dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis tools We have also integrated various tools in VIRmiRNA like Seed-Align, BLAST, Map and TarFind for the analysis of the miRNA sequences. In VIRmiRNA-Seed-Align, user can align any given miRNA seed sequence against VIRmiRNA as well as cellular (miRBase) miRNAs. User has to provide the miRNA seed sequence in FASTA format. The output provides a list of matching seed sequences along with their  database accession numbers, nomenclature, alignment positions and percent identity values. VIRmiRNA-BLAST searches the presence of miRNA sequences in the database matching the user provided miRNA sequence. The output shows both graphical and text alignment of the matching miRNA sequences along with their alignment score. VIRmiRNA-Map displays the perfectly matching miRNAs available in our database against a given nucleotide sequence.
It helps user to find how many miRNAs are available in the database corresponding to the user provided sequence. VIRmiRNA-TarFind predicts the target genes in the human reference gene sequence against the user provided miRNA or seed sequence. VIRmiRNA-Update helps user to submit new viral miRNA information, which will be included in the main database after cross checking. We have also included 'How to use' and Frequently Asked Questions (FAQ) pages as well as 'help pop-ups' on individual pages to further assist the user to effectively use this resource. The 'How to use' page is given with snapshots of input and output examples. Besides, user can download any of the three subdatabases from the 'Downloads' section.
Discussion miRNAs are considered as important noncoding RNA molecules playing a fundamental role in the regulation of cellular pathways  (53, 54)  and viral infection  (16, 55). There are various experimentally validated miRNA databases reported in the literature like miRBase-a general miRNA storehouse  (56) , PMRD-a plant miRNA repository  (34) , miRCancer-a cancer-associated miRNA assemblage (57) etc. Of these only miRBase  (56) , the largest primary miRNA sequence repository, contains limited information on viral miRNAs. In VIRmiRNA, we have provided updated and comprehensive information on viral miRNAs from as many as 42 different viruses, which is 2-fold more than any existing resource. In addition, many resources of predicted miRNAs like miRGen  (58) , miRWalk  (39) , mirDB  (38) , microRNA.
org (35) also exist. However, for viral miRNA only a few predicted databases have been described like VirMirDb-a predicted viral miRNA database  (41) , vhotdb-a collection of predicted host-virus miRNA interactions  (42)  and miRNEST (43) but they do not furnish information on experimental viral miRNAs. It has been reported that a single miRNA gene can express multiple isomiRs which differ between cell and tissue types  (18). The isomiRs may either form an miRNA locus to coordinate target mRNA regulation  (19)  or their target transcripts could also potentially differ  (17). VIRmiRNA also harbors knowledge about viral isomiRs that would be helpful in miRNA analysis. Studies have shown that some viral miRNAs have their orthologous cellular counterparts due to identical or partial overlap seed region.
For e. g. kshv-miR-K12-11 has been reported to be orthologous to cellular miR-155  (59, 60) , kshv-miR-K10a to miR-142-3p  (21)  and BLV-miR-B4 to hsa-miR-29  (61). For the analysis of miRNA mimics, we have integrated seed align tool, which will be helpful to find miRNAs with identical seed region in both cellular and viral miRNAs. There are a few repositories of experimentally validated miRNA targets viz. TarBase (36), miRNAMap 2. 0 (62) and miRTarBase  (37). Although miRTarBase has comprehensive information of miRNA targets from several organisms, but it has scarce data on viral miRNA targets. Simultaneously, there are many catalogues of predicted miRNA targets viz. TargetScan  (63) , PicTar  (64) , mirDB  (38) , DIANA microT  (65) , miRanda  (51) , ComiR  (66). Moreover, databases of predicted viral miRNA targets are also available including ViTa-a database of predicted host miRNA targets on the viral genome  (44)  and RepTar-a repository of predicted cellular targets of mammalian as well as viral miRNAs  (45).
