dataesr/scientific-paragraphs-categorization huggingface dataset:
Data streaming interface The driving issue of MASER is the science ready and remote access to low frequency radio astronomy data collections, and more specifically to long time-series or high-resolution datasets. For instance, each Solar transit observation (8 hours) data from the NewRoutine receiver of the Nancay Decameter Array (NDA) is stored in a 768 MB file, with 57,600 consecutive spectra (1 spectrum every 500 ms)  (Lamy 2017). Displaying data on a typical computer screen requires about 2,000 pixels on the horizontal (i. e. , temporal) axis, so that displaying the aforementioned Solar transit only requires transferring about 27 MB after temporal resampling (a reduction factor of about 30). As presented by  Lamy (2017) , some datasets from Nançay are reaching a few milliseconds of temporal resolution, so that the daily file size reaches several TBytes, and the data collection spans over several years.
Remote visualisation of such data with adaptive temporal resolution streaming capabilities is thus needed. The Das2 data streaming technology allows to visualize data with a server-side time-axis resampling. The transmitted data are adjusted to the client temporal resolution, leading to a reduction of the data transfer over Internet. This reduces the delay for displaying the data, proportionally to the resampling rate, as specified by das2 clients, such as Autoplot or the das2py library (https://github. com/das-developers/das2py). The installation and configuration of the das2 server framework (https://github. com/das-developers/das2-pyserver), is simple and straightforward. Data providers have to develop a data reader script, which writes out the data as a das2stream into the local standard output for a given input time interval.
The das2stream format is documented in its Interface Control Document (ICD)  (Piker et al 2017). In addition to the original das2 servers at University of Iowa, two other das2 servers are running to serve LESIA (http://voparis-das-maser. obspm. fr/das2/server) and Nançay (https://das2server. obs-nancay. fr/das2/server) datasets. Data readers are using the maser4py library (see section 5) for reading the data files from the local repositories. Figure  2  shows a dynamic spectrum of calibrated Cassini/RPWS/HFR data during the Jupiter flyby on December 31 st 2000 and January 1 st 2001, using Autoplot and accessing the data through the MASER/LESIA das2 server.
Data discovery interface VESPA is providing a data discovery framework with a metadata dictionary, a query protocol and a registry of services. Each VESPA service consists in a metadata table following the EPNcore metadata dictionary  (Erard 2018). Each row contains the metadata corresponding to a single product, including a data access URL. The VESPA services are running over the Table  Access  Protocol (TAP, http://www. ivoa. net/documents/TAP/) from the IVOA. MASER teams are currently sharing data files (Raw, CDF or FITS formats) through VESPA. The VESPA services are built upon the Data Centre Helper Suite (DaCHS, http://dachsdoc. readthedocs. io) framework, and the tables are fed directly from reading the CDF or FITS headers. The VESPA main query portal (http://vespa. obspm. fr) also includes capabilities to interact directly with Autoplot (with the Simple Application Messaging Protocol (SAMP, http://www.
ivoa. net/documents/SAMP/) of IVOA). Several TAP servers dedicated to distributing MASER VESPA catalogue tables are available (http://voparis-tap-maser. obspm. fr at ObsParis, http://vogate. obs-nancay. fr in Nançay). They serve data collections from space mission with radio instruments (Cassini, Voyager, STEREO), from ground instruments (NDA) or modelled data (ExPRES, see section 6).
Run-on-demand interface The IVOA has developed a computing job management system called Universal Worker Service (UWS, http://www. ivoa. net/documents/UWS/). MASER has implemented an instance of the Observatoire de Paris UWS System (OPUS, https://uws-server. readthedocs. io/en/latest/) available at https://voparis-uws-maser. obspm. fr. This server allows to submit jobs on a local computing cluster, either for automated data production pipelines (through a command line scripting interface), or for external users (through the web interface).
Maser4py Library The maser4py library (https://github. com/maserlib/maser4py/) is providing data reader modules (for Python 3. 6 and up) for legacy and non-standard format radio data collections. It currently includes modules for data collections hosted or produced by LESIA (Cassini/RPWS, Voyager/PRA, Solar Orbiter/RPW), by the Centre de Données de la Physique des Plasmas (CDPP, http://cdpp. eu) (Demeter, Interball, Viking (Swedish auroral mission), ISEE3, Wind), by the Planetary Plasma Interaction (PPI) node of NASA/PDS (Cassini/RPWS, Voyager/PRA), by the Nançay radio telescopes (NDA, NenuFAR), as well as by the radio amateur RadioJOVE project. It also includes generic modules developed for the ground segment of Solar Orbiter/RPW and a query interface for the HELIO-HFC (Heliophysics Integrated Observatory Feature Catalog)  (Bonnin 2013).
