dataesr/scientific-paragraphs-categorization huggingface dataset:
phoenia (at least 2. 8 mya;  (Lessios, 2008) ), the divergence times within the Ophioderma longicauda species complex were inferred to be at least 537,000 years ago  [95% CI: 445, 795]  (Table  4 ; Fig. S5 ). It is noteworthy that the BPP analyses run on a sub-dataset of nine C3 individuals and eleven C5 individuals considering each individual as a candidate species in the starting tree gave unsupported results, with unstable numbers of estimated species and low posterior probabilities among replicate analyses (6, 3 and 2 species; Table  S5 ). Therefore, BPP performed poorly to delimit species without a meaningful starting species tree. On the opposite, the DAPC succeeded in finding the true number of species and affecting individuals to them (Fig. S6 ).
, 2013; see Hugall et al. , 2015  for exon-capture specific to brittle stars). Until now, these data have mainly been used for phylogenomic purposes  (O'Hara et al. , 2017 (O'Hara et al. , , 2014) )  but they could as well be used for cryptic species delimitation with multilocus genotype approaches, which are powerful to detect recent genetic isolation Although 30 markers allowed us to use computationally intensive methods such as BPP and ABC, a very small part of the genome was examined. Therefore, a next step would be to test the validity of our results using a phylogenomic dataset and multilocus genotype approaches, but as well inferring a species tree in a multispecies coalescent framework (e. g. ASTRAL  (Mirarab et al. , 2014) , MP-EST  (Liu et al. , 2010)  or SVDquartets  (Chifman and Kubatko, 2014) ) or in a reticulated evolution framework taking incomplete lineage sorting and hybridization into account (e.
Data statement The raw Miseq reads were deposited on Dryad Digital Repository and are accessible on https://doi. org/10. 5061/dryad. 5ks03 (The raw data of the present study are from the same Miseq run as for the study:  Weber et al. , 2015). thank the support team of sciCORE (center for scientific computing, University of Basel, http://scicore. unibas. ch/) for providing access to computational resources, especially Pablo Escobar Lopez. Finally, we would like to thank two anonymous reviewers for their comments on a previous version of the manuscript.
Acknowledgments We are very grateful to the many people who contributed to sampling of Ophioderma longicauda specimens:  Helmut Zibrowius ,  Christos Arvanitidis ,  Thanos Dailianis ,  Elena Sarropoulou ,  Magdalini Christodoulou ,  Zined Marzouk ,  Didier Weber ,  Thi Weber  and  Philipp Moser. Many thanks to  Francisco Alonso Solis-Marin  and  Harilaos Lessios  for providing Ophioderma outgroup samples. We also would like to thank  Laurent Abi-Rached  for his advice on phylogenetic analyses,  Arnaud Estoup  for his advice on DIYABC and the genomic sequencing platform Genotoul ( INRA, Toulouse ) for the Illumina sequencing. We.
At the last step (the n -1th step), we obtain H 2n-2. H 2 H 1 A(H 2n-2. H 2 H 1 ) J = H 11 H 12 H 21 H 22 = H ∈ R 2n×2n , with H 11 , H 21 , H 22 upper triangular and H 12 upper Hessenberg. We get A = S J HS with S = H 2n-2. H 1. The entries of the diagonal of H 11 are the free parameters A (2j-1) (j, j), ie. H 11 (j, j) = A (2j-1) (j, j) for j = 1,. , n. Also, The entries of the sub-diagonal of H 12 are the free parameters A (2j) (j + 1, n + j), ie. H 12 (j + 1, j) = A (2j) (j + 1, n + j) for j = 1,. , n -1. We propose here the algorithm in its general version, written in pseudo Matlab code, for computing the reduction of a matrix to the upper J-Hessenberg form, via symplectic Householder transformations (JHSH algorithm).
For this reason, almost all Big Data systems use some form of distributed file system, in order to cope with both the volume of data, and the need of reading the data at fast pace. The Hadoop Distributed File System is generally used not only by Hadoop, but also by other systems, such as Spark and Impala. The usual Hadoop stack is composed of HDFS at the bottom, the job engine (like Hadoop YARN or MapReduce) over it, and the application layer on the top, with Hive, Spark or MapReduce programs solving the user queries. This stack is shown in Figure  1. In these huge systems, there is a constant pursuit for performance, since the execution time for jobs can take several hours, even days  [1]. In this environment, even a minor improvement in performance can save a considerable amount of execution time.
