dataesr/scientific-paragraphs-categorization huggingface dataset:
The Agalma pipeline produces a large, but sparse matrix as compared to OF-PTP (Fig. 4 ). In cases where contamination is common, as with myxozoan data, sparse data matrices have high numbers of partitions with single species represented per clade, which in turn are enriched for contaminant sequences. Partitions with greater than one species of myxozoan present have a lower potential to include contamination. The OF-PTP pipeline produces a denser data matrix, which makes it inherently less prone to selecting contaminants Additional files 3, 5 and 6). Similarly, the OF-PTP datasets were substantially denser, but smaller in size (Fig. 4a-b ) than the dataset reported by Zapata et al. [10] , which was also constructed using the same Agalma pipeline  [45]  (Fig. 4b , Additional files 3, 5 and 6).
The data matrix reported by Chang et al. [11] , a manually curated dataset, had the highest density, but the lowest number of ingroup taxa present (Fig. 4b ). Overall, our findings suggested significant differences between the composition of datasets produced by OF-PTP, Agalma and those reported in recent phylogenomic analyses of Cnidaria  [11, 10]. Next, we sought to understand how these datasets differed in terms of the functional classes of genes present in each. We first compared the number of N. vectensis genes shared in each dataset and found surprisingly low levels of overlap among datasets (Fig. 4c ). This estimation could be confounded if different pipelines retained different, but closely related, N. vectensis paralogs during their distinct tree pruning procedures, potentially exaggerating differences between them.
We therefore conducted gene ontology (GO) analyses of enrichment and depletion by comparing the relative proportions of each GO term for the N. vectensis genes present in each dataset to their relative proportion in the background N. vectensis v1. 0 protein set  [48]. These analyses show that significant differences in GO term representation, relative to the background, pervade each dataset across each GO category examined (e. g. cellular component, molecular function and biological process). These differences are evident by comparing the composition and relative enrichment and depletion of GO terms between each of the data matrices (Fig. 4d , Additional file 7). Our findings demonstrate that the two independent datasets produced here, together with those from the two most recent phylogenomic analyses of Cnidaria  [11, 10] , are comprised of data partitions that differ profoundly in terms of gene identity and functional class (Fig.
Consistent phylogenomic results from different data matrices Preliminary phylogenetic analyses of the AG_62tx and OF-PTP_62tx matrices were largely congruent in our ML analyses, with the exception that the Myxozoa was unexpectedly positioned within the vertebrates in our analyses of AG_62tx (see above; Additional file 3). The unexpected position of Myxozoa in the AG_62tx analysis is likely due to contamination exaggerated by data sparseness in that matrix. On average, myxozoan species are represented in a significantly smaller proportion (Fisher's Exact Test) of partitions in the AG_62tx dataset as compared to the OF-PTP_62tx dataset: Myxobolus pendula P = 0. 027; Thelohanellus kitauei P = 0. 0001; Myxobolus cerebralis P = 0. 0001 (Fig. 5 , Additional file 3). We hypothesized that if contaminants were still present in the myxozoan datasets despite our filtering efforts, they would be minimized in partitions that had more than one myxozoan species present, as the likelihood of selecting more than one orthologous contaminant sequences from multiple datasets for the same partition would be low.
In fact, we recovered a monophyletic Cnidaria with the AG_62x matrix after pruning myxozoan partitions with fewer than three myxozoan species (Additional file 4). The sparse representation of myxozoan sequences across AG_62tx makes it more likely that a single myxozoan species is represented per partition, as compared to the less sparse OF-PTP matrix (Fig. 3 ). This relationship, in turn, makes it more likely that contaminants are incorporated into the Agalma matrix. Given the sparse nature of the matrix produced by Agalma under default settings, we decided to focus our deeper analyses on the matrix derived from the OF-PTP approach. After our initial analyses, we added new data for 13 additional taxa derived from our own sequencing efforts and from newly published studies, for a total of 67 cnidarian taxa (75 species in total, Additional files 1 and 2) and produced a new dataset containing 357 partitions (53,539 positions) which we refer to as OF-PTP_75tx (Additional file 8).
