dataesr/scientific-paragraphs-categorization huggingface dataset:
The presence of these viruses was corroborated via RT-PCR: GPGV and GRVFV in sample Pa10, GSyV-1 in sample Pa5 and GLRaV2 in Py14 (data not shown). These results confirm that grapevine is rarely infected by a single virus species, or even a single variant of that species, but rather by multiple viruses in a vineyard. Only samples Pa3 and Py13 were only co-infected with GRSPaV and viroids that are present in most grapevines worldwide and are considered as part of the 'microbiome' of grapevine  (Saldarelli et al. , 2017). All the other samples were super-infected with at least one (samples Pa7, Pa9, Py16, and Py19) and up to five different viral species (sample Py17). Nonetheless, the virus count might not be final, knowing that RNAseq does not provide an exhaustive view of the virome.
Interestingly, detecting the presence of GFLV was more sensitive when targeting RNA2 compared to RNA1. Likewise, when dealing with RNAseq datasets, RPKMs (which reflect viral accumulation) were always higher for RNA2 compared to RNA1 sequences (Table  5 ). This higher accumulation of RNA2 was previously described from the estimation of the quantity of RNA species from purified GFLV particles  (Pinck et al. , 1988). As expected, no correlation in GFLV quantification was observed between the different diagnostic techniques (Supplementary Table  S8 ). What was more startling was that no correlation in viral quantification was found between techniques that identify the same target, for example total RNA by RT-qPCR and RNAseq. This could be due to an inherent bias of each method such as the use of RPKM that normalize the coverage/quantification along the whole molecule for RNAseq compared to the involvement of anchored primers in the RT-qPCR method, amplifying only one small targeted portion of the viral genome.
The use of specific primers might also prevent the amplification of all variants by RT-qPCR due to mismatches. However, correlations were found when comparing GFLV RNA1 and RNA2 quantification by the same detection technique, strongly supporting the fact that both RNAs are necessary for a successful infection to take place and probably at a steady equilibrium, with a RNA2/RNA1 ratio = 2. 37 ± 0. 16 according to our RNAseq dataset. This ratio was quite similar to the 2. 33 ± 0. 06 ratio from one of our previous metagenomics study  (Hily et al. , 2018c). This result reinforces the need to target RNA2 for a more efficient GFLV detection by molecular techniques. High throughput sequencing offers unparalleled amount of sequence information from a single run. From this work using either RNAseq or smallRNAseq libraries, we learned that the number of reads or the genome coverage are not sufficient to proclaim a sample positive for a virus (Table  1 ).
After a direct mapping analysis from the RNAseq datasets, both HRC and LRC samples displayed over 80% coverage of the GFLV genome. By performing a simple de novo assembly from reads mapping to GFLV-consensus sequences in the LRC samples, not a single complete GFLV sequence was obtained but rather a number of short contigs that matched complete GFLV genomes de novo assembled from HRC samples present in the same sequencing lane. From these observations, we developed a bioinformatics protocol to remove highly identical reads from different samples within the same sequencing lane (Figure  2  and Supplementary Data Sheet 1), allowing for a discrimination between potential low viral titer infection and intra-lane cross-contamination, also known as 'index misassignment' or 'index hopping'  (Kircher et al.
, 2012). Such contamination arises when multiple libraries are pooled in a single lane and sequenced simultaneously. To address this issue, a unique sequence (called index) per sample is added to individual DNA fragments during a library preparation. Index hopping occurs post-sequencing and before final data analysis during a process called 'demultiplexing. ' Levels of index hooping are different depending on the indexes being used 3. This step consists in identifying all reads by sorting them computationally to its correct library. While index hopping is inevitable when multiplexing samples, few practices offer mitigation options, with the best of them consisting in the use of a unique dual indexing combinations  (Kircher et al. , 2012). However, we clearly show that the use of dual indexing is not sufficient to completely abolish such index hopping (see results from Lane 1).
, 2017). Our data highlighted some advantages and drawbacks of RNAseq and smallRNAseq libraries. For example, the latter offers a wider viral detection panel, not restricted to poly-A-tailed viruses (Table  3 ). On the other hand, de novo assembly and full-length genome sequence recovery was more difficult than from RNAseq libraries. Often some viral genomes were not fully recovered from smallRNAseq datasets, with small gaps along the genome (data not shown). This was also previously reported for other sequencing techniques  (Beuve et al. , 2018). Except for GFkV or other viruses with a cytosine-rich genome, RNAseq provided data that can yield complete viral genomes as a result of a straightforward de novo assembly protocol. Moreover, viral genetic diversity was better defined using RNAseq.
