dataesr/scientific-paragraphs-categorization huggingface dataset:
Database overview and usage Users can access the list of srn genes through the Web interface (Fig. 5A ). From the SRD home page they can (i) access the data set corresponding to the four genomes used to construct SRD, (ii) retrieve a short list of srn genes with a unified annotation, (iii) browse for predictions in other Staphylococci (described elsewhere in the text), (iv) BLAST  (Altschul et al. 1990 ) their own sRNA sequences against the entire SRD database, or (v) search for RNA-RNA interactions using the intaRNA program  (Busch et al. 2008). After entering in the webpage of one of the four curated genomes, the users will have access to the full list of srn reported so far. For each table, the new nomenclature, the genomic coordinates, the orientation, previous names, and experimental support are provided.
SRD specific features Several functions have been included in the SRD website to provide an efficient device for the community working on sRNAs. BLAST and intaRNA  (Altschul et al. 1990; Busch et al. 2008)  were implemented in the database while external links to RNAtarget2  (Kery et al. 2014 ) and to RNA predator  (Eggenhofer et 2011)  are easily accessible from the webpage describing each sRNA. For BLAST comparisons, our databank includes the four initial reference genomes, the lists of validated sRNAs, and all the srn curated in the four strains. IntaRNA can be activated from the intaRNA page or directly from any srn sequences by clicking on the dedicated symbol. The users will only have to paste their mRNA sequences to see whether they may interact with each sRNA from SRD. MFold structure predictions  (Zuker 2003) , defined for the 575 srn RNA sequences, can be downloaded also directly from the SRD website.
Evolution of the database and future directions SRD is a repository for the Staphylococcal sRNAs with primary focus on S. aureus. By combining an in-depth cleaning and novel RNA-seq analysis, it clarifies the status regarding the absence of a consensus nomenclature and also to the actual expanding number of individual sRNA genes. With the rising interest of the community in the field of sRNAs, the number of sRNAs detected or characterized in bacteria is predicted to constantly increase. SRD will therefore evolve, based on the identification of novel sRNAs published in the literature. In addition, researchers who would be willing to unify new discoveries under the srn nomenclature will have the possibility to submit their sRNA sequence(s) to SRD under the contact information box. SRD would allow both already published and nonpublished submission.
DISCUSSION Over the last 20 yr, the number of sequences identified as sRNAs dramatically increased in Staphylococcus aureus. However, the democratization of Next-Generation Sequencing and the absence of a consensus in the community for annotating newly identified sRNAs led to a growing confusion that become detrimental to the field. SRD compiled the existing data in a single interface, removed the repeated and or redundant sequences and proposed a novel, simplified, nomenclature. Compared with the databases on other prokaryotes sRNAs, SRD is the first sRNA database dedicated to the Staphylococcus genus. SRD assigned a single identifier for the whole genus while other sRNA databases, such as BSRD  (Li et al. 2013) , provide an index per strain. A unique identifier offers a substantial advantage when comparing different strains, as it should avoid the dissemination of multiple redundancies.
Therefore, the data provided by SRD would have been difficult to fuse with other databases. SRD hosts a  large collection of manually curated Staphylococcal sRNAs (575 srn genes) mostly exempted of repeated sequences and of redundancies. Furthermore, coupled with an SRD's RNA-seq analysis, a list of 159 RNA-seq transcribed srn is provided that includes all the 60 previously experimentally supported sRNAs. This suggests that the SRD's criteria used to describe the srn transcripts that combined previously published cut offs  (Yoder-Himes et al. 2009; Howden et al. 2013)  with high mapping quality scores and FPKM normalization (to prevent the detection of incorrectly mapped reads or of long transcripts that do not arise from transcriptional noise) were relevant. However, among the nearly 300 srn genes not transcribed under the physiological conditions tested, some may be later annotated as sRNA transcripts once additional data and work will provide transcriptional evidence.
From this set of 159 srn genes, predictions were issued by sequence similarity to identify homologous genes in other Staphylococcal species. A comparison of the SRD's predictions with the BSRD entries for Staphylococcus strains shows that a genus-specific database is relevant. Indeed, in BSRD there is a huge discrepancy in the number of sRNAs sequences being available within the S. aureus subps. aureus species. While 154 sRNAs were listed for strain N315, mostly based on the work of  Beaume et al. (2010) , there were <60 sequences inventoried by similarity for other Staphylococcal strains  (Li et al. 2013). All BSRD S. aureus entries are present in our database, and the predictions performed within the genus from a set of 575 srn genes (versus 8248 genes for BSRD), led to a larger set of predicted genes in SRD (Table  4 ).