It requires the use of small port-count converter-switches. Hybrid electro-optical systems have also been recently proposed to improve performances, such as the Hybrid Fat Tree (HFT)  [5]. By introducing wavelengthdivision multiplexing (WDM) and optical switches in Fat-Tree like topologies, the authors find that these technologies reduce the required number of switches by 45% in their minimum hybrid networks. Inter-rack optical rings have also been proposed for improving the overall throughput of the DCN, by using flexible spectrum allocation with ROADMs  [6]. Software-Defined Networking (SDN) is increasingly being deployed in modern DCs and can benefit big data applications in many ways  [7] , including big data processing, data delivery, programming at runtime for optimizing big data applications, scientific big data architectures, and more specifically for Hadoop scheduling as shown in  [8].
All the experiments used the HDFS default values of 128 MB for the block size and 3 for the replication factor. Those are the values usually found in real clusters, and used for general data. The network connecting the nodes and the racks is a Gigabit Ethernet, with delays of 1 ms per switch, frame size of 1518 bytes and not considering the delays caused by the TCP receiving buffers (usually with 64 KB) due to the dynamic buffering option found in modern Linux operating system kernels. Three sizes of clusters were used in the tests: • 500 nodes (25 racks with 20 nodes each) • 1000 nodes (50 racks with 20 nodes each) • 2000 nodes (100 racks with 20 nodes each) For these clusters, the following graph configurations were used in the tests: • 500 nodes: 5 layer-1 meshes with 5 racks in each. • 1000 nodes: 5 layer-1 meshes with 10 racks in each.
All the results are average values from 100 consecutive runs, to avoid bias due to the utilisation of random variables used to define data placement and so on. The dataset size used in the experiments is calculated to use all the available resources in the cluster, and is equal to the block size multiplied by the total number of available processing slots in all the nodes. In that way, all the cluster processing resources are used in all the tests, with resulting dataset sizes of approximately 0. 5 GB (500 nodes), 1 TB (1000 nodes) and 2 TB (2000 nodes) per concurrent job.
Material and Methods Data mining and annotation with Scipio. An exhaustive data mining procedure was performed on the fungal genomes available from BROAD institute, JGI and NCBI to extract all the available ligninase sequences. All sequences used in this study have been annotated by an expert process to discard prediction errors subsequent to automatic annotations. First, a search by keyword and profile number has been done on different databases. These searches have been completed by homology searches against predicted proteomes (BLASTP), transcriptomes (TBLASTN) or whole genomes using well known ligninase sequences from Phanerochaetes. This data mining allowed the creation of initial batches corresponding to the different protein families. Then, a manual and individual curation of each predicted sequence was performed: gene structure (intron number and position), length and the presence of key residues were mandatory controls.
Phylogenetic analysis. All protein sequences used for the in silico analyses are available from the RedoxiBase database (http://peroxibase. toulouse. inra. fr)  33, 34. First, protein sequences were aligned using PRANK  35  with default parameters. Then phylogenies were estimated by maximum likelihood using RaxML (version 8. 1. 5)  36 , under the PROTGAMMAWAG model, as the substitution model determined by protTest  37  was WAG  38  and a gamma distribution (4 discrete categories of sites and an estimated alpha parameter). Finally, the trees were edited and analyzed using iTOL (https://itol. embl. de/). Gene structure analysis. The intron/exon coordinates together with the corresponding genomic sequences of all identified genes were determined with Scipio 32 , with maximal intron size set to 1000 nt, and minimum percent identity set to 30%.
The intron/exon conservation within the different families was verified with CIWOG  39  and GECA  40. They both analyzed the evolution or conservation of introns between paralogs as well as between species. Intron size changes were visualized through the graphical representation provided by GECA. Conserved common introns analysis. Gene structure and common introns (or cintrons) were analyzed from all fungi sequences. First, the protein alignment generated with MAFFT  41  was completed with the identification of common introns in the corresponding genes with CIWOG. Cintrons were extracted from the CIWOG database and only those present in one or more sub-classes with a conservation rate higher than 50% were considered as conserved. Finally, the sequences were placed in order of appearance in the phylogenetic tree and the conserved cintrons were highlighted for each sequence.
Acknowledgements The authors are thankful to the  Paul Sabatier-Toulouse 3 University  and to the  Centre National de la Recherche Scientifique (CNRS)  for granting their work. We are grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees (Bioinfo Genotoul)  for providing help and computing and storage resources and to  Mark Weber  for careful English corrections. The RedoxiBase is hosted by the  Toulouse Midi-Pyrénées bioinformatics platform.
