dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2008; LPWG, 2017). Moreover, previous legume phylogenies have been exclusively inferred from a handful of chloroplast markers  (Doyle et al. , 1997; Wojciechowski et al. , 2004; Lavin et al. , 2005; Bruneau et al. , 2008; Simon et al. , 2009; Cardoso et al. , 2012 Cardoso et al. , , 2013;; LPWG, 2017) , even though it is preferable to infer species trees based on analysis of unlinked nuclear loci to account for different evolutionary histories of individual genes  (Maddison, 1997). Alongside improving resolution in the legume phylogeny, our main objective is to investigate the causes of the lack of resolution surrounding the initial divergence and deep-branching relationships of legumes. First, we ask whether lack of phylogenetic signal is causing lack of resolution, or whether previous studies simply did not analyse sufficiently large datasets.
Second, considering gene tree discordance, can we choose among alternative topologies to reject a hard polytomy and find support for a fully bifurcating topology? In addition to analysing sequences of nearly all protein-coding genes from the chloroplast genome, we analyse thousands of nuclear gene alignments harvested from transcriptomes and completely sequenced genomes, with a total aligned length several orders of magnitude longer than those previously used in legume phylogenetics. This means we can dissect and analyse phylogenetic signal and conflict across unlinked loci, and most likely rule out data deficiency as causing lack of resolution. We analyse these new datasets with maximum likelihood (ML) analysis, Bayesian inference (BI) and a multispecies coalescent summary method to infer the most likely relationships among the legume subfamilies.
Chloroplast proteome alignment DNA sequences of protein-coding chloroplast genes were newly generated for 49 accessions, or extracted from data sources (Supporting Information Table  S1 ). Sequence data were extracted directly from annotated plastomes in GenBank or by BLAST searches from de novo assembled contigs and transcriptomes against Medicago truncatula plastid coding reference sequences using custom PYTHON scripts. Sequences for some outgroup taxa (data from  Moore et al. , 2010)  were downloaded separately per gene from GenBank. For each gene, a codon alignment was inferred using MACSE v. 1. 01b (Ranwez et al. , 2011)  with default settings. Phylogenetic trees were inferred  with RAXML v. 8. 2 (Stamatakis, 2014)  for each gene separately to screen for erroneously aligned sequences.
Nuclear gene data and matrix assembly Whole-genome and transcriptome data were downloaded from various sources and augmented with newly generated transcriptome sequence data for six Caesalpinioideae and Detarioideae taxa (Table  S2 ). Peptide sequences were downloaded from annotated genomes, or extracted from transcriptome assemblies using TRANSDECODER (http://transdecoder. github. io/). To assemble the nuclear peptide sequence data into aligned gene matrices, we performed mcl clustering using the pipeline of  Yang & Smith (2014) , with a hit fraction cut-off of 0. 75, inflation value of 2 and a minimum log-transformed e-value of 30. These settings yield clusters with good overlap between sequences and good alignability (omitting genes that are too variable), with loss of only a few short gene clusters.
A majority-rule consensus tree was constructed using sumtrees. py (from the Dendropy library  www. newphytologist. com (Sukumaran & Holder, 2010 )) from 12 500 total posterior trees, representing the MCMC cycles 501-1000 of each replicate. BI analyses were also not partitioned, as the CATGTR model describes heterogeneity across alignments more accurately than partitioning by gene and/or codon as the substitution process also varies across gene sequences and codon positions. The LG4X and CATGTR models have been shown to provide a better fit to empirical amino acid sequence data  (Lartillot & Philippe, 2004; Le et al. , 2012)  and amino acid sequences are more suitable for resolving deep divergences because they are less saturated with substitutions (silent substitutions are absent), and thus less prone to long branch attraction.
We have not analysed nucleotide sequences for the large nuclear gene dataset to avoid costly computation time to generate analyses that would be inferior to those presented here. For the multispecies coalescent analysis with ASTRAL, we used ML topologies (not bootstrapped gene trees; see  Mirarab et al. (2014b) ) of the one-to-one orthologue gene trees estimated with RAxML, using local posterior probability and quartet support to evaluate the inferred topology  (Sayyari & Mirarab, 2016). We also used the polytomy test in ASTRAL  (Sayyari & Mirarab, 2018)  to evaluate whether a hard polytomy can be rejected for the relationships among subfamilies, where P < 0. 05 is considered to reject the null hypothesis of a polytomy. We used SPLITSTREE4 to draw a filtered supernetwork  (Whitfield et al.