The comparative analysis performed for the srn content confirmed a low level of conservation within the genus and therefore in the bacterial kingdom. The weak sequence conservation at the DNA level in comparison with the protein level is therefore a serious limitation for retrieving genes, and therefore sRNAs, between species  (Konstantinidis and Tiedje 2007; Sentausa and Fournier 2013). To our knowledge, there is no recognized standard to assign identifiers or nomenclature to sRNAs. Therefore, to not favor anyone name (spr, rsa, sau, teg, and others) we created the srn identifier. In addition, a most common name was assigned based on the chronological discovery of experimental confirmation to avoid the community to name some of the already very well-described sRNAs under a srn identifier while using the database.
However, for the sRNAs that were only described by RNA-seq, the community is encouraged to use the srn identifier in an effort for unifying the work done on the sRNAs so far. We believe that SRD will help the scientific community working on Staphylococcal sRNA identification, function, and biology. SRD provides a simple and unified sRNA resource, a detailed annotation for each sRNA, a direct access to various RNA and genomic analysis tools. Finally, it shall encourage the community to participate in an effort to submitting new Staphylococcus sRNAs in SRD and to develop other genus specific sRNA databases that should be an essential extension to the generalist sRNA databases.
Data set building and comparative analysis The 32 genomes of Staphylococcus used in this study were downloaded from GenBank (Table  2 ). The published sRNA sequences were extracted from their respective genomes to construct a pool of sRNA. This pool was used to predict the srn genes in reference genomes. Then a pool of SRD's RNA-seq transcribed sRNA was used to predict srn genes in 28 Staphylococcus genomes. The predictions were done using "BLASTN" with a cutoff E-value <1 × 10 -20 , percentage similarity >80% and an alignment length >60 nt of the query length. The 32 Staphylococcus genomes and the predicted srn genes sequences were aligned using Muscle aligner implemented in Mauve software  (Darling et al. 2010). Mauve alignment generated a genome content matrix for which the identity scores range between 0 and 1, where 0 indicates that no identical homologous nucleotides were found, and 1 indicates that every homologous nucleotide was identical.
A matrix based on the srn gene content was generated (the similarity between two species is defined as the number of genes that they have in common divided by the total number of srn genes)  (Snel et al. 1999 (Snel et al. , 2005;; Huson and Bryant 2006). The genome content based matrix and the srn gene content based matrix were then used to construct, respectively, a Staphylococcus "phylogenomic" tree-based on genome content and a Staphylococcus tree-based on srn content, using Neighborjoining algorithm in the package SplitsTree4  (Huson and Bryant 2006). A "heatmap" clusterization was constructed using a matrix based on presence and absence of srn genes using the R package (http://www. r-project. org/).
Database design The web server has been designed in PHP with the "Symfony" framework (http://symfony. com). It includes a set of scripts that automatically parse the raw input files (srn, genomes), fill in a MySQL database for each set of genome/srn, and build some templates for all input predictions. Those scripts allow the addition of other genomes easily by simply adding the new files to the directory structure. They will update the existing information and insert the new ones. The website menus are automatically adapted to the list of analyzed genomes, removing the need to modify the website when new information is added (new genomes, new predictions, etc. ). The scripts also (i) extract the FASTA sequence of each srn (to prefill IntaRNA form for example), (ii) execute MFold to create the pdf files containing the structure, and (iii) prepare a blast index for all genomes and set of srn.
ACKNOWLEDGMENTS This research was supported by a  Marie Curie International Incoming Fellowship  (project  621959-SarHyb ) within the 7th European Community Framework Programme and by the  region Bretagne  grant  SAD 2013-SARS 8254. T. M. is a recipient of a fellowship from the  Direction Générale pour l'Armement. We thank  Dr. Philippe Bouloc  and  Dr. Tatiana  ( Orsay-Université Paris Sud) Rochat  for providing a BAM file combining 16 growth conditions for strain NCTC8325. We are most grateful to the  "plate-forme Génomique Santé" Biogenouest Genomics, Biosit core facility  for its technical support and to the  CNRS-UPMC ABiMS bioinformatics platform  (http://abims. sb-roscoff. fr)for providing computational resources and support. This work has benefited from the facilities and expertise of the high-throughput sequencing platform of  IMAGIF (Centre de Recherche de Gif -www.