A total of 1000 out of the 1784 SCOs have at least one tag, with an average of 2. 3 tags per SCO (ranging from 1 to 14). Of the 2339 candidate tags, 483 did not pass the post-assembly tests (Step 5, Fig. 1 ). In details, 46 tags were removed due to unspecific binding of their primer pairs to the haploid reference genome sequence of Rosa 'Old Blush'; 224 tags did not pass the RBB test of orthology; 47 tags did not have a consistent allele number in aTRAM contigs regarding the ploidy level of the unassembled Rosa genome; 166 tags did not have a consistent hit number regarding the ploidy level of the Rosa genome when BLAST-searched on already assembled Rosa datasets. The final set contains 1856 tags that could be used for phylogenomic analyses (Additional file 2). These tags will now be referred to as Single-Copy Orthologous Tags (SCO Tag s) in the text, to denote that they are short, PCR-amplifiable sequence tags, derived from primers in conserved sequences that flank variable sequence regions in single-copy orthologous genes identified across a set of closely-related species.
Species sequences from each of the 1856  SCO Tag s are available in Additional file 3. Species sequences from each SCO Tag were aligned using mafft and cleaned with Gblocks, leading to a supermatrix of 669,354 bp for the ingroup species with 28% of missing data, after the removal of 4843 (0. 7%) poorly-aligned sites. For the dataset with ingroup plus outgroup species, the supermatrix contained 676,389 bp with 34% of missing data after the removal of 16,978 (2. 4%) poorlyaligned sites.
3 ). Both datasets support a distinct Chinenses-Gallicanae-Synstylae clade but have slightly different tree structures for the remaining species. While only the nuclear SCO Tag s support monophyly for the Chinenses and three of the four Synstylae, both datasets exhibit strong support (> 99% BS) for the position of Rosa moschata and R. minutifolia near the Rosa clade. In addition, the nuclear SCO Tag s dates the R. laevigata speciation event as being more ancient (26 MYa) than the plastid dataset suggests (16 MYa) and supports the monophyly of the two bright yellow-flowered species, R. persica and R. xanthina.
Numbers in table  (2)  correspond to the number of SCO Tag s that were retrieved for each of the four Rosa species with already assembled datasets. The procedure is described in detail in the Methods section. RBB: Reciprocal Best Blast; mcl: Markov CLuster algorithm criteria to ensure that highly informative SCO Tag s are concordant with the species-tree and do not result from regions with too many fast evolving sites. We first constructed a network to summarize conflicts between all SCO Tag s trees (Additional file 1: Figure  S5 ). Species groups identified in the network are mostly consistent with the clades found in the concatenated analysis (Fig. 3b ). The reticulation pattern show conflict between SCO Tag trees for both recent and ancient speciations. For recent speciations, links between species are short and packed while they are long and slack for more ancient speciations.
3b , but with slight modification of BS for node 7 (increase from 60 to 75%), node 13 (decrease from 99 to 91%), node 14 (decrease from 86 to 65%), node 16 (decrease from 100 to 79%). In addition to BS, we computed two other support values: (1) Local Posterior Probabilities (LPP) that derive from frequencies of quadripartitions observed in the set of SCO Tag trees and (2) Internode Certainty All (ICA) scores that provide information on the amount of conflict at each node. Although not directly related, these three support values each explain in their own way the phylogenetic signal present in the dataset. We observe that low LPP generally correspond to less supported branches (BS < 100%), except for node 16. However, we often observe that high LPP and BS value do not always correspond to high ICA scores (Node 6, 8, 9 and 10).
Conclusion The method implemented here to mine genome-scale sequencing data successfully recovered hundreds of nuclear single-copy orthologous sequence tags suitable for species-level phylogenomics in the highly complex genus Rosa. We emphasize that a thorough analysis must be performed on phylogenomic datasets in order to choose the most informative markers. While the sequence content of variable sites is obviously important, it does not predict better topology resolution. Computing phylogenetic informativeness and topological conflict of SCO Tag s ensures the selection of a comprehensive set of SCO Tag s containing appropriate sequence variations to cover the entire period of species divergence and simultaneously reveals potential sources of topological conflict that may have biological meanings, such as hybridization events or unwanted selection of paralogous copies.