Comparatively, VIRmiRtar is the only database that holds updated and inclusive information of over 7000 experimental viral miRNA targets from 15 viruses. It has been reported that multiple miRNAs bind to a single target gene, a phenomenon known as cooperativity  (51, 52). Analysis of the target genes in VIRmiRtar showed that CCNT2, BRWD1, BTBD3, FNDC3A and LCOR etc. genes were targeted by several miRNAs. CCNT2 or Cyclin-T2 that functions as a regulator of CDK9 kinase (67) is being targeted by six miRNAs, belonging to KSHV and EBV, namely kshv-miR-k12-7, ebv-miR-bart9, ebv-miR-bart1-3p, ebv-miR-bart10, kshv-miR-k12-5 and kshv-miR-k12-1. Similarly, BRWD1 or Bromodomain and WD repeat-containing protein-1, that controls cell shape via the regulation of cytoskeletal organization (  68 ) is being regulated by five viral miRNAs.
Last, fluorescent reporters can cause subtle phenotypic changes, introducing biases in quantitative analysis. We therefore developed a computer program that detects centrosome position over time from DIC images. It analyzes spindle positioning quantitatively in various worm strains without requiring genetic manipulations. The computer program is a unique macro for the image-and movie-processing platform ImageJ. Within 2 minutes, movies are processed and the trajectories of the centrosomes are detected with high accuracy. The program generates a movie showing the position of centrosomes over time on the input DIC movie, which allows a visual check of the tracking quality. The program generates timeseries graphs of the centrosome positions as well as a text file containing the position of each centrosome over time.
Tracking variability between human manipulators For a critical evaluation of the macro, it was essential to know the precision of the standard procedure for manual tracking. Thus far, we performed manual centrosome tracking using the ''Manual Tracking'' plugin of the program ImageJ. As a control experiment, we determined the variability between different human manipulators using this plugin. Two manipulators tracked the trajectories of the anterior and posterior centrosomes for a heterogeneous collection of 18 mitosis events (Table  S1 ). To evaluate the similarity between the detected trajectories, we measured the distribution of the root-mean-square deviation (RMSD) values for the anterior and posterior trajectories (Figure  3 ). RMSD is a measure for the overall deviation between two series of points (see Methods).
Time-lapse DIC video microscopy Adults were dissected in M9 medium. Eggs were collected and mounted on 2% agarose pads, covered by a coverslip and recorded at 23uC-unless otherwise stated. DIC movies were acquired using an AxioImager A2 Zeiss (objective lens 1006, NA 1. 4) equipped with a Kappa camera and its accompanying timelapse module. The resolution was 0. 129 mm/pixel. Images were taken every 0,5 seconds and TIFF files were converted in Quicktime movies with the ImageJ software.
Acknowledgments We thank  Gae ¨l Yvert  for critical reading of the manuscript. We are grateful to the  Federation Lyonnaise de Modelisation et Sciences Numeriques (FLMSN)  and the  Pole Scientifique et Modelisation Numerique (PSMN)  for providing CPU time and technical assistance. We thank the  Hyman and Felix laboratories  for providing strains. Some nematode strains used in this work were also provided by the  Caenorhabditis Genetics Center , which is funded by the  NIH National Center for Research Resources (NCRR).
This project has been funded by grants from the  CNRS (ATIP)  to M. D. and from the  Grand Equipement National de Calcul Intensif (GENCI ,  x2013076381 ) to M. S. ; S. R. and P-N. S. are supported by a PhD fellowship from the  Region Rhone-Alpes. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This paper presents the characterization of the in-flight beams, the beam window functions and the associated uncertainties for the Planck Low Frequency Instrument (LFI). Knowledge of the beam profiles is necessary for determining the transfer function to go from the observed to the actual sky anisotropy power spectrum. The main beam distortions affect the beam window function, complicating the reconstruction of the anisotropy power spectrum at high multipoles, whereas the sidelobes affect the low and intermediate multipoles. The in-flight assessment of the LFI main beams relies on the measurements performed during Jupiter observations. By stacking the data from multiple Jupiter transits, the main beam profiles are measured down to -20 dB at 30 and 44 GHz, and down to -25 dB at 70 GHz. The main beam solid angles are determined to better than 0.