HyLiTE accommodates any number of parent species, multiple data sources (including genomic DNA reads to improve SNP detection), and implements a statistical framework optimized for genes with low to moderate expression. Conclusions: HyLiTE is a flexible and easy-to-use program designed for bench biologists to explore patterns of gene expression following genome merger. HyLiTE offers practical advantages over manual methods and existing programs, has been designed to accommodate a wide range of genome merger systems, can identify SNPs that arose following genome merger, and offers accurate performance on non-model organisms.
• Improving SNP calling by using (optional) genomic DNA information in addition to high throughput mRNA sequences. • Providing statistical validation of SNP calls and automatically masking 'polymorphisms' with low statistical support. • An experimental feature that identifies putative chimeric genes (i. e. , genes in which the homeologs have recombined within the hybrid or allopolyploid)  [13] , but see Additional file 1 for details on current limits of accuracy. The standard HyLiTE analysis, which will be adequate for most users, comprises a single, short command line. However, advanced users have complete flexibility to override individual steps. For instance, by default, Bowtie2 is used for read mapping, but HyLiTE can be run with any mapping software that returns the standard SAM mapping format.
Because HyLiTE analyzes each gene independently, the software has low RAM requirements and runtime is linear with the number of genes under study. This independence between genes also allows HyLiTE to be parallelized via optional executables (see project website for details; http://hylite. sourceforge. net). HyLiTE regularly autosaves the run state, and analyses can therefore be stopped and re-started from the last checkpoint. Extensive documentation about the algorithms implemented in HyLiTE, software validation and benchmarking against alternative pipelines is provided in Additional file 1.
Software comparison A major point of difference between HyLiTE and alternative approaches (e. g. , PolyCat  [14] ) is its robust statistical assessment of SNP calls and automatic masking of 'polymorphisms' with low statistical support. Due to the substantial error rate of high throughput sequencing technologies, sequencing errors can easily be confused with genuine polymorphisms in genes with low expression (and hence, low read coverage). The probability that a polymorphism at any given nucleotide position is a SNP rather than an error is given by a binomial distribution conditioned on the coverage level. Nucleotides with coverage less than this threshold are masked, but because coverage varies widely across even a single gene, typically only small, uninformative regions of any given gene are masked.
6%; see Additional file 1 for details). It also identified 46,206 new SNPs specific to G. hirsutum. Animals. Finally, we analyzed gene expression in a synthetic allotetraploid fish derived from diploid goldfish (Carassius auratus) and diploid common carp (Cyprinus carpio) (∼6% divergence) (NCBI BioProject accession number: PRJNA82763). The very small number of reads available per gene (an average of only 15) caused HyLiTE to reject most SNP calls and therefore classify the majority of reads as parentally uninformative. However, the reads for which sufficient information was available to assign parental ancestry showed a very low error rate (0. 22%).
Acknowledgements Research support was provided to MPC by the  Royal Society of New Zealand  via a  Rutherford Fellowship  ( RDF-10-MAU-001 ) and by the  BioProtection Research Center , a  New Zealand Center of Research Excellence (CoRE) , via a Principal Investigator award. These funding bodies played no role in study design; collection, analysis or interpretation of data; writing of the manuscript; or the decision to submit this manuscript for publication.
Materials The PerFix EXPOSE (Phospho Epitopes Exposure Kit; Beckman Coulter) was used to fix and permeabilize cells. Samples were acquired on a FC500-MPL cytometer from Beckman Coulter (2 lasers: 5 colors, equipped with the Multi-Plate Loader that allows direct reading of 96-well plates), and data were analyzed with Kaluza* software (Beckman Coulter). Human lymphoblast cells IM-9 were derived from the blood of a patient with multiple myeloma and have been shown to be an Epstein-Barr virus (EBV)-transformed B lymphoblastoid cell line. Cells were maintained in RPMI 1640, 10% fetal calf serum, penicillin, streptomycin, containing 4. 5 g/L glucose and 1 mM sodium pyruvate. hGH was purchased from R&D Systems (Minneapolis, MN) and aliquoted in phosphate buffered saline (PBS)-0. 2% bovine serum albumin (BSA) at 1 µg/mL.