Modelling The Exoplanetary and Planetary Radio Emission Simulator (ExPRES) code computes the geometric visibility of modelled auroral planetary radio source  (Louis 2017 (Louis , 2019)). ExPRES is based on the Cyclotron Maser Instability (CMI) theory. It needs a planetary magnetic field model as well as parameters of the particle distributions in the modelled radio source. The code outputs time-frequency arrays of the visible auroral planetary radio source parameters (3D locus in the selected planetary frame and other radio source parameters). It is now used routinely to produce modelled daily spectrograms of simulated radio emissions induced by the Jovian Galilean satellites, for various observatory locations (Juno, Earth, STEREO). Precomputed simulation runs are available are available through different interfaces as defined in the MASER Data Management Plan (see  Louis (2019)  for more details).
ExPRES is open-source and its code is available at: https://github. com/maserlib/ExPRES. Runon-demand is also available from the MASER OPUS server (see section 2. 3). This computing interface requires an ExPRES JSON input configuration file. Examples of such configuration files are available through the web directory listing or virtual observatory catalogue: each of the precomputed file is provided with its input configuration file. The JSON input files must comply with the ExPRES JSON-schema specification, the current version of which is available at https://voparis-ns. obspm. fr/maser/expres/v1. 0/schema#. Figure  3  shows the run-on-demand web interface where the user can manage his jobs. Figure  4  shows a simulation run compared to Juno/Waves data (courtesy of C. Louis). We also plan to distribute the electromagnetic ray tracing code ARTEMIS-P (Gautier 2013) through MASER and UWS.
Future Steps New data readers will be continuously included in the maser4py library. The MASER team will also reach out to the community for participation. This requires a consolidation of the maser4py interfaces (classes and methods) and tests. The ExPRES simulations are now used by the Juno/Waves instrument team. Discussions are ongoing with ESA, for using ExPRES as an observation planning support tool for the JUICE mission. The need for a radio ground support has also been identified by the Solar Orbiter and Parker Solar Probe teams. The MASER tools and data collections are already available and serve those needs. Finally, there is a growing need for community coordinated open source library and software developments (especially for python-based developments). Several groups are pushing for this, and MASER will participate to these efforts (e.
Acknowledgments We thank  Marilou Bourgeon  for her technical support and  Catherine Llanes  and  Thilo Köhler  for helpful discussions. We also thank  Dr Beryl Oppenheim  for the gift of a ST395 isolate from the  Queen Elizabeth Hospital, Birmingham, UK. Computations were performed at the  Mésocentre de Calculs de Franche-comté (Besançon, France ). X. B. , B. V. , and D. H. designed the research; M. P. , P. J. , A. M. , H. P. , and B. V. performed the research; B. V. contributed new reagents/analytical tools; M. P. , P. J. , E. D. , B. V. , and D. H. analyzed the data; and M. P. , P. J. , X. B. , B. V. , and D. H. wrote the paper. This work was supported by a grant from the  Région de Franche-Comté  and from the  University Hospital of Besançon, France  ( API 3A -Promes 2013 ) to M. P. , the  French "Ministère de l'Enseignement Supérieur et de la Recherche  to  P.
Introduction Decrease in sequencing cost has widened the scope of genome-wide association studies (GWAS). Large cohorts are now built for an ever growing number of diseases. In common ones, the disease risk depends on a large number of genes connected through complex interaction networks. The classical approach and still widespread methodology in GWAS is to implement univariate association tests between each single nucleotide polymorphism (SNP) and the phenotype of interest. Such an approach is limited for common diseases, where the interactions between distant genes, or epistasis, need to be taken into account. For instance, several epistatic mechanisms have been highlighted in the onset of Alzheimer's disease  [1]. Most notably, the interaction between the two genes BACE1 and APOE4 was found to be significant on four distinct datasets.