Introduction This paper, one of a set associated with the 2013 release of data from the Planck 1 mission (Planck Collaboration I-XXXI, Detailed knowledge of the instrumental angular response is a key requirement for the analysis of high precision measurements of the cosmic microwave background (CMB). Modern experiments employ multi-frequency focal plane arrays whose off-axis beams necessarily deviate, to some extent, from an ideal, axisymmetric (circular), Gaussian shape. The radiation patterns of the individual detector and their projected angular locations need to be reconstructed with great precision to avoid significant systematic effects in the data  (Hill et al. 2009; Nolta et al. 2009; Huffenberger et al. 2010). The Planck optical system is designed to ensure high image quality over a wide field of view, for detectors spanning over 1.
The optimization of the LFI optical system leading to the focal plane configuration used in flight is described in  Sandri et al. (2010) , while the preliminary characterization of the LFI beams based on the first in-flight data are reported in  Mennella et al. (2011)  and  Zacchei et al. (2011). The LFI map-making procedure does not take into account the beam profile, which is effectively assumed to be a pencil beam. To correct for the beam shape, the angular power spectrum computed from the observed map is divided by the beam window function to reveal the intrinsic angular power spectrum of the sky. For this reason, beam knowledge directly affects the cosmological analysis. Typically, the beam should be mapped to less than -30 dB of the peak to achieve 1% accuracy on the angular power spectrum  (Page et al.
2003). By stacking the data from the first four Jupiter transits, the LFI beams have been measured down to -20 dB at 30 and 44 GHz, and down to -25 dB at 70 GHz with an uncertainty of about 0. 3% on the angular resolution and about 0. 5% on the main beam ellipticity. In order to achieve the beam knowledge at lower power levels and improve the accuracy on the angular power spectrum, a substantial effort has been made to tune the Planck optical model, presented in  Tauber et al. (2010) , to fit the in-flight measurements of the LFI beams. This ensures a good representation of the LFI optics, for both the main beam and sidelobes. The separation of the instrumental angular response into main beam and sidelobes can be somewhat arbitrary. In the framework of this paper, we consider three regions defined with respect to the beam boresight and shown in Fig.
Planet Data Handling The LFI in-flight main beam reconstruction is based on a minimization code described in  Burigana et al. (2001)  and incorporated into the Level 2 Planck LFI DPC pipeline. The code uses the calibrated timelines of Jupiter transits observed by the LFI beams, to fit the beam shape to an elliptical Gaussian function. With this Gaussian approximation, the angular resolution is defined in terms of the full width half maximum (FWHM), the beam ellipticity (e), and the beam orientation (ψ ell , see Fig. 2 ). Moreover, this fit is used to define the beam center so that the beam pointing directions agree with the convention adopted in Planck Collaboration II (2014). The fit is performed in the plane of the Planck field of view, centered along the nominal line of sight (LOS) defined in Tauber et al.
(  2010 ). In Fig. 3  the LFI footprint on the sky is reported for both polarization arms. The data selection is done using the pointing information contained in the satellite Attitude History File (Planck Collaboration II 2014), which in turn is used to infer the nominal LOS direction synchronously with the sampled data. The data selected for fits to Jupiter lie on square grids centered with respect to the main beam pointing direction, of about 1. 7 • in total size at 70 GHz, 2. 6 • at 44 GHz, and 3. 8 • at 30 GHz. For each radiometer arm, the selected data are characterized by an array of data samples specified by the signal amplitude (in thermodynamic temperature) during the transit, the positions (x, corresponding to the scan circles, and y, corresponding to the positions along each scan circle) of Jupiter during the transit, and the distances between Planck and the planet itself during the transit.
Beam validation through deconvolution To test the goodness of the beam representation, the maps for each individual horn at 30 GHz and 44 GHz have been deconvolved using the ArtDeco beam deconvolution algorithm described in  Keihanen & Reinecke (2012). The code takes as input the time-ordered data stream, along with pointing information and the harmonic representation of the simulated beam, to construct the harmonic a slm coefficients that represent the sky signal. From the harmonic coefficients we further construct a sky map, which is now free from the effects of beam asymmetry, assuming that our beam representation is correct. Before deconvolution we ran the time-ordered data through the Madam map-making code  (Keihänen et al. 2010) , to remove low-frequency noise. We saved the baselines that represent the correlated noise component, and subtracted them from the original data stream.