Data Analysis All needs assessment interviews and CWs were analyzed by thematic analysis  [18]. Both coders (SDL and CvU) read all transcripts to familiarize themselves with the data. The coders independently selected citations from the transcripts of all needs assessment interviews relating to current follow-up cancer care and needs of health care professionals with respect to an eHealth application. These were coded into themes. To analyze the usability of OncoKompas, we made use of the CW transcripts, supported by the Morae recordings. In total, 9 transcripts were coded by 2 coders. Initial codes for the CWs were generated focusing on system quality (ease of use), content quality (usefulness and relevance), and service quality (the process of care provided)  [15, 19, 20]. Additionally, both coders independently selected citations for 9 of the semistructured interviews concerning the implementation process and coded these into categories.
We present a high resolution spectro-imaging study with GEMINI/NIFS+Altair of the RY Tau jet in the [Fe ii] 1. 64 µm emission line. We also present a study of the disk kinematics conducted with IRAM/PbBI in millimetric CO lines. Together these data sets allow us to search for velocity gradients both in the jet and disk, and simultaneously perform the two consistency checks for jet rotation signatures discussed before. Furthermore, the imaging capabilities of NIFS/GEMINI provides an insight into the impact of variability within the jet on the derivation of rotation signatures. Our observations are described in section 2, while the method of analysis and results are detailed in sections 3, 4 and 5. In section 6, we discuss the measurements in the context of the magnetocentrifugal acceleration process, and we summarise our conclusions in section 7.
OBSERVATIONS To observe the outflow from RY Tau (R=9. 67), GEM-INI/NIFS+Altair was used to obtain AO-corrected (with natural guide star) IFU data of the approaching jet (PA=294 • ), with the H-band filter/grating at slit/slice position angles both parallel (24 • ) and anti-parallel (204 • ) to the jet axis (Program ID: GN-2009B-Q-43). This configuration positioned the IFU 0. 1 ′′ ×0. 043 ′′ spaxels with the smallest sampling in the direction perpendicular to the jet axis. Additional arc lamp exposures were taken during observations to ensure high accuracy wavelength calibration. Velocity resolution of the instrument was 57 km s - 1  , but for good signal-to-noise we could achieve higher velocity precision in emission line velocity centroids, via Gaussian fitting (section 4). Spatial resolution with AO correction (measured as the FWHM of the stellar PSF core) in the direction across the jet is 0.
Therefore, these observations were not included in the co-adds and hence the jet was not observed with the same signal-to-noise at each position angle. It subsequently transpired that the anti-parallel slit data could not be used as it did not reach sufficiently high signal-to-noise. To observe the RY Tau disk, 12 CO J=2-1 observations of the system were performed with the IRAM Plateau de Bure Interferometer on 2007 December 18 using the extended C configuration with 6 antennas and baselines ranging from 24m to 176m (project Q010). The 1 mm receivers were tuned single sideband at 230. 538 GHz. One correlator band of 40 MHz was centered on the 12 CO J = 2-1, implying a channel spacing of 78 kHz or 0. 1 km s -1. The total telescope time, including phase and amplitude calibrators, amounted to 24h with 6 antennas.
DATA REDUCTION The NIFS data were processed through the standard GEMINI calibration pipeline, but using the additional arc lamp files to improve wavelength calibration. It was not necessary to perform telluric subtraction during the pipeline, because subsequent continuum subtraction took into account the telluric features. The output IFU cubes for a given position angle were then combined, and continuum subtraction performed as follows. We selected a high signal-to-noise reference photospheric spectrum free of jet emission. This reference spectrum was then scaled down to the level of continuum in a region near the emission line of interest, and was subtracted from the jet spectrum. The residual emission line profile is not only free of photospheric absorption lines but also of telluric features.