In addition, the high dimensionality of the data leads us to use stability selection  [18, 19]  to select the regularization parameter of the ℓ 1 -penalized regressions. In summary, we develop a new framework to study epistasis by solely focusing on the synergies with a predetermined target. By proceeding this way, our methods improve the recovery of interacting SNPs compared to standard methods like GBOOST or a LASSO with interaction terms. We demonstrate the performance of our methods against both of them for several types of disease models. We also conduct a case study on a real GWAS dataset of type II diabetes to demonstrate the scalability of our methods and to investigate the resulting differences between them.
In the context of genomic data, we can interpret these synergies as pure epistatic effects: the main effects are accounted for by μ(X) and δ 0. Furthermore, if δ 1 (X) is sparse, meaning that it only depends on a subset of elements of X, referred to as the support of δ 1 (X), then the SNPs in this support are the ones interacting with A. In other words, searching for epistatic interactions between A and SNPs in X amounts to searching for the support of δ. A GWAS dataset is a set of n triplets (X i , A i , Y i ) i=1,. ,n , which we model as independent random variables identically distributed according to P. To estimate the support of δ(X) from a GWAS dataset, we propose several models based on sparse regression. The common thread between them is the use of propensity scores to estimate δ(X) and its support without estimating μ(X).
Based on this notion of propensity score, the first family of methods we propose falls under the modified outcome banner  [14]. In these models, an outcome that combines the phenotype Y with the target SNP A and the propensity score e(X) is fit linearly to the genomic covariates X. We propose several variants of this approach, which differ in their control of estimation errors. Our second proposal is a case-only method based on the framework of outcome weighted learning  [15]. In this model, which is a weighted binary classification problem, the outcome is the target SNP A, the covariates are the rest of the genotype X, while the phenotype Y and the propensity score e(X) are incorporated in the sample weights. If not stated otherwise, the full data pipeline is written in the R language. The methods presented in this work are implemented in the R package epiGWAS, which is directly available via CRAN.
[19]. The main intuition behind the better performance is the early entry of causal variables into the LASSO path. Finally, to determine the grid Λ, we use the R package glmnet  [35]. We generate a logscaled grid of 200 values (λ l ) l=1,� � �,200 between λ 1 = λ max and λ 200 = λ max /100, where λ max is the maximum λ leading to a non-zero model. To improve inference, we only retain the first half of the path comprised between λ 1 and λ 100. The benefit of a thresholded regularization path is to discard a large number of irrelevant covariates that enter the support for low values of λ.
t. λ l > λ l+1 instead of the value of λ l for computing the area under the curve. In Fig  1 , we provide the ROC and PR curves for the fourth scenario which corresponds to a partial overlap between synergistic and quadratic/marginal effects and for a sample size n = 500. Because of space constraints, all ROC/PR figures and corresponding AUC tables are listed in the S2 File. The figures represent the average ROC and PR curves of the 125 simulations in each of the four scenarios. To generate those figures, we used the R package precrec  [40]. It performs nonlinear interpolation in the PR space. The AUCs are computed with same package. Regardless of the scenario and the sample size, the areas under all ROC curves are higher than 0. 5. This confirms that all of them perform better than random, yet with varying degrees of success.
Case study: Type II diabetes dataset of the WTCCC As a case study, we selected the type II diabetes dataset of the WTCCC  [39]  to illustrate the scalability of our methods to real datasets. To the best of our knowledge, no confirmed epistatic interactions exist for type II diabetes. We instead propose to study the synergies with a particular target: rs41475248 on chromosome 8. We focus on this target SNP because (i) GBOOST finds that it is involved in 3 epistatic interactions, when controlling for a false discovery rate of 0. 05, and (ii) it is a common variant, with a MAF of 0. 45. Before running our methods on the WTCCC dataset, we applied the same QC procedures with the following thresholds: 0. 01 for minor-allele frequencies and p > 10 -6 for the Hardy-Weinberg equilibrium. No additional pruning is performed.
The number of remaining variants is 354439 SNPs. The number of samples is 4897, split between 1953 cases and 2944 controls. To solve the different L 1 -penalized regressions, we abandoned the glmnet package in favor of another one, biglasso  [41]. Indeed, glmnet does not accept as input such ultra-high dimensional design matrices. On the other hand, biglasso was specifically developed for similar settings thanks to its multi-threaded implementation and utilization of memory-mapped files. Because biglasso does not implement sample weighting, it cannot be used to run outcome weighted learning. Since this approach performs worse than the modified outcome approaches on simulated data, we simply exclude it from this case study. The main difficulty for the evaluation of GWAS methods is the biological validation of the study results.