Sidelobes The response of the beam pattern outside the main beam needs to be carefully understood, as it may have significant impact on the Planck data analysis. Although a full physical optics computation could be developed to predict accurately the antenna pattern of the telescope, this is not feasible for the whole-spacecraft simulations since the physical optics approach is very complicated when multiple diffractions and reflections between scattering surfaces are involved. For this reason, we have calculated the sidelobe patterns through the GRASP multi-reflector geometrical theory of diffraction (MrGTD), which computes the scattered field from the reflectors by performing backward ray tracing. This represents a suitable method for predicting the fullsky radiation pattern of complex mm-wavelength optical systems in a reasonable time.
The contributions to beam solid angle found in this simulation of the sidelobe region using the MrGTD up to the first order are about a factor two lower than the expected value derived from physical optics calculations (see f sl reported in Table  3 ). That suggests that the first order approximation adopted in this subsection underestimates the actual integrated power in the sidelobes. In the future, it will be necessary to take into account the impact of higher order contributions in combination with physical optics analyses. Careful analysis of the LFI 30 GHz data reveals the imprint of Galactic radiation received through the far sidelobes. Such a detection is amplified when taking the difference between maps of even and odd surveys: the different satellite orientation during odd and even surveys reverses the sidelobe pattern with respect to the Galactic radiation.
A detailed discussion of the systematic effects introduced by sidelobe pickup at 30 GHz is given in Planck Collaboration III (2014). The expectations of the sidelobe pick-up based on the known level of Galactic emission (as measured by Planck itself) and our sidelobe model, are in good qualitative agreement with the observed effect, as shown in Fig. 10. The residual ring clearly visible in the third panel of Fig. 10  demonstrates the need to improve the sidelobe model with higher order contributions, possibly combined with full physical optics analyses. In the bottom panel of the Fig. 10  we show the difference between data and the simulations amplified by a factor equal to the ratio of the power entering the sidelobes (computed from the main beam efficiency) and the integral of the simulated sidelobes.
The beam data were delivered to the Planck data processing centers (Planck Collaboration II 2014; Planck Collaboration VI 2014) over the network, on tape and disk, and ingested into the Data Management Component (DMC). FEBeCoP associated application software was developed and installed to use the effective beams, e. g. , fast Monte Carlo full sky convolution codes. In estimating the effective beams, a cut-off is applied to the input simulated beams. The several tests performed converged to a cut-off radius of 2. 5 × FWHM. The beam within this cut-off radius is named nominal beam and it is the portion of the beam used to create the beam window function. The beam efficiency of the simulated beams within this cut-off radius is reported in Table  4. For a detailed account of the algebra involving the effective beams for temperature and polarization see  Mitra et al.
The same Madam parameter settings were used as for the flight maps  (Zacchei et al. 2011; Planck Collaboration II 2014). The calibration step was not simulated, as the simulated signal was constructed as already calibrated, except for the effect of power lost to the sidelobes. The impact of sidelobes on the calibration of flight data is discussed in Planck Collaboration V (2014), while its effect on the beam window function will be discussed in Sect. 5 of this paper. For the MC, we assumed that the calibration compensates for the missing power in the main beams, according to the discussion in Sect. 2. 2 of Planck Collaboration V (2014). We produced in this way 30 GHz, 44 GHz, and 70 GHz frequency maps, and the "horn-pair" maps for 70 GHz 18/23, 19/22, and 20/21 from the 15. 5 month nominal survey.
Hence, the FEBeCoP window functions will be used for the power spectrum analysis (Planck Collaboration II 2014; Planck Collaboration XV 2014), and will be distributed within the data release. With FEBeCoP we also estimate the level of contamination of the transfer functions due to a non-uniform sky sampling within the pixels, comparing the ideal HEALpix pixel window function, which is derived under the assumption of uniform coverage, with the true one computed with FEBeCoP. This effect acts as a noise term in the maps and it becomes important only at very high. An analytic treatment of the contamination on the maps is described in Appendix F of the HFI Beam paper  (Planck Collaboration VII 2014). The level of distortion of the window function is: 0. 1% at = 600 for 30 GHz, 0. 4% at = 800 for 30 GHz, and 0.