Methods Identification of single-copy orthologs in Rosa 'Old Blush' and Fragaria vesca Single-copy nuclear genes were identified by comparing annotated protein sets in the haploid reference genome sequences of Rosa 'Old Blush'  [67]  and Fragaria vesca  [71]. First, the annotated protein set from each genome was compared to itself using an all-against-all BLAST+ [90] search. Outputs were parsed using the tcl script  [91]  with an e-value cutoff of 1e-10, identity of at least 30% and coverage above 70% of the query. Single-copy nuclear genes were identified as those with a unique blast hit to themselves (Step 1, Fig. 1 ). Next, two methods were used to identify single-copy orthologs (SCOs) shared between Rosa 'Old Blush' and F. vesca. In the first method, a reciprocal best-hit blast (RBB) was performed between Rosa 'Old Blush' and F.
1 ). We also used an assembled transcriptome of Rosa palustris  [70]  because it belongs to the Rosa sect. Carolinae and is related to several wild roses native to North America. Only exonic tag can be retrieved from transcriptome sequencing data of R. palustris. We used a BLAST search to retrieve tag sequences from assembled genomes/transcriptome of Rosa species (e-value ≤1e-10; identity ≥65%; 100% coverage of the consensus query tag; maximum query-subject length difference of ±20%). If multiple best hits were found, we arbitrary choose one of them as the representing sequence for the Rosa species. If the number of best hits was not consistent with the ploidy level of the Rosa species genome, we discarded all sequences related to this tag for downstream analysis. We additionally checked that edges of retrieved sequences corresponded to primer pairs.
Assessment of phylogenomic utility SCO Tag sequences from different species were aligned using mafft  [96] , and Gblock  [100]  was used to trim poorly aligned regions. Variable and parsimony-informative site (PIS) contents were calculated per SCO Tag alignment. Gaps were treated as a fifth base. We then computed phylogenetic informativeness (PI) per SCO Tag using the formula presented in  [50]  and implemented in the PhyDesign online application  [101]. For this analysis, all SCO Tag alignments were concatenated into a super-matrix and the best partition scheme was searched with PartitionFinder v2. 1. 1  [102]. The partitioned matrix served to construct a Maximum Likelihood (ML) species-tree using RAxML v8. 1. 5 [103]. The species-tree was then converted to a chronogram in R using the function chronos in the package ape  [104]  by applying one calibration point on the crown node of Rosa, dated at 30 MYa  [51].
We repeated this second step one time to totally remove high spikes from PI profiles. The python script (PhantomSpikesRemover. py) that we developed to trim SCO Tag PI profiles and alignments is available at https://github. com/kdebray/SCOtags. The same procedure was applied to the three plastid loci to recover their PIS content and PI profiles. To further determine the underlying phylogenetic conflicts between SCO Tag s, we looked for well-supported incongruences between SCO Tag tree topologies. For each SCO Tag alignment with at least one outgroup sequence, we determined the best nucleotide substitution model using jModelTest v2  [106] , and we estimated corresponding ML tree with PhyML  [107]. We then used PhyParts  [108]  to map resulting SCO Tag -trees onto the species-tree topology, previously obtained by concatenation of all SCO Tag s followed by a ML tree estimation.
We also calculated the Internode Certainty All (ICA) for each node of the species-tree topology, as implemented in PhyParts. ICA values near 0 indicate major conflicts with similar frequencies among conflicting bipartitions. ICA values near 1 indicate a strong certainty in the bipartition, meaning that few alternative bipartitions with low frequencies have been found. Although ICA score is not directly comparable to bootstrap support (BS), it provides more information about the distribution of conflicts among phylogenomic loci for a specific bipartition  [108]. In addition, we also summarized topological conflict between SCO Tag trees through a species network. For this analysis, we first collapsed branches that were poorly supported (ie. BS < 70%) using a custom R script and the function di2multi in the ape package.
Funding This research was conducted within the framework of the regional program "Objectif Végétal, Research,  Education and Innovation in Pays de la Loire ", supported by the  French Pays de la Loire region, Angers Loire Métropole  and the  European Regional Development Fund. Additional financial support to KD was granted by the  French Ministry of Higher Education and Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Availability of data and materials The 1,856 SCO Tag s sequences are available in Additional file 4. The 1,856 primer pairs are available in Additional file 2. All SCO Tag sequences used in this study are available in Additional file 3 and at DOI:https://doi. org/10. 6084/ m9. figshare. 7907249. All metrics regarding structural annotation, variability content, topological conflict and phylogenetic informativeness are available in Additional file 4. Some custom scripts developed for this study are available at https://github. com/kdebray/SCOtags. The entire set of Rosa wichurana genome assembly scaffolds have been deposited at DDBJ/ENA/ GenBank under the accession RQIQ00000000. The version described in this paper is version RQIQ01000000.