Counting supporting bipartitions for key nodes across gene trees Using a custom PYTHON script (Notes S1), numbers of matching and alternative bipartitions across the RT gene trees were counted for the nodes labelled A-H in Figs  3  and 4 (b) (see later), to assess monophyly of each of the subfamilies and combinations (clades) of subfamilies, against the outgroup, across all gene trees. For each gene tree, we first assessed whether all six groups (five subfamilies plus the outgroup) are present and gene trees with missing groups were not taken into account. Next, we evaluated whether the gene tree includes a matching bipartition for the family, each subfamily and all possible combinations of subfamilies. A matching bipartition means that all taxa of a subfamily or combination of subfamilies are separated from all other taxa in the gene tree, including the outgroup, thus constituting support for that clade to be monophyletic.
Results The chloroplast alignment includes 72 protein-coding genes, for 157 taxa (including 111 legume species; Table  S1 ), with a total aligned length of 75 282 bp or 25 094 amino acid residues. From transcriptomes and fully sequenced genomes, we gathered 9282 homologous nuclear-encoded gene clusters for 76 taxa, including 42 legume species (Table  S2 ). From these clusters, we extracted protein alignments of 1103 one-to-one orthologues for species tree inference with a total aligned length of 325 134 amino acids We used different accessions for the chloroplast and nuclear datasets for some species that are present in both (nine altogether; see Table  S1 ), meaning they are probably derived from different individuals of the same species. This is unlikely to cause conflicting results as we focus on deep-branching relationships, and moreover their phylogenetic positions are fully congruent in the chloroplast and nuclear species trees.
Inferring the species tree Our analyses reveal that both the chloroplast and nuclear datasets resolve all subfamilies as monophyletic with (nearly) full support (Figs S1  -S7 ). The relationships among the subfamilies are less robustly resolved (Figs 2-4, S1-S7), with, in particular, the position of the root of the family and the Caesalpinioideae-Papilionoideae sister relationship receiving low support in some analyses, and indeed a polytomy could not be rejected for the latter. The clade consisting of Papilionoideae, Caesalpinioideae and Dialioideae is recovered in all analyses, with Duparquetia as the sister group to this clade as inferred from chloroplast data. Duparquetia is not sampled for nuclear data. Transcriptome or genome sequencing is necessary for this taxon to confirm the relationship found by chloroplast data.
The position of the root of the legume family (i. e. the relationships between Cercidoideae, Detarioideae and the clade comprising the remaining subfamilies) is more difficult to resolve, presumably because of the long stem branch, and here the chloroplast and nuclear datasets estimate conflicting topologies. The chloroplast alignment weakly supports Cercidoideae as sister to the rest of the family (Figs 2c, S1-S4), except in the BI analysis of nucleotide sequences. This suggests that the sister-group relationship of Cercidoideae with the rest of the family is the most likely rooting as inferred from chloroplast data, but given the low BS values and lack of resolution in the BI analysis of nucleotide sequences, phylogenetic signal in the chloroplast data with regard to the root node appears to be limited.
Evaluation of gene tree support and conflict While the chloroplast and nuclear phylogenies show different topologies with regard to the first two dichotomies within legumes, all analyses of the nuclear data (ML, BI and multispecies coalescent) yield the same topology at the base of the family  (Figs 3, 4) , showing a sister-group relationship of Cercidoideae and Detarioideae and a clade comprising the remaining three sampled subfamilies as their sister clade. Because the nuclear dataset comprises 1103 loci sampled from across the nuclear genome that are therefore probably largely unlinked, while the recombination-free chloroplast genome constitutes just a single locus, the nuclear topology should be considered a more realistic estimate of the species tree topology. However, when evaluating gene tree conflict, it is clear that many conflicting bipartitions exist, with the most prevalent being nearly as frequent across gene trees as compatible bipartitions (Fig.