We often lack evidence to correctly label each SNP as being involved or not in an epistatic interaction. Evaluating the real model selection performance of the different methods on real datasets is then impossible. However, we can study the concordance between them. A common way to proceed is Kendall's tau which is a measure of rank correlation. In Table  1 , we give the correlation matrix of our four variants of modified outcome methods, and of the two baseline methods GBOOST and product LASSO. All elements are positive which indicates a relative agreement between the methods. While methods using different mathematical definitions of epistasis cannot be expected to return the same results, those with similar or identical underlying models should capture similar genetic architectures and return more similar results.
This is particularly true for small number of samples (n = 500), which is the closest setup to real GWAS datasets. However, the low PR AUCs show that there is still room for improvement. The highest observed PR AUC is 0. 17. Interestingly, we note that several of our methods clearly outperform GBOOST across all scenarios and all sample sizes in the PR space. Nonetheless, GBOOST behaves similarly to our methods in the ROC space. Such differences between ROC and PR curves are common for highly-imbalanced datasets where PR curves are more informative and discriminative  [42]. In our simulations, ROC and PR AUCs were relatively close between all methods. On the other hand, according to two rank correlation measures (Kendall's tau and weighted Kendall's tau), the results do not strongly overlap between the different methods (values far from 1).
| Taxon sampling A total of 74 taxa (64 clitellates and 10 polychaetes) were sampled for this study (Table  1 ). Outgroups were selected based on examination of phylogenomic studies of Annelida  (Andrade et al. , 2015; Struck et al. , 2011 Struck et al. , , 2015;; Weigert & Bleidorn, 2016; Weigert et al. , 2014)  and preliminary results from phylogenomic analyses of Annelida conducted by our research group. Representatives of several marine lineages-Arenicolidae, Capitellidae, Flabelligeridae, Opheliidae, Sabellidae, Scalibregmatidae, Sternaspidae and Terebellidae-were included, as were representatives of Aeolosomatidae and the terrestrial lineage Hrabeiellidae. The clitellate samples represented most major taxa, that is, Branchiobdellida, Capilloventridae, Crassiclitellata, Enchytraeidae, Haplotaxidae, Hirudinida, Lumbriculida, Moniligastridae, Naididae, Parvidrilidae, Phreodrilidae, T A B L E 1 Collection locality, museum voucher information and (where applicable) GenBank SRA assembly accession number for all data used in this study.
| Sequence assembly and processing Raw PE Illumina reads were digitally normalized using khmer (normalize-by-median. py -C 30 -k 20 -N 4 -x 2. 5e9)  (Crusoe et al. , 2015)  and assembled using Trinity (October 5, 2012 release)  (Grabherr et al. , 2011). We used TransDecoder (http://trans decod er. github. io) to find open reading frames and translate nucleotide sequences into amino acid sequences at least 100 amino acids in length.
We used a custom script to sort these sequences into FASTA-formatted files corresponding to each core ortholog pHMM that included all sequences except duplicated contigs. Each of these files represents a putative orthogroup (i. e. a set of sequences from our transcriptomes that are derived from a single gene copy in the last common ancestor of the taxa in our data set). Our pipeline is similar to those used in recent phylogenomic studies on earthworms and cephalopods  (Anderson et al. , 2017; Lindgren & Anderson, 2018). Briefly, each orthogroup was aligned with MAFFT (L-INS-i)  (Katoh, Kuma, Toh, & Miyata, 2005) , characters that were all gaps or had four or fewer non-gap characters were deleted, and sequences that overlapped all other sequences by less than 20 amino acids were deleted. We chose not to use any automated alignment filtering methods (e.