g. [31, 32] ). This placement of Zoantharia has several implications for morphological evolution within Hexacorallia. For example, Zoantharia, like Ceriantharia and Octorallia, but unlike most other Hexacorallia, have a single siphonoglyph (a ciliated groove located in the actinopharynx) and are therefore bilaterally symmetrical  [63]. Thus, our placement of Zoantharia as sister to the remaining Hexacorallia adds support to the idea that the last common ancestor of Anthozoa (and perhaps Cnidaria) was bilaterally symmetrical. We were unable to include Antipatharia in our dataset and data from this group will be important in future phylogenomic studies for solidifying the hexacorallian topology. Our analyses corroborate previous findings of a sister relationship between Medusozoa and Endocnidozoa (Myxozoa and Polypodium hydriforme)  [11, 64, 65].
In our analyses, the data matrix produced by the Agalma pipeline resulted in the misplacement of the data-poor Myxozoa within vertebrates due to contamination (Fig. 3 , Additional files 3 and 4) despite our extensive data filtering steps. We note that our analyses are based on the earlier version of Agalama v0. 5-devel and a subsequent update has been made available (https://bitbucket. org/caseywdunn/agalma). The minuscule amount of overlap between the Agalma and OF-PTP datasets (only 53 N. vectensis loci shared across partitions between AG_62tx and OF_62tx; Fig. 4c ) was surprising, given that both pipelines use similar approaches: TransDecoder  [68]  to produce translated peptidomes, all-by-all BLAST to generate similarity graphs and Markov clustering (MCL;  [69] ) to define orthologous gene clusters.
Why would similar approaches produce largely different datasets using identical input data? Minor differences between the Agalma and OF-PTP pipelines include the default MCL inflation parameters (2. 1 and 1. 5 respectively). However, it has been demonstrated that varying the inflation parameter in MCL clustering does not have a major effect on resulting orthogroups (Li et al. [70] ; but see Gibbons et al. [71] ). One important difference that could explain the construction of largely different datasets by the two pipelines is the procedure used for pruning orthogroups when multiple representative sequences from each taxon are present. The treeprune procedure in Agalma may produce several partitions per orthogroup, while the OF-PTP pipeline uses PhyloTreePruner  [47] , which produces only a single partition (the largest monophyletic group) from each orthogroup, regardless of the topology.
Deciphering relationships within Anthozoa This study is the first to confidently determine the position of the ceriantharian tube anemones (Fig. 2 ) as the sister group to Hexacorallia within Anthozoa (Figs. 5  and 7 ). Our result contradicts the favored hypothesis of Stampar et al. [6]  that Ceriantharia is the sister to the remaining Anthozoa, and corroborates earlier but weakly supported hypotheses based on morphology  [31, 32] , Fig. 7  Summary of results. Our working hypothesis for the topology of major cnidarian lineages based on the present study mitochondrial genomes  [7]  and phylogenomic datasets  [10]. Ceriantharians possess several unique characteristics  [31, 32]  and following the suggestion of Stampar et al. [6]  we treat it as a unique taxon among the other anthozoan clades (Fig.
Evolution of complex characters in Cnidaria Our phylogenomic analyses of Cnidaria provide a framework for understanding the evolutionary histories of several important organismal traits that likely contributed to the success of the phylum. Our inclusion of all major lineages makes this phylogenetic hypothesis particularly suitable for reconstructing ancestral states for the last common ancestor of Cnidaria. We scored each taxon in our dataset for the presence or absence of recognizable traits including photosynthetic eukaryotic endosymbionts, colonial body plans, a medusa stage and a polyp stage as discrete characters (Fig. 6 ). We used explicit models of character evolution to reconstruct individual character histories  [53, 54, 55] , rates of gain and loss  [57]  and numbers of origins  [56].
These methods provide powerful tools for understanding the evolutionary histories of selected traits, but several caveats regarding their application are in order. First, the efficacy of ancestral state reconstruction is dependent upon taxon sampling and, while our dataset is larger than previous phylogenomic analyses of cnidarians (Fig. 4b ), we emphasize that taxon sampling in certain octocoral and hydrozoan subclades remains sparse and characters that vary within orders may be obscured. In addition, our taxon selection likely over-represents nearshore, shallow-water taxa, which could bias our attempts to reconstruct the ancestry of traits like symbiosis or even life history. Finally, the statistical approaches employed here depend on parameters that define the rates of character transitions.