Acknowledgements This work was supported by the  Swiss National Science Foundation  (grant  31003A_135522  to CEH), the  Department of Systematic and Evolutionary Botany, University of Zurich , the  Natural Sciences and Engineering Research Council of Canada  (grant to AB), the  UK National Environment Research Council  (grant  NE/1027797/1  to RTP) and the  Fonds de la Recherche Scientifique of Belgium  (grant J. 0292. 17 to OH). We thank the  S3IT of the University of Zurich  for use of the ScienceCloud computational infrastructure and the  Functional Genomics Center Zurich (FGCZ)  for library preparation and sequencing. We thank  Robin van Velzen ,  Pascal-Antoine Christin ,  Donovan Bailey  and two anonymous reviewers for insightful comments that improved the manuscript.
The representative cloned rtL180M+A181C+M204V sequences from each patient, together with other rtA181C-containing sequences, have been deposited in GenBank (accession number: MF682469-MF682490). Among the 18 patients, 15 (83. 33%) were infected with HBV/C and three (16. 67%) with HBV/B. The HBV genotype distribution showed no significant difference when compared with that of the rtA181C-negative patients across the studied population (HBV/B 14. 0%, HBV/C 85. 2%, HBV/D 0. 8%). The clinical data and HBV mutation patterns, by direct and clonal sequencing, of the 18 patients are summarized in Table  2. Clinical course of five representative cases with rtA181C substitution and HBV mutant evolution during antiviral treatment Serial serum samples were obtained from five rtA181C-positive patients (patient 1 through patient 5 in Table  2 ) whose clinical information, and dynamic changes of their mutant viruses, were longitudinally analysed.
Replication capacity and drug susceptibility of rtA181C mutants Phenotypic analysis was performed for four mutants and one wild-type strain derived from serial serum samples of patient 5, as well as for two classical ETVresistance mutants and one wild-type strain of genotype C HBV (GenBank accession number: GQ402156, GQ402157, and GQ402151) derived from another ETV-refractory patient  [16]. In addition, a laboratory strain rtL180M+M204V(lab) which was generated by eliminating rt181C mutation of the rtL180M +A181C+M204V was taken into the phenotypic analysis of drug resistance. Compared to the wild-type strain, rtL180M+A181C+M204V and rtL180M +A181V+M204V mutants had a modest decrease in viral replication capacity (45. 7% and 43. 1% of the wild-type, respectively). The two classical ETV-resistance mutants also exhibited a modest decrease in viral replication (53.
The successive resistance history to LAM and ADV was likely a favourite factor for the development of the rtL180M+A181C+M204V mutation, which could contribute to the rare occurrence of rtA181C as an ETV resistance mutation. HBV genotype might be relevant in the evolution and development of drug resistance  [28]. In North China, HBV/C is dominant while HBV/B is subdominant. We have recently reported that HBV/C and HBV/B-infected patients had a similar rate of ETVresistance mutations, but different ETV-resistance mutation patterns  [29]. In this study, the occurrence of an rtA181C mutation was independent of HBV genotypes. The rtL180M+A181C+M204V mutant sequences were not found being documented previously in GenBank. Unlike rtA181 T mutation which may cause sW172stop and non-stop (sW172S, sW172L) mutations and the stop mutation will delete an HLA-A2-restricted s172-180 (env335-343) epitope of cytotoxic T lymphocytes (CTL)  [27]  the rtA181C mutation only caused sW172C mutation in this study without deleting the CTL epitope.
These patients were from the Database of Beijing 302 Hospital, and all provided their informed consent for the use of their samples for research before enrolment in the Database of Beijing 302 Hospital. The study was approved by the Ethics Committee of Beijing 302 Hospital. Serological markers, quantitation of HBV DNA, and sequencing of HBV RT gene Biochemical and serological markers, and HBV DNA level, of the patients were routinely detected in the Central Clinical Laboratory of Beijing 302 Hospital. HBV DNA level was determined using a real-time quantitative PCR kit (Fosun Pharmaceutical Co. , Ltd. , Shanghai, China) with a lower limit of detection (LLOD) of 100 IU/ml before April 2012 and 40 IU/ ml afterwards. If sufficient amount of serum was available, HBV DNA of interesting samples below the LLOD was further quantitated using AmpliPrep/ COBAS TaqMan (Roche Diagnostics) with an LLOD of 20 IU/ml as described previously  [34].