For example, within our parameters (quality and sequencing depth), little to no difference in reads mapping were observed between GFLV-consensus and de novo assembled sequences from our RNAseq datasets. This indicated that using a consensus genome might be the best approach to recover a maximum viral diversity within a sample even when dealing with a highly divergent virus such as GFLV. On the other hand, read numbers nearly tripled when dealing with smallRNAseq data. More work in parameter setting is needed to easily detect the full genetic diversity within a sample when using smallRNAseq libraries. Another advantage that HTS offers compared to traditional methods is the possibility to go back to datasets which can be considered as 'evergreen' and can be analyzed endlessly. This is particularly advantageous because new viruses are detected almost monthly.
All detection techniques were in agreement for the characterization of the presence or absence of GFLV in samples from a grapevine virus collection or naturally infected vineyards. We can conclude that (i) DAS-ELISA is a robust and reliable method detecting as low as 117 pg of virion, (ii) Molecular approaches are, as expected, more sensitive than serological methods, regardless of the isolate tested with IC-RT-qPCR allowing the detection of GFLV as low as 0. 44 pg of virions when targeting RNA2, (iii) SmallRNAseq libraries are useful for an exhaustive definition of the sanitary status of a sample, (iv) RNAseq is the method of choice for an easy detection of distinct GFLV variants, and (v) a suitable protocol discriminating between contamination and the potential low GFLV infection level needs to be implemented for RNAseq and small RNAseq datasets to confidently decide on the infectious status of grapevine samples.
ACKNOWLEDGMENTS We would like to particularly recognize  Géraldine Uriel  (CIVC),  Véronique Bonnet  ( Moët and Chandon ),  Olivier Jacquet  (the  Chamber of Agriculture of Vaucluse), and Pascal Blois (IFV)  for their help and support in the field and for providing samples used in this study. We thank  Dr. Marc Fuchs  for proofreading the manuscript. We acknowledge  J. Misbach  and the greenhouse team for technical support,  L. Ley  and the members of the experimental unit of  INRA-Colmar  for the production of plants.
FUNDING This work was supported by the  Institut National de la Recherche Agronomique (INRA)  and the project  VACCIVINE  funded by the  'Plan National Dépérissement du Vignoble' (French Ministry of Agriculture , FranceAgrimer and CNIV), class of 2017. Additional funds were kindly provided by three French professional committees for viticulture ( Interprofessional Committee of Champagne Wine, CIVC, Epernay; Interprofessional Office of Burgundy Wines, BIVB, Beaune; and Interprofessional Committee of Wines from Alsace, CIVA, Colmar ), the  Chamber of Agriculture of Vaucluse and Moët and Chandon.
For example, we crossed Df(1)BSC531, w 1118 /FM7h female virgins with "w 1118 /Y; nub>Cdk8 + /CyO" males, and then scored F1 females with the following genotype: "Df(1)BSC531, w 1118 / w 1118 ; nub>Cdk8 + /+". Similarly, we crossed "w 1118 ; nub-Gal4; UAS-Cdk8-RNAi" female virgins with "Df(2R)Exel6064/CyO" males, and then scored F1 females with the following genotype: "w 1118 /+; nub-Gal4/Df(2R)Exel6064; UAS-Cdk8-RNAi/+". Adult Drosophila wing imaging. The wings from adult females were dissected onto slides, briefly washed using isopropanol, and then mounted in 50% Canada balsam in isopropanol. Images were taken under 5X objective of a microscope (Leica DM2500) and then processed by Adobe Photoshop CS6 software.
Wing discs were incubated with these primary antibodies overnight at 4˚C on a rotator. After rinsing with PBS-Triton X-100, the discs were then incubated with the fluorophore conjugated secondary antibodies: goat antiguinea pig (106-545-003), goat anti-mouse (115-545-003), or goat anti-rabbit (111-545-003), all purchased from Jackson Immunological Laboratories. These secondary antibodies were diluted 1:1000 in PBS-Triton X-100-NGS-BSA, and incubated with the samples for one hour at the room temperature. All samples were then stained with 1 µM DAPI at room temperature for 10 minutes, rinsed for another two times with PBS-Triton X-100 and mounted in the Vectashield mounting media (Vector Laboratories, H-1000). Confocal images were taken with a Nikon Ti Eclipse microscope system, and images were processed using the Adobe Photoshop CS6 software.