Conclusions The optics and electronics of the Planck detectors, combined with the satellite motion, determine the instrumental angular response to a sky signal. An accurate characterization and a thorough understanding of the LFI beam patterns is the key to determining their imprint on the transfer function from the observed to the true sky anisotropy spectrum. In this paper we discussed the algorithms used to reveal the most significant LFI beam features that impact the exploration of the underlying cosmology. The in-flight assessment of the LFI main beams relied mainly on the measurements performed during four Jupiter crossings. The calibrated data from the Jupiter scans were used to determine the scanning beams: the signal-to-noise ratio for this data is such as to make it possible to follow the LFI beams profile down to -20 dB from the peak, corresponding to distances from the beam line of sight of about 1.
INTRODUCTION Vibrio is a ubiquitous, speciose and commercially important bacterial genus with both host associated and free-living representatives. Several species within the genus are pathogenic to humans and animals. Vibrio cholerae has caused six historic and one ongoing cholera pandemic, and countless epidemics  (Mutreja et al. , 2011)  including a recent outbreak in Haiti that killed more than 8000 people  (Chin et al. , 2011). Vibrio pathogens are also important to the aquaculture industry, where they inflict costly losses on farmed fish, mollusks and shrimp  (Austin and Austin, 2007) , limiting the development of an industry poised to help bridge global food gaps and preserve wild fisheries  (FAO, 2012). Due to their importance to human and animal welfare, and the ease with which they are cultured, vibrios are relatively well studied, with over 570 publicly available annotated genomes and over 64,000 16S rRNA gene sequences annotated as vibrios in GenBank as of March 2014.
Analyses of this type have historically involved culturing isolates from target habitats and sequencing multiple loci in order to gain sufficient taxonomic resolution within a sample, requiring the use of Vibrio-specific primers  (Preheim et al. , 2011a; Szabo et al. , 2013) , and making-large scale, non-targeted, multi-habitat analyses challenging and costly. More recently, oligotyping rRNA gene amplicon sequences affords extremely high resolution analysis of community structure by selecting a subset of highly informative nucleotide sites within single loci of 16S rRNA gene hypervariable regions alone  (Eren et al. , 2013a (Eren et al. , , 2014;; Reveillaud et al. , 2014). At the same time, repositories of 16S rRNA gene sequences have grown in size and scope. The Visualization and Analysis of Microbial Population Structures (VAMPS) database is one such repository that contains over 1000 datasets representing hundred of millions of publically available 16S rRNA gene sequences  (Huse et al.
SEQUENCE COLLECTION The VAMPS database houses 16S rRNA gene amplicon sequence data projects from a wide variety of environmental and hostassociated habitats. We identified seven existing projects to target for analyses of Vibrio diversity, representing free-living and host (abiotic and biotic) substrate associated habitats (Table  1 ). We chose projects with the occurrence of at least three samples with >300 sequences identified as Vibrio by the Global Alignment for Sequence Taxonomy (GAST) pipeline  (Huse et al. , 2008)  in the VAMPS database. In rare cases, a sample was included that had less than 300 sequences in order to increase the number of samples for habitats with low sample numbers, or when water associated with a specific substrate was of interest but possessed low Vibrio sequence representation.
OLIGOTYPE GENERATION AND ANALYSIS Oligotyping is a supervised method that allows the identification of closely related but distinct bacterial taxa in high-throughput sequencing datasets of marker genes. This novel bioinformatics approach is capable of uncovering ecological patterns of microbial communities at finer scales than previously possible with de novo approaches  (Eren et al. , 2013a). Oligotying exploits the fact that some positions within a DNA marker sequence are more ecologically informative than others. The method identifies highly variable locations using Shannon entropy (that is, "entropy components"), and uses only these positions to discriminate ecological units, so called oligotypes. This process reduces the impact of noise caused by sequencing error by relying on only a small number of nucleotide positions, discarding the redundant parts of reads for the identification of oligotypes.