Molecular modelling of HBV RT binding to ETV triphosphate (ETV-TP) The modelling structure of the HBV RT was constructed by SWISS-MODEL (https://www. swissmodel. expasy. org) based on the crystal structure of the HIV RT [Protein Data Bank (PDB) accession number; 1RTD]. This HBV RT modelling structure was used as a wild-type to build the structures of the HBV NA-resistant mutants. Autodock software (version 4. 2. 6, molecular graphics laboratory) was used to simulate the docking process and evaluate the binding energy of HBV RT and ETV-TP. Autodock is a component of the MGLTools that is freely available for academic use. More detailed information about Autodock could be found at the following website: http:// mgltools. scripps. edu/. To further understand the conformational change of the rtA181C mutant protein, a 2000-picosecond molecular dynamics simulation was performed by NAMD (version 2.
Acknowledgements DX, FZ, and ML participated in substantial contributions to the conception or design of the work. YL, YZ, XL, and MN oversaw the acquisition, analysis, or interpretation of data. RC, JS, LS, DL, YL, LL, KZ, XX, and ZX collected clinical information and facilitated the establishment of the HBV gene database. YL, YZ, XL, MN, DX, ML, and FZ drafted the work or critically revised it for important intellectual content. All authors read and approved this version of the paper to be published.
Result Production and purification of Der p 5 allergen We produced a recombinant form of the mite allergen by molecular cloning using the Pichia pastoris expression system. The form produced of Der p 5 allergen was purified using chromatography methods. The purification of Der p 5 allergen was carried out in three stages; the containing fractions were analysed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Fig. 1(A)  shows the chromatogram corresponding to the last purification stage using the HiTrap QHR column. The purified allergen was analysed by SDS-PAGE. As indicated in Fig. 1 (B), the band was present corresponding to the recombinant Der p 5 allergen with 14 kDa. By amino acid (Nt) sequencing and electrospray ionization and quadrupole time-of-flight mass spectrometry, we confirmed that this band corresponds to the mature form of the Der p 5 mite allergen (data not shown).
Experts identified 34,640 images of modern (extant) planktonic foraminifera to the species level. These images are served as species exemplars through the online portal Endless Forams (endlessforams. org) and a taxonomic training portal hosted on the citizen science platform Zooniverse (zooniverse. org/projects/ahsiang/ endless-forams/). A supervised machine learning classifier was then trained with ~27,000 images of these identified planktonic foraminifera. The best-performing model provided the correct species name for an image in the validation set 87. 4% of the time and included the correct name in its top three guesses 97. 7% of the time. Together, these resources provide a rigorous set of training tools in modern planktonic foraminiferal taxonomy and a means of rapidly generating assemblage data via machine learning in future studies for applications such as paleotemperature reconstruction.
10. 1029/2019PA003612 Key Points: • We built an extensive image database of modern planktonic foraminifera with high-quality species labels, available on an online portal • Using this database, we trained a supervised machine learning classifier that automatically identifies foraminifera with high accuracy • Our database and machine classifier represent important resources for facilitating future paleoceanographic research using foraminifera Supporting Information: • Supporting Information S1 • Table  S1  • Table  S2  • Table  S3  • Table  S4  1. Introduction When a young naturalist commences the study of a group of organisms quite unknown to him, he is at first much perplexed to determine what differences to consider as specific, and what as varieties, for he knows nothing of the amount and kind of variation to which a group is subject; and this shows, at least, how very generally there is some variation.
Regardless, this variation requires the practitioner to demarcate species at some point along a morphological continuum. As a result, the circumstances of one's taxonomic training has a significant effect on the boundary conditions of the morphospace definition of a specific species that is used in practice. Different groups of taxonomists have developed different concepts for the morphological identity of species over time, with self-trained taxonomists having the most divergent opinions of species identity  (Al-Sabouni et al. , 2018). One potential reason for such diverging opinions is the limited number of published exemplar images for species in taxonomic references and online resources. Here we have generated the largest image database of modern planktonic foraminifera species to date through the combined efforts of more than twenty taxonomic experts.