Acknowledgements: We are grateful to  Lauren Bridges ,  Christine Hermann , and  Suzie Park  for their technical assistance with the genetic screen,  Liz Perkins  for the TRiP lines, and the  Bloomington Drosophila Stock Center  ( NIH P40OD018537 ) for the fly strains. We also thank  Xiuren Zhang  for advice in the yeast two-hybrid assay, and  Laurel Raftery  and  Fajun Yang  for helpful discussions. The monoclonal antibody against β-Gal (DSHB-40-1a-s) was deposited to the  Developmental Studies Hybridoma Bank at the University of Iowa by Joshua Sanes. This work was supported by grants from the  National Institute of Health  ( DK095013  to J. Y. J. ) and the  "Association contre la Cancer" (ARC , to H. M. B. ).
Microscopy, with transmission electron microscopy (TEM) and atomic force microscopy (AFM) are methods of choice for the structural characterisation. Microscopy approach has the advantage of a direct visualization of the morphology and the disadvantage of a limited representativeness of the collected pictures. To complement microscopy, scattering techniques such as SAXS and SANS, are frequently performed. Indeed, both SAXS and SANS permit to collect structural averaged data in a large volume of sample; the difficulty is the need of mathematical models to transfer the data from the reciprocal space to the real space. [11]  Also, SAXS and SANS are well complementary to each other; for instance, the high brilliance of the X-ray sources allows a fast data acquisition (below the second) together with a high resolution which is essential to investigate fast structural kinetic; on the other hand, neutron contrast variation approach allows to selectively enhance or match the contrast in multiphasic systems, which is essential for a detailed description of complex morphologies.
This model is based on SAXS and SANS data collected at much smaller q compare to Gierke's model, i. e. information on the larger dimension (cf. figure  4b ) and also over a wide range of water content. [23, 27]  The systematic presence of a q -1 slope at small q was a key clue, suggesting elongated scattering objects. In addition, this model is able to describe the orientation of the structure under an uniaxial stretching (cf. figure  5 ). [20, 29]  In 2008, an inverted cylinder model was proposed based on new analysis of existing SAXS curves using numerical Fourier transform techniques. [28]  This model describes Nafion as parallel cylinders decorated inside with ionic groups and water within a fluorocarbon matrix (cf. figure  4c ). More recently, Nafion was described by locally flat and narrow water domains (about 1 nm) in a fluorocarbon matrix.
Adding salt changes considerably the interaction between the blocks, as demonstrated by Ruzette et al. ; then equation 1 can be adapted, assuming that the ions are dissolved in only one of the phases. The Flory-Huggins parameter χeff for block copolymers blended with salt becomes:  (5)  with r the molar ratio between the ions and the monomers of ion-containing blocks. Experimentally, χ eff can been determined from scattering data, considering (i) the structure factor changes in the disordered phase, (ii) the change in the domain spacing of the ordered phases, or also (iii) the shift in the phase boundaries. Russell and coworkers  [76]  have fitted the changes in structure factor of the disordered phase using the RPA theory (cf. annex D). Their experimental data was collected by SANS on PMMA-b-PS diblock copolymers blended with LiCl salt (cf.
Acknowledgements I am grateful to  M. Wolf ,  F. Cousin ,  M. Månssson  and  I. Grillo  for their kind invitation to the  French & Swedish Winter School in Neutron Scattering (Uppsala ,  2016 ). Also, I would like to thanks my supervisors and colleagues  O. Diat ,  G. Gebel ,  B. Frisken ,  S. Holdcroft ,  R. Mezzenga ,  J. Ruokolainen ,  D. Mecerreyes ,  T. Bowden  and  D. Brandell  for our fruitful collaborations. Finally, I thank  LLB ,  ILL , SINQ,  NIST-CNR  and  ISIS  for the neutrons beamtime allocations.