Strong evidence for loss of coloniality in our dataset is found only in the Aplanulata hydrozoans, which include species within the genus Hydra. The polyp is generally regarded as the ancestral life history state in Cnidaria, to which the medusa was added in one or more lineages (  [5, 61] ). Defining different cnidarian life history stages is often difficult because of the many variations and exceptions exhibited within the wide variety of taxa  [58, 59, 23]. Our definition of the medusa as a liberated, propulsive form bearing gonads  [58]  requires that the solitary, benthic members of Staurozoa are scored as possessing a derived polyp rather than a degenerated medusa as suggested earlier  [4, 7, 66, 101, 95]  and the stolon stage of Polypodium hydriforme is scored as neither a medusa nor a polyp  [102].
Taxon sampling and sequencing We generated new transcriptome data from a range of cnidarian taxa including five staurozoans (Calvadosia cruxmelitensis, Craterolophus convolvulus, Haliclystus auricula, Halyclystus "sanjuanensis" (nomen nudum) and Leucernaria quadricornis), one cerianthid (Cerianthus borealis), one scyphozoan (Cassiopea xamachana) and gene model data from whole-genome sequencing of one octocoral (Renilla reniformis). To these we added the following previously published data: 13 cnidarian transcriptomes from Zapata et al. [10] , 30 RNA-seq datasets from the NCBI SRA Archive and 16 transcriptomes and gene models from whole-genome data. We included the same seven outgroups used by Zapata et al. [10]  to which we added Lottia gigantea  [104]. A single adult sample of Calvadosia cruxmelitensis was collected from Penzance, Cornwall, England.
Renilla reniformis adult sample was collected in the surf at Fort George Inlet, Jacksonville, Florida, USA. Washington specimens were collected with permission from Friday Harbor Marine Labs. Florida specimens were collected within allowable limits as stipulated by Florida Fish and Wildlife Conservation Commission. New Hampshire collections were done under a permit from the New Hampshire Fish and Wildlife Department. Maine collections were done under a permit from the State of Maine Department of Marine Resources. England collections were done under a permit from Natural England. Additional details on data sources are provided in Additional files 1 and 2. Materials used for sequencing were either sampled from whole organisms, or from multiple tissue types per taxon as to broaden transcript diversity.
Sequence assembly and translation After adaptor filtering using Trimmomatic v0. 33  [105]  with default settings and retaining reads greater than 80 bp for 100 bp-length sequencing runs and 100 bp for 150 bp-length sequencing runs, we assembled all de novo transcriptomes using Trinity v2. 0. 6  [106]  with default parameters (Trinity v2. 3 was used for H. auricula, L. quadricornis and C. borealis). For each transcriptome, transcripts were translated into peptides using default settings in TransDecoder v2. 0. 1  [68]. We generated an assembly of the Renilla reniformis nuclear genome from Illumina paired-end reads as follows: we trimmed adapters with Trimmomatic v0. 32  [105] , performed error-correction with Allpaths-LG version 44,837  [107]  and assembled the processed reads using Platanus version 1.
2. 1 (with default parameters except k = 48)  [108]. We created a coding-region training set using the JGI genome annotations of Nematostella vectensis v1. 0  [48]  and then used Augustus 3. 0. 3 [109]  with default parameters to generate Renilla reniformis protein predictions. To minimize the possibility of integrating contaminant or laterally transferred sequences, we removed all sequences that had better BLAST  [110]  hits to outgroups than to ingroups. We did this in two steps: first against a database that included a representative set of metazoan and non-metazoan sequences (http://ryanlab. whitney. ufl. edu/ downloads/alien_index/), and then against a database that included a set of representative cnidarian sequences and a set of representative bilaterian sequences (in GitHub repository).
We used alien_index version 3. 0  [111]  to identify sequences with better hits to each outgroup and the remove_aliens script from the alien_index package to build a new FASTA sequence file that excluded potential contaminants. While this process likely removed numerous non-contaminant/ non-laterally transferred sequences, our conservative approach made it less likely that we included contaminant sequences and the loss of data was acceptable given the great number of sequences that passed our conservative filter. All commands and scripts used for sequence assembly and translation are given in Additional file 13 and at https:// github. com/josephryan/2017-Kayal_et_al.