New images are then used only to train the unfrozen layers at the end of the CNN in order to fine-tune the model to the task at hand. This can be an efficient and effective strategy when one does not have a very large data set with which to train a CNN from scratch. For example, ImageNet, which contains over 14 million images of various objects and activities, has been used to train many CNNs, and the resulting weights are freely available. Transfer learning thus allows accurate models to be trained with hundreds to thousands, rather than millions, of images. In this study, we generate a large image data set of planktonic foraminifera with associated high-quality species labels assigned by taxonomic experts. We then use these data to train a supervised machine learning classifier using deep CNNs that is able to automatically identify planktonic foraminifera with high accuracies that are comparable to those of human experts.
Paleoceanography and Paleoclimatology or damaged planktonic foraminifera by human classifiers. To identify these images to the species level, we used the online platform Zooniverse to create a private portal for taxonomic experts to identify images. As several taxonomies exist for extant planktonic foraminifera, we standardized the species list by using the SCOR/IGBP Working Group 138 taxonomy  (Hottinger et al. , 2006). Further details regarding the Zooniverse interface and data collection are available in the supporting information. To collect statistics on classifier accuracy and avoid inaccurate labels resulting from single-user errors, four independent taxonomists classified each image before it was considered complete. Classifiers were required to identify each image they encountered.
A total of 24 taxonomists submitted at least one classification and of these, 16 submitted more than 5,000 classifications and are co-authors on this manuscript. In sum, 140,616 unique classifications were collected on the 40,000 unique images uploaded. The raw data was processed to determine how many objects received four independent classifications, and what degree of agreement existed between the independent object classifications. All objects with 75% agreement or higher were considered to have high-quality classifications and were retained for CNN training and validation. The final set of YPM Coretop Collection images with high-quality species labels includes representatives from 34 species and comprises a total of 24,569 individuals.
Species Identification of the Buckley Collection We included images of planktonic foraminifera from the Henry A. Buckley collection at the Natural History Museum in London, which includes samples from various localities worldwide, primarily from the Pacific, Atlantic, and Indian oceans (see  Rillo et al. , 2016 , for details regarding the Buckley specimens). A total of 1,355 slides containing identified modern specimens were segmented using AutoMorph  (Hsiang et al. , 2017;  available at https://github. com/HullLab/AutoMorph) for inclusion in the machine learning data set. All slides were segmented using an object size range of 50-800 pixels and a test threshold value range of 0. 5-0. 7, incremented by 0. 01, resulting in 30 segmentations per slide. The segmentation results were examined by eye to determine the ideal threshold value that maximizes the number of correctly identified foraminifera and minimizes the number of spurious objects segmented by the algorithm.
A total of 17,383 objects were segmented out of the 1,355 modern slides. After segmentation, A. B. and M. C. R. examined all segmented objects using a modified version of the Specify software  (Hsiang et al. , 2016)  in order to (1) flag spurious identified objects for removal (usually bits of background or slide labels) and (  2 ) mark whether the original species identification provided by Buckley matched modern species concepts unambiguously. Objects for which both A. B. and M. C. R. agreed with Buckley's original label were retained for the final image data set, resulting in a total of 10,071 images. For certain species, Buckley used antiquated names that mapped unambiguously onto modern synonyms; these names were updated to follow the SCOR taxonomy (Table  S1 ), and the species images were included in the final data set.
Supervised Machine Learning Following convention, we withheld 20% of the combined YPM + Buckley data set as a test data set (i. e. , validation set), with the remaining 80% used for training. For the YPM data set, 20% of the data set had already been designated as withheld objects during data set creation (see  Elder et al. , 2018). We used this list to split those images with high-quality species labels into a test set (i. e. , all images with originally "withheld" object IDs) and a training set. We initially tested three CNNs: VGG16  (Simonyan & Zisserman, 2014) , DenseNet121  (Huang et al. , 2016) , and InceptionV3  (Szegedy et al. , 2015). These CNNs are commonly used neural networks for image classification. We used the implementation of these CNNs from the Keras Python API  (Chollet et al.