2(A) )). The results show that taking the highest algorithmically complex compounds would profile all pyrimidines (558) with high accuracy, of all the other compounds considered in this  database (44 089), and likewise the lowest complexity retrieves all inorganic compounds followed by heavy and iodinated molecules. The total number of compounds per class can be found in Table  1. It is also of interest to note that classes of compounds whose algorithmic complexity estimation median values are close to each other are causally related. For example, acids and alcohols appear to have very similar algorithmic information content, notwithstanding the fact that alcohol structures are much larger in size than acids. Their structure and causal origin can be regarded as similar as they can be derived from each other with their main structure unchanged.
Statistical overlapping of complexity estimations is to be expected, given that classes are not distinct. Aspirin is a drug, but it is also classified as a biomolecule, organic, solid and aromatic. Many classes also share elements Figure  3 : Graph branching is one of the most important measures proposed in molecular complexity. Here it is shown how algorithmic-based measures such as lossless compression (Compress and Bzip2) are correlated with graph spectra, which in turn capture graph branching  [18]. The correlation is stronger than that obtained when using only Shannon entropy. All data come from the Wolfram Language database retrieved by the ChemicalData[] function with chirality properties. However, a significant divergence between contrasting classes such as those not closely causally related is to be expected, as measured by the algorithmic probability of a compound being in the causal path of another class of compounds (i.
Initial Material To produce our custom multi-scale map, we choose as initial situation the "Scan Express multi-scale map", produced by the French National Mapping Agency. This multi-scale map uses a similar style at all scales, which ensures a homogeneous symbolization. However, as the different maps have been derived from different databases, some inconsistencies appear. For instance, Figure  3  shows a salient highway which changes color across scales. We choose to correct these inconsistencies in existing maps, to avoid their influence on task performances during the test. After checking on the study area, the existing maps were found consistent in terms of content. To limit the time of map making and the quantity of variables to test, we choose to only consider the scale range between 1:25k and 1:100k.
Three cartographic datasets exist on this scale range, produced at 1: 25k, 1: 50k and 1: 100k scales. Extracts of these maps are presented in Figure  3. We only use the following geographic themes of topographic maps: land use, hydrography, roads, settlement areas and place names. This initial multi-scale map will be included in the user evaluation to serve as reference and confirm (or infirm) our hypotheses about transitions improvements.
Production of Alternative Transitions This section describes the processes used to build each custom multi-scale map, called an "alternative" to the reference multi-scale map. To enable the comparison between alternatives and the reference multi-scale map, we use the existing datasets at 1:25k and 1: 100k scales to produce them. However, to limit the constraints on intermediate representations design, we choose not to use the initial dataset at 1:50k in the alternatives. Then in each alternative, we derive several intermediate representations, depending on the hypothesis to test. We only discuss about the algorithms we use to produce alternative maps, so we do not discuss about the number and display scales of intermediate representations in this 1 25 1 50 1 1 1 part. Besides, we particularly focus on the simplification of roads and settlement areas, as they are very salient cartographic themes.
They perform a systematic and detailed analysis of the limitations of the kinematics parameters, and in particular the velocity dispersion in the CALIFA dataset. Finally, they present the largest and more precise estimation of individual velocity dispersions based on CAL-IFA observations, as a public accessible table. The current study would explore the nature of these scaling relations. The structure of this article is as follows. In Section 2 we briefly describe the CALIFA sample, including a summary of the delivered datasets. Details of the analysis perform over the data are presented in Section 3. 1. In Section 3. 2 we estimate the kinematics parameters within 1r e , following the same methodology as C14. In Section 3. 3 we perform a detailed modeling of the 2D spatially resolved velocity maps for a subsample of good quality datasets.
7 Å). The delivered dataset was reduced using version 2. 2 of the CALIFA pipeline, whose modifications with respect to the previous ones  (Sánchez et al. 2012b; Husemann et al. 2013; García-Benito et al. 2015 ) are described in  Sánchez et al. (2016d). The final dataproduct of the reduction is a datacube comprising the spatial information in the x and y axis, and the spectral one in the z one. For further details of the adopted dataformat and the quality of the data consult  Sánchez et al. (2016d).