, 2013). Sequences falling outside the maximally inclusive subtree were deleted. If multiple in-paralogs were initially retained, all but the longest one were deleted by PhyloTreePruner. This yielded an alignment for each orthogroup that included no more than a single, putatively orthologous sequence for each taxon. PhyloTreePruner was used to retain only orthogroups found in at least ~25% (19 taxa), 50% (37 taxa) and ~75% (56 taxa) of the transcriptomes. All loci were then realigned with MAFFT (L-INS-i), and FASconCAT v1. 0. pl  (Kück & Meusemann, 2010)  was used to concatenate orthogroups. All data matrices, tree files and scripts are available via Mendeley Data (http://dx. doi. org/10. 17632/ rkcw5 2fxdc. 1).
Orthogroups that returned a value equal to or greater than 1. 5 times the interquartile range above the median for any of the three indices were deleted, and the remaining loci were concatenated with FASconCAT. This new TreSpEx-filtered concatenated matrix was then evaluated with BaCoCa v. 1. 104r  (Kück & Struck, 2014). Data subsets (i. e. orthogroups) with a p-value of <. 05 for a chi-square test of homogeneity were removed, as were all orthogroups that were 1. 5 times the interquartile range above the median RCFV  (Zhong et al. , 2011). These filtering steps should remove all orthogroups that show signs of especially high levels of branch-length and/or compositional heterogeneity among lineages, potentially including contaminant sequences. We also employed the MARE v. 0. 1. 2 software package  (Meyer, Meusemann, & Misof, 2011)  to reduce the number of orthogroups in the 25%, 50% and 75% matrices.
Another leech sampled here, Glyptonotobdella antarctica, was not sampled by Phillips et al. To detect possible host contamination, we extracted all Piscicolidae sp. and Glyptonotobdella sequences from the 25% unfiltered set of loci. We then performed a local blast search of these sequences against the non-redundant (nr) protein database (downloaded on 15 June 2019), retaining the 10 best hits with e-values of less than 1e-5 for each Piscicolidae sp. and Glyptonotobdella sequence. We extracted all vertebrate sequences from this collection of top blast hits, added them to the appropriate fasta file containing our annelid sequences, aligned each file with MAFFT (linsi) and analysed each resulting alignment under ML using IQ-TREE v. 1. 6. 11 (Nguyen, Schmidt, von Haeseler, & Minh, 2015)  with 1,000 ultrafast bootstrap replicates  (Hoang, Chernomor, von Haeseler, Minh, & Vinh, 2018)  and automated model selection  (Kalyaanamoorthy, Minh, Wong, von Haeseler, & Jermiin, 2017).
| Maximum-likelihood (ML) Analyses We used the ProteinModelSelection. pl script (https://github. com/stama tak/stand ard-RAxML/ blob/maste r/usefu lScri pts/ Prote inMod elSel ection. pl) to select best-fitting amino acid substitution models for each orthogroup. Maximum-likelihood (ML) analyses (partitioned by orthogroup) were conducted for most data matrices with RAxML versions 8. 2. 8 and 8. 2. 9  (Stamatakis, 2014)  using XSEDE resources  (Towns et al. , 2014)  via the CIPRES Scientific Gateway  (Miller, Pfeiffer, & Schwartz, 2010)  with 1,000 rapid bootstrap pseudoreplicates, using: -f a -x < random number seed for rapid bootstrapping; unique for each analysis> -p < random number seed for initial parsimony inferences; unique for each analysis> -# 1,000 -m PROTGAMMA < amino acid model> -s < inputfile> -n < outputfile> -q < partitionfile> (Table  S1 ).
We subsequently used the bootstopping approach for all other 25% matrices (i. e. the TreSpExand-BaCoCa-filtered and MARE-reduced matrices). Conflict and congruence among gene trees were assessed in two ways. First, we used phyparts  (Smith, Moore, Brown, & Yang, 2015)  to map single-gene ML trees to the ML tree for the TreSpExand-BaCoCa-filtered 50% data matrix. These analyses require rooted trees, so we only mapped gene trees that included at least one of the ten outgroup taxa and used Mesquite version 3. 6  (Maddison & Maddison, 2018)  to root the single-gene trees. Pie charts were generated with the phypartspiecharts. py script (available at https://github. com/mossm atter s/MJPyt honNo teboo ks/). We also calculated gene concordance factors (gCF;  Ané, Larget, Baum, Smith, & Rokas, 2006; Gadagkar, Rosenberg, & Kumar, 2005)  and site concordance factors (sCF;  Minh, Hahn, & Lanfear, 2018)  for this matrix using IQ-TREE 1.