Construction of phylogenomic datasets We built two preliminary datasets consisting of 54 cnidarian taxa and eight outgroups using 1) Agalma v0. 5devel with nucleotide sequences as input (https://bitbucket. org/caseywdunn/agalma) as in Zapata et al. [10]  and, 2) a custom phylogenomics pipeline consisting of OrthoFinder v0. 4. 0  [46]  followed by PhyloTreePruner  [47]  and our associated wrapper scripts that we refer to as OF-PTP. OF-PTP takes the TransDecoder-translated peptide sequences from each transcriptome as input. The final supermatrices produced by both approaches were filtered to include partitions with greater than 50% taxon occupancy, which were then used for phylogenetic analyses. Following preliminary phylogenetic analyses we noticed that the myxozoan taxa showed evidence of contamination even after extensive filtering with alien_index and this contamination resulted in Endocnidozoa being placed within Verterbrata in the Agalma, but not the OF-PTP dataset.
To investigate this further, we obtained all data partitions from AG_62tx and OF_PTP_62tx that had myxozoan data and assessed how many putative contaminant sequences were present in each. We also conducted BLAST  [110]  similarity searches for each myxozoan sequence captured by both pipelines against a BLAST database comprised of the protein models from ten high-quality, phylogenetically disparate metazoan genomes, including teleosts and cnidarians. The frequency that the top BLAST hit for each myxozoan sequence resided in a data partition with one, two, three or four other myxozoan species was determined for each data matrix using custom scripts. In addition, while analyzing our preliminary datasets, our efforts and additional publications made available transcriptomic data for several additional cnidarian taxa.
We therefore created a final dataset using the OF-PTP pipeline (OF-PTP_75tx) that included 13 of these taxa. We estimated overlap in data composition between the OF-PTP and Agalma matrices by directly comparing the complement of N. vectensis sequences present in each data partition. To do this, we first cross referenced the N. vectensis sequences from each partition for each dataset to their full length protein model in the N. vectensis v1. 0 genome release  [48]  using BLAST  [110]. We then compared the single best hits for each partition recovered from each dataset. We also explored possible differences in the functional classes represented by sequences present in each data matrix by assigning GO terms to the N. vectensis sequences recovered by each pipeline, and to the global N. vectensis protein models, using Interproscan v5  [112].
GO terms were summarized using REVIGO  [113]  and their relative enrichment and/or depletion compared to the N. vectensis v1. 0 protein models  [48]  was assessed using Fisher's Exact Tests in custom R scripts. In addition, we determined the number of partitions that included data-per-taxon (taxon occupancy) in our final supermatrices using custom R scripts. Commands and scripts used in the construction of phylogenomic datasets can be found at https://github. com/josephryan/ 2017-Kayal_et_al.
Phylogenetic analyses and character mapping For all datasets, preliminary phylogenetic analyses were conducted under the Maximum Likelihood (ML) framework with the best-fit model (PROTGAMMAAUTO) on a single partition using RAxML v8  [114]. In addition, we estimated an ML tree for our final dataset (OF-PTP_75tx) using the partitioning scheme predicted by PartitionFinder2  [115]. For all ML analyses, we first performed 20 independent runs using random starting trees under the best-fit model (preliminary analyses) or the modeling scheme predicted by Par-titionFinder2 and from these, chose the best scoring tree. In addition, for each analysis, we generated 500 bootstrap replicates under the cognate model as a measure of nodal support. We also conducted Bayesian analyses of OF-PTP_75tx by running two independent chains with PhyloBayes MPI v.
We scored each taxon for presence or absence of photosynthetic endosymbionts (including Symbiodinium and zoochlorellae), colonial body plan, a medusa stage, and a polyp stage as discrete characters (Fig. 6 ). In addition, we estimated the rate of gain or loss of each character under a two-rate Markov process using corHMM  [57]  and, using these estimated rates, we then estimated the marginal likelihoods of single vs. multiple origins for each trait using indorigins  [56, 118]. To test the robustness of analyses of independent origins to differences in rate parameters we also conducted these analyses with arbitrary, exaggerated rate parameters (Table  1 ). Commands and R scripts used for phylogenetic analyses and character mapping can be found at https://github. com/josephryan/2017-Kayal_et_al.