Spectroscopic Analysis. In this paper we use the data-products (ionized gas kinematic maps) derived for the CALIFA V500 setup dataset by Pipe3D pipeline  (Sánchez et al. 2016c ) based on the Fit3D fitting tool  (Sánchez et al. 2016a ), together with the stellar line-of-sight velocity and intrinsic dispersion maps for the V1200 setup performed using pPXF by  Falcón-Barroso et al. (2017). Pipe3D models the stellar continuum adopting a multi Single Stellar Population (SSP) template library, taking into account the velocity, dispersion and dust attenuation of the stellar populations. Then, it estimates the main properties of the nebular emission lines. The current implementation of Pipe3D adopted the GSD156  (Cid Fernandes et al. 2013)  template library for the analysis of the stellar population properties.
Integrated kinematics. The original dataset comprises 734 galaxies for the V500 dataset observed within the frame-work of the CALIFA survey  Sánchez et al. (2017) , and the 300 galaxies for the V1200 dataset described by  Falcón-Barroso et al. (2017). From this dataset we perform a selection of the optimal data for the proposed analysis following the methodology described by C14: First, spaxels are discarded if the error in velocities is greater than 20 km/s and 50 km/s for gaseous and stellar kinematics, respectively. This conventional cut correspond to one third of the spectral FWHM (∼ 6 Å, i. e. , ∼ 150 km/s) of V500 CALIFA data. Second, we selected only those galaxies for which at least 80% of the spaxels within an ellipse of semi-major axis equal to 1r e fulfill this quality criteria.
Velocity dispersion: σ. Stellar velocity dispersions were estimated as the linear average of the velocity dispersion of all spaxels within the ellipse mentioned in the previous section using the velocity dispersion maps from the V1200 dataset without correction for inclination. Following C14 we use linear instead luminosityweighted averages to be consistent with our velocity width measurements which are not luminosity-weighted. Ionized gas velocity dispersions were estimated fitting the integrated spectrum within a diameter of 5" with Pipe3D for the V500 dataset using the template library described above. Regarding the determination of the stellar and gaseous velocity dispersions (up to 1r e ), which dominate in early-type galaxies, we rely on the detailed kinematic analysis presented in  Zhu et al.
Rotation velocity : V rot. Once more, we followed C14, to derive the stellar (V1200 dataset) and gaseous (V500 dataset) rotation velocities. They adopted the same classical procedure developed to analyze the integrated HI emission profiles in galaxies, i. e. , through the width parameter, W (e. g. Mathewson et al. 1992; Vogt et al. 2004 ). First, a histogram is derived of the velocities estimated for all the good spaxels within the r e. Then, it is calculated the difference between the 10th and 90th percentile points of this velocity histogram, defined as the width: W = V 90 -V 10 (e. g. Catinella et al. 2005). Finally, the rotation velocity is defined as: V rot = W 2(1 + z)sin(i) , ( 2 ) where z is the redshift and i is the galaxy inclination determined from the observed ellipticity ε as: cos(i) = (1 -ε) 2 -q 2 0 1 -q 2 0 , (3) with q 0 being the intrinsic axial ratio of edge-on galaxies.
A more precise kinematic analysis requires tools that consider non-circular motions. Spekkens & Sellwood (2007)  and  Sellwood & Sánchez (2010)  developed the VELFIT code specifically to characterize the non-circular motions in the kinematics of spiral galaxies expressed in a Fourier series. We used this code with some improvements (Aquino-Ortíz in prep. ) to derive the properties of the velocity maps. This fit provides an estimate of the rotation curve, the kinematic inclination and position angle of the galaxy, together with the amplitude of the non-circular motions as a function of radius. A bootstrap procedure is adopted to estimate the uncertainties on the derived parameters. The current procedure is not performed over the full dataset, since in many cases the kinematics present clear deviations due to external perturbations or is strongly affected by random motions.
RESULTS. Figure  2  shows the analyzed kinematic scaling relations using the total stellar mass (unless noted otherwise) and the integrated kinematics, segregated by stellar and ionized gas kinematics (upper panels) and by morphology (lower panels), respectively. In each panel is included some reference relations found by previous results, and the best-fit relations for the M -S 0. 5 distribution. Figure  3  shows the same distributions for the resolved kinematics. Table  1  summarizes the results of an orthogonal linear fit along the horizontal axis, considering the total stellar mass on the vertical axis as the independent variable, using the routines presented by  Akritas & Bershady (1996) , for each of the kinematic scaling relations and dataset. It includes the zero-points and slopes, together with the scatter around the best-fit relations estimated from the 'error-invariable' of the corresponding fit as the standard deviation of residuals.