2x Roseraie du Val-de-Marne, L' SRX4006792 [67] Hay-les-Roses, France Rosa majalis Herrm. 2x ENS Lyon, Lyon, France SRX3286287 [66] Rosa minutifolia var. alba Engelm. 2x Roseraie du Val-de-Marne, L' SRX4006787 [67] Hay-les-Roses, France Rosa moschata Herrm. 2x Roses Loubert rose garden, SRX4006793 [67] Les Rosiers-sur-Loire, France Rosa multiflora Thunb. ex Murr. 2x Keisei Rose Nurseries, Chiba, PRJDB4738 [69] Japan Rosa odorata (Andr. ) Sweet 2x Lyon botanical garden, Lyon, SRX3286293 [66] France Rosa palustris Marsh. 2x NA ERS1829481 [70] Rosa pendulina L 2x Lyon botanical garden, SRX3286278 [66] Lyon, France Rosa persica Michx. ex Jussieu 2x Roses Loubert nurseries, SRX4006789 [67] Les Rosiers-sur-Loire, France Rosa rugosa Thunb. 2x Roseraie du Val-de-Marne, L' SRX4006791 [67] Hay-les-Roses, France Rosa wichurana Crép 2x ILVO, Melle, Belgium PRJNA504542 - Rosa xanthina var.
xanthina f. spontanea Rehd. 2x Roses Loubert rose garden, SRX4006788 [67] Les Rosiers-sur-Loire, France Outgroup Fragaria vesca L. 1x NCGR, Corvallis, OR, USA PRJNA66853 [71] Fragaria iinumae Makino 2x Kagawa University, Kagawa, PRJDB1478 [72] Japan Fragaria nipponica Makino 2x Kagawa University, Kagawa, PRJDB1479 [72] Japan Fragaria nubicola Lindl. ex Lacaita 2x NCGR, Corvallis, OR, USA PRJDB1480 [72] Geum urbanum L. 2x Punnets Town, UK PRJEB23412 [73] Potentilla micrantha Ramond ex DC. 6x Avala, Serbia PRJEB18433 [74] Rubus occidentalis L. 2x Rich Mountain, South Carolina, - [75] USA.
Data extraction and mapping Our data extraction tool was developed in Microsoft Excel, with drop-down menus for categorical items, and free-text cells as appropriate. It was piloted on six included studies, after which further refinements were made where necessary. Investigators extracted data from the full texts, highlighting areas of uncertainty for resolution through discussion. Data headings fell into five broad categories: 1 Study characteristics, e. g. aims, design, setting 2 Sample characteristics, e. g. age range, comorbidities, gender, sample size, diagnostic criteria 3 Treatment details, e. g. treatment duration, MPH formulation, concomitant treatments, comparator treatment where relevant 4 Potential adverse outcomes addressed 1 5 Study conclusions, categorised using a "Traffic-light" system ("Yes", "Proceed with Caution"; "No"; "Unclear") • For comparative studies: Does the study overall favour MPH? • For non-comparative studies: Do the authors conclude that MPH is safe/well-tolerated? 2 For the comparative studies, if the comparator was another active treatment, we coded the result as "Yes" if the outcome favoured MPH, or "No" if it favoured an alternative.
Atomoxetine Of the six studies that included a comparison with atomoxetine, two were investigations of adverse effects of treatment. In a large pharmacovigilance study,  Cortese et al. (2015)  found significantly fewer neuropsychiatric AEs overall for MPH than for Atomoxetine 4. Haynes et al. (2015)  investigated factors predicting worsening ADHD severity, and measured a range of AEs to MPH and atomoxetine treatment; and these included sleep AEs 5. We coded the result of this study as Unclear. The other three studies used large national databases and each considered a specific long-term outcome: Lee et al. (  2016 ) investigated mood disorder 6 ;  Wang et al. (2016a) , considered bipolar disorder 7 and Liang et al. (  2018 ) considered suicidal behaviour. 8 For all three outcomes, as neither MPH nor atomoxetine treatment was associated with increased risk, and long-term MPH treatment was associated with reduced risk, we coded these results as Favours MPH.