Acknowledgements The authors would like to acknowledge the fine work of the groups that generated the publicly available data that we were able to integrate into our analyses. The Renilla reniformis samples were collected by  José Núñez  and  Ian Hamilton. Marta Chiodin  collected Haliclystus "sanjuanensis" samples with the help of  Claudia Mills. David Fenwick  collected Calvadosia cruxmeletensis samples. We thank  Larry Harris  for advice and collection of the Haliclystus auricula samples. Jarrett Byrnes  collected the Lucernaria quadricornis samples. James Coyer  collected the Cerianthus borealis samples. We thank  Shoals Marine Laboratories  for lab space during collections. We thank  Yanping Zhang  and  David Moraga Amador  from the  University of Florida's Interdisciplinary Center for Biotechnology Research core , and the  Hubbard Center for Genome Studies (UNH)  for sequencing and library prep.
Availability of data and materials Data and bioinformatics code utilized in this study is freely available at https:// github. com/josephryan/2017-Kayal_et_al. Transcriptome assemblies and raw reads are available at the European DNA Archive https://www. ebi. ac. uk/ena. Transcriptomes are available under study PRJEB19215. Raw reads for C. xamachana are available under runs ERR1991652-ERR1991663. All other data are available under runs ERR2248379 -ERR2248395.
To match the frequencies at which the moduli are computed when using a chirp signal with those of the frequency sweep, we performed a cubic spline interpolation of the discrete frequency sweep data set that was obtained with 30 points per decade. The experiments were repeated at least 6 times and the values of the error obtained for each r, averaged over all the experiments, are shown with dark gray circles in Fig. 3 (a) and (b) for the elastic and loss moduli respectively. The error-bars represent one standard deviation. The observed trends are robust for both contributions to the complex modulus: there is an initial region for small values of the tapering parameter (r ≤ 1%) that shows an approximately exponential decay of the form ε ∼ exp(-200r) as evidenced by the dashed black line. For larger values of r the decrease is less pronounced until the error reaches an intermediate minimum plateau.
Before gelation begins, the solution is essentially a lowviscosity Newtonian fluid and can easily be transferred to the rheometer for measurements without any residual memory of loading history. To quantify the casein gelation process with the ARES-G2 rheometer we monitor the acid-induced agglomeration of the colloidal proteins with a sequence of continuous OWCh signals. Immediately after preparation, the mixture is poured into a double gap Couette cell geometry, which has been prealigned and set in advance at T = 25 • C. The rotor is then lowered to the working gap and the experimental procedure, already prepared in the rheometer software, is initiated. The procedure consists of applying repeated OWCh signals over a duration of about 6 hours. The force transducers are zeroed at the beginning of the experiment, while the strain is zeroed before each signal by imposing γ = 0 for a waiting time t w = 1 s.
All the experimental data acquired with the ARES-G2 rheometer for different chirp sequences were exported and post-processed separately in Matlab. The signals, measured in time with an acquisition frequency f s = 500Hz for all the experiments presented in this work, were first checked for any DC bias in the baseline average value due to either the initial motor position or an inaccurate zeroing of the torque transducer, and then shifted to correct for such bias whenever necessary. After several experimental and numerical trials, this procedure was found to be essential in order to obtain the most accurate values of the viscoelastic moduli. We later found that a similar suggestion had been reported by John W. Tukey and coworkers  [78]  as well. Before being processed via the inbuilt FFT functions, each signal was also cut to eliminate the initial waiting time t w and then zero-padded after the last data point to extend the signal to the closest power of two to improve the performance of FFT algorithms.
Time-dependent data generated with highly resolved numerical simulations were also down-sampled to the same acquisition frequency and processed using the exact same functions written for experimental data, the only difference being the unnecessary step of checking for possible biases. Based on the files written for the analysis of the data presented in this work, we have developed a Matlab GUI to facilitate the application of the OWCh techique. The GUI has two main sections: one to generate the signal in the time domain that is then used as an input in the rheometer, and one to perform the steps for post-processing the measured data and extract the linear viscoelastic response G (ω) = G (ω) + iG (ω). The GUI is free and available upon request to the following email address owch@mit. edu. ) and TB = 107 (G , G ).