The Plateau de Bure data were reduced using the GILDAS 1 software. Standard calibration methods, using close calibrators, were applied. The phase and am-plitude calibrators 0528+134, J0418+380 were observed along with 3C454. 3 as radio-frequency bandpass calibrator. The low quality data were filtered out. In particular, data with phase noise rms above 50 • were not used in subsequent data analysis. 12 CO J=2-1 images were produced using the CLEAN algorithm. The clean beam size is 1. ′′ 4500 × 1. ′′ 0100 at PA=41 •. Velocities are expressed in the LSR frame with a systemic velocity of 7. 8 km s -1.
DATA ANALYSIS The individual NIFS spectra showed that the [Fe II] line was well fitted with a single Gaussian. Emission line profile fitting was conducted at every position along and across the jet. The resulting velocities were corrected by -17 km s -1 to the LSR velocity frame. The overall error in centroid velocity was determined by measuring the velocities of the OH skylines. To do this, four of the brightest sky lines were selected (at 1. 60307, 1. 61283, 1. 66924 and 1. 69035 µm) based on their close proximity to the [Fe II] 1. 644 µm jet emission line. The velocities were measured in each spaxel, and their RMS was calculated separately for each slicing mirror, as this is the direction across the jet. Hence, we could identify wavelength calibration drifts which may affect our measurement of the transverse radial velocity profile of the jet.
RESULTS While the NIFS and PDBI datasets presents a wealth of information on the jet and disk morphology and kinematics, we choose to restrict our current focus to the case for jet rotation. Two separate more comprehensive studies of jet and disk are to follow  (Coffey et al. in preparation; Dougados et al. in preparation) , with just a brief commentary on the jet given below by way of introduction. The brightest skyline (snr=10) was then binned (×4) along the slitlet and the improved RMS was plotted (see points at snr=25). The dashed line represents the theoretical 1σ error on the Gaussian centroid fit, calculated according to equation 1. the radial velocity was obtained by Gaussian fitting the spectral emission peak where signal-to-noise ≥ 5. Figure  2  shows how the contours of the jet intensity relate to the velocity colour map.
The resulting coordinate positioning indicates an upper limit on the launch radius in the disk, r 0 ≤ 0. 45 AU, and a small magnetic lever arm parameter, λ=(r A /r 0 ) 2 ≤ 7. Note that our constraint on r 0 is only applicable for the part of the flow with radial velocities larger than the value at the radius from the jet axis of r=0. ′′ 1 and at 70 AU from the source. There are clearly data points with lower radial velocities in the outer borders of the jet, and this jet material could be originating from larger radii than r 0 ≤0. 45 AU. However, in our observations, these data are too faint to analyse. We briefly consider the implications of mass fluxes for the launching radius in the RY Tau system. In a steady MHD disk wind that carries away all of the accretion angular momentum flux from a Keplerian disk, there is a single inverse relation which has to hold between the magnetic lever arm parameter, λ, and the ratio of ejected to accreted mass-flux.
CONCLUSIONS High resolution observations of jets close to their base are critical in constraining the jet ejection mechanism. Here, we present results for RY Tau, via an IFU dataset of high resolution both spatially and spectrally. We also present Plateau de Bure observations of the disk, which reveal a velocity field consistent with disk rotation, and we attempt to determine if we can observe a transfer of angular momentum from disk to jet. A radial velocity analysis of the jet revealed that there is no systematic difference across the jet at the 3 σ level, i. e. no indication of jet rotation. Nevertheless, we can use the 3 σ error bars to obtain a useful upper limit on the current jet toroidal velocity. For RY Tau, at 0. ′′ 5 (70 AU) from star and 0. ′′ 1 (14 AU) from axis, we find an upper limit of 10 km s -1 on the toroidal velocity.
In this case, no firm constraint can be placed on the jet launch point since most of its angular momentum could be stored in magnetic form, rather than in rotation of matter. Although we do not reach the velocity accuracy anticipated, the variations within the IFU dataset make it clear that a three-dimensional datacube is critical to interpretations of velocity asymmetries across T Tauri jets. sities for Research in Astronomy, Inc. , under a cooperative agreement with the NSF on behalf of the Gemini partnership: the National Science Foundation (United States), the National Research Council (Canada), CON-ICYT (Chile), the Australian Research Council (Australia), Ministério da Ciência, Tecnologia e Inovação (Brazil) and Ministerio de Ciencia, Tecnología e Innovación Productiva (Argentina). Based on observations carried out with the IRAM Plateau de Bure Interferometer.