of light simulations. We show that the error is driven by the hydrogen -photon chemistry by considering the analytical solution for a strongly ionized hydrogen gas in photoionization equilibrium. In this regime, reducing the speed of light has the same effect as artificially reducing the photon density or the hydrogen photoionization cross section and leads to an underestimated ionizing intensity. We confirm this interpretation by running additional simulations using a reduced speed of light in the photon propagation module, but this time we keep the true speed of light in the chemistry module.
Reduced speed of light, dual speed of light The ATON RT module consists of two main sub-modules: an advection (i. e. photon propagation) module and a thermochemistry module, which is purely local and models the interaction between the photons and the hydrogen gas. We therefore consider the two following speeds of light: c pr : the speed of light used in the propagation module; and c ch : the speed of light used in the thermochemistry module. With these definitions, the usual RSL approximation is obtained by using the same reduced speed of light c rsl =c pr =c ch in both modules. This is the approach of  Rosdahl et al. (2013 ), Trebitsch et al. (2017 ), and Katz et al. (2017)  for instance. As we will see, this has a strong impact on the volumeweighted average neutral ionized fraction after reionization.
Conclusions We have investigated the impact of the RSL approximation on the properties of the IGM using fully coupled RHD RAMSES-CUDATON simulations. We find that reducing the speed of light by a factor 5 (20, 100) leads to overestimating the post-reionization neutral hydrogen fraction by the same factor ∼5 (20, 100, respectively) with respect to our reference simulation using the full speed of light. We consider the simple analytical expression for a hydrogen gas in photoionization equilibrium to show that the error is driven by the hydrogen -photon chemistry. Reducing the speed of light has the same effect as artificially reducing the reaction cross section or reducing the photon density, and results in underestimating the ionizing intensity J 21. We confirm this interpretation by running additional simulations using a reduced speed of light in the propagation module, but keeping this time the true speed of light in the chemistry module.
Acknowledgements The simulations used in this study were performed on CSCS/ Piz Daint  ( Swiss National Supercomputing Centre ), as part of the "Shining a light through the dark ages" PRACE allocation obtained via the 16 th call for  PRACE  Project Access. Auxiliary simulations were performed at the  HPC center of the Strasbourg university. PO acknowledges funding from the  Agence Nationale de la Recherche  through the project  ORAGE. The author thanks  D. Munro  for freely distributing his Yorick programming language 2.
Functionally, we summarize recent and ongoing work that leverages whole-genome sequences to identify and characterize genes controlling 22 agronomically important Prunus traits. These include phenology, fruit quality, allergens, disease resistance, tree architecture, and self-incompatibility. Translationally, we explore the application of sequence-based marker-assisted breeding technologies and other sequence-guided biotechnological approaches for Prunus crop improvement. Finally, we present the current status of publically available Prunus genomics and genetics data housed mainly in the Genome Database for Rosaceae (GDR) and its updated functionalities for future bioinformatics-based Prunus genetics and genomics inquiry.
SNP Melting vs. nonmelting flesh and clingston vs. freestone P. persica G4 (M and F) Endopolygalacturonase (Prupe. 4G261900 and Prupe. 4G262200) Fine mapping and CG functional classification  5, 6  SNPs and small indels Stony hard texture P. persica G6 (Hd) PpYUC11-like (ppa008176m) Fine mapping, GWAS, and CG approach 33       In brackets, assembled scaffolds prior the build of pseudomolecules genome was sequenced  13  , producing an assembly of 323. 8 Mb with 41,294 protein-coding genes (Table  2 ). The sequence of "Texas" almond (P. Arús, unpublished results) is already publicly available at the Genome Database for Rosaceae (GDR). Other de novo assemblies of Prunus genomes are underway, such as that of another sweet cherry cultivar, "Regina" (E. Dirlewanger, unpublished results), while those for other Prunus crops, plum, apricot, and other wild crop relatives are expected to be released in the next several years.
Although SSRs and SNPs are and will remain the principal genotyping tools in Prunus species lacking a reference genome, the continuous cost reduction of sequencing techniques forecasts the routine use of resequencing data in future Prunus variability studies. Currently, increased sequencing efforts are feeding the databases and providing important sequence resources for diversity and domestication analyses. In a near future, we may expect an increasing number of studies focused on the identification of particular genomic regions, such as those showing differential patterns of diversity as a consequence of domestication or breeding, gene families, or DNA features such as transposable elements (TEs). Such studies may answer fundamental questions concerning when, where, and how domestication events took place in the development of certain Prunus species.
If a breeding parent does not have coupling-phase linkage for desirable alleles within a haploblock containing multiple quantitative trait loci, such tight linkage might be targeted for recombination in the next generation of the target region, where a small set of genes can be identified. This approach, supported by many RNA-seq datasets available for most Prunus species 10 and epigenetic analysis, has led to the discovery of several causal genes for certain traits and of strong candidates for others in peach and other Prunus (Table  1 ) that are reviewed below.
The second component is conceptual, giving explicit attention to each elite germplasm individual (parents, selections, and cultivars) just as breeders do. The common way to deal with large genotypic datasets is a quantitative summary across the germplasm, but this approach easily overlooks each germplasm individual. The third component is an analytical synthesis of genome-wide genotypic data into three genetic vectors of breeding relevance: (1) allelic variation (patterns of similarity among alleles within each locus); (2) linkage (patterns of recombination among loci within each individual); and (3) relatedness (patterns of shared ancestry among individuals within a population). Together, the patterns of alleles over loci over generations can provide emergent genetic knowledge on elite germplasm individuals to improve the precision with which individuals are used in breeding.
The key qualitative leap in information now available is the positioning of all recombination events that have led to each plant. SNP datasets of pedigreeconnected material must be carefully curated by removing errors, ascertaining all pedigree connections, phasing each individual, and imputing missing data  186. Haploblocking (section "Haploblocking Prunus genomes") also helps. The fourth component is enabling user experience of the holistic "genotype" of each elite individual. It is proposed that sensory experience of DNA information that focuses on elite individuals, just as for phenotypic information, would help breeders better understand what they have and then target development of what they want. While DNA-based genetic information cannot yet be heard or tasted, one way for breeders to "experience the genotype" of an elite individual is by seeing it.
The target sequence recognized by the spacer will be a protospacer sequence, located contiguous to an adjacent motif recognized by Cas9 required for DNA cleavage, that is an NGG nucleotide arrangement called protospacer adjacent motif (PAM). Currently, new nucleases replacing Cas9 are the focus of new research that uses different PAM motifs  192. By computing on described genomes, PAM datasets and their contiguous regions can be reviewed and summarized in datasets. In this way, several datasets for gRNA and PAM required in DNA editing have been described and used for on-target and off-target predicting activity of gRNAs inside a genome  193. In the case of Prunus spp. , P. mume datasets with information about the target sites of Cas9 (i. e. , NGG) and newer nucleases such as Cpf1 (which uses TTTNs and TTNs as PAMs) are available at the CRISPR-Local 194 website (http://crispr.
Existing and new bioinformatics tools and resources for Prunus The availability of extensive sequence data forms an essential genomics resource in designing various research platforms to understand the biology of crops and to apply the knowledge in their improvement. Three primary sequence databases are GenBank  219  , European Nucleotide Archive  220  , and the DNA Data Bank of Japan  221. As of September 12, 2018, a search of NCBI for data on Prunus revealed 4 genomes, 85,014 genes, 196,367 proteins, 2,994,795 nucleotide sequences, 2481 probes, and 2712 records in the short read archive (SRA). The WGS data include P. persica v2. 0  10, 16  , P. avium v1. 0 12 , P. mume v1. 0 11 , and P. yedoensis var. nudiflora (assembly Pyn. v1)  13. Users can also download the WGS data from their FTP site and perform BLAST analyses.
In the GDR  222  , in addition to two assemblies of peach, P. persica genome v1. 0 (IPGI 2013) and v2. 0, the whole-genome assembly of sweet cherry P. avium genome v1. 0 and almond P. dulcis Texas v2. 0 are available for Prunus. Additional data provided by GDR on these assemblies include computational annotation of predicted genes with homology to genes of closely related or model plant species and assignment of InterPro protein domains  223  and GO terms  224, 225. NCBI runs a separate gene annotation pipeline to annotate WGS data. To help researchers compare two different gene annotation sets, GDR performs BLAST analysis between the NCBI annotated genes and genes from the original genome assemblies. The WGS data in GDR can be accessed through the species page, gene/transcript search page, JBrowse  226  , and BLASTX  227.
Newly added functionality in the gene/transcript search page allows users customize output to include various functional annotation data in the result table. The Prunus SNP array data and the genotyping data using these SNPs are available to view, search, and download in JBrowse and other pages in GDR. In the SSR and SNP genotype search pages, data from nine SSR and four SNP genotyping projects are available for Prunus. The new SNP genotype search page as well as a new plugin in JBrowse in GDR allows users to choose a specific genomic region and peach accessions to view the individual genotype. The latest and most efficient tool for transcriptome analysis, RNA-seq, allows not only the assessment of the expression level of specific genes but also the detection of less-represented transcripts, allelic-specific expression of transcripts, post-transcriptional mutations, and the expression